Datasets:

SivilTaram

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code-document-sample

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adeptdata

cran

Package ‘adeptdata’ October 12, 2022 Type Package Title Accelerometry Data Sets Version 1.1 Description Created to host raw accelerometry data sets and their deriva- tives which are used in the corresponding 'adept' package. License GPL-3 Encoding UTF-8 LazyData true LazyDataCompression xz RoxygenNote 7.1.1 Depends R (>= 2.10) Suggests spelling Language en-US NeedsCompilation no Author Marta Karas [aut, cre] (<https://orcid.org/0000-0001-5889-3970>), Jacek Urbanek [aut] (<https://orcid.org/0000-0002-1890-8899>), Jaroslaw Harezlak [aut] (<https://orcid.org/0000-0002-3070-7686>), William Fadel [aut] (<https://orcid.org/0000-0002-0292-6734>) Maintainer Marta Karas <marta.karass@gmail.com> Repository CRAN Date/Publication 2021-03-28 00:10:06 UTC R topics documented: acc_running . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 acc_walking_IU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 stride_template . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Index 6 1 2 acc_running acc_running Outdoor Run Raw Accelerometry Data Description Raw accelerometry data collected during 25 minutes of an outdoor run. Data were collected at frequency 100 Hz simultaneously with two wearable accelerometers located at left hip and left ankle. Usage acc_running Format A data.frame with 300000 observations of 5 variables: • loc_id - sensor location, one of: "left_hip", "left_ankle", • date_time - date and time of acceleration measurement collection stored as POSIXct, • x - acceleration measurement time-series collected from a "x" axis of the sensor accelerometer, • y - acceleration measurement time-series collected from a "y" axis of the sensor accelerometer, • z - acceleration measurement time-series collected from a "z" axis of the sensor accelerometer. Details Data were collected during 25 minutes of an outdoor run performed by an adult healthy female, 180 cm tall and of 67 kg weight The data were collected at frequency 100 Hz with two ActiGraph GT9X Link sensors. One of the sensors was attached to the shoe with a clip on the outside side of a left foot, just below the left ankle. The other sensor was attached to the elastic belt located at hip, on the left side of a hip. Based on a mobile tracking application output, the ground elevation difference between start and end point of the data collection is approximately 36 m (17 m at the start point, 53 m at the finish point). The distance covered is approximately 3.35 km. The person from which the data were collected is Marta Karas, a co-author of the package. The IRB Office Determination Request Form for Primary (New) Data Collection request form was submitted in regard to the collection and further publishing of these data. Based on preliminary review of the request form submitted, it was determined that the data collection and further data publishing ac- tivity described in the determination request does not qualify as human subjects research as defined by DHHS regulations 45 CFR 46.102, and does not require IRB oversight. acc_walking_IU 3 acc_walking_IU Outdoor Continuous Walking Raw Accelerometry Data Description Raw accelerometry data collected during outdoor continuous walking from 32 healthy participants between 23 and 52 years of age. Data were collected at frequency 100 Hz simultaneously with four wearable accelerometers located at left wrist, left hip and both ankles. Usage acc_walking_IU Format A data.frame with 2590448 observations of 6 variables: • subj_id - study participant ID, • loc_id - sensor location, one of: "left_wrist", "left_hip", "left_ankle", "right_ankle", • time_s - duration of recorded exercise for a study participant, expressed in seconds, • x - acceleration measurement time-series collected from a "x" axis of the sensor accelerometer, • y - acceleration measurement time-series collected from a "y" axis of the sensor accelerometer, • z - acceleration measurement time-series collected from a "z" axis of the sensor accelerometer. Details Raw accelerometry data of continuous walking were collected as a part of the study on Identification of Walking, Stair Climbing, and Driving Using Wearable Accelerometers, sponsored by the Indiana University CTSI grant and conducted at the Department of Biostatistics, Fairbanks School of Public Health at Indiana University. The study was led by Dr. Jaroslaw Harezlak, assisted by Drs. William Fadel and Jacek Urbanek. The study was approved by the IRB of Indiana University; all participants provided written informed consent. Attached data set is anonymized. Study enrolled 32 healthy participants between 23 and 52 years of age. Participants were asked, among others, to perform self-paced, undisturbed, outdoor walking on the sidewalk. Accelerometry data were collected simultaneously at four body locations: left wrist, left hip, left ankle, and right ankle, at frequency 100 Hz. Duration time of outdoor walking exercise ranges between 2,5 to 4 minutes for study participants. 4 stride_template stride_template Walking Stride Pattern Templates Description Walking stride pattern templates derived from raw accelerometry data collected at four body loca- tions: left wrist, left hip, left ankle, and right ankle. Usage stride_template Format A list with four named elements: • left_wrist, • left_hip, • left_ankle, • right_ankle. Each of the above is a five-element list of matrix objects. The matrices are collection of (sub)population- specific stride pattern templates. For example, • stride_template$left_wrist[[1]] is a 1 x 200 matrix of one population-specific stride template derived from accelerometry data collected at left wrist. • stride_template$left_wrist[[2]] is a 2 x 200 matrix of two distinct subpopulation- specific stride templates derived from accelerometry data collected at left wrist. Each row is a one subpopulation-specific stride template. • stride_template$right_ankle[[5]] is a 5 x 200 matrix of five distinct subpopulation- specific stride templates derived from accelerometry data collected at right ankle. Details Raw accelerometry data used to derive walking stride pattern templates were collected as a part of the study on Identification of Walking, Stair Climbing, and Driving Using Wearable Accelerome- ters, sponsored by the Indiana University CTSI grant and conducted at the Department of Biostatis- tics, Fairbanks School of Public Health at Indiana University. The study was led by Dr. Jaroslaw Harezlak, assisted by Drs. William Fadel and Jacek Urbanek. Study enrolled 32 healthy partici- pants between 23 and 52 years of age. Participants were asked, among others, to perform self-paced, undisturbed, outdoor walking on the sidewalk. Accelerometry data were collected at four body lo- cations: left wrist, left hip, left ankle, and right ankle. To derive empirical stride pattern, firstly, from each body location, 642 data segments corresponding to individual walking strides were manually segmented. Secondly, Vector Magnitude (VM), which is a univariate summary of three-dimensional time-series of raw accelerometry data, was computed. Lastly, 642 univariate vectors of VM were interpolated to have the same vector length, scaled, stride_template 5 and clustered into 1,2,3,4 and 5 clusters via correlation clustering. The vectors obtained as point- wise means within each cluster were defined to be subpopulation-specific stride pattern templates, respectively. Index ∗ datasets acc_running, 2 acc_walking_IU, 3 stride_template, 4 acc_running, 2 acc_walking_IU, 3 stride_template, 4 6

BiDAG

cran

Package ‘BiDAG’ May 16, 2023 Type Package Title Bayesian Inference for Directed Acyclic Graphs Version 2.1.4 Author Polina Suter [aut, cre], Jack Kuipers [aut] Maintainer Polina Suter <polina.suter@gmail.com> Description Implementation of a collection of MCMC methods for Bayesian structure learning of directed acyclic graphs (DAGs), both from continuous and discrete data. For efficient inference on larger DAGs, the space of DAGs is pruned according to the data. To filter the search space, the algorithm employs a hybrid approach, combining constraint-based learning with search and score. A reduced search space is initially defined on the basis of a skeleton obtained by means of the PC-algorithm, and then iteratively improved with search and score. Search and score is then performed following two approaches: Order MCMC, or Partition MCMC. The BGe score is implemented for continuous data and the BDe score is implemented for binary data or categorical data. The algorithms may provide the maximum a posteriori (MAP) graph or a sample (a collection of DAGs) from the posterior distribution given the data. All algorithms are also applicable for structure learning and sampling for dynamic Bayesian net- works. References: J. Kuipers, P. Suter, G. Moffa (2022) <doi:10.1080/10618600.2021.2020127>, N. Friedman and D. Koller (2003) <doi:10.1023/A:1020249912095>, J. Kuipers and G. Moffa (2017) <doi:10.1080/01621459.2015.1133426>, M. Kalisch et al. (2012) <doi:10.18637/jss.v047.i11>, D. Geiger and D. Heckerman (2002) <doi:10.1214/aos/1035844981>, P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09>. Acknowledgments We would like to thank Giusi Moffa for discussion and comments on the package and its manual. License GPL (>= 2) Depends R (>= 3.5.0) Imports Rcpp (>= 0.12.7), methods, graph, Rgraphviz, RBGL, pcalg, graphics, Matrix, coda LinkingTo Rcpp RoxygenNote 7.2.0 1 2 R topics documented: Encoding UTF-8 LazyData TRUE NeedsCompilation yes Repository CRAN Date/Publication 2023-05-16 12:46:02 UTC R topics documented: Asia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Asiamat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 bidag2coda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 bidag2codalist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Boston . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 compact2full . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 compareDAGs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 compareDBNs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 connectedSubGraph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 DAGscore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 DBNdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 DBNmat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 DBNscore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 DBNunrolled . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 edgep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 full2compact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 getDAG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 getMCMCscore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 getRuntime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 getSpace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 getSubGraph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 getTrace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 graph2m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 gsim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 gsim100 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 gsimmat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 iterativeMCMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 iterativeMCMC class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 itercomp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 kirc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 kirp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 learnBN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 m2graph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 modelp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 orderMCMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 orderMCMC class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 Asia 3 partitionMCMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 partitionMCMC class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 plot2in1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 plotDBN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 plotdiffs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 plotdiffsDBN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 plotpcor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 plotpedges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 sampleBN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 samplecomp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 scoreagainstDAG . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 scoreagainstDBN . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58 scoreparameters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 scorespace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 scorespace class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 string2mat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 Index 65 Asia Asia dataset Description A synthetic dataset from Lauritzen and Spiegelhalter (1988) about lung diseases (tuberculosis, lung cancer or bronchitis) and visits to Asia. Usage Asia Format A data frame with 5000 rows and 8 binary variables: • D (dyspnoea), binary 1/0 corresponding to "yes" and "no" • T (tuberculosis), binary 1/0 corresponding to "yes" and "no" • L (lung cancer), binary 1/0 corresponding to "yes" and "no" • B (bronchitis), binary 1/0 corresponding to "yes" and "no" • A (visit to Asia), binary 1/0 corresponding to "yes" and "no" • S (smoking), binary 1/0 corresponding to "yes" and "no" • X (chest X-ray), binary 1/0 corresponding to "yes" and "no" • E (tuberculosis versus lung cancer/bronchitis), binary 1/0 corresponding to "yes" and "no" Source https://www.bnlearn.com/bnrepository/ 4 Asiamat References Lauritzen S, Spiegelhalter D (1988). ‘Local Computation with Probabilities on Graphical Structures and their Application to Expert Systems (with discussion)’. Journal of the Royal Statistical Society: Series B 50, 157-224. Asiamat Asiamat Description An adjacency matrix representing the ground truth DAG used to generate a synthetic dataset from Lauritzen and Spiegelhalter (1988) about lung diseases (tuberculosis, lung cancer or bronchitis) and visits to Asia. Usage Asiamat Format A binary matrix with 8 rows and 8 columns representing an adjacency matrix of a DAG with 8 nodes: • D (dyspnoea), binary 1/0 corresponding to "yes" and "no" • T (tuberculosis), binary 1/0 corresponding to "yes" and "no" • L (lung cancer), binary 1/0 corresponding to "yes" and "no" • B (bronchitis), binary 1/0 corresponding to "yes" and "no" • A (visit to Asia), binary 1/0 corresponding to "yes" and "no" • S (smoking), binary 1/0 corresponding to "yes" and "no" • X (chest X-ray), binary 1/0 corresponding to "yes" and "no" • E (tuberculosis versus lung cancer/bronchitis), binary 1/0 corresponding to "yes" and "no" Source https://www.bnlearn.com/bnrepository/ References Lauritzen S, Spiegelhalter D (1988). ‘Local Computation with Probabilities on Graphical Structures and their Application to Expert Systems (with discussion)’. Journal of the Royal Statistical Society: Series B 50, 157-224. bidag2coda 5 bidag2coda Converting a single BiDAG chain to mcmc object Description This function converts a single object of one of the BiDAG classes, namely ’orderMCMC’ or ’par- titionMCMC’ to an object of class ’mcmc’. This object can be further used for convergence and mixing diagnostics implemented in the package coda Usage bidag2coda( MCMCtrace, edges = FALSE, pdag = TRUE, p = 0.1, burnin = 0.2, window = 100, cumulative = FALSE ) Arguments MCMCtrace object of class orderMCMC or partitionMCMC edges logical, when FALSE (default), then only DAG score trace is extracted; when TRUE, a trace of posterior probabilities is extracted for every edge (based on the sampled DAGs defined by parameters ’window’ and ’cumulative’) resulting in up to n^2 trace vectors, where n is the number of nodes in the network pdag logical, when edges=TRUE, defines if the DAGs are converted to CPDAGs prior to computing posterior probabilities; ignored otherwise p numeric, between 0 and 1; defines the minimum probability for including poste- rior traces in the returned objects (for probabilities close to 0 PRSF diagnostics maybe too conservative) burnin numeric between 0 and 1, indicates the percentage of the samples which will be discarded as ’burn-in’ of the MCMC chain; the rest of the samples will be used to calculate the posterior probabilities; 0.2 by default window integer, defines a number of DAG samples for averaging and computing edges’ posterior probabilities; ignored when edges=FALSE cumulative logical, indicates if posterior probabilities should be calculated based on a cu- mulative sample of DAGs, where 25% of the first samples are discarded Value Object of class mcmc from the package coda 6 bidag2codalist Author(s) Polina Suter Examples ## Not run: library(coda) myscore<-scoreparameters("bde",Asia) ordersample<-sampleBN(myscore,"order") order_mcmc<-bidag2coda(ordersample) par(mfrow=c(1,2)) densplot(order_mcmc) traceplot(order_mcmc) ## End(Not run) bidag2codalist Converting multiple BiDAG chains to mcmc.list Description This function converts a list of objects of classes ’orderMCMC’ or ’partitionMCMC’ to an ob- ject of class ’mcmc.list’. This object can be further used for convergence and mixing diagnostics implemented in the R-package coda. Usage bidag2codalist( MCMClist, edges = FALSE, pdag = TRUE, p = 0.1, burnin = 0.2, window = 10, cumulative = FALSE ) Arguments MCMClist a list of objects of classes orderMCMC or partitionMCMC edges logical, when FALSE (default), then only DAG score trace is extracted; when TRUE, a trace of posterior probabilities is extracted for every edge (based on the sampled DAGs defined by parameters ’window’ and ’cumulative’) resulting in up to n^2 trace vectors, where n is the number of nodes in the network pdag logical, when edges=TRUE, defines if the DAGs are converted to CPDAGs prior to computing posterior probabilities; ignored otherwise Boston 7 p numeric, between 0 and 1; defines the minimum probability for including poste- rior traces in the returned objects (for probabilities close to 0, PRSF diagnostics maybe too conservative; the threshold above 0 is recommended) burnin numeric between 0 and 1, indicates the percentage of the samples which will be discarded as ’burn-in’ of the MCMC chain; the rest of the samples will be used to calculate the posterior probabilities; 0.2 by default window integer, defines a number of DAG samples for averaging and computing edges’ posterior probabilities; ignored when edges=FALSE cumulative logical, indicates if posterior probabilities should be calculated based on a cu- mulative sample of DAGs, where 25% of the first samples are discarded Value Object of class mcmc.list from the package coda Author(s) Polina Suter References Robert J. B. Goudie and Sach Mukherjee (2016). A Gibbs Sampler for Learning DAGs. J Mach Learn Res. 2016 Apr; 17(30): 1–39. Examples ## Not run: library(coda) scoreBoston<-scoreparameters("bge",Boston) ordershort<-list() #run very short chains -> convergence issues ordershort[[1]] <- sampleBN(scoreBoston, algorithm = "order", iterations=2000) ordershort[[2]] <- sampleBN(scoreBoston, algorithm = "order", iterations=2000) codashort_edges<-bidag2codalist(ordershort,edges=TRUE,pdag=TRUE,p=0.05,burnin=0.2,window=10) gd_short<-gelman.diag(codashort_edges, transform=FALSE, autoburnin=FALSE, multivariate=FALSE) length(which(gd_short$psrf[,1]>1.1))/(length(gd_short$psrf[,1])) #=>more MCMC iterations are needed, try 100000 ## End(Not run) Boston Boston housing data Description A dataset containing information collected by the U.S Census Service concerning housing in the area of Boston, originally published by Harrison and Rubinfeld (1978). 8 compact2full Usage Boston Format A data frame with 506 rows and 14 variables: • CRIM - per capita crime rate by town • ZN - proportion of residential land zoned for lots over 25,000 sq.ft. • INDUS - proportion of non-retail business acres per town. • CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise) • NOX - nitric oxides concentration (parts per 10 million) • RM - average number of rooms per dwelling • AGE - proportion of owner-occupied units built prior to 1940 • DIS - weighted distances to five Boston employment centres • TAX - full-value property-tax rate per $10,000 • RAD - index of accessibility to radial highways • PTRATIO - pupil-teacher ratio by town • B - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town • LSTAT - percentage lower status of the population • MEDV - Median value of owner-occupied homes in $1000’s Source http://lib.stat.cmu.edu/datasets/boston References Harrison, D and Rubinfeld, DL (1978) ‘Hedonic prices and the demand for clean air’, Journal of Environmental Economics and Management 5, 81-102. compact2full Deriving an adjecency matrix of a full DBN Description This function transforms a compact 2-slice adjacency matrix of DBN into full T-slice adjacency matrix Usage compact2full(DBNmat, slices, b = 0) compareDAGs 9 Arguments DBNmat a square matrix, representing initial and transitional structure of a DBN; the size of matrix is 2*dyn+b slices integer, number of slices in an unrolled DBN b integer, number of static variables Value an adjacency matrix of an unrolled DBN Examples compact2full(DBNmat, slices=5, b=3) compareDAGs Comparing two graphs Description This function compares one (estimated) graph to another graph (true graph), returning a vector of 8 values: • the number of true positive edges (’TP’) is the number of edges in the skeleton of ’egraph’ which are also present in the skeleton of ’truegraph’ • the number of false positive edges (’FP’) is the number of edges in the skeleton of ’egraph’ which are absent in the skeleton of ’truegraph’ • the number of fralse negative edges (’FN’) is the number of edges in the skeleton of ’truegraph’ which are absent in the skeleton of ’egraph’ • structural Hamming distance (’SHD’) between 2 graphs is computed as TP+FP+the number of edges with an error in direction • TPR equals TP/(TP+FN) • FPR equals FP/(TN+FP) (TN stands for true negative edges) • FPRn equals FP/(TP+FN) • FDR equals FP/(TP+FP) Usage compareDAGs(egraph, truegraph, cpdag = FALSE, rnd = 2) 10 compareDBNs Arguments egraph an object of class graphNEL (package ‘graph’), representing the graph which should be compared to a ground truth graph or an ajecency matrix corresponding to the graph truegraph an object of class graphNEL (package ‘graph’), representing the ground truth graph or an ajecency matrix corresponding to this graph cpdag logical, if TRUE (FALSE by default) both graphs are first converted to their respective equivalence class (CPDAG); this affects SHD calculation rnd integer, rounding integer indicating the number of decimal places (round) when computing TPR, FPR, FPRn and FDR Value a named numeric vector 8 elements: SHD, number of true positive edges (TP), number of false positive edges (FP), number of false negative edges (FN), true positive rate (TPR), false positive rate (FPR), false positive rate normalized to the true number of edges (FPRn) and false discovery rate (FDR) Examples Asiascore<-scoreparameters("bde", Asia) ## Not run: eDAG<-learnBN(Asiascore,algorithm="order") compareDAGs(eDAG$DAG,Asiamat) ## End(Not run) compareDBNs Comparing two DBNs Description This function compares one (estimated) DBN structure to another DBN (true DBN). Comparisons for initial and transitional structures are returned separately if equalstruct equals TRUE. Usage compareDBNs(eDBN, trueDBN, struct = c("init", "trans"), b = 0) Arguments eDBN an object of class graphNEL (or an ajacency matrix corresponding to this DBN), representing the DBN which should be compared to a ground truth DBN trueDBN an object of class graphNEL (or an ajacency matrix corresponding to this DBN), representing the ground truth DBN connectedSubGraph 11 struct option used to determine if the initial or the transitional structure should be compared; accaptable values are init or trans b number of static variables in one time slice of a DBN; note that for function to work correctly all static variables have to be in the first b columns of the matrix Value a vector of 5: SHD, number of true positive edges, number of false positive edges, number of false negative edges and true positive rate Examples testscore<-scoreparameters("bge", DBNdata, DBN=TRUE, dbnpar=list(samestruct=TRUE, slices=5, b=3)) ## Not run: DBNfit<-learnBN(testscore, algorithm="orderIter",moveprobs=c(0.11,0.84,0.04,0.01)) compareDBNs(DBNfit$DAG,DBNmat, struct="trans", b=3) ## End(Not run) connectedSubGraph Deriving connected subgraph Description This function derives an adjacency matrix of a subgraph whose nodes are connected to at least one other node in a graph Usage connectedSubGraph(adj) Arguments adj square adjacency matrix with elements in {0,1}, representing a graph Value adjacency matrix of a subgraph of graph represented by ’adj’ whose nodes have at least one con- nection Examples dim(gsimmat) #full graph contains 100 nodes gconn<-connectedSubGraph(gsimmat) #removing disconnected nodes dim(gconn) #connected subgraph contains 93 nodes 12 DAGscore DAGscore Calculating the BGe/BDe score of a single DAG Description This function calculates the score of a DAG defined by its adjacency matrix. Acceptable data matrices are homogeneous with all variables of the same type: continuous, binary or categorical. The BGe score is evaluated in the case of continuous data and the BDe score is evaluated for binary and categorical variables. Usage DAGscore(scorepar, incidence) Arguments scorepar an object of class scoreparameters, containing the data and scoring parame- ters; see constructor function scoreparameters incidence a square matrix of dimensions equal to the number of nodes, representing the ad- jacency matrix of a DAG; the matrix entries are in {0,1} such that incidence[i,j] equals 1 if there is a directed edge from node i to node j in the DAG and incidence[i,j] equals 0 otherwise Value the log of the BGe or BDe score of the DAG Author(s) Jack Kuipers, Polina Suter, the code partly derived from the order MCMC implementation from Kuipers J, Moffa G (2017) <doi:10.1080/01621459.2015.1133426> References Geiger D and Heckerman D (2002). Parameter priors for directed acyclic graphical models and the characterization of several probability distributions. The Annals of Statistics 30, 1412-1440. Heckerman D and Geiger D (1995). Learning Bayesian networks: A unification for discrete and Gaussian domains. In Eleventh Conference on Uncertainty in Artificial Intelligence, pages 274-284. Kuipers J, Moffa G and Heckerman D (2014). Addendum on the scoring of Gaussian directed acyclic graphical models. The Annals of Statistics 42, 1689-1691. Examples myScore<-scoreparameters("bde", Asia) DAGscore(myScore, Asiamat) DBNdata 13 DBNdata Simulated data set from a 2-step dynamic Bayesian network Description A synthetic dataset containing 100 observations generated from a random dynamic Bayesian net- work with 12 continuous dynamic nodes and 3 static nodes. The DBN includes observations from 5 time slices. Usage DBNdata Format A data frame with 100 rows and 63 (3+12*5) columns representing observations of 15 variables: 3 static variables (first 3 columns) which do not change over time and 12 dynamic variables observed in 5 conseecutive time slices. DBNmat An adjacency matrix of a dynamic Bayesian network Description An adjacency matrix representing the ground truth DBN used to generate a synthetic dataset DBNdata. The matrix is a compact representation of a 2-step DBN, such that initial structure is stored in the first 15 columns of the matrix and transitional structure is stored in the last 12 columns of the matrix. Usage DBNmat Format A binary matrix with 27 rows and 27 columns representing an adjacency matrix of a DBN. Rows and columns of the matrix correspond to 15 variables of a DBN across 2 time slices. 14 DBNscore DBNscore Calculating the BGe/BDe score of a single DBN Description This function calculates the score of a DBN defined by its compact adjacency matrix. Acceptable data matrices are homogeneous with all variables of the same type: continuous, binary or categor- ical. The BGe score is evaluated in the case of continuous data and the BDe score is evaluated for binary and categorical variables. Usage DBNscore(scorepar, incidence) Arguments scorepar an object of class scoreparameters, containing the data and scoring parame- ters; see constructor function scoreparameters incidence a square matrix, representing initial and transitional structure of a DBN; the size of matrix is 2*nsmall+bgn, where nsmall is the number of variables per time slice excluding static nodes and bgn is the number of static variables the matrix entries are in {0,1} such that incidence[i,j] equals 1 if there is a directed edge from node i to node j in the DAG and incidence[i,j] equals 0 otherwise Value the log of the BGe or BDe score of the DBN Author(s) Polina Suter, Jack Kuipers Examples testscore<-scoreparameters("bge", DBNdata, DBN=TRUE, dbnpar=list(slices=5, b=3)) DBNscore(testscore, DBNmat) DBNunrolled 15 DBNunrolled An unrolled adjacency matrix of a dynamic Bayesian network Description An adjacency matrix representing the ground truth DBN used to generate a synthetic dataset DBNdata. The matrix is an unrolled representation of a 2-step DBN, such that the static variables are repre- sented in the first 3 columns/rows of the matrix. Usage DBNunrolled Format A binary matrix with 63 rows and 63 columns representing an adjacency matrix of a DBN. Rows and columns of the matrix correspond to 15 variables (s1, s2, s3, v1, v2, v3, v4, v5, v6, v7, v8, v9, v10, v11, v12) of a DBN across 5 time slices. edgep Estimating posterior probabilities of single edges Description This function estimates the posterior probabilities of edges by averaging over a sample of DAGs obtained via an MCMC scheme. Usage edgep(MCMCchain, pdag = FALSE, burnin = 0.2, endstep = 1) Arguments MCMCchain an object of class partitionMCMC, orderMCMC or iterativeMCMC, representing the output of structure sampling function partitionMCMC or orderMCMC (the latter when parameter chainout=TRUE; pdag logical, if TRUE (FALSE by default) all DAGs in the MCMCchain are first converted to equivalence class (CPDAG) before the averaging burnin number between 0 and 1, indicates the percentage of the samples which will be discarded as ‘burn-in’ of the MCMC chain; the rest of the samples will be used to calculate the posterior probabilities; 0.2 by default endstep number between 0 and 1; 1 by default 16 full2compact Value a square matrix with dimensions equal to the number of variables; each entry [i,j] is an estimate of the posterior probability of the edge from node i to node j Author(s) Polina Suter Examples Bostonscore<-scoreparameters("bge", Boston) ## Not run: samplefit<-sampleBN(Bostonscore, "order") edgesposterior<-edgep(samplefit, pdag=TRUE, burnin=0.2) ## End(Not run) full2compact Deriving a compact adjacency matrix of a DBN Description This function transforms an unrolled adjacency matrix of DBN into a compact representation Usage full2compact(DBNmat, b = 0) Arguments DBNmat a square matrix, representing the structure of an unrolled DBN; the size of matrix is slices*dyn+b; all static variables are assumed to be in the first b rows and columns of the matrix b integer, number of static variables; 0 by default Examples full2compact(DBNunrolled,b=3) getDAG 17 getDAG Extracting adjacency matrix (DAG) from MCMC object Description This function extracts an adjacency matrix of a maximum scoring DAG from the result of the MCMC run. Usage getDAG(x, amat = TRUE, cp = FALSE) Arguments x object of class ’orderMCMC’,’partitionMCMC’ or ’iterativeMCMC’ amat logical, when TRUE adjacency matrix is returned and object of class ’graph- NEL’ otherwise cp logical, when TRUE the CPDAG (equivalence class) is returned and DAG oth- erwise; FALSE by default Value adjacency matrix of a maximum scoring DAG (or CPDAG) discovered/sampled in one MCMC run Examples myscore<-scoreparameters("bge", Boston) ## Not run: itfit<-learnBN(myscore,algorithm="orderIter") maxEC<-getDAG(itfit,cp=TRUE) ## End(Not run) getMCMCscore Extracting score from MCMC object Description This function extracts the score of a maximum DAG sampled in the MCMC run. Usage getMCMCscore(x) Arguments x object of class ’orderMCMC’,’partitionMCMC’ or ’iterativeMCMC’ 18 getRuntime Value a score of a maximum-scoring DAG found/sampled in one MCMC run Examples myscore<-scoreparameters("bge", Boston) ## Not run: itfit<-learnBN(myscore,algorithm="orderIter") getMCMCscore(itfit) ## End(Not run) getRuntime Extracting runtime Description This function extracts runtime of a particular step of order and partition MCMC. Usage getRuntime(x, which = 0) Arguments x object of class ’orderMCMC’or ’partitionMCMC’ which integer, defines if the runtime is extracted for: computing score tables (which = 1), running MCMC chain (which = 2) Value runtime of a particular step of MCMC scheme or total runtime Examples myscore<-scoreparameters("bge",Boston) ## Not run: orderfit<-sampleBN(myscore,algorithm="order") (getRuntime(orderfit,1)) (getRuntime(orderfit,2)) ## End(Not run) getSpace 19 getSpace Extracting scorespace from MCMC object Description This function extracts an object of class ’scorespace’ from the result of the MCMC run when the parameter ’scoreout’ was set to TRUE; otherwise extracts only adjacency matrix of the final search space without the score tables. Usage getSpace(x) Arguments x object of class ’orderMCMC’,’partitionMCMC’ or ’iterativeMCMC’ Value an object of class ’scorespace’ or an adjacency binary matrix corresponding to a search space last used in MCMC Examples myscore<-scoreparameters("bge", Boston) ## Not run: itfit<-learnBN(myscore,algorithm="orderIter",scoreout=TRUE) itspace<-getSpace(itfit) ## End(Not run) getSubGraph Deriving subgraph Description This function derives an adjacency matrix of a subgraph based on the adjacency matrix of a full graph and a list of nodes Usage getSubGraph(adj, nodes) Arguments adj square adjacency matrix with elements in {0,1}, representing a graph nodes vector of node names of the subgraph; should be a subset of column names of ’adj’ 20 getTrace Value adjacency matrix of a subgraph which includes all ’nodes’ Examples getSubGraph(Asiamat,c("E","B","D","X")) getTrace Extracting trace from MCMC object Description This function extracts a trace of • DAG scores • DAG adjacency matrices • orders • order scores from the result of the MCMC run. Note that the last three options work only when the parameter ’scoreout’ was set to TRUE. Usage getTrace(x, which = 0) Arguments x object of class ’orderMCMC’,’partitionMCMC’ or ’iterativeMCMC’ which integer, indication which trace is returned: DAG scores (which = 0), DAGs (which = 1), orders (which = 2), order scores (which = 3) Value a list or a vector of objects representing MCMC trace, depends on parameter ’which’; by default, the trace of DAG scores is returned Examples myscore<-scoreparameters("bge",Boston) ## Not run: orderfit<-sampleBN(myscore,algorithm="order") DAGscores<-getTrace(orderfit,which=0) DAGtrace<-getTrace(orderfit,which=1) orderscores<-getTrace(orderfit,which=3) ## End(Not run) graph2m 21 graph2m Deriving an adjacency matrix of a graph Description This function derives the adjacency matrix corresponding to a graph object Usage graph2m(g) Arguments g graph, object of class graphNEL (package ‘graph’) Value a square matrix whose dimensions are the number of nodes in the graph g, where element [i,j] equals 1 if there is a directed edge from node i to node j in the graph g, and 0 otherwise Examples Asiagraph<-m2graph(Asiamat) Asia.adj<-graph2m(Asiagraph) gsim A simulated data set from a Gaussian continuous Bayesian network Description A synthetic dataset containing 1000 observations generated from a random DAG with 100 continu- ous nodes. Functions ’randomDAG’ and ’rmvDAG’ from R-packages ’pcalg’ were used to generate the data. Usage gsim Format A data frame with 1000 rows representing observations of 100 continuous variables: V1, ..., V100 22 gsimmat gsim100 A simulated data set from a Gaussian continuous Bayesian network Description A synthetic dataset containing 100 observations generated from a random DAG with 100 continuous nodes. Functions ’randomDAG’ and ’rmvDAG’ from R-packages ’pcalg’ were used to generate the data. Usage gsim100 Format A data frame with 100 rows representing observations of 100 continuous variables: V1, ..., V100 gsimmat An adjacency matrix of a simulated dataset Description An adjacency matrix representing the ground truth DAG used to generate a synthetic dataset with observations of 100 continuous variables. Usage gsimmat Format A binary matrix with 100 rows and 100 columns representing an adjacency matrix of a DAG with 100 nodes: V1, ..., V100 interactions 23 interactions interactions dataset Description A data frame containing possible interactions between genes from kirp and kirc data sets Usage interactions Format A data frame with 179 rows and 3 columns; • node1 character, name of a gene • node2 character, name of a gene • combined_score interaction score, reflecting confidence in the fact that interaction between gene1 and gene2 is possible each row represents a possible interaction between two genes Source https://string-db.org/ iterativeMCMC Structure learning with an iterative order MCMC algorithm on an ex- panded search space Description This function implements an iterative search for the maximum a posteriori (MAP) DAG, by means of order MCMC (arXiv:1803.07859v3). At each iteration, the current search space is expanded by allowing each node to have up to one additional parent not already included in the search space. By default the initial search space is obtained through the PC-algorithm (using the functions skeleton and pc from the ‘pcalg’ package [Kalisch et al, 2012]). At each iteration order MCMC is employed to search for the MAP DAG. The edges in the MAP DAG are added to the initial search space to provide the search space for the next iteration. The algorithm iterates until no further score improvements can be achieved by expanding the search space. The final search space may be used for the sampling versions of orderMCMC and partitionMCMC. 24 iterativeMCMC Usage iterativeMCMC( scorepar, MAP = TRUE, posterior = 0.5, softlimit = 9, hardlimit = 12, alpha = 0.05, gamma = 1, verbose = TRUE, chainout = FALSE, scoreout = FALSE, cpdag = FALSE, mergetype = "skeleton", iterations = NULL, moveprobs = NULL, stepsave = NULL, startorder = NULL, accum = FALSE, compress = TRUE, plus1it = NULL, startspace = NULL, blacklist = NULL, addspace = NULL, scoretable = NULL, alphainit = NULL ) ## S3 method for class 'iterativeMCMC' plot( x, ..., main = "iterative MCMC, DAG scores", xlab = "MCMC step", ylab = "DAG logscore", type = "l", col = "blue" ) ## S3 method for class 'iterativeMCMC' print(x, ...) ## S3 method for class 'iterativeMCMC' summary(object, ...) iterativeMCMC 25 Arguments scorepar an object of class scoreparameters, containing the data and scoring parame- ters; see constructor function scoreparameters MAP logical, if TRUE (default) the search targets the MAP DAG (a DAG with max- imum score), if FALSE at each MCMC step a DAG is sampled from the order proportionally to its score; when expanding a search space when MAP=TRUE all edges from the maximum scoring DAG are added to the new space, when MAP=FALSE only edges with posterior probability higher than defined by pa- rameter posterior are added to the search space posterior logical, when MAP set to FALSE defines posterior probability threshold for adding the edges to the search space softlimit integer, limit on the size of parent sets beyond which adding undirected edges is restricted; below this limit edges are added to expand the parent sets based on the undirected skeleton of the MAP DAG (or from its CPDAG, depending on the parameter mergecp), above the limit only the directed edges are added from the MAP DAG; the limit is 9 by default hardlimit integer, limit on the size of parent sets beyond which the search space is not further expanded to prevent long runtimes; the limit is 12 by default alpha numerical significance value in {0,1} for the conditional independence tests in the PC-stage gamma tuning parameter which transforms the score by raising it to this power, 1 by default verbose logical, if TRUE (default) prints messages on the progress of execution chainout logical, if TRUE the saved MCMC steps are returned, FALSE by default scoreout logical, if TRUE the search space from the last plus1 iterations and the corre- sponding score tables are returned, FALSE by default cpdag logical, if set to TRUE the equivalence class (CPDAG) found by the PC algo- rithm is used as a search space, when FALSE (default) the undirected skeleton used as a search space mergetype defines which edges are added to the search space at each expansion iteration; three options are available ’dag’, ’cpdag’, ’skeleton’; ’skeleton’ by default iterations integer, the number of MCMC steps, the default value is 3.5n2 log n moveprobs a numerical vector of 4 values in {0,1} corresponding to the probabilities of the following MCMC moves in the order space: • exchanging 2 random nodes in the order • exchanging 2 adjacent nodes in the order • placing a single node elsewhere in the order • staying still stepsave integer, thinning interval for the MCMC chain, indicating the number of steps between two output iterations, the default is iterations/1000 startorder integer vector of length n, which will be used as the starting order in the MCMC algorithm, the default order is random 26 iterativeMCMC accum logical, when TRUE at each search step expansion new edges are added to the current search space; when FALSE (default) the new edges are added to the starting space compress logical, if TRUE adjacency matrices representing sampled graphs will be stored as a sparse Matrix (recommended); TRUE by default plus1it (optional) integer, a number of iterations of search space expansion; by default the algorithm iterates until no score improvement can be achieved by further expanding the search space startspace (optional) a square matrix, of dimensions equal to the number of nodes, which defines the search space for the order MCMC in the form of an adjacency ma- trix; if NULL, the skeleton obtained from the PC-algorithm will be used; if startspace[i,j] equals to 1 (0) it means that the edge from node i to node j is included (excluded) from the search space; to include an edge in both direc- tions, both startspace[i,j] and startspace[j,i] should be 1 blacklist (optional) a square matrix, of dimensions equal to the number of nodes, which defines edges to exclude from the search space; if blacklist[i,j] equals to 1 it means that the edge from node i to node j is excluded from the search space • "dag", then edges from maximum scoring DAG are added; • "cpdag", then the maximum scoring DAG is first converted to the CPDAG, from which all edges are added to the search space; • "skeleton", then the maximum scoring DAG is first converted to the skele- ton, from which all edges are added to the search space addspace (optional) a square matrix, of dimensions equal to the number of nodes, which defines the edges, which are added at to the search space only at the first iteration of iterative seach and do not necessarily stay afterwards; defined in the form of an adjacency matrix; if addspace[i,j] equals to 1 (0) it means that the edge from node i to node j is included (excluded) from the search space; to include an edge in both directions, both addspace[i,j] and addspace[j,i] should be 1 scoretable (optional) object of class scorespace. When not NULL, parameters startspace and addspace are ignored. alphainit (optional) numerical, defines alpha that is used by the PC algorithm to learn initial structure of a DBN, ignored in static case x object of class ’iterativeMCMC’ ... ignored main name of the graph; "iterative MCMC, DAG scores" by default xlab name of x-axis; "MCMC step" ylab name of y-axis; "DAG logscore" type type of line in the plot; "l" by default col colour of line in the plot; "blue" by default object object of class ’iterativeMCMC’ iterativeMCMC 27 Value Object of class iterativeMCMC, which contains log-score trace as well as adjacency matrix of the maximum scoring DAG, its score and the order score. The output can optionally include DAGs sampled in MCMC iterations and the score tables. Optional output is regulated by the parameters chainout and scoreout. See iterativeMCMC class for a detailed class structure. Note see also extractor functions getDAG, getTrace, getSpace, getMCMCscore. Author(s) Polina Suter, Jack Kuipers References P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09> Kuipers J, Super P and Moffa G (2020). Efficient Sampling and Structure Learning of Bayesian Networks. (arXiv:1803.07859v3) Friedman N and Koller D (2003). A Bayesian approach to structure discovery in bayesian networks. Machine Learning 50, 95-125. Kalisch M, Maechler M, Colombo D, Maathuis M and Buehlmann P (2012). Causal inference using graphical models with the R package pcalg. Journal of Statistical Software 47, 1-26. Geiger D and Heckerman D (2002). Parameter priors for directed acyclic graphical models and the characterization of several probability distributions. The Annals of Statistics 30, 1412-1440. Kuipers J, Moffa G and Heckerman D (2014). Addendum on the scoring of Gaussian directed acyclic graphical models. The Annals of Statistics 42, 1689-1691. Spirtes P, Glymour C and Scheines R (2000). Causation, Prediction, and Search, 2nd edition. The MIT Press. Examples ## Not run: Bostonpar<-scoreparameters("bge",Boston) itfit<-iterativeMCMC(Bostonpar, chainout=TRUE, scoreout=TRUE) plot(itfit) ## End(Not run) 28 iterativeMCMC class iterativeMCMC class iterativeMCMC class structure Description The structure of an object of S3 class iterativeMCMC. Details An object of class iterativeMCMC is a list containing at least the following components: • DAG: adjacency matrix of a maximum scoring DAG found/sampled in MCMC. • CPDAG: adjacency matrix representing equivalence class of a maximum scoring DAG found/sampled in MCMC. • score: score of a maximum scoring DAG found/sampled in MCMC. • maxorder: order of a maximum scoring DAG found/sampled in MCMC. • maxtrace: a list of maximum score graphs uncovered at each expansion of the search space; their scores and orders • info: a list containing information about parameters and results of MCMC • trace: a list of vectors containing log-scores of sampled DAGs, each element of the list corre- sponds to a single expansion of a search space • startspace: adjacency matrix representing the initial core space where MCMC was ran • endspace: adjacency matrix representing the final core space where MCMC was ran Optional components: – traceadd: list which consists of three elements: * incidence: list containg adjacency matrices of sampled DAGs * order: list of orders from which the DAGs were sampled * orderscores: a list of vectors with order log-scores – scoretable: object of class scorespace class Author(s) Polina Suter itercomp 29 itercomp Performance assessment of iterative MCMC scheme against a known Bayesian network Description This function compute 8 different metrics of structure fit of an object of class iterativeMCMC to the ground truth DAG (or CPDAG). Object of class iterativeMCMC stores MAP graph at from each search space expansion step. This function computes structure fit of each of the stored graphs to the ground truth one. Computed metrics include: TP, FP, TPR, FPR, FPRn, FDR, SHD. See metrics description in see also compareDAGs. Usage itercomp(MCMCmult, truedag, cpdag = TRUE, p = 0.5, trans = TRUE) ## S3 method for class 'itercomp' plot(x, ..., vars = c("FP", "TP"), type = "b", col = "blue", showit = c()) ## S3 method for class 'itercomp' print(x, ...) ## S3 method for class 'itercomp' summary(object, ...) Arguments MCMCmult an object which of class iterativeMCMC, see also iterativeMCMC) truedag ground truth DAG which generated the data used in the search procedure; rep- resented by an object of class graphNEL or an adjacency matrix cpdag logical, if TRUE (FALSE by default) all DAGs are first converted to their re- spective equivalence classes (CPDAG) p threshold such that only edges with a higher posterior probability will be re- tained in the directed graph summarising the sample of DAGs at each iteration from MCMCmult if parameter sample set to TRUE trans logical, for DBNs indicates if model comparions are performed for transition structure; when trans equals FALSE the comparison is performed for initial structures of estimated models and the ground truth DBN; for usual BNs the parameter is disregarded x object of class ’itercomp’ ... ignored vars a tuple of variables which will be used for ’x’ and ’y’ axes; possible values: "SHD", "TP", "FP", "TPR", "FPR", "FPRn", "FDR", "score" type type of line in the plot;"b" by default 30 kirc col colour of line in the plot; "blue" by default showit (optional) vector of integers specifying indices of search expansion iterations to be labelled; by default no iterations are labelled object object of class ’itercomp’ Value an object if class itersim, a matrix with the number of rows equal to the number of expansion iterations in iterativeMCMC, and 8 columns reporting for the maximally scoring DAG uncovered at each iteration: the number of true positive edges (’TP’), the number of false positive edges (’FP’), the true positive rate (’TPR’), the structural Hamming distance (’SHD’), false positive rate (’FPR’), false discovery rate (’FDR’) and the score of the DAG (‘score’). Author(s) Polina Suter Examples gsim.score<-scoreparameters("bge", gsim) ## Not run: MAPestimate<-learnBN(gsim.score,"orderIter") itercomp(MAPestimate, gsimmat) ## End(Not run) kirc kirc dataset Description Mutation data from TCGA kidney renal clear cell cohort (KIRC). Mutations are picked according to q-value computed by MutSig2CV (q<0.1) or connected in networks discovered by Kuipers et al. 2018. Usage kirc Format An object of class matrix (inherits from array) with 476 rows and 70 columns. Details Each variable represents a gene. If in sample i gene j contains a mutation, than j-th element in row i equals 1, and 0 otherwise. The rows are named according to sample names in TCGA. The columns are named according to gene symbols. kirp 31 References https://portal.gdc.cancer.gov/ http://firebrowse.org/iCoMut/?cohort=kirc Lawrence, M. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214-218 (2013) kirp kirp dataset Description Mutation data from TCGA kidney renal papillary cell cohort (KIRP). Mutations are picked accord- ing to q-value computed by MutSigCV (q<0.1) or connected in networks discovered by Kuipers et al. 2018. Usage kirp Format An object of class matrix (inherits from array) with 282 rows and 70 columns. Details Each variable represents a gene. If in sample i gene j contains a mutation, than j-th element in row i equals 1, and 0 otherwise. The rows are named according to sample names in TCGA. The columns are named according to gene symbols. References https://portal.gdc.cancer.gov/ http://firebrowse.org/iCoMut/?cohort=kirp Lawrence, M. et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature 499, 214-218 (2013) 32 learnBN learnBN Bayesian network structure learning Description This function can be used finding the maximum a posteriori (MAP) DAG using stochastic search relying on MCMC schemes. Due to the superexponential size of the search space, it must be re- duced. By default the search space is limited to the skeleton found through the PC algorithm by means of conditional independence tests (using the functions skeleton and pc from the ‘pcalg’ package [Kalisch et al, 2012]). It is also possible to define an arbitrary search space by inputting an adjacency matrix, for example estimated by partial correlations or other network algorithms. Or- der MCMC scheme (algorithm="order") performs the search of a maximum scoring order and selects a maximum scoring DAG from this order as MAP. To avoid discovering a suboptimal graph due to the absence of some of the true positive edges in the search space, the function includes the possibility to expand the default or input search space, by allowing each node in the network to have one additional parent (plus1="TRUE"). This offers improvements in the learning of Bayesian networks. The iterative MCMC (algorithm="orderIter") scheme allows for iterative expansions of the search space. This is useful in cases when the initial search space is poor in a sense that it contains only a limited number of true positive edges. Iterative expansions of the search space efficiently solve this issue. However this scheme requires longer runtimes due to the need of run- ning multiple consecutive MCMC chains. This function is a wrapper for the individual structure learning functions that implement each of the described algorithms; for details see orderMCMC, and iterativeMCMC. Usage learnBN( scorepar, algorithm = c("order", "orderIter"), chainout = FALSE, scoreout = ifelse(algorithm == "orderIter", TRUE, FALSE), alpha = 0.05, moveprobs = NULL, iterations = NULL, stepsave = NULL, gamma = 1, verbose = FALSE, compress = TRUE, startspace = NULL, blacklist = NULL, scoretable = NULL, startpoint = NULL, plus1 = TRUE, iterpar = list(softlimit = 9, mergetype = "skeleton", accum = FALSE, plus1it = NULL, addspace = NULL, alphainit = NULL), cpdag = FALSE, hardlimit = 12 learnBN 33 ) Arguments scorepar an object of class scoreparameters, containing the data and score parameters, see constructor function scoreparameters algorithm MCMC scheme to be used for MAP structure learning; possible options are "order" (orderMCMC) or "orderIter" (iterativeMCMC) chainout logical, if TRUE the saved MCMC steps are returned, TRUE by default scoreout logical, if TRUE the search space and score tables are returned; FALSE by de- fault for "order", TRUE for "orderIter" alpha numerical significance value in {0,1} for the conditional independence tests at the PC algorithm stage moveprobs a numerical vector of 4 (for "order" and "orderIter" algorithms) or 5 values (for "partition" algorithm) representing probabilities of the different moves in the space of order and partitions accordingly. The moves are described in the corre- sponding algorithm specific functions orderMCMC and partitionMCMC iterations integer, the number of MCMC steps, the default value is 6n2 log n orderMCMC, 20n2 log n for partitionMCMC and 3.5n2 log n for iterativeMCMC; where n is the number of nodes in the Bayesian network stepsave integer, thinning interval for the MCMC chain, indicating the number of steps between two output iterations, the default is iterations/1000 gamma tuning parameter which transforms the score by raising it to this power, 1 by default verbose logical, if TRUE messages about the algorithm’s progress will be printed, FALSE by default compress logical, if TRUE adjacency matrices representing sampled graphs will be stored as a sparse Matrix (recommended); TRUE by default startspace (optional) a square sparse or ordinary matrix, of dimensions equal to the number of nodes, which defines the search space for the order MCMC in the form of an adjacency matrix. If NULL, the skeleton obtained from the PC-algorithm will be used. If startspace[i,j] equals to 1 (0) it means that the edge from node i to node j is included (excluded) from the search space. To include an edge in both directions, both startspace[i,j] and startspace[j,i] should be 1. blacklist (optional) a square sparse or ordinary matrix, of dimensions equal to the number of nodes, which defines edges to exclude from the search space. If blacklist[i,j] equals to 1 it means that the edge from node i to node j is excluded from the search space. scoretable (optional) object of class scorespace containing list of score tables calculated for example by the last iteration of the function iterativeMCMC. When not NULL, parameter startspace is ignored. startpoint (optional) integer vector of length n (representing an order when algorithm="order" or algorithm="orderIter") or an adjacency matrix or sparse adjacency ma- trix (representing a DAG when algorithm="partition"), which will be used as the starting point in the MCMC algorithm, the default starting point is random 34 learnBN plus1 logical, if TRUE (default) the search is performed on the extended search space; only changable for orderMCMC; for other algorithms is fixed to TRUE iterpar addition list of parameters for the MCMC scheme implemeting iterative expan- sions of the search space; for more details see iterativeMCMC; list(posterior = 0.5, softlimit = 9, mergetype = "skeleton", accum = FALSE, plus1it = NULL, addspace = NULL, alphainit = NULL) cpdag logical, if TRUE the CPDAG returned by the PC algorithm will be used as the search space, if FALSE (default) the full undirected skeleton will be used as the search space hardlimit integer, limit on the size of parent sets in the search space; by default 14 when MAP=TRUE and 20 when MAP=FALSE Value Depending on the value or the parameter algorithm returns an object of class orderMCMC or iterativeMCMC which contains log-score trace of sampled DAGs as well as adjacency matrix of the maximum scoring DAG(s), its score and the order or partition score. The output can optionally include DAGs sampled in MCMC iterations and the score tables. Optional output is regulated by the parameters chainout and scoreout. See orderMCMC class, iterativeMCMC class for a detailed description of the classes’ structures. Note see also extractor functions getDAG, getTrace, getSpace, getMCMCscore. Author(s) Polina Suter, Jack Kuipers, the code partly derived from the order MCMC implementation from Kuipers J, Moffa G (2017) <doi:10.1080/01621459.2015.1133426> References P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09> Friedman N and Koller D (2003). A Bayesian approach to structure discovery in bayesian networks. Machine Learning 50, 95-125. Kalisch M, Maechler M, Colombo D, Maathuis M and Buehlmann P (2012). Causal inference using graphical models with the R package pcalg. Journal of Statistical Software 47, 1-26. Geiger D and Heckerman D (2002). Parameter priors for directed acyclic graphical models and the characterization of several probability distributions. The Annals of Statistics 30, 1412-1440. Kuipers J, Moffa G and Heckerman D (2014). Addendum on the scoring of Gaussian acyclic graphical models. The Annals of Statistics 42, 1689-1691. Spirtes P, Glymour C and Scheines R (2000). Causation, Prediction, and Search, 2nd edition. The MIT Press. m2graph 35 Examples ## Not run: myScore<-scoreparameters("bge",Boston) mapfit<-learnBN(myScore,"orderIter") summary(mapfit) plot(mapfit) ## End(Not run) m2graph Deriving a graph from an adjacancy matrix Description This function derives a graph object corresponding to an adjacency matrix Usage m2graph(adj, nodes = NULL) Arguments adj square adjacency matrix with elements in {0,1}, representing a graph nodes (optional) labels of the nodes, c(1:n) are used by default Value object of class graphNEL (package ‘graph’); if element adj[i,j] equals 1, then there is a directed edge from node i to node j in the graph, and no edge otherwise Examples m2graph(Asiamat) mapping mapping dataset Description A data frame containing mapping between names of genes used in kirp/kirc data sets and names used in STRING interactions list (see interactions). Usage mapping 36 modelp Format A data frame with 46 rows and two columns: • queryItem character, name used for structure learning • preferredName character, name used in STRING interactions data set Source https://string-db.org/ modelp Estimating a graph corresponding to a posterior probability threshold Description This function constructs a directed graph (not necessarily acyclic) including all edges with a poste- rior probability above a certain threshold. The posterior probability is evaluated as the Monte Carlo estimate from a sample of DAGs obtained via an MCMC scheme. Usage modelp(MCMCchain, p, pdag = FALSE, burnin = 0.2) Arguments MCMCchain object of class partitionMCMC, orderMCMC or iterativeMCMC, representing the output of structure sampling function partitionMCMC or orderMCMC (the latter when parameter chainout=TRUE; p threshold such that only edges with a higher posterior probability will be re- tained in the directed graph summarising the sample of DAGs pdag logical, if TRUE (FALSE by default) all DAGs in the MCMCchain are first converted to equivalence class (CPDAG) before the averaging burnin number between 0 and 1, indicates the percentage of the samples which will be the discarded as ‘burn-in’ of the MCMC chain; the rest of the samples will be used to calculate the posterior probabilities; 0.2 by default Value a square matrix with dimensions equal to the number of variables representing the adjacency matrix of the directed graph summarising the sample of DAGs Author(s) Polina Suter orderMCMC 37 Examples Bostonscore<-scoreparameters("bge", Boston) ## Not run: partfit<-sampleBN(Bostonscore, "partition") hdag<-modelp(partfit, p=0.9) ## End(Not run) orderMCMC Structure learning with the order MCMC algorithm Description This function implements the order MCMC algorithm for the structure learning of Bayesian net- works. This function can be used for MAP discovery and for sampling from the posterior distribu- tion of DAGs given the data. Due to the superexponential size of the search space as the number of nodes increases, the MCMC search is performed on a reduced search space. By default the search space is limited to the skeleton found through the PC algorithm by means of conditional indepen- dence tests (using the functions skeleton and pc from the ‘pcalg’ package [Kalisch et al, 2012]). It is also possible to define an arbitrary search space by inputting an adjacency matrix, for example estimated by partial correlations or other network algorithms. Also implemented is the possibil- ity to expand the default or input search space, by allowing each node in the network to have one additional parent. This offers improvements in the learning and sampling of Bayesian networks. Usage orderMCMC( scorepar, MAP = TRUE, plus1 = TRUE, chainout = FALSE, scoreout = FALSE, moveprobs = NULL, iterations = NULL, stepsave = NULL, alpha = 0.05, cpdag = FALSE, gamma = 1, hardlimit = ifelse(plus1, 14, 20), verbose = FALSE, compress = TRUE, startspace = NULL, blacklist = NULL, startorder = NULL, scoretable = NULL ) 38 orderMCMC ## S3 method for class 'orderMCMC' plot( x, ..., burnin = 0.2, main = "DAG logscores", xlab = "iteration", ylab = "logscore", type = "l", col = "#0c2c84" ) ## S3 method for class 'orderMCMC' print(x, ...) ## S3 method for class 'orderMCMC' summary(object, ...) Arguments scorepar an object of class scoreparameters, containing the data and score parameters, see constructor function scoreparameters MAP logical, if TRUE (default) the search targets the MAP DAG (a DAG with max- imum score), if FALSE at each MCMC step a DAG is sampled from the order proportionally to its score plus1 logical, if TRUE (default) the search is performed on the extended search space chainout logical, if TRUE the saved MCMC steps are returned, TRUE by default scoreout logical, if TRUE the search space and score tables are returned, FALSE by de- fault moveprobs a numerical vector of 4 values in {0,1} corresponding to the probabilities of the following MCMC moves in the order space • exchanging 2 random nodes in the order • exchanging 2 adjacent nodes in the order • placing a single node elsewhere in the order • staying still iterations integer, the number of MCMC steps, the default value is 6n2 log n stepsave integer, thinning interval for the MCMC chain, indicating the number of steps between two output iterations, the default is iterations/1000 alpha numerical significance value in {0,1} for the conditional independence tests at the PC algorithm stage cpdag logical, if TRUE the CPDAG returned by the PC algorithm will be used as the search space, if FALSE (default) the full undirected skeleton will be used as the search space gamma tuning parameter which transforms the score by raising it to this power, 1 by default orderMCMC 39 hardlimit integer, limit on the size of parent sets in the search space; by default 14 when MAP=TRUE and 20 when MAP=FALSE verbose logical, if TRUE messages about the algorithm’s progress will be printed, FALSE by default compress logical, if TRUE adjacency matrices representing sampled graphs will be stored as a sparse Matrix (recommended); TRUE by default startspace (optional) a square matrix, of dimensions equal to the number of nodes, which defines the search space for the order MCMC in the form of an adjacency ma- trix. If NULL, the skeleton obtained from the PC-algorithm will be used. If startspace[i,j] equals to 1 (0) it means that the edge from node i to node j is included (excluded) from the search space. To include an edge in both direc- tions, both startspace[i,j] and startspace[j,i] should be 1. blacklist (optional) a square matrix, of dimensions equal to the number of nodes, which defines edges to exclude from the search space. If blacklist[i,j] equals to 1 it means that the edge from node i to node j is excluded from the search space. startorder (optional) integer vector of length n, which will be used as the starting order in the MCMC algorithm, the default order is random scoretable (optional) object of class scorespace containing list of score tables calculated for example by the last iteration of the function iterativeMCMC. When not NULL, parameter startspace is ignored. x object of class ’orderMCMC’ ... ignored burnin number between 0 and 1, indicates the percentage of the samples which will be discarded as ‘burn-in’ of the MCMC chain; the rest of the samples will be used to calculate the posterior probabilities; 0.2 by default main name of the graph; "DAG logscores" by default xlab name of x-axis; "iteration" ylab name of y-axis; "logscore" type type of line in the plot; "l" by default col colour of line in the plot; "#0c2c84" by default object object of class ’orderMCMC’ Value Object of class orderMCMC, which contains log-score trace of sampled DAGs as well as adjacency matrix of the maximum scoring DAG, its score and the order score. The output can optionally include DAGs sampled in MCMC iterations and the score tables. Optional output is regulated by the parameters chainout and scoreout. See orderMCMC class for a detailed class structure. Note see also extractor functions getDAG, getTrace, getSpace, getMCMCscore. 40 orderMCMC class Author(s) Polina Suter, Jack Kuipers, the code partly derived from the order MCMC implementation from Kuipers J, Moffa G (2017) <doi:10.1080/01621459.2015.1133426> References P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09> Friedman N and Koller D (2003). A Bayesian approach to structure discovery in bayesian networks. Machine Learning 50, 95-125. Kalisch M, Maechler M, Colombo D, Maathuis M and Buehlmann P (2012). Causal inference using graphical models with the R package pcalg. Journal of Statistical Software 47, 1-26. Geiger D and Heckerman D (2002). Parameter priors for directed acyclic graphical models and the characterization of several probability distributions. The Annals of Statistics 30, 1412-1440. Kuipers J, Moffa G and Heckerman D (2014). Addendum on the scoring of Gaussian acyclic graphical models. The Annals of Statistics 42, 1689-1691. Spirtes P, Glymour C and Scheines R (2000). Causation, Prediction, and Search, 2nd edition. The MIT Press. Examples ## Not run: #find a MAP DAG with search space defined by PC and plus1 neighbourhood Bostonscore<-scoreparameters("bge",Boston) #estimate MAP DAG orderMAPfit<-orderMCMC(Bostonscore) summary(orderMAPfit) #sample DAGs from the posterior distribution ordersamplefit<-orderMCMC(Bostonscore,MAP=FALSE,chainout=TRUE) plot(ordersamplefit) ## End(Not run) orderMCMC class orderMCMC class structure Description The structure of an object of S3 class orderMCMC. Details An object of class orderMCMC is a list containing at least the following components: • DAG: adjacency matrix of a maximum scoring DAG found/sampled in the MCMC scheme. • CPDAG: adjacency matrix representing equivalence class of a maximum scoring DAG found/sampled in MCMC. partitionMCMC 41 • score: score of a maximum scoring DAG found/sampled in MCMC. • maxorder: order of a maximum scoring DAG found/sampled in MCMC. • info: a list containing information about parameters and results of MCMC. • trace: a vector containing log-scores of sampled DAGs. Optional components: – traceadd: list which consists of three or four elements (depending on MCMC scheme used for sampling): * incidence: list containg adjacency matrices of sampled DAGs * order: list of orders from which the DAGs were sampled * orderscores: order log-scores – scoretable: object of class scorespace class Author(s) Polina Suter partitionMCMC DAG structure sampling with partition MCMC Description This function implements the partition MCMC algorithm for the structure learning of Bayesian net- works. This procedure provides an unbiased sample from the posterior distribution of DAGs given the data. The search space can be defined either by a preliminary run of the function iterativeMCMC or by a given adjacency matrix (which can be the full matrix with zero on the diagonal, to consider the entire space of DAGs, feasible only for a limited number of nodes). Usage partitionMCMC( scorepar, moveprobs = NULL, iterations = NULL, stepsave = NULL, alpha = 0.05, gamma = 1, verbose = FALSE, scoreout = FALSE, compress = TRUE, startspace = NULL, blacklist = NULL, scoretable = NULL, startDAG = NULL ) 42 partitionMCMC ## S3 method for class 'partitionMCMC' plot( x, ..., burnin = 0.2, main = "DAG logscores", xlab = "iteration", ylab = "logscore", type = "l", col = "#0c2c84" ) ## S3 method for class 'partitionMCMC' print(x, ...) ## S3 method for class 'partitionMCMC' summary(object, ...) Arguments scorepar an object of class scoreparameters, containing the data and scoring parame- ters; see constructor function scoreparameters. moveprobs (optional) a numerical vector of 5 values in {0,1} corresponding to the follow- ing MCMC move probabilities in the space of partitions: • swap any two elements from different partition elements • swap any two elements in adjacent partition elements • split a partition element or join one • move a single node into another partition element or into a new one • stay still iterations integer, the number of MCMC steps, the default value is 20n2 log n stepsave integer, thinning interval for the MCMC chain, indicating the number of steps between two output iterations, the default is iterations/1000 alpha numerical significance value in {0,1} for the conditional independence tests at the PC algorithm stage gamma tuning parameter which transforms the score by raising it to this power, 1 by default verbose logical, if set to TRUE (default) messages about progress will be printed scoreout logical, if TRUE the search space and score tables are returned, FALSE by de- fault compress logical, if TRUE adjacency matrices representing sampled graphs will be stored as a sparse Matrix (recommended); TRUE by default startspace (optional) a square matrix, of dimensions equal to the number of nodes, which defines the search space for the order MCMC in the form of an adjacency ma- trix; if NULL, the skeleton obtained from the PC-algorithm will be used. If startspace[i,j] equals to 1 (0) it means that the edge from node i to node j partitionMCMC 43 is included (excluded) from the search space. To include an edge in both direc- tions, both startspace[i,j] and startspace[j,i] should be 1. blacklist (optional) a square matrix, of dimensions equal to the number of nodes, which defines edges to exclude from the search space; if blacklist[i,j]=1 it means that the edge from node i to node j is excluded from the search space scoretable (optional) object of class scorespace containing list of score tables calculated for example by the last iteration of the function iterativeMCMC. When not NULL, parameter startspace is ignored startDAG (optional) an adjacency matrix of dimensions equal to the number of nodes, representing a DAG in the search space defined by startspace. If startspace is defined but startDAG is not, an empty DAG will be used by default x object of class ’partitionMCMC’ ... ignored burnin number between 0 and 1, indicates the percentage of the samples which will be discarded as ‘burn-in’ of the MCMC chain; the rest of the samples will be used to calculate the posterior probabilities; 0.2 by default main name of the graph; "DAG logscores" by default xlab name of x-axis; "iteration" ylab name of y-axis; "logscore" type type of line in the plot; "l" by default col colour of line in the plot; "#0c2c84" by default object object of class ’partitionMCMC’ Value Object of class partitionMCMC, which contains log-score trace as well as adjacency matrix of the maximum scoring DAG, its score and the order score. Additionally, returns all sampled DAGs (represented by their adjacency matrices), their scores, orders and partitions See partitionMCMC class. Note see also extractor functions getDAG, getTrace, getSpace, getMCMCscore. Author(s) Jack Kuipers, Polina Suter, the code partly derived from the partition MCMC implementation from Kuipers J, Moffa G (2017) <doi:10.1080/01621459.2015.1133426> References P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09> Kuipers J and Moffa G (2017). Partition MCMC for inference on acyclic digraphs. Journal of the American Statistical Association 112, 282-299. 44 partitionMCMC class Geiger D and Heckerman D (2002). Parameter priors for directed acyclic graphical models and the characterization of several probability distributions. The Annals of Statistics 30, 1412-1440. Heckerman D and Geiger D (1995). Learning Bayesian networks: A unification for discrete and Gaussian domains. In Eleventh Conference on Uncertainty in Artificial Intelligence, pages 274-284. Kalisch M, Maechler M, Colombo D, Maathuis M and Buehlmann P (2012). Causal inference using graphical models with the R package pcalg. Journal of Statistical Software 47, 1-26. Kuipers J, Moffa G and Heckerman D (2014). Addendum on the scoring of Gaussian directed acyclic graphical models. The Annals of Statistics 42, 1689-1691. Examples ## Not run: myScore<-scoreparameters("bge", Boston) partfit<-partitionMCMC(myScore) plot(partfit) ## End(Not run) partitionMCMC class partitionMCMC class structure Description The structure of an object of S3 class partitionMCMC. Details An object of class partitionMCMC is a list containing at least the following components: • DAG: adjacency matrix of a maximum scoring DAG found/sampled in the MCMC scheme. • CPDAG: adjacency matrix representing equivalence class of a maximum scoring DAG found/sampled in MCMC. • score: score of a maximum scoring DAG found/sampled in MCMC. • maxorder: order of a maximum scoring DAG found/sampled in MCMC. • info: a list containing information about parameters and results of MCMC. • trace: a vector containing log-scores of sampled DAGs. Optional components: – traceadd: list which consists of three or four elements (depending on MCMC scheme used for sampling): * incidence: list containg adjacency matrices of sampled DAGs * order: list of orders from which the DAGs were sampled * partition: list of partition from which the DAGs were sampled * partitionscores: partition log-scores – scoretable: object of class scorespace class plot2in1 45 Author(s) Polina Suter plot2in1 Highlighting similarities between two graphs Description This function plots nodes and edges from two graphs in one and indicates similarities between these graphs. Usage plot2in1(graph1, graph2, name1 = NULL, name2 = NULL, bidir = FALSE, ...) Arguments graph1 binary adjacency matrix of a graph graph2 binary adjacency matrix of a graph, column names should coincide with column names of ’graph1’ name1 character, custom name for ’graph1’; when NULL no legend will be plotted name2 character, custom name for ’graph2’ bidir logical, defines if arrows of bidirected edges are drawn; FALSE by defauls. ... optional parameters passed to Rgraphviz plotting functions e.g. main, fontsize Value plots the graph which includes nodes and edges two graphs; nodes which are connected to at least one other node in both graphs are plotted only once and coloured orange, edges which are shared by two graphs are coloured orange; all other nodes and edges a plotted once for each ’graph1’ and ’graph2’ and coloured blue and green accordingly. Author(s) Polina Suter 46 plotDBN plotDBN Plotting a DBN Description This function can be used for plotting initial and transition structures of a dynamic Bayesian net- work. Usage plotDBN(DBN, struct = c("init", "trans"), b = 0, shape = "circle", ...) Arguments DBN binary matrix (or a graph object) representing a 2-step DBN (compact or un- rolled) struct option used to determine if the initial or the transition structure should be plot- ted; acceptable values are init or trans b number of static variables in the DBN, 0 by default; note that for function to work correctly all static variables have to be in the first b columns of the matrix shape string, defining the shape of the box around each node; possible values are circle, ellipse, box ... optional parameters passed to Rgraphviz plotting functions e.g. main, fontsize Value plots the DBN defined by the adjacency matrix ’DBN’ and number of static and dynamic variables. When ’struct’ equals "trans" the transition structure is plotted, otherwise initial structure is plotted Author(s) Polina Suter Examples plotDBN(DBNmat, "init", b=3) plotDBN(DBNmat, "trans", b=3) plotdiffs 47 plotdiffs Plotting difference between two graphs Description This function plots edges from two graphs in one and indicates similarities and differences between these graphs. It is also possible to use this function for plotting mistakes in estimated graph when the ground truth graph is known. Usage plotdiffs( graph1, graph2, estimated = TRUE, name1 = "graph1", name2 = "graph2", clusters = NULL, ... ) Arguments graph1 object of class graphNEL or its adjacency matrix graph2 object of class graphNEL or its adjacency matrix estimated logical, indicates if graph1 is estimated graph and graph2 is ground truth DAG, TRUE by default; this affects the legend and colouring of the edges name1 character, custom name for ’graph1’ name2 character, custom name for ’graph2’ clusters (optional) a list of nodes to be represented on the graph as clusters ... optional parameters passed to Rgraphviz plotting functions e.g. main, fontsize Value plots the graph which includes edges from graph1 and graph2; edges which are different in graph1 compared to graph2 are coloured according to the type of a difference Author(s) Polina Suter 48 plotdiffsDBN Examples Asiascore<-scoreparameters("bde",Asia) Asiamap<-orderMCMC(Asiascore) plotdiffs(Asiamap$DAG,Asiamat) Asiacp<-pcalg::dag2cpdag(m2graph(Asiamat)) mapcp<-pcalg::dag2cpdag(m2graph(Asiamap$DAG)) plotdiffs(mapcp,Asiacp) plotdiffsDBN Plotting difference between two DBNs Description This function plots an estimated DBN such that the edges which are different to the ground truth DBN are highlighted. Usage plotdiffsDBN( eDBN, trueDBN, struct = c("init", "trans"), b = 0, showcl = TRUE, orientation = "TB", ... ) Arguments eDBN object of class graphNEL (or its adjacency matrix), representing estimated struc- ture (not necessarily acyclic) to be compared to the ground truth graph trueDBN object of class graphNEL (or its adjacency matrix), representing the ground truth structure (not necessarily acyclic) struct option used to determine if the initial or the transition structure should be plot- ted; accaptable values are init or trans b number of static variables in one time slice of a DBN; note that for function to work correctly all static variables have to be in the first b columns of the matrix showcl logical, when TRUE (default) nodes are shown in clusters according to the time slice the belong to orientation orientation of the graph layout, possible options are ’TB’ (top-bottom) and ’LR’ (left-right) ... optional parameters passed to Rgraphviz plotting functions e.g. main, fontsize plotpcor 49 Value plots the graph highlights differences between ’eDBN’ (estimated DBN) and ’trueDBN’ (ground truth); edges which are different in ’eDBN’ compared to ’trueDBN’ are coloured according to the type of a difference: false-positive, false-negative and error in direction. Author(s) Polina Suter Examples dbnscore<-scoreparameters("bge",DBNdata, dbnpar = list(samestruct=TRUE, slices=5, b=3), DBN=TRUE) ## Not run: orderDBNfit<-learnBN(dbnscore,algorithm="order") iterDBNfit<-learnBN(dbnscore,algorithm="orderIter") plotdiffsDBN(getDAG(orderDBNfit),DBNmat,struct="trans",b=3) plotdiffsDBN(getDAG(iterDBNfit),DBNmat,struct="trans",b=3) ## End(Not run) plotpcor Comparing posterior probabilitites of single edges Description This function can be used to compare posterior probabilities of edges in a graph Usage plotpcor(pmat, highlight = 0.3, printedges = FALSE, cut = 0.05, ...) Arguments pmat a list of square matrices, representing posterior probabilities of single edges in a Bayesian network; see edgep for obtaining such a matrix from a single MCMC run highlight numeric, defines maximum acceptable difference between posterior probabili- ties of an edge in two samples; points corresponding to higher differences are highlighted in red printedges when TRUE the function also returns squared correlation and RMSE of posterior probabilities higher than the value defined by the argument ’cut’ as well as the list of all edges whose posterior probabilities in the first two matrices differ more than ’highlight’; FALSE by default cut numeric value corresponding to a minimum posterior probabilitity which is included into calculation of squared correlation and MSE when ’printedges’ equals TRUE 50 plotpedges ... prameters passed further to the plot function (e.g. xlab, ylab, main) in case when the length of pmat equals 2 Value plots concordance of posterior probabilitites of single edges based on several matrices (minimum 2 matrices); highlights the edges whose posterior probabilities in a pair of matrices differ by more than ’highlight’; when ’printedges’ set to TRUE, the function returns also squared correlation and RMSE of posterior probabilities higher than the value defined by the argument ’cut’ as well as the list of all edges whose posterior probabilities in the first two matrices differ by more than ’highlight’. Author(s) Polina Suter Examples Asiascore<-scoreparameters("bde", Asia) ## Not run: orderfit<-list() orderfit[[1]]<-sampleBN(Asiascore,algorithm="order") orderfit[[2]]<-sampleBN(Asiascore,algorithm="order") orderfit[[3]]<-sampleBN(Asiascore,algorithm="order") pedges<-lapply(orderfit,edgep,pdag=TRUE) plotpcor(pedges, xlab="run1", ylab="run2",printedges=TRUE) ## End(Not run) plotpedges Plotting posterior probabilities of single edges Description This function plots posterior probabilities of all possible edges in the graph as a function of MCMC iterations. It can be used for convergence diagnostics of MCMC sampling algorithms order MCMC and partition MCMC. Usage plotpedges( MCMCtrace, cutoff = 0.2, pdag = FALSE, onlyedges = NULL, highlight = NULL, ... ) sampleBN 51 Arguments MCMCtrace an object of class MCMCres cutoff number representing a threshold of posterior probability below which lines will not be plotted pdag logical, when true DAGs in a sample will be first coverted to CPDAGs onlyedges (optional) binary matrix, only edges corresponding to entries which equal 1 will be plotted highlight (optional) binary matrix, edges corresponding to entries which equal 1 are high- lighted with "red" ... (optional) parameters passed to the plot function Value plots posterior probabilities of edges in the graph as a function of MCMC iterations Author(s) Polina Suter Examples score100<-scoreparameters("bde", Asia[1:100,]) orderfit100<-orderMCMC(score100,plus1=TRUE,chainout=TRUE) ## Not run: score5000<-scoreparameters("bde", Asia) orderfit5000<-orderMCMC(score5000,plus1=TRUE,chainout=TRUE) plotpedges(orderfit100, pdag=TRUE) plotpedges(orderfit5000, pdag=TRUE) ## End(Not run) sampleBN Bayesian network structure sampling from the posterior distribution Description This function can be used for structure sampling using three different MCMC schemes. Order MCMC scheme (algorithm="order") is the most computationally efficient however it imposes a non-uniform prior in the space of DAGs. Partition MCMC (algorithm="partition") is less computationally efficient and requires more iterations to reach convergence, however it implements sampling using a uniform prior in the space of DAGs. Due to the superexponential size of the search space as the number of nodes increases, the MCMC search is performed on a reduced search space. By default the search space is limited to the skeleton found through the PC algorithm by means of conditional independence tests (using the functions skeleton and pc from the ‘pcalg’ package [Kalisch et al, 2012]). It is also possible to define an arbitrary search space by inputting an adjacency matrix, for example estimated by partial correlations or other network algorithms. 52 sampleBN Also implemented is the possibility to expand the default or input search space, by allowing each node in the network to have one additional parent. This offers improvements in the learning and sampling of Bayesian networks. The iterative MCMC scheme (algorithm="orderIter") allows for iterative expansions of the search space. This is useful in cases when the initial search space is poor in a sense that it contains only a limited number of true positive edges. Iterative expansions of the search space efficiently solve this issue. However this scheme requires longer runtimes due to the need of running multiple consecutive MCMC chains. This function is a wrapper for the three individual structure learning and sampling functions that implement each of the described algorithms; for details see orderMCMC, partitionMCMC,iterativeMCMC. Usage sampleBN( scorepar, algorithm = c("order", "orderIter", "partition"), chainout = TRUE, scoreout = FALSE, alpha = 0.05, moveprobs = NULL, iterations = NULL, stepsave = NULL, gamma = 1, verbose = FALSE, compress = TRUE, startspace = NULL, blacklist = NULL, scoretable = NULL, startpoint = NULL, plus1 = TRUE, cpdag = FALSE, hardlimit = 12, iterpar = list(posterior = 0.5, softlimit = 9, mergetype = "skeleton", accum = FALSE, plus1it = NULL, addspace = NULL, alphainit = NULL) ) Arguments scorepar an object of class scoreparameters, containing the data and score parameters, see constructor function scoreparameters algorithm MCMC scheme to be used for sampling from posterior distribution; possible options are "order" (orderMCMC), "orderIter" (iterativeMCMC) or "partition" (partitionMCMC) chainout logical, if TRUE the saved MCMC steps are returned, TRUE by default scoreout logical, if TRUE the search space and score tables are returned, FALSE by de- fault alpha numerical significance value in {0,1} for the conditional independence tests at the PC algorithm stage sampleBN 53 moveprobs a numerical vector of 4 (for "order" and "orderIter" algorithms) or 5 values (for "partition" algorithm) representing probabilities of the different moves in the space of order and partitions accordingly. The moves are described in the corre- sponding algorithm specific functions orderMCMC and partitionMCMC iterations integer, the number of MCMC steps, the default value is 6n2 log n orderMCMC, 20n2 log n for partitionMCMC and 3.5n2 log n for iterativeMCMC; where n is the number of nodes in the Bayesian network stepsave integer, thinning interval for the MCMC chain, indicating the number of steps between two output iterations, the default is iterations/1000 gamma tuning parameter which transforms the score by raising it to this power, 1 by default verbose logical, if TRUE messages about the algorithm’s progress will be printed, FALSE by default compress logical, if TRUE adjacency matrices representing sampled graphs will be stored as a sparse Matrix (recommended); TRUE by default startspace (optional) a square sparse or ordinary matrix, of dimensions equal to the number of nodes, which defines the search space for the order MCMC in the form of an adjacency matrix. If NULL, the skeleton obtained from the PC-algorithm will be used. If startspace[i,j] equals to 1 (0) it means that the edge from node i to node j is included (excluded) from the search space. To include an edge in both directions, both startspace[i,j] and startspace[j,i] should be 1. blacklist (optional) a square sparse or ordinary matrix, of dimensions equal to the number of nodes, which defines edges to exclude from the search space. If blacklist[i,j] equals to 1 it means that the edge from node i to node j is excluded from the search space. scoretable (optional) object of class scorespace containing list of score tables calculated for example by the last iteration of the function iterativeMCMC. When not NULL, parameter startspace is ignored. startpoint (optional) integer vector of length n (representing an order when algorithm="order" or algorithm="orderIter") or an adjacency matrix or sparse adjacency ma- trix (representing a DAG when algorithm="partition"), which will be used as the starting point in the MCMC algorithm, the default starting point is random plus1 logical, if TRUE (default) the search is performed on the extended search space; only changable for orderMCMC; for other algorithms is fixed to TRUE cpdag logical, if TRUE the CPDAG returned by the PC algorithm will be used as the search space, if FALSE (default) the full undirected skeleton will be used as the search space hardlimit integer, limit on the size of parent sets in the search space; iterpar addition list of parameters for the MCMC scheme implemeting iterative expan- sions of the search space; for more details see iterativeMCMC; list(posterior = 0.5, softlimit = 9, mergetype = "skeleton", accum = FALSE, plus1it = NULL, addspace = NULL, alphainit = NULL) 54 sampleBN Value Depending on the value or the parameter algorithm returns an object of class orderMCMC, partitionMCMC or iterativeMCMC which contains log-score trace of sampled DAGs as well as adjacency matrix of the maximum scoring DAG(s), its score and the order or partition score. The output can op- tionally include DAGs sampled in MCMC iterations and the score tables. Optional output is regu- lated by the parameters chainout and scoreout. See orderMCMC class, partitionMCMC class, iterativeMCMC class for a detailed description of the classes’ structures. Note see also extractor functions getDAG, getTrace, getSpace, getMCMCscore. Author(s) Polina Suter, Jack Kuipers, the code partly derived from the order MCMC implementation from Kuipers J, Moffa G (2017) <doi:10.1080/01621459.2015.1133426> References P. Suter, J. Kuipers, G. Moffa, N.Beerenwinkel (2023) <doi:10.18637/jss.v105.i09> Friedman N and Koller D (2003). A Bayesian approach to structure discovery in bayesian networks. Machine Learning 50, 95-125. Kalisch M, Maechler M, Colombo D, Maathuis M and Buehlmann P (2012). Causal inference using graphical models with the R package pcalg. Journal of Statistical Software 47, 1-26. Geiger D and Heckerman D (2002). Parameter priors for directed acyclic graphical models and the characterization of several probability distributions. The Annals of Statistics 30, 1412-1440. Kuipers J, Moffa G and Heckerman D (2014). Addendum on the scoring of Gaussian acyclic graphical models. The Annals of Statistics 42, 1689-1691. Spirtes P, Glymour C and Scheines R (2000). Causation, Prediction, and Search, 2nd edition. The MIT Press. Examples ## Not run: Asiascore <- scoreparameters("bde", Asia) iterativefit <- learnBN(Asiascore, algorithm = "orderIter") orderfit <- sampleBN(Asiascore, scoretable = iterativefit) myScore<-scoreparameters("bge",Boston) MCMCchains<-list() MCMCchains[[1]]<-sampleBN(myScore,"partition") MCMCchains[[2]]<-sampleBN(myScore,"partition") edge_posterior<-lapply(MCMCchains,edgep,pdag=TRUE) plotpcor(edge_posterior) ## End(Not run) samplecomp 55 samplecomp Performance assessment of sampling algorithms against a known Bayesian network Description This function compute 8 different metrics of structure fit of an object of classes orderMCMC and partitionMCMC to the ground truth DAG (or CPDAG). First posterior probabilities of single edges are calculated based on a sample stores in the object of class orderMCMC or partitionMCMC. This function computes structure fit of each of the consensus graphs to the ground truth one based on a defined range of posterior thresholds. Computed metrics include: TP, FP, TPR, FPR, FPRn, FDR, SHD. See metrics description in see also compareDAGs. Usage samplecomp( MCMCchain, truedag, p = c(0.99, 0.95, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2), pdag = TRUE, burnin = 0.2, trans = TRUE ) ## S3 method for class 'samplecomp' plot(x, ..., vars = c("FP", "TP"), type = "b", col = "blue", showp = NULL) ## S3 method for class 'samplecomp' print(x, ...) ## S3 method for class 'samplecomp' summary(object, ...) Arguments MCMCchain an object of class partitionMCMC or orderMCMC, representing the output of structure sampling function partitionMCMC or orderMCMC (the latter when pa- rameter chainout=TRUE; truedag ground truth DAG which generated the data used in the search procedure; rep- resented by an object of class graphNEL p a vector of numeric values between 0 and 1, defining posterior probabilities ac- cording to which the edges of assessed structures are drawn, please note very low barriers can lead to very dense structures; by default p = c(0.99, 0.95, 0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3, 0.2) pdag logical, if TRUE (default) all DAGs in the MCMCchain are first converted to equivalence class (CPDAG) before the averaging 56 samplecomp burnin number between 0 and 1, indicates the percentage of the samples which will be the discarded as ‘burn-in’ of the MCMC chain; the rest of the samples will be used to calculate the posterior probabilities; 0.2 by default trans logical, for DBNs indicates if model comparions are performed for transition structure; when trans equals FALSE the comparison is performed for initial structures of estimated models and the ground truth DBN; for usual BNs the parameter is disregarded x object of class ’samplecomp’ ... ignored vars a tuple of variables which will be used for ’x’ and ’y’ axes; possible values: "SHD", "TP", "FP", "TPR", "FPR", "FPRn", "FDR" type type of line in the plot; "b" by default col colour of line in the plotl; "blue" by default showp logical, defines if points are labelled with the posterior threshold corresponding to the assessed model object object of class ’samplecomp’ Value an object if class samplesim, a matrix with the number of rows equal to the number of elements in ’p’, and 8 columns reporting for the consensus graphss (corresponfing to each of the values in ’p’) the number of true positive edges (’TP’), the number of false positive edges (’FP’), the number of false negative edges (’FN’), the true positive rate (’TPR’), the structural Hamming distance (’SHD’), false positive rate (’FPR’), false discovery rate (’FDR’) and false positive rate normalized by TP+FN (’FPRn’). Author(s) Polina Suter Examples gsim.score<-scoreparameters("bge", gsim) ## Not run: MAPestimate<-learnBN(gsim.score,"orderIter",scoreout=TRUE) ordersample<-sampleBN(gsim.score, "order", scoretable=getSpace(MAPestimate)) samplecomp(ordersample, gsimmat) ## End(Not run) scoreagainstDAG 57 scoreagainstDAG Calculating the score of a sample against a DAG Description This function calculates the score of a given sample against a DAG represented by its incidence matrix. Usage scoreagainstDAG( scorepar, incidence, datatoscore = NULL, marginalise = FALSE, onlymain = FALSE, bdecatCvec = NULL ) Arguments scorepar an object of class scoreparameters; see constructor function scoreparameters incidence a square matrix of dimensions equal to the number of variables with entries in {0,1}, representing the adjacency matrix of the DAG against which the score is calculated datatoscore (optional) a matrix (vector) containing binary (for BDe score) or continuous (for the BGe score) observations (or just one observation) to be scored; the number of columns should be equal to the number of variables in the Bayesian network, the number of rows should be equal to the number of observations; by default all data from scorepar parameter is used marginalise (optional for continuous data) defines, whether to use the posterior mean for scoring (default) or to marginalise over the posterior distribution (more compu- tationally costly) onlymain (optional), defines the the score is computed for nodes excluding ’bgnodes’; FALSE by default bdecatCvec (optional for categorical data) Value the log of the BDe/BGe score of given observations against a DAG Author(s) Jack Kuipers, Polina Suter 58 scoreagainstDBN References Heckerman D and Geiger D, (1995). Learning Bayesian networks: A unification for discrete and Gaussian domains. In Eleventh Conference on Uncertainty in Artificial Intelligence, pages 274-284, 1995. Examples Asiascore<-scoreparameters("bde", Asia[1:100,]) #we wish to score only first 100 observations scoreagainstDAG(Asiascore, Asiamat) scoreagainstDBN Score against DBN Description Scoring observations against a DBN structure Usage scoreagainstDBN( scorepar, incidence, datatoscore = NULL, marginalise = FALSE, onlymain = FALSE, datainit = NULL ) Arguments scorepar object of class ’scoreparameters’ incidence adjacency matrix of a DAG datatoscore matrix or vector containing observations to be scored marginalise (logical) should marginal score be used? onlymain (logical) should static nodes be included in the score? datainit optional, in case of unbalanced design, the mean score of available samples for T0 are computed Value vector of log-scores Author(s) Polina Suter scoreparameters 59 scoreparameters Initializing score object Description This function returns an object of class scoreparameters containing the data and parameters needed for calculation of the BDe/BGe score, or a user defined score. Usage scoreparameters( scoretype = c("bge", "bde", "bdecat", "usr"), data, bgepar = list(am = 1, aw = NULL, edgepf = 1), bdepar = list(chi = 0.5, edgepf = 2), bdecatpar = list(chi = 0.5, edgepf = 2), dbnpar = list(samestruct = TRUE, slices = 2, b = 0, stationary = TRUE, rowids = NULL, datalist = NULL, learninit = TRUE), usrpar = list(pctesttype = c("bge", "bde", "bdecat")), mixedpar = list(nbin = 0), MDAG = FALSE, DBN = FALSE, weightvector = NULL, bgnodes = NULL, edgepmat = NULL, nodeslabels = NULL ) ## S3 method for class 'scoreparameters' print(x, ...) ## S3 method for class 'scoreparameters' summary(object, ...) Arguments scoretype the score to be used to assess the DAG structure: "bge" for Gaussian data, "bde" for binary data, "bdecat" for categorical data, "usr" for a user defined score; when "usr" score is chosen, one must define a function (which evaluates the log score of a node given its parents) in the following format: usrDAG- corescore(j,parentnodes,n,param), where ’j’ is node to be scores, ’parentnodes’ are the parents of this node, ’n’ number of nodes in the netwrok and ’param’ is an object of class ’scoreparameters’ data the data matrix with n columns (the number of variables) and a number of rows equal to the number of observations bgepar a list which contains parameters for BGe score: 60 scoreparameters • am (optional) a positive numerical value, 1 by default • aw (optional) a positive numerical value should be more than n+1, n+am+1 by default • edgepf (optional) a positive numerical value providing the edge penaliza- tion factor to be combined with the BGe score, 1 by default (no penaliza- tion) bdepar a list which contains parameters for BDe score for binary data: • chi (optional) a positive number of prior pseudo counts used by the BDe score, 0.5 by default • edgepf (optional) a positive numerical value providing the edge penaliza- tion factor to be combined with the BDe score, 2 by default bdecatpar a list which contains parameters for BDe score for categorical data: • chi (optional) a positive number of prior pseudo counts used by the BDe score, 0.5 by default • edgepf (optional) a positive numerical value providing the edge penaliza- tion factor to be combined with the BDe score, 2 by default dbnpar which type of score to use for the slices • samestruct logical, when TRUE the structure of the first time slice is as- sumed to be the same as internal structure of all other time slices • slices integer representing the number of time slices in a DBN • b the number of static variables; all static variables have to be in the first b columns of the data; for DBNs static variables have the same meaning as bgnodes for usual Bayesian networks; for DBNs parameters parameter bgnodes is ignored • rowids optional vector of time IDs; usefull for identifying data for initial time slice • datalist indicates is data is passed as a list for a two step DBN; useful for unbalanced number of samples in timi slices usrpar a list which contains parameters for the user defined score • pctesttype (optional) conditional independence test ("bde","bge","bdecat") mixedpar a list which contains parameters for the BGe and BDe score for mixed data • nbin a positive integer number of binary nodes in the network (the binary nodes are always assumed in first nbin columns of the data) MDAG logical, when TRUE the score is initialized for a model with multiple sets of parameters but the same structure DBN logical, when TRUE the score is initialized for a dynamic Baysian network; FALSE by default weightvector (optional) a numerical vector of positive values representing the weight of each observation; should be NULL(default) for non-weighted data bgnodes (optional) a vector that contains column indices in the data defining the nodes that are forced to be root nodes in the sampled graphs; root nodes are nodes which have no parents but can be parents of other nodes in the network; in case of DBNs bgnodes represent static variables and defined via element b of the parameters dbnpar; parameter bgnodes is ignored for DBNs scorespace 61 edgepmat (optional) a matrix of positive numerical values providing the per edge penal- ization factor to be added to the score, NULL by default nodeslabels (optional) a vector of characters which denote the names of nodes in the Bayesian network; by default column names of the data will be taken x object of class ’scoreparameters’ ... ignored object object of class ’scoreparameters’ Value an object of class scoreparameters, which includes all necessary information for calculating the BDe/BGe score Author(s) Polina Suter, Jack kuipers References Geiger D and Heckerman D (2002). Parameter priors for directed acyclic graphical models and the characterization of several probability distributions. The Annals of Statistics 30, 1412-1440. Kuipers J, Moffa G and Heckerman D (2014). Addendum on the scoring of Gaussian acyclic graphical models. The Annals of Statistics 42, 1689-1691. Heckerman D and Geiger D (1995). Learning Bayesian networks: A unification for discrete and Gaussian domains. In Eleventh Conference on Uncertainty in Artificial Intelligence, pages 274-284. Scutari M (2016). An Empirical-Bayes Score for Discrete Bayesian Networks. Journal of Machine Learning Research 52, 438-448 Examples myDAG<-pcalg::randomDAG(20, prob=0.15, lB = 0.4, uB = 2) myData<-pcalg::rmvDAG(200, myDAG) myScore<-scoreparameters("bge", myData) scorespace Prints ’scorespace’ object Description Prints ’scorespace’ object Summary of object of class ’scorespace’ 62 scorespace Usage scorespace( scorepar, alpha = 0.05, hardlimit = 14, plus1 = TRUE, cpdag = TRUE, startspace = NULL, blacklist = NULL, verbose = FALSE ) ## S3 method for class 'scorespace' print(x, ...) ## S3 method for class 'scorespace' summary(object, ...) Arguments scorepar an object of class scoreparameters, containing the data and score scorepareters, see constructor function scoreparameters alpha numerical significance value in {0,1} for the conditional independence tests at the PC algorithm stage (by default 0.4 for n < 50, 20/n for n > 50) hardlimit integer, limit on the size of parent sets in the search space; by default 14 when MAP=TRUE and 20 when MAP=FALSE plus1 logical, if TRUE (default) the search is performed on the extended search space cpdag logical, if TRUE the CPDAG returned by the PC algorithm will be used as the search space, if FALSE (default) the full undirected skeleton will be used as the search space startspace (optional) a square matrix, of dimensions equal to the number of nodes, which defines the search space for the order MCMC in the form of an adjacency ma- trix. If NULL, the skeleton obtained from the PC-algorithm will be used. If startspace[i,j] equals to 1 (0) it means that the edge from node i to node j is included (excluded) from the search space. To include an edge in both direc- tions, both startspace[i,j] and startspace[j,i] should be 1. blacklist (optional) a square matrix, of dimensions equal to the number of nodes, which defines edges to exclude from the search space. If blacklist[i,j] equals to 1 it means that the edge from node i to node j is excluded from the search space. verbose logical, if TRUE messages about the algorithm’s progress will be printed, FALSE by default x object of class ’scorespace’ ... ignored object object of class ’scorespace’ scorespace class 63 Value Object of class scorespace, a list of three objects: ’adjacency’ matrix representiong the search space, ’blacklist’ used to exclude edges from the search space and ’tables’ containing score quanti- ties for each node needed to run MCMC schemes Author(s) Polina Suter, Jack Kuipers References Friedman N and Koller D (2003). A Bayesian approach to structure discovery in bayesian networks. Machine Learning 50, 95-125. Examples #' #find a MAP DAG with search space defined by PC and plus1 neighbourhood Bostonscore<-scoreparameters("bge",Boston) Bostonspace<-scorespace(Bostonscore, 0.05, 14) ## Not run: orderfit<-orderMCMC(Bostonscore, scoretable=Bostonspace) partitionfit<-orderMCMC(Bostonscore, scoretable=Bostonspace) ## End(Not run) scorespace class scorespace class structure Description The structure of an object of S3 class scorespace. Details An object of class scorespace is a list containing at least the following components: • adjacency: adjacency martrix representing the core search space • blacklist: adjacency martrix representing the blacklist used for computing score tables tables • tables: a list of matrices (for core search space) or a list of lists of matrices (for extended search space) containing quantities needed for scoring orders and sampling DAGs in MCMC schemes; this list corresponds to adjacency and blacklist Author(s) Polina Suter 64 string2mat string2mat Deriving interactions matrix Description This transforms a list of possible interactions between proteins downloaded from STRING database into a matrix which can be used for blacklisting/penalization in BiDAG. Usage string2mat(curnames, int, mapping = NULL, type = c("int"), pf = 2) Arguments curnames character vector with gene names which will be used in BiDAG learning function int data frame, representing a interactions between genes/proteins downloaded from STRING (https://string-db.org/); two columns are necessary ’node1’ and ’node2’ mapping (optional) data frame, representing a mapping between ’curnames’ (gene names, usually the column names of ’data’) and gene names used in interactions down- loaded from STRING (https://string-db.org/); two columns are necessary ’queryItem’ and ’preferredName’ type character, defines how interactions will be reflected in the output matrix; int will result in a matrix whose entries equal 1 if interaction is present in the list of interactions int and 0 otherwise; blacklist results in a matrix whose entries equal 0 when interaction is present in the list of interactions and 1 otherwise; pf results in a matrix results in a matrix whose entries equal 1 is interaction is present in the list of interactions int and pf otherwise$ "int" by default pf penalization factor for interactions, needed if type=pf Value square matrix whose entries correspond to the list of interactions and parameter type Examples curnames<-colnames(kirp) intmat<-string2mat(curnames, mapping, interactions, type="pf") Index ∗ classes getDAG, 17, 27, 34, 39, 43, 54 iterativeMCMC class, 28 getMCMCscore, 17, 27, 34, 39, 43, 54 orderMCMC class, 40 getRuntime, 18 partitionMCMC class, 44 getSpace, 19, 27, 34, 39, 43, 54 scorespace class, 63 getSubGraph, 19 ∗ datasets getTrace, 20, 27, 34, 39, 43, 54 Asia, 3 graph2m, 21 Asiamat, 4 graphNEL, 10, 21, 29, 35, 55 Boston, 7 gsim, 21 DBNdata, 13 gsim100, 22 DBNmat, 13 gsimmat, 22 DBNunrolled, 15 gsim, 21 interactions, 23, 35 gsim100, 22 iterativeMCMC, 23, 29, 32–34, 52, 53 gsimmat, 22 iterativeMCMC class, 28 interactions, 23 itercomp, 29 kirc, 30 kirp, 31 kirc, 30 mapping, 35 kirp, 31 Asia, 3 learnBN, 32 Asiamat, 4 m2graph, 35 bidag2coda, 5 mapping, 35 bidag2codalist, 6 modelp, 36 Boston, 7 orderMCMC, 15, 23, 32, 33, 36, 37, 52, 53, 55 compact2full, 8 orderMCMC class, 40 compareDAGs, 9, 29, 55 partitionMCMC, 15, 23, 33, 36, 41, 52, 53, 55 compareDBNs, 10 partitionMCMC class, 44 connectedSubGraph, 11 pc, 23, 32, 37, 51 DAGscore, 12 plot.iterativeMCMC (iterativeMCMC), 23 DBNdata, 13, 13, 15 plot.itercomp (itercomp), 29 DBNmat, 13 plot.orderMCMC (orderMCMC), 37 DBNscore, 14 plot.partitionMCMC (partitionMCMC), 41 DBNunrolled, 15 plot.samplecomp (samplecomp), 55 plot2in1, 45 edgep, 15, 49 plotDBN, 46 plotdiffs, 47 full2compact, 16 plotdiffsDBN, 48 65 66 INDEX plotpcor, 49 plotpedges, 50 print.iterativeMCMC (iterativeMCMC), 23 print.itercomp (itercomp), 29 print.orderMCMC (orderMCMC), 37 print.partitionMCMC (partitionMCMC), 41 print.samplecomp (samplecomp), 55 print.scoreparameters (scoreparameters), 59 print.scorespace (scorespace), 61 sampleBN, 51 samplecomp, 55 scoreagainstDAG, 57 scoreagainstDBN, 58 scoreparameters, 12, 14, 25, 33, 38, 42, 52, 57, 59, 62 scorespace, 61 scorespace class, 63 skeleton, 23, 32, 37, 51 string2mat, 64 summary.iterativeMCMC (iterativeMCMC), 23 summary.itercomp (itercomp), 29 summary.orderMCMC (orderMCMC), 37 summary.partitionMCMC (partitionMCMC), 41 summary.samplecomp (samplecomp), 55 summary.scoreparameters (scoreparameters), 59 summary.scorespace (scorespace), 61

mapedit

cran

Package ‘mapedit’ October 13, 2022 Title Interactive Editing of Spatial Data in R Description Suite of interactive functions and helpers for selecting and editing geospatial data. Version 0.6.0 Date 2020-02-01 URL https://github.com/r-spatial/mapedit BugReports https://github.com/r-spatial/mapedit/issues License MIT + file LICENSE Depends R (>= 3.1.0) Imports dplyr, htmltools (>= 0.3), htmlwidgets, jsonlite, leafem, leaflet (>= 2.0.1), leaflet.extras (>= 1.0), leafpm, mapview, methods, miniUI, raster, scales, sf (>= 0.5-2), shiny, sp Suggests crayon Enhances geojsonio Encoding UTF-8 LazyData true RoxygenNote 7.0.2 NeedsCompilation no Author Tim Appelhans [aut, cre], Kenton Russell [aut], Lorenzo Busetto [aut], Josh O'Brien [ctb], Jakob Gutschlhofer [ctb] Maintainer Tim Appelhans <tim.appelhans@gmail.com> Repository CRAN Date/Publication 2020-02-02 17:20:02 UTC 1 2 mapedit-package R topics documented: mapedit-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 addToolbar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 drawFeatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 editFeatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 editMap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 editMod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 editModUI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 processOpts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 selectFeatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 selectMap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 selectMod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 selectModUI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 Index 18 mapedit-package mapedit: interactive editing and selection for geospatial data Description mapedit, a RConsortium funded project, provides interactive tools to incorporate in geospatial work- flows that require editing or selection of spatial data. Edit • editMap • editFeatures • Shiny edit module editModUI, editMod #’ @section Edit: • selectMap • selectFeatures • Shiny edit module selectModUI, selectMod Author(s) Maintainer: Tim Appelhans <tim.appelhans@gmail.com> Authors: • Kenton Russell • Lorenzo Busetto Other contributors: • Josh O’Brien [contributor] • Jakob Gutschlhofer [contributor] addToolbar 3 See Also Useful links: • https://github.com/r-spatial/mapedit • Report bugs at https://github.com/r-spatial/mapedit/issues addToolbar Add a (possibly customized) toolbar to a leaflet map Description Add a (possibly customized) toolbar to a leaflet map Usage addToolbar(leafmap, editorOptions, editor, targetLayerId) Arguments leafmap leaflet map to use for Selection editorOptions A list of options to be passed on to either leaflet.extras::addDrawToolbar or leafpm::addPmToolbar. editor Character string giving editor to be used for the current map. Either "leafpm" or "leaflet.extras". targetLayerId string name of the map layer group to use with edit Value The leaflet map supplied to leafmap, now with an added toolbar. drawFeatures Draw (simple) features on a map Description Draw (simple) features on a map 4 drawFeatures Usage drawFeatures( map = NULL, sf = TRUE, record = FALSE, viewer = shiny::paneViewer(), title = "Draw Features", editor = c("leaflet.extras", "leafpm"), editorOptions = list(), ... ) Arguments map a background leaflet or mapview map to be used for editing. If NULL a blank mapview canvas will be provided. sf logical return simple features. The default is TRUE. If sf = FALSE, GeoJSON will be returned. record logical to record all edits for future playback. viewer function for the viewer. See Shiny viewer. NOTE: when using browserViewer(browser = getOption("browser")) to open the app in the default browser, the browser window will automatically close when closing the app (by pressing "done" or "cancel") in most browsers. Firefox is an exception. See Details for instructions on how to enable this behaviour in Firefox. title string to customize the title of the UI window. editor character either "leaflet.extras" or "leafpm" editorOptions list of options suitable for passing to either leaflet.extras::addDrawToolbar or leafpm::addPmToolbar. ... additional arguments passed on to editMap. Details When setting viewer = browserViewer(browser = getOption("browser")) and the systems de- fault browser is Firefox, the browser window will likely not automatically close when the app is closed (by pressing "done" or "cancel"). To enable automatic closing of tabs/windows in Firefox try the following: • input "about:config " to your firefox address bar and hit enter • make sure your "dom.allow_scripts_to_close_windows" is true editFeatures 5 editFeatures Interactively Edit Map Features Description Interactively Edit Map Features Usage editFeatures(x, ...) ## S3 method for class 'sf' editFeatures( x, map = NULL, mergeOrder = c("add", "edit", "delete"), record = FALSE, viewer = shiny::paneViewer(), crs = 4326, label = NULL, title = "Edit Map", editor = c("leaflet.extras", "leafpm"), editorOptions = list(), ... ) ## S3 method for class 'Spatial' editFeatures(x, ...) Arguments x features to edit ... other arguments map a background leaflet or mapview map to be used for editing. If NULL a blank mapview canvas will be provided. mergeOrder vector or character arguments to specify the order of merge operations. By default, merges will proceed in the order of add, edit, delete. record logical to record all edits for future playback. viewer function for the viewer. See Shiny viewer. NOTE: when using browserViewer(browser = getOption("browser")) to open the app in the default browser, the browser window will automatically close when closing the app (by pressing "done" or "cancel") in most browsers. Firefox is an exception. See Details for instructions on how to enable this behaviour in Firefox. crs see st_crs. label character vector or formula for the content that will appear in label/tooltip. 6 editFeatures title string to customize the title of the UI window. The default is "Edit Map". editor character either "leaflet.extras" or "leafpm" editorOptions list of options suitable for passing to either leaflet.extras::addDrawToolbar or leafpm::addPmToolbar. Details When setting viewer = browserViewer(browser = getOption("browser")) and the systems de- fault browser is Firefox, the browser window will likely not automatically close when the app is closed (by pressing "done" or "cancel"). To enable automatic closing of tabs/windows in Firefox try the following: • input "about:config " to your firefox address bar and hit enter • make sure your "dom.allow_scripts_to_close_windows" is true Examples ## Not run: library(mapedit) library(mapview) lf <- mapview() # draw some polygons that we will select later drawing <- lf %>% editMap() # little easier now with sf mapview(drawing$finished) # especially easy with selectFeatures selectFeatures(drawing$finished) # use @bhaskarvk USA Albers with leaflet code # https://bhaskarvk.github.io/leaflet/examples/proj4Leaflet.html #devtools::install_github("hrbrmstr/albersusa") library(albersusa) library(sf) library(leaflet) library(mapedit) spdf <- usa_sf() pal <- colorNumeric( palette = "Blues", domain = spdf$pop_2014 ) bounds <- c(-125, 24 ,-75, 45) (lf <- leaflet( editMap 7 options= leafletOptions( worldCopyJump = FALSE, crs=leafletCRS( crsClass="L.Proj.CRS", code='EPSG:2163', proj4def=paste0( '+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 ', '+b=6370997 +units=m +no_defs' ), resolutions = c(65536, 32768, 16384, 8192, 4096, 2048,1024, 512, 256, 128) ) ) ) %>% fitBounds(bounds[1], bounds[2], bounds[3], bounds[4]) %>% setMaxBounds(bounds[1], bounds[2], bounds[3], bounds[4]) %>% mapview::addFeatures( data=spdf, weight = 1, color = "#000000", # adding group necessary for identification layerId = ~iso_3166_2, fillColor=~pal(pop_2014), fillOpacity=0.7, label=~stringr::str_c(name,' ', format(pop_2014, big.mark=",")), labelOptions= labelOptions(direction = 'auto') ) ) # test out selectMap with albers example selectMap( lf, styleFalse = list(weight = 1), styleTrue = list(weight = 4) ) ## End(Not run) editMap Interactively Edit a Map Description Interactively Edit a Map Usage editMap(x, ...) ## S3 method for class 'leaflet' editMap( 8 editMap x = NULL, targetLayerId = NULL, sf = TRUE, ns = "mapedit-edit", record = FALSE, viewer = shiny::paneViewer(), crs = 4326, title = "Edit Map", editor = c("leaflet.extras", "leafpm"), editorOptions = list(), ... ) ## S3 method for class 'mapview' editMap( x = NULL, targetLayerId = NULL, sf = TRUE, ns = "mapedit-edit", record = FALSE, viewer = shiny::paneViewer(), crs = 4326, title = "Edit Map", editor = c("leaflet.extras", "leafpm"), editorOptions = list(), ... ) ## S3 method for class '`NULL`' editMap(x, editor = c("leaflet.extras", "leafpm"), editorOptions = list(), ...) Arguments x leaflet or mapview map to edit ... other arguments for leafem::addFeatures() when using editMap.NULL or selectFeatures targetLayerId string name of the map layer group to use with edit sf logical return simple features. The default is TRUE. If sf = FALSE, GeoJSON will be returned. ns string name for the Shiny namespace to use. The ns is unlikely to require a change. record logical to record all edits for future playback. viewer function for the viewer. See Shiny viewer. NOTE: when using browserViewer(browser = getOption("browser")) to open the app in the default browser, the browser window will automatically close when closing the app (by pressing "done" or "cancel") in most browsers. Firefox is an exception. See Details for instructions on how to enable this behaviour in Firefox. editMap 9 crs see st_crs. title string to customize the title of the UI window. The default is "Edit Map". editor character either "leaflet.extras" or "leafpm" editorOptions list of options suitable for passing to either leaflet.extras::addDrawToolbar or leafpm::addPmToolbar. Details When setting viewer = browserViewer(browser = getOption("browser")) and the systems de- fault browser is Firefox, the browser window will likely not automatically close when the app is closed (by pressing "done" or "cancel"). To enable automatic closing of tabs/windows in Firefox try the following: • input "about:config " to your firefox address bar and hit enter • make sure your "dom.allow_scripts_to_close_windows" is true Value sf simple features or GeoJSON Examples ## Not run: library(leaflet) library(mapedit) editMap(leaflet() %>% addTiles()) ## End(Not run) ## Not run: # demonstrate Leaflet.Draw on a layer library(sf) library(mapview) library(leaflet.extras) library(mapedit) # ?sf::sf pol = st_sfc( st_polygon(list(cbind(c(0,3,3,0,0),c(0,0,3,3,0)))), crs = 4326 ) mapview(pol) %>% editMap(targetLayerId = "pol") mapview(franconia[1:2,]) %>% editMap(targetLayerId = "franconia[1:2, ]") ## End(Not run) 10 editMod editMod Shiny Module Server for Geo Create, Edit, Delete Description Shiny Module Server for Geo Create, Edit, Delete Usage editMod( input, output, session, leafmap, targetLayerId = NULL, sf = TRUE, record = FALSE, crs = 4326, editor = c("leaflet.extras", "leafpm"), editorOptions = list() ) Arguments input Shiny server function input output Shiny server function output session Shiny server function session leafmap leaflet map to use for Selection targetLayerId character identifier of layer to edit, delete sf logical to return simple features. sf=FALSE will return GeoJSON. record logical to record all edits for future playback. crs see st_crs. editor character either "leaflet.extras" or "leafpm" editorOptions list of options suitable for passing to either leaflet.extras::addDrawToolbar or leafpm::addPmToolbar. Value server function for Shiny module editModUI 11 editModUI Shiny Module UI for Geo Create, Edit, Delete Description Shiny Module UI for Geo Create, Edit, Delete Usage editModUI(id, ...) Arguments id character id for the the Shiny namespace ... other arguments to leafletOutput() Value ui for Shiny module processOpts Prepare arguments for addDrawToolbar or addPmToolbar Description Prepare arguments for addDrawToolbar or addPmToolbar Usage processOpts(fun, args) Arguments fun Function used by editor package (leafpm or leaflet.extras) to set defaults args Either a (possibly nested) list of named options of the form suitable for passage to fun or (if the chosen editor is "leaflet.extras") FALSE. Value An object suitable for passing in as the supplied argument to either leaflet.extras::addDrawToolbar or leafpm::addPmToolbar. 12 selectFeatures selectFeatures Interactively Select Map Features Description Interactively Select Map Features Usage selectFeatures(x, ...) ## S3 method for class 'sf' selectFeatures( x = NULL, mode = c("click", "draw"), op = sf::st_intersects, map = NULL, index = FALSE, viewer = shiny::paneViewer(), label = NULL, title = "Select features", ... ) ## S3 method for class 'Spatial' selectFeatures(x, ...) Arguments x features to select ... other arguments mode one of "click" or "draw". op the geometric binary predicate to use for the selection. Can be any of geos_binary_pred. In the spatial operation the drawn features will be evaluated as x and the supplied feature as y. Ignored if mode = "click". map a background leaflet or mapview map to be used for editing. If NULL a blank mapview canvas will be provided. index logical with index=TRUE indicating return the index of selected features rather than the actual selected features viewer function for the viewer. See Shiny viewer. NOTE: when using browserViewer(browser = getOption("browser")) to open the app in the default browser, the browser window will automatically close when closing the app (by pressing "done" or "cancel") in most browsers. Firefox is an exception. See Details for instructions on how to enable this behaviour in Firefox. label character vector or formula for the content that will appear in label/tooltip. title string to customize the title of the UI window. The default is "Select features". selectFeatures 13 Details When setting viewer = browserViewer(browser = getOption("browser")) and the systems de- fault browser is Firefox, the browser window will likely not automatically close when the app is closed (by pressing "done" or "cancel"). To enable automatic closing of tabs/windows in Firefox try the following: • input "about:config " to your firefox address bar and hit enter • make sure your "dom.allow_scripts_to_close_windows" is true Examples ## Not run: library(mapedit) library(mapview) lf <- mapview() # draw some polygons that we will select later drawing <- lf %>% editMap() # little easier now with sf mapview(drawing$finished) # especially easy with selectFeatures selectFeatures(drawing$finished) # use @bhaskarvk USA Albers with leaflet code # https://bhaskarvk.github.io/leaflet/examples/proj4Leaflet.html #devtools::install_github("hrbrmstr/albersusa") library(albersusa) library(sf) library(leaflet) library(mapedit) spdf <- usa_sf() pal <- colorNumeric( palette = "Blues", domain = spdf$pop_2014 ) bounds <- c(-125, 24 ,-75, 45) (lf <- leaflet( options= leafletOptions( worldCopyJump = FALSE, crs=leafletCRS( crsClass="L.Proj.CRS", code='EPSG:2163', proj4def=paste0( 14 selectMap '+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 ', '+b=6370997 +units=m +no_defs' ), resolutions = c(65536, 32768, 16384, 8192, 4096, 2048,1024, 512, 256, 128) ) ) ) %>% fitBounds(bounds[1], bounds[2], bounds[3], bounds[4]) %>% setMaxBounds(bounds[1], bounds[2], bounds[3], bounds[4]) %>% mapview::addFeatures( data=spdf, weight = 1, color = "#000000", # adding group necessary for identification layerId = ~iso_3166_2, fillColor=~pal(pop_2014), fillOpacity=0.7, label=~stringr::str_c(name,' ', format(pop_2014, big.mark=",")), labelOptions= labelOptions(direction = 'auto') ) ) # test out selectMap with albers example selectMap( lf, styleFalse = list(weight = 1), styleTrue = list(weight = 4) ) ## End(Not run) selectMap Interactively Select Map Features Description Interactively Select Map Features Usage selectMap(x, ...) ## S3 method for class 'leaflet' selectMap( x = NULL, styleFalse = list(fillOpacity = 0.2, weight = 1, opacity = 0.4), styleTrue = list(fillOpacity = 0.7, weight = 3, opacity = 0.7), ns = "mapedit-select", viewer = shiny::paneViewer(), title = "Select features", ... ) selectMap 15 Arguments x leaflet or mapview map to use for selection ... other arguments styleFalse, styleTrue names list of CSS styles used for selected (styleTrue) and deselected (styleFalse) ns string name for the Shiny namespace to use. The ns is unlikely to require a change. viewer function for the viewer. See Shiny viewer. NOTE: when using browserViewer(browser = getOption("browser")) to open the app in the default browser, the browser window will automatically close when closing the app (by pressing "done" or "cancel") in most browsers. Firefox is an exception. See Details for instructions on how to enable this behaviour in Firefox. title string to customize the title of the UI window. The default is "Select features". Details When setting viewer = browserViewer(browser = getOption("browser")) and the systems de- fault browser is Firefox, the browser window will likely not automatically close when the app is closed (by pressing "done" or "cancel"). To enable automatic closing of tabs/windows in Firefox try the following: • input "about:config " to your firefox address bar and hit enter • make sure your "dom.allow_scripts_to_close_windows" is true Examples ## Not run: library(mapedit) library(mapview) lf <- mapview() # draw some polygons that we will select later drawing <- lf %>% editMap() # little easier now with sf mapview(drawing$finished) # especially easy with selectFeatures selectFeatures(drawing$finished) # use @bhaskarvk USA Albers with leaflet code # https://bhaskarvk.github.io/leaflet/examples/proj4Leaflet.html #devtools::install_github("hrbrmstr/albersusa") library(albersusa) library(sf) library(leaflet) 16 selectMod library(mapedit) spdf <- usa_sf() pal <- colorNumeric( palette = "Blues", domain = spdf$pop_2014 ) bounds <- c(-125, 24 ,-75, 45) (lf <- leaflet( options= leafletOptions( worldCopyJump = FALSE, crs=leafletCRS( crsClass="L.Proj.CRS", code='EPSG:2163', proj4def=paste0( '+proj=laea +lat_0=45 +lon_0=-100 +x_0=0 +y_0=0 +a=6370997 ', '+b=6370997 +units=m +no_defs' ), resolutions = c(65536, 32768, 16384, 8192, 4096, 2048,1024, 512, 256, 128) ) ) ) %>% fitBounds(bounds[1], bounds[2], bounds[3], bounds[4]) %>% setMaxBounds(bounds[1], bounds[2], bounds[3], bounds[4]) %>% mapview::addFeatures( data=spdf, weight = 1, color = "#000000", # adding group necessary for identification layerId = ~iso_3166_2, fillColor=~pal(pop_2014), fillOpacity=0.7, label=~stringr::str_c(name,' ', format(pop_2014, big.mark=",")), labelOptions= labelOptions(direction = 'auto') ) ) # test out selectMap with albers example selectMap( lf, styleFalse = list(weight = 1), styleTrue = list(weight = 4) ) ## End(Not run) selectMod Shiny Module Server for Geo Selection selectModUI 17 Description Shiny Module Server for Geo Selection Usage selectMod( input, output, session, leafmap, styleFalse = list(fillOpacity = 0.2, weight = 1, opacity = 0.4), styleTrue = list(fillOpacity = 0.7, weight = 3, opacity = 0.7) ) Arguments input Shiny server function input output Shiny server function output session Shiny server function session leafmap leaflet map to use for Selection styleFalse named list of valid CSS for non-selected features styleTrue named list of valid CSS for selected features Value server function for Shiny module selectModUI Shiny Module UI for Geo Selection Description Shiny Module UI for Geo Selection Usage selectModUI(id, ...) Arguments id character id for the the Shiny namespace ... other arguments to leafletOutput() Value ui for Shiny module Index addToolbar, 3 drawFeatures, 3 editFeatures, 2, 5 editMap, 2, 4, 7 editMod, 2, 10 editModUI, 2, 11 geos_binary_pred, 12 mapedit (mapedit-package), 2 mapedit-package, 2 processOpts, 11 selectFeatures, 2, 12 selectMap, 2, 14 selectMod, 2, 16 selectModUI, 2, 17 st_crs, 5, 9, 10 viewer, 4, 5, 8, 12, 15 18

dice

cran

Package ‘dice’ October 13, 2022 Type Package Title Calculate probabilities of various dice-rolling events Version 1.2 Date 2014-10-13 Author Dylan Arena Maintainer Dylan Arena <dylanarena1@gmail.com> Description This package provides utilities to calculate the probabilities of various dice- rolling events, such as the probability of rolling a four-sided die six times and get- ting a 4, a 3, and either a 1 or 2 among the six rolls (in any order); the probabil- ity of rolling two six-sided dice three times and getting a 10 on the first roll, fol- lowed by a 4 on the second roll, followed by anything but a 7 on the third roll; or the probabili- ties of each possible sum of rolling five six-sided dice, dropping the lowest two rolls, and sum- ming the remaining dice. License GPL (>= 2) Depends R (>= 2.0.0), gtools NeedsCompilation no Repository CRAN Date/Publication 2014-10-14 08:25:25 R topics documented: dice-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 getEventProb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 getSumProbs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Index 7 1 2 dice-package dice-package Calculate probabilities of various dice-rolling events Description This package provides utilities to calculate the probabilities of various dice-rolling events, such as the probability of rolling a four-sided die six times and getting a 4, a 3, and either a 1 or 2 among the six rolls (in any order); the probability of rolling two six-sided dice three times and getting a 10 on the first roll, followed by a 4 on the second roll, followed by anything but a 7 on the third roll; or the probabilities of each possible sum of rolling five six-sided dice, dropping the lowest two rolls, and summing the remaining dice. Details Package: dice Type: Package Version: 1.2 Date: 2014-10-13 License: GPL (>= 2) Although initially conceived as a utility for role-playing game calculations, functions in the dice package can be used to answer questions in any dice-rolling context (e.g., calculating probabilities for the game of craps, solving problems for an introductory probability course, etc.) The dice package requires the gtools package. For a complete list of functions, use library(help="dice"). Author(s) Dylan Arena <dylanarena1@gmail.com> References The implementation for the getSumProbs function originated with the ideas presented in the fol- lowing forum thread: http://www.enworld.org/showthread.php?t=56352&page=1&pp=40 Examples getEventProb(nrolls = 6, ndicePerRoll = 1, nsidesPerDie = 4, eventList = list(4, 3, c(1,2)), orderMatters = FALSE) getEventProb(nrolls = 3, getEventProb 3 ndicePerRoll = 2, nsidesPerDie = 6, eventList = list(10, 4, c(2:6, 8:12)), orderMatters = TRUE) getSumProbs(ndicePerRoll = 5, nsidesPerDie = 6, nkept = 3, dropLowest = TRUE) getEventProb Calculate the probability of a specified set of dice-rolling events Description For a specified dice-rolling process, getEventProb calculates the probability of an event (i.e., a non-empty set of outcomes) that is specified by passing a list object in to eventList. Usage getEventProb(nrolls, ndicePerRoll, nsidesPerDie, eventList, orderMatters = FALSE) Arguments nrolls A single positive integer representing the number of dice rolls to make ndicePerRoll A single positive integer representing the number of dice to use in each dice roll nsidesPerDie A single positive integer representing the number of sides on each die (getEventProb’s dice-rolling process involves only one type of die per call) eventList A list object, each element of which is a vector that constrains a single dice roll in the dice-rolling process (see Details below) orderMatters A logical flag indicating whether the order of the elements of eventList should constrain the event space; if TRUE, eventList must specify constraints for every dice roll–i.e., it must contain exactly nrolls elements (some of which may be "empty" constraints listing all possible outcomes of a dice roll, i.e., a vector from ndicePerRoll to (ndicePerRoll * nsidesPerDie)) Details The crux of this function is eventList, which sets the conditions that acceptable dice-rolls must meet. E.g., to get the probability of rolling at least one 6 when rolling four six-sided dice, eventList would be list(6) and orderMatters would be FALSE; to get the probability of rolling a 6, fol- lowed by a 5, followed by either a 1, 2, or 3 when rolling three six-sided dice, eventList would be list(6,5,1:3) and orderMatters would be TRUE. Value A single number representing the probability of an event that meets the constraints of the specified dice-rolling process 4 getSumProbs Author(s) Dylan Arena Examples ## Probability of rolling at least one 6 when rolling four six-sided dice getEventProb(nrolls = 4, ndicePerRoll = 1, nsidesPerDie = 6, eventList = list(6)) ## Probability of rolling a 6, followed by a 5, followed by either a 1, 2, ## or 3 when rolling three six-sided dice getEventProb(nrolls = 3, ndicePerRoll = 1, nsidesPerDie = 6, eventList = list(6, 5, 1:3), orderMatters = TRUE) ## Probability of rolling no 10's when rolling two ten-sided dice getEventProb(nrolls = 2, ndicePerRoll = 1, nsidesPerDie = 10, eventList = list(1:9,1:9)) getSumProbs Calculate the probabilities of all possible outcome sums of a dice roll Description For a specified number of dice with a specified number of sides per die (and dropping a specified number of dice–those with either the lowest or highest values), getSumProbs calculates the prob- abilities of all possible outcome sums (i.e., all possible sums of those dice whose results are not dropped); the function also accommodates modifiers (either to each die roll or to the sum), such as rolling five four-sided dice and adding 1 to the outcome of each roll, or rolling one twenty-sided die and adding 12 to the outcome. (Such modified rolls frequently occur in the context of role-playing games, e.g., Dungeons & Dragons, Mutants & Masterminds, or BESM.) getSumProbs 5 Usage getSumProbs(ndicePerRoll, nsidesPerDie, nkept = ndicePerRoll, dropLowest = TRUE, sumModifier = 0, perDieModifier = 0, perDieMinOfOne = TRUE) Arguments ndicePerRoll A single positive integer representing the number of dice to roll nsidesPerDie A single positive integer representing the number of sides on each die (getSumProbs’s dice-rolling process involves only one type of die per call) nkept A single positive integer representing the number of dice whose values to in- clude when calculating the sum (the dice to be kept will always be those with the highest values) dropLowest A single logical indicating whether to drop the lowest outcome values (FALSE drops the highest values instead) sumModifier A single integer representing an amount to add to or subtract from the outcome sum perDieModifier A single integer representing an amount to add to or subtract from each die roll perDieMinOfOne A logical flag indicating whether each die roll should be considered to have a minimum value of 1 (as is often true in role-playing-game contexts) Value probabilities A matrix with a row for each possible outcome sum and three columns: one that lists each sum, one for the probability of that sum, and one for the number of ways to roll that sum average A single number representing the expected value of the specified dice-rolling process Author(s) Dylan Arena References This function’s implementation originated with the ideas presented in the following forum thread: http://www.enworld.org/showthread.php?t=56352&page=1&pp=40 6 getSumProbs Examples ## Rolling four six-sided dice and keeping the three highest die rolls getSumProbs(ndicePerRoll = 4, nsidesPerDie = 6, nkept = 3) ## Rolling five four-sided dice and adding 1 to each die roll getSumProbs(ndicePerRoll = 5, nsidesPerDie = 4, perDieModifier = 1) ## Rolling one twenty-sided die and adding 12 to the result getSumProbs(ndicePerRoll = 1, nsidesPerDie = 20, sumModifier = 12) Index ∗ distribution getEventProb, 3 getSumProbs, 4 ∗ package dice-package, 2 dice (dice-package), 2 dice-package, 2 getEventProb, 3 getSumProbs, 2, 4 7

mcount

cran

Package ‘mcount’ October 13, 2022 Type Package Title Marginalized Count Regression Models Version 1.0.0 Author Zhengyang Zhou [aut, cre] Dateng Li [aut] David Huh [aut] Eun-Young Mun [aut] Depends R (>= 3.6) Maintainer Zhengyang Zhou <zhengyang.zhou@unthsc.edu> Description Implementation of marginalized models for zero-inflated count data. This package pro- vides a tool to implement an estimation algorithm for the marginalized count models, which directly makes inference on the effect of each covariate on the marginal mean of the outcome. The method involves the marginalized zero-inflated Poisson model described in Long et al. (2014) <doi:10.1002/sim.6293>. License GPL-3 Encoding UTF-8 LazyData true Imports bbmle, stats NeedsCompilation no RoxygenNote 7.1.2 Repository CRAN Date/Publication 2022-03-11 10:30:05 UTC R topics documented: dat.pfi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 mzip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Index 4 1 2 mzip dat.pfi Example Data Description A data set from White et al. (2008), which is also described in Mun et al. (2015, 2022) Usage data(dat.pfi) Format The data fram contains 194 rows and 5 columns: m0 the number of standard alcohol drinks consumed at baseline int_PF 1: received personalized feedback interventions (PFI); 0: did not receive PFI year_new 1: first-year college student; 0: otherwise race_new 1: white; 0: non-white y the number of standard alcohol drinks consumed at post-intervention; the response variable References Mun, E.-Y., Zhou, Z., Huh, D., Tan, L., Li, D., Tanner-Smith, E. E., Walters, S. T., & Larimer, M.E. (2022). Brief alcohol interventions are effective through six months: Findings from marginalized zero-inflated Poisson and negative binomial models in a two-step IPD meta-analysis. Prevention Science. (under review) Mun, E. Y., De La Torre, J., Atkins, D. C., White, H. R., Ray, A. E., Kim, S. Y., ... & The Project INTEGRATE Team. (2015). Project INTEGRATE: An integrative study of brief alcohol interventions for college students. Psychology of Addictive Behaviors, 29(1), 34-48. White, H. R., Mun, E.-Y., & Morgan, T. J. (2008). Do brief personalized feedback interventions work for mandated students or is it just getting caught that works? Psychology of Addictive Behav- iors, 22 (1), 107–116. https://doi.org/10.1037/0893-164X.22.1.107. mzip Estimating marginalized zero-inflated Poisson model Description Function to estimate a marginalized zero-inflated Poisson model mzip 3 Usage mzip(formula, data) Arguments formula an object of class "formula" (or one that can be coerced to that class): a sym- bolic description of the model to be fitted. A typical formula has the form response ~ terms where response is the count response vector and terms is a series of terms that predict response. For example, formula = y ~ x1 + x2 + x3. Do not write intercept in the formula; intercept will be automatically added in model fitting. data a data frame containing variables in the model. Details Function returns an object of class "mle2" from bbmle R package. Apply summary function to the resulting object from the function to obtain more estimation information. Value Suffix _zero corresponds to the parameters associated with the structrual zero rate part of a model. Suffix _mean corresponds to the parameters associated with the overall mean, which evaluate the effects of covariates on the overall mean. References Long, D. L., Preisser, J. S., Herring, A. H., & Golin, C. E. (2014). A marginalized zero-inflated Poisson regression model with overall exposure effects. Statistics in Medicine, 33(29), 5151-5165. Examples head(dat.pfi) #Fit a marginalized zero-inflated Poisson model res = mzip(formula = y ~ m0 + int_PF + year_new + race_new, data = dat.pfi) #Obtain estimation results bbmle::summary(res) Index ∗ datasets dat.pfi, 2 dat.pfi, 2 mzip, 2 4

DoseFinding

cran

Package ‘DoseFinding’ June 27, 2023 Type Package Title Planning and Analyzing Dose Finding Experiments Version 1.0-5 Date 2023-06-27 Depends ggplot2, lattice, mvtnorm, R (>= 2.15.0) Suggests numDeriv, Rsolnp, quadprog, parallel, multcomp, knitr, rmarkdown, MASS, testthat Maintainer Bjoern Bornkamp <bbnkmp@mail.de> Description The DoseFinding package provides functions for the design and analysis of dose-finding experiments (with focus on pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests, fitting non-linear dose-response models (using Bayesian and non-Bayesian estimation), calculating optimal designs and an implementation of the MCPMod methodology (Pinheiro et al. (2014) <doi:10.1002/sim.6052>). VignetteBuilder knitr License GPL-3 LazyLoad yes NeedsCompilation yes Author Bjoern Bornkamp [aut, cre] (<https://orcid.org/0000-0002-6294-8185>), Jose Pinheiro [aut], Frank Bretz [aut], Ludger Sandig [aut] Repository CRAN Date/Publication 2023-06-27 09:30:02 UTC R topics documented: DoseFinding-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 bFitMod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 biom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1 2 DoseFinding-package defBnds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 DesignMCPModApp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 DR-Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 fitMod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 glycobrom . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 guesst . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 IBScovars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 MCPMod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 MCTpval . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 MCTtest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 migraine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Mods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 mvtnorm.control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 neurodeg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 optContr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 optDesign . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 planMod . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 powMCT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 sampSize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Target Doses . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Index 56 DoseFinding-package Design and Analysis of dose-finding studies Description The DoseFinding package provides functions for the design and analysis of dose-finding exper- iments (for example pharmaceutical Phase II clinical trials). It provides functions for: multiple contrast tests (MCTtest), fitting non-linear dose-response models (fitMod), a combination of test- ing and dose-response modelling (MCPMod), and calculating optimal designs (optDesign), both for normal and general response variable. Details Package: DoseFinding Type: Package Version: 1.0-5 Date: 2023-06-27 License: GPL-3 The main functions are: MCTtest: Implements a multiple contrast tests powMCT: Power calculations for multiple contrast tests fitMod: Fits non-linear dose-response models optDesign: Calculates optimal designs for dose-response models DoseFinding-package 3 MCPMod: Performs MCPMod methodology sampSize: General function for sample size calculation Author(s) Bjoern Bornkamp, Jose Pinheiro, Frank Bretz Maintainer: Bjoern Bornkamp <bbnkmp@gmail.com> References Bornkamp, B., Bretz, F., Dette, H. and Pinheiro, J. C. (2011). Response-Adaptive Dose-Finding under model uncertainty, Annals of Applied Statistics, 5, 1611–1631 Bornkamp B., Pinheiro J. C., and Bretz, F. (2009). MCPMod: An R Package for the Design and Analysis of Dose-Finding Studies, Journal of Statistical Software, 29(7), 1–23 Bretz, F., Pinheiro, J. C., and Branson, M. (2005), Combining multiple comparisons and modeling techniques in dose-response studies, Biometrics, 61, 738–748 Dette, H., Bretz, F., Pepelyshev, A. and Pinheiro, J. C. (2008). Optimal Designs for Dose Finding Studies, Journal of the American Statisical Association, 103, 1225–1237 O’Quigley, J., Iasonos, A. and Bornkamp, B. (2017) Handbook of methods for designing, monitor- ing, and analyzing dose-finding trials, CRC press, Part 3: Dose-Finding Studies in Phase II Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of dose finding studies combining multiple comparisons and modeling procedures, Journal of Biopharmaceutical Statis- tics, 16, 639–656 Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) Model-based dose finding under model uncertainty using general parametric models, Statistics in Medicine, 33, 1646–1661 Seber, G.A.F. and Wild, C.J. (2003). Nonlinear Regression, Wiley Examples data(IBScovars) ## perform (model based) multiple contrast test ## define candidate dose-response shapes models <- Mods(linear = NULL, emax = 0.2, quadratic = -0.17, doses = c(0, 1, 2, 3, 4)) ## plot models plot(models) ## perform multiple contrast test test <- MCTtest(dose, resp, IBScovars, models=models, addCovars = ~ gender) ## fit non-linear emax dose-response model fitemax <- fitMod(dose, resp, data=IBScovars, model="emax", bnds = c(0.01,5)) ## display fitted dose-effect curve plot(fitemax, CI=TRUE, plotData="meansCI") 4 bFitMod ## Calculate optimal designs for target dose (TD) estimation doses <- c(0, 10, 25, 50, 100, 150) fmodels <- Mods(linear = NULL, emax = 25, exponential = 85, logistic = c(50, 10.8811), doses = doses, placEff=0, maxEff=0.4) plot(fmodels, plotTD = TRUE, Delta = 0.2) weights <- rep(1/4, 4) desTD <- optDesign(fmodels, weights, Delta=0.2, designCrit="TD") bFitMod Fit a dose-response model using Bayesian or bootstrap methods. Description For ‘type = "Bayes"’, MCMC sampling from the posterior distribution of the dose-response model is done. The function assumes a multivariate normal distribution for resp with covariance matrix S, and this is taken as likelihood function and combined with the prior distributions specified in prior to form the posterior distribution. For ‘type = "bootstrap"’, a multivariate normal distribution for resp with covariance matrix S is assumed, and a large number of samples is drawn from this distribution. For each draw the fitMod function with ‘type = "general"’ is used to fit the draws from the multivariate normal distribution. Usage bFitMod(dose, resp, model, S, placAdj = FALSE, type = c("Bayes", "bootstrap"), start = NULL, prior = NULL, nSim = 1000, MCMCcontrol = list(), control = NULL, bnds, addArgs = NULL) ## S3 method for class 'bFitMod' coef(object, ...) ## S3 method for class 'bFitMod' predict(object, predType = c("full-model", "effect-curve"), summaryFct = function(x) quantile(x, probs = c(0.025, 0.25, 0.5, 0.75, 0.975)), doseSeq = NULL, lenSeq = 101, ...) ## S3 method for class 'bFitMod' plot(x, plotType = c("dr-curve", "effect-curve"), quant = c(0.025, 0.5, 0.975), plotData = c("means", "meansCI", "none"), level = 0.95, lenDose = 201, ...) bFitMod 5 Arguments dose Numeric specifying the dose variable. resp Numeric specifying the response estimate corresponding to the doses in dose S Covariance matrix associated with the dose-response estimate specified via resp model Dose-response model to fit, possible models are "linlog", "linear", "quadratic", "emax", "exponential", "sigEmax", "betaMod" and "logistic", see drmodels. placAdj Whether or not estimates in "placAdj" are placebo-adjusted (note that the linear in log and the logistic model cannot be fitted for placebo-adjusted data) type Character with allowed values "Bayes" and "bootstrap", Determining whether samples are drawn from the posterior, or the bootstrap distribution. start Optional starting values for the dose-response parameters in the MCMC algo- rithm. prior List containing the information regarding the prior distributions for ‘type = "Bayes"’. The list needs to have as many entries as there are model param- eters. The ordering of the list entries should be the same as in the arguments list of the model see (see drmodels). For example for the Emax model the first entry determines the prior for e0, the second to eMax and the third to ed50. For each list entry the user has the choice to choose from 4 possible distributions: • norm: Vector of length 2 giving mean and standard deviation. • t: Vector of length 3 giving median, scale and degrees of freedom of the t-distribution. • lnorm: Vector of length 2 giving mean and standard deviation on log scale. • beta: Vector of length 4 giving lower and upper bound of the beta prior as well as the alpha and beta parameters of the beta distribution nSim Desired number of samples to produce with the algorithm MCMCcontrol List of control parameters for the MCMC algorithm • thin Thinning rate. Must be a positive integer. • w Numeric of same length as number of parameters in the model, specifies the width parameters of the slice sampler. • adapt Logical whether to adapt the w (width) parameter of the slice sampler in a short trial run. The widths are chosen as IQR/1.3 of the trial run. control Same as the control argument in fitMod. bnds Bounds for non-linear parameters, in case ‘type = "bootstrap"’. If missing the the default bounds from defBnds is used. addArgs List containing two entries named "scal" and "off" for the "betaMod" and "lin- log" model. When addArgs is NULL the following defaults are used ‘list(scal = 1.2*max(doses), off = 0.01*max(doses))’ x, object A bFitMod object predType, summaryFct, doseSeq, lenSeq Arguments for the predict method. ‘predType’: predType determines whether predictions are returned for the dose- response curve or the effect curve (difference to placebo). 6 bFitMod ‘summaryFct’: If equal to NULL predictions are calculated for each sampled parameter value. Otherwise a summary function is applied to the dose-response predictions for each parameter value. The default is to calculate 0.025, 0.25, 0.5, 0.75, 0.975 quantiles of the predictions for each dose. ‘doseSeq’: Where to calculate predictions. If not specified predictions are cal- culated on a grid of length ‘lenSeq’ between minimum and maximum dose. ‘lenSeq’: Length of the default grid where to calculate predictions. plotType, quant, plotData, level, lenDose Arguments for plot method. ‘plotType’: Determining whether the dose-response curve or the effect curve should be plotted. ‘quant’: Vector of quantiles to display in plot ‘plotData’: Determines how the original data are plotted: Either as means or as means with CI or not. The level of the CI is determined by the argument ‘level’. ‘level’: Level for CI, when plotData is equal to ‘meansCI’. ‘lenDose’: Number of grid values to use for display. ... Additional arguments are ignored. Details Componentwise univariate slice samplers are implemented (see Neal, 2003) to sample from the posterior distribution. Value An object of class bFitMod, which is a list containing the matrix of posterior simulations plus some additional information on the fitted model. Author(s) Bjoern Bornkamp References Neal, R. M. (2003), Slice sampling, Annals of Statistics, 31, 705-767 See Also fitMod Examples data(biom) ## produce first stage fit (using dose as factor) anMod <- lm(resp~factor(dose)-1, data=biom) drFit <- coef(anMod) S <- vcov(anMod) dose <- sort(unique(biom$dose)) bFitMod 7 ## define prior list ## normal prior for E0 (mean=0 and sdev=10) ## normal prior for Emax (mean=0 and sdev=100) ## beta prior for ED50: bounds: [0,1.5] parameters shape1=0.45, shape2=1.7 prior <- list(norm = c(0, 10), norm = c(0,100), beta=c(0,1.5,0.45,1.7)) ## now fit an emax model gsample <- bFitMod(dose, drFit, S, model = "emax", start = c(0, 1, 0.1), nSim = 1000, prior = prior) ## summary information gsample ## samples are stored in head(gsample$samples) ## predict 0.025, 0.25, 0.5, 0.75, 0.975 Quantile at 0, 0.5 and 1 predict(gsample, doseSeq = c(0, 0.5, 1)) ## simple plot function plot(gsample) ## now look at bootstrap distribution gsample <- bFitMod(dose, drFit, S, model = "emax", type = "bootstrap", nSim = 100, bnds = defBnds(1)$emax) plot(gsample) ## now fit linear interpolation prior <- list(norm = c(0,1000), norm = c(0,1000), norm = c(0,1000), norm = c(0,1000), norm = c(0,100)) gsample <- bFitMod(dose, drFit, S, model = "linInt", start = rep(1,5), nSim = 1000, prior = prior) gsample <- bFitMod(dose, drFit, S, model = "linInt", type = "bootstrap", nSim = 100) ## data fitted on placebo adjusted scale data(IBScovars) anovaMod <- lm(resp~factor(dose)+gender, data=IBScovars) drFit <- coef(anovaMod)[2:5] # placebo adjusted estimates at doses vCov <- vcov(anovaMod)[2:5,2:5] dose <- sort(unique(IBScovars$dose))[-1] prior <- list(norm = c(0,100), beta=c(0,6,0.45,1.7)) ## Bayes fit gsample <- bFitMod(dose, drFit, vCov, model = "emax", placAdj=TRUE, start = c(1, 0.1), nSim = 1000, prior = prior) ## bootstrap fit gsample <- bFitMod(dose, drFit, vCov, model = "emax", placAdj=TRUE, type = "bootstrap", start = c(1, 0.1), nSim = 100, prior = prior, bnds = c(0.01,6)) ## calculate target dose estimate TD(gsample, Delta = 0.2) ## now fit linear interpolation prior <- list(norm = c(0,1000), norm = c(0,1000), norm = c(0,1000), norm = c(0,100)) gsample <- bFitMod(dose, drFit, vCov, model = "linInt", placAdj=TRUE, start = rep(1,4), nSim = 1000, prior = prior) gsample <- bFitMod(dose, drFit, vCov, model = "linInt", type = "bootstrap", placAdj = TRUE, nSim = 100) 8 defBnds biom Biometrics Dose Response data Description An example data set for dose response studies. This data set was used in Bretz et al. (2005) to illustrate the MCPMod methodology. Usage data(biom) Format A data frame with 100 observations on the following 2 variables. resp a numeric vector containing the response values dose a numeric vector containing the dose values Source Bretz, F., Pinheiro, J. C., and Branson, M. (2005), Combining multiple comparisons and modeling techniques in dose-response studies, Biometrics, 61, 738–748 defBnds Calculates default bounds for non-linear parameters in dose-response models Description Calculates reasonable bounds for non-linear parameters for the built-in non-linear regression model based on the dose range under investigation. For the logistic model the first row corresponds to the ED50 parameter and the second row to the delta parameter. For the sigmoid Emax model the first row corresponds to the ED50 parameter and the second row to the h parameter, while for the beta model first and second row correspond to the delta1 and delta2 parameters. See logistic, sigEmax and betaMod for details. Usage defBnds(mD, emax = c(0.001, 1.5)*mD, exponential = c(0.1, 2)*mD, logistic = matrix(c(0.001, 0.01, 1.5, 1/2)*mD, 2), sigEmax = matrix(c(0.001*mD, 0.5, 1.5*mD, 10), 2), betaMod = matrix(c(0.05,0.05,4,4), 2)) DesignMCPModApp 9 Arguments mD Maximum dose in the study. emax, exponential, logistic, sigEmax, betaMod values for the nonlinear parameters for these model-functions Value List containing bounds for the model parameters. Author(s) Bjoern Bornkamp See Also fitMod Examples defBnds(mD = 1) defBnds(mD = 200) DesignMCPModApp Start externally hosted DesignMCPMod Shiny App Description This function starts the externally hosted DesignMCPMod Shiny App in a browser window. The app was developed by Sophie Sun [aut, cre], Danyi Xiong [aut], Bjoern Bornkamp [ctb], Frank Bretz [ctb], Ardalan Mirshani [ctb]. This app performs power and sample size calculations for a multiple contrast test for normal, binary and negative binomial outcomes. The app uses the DoseFinding package as calculation backend and the R code underlying the calculations in the app can be ex- tracted from the app. Usage DesignMCPModApp() 10 DR-Models DR-Models Built-in dose-response models in DoseFinding Description Dose-response model functions and gradients. Below are the definitions of the model functions: Emax model d f (d, θ) = E0 + Emax ED50 + d Sigmoid Emax Model dh f (d, θ) = E0 + Emax h + dh ED50 Exponential Model f (d, θ) = E0 + E1 (exp(d/δ) − 1) Beta model f (d, θ) = E0 + Emax B(δ1 , δ2 )(d/scal)δ1 (1 − d/scal)δ2 here B(δ1 , δ2 ) = (δ1 + δ2 )δ1 +δ2 /(δ1δ1 δ2δ2 ) and scal is a fixed dose scaling parameter. Linear Model f (d, θ) = E0 + δd Linear in log Model f (d, θ) = E0 + δ log(d + of f ) here of f is a fixed offset parameter. Logistic Model f (d, θ) = E0 + Emax / {1 + exp [(ED50 − d) /δ]} Quadratic Model f (d, θ) = E0 + β1 d + β2 d2 The standardized model equation for the quadratic model is d + δd2 , with δ = β2 /β1 . Linear Interpolation model The linInt model provides linear interpolation at the values defined by the nodes vector. In virtually all situations the nodes vector is equal to the doses used in the analysis. For example the Mods and the fitMod function automatically use the doses that are used in the context of the function call as nodes. The guesstimates specified in the Mods function need to be the treatment effects at the active doses standardized to the interval [0,1] (see the examples in the Mods function). DR-Models 11 Usage emax(dose, e0, eMax, ed50) emaxGrad(dose, eMax, ed50, ...) sigEmax(dose, e0, eMax, ed50, h) sigEmaxGrad(dose, eMax, ed50, h, ...) exponential(dose, e0, e1, delta) exponentialGrad(dose, e1, delta, ...) quadratic(dose, e0, b1, b2) quadraticGrad(dose, ...) betaMod(dose, e0, eMax, delta1, delta2, scal) betaModGrad(dose, eMax, delta1, delta2, scal, ...) linear(dose, e0, delta) linearGrad(dose, ...) linlog(dose, e0, delta, off = 1) linlogGrad(dose, off, ...) logistic(dose, e0, eMax, ed50, delta) logisticGrad(dose, eMax, ed50, delta, ...) linInt(dose, resp, nodes) linIntGrad(dose, resp, nodes, ...) Arguments dose Dose variable e0 For most models placebo effect. For logistic model left-asymptote parameter, corresponding to a basal effect level (not the placebo effect) eMax Beta Model: Maximum effect within dose-range Emax, sigmoid Emax, logistic Model: Asymptotic maximum effect ed50 Dose giving half of the asymptotic maximum effect h Hill parameter, determining the steepness of the model at the ED50 e1 Slope parameter for exponential model delta Exponential model: Parameter, controlling the convexity of the model. Linear and linlog model: Slope parameter Logistic model: Parameter controlling determining the steepness of the curve delta1 delta1 parameter for beta model delta2 delta2 parameter for beta model b1 first parameter of quadratic model b2 second parameter of quadratic model (controls, whether model is convex or con- cave) 12 DR-Models resp Response values at the nodes for the linInt model off Offset value to avoid problems with dose=0 (treated as a fixed value, not esti- mated) scal Scale parameter (treated as a fixed value, not estimated) nodes Interpolation nodes for the linear interpolation for the linInt model (treated as a fixed value, not estimated) ... Just included for convenience in the gradient functions, so that for example quadratic(dose, e0=0, b1=1, b2=3) will not throw an error (although the gradient of the quadratic model is independent of e0, b1 and b2). Details The Emax model is used to represent monotone, concave dose-response shapes. To distinguish it from the more general sigmoid emax model it is sometimes also called hyperbolic emax model. The sigmoid Emax model is an extension of the (hyperbolic) Emax model by introducing an addi- tional parameter h, that determines the steepness of the curve at the ed50 value. The sigmoid Emax model describes monotonic, sigmoid dose-response relationships. In the toxicology literature this model is also called four-parameter log-logistic (4pLL) model. The quadratic model is intended to capture a possible non-monotonic dose-response relationship. The exponential model is intended to capture a possible sub-linear or a convex dose-response relationship. The beta model is intended to capture non-monotone dose-response relationships and is more flex- ible than the quadratic model. The kernel of the beta model function consists of the kernel of the density function of a beta distribution on the interval [0,scal]. The parameter scal is not esti- mated but needs to be set to a value larger than the maximum dose. It can be set in most functions (‘fitMod’, ‘Mods’) via the ‘addArgs’ argument, when omitted a value of ‘1.2*(maximum dose)’ is used as default, where the maximum dose is inferred from other input to the respective function. The linear in log-dose model is intended to capture concave shapes. The parameter off is not estimated in the code but set to a pre-specified value. It can be set in most functions (‘fitMod’, ‘Mods’) via the ‘addArgs’ argument, when omitted a value of ‘0.01*(maximum dose)’ is used as default, where the maximum dose is inferred from other input to the respective function. The logistic model is intended to capture general monotone, sigmoid dose-response relationships. The logistic model and the sigmoid Emax model are closely related: The sigmoid Emax model is a logistic model in log(dose). The linInt model provids linear interpolation of the means at the doses. This can be used as a "nonparametric" estimate of the dose-response curve, but is probably most interesting for specify- ing a "nonparametric" truth during planning and assess how well parametric models work under a nonparametric truth. For the function ‘Mods’ and ‘fitMod’ the interpolation ‘nodes’ are selected equal to the dose-levels specified. Value Response value for model functions or matrix containing the gradient evaluations. DR-Models 13 References MacDougall, J. (2006). Analysis of dose-response studies - Emax model,in N. Ting (ed.), Dose Finding in Drug Development, Springer, New York, pp. 127–145 Pinheiro, J. C., Bretz, F. and Branson, M. (2006). Analysis of dose-response studies - modeling approaches, in N. Ting (ed.). Dose Finding in Drug Development, Springer, New York, pp. 146– 171 See Also fitMod Examples ## some quadratic example shapes quadModList <- Mods(quadratic = c(-0.5, -0.75, -0.85, -1), doses = c(0,1)) plotMods(quadModList) ## some emax example shapes emaxModList <- Mods(emax = c(0.02,0.1,0.5,1), doses = c(0,1)) plotMods(emaxModList) ## example for gradient emaxGrad(dose = (0:4)/4, eMax = 1, ed50 = 0.5) ## some sigmoid emax example shapes sigEmaxModList <- Mods(sigEmax = rbind(c(0.05,1), c(0.15,3), c(0.4,8), c(0.7,8)), doses = c(0,1)) plotMods(sigEmaxModList) sigEmaxGrad(dose = (0:4)/4, eMax = 1, ed50 = 0.5, h = 8) ## some exponential example shapes expoModList <- Mods(exponential = c(0.1,0.25,0.5,2), doses=c(0,1)) plotMods(expoModList) exponentialGrad(dose = (0:4)/4, e1 = 1, delta = 2) ## some beta model example shapes betaModList <- Mods(betaMod = rbind(c(1,1), c(1.5,0.75), c(0.8,2.5), c(0.4,0.9)), doses=c(0,1), addArgs=list(scal = 1.2)) plotMods(betaModList) betaModGrad(dose = (0:4)/4, eMax = 1, delta1 = 1, delta2 = 1, scal = 5) ## some logistic model example shapes logistModList <- Mods(logistic = rbind(c(0.5,0.05), c(0.5,0.15), c(0.2,0.05), c(0.2,0.15)), doses=c(0,1)) plotMods(logistModList) logisticGrad(dose = (0:4)/4, eMax = 1, ed50 = 0.5, delta = 0.05) ## some linInt shapes genModList <- Mods(linInt = rbind(c(0.5,1,1), c(0,1,1), c(0,0,1)), doses=c(0,0.5,1,1.5)) plotMods(genModList) linIntGrad(dose = (0:4)/4, resp=c(0,0.5,1,1,1), nodes=(0:4)/4) 14 fitMod fitMod Fit non-linear dose-response model Description Fits a dose-response model. Built-in dose-response models are "linlog", "linear", "quadratic", "emax", "exponential", "sigEmax", "betaMod" and "logistic" (see drmodels). When ‘type = "normal"’ ordinary least squares is used and additional additive covariates can be specified in ‘addCovars’. The underlying assumption is hence normally distributed data and ho- moscedastic variance. For ‘type = "general"’ a generalized least squares criterion is used (f (dose, θ) − resp)0 S −1 (f (dose, θ) − resp) and an inverse weighting matrix is specified in ‘S’, ‘type = "general"’ is primarily of interest, when fitting a model to AN(C)OVA type estimates obtained in a first stage fit, then ‘resp’ contains the estimates and ‘S’ is the estimated covariance matrix for the estimates in ‘resp’. Statistical inference (e.g. confidence intervals) rely on asymptotic normality of the first stage estimates, which makes this method of interest only for sufficiently large sample size for the first stage fit. A modified model-selection criterion can be applied to these model fits (see also Pinheiro et al. 2014 for details). For details on the implemented numerical optimizer see the Details section below. Usage fitMod(dose, resp, data = NULL, model, S = NULL, type = c("normal", "general"), addCovars = ~1, placAdj = FALSE, bnds, df = NULL, start = NULL, na.action = na.fail, control = NULL, addArgs = NULL) ## S3 method for class 'DRMod' coef(object, sep = FALSE, ...) ## S3 method for class 'DRMod' predict(object, predType = c("full-model", "ls-means", "effect-curve"), newdata = NULL, doseSeq = NULL, se.fit = FALSE, ...) ## S3 method for class 'DRMod' vcov(object, ...) ## S3 method for class 'DRMod' plot(x, CI = FALSE, level = 0.95, plotData = c("means", "meansCI", "raw", "none"), plotGrid = TRUE, colMn = 1, colFit = 1, ...) fitMod 15 ## S3 method for class 'DRMod' logLik(object, ...) ## S3 method for class 'DRMod' AIC(object, ..., k = 2) ## S3 method for class 'DRMod' gAIC(object, ..., k = 2) Arguments dose, resp Either vectors of equal length specifying dose and response values, or names of variables in the data frame specified in ‘data’. data Data frame containing the variables referenced in dose and resp if ‘data’ is not specified it is assumed that ‘dose’ and ‘resp’ are variables referenced from data (and no vectors) model The dose-response model to be used for fitting the data. Built-in models are "linlog", "linear", "quadratic", "emax", "exponential", "sigEmax", "betaMod" and "logistic" (see drmodels). S The inverse weighting matrix used in case, when ‘type = "general"’, see De- scription. For later inference statements (vcov or predict methods) it is assumed this is the estimated covariance of the estimates in the first stage fit. type Determines whether inference is based on an ANCOVA model under a ho- moscedastic normality assumption (when ‘type = "normal"’), or estimates at the doses and their covariance matrix and degrees of freedom are specified di- rectly in ‘resp’, ‘S’ and ‘df’. See also the Description above and Pinheiro et al. (2014). addCovars Formula specifying additional additive linear covariates (only for ‘type = "normal"’) placAdj Logical, if true, it is assumed that placebo-adjusted estimates are specified in ‘resp’ (only possible for ‘type = "general"’). bnds Bounds for non-linear parameters. If missing the the default bounds from defBnds is used. When the dose-response model has only one non-linear parameter (for example Emax or exponential model), ‘bnds’ needs to be a vector containing upper and lower bound. For models with two non-linear parameters ‘bnds’ needs to be a matrix containing the bounds in the rows, see the Description section of defBnds for details on the formatting of the bounds for the individual models. df Degrees of freedom to use in case of ‘type = "general"’. If this argument is missing ‘df = Inf’ is used. For ‘type = "normal"’ this argument is ignored as the exact degrees of freedom can be deduced from the model. start Vector of starting values for the nonlinear parameters (ignored for linear mod- els). When equal to NULL, a grid optimization is performed and the best value is used as starting value for the local optimizer. na.action A function which indicates what should happen when the data contain NAs. 16 fitMod control A list with entries: "nlminbcontrol", "optimizetol" and "gridSize". The entry nlminbcontrol needs to be a list and it is passed directly to control argument in the nlminb function, that is used internally for models with 2 non- linear parameters. The entry optimizetol is passed directly to the tol argument of the optimize func- tion, which is used for models with 1 nonlinear parameters. The entry gridSize needs to be a list with entries dim1 and dim2 giving the size of the grid for the gridsearch in 1d or 2d models. addArgs List containing two entries named "scal" and "off" for the "betaMod" and "lin- log" model. When addArgs is NULL the following defaults is used ‘list(scal = 1.2*max(doses), off = 0.01*max(doses))’. object, x DRMod object sep Logical determining whether all coefficients should be returned in one numeric or separated in a list. predType, newdata, doseSeq, se.fit predType determines whether predictions are returned for the full model (includ- ing potential covariates), the ls-means (SAS type) or the effect curve (difference to placebo). newdata gives the covariates to use in producing the predictions (for predType = "full-model"), if missing the covariates used for fitting are used. doseSeq dose-sequence on where to produce predictions (for predType = "effect- curve" and predType = "ls-means"). If missing the doses used for fitting are used. se.fit: logical determining, whether the standard error should be calculated. CI, level, plotData, plotGrid, colMn, colFit Arguments for plot method: ‘CI’ determines whether confidence intervals should be plotted. ‘level’ determines the level of the confidence intervals. ‘plotData’ determines how the data are plotted: Either as means or as means with CI, raw data or none. In case of ‘type = "normal"’ and covariates the ls-means are dis- played, when ‘type = "general"’ the option "raw" is not available. ‘colMn’ and ‘colFit’ determine the colors of fitted model and the raw means. k Penalty to use for model-selection criterion (AIC uses 2, BIC uses log(n)). ... Additional arguments for plotting for the ‘plot’ method. For all other cases additional arguments are ignored. Details Details on numerical optimizer for model-fitting: For linear models fitting is done using numerical linear algebra based on the QR decomposition. For nonlinear models numerical optimization is performed only in the nonlinear parameters in the model and optimizing over the linear parameters in each iteration (similar as the Golub-Pereyra implemented in nls). For models with 1 nonlinear parameter the optimize function is used for 2 nonlinear parameters the nlminb function is used. The starting value is generated using a grid- search (with the grid size specified via ‘control$gridSize’), or can directly be handed over via ‘start’. fitMod 17 For details on the asymptotic approximation used for ‘type = "normal"’, see Seber and Wild (2003, chapter 5). For details on the asymptotic approximation used for ‘type = "general"’, and the gAIC, see Pinheiro et al. (2014). Value An object of class DRMod. Essentially a list containing information about the fitted model coeffi- cients, the residual sum of squares (or generalized residual sum of squares), Author(s) Bjoern Bornkamp References Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) Model-based dose finding under model uncertainty using general parametric models, Statistics in Medicine, 33, 1646–1661 Seber, G.A.F. and Wild, C.J. (2003). Nonlinear Regression, Wiley. See Also defBnds, drmodels Examples ## Fit the emax model to the IBScovars data set data(IBScovars) fitemax <- fitMod(dose, resp, data=IBScovars, model="emax", bnds = c(0.01, 4)) ## methods for DRMod objects summary(fitemax) ## extracting coefficients coef(fitemax) ## (asymptotic) covariance matrix of estimates vcov(fitemax) ## predicting newdat <- data.frame(dose = c(0,0.5,1), gender=factor(1)) predict(fitemax, newdata=newdat, predType = "full-model", se.fit = TRUE) ## plotting plot(fitemax, plotData = "meansCI", CI=TRUE) ## now include (additive) covariate gender fitemax2 <- fitMod(dose, resp, data=IBScovars, model="emax", addCovars = ~gender, bnds = c(0.01, 4)) vcov(fitemax2) plot(fitemax2) ## fitted log-likelihood logLik(fitemax2) ## extracting AIC (or BIC) AIC(fitemax2) 18 glycobrom ## Illustrating the "general" approach for a binary regression ## produce first stage fit (using dose as factor) data(migraine) PFrate <- migraine$painfree/migraine$ntrt doseVec <- migraine$dose doseVecFac <- as.factor(migraine$dose) ## fit logistic regression with dose as factor fitBin <- glm(PFrate~doseVecFac-1, family = binomial, weights = migraine$ntrt) drEst <- coef(fitBin) vCov <- vcov(fitBin) ## now fit an Emax model (on logit scale) gfit <- fitMod(doseVec, drEst, S=vCov, model = "emax", bnds = c(0,100), type = "general") ## model fit on logit scale plot(gfit, plotData = "meansCI", CI = TRUE) ## model on probability scale logitPred <- predict(gfit, predType ="ls-means", doseSeq = 0:200, se.fit=TRUE) plot(0:200, 1/(1+exp(-logitPred$fit)), type = "l", ylim = c(0, 0.5), ylab = "Probability of being painfree", xlab = "Dose") LB <- logitPred$fit-qnorm(0.975)*logitPred$se.fit UB <- logitPred$fit+qnorm(0.975)*logitPred$se.fit lines(0:200, 1/(1+exp(-LB))) lines(0:200, 1/(1+exp(-UB))) ## now illustrate "general" approach for placebo-adjusted data (on ## IBScovars) note that the estimates are identical to fitemax2 above) anovaMod <- lm(resp~factor(dose)+gender, data=IBScovars) drFit <- coef(anovaMod)[2:5] # placebo adjusted estimates at doses vCov <- vcov(anovaMod)[2:5,2:5] dose <- sort(unique(IBScovars$dose))[-1] ## now fit an emax model to these estimates gfit2 <- fitMod(dose, drFit, S=vCov, model = "emax", type = "general", placAdj = TRUE, bnds = c(0.01, 2)) ## some outputs summary(gfit2) coef(gfit2) vcov(gfit2) predict(gfit2, se.fit = TRUE, doseSeq = c(1,2,3,4), predType = "effect-curve") plot(gfit2, CI=TRUE, plotData = "meansCI") gAIC(gfit2) glycobrom Glycopyrronium Bromide dose-response data glycobrom 19 Description Data from a clinical study evaluating Efficacy and Safety of Four Doses of Glycopyrronium Bro- mide in Patients With Stable Chronic Obstructive Pulmonary Disease (COPD). This data set was obtained from clinicaltrials.gov (NCT00501852). The study design was a 4 period incomplete cross-over design. The primary endpoint is the trough forced expiratory volume in 1 second (FEV1) following 7 days of Treatment. The data given here are summary estimates (least-square means) for each dose. Usage data(glycobrom) Format A data frame with 5 summary estimates (one per dose). Variables: dose a numeric vector containing the dose values fev1 a numeric vector containing the least square mean per dose sdev a numeric vector containing the standard errors of the least square means per dose n Number of participants analyzed per treatment group Source http://clinicaltrials.gov/ct2/show/results/NCT00501852 Examples ## simulate a full data set with given means and sdv (here we ignore ## the original study was a cross-over design, and simulate a parallel ## group design) simData <- function(mn, sd, n, doses, fixed = TRUE){ ## simulate data with means (mns) and standard deviations (sd), for ## fixed = TRUE, the data set will have observed means and standard ## deviations as given in mns and sd resp <- numeric(sum(n)) uppind <- cumsum(n) lowind <- c(0,uppind)+1 for(i in 1:length(n)){ rv <- rnorm(n[i]) if(fixed) rv <- scale(rv) resp[lowind[i]:uppind[i]] <- mn[i] + sd[i]*rv } data.frame(doses=rep(doses, n), resp=resp) } data(glycobrom) fullDat <- simData(glycobrom$fev1, glycobrom$sdev, glycobrom$n, glycobrom$dose) 20 guesst guesst Calculate guesstimates based on prior knowledge Description Calculates guesstimates for standardized model parameter(s) using the general approach described in Pinheiro et al. (2006). Usage guesst(d, p, model = c("emax", "exponential", "logistic", "quadratic", "betaMod", "sigEmax"), less = TRUE, local = FALSE, dMax, Maxd, scal) Arguments d Vector containing dose value(s). p Vector of expected percentages of the maximum effect achieved at d. model Character string. Should be one of "emax", "exponential", "quadratic", "beta- Mod", "sigEmax", "logistic". less Logical, only needed in case of quadratic model. Determines if d is smaller (‘less=TRUE’) or larger (‘less=FALSE’) than dopt (see Pinheiro et al. (2006) for details). local Logical indicating whether local or asymptotic version of guesstimate should be derived (defaults to ‘FALSE’). Only needed for emax, logistic and sigEmax model. When ‘local=TRUE’ the maximum dose must be provided via ‘Maxd’. dMax Dose at which maximum effect occurs, only needed for the beta model Maxd Maximum dose to be administered in the trial scal Scale parameter, only needed for the beta model Details Calculates guesstimates for the parameters θ2 of the standardized model function based on the prior expected percentage of the maximum effect at certain dose levels. Note that this function should be used together with the plot.Mods function to ensure that the guesstimates are reflecting the prior beliefs. For the logistic and sigmoid emax models at least two pairs (d,p) need to be specified. For the beta model the dose at which the maximum effect occurs (dMax) has to be specified in addition to the (d,p) pair. For the exponential model the maximum dose administered (Maxd) needs to be specified in addition to the (d,p) pair. For the quadratic model one (d,p) pair is needed. It is advisable to specify the location of the maximum within the dose range with this pair. guesst 21 For the emax, sigmoid Emax and logistic model one can choose between a local and an asymptotic version. In the local version one explicitly forces the standardized model function to pass through the specified points (d,p). For the asymptotic version it assumed that the standardized model func- tion is equal to 1 at the largest dose (this is the approach described in Pinheiro et al. (2006)). If the local version is used, convergence problems with the underlying nonlinear optimization can occur. Value Returns a numeric vector containing the guesstimates. References Bornkamp B., Pinheiro J. C., and Bretz, F. (2009). MCPMod: An R Package for the Design and Analysis of Dose-Finding Studies, Journal of Statistical Software, 29(7), 1–23 Pinheiro, J. C., Bretz, F., and Branson, M. (2006). Analysis of dose-response studies - modeling approaches, in N. Ting (ed.), Dose Finding in Drug Development, Springer, New York, pp. 146–171 See Also emax, logistic, betaMod, sigEmax, quadratic, exponential, plot.Mods Examples ## Emax model ## Expected percentage of maximum effect: 0.8 is associated with ## dose 0.3 (d,p)=(0.3, 0.8), dose range [0,1] emx1 <- guesst(d=0.3, p=0.8, model="emax") emax(0.3,0,1,emx1) ## local approach emx2 <- guesst(d=0.3, p=0.8, model="emax", local = TRUE, Maxd = 1) emax(0.3,0,1,emx2)/emax(1,0,1,emx2) ## plot models models <- Mods(emax=c(emx1, emx2), doses=c(0,1)) plot(models) ## Logistic model ## Select two (d,p) pairs (0.2, 0.6) and (0.2, 0.95) lgc1 <- guesst(d = c(0.2, 0.6), p = c(0.2, 0.95), "logistic") logistic(c(0.2,0.6), 0, 1, lgc1[1], lgc1[2]) ## local approach lgc2 <- guesst(d = c(0.2, 0.6), p = c(0.2, 0.95), "logistic", local = TRUE, Maxd = 1) r0 <- logistic(0, 0, 1, lgc2[1], lgc2[2]) r1 <- logistic(1, 0, 1, lgc2[1], lgc2[2]) (logistic(c(0.2,0.6), 0, 1, lgc2[1], lgc2[2])-r0)/(r1-r0) ## plot models models <- Mods(logistic = rbind(lgc1, lgc2), doses=c(0,1)) plot(models) ## Beta Model ## Select one pair (d,p): (0.4,0.8) 22 IBScovars ## dose, where maximum occurs: 0.8 bta <- guesst(d=0.4, p=0.8, model="betaMod", dMax=0.8, scal=1.2, Maxd=1) ## plot models <- Mods(betaMod = bta, doses=c(0,1), addArgs = list(scal = 1.2)) plot(models) ## Sigmoid Emax model ## Select two (d,p) pairs (0.2, 0.6) and (0.2, 0.95) sgE1 <- guesst(d = c(0.2, 0.6), p = c(0.2, 0.95), "sigEmax") sigEmax(c(0.2,0.6), 0, 1, sgE1[1], sgE1[2]) ## local approach sgE2 <- guesst(d = c(0.2, 0.6), p = c(0.2, 0.95), "sigEmax", local = TRUE, Maxd = 1) sigEmax(c(0.2,0.6), 0, 1, sgE2[1], sgE2[2])/sigEmax(1, 0, 1, sgE2[1], sgE2[2]) models <- Mods(sigEmax = rbind(sgE1, sgE2), doses=c(0,1)) plot(models) ## Quadratic model ## For the quadratic model it is assumed that the maximum effect occurs at ## dose 0.7 quad <- guesst(d = 0.7, p = 1, "quadratic") models <- Mods(quadratic = quad, doses=c(0,1)) plot(models) ## exponential model ## (d,p) = (0.8,0.5) expo <- guesst(d = 0.8, p = 0.5, "exponential", Maxd=1) models <- Mods(exponential = expo, doses=c(0,1)) plot(models) IBScovars Irritable Bowel Syndrome Dose Response data with covariates Description A subset of the data used by (Biesheuvel and Hothorn, 2002). The data are part of a dose ranging trial on a compound for the treatment of the irritable bowel syndrome with four active treatment arms, corresponding to doses 1,2,3,4 and placebo. Note that the original dose levels have been blinded in this data set for confidentiality. The primary endpoint was a baseline adjusted abdom- inal pain score with larger values corresponding to a better treatment effect. In total 369 patients completed the study, with nearly balanced allocation across the doses. Usage data(IBScovars) Format A data frame with 369 observations on the following 2 variables. MCPMod 23 gender a factor specifying the gender dose a numeric vector resp a numeric vector Source Biesheuvel, E. and Hothorn, L. A. (2002). Many-to-one comparisons in stratified designs, Biomet- rical Journal, 44, 101–116 MCPMod MCPMod - Multiple Comparisons and Modeling Description Tests for a dose-response effect using a model-based multiple contrast test (see MCTtest), selects one (or several) model(s) from the significant shapes, fits them using fitMod. For details on the method see Bretz et al. (2005). Usage MCPMod(dose, resp, data, models, S = NULL, type = c("normal", "general"), addCovars = ~1, placAdj = FALSE, selModel = c("AIC", "maxT", "aveAIC"), alpha = 0.025, df = NULL, critV = NULL, doseType = c("TD", "ED"), Delta, p, pVal = TRUE, alternative = c("one.sided", "two.sided"), na.action = na.fail, mvtcontrol = mvtnorm.control(), bnds, control = NULL) ## S3 method for class 'MCPMod' predict(object, predType = c("full-model", "ls-means", "effect-curve"), newdata = NULL, doseSeq = NULL, se.fit = FALSE, ...) ## S3 method for class 'MCPMod' plot(x, CI = FALSE, level = 0.95, plotData = c("means", "meansCI", "raw", "none"), plotGrid = TRUE, colMn = 1, colFit = 1, ...) Arguments dose, resp Either vectors of equal length specifying dose and response values, or names of variables in the data frame specified in ‘data’. data Data frame containing the variables referenced in dose and resp if ‘data’ is not specified it is assumed that ‘dose’ and ‘resp’ are variables referenced from data (and no vectors) 24 MCPMod models An object of class ‘"Mods"’, see Mods for details S The covariance matrix of ‘resp’ when ‘type = "general"’, see Description. type Determines whether inference is based on an ANCOVA model under a ho- moscedastic normality assumption (when ‘type = "normal"’), or estimates at the doses and their covariance matrix and degrees of freedom are specified di- rectly in ‘resp’, ‘S’ and ‘df’. See also fitMod and Pinheiro et al. (2014). addCovars Formula specifying additive linear covariates (for ‘type = "normal"’) placAdj Logical, if true, it is assumed that placebo-adjusted estimates are specified in ‘resp’ (only possible for ‘type = "general"’). selModel Optional character vector specifying the model selection criterion for dose esti- mation. Possible values are • AIC: Selects model with smallest AIC (this is the default) • maxT: Selects the model corresponding to the largest t-statistic. • aveAIC: Uses a weighted average of the models corresponding to the sig- nificant contrasts. PThe model weights are chosen by the formula: wi = exp(−0.5AICi )/ i (exp(−0.5AICi )) See Buckland et al. (1997) for de- tails. For ‘type = "general"’ the "gAIC" is used. alpha Significance level for the multiple contrast test df Specify the degrees of freedom to use in case ‘type = "general"’, for the call to MCTtest and fitMod. Infinite degrees of (‘df=Inf’) correspond to the multi- variate normal distribution. For type = "normal" the degrees of freedom deduced from the AN(C)OVA fit are used and this argument is ignored. critV Supply a pre-calculated critical value. If this argument is NULL, no critical value will be calculated and the test decision is based on the p-values. If ‘critV = TRUE’ the critical value will be calculated. doseType, Delta, p ‘doseType’ determines the dose to estimate, ED or TD (see also Mods), and ‘Delta’ and ‘p’ need to be specified depending on whether TD or ED is to be estimated. See TD and ED for details. pVal Logical determining, whether p-values should be calculated. alternative Character determining the alternative for the multiple contrast trend test. na.action A function which indicates what should happen when the data contain NAs. mvtcontrol A list specifying additional control parameters for the ‘qmvt’ and ‘pmvt’ calls in the code, see also mvtnorm.control for details. bnds Bounds for non-linear parameters. This needs to be a list with list entries corre- sponding to the selected bounds. The names of the list entries need to correspond to the model names. The defBnds function provides the default selection. control Control list for the optimization. A list with entries: "nlminbcontrol", "optimizetol" and "gridSize". The entry nlminbcontrol needs to be a list and is passed directly to control argu- ment in the nlminb function, that is used internally for models with 2 nonlinear parameters (e.g. sigmoid Emax or beta model). MCPMod 25 The entry optimizetol is passed directly to the tol argument of the optimize func- tion, which is used for models with 1 nonlinear parameters (e.g. Emax or expo- nential model). The entry gridSize needs to be a list with entries dim1 and dim2 giving the size of the grid for the gridsearch in 1d or 2d models. object, x MCPMod object predType, newdata, doseSeq, se.fit, ... predType determines whether predictions are returned for the full model (includ- ing potential covariates), the ls-means (SAS type) or the effect curve (difference to placebo). newdata gives the covariates to use in producing the predictions (for ‘predType = "full-model"’), if missing the covariates used for fitting are used. doseSeq dose-sequence on where to produce predictions (for ‘predType = "effect-curve"’ and ‘predType = "ls-means"’). If missing the doses used for fitting are used. se.fit: logical determining, whether the standard error should be calculated. . . . : Additional arguments, for plot.MCPMod these are passed to plot.DRMod. CI, level, plotData, plotGrid, colMn, colFit Arguments for plot method: ‘CI’ determines whether confidence intervals should be plotted. ‘level’ determines the level of the confidence intervals. ‘plotData’ determines how the data are plotted: Either as means or as means with CI, raw data or none. In case of ‘type = "normal"’ and covariates the ls-means are dis- played, when ‘type = "general"’ the option "raw" is not available. ‘colMn’ and ‘colFit’ determine the colors of fitted model and the raw means. Value An object of class ‘MCPMod’, which contains the fitted ‘MCTtest’ object as well as the ‘DRMod’ objects and additional information (model selection criteria, dose estimates, selected models). Author(s) Bjoern Bornkamp References Bretz, F., Pinheiro, J. C., and Branson, M. (2005), Combining multiple comparisons and modeling techniques in dose-response studies, Biometrics, 61, 738–748 Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of dose finding studies combining multiple comparisons and modeling procedures, Journal of Biopharmaceutical Statis- tics, 16, 639–656 Pinheiro, J. C., Bretz, F., and Branson, M. (2006). Analysis of dose-response studies - modeling approaches, in N. Ting (ed.). Dose Finding in Drug Development, Springer, New York, pp. 146–171 Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) Model-based dose finding under model uncertainty using general parametric models, Statistics in Medicine, 33, 1646–1661 Schorning, K., Bornkamp, B., Bretz, F., & Dette, H. (2016). Model selection versus model averag- ing in dose finding studies. Statistics in Medicine, 35, 4021–4040 26 MCPMod Xun, X. and Bretz, F. (2017) The MCP-Mod methodology: Practical Considerations and The DoseFinding R package, in O’Quigley, J., Iasonos, A. and Bornkamp, B. (eds) Handbook of meth- ods for designing, monitoring, and analyzing dose-finding trials, CRC press Buckland, S. T., Burnham, K. P. and Augustin, N. H. (1997). Model selection an integral part of inference, Biometrics, 53, 603–618 Seber, G.A.F. and Wild, C.J. (2003). Nonlinear Regression, Wiley. See Also MCTtest, fitMod, drmodels Examples data(biom) ## first define candidate model set (only need "standardized" models) models <- Mods(linear = NULL, emax=c(0.05,0.2), linInt=c(1, 1, 1, 1), doses=c(0,0.05,0.2,0.6,1)) plot(models) ## perform MCPMod procedure MM <- MCPMod(dose, resp, biom, models, Delta=0.5) ## a number of things can be done with an MCPMod object MM # print method provides basic information summary(MM) # more information ## predict all significant dose-response models predict(MM, se.fit=TRUE, doseSeq=c(0,0.2,0.4, 0.9, 1), predType="ls-means") ## display all model functions plot(MM, plotData="meansCI", CI=TRUE) ## now perform model-averaging MM2 <- MCPMod(dose, resp, biom, models, Delta=0.5, selModel = "aveAIC") sq <- seq(0,1,length=11) pred <- predict(MM, doseSeq=sq, predType="ls-means") modWeights <- MM2$selMod ## model averaged predictions pred <- do.call("cbind", pred)%*%modWeights ## model averaged dose-estimate TDEst <- MM2$doseEst%*%modWeights ## now an example using a general fit and fitting based on placebo ## adjusted first-stage estimates data(IBScovars) ## ANCOVA fit model including covariates anovaMod <- lm(resp~factor(dose)+gender, data=IBScovars) drFit <- coef(anovaMod)[2:5] # placebo adjusted estimates at doses vCov <- vcov(anovaMod)[2:5,2:5] dose <- sort(unique(IBScovars$dose))[-1] # no estimate for placebo ## candidate models models <- Mods(emax = c(0.5, 1), betaMod=c(1,1), doses=c(0,4)) plot(models) ## hand over placebo-adjusted estimates drFit to MCPMod MM3 <- MCPMod(dose, drFit, S=vCov, models = models, type = "general", MCTpval 27 placAdj = TRUE, Delta=0.2) plot(MM3, plotData="meansCI") ## The first example, but with critical value handed over ## this is useful, e.g. in simulation studies MM4 <- MCPMod(dose, resp, biom, models, Delta=0.5, critV = 2.31) MCTpval Calculate multiplicity adjusted p-values for multiple contrast test Description Calculate multiplicity adjusted p-values for a maximum contrast test corresponding to a set of con- trasts and given a set of observed test statistics. This function is exported as it may be a useful building block and used in more complex testing situations that are not covered by MCTtest. Most users probably don’t need to use this function. Usage MCTpval(contMat, corMat, df, tStat, alternative = c("one.sided", "two.sided"), control = mvtnorm.control()) Arguments contMat Contrast matrix to use. The individual contrasts should be saved in the columns of the matrix corMat correlation matrix of the contrasts df Degrees of freedom to assume in case ‘S’ (a general covariance matrix) is spec- ified. When ‘n’ and ‘sigma’ are specified the ones from the corresponding ANOVA model are calculated. tStat Vector of contrast test statistics alternative Character determining the alternative for the multiple contrast trend test. control A list specifying additional control parameters for the ‘qmvt’ and ‘pmvt’ calls in the code, see also ‘mvtnorm.control’ for details. Value Numeric containing the calculated p-values. Author(s) Bjoern Bornkamp 28 MCTtest References Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of dose finding studies combining multiple comparisons and modeling procedures, Journal of Biopharmaceutical Statis- tics, 16, 639–656 See Also MCTtest, optContr Examples ## need to add example MCTtest Performs multiple contrast test Description This function performs a multiple contrast test. The contrasts are either directly specified in ‘contMat’ or optimal contrasts derived from the ‘models’ argument. The directionality of the data (i.e. whether an increase or decrease in the response variable is beneficial is inferred from the ‘models’ object, see Mods). For ‘type = "normal"’ an ANCOVA model based on a homoscedastic normality assumption (with additive covariates specified in ‘addCovars’) is fitted. For ‘type = "general"’ it is assumed multivariate normally distributed estimates are specified in ‘resp’ with covariance given by ‘S’, and the contrast test statistic is calculated based on this as- sumption. Degrees of freedom specified in ‘df’. Usage MCTtest(dose, resp, data = NULL, models, S = NULL, type = c("normal", "general"), addCovars = ~1, placAdj = FALSE, alpha = 0.025, df = NULL, critV = NULL, pVal = TRUE, alternative = c("one.sided", "two.sided"), na.action = na.fail, mvtcontrol = mvtnorm.control(), contMat = NULL) Arguments dose, resp Either vectors of equal length specifying dose and response values, or names of variables in the data frame specified in ‘data’. data Data frame containing the variables referenced in dose and resp if ‘data’ is not specified it is assumed that ‘dose’ and ‘resp’ are variables referenced from data (and no vectors) models An object of class ‘Mods’, see Mods for details S The covariance matrix of ‘resp’ when ‘type = "general"’, see Description. MCTtest 29 type Determines whether inference is based on an ANCOVA model under a ho- moscedastic normality assumption (when ‘type = "normal"’), or estimates at the doses and their covariance matrix and degrees of freedom are specified di- rectly in ‘resp’, ‘S’ and ‘df’. See also fitMod and Pinheiro et al. (2014). addCovars Formula specifying additive linear covariates (for ‘type = "normal"’) placAdj Logical, if true, it is assumed that placebo-adjusted estimates are specified in ‘resp’ (only possible for ‘type = "general"’). alpha Significance level for the multiple contrast test df Specify the degrees of freedom to use in case ‘type = "general"’. If this ar- gument is missing ‘df = Inf’ is used (which corresponds to the multivariate normal distribution). For type = "normal" the degrees of freedom deduced from the AN(C)OVA fit are used and this argument is ignored. critV Supply a pre-calculated critical value. If this argument is NULL, no critical value will be calculated and the test decision is based on the p-values. If ‘critV = TRUE’ the critical value will be calculated. pVal Logical determining, whether p-values should be calculated. alternative Character determining the alternative for the multiple contrast trend test. na.action A function which indicates what should happen when the data contain NAs. mvtcontrol A list specifying additional control parameters for the ‘qmvt’ and ‘pmvt’ calls in the code, see also mvtnorm.control for details. contMat Contrast matrix to apply to the ANCOVA dose-response estimates. The con- trasts need to be in the columns of the matrix (i.e. the column sums need to be 0). Details Integrals over the multivariate t and multivariate normal distribution are calculated using the ‘mvtnorm’ package. Value An object of class MCTtest, a list containing the output. Author(s) Bjoern Bornkamp References Hothorn, T., Bretz, F., and Westfall, P. (2008). Simultaneous Inference in General Parametric Mod- els, Biometrical Journal, 50, 346–363 Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) Model-based dose finding under model uncertainty using general parametric models, Statistics in Medicine, 33, 1646–1661 See Also powMCT, optContr 30 MCTtest Examples ## example without covariates data(biom) ## define shapes for which to calculate optimal contrasts modlist <- Mods(emax = 0.05, linear = NULL, logistic = c(0.5, 0.1), linInt = c(0, 1, 1, 1), doses = c(0, 0.05, 0.2, 0.6, 1)) m1 <- MCTtest(dose, resp, biom, models=modlist) ## now calculate critical value (but not p-values) m2 <- MCTtest(dose, resp, biom, models=modlist, critV = TRUE, pVal = FALSE) ## now hand over critical value m3 <- MCTtest(dose, resp, biom, models=modlist, critV = 2.24) ## example with covariates data(IBScovars) modlist <- Mods(emax = 0.05, linear = NULL, logistic = c(0.5, 0.1), linInt = c(0, 1, 1, 1), doses = c(0, 1, 2, 3, 4)) MCTtest(dose, resp, IBScovars, models = modlist, addCovars = ~gender) ## example using general approach (fitted on placebo-adjusted scale) ancMod <- lm(resp~factor(dose)+gender, data=IBScovars) ## extract estimates and information to feed into MCTtest drEst <- coef(ancMod)[2:5] vc <- vcov(ancMod)[2:5, 2:5] doses <- 1:4 MCTtest(doses, drEst, S = vc, models = modlist, placAdj = TRUE, type = "general", df = Inf) ## example with general alternatives handed over data(biom) ## calculate contrast matrix for the step-contrasts ## represent them as linInt models models <- Mods(linInt=rbind(c(1,1,1,1), c(0,1,1,1), c(0,0,1,1), c(0,0,0,1)), doses=c(0,0.05,0.2,0.6,1)) plot(models) ## now calculate optimal contrasts for these means ## use weights from actual sample sizes weights <- as.numeric(table(biom$dose)) contMat <- optContr(models, w = weights) ## plot contrasts plot(contMat) ## perform multiple contrast test MCTtest(dose, resp, data=biom, contMat = contMat) ## example for using the Dunnett contrasts ## Dunnett contrasts doses <- sort(unique(biom$dose)) contMat <- rbind(-1, diag(4)) rownames(contMat) <- doses colnames(contMat) <- paste("D", doses[-1], sep="") migraine 31 MCTtest(dose, resp, data=biom, contMat = contMat) migraine Migraine Dose Response data Description Data set obtained from clinicaltrials.gov (NCT00712725). This was randomized placebo controlled dose-response trial for treatment of acute migraine. The primary endpoint was "pain freedom at 2 hours postdose" (a binary measurement). Usage data(migraine) Format A data frame with 517 columns corresponding to the patients that completed the trial dose a numeric vector containing the dose values painfree number of treatment responders ntrt number of subject per treatment group Source http://clinicaltrials.gov/ct2/show/results/NCT00712725 Mods Define dose-response models Description The Mods functions allows to define a set of dose-response models. The function is used as input object for a number of other different functions. The dose-response models used in this package (see drmodels for details) are of form f (d) = θ0 + θ1 f 0 (d, θ2 ) where the parameter θ2 is the only non-linear parameter and can be one- or two-dimensional, de- pending on the used model. One needs to hand over the effect at placebo and the maximum effect in the dose range, from which θ0 , θ1 are then back-calculated, the output object is of class ‘"Mods"’. This object can form the input for other functions to extract the mean response (‘getResp’) or target doses (TD and ED) corresponding to the models. It is also needed as input to the functions powMCT, optDesign 32 Mods Some models, for example the beta model (‘scal’) and the linlog model (‘off’) have parameters that are not estimated from the data, they need to be specified via the ‘addArgs’ argument. The default plot method for ‘Mods’ objects is based on a plot using the ‘lattice’ package for backward compatibility. The function ‘plotMods’ function implements a plot using the ‘ggplot2’ package. NOTE: If a decreasing effect is beneficial for the considered response variable it needs to specified here, either by using ‘direction = "decreasing"’ or by specifying a negative "maxEff" argument. Usage Mods(..., doses, placEff = 0, maxEff, direction = c("increasing", "decreasing"), addArgs=NULL, fullMod = FALSE) getResp(fmodels, doses) ## S3 method for class 'Mods' plot(x, nPoints = 200, superpose = FALSE, xlab = "Dose", ylab = "Model means", modNams = NULL, plotTD = FALSE, Delta, ...) plotMods(ModsObj, nPoints = 200, superpose = FALSE, xlab = "Dose", ylab = "Model means", modNams = NULL, trafo = function(x) x) Arguments ... In function Mods: Dose-response model names with parameter values specifying the guesstimates for the θ2 parameters. See drmodels for a complete list of dose-response mod- els implemented. See below for an example specification. In function plot.Mods: Additional arguments to the ‘xyplot’ call. doses Dose levels to be used, this needs to include placebo. addArgs List containing two entries named "scal" and "off" for the "betaMod" and "lin- log". When addArgs is NULL the following defaults are used ‘list(scal = 1.2*max(doses), off = 0.01*max(doses), nodes = doses)’. fullMod Logical determining, whether the model parameters specified in the Mods func- tion (via the ... argument) should be interpreted as standardized or the full model parameters. placEff, maxEff Specify used placebo effect and the maximum effect over placebo. Either a numeric vector of the same size as the number of candidate models or of length one. When these parameters are not specified ‘placEff = 0’ is assumed, for ‘maxEff Mods 33 = 1’ is assumed, if ‘direction = "increasing"’ and ‘maxEff = -1’ is assumed, for ‘direction = "decreasing"’. direction Character determining whether the beneficial direction is ‘increasing’ or ‘decreasing’ with increasing dose levels. This argument is ignored if ‘maxEff’ is specified. fmodels An object of class Mods Delta Delta: The target effect size use for the target dose (TD) (Delta should be > 0). x Object of class Mods with type Mods nPoints Number of points for plotting superpose Logical determining, whether model plots should be superposed xlab, ylab Label for y-axis and x-axis. modNams When ‘modNams == NULL’, the names for the panels are determined by the un- derlying model functions, otherwise the contents of ‘modNams’ are used. plotTD ‘plotTD’ is a logical determining, whether the TD should be plotted. ‘Delta’ is the target effect to estimate for the TD. ModsObj For function ‘plotMods’ the ‘ModsObj’ should contain an object of class ‘Mods’. trafo For function ‘plotMods’ there is the option to plot the candidate model set on a transformed scale (e.g. probability scale if the candidate models are formulated on log-odds scale). The default for ‘trafo’ is the identity function. Value Returns an object of class ‘"Mods"’. The object contains the specified model parameter values and the derived linear parameters (based on ‘"placEff"’ and ‘"maxEff"’) in a list. Author(s) Bjoern Bornkamp References Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of dose finding studies combining multiple comparisons and modeling procedures, Journal of Biopharmaceutical Statis- tics, 16, 639–656 See Also Mods, drmodels, optDesign, powMCT Examples ## Example on how to specify candidate models ## Suppose one would like to use the following models with the specified ## guesstimates for theta2, in a situation where the doses to be used are ## 0, 0.05, 0.2, 0.6, 1 ## Model guesstimate(s) for theta2 parameter(s) (name) 34 Mods ## linear - ## linear in log - ## Emax 0.05 (ED50) ## Emax 0.3 (ED50) ## exponential 0.7 (delta) ## quadratic -0.85 (delta) ## logistic 0.4 0.09 (ED50, delta) ## logistic 0.3 0.1 (ED50, delta) ## betaMod 0.3 1.3 (delta1, delta2) ## sigmoid Emax 0.5 2 (ED50, h) ## linInt 0.5 0.75 1 1 (perc of max-effect at doses) ## linInt 0.5 1 0.7 0.5 (perc of max-effect at doses) ## for the linInt model one specifies the effect over placebo for ## each active dose. ## The fixed "scal" parameter of the betaMod is set to 1.2 ## The fixed "off" parameter of the linlog is set to 0.1 ## These (standardized) candidate models can be specified as follows models <- Mods(linear = NULL, linlog = NULL, emax = c(0.05, 0.3), exponential = 0.7, quadratic = -0.85, logistic = rbind(c(0.4, 0.09), c(0.3, 0.1)), betaMod = c(0.3, 1.3), sigEmax = c(0.5, 2), linInt = rbind(c(0.5, 0.75, 1, 1), c(0.5, 1, 0.7, 0.5)), doses = c(0, 0.05, 0.2, 0.6, 1), addArgs = list(scal=1.2, off=0.1)) ## "models" now contains the candidate model set, as placEff, maxEff and ## direction were not specified a placebo effect of 0 and an effect of 1 ## is assumed ## display of specified candidate set using default plot (based on lattice) plot(models) ## display using ggplot2 plotMods(models) ## example for creating a candidate set with decreasing response doses <- c(0, 10, 25, 50, 100, 150) fmodels <- Mods(linear = NULL, emax = 25, logistic = c(50, 10.88111), exponential = 85, betaMod = rbind(c(0.33, 2.31), c(1.39, 1.39)), linInt = rbind(c(0, 1, 1, 1, 1), c(0, 0, 1, 1, 0.8)), doses=doses, placEff = 0.5, maxEff = -0.4, addArgs=list(scal=200)) plot(fmodels) plotMods(fmodels) ## some customizations (different model names, symbols, line-width) plot(fmodels, lwd = 3, pch = 3, cex=1.2, col="red", modNams = paste("mod", 1:8, sep="-")) ## for a full-model object one can calculate the responses ## in a matrix getResp(fmodels, doses=c(0, 20, 100, 150)) mvtnorm.control 35 ## calculate doses giving an improvement of 0.3 over placebo TD(fmodels, Delta=0.3, direction = "decreasing") ## discrete version TD(fmodels, Delta=0.3, TDtype = "discrete", doses=doses, direction = "decreasing") ## doses giving 50% of the maximum effect ED(fmodels, p=0.5) ED(fmodels, p=0.5, EDtype = "discrete", doses=doses) plot(fmodels, plotTD = TRUE, Delta = 0.3) ## example for specifying all model parameters (fullMod=TRUE) fmods <- Mods(emax = c(0, 1, 0.1), linear = cbind(c(-0.4,0), c(0.2,0.1)), sigEmax = c(0, 1.1, 0.5, 3), doses = 0:4, fullMod = TRUE) getResp(fmods, doses=seq(0,4,length=11)) ## calculate doses giving an improvement of 0.3 over placebo TD(fmods, Delta=0.3) ## discrete version TD(fmods, Delta=0.3, TDtype = "discrete", doses=0:4) ## doses giving 50% of the maximum effect ED(fmods, p=0.5) ED(fmods, p=0.5, EDtype = "discrete", doses=0:4) plot(fmods) mvtnorm.control Control options for pmvt and qmvt functions Description Returns a list (an object of class "GenzBretz") with control parameters for the ‘pmvt’ and ‘qmvt’ functions from the ‘mvtnorm’ package. Note that the DoseFinding package always uses "GenzBretz" algorithm. See the mvtnorm documentation for more information. Usage mvtnorm.control(maxpts = 30000, abseps = 0.001, releps = 0, interval = NULL) Arguments maxpts Maximum number of function values as integer. abseps Absolute error tolerance as double. releps Relative error tolerance as double. interval Interval to be searched, when the quantile is calculated. 36 neurodeg neurodeg Neurodegenerative disease simulated longitudinal dose-finding data set Description This simulated data set is motivated by a real Phase 2 clinical study of a new drug for a neurode- generative disease. The state of the disease is measured through a functional scale, with smaller values corresponding to more severe neurodeterioration. The goal of the drug is to reduce the rate of disease progression, which is measured by the linear slope of the functional scale over time. The trial design includes placebo and four doses: 1, 3, 10, and 30 mg, with balanced allocation of 50 patients per arm. Patients are followed up for one year, with measurements of the functional scale being taken at baseline and then every three months. The functional scale response is assumed to be normally distributed and, based on historical data, it is believed that the longitudinal progression of the functional scale over the one year of follow up can be modeled a simple linear trend. See the example below on how to analyse this type of data. This data set was used in Pinheiro et al. (2014) to illustrate the generalized MCPMod methodology. Usage data(neurodeg) Format A data frame with 100 observations on the following 2 variables. resp a numeric vector containing the response values dose a numeric vector containing the dose values id Patient ID time time of measurement Source Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) Model-based dose finding under model uncertainty using general parametric models, Statistics in Medicine, 33, 1646–1661 Examples ## Not run: ## reproduce analysis from Pinheiro et al. (2014) data(neurodeg) ## first fit the linear mixed effect model library(nlme) fm <- lme(resp ~ as.factor(dose):time, neurodeg, ~time|id, method = "ML") muH <- fixef(fm)[-1] # extract estimates covH <- vcov(fm)[-1,-1] optContr 37 ## derive optimal contrasts for candidate shapes doses <- c(0, 1, 3, 10, 30) mod <- Mods(emax = 1.11, quadratic= -0.022, exponential = 8.867, linear = NULL, doses = doses) # contMat <- optContr(mod, S=covH) # calculate optimal contrasts ## multiple contrast test MCTtest(doses, muH, S=covH, type = "general", critV = TRUE, contMat=contMat) ## fit the emax model fitMod(doses, muH, S=covH, model="emax", type = "general", bnds=c(0.1, 10)) ## alternatively one can also fit the model using nlme nlme(resp ~ b0 + (e0 + eM * dose/(ed50 + dose))*time, neurodeg, fixed = b0 + e0 + eM + ed50 ~ 1, random = b0 + e0 ~ 1 | id, start = c(200, -4.6, 1.6, 3.2)) ## both approaches lead to rather similar results ## End(Not run) optContr Calculate optimal contrasts Description This function calculates a contrast vectors that are optimal for detecting certain alternatives. The contrast is optimal in the sense of maximizing the non-centrality parameter of the underlying con- trast test statistic: c0 µ √ c0 Sc Here µ is the mean vector under the alternative and S the covariance matrix associated with the estimate of µ. The optimal contrast is given by µ0 S −1 1   opt −1 c ∝S µ − 0 −1 , 1S 1 see Pinheiro et al. (2014). Note that the directionality (i.e. whether in "increase" in the response variable is beneficial or a "decrease", is inferred from the specified ‘models’ object, see Mods for details). Constrained contrasts (type = "constrained") add the additional constraint in the optimization that the sign of the contrast coefficient for control and active treatments need to be different. The quadratic programming algorithm from the quadprog package is used to calculate the contrasts. 38 optContr Usage optContr(models, doses, w, S, placAdj = FALSE, type = c("unconstrained", "constrained")) ## S3 method for class 'optContr' plot(x, superpose = TRUE, xlab = "Dose", ylab = NULL, plotType = c("contrasts", "means"), ...) plotContr(optContrObj, xlab = "Dose", ylab = "Contrast coefficients") Arguments models An object of class ‘Mods’ defining the dose-response shapes for which to calcu- late optimal contrasts. doses Optional argument. If this argument is missing the doses attribute in the ‘Mods’ object specified in ‘models’ is used. w, S Arguments determining the matrix S used in the formula for the optimal con- trasts. Exactly one of ‘w’ and ‘S’ has to be specified. Note that ‘w’ and ‘S’ only have to be specified up to proportionality • w Vector specifying weights for the different doses, in the formula for cal- culation of the optimal contrasts. Specifying a weights vector is equivalent to specifying S=diag(1/w) (e.g. in a homoscedastic case with unequal sam- ple sizes, ‘w’ should be proportional to the group sample sizes). • S Directly specify a matrix proportional to the covariance matrix to use. placAdj Logical determining, whether the contrasts should be applied to placebo-adjusted estimates. If yes the returned coefficients are no longer contrasts (i.e. do not sum to 0). However, the result of multiplying of this "contrast" matrix with the placebo adjusted estimates, will give the same results as multiplying the original contrast matrix to the unadjusted estimates. type For ‘type = "constrained"’ the contrast coefficients of the zero dose group are constrained to be different from the coefficients of the active treatment groups. So that a weighted sum of the active treatments is compared against the zero dose group. For an increasing trend the coefficient of the zero dose group is negative and all other coefficients have to be positive (for a decreasing trend the other way round). x, superpose, xlab, ylab, plotType Arguments for the plot method for optContr objects. plotType determines, whether the contrasts or the underlying (standardized) mean matrix should be plotted. optContrObj For function ‘plotContr’ the ‘optContrObj’ should contain an object of class ‘optContr’. ... Additional arguments for plot method optContr 39 Value Object of class ‘optContr’. A list containing entries contMat and muMat (i.e. contrast, mean and correlation matrix). Author(s) Bjoern Bornkamp References Bretz, F., Pinheiro, J. C., and Branson, M. (2005), Combining multiple comparisons and modeling techniques in dose-response studies, Biometrics, 61, 738–748 Pinheiro, J. C., Bornkamp, B., Glimm, E. and Bretz, F. (2014) Model-based dose finding under model uncertainty using general parametric models, Statistics in Medicine, 33, 1646–1661 See Also MCTtest Examples doses <- c(0,10,25,50,100,150) models <- Mods(linear = NULL, emax = 25, logistic = c(50, 10.88111), exponential= 85, betaMod=rbind(c(0.33,2.31), c(1.39,1.39)), doses = doses, addArgs = list(scal = 200)) contMat <- optContr(models, w = rep(50,6)) plot(contMat) plotContr(contMat) # display contrasts using ggplot2 ## now we would like the "contrasts" for placebo adjusted estimates dosPlac <- doses[-1] ## matrix proportional to cov-matrix of plac. adj. estimates for balanced data S <- diag(5)+matrix(1, 5,5) ## note that we explicitly hand over the doses here contMat0 <- optContr(models, doses=dosPlac, S = S, placAdj = TRUE) ## -> contMat0 is no longer a contrast matrix (columns do not sum to 0) colSums(contMat0$contMat) ## calculate contrast matrix for unadjusted estimates from this matrix ## (should be same as above) aux <- rbind(-colSums(contMat0$contMat), contMat0$contMat) t(t(aux)/sqrt(colSums(aux^2))) ## compare to contMat$contMat ## now calculate constrained contrasts optContr(models, w = rep(50,6), type = "constrained") optContr(models, doses=dosPlac, S = S, placAdj = TRUE, type = "constrained") 40 optDesign optDesign Function to calculate optimal designs Description Given a set of models (with full parameter values and model probabilities) the ‘optDesign’ function calculates the optimal design for estimating the dose-response model parameters (D-optimal) or the design for estimating the target dose (TD-optimal design) (see Dette, Bretz, Pepelyshev and Pinheiro (2008)), or a mixture of these two criteria. The design can be plotted (together with the candidate models) using ‘plot.design’. ‘calcCrit’ calculates the design criterion for a discrete set of design(s). ‘rndDesign’ provides efficient rounding for the calculated continous design to a finite sample size. Usage optDesign(models, probs, doses, designCrit = c("Dopt", "TD", "Dopt&TD", "userCrit"), Delta, standDopt = TRUE, weights, nold = rep(0, length(doses)), n, control=list(), optimizer = c("solnp", "Nelder-Mead", "nlminb", "exact"), lowbnd = rep(0, length(doses)), uppbnd = rep(1, length(doses)), userCrit, ...) ## S3 method for class 'DRdesign' plot(x, models, lwdDes = 10, colDes = rgb(0,0,0,0.3), ...) calcCrit(design, models, probs, doses, designCrit = c("Dopt", "TD", "Dopt&TD"), Delta, standDopt = TRUE, weights, nold = rep(0, length(doses)), n) rndDesign(design, n, eps = 0.0001) Arguments models An object of class ‘c(Mods, fullMod)’, see the Mods function for details. When an TD optimal design should be calculated, the TD needs to exist for all models. If a D-optimal design should be calculated, you need at least as many doses as there are parameters in the specified models. probs Vector of model probabilities for the models specified in ‘models’, assumed in the same order as specified in models doses Optional argument. If this argument is missing the doses attribute in the ‘c(Mods, fullMod)’ object specified in ‘models’ is used. designCrit Determines which type of design to calculate. "TD&Dopt" uses both optimality criteria with equal weight. optDesign 41 Delta Target effect needed for calculating "TD" and "TD&Dopt" type designs. standDopt Logical determining, whether the D-optimality criterion (specifically the log- determinant) should be standardized by the number of parameters in the model or not (only of interest if type = "Dopt" or type = "TD&Dopt"). This is of interest, when there is more than one model class in the candidate model set (traditionally standardization this is done in the optimal design literature). weights Vector of weights associated with the response at the doses. Needs to be of the same length as the ‘doses’. This can be used to calculate designs for het- eroscedastic or for generalized linear model situations. nold, n When calculating an optimal design at an interim analysis, ‘nold’ specifies the vector of sample sizes already allocated to the different doses, and ‘n’ gives sample size for the next cohort. For ‘optimizer = "exact"’ one always needs to specify the total sample size via ‘n’. control List containing control parameters passed down to numerical optimization algo- rithms (optim, nlminb or solnp function). For ‘type = "exact"’ this should be a list with possible entries ‘maxvls1’ and ‘maxvls2’, determining the maximum number of designs allowed for passing to the criterion function (default ‘maxvls2=1e5’) and for creating the initial unre- stricted matrix of designs (default ‘maxvls1=1e6’). In addition there can be an entry ‘groupSize’ in case the patients are allocated a minimum group size is required. optimizer Algorithm used for calculating the optimal design. Options "Nelder-Mead" and "nlminb" use the optim and nlminb function and use a trigonometric transfor- mation to turn the constrained optimization problem into an unconstrained one (see Atkinson, Donev and Tobias, 2007, pages 130,131). Option "solnp" uses the solnp function from the Rsolnp package, which imple- ments an optimizer for non-linear optimization under general constraints. Option "exact" tries all given combinations of ‘n’ patients to the given dose groups (subject to the bounds specified via ‘lowbnd’ and ‘uppbnd’) and reports the best design. When patients are only allowed to be allocated in groups of a certain ‘groupSize’, this can be adjusted via the control argument. ‘n/groupSize’ and ‘length(doses)’ should be rather small for this approach to be feasible. When the number of doses is small (<8) usually ‘"Nelder-Mead"’ and ‘"nlminb"’ are best suited (‘"nlminb"’ is usually a bit faster but less stable than ‘"Nelder-Mead"’). For a larger number of doses ‘"solnp"’ is the most reliable option (but also slowest) (‘"Nelder-Mead"’ and ‘"nlminb"’ often fail). When the sample size is small ‘"exact"’ provides the optimal solution rather quickly. lowbnd, uppbnd Vectors of the same length as dose vector specifying upper and lower limits for the allocation weights. This option is only available when using the "solnp" and "exact" optimizers. userCrit User defined design criterion, should be a function that given a vector of al- location weights and the doses returns the criterion function. When specified ‘models’ does not need to be handed over. 42 optDesign The first argument of ‘userCrit’ should be the vector of design weights, while the second argument should be the ‘doses’ argument (see example below). Ad- ditional arguments to ‘userCrit’ can be passed via ... ... For function ‘optDesign’ these are additional arguments passed to ‘userCrit’. For function ‘plot.design’ these are additional parameters passed to plot.Mods. design Argument for ‘rndDesign’ and ‘calcCrit’ functions: Numeric vector (or ma- trix) of allocation weights for the different doses. The rows of the matrices need to sum to 1. Alternatively also an object of class "DRdesign" can be used for ‘rndDesign’. Note that there should be at least as many design points available as there are parameters in the dose-response models selected in models (other- wise the code returns an NA). eps Argument for ‘rndDesign’ function: Value under which elements of w will be regarded as 0. x Object of class ‘DRdesign’ (for ‘plot.design’) lwdDes, colDes Line width and color of the lines plotted for the design (in ‘plot.design’) Details LetPMm denote the Fisher information matrix under model m (up to proportionality). Mm is given by ai wi giT gi , where ai is the allocation weight to dose i, wi the weight for dose i specified via ‘weights’ and gi the gradient vector of model m evaluated at dose i. For ‘designCrit = "Dopt"’ the code minimizes the design criterion X − pm /km log(det(Mm )) m where pm is the probability for model m and km is the number of parameters for model m. When ‘standDopt = FALSE’ the km are all assumed to be equal to one. For ‘designCrit = "TD"’ the code minimizes the design criterion X pm log(vm ) m where pm is the probability for model m and vm is proportional to the asymptotic variance of the TD estimate and given by b0m Mm − bm (see Dette et al. (2008), p. 1227 for details). For ‘designCrit = "Dopt&TD"’ the code minimizes the design criterion X pm (−0.5 log(det(Mm ))/km + 0.5 log(vm )) m Again, for ‘standDopt = FALSE’ the km are all assumed to be equal to one. For details on the ‘rndDesign’ function, see Pukelsheim (1993), Chapter 12. optDesign 43 Note In some cases (particularly when the number of doses is large, e.g. 7 or larger) it might be necessary to allow a larger number of iterations in the algorithm (via the argument ‘control’), particularly for the Nelder-Mead algorithm. Alternatively one can use the solnp optimizer that is usually the most reliable, but not fastest option. Author(s) Bjoern Bornkamp References Atkinson, A.C., Donev, A.N. and Tobias, R.D. (2007). Optimum Experimental Designs, with SAS, Oxford University Press Dette, H., Bretz, F., Pepelyshev, A. and Pinheiro, J. C. (2008). Optimal Designs for Dose Finding Studies, Journal of the American Statisical Association, 103, 1225–1237 Pinheiro, J.C., Bornkamp, B. (2017) Designing Phase II Dose-Finding Studies: Sample Size, Doses and Dose Allocation Weights, in O’Quigley, J., Iasonos, A. and Bornkamp, B. (eds) Handbook of methods for designing, monitoring, and analyzing dose-finding trials, CRC press Pukelsheim, F. (1993). Optimal Design of Experiments, Wiley See Also Mods, drmodels Examples ## calculate designs for Emax model doses <- c(0, 10, 100) emodel <- Mods(emax = 15, doses=doses, placEff = 0, maxEff = 1) optDesign(emodel, probs = 1) ## TD-optimal design optDesign(emodel, probs = 1, designCrit = "TD", Delta=0.5) ## 50-50 mixture of Dopt and TD optDesign(emodel, probs = 1, designCrit = "Dopt&TD", Delta=0.5) ## use dose levels different from the ones specified in emodel object des <- optDesign(emodel, probs = 1, doses = c(0, 5, 20, 100)) ## plot models overlaid by design plot(des, emodel) ## round des to a sample size of exactly 90 patients rndDesign(des, n=90) ## using the round function would lead to 91 patients ## illustrating different optimizers (see Note above for more comparison) optDesign(emodel, probs=1, optimizer="Nelder-Mead") optDesign(emodel, probs=1, optimizer="nlminb") ## optimizer solnp (the default) can deal with lower and upper bounds: optDesign(emodel, probs=1, designCrit = "TD", Delta=0.5, optimizer="solnp", lowbnd = rep(0.2,3)) ## exact design using enumeration of all possibilites optDesign(emodel, probs=1, optimizer="exact", n = 30) 44 optDesign ## also allows to fix minimum groupSize optDesign(emodel, probs=1, designCrit = "TD", Delta=0.5, optimizer="exact", n = 30, control = list(groupSize=5)) ## optimal design at interim analysis ## assume there are already 10 patients on each dose and there are 30 ## left to randomize, this calculates the optimal increment design optDesign(emodel, 1, designCrit = "TD", Delta=0.5, nold = c(10, 10, 10), n=30) ## use a larger candidate model set doses <- c(0, 10, 25, 50, 100, 150) fmods <- Mods(linear = NULL, emax = 25, exponential = 85, linlog = NULL, logistic = c(50, 10.8811), doses = doses, addArgs=list(off=1), placEff=0, maxEff=0.4) probs <- rep(1/5, 5) # assume uniform prior desDopt <- optDesign(fmods, probs, optimizer = "nlminb") desTD <- optDesign(fmods, probs, designCrit = "TD", Delta = 0.2, optimizer = "nlminb") desMix <- optDesign(fmods, probs, designCrit = "Dopt&TD", Delta = 0.2) ## plot design and truth plot(desMix, fmods) ## illustrate calcCrit function ## calculate optimal design for beta model doses <- c(0, 0.49, 25.2, 108.07, 150) models <- Mods(betaMod = c(0.33, 2.31), doses=doses, addArgs=list(scal=200), placEff=0, maxEff=0.4) probs <- 1 deswgts <- optDesign(models, probs, designCrit = "Dopt", control=list(maxit=1000)) ## now compare this design to equal allocations on ## 0, 10, 25, 50, 100, 150 doses2 <- c(0, 10, 25, 50, 100, 150) design2 <- c(1/6, 1/6, 1/6, 1/6, 1/6, 1/6) crit2 <- calcCrit(design2, models, probs, doses2, designCrit = "Dopt") ## ratio of determinants (returned criterion value is on log scale) exp(deswgts$crit-crit2) ## example for calculating an optimal design for logistic regression doses <- c(0, 0.35, 0.5, 0.65, 1) fMod <- Mods(linear = NULL, doses=doses, placEff=-5, maxEff = 10) ## now calculate weights to use in the covariance matrix mu <- as.numeric(getResp(fMod, doses=doses)) mu <- 1/(1+exp(-mu)) weights <- mu*(1-mu) des <- optDesign(fMod, 1, doses, weights = weights) ## one can also specify a user defined criterion function ## here D-optimality for cubic polynomial planMod 45 CubeCrit <- function(w, doses){ X <- cbind(1, doses, doses^2, doses^3) CVinv <- crossprod(X*w) -log(det(CVinv)) } optDesign(doses = c(0,0.05,0.2,0.6,1), designCrit = "userCrit", userCrit = CubeCrit, optimizer = "nlminb") planMod Evaluate performance metrics for fitting dose-response models Description This function evaluates, the performance metrics for fitting dose-response models (using asymptotic approximations or simulations). Note that some metrics are available via the print method and others only via the summary method applied to planMod objects. The implemented metrics are • Root of the mean-squared error to estimate the placebo-adjusted dose-response averaged over the used dose-levels, i.e. a rather discrete set (dRMSE). Available via the print method of planMod objects. • Root of the mean-squared error to estimate the placebo-adjusted dose-response (cRMSE) aver- aged over fine (almost continuous) grid at 101 equally spaced values between placebo and the maximum dose. NOTE: Available via the summary method applied to planMod objects. • Ratio of the placebo-adjusted mean-squared error (at the observed doses) of model-based vs ANOVA approach (Eff-vs-ANOVA). This can be interpreted on the sample size scale. NOTE: Available via the summary method applied to planMod objects. • Power that the (unadjusted) one-sided ‘1-alpha’ confidence interval comparing the dose with maximum effect vs placebo is larger than ‘tau’. By default ‘alpha = 0.025’ and ‘tau = 0’ (Pow(maxDose)). Available via the print method of planMod objects. • Probability that the EDp estimate is within the true [EDpLB, EDpUB] (by default ‘p=0.5’, ‘pLB=0.25’ and ‘pUB=0.75’). This metric gives an idea on the ability to characterize the in- creasing part of the dose-response curve (P(EDp)). Available via the print method of planMod objects. • Length of the quantile range for a target dose (TD or EDp). This is calculated by taking the difference of the dUB and dLB quantile of the empirical distribution of the dose esti- mates. (lengthTDCI and lengthEDpCI). It is NOT calculated by calculating confidence inter- val lengths in each simulated data-set and taking the mean. NOTE: Available via the summary method of planMod objects. A plot method exists to summarize dose-response and dose estimations graphically. 46 planMod Usage planMod(model, altModels, n, sigma, S, doses, asyApprox = TRUE, simulation = FALSE, alpha = 0.025, tau = 0, p = 0.5, pLB = 0.25, pUB = 0.75, nSim = 100, cores = 1, showSimProgress = TRUE, bnds, addArgs = NULL) ## S3 method for class 'planMod' plot(x, type = c("dose-response", "ED", "TD"), p, Delta, placAdj = FALSE, xlab, ylab, ...) ## S3 method for class 'planMod' summary(object, digits = 3, len = 101, Delta, p, dLB = 0.05, dUB = 0.95, ...) Arguments model Character vector determining the dose-response model(s) to be used for fit- ting the data. When more than one dose-response model is provided the best fitting model is chosen using the AIC. Built-in models are "linlog", "linear", "quadratic", "emax", "exponential", "sigEmax", "betaMod" and "logistic" (see drmodels). altModels An object of class ‘Mods’, defining the true mean vectors under which operating characteristics should be calculated. n, sigma, S Either a vector ‘n’ and ‘sigma’ or ‘S’ need to be specified. When ‘n’ and ‘sigma’ are specified it is assumed computations are made for a normal ho- moscedastic ANOVA model with group sample sizes given by ‘n’ and resid- ual standard deviation ‘sigma’, i.e. the covariance matrix used for the esti- mates is thus sigma^2*diag(1/n) and the degrees of freedom are calculated as sum(n)-nrow(contMat). When a single number is specified for ‘n’ it is as- sumed this is the sample size per group and balanced allocations are used. When ‘S’ is specified this will be used as covariance matrix for the estimates. doses Doses to use asyApprox, simulation Logicals determining, whether asymptotic approximations or simulations should be calculated. If multiple models are specified in ‘model’ asymptotic approxi- mations are not available. alpha, tau Significance level for the one-sided confidence interval for model-based contrast of best dose vs placebo. Tau is the threshold to compare the confidence interval limit to. CI(MaxDCont) gives the percentage that the bound of the confidence interval was larger than tau. p, pLB, pUB p determines the type of EDp to estimate. pLB and pUB define the bounds for the EDp estimate. The performance metric Pr(Id-ED) gives the percentage that the estimated EDp was within the true EDpLB and EDpUB. nSim Number of simulations planMod 47 cores Number of cores to use for simulations. By default 1 cores is used, note that cores > 1 will have no effect Windows, as the mclapply function is used inter- nally. showSimProgress In case of simulations show the progress using a progress-bar. bnds Bounds for non-linear parameters. This needs to be a list with list entries corre- sponding to the selected bounds. The names of the list entries need to correspond to the model names. The defBnds function provides the default selection. addArgs See the corresponding argument in function fitMod. This argument is directly passed to fitMod. x An object of class planMod type Type of plot to produce Delta Additional arguments determining what dose estimate to plot, when ‘type = "ED"’ or ‘type = "TD"’ placAdj When ‘type = "dose-response"’, this determines whether dose-response esti- mates are shown on placebo-adjusted or original scale xlab, ylab Labels for the plot (ylab only applies for ‘type = "dose-response"’) len Number of equally spaced points to determine the mean-squared error on a grid (cRMSE). dLB, dUB Which quantiles to use for calculation of lengthTDCI and lengthEDpCI. By default dLB = 0.05 and dUB = 0.95, so that this corresponds to a 90% interval. object, digits object: A planMod object. digits: Digits in summary output ... Additional arguments (currently ignored) Author(s) Bjoern Bornkamp References TBD See Also fitMod Examples ## Not run: doses <- c(0,10,25,50,100,150) fmodels <- Mods(linear = NULL, emax = 25, logistic = c(50, 10.88111), exponential= 85, betaMod=rbind(c(0.33,2.31),c(1.39,1.39)), doses = doses, addArgs=list(scal = 200), placEff = 0, maxEff = 0.4) sigma <- 1 48 powMCT n <- rep(62, 6)*2 model <- "quadratic" pObj <- planMod(model, fmodels, n, sigma, doses=doses, simulation = TRUE, alpha = 0.025, nSim = 200, p = 0.5, pLB = 0.25, pUB = 0.75) print(pObj) ## to get additional metrics (e.g. Eff-vs-ANOVA, cRMSE, lengthTDCI, ...) summary(pObj, p = 0.5, Delta = 0.3) plot(pObj) plot(pObj, type = "TD", Delta=0.3) plot(pObj, type = "ED", p = 0.5) ## End(Not run) powMCT Calculate power for multiple contrast test Description Calculate power for a multiple contrast test for a set of specified alternatives. Usage powMCT(contMat, alpha = 0.025, altModels, n, sigma, S, placAdj=FALSE, alternative = c("one.sided", "two.sided"), df, critV, control = mvtnorm.control()) Arguments contMat Contrast matrix to use. The individual contrasts should be saved in the columns of the matrix alpha Significance level to use altModels An object of class ‘Mods’, defining the mean vectors under which the power should be calculated n, sigma, S Either a vector ‘n’ and ‘sigma’ or ‘S’ need to be specified. When ‘n’ and ‘sigma’ are specified it is assumed computations are made for a normal ho- moscedastic ANOVA model with group sample sizes given by ‘n’ and resid- ual standard deviation ‘sigma’, i.e. the covariance matrix used for the esti- mates is thus sigma^2*diag(1/n) and the degrees of freedom are calculated as sum(n)-nrow(contMat). When a single number is specified for ‘n’ it is as- sumed this is the sample size per group and balanced allocations are used. When ‘S’ is specified this will be used as covariance matrix for the estimates. powMCT 49 placAdj Logical, if true, it is assumed that the standard deviation or variance matrix of the placebo-adjusted estimates are specified in ‘sigma’ or ‘S’, respectively. The contrast matrix has to be produced on placebo-adjusted scale, see optContr, so that the coefficients are no longer contrasts (i.e. do not sum to 0). alternative Character determining the alternative for the multiple contrast trend test. df Degrees of freedom to assume in case ‘S’ (a general covariance matrix) is spec- ified. When ‘n’ and ‘sigma’ are specified the ones from the corresponding ANOVA model are calculated. critV Critical value, if equal to ‘TRUE’ the critical value will be calculated. Otherwise one can directly specify the critical value here. control A list specifying additional control parameters for the ‘qmvt’ and ‘pmvt’ calls in the code, see also ‘mvtnorm.control’ for details. Value Numeric containing the calculated power values Author(s) Bjoern Bornkamp References Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of dose finding studies combining multiple comparisons and modeling procedures, Journal of Biopharmaceutical Statis- tics, 16, 639–656 See Also powN, sampSizeMCT, MCTtest, optContr, Mods Examples ## look at power under some dose-response alternatives ## first the candidate models used for the contrasts doses <- c(0,10,25,50,100,150) ## define models to use as alternative fmodels <- Mods(linear = NULL, emax = 25, logistic = c(50, 10.88111), exponential= 85, betaMod=rbind(c(0.33,2.31),c(1.39,1.39)), doses = doses, addArgs=list(scal = 200), placEff = 0, maxEff = 0.4) ## plot alternatives plot(fmodels) ## power for to detect a trend contMat <- optContr(fmodels, w = 1) powMCT(contMat, altModels = fmodels, n = 50, alpha = 0.05, sigma = 1) ## Not run: ## power under the Dunnett test 50 sampSize ## contrast matrix for Dunnett test with informative names contMatD <- rbind(-1, diag(5)) rownames(contMatD) <- doses colnames(contMatD) <- paste("D", doses[-1], sep="") powMCT(contMatD, altModels = fmodels, n = 50, alpha = 0.05, sigma = 1) ## now investigate power of the contrasts in contMat under "general" alternatives altFmods <- Mods(linInt = rbind(c(0, 1, 1, 1, 1), c(0.5, 1, 1, 1, 0.5)), doses=doses, placEff=0, maxEff=0.5) plot(altFmods) powMCT(contMat, altModels = altFmods, n = 50, alpha = 0.05, sigma = 1) ## now the first example but assume information only on the ## placebo-adjusted scale ## for balanced allocations and 50 patients with sigma = 1 one obtains ## the following covariance matrix S <- 1^2/50*diag(6) ## now calculate variance of placebo adjusted estimates CC <- cbind(-1,diag(5)) V <- (CC)%*%S%*%t(CC) linMat <- optContr(fmodels, doses = c(10,25,50,100,150), S = V, placAdj = TRUE) powMCT(linMat, altModels = fmodels, placAdj=TRUE, alpha = 0.05, S = V, df=6*50-6) # match df with the df above ## End(Not run) sampSize Sample size calculations Description The ‘sampSize’ function implements a bisection search algorithm for sample size calculation. The user can hand over a general target function (via ‘targFunc’) that is then iterated so that a certain ‘target’ is achieved. The ‘sampSizeMCT’ is a convenience wrapper of ‘sampSize’ for multiple contrast tests using the power as target function. The ‘targN’ functions calculates a general target function for different given sample sizes. The ‘powN’ function is a convenience wrapper of ‘targN’ for multiple contrast tests using the power as target function. Usage sampSize(upperN, lowerN = floor(upperN/2), targFunc, target, tol = 0.001, alRatio, Ntype = c("arm", "total"), verbose = FALSE) sampSizeMCT(upperN, lowerN = floor(upperN/2), ..., power, sumFct = mean, tol = 0.001, alRatio, Ntype = c("arm", "total"), sampSize 51 verbose = FALSE) targN(upperN, lowerN, step, targFunc, alRatio, Ntype = c("arm", "total"), sumFct = c("min", "mean", "max")) powN(upperN, lowerN, step, ..., alRatio, Ntype = c("arm", "total"), sumFct = c("min", "mean", "max")) ## S3 method for class 'targN' plot(x, superpose = TRUE, line.at = NULL, xlab = NULL, ylab = NULL, ...) Arguments upperN, lowerN Upper and lower bound for the target sample size. lowerN defaults to floor(upperN/2). step Only needed for functions ‘targN’ and ‘powN’. Stepsize for the sample size at which the target function is calculated. The steps are calculated via seq(lowerN,upperN,by=step). targFunc, target The target function needs to take as an input the vector of sample sizes in the different dose groups. For ‘sampSize’ it needs to return a univariate number. For function ‘targN’ it should return a numerical vector. Example: ‘targFunc’ could be a function that calculates the power of a test, and ‘target’ the desired target power value. For function ‘sampSize’ the bisection search iterates the sample size so that a specific target value is achieved (the implicit assumption is that targFunc is monotonically increasing in the sample size). Function ‘targN’ simply calculates ‘targFunc’ for a given set of sample sizes. tol A positive numeric value specifying the tolerance level for the bisection search algorithm. Bisection is stopped if the ‘targFunc’ value is within ‘tol’ of ‘target’. alRatio Vector describing the relative patient allocations to the dose groups up to propor- tionality, e.g. ‘rep(1, length(doses))’ corresponds to balanced allocations. Ntype One of "arm" or "total". Determines, whether the sample size in the smallest arm or the total sample size is iterated in bisection search algorithm. verbose Logical value indicating if a trace of the iteration progress of the bisection search algorithm should be displayed. ... Arguments directly passed to the powMCT function in the ‘sampSizeMCT’ and ‘powN’ function. The ‘placAdj’ argument needs to be ‘FALSE’ (which is the default value for this argument). If sample size calculations are desired for a placebo-adjusted formulation use ‘sampSize’ or ‘targN’ directly. In case S is specified, the specified matrix needs to be proportional to the (hy- pothetical) covariance matrix of one single observation. The covariance matrix 52 sampSize used for sample size calculation is 1/N*S, where N is the total sample size. Hence ‘Ntype == "total"’ needs to be used if S is specified. When S is speci- fied, automatically ‘df = Inf’ is assumed in the underlying ‘powMCT’ calls. For a homoscedastic normally distributed response variable only ‘sigma’ needs to be specified, as the sample size ‘n’ is iterated in the different ‘powMCT’ calls. power, sumFct power is a numeric defining the desired summary power to achieve (in ‘sampSizeMCT’). sumFct needs to be a function that combines the power values under the different alternatives into one value (in ‘sampSizeMCT’). x, superpose, line.at, xlab, ylab arguments for the plot method of ‘targN’ and ‘powN’, additional arguments are passed down to the low-level lattice plotting routines. Author(s) Jose Pinheiro, Bjoern Bornkamp References Pinheiro, J. C., Bornkamp, B., and Bretz, F. (2006). Design and analysis of dose finding studies combining multiple comparisons and modeling procedures, Journal of Biopharmaceutical Statis- tics, 16, 639–656 Pinheiro, J.C., Bornkamp, B. (2017) Designing Phase II Dose-Finding Studies: Sample Size, Doses and Dose Allocation Weights, in O’Quigley, J., Iasonos, A. and Bornkamp, B. (eds) Handbook of methods for designing, monitoring, and analyzing dose-finding trials, CRC press See Also powMCT Examples ## sampSize examples ## first define the target function ## first calculate the power to detect all of the models in the candidate set fmodels <- Mods(linear = NULL, emax = c(25), logistic = c(50, 10.88111), exponential=c(85), betaMod=matrix(c(0.33,2.31,1.39,1.39), byrow=TRUE, nrow=2), doses = c(0,10,25,50,100,150), placEff=0, maxEff=0.4, addArgs = list(scal=200)) ## contrast matrix to use contMat <- optContr(fmodels, w=1) ## this function calculates the power under each model and then returns ## the average power under all models tFunc <- function(n){ powVals <- powMCT(contMat, altModels=fmodels, n=n, sigma = 1, alpha=0.05) mean(powVals) } Target Doses 53 ## assume we want to achieve 80% average power over the selected shapes ## and want to use a balanced allocations ## Not run: sSize <- sampSize(upperN = 80, targFunc = tFunc, target=0.8, alRatio = rep(1,6), verbose = TRUE) sSize ## Now the same using the convenience sampSizeMCT function sampSizeMCT(upperN=80, contMat = contMat, sigma = 1, altModels=fmodels, power = 0.8, alRatio = rep(1, 6), alpha = 0.05) ## Alternatively one can also specify an S matrix ## covariance matrix in one observation (6 total observation result in a ## variance of 1 in each group) S <- 6*diag(6) ## this uses df = Inf, hence a slightly smaller sample size results sampSizeMCT(upperN=500, contMat = contMat, S=S, altModels=fmodels, power = 0.8, alRatio = rep(1, 6), alpha = 0.05, Ntype = "total") ## targN examples ## first calculate the power to detect all of the models in the candidate set fmodels <- Mods(linear = NULL, emax = c(25), logistic = c(50, 10.88111), exponential=c(85), betaMod=matrix(c(0.33,2.31,1.39,1.39), byrow=TRUE, nrow=2), doses = c(0,10,25,50,100,150), placEff=0, maxEff=0.4, addArgs = list(scal=200)) ## corresponding contrast matrix contMat <- optContr(fmodels, w=1) ## define target function tFunc <- function(n){ powMCT(contMat, altModels=fmodels, n=n, sigma = 1, alpha=0.05) } powVsN <- targN(upperN = 100, lowerN = 10, step = 10, tFunc, alRatio = rep(1, 6)) plot(powVsN) ## the same can be achieved using the convenience powN function ## without the need to specify a target function powN(upperN = 100, lowerN=10, step = 10, contMat = contMat, sigma = 1, altModels = fmodels, alpha = 0.05, alRatio = rep(1, 6)) ## End(Not run) Target Doses Calculate dose estimates for a fitted dose-response model (via fitMod or bFitMod) or a Mods object. 54 Target Doses Description The TD (target dose) is defined as the dose that achieves a target effect of Delta over placebo (if there are multiple such doses, the smallest is chosen): T D∆ = min{x|f (x) > f (0) + ∆} If a decreasing trend is beneficial the definition of the TD is T D∆ = min{x|f (x) < f (0) − ∆} When ∆ is the clinical relevance threshold, then the TD is similar to the usual definition of the minimum effective dose (MED). The ED (effective dose) is defined as the dose that achieves a certain percentage p of the full effect size (within the observed dose-range!) over placebo (if there are multiple such doses, the smallest is chosen). EDp = min{x|f (x) > f (0) + p(f (dmax) − f (0)) Note that this definition of the EDp is different from traditional definition based on the Emax model, where the EDp is defined relative to the asymptotic maximum effect (rather than the maximum effect in the observed dose-range). Usage TD(object, Delta, TDtype = c("continuous", "discrete"), direction = c("increasing", "decreasing"), doses) ED(object, p, EDtype = c("continuous", "discrete"), doses) Arguments object An object of class c(Mods, fullMod), DRMod or bFitMod Delta, p Delta: The target effect size use for the target dose (TD) (Delta should be > 0). p: The percentage of the dose to use for the effective dose. TDtype, EDtype character that determines, whether the dose should be treated as a continuous variable when calculating the TD/ED or whether the TD/ED should be calcu- lated based on a grid of doses specified in ‘doses’ direction Direction to be used in defining the TD. This depends on whether an increasing or decreasing of the response variable is beneficial. doses Dose levels to be used, this needs to include placebo, ‘TDtype’ or ‘EDtype’ are equal to ‘"discrete"’. Value Returns the dose estimate Author(s) Bjoern Bornkamp Target Doses 55 See Also Mods, fitMod, bFitMod, drmodels Examples ## example for creating a "full-model" candidate set placebo response ## and maxEff already fixed in Mods call doses <- c(0, 10, 25, 50, 100, 150) fmodels <- Mods(linear = NULL, emax = 25, logistic = c(50, 10.88111), exponential = 85, betaMod = rbind(c(0.33, 2.31), c(1.39, 1.39)), linInt = rbind(c(0, 1, 1, 1, 1), c(0, 0, 1, 1, 0.8)), doses=doses, placEff = 0, maxEff = 0.4, addArgs=list(scal=200)) ## calculate doses giving an improvement of 0.3 over placebo TD(fmodels, Delta=0.3) ## discrete version TD(fmodels, Delta=0.3, TDtype = "discrete", doses=doses) ## doses giving 50% of the maximum effect ED(fmodels, p=0.5) ED(fmodels, p=0.5, EDtype = "discrete", doses=doses) plot(fmodels, plotTD = TRUE, Delta = 0.3) Index ∗ datasets gAIC (fitMod), 14 biom, 8 getResp (Mods), 31 glycobrom, 18 glycobrom, 18 IBScovars, 22 guesst, 20 migraine, 31 neurodeg, 36 IBScovars, 22 ∗ package linear (DR-Models), 10 DoseFinding-package, 2 linearGrad (DR-Models), 10 AIC.DRMod (fitMod), 14 linInt (DR-Models), 10 linIntGrad (DR-Models), 10 betaMod, 8, 21 linlog (DR-Models), 10 betaMod (DR-Models), 10 linlogGrad (DR-Models), 10 betaModGrad (DR-Models), 10 logistic, 8, 21 bFitMod, 4, 53, 55 logistic (DR-Models), 10 biom, 8 logisticGrad (DR-Models), 10 logLik.DRMod (fitMod), 14 calcCrit (optDesign), 40 MCPMod, 23 coef.bFitMod (bFitMod), 4 MCTpval, 27 coef.DRMod (fitMod), 14 MCTtest, 23, 24, 26–28, 28, 39, 49 migraine, 31 defBnds, 5, 8, 15, 17, 24, 47 Mods, 10, 24, 28, 31, 33, 37, 40, 43, 49, 53, 55 DesignMCPModApp, 9 mvtnorm.control, 24, 29, 35 DoseFinding (DoseFinding-package), 2 DoseFinding-package, 2 neurodeg, 36 DR-Models, 10 nlminb, 16, 41 drmodels, 5, 14, 15, 17, 26, 31–33, 43, 46, 55 nls, 16 drmodels (DR-Models), 10 optContr, 28, 29, 37, 49 ED, 24, 31 optDesign, 31, 33, 40 ED (Target Doses), 53 optim, 41 emax, 21 optimize, 16 emax (DR-Models), 10 emaxGrad (DR-Models), 10 planMod, 45 exponential, 21 plot.bFitMod (bFitMod), 4 exponential (DR-Models), 10 plot.DRdesign (optDesign), 40 exponentialGrad (DR-Models), 10 plot.DRMod (fitMod), 14 plot.MCPMod (MCPMod), 23 fitMod, 5, 6, 9, 10, 13, 14, 23, 24, 26, 29, 47, plot.Mods, 20, 21, 42 53, 55 plot.Mods (Mods), 31 56 INDEX 57 plot.optContr (optContr), 37 plot.planMod (planMod), 45 plot.targN (sampSize), 50 plotContr (optContr), 37 plotMods (Mods), 31 powMCT, 29, 31, 33, 48, 51, 52 powN, 49 powN (sampSize), 50 predict.bFitMod (bFitMod), 4 predict.DRMod (fitMod), 14 predict.MCPMod (MCPMod), 23 quadratic, 21 quadratic (DR-Models), 10 quadraticGrad (DR-Models), 10 rndDesign (optDesign), 40 sampSize, 50 sampSizeMCT, 49 sampSizeMCT (sampSize), 50 sigEmax, 8, 21 sigEmax (DR-Models), 10 sigEmaxGrad (DR-Models), 10 summary.planMod (planMod), 45 Target Doses, 53 targN (sampSize), 50 TD, 24, 31 TD (Target Doses), 53 vcov.DRMod (fitMod), 14

SemiMarkov

cran

Package ‘SemiMarkov’ October 12, 2022 Type Package Title Multi-States Semi-Markov Models Version 1.4.6 Date 2019-06-27 Author Agnieszka Listwon-Krol, Philippe Saint-Pierre Maintainer Agnieszka Listwon-Krol <krol@lunenfeld.ca> Description Functions for fitting multi-state semi-Markov models to longitudinal data. A paramet- ric maximum likelihood estimation method adapted to deal with Exponential, Weibull and Expo- nentiated Weibull distributions is considered. Right-censoring can be taken into ac- count and both constant and time-varying covariates can be included using a Cox propor- tional model. Reference: A. Krol and P. Saint-Pierre (2015) <doi:10.18637/jss.v066.i06>. Depends R (>= 2.10.0), numDeriv, MASS, Rsolnp License GPL (>= 2) NeedsCompilation no Repository CRAN Date/Publication 2019-07-02 11:10:03 UTC R topics documented: asthma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 hazard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 param.init . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 plot.hazard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 print.hazard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 print.semiMarkov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 semiMarkov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 summary.hazard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 summary.semiMarkov . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 table.state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Index 24 1 2 asthma asthma Asthma control data Description Data from a follow-up study of severe asthmatic patients. At each visit, covariates are recorded and asthma was evaluated using the concept of control scores. Such scores reflect a global judgement of the disease gravity based on official criteria. Three levels are considered (optimal, suboptimal and unacceptable control) and can be used to define the subject’s state at each visit. The aim is to investigate the evolution of asthma control and to evaluate the effect of covariates. The data contains an extraction of 371 patients with at least two visits. The table is presented in long format with one row for each observed transition between two states. The rows corresponding to the same subject are ordered chronologically. The last sojourn time is right-censored by the end of the study and represent the time until censoring. A censored transition is defined as a transition to the same state h->h. Usage data(asthma) Format A data frame containing 928 rows. Each row represents a patient examination and contains several covariates. id Patient identification number state.h Starting state (1 for optimal, 2 for suboptimal and 3 for unacceptable control state) state.j Arrival state (1 for optimal, 2 for suboptimal and 3 for unacceptable control state) time Waiting (sojourn) time in state state.h Severity Disease severity (1=severe, 0=mild-moderate asthma) BMI Body Mass Index (1=BMI>=25, 0=otherwise) Sex Sex (1=men, 0=women) This presentation of the data implies that, for a given patient, the visited states are the sequence of state.h and the follow-up time is the the cumulated sum of time. Source ARIA (Association pour la Recherche en Intelligence Artificielle), France. References Saint-Pierre P., Combescure C., Daures J.P., Godard P. (2003). The analysis of asthma control under a Markov assumption with use of covariates. Statistics in Medicine, 22(24):3755-70. hazard 3 Examples data(asthma) head(asthma) hazard Computes hazard rates using an object of class semiMarkov or param.init Description For a given vector of times, the function computes the hazard rates values of an object of class semiMarkov or param.init (which provided the hazard rates). Both, values of hazard rate of waiting time of semi-Markov process can be obtained. Usage hazard(object, type = "alpha", time = NULL, cov = NULL, s = 0, t = "last", Length = 1000) Arguments object Object of class semiMarkov or param.init. type Type of hazard to be computed: "alpha" for the hazard rates of waiting times of the embedded Markov chain and "lambda" for the hazard rates of the semi- Markov process. Default is "alpha". time A vector containing the time values for which the hazard rate is computed. Default value is a vector seq(0, last, length = Length) where last is the largest duration observed in the data set and Length is the length of the vector. cov A list with one component for each covariate. Each component gives values of covariates that are to be used for the hazard rates computation. For a time- fixed covariate a single value can be given whereas a whole vector of values is required for time dependent covariates. Default is NULL which corresponds to time-independent covariates all equal to 0. Note that the same covariates values are used for all transitions. s Starting value of the time interval [s, t] which is used to compute the hazard rate. This argument is not considered when the vector time is defined. Default value is 0. t Ending value of the time interval [s, t] which is used to compute the hazard rate. This argument is not considered when the vector time is defined. Default value is last which is the the largest duration observed in the data set. Length The number of points of the time interval [s, t] for which the hazard rate is computed. These points are equally distributed in the time interval [s, t]. This argument is not considered when the vector time is defined. Default value is 1000. 4 hazard Details This function computes the hazard rates of waiting (or sojourn) times and the hazard rates of semi- Markov process defined in the parametric multi-state semi-Markov model described in Listwon and Saint-Pierre (2013). Additional details about the methodology behind the SemiMarkov package can be found in Limnios and Oprisan (2001), Foucher et al. (2006) and Perez-Ocon and Ruiz-Castro (1999). The hazard rate of waiting time at time t represents the conditional probability that a transition from state h to state j is observed given that no event occurs until time t. In a parametric framework, the expression of the hazard rates can easily be obtained as the distributions of waiting time belong to a parametric family. The hazard rate values are calculated using the chosen distribution and the given values of the parameters. The effects of both constant and time-varying covariates on the hazard of waiting time can be studied using a proportional intensities model. The effects of covariates can then be interpreted in terms of relative risk. The hazard rate of the semi-Markov process at time t represents the conditional probability that a transition into state j is observed given that the subject is in state h and that no event occurs until time t. The hazard rate of the semi-Markov process can be interpreted as the subject’s risk of passing from state h to state j. This quantity can be deduced from the transition probabilities of the embedded Markov chain and from the distributions of waiting times. This function can be used to compute the hazard rates for different values of the covariates or different values of the parameters. These hazard rates can then be plotted using plot.hazard. Objects of classes semiMarkov and param.init can be used in the function hazard. These objects contain informations on the model and the values of the parameters for the waiting time distribution, the transition probability of Markov chain and the regression coefficients. Value Returns an object of class hazard. Type The type of hazard computed by the function hazard: the hazard of waiting time (alpha) or the hazard of the semi-Markov process (lambda). vector A data frame containing one vector for each possible transition. A vector con- tains values of the hazard rate associated to the vector of times. Time The vector of times used to compute the hazard rate. Covariates A list containing the values of the covariates (fixed or time-dependent). Summary A list of data frames (one for each possible transition). A dataframe contains quantiles, means, minimums and maximums of the hazard rate values. Transition_matrix A matrix containing informations on the model: the possible transitions and the distribution of waiting times for each transition (Exponential, Weibull or Exponentiated Weibull). call Recall the name of the model. Author(s) Agnieszka Listwon-Krol hazard 5 References Krol, A., Saint-Pierre P. (2015). SemiMarkov : An R Package for Parametric Estimation in Multi- State Semi-Markov Models. 66(6), 1-16. Limnios, N., Oprisan, G. (2001). Semi-Markov processes and reliability. Statistics for Industry and Technology. Birkhauser Boston. Foucher, Y., Mathieu, E., Saint-Pierre, P., Durand, J.F., Daures, J.P. (2006). A semi-Markov model based on Generalized Weibull distribution with an illustration for HIV disease. Biometrical Journal, 47(6), 825-833. Perez-Ocon, R., Ruiz-Castro, J. E. (1999). Semi-markov models and applications, chapter 14, pages 229-238. Kluwer Academic Publishers. See Also plot.hazard, semiMarkov, param.init, summary.hazard, print.hazard Examples ## Asthma control data data(asthma) ## Definition of the model: states, names, # possible transtions and waiting times distributions states_1 <- c("1","2","3") mtrans_1 <- matrix(FALSE, nrow = 3, ncol = 3) mtrans_1[1, 2:3] <- c("E","E") mtrans_1[2, c(1,3)] <- c("E","E") mtrans_1[3, c(1,2)] <- c("W","E") ## semi-Markov model without covariates fit1 <- semiMarkov(data = asthma, states = states_1, mtrans = mtrans_1) ## Hazard rates of waiting time alpha1 <- hazard(fit1) plot(alpha1) ## Hazard rates of the semi-Markov process lambda1 <- hazard(fit1, type = "lambda") plot(lambda1) ## Defining a vector of equally distributed times alpha2 <- hazard(fit1, s=0, t=3, Length=300) plot(alpha2) ## Considering times observed in the data set alpha3 <- hazard(fit1, time=sort(unique(asthma$time))) plot(alpha3) 6 param.init ## semi-Markov model with a covariate "BMI" fit2 <- semiMarkov(data = asthma, cov = as.data.frame(asthma$BMI), states = states_1, mtrans = mtrans_1) ## Time fixed covariate ## Covariate equal to 0 and 1 for each transition alpha4 <- hazard(fit2) alpha5 <- hazard(fit2, cov=1) plot(alpha4,alpha5) ## Time dependent covariate ## Suppose that the covariate value is known for all times values Time<-sort(unique(asthma$time)) # observed times in ascending order Cov1<-sort(rbinom(length(Time), 1, 0.3)) # simulation of binary covariate Cov2<-sort(rexp(length(Time), 5)) # simulation of numeric covariate alpha6 <- hazard(fit2, time=Time, cov=Cov1) plot(alpha6) alpha7 <- hazard(fit2, time=Time, cov=Cov2) plot(alpha7) ## semi-Markov model with two covariates ## "BMI" affects transitions "1->3" and "3->1" ## "Sex" affects transition "3->1" SEX <- as.data.frame(asthma$Sex) BMI <- as.data.frame(asthma$BMI) fit3 <- semiMarkov(data = asthma, cov = as.data.frame(cbind(BMI,SEX)), states = states_1, mtrans = mtrans_1, cov_tra = list(c("13","31"),c("31"))) alpha8 <- hazard(fit3, cov=c(0,0)) alpha9 <- hazard(fit3, cov=c(1,1)) plot(alpha8,alpha9) param.init Defines the initial values of parameters for a semi-Markov model Description Function defining initial values of parameters of the waiting time distributions, probabilities of the Markov chain and optional regression coefficients associated with covariates. The function can either provides the default initial values (the same as those considered in the function semiMarkov) or can be used to specify particular initial values. Usage param.init(data = NULL, cov = NULL, states, mtrans, cov_tra = NULL, cens = NULL, dist_init = NULL, proba_init=NULL, coef_init = NULL) param.init 7 Arguments data data frame of the form data.frame(id,state.h,state.j,time), where • id: the individual identification number • state.h: state left by the process • state.j: state entered by the process • time: waiting time in state state.h The data.frame containts one row per transition (possibly several rows per pa- tient). The data frame data is not needed if proba_init is provided. cov Optional data frame containing the covariates values. states A numeric vector giving the names of the states (names are values used in state.h). mtrans A quadratic matrix of characters describing the possible transitions and the dis- tributions of waiting time. The rows represent the left states, and the columns represent the entered states. If an instantaneous transition is not allowed from state h to state j, then mtrans should have (h, j) entry FALSE, otherwise it should be "E" (or "Exp" or "Exponential") for Exponential distribution, "W" (or "Weibull") for Weibull distribution or "EW" (or "EWeibull" or "Exponentiated Weibull") for Exponentiated Weibull distribution. If TRUE is used instead of the name of the distribution, then a Weibull distribution is considered. By definition of a semi-Markov model, the transitions into the same state are not possible. The diagonal elements of mtrans must be set to FALSE otherwise the function will stop. cov_tra Optional list of vectors: a vector is associated with covariates included in the model. For a given covariate, the vector contains the transitions "hj" for which the covariate have an effect (only the transitions not equal to FALSE in mtrans are allowed). The effect of covariates can then be considered only on specific transitions. By default, the effects of covariates on all the possible transitions are included in a model. cens A character giving the code for censored observations in the column state.j of the data. Default is NULL which means that the censoring is defined as a transition fron state h to state h. dist_init Optional numeric vector giving the initial values of the distribution parameters. Default is 1 for each distribution parameter. The length of the vector depends on the chosen distribution, number of transitions and states. proba_init Optional numeric vector giving the initial values of the transition probabilities. The sum of the probabilities in the same raw must be equal to 1. According to semi-Markov model, the probability to stay in the same state must be equal to 0. The default values for the transition probabilities are estimated from the data. If data = NULL, the argument proba_init is obligatory. coef_init Optional numeric vector giving the initial values of the regression coefficients associated with the covariates. Default is 0 for each regression coefficient mean- ing no effect of the covariate. 8 param.init Details This function returns a data frame containing the initial values of parameters of a semi-Markov model. The model parameters are the distribution parameters, the transition probabilities of the Markov chain and the regression coefficients associated with covariates. The number of parameters depends on the chosen model: the distributions of the sojourn times, the number of states and the transitions between states specified with the matrix mtrans, the number of covariates (cov) and their effects or not on transitions (cov_tra). The default initial values for the distribution parameters are fixed to 1. As the three possible dis- tributions are nested for respective parameters equal to 1 (See details of the semiMarkov function), the initial distribution corresponds to the exponential distribution with parameter equal to 1 (what- ever the chosen distribution). The default initial values for the regression coefficients are fixed to 0 meaning that the covariates have no effect on the hazard rates of the sojourn times. These initial values may be changed using the arguments dist_init and coef_init. By default, the initial probabilities are calculated by simple proportions. The probability associated to the transition from h to j is estimed by the number of observed transitions from state h to state j divided by the total number of transitions from state h observed in the data. The results are displayed in matrix matrix.P. The number of parameters for transition probabilities is smaller than the number of possible transitions as the probabilities in the same row sum up to one. Considering this point and that the probability to stay in the same state is zero, the user can change the initial values using the argument proba_init. Value This function returns an object of class param.init to be used in functions semiMarkov and hazard. An object of class param.init consists of nstates The length of vector states interpreted as the number of possible states for the process. table.state A table, with starting states as rows and arrival states as columns, which pro- vides the number of times that a transition between two states is observed. This argument is only returned when data is provided. It can be used to quickly summarize multi-state data. Ncens Number of individuals subjected to censoring. matrix.P Quadratic matrix, with starting states as rows and arrival states as columns, giv- ing the default initial values for the transition propabilities of the embedded Markov chain. All diagonal values are zero. The sum of all probabilities of the same row is equal to one. last The largest duration observed in data if data is given. Transition_matrix A matrix containing the informations on the model definition : the possible tran- sitions o and the distribution of waiting times for each transition (Exponential, Weibull or Exponentiated Weibull). dist.init A data frame giving the names of the parameters, transitions associated with and initial values of the distribution parameters. proba.init A data frame giving names of the parameters, transitions associated with and initial values of the probabilities of the embedded Markov chain. param.init 9 coef.init A data frame giving the names of covariates, transitions associated with and initial values of the regression coefficients. Author(s) Agnieszka Listwon-Krol See Also hazard, semiMarkov Examples ## Asthma control data data(asthma) ## Definition of the model: states, names, # possible transtions and waiting time distributions states_1 <- c("1","2","3") mtrans_1 <- matrix(FALSE, nrow = 3, ncol = 3) mtrans_1[1, 2:3] <- c("W","W") mtrans_1[2, c(1,3)] <- c("EW","EW") mtrans_1[3, c(1,2)] <- c("W","W") ## Default initial values in a model without covariates init_1 <- param.init(data = asthma, states = states_1, mtrans = mtrans_1) ## Definition of initial values in a model without covariates init_2 <- param.init(data = asthma, states = states_1, mtrans = mtrans_1, dist_init=c(rep(1.5,6),rep(1.8,6),rep(2,2)), proba_init=c(0.2,0.8,0.3,0.7,0.35,0.65)) ## Default initial values with a covariate "Sex" # influencing transitions " 1->2" and "3->2" init_3 <- param.init(data = asthma, cov=as.data.frame(asthma$Sex), states = states_1, mtrans = mtrans_1, cov_tra=list(c("12","32"))) ## Definition of initial values with a covariate "Sex" # influencing transitions " 1->2" and "3->2" init_4 <- param.init(data = asthma, cov=as.data.frame(asthma$Sex), states = states_1, mtrans = mtrans_1, cov_tra=list(c("12","32")), dist_init=c(rep(1.5,6),rep(1.8,6),rep(2,2)), proba_init=c(0.2,0.8,0.3,0.7,0.35,0.65), coef_init=rep(0.3,2)) init_5 <- param.init(data = asthma, cov=as.data.frame(asthma$Sex), states = states_1, mtrans = mtrans_1, cov_tra=list(c("12","32")), coef_init=c(0.2,0.5)) ## Definition of initial values without dataset in an illness-death model ## 1 - healthy, 2 - illness, 3 - death states_2 <- c("1","2","3") 10 plot.hazard mtrans_2 <- matrix(FALSE, nrow = 3, ncol = 3) mtrans_2[1,c(2,3)] <- c("E","W") mtrans_2[2,c(1,3)] <- c("EW","EW") init_6<-param.init(states=states_2, mtrans=mtrans_2, proba_init=c(0.7,0.3,0.2,0.8)) plot.hazard Plot method for objects of class hazard Description Plot method for one or several (maximum 10) objects of class hazard. Depending on the hazard rate chosen in the function hazard, the function plots either the hazard rates of sojourn times or the semi-Markov process hazard rate for each considered transition (one plot for each transition). Usage ## S3 method for class 'hazard' plot(x, x2 = NULL, x3 = NULL, x4 = NULL, x5 = NULL, x6 = NULL, x7 = NULL, x8 = NULL, x9 = NULL, x10 = NULL, transitions = NULL, names = NULL, legend = TRUE, legend.pos = NULL, cex = NULL, colors = NULL, xlab = "Time", ylab = "Hazard function", lwd = 3, type = "p", ...) Arguments x Object of class hazard. At least one hazard object is needed. x2 Object of class hazard. Default is NULL. x3 Object of class hazard. Default is NULL. x4 Object of class hazard. Default is NULL. x5 Object of class hazard. Default is NULL. x6 Object of class hazard. Default is NULL. x7 Object of class hazard. Default is NULL. x8 Object of class hazard. Default is NULL. x9 Object of class hazard. Default is NULL. x10 Object of class hazard. A maximum of ten hazard objects is possible. Default is NULL. transitions A character vector giving the transitions to be plotted. Default is NULL which means that all the possible transitions are displayed. names Names of the hazard rates. Default is NULL which means that the names used in the semiMarkov object are applied. legend A logical value specifying if a legend should be added. Default is TRUE. legend.pos A vector giving the legend position. cex character expansion factor relative to current par("cex"). plot.hazard 11 colors A vector of colours for the hazard rates. xlab x-axis label. Default is Time. ylab y-axis label. Default is Hazard function. lwd Thickness of lines or points. type Type of graph. Default are points p. ... Further arguments for plot. Value No value returned. Author(s) Agnieszka Listwon-Krol See Also hazard, semiMarkov Examples ## Asthma control data data(asthma) ## Definition of the model: states, names, possible transtions # and waiting times distributions states_1 <- c("1","2","3") mtrans_1 <- matrix(FALSE, nrow = 3, ncol = 3) mtrans_1[1, 2:3] <- c("E","E") mtrans_1[2, c(1,3)] <- c("E","E") mtrans_1[3, c(1,2)] <- c("W","E") fit <- semiMarkov(data = asthma, states = states_1, mtrans = mtrans_1) lambda<-hazard (fit, type = "lambda") plot(lambda, names = c("lambda"),legend=FALSE) plot(lambda, transitions = c("13","31"), names = c("lambda"), legend.pos=c(2,0.09,2,0.4)) ## semi-Markov model in each stratum of Severity fit0 <- semiMarkov(data = asthma[asthma$Severity==0,], states = states_1, mtrans = mtrans_1) fit1 <- semiMarkov(data = asthma[asthma$Severity==1,], states = states_1, mtrans = mtrans_1) lambda0<-hazard (fit0, type = "lambda",s=0,t=5,Length=1000) lambda1<-hazard (fit1, type = "lambda",s=0,t=5,Length=1000) plot(lambda0,lambda1, names = c("lambda0", "lambda1"), legend.pos=c(4,0.18,4,0.8,4,0.2,4,0.09,4,0.7,4,0.21)) 12 print.hazard ## semi-Markov model with covariate "BMI" fitcov <- semiMarkov(data = asthma, cov = as.data.frame(asthma$BMI), states = states_1, mtrans = mtrans_1) lambda0<-hazard (fitcov, type = "lambda",cov = c(0)) lambda1<-hazard (fitcov, type = "lambda",cov = c(1)) plot(lambda0,lambda1, names = c("lambda0", "lambda1")) print.hazard Print method for object of class hazard Description Print method for objects of class hazard. Usage ## S3 method for class 'hazard' print(x, whole = FALSE, ...) Arguments x An object of class hazard. whole A logical value indicating if the whole vectors of the object hazard should be displayed. Default is FALSE which means that only the first six values are re- turned. ... Further arguments for print or summary. Value No value returned. Author(s) Agnieszka Listwon-Krol See Also hazard, plot.hazard print.semiMarkov 13 print.semiMarkov Print method for object of class semiMarkov Description Print method for objects of class semiMarkov. Usage ## S3 method for class 'semiMarkov' print(x, CI=TRUE, Wald.test=TRUE, ...) Arguments x An object of class semiMarkov. CI A logical value indicating if the confidence intervals for each parameter should be returned. Default is TRUE. The confidence level is chosen in semiMarkov. Wald.test A logical value indicating if the results of the Wald test for each parameter should be returned. Default is TRUE. ... Further arguments for print or summary. Value No value returned. Author(s) Agnieszka Listwon-Krol See Also semiMarkov, summary.semiMarkov semiMarkov Parametric estimation in multi-state semi-Markov models Description This function computes the parametric maximum likelihood estimation in multi-state semi-Markov models in continuous-time. The effect of time varying or fixed covariates can be studied using a proportional intensities model for the hazard of the sojourn time. 14 semiMarkov Usage semiMarkov(data, cov = NULL, states, mtrans, cov_tra = NULL, cens = NULL, dist_init=NULL, proba_init = NULL, coef_init = NULL, alpha_ci = 0.95, Wald_par = NULL, eqfun = NULL, ineqLB = NULL,ineqUB = NULL, control = list() ) Arguments data data frame of the form data.frame(id,state.h,state.j,time), where • id: the individual identification number • state.h: state left by the process • state.j: state entered by the process • time: waiting time in state state.h This data.frame containts one row per transition (possibly several rows per pa- tient). cov Optional data frame containing covariates values. states A numeric vector giving names of the states (names are values used in state.h). mtrans A quadratic matrix of characters describing the possible transitions and the dis- tributions of waiting time. The rows represent the left states, and the columns represent the entered states. If an instantaneous transition is not allowed from state h to state j, then mtrans should have (h, j) entry FALSE, otherwise it should be "E" (or "Exp" or "Exponential") for Exponential distribution, "W" (or "Weibull") for Weibull distribution or "EW" (or "EWeibull" or "Exponentiated Weibull") for Exponentiated Weibull distribution. If TRUE is used instead of the name of the distribution, then a Weibull distribution is considered. By definition of a semi-Markov model, the transitions into the same state are not possible. The diagonal elements of mtrans must be set to FALSE otherwise the function will stop. cov_tra Optional list of vectors: a vector is associated with covariate included in the model. For a given covariate, the vector contains the transitions "hj" for which the covariate have an effect (only the transitions specified in mtrans are al- lowed). The effect of covariates can then be considered only on specific tran- sitions. By default, the effects of covariates on all the possible transitions are studied. cens A character giving the code for censored observations in the column state.j of the data. Default is NULL which means that the censoring is defined as a transition fron state h to state h. dist_init Optional numeric vector giving the initial values of the distribution parameters. Default is 1 for each distribution parameter. The length of the vector depend on the chosen distribution, number of transitions and states. proba_init Optional numeric vector giving the initial values of the transition probabilities. The sum of the probabilities in the same row must be equal to 1. According to semi-Markov model, the probability to stay in the same state must be equal to 0. The default values for the transition probabilities are estimated from the data. If data = NULL, the argument proba_init is obligatory. semiMarkov 15 coef_init Optional numeric vector giving the initial values of the regression coefficients associated with the covariates. Default is 0 for each regression coefficient which means that the covariate has no effect. alpha_ci Confidence level to be considered for the confidence intervals. The default value is 0.95. Wald_par Optional numeric vector giving the values to be tested (null hypothesis) by the Wald test for each parameter. The Wald statistics are evaluated only for the parameters of distributions and regression coefficients. The length of this vector must then be equal to the number of those parameters. The order of the values must be as in the parameters table given by objects semiMarkov or param.init (excluding the parameters associated to the transition probabilities). The default values for the elements of Wald_par vector are 1 for the distribution parameters and 0 for the regression coefficients. eqfun Optional list given equality constraints between parameters. These constraints are passed using the equality constraint function that can be defined in the solnp optimization function. See below for details. ineqLB Optional list given values of lower bound for parameters. These values are used in the inequality constraint that can be defined in the solnp optimization func- tion. See below for details. ineqUB Optional list given values of upper bound for parameters. These values are used in the inequality constraint that can be defined in the solnp optimization func- tion. See below for details. control The control list of optimization parameters for solnp optimization function. Details This function fits parametric multi-state semi-Markov model described in Listwon and Saint-Pierre (2013) to longitudinal data. Additional details about the methodology behind the SemiMarkov pack- age can be found in Limnios and Oprisan (2001), Foucher et al. (2006) and Perez-Ocon and Ruiz- Castro (1999). Consider an homogeneous semi-Markov process with a finite state space. In a parametric frame- work, distributions of the waiting time belong to parametric families. The distribution of the waiting time can be chosen between the exponential, the Weibull and the exponentiated Weibull distribu- tions. The exponential distribution with scale parameter σ > 0 has a density defined as follows f (x) = (1/σ)exp(−x/σ). The Weibull distribution with scale parameter σ > 0 and shape parameter ν > 0 has a density given by (same as one defined in dweibull) g(x) = (ν/σ)(x/σ)ν−1 exp(−(x/σ)ν ). The exponentiated Weibull distribution (or generalized Weibull) with scale parameter σ > 0, shape parameter ν > 0 and family parameter equal to θ > 0 has a density given by (same as one defined in function dgweibull from the R package rmutil) h(x) = θ(ν/σ)(x/σ)ν−1 exp(−(x/σ)ν )(1 − exp(−(x/σ)ν ))θ−1 . 16 semiMarkov These three distributions are nested. The exponentiated Weibull density with θ = 1 gives a Weibull distribution and the Weibull density with ν = 1 gives the exponential density. Note that the effects of both constant and time-varying covariates on the hazards of sojourn time can be studied using a proportional intensities model. The effects of covariates can then be interpreted in terms of relative risk. The model parameters are the distribution parameters, the transition probabilities of the Markov chain and the regression coefficients associated with covariates. The number of parameters depends on the chosen model: the distributions of the sojourn times, the number of states and the transitions between states specified with the matrix mtrans, the number of covariates (cov) and their effects or not on transitions (cov_tra). The default initial values for the distribution parameters are fixed to 1. As the three possible dis- tributions are nested for parameters equal to 1 (See details of the semiMarkov function), the initial distribution corresponds to an exponential with parameter equal to 1 (whatever the chosen distri- bution). The default initial values for the regression coefficients are fixed to 0 meaning that the covariates have no effect on the hazard rates of the sojourn times. These initial values may be changed using the arguments dist_init and coef_init. By default, the initial probabilities are calculated by simple proportions. The probability associated to the transition from h to j is estimed by the number of observed transitions from state h to state j divided by the total number of transitions from state h observed in the data. The results are displayed in matrix matrix.P. The number of parameters for transition probabilities is smaller than the number of possible transitions as the probabilities in the same row sum up to one. Considering this point and that the probability to stay in the same state is zero, the user can changed the initial values using the argument proba_init. The Yinyu Ye optimization solver to nonlinear problem is applied to maximize the log-likelihood using the function solnp created by A. Ghalanos and S. Theussl. In order to modify the optimization parameters refer to the package Rsolnp documentation. Some optimization difficulties can arrise when there is not enough information in the data to esti- mate each transition rate. The users can change the optimization parameters and the initial values. It may be appropriate to reduce the number of states in the model, the number of allowed transitions, or the number of covariate effects, to ensure convergence. Some additionals constraints can be introduced using eqfun, ineqLB and ineqUB. These constraints on distribution parameters, transition probabilities and regression coefficients can be defined using lists of vectors. The argument eqfun gives the possibility to add constraints of type par1 = a∗par2 (a is a constant). This equality constraint must be expressed with a vector of 3 elements where the first element is the identifier of the parameters type ("dist" for distribution parameters, "proba" for the transition probabilities and "coef" for the regression coefficients), the second and the third elements are the index of par1 and par2, respectively. The index values of distribution parameters, transition probabilities and regression coefficients can be found in the table provided by an object semiMarkov. The last element of the vector corresponds to the constant a. The arguments ineqLB and ineqUB allow to add constraints of type par ≥ a and par ≤ a, respectively. These arguments are lists of vectors of length 3 where the first element is the type of the parameter ("dist", "proba" or "coef"), the second element is the index of parameter par and the last one is the constant a. If a chosen constraint corresponds to a transition probability, it should be considered that the last probabilities in a row of the transition matrix are not estimated but obtained directly since the sum of transition probabilities in the same row is equal to 1. Thus, no additional constraints related to these parameters are permitted. Moreover, note that the argument eqfun does not allowed to define semiMarkov 17 relationships between parameters of different types (for instance, a transition probability can not be equal to a regression coefficient). The optional constraints on parameters should be used prudently since they may induce problems in the convergence of the optimization method. In particular, the Wald statistic and the standard deviation may not be computed for some parameters due to negative values in the hessian matrix. Note that the default constraints induce by the model definition are treated in priority. Value call The original call to semiMarkov. minus2loglik Minus twice the maximized log-likelihood. solution Etimations of the distribution parameters, probabilities of the embedded Markov chain and regression coefficients (if any considered) for each transition specified in the matrix mtrans. This is a data frame with three columns: the label of the parameter, the transition associated with and the estimated value. opt.message The message giving the information on the optimization result returned by the constrOptim.nl function. opt.iter Number of outer iterations of the optimization method. nstates The length of vector states interpreted as the number of possible states for the process. table.state A table, with starting states as rows and arrival states as columns, which provides the number of observed transitions between two states. This argument can be used to quickly summarize multi-state data. Ncens Number of individuals subjected to censoring. Transition_matrix A matrix containing the informations on the model definition : the possible transitions and the distribution of waiting times for each transition (Exponential, Weibull or Exponentiated Weibull). param.init Recall the initial values of the parameters. The third column of this object can be used in hazard function. table.dist Statistics for the estimations of distribution parameters of waiting time distribu- tions. For the exponential distribution one data frame for the parameter sigma is returned, for the Weibull distribution two data frames for sigma and nu are returned, and for the Exponentiated Weibull distribution three data frames for sigma, nu and theta are returned. The columns of each data frame are the possible transitions, the estimations, the standard deviations, the lower and up- per bounds of confidence intervals, the Wald test null hypothesis, the Wald test statistics and the p-values of the Wald test when testing hypothesis sigma=1, nu=1 or theta=1. table.proba A data frame giving the estimations of the transition probabilities of the Markov chain and their standard deviations. By definition, the probability associated to the last possible transition of each row of the matrix mtrans is equal to 1 − pr, where pr is the sum of all other probabilities from the row. table.coef If some covariates are included in the model it returns a data frame with the statistics for the estimated values of the regression coefficients. The columns 18 semiMarkov of the data frame are the transitions associated with the coefficients, the es- timations, the standard deviations, the lower and upper bounds of confidence intervals, the Wald test null hypothesis, the Wald test statistics and the p-values of the Wald test when testing hypothesis coef=0. table.param Data frame with the statistics for all the model parameters, that is table.dist, table.proba and table.coef in a single data frame. Note Printing a semiMarkov object by typing the object’s name at the command line implicitly invokes print.semiMarkov. Author(s) Agnieszka Listwon-Krol, Philippe Saint-Pierre References Krol, A., Saint-Pierre P. (2015). SemiMarkov : An R Package for Parametric Estimation in Multi- State Semi-Markov Models. 66(6), 1-16. Ghalanos, A. and Theussl S. (2012). Rsolnp: General Non-linear Optimization Using Augmented Lagrange Multiplier Method. R package version 1.14. Limnios, N., Oprisan, G. (2001). Semi-Markov processes and reliability. Statistics for Industry and Technology. Birkhauser Boston. Foucher, Y., Mathieu, E., Saint-Pierre, P., Durand, J.F., Daures, J.P. (2006). A semi-Markov model based on Generalized Weibull distribution with an illustration for HIV disease. Biometrical Journal, 47(6), 825-833. Perez-Ocon, R., Ruiz-Castro, J. E. (1999). Semi-markov models and applications, chapter 14, pages 229-238. Kluwer Academic Publishers. See Also param.init, hazard, summary.semiMarkov, print.semiMarkov Examples ## Asthma control data data(asthma) ## Definition of the model: states, names, possible transtions and waiting time ## distributions states_1 <- c("1","2","3") mtrans_1 <- matrix(FALSE, nrow = 3, ncol = 3) mtrans_1[1, 2:3] <- c("E","E") mtrans_1[2, c(1,3)] <- c("E","E") mtrans_1[3, c(1,2)] <- c("W","E") semiMarkov 19 ## semi-Markov model without covariates fit1 <- semiMarkov(data = asthma, states = states_1, mtrans = mtrans_1) ## semi-Markov model with one covariate ## "BMI" affects all transitions BMI <- as.data.frame(asthma$BMI) fit2 <- semiMarkov(data = asthma, cov = BMI, states = states_1, mtrans = mtrans_1) ## semi-Markov model with one covariate ## "BMI" affects the transitions "1->3" and "3->1" fit3 <- semiMarkov(data = asthma, cov = BMI, states = states_1, mtrans = mtrans_1, cov_tra = list(c("13","31"))) ## semi-Markov model with two covariates ## "BMI" affects the transitions "1->3" and "3->1" ## "Sex" affects the transition "3->1" SEX <- as.data.frame(asthma$Sex) fit4 <- semiMarkov(data = asthma, cov = as.data.frame(cbind(BMI,SEX)), states = states_1, mtrans = mtrans_1, cov_tra = list(c("13","31"),c("31"))) ## semi-Markov model using specific initial values ## same model as "fit1" but using different initial values ## "fit5" and "fit6" are equivalent init <- param.init(data = asthma, states = states_1, mtrans = mtrans_1, dist_init=c(rep(1.5,6),c(1.8)), proba_init=c(0.2,0.8,0.3,0.7,0.35,0.65)) fit5 <- semiMarkov(data = asthma, states = states_1, mtrans = mtrans_1, dist_init=init$dist.init[,3], proba_init=init$proba.init[,3]) fit6 <- semiMarkov(data = asthma, states = states_1, mtrans = mtrans_1, dist_init=c(rep(1.5,6),c(1.8)), proba_init=c(0.2,0.8,0.3,0.7,0.35,0.65)) ## The Wald test null hypothesis is modified ## Wald statistics when testing nullity of distribution parameters ## and regression coefficients equal to -1 fit7 <- semiMarkov(data = asthma, cov = BMI, states = states_1, mtrans = mtrans_1, Wald_par = c(rep(0,7),rep(-1,6))) ## semi-Markov model with additional constraints ## distribution parameters sigma for transition "1->3" = sigma for transition "2->1" fit8 <- semiMarkov(data = asthma, cov = BMI, states = states_1, mtrans = mtrans_1, eqfun = list(c("dist",2,3,1))) ## semi-Markov model with additional constraints ## regression coefficients beta for transition "1->2" = beta for transition "2->1" ## = beta for transition "2->3" fit9 <- semiMarkov(data = asthma, cov = BMI, states = states_1, mtrans = mtrans_1, eqfun = list(c("coef",1,3,1),c("coef",1,4,1))) ## semi-Markov model with additional constraints ## regression coeficient beta for transition "1->2" belongs to [-0.2,0.2] 20 summary.hazard ## and regression coeficient beta for transition "2->3" belongs to [-0.05,0.05] fit10 <- semiMarkov(data = asthma, cov = BMI, states = states_1, mtrans = mtrans_1, ineqLB = list(c("coef",1,-0.2),c("coef",4,-0.05)), ineqUB = list(c("coef",1,0.2),c("coef",4,0.05))) summary.hazard Summary method for objects of class hazard Description Summary method for objects of class hazard. Usage ## S3 method for class 'hazard' summary(object, ...) Arguments object An object of class hazard. ... Further arguments for summary. Details For an object of class hazard, this function gives the informations on the type of hazard rates (sojourn time or semi-Markov process), the chosen model, the distribution of the sojourn times, the covariates and the vector of times. Value No value returned. Author(s) Agnieszka Listwon-Krol See Also hazard, print.hazard summary.semiMarkov 21 summary.semiMarkov Summary method for objects of class semiMarkov Description Summary method for objects of class semiMarkov. Usage ## S3 method for class 'semiMarkov' summary(object, all = TRUE, transitions = NULL, ...) Arguments object An object of class semiMarkov. all A logical value indicating if the results should be displayed for all the possible transitions. If set to FALSE, the transitions to be displayed must be specified using the argument transitions. Default is TRUE. transitions A vector of characters specifying the transitions to be displayed when the argu- ment all is set to FALSE. ... Further arguments for summary. Value A list of data frames giving Transition_matrix A matrix containing the informations on the model definition : the possible transitions and the distribution of waiting times for each transition (Exponential, Weibull or Exponentiated Weibull). param.init Recall the initial values of the parameters. The third column of this object can be used in hazard function. table.state A table, with starting states as rows and arrival states as columns, which provides the number of observed transitions between two states. This argument can be used to quickly summarize multi-state data. Ncens Number of individuals subjected to censoring. table.param List of data frames (one for each transition). A data frame includes, for each parameter (distribution parameters, the transition probabilities and the regres- sion coefficients), the estimation, the standard deviation, the lower and upper bounds of confidence interval, the Wald test statistic and Wald test p-value (for the distribution parameters and the regression coefficients). Author(s) Agnieszka Listwon-Krol 22 table.state See Also semiMarkov, print.semiMarkov table.state Table giving the numbers of observed transitions Description Function returning a table with numbers of transitions between two states observed in the data set. This table can be a used to summarize a multi-state data or to define the matrix mtrans required in the semiMarkov function. Usage table.state( data, states = NULL, mtrans = NULL, cens = NULL) Arguments data data frame in form data.frame(id,state.h,state.j,time), where • id: the individual identification number • state.h: state left by the process • state.j: state entered by the process • time: waiting time in state state.h This data.frame containts one row per transition (possibly several rows per pa- tient). states A numeric vector giving the names of the states (names are values used in state.h). mtrans A quadratic matrix of logical values describing the possible transitions. The rows represent the left states, and the columns represent the entered states. If an instantaneous transition is not allowed from state h to state j, then mtrans should have (h, j) entry FALSE, otherwise it should be TRUE. Default value is a matrix which allows all the possible transitions between states. cens A character giving the code for censored observations in the column state.j of the data. Default is NULL which means that the censoring is defined as a transi- tion fron state i to state i (by definition of a semi-Markov model, the transitions into the same state are not possible). Value table.state A table, with starting states as rows and arrival states as columns, which provides the number of observed transitions between two states. This argument can be used to quickly summarize multi-state data. Ncens Number of individuals subjected to censoring. table.state 23 Author(s) Agnieszka Listwon-Krol See Also param.init, semiMarkov Examples ## Asthma control data data(asthma) # default description # censoring is implicitly defined as a transition "h->h" table.state(asthma) table.state(asthma)$Ncens # censoring defined as a transition to state "4" asthma_bis<-asthma for(i in 1:dim(asthma)[1]){if(asthma[i,2]==asthma[i,3]) asthma_bis[i,3]<-4} table.state (asthma_bis, cens = 4) ## Definition of the model: states names and possible transtions states_1 <- c("1","2","3") mtrans_1 <- matrix(FALSE, nrow = 3, ncol = 3) mtrans_1[1, 2:3] <- TRUE mtrans_1[2, c(1,3)] <- c("W","E") table.state(asthma, states = states_1, mtrans = mtrans_1) Index ∗ datasets asthma, 2 ∗ documentation hazard, 3 param.init, 6 plot.hazard, 10 print.hazard, 12 print.semiMarkov, 13 semiMarkov, 13 summary.hazard, 20 summary.semiMarkov, 21 table.state, 22 asthma, 2 hazard, 3, 8, 9, 11, 12, 18, 20 param.init, 5, 6, 18, 23 plot.hazard, 4, 5, 10, 12 print.hazard, 5, 12, 20 print.semiMarkov, 13, 18, 22 SemiMarkov (semiMarkov), 13 semiMarkov, 5, 8, 9, 11, 13, 13, 22, 23 summary.hazard, 5, 20 summary.semiMarkov, 13, 18, 21 table.state, 22 24

sqliteutils

cran

Package ‘sqliteutils’ October 14, 2022 Title Utility Functions for 'SQLite' Version 0.1.0 Description A tool for working with 'SQLite' databases. 'SQLite' has some idiosyncrasies and limita- tions that impose some hurdles to the R developer who is using this database as a repository. For in- stance, 'SQLite' doesn't have a date type and 'sqliteutils' has some functions to deal with that. License MIT + file LICENSE Suggests testthat (>= 3.0.0) Config/testthat/edition 3 Encoding UTF-8 RoxygenNote 7.1.2 Imports RSQLite, DBI, dplyr, dbplyr, magrittr NeedsCompilation no Author Bruno Crotman [aut, cre] Maintainer Bruno Crotman <crotman@gmail.com> Repository CRAN Date/Publication 2021-09-21 14:10:02 UTC R topics documented: slu_date_to_r . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 slu_date_to_sqlite . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Index 4 1 2 slu_date_to_sqlite slu_date_to_r Converts dates stored on ’SQLite’ to their original values Description Converts dates stored on ’SQLite’ to their original values Usage slu_date_to_r(date_sqlite) Arguments date_sqlite the numbers that result from inserting dates on ’SQLite’ Value the dates that were originally inserted Examples data <- data.frame(date = as.Date("2021-09-18")) con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") DBI::dbWriteTable(conn = con, name = "dates", value = data ) data_from_bd <- DBI::dbReadTable(conn = con, name = "dates") original_date <- slu_date_to_r(data_from_bd$date) DBI::dbDisconnect(con) slu_date_to_sqlite Converts dates to the numeric values as which they would be stored on SQLite Description Converts dates to the numeric values as which they would be stored on SQLite Usage slu_date_to_sqlite(date_r) Arguments date_r dates as returned by as.Date() in R slu_date_to_sqlite 3 Value integers that correspond to the numbers that are stored on SQLite when DBI:dbWriteTable is used Examples con <- DBI::dbConnect(RSQLite::SQLite(), ":memory:") data <- data.frame( date = as.Date("2021-09-19") ) DBI::dbWriteTable(conn = con, name = "dates", value = data ) data_from_bd <- dplyr::tbl(src = con, "dates") %>% dplyr::collect() data_with_sqlite_dates <- data %>% dplyr::mutate( date = slu_date_to_sqlite(date) ) print(data_from_bd) print(data_with_sqlite_dates) DBI::dbDisconnect(con) Index slu_date_to_r, 2 slu_date_to_sqlite, 2 4

mcompanion

cran

Package ‘mcompanion’ September 22, 2023 Type Package Title Objects and Methods for Multi-Companion Matrices Version 0.5.8 Description Provides a class for multi-companion matrices with methods for arithmetic and factorization. A method for generation of multi-companion matrices with prespecified spectral properties is provided, as well as some utilities for periodically correlated and multivariate time series models. See Boshnakov (2002) <doi:10.1016/S0024-3795(01)00475-X> and Boshnakov & Iqelan (2009) <doi:10.1111/j.1467-9892.2009.00617.x>. URL https://geobosh.github.io/mcompanion/ (doc), https://github.com/GeoBosh/mcompanion (devel) BugReports https://github.com/GeoBosh/mcompanion/issues Imports methods, Matrix (>= 1.5-0), gbutils, MASS, Rdpack Suggests testthat, lagged RdMacros Rdpack License GPL (>= 2) Collate mc.R mcompanion.R utils_Jordan.R mat.R sim.R class_MC.R class_MF.R class_Jordan.R chains_smc.R class_SMC.R class_mcSpec.R NeedsCompilation no Author Georgi N. Boshnakov [aut, cre] Maintainer Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk> Repository CRAN Date/Publication 2023-09-22 14:50:02 UTC R topics documented: mcompanion-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 jordan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1 2 mcompanion-package JordanDecomposition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 JordanDecompositionDefault-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 make_mcev . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 make_mcmatrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 mCompanion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 mcSpec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 mcSpec-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 mcStable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 mc_chain_extend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 mc_eigen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 mc_factorize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 mc_factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 mc_from_factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 mc_matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 mf_VSform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 MultiCompanion-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 MultiFilter-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 null_complement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 permute_var . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 rblockmult . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 sim_mc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 sim_pcfilter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 SmallMultiCompanion-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 spec_core . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 spec_root0 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 spec_root1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 spec_seeds1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 VAR2pcfilter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Index 56 mcompanion-package Objects and Methods for Multi-Companion Matrices Description Provides a class for multi-companion matrices with methods for arithmetic and factorization. A method for generation of multi-companion matrices with prespecified spectral properties is pro- vided, as well as some utilities for periodically correlated and multivariate time series models. See Boshnakov (2002) <doi:10.1016/S0024-3795(01)00475-X> and Boshnakov & Iqelan (2009) <doi:10.1111/j.1467-9892.2009.00617.x>. Details Index of the main exported objects, classes and methods: Classes and generators: mcompanion-package 3 MultiCompanion-class Class "MultiCompanion" MultiFilter-class Class "MultiFilter" VAR2pcfilter PAR representations of VAR models mCompanion Create objects from class MultiCompanion mcSpec Generate objects of class mcSpec mcSpec-class A class for spectral specifications of multi-companion matrices mf_VSform Extract properties of multi-filters Utilities for multi-companion matrices: mc_eigen The eigen decomposition of a multi-companion matrix mc_factorize Factorise multi-companion matrices mc_factors Factors of multi-companion matrices mc_from_factors Multi-companion matrix from factors Simulation: sim_mc Simulate a multi-companion matrix sim_pcfilter Generate periodic filters Generic matrix utilities: Jordan_matrix Utilities for Jordan matrices mcStable Check if an object is stable rblockmult Right-multiply a matrix by a block Spectral description of mc-matrices: spec_core Parameterise Jordan chains of multi-companion matrices spec_root0 Give the spectral parameters for zero eigenvalues of mc-matrices spec_root1 Give the spectral parameters for eigenvalues of mc-matrices equal to one spec_seeds1 Generate seed parameters for unit mc-eigenvectors Low-level functions: mc_chain_extend Extend multi-companion eigenvectors Overview of the package Package "mcompanion" implements multi-companion matrices as discussed by Boshnakov (2002) and Boshnakov and Iqelan (2009). The main feature is the provided parsimonious parameterisation of such matrices based on their eigenvalues and the seeds for their eigenvectors. This can be used for specification and parameterisation of models for time series and dynamical systems in terms of spectral characteristics, such as the poles of the associated filters or transition matrices. A multi-companion matrix of order k is a square n × n matrix with arbitrary k rows put on top of an identity (n − k) × (n − k) matrix and a zero (n − k) × k matrix. The number k is the multi- companion order of the matrix. It may happen that the top k × n block, say T, of an mc-matrix has 4 jordan columns of zeroes at its end. In this documentation we say that an n × n matrix has dimension n and size n × n. Multi-companion matrices can be created by the functions new and mCompanion, the latter being more versatile. Some of the other functions in this package return such objects, as well. sim_mc generates a multi-companion matrix with partially or fully specified spectral properties. If the specification is incomplete, it completes it with simulated values. sim_pcfilter is a convenience function (it uses sim_mc) for generation of filters for periodically correlated models. These can be converted to various multivariate models, such as VAR, most conveniently using class MultiFilter, see below. Class "MultiFilter" is a formal representation of periodic filters with methods for conversion be- tween periodic and (non-periodic) multivariate filters. Several forms of VAR models are provided, see mf_VSform, VAR2pcfilter, MultiFilter, and the examples there. Author(s) Georgi N. Boshnakov [aut, cre] Maintainer: Georgi N. Boshnakov <georgi.boshnakov@manchester.ac.uk> References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. Boshnakov GN (2007). “Singular value decomposition of multi-companion matrices.” Linear Al- gebra Appl., 424(2-3), 393–404. ISSN 0024-3795, doi:10.1016/j.laa.2007.02.010. Boshnakov GN, Iqelan BM (2009). “Generation of time series models with given spectral proper- ties.” J. Time Series Anal., 30(3), 349–368. ISSN 0143-9782, doi:10.1111/j.14679892.2009.00617.x. See Also for examples, see mCompanion, sim_mc, sim_pcfilter, mf_VSform, VAR2pcfilter, MultiFilter, MultiCompanion, Examples ## see the examples in the links in section 'See Also' above. jordan Utilities for Jordan matrices Description Utility functions for work with the Jordan decompositions of matrices: create a block diagonal matrix of Jordan blocks, restore a matrix from its Jordan decomposition, locate specific chains. jordan 5 Usage Jordan_matrix(eigval, len.block) from_Jordan(x, jmat, ...) chain_ind(chainno, len.block) chains_to_list(vectors, heights) Arguments eigval eigenvalues, a numeric or complex vector. len.block lengths of Jordan chains, a vector of positive integers. x generalised eigenvectors, a matrix with one column for each (generalised) eigen- vector. jmat a Jordan matrix. chainno a vector of positive integers between 1 and length(eigval) specifying which Jordan chains to locate, see Details. ... further arguments to pass on to solve. vectors a matrix of generalised eigenvectors of a matrix. heights a vector of chain lengths, heights[i] is the length of the i-th chain. Details Jordan_matrix creates a Jordan matrix (block-diagonal matrix with Jordan blocks on the diagonal) whose i-th diagonal block corresponds to eigval[i] and is of size len.block[i]. If len.block is missing, Jordan_matrix returns diag(eigenvalues). from_Jordan computes the matrix whose Jordan decomposition is represented by arguments X (chains) and J (Jordan matrix). Conceptually, the result is equivalent to XJX −1 but without ex- plicitly inverting matrices (currently the result is the transpose of solve(t(x), t(x %*% jmat), ...)). chain_ind computes the columns of specified Jordan chains in a matrix of generalised eigenvectors. It is mostly internal function. If x is a matrix whose columns are generalised eigenvectors and the i-th Jordan chain is of length len.block[i], then this function gives the column numbers of x containing the specified chains. Note that chain_ind is not able to deduce the total number of eigenvalues. It is therefore an error to omit argument len.block when calling it. chains_to_list converts the matrix vectors into a list of matrices. The i-th element of this list is a matrix whose columns are the vectors in the i-th chain. Value for Jordan_matrix, a matrix with the specified Jordan blocks on its diagonal. for from_Jordan, the matrix with the specified Jordan decomposition. for chain_ind, a vector of positive integers giving the columns of the requested chains. for chains_to_list, a list of matrices. Level 0 6 jordan Author(s) Georgi N. Boshnakov Examples ## single Jordan blocks Jordan_matrix(4, 2) Jordan_matrix(5, 3) Jordan_matrix(6, 1) ## a matrix with the above 3 blocks Jordan_matrix(c(4, 5, 6), c(2, 3, 1)) ## a matrix with a 2x2 Jordan block for eval 1 and two simple 0 eval's m <- make_mcmatrix(eigval = c(1), co = cbind(c(1,1,1,1), c(0,1,0,0)), dim = 4, len.block = c(2)) m m.X <- cbind(c(1,1,1,1), c(0,1,0,0), c(0,0,1,0), c(0,0,0,1)) m.X m.J <- cbind(c(1,0,0,0), c(1,1,0,0), rep(0,4), rep(0,4)) m.J from_Jordan(m.X, m.J) # == m m.X %*% m.J %*% solve(m.X) # == m all(m == from_Jordan(m.X, m.J)) && all(m == m.X %*% m.J %*% solve(m.X)) ## TRUE ## which column(s) in m.X correspond to 1st Jordan block? chain_ind(1, c(2,1,1)) # c(1, 2) since 2x2 Jordan block ## which column(s) in m.X correspond to 2nd Jordan block? chain_ind(2, c(2,1,1)) # 3, simple eval ## which column(s) in m.X correspond to 1st and 2nd Jordan blocks? chain_ind(c(1, 2), c(2,1,1)) # c(1,2,3) ## non-contiguous subset are ok: chain_ind(c(1, 3), c(2,1,1)) # c(1,2,4) ## split the chains into a list of matrices chains_to_list(m.X, c(2,1,1)) m.X %*% m.J m %*% m.X # same all(m.X %*% m.J == m %*% m.X) # TRUE m %*% c(1,1,1,1) # = c(1,1,1,1), evec for eigenvalue 1 m %*% c(0,1,0,0) # gen.e.v. for eigenvalue 1 ## indeed: all( m %*% c(0,1,0,0) == c(0,1,0,0) + c(1,1,1,1) ) # TRUE ## m X = X jordan.block cbind(c(1,1,1,1), c(0,1,0,0)) %*% cbind(c(1,0), c(1,1)) m %*% cbind(c(1,1,1,1), c(0,1,0,0)) JordanDecomposition 7 JordanDecomposition Create objects representing Jordan decompositions Description Create objects representing Jordan decompositions. Usage JordanDecomposition(values, vectors, heights, ...) Arguments values eigenvalues, a vector of length equal to the number of Jordan chains. vectors the (generalised) eigenvectors, a matrix. heights a vector of positive integers, heights[i] is the height of values[i]. ... further arguments that may be needed by methods. Details JordanDecomposition is an S4 generic function. It creates objects representing Jordan decompo- sitions. Dispatch is on the first two arguments, values and vectors. The names of the arguments correspond to slots in class "JordanDecompositionDefault", which is the class of the objects created by methods in package mcompanion and inherits from the virtual class "JordanDecomposition". Value an object inheriting from "JordanDecomposition" Methods signature(values = "ANY", vectors = "ANY") the default method; currently raises an error. signature(values = "JordanDecomposition", vectors = "missing") simply returns values. signature(values = "list", vectors = "missing") In this case values can be a list with com- ponents "values", "vectors" and "heights". This method has an additional argument "names" which can be used when the components of the list are different, e.g. names = c(values = "eigval", vectors = "eigvec", heights = "len.block"). signature(values = "missing", vectors = "matrix") This is equivalent to the case values = "number" with values set to a vector of missing values. signature(values = "missing", vectors = "missing") values (vectors) is set to a vector (matrix) of missing values. The dimensions are deduced from argument heights, so heights cannot be missing for this signature. signature(values = "number", vectors = "matrix") This is equivalent to calling new for class "JordanDecompositionDefault" with arguments values, vectors and heights. 8 JordanDecompositionDefault-class signature(values = "number", vectors = "missing") This is equivalent to the case vectors = "matrix" with vectors set to a matrix of missing values. signature(values = "SmallMultiCompanion", vectors = "missing") This computes the Jor- dan decomposition of an object from class "SmallMultiCompanion". Author(s) Georgi N. Boshnakov Examples m <- matrix(c(1,2,4,10), nrow = 2) m <- matrix(c(1,2,4,10), nrow = 2) m <- matrix(c(5, 12, 3, 4), nrow = 2) JordanDecomposition(values = rep(0,2), vectors = m) jd <- JordanDecomposition(values = c(0.9, 0.3), vectors = m) as(jd, "matrix") eigen(jd) ## the eigenvectors are scaled versions of m's columns: eigen(jd)$vectors %*% diag(c(5 / eigen(jd)$vectors[1,1], -5)) ## == m ## eigenvalues are not supplied, so set to NA's here: JordanDecomposition(vectors = m) ## eigenvectors are set to vectors of NA's here: JordanDecomposition(values = rep(0,2), height = c(1,1)) JordanDecompositionDefault-class A basic class for Jordan decompositions Description A basic class for Jordan decompositions. Details Class "JordanDecompositionDefault" represents Jordan decompositions. It inherits from the virtual class "JordanDecomposition", which serves as a base class for Jordan decompositions. These classes should be considered internal. Objects from the Class Objects from class "JordanDecompositionDefault" can be created by a call to JordanDecomposition(). Objects can be created by calls of the form new("JordanDecompositionDefault", heights, ...). make_mcev 9 Slots values: Object of class "number", vector of eigenvalues (one value for each Jordan chain). heights: Object of class "integer", the heights of the Jordan chains. vectors: Object of class "matrix", the (generalised) eigenvectors (similarity matrix). Extends Class "JordanDecomposition", directly. Methods coerce signature(from = "JordanDecompositionDefault",to = "matrix"): gives the matrix represented by the Jordan decomposition, i.e. XJX −1 . As with other coerce methods, use as(obj, "matrix"), where obj is the Jordan decomposition object. initialize signature(.Object = "JordanDecompositionDefault"): ... Author(s) Georgi N. Boshnakov See Also JordanDecomposition Examples showClass("JordanDecompositionDefault") m <- matrix(c(1,2,4,3), nrow = 2) new("JordanDecompositionDefault", values = rep(0,2), vectors = m) make_mcev Create a multi-companion eigenvector Description Creates an eigenvector of a multicompanion matrix from the eigenvalue and the seed parameters. Usage make_mcev(eigval, co, dim, what.co = "bottom") make_mcgev(eigval, co, v, what.co = "bottom") 10 make_mcev Arguments eigval the eigenvalue. co the bottom (default) or the top seed elements of the vector. dim the size of the matrix. what.co type of co: "bottom" or "top". v the previous vector in the chain. Details make_mcev computes an eigenvector for a multi-companion dim x dim matrix by filling its top or bottom part with co and completing the remaining elements using the general pattern of eigenvec- tors of such matrices (Boshnakov 2002). Similarly, make_mcgev computes the next generalised eigenvector in a chain whose previous ele- ment is v. what.co cannot be "top" if the eigenvalue is 0. Generalised eigenvectors corresponding to the zero eigenvalue have some specifics, so it is better to use the specialised functions in that case. Value make_mcev returns the required eigenvector. make_mcgev returns the required generalised eigenvector. Author(s) Georgi N. Boshnakov References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. Examples v1 <- make_mcev(0.5, c(1, 1), dim = 4) v1 make_mcev(0.5, c(1, 1), dim = 4, what.co = "top") v2 <- make_mcgev(0.5, c(0, 1), v = v1, what.co = "top") v2 make_mcgev(0.5, c(0, 1), v = v2, what.co = "top") make_mcmatrix 11 make_mcmatrix Generate a multi-companion matrix from spectral description Description Generate a multi-companion matrix or its Jordan decomposition from spectral parameters. Usage make_mcmatrix(type = "real", what.res = "matrix", ..., eigval0) make_mcchains(eigval, co, dim, len.block, eigval0 = FALSE, mo.col = NULL, what.co = "bottom", ...) Arguments eigval the eigenvalues, a numeric vector co the seeding parameters for the eigenvectors, a matrix dim the dimension of the matrix, a positive integer len.block lengths of Jordan chains, len.block[i] is for eigval[i] type mode of the matrix, real or complex what.res format of the result, see details eigval0 If TRUE completes the matrix to a square matrix, see details. eigval0 is ignored by make_mcmatrix (it always sets it to TRUE). ... for make_mcmatrix, these are additional arguments to be passed to make_mcchains. For make_mcchains, arguments in "..." are passed on to mc_0chains. mo.col the last non-zero column in the top of the mc-matrix. The default is dim. what.co a character string equal to "bottom" (default) or "top", specifying whether the ’co’ parameters give the last or the first few elements of the (generalised) eigen- vectors. Details make_mcmatrix creates a multi-companion matrix specified by spectral parameters. make_mcchains creates a matrix of eigenvectors and generalised eigenvectors from the given spectral parameters. make_mcmatrix passes the spectral parameters to make_mcchains to generate the (generalised) eigenvectors. It then calls Jordan_matrix to create the corresponding Jordan matrix. The re- sults are combined to produce the multicompanion matrix. By default, the real part is returned, which is appropriate if all complex spectral parameters come in complex conjugate pairs. This may be changed by argument type. A list containing the matrix and the Jordan factors is returned if what.res = "list". The closely related function sim_mc is like make_mcmatrix but it does not need complete specifi- cation of the matrix - it completes any missing information (eigenvalues, co) with randomly gener- ated entries. The result of both functions is a list or ordinary matrix, use mCompanion to obtain a MultiCompanion object directly. 12 make_mcmatrix make_mcchains constructs the eigensystem, make_mcmatrix calls make_mcchains (passing the ... arguments to it) and forms the matrix. make_mcchains passes the ... arguments to mc_0chains. make_mcchains creates the full eigenvectors from the co parameters. If the number of vectors is smaller then dim and eigval0 is TRUE it then completes the system with chains for the zero eigenvalue. More specifically, it assumes that the number of the given chains is mo.col, takes chains corresponding to the zero eigenvalue, if any, and adds additional eigenvectors and/or generalised eigenvectors to construct the complete system. The mc-order is determined from the dimension of the ’co’ parameters. If that is equal to dim, the mc-matrix is actually a general matrix. TODO: cover the case mo < mo.col? Value make_mcmatrix normally returns the multi-companion matrix (as an ordinary matrix) having the given spectral properties but if what.res = "list", it returns a list containing the matrix and the spectral information: eigval eigenvalues, a vector len.block lengths of Jordan chains, a vector mo multi-companion order, positive integer eigvec generalied eigenvectors, a matrix co seeding parameters mo.col top order mat the multi-companion matrix, a matrix make_mcchains returns a similar list without the component mat. Note The result is an ordinary matrix. Also, some entries that should be 0 may be non-zero due to numerical error. To get a MultiCompanion object use mCompanion. Author(s) Georgi N. Boshnakov References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. Boshnakov GN, Iqelan BM (2009). “Generation of time series models with given spectral proper- ties.” J. Time Series Anal., 30(3), 349–368. ISSN 0143-9782, doi:10.1111/j.14679892.2009.00617.x. See Also make_mcev, make_mcgev, sim_mc, mCompanion make_mcmatrix 13 Examples make_mcmatrix(eigval = c(1, 0.5), co = cbind(c(1,1), c(1, -1)), dim = 4, mo.col = 2, len.block = c(1, 1)) ## one unit root, one root = 0.5 make_mcmatrix(eigval = c(1, 0.5), co = cbind(c(1,1), c(1, -1)), dim = 6, mo.col = 2, len.block = c(1, 1)) ## two simple unit roots, one root = 0.5 make_mcmatrix(eigval = c(1, 1, 0.5), co = cbind(c(1,1), c(1, -1), c(1, 1)), dim = 6, mo.col = 3, len.block = c(1, 1, 1)) ## two unit roots with a single Jordan chain, one root = 0.5 make_mcmatrix(eigval = c(1, 0.5), co = cbind(c(1,1), c(0, 1), c(1, 1)), dim = 6, len.block = c(2, 1)) ## make_mcchains make_mcchains(c(1, 0.5), co = cbind(c(1,1), c(1, 1)), dim = 4, len.block = c(1, 1), eigval0 = TRUE) ## one unit root, one root = 0.5 make_mcchains(c(1, 0.5), co = cbind(c(1,1), c(1, 1)), dim = 6, len.block = c(1, 1), eigval0 = TRUE) ## two simple unit roots, one root = 0.5 make_mcchains(c(1, 1, 0.5), co = cbind(c(1,1), c(1, -1), c(1, 1)), dim = 6, len.block = c(1, 1, 1), eigval0 = TRUE) ## two unit roots with a single Jordan chain, one root = 0.5 make_mcchains(c(1, 0.5), co = cbind(c(1,1), c(1, -1), c(1, 1)), dim = 6, len.block = c(2, 1), eigval0 = TRUE) ## examples with mc-order = dim make_mcchains(c(1), co = cbind(c(1,1,1,1), c(1,2,1,1)), dim = 4, len.block = c(2), eigval0 = TRUE) ## do not complete with chians for the 0 eigval: make_mcchains(c(1), co = cbind(c(1,1,1,1), c(1,2,1,1)), dim = 4, len.block = c(2), eigval0 = FALSE) make_mcmatrix(eigval = c(1), co = cbind(c(1,1,1,1), c(1,2,1,1)), dim = 4, len.block = c(2)) make_mcmatrix(eigval = c(1), co = cbind(c(1,1,1,1), c(1,2,3,4)), dim = 4, len.block = c(2)) 14 mCompanion mCompanion Create objects from class MultiCompanion Description Create, generate, or simulate objects from class "MultiCompanion" by specifying the matrix in several ways. Usage mCompanion(x, detect = "nothing", misc = list(), ...) ## S4 method for signature 'MultiCompanion' initialize(.Object, xtop, mo, n, mo.col, ido, x, dimnames, detect = "nothing", misc = list()) Arguments x the matrix or, for mCompanion only, the top of the matrix or a character string, see section ‘Details’. misc information to be stored in the object’s pad. ... other arguments to be passed down to generator functions, see section ‘Details’. xtop the top of the matrix. mo the multi-companion order of the matrix. n the dimension. mo.col the top order, meaniing that columns mo.col+1,...,n of the top of the matrix are zeros. mo.col may also be set to "detect", in which case it is determined by scanning xtop or x. ido the dimension of the identity sub-matrix. dimnames is not used currently. detect controls whether automatic detection of mo and mo.col should be attempted. The values tested are "mo", "mo.col", "all", and "nothing" with obvious mean- ings. .Object this is set implicitly by package "methods". Details Objects from class "MultiCompanion" can be created by calling mCompanion() or new("MultiCompanion", ...). In the latter case the “. . . ” arguments are as for the initialize method, except .Object. Do not call initialize directly. mCompanion can generate multi-companion matrices from spectral information, full or partial, using the methodology developed by Boshnakov and Iqelan (2009). If the specification is not given in full, the missing information is filled with suitably simulated values. For example, unspecifies eigenvalues are generated inside the unit circle, sim_mc. mCompanion 15 If argument x is the string "sim" or "gen", then mCompanion calls sim_mc or make_mcmatrix, re- spectively, with the arguments ... and converts the result to class MultiCompanion. See the doc- umentation of those functions for further details and examples. The conversion may be the main reason to use mCompanion in this way rather than call sim_mc and make_mcmatrix directly. Otherwise, if x is numeric it is taken to specify the top of the matrix unless detect="mo" in which case it is the whole matrix. In both cases all arguments are passed down to new, the only (more or less) change being that x is passed down as xtop=x and x=x, respectively, see MultiCompanion. detect=="gen" signifies that x has the format of the output from sim_mc or make_mcmatrix, so that mCompanion may use the additional information in such objects. The multi-companion order is determined automatically from the content of the matrix if detect=="mo". Value a multi-companion matrix, an object of class "MultiCompanion" Author(s) Georgi N. Boshnakov References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. Boshnakov GN (2007). “Singular value decomposition of multi-companion matrices.” Linear Al- gebra Appl., 424(2-3), 393–404. ISSN 0024-3795, doi:10.1016/j.laa.2007.02.010. Boshnakov GN, Iqelan BM (2009). “Generation of time series models with given spectral proper- ties.” J. Time Series Anal., 30(3), 349–368. ISSN 0143-9782, doi:10.1111/j.14679892.2009.00617.x. See Also sim_mc, make_mcmatrix, MultiCompanion Examples # simulate a 6x6 mc matrix with 2 non-trivial rows mCompanion("sim", dim = 4, mo = 2) # simulate a 6x6 mc matrix with 4 non-trivial rows mCompanion("sim", dim = 6, mo = 4) # similar to above but top rows with 2 non-zero columns mCompanion("sim", dim = 6, mo = 4, mo.col = 2) ## specify the non-trivial top rows (as a matrix): m1 <- matrix(1:24, nrow = 4) mCompanion(m1) # mc matrix with m1 on top m2 <- rbind(c(1, 2, 0, 0), c(3, 4, 0, 0)) x2a <- mCompanion(m2) # mc matrix with m2 on top x2a@mo.col # = 4 16 mcSpec x2 <- mCompanion(m2, mo.col = "detect") x2@mo.col # = 2, detects the 0 columns in m2 mCompanion(m2, mo.col = 2) # same # create manually an mc matrix (m3 <- rbind(m1, c(1, rep(0, 5)), c(0, 1, rep(0, 4)))) # turn it into a MultiCompanion object x3 <- mCompanion(x = m3, detect = "mo") x3@mo x3 <- mCompanion(m3) x3@mo m4 <- rbind(c(1, 2, rep(0, 4)), c(3, 4, rep(0, 4))) x4 <- mCompanion(m4, mo = 2) x4@mo.col # = 6, ## special structure not incorporated in x4, ## eigen and mc_eigen are equiv. in this case eigen(x4) mc_eigen(x4) x4a <- mCompanion(m4, mo = 2, mo.col = 2) x4a@mo.col # = 2, has Jordan blocks of size > 1 ## the eigenvectors do not span the space: eigen(x4a) ## mc_eigen exploits the Jordan structure, e.g.2x2 Jordan blocks, ## and gives the generalised eigenvectors: (ev <- mc_eigen(x4a)) x4a %*% ev$vectors ## construct the Jordan matrix of x4a from eigenvalues and eigenvectors (x4a.j <- Jordan_matrix(ev$values, ev$len.block)) ## check that AX = XJ and A = XJX^-1, up to numerical precision: x4a %*% ev$vectors - ev$vectors %*% x4a.j x4a - ev$vectors %*% x4a.j %*% solve(ev$vectors) mcSpec Generate objects of class mcSpec Description Generate objects of class mcSpec. Usage mcSpec(...) ## S4 method for signature 'mcSpec' mcSpec 17 initialize(.Object, dim, mo, root1 = numeric(0), iorder = 0, siorder = 0, order = rep(dim, mo), evtypes = NULL, mo.col = NULL, n.roots = mo.col, ...) Arguments dim the dimension, a positive integer. mo multi-companion order, a.k.a. number of seasons. root1 roots equal to one, a vector of positive integers of length at most mo. iorder integration order, a non-negative integer. siorder seasonal integration order, a non-negative integer. order order of the periodic filter, a vector of length mo. evtypes types of additional eigenvalues, see Details. mo.col number of non-zero columns in the top part of the multicompanion matrix, see Details. n.roots number of non-zero roots ... further arguments to be passed on. .Object An object. This argument is not used in calls of mcSpec and new, see the details section. Details mcSpec(...) and new("mcSpec", ...) create objects from class mcSpec. The two calls are equiv- alent and may contain any of the arguments of the initialize method described here, except .Object which is generated automatically. In both cases the initialize method is called and passed all the arguments. Several ways are provided for the specification of unit roots and they may be combined, as long as the specification is consistent. roots1 specifies eigenvalues equal to 1 and the size of their Jordan chains. iorder and siorder provide convenient shortcuts for the special cases which they cover. iorder specifies the integration order. This corresponds to operator (1 − B) applied iorder times. Similarly, siorder specifies the seasonal integration order, which corresponds to the operator (1 − B s ) applied siorder times, where s is equal to mo. This argument generates mo unit roots, each of height (dimension of its Jordan chain) siorder. It is possible to use combinations of these arguments to specify the unit roots and all specifications are combined. Care must be taken not to exceed dim. If mo.col is missing, it is set to max(order). mo.col may also be the character string "+ones". In this case the dimension of the unit roots is added to max(order). mo.col may also be set directly by giving it an appropriate integer value. TODO: Need more checks for consistency here! TODO: describe other roots and eigenvectors! After all specified quantities are prepared, the rest are set to NA’s. If not all eigenvalues are specified, additional eigenvalues are introduced to reach dimension dim. By default, if an even number of eigenvalues is needed, all of them are specified as complex pairs, "cp". If the number is odd, one real eigenvalue is specified and the rest are set again to "cp". 18 mcSpec-class Argument evtypes can be used to select a different setting for the additional eigenvalues. It is a character vector in which "r" stands for real eigenavalues and "cp" stands for a complex pair. For example, if there are two "free" eigenvalues, the automatic choice would be a complex pair, "cp". If two real eigenvalues are desired set evtypes to c("r","r"). Note: evtypes is for types of additional eigenvalues. Do not specify types for eigenvalues equal to one or zero. Value an object of class mcSpec Author(s) Georgi N. Boshnakov References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. Boshnakov GN, Iqelan BM (2009). “Generation of time series models with given spectral proper- ties.” J. Time Series Anal., 30(3), 349–368. ISSN 0143-9782, doi:10.1111/j.14679892.2009.00617.x. See Also mcSpec-class Examples spec2 <- mcSpec(21, 4, siorder=2, iorder=1) spec4 <- mcSpec(11, 4, siorder=1, iorder=1) spec.co2 <- mcSpec(dim = 5, mo = 4, siorder = 1) spec.co2new <- mcSpec(dim = 5, mo = 4, siorder = 1) # after correcting ev.arg spec.co2alt <- mcSpec(dim = 6, mo = 4, siorder = 1) spec.co3 <- mcSpec(dim = 5, mo = 4, root1 = c(1,1,1)) spec.coz1 <- mcSpec(dim = 4, mo = 4, root1 = c(1,1), order = rep(2,4)) # test0 roots spec.coz2 <- mcSpec(dim = 5, mo = 4, root1 = c(1,1), order = rep(2,4)) # test0 roots spec.coz3 <- mcSpec(dim = 4, mo = 4, root1 = c(1), order = rep(2,4)) # test0 roots spec.co4 <- mcSpec(dim = 4, mo = 4, root1 = c(1,1,1)) mcSpec-class A class for spectral specifications of multi-companion matrices Description A class for spectral specifications of multi-companion matrices. mcSpec-class 19 Objects from the Class Objects can be created by calls of one of the following equivalent forms: • mcSpec(dim, mo, root1, iorder, siorder, order, evtypes, ...), • new("mcSpec", dim, mo, root1, iorder, siorder, order, evtypes, ...). An object of class "mcSpec" holds a spectral specification of a square multi-companion matrix. The specification may be only partial. In that case unspecified components are set to NA. Eigenvalues are represented by their modulus and complex argument. The argument is in cycles per unit time. So, a negative real number has argument 0.5. The complex eigenvalues come in pairs and only one needs to be specified. If an eigenvalue is not simple, it should not be repeated. Rather, the size of the corresponding Jordan block should be specified. The types of the eigenvalues may be "r" (real) or "cp" (complex pair). See mcSpec for full details about the initialization function for class mcSpec. Slots dim: dimension of the matrix, a positive integer. mo: multi-companion order, a positive integer. ev.type: Types of eigenvalues, "r" or "cp", a character vector. co.type: Types of the co parameters, a character vector. order: orders of the factors, the default is rep(dim,mo). n.root: number of nonzero roots. ev.abs: absolute values (moduli) of the roots. ev.arg: complex arguments of the roots (cycles per unit time). In particular, zero for positive reals, 0.5 for negative reals. (TODO: check that functions that use this specification know that!) block.length: sizes of Jordan blocks corresponding to the eigenvalues, a vector of positive inte- gers. By default the eigenvalues are simple. co.abs: moduli of the co parameters, a matrix. co.arg: arguments of the co parameters, a matrix. mo.col: Object of class "numeric". F0bot: Object of class "optionalMatrix". Methods initialize signature(.Object = "mcSpec"): see mcSpec. Note The initialization function for mcSpec class is incomplete, in the sense that it does not cover all cases. 20 mcStable Author(s) Georgi N. Boshnakov See Also mcSpec Examples mcSpec(dim = 5, mo = 4, root1 = c(1,1), order = rep(3,4)) mcSpec(dim = 5, mo = 4, root1 = c(1,1,1), order = rep(5,4)) mcSpec(dim = 5, mo = 4, root1 = c(1,1,1,1), order = rep(5,4)) mcStable Check if an object is stable Description Check if an object is stable. Usage mcStable(x) Arguments x the object to be checked Details A stable matrix is a matrix all of whose eigenvalues have moduli less than one. Other objects are stable if the associated matrix is stable. This is a generic function. The default method works as follows. x is a square matrix, the method checks if its eigenvalues satisfy the stability condition and returns the result. Otherwise, if x is a rectangular matrix with more columns than rows, it is assumed to be the top of a multi-companion matrix. If x is a vector, it is assumed to represent the top row of a companion matrix. In all other cases x is converted to matrix with as.matrix(x). The result should be a square matrix whose eigenvalues are checked. It is an error for the matrix to have more rows than columns. Value TRUE if the object is stable and FALSE otherwise mcStable 21 Note An argument ... may be a good idea since methods may wish to provide options. For example, for continuous time systems, the stability condition is that the real parts of the eigenvalues are negative. For example, an option to choose the left half-plane for the stable region, instead of the unit circle, would handle stability for continuous time systems. Author(s) Georgi N. Boshnakov Examples ## a simulated matrix (it is stable by default) mc <- mCompanion("sim", dim=4, mo=2) mcStable(mc) ## a square matrix m <- matrix(1:9, nrow=3) eigen(m)$values mcStable(m) ## a 2x4 matrix, taken to be the top of an mc matrix m <- matrix(1:8, nrow=2) mcStable(m) mCompanion(m) ## a vector, taken to be the top row of an mc matrix v <- 1:4 mcStable(v) mCompanion(v) abs(mc_eigen(mCompanion(v))$values) co1 <- cbind(c(1,1,1,1), c(0,1,0,0)) ## a matrix with eigenvalues equal to 1 mat2 <- make_mcmatrix(eigval = c(1), co = co1, dim = 4, len.block = c(2)) ## mat2 is ordinary matrix, eigenvalues are computed numerically eigen(mat2) mcStable(mat2) # FALSE but in general depends on floating point arithmetic mat2a <- mCompanion(x="gen", eigval = c(1), co = co1, dim = 4, len.block = c(2), what.res = "list") mc_eigen(mat2a) mcStable(mat2a) mat2b0 <- make_mcmatrix(eigval = c(1), co = co1, dim = 4, len.block = c(2), what = "list") mat2b <- mCompanion(mat2b0, "gen") mc_eigen(mat2b) mcStable(mat2b) ## mat2c is a MultiCompanion object with the eigenvalues stored in it 22 mc_chain_extend mat2c <- mCompanion(x="sim", eigval = c(1,0,0), co = cbind(co1, c(0,0,1,0), c(0,0,0,1)), dim = 4, len.block = c(2,1,1)) mat2c ## since the eigenvalues are directly available here, no need to compute them mc_eigen(mat2c) # contains a 2x2 Jordan block. mcStable(mat2c) mc_chain_extend Extend multi-companion eigenvectors Description Extend Jordan chains of a multi-companion matrix to higher dimension and complete them to a full system by adding eigenchains for zero eigenvalues. Usage mc_chain_extend(ev, newdim) Arguments ev eigenvalues and eigenvectors, a list with components values and vectors. newdim the new dimension of the vectors. Details The eigenvectors of a multi-companion matrix have a special structure. This function extends the supplied eigenvectors to be eigenvectors of a higher-dimensional multi-companion matrix of the same multi-companion order with the same top rows extended with zeroes. ev is a list with components values, vectors and possibly others. In particular, ev may be the value returned by a call to the base function eigen(). A component len.block may be used to specify the lengths of the Jordan chains, by default all are of length one. The function handles also the case when only the first mo.col columns of the top of the origi- nal multi-companion matrix are non-zero. This may be specified by a component mo.col in ev, otherwise mo.col is set to the dimension of the space spanned by the non-zero eigenvalues. When mo.col is smaller than the multi-companion order, the information in the eigenvectors is not sufficient to extend them. The missing entries are supplied via the argument F0bot (TODO: describe!). Chains corresponding to zero eigenvalues come last in the result. Value The eigenvectors extended to the new dimension. Author(s) Georgi N. Boshnakov mc_eigen 23 References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. Boshnakov GN, Iqelan BM (2009). “Generation of time series models with given spectral proper- ties.” J. Time Series Anal., 30(3), 349–368. ISSN 0143-9782, doi:10.1111/j.14679892.2009.00617.x. See Also mc_eigen; the main work is done by mC.non0chain.extend and mc_0chains. Examples ev <- make_mcchains(eigval = c(1, 0.5), co = cbind(c(1,1), c(1, -1)), dim = 4, mo.col = 2, len.block = c(1, 1)) ev ## extend evecs in ev to the requested dim and complete with chains for eval 0. mc_chain_extend(ev = ev, newdim = 6) mc_chain_extend(ev = ev, newdim = 7) mc_eigen The eigen decomposition of a multi-companion matrix Description Give the eigenvalues or the entire eigen decomposition of a multi-companion matrix Usage mc_eigen(x, ...) mc_eigenvalues(x, ...) Arguments x a multi-companion matrix, an object of class MultiCompanion. ... additional arguments, currently not used. Details Both functions first check if the decomposition is stored in x and, if that is the case, return the result without computations. This is particularly useful when the matrix is created from its spectral decomposition in the first place. The only restrictions on the result in this case come from the structure of multi-companion matrices. Otherwise they use eigen to do the main computation. In addition, if the top of the matrix has struc- tural columns of zeroes, mc_eigen takes care to call eigen with a sub-matrix whose last column is not zero, and handles the zero eigenvalues separately. 24 mc_eigen Note that x@mo.col is the last column containing nonzero elements in the top of the matrix. By calling eigen on the top left x@mo.col square block, rather than on the entire matrix, we achieve several things. Firstly, this block may turn out to be non-singular. In that case, the chains corre- sponding to zero eigenvalues, if any, are structural and straightforward. Secondly, if this block turns out to be singular, we know that by reducing the dimension we have left out only elements corre- sponding to zero eigenvalues. The vectors associated with zero eigenvalues are somewhat tricky in this case, but manageable. The net effect is that the only restriction comes from the use of eigen, which does not handle Jordan chains of length larger than one. In general, this is not a problem, since chains with more than one vector are not likely to occur numerically. In particular, it is relatively safe to assume that the space spanned by the non-zero eigenvalues of the multicompanion matrix has a basis of eigenvectors. However, when x@mo.col is smaller than the dimension of the matrix, eigenchains associated with the zero value can easily occur, due to the structure of the matrix. That is why we pay special attention to them. In mc_eigen the handling of the zero eigenvalues is based on mc_chain_extend. The latter takes care also of zero eigenvalues whose Jordan blocks are of size larger than one. Value For mc_eigenvalues, the eigenvalues as a vector. For mc_eigen, the eigenvalues and eigenvectors as a list with components values and vectors. In addition the list contains a component len.block with the lengths of the Jordan chains. Note mc_eigenvalues currently simply calls eigen if the eigenvalues are not stored in the object. It is probably mostly useful when the interest is in the nonzero eigenvalues. Author(s) Georgi N. Boshnakov References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. Examples x <- sim_mc(6,4,mo.col=2) x y <- mCompanion(x,detect="gen") y z <- as.matrix(y) xx <- mCompanion(x=z,mo.col=2) mc_eigen(xx) mc_factorize 25 mc_factorize Factorise multi-companion matrices Description Companion factorization of multi-companion matrices. Usage mc_factorize(x, mo, mo.col) mc_leftc(x, mo, mo.col) Arguments x a multi-companion matrix or its top. mo multi-companion order, number of structural top rows. mo.col number of non-trivial columns in the top of the matrix. Details The companion factorization of a multi-companion matrix, X, of (multi-companion) order p is X = A1 × · · · × Ap , where Ai , i = 1, . . . , p, are companion matrices. mc_leftc factorises a multi-companion matrix into a product of companion times multi-companion. mc_factorize calls mc_leftc a number of times to compute the full factorisation. If x is not a matrix an attempt is made to convert it to matrix. If x is a vector it is converted to a matrix with 1 row. x may be the whole matrix or its top. If mo is missing x is assumed to be the top of the matrix and the multi-companion order is set to its number of rows. mo.col defaults to the number of columns of x. It is important to specify mo.col if there are columns of zeroes in the top of the matrix. Otherwise the factorisation usually fails with a message (from solve) that the system is exactly singular. Note however that for objects of class MultiCom- panion this situation is handled automatically (unless the user overwrites the default behaviour). Value for mc_factorize, a matrix whose i-th row is the first row of the i-th companion factor. for mc_leftc, a numeric vector containing the first row of the companion factor. Level 0 26 mc_factors Note The companion factorisation does not always exist but currently this possibility is not handled. Even if it exists, it may be numerically unstable. Also, if mo.col is smaller than the number of columns, then the factorisation is not unique, the one having mo.col non-zero entries is computed. The existence is not treated. mc_leftc is probably the first function I wrote for multi-companion matrices. It does not do checks consistently. The MultiCompanion class can be used here. Author(s) Georgi N. Boshnakov References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. See Also mc_from_factors Examples mat2 <- make_mcmatrix(eigval = c(1), co = cbind(c(1,1,1,1), c(0,1,0,0)), dim = 4, len.block = c(2)) mat2 eigen(mat2) mc_leftc(mat2, mo = 4, mo.col = 2) mCompanion(mat2) mCompanion(mat2, mo=4, mo.col=2) mc_leftc(mCompanion(mat2), mo = 4, mo.col = 2) mc_eigen(mCompanion(mat2), mo = 4, mo.col = 2) mc_eigen(mCompanion(mat2, mo=4, mo.col=2), mo = 4, mo.col = 2) mc_factors Factors of multi-companion matrices Description Gives the factors comprising the companion factorisation of a multi-companion matrix. Usage mc_factors(x, what = "mc") Arguments x a multi-companion matrix, an object of class MultiCompanion. what format of the result, see below. mc_from_factors 27 Details If the factors are available in the object’s pad in the requested format, they are returned without further processing. The factors may be available if they have been previously computed or if the matrix has been created from the factors. If the factors are available, but not in the requested format, they are converted to it. Otherwise the factors are computed. The factors are stored in the object’s pad under the name "mC.factors" when what == "mc", and in "mC.factorsmat" otherwise. Value If what == "mc" the companion factors of x as a list of MultiCompanion objects. Otherwise a matrix with i-th row representing the i-th factor. As a side effect, the factors are stored in the object’s pad, see ‘Details’. Author(s) Georgi N. Boshnakov References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. Examples m <- mCompanion(matrix(1:8, nrow = 2)) mc_factors(m) mc_from_factors Multi-companion matrix from factors Description Compute a multi-companion matrix from its companion factors or from a periodic filter. Create the multi-companion matrix corresponding to a periodic filter by multiplying the relevant companion matrices in reverse order. Usage mc_from_factors(x) mc_from_filter(x) Arguments x a matrix with a row for each companion factor, see details. 28 mc_from_factors Details x is a matrix whose i-th row is the top row of the i-th companion factor (for mc_from_factors) or the filter coefficients for the i-th season (for mc_from_filter). mc_from_factors is, effectively, the inverse of mc_factorize. The companion matrices specified by the argument are multiplied. mc_from_filter is similar except that the relevant companion matrices are multiplied in reverse order. After all, it is natural to have the coefficients for the i-th season in the i-th row! todo: add an argument to specify the "first" season. Value The top of the resulting multi-companion matrix. Level Currently mc_from_factors calls mCompanion, which it probably should not do. Author(s) Georgi N. Boshnakov References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. See Also mc_factorize Examples x <- matrix(1:8, nrow = 2) mc_from_factors(x) mCompanion(mc_from_factors(x)) mc_from_filter(x) mCompanion(mc_from_filter(x)) mc_matrix 29 mc_matrix Basic utilities for multi-companion matrices Description Compute the dense matrix representation of a multi-companion matrix or convert the argument to an ordinary matrix. Usage mc_full(x) mc_matrix(x) mc_order(x) is_mc_bottom(x) Arguments x the top part of the multi-companion matrix or the whole matrix, see Details. Details mc_matrix returns an ordinary matrix. It returns x if x is an ordinary matrix (is.matrix(x) == TRUE), converts x to a matrix with one row if x is a vector, and returns as.matrix(x) otherwise. mc_matrix is used by some functions in package mcompanion that want to allow flexible format for the top of a multicompanion matrix or even the whole matrix (e.g. x may be a MultiCompanion object) but are not really multi-companion aware. For mc_full, x is normally the top part of a multi-companion matrix. Rows are appended as necessary to obtain the dense representation of the matrix and the result is guaranteed to be a multi- companion matrix. It is an error to have more rows than columns. If the number of rows is equal to the number of columns, i.e. x is the whole matrix, the effect is that x is converted to an ordinary matrix but no check is made to see if the result is indeed a multi-companion matrix. x may be a vector if the multi-companion order is 1. Give the multi-companion order of a square matrix Determine the multi-companion order of a square matrix or check if a matrix may be the bottom part of a multi-companion matrix. In mc_order(x) should be a square matrix, while in is_mc_bottom(x) the matrix is usually rect- angular. The bottom part of a multi-companion matrix is of the form [I 0], where I is an identity matrix and 0 is a matrix of zeroes. The top consists of the rows above the bottom part. The multi-companion order is the number of rows in the top of a multi-companion matrix. Identity matrices have mc_order zero. Other general matrices have mc_order equal to the number of rows. In particular, an 1 × 1 matrix has mc_order zero, if its only element is equal to one, and mc_order one otherwise. Acordingly, is_mc_bottom(x) returns TRUE if x is the identity matrix or a matrix with zero rows. This is consistent with the treatment of the identity matrix as multi-companion of multi order 0 and a general matrix as multi-companion of multi-companion order equal to the number of its rows. 30 mc_matrix Value for mc_full, the multi-companion matrix as an ordinary dense matrix object. For mc_matrix, an ordinary matrix. for mc_order, the multi-companion order of x, a non-negative integer for is_mc_bottom, TRUE if x may be the bottom part of a multi-companion matrix and FALSE otherwise. Note mc_matrix is not multi-companion specific, except that it converts a vector to a matrix with one row (not column). For square matrices these functions are not really multi-companion specific. It may make sense to allow non-square matrices also for mc_order. Author(s) Georgi N. Boshnakov References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. See Also mcStable Examples mc <- mCompanion("sim", dim = 4, mo = 2) mc mc_order(mc) x <- mc[1:2, ] # the top of mc x x2 <- mc[] # whole mc as ordinary matrix x2 mc_matrix(mc) mc_matrix(x2) ## mc_matrix() doesn't append rows to its argument mc_matrix(x) ## mc_full() appends rows, to make the matrix square multicompanion mc_full(x) ## mc and x2 are square, so not amended: mc_full(mc) mc_full(x2) ## a vector argument is treated as a matrix with 1 row: mc_matrix(1:4) mc_full(1:4) mf_VSform 31 ## mc_order(1:4) # not by mc_order m <- mCompanion(matrix(1:8, nrow = 2)) mc_matrix(m) mc_order(m) m[-c(1,2), ] is_mc_bottom(m[-c(1,2), ]) # TRUE ## TRUE for reactangular diagonal matrix with nrow < ncol is_mc_bottom(diag(1, nrow = 3, ncol = 5)) ## border cases is_mc_bottom(matrix(0, nrow = 0, ncol = 4)) # TRUE, 0 rows is_mc_bottom(diag(4)) # TRUE, square diagonal matrix mf_VSform Extract properties of multi-filters Description Extract properties for scalar and vector of seasons forms of multi-filters. Usage mf_order(x, i = "max", form = "pc", perm) mf_period(x) mf_poles(x, blocks = FALSE) mf_VSform(x, first = 1, form = "U", perm) Arguments x the filter, an object of class "MultiFilter". i index, integer vector or a string. first the first season of the year. form the form of the filter to which the result refers, one of "pc", "I", "U", or "L", see Details. perm permutation of the seasons within the year. blocks request lengths of Jordan chains. Details With the default i=="max" the function mf_order returns a single number, the order of the filter in the representation requested by form. The orders of the components may be obtained with the setting i=="all" which gives a vector whose j-th element is the order of the j-th component of the filter. A subset of these may be obtained with numeric i which is treated as standard index vector. Values for i other than the default are meaningful mainly for form="pc". 32 mf_VSform mf_VSform arranges the filter coefficients in one of the vector of seasons forms (todo: cite me). The component Phi of the result is a matrix obtained by putting the coefficient matrices next to each other, [A1 ... Ad]. If perm is provided, then the result is the same for "U" and "L". mf_VSform is called implicitly by the subscripting operation ("[") when needed, it is more flexible and is recommended for general use. For the vector forms ("I", "U", and "L") the argument perm specifies the arrangement of the com- ponents of the filter in that form. For the I- and U-forms the default is mf_period(x):1, for the L-form it is 1:mf_period(x). Currently perm may take on values that can be obtained from the default by rotation, e.g. if the period is 4, perm may be one of (4,3,2,1), (1,4,3,2), (2,1,4,3), (3,2,1,4) for the U-form, and (1,2,3,4), (4,1,2,3), (3,4,1,2), (2,3,4,1) for the L-form. Other permutations may be usefull in some situations but may not result in U- or L- forms (without further transformations). For I-form any permutation should be permissible when implemented (todo:). For mf_order the argument perm affects the computation only, not the ordering in the result. The result (if vector) is not permuted unless the argument i asks for this. For mf_VSform however such a behaviour would be very peculiar and the rows of the result are for the permuted seasons. In short, the i-th element of the result of mf_order (if vector) gives the order (in the requested form) of the i-th season but the i-th row of any of the matrices returned by mf_VSform depends on perm and form. Note: the terminology here reflects application to pc processes, probably should be made more neutral in this respect. todo: (2013-03-26) mf_order seems unfinished. Value For mf_order, if i = "max" a positive integer, otherwise a vector of positive integers. For mf_period the period of the filter, a positive integer. For mf_poles, if blocks = FALSE, a vector of the eigenvalues of the associated multi-companion matrix, each eigenvalue repeated according to its algebraic multiplicity. If blocks = TRUE, a 2- column matrix with the eigenvalues in the first column and the lengths of the Jordan chains in the second. There is one row for each chain (i.e. multiple eigenvalues are repeated according to their geometric multiplicity). For mf_VSform a list with components: Phi0 the zero lag coefficient, a matrix, Phi the remaining coefficients, a matrix, Phi0inv (form=="I" only) the inverse of the zero lag coefficient matrix of the vs-form, a matrix. (TODO: the name of this component is misleading since in the case form = "I" Phi0 is the identity matrix and Phi0inv is not equal to the inverse of Phi0.) Author(s) Georgi N. Boshnakov mf_VSform 33 References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. Boshnakov GN, Iqelan BM (2009). “Generation of time series models with given spectral proper- ties.” J. Time Series Anal., 30(3), 349–368. ISSN 0143-9782, doi:10.1111/j.14679892.2009.00617.x. See Also MultiFilter and the examples there, mcStable Examples ## simulate a 3x3 2-companion matrix ## and turn it into a multi-filter (m <- mCompanion("sim", dim=3, mo=2)) (flt <- new("MultiFilter", mc = m )) mf_period(flt) mf_poles(flt) abs(mf_poles(flt)) mf_VSform(flt,form="U") mf_VSform(flt,form="L") mf_VSform(flt,form="I") ## simulate a pc filter (2 seasons) ## and turn it into a multi-filter object (rfi <- sim_pcfilter(2, 3)) (flt <- new("MultiFilter", coef = rfi$pcfilter)) mf_period(flt) mf_poles(flt) abs(mf_poles(flt)) mf_VSform(flt, form="U") mf_VSform(flt, form="I") mf_VSform(flt, form="L") ## indexing can be used to extract filter coefficients flt[] flt[1,] ## the rest are some checks of numerical performance. rfi rfi$mat==0 zapsmall(rfi$mat) mCompanion(zapsmall(rfi$mat)) unclass(mCompanion(zapsmall(rfi$mat))) unclass(mCompanion(rfi$mat)) flt1 <- new("MultiFilter", mc = mCompanion(zapsmall(rfi$mat))) flt2 <- flt flt1[] flt2[] 34 MultiCompanion-class flt1[] - flt2[] rfi$pcfilter - rfi$mat[1:2,] mf_poles(flt1) abs(mf_poles(flt1)) svd(rfi$mat) rcond(rfi$mat) Matrix::rcond(Matrix::Matrix(rfi$mat),"O") 1/Matrix::rcond(Matrix::Matrix(rfi$mat),"O") MultiCompanion-class Class "MultiCompanion" Description Objects and methods for multi-companion matrices Objects from the Class For ordinary usage objects from this class should behave as matrices and there should be no need to access the slots directly. Objects can be created with the function mCompanion. Other functions in the mcompanion package also produce MultiCompanion objects. It is possible also to call new() directly: new("MultiCompanion", xtop, mo, n, mo.col, ido, x, dimnames, detect, misc) Arguments: xtop is the top of the matrix. mo is the multi-companion order of the matrix. n is the dimension. mo.col is the top order, meaniing that columns mo.col+1,...,n of the top of the matrix are zeros. mo.col may also be set to "detect", in which case it is determined by scanning xtop or x. ido the dimension of the identity sub-matrix. x the whole matrix. dimnames is not used currently. detect controls whether automatic detection of mo and mo.col should be attempted. The values tested are "mo", "mo.col", "all", and "nothing" with obvious meanings. misc todo: describe this argument! MultiCompanion-class 35 Normally one of xtop and x is supplied but if both are, they are checked for consistency, including the elements of the matrix (equality is tested with ==). To facilitate calls with one unnamed argu- ment, when xtop is a square matrix it is taken to be the entire matrix (provided that x is missing). Aside from xtop (or x), most of the remaining arguments can be deduced automatically. The number of rows and columns of xtop give the multi-companion order and the dimension of the matrix, respectively. A vector xtop is taken to stand for a matrix with one row. x needs to be square or a vector of length equal to exact square. mo and mo.col may be determined from the contents of x and xtop. There is no harm in ignoring mo.col but it is useful for our applications. Note that by default it is to set to the number of columns and not determined by scanning the matrix. The contents of the misc argument are stored in the pad of the new object. Slots xtop: The top of the matrix, an object of class "matrix" mo: Multi-companion order, an object of class "numeric" ido: dimension of the identity submatrix, object of class "numeric" mo.col: number of non-zero columns in top rows, object of class "numeric" pad: storage for additional info, object of class "objectPad" x: inherited, object of class "numeric" Dim: inherited, object of class "integer" Dimnames: inherited, object of class "list" factors: inherited, object of class "list" Extends Class "ddenseMatrix", directly. Class "generalMatrix", directly. Class "dMatrix", by class "ddenseMatrix". Class "denseMatrix", by class "ddenseMatrix". Class "Matrix", by class "ddenseMatrix". Class "Matrix", by class "ddenseMatrix". Class "compMatrix", by class "generalMatrix". Class "Matrix", by class "generalMatrix". Methods %*% signature(x = "ANY", y = "MultiCompanion"): ... %*% signature(x = "MultiCompanion", y = "MultiCompanion"): ... %*% signature(x = "MultiCompanion", y = "ANY"): ... [ signature(x = "MultiCompanion", i = "index", j = "index", drop = "logical"): ... [ signature(x = "MultiCompanion", i = "index", j = "missing", drop = "logical"): ... [ signature(x = "MultiCompanion", i = "missing", j = "index", drop = "logical"): ... coerce signature(from = "dgeMatrix", to = "MultiCompanion"): ... coerce signature(from = "matrix", to = "MultiCompanion"): ... coerce signature(from = "MultiCompanion", to = "matrix"): ... coerce signature(from = "MultiCompanion", to = "dgeMatrix"): ... 36 MultiCompanion-class initialize signature(.Object = "MultiCompanion"): This method is called implicitly when the user calls new("MultiCompanion",...). mcStable signature(x = "MultiCompanion"): ... t signature(x = "MultiCompanion"): ... %*% signature(x = "matrix", y = "MultiCompanion"): ... %*% signature(x = "MultiCompanion", y = "matrix"): ... [ signature(x = "MultiCompanion", i = "index", j = "index", drop = "missing"): ... [ signature(x = "MultiCompanion", i = "index", j = "missing", drop = "missing"): ... [ signature(x = "MultiCompanion", i = "missing", j = "index", drop = "missing"): ... %*% signature(x = "MultiCompanion", y = "vector"): ... %*% signature(x = "vector", y = "MultiCompanion"): ... Note The implementation is rather redundant, this class probably should inherit in a different way from classes in Matrix package or may be not inherit at all. Methods to get the multi-order, mo.col, and others, would be useful but first the terminology needs to be made consistent. Other matrix arithmetic operations? Argument n is called dim in other functions. Author(s) Georgi N. Boshnakov References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. Boshnakov GN, Iqelan BM (2009). “Generation of time series models with given spectral proper- ties.” J. Time Series Anal., 30(3), 349–368. ISSN 0143-9782, doi:10.1111/j.14679892.2009.00617.x. See Also mCompanion and the examples there; the following functions produce multi-companion matrices but do not return MultiCompanion objects: sim_mc, make_mcmatrix Examples a1 <- matrix(1:12,nrow=2) mc1 <- new("MultiCompanion",xtop=a1) new("MultiCompanion",a1) # same a2 <- matrix(c(1:6,rep(0,4)),nrow=2) # 1st 3 columns of a2 are non-zero mc2 <- new("MultiCompanion",a2) MultiFilter-class 37 mc2 mc2@mo.col # =5, because the default is to set mo.col to ncol mc2a <- new("MultiCompanion",a2,detect="mo.col") mc2a@mo.col # =3, compare with above b <- as(mc2,"matrix") # b is ordinary R matrix mcb <- new("MultiCompanion",x=b) new("MultiCompanion",b) # same as mcb mcb@mo # 2 (mo detected) mcb@mo.col # 5 (no attempt to detect mo.col) mcba <- new("MultiCompanion",b,detect="all") mcba@mo # 2 (mo detected) mcba@mo.col # 3 (mo.col detected) MultiFilter-class Class "MultiFilter" Description Objects and methods for filters with more than one set of coefficients. Objects from the Class Objects can be created by calls of the form new("MultiFilter", coef, mc, order, sign). Objects from this class represent periodic filters. A d-periodic filter relates an input series εt to an output series yt by the following formula: Xpt yt = φt (i)yt−i + εt , i=1 where the coefficients φt (i) are d-periodic in t, i.e. φt+d (i) = φt (i) and pt+d = pt . The periodicity means that it is sufficient to store the coefficients in a d × p matrix, where p = max(p1 , . . . , pt ). Slot coef contains such a matrix. The filter may be specified either by its coefficients or by its multi-companion form. Slots mc: the multi-companion form of the filter, an object of class "MultiCompanion" coef: the coefficients of the filter, an object of class "matrix", whose sth row contains the coeffi- cients for t = k × d + s. order: the periodic order of the filter, a numeric vector giving the orders of the individual seasons. sign: 1 or -1. The default value, 1, corresponds to the formula given in section "Objects from the Class". It can also be -1, if the sum on the right-hand side of that formula is preceded by a minus (usual convention in signal processing). 38 MultiFilter-class Methods [ signature(x = "MultiFilter", i = "ANY", j = "ANY",drop = "ANY"): take subset of the coef- ficients of the filter in various forms. To do: the function needs more work! Document the function and the additional arguments! initialize signature(.Object = "MultiFilter"): This function is called implicitly by new, see the signature for new above. One of mc and coef must be supplied, the other arguments are optional. If mc is missing it is computed from coef. In this case, component mC.factorsmat of slot misc of mc is set to the companion factorisation of mc (essentially the reversed rows of coef). If coef is missing it is computed from mc, see mc_factors. mcStable signature(x = "MultiFilter"): Check if the filter is stable. See also the documentation for the following functions which are effectively methods for class "MuliFilter" but are not defined as formal methods: mf_period, mf_order, mf_poles, mf_VSform. Author(s) Georgi N. Boshnakov References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. Boshnakov GN, Iqelan BM (2009). “Generation of time series models with given spectral proper- ties.” J. Time Series Anal., 30(3), 349–368. ISSN 0143-9782, doi:10.1111/j.14679892.2009.00617.x. See Also MultiCompanion, mf_period, mf_order, mf_poles, mf_VSform. Examples showClass("MultiFilter") m <- mCompanion("sim",dim=3,mo=2) # simulate a 3x3 2-comp. matrix flt <- new("MultiFilter", mc = m ) flt[] mf_period(flt) mf_poles(flt) abs(mf_poles(flt)) mf_VSform(flt,form="U") mf_VSform(flt,form="L") mf_VSform(flt,form="I") # try arguments "coef" and "mc", for comparison rfi <- sim_pcfilter(2,3) # period=2, order=c(3,3) # per. filter from a multi-companion matrix null_complement 39 flt1 <- new("MultiFilter",mc= mCompanion(zapsmall(rfi$mat)) ) flt1[] mf_period(flt1) mf_poles(flt1) abs(mf_poles(flt1)) mf_VSform(flt1,form="U") mf_VSform(flt1,form="L") mf_VSform(flt1,form="I") # per. filter from coefficients, should be the same (numerically) flt2 <- new("MultiFilter",coef=rfi$pcfilter) flt2[] mf_period(flt2) mf_poles(flt2) abs(mf_poles(flt2)) mf_VSform(flt2,form="U") mf_VSform(flt2,form="L") mf_VSform(flt2,form="I") null_complement Compute the orthogonal complement of a subspace Description Computes the orthogonal complement of a subspace relative to a universe. Usage null_complement(m, universe = NULL, na.allow = TRUE) Arguments m NA or a matrix whose columns define the subspace, a vector is treated as a matrix with one column. universe a matrix whose columns specify the subspace relative to which to compute the complement, the default is the full space. na.allow if TRUE, default, treat NA’s specially, see Details. Details null_complement computes the orthogonal complement of a subspace (spanned by the columns of m) relative to a universe. Argument universe can be used to specify a subspace w.r.t. which to compute the complement. If universe is NULL (the default), the complement w.r.t. the full space is computed. The full space is the n-dimensional space, where n is the number of rows of argument m. 40 permute_var null_complement returns a matrix whose columns give a basis of the required subspace. null_complement uses Null() from package MASS for the actual computation. null_complement(m, na.allow = FALSE) is equivalent to Null(m). m is typically a matrix whose columns represent the subspace w.r.t. which to compute the comple- ment. null_complement can also deal with NA’s in m. This facility can be turned off by specifying na.allow = FALSE. If na.allow = TRUE, the default, and m is identical to NA, universe is returned (i.e. m = NA represents the empty subspace). Note that in this case universe cannot be NULL, since there is no way to determine the dimension of the full space. Otherwise, m is a matrix. If all elements of m are NA, a matrix of NA’s is returned with number of columns equal to ncol(universe) - ncol(m). Value a matrix representing a basis of the requested subspace Author(s) Georgi N. Boshnakov Examples m1 <- diag(1, nrow = 3, ncol = 2) null_complement(m1) null_complement(c(1,1,0)) null_complement(c(1,1,0), m1) ## the columns of the result from null_complement() are orthogonal ## to the 1st argument: t(c(1,1,0)) %*% null_complement(c(1,1,0)) t(c(1,1,0)) %*% null_complement(c(1,1,0), m1) null_complement(rep(NA_real_, 3), m1) null_complement(NA, m1) permute_var Permute rows and columns of matrices Description Permute rows and columns of matrices. Usage permute_var(mat, perm = nrow(mat):1) permute_synch(param, perm) permute_var 41 Arguments mat a matrix. param a matrix or list, see Details. perm permutation, defaults to nrow:1. Details Given a permutation, permute_var permutes the rows and columns of a matrix in such a way that if mat is the covariance matrix of a vector x, then the rearranged matrix is the covariance matrix of x[perm]. If P is the permutation matrix corresponding to perm, then the computed value is P %*% mat %*% t(P). permute_synch performs the above transformation on all matrices found in param. More precisely, if param is a matrix, then the result is the same as for permute_var. Otherwise param should be a list and, conceptually, permute_synch is applied recursively on each element of this list. The net result is that each matrix, say M , in param is replaced by P M P 0 and each vector, say v, by P v. The idea is that param may contain specification of a VAR model, all components of which need to be reshuffled if the components of the multivariate vector are permuted. All matrices in param must have the same number of rows, say d, but this is not checked. perm should be a permutation of 1:d. Value for permute_var, a matrix, for permute_synch, a matrix or list of the same shape as param in which each matrix is transformed as described in Details. Author(s) Georgi N. Boshnakov Examples Cl <- cor(longley) # from example for 'cor()' nc <- ncol(Cl) v <- 1:nc names(v) <- colnames(Cl) permute_var(Cl) all(permute_var(Cl) == Cl[ncol(Cl):1, ncol(Cl):1]) 42 sim_mc rblockmult Right-multiply a matrix by a block Description Treats a matrix as a block matrix and multiplies each block by a given block. Usage rblockmult(x, b) Arguments x the matrix. b the block. Details x is split into blocks [x1 ... xn] so that ncol(xi)==nrow(b) and each block is multiplied by b. The result is the matrix [x1 b ... xn b]. Value the matrix obtained as described above Author(s) Georgi N. Boshnakov Examples m <- matrix(1:12, nrow = 2) b <- matrix(c(0, 1, 1, 0), nrow = 2) rblockmult(m,b) sim_mc Simulate a multi-companion matrix Description Simulate a multi-companion matrix with partially or fully specified spectral properties. Usage sim_mc(dim, mo, mo.col = dim, eigval, len.block, type.eigval = NULL, co, eigabs, eigsign, type = "real", value = "real", value.type = "", ...) sim_mc 43 Arguments dim dimension of the matrix. mo multi-companion order. mo.col number of structural columns. eigval eigenvalues, one for each Jordan block. len.block lengths of the Jordan blocks corresponding to eigval. type.eigval types of the eigenvalues, a character vector co co parameters, see Details. eigabs moduli (absolute values) of eigenvalues, see Details. eigsign signs or complex arguments of eigenvalues, see Details. type passed down to generators (???) value what to return value.type type of the value (???) ... further arguments to passed on to sim_chains and sim_numbers, see Details. Details sim_mc generates a multi-companion matrix of dimension dim x dim and multi-companion order mo. The matrix has the spectral properties specified by the arguments. Values that cannot be inferred from the arguments are simulated. Arguments dim, mo, and mo.col define the structure of the matrix. The first two are compulsory but the last one, mo.col, is optional. If no other arguments are supplied sim_mc produces a matrix with all spectral parameters simulated. The number of non-zero eigenvalues is at most mo.col. If mo.col < dim the multi-companion ma- trix has structural eigenvectors/chains corresponding to the zero eigenvalue(s), see the references. These chains are generated automatically. Arguments type.eigval, eigabs, eigsign and eigval are vectors used to specify the types and the values of the eigenvalues. Any or all of them may be missing or NULL. Those present must have the same length. It is not necessary to specify eigenvalues and eigenvectors corresponding to eigenvalues equal to zero, since the structural eigenchains needed when mo.col < dim are created automatically. In practice, the number of the non-zero eigenvalues is usually equal to mo.col. The net effect is that the arguments specifying the spectral structure of the matrix normally need to specify the spectral information about the non-zero eigval only. Some or all of the eigenvalues may be specified partially or fully using arguments eigabs, eigsign, and eigval. Non-NA entries in eigval specify complete eigenvalues. Non-NA entries in eigabs specify absolute values of eigenvalues. Non-NA entries in eigsign specify signs of real eigenvalues or complex arguments of complex eigenvalues. Generally, if the entry for an eigenvalue in eigval is a number (not NA), then the corresponding entries in eigabs and eigsign will be NA. This is not enforced and a limited check for consistency is made in case of redundant information. type.eigval is a character vector describing the types of the eigenvalues, where "r", "c", and "cp" stand for real, complex, and complex pair, respectively. It is best to have one entry only for each complex pair (specified by "cp"), rather than two "c" entries. 44 sim_mc If type.eigval is NULL (default) and eigval is supplied, then type.eigval is inferred from the imaginary part of eigval ("r" or "cp"), if it is complex. For compatibility with older versions of this function eigval may be a character vector in which case it is simply assigned to type.eigval. If both, type.eigval and eigval, are missing a default allocation of the types of the eigenvalues is chosen. TODO: complete the description below. The remaining spectral parameters may be specified with the argument co with missing entries for the "free" entries. (!!! This is not complete, it may be better to have separate arguments for the ab- solute value and the angle, as for eigenvalues, and an option for normalisation of these coefficients. ???) Generators other than the default ones may be specified in the ... argument. These are passed to sim_numbers and sim_chains. Again, for the "co" arguments the support is not finished. Value if value.type is the character string "matrix", the required multi-companion matrix. Otherwise, if value.type=="list", a list containing also the spectral information (this list is the same as the one from make_mcmatrix)). Note A canonical form is needed, especially when there are repeated eigenvalues whose eigenvectors may be chosen to be orthogonal, at least. (nyakade v zapiskite mi tryabva da ima kanonichna forma!) Author(s) Georgi N. Boshnakov References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. Boshnakov GN, Iqelan BM (2009). “Generation of time series models with given spectral proper- ties.” J. Time Series Anal., 30(3), 349–368. ISSN 0143-9782, doi:10.1111/j.14679892.2009.00617.x. See Also gbutils::sim_numbers and sim_chains for arguments that control the distributions of the random numbers. make_mcmatrix creates the matrix. Examples m0 <- sim_mc(3,2) # simulate 3x3 2-companion matrix abs(m0$eigval) # eigval random, so their abs values # now fix moduli of eigenvalues, and sim_pcfilter 45 # ask for one real ev and one complex pair of ev's m1 <- sim_mc(3,2,eigabs=c(0.25,0.5), type.eigval=c("r","cp")) m1$eigval abs(m1$eigval) # same as above, since type.eigval happens to be the default # dim is odd, by default first ev is real, rest are complex pairs m1a <- sim_mc(3,2,eigabs=c(0.25,0.5)) m1a$eigval abs(m1a$eigval) # simulate 6x6 4-companion matrix # with ev's at the seasonal frequencies (1.57 3.141593 -1.57) # and random moduli. 3 complex pairs of ev's m2 <- sim_mc(6,4, eigsign = pi*c(1/2,1,-1/2) ) Arg(m2$eigval) sim_pcfilter Generate periodic filters Description Generates periodic filters. Usage sim_pcfilter(period, n.root, order = n.root, mo.col, ...) Arguments period the period. n.root number of non-zero roots (poles). order order of the filter. ... additional parameters to be passed down to sim_mc. mo.col the last non-zero column in the top of the mc-matrix. The default is dim. Details Generates periodic filters using the multicompanion approach (Boshnakov and Iqelan 2009). By default the generated filter is stable and may be used as the autoregressive or moving average part of a periodic autoregressive moving average model. The filter is generated from the specified spectral information by factoring a multi-companion matrix. Any non-specified quantities are gen- erated randomly. Randomly generated eigenvalues correspond to stable filter. The user may specify non-stable roots, unit roots in particular, see sim_mc. 46 SmallMultiCompanion-class Value A list as obtained from sim_mc with an addtional component for the filter. pcfilter a matrix with the filter coefficients for the i-th season in the i-th row. Note todo: a) Allow different orders for the individual seasons. This is not trivial and maybe not natural for this method. In the singular case it may make sense to implement different strategies for choos- ing the factorization (when it is not unique) and to choose more carefully the order of the filter to ensure existence of factorization, see my paper. Author(s) Georgi N. Boshnakov References Boshnakov GN (2002). “Multi-companion matrices.” Linear Algebra Appl., 354, 53–83. ISSN 0024-3795, doi:10.1016/S00243795(01)00475X. Boshnakov GN, Iqelan BM (2009). “Generation of time series models with given spectral proper- ties.” J. Time Series Anal., 30(3), 349–368. ISSN 0143-9782, doi:10.1111/j.14679892.2009.00617.x. See Also sim_mc Examples rfi <- sim_pcfilter(2,3) rfi mo <- cbind(c(1,1),rfi$pcfilter) mo SmallMultiCompanion-class A class for small multi-companion matrices Description A class for internal use for work with small multi-companion matrices. Objects from the Class This class is for internal use. Objects can be created by calls of the form new("SmallMultiCompanion", Mtop, Mbot, jdMtop, MbotXtop). spec_core 47 Slots jdMtop: Object of class "JordanDecomposition" ~~ Mtop: Object of class "matrix" ~~ Mbot: Object of class "matrix" ~~ MbotXtop: Object of class "matrix" ~~ Methods coerce signature(from = "SmallMultiCompanion", to = "matrix"): ... initialize signature(.Object = "SmallMultiCompanion"): ... JordanDecomposition signature(values = "SmallMultiCompanion", vectors = "missing"): ... Author(s) Georgi N. Boshnakov See Also MultiCompanion Examples mat2 <- make_mcmatrix(eigval = c(1), co = cbind(c(1,1,1,1), c(0,1,0,0)), dim = 4, len.block = c(2)) mat2 ## Jordan decomp. of mat2[1:2,1:2]: x2 <- matrix(c(1,1,-1,0), ncol =2) jd <- matrix(c(1,0,1,1), ncol = 2) mat2[1:2,1:2] - x2 %*% jd %*% solve(x2) jdobj <- JordanDecomposition(values = 1, vectors = x2, heights = 2) m1 <- new("SmallMultiCompanion", mat2[1:2, 1:2], Mbot = mat2[3:4, 1:2], jdMtop = jdobj) m1a <- new("SmallMultiCompanion", Mbot = mat2[3:4, 1:2], jdMtop = jdobj) as.matrix(m1) - as.matrix(m1a) # (approx.) 0's spec_core Parameterise Jordan chains of multi-companion matrices Description Parameterise the Jordan chains corresponding to a given eigenvalue of a multi-companion matrix. Usage spec_core(mo, evalue, heights, ubasis = NULL, uorth = NULL, evspace = NULL) 48 spec_core Arguments mo multi-companion order, a positive integer. evalue eigenvalue, a real or complex number. heights dimensions of Jordan blocks of evalue, a vector of positive integers. ubasis basis of the universe, a matrix. uorth orthogonal complement of ubasis w.r.t. the full core basis, see Details. evspace The space spanned by the eigenvectors, see Details. Details spec_core prepares a canonical representation of the parameters of a multi-companion matrix core- sponding to an eigenvalue. Roughly speaking, free parameters are represented by NA’s in the re- turned object. For no-repeated eigenvalues the parameterisation consists of the eigenvalue and the seed parameters of the eigenvector. Even then, for uniqueness some convention needs to be adopted. So, in general the parameterisation is effectively in terms of subspaces. TODO: Currently this is not documented and is work in progress, there are only some working notes (rakopis: "Some technical details about the parameterisation of mc-matrices"). Value a list representing the parameterised chains corresponding to the eigenvalue. Currently it contains the following elements: evalue heights co core.vectors param.tall param.hang generators Author(s) Georgi N. Boshnakov Examples spec_core(4, 1, c(1,1,1,1)) spec_core(4, 1, c(2,1,1,1)) spec_seeds1(c(2,2,2,2), 4) spec_seeds1(c(2,1,1,1), 4) spec_core(4, 1, c(2,1,1,1))$co spec_core(4, 1, c(2,1,1,1))$generators spec_root0 49 spec_root0 Give the spectral parameters for zero eigenvalues of mc-matrices Description Give the spectral parameters for zero eigenvalues of mc-matrices. Usage spec_root0(dim, mo, mo.col) Arguments dim dimension of the matrix, a positive integer. mo multi-companion order, a positive integer. mo.col last non-zero column in the top of the mc-matrix, a non-negative integer. Details spec_root0 prepares a structure for the zero roots of an mc-matrix. Value a list with the following components: mo multi-companion order ev.type type of the eigenvalues co.type not used currently (:todo:) n.root number of non-zero roots ev.abs absolute values of roots ev.arg arguments of eigenvalues (0 for positive ev) block.length lengths of Jordan blocks co.abs absolute values of seed parameters co.arg arguments of seed parameters (Hz: 0 for positive; 1/2 for negative) co0 redundant but keep it for now. Author(s) Georgi N. Boshnakov See Also spec_root1, mcSpec 50 spec_root1 Examples spec_root0(4,2,3) spec_root0(4,2,2) spec_root0(4,2,1) spec_root0(5,2,3) spec_root1(4,2,2) spec_root0(6,4,2) spec_root0(6,4,4) spec_root0(10,4,8) spec_root1 Give the spectral parameters for eigenvalues of mc-matrices equal to one Description Give the spectral parameters for eigenvalues of mc-matrices equal to one. Usage spec_root1(mo, root1 = numeric(0), iorder = 0, siorder = 0) Arguments mo mc order. root1 Jordan block lengths for the unit roots, a vector of positive integer numbers. iorder order of integration, a non-negative integer. siorder order of seasonal integration, a non-negative integer. Details The specifications given by root1, iorder and siorder are combined and the spectral parameters prepared. In principle, argument root1 is sufficient, the other two are for convenient specification of integra- tion and seasonal integration. TODO: rename argument root1! Value a list with the following components: mo multi-companion order ev.type type of the eigenvalues co.type not used currently (:todo:) n.root number of non-zero roots spec_seeds1 51 ev.abs absolute values of roots ev.arg arguments of eigenvalues (0 for positive ev) block.length lengths of Jordan blocks co.abs absolute values of seed parameters co.arg arguments of seed parameters (Hz: 0 for positive; 1/2 for negative) co1 temporary hack; TODO: check the calling code and remove it! Author(s) Georgi N. Boshnakov See Also mcSpec, spec_root0 Examples spec_root1(4, root1 = 1) spec_root1(4, root1 = c(1,0,0,0)) # same spec_root1(4, iorder = 1) # same spec_root1(4, root1 = 2) spec_root1(4, root1 = c(2,0,0,0)) # same spec_root1(4, iorder = 2) # same spec_root1(4, root1 = c(1,1,1,1)) spec_root1(4, siorder = 1) # same spec_root1(4, root1 = c(2,2,2,2)) spec_root1(4, siorder = 2) # same spec_root1(4, root1 = c(2,1,1,1)) spec_root1(4, iorder = 1, siorder = 1) # same spec_root1(4, root1 = c(2,1)) spec_root1(4, root1 = c(2,1,1)) spec_seeds1 Generate seed parameters for unit mc-eigenvectors Description Generates seed parameters for mc-eigenvectors corresponding to unit roots. 52 spec_seeds1 Usage spec_seeds1(len.block, mo) Arguments len.block lengths of Jordan blocks, a vector of positive integers. mo multi-companion order. Details Creates a matrix of seed parameters corresponding to unit eigenvalues of a multi-companion matrix of multi-companion order mo. len.block gives the sizes of the Jordan blocks corresponding to eigenvalues equal to one. In general, the entries are filled with NA’s but for some configurations some (or even all) of the entries are uniquely determined up to a linear transformation. In such cases a “canonical” choice is made. The generated seed parameters can be considered to be "top" or "bottom", as needed. (TODO: check this claim, I have forgotten the details but think that this is the reason that it is not necessary to have an argument for the dimension of the matrix). codespec_seeds1 can be used by model fitting functions to prepare parameters for estimation but see spec_root1 and mcSpec for a more comprehensive treatment. Value a matrix with mo rows and sum(len.block) columns Note TODO: the treatment of “canonical” cases is incomplete, see also the comments in the source code of the function. TODO: explain the Inf and -Inf output entries for some configurations (e.g. the last example below). "co" in the name of spec_seeds1 is short for coefficient. Author(s) Georgi N. Boshnakov See Also spec_root1, mcSpec VAR2pcfilter 53 Examples spec_seeds1(c(1), mo = 4) # NA's spec_seeds1(c(1,1), mo = 4) # NA's spec_seeds1(c(1,1,1), mo = 4) # NA's (but for parameterisation # a different approach is used) spec_seeds1(c(1,1,1,1), mo = 4) # identity matrix but other bases are good too spec_seeds1(c(2,2,2,2), mo = 4) # no NA's, tops of gen.evecs can be chosen 0 spec_seeds1(c(2,1,1,1), mo = 4) # (can be improved) spec_seeds1(c(2,1), mo = 4) # NA's VAR2pcfilter PAR representations of VAR models Description Give the univariate periodic autoregression representation of a VAR model. Several arrangements are supported as discussed by Boshnakov and Iqelan (2009). If the VAR model contains unit roots on the unit circle, then the univariate model is periodically integrated. Usage VAR2pcfilter(phi, ..., Sigma, Phi0, Phi0inv, D, what = "coef", perm) Arguments phi VAR coefficients, a matrix, see Details. ... alternative way to specify the VAR coefficients by giving a matrix for each lag in separate arguments, see section ‘Details’. Sigma covariance matrix of innovations. Phi0 coefficient matrix at lag 0 (alternative to Sigma). Phi0inv inverse of Phi0 (alternative to Sigma and Phi0). If Phi0inv is lower triangular, then it is the Cholesky factor of Sigma (in Sigma = LDL0 ). D the diagonal matrix corresponding to Phi0, not used if Sigma is specified. what what to return, a string. If equal to "coef", return the PAR coefficients only (as a matrix with one row for each “season”); if equal to "coef.and.var" return also the innovation variances. Otherwise return additional quantities (useful for exploration). perm a permutation specifying the ordering of the variables when treated as “seasons”. The default, d:1, corresponds to the U-form, see section ‘Details’. 54 VAR2pcfilter Details VAR2pcfilter converts a VAR model to a scalar periodic autoregressive (PAR) model. There are various ways to specify a VAR model and associate its variables with seasons of the scalar representation, see Boshnakov and Iqelan (2009) for a detailed discussion and the terminology used here. The VAR coefficients phi,... are those in the standard form of the VAR model (e.g., see Bosh- nakov and Iqelan 2009). There are two ways to specify them. The first is to put them side by side in a matrix [Φ1 , . . . , Φp ] and give this matrix as argument phi. Alternatively, the matrices Φi may be given directly as arguments to VAR2pcfilter, as in VAR2pcfilter(Phi1, Phi2, Phi3, Sigma = Sigma). The specification of the model can be completed by giving the covariance matrix, Sigma, of the innovations. Alternatively, it is possible to give the components of the U DU 0 decomposition of Sigma. In this case argument D is a vector giving the diagonal of the matrix D, while Phi0inv represents the upper triangular matrix U . A further option is to use argument Phi0 to specify the inverse of U . In summary, give either Sigma or D and one of Phi0inv and Phi0. Phi0 can e interpreted as the coefficient at lag zero in the U-form (Boshnakov and Iqelan 2009) of the VAR model. diag(D) is the variance matrix of the innovations in that form. D also gives the variances of the innovations in the PAR (periodic autoregression) form. By default, VAR2pcfilter constructs the U-form of the VAR model and extracts the coefficients of the PAR filter from it. This means that the variables in the multivariate vector are given “seasons” in reverse order (the first variable takes the last season, and so on). For the reasons behind this default, see Boshnakov and Iqelan (2009). Another arrangement can be chosen with the help of argument perm. perm should be a permutation specifying the desired allocation of variables to seasons. The default corresponds to perm=d:1, where d is the number of seasons. perm=1:d could be used to request the “natural” order. When D and Phi0inv (or Phi0) are given, the matrix Sigma is not computed if argument perm is missing but it is if perm is present. This means that perm = d:1 may be used to force the formation of Sigma and recomputation of Phi0 and Phi0inv. This is redundant if the latter two are unit upper- triangular (which is assumed but not checked) but may be handy if, for example, the Cholesky decomposition with a lower triangular matrix is available. Value If what="coef", a matrix containing the periodic model coefficients (one row for each season). If what="coef.and.var", a list containing the coefficients and the innovations’ variances: pcfilter PAR coefficients, a matrix var innovation variances, a vector Otherwise the returned list contains an additional component, Uform, which is itself a list with components: Sigma covariance matrix of innovations, U0 coefficient for lag zero, U the remaining AR coefficients, U0inv the inverse of U0, VAR2pcfilter 55 perm permutation giving the season of each variable. Note: U0 and U correspond to A0 and A in the reference (Boshnakov and Iqelan 2009). Note This function uses some non-exported internal functions: .ldl Computes the LDL’ Cholesky decomposition with unit lower-triangular matrix L, .udu Computes the UDU’ Cholesky decomposition with unit upper-triangular matrix U. Could export these if they are deemed more widely useful. Author(s) Georgi N. Boshnakov References Boshnakov GN, Iqelan BM (2009). “Generation of time series models with given spectral proper- ties.” J. Time Series Anal., 30(3), 349–368. ISSN 0143-9782, doi:10.1111/j.14679892.2009.00617.x. See Also mf_VSform, sim_pcfilter Examples ## create a pc filter rfi <- sim_pcfilter(2,3) rfi$pcfilter ## turn it into VAR form flt <- new("MultiFilter", coef = rfi$pcfilter) I1 <- mf_VSform(flt, form="I") I1 ## from VAR to scalar form flt2 <- VAR2pcfilter(I1$Phi, Sigma = I1$Phi0inv %*% t(I1$Phi0inv)) flt2 ## confirm that we are back to the original ## (VAR2pcfilter doesn't drop redundant zeroes, so we do it manually) all.equal(flt2[ , 1:3], rfi$pcfilter) ## TRUE Index ∗ algebra ∗ ts jordan, 4 mf_VSform, 31 JordanDecomposition, 7 sim_pcfilter, 45 null_complement, 39 VAR2pcfilter, 53 ∗ classes [,MultiCompanion,index,index,logical-method JordanDecompositionDefault-class, (MultiCompanion-class), 34 8 [,MultiCompanion,index,index,missing-method mcSpec-class, 18 (MultiCompanion-class), 34 MultiCompanion-class, 34 [,MultiCompanion,index,missing,logical-method MultiFilter-class, 37 (MultiCompanion-class), 34 SmallMultiCompanion-class, 46 [,MultiCompanion,index,missing,missing-method ∗ datagen (MultiCompanion-class), 34 sim_mc, 42 [,MultiCompanion,missing,index,logical-method sim_pcfilter, 45 (MultiCompanion-class), 34 ∗ matrices [,MultiCompanion,missing,index,missing-method jordan, 4 (MultiCompanion-class), 34 make_mcev, 9 [,MultiFilter,ANY,ANY,ANY-method make_mcmatrix, 11 (MultiFilter-class), 37 mc_chain_extend, 22 %*%,ANY,MultiCompanion-method (MultiCompanion-class), 34 mc_eigen, 23 %*%,MultiCompanion,ANY-method mc_factorize, 25 (MultiCompanion-class), 34 mc_factors, 26 %*%,MultiCompanion,MultiCompanion-method mc_from_factors, 27 (MultiCompanion-class), 34 mc_matrix, 29 %*%,MultiCompanion,matrix-method mCompanion, 14 (MultiCompanion-class), 34 mcStable, 20 %*%,MultiCompanion,vector-method null_complement, 39 (MultiCompanion-class), 34 permute_var, 40 %*%,matrix,MultiCompanion-method rblockmult, 42 (MultiCompanion-class), 34 ∗ mcspec %*%,vector,MultiCompanion-method spec_core, 47 (MultiCompanion-class), 34 spec_root0, 49 spec_root1, 50 chain_ind (jordan), 4 spec_seeds1, 51 chains_to_list (jordan), 4 ∗ methods coerce,dgeMatrix,MultiCompanion-method JordanDecomposition, 7 (MultiCompanion-class), 34 mcSpec, 16 coerce,JordanDecompositionDefault,matrix-method ∗ package (JordanDecompositionDefault-class), mcompanion-package, 2 8 56 INDEX 57 coerce,matrix,MultiCompanion-method make_mcev, 9, 12 (MultiCompanion-class), 34 make_mcgev, 12 coerce,MultiCompanion,dgeMatrix-method make_mcgev (make_mcev), 9 (MultiCompanion-class), 34 make_mcmatrix, 11, 15, 36, 44 coerce,MultiCompanion,matrix-method mC.non0chain.extend, 23 (MultiCompanion-class), 34 mc_0chains, 23 coerce,SmallMultiCompanion,matrix-method mc_chain_extend, 22, 24 (SmallMultiCompanion-class), 46 mc_eigen, 23, 23 mc_eigenvalues (mc_eigen), 23 from_Jordan (jordan), 4 mc_factorize, 25, 28 initialize,JordanDecompositionDefault-method mc_factors, 26, 38 (JordanDecompositionDefault-class), mc_from_factors, 26, 27 8 mc_from_filter (mc_from_factors), 27 initialize,mcSpec-method (mcSpec), 16 mc_full (mc_matrix), 29 initialize,MultiCompanion-method mc_leftc (mc_factorize), 25 (mCompanion), 14 mc_matrix, 29 initialize,MultiFilter-method mc_order (mc_matrix), 29 (MultiFilter-class), 37 mCompanion, 4, 11, 12, 14, 34, 36 initialize,SmallMultiCompanion-method mcompanion (mcompanion-package), 2 (SmallMultiCompanion-class), 46 mcompanion-package, 2 is_mc_bottom (mc_matrix), 29 mcSpec, 16, 19, 20, 49, 51, 52 mcSpec-class, 18 jordan, 4 mcStable, 20, 30, 33 Jordan_matrix (jordan), 4 mcStable,MultiCompanion-method JordanDecomposition, 7, 9 (MultiCompanion-class), 34 JordanDecomposition,ANY,ANY-method mcStable,MultiFilter-method (JordanDecomposition), 7 (MultiFilter-class), 37 mcStable-methods (mcStable), 20 JordanDecomposition,JordanDecomposition,missing-method (JordanDecomposition), 7 mf_order, 38 JordanDecomposition,list,missing-method mf_order (mf_VSform), 31 (JordanDecomposition), 7 mf_period, 38 JordanDecomposition,missing,matrix-method mf_period (mf_VSform), 31 (JordanDecomposition), 7 mf_poles, 38 JordanDecomposition,missing,missing-method mf_poles (mf_VSform), 31 (JordanDecomposition), 7 mf_VSform, 4, 31, 38, 55 JordanDecomposition,number,matrix-method MultiCompanion, 4, 15, 38, 47 (JordanDecomposition), 7 MultiCompanion-class, 34 JordanDecomposition,number,missing-method MultiFilter, 4, 33 (JordanDecomposition), 7 MultiFilter-class, 37 JordanDecomposition,SmallMultiCompanion,missing-method (JordanDecomposition), 7 null_complement, 39 JordanDecomposition-class (JordanDecompositionDefault-class), permute_synch (permute_var), 40 8 permute_var, 40 JordanDecomposition-methods (JordanDecomposition), 7 rblockmult, 42 JordanDecompositionDefault-class, 8 sim_chains, 44 make_mcchains (make_mcmatrix), 11 sim_mc, 4, 11, 12, 14, 15, 36, 42, 46 58 INDEX sim_pcfilter, 4, 45, 55 SmallMultiCompanion-class, 46 spec_core, 47 spec_root0, 49, 51 spec_root1, 49, 50, 52 spec_seeds1, 51 t,MultiCompanion-method (MultiCompanion-class), 34 VAR2pcfilter, 4, 53

ClusterStability

cran

Package ‘ClusterStability’ March 8, 2023 Type Package Depends R (>= 2.2.4), Rcpp, cluster, copula (>= 0.999), WeightedCluster LinkingTo Rcpp Title Assessment of Stability of Individual Objects or Clusters in Partitioning Solutions Version 1.0.4 Date 2023-03-07 Author Etienne Lord, Matthieu Willems, Francois-Joseph Lapointe, and Vladimir Makarenkov Maintainer Etienne Lord <m.etienne.lord@gmail.com> Description Allows one to assess the stability of individual objects, clusters and whole clustering solutions based on repeated runs of the K-means and K-medoids partitioning algorithms. License GPL-3 LazyLoad yes NeedsCompilation yes Repository CRAN Date/Publication 2023-03-07 23:20:02 UTC R topics documented: ClusterStability-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 calinski_harabasz_score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 ClusterStability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 ClusterStability_exact . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 davies_bouldin_score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 dunn_score . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Kcombination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Reorder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Stirling2nd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Undocumented functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1 2 ClusterStability-package Index 9 ClusterStability-package Assessment of the stability of individual objects, clusters and a whole clustering solution based on repeated runs of a clustering algorithm. Description The ClusterStability package uses a probabilistic framework and some well-known clustering crite- ria (e.g. Calinski-Harabasz, Silhouette, Dunn and Davies-Bouldin) to compute the stability scores (ST) of each individual object (i.e., element) in the clustering solution provided by the K-means and K-medoids partitioning algorithms. Details Package: ClusterStability Type: Package Version: 1.0.2 Date: 2015-10-14 License: GPL-2 Maintainer: Etienne Lord <m.etienne.lord@gmail.com>, Vladimir Makarenkov <makarenkov.vladimir@uqam.ca> Function ClusterStability computes the individual and global stability scores (ST) for a parti- tioning solution using either K-means or K-medoids (the approximate solution is provided). Function ClusterStability_exact is similar to the ClusterStability function but uses the Stirling numbers of the second kind to compute the exact stability scores (but is limited to a small number of objects). Function Kcombination computes the k-combination of a set of numbers for a given k. Function Reorder returns the re-ordered partitioning of a series of clusters. Function Stirling2nd computes the Stirling numbers of the second kind. Author(s) Etienne Lord, François-Joseph Lapointe and Vladimir Makarenkov See Also ClusterStability, ClusterStability_exact, Kcombination, Reorder, Stirling2nd calinski_harabasz_score 3 calinski_harabasz_score This function returns the Calinski Harabasz score. Description This function returns the Calinski Harabasz score of a partition (also known as the Variance Ratio Criterion). Usage calinski_harabasz_score(X, labels) Arguments X the input dataset: either a matrix or a dataframe. labels the partition vector. Value The Calinski Harabasz score for this data. References T. Calinski and J. Harabasz. A dendrite method for cluster analysis. Communications in Statistics, 3, no. 1:1–27, 1974 Examples calinski_harabasz_score(iris[1:10,1:4], c(3,2,2,2,3,1,2,3,2,2)) # Expected : 11.34223 ClusterStability Calculates the approximate stability score (ST) of individual objects in a clustering solution (the approximate version allowing one to avoid possible variable overflow errors). Description This function will return the individual stability score ST and the global score STglobal using ei- ther the K-means or K-medoids algorithm and four different clustering indices: Calinski-Harabasz, Silhouette, Dunn or Davies-Bouldin. Usage ClusterStability(dat, k, replicate, type) 4 ClusterStability_exact Arguments dat the input dataset: either a matrix or a dataframe. k the number of classes for the K-means or K-medoids algorithm (default=3). replicate the number of replicates to perform (default=1000). type the algorithm used in the partitioning: either ’kmeans’ or ’kmedoids’ algorithm (default=kmeans). Value Returns the individual (ST) and global (ST_global) stability scores for the four clustering indices: Calinski-Harabasz (ch), Silhouette (sil), Dunn (dunn) or Davies-Bouldin (db). Examples ## Calculates the stability scores of individual objects of the Iris dataset ## using K-means, 100 replicates (random starts) and k=3 ClusterStability(dat=iris[1:4],k=3,replicate=100,type='kmeans'); ClusterStability_exact Calculates the exact stability score (ST) for individual objects in a clustering solution. Description This function will return the exact individual stability score ST and the exact global score STglobal using either the K-means or K-medoids algorithm and four different clustering indices: Calinski- Harabasz, Silhouette, Dunn or Davies-Bouldin. Variable overflow errors are possible for large numbers of objects. Usage ClusterStability_exact(dat, k, replicate, type) Arguments dat the input dataset: either a matrix or a dataframe. k the number of classes for the K-means or K-medoids algorithm (default=3). replicate the number of replicates to perform (default=1000). type the algorithm used in the partitioning: either ’kmeans’ or ’kmedoids’ algorithm (default=kmeans). Value Returns the exact individual (ST) and global (ST_global) stability scores for the four clustering indices: Calinski-Harabasz (ch), Silhouette (sil), Dunn (dunn) or Davies-Bouldin (db). davies_bouldin_score 5 Examples ## Calculate the stability scores of individual objects of the Iris dataset ## using K-means, 100 replicates (random starts) and k=3 ClusterStability_exact(dat=iris[1:4],k=3,replicate=100,type='kmeans'); davies_bouldin_score This function returns the Davies Bouldin score. Description This function returns the Davies Bouldin score of a partition. Usage davies_bouldin_score(X, labels) Arguments X the input dataset: either a matrix or a dataframe. labels the partition vector. Value The Davies Bouldin score for this data. References D. L. Davies and D. W. Bouldin. A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence, PAMI-1, no. 2:224-227, 1979 Examples davies_bouldin_score(iris[1:10,1:4], c(3,2,2,2,3,1,2,3,2,2)) # Expected : 0.5103277 6 Kcombination dunn_score This function returns the Dunn_score. Description This function returns the Dunn score (also known as the e Dunn index) of a partition . Usage dunn_score(X, labels) Arguments X the input dataset: either a matrix or a dataframe. labels the partition vector. Value The Dunn index score for this data. References J. Dunn. Well separated clusters and optimal fuzzy partitions. Journal of Cybernetics, 4:95–104, 1974. Examples dunn_score(iris[1:10,1:4], c(3,2,2,2,3,1,2,3,2,2)) # Expected : 0.5956834 Kcombination Kcombination returns the list of all possible combinations of a set of numbers of a given length k. Description This function, given a vector of numbers, will return all the possible combinations of a given length k. Usage Kcombination(data, k, selector) Reorder 7 Arguments data the vector of numbers (i.e. elements) to consider. k the length of the returned combination (between 2 and 6 in this version). selector if set, returns only the combinations containing this number. Value Return a list of all possible combinations for the given vector of numbers. Examples ## Returns the k-combination of the list of numbers: 1,2,3 of length=2. ## i.e. (1,2), (1,3), (2,3) Kcombination(c(1,2,3),k=2) ## Returns only the k-combination containing the number 1. ## i.e. (1,2), (1,3) Kcombination(c(1,2,3),k=2,selector=1) Reorder This function returns the ordering of a partitioning solution in ascend- ing order. Description This function returns the ordered partition of a set of numbers in ascending order and reorderd to start at one. This is an auxiliary function. Usage Reorder(data) Arguments data vector of partition numbers to reorder. Value A vector of ordered partition numbers for this data. Examples Reorder(c(1,3,4,4,3,1)) # Expected : 1 2 3 3 2 1 8 Undocumented functions Stirling2nd Stirling2nd function computes the Stirling numbers of the second kind. Description This function returns the estimated Stirling numbers of the second kind i.e., the number of ways of partitioning a set of n objects into k nonempty groups. Usage Stirling2nd(n,k) Arguments n number of objects. k number of groups (i.e. classes). Value The Stirling number of the 2nd kind for n elements and k groups or NaN (if the Stirling number for those n and k is greater than 1e300). Examples Stirling2nd(n=3,k=2) # Expected value=3 Stirling2nd(n=300,k=20) # Expected value=NaN Undocumented functions Undocumented functions Description The following functions are for internal computation only: calculate_global_PSG, calculate_indices, calculate_singleton, is_partition_group, p_n_k, p_tilde_n_k, calculate_individual_PSG_approximative, calculate_individual_PSG_exact, calculate_individual_PSG. Index ∗ k-combination Stirling2nd, 2, 8 Kcombination, 6 ∗ package Undocumented functions, 8 ClusterStability-package, 2 ∗ partitioning criteria ClusterStability-package, 2 ∗ stability score ClusterStability-package, 2 a2combination (Undocumented functions), 8 calculate_global_PSG (Undocumented functions), 8 calculate_indices (Undocumented functions), 8 calculate_individual_PSG (Undocumented functions), 8 calculate_individual_PSG_approximative (Undocumented functions), 8 calculate_individual_PSG_exact (Undocumented functions), 8 calculate_singleton (Undocumented functions), 8 calinski_harabasz_score, 3 ClusterStability, 2, 3 ClusterStability-package, 2 ClusterStability_exact, 2, 4 davies_bouldin_score, 5 dunn_score, 6 is_partition_group (Undocumented functions), 8 Kcombination, 2, 6 p_n_k (Undocumented functions), 8 p_tilde_n_k (Undocumented functions), 8 Reorder, 2, 7 9

longsurr

cran

Package ‘longsurr’ October 13, 2022 Type Package Title Longitudinal Surrogate Marker Analysis Version 1.0 Description Assess the proportion of treatment effect explained by a longitudinal surro- gate marker as described in Agniel D and Parast L (2021) <doi:10.1111/biom.13310>. License GPL Imports stringr, splines, mgcv, Rsurrogate, dplyr, here, tidyr, fs, KernSmooth, stats, fdapace, grf, lme4, mvnfast, plyr, tibble, magrittr, glue, purrr, readr, refund, fda, fda.usc NeedsCompilation no Author Layla Parast [aut, cre], Denis Agniel [aut] Maintainer Layla Parast <parast@austin.utexas.edu> Depends R (>= 3.5.0) Repository CRAN Date/Publication 2022-09-29 10:00:02 UTC R topics documented: estimate_surrogate_value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 full_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 presmooth_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Index 5 1 2 estimate_surrogate_value estimate_surrogate_value Estimate the surrogate value of a longitudinal marker Description Estimate the surrogate value of a longitudinal marker Usage estimate_surrogate_value(y_t, y_c, X_t, X_c, method = c("gam", "linear", "kernel"), k = 3, var = FALSE, bootstrap_samples = 50, alpha = 0.05) Arguments y_t vector of n1 outcome measurements for treatment group y_c vector of n0 outcome measurements for control or reference group X_t n1 x T matrix of longitudinal surrogate measurements for treatment group, where T is the number of time points X_c n0 x T matrix of longitudinal surrogate measurements for control or reference group, where T is the number of time points method method for dimension-reduction of longitudinal surrogate, either ’gam’, ’linear’, or ’kernel’ k number of eigenfunctions to use in semimetric var logical, if TRUE then standard error estimates and confidence intervals are pro- vided bootstrap_samples number of bootstrap samples to use for standard error estimation, used if var = TRUE, default is 50 alpha alpha level, default is 0.05 Value a tibble containing estimates of the treatment effect (Deltahat), the residual treatment effect (Delta- hat_S), and the proportion of treatment effect explained (R); if var = TRUE, then standard errors of Deltahat_S and R are also provided (Deltahat_S_se and R_se), and quantile-based 95% confidence intervals for Deltahat_S and R are provided (Deltahat_S_ci_l [lower], Deltahat_S_ci_h [upper], R_ci_l [lower], R_ci_u [upper]) References Agniel D and Parast L (2021). Evaluation of Longitudinal Surrogate Markers. Biometrics, 77(2): 477-489. full_data 3 Examples library(dplyr) data(full_data) wide_ds <- full_data %>% dplyr::select(id, a, tt, x, y) %>% tidyr::spread(tt, x) wide_ds_0 <- wide_ds %>% filter(a == 0) wide_ds_1 <- wide_ds %>% filter(a == 1) X_t <- wide_ds_1 %>% dplyr::select(`-1`:`1`) %>% as.matrix y_t <- wide_ds_1 %>% pull(y) X_c <- wide_ds_0 %>% dplyr::select(`-1`:`1`) %>% as.matrix y_c <- wide_ds_0 %>% pull(y) estimate_surrogate_value(y_t = y_t, y_c = y_c, X_t = X_t, X_c = X_c, method = 'gam', var = FALSE) estimate_surrogate_value(y_t = y_t, y_c = y_c, X_t = X_t, X_c = X_c, method = 'linear', var = TRUE, bootstrap_sample = 50) full_data Example data to illustrate functions Description Simulated nonsmooth data to illustrate functions Usage data("full_data") Format A data frame with 10100 observations on the following 5 variables. id a unique person ID a treatment group, 0 or 1 tt time x surrogate marker value y primary outcome 4 presmooth_data presmooth_data Pre-smooth sparse longitudinal data Description Pre-smooth sparse longitudinal data Usage presmooth_data(obs_data, ...) Arguments obs_data data.frame or tibble containing the observed data, with columns id identifying the individual measured, tt identifying the time of the observation, x the value of the surrogate at time tt, and a indicating 1 for treatment arm and 0 for control arm. ... additional arguments passed on to fpca Value list containing matrices X_t and X_c, which are the smoothed surrogate values for the treated and control groups, respectively, for use in downstream analyses Examples library(dplyr) data(full_data) obs_ds <- group_by(full_data, id) obs_data <- sample_n(obs_ds, 5) obs_data <- ungroup(obs_data) head(obs_data) presmooth_X <- presmooth_data(obs_data) Index ∗ datasets full_data, 3 estimate_surrogate_value, 2 full_data, 3 presmooth_data, 4 5

neutralitytestr

cran

Package ‘neutralitytestr’ October 13, 2022 Title Test for a Neutral Evolutionary Model in Cancer Sequencing Data Version 0.0.3 Description Package takes frequencies of mutations as reported by high throughput sequenc- ing data from cancer and fits a theoretical neutral model of tumour evolution. Package out- puts summary statistics and contains code for plot- ting the data and model fits. See Williams et al 2016 <doi:10.1038/ng.3489> and Williams et al 2017 <doi:10.1101/096305> ther details of the method. Depends R (>= 3.4) License MIT + file LICENSE Encoding UTF-8 LazyData true Imports dplyr, ggplot2, scales, pracma, ggpmisc, cowplot Suggests knitr, rmarkdown, testthat VignetteBuilder knitr URL https://github.com/marcjwilliams1/neutralitytestr BugReports https://github.com/marcjwilliams1/neutralitytestr/issues RoxygenNote 7.1.1 NeedsCompilation no Author Marc Williams [aut, cre] Maintainer Marc Williams <marcjwilliams1@gmail.com> Repository CRAN Date/Publication 2021-02-16 18:00:06 UTC R topics documented: lsq_plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 neutralitytest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 neutralitytestr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 normalized_plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1 2 neutralitytest plot_all . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 VAFneutral . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 VAFselection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 vaf_histogram . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Index 7 lsq_plot Plot cumulative distribution lsq_plot Plots the cumulative distribu- tion of the data as well as the best fit linear model line. Description Plot cumulative distribution lsq_plot Plots the cumulative distribution of the data as well as the best fit linear model line. Usage lsq_plot(object) Arguments object neutrality test object Value ggplot object. Examples lsq_plot(neutralitytest(VAFselection, fmin = 0.1, fmax = 0.25)) neutralitytest Testing for neutrality on cancer sequencing data Description neutralitytest returns a neutralitytest object which contains the result of various test statistics to test for neutrality as described in Williams et al. Nature Genetics 2018. WARNING: This package has been superseded by MOBSTER, see Caravagna et al. Nature Genetics 2020. neutralitytest 3 Usage neutralitytest( VAF, fmin = 0.1, fmax = 0.25, read_depth = NULL, rho = 0, cellularity = 1, ploidy = 2 ) Arguments VAF Vector of variant allele frequencies (VAFs) from a deep sequencing experiment, numbers should be between 0 and 1 fmin Minimum VAF of integration range, default is 0.1 fmax Maximum VAF of integration range, default is 0.25 read_depth Read depth of sample, if this is specified it will be used to calculate an approp- tiate integration range. default is NULL in which case the default or inputted fmin and fmax will be used. rho Overdispersion of sample if known, default is 0.0. Will be used to calculate integration range if read_depth != NULL cellularity Cellularity of sample, default is 1.0. Will be used to calculate integration range if read_depth != NULL ploidy Ploidy of the genome, default is 2. Ideally mutations should be filtered for this ploidy before running the test. Will be used to calculate integration range if read_depth != NULL Value neutralitytest object which contains test statistics which tests if the sequencing data is consistent a neutral evolutionary model. Test statistics are area between theoretical and empirical curves, kol- mogorov distance, mean distance and R^2 statistics from linear model fit. Also returns an estimate of the mutation rate per tumour tumour doubling, the raw VAFs and cumulative distribution Examples neutralitytest(runif(100)) neutralitytest(VAFselection, fmin = 0.1, fmax = 0.25) neutralitytest(VAFneutral, read_depth = 100.0, cellularity = 0.8) 4 normalized_plot neutralitytestr neutralitytestr package Description Package to test a neutral evolutionary model on deep sequencing data. Details See the README on GitHub normalized_plot Plot normalized cumulative distribution normalized_plot Plots the (normalized) cumulative distribution of the data as well as the theo- retical expectation from a neutral evolutionary model. Description Plot normalized cumulative distribution normalized_plot Plots the (normalized) cumulative dis- tribution of the data as well as the theoretical expectation from a neutral evolutionary model. Usage normalized_plot(object) Arguments object neutrality test object Value ggplot object. Examples normalized_plot(neutralitytest(VAFselection, fmin = 0.1, fmax = 0.25)) plot_all 5 plot_all Plot all plots in the package and make composite figure. plot_all Plots histogram, linear model best fit plot and normalized plot and plot and makes composite figure. Description Plot all plots in the package and make composite figure. plot_all Plots histogram, linear model best fit plot and normalized plot and plot and makes composite figure. Usage plot_all(object) Arguments object neutrality test object Value ggplot object. Examples plot_all(neutralitytest(VAFselection, fmin = 0.1, fmax = 0.25)) VAFneutral Synthetic sequencing data generated from a evolutionary based cancer simulation. Description This data is generated from a neutral evolutionary model where all subclonal mutations are neutral. Usage VAFneutral Format A vector with variant allele frequencies (VAFs) ranging from 0 to 1 Source Generated using cancer sequencing simulation https://github.com/marcjwilliams1/CancerSeqSim. jl 6 vaf_histogram VAFselection Synthetic sequencing data generated from a evolutionary based cancer simulation. Description This data is generated from an evolutionary model where there is on subclonal population and all other mutations are neutral passengers. Usage VAFselection Format A vector with variant allele frequencies (VAFs) ranging from 0 to 1 Source Generated using cancer sequencing simulation https://github.com/marcjwilliams1/CancerSeqSim. jl vaf_histogram Plot VAF histogram vaf_histogram Plots a histogram of the variant allele frequencies. Description Plot VAF histogram vaf_histogram Plots a histogram of the variant allele frequencies. Usage vaf_histogram(object) Arguments object neutrality test object Value ggplot object. Examples vaf_histogram(neutralitytest(VAFselection, fmin = 0.1, fmax = 0.25)) Index ∗ datasets VAFneutral, 5 VAFselection, 6 lsq_plot, 2 neutralitytest, 2 neutralitytestr, 4 normalized_plot, 4 plot_all, 5 vaf_histogram, 6 VAFneutral, 5 VAFselection, 6 7

WASP

cran

Package ‘WASP’ October 12, 2022 Title Wavelet System Prediction Version 1.4.3 Author Ze Jiang [aut, cre] (<https://orcid.org/0000-0002-3472-0829>), Md. Mamunur Rashid [aut] (<https://orcid.org/0000-0002-0315-9055>), Ashish Sharma [aut] (<https://orcid.org/0000-0002-6758-0519>), Fiona Johnson [aut] (<https://orcid.org/0000-0001-5708-1807>) Maintainer Ze Jiang <ze.jiang@unsw.edu.au> Description The wavelet-based variance transformation method is used for system modelling and pre- diction. It refines predictor spectral representation using Wavelet Theory, which leads to im- proved model specifications and prediction accuracy. Details of methodologies used in the pack- age can be found in Jiang, Z., Sharma, A., & John- son, F. (2020) <doi:10.1029/2019WR026962>, Jiang, Z., Rashid, M. M., John- son, F., & Sharma, A. (2020) <doi:10.1016/j.envsoft.2020.104907>, and Jiang, Z., Sharma, A., & John- son, F. (2021) <doi:10.1016/J.JHYDROL.2021.126816>. License GPL-3 Encoding UTF-8 Depends R (>= 3.6.0) URL https://github.com/zejiang-unsw/WASP#readme BugReports https://github.com/zejiang-unsw/WASP/issues Imports waveslim, stats, tidyr, ggplot2, sp, rlang (>= 1.0.0) Suggests zoo, FNN, readr, knitr, cowplot, gridGraphics, bookdown, rmarkdown, SPEI, NPRED, synthesis, kableExtra, fitdistrplus, devtools, testthat RoxygenNote 7.2.1 VignetteBuilder knitr NeedsCompilation no Repository CRAN Date/Publication 2022-08-22 07:50:24 UTC 1 2 R topics documented: R topics documented: WASP-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 at.vt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 at.vt.val . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 at.wd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 aus.coast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 data.AWAP.2.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 data.CI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 data.gen.ar1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 data.gen.ar4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 data.gen.ar9 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 data.gen.HL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 data.gen.Rossler . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 data.gen.SW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 data.gen.tar1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 data.gen.tar2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 data.HL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 data.SW1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 data.SW3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 dwt.vt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 dwt.vt.val . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 fig.dwt.vt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Ind_AWAP.2.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 knn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 knnregl1cv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 lat_lon.2.5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 modwt.vt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 modwt.vt.val . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 mra.plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 non.bdy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 obs.mon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 padding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 r2.boot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 rain.mon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 scal2freqM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 scal2freqR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 SPI.12 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 stepwise.VT . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 stepwise.VT.val . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 wave.var . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Index 38 WASP-package 3 WASP-package WASP: WAvelet System Prediction Description The package WASP (variance transformation) is used for system modelling and prediction. Details Package: WASP Type: Package Version: 1.0.1 Date: 2020-03-17 License: GPL-3 WASP functions Variance transformation functions: dwt.vt, modwt.vt, at.vt and associated K-nearest neighbor function: knn Synthetic data generator functions: data.gen.SW, data.gen.HL, data.gen.Rossler; data.gen.ar1, data.gen.ar4, data.gen.ar9, data.gen.tar1, data.gen.tar2. Author(s) Ze Jiang Maintainer: Ze Jiang <ze.jiang@unsw.edu.au> References Jiang, Z., Sharma, A., & Johnson, F. (2020). Refining Predictor Spectral Representation Us- ing Wavelet Theory for Improved Natural System Modeling. Water Resources Research, 56(3), e2019WR026962. doi:10.1029/2019wr026962 Percival, D. B. and A. T. Walden (2000) Wavelet Methods for Time Series Analysis, Cambridge: Cambridge University Press. 4 at.vt at.vt Variance Transformation Operation - AT(a trous) Description Variance Transformation Operation - AT(a trous) Usage at.vt( data, wf, J, boundary, cov.opt = "auto", flag = "biased", detrend = FALSE ) Arguments data A list of response x and dependent variables dp. wf Name of the wavelet filter to use in the decomposition. J Specifies the depth of the decomposition. This must be a number less than or equal to log(length(x),2). boundary Character string specifying the boundary condition. If boundary=="periodic" the default, then the vector you decompose is assumed to be periodic on its defined interval, if boundary=="reflection", the vector beyond its boundaries is assumed to be a symmetric reflection of itself. cov.opt Options of Covariance matrix sign. Use "pos", "neg", or "auto". flag Biased or Unbiased variance transformation, c("biased","unbiased"). detrend Detrend the input time series or just center, default (F) Value A list of 8 elements: wf, J, boundary, x (data), dp (data), dp.n (variance transformed dp), and S (covariance matrix). References Jiang, Z., Sharma, A., & Johnson, F. (2020). Refining Predictor Spectral Representation Us- ing Wavelet Theory for Improved Natural System Modeling. Water Resources Research, 56(3), e2019WR026962. Jiang, Z., Sharma, A., & Johnson, F. (2021). Variable transformations in the spectral domain – Implications for hydrologic forecasting. Journal of Hydrology, 126816. at.vt.val 5 Examples data(rain.mon) data(obs.mon) ## response SPI - calibration # SPI.cal <- SPI.calc(window(rain.mon, start=c(1949,1), end=c(1979,12)),sc=12) SPI.cal <- SPEI::spi(window(rain.mon, start = c(1949, 1), end = c(1979, 12)), scale = 12)$fitted ## create paired response and predictors dataset for each station data.list <- list() for (id in seq_len(ncol(SPI.cal))) { x <- window(SPI.cal[, id], start = c(1950, 1), end = c(1979, 12)) dp <- window(obs.mon, start = c(1950, 1), end = c(1979, 12)) data.list[[id]] <- list(x = as.numeric(x), dp = matrix(dp, nrow = nrow(dp))) } ## variance transformation dwt.list <- lapply( data.list, function(x) at.vt(x, wf = "d4", J = 7, boundary = "periodic", cov.opt = "auto") ) ## plot original and reconstrcuted predictors for each station for (i in seq_len(length(dwt.list))) { # extract data dwt <- dwt.list[[i]] x <- dwt$x # response dp <- dwt$dp # original predictors dp.n <- dwt$dp.n # variance transformed predictors plot.ts(cbind(x, dp)) plot.ts(cbind(x, dp.n)) } at.vt.val Variance Transformation Operation for Validation Description Variance Transformation Operation for Validation Usage at.vt.val(data, J, dwt, detrend = FALSE) Arguments data A list of response x and dependent variables dp. 6 at.vt.val J Specifies the depth of the decomposition. This must be a number less than or equal to log(length(x),2). dwt A class of "at" data. Output from at.vt(). detrend Detrend the input time series or just center, default (F) Value A list of 8 elements: wf, J, boundary, x (data), dp (data), dp.n (variance transformed dp), and S (covariance matrix). References Jiang, Z., Sharma, A., & Johnson, F. (2020). Refining Predictor Spectral Representation Us- ing Wavelet Theory for Improved Natural System Modeling. Water Resources Research, 56(3), e2019WR026962. doi:10.1029/2019wr026962 Examples data(rain.mon) data(obs.mon) ## response SPI - calibration # SPI.cal <- SPI.calc(window(rain.mon, start=c(1949,1), end=c(1979,12)),sc=12) SPI.cal <- SPEI::spi(window(rain.mon, start = c(1949, 1), end = c(1979, 12)), scale = 12)$fitted ## create paired response and predictors dataset for each station data.list <- list() for (id in seq_len(ncol(SPI.cal))) { x <- window(SPI.cal[, id], start = c(1950, 1), end = c(1979, 12)) dp <- window(obs.mon, start = c(1950, 1), end = c(1979, 12)) data.list[[id]] <- list(x = as.numeric(x), dp = matrix(dp, nrow = nrow(dp))) } ## variance transformation - calibration dwt.list <- lapply( data.list, function(x) at.vt(x, wf = "d4", J = 7, boundary = "periodic", cov.opt = "auto") ) ## response SPI - validation # SPI.val <- SPI.calc(window(rain.mon, start=c(1979,1), end=c(2009,12)),sc=12) SPI.val <- SPEI::spi(window(rain.mon, start = c(1979, 1), end = c(2009, 12)), scale = 12)$fitted ## create paired response and predictors dataset for each station data.list <- list() for (id in seq_len(ncol(SPI.val))) { x <- window(SPI.val[, id], start = c(1980, 1), end = c(2009, 12)) dp <- window(obs.mon, start = c(1980, 1), end = c(2009, 12)) data.list[[id]] <- list(x = as.numeric(x), dp = matrix(dp, nrow = nrow(dp))) } at.wd 7 # variance transformation - validation dwt.list.val <- lapply( seq_len(length(data.list)), function(i) at.vt.val(data.list[[i]], J = 7, dwt.list[[i]]) ) ## plot original and reconstrcuted predictors for each station for (i in seq_len(length(dwt.list.val))) { # extract data dwt <- dwt.list.val[[i]] x <- dwt$x # response dp <- dwt$dp # original predictors dp.n <- dwt$dp.n # variance transformed predictors plot.ts(cbind(x, dp)) plot.ts(cbind(x, dp.n)) } at.wd a trous (AT) based additive decompostion using Daubechies family wavelet Description a trous (AT) based additive decompostion using Daubechies family wavelet Usage at.wd(x, wf, J, boundary = "periodic") Arguments x The input time series. wf Name of the wavelet filter to use in the decomposition. J Specifies the depth of the decomposition. This must be a number less than or equal to log(length(x),2). boundary Character string specifying the boundary condition. If boundary=="periodic" the default, then the vector you decompose is assumed to be periodic on its defined interval, if boundary=="reflection", the vector beyond its boundaries is assumed to be a symmetric reflection of itself. Value A matrix of decomposed sub-time series. References Nason, G. P. (1996). Wavelet shrinkage using cross-validation. Journal of the Royal Statistical Society: Series B (Methodological), 58(2), 463-479. 8 data.AWAP.2.5 Examples data(obs.mon) n <- nrow(obs.mon) v <- 1 J <- floor(log(n / (2 * v - 1)) / log(2)) # (Kaiser, 1994) names <- colnames(obs.mon) at.atm <- vector("list", ncol(obs.mon)) for (i in seq_len(ncol(obs.mon))) { tmp <- as.numeric(scale(obs.mon[, i], scale = FALSE)) at.atm <- do.call(cbind, at.wd(tmp, wf = "haar", J = J, boundary = "periodic")) plot.ts(cbind(obs.mon[1:n, i], at.atm[1:n, 1:9]), main = names[i]) print(sum(abs(scale(obs.mon[1:n, i], scale = FALSE) - rowSums(at.atm[1:n, ])))) } aus.coast Sample data: Australia map Description A dataset containing the Australia map. Usage data(aus.coast) data.AWAP.2.5 Sample data: AWAP rainfall data over Australia Description A dataset containing 1320 rows (data length) and 252 columns (grids). Usage data(data.AWAP.2.5) data.CI 9 data.CI Sample data: Climate indices strongly influencing Australia climate Description A dataset containing 1332 rows (data length) and 6 columns (indices). Usage data(data.CI) data.gen.ar1 Generate predictor and response data from AR1 model. Description Generate predictor and response data from AR1 model. Usage data.gen.ar1(nobs, ndim = 9) Arguments nobs The data length to be generated. ndim The number of potential predictors (default is 9). Value A list of 2 elements: a vector of response (x), and a matrix of potential predictors (dp) with each column containing one potential predictor. Examples # AR1 model from paper with 9 dummy variables data.ar1 <- data.gen.ar1(500) plot.ts(cbind(data.ar1$x, data.ar1$dp)) 10 data.gen.ar9 data.gen.ar4 Generate predictor and response data from AR4 model. Description Generate predictor and response data from AR4 model. Usage data.gen.ar4(nobs, ndim = 9) Arguments nobs The data length to be generated. ndim The number of potential predictors (default is 9). Value A list of 2 elements: a vector of response (x), and a matrix of potential predictors (dp) with each column containing one potential predictor. Examples # AR4 model from paper with total 9 dimensions data.ar4 <- data.gen.ar4(500) plot.ts(cbind(data.ar4$x, data.ar4$dp)) data.gen.ar9 Generate predictor and response data from AR9 model. Description Generate predictor and response data from AR9 model. Usage data.gen.ar9(nobs, ndim = 9) Arguments nobs The data length to be generated. ndim The number of potential predictors (default is 9). Value A list of 2 elements: a vector of response (x), and a matrix of potential predictors (dp) with each column containing one potential predictor. data.gen.HL 11 Examples # AR9 model from paper with total 9 dimensions data.ar9 <- data.gen.ar9(500) plot.ts(cbind(data.ar9$x, data.ar9$dp)) data.gen.HL Generate predictor and response data: Hysteresis Loop Description Generate predictor and response data: Hysteresis Loop Usage data.gen.HL(n = 3, m = 5, nobs = 512, fp = 25, fd, sd.x = 0.1, sd.y = 0.1) Arguments n Positive integer for the split line parameter. If n=1, split line is linear; If n is even, split line has a u shape; If n is odd and higher than 1, split line has a chair or classical shape. m Positive odd integer for the bulging parameter, indicates degree of outward curv- ing (1=highest level of bulging). nobs The data length to be generated. fp The frequency in the generated response.fp = 25 used in the WRR paper. fd A vector of frequencies for potential predictors. fd = c(3,5,10,15,25,30,55,70,95) used in the WRR paper. sd.x The noise level in the predictor. sd.y The noise level in the response. Details The Hysteresis is a common nonlinear phenomenon in natural systems and it can be numerical simulated by the following formulas: xt = a ∗ cos(2pi ∗ f ∗ t) yt = b ∗ cos(2pi ∗ f ∗ t)n + c ∗ sin(2pi ∗ f ∗ t)m The default selection for the system parameters (a = 0.8, b = 0.6, c = -0.2, n = 3, m = 5) is known to generate a classical hysteresis loop. Value A list of 3 elements: a vector of response (x), a matrix of potential predictors (dp) with each column containing one potential predictor, and a vector of true predictor numbers. 12 data.gen.Rossler References LAPSHIN, R. V. 1995. Analytical model for the approximation of hysteresis loop and its application to the scanning tunneling microscope. Review of Scientific Instruments, 66, 4718-4730. Examples ###synthetic example - Hysteresis loop #frequency, sampled from a given range fd <- c(3,5,10,15,25,30,55,70,95) data.HL <- data.gen.HL(n=3,m=5,nobs=512,fp=25,fd=fd) plot.ts(cbind(data.HL$x,data.HL$dp)) data.gen.Rossler Generate predictor and response data: Rossler system Description Generates a 3-dimensional time series using the Rossler equations. Usage data.gen.Rossler( a = 0.2, b = 0.2, w = 5.7, start = c(-2, -10, 0.2), time = seq(0, 50, length.out = 5000) ) Arguments a The a parameter. Default:0.2. b The b parameter. Default: 0.2. w The w parameter. Default: 5.7. start A 3-dimensional numeric vector indicating the starting point for the time series. Default: c(-2, -10, 0.2). time The temporal interval at which the system will be generated. Default: time=seq(0,50,length.out = 5000). data.gen.SW 13 Details The Rossler system is a system of ordinary differential equations defined as: ẋ = −(y + z) ẏ = x + a · y ż = b + z ∗ (x − w) The default selection for the system parameters (a = 0.2, b = 0.2, w = 5.7) is known to produce a deterministic chaotic time series. Value A list with four vectors named time, x, y and z containing the time, the x-components, the y- components and the z-components of the Rossler system, respectively. Note Some initial values may lead to an unstable system that will tend to infinity. References RÖSSLER, O. E. 1976. An equation for continuous chaos. Physics Letters A, 57, 397-398. Examples ### synthetic example - Rossler ts.r <- data.gen.Rossler( a = 0.2, b = 0.2, w = 5.7, start = c(-2, -10, 0.2), time = seq(0, 50, length.out = 1000) ) # add noise ts.r$x <- ts(ts.r$x + rnorm(length(ts.r$time), mean = 0, sd = 1)) ts.r$y <- ts(ts.r$y + rnorm(length(ts.r$time), mean = 0, sd = 1)) ts.r$z <- ts(ts.r$z + rnorm(length(ts.r$time), mean = 0, sd = 1)) ts.plot(ts.r$x, ts.r$y, ts.r$z, col = c("black", "red", "blue")) data.gen.SW Generate predictor and response data: Sinewave model Description Generate predictor and response data: Sinewave model Usage data.gen.SW(nobs = 512, fp = 25, fd, sd.x = 0.1, sd.y = 0.1) 14 data.gen.tar1 Arguments nobs The data length to be generated. fp The frequencies in the generated response. fd A vector of frequencies for potential predictors. fd = c(3,5,10,15,25,30,55,70,95) used in the WRR paper. sd.x The noise level in the predictor. sd.y The noise level in the response. Value A list of 3 elements: a vector of response (x), a matrix of potential predictors (dp) with each column containing one potential predictor, and a vector of true predictor numbers. Examples ###synthetic example #frequency, sampled from a given range fd <- c(3,5,10,15,25,30,55,70,95) data.SW1 <- data.gen.SW(nobs=512,fp=25,fd=fd) data.SW3 <- data.gen.SW(nobs=512,fp=c(15,25,30),fd=fd) ts.plot(ts(data.SW1$x),ts(data.SW3$x),col=c("black","red")) plot.ts(cbind(data.SW1$x,data.SW1$dp)) plot.ts(cbind(data.SW3$x,data.SW3$dp)) data.gen.tar1 Generate predictor and response data from TAR1 model. Description Generate predictor and response data from TAR1 model. Usage data.gen.tar1(nobs, ndim = 9, noise = 0.1) Arguments nobs The data length to be generated. ndim The number of potential predictors (default is 9). noise The white noise in the data Value A list of 2 elements: a vector of response (x), and a matrix of potential predictors (dp) with each column containing one potential predictor. data.gen.tar2 15 References Sharma, A. (2000). Seasonal to interannual rainfall probabilistic forecasts for improved water sup- ply management: Part 1—A strategy for system predictor identification. Journal of Hydrology, 239(1-4), 232-239. Examples # TAR1 model from paper with total 9 dimensions data.tar1 <- data.gen.tar1(500) plot.ts(cbind(data.tar1$x, data.tar1$dp)) data.gen.tar2 Generate predictor and response data from TAR2 model. Description Generate predictor and response data from TAR2 model. Usage data.gen.tar2(nobs, ndim = 9, noise = 0.1) Arguments nobs The data length to be generated. ndim The number of potential predictors (default is 9). noise The white noise in the data Value A list of 2 elements: a vector of response (x), and a matrix of potential predictors (dp) with each column containing one potential predictor. References Sharma, A. (2000). Seasonal to interannual rainfall probabilistic forecasts for improved water sup- ply management: Part 1—A strategy for system predictor identification. Journal of Hydrology, 239(1-4), 232-239. Examples # TAR2 model from paper with total 9 dimensions data.tar2 <- data.gen.tar2(500) plot.ts(cbind(data.tar2$x, data.tar2$dp)) 16 data.SW3 data.HL Sample data: Hysteresis loop Description A dataset containing 3 lists: a vector of response (x), a matrix of 9 potential predictors (dp) with each column containing one potential predictor, and a vector of true predictor numbers. Usage data(data.HL) data.SW1 Sample data: Sinewave model 1 (SW1) Description A dataset containing 3 lists: a vector of response (x), a matrix of 9 potential predictors (dp) with each column containing one potential predictor, and a vector of true predictor numbers. The Sinewave model 1 (SW1) is defined as: xt = sin(2pi ∗ f ∗ t) + eps Usage data(data.SW1) data.SW3 Sample data: Sinewave model 3 (SW3) Description A dataset containing 3 lists: a vector of response (x), a matrix of 9 potential predictors (dp) with each column containing one potential predictor, and a vector of true predictor numbers. The Sinewave model 3 (SW3) is defined as: X3 xt = sin(2pi ∗ fi ∗ t) + eps i=1 Usage data(data.SW3) dwt.vt 17 dwt.vt Variance Transformation Operation - MRA Description Variance Transformation Operation - MRA Usage dwt.vt( data, wf, J, method, pad, boundary, cov.opt = "auto", flag = "biased", detrend = FALSE ) Arguments data A list of response x and dependent variables dp. wf Name of the wavelet filter to use in the decomposition. J Specifies the depth of the decomposition. This must be a number less than or equal to log(length(x),2). method Either "dwt" or "modwt". pad The method used for extend data to dyadic size. Use "per", "zero", or "sym". boundary Character string specifying the boundary condition. If boundary=="periodic" the default, then the vector you decompose is assumed to be periodic on its defined interval, if boundary=="reflection", the vector beyond its boundaries is assumed to be a symmetric reflection of itself. cov.opt Options of Covariance matrix sign. Use "pos", "neg", or "auto". flag Biased or Unbiased variance transformation, c("biased","unbiased"). detrend Detrend the input time series or just center, default (F) Value A list of 8 elements: wf, method, boundary, pad, x (data), dp (data), dp.n (variance trasnformed dp), and S (covariance matrix). 18 dwt.vt.val References Jiang, Z., Sharma, A., & Johnson, F. (2020). Refining Predictor Spectral Representation Us- ing Wavelet Theory for Improved Natural System Modeling. Water Resources Research, 56(3), e2019WR026962. Examples data(rain.mon) data(obs.mon) ## response SPI - calibration # SPI.cal <- SPI.calc(window(rain.mon, start=c(1949,1), end=c(1979,12)),sc=12) SPI.cal <- SPEI::spi(window(rain.mon, start = c(1949, 1), end = c(1979, 12)), scale = 12)$fitted ## create paired response and predictors dataset for each station data.list <- list() for (id in seq_len(ncol(SPI.cal))) { x <- window(SPI.cal[, id], start = c(1950, 1), end = c(1979, 12)) dp <- window(obs.mon, start = c(1950, 1), end = c(1979, 12)) data.list[[id]] <- list(x = as.numeric(x), dp = matrix(dp, nrow = nrow(dp))) } ## variance transformation dwt.list <- lapply(data.list, function(x) { dwt.vt(x, wf = "d4", J = 7, method = "dwt", pad = "zero", boundary = "periodic", cov.opt = "auto") }) ## plot original and reconstrcuted predictors for each station for (i in seq_len(length(dwt.list))) { # extract data dwt <- dwt.list[[i]] x <- dwt$x # response dp <- dwt$dp # original predictors dp.n <- dwt$dp.n # variance transformed predictors plot.ts(cbind(x, dp)) plot.ts(cbind(x, dp.n)) } dwt.vt.val Variance Transformation Operation for Validation Description Variance Transformation Operation for Validation Usage dwt.vt.val(data, J, dwt, detrend = FALSE) dwt.vt.val 19 Arguments data A list of response x and dependent variables dp. J Specifies the depth of the decomposition. This must be a number less than or equal to log(length(x),2). dwt A class of "dwt" data. Output from dwt.vt(). detrend Detrend the input time series or just center, default (F) Value A list of 8 elements: wf, method, boundary, pad, x (data), dp (data), dp.n (variance trasnformed dp), and S (covariance matrix). References Jiang, Z., Sharma, A., & Johnson, F. (2020). Refining Predictor Spectral Representation Us- ing Wavelet Theory for Improved Natural System Modeling. Water Resources Research, 56(3), e2019WR026962. doi:10.1029/2019wr026962 Examples data(rain.mon) data(obs.mon) ## response SPI - calibration # SPI.cal <- SPI.calc(window(rain.mon, start=c(1949,1), end=c(1979,12)),sc=12) SPI.cal <- SPEI::spi(window(rain.mon, start = c(1949, 1), end = c(1979, 12)), scale = 12)$fitted ## create paired response and predictors dataset for each station data.list <- list() for (id in seq_len(ncol(SPI.cal))) { x <- window(SPI.cal[, id], start = c(1950, 1), end = c(1979, 12)) dp <- window(obs.mon, start = c(1950, 1), end = c(1979, 12)) data.list[[id]] <- list(x = as.numeric(x), dp = matrix(dp, nrow = nrow(dp))) } ## variance transformation - calibration dwt.list <- lapply(data.list, function(x) { dwt.vt(x, wf = "d4", J = 7, method = "dwt", pad = "zero", boundary = "periodic", cov.opt = "auto") }) ## response SPI - validation # SPI.val <- SPI.calc(window(rain.mon, start=c(1979,1), end=c(2009,12)),sc=12) SPI.val <- SPEI::spi(window(rain.mon, start = c(1979, 1), end = c(2009, 12)), scale = 12)$fitted ## create paired response and predictors dataset for each station data.list <- list() for (id in seq_len(ncol(SPI.val))) { x <- window(SPI.val[, id], start = c(1980, 1), end = c(2009, 12)) dp <- window(obs.mon, start = c(1980, 1), end = c(2009, 12)) data.list[[id]] <- list(x = as.numeric(x), dp = matrix(dp, nrow = nrow(dp))) 20 fig.dwt.vt } # variance transformation - validation dwt.list.val <- lapply( seq_len(length(data.list)), function(i) dwt.vt.val(data.list[[i]], J = 7, dwt.list[[i]]) ) ## plot original and reconstrcuted predictors for each station for (i in seq_len(length(dwt.list.val))) { # extract data dwt <- dwt.list.val[[i]] x <- dwt$x # response dp <- dwt$dp # original predictors dp.n <- dwt$dp.n # variance transformed predictors plot.ts(cbind(x, dp)) plot.ts(cbind(x, dp.n)) } fig.dwt.vt Plot function: Variance structure before and after variance transfor- mation Description Plot function: Variance structure before and after variance transformation Usage fig.dwt.vt(dwt.data) Arguments dwt.data Output data from variance transformation function Value A plot with variance structure before and after variance transformation. Examples data("data.HL") data("data.SW1") # variance transfrom dwt.SW1 <- dwt.vt(data.SW1[[1]], wf = "d4", J = 7, method = "dwt", pad = "zero", boundary = "periodic", cov.opt = "auto" ) Ind_AWAP.2.5 21 # plot fig1 <- fig.dwt.vt(dwt.SW1) fig1 # variance transfrom dwt.HL <- dwt.vt(data.HL[[1]], wf = "d4", J = 7, method = "dwt", pad = "zero", boundary = "periodic", cov.opt = "auto" ) # plot fig2 <- fig.dwt.vt(dwt.HL) fig2 Ind_AWAP.2.5 Sample data: Index of AWAP grids with no missing data Description A dataset containing 145 numbers. Usage data(Ind_AWAP.2.5) knn Modified k-nearest neighbour conditional bootstrap or regression function estimation with extrapolation Description Modified k-nearest neighbour conditional bootstrap or regression function estimation with extrapo- lation Usage knn( x, z, zout, k = 0, pw, reg = TRUE, nensemble = 100, tailcorrection = TRUE, 22 knn tailprob = 0.25, tailfac = 0.2, extrap = TRUE ) Arguments x A vector of response. z A matrix of existing predictors. zout A matrix of predictor values the response is to be estimated at. k The number of nearest neighbours used. The default value is 0, indicating Lall and Sharma default is used. pw A vector of partial weights of the same length of z. reg A logical operator to inform whether a conditional expectation should be output or not nensemble, Used if reg=F and represents the number of realisations that are generated Value. nensemble An integer the specifies the number of ensembles used. The default is 100. tailcorrection A logical value, T (default) or F, that denotes whether a reduced value of k (number of nearest neighbours) should be used in the tails of any conditioning plane. Whether one is in the tails or not is determined based on the nearest neighbour response value. tailprob A scalar that denotes the p-value of the cdf (on either extreme) the tailcorrection takes effect. The default value is 0.25. tailfac A scalar that specifies the lowest fraction of the default k that can be used in the tails. Depending on the how extreme one is in the tails, the actual k decreases linearly from k (for a p-value greater than tailprob) to tailfac*k proportional to the actual p-value of the nearest neighbour response, divided by tailprob. The default value is 0.2. extrap A logical value, T (default) or F, that denotes whether a kernel extraplation method is used to predict x. Value A matrix of responses having same rows as zout if reg=T, or having nensemble columns is reg=F. References Sharma, A., Tarboton, D.G. and Lall, U., 1997. Streamflow simulation: A nonparametric approach. Water resources research, 33(2), pp.291-308. Sharma, A. and O’Neill, R., 2002. A nonparametric approach for representing interannual depen- dence in monthly streamflow sequences. Water resources research, 38(7), pp.5-1. knnregl1cv 23 Examples # AR9 model x(i)=0.3*x(i-1)-0.6*x(i-4)-0.5*x(i-9)+eps data.ar9 <- data.gen.ar9(500) x <- data.ar9$x # response py <- data.ar9$dp # possible predictors # identify the meaningful predictors and estimate partial weights ans.ar9 <- NPRED::stepwise.PIC(x, py) z <- py[, ans.ar9$cpy] # predictor matrix pw <- ans.ar9$wt # partial weights # vector denoting where we want outputs, can be a matrix representing grid. zout <- apply(z, 2, mean) knn(x, z, zout, reg = TRUE, pw = pw) # knn regression estimate using partial weights. knn(x, z, zout, reg = FALSE, pw = pw) # alternatively, knn conditional bootstrap (100 realisations). # Mean of the conditional bootstrap estimate should be # approximately the same as the regression estimate. zout <- ts(data.gen.ar9(500, ndim = length(ans.ar9$cpy))$dp) # new input xhat1 <- xhat2 <- x xhat1 <- knn(x, z, zout, k = 5, reg = TRUE, extrap = FALSE) # without extrapolation xhat2 <- knn(x, z, zout, k = 5, reg = TRUE, extrap = TRUE) # with extrapolation ts.plot(ts(x), ts(xhat1), ts(xhat2), col = c("black", "red", "blue"), ylim = c(-5, 5), lwd = c(2, 2, 1)) knnregl1cv Leave one out cross validation. Description Leave one out cross validation. Usage knnregl1cv(x, z, k = 0, pw) Arguments x A vector of response. z A matrix of predictors. k The number of nearest neighbours used. The default is 0, indicating Lall and Sharma default is used. pw A vector of partial weights of the same length of z. 24 modwt.vt Value A vector of L1CV estimates of the response. References Lall, U., Sharma, A., 1996. A Nearest Neighbor Bootstrap For Resampling Hydrologic Time Series. Water Resources Research, 32(3): 679-693. Sharma, A., Mehrotra, R., 2014. An information theoretic alternative to model a natural system using observational information alone. Water Resources Research, 50(1): 650-660. lat_lon.2.5 Sample data: Latitude and longitude of AWAP grids Description A dataset containing 252 rows (grids) and 2 columns (lat and lon). Usage data(lat_lon.2.5) modwt.vt Variance Transformation Operation - MODWT Description Variance Transformation Operation - MODWT Usage modwt.vt( data, wf, J, boundary, cov.opt = "auto", flag = "biased", detrend = FALSE ) modwt.vt 25 Arguments data A list of response x and dependent variables dp. wf Name of the wavelet filter to use in the decomposition. J Specifies the depth of the decomposition. This must be a number less than or equal to log(length(x),2). boundary Character string specifying the boundary condition. If boundary=="periodic" the default, then the vector you decompose is assumed to be periodic on its defined interval, if boundary=="reflection", the vector beyond its boundaries is assumed to be a symmetric reflection of itself. cov.opt Options of Covariance matrix sign. Use "pos", "neg", or "auto". flag Biased or Unbiased variance transformation, c("biased","unbiased"). detrend Detrend the input time series or just center, default (F) Value A list of 8 elements: wf, J, boundary, x (data), dp (data), dp.n (variance transformed dp), and S (covariance matrix). References Jiang, Z., Sharma, A., & Johnson, F. (2020). Refining Predictor Spectral Representation Us- ing Wavelet Theory for Improved Natural System Modeling. Water Resources Research, 56(3), e2019WR026962. Jiang, Z., Rashid, M. M., Johnson, F., & Sharma, A. (2020). A wavelet-based tool to modulate variance in predictors: an application to predicting drought anomalies. Environmental Modelling & Software, 135, 104907. Examples ### real-world example data(Ind_AWAP.2.5) data(obs.mon) data(SPI.12) x <- window(SPI.12, start = c(1950, 1), end = c(2009, 12)) dp <- window(obs.mon, start = c(1950, 1), end = c(2009, 12)) op <- par(mfrow = c(ncol(dp), 1), pty = "m", mar = c(1, 4, 1, 2)) for (id in sample(Ind_AWAP.2.5, 1)) { data <- list(x = x[, id], dp = dp) dwt <- modwt.vt(data, wf = "d4", J = 7, boundary = "periodic", cov.opt = "auto") for (i in 1:ncol(dp)) { ts.plot(dwt$dp[, i], dwt$dp.n[, i], xlab = NA, col = c("black", "red"), lwd = c(2, 1)) } } par(op) ### synthetic example 26 modwt.vt.val # frequency, sampled from a given range fd <- c(3, 5, 10, 15, 25, 30, 55, 70, 95) data.SW1 <- data.gen.SW(nobs = 512, fp = 25, fd = fd) dwt.SW1 <- modwt.vt(data.SW1, wf = "d4", J = 7, boundary = "periodic", cov.opt = "auto") x.modwt <- waveslim::modwt(dwt.SW1$x, wf = "d4", n.levels = 7, boundary = "periodic") dp.modwt <- waveslim::modwt(dwt.SW1$dp[, 1], wf = "d4", n.levels = 7, boundary = "periodic") dp.vt.modwt <- waveslim::modwt(dwt.SW1$dp.n[, 1], wf = "d4", n.levels = 7, boundary = "periodic") sum(sapply(dp.modwt, var)) var(dwt.SW1$dp[, 1]) sum(sapply(dp.vt.modwt, var)) var(dwt.SW1$dp.n[, 1]) data <- rbind( sapply(dp.modwt, var) / sum(sapply(dp.modwt, var)), sapply(dp.vt.modwt, var) / sum(sapply(dp.vt.modwt, var)) ) bar <- barplot(data, beside = TRUE, col = c("red", "blue")) lines(x = bar[2, ], y = sapply(x.modwt, var) / sum(sapply(x.modwt, var))) points(x = bar[2, ], y = sapply(x.modwt, var) / sum(sapply(x.modwt, var))) modwt.vt.val Variance Transformation Operation for Validation Description Variance Transformation Operation for Validation Usage modwt.vt.val(data, J, dwt, detrend = FALSE) Arguments data A list of response x and dependent variables dp. J Specifies the depth of the decomposition. This must be a number less than or equal to log(length(x),2). dwt A class of "modwt" data. Output from modwt.vt(). detrend Detrend the input time series or just center, default (F) Value A list of 8 elements: wf, J, boundary, x (data), dp (data), dp.n (variance transformed dp), and S (covariance matrix). modwt.vt.val 27 References Z Jiang, A Sharma, and F Johnson. WRR Examples data(rain.mon) data(obs.mon) ## response SPI - calibration # SPI.cal <- SPI.calc(window(rain.mon, start=c(1949,1), end=c(1979,12)),sc=12) SPI.cal <- SPEI::spi(window(rain.mon, start = c(1949, 1), end = c(1979, 12)), scale = 12)$fitted ## create paired response and predictors dataset for each station data.list <- list() for (id in 1:ncol(SPI.cal)) { x <- window(SPI.cal[, id], start = c(1950, 1), end = c(1979, 12)) dp <- window(obs.mon, start = c(1950, 1), end = c(1979, 12)) data.list[[id]] <- list(x = as.numeric(x), dp = matrix(dp, nrow = nrow(dp))) } ## variance transformation - calibration dwt.list <- lapply(data.list, function(x) { modwt.vt(x, wf = "d4", J = 7, boundary = "periodic", cov.opt = "auto") }) ## response SPI - validation # SPI.val <- SPI.calc(window(rain.mon, start=c(1979,1), end=c(2009,12)),sc=12) SPI.val <- SPEI::spi(window(rain.mon, start = c(1979, 1), end = c(2009, 12)), scale = 12)$fitted ## create paired response and predictors dataset for each station data.list <- list() for (id in 1:ncol(SPI.val)) { x <- window(SPI.val[, id], start = c(1980, 1), end = c(2009, 12)) dp <- window(obs.mon, start = c(1980, 1), end = c(2009, 12)) data.list[[id]] <- list(x = as.numeric(x), dp = matrix(dp, nrow = nrow(dp))) } # variance transformation - validation dwt.list.val <- lapply( seq_along(data.list), function(i) modwt.vt.val(data.list[[i]], J = 7, dwt.list[[i]]) ) ## plot original and reconstrcuted predictors for each station for (i in seq_along(dwt.list.val)) { # extract data dwt <- dwt.list.val[[i]] x <- dwt$x # response dp <- dwt$dp # original predictors dp.n <- dwt$dp.n # variance transformed predictors plot.ts(cbind(x, dp)) 28 mra.plot plot.ts(cbind(x, dp.n)) } mra.plot Plot function: Plot original time series and decomposed frequency components Description Plot function: Plot original time series and decomposed frequency components Usage mra.plot( y, y.mra, limits.x, limits.y, type = c("details", "coefs"), ps = 12, ... ) Arguments y Original time series (Y). y.mra Decomposed frequency components (d1,d2,..,aJ). limits.x x limit for plot. limits.y y limit for plot. type type of wavelet coefficients, details or approximations. ps integer; the point size of text (but not symbols). ... arguments for plot(). Value A plot with original time series and decomposed frequency components. Examples ### synthetic example # frequency, sampled from a given range fd <- c(3, 5, 10, 15, 25, 30, 55, 70, 95) data.SW3 <- data.gen.SW(nobs = 512, fp = c(15, 25, 30), fd = fd) x <- data.SW3$x xx <- padding(x, pad = "zero") non.bdy 29 ### wavelet transfrom # wavelet family, extension mode and package wf <- "d4" # wavelet family D8 or db4 boundary <- "periodic" pad <- "zero" if (wf != "haar") v <- as.integer(as.numeric(substr(wf, 2, 3)) / 2) else v <- 1 # Maximum decomposition level J n <- length(x) J <- ceiling(log(n / (2 * v - 1)) / log(2)) # (Kaiser, 1994) ### decomposition x.mra <- waveslim::mra(xx, wf = wf, J = J, method = "dwt", boundary = "periodic") x.mra.m <- matrix(unlist(x.mra), ncol = J + 1) print(sum(abs(x - rowSums(x.mra.m[1:n, ])))) # additive check var(x) sum(apply(x.mra.m[1:n, ], 2, var)) # variance check limits.x <- c(0, n) limits.y <- c(-3, 3) mra.plot(x, x.mra.m, limits.x, limits.y, type = "details") non.bdy Replace Boundary Wavelet Coefficients with Missing Values (NA). Description Replace Boundary Wavelet Coefficients with Missing Values (NA). Usage non.bdy(x, wf, method = c("dwt", "modwt", "mra")) Arguments x DWT/MODWT/AT object wf Character string; name of wavelet filter method Either dwt or modwt or mra Value Same object as x only with some missing values (NA). References Cornish, C. R., Bretherton, C. S., & Percival, D. B. (2006). Maximal overlap wavelet statistical analysis with application to atmospheric turbulence. Boundary-Layer Meteorology, 119(2), 339- 374. 30 padding obs.mon Sample data: NCEP reanalysis data averaged over Sydney region Description A dataset containing 720 rows (data length) and 7 columns (atmospheric variables). Usage data(obs.mon) padding Padding data to dyadic sample size Description Padding data to dyadic sample size Usage padding(x, pad = c("per", "zero", "sym")) Arguments x A vector or time series containing the data be to decomposed. pad Method for padding, including periodic, zero and symetric padding. Value A dyadic length (power of 2) vector or time series. Examples x <- rnorm(360) x1 <- padding(x, pad = "per") x2 <- padding(x, pad = "zero") x3 <- padding(x, pad = "sym") ts.plot(cbind(x, x1, x2, x3), col = 1:4) r2.boot 31 r2.boot R2 threshold by re-sampling approach Description R2 threshold by re-sampling approach Usage r2.boot(z.vt, x, prob) Arguments z.vt Identified independent variables x Response or dependent variable prob Probability with values in [0,1]. Value A quantile assosciated with prob. rain.mon Sample data: Rainfall station data over Sydney region Description A dataset containing 732 rows (data length) and 15 columns (stations). Usage data(rain.mon) 32 scal2freqR scal2freqM Scale to frequency by Matlab Description Scale to frequency by Matlab Usage scal2freqM(wf, scale, delta) Arguments wf wavelet name scale a scale delta the sampling period. Value A vector of two numbers: frequency and period. Examples delta <- 1 / 12 # monthly data scales <- 2^(1:7) for (wf in c("haar", "d4", "d6", "d8", "d16")[1:5]) { df1 <- scal2freqM(wf, scales, delta) df2 <- scal2freqR(wf, scales, delta) print(cbind(df1$frequency, df2$frequency)) } scal2freqR Scale to frequency by R Description Scale to frequency by R Usage scal2freqR(wf, scale, delta) SPI.12 33 Arguments wf wavelet name scale a scale delta the sampling period. Value A vector of two numbers: frequency and period. Examples delta <- 1 / 12 # monthly data scales <- 2^(1:7) for (wf in c("haar", "d4", "d6", "d8", "d16")[1:5]) { df1 <- scal2freqM(wf, scales, delta) df2 <- scal2freqR(wf, scales, delta) print(cbind(df1$frequency, df2$frequency)) } SPI.12 Sample data: Standardized Precipitation Index with 12 month accu- mulation period. Description A dataset containing 1200 rows (data length) and 252 columns. Usage data(SPI.12) stepwise.VT Calculate stepwise high order VT in calibration Description Calculate stepwise high order VT in calibration 34 stepwise.VT Usage stepwise.VT( data, alpha = 0.1, nvarmax = 4, mode = c("MRA", "MODWT", "AT"), wf, J, method = "dwt", pad = "zero", boundary = "periodic", cov.opt = "auto", flag = "biased", detrend = FALSE ) Arguments data A list of data, including response and predictors alpha The significance level used to judge whether the sample estimate is significant. A default alpha value is 0.1. nvarmax The maximum number of variables to be selected. mode A mode of variance transformation, i.e., MRA, MODWT, or AT wf Wavelet family J Specifies the depth of the decomposition. This must be a number less than or equal to log(length(x),2). method Either "dwt" or "modwt" of MRA. pad The method used for extend data to dyadic size. Use "per", "zero", or "sym". boundary Character string specifying the boundary condition. If boundary=="periodic" the default, then the vector you decompose is assumed to be periodic on its defined interval, if boundary=="reflection", the vector beyond its boundaries is assumed to be a symmetric reflection of itself. cov.opt Options of Covariance matrix sign. Use "pos", "neg", or "auto". flag Biased or Unbiased variance transformation. detrend Detrend the input time series or just center, default (F). Value A list of 2 elements: the column numbers of the meaningful predictors (cpy), and partial informa- tional correlation (cpyPIC). References Sharma, A., Mehrotra, R., 2014. An information theoretic alternative to model a natural system using observational information alone. Water Resources Research, 50(1): 650-660. Jiang, Z., Sharma, A., & Johnson, F. (2021). Variable transformations in the spectral domain – Implications for hydrologic forecasting. Journal of Hydrology, 126816. stepwise.VT.val 35 Examples ### Real-world example data("rain.mon") data("obs.mon") mode <- switch(1, "MRA", "MODWT", "AT" ) wf <- "d4" station.id <- 5 # station to investigate SPI.12 <- SPEI::spi(rain.mon, scale = 12)$fitted lab.names <- colnames(obs.mon) # plot.ts(SPI.12[,1:10]) x <- window(SPI.12[, station.id], start = c(1950, 1), end = c(1979, 12)) dp <- window(obs.mon[, lab.names], start = c(1950, 1), end = c(1979, 12)) data <- list(x = x, dp = matrix(dp, ncol = ncol(dp))) dwt <- stepwise.VT(data, mode = mode, wf = wf, flag = "biased") ### plot transformed predictor before and after cpy <- dwt$cpy op <- par(mfrow = c(length(cpy), 1), mar = c(2, 3, 2, 1)) for (i in seq_along(cpy)) { ts.plot(cbind(dwt$dp[, i], dwt$dp.n[, i]), xlab = "NA", col = 1:2) } par(op) stepwise.VT.val Calculate stepwise high order VT in validation Description Calculate stepwise high order VT in validation Usage stepwise.VT.val(data, J, dwt, mode = c("MRA", "MODWT", "AT"), detrend = FALSE) Arguments data A list of data, including response and predictors J Specifies the depth of the decomposition. This must be a number less than or equal to log(length(x),2). dwt Output from dwt.vt(), including the transformation covariance mode A mode of variance transformation, i.e., MRA, MODWT, or AT detrend Detrend the input time series or just center, default (F) 36 wave.var Value A list of objects, including transformed predictors Examples ### Real-world example data("rain.mon") data("obs.mon") mode <- switch(1, "MRA", "MODWT", "a trous" ) wf <- "d4" station.id <- 5 # station to investigate SPI.12 <- SPEI::spi(rain.mon, scale = 12)$fitted lab.names <- colnames(obs.mon) # plot.ts(SPI.12[,1:10]) #-------------------------------------- ### calibration x <- window(SPI.12[, station.id], start = c(1950, 1), end = c(1979, 12)) dp <- window(obs.mon[, lab.names], start = c(1950, 1), end = c(1979, 12)) data <- list(x = x, dp = matrix(dp, ncol = ncol(dp))) dwt <- stepwise.VT(data, mode = mode, wf = wf, flag = "biased") cpy <- dwt$cpy #-------------------------------------- ### validation x <- window(SPI.12[, station.id], start = c(1980, 1), end = c(2009, 12)) dp <- window(obs.mon[, lab.names], start = c(1980, 1), end = c(2009, 12)) data.n <- list(x = x, dp = matrix(dp, ncol = ncol(dp))) dwt.val <- stepwise.VT.val(data = data.n, dwt = dwt, mode = mode) ### plot transformed predictor before and after op <- par(mfrow = c(length(cpy), 1), mar = c(0, 3, 2, 1)) for (i in seq_along(cpy)) { ts.plot(cbind(dwt.val$dp[, i], dwt.val$dp.n[, i]), xlab = "NA", col = 1:2) } par(op) wave.var Produces an estimate of the multiscale variance along with approxi- mate confidence intervals. Description Produces an estimate of the multiscale variance along with approximate confidence intervals. wave.var 37 Usage wave.var(x, type = "eta3", p = 0.025) Arguments x DWT/MODWT/AT object type character string describing confidence interval calculation; valid methods are gaussian, eta1, eta2, eta3, nongaussian p (one minus the) two-sided p-value for the confidence interval Value Dataframe with as many rows as levels in the wavelet transform object. The first column provides the point estimate for the wavelet variance followed by the lower and upper bounds from the confi- dence interval. References Percival, D. B. (1995) Biometrika, 82, No. 3, 619-631. Index ∗ datasets lat_lon.2.5, 24 aus.coast, 8 data.AWAP.2.5, 8 modwt.vt, 24 data.CI, 9 modwt.vt.val, 26 data.HL, 16 mra.plot, 28 data.SW1, 16 data.SW3, 16 non.bdy, 29 Ind_AWAP.2.5, 21 obs.mon, 30 lat_lon.2.5, 24 obs.mon, 30 padding, 30 rain.mon, 31 SPI.12, 33 r2.boot, 31 rain.mon, 31 at.vt, 4 at.vt.val, 5 scal2freqM, 32 at.wd, 7 scal2freqR, 32 aus.coast, 8 SPI.12, 33 stepwise.VT, 33 data.AWAP.2.5, 8 stepwise.VT.val, 35 data.CI, 9 data.gen.ar1, 9 WASP-package, 3 data.gen.ar4, 10 wave.var, 36 data.gen.ar9, 10 data.gen.HL, 11 data.gen.Rossler, 12 data.gen.SW, 13 data.gen.tar1, 14 data.gen.tar2, 15 data.HL, 16 data.SW1, 16 data.SW3, 16 dwt.vt, 17 dwt.vt.val, 18 fig.dwt.vt, 20 Ind_AWAP.2.5, 21 knn, 21 knnregl1cv, 23 38

mplot

cran

Package ‘mplot’ October 13, 2022 Type Package Title Graphical Model Stability and Variable Selection Procedures Version 1.0.6 Date 2021-07-10 Description Model stability and variable inclusion plots [Mueller and Welsh (2010, <doi:10.1111/j.1751-5823.2010.00108.x>); Murray, Heritier and Mueller (2013, <doi:10.1002/sim.5855>)] as well as the adaptive fence [Jiang et al. (2008, <doi:10.1214/07-AOS517>); Jiang et al. (2009, <doi:10.1016/j.spl.2008.10.014>)] for linear and generalised linear models. License GPL (>= 2) Suggests knitr, mvoutlier, glmulti, rmarkdown, DT, MASS Imports leaps, foreach, parallel, bestglm, doParallel, doRNG, plyr, shinydashboard, shiny, glmnet, graphics, stats, googleVis, ggplot2, reshape2, scales, dplyr, tidyr, magrittr URL https://garthtarr.github.io/mplot/, https://github.com/garthtarr/mplot LazyData TRUE RoxygenNote 7.1.1 Encoding UTF-8 NeedsCompilation no Author Garth Tarr [aut, cre] (<https://orcid.org/0000-0002-6605-7478>), Samuel Mueller [aut] (<https://orcid.org/0000-0002-3087-8127>), Alan H Welsh [aut] (<https://orcid.org/0000-0002-3165-9559>) Maintainer Garth Tarr <garth.tarr@gmail.com> Repository CRAN Date/Publication 2021-07-10 16:20:02 UTC 1 2 mplot-package R topics documented: mplot-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 af . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 artificialeg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 bglmnet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 bodyfat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 diabetes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 fev . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 glmfence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 lmfence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 mplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 plot.af . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 plot.bglmnet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 plot.vis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 print.af . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 print.vis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 process.fn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 summary.af . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 vis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 wallabies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Index 26 mplot-package Graphical model stability and model selection procedures Description Graphical model stability and model selection procedures References Tarr G, Mueller S and Welsh AH (2018). mplot: An R Package for Graphical Model Stabil- ity and Variable Selection Procedures. Journal of Statistical Software, 83(9), pp. 1-28. doi: 10.18637/jss.v083.i09 af 3 af The adaptive fence procedure Description This function implements the adaptive fence procedure to first find the optimal cstar value and then finds the corresponding best model as described in Jiang et. al. (2009) with some practical modifications. Usage af( mf, B = 60, n.c = 20, initial.stepwise = FALSE, force.in = NULL, cores, nvmax, c.max, screen = FALSE, seed = NULL, ... ) Arguments mf a fitted ’full’ model, the result of a call to lm or glm (and in the future lme or lmer). B number of bootstrap replications at each fence boundary value n.c number of boundary values to be considered initial.stepwise logical. Performs an initial stepwise procedure to look for the range of model sizes where attention should be focussed. See details for implementation. force.in the names of variables that should be forced into all estimated models cores number of cores to be used when parallel processing the bootstrap nvmax size of the largest model that can still be considered as a viable candidate. In- cluded for performance reasons but if it is an active constraint it could lead to misleading results. c.max manually specify the upper boundary limit. Only applies when initial.stepwise=FALSE. screen logical, whether or not to perform an initial screen for outliers. Highly experi- mental, use at own risk. Default = FALSE. seed random seed for reproducible results ... further arguments (currently unused) 4 af Details The initial stepwise procedure performs forward stepwise model selection using the AIC and back- ward stepwise model selection using BIC. In general the backwise selection via the more conser- vative BIC will tend to select a smaller model than that of the forward selection AIC approach. The size of these two models is found, and we go two dimensions smaller and larger to estimate a sensible range of c values over which to perform a parametric bootstrap. This procedure can take some time. It is recommended that you start with a relatively small number of bootstrap samples (B) and grid of boundary values (n.c) and increase both as required. If you use initial.stepwise=TRUE then in general you will need a smaller grid of boundary values than if you select initial.stepwise=FALSE. It can be useful to check initial.stepwise=FALSE with a small number of bootstrap replications over a sparse grid to ensure that the initial.stepwise=TRUE has landed you in a reasonable region. The best.only=FALSE option when plotting the results of the adaptive fence is a modification to the adaptive fence procedure which considers all models at a particular size that pass the fence hurdle when calculating the p* values. In particular, for each value of c and at each bootstrap replication, if a candidate model is found that passes the fence, then we look to see if there are any other models of the same size that also pass the fence. If no other models of the same size pass the fence, then that model is allocated a weight of 1. If there are two models that pass the fence, then the best model is allocated a weight of 1/2. If three models pass the fence, the best model gets a weight of 1/3, and so on. After B bootstrap replications, we aggregate the weights by summing over the various models. The p* value is the maximum aggregated weight divided by the number of bootstrap replications. This correction penalises the probability associated with the best model if there were other models of the same size that also passed the fence hurdle. The rationale being that if a model has no redundant variables then it will be the only model at that size that passes the fence over a range of values of c. The result is more pronounced peaks which can help to determine the location of the correct peak and identify the optimal c*. See ?plot.af or help("plot.af") for details of the plot method associated with the result. References Jiang J., Nguyen T., Sunil Rao J. (2009), A simplified adaptive fence procedure, Statistics & Prob- ability Letters, 79(5):625-629. doi: 10.1016/j.spl.2008.10.014 Jiang J., Sunil Rao J., Gu Z, Nguyen T. (2008), Fence methods for mixed model selection, Annals of Statistics, 36(4):1669-1692. doi: 10.1214/07-AOS517 See Also plot.af Other fence: glmfence(), lmfence() Examples n = 100 set.seed(11) e = rnorm(n) x1 = rnorm(n) x2 = rnorm(n) artificialeg 5 x3 = x1^2 x4 = x2^2 x5 = x1*x2 y = 1 + x1 + x2 + e dat = data.frame(y,x1,x2,x3,x4,x5) lm1 = lm(y ~ ., data = dat) ## Not run: af1 = af(lm1, initial.stepwise = TRUE, seed = 1) summary(af1) plot(af1) ## End(Not run) artificialeg Artificial example Description An artificial data set which causes stepwise regression procedures to select a non-parsimonious model. The true model is a simple linear regression of y against x8. Usage data(artificialeg) Format A data frame with 50 observations on 10 variables. Details Inspired by the pathoeg data set in the MPV pacakge. Examples data(artificialeg) full.mod = lm(y~.,data=artificialeg) step(full.mod) # generating model n=50 set.seed(8) # a seed of 2 also works x1 = rnorm(n,0.22,2) x7 = 0.5*x1 + rnorm(n,0,sd=2) x6 = -0.75*x1 + rnorm(n,0,3) x3 = -0.5-0.5*x6 + rnorm(n,0,2) x9 = rnorm(n,0.6,3.5) x4 = 0.5*x9 + rnorm(n,0,sd=3) x2 = -0.5 + 0.5*x9 + rnorm(n,0,sd=2) x5 = -0.5*x2+0.5*x3+0.5*x6-0.5*x9+rnorm(n,0,1.5) 6 bglmnet x8 = x1 + x2 -2*x3 - 0.3*x4 + x5 - 1.6*x6 - 1*x7 + x9 +rnorm(n,0,0.5) y = 0.6*x8 + rnorm(n,0,2) artificialeg = round(data.frame(x1,x2,x3,x4,x5,x6,x7,x8,x9,y),1) bglmnet Model stability and variable importance plots for glmnet Description Model stability and variable importance plots for glmnet Usage bglmnet( mf, nlambda = 100, lambda = NULL, B = 100, penalty.factor, screen = FALSE, redundant = TRUE, cores = NULL, force.in = NULL, seed = NULL ) Arguments mf a fitted ’full’ model, the result of a call to lm or glm. nlambda how many penalty values to consider. Default = 100. lambda manually specify the penalty values (optional). B number of bootstrap replications penalty.factor Separate penalty factors can be applied to each coefficient. This is a number that multiplies lambda to allow differential shrinkage. Can be 0 for some variables, which implies no shrinkage, and that variable is always included in the model. Default is 1 for all variables (and implicitly infinity for variables listed in ex- clude). Note: the penalty factors are internally rescaled to sum to nvars, and the lambda sequence will reflect this change. screen logical, whether or not to perform an initial screen for outliers. Highly experi- mental, use at own risk. Default = FALSE. redundant logical, whether or not to add a redundant variable. Default = TRUE. cores number of cores to be used when parallel processing the bootstrap (Not yet implemented.) force.in the names of variables that should be forced into all estimated models. (Not yet implemented.) seed random seed for reproducible results bodyfat 7 Details The result of this function is essentially just a list. The supplied plot method provides a way to visualise the results. See Also plot.bglmnet Examples n = 100 set.seed(11) e = rnorm(n) x1 = rnorm(n) x2 = rnorm(n) x3 = x1^2 x4 = x2^2 x5 = x1*x2 y = 1 + x1 + x2 + e dat = data.frame(y, x1, x2, x3, x4, x5) lm1 = lm(y ~ ., data = dat) ## Not run: bg1 = bglmnet(lm1, seed = 1) # plot(bg1, which = "boot_size", interactive = TRUE) plot(bg1, which = "boot_size", interactive = FALSE) # plot(bg1, which = "vip", interactive = TRUE) plot(bg1, which = "vip", interactive = FALSE) ## End(Not run) bodyfat Body fat data set Description A data frame with 128 observations on 15 variables. Usage data(bodyfat) Format A data frame with 128 observations on 15 variables. Id Identifier Bodyfat Bodyfat percentage 8 diabetes Age Age (years) Weight Weight (kg) Height Height (inches) Neck Neck circumference (cm) Chest Chest circumference (cm) Abdo Abdomen circumference (cm) "at the umbilicus and level with the iliac crest" Hip Hip circumference (cm) Thigh Thigh circumference (cm) Knee Knee circumference (cm) Ankle Ankle circumference (cm) Bic Extended biceps circumference (cm) Fore Forearm circumference (cm) Wrist Wrist circumference (cm) "distal to the styloid processes" Details A subset of the 252 observations available in the mfp package. The selected observations avoid known high leverage points and outliers. The unused points from the data set could be used to validate selected models. References Johnson W (1996, Vol 4). Fitting percentage of body fat to simple body measurements. Journal of Statistics Education. Bodyfat data retrieved from http://www.amstat.org/publications/jse/v4n1/datasets.johnson.html An expanded version is included in the mfp R package. Examples data(bodyfat) full.mod = lm(Bodyfat~.,data=subset(bodyfat,select=-Id)) diabetes Blood and other measurements in diabetics Description The diabetes data frame has 442 rows and 11 columns. These are the data used in Efron et al. (2004). Usage data(diabetes) fev 9 Format A data frame with 442 observations on 11 variables. age Age sex Gender bmi Body mass index map Mean arterial pressure (average blood pressure) tc Total cholesterol (mg/dL)? Desirable range: below 200 mg/dL ldl Low-density lipoprotein ("bad" cholesterol)? Desirable range: below 130 mg/dL hdl High-density lipoprotein ("good" cholesterol)? Desirable range: above 40 mg/dL tch Blood serum measurement ltg Blood serum measurement glu Blood serum measurement (glucose?) y A quantitative measure of disease progression one year after baseline Details Data sourced from http://web.stanford.edu/~hastie/Papers/LARS References Efron, B., Hastie, T., Johnstone, I., Tibshirani, R., (2004). Least angle regression. The Annals of Statistics 32(2) 407-499. DOI: 10.1214/009053604000000067 Examples data(diabetes) full.mod = lm(y~.,data=diabetes) fev Forced Expiratory Volume Description This data set consists of 654 observations on youths aged 3 to 19 from East Boston recorded duing the middle to late 1970’s. Forced expiratory volume (FEV), a measure of lung capacity, is the vari- able of interest. Age and height are two continuous predictors. Sex and smoke are two categorical predictors. Usage data(fev) 10 glmfence Format A data frame with 654 observations on 5 variables. age Age (years) fev Forced expiratory volume (liters). Roughly the amount of air an individual can exhale in the first second of a forceful breath. height Height (inches). sex Female is 0. Male is 1. smoke A binary variable indicating whether or not the youth smokes. Nonsmoker is 0. Smoker is 1. Details Copies of this data set can also be found in the coneproj and tmle packages. References Tager, I. B., Weiss, S. T., Rosner, B., and Speizer, F. E. (1979). Effect of parental cigarette smoking on pulmonary function in children. American Journal of Epidemiology, 110, 15-26. Rosner, B. (1999). Fundamentals of Biostatistics, 5th Ed., Pacific Grove, CA: Duxbury. Kahn, M.J. (2005). An Exhalent Problem for Teaching Statistics. Journal of Statistics Education, 13(2). http://www.amstat.org/publications/jse/v13n2/datasets.kahn.html Examples data(fev) full.mod = lm(fev~.,data=fev) step(full.mod) glmfence The fence procedure for generalised linear models Description This function implements the fence procedure to find the best generalised linear model. Usage glmfence(mf, cstar, nvmax, adaptive = TRUE, trace = TRUE, ...) lmfence 11 Arguments mf an object of class glm specifying the full model. cstar the boundary of the fence, typically found through bootstrapping. nvmax the maximum number of variables that will be be considered in the model. adaptive logical. If TRUE the boundary of the fence is given by cstar. Otherwise, it the original (non-adaptive) fence is performed where the boundary is cstar*hat(sigma)_M,tildeM. trace logical. If TRUE the function prints out its progress as it iterates up through the dimensions. ... further arguments (currently unused) References Jiming Jiang, Thuan Nguyen, J. Sunil Rao, A simplified adaptive fence procedure, Statistics & Prob- ability Letters, Volume 79, Issue 5, 1 March 2009, Pages 625-629, http://dx.doi.org/10.1016/j.spl.2008.10.014. See Also af, lmfence Other fence: af(), lmfence() lmfence The fence procedure for linear models Description This function implements the fence procedure to find the best linear model. Usage lmfence(mf, cstar, nvmax, adaptive = TRUE, trace = TRUE, force.in = NULL, ...) Arguments mf an object of class lm specifying the full model. cstar the boundary of the fence, typically found through bootstrapping. nvmax the maximum number of variables that will be be considered in the model. adaptive logical. If TRUE the boundary of the fence is given by cstar. Otherwise, it the original (non-adaptive) fence is performed where the boundary is cstar*hat(sigma)_M,tildeM. trace logical. If TRUE the function prints out its progress as it iterates up through the dimensions. force.in the names of variables that should be forced into all estimated models. ... further arguments (currently unused) 12 mplot References Jiming Jiang, Thuan Nguyen, J. Sunil Rao, A simplified adaptive fence procedure, Statistics & Prob- ability Letters, Volume 79, Issue 5, 1 March 2009, Pages 625-629, http://dx.doi.org/10.1016/j.spl.2008.10.014. See Also af, glmfence Other fence: af(), glmfence() Examples n = 40 # sample size beta = c(1,2,3,0,0) K=length(beta) set.seed(198) X = cbind(1,matrix(rnorm(n*(K-1)),ncol=K-1)) e = rnorm(n) y = X%*%beta + e dat = data.frame(y,X[,-1]) # Non-adaptive approach (not recommended) lm1 = lm(y~.,data=dat) lmfence(lm1,cstar=log(n),adaptive=FALSE) mplot Model selection and stability curves Description Opens a shiny GUI to investigate a range of model selection and stability issues Usage mplot(mf, ...) Arguments mf a fitted model. ... objects of type vis or af or bglmnet. References Tarr G, Mueller S and Welsh AH (2018). mplot: An R Package for Graphical Model Stabil- ity and Variable Selection Procedures. Journal of Statistical Software, 83(9), pp. 1-28. doi: 10.18637/jss.v083.i09 plot.af 13 Examples n = 100 set.seed(11) e = rnorm(n) x1 = rnorm(n) x2 = rnorm(n) x3 = x1^2 x4 = x2^2 x5 = x1*x2 y = 1 + x1 + x2 + e dat = round(data.frame(y,x1,x2,x3,x4,x5),2) lm1 = lm(y ~ ., data = dat) ## Not run: v1 = vis(lm1) af1 = af(lm1) bg1 = bglmnet(lm1) mplot(lm1, v1, af1, bg1) ## End(Not run) plot.af Plot diagnostics for an af object Description Summary plot of the bootstrap results of an af object. Usage ## S3 method for class 'af' plot( x, pch, interactive = FALSE, classic = NULL, tag = NULL, shiny = FALSE, best.only = FALSE, width = 800, height = 400, fontSize = 12, left = 50, top = 30, chartWidth = "60%", chartHeight = "80%", backgroundColor = "transparent", legend.position = "top", 14 plot.af model.wrap = NULL, legend.space = NULL, options = NULL, ... ) Arguments x af object, the result of af pch plotting character, i.e., symbol to use interactive logical. If interactive=TRUE a googleVis plot is provided instead of the base graphics plot. Default is interactive=FALSE. classic logical. Depricated. If classic=TRUE a base graphics plot is provided instead of a googleVis plot. For now specifying classic will overwrite the default interactive behaviour, though this is likely to be removed in the future. tag Default NULL. Name tag of the objects to be extracted from a gvis (googleVis) object. The default tag for is NULL, which will result in R opening a browser window. Setting tag='chart' or setting options(gvis.plot.tag='chart') is useful when googleVis is used in scripts, like knitr or rmarkdown. shiny Default FALSE. Set to TRUE when using in a shiny interface. best.only logical determining whether the output used the standard fence approach of only considering the best models that pass the fence (TRUE) or if it should take into account all models that pass the fence at each boundary value (FALSE). width Width of the googleVis chart canvas area, in pixels. Default: 800. height Height of the googleVis chart canvas area, in pixels. Default: 400. fontSize font size used in googleVis chart. Default: 12. left space at left of chart (pixels?). Default: "50". top space at top of chart (pixels?). Default: "30". chartWidth googleVis chart area width. A simple number is a value in pixels; a string con- taining a number followed by % is a percentage. Default: "60%" chartHeight googleVis chart area height. A simple number is a value in pixels; a string containing a number followed by % is a percentage. Default: "80%" backgroundColor The background colour for the main area of the chart. A simple HTML color string, for example: ’red’ or ’#00cc00’. Default: ’transparent’ legend.position legend position, e.g. "topleft" or "bottomright" model.wrap Optional parameter to split the legend names if they are too long for classic plots. model.wrap=2 means that there will be two variables per line, model.wrap=2 gives three variables per line and model.wrap=4 gives 4 variables per line. legend.space Optional parameter to add additional space between the legend items for the classic plot. options If you want to specify the full set of googleVis options. ... further arguments (currently unused) plot.bglmnet 15 Details For each value of c a parametric bootstrap is performed under the full model. For each bootstrap sample we identify the smallest model inside the fence, α̂(c). We calculate the empirical probability of selecting model α for a given value of c as p∗ (c, α) = P ∗ {α̂(c) = α}. Hence, if B bootstrap replications are performed, p∗ (c, α) is the proportion of times that model α is selected. Finally, define an overall selection probability, p∗ (c) = max p∗ (c, α) α∈A and we plot p∗ (c) against c. The points on the scatter plot are colour coded by the model that yielded the highest inclusion probability. plot.bglmnet Plot diagnostics for a bglmnet object Description A plot method to visualise the results of a bglmnet object. Usage ## S3 method for class 'bglmnet' plot( x, highlight, interactive = FALSE, classic = NULL, tag = NULL, shiny = FALSE, which = c("vip", "boot", "boot_size"), width = 800, height = 400, fontSize = 12, left = 50, top = 30, chartWidth = "60%", chartHeight = "80%", axisTitlesPosition = "out", dataOpacity = 0.5, options = NULL, hAxis.logScale = TRUE, ylim, text = FALSE, backgroundColor = "transparent", 16 plot.bglmnet legend.position = "right", jitterk = 0.1, srt = 45, max.circle = 15, min.prob = 0.1, ... ) Arguments x bglmnet object, the result of bglmnet highlight the name of a variable that will be highlighted. interactive logical. If interactive=TRUE a googleVis plot is provided instead of the base graphics plot. Default is interactive=FALSE. classic logical. Depricated. If classic=TRUE a base graphics plot is provided instead of a googleVis plot. For now specifying classic will overwrite the default interactive behaviour, though this is likely to be removed in the future. tag Default NULL. Name tag of the objects to be extracted from a gvis (googleVis) object. The default tag for is NULL, which will result in R opening a browser window. Setting tag='chart' or setting options(gvis.plot.tag='chart') is useful when googleVis is used in scripts, like knitr or rmarkdown. shiny Default FALSE. Set to TRUE when using in a shiny interface. which a vector specifying the plots to be output. Variable inclusion type plots which = "vip" or plots where the size of the point representing each model is pro- portional to selection probabilities by model size which = "boot_size" or by penalty paramter which = "boot". width Width of the googleVis chart canvas area, in pixels. Default: 800. height Height of the googleVis chart canvas area, in pixels. Default: 400. fontSize font size used in googleVis chart. Default: 12. left space at left of chart (pixels?). Default: "50". top space at top of chart (pixels?). Default: "30". chartWidth googleVis chart area width. A simple number is a value in pixels; a string con- taining a number followed by % is a percentage. Default: "60%" chartHeight googleVis chart area height. A simple number is a value in pixels; a string containing a number followed by % is a percentage. Default: "80%" axisTitlesPosition Where to place the googleVis axis titles, compared to the chart area. Supported values: "in" - Draw the axis titles inside the the chart area. "out" - Draw the axis titles outside the chart area. "none" - Omit the axis titles. dataOpacity The transparency of googleVis data points, with 1.0 being completely opaque and 0.0 fully transparent. options a list to be passed to the googleVis function giving complete control over the output. Specifying a value for options overwrites all other plotting variables. plot.vis 17 hAxis.logScale logical, whether or not to use a log scale on the horizontal axis. Default = TRUE. ylim the y limits of the which="boot" plots. text logical, whether or not to add text labels to classic boot plot. Default = FALSE. backgroundColor The background colour for the main area of the chart. A simple HTML color string, for example: ’red’ or ’#00cc00’. Default: ’transparent’ legend.position the postion of the legend for classic plots. Default legend.position="right" alternatives include legend.position="top" and legend.position="bottom" jitterk amount of jittering of the model size in the lvk and boot plots. Default = 0.1. srt when text=TRUE, the angle of rotation for the text labels. Default = 45. max.circle determines the maximum circle size. Default = 15. min.prob lower bound on the probability of a model being selected. If a model has a selection probability lower than min.prob it will not be plotted. ... further arguments (currently unused) See Also bglmnet plot.vis Plot diagnostics for a vis object Description A plot method to visualise the results of a vis object. Usage ## S3 method for class 'vis' plot( x, highlight, interactive = FALSE, classic = NULL, tag = NULL, shiny = FALSE, nbest = "all", which = c("vip", "lvk", "boot"), width = 800, height = 400, fontSize = 12, left = 50, top = 30, chartWidth = "60%", 18 plot.vis chartHeight = "80%", axisTitlesPosition = "out", dataOpacity = 0.5, options = NULL, ylim, legend.position = "right", backgroundColor = "transparent", text = FALSE, min.prob = 0.4, srt = 45, max.circle = 15, print.full.model = FALSE, jitterk = 0.1, seed = NULL, ... ) Arguments x vis object, the result of vis highlight the name of a variable that will be highlighted interactive logical. If interactive=TRUE a googleVis plot is provided instead of the base graphics plot. Default is interactive=FALSE. classic logical. Depricated. If classic=TRUE a base graphics plot is provided instead of a googleVis plot. For now specifying classic will overwrite the default interactive behaviour, though this is likely to be removed in the future. tag Default NULL. Name tag of the objects to be extracted from a gvis (googleVis) object. The default tag for is NULL, which will result in R opening a browser window. Setting tag='chart' or setting options(gvis.plot.tag='chart') is useful when googleVis is used in scripts, like knitr or rmarkdown. shiny Default FALSE. Set to TRUE when using in a shiny interface. nbest maximum number of models at each model size that will be considered for the lvk plot. Can also take a value of "all" which displays all models (default). which a vector specifying the plots to be output. Variable inclusion plots which="vip"; description loss against model size which="lvk"; bootstrapped description loss against model size which="boot". width Width of the googleVis chart canvas area, in pixels. Default: 800. height Height of the googleVis chart canvas area, in pixels. Default: 400. fontSize font size used in googleVis chart. Default: 12. left space at left of chart (pixels?). Default: "50". top space at top of chart (pixels?). Default: "30". chartWidth googleVis chart area width. A simple number is a value in pixels; a string con- taining a number followed by % is a percentage. Default: "60%" plot.vis 19 chartHeight googleVis chart area height. A simple number is a value in pixels; a string containing a number followed by % is a percentage. Default: "80%" axisTitlesPosition Where to place the googleVis axis titles, compared to the chart area. Supported values: "in" - Draw the axis titles inside the the chart area. "out" - Draw the axis titles outside the chart area. "none" - Omit the axis titles. dataOpacity The transparency of googleVis data points, with 1.0 being completely opaque and 0.0 fully transparent. options a list to be passed to the googleVis function giving complete control over the output. Specifying a value for options overwrites all other plotting variables. ylim the y limits of the lvk and boot plots. legend.position the postion of the legend for classic plots. Default legend.position="right" alternatives include legend.position="top" and legend.position="bottom" backgroundColor The background colour for the main area of the chart. A simple HTML color string, for example: ’red’ or ’#00cc00’. Default: ’null’ (there is an issue with GoogleCharts when setting ’transparent’ related to the zoom window sticking - once that’s sorted out, the default will change back to ’transparent’) text logical, whether or not to add text labels to classic boot plot. Default = FALSE. min.prob when text=TRUE, a lower bound on the probability of selection before a text label is shown. srt when text=TRUE, the angle of rotation for the text labels. Default = 45. max.circle determines the maximum circle size. Default = 15. print.full.model logical, when text=TRUE this determines if the full model gets a label or not. Default=FALSE. jitterk amount of jittering of the model size in the lvk and boot plots. Default = 0.1. seed random seed for reproducible results ... further arguments (currently unused) Details Specifying which = "lvk" generates a scatter plot where the points correspond to description loss is plot against model size for each model considered. The highlight argument is used to differentiate models that contain a particular variable from those that do not. Specifying which = "boot" generates a scatter plot where each circle represents a model with a non-zero bootstrap probability, that is, each model that was selected as the best model of a partic- ular dimension in at least one bootstrap replication. The area of each circle is proportional to the corresponding model’s bootstrapped selection probability. References Mueller, S. and Welsh, A. H. (2010), On model selection curves. International Statistical Review, 78:240-256. doi: 10.1111/j.1751-5823.2010.00108.x 20 print.af Murray, K., Heritier, S. and Mueller, S. (2013), Graphical tools for model selection in generalized linear models. Statistics in Medicine, 32:4438-4451. doi: 10.1002/sim.5855 Tarr G, Mueller S and Welsh AH (2018). mplot: An R Package for Graphical Model Stabil- ity and Variable Selection Procedures. Journal of Statistical Software, 83(9), pp. 1-28. doi: 10.18637/jss.v083.i09 See Also vis Examples n = 100 set.seed(11) e = rnorm(n) x1 = rnorm(n) x2 = rnorm(n) x3 = x1^2 x4 = x2^2 x5 = x1*x2 y = 1 + x1 + x2 + e dat = data.frame(y,x1,x2,x3,x4,x5) lm1 = lm(y~.,data=dat) ## Not run: v1 = vis(lm1, seed = 1) plot(v1, highlight = "x1", which = "lvk") plot(v1, which = "boot") plot(v1, which = "vip") ## End(Not run) print.af Print method for an af object Description Prints basic output of the bootstrap results of an af object. Usage ## S3 method for class 'af' print(x, best.only = TRUE, ...) print.vis 21 Arguments x an af object, the result of af best.only logical determining whether the output used the standard fence approach of only considering the best models that pass the fence (TRUE) or if it should take into account all models that pass the fence at each boundary value (FALSE). ... further arguments (currently unused) print.vis Print method for a vis object Description Prints basic output of the bootstrap results of an vis object. Usage ## S3 method for class 'vis' print(x, min.prob = 0.3, print.full.model = FALSE, ...) Arguments x a vis object, the result of vis min.prob a lower bound on the probability of selection before the result is printed print.full.model logical, determines if the full model gets printed or not. Default=FALSE. ... further arguments (currently unused) process.fn Process results within af function Description This function is used by the af function to process the results when iterating over different boundary values Usage process.fn(fence.mod, fence.rank) Arguments fence.mod set of fence models fence.rank set of fence model ranks 22 vis summary.af Summary method for an af object Description Provides comprehensive output of the bootstrap results of an af object. Usage ## S3 method for class 'af' summary(object, best.only = TRUE, ...) Arguments object af object, the result of af best.only logical determining whether the output used the standard fence approach of only considering the best models that pass the fence (TRUE) or if it should take into account all models that pass the fence at each boundary value (FALSE). ... further arguments (currently unused) vis Model stability and variable inclusion plots Description Calculates and provides the plot methods for standard and bootstrap enhanced model stability plots (lvk and boot) as well as variable inclusion plots (vip). Usage vis( mf, nvmax, B = 100, lambda.max, nbest = "all", use.glmulti = FALSE, cores, force.in = NULL, screen = FALSE, redundant = TRUE, seed = NULL, ... ) vis 23 Arguments mf a fitted ’full’ model, the result of a call to lm or glm (and in the future lme or lmer) nvmax size of the largest model that can still be considered as a viable candidate B number of bootstrap replications lambda.max maximum penalty value for the vip plot, defaults to 2*log(n) nbest maximum number of models at each model size that will be considered for the lvk plot. Can also take a value of "all" which displays all models. use.glmulti logical. Whether to use the glmulti package instead of bestglm. Default use.glmulti=FALSE. cores number of cores to be used when parallel processing the bootstrap force.in the names of variables that should be forced into all estimated models. (Not yet implemented.) screen logical, whether or not to perform an initial screen for outliers. Highly experi- mental, use at own risk. Default = FALSE. redundant logical, whether or not to add a redundant variable. Default = TRUE. seed random seed for reproducible results ... further arguments (currently unused) Details The result of this function is essentially just a list. The supplied plot method provides a way to visualise the results. See ?plot.vis or help("plot.vis") for details of the plot method associated with the result. References Mueller, S. and Welsh, A. H. (2010), On model selection curves. International Statistical Review, 78:240-256. doi: 10.1111/j.1751-5823.2010.00108.x Murray, K., Heritier, S. and Mueller, S. (2013), Graphical tools for model selection in generalized linear models. Statistics in Medicine, 32:4438-4451. doi: 10.1002/sim.5855 Tarr G, Mueller S and Welsh AH (2018). mplot: An R Package for Graphical Model Stabil- ity and Variable Selection Procedures. Journal of Statistical Software, 83(9), pp. 1-28. doi: 10.18637/jss.v083.i09 See Also plot.vis Examples n = 100 set.seed(11) e = rnorm(n) x1 = rnorm(n) x2 = rnorm(n) 24 wallabies x3 = x1^2 x4 = x2^2 x5 = x1*x2 y = 1 + x1 + x2 + e dat = data.frame(y, x1, x2, x3, x4, x5) lm1 = lm(y ~ ., data = dat) ## Not run: v1 = vis(lm1, seed = 1) plot(v1, highlight = "x1", which = "lvk") plot(v1, which = "boot") plot(v1, which = "vip") ## End(Not run) wallabies Rock-wallabies data set Description On Chalkers Top in the Warrumbungles (NSW, Australia) 200 evenly distributed one metre squared plots were surveyed. Plots were placed at a density of 7-13 per hectare. The presence or absence of fresh (<1 month old) scats of rock-wallabies was recorded for each plot along with location and a selection of predictor variables. Usage data(wallabies) Format A data frame with 200 observations on 9 variables. rw Presence of rock-wallaby scat edible Percentage cover of edible vegetation inedible Percentage cover of inedible vegetation canopy Percentage canopy cover distance Distance from diurnal refuge shelter Whether or not a plot occurred within a shelter point (large rock or boulder pile) lat Latitude of the plot location long Longitude of the plot location wallabies 25 Details Macropods defaecate randomly as they forage and scat (faecal pellet) surveys are a reliable method for detecting the presence of rock-wallabies and other macropods. Scats are used as an indication of spatial foraging patterns of rock-wallabies and sympatric macropods. Scats deposited while foraging were not confused with scats deposited while resting because the daytime refuge areas of rock-wallabies were known in detail for each colony and no samples were taken from those areas. Each of the 200 sites were examined separately to account for the different levels of predation risk and the abundance of rock-wallabies. References Tuft KD, Crowther MS, Connell K, Mueller S and McArthur C (2011), Predation risk and compet- itive interactions affect foraging of an endangered refuge-dependent herbivore. Animal Conserva- tion, 14: 447-457. doi: 10.1111/j.1469-1795.2011.00446.x Examples data(wallabies) wdat = data.frame(subset(wallabies,select=-c(lat,long)), EaD = wallabies$edible*wallabies$distance, EaS = wallabies$edible*wallabies$shelter, DaS = wallabies$distance*wallabies$shelter) M1 = glm(rw~., family = binomial(link = "logit"), data = wdat) Index ∗ Internal process.fn, 21 glmfence, 10 lmfence, 11 summary.af, 22 process.fn, 21 ∗ datasets vis, 18, 20, 21, 22 artificialeg, 5 wallabies, 24 bodyfat, 7 diabetes, 8 fev, 9 wallabies, 24 ∗ fence af, 3 glmfence, 10 lmfence, 11 ∗ package mplot-package, 2 af, 3, 11, 12, 14, 21, 22 artificialeg, 5 bglmnet, 6, 16, 17 bodyfat, 7 diabetes, 8 fev, 9 glm, 11 glmfence, 4, 10, 12 lm, 11 lmfence, 4, 11, 11 mplot, 12 mplot-package, 2 plot.af, 4, 13 plot.bglmnet, 7, 15 plot.vis, 17, 23 print.af, 20 print.vis, 21 26

scorecard

cran

Package ‘scorecard’ August 8, 2023 Version 0.4.3 Title Credit Risk Scorecard Description The `scorecard` package makes the development of credit risk scorecard easier and efficient by providing functions for some common tasks, such as data partition, variable selection, woe binning, scorecard scaling, performance evaluation and report generation. These functions can also used in the development of machine learning models. The references including: 1. Refaat, M. (2011, ISBN: 9781447511199). Credit Risk Scorecard: Development and Implementation Using SAS. 2. Siddiqi, N. (2006, ISBN: 9780471754510). Credit risk scorecards. Developing and Implementing Intelligent Credit Scoring. Depends R (>= 3.5.0) Imports data.table (>= 1.10.0), ggplot2, gridExtra, foreach, doParallel, parallel, openxlsx, stringi, cli, xml2, xefun (>= 0.1.3) Suggests knitr, rmarkdown, pkgdown, testthat License MIT + file LICENSE URL https://github.com/ShichenXie/scorecard, http://shichen.name/scorecard/ BugReports https://github.com/ShichenXie/scorecard/issues LazyData true VignetteBuilder knitr RoxygenNote 7.2.3 Encoding UTF-8 NeedsCompilation no Author Shichen Xie [aut, cre] Maintainer Shichen Xie <xie@shichen.name> Repository CRAN Date/Publication 2023-08-08 14:40:02 UTC 1 2 describe R topics documented: describe . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 gains_table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 germancredit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 iv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 one_hot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 perf_cv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 perf_eva . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 perf_psi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 replace_na . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 scorecard . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 scorecard2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 scorecard_ply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 scorecard_pmml . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 split_df . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 var_filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 var_scale . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 vif . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 woebin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 woebin_adj . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 woebin_plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 woebin_ply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Index 32 describe Variable Describe Description This function provides descriptive statistic for exploratory data analysis. Usage describe(dt) Arguments dt A data frame. gains_table 3 Examples library(data.table) data("germancredit") dat = rbind( setDT(germancredit), data.table(creditability=sample(c("good","bad"),100,replace=TRUE)), fill=TRUE) eda = describe(dat) eda gains_table Gains Table Description gains_table creates a data frame including distribution of total, negative, positive, positive rate and rejected rate by score bins. The gains table is used in conjunction with financial and operational considerations to make cutoff decisions. Usage gains_table(score, label, bin_num = 10, method = "freq", width_by = NULL, breaks_by = NULL, positive = "bad|1", ...) Arguments score A list of credit score for actual and expected data samples. For example, score = list(actual = scoreA, expect = scoreE). label A list of label value for actual and expected data samples. For example, label = list(actual = labelA, expect = labelE). bin_num Integer, the number of score bins. Defaults to 10. If it is ’max’, then individual scores are used as bins. method The score is binning by equal frequency or equal width. Accepted values are ’freq’ and ’width’. Defaults to ’freq’. width_by Number, increment of the score breaks when method is set as ’width’. If it is provided the above parameter bin_num will not be used. Defaults to NULL. breaks_by The name of data set to create breakpoints. Defaults to the first data set. Or numeric values to set breakpoints manually. positive Value of positive class, Defaults to "bad|1". ... Additional parameters. 4 germancredit Value A data frame See Also perf_eva perf_psi Examples # load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dtvf = var_filter(germancredit, "creditability") # breaking dt into train and test dt_list = split_df(dtvf, "creditability") label_list = lapply(dt_list, function(x) x$creditability) # binning bins = woebin(dt_list$train, "creditability") # scorecard card = scorecard2(bins, dt = dt_list$train, y = 'creditability') # credit score score_list = lapply(dt_list, function(x) scorecard_ply(x, card)) ###### gains_table examples ###### # Example I, input score and label can be a vector or a list g1 = gains_table(score = unlist(score_list), label = unlist(label_list)) g2 = gains_table(score = score_list, label = label_list) # Example II, specify the bins number and type g3 = gains_table(score = unlist(score_list), label = unlist(label_list), bin_num = 20) g4 = gains_table(score = unlist(score_list), label = unlist(label_list), method = 'width') germancredit German Credit Data Description Credit data that classifies debtors described by a set of attributes as good or bad credit risks. See source link below for detailed information. Usage data(germancredit) iv 5 Format A data frame with 21 variables (numeric and factors) and 1000 observations. Source http://archive.ics.uci.edu/ml/datasets/Statlog+(German+Credit+Data) Examples # load German credit data data(germancredit) # structure of germancredit str(germancredit) # summary of germancredit # lapply(germancredit, summary) iv Information Value Description This function calculates information value (IV) for multiple x variables. It treats each unique value in x variables as a group. If there is a zero number of y class, it will be replaced by 0.99 to make sure woe/iv is calculable. Usage iv(dt, y, x = NULL, positive = "bad|1", order = TRUE) Arguments dt A data frame with both x (predictor/feature) and y (response/label) variables. y Name of y variable. x Name of x variables. Defaults to NULL. If x is NULL, then all columns except y are counted as x variables. positive Value of positive class, Defaults to "bad|1". order Logical, Defaults to TRUE. If it is TRUE, the output will descending order via iv. 6 one_hot Details IV is a very useful concept for variable selection while developing credit scorecards. The formula for information value is shown below: X DistributionP ositivei IV = (DistributionP ositivei − DistributionN egativei ) ∗ ln( ). DistributionN egativei The log component in information value is defined as weight of evidence (WOE), which is shown as DistributionP ositivei W eightof Evidence = ln( ). DistributionN egativei The relationship between information value and predictive power is as follows: Information Value Predictive Power —————– —————- < 0.02 useless for prediction 0.02 to 0.1 Weak predictor 0.1 to 0.3 Medium predictor > 0.3 Strong predictor Value A data frame with columns for variable and info_value Examples # Load German credit data data(germancredit) # information values info_value = iv(germancredit, y = "creditability") str(info_value) one_hot One Hot Encoding Description One-hot encoding on categorical variables and replace missing values. It is not needed when creat- ing a standard scorecard model, but required in models that without doing woe transformation. Usage one_hot(dt, var_skip = NULL, var_encode = NULL, nacol_rm = FALSE, ...) perf_cv 7 Arguments dt A data frame. var_skip Name of categorical variables that will skip for one-hot encoding. Defaults to NULL. var_encode Name of categorical variables to be one-hot encoded, Defaults to NULL. If it is NULL, then all categorical variables except in var_skip are counted. nacol_rm Logical. One-hot encoding on categorical variable contains missing values, whether to remove the column generated to indicate the presence of NAs. De- faults to FALSE. ... Additional parameters. Value A data frame Examples # load germancredit data data(germancredit) library(data.table) dat = rbind( setDT(germancredit)[, c(sample(20,3),21)], data.table(creditability=sample(c("good","bad"),10,replace=TRUE)), fill=TRUE) # one hot encoding ## keep na columns from categorical variable dat_onehot1 = one_hot(dat, var_skip = 'creditability', nacol_rm = FALSE) # default str(dat_onehot1) ## remove na columns from categorical variable dat_onehot2 = one_hot(dat, var_skip = 'creditability', nacol_rm = TRUE) str(dat_onehot2) perf_cv Cross Validation Description perf_cv provides cross validation on logistic regression and other binomial classification models. Usage perf_cv(dt, y, x = NULL, no_folds = 5, seeds = NULL, binomial_metric = "ks", positive = "bad|1", breaks_list = NULL, ...) 8 perf_cv Arguments dt A data frame with both x (predictor/feature) and y (response/label) variables. y Name of y variable. x Name of x variables. Defaults to NULL. If x is NULL, then all columns except y are counted as x variables. no_folds Number of folds for K-fold cross-validation. Defaults to 5. seeds The seeds to create multiple random splits of the input dataset into training and validation data by using split_df function. Defaults to NULL. binomial_metric Defaults to ks. positive Value of positive class, defaults to "bad|1". breaks_list List of break points, defaults to NULL. If it is NULL, then using original values of the input data to fitting model, otherwise converting into woe values based on training data. ... Additional parameters. Value A list of data frames of binomial metrics for each datasets. Examples ## Not run: data("germancredit") dt = var_filter(germancredit, y = 'creditability') bins = woebin(dt, y = 'creditability') dt_woe = woebin_ply(dt, bins) perf1 = perf_cv(dt_woe, y = 'creditability', no_folds = 5) perf2 = perf_cv(dt_woe, y = 'creditability', no_folds = 5, seeds = sample(1000, 10)) perf3 = perf_cv(dt_woe, y = 'creditability', no_folds = 5, binomial_metric = c('ks', 'auc')) ## End(Not run) perf_eva 9 perf_eva Binomial Metrics Description perf_eva calculates metrics to evaluate the performance of binomial classification model. It can also creates confusion matrix and model performance graphics. Usage perf_eva(pred, label, title = NULL, binomial_metric = c("mse", "rmse", "logloss", "r2", "ks", "auc", "gini"), confusion_matrix = FALSE, threshold = NULL, show_plot = c("ks", "lift"), pred_desc = TRUE, positive = "bad|1", ...) Arguments pred A list or vector of predicted probability or score. label A list or vector of label values. title The title of plot. Defaults to NULL. binomial_metric Defaults to c(’mse’, ’rmse’, ’logloss’, ’r2’, ’ks’, ’auc’, ’gini’). If it is NULL, then no metric will calculated. confusion_matrix Logical, whether to create a confusion matrix. Defaults to TRUE. threshold Confusion matrix threshold. Defaults to the pred on maximum F1. show_plot Defaults to c(’ks’, ’roc’). Accepted values including c(’ks’, ’lift’, ’gain’, ’roc’, ’lz’, ’pr’, ’f1’, ’density’). pred_desc whether to sort the argument of pred in descending order. Defaults to TRUE. positive Value of positive class. Defaults to "bad|1". ... Additional parameters. Details Accuracy = true positive and true negative/total cases Error rate = false positive and false negative/total cases TPR, True Positive Rate(Recall or Sensitivity) = true positive/total actual positive PPV, Positive Predicted Value(Precision) = true positive/total predicted positive TNR, True Negative Rate(Specificity) = true negative/total actual negative = 1-FPR NPV, Negative Predicted Value = true negative/total predicted negative Value A list of binomial metric, confusion matrix and graphics 10 perf_psi See Also perf_psi Examples # load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dtvf = var_filter(germancredit, "creditability") # breaking dt into train and test dt_list = split_df(dtvf, "creditability") label_list = lapply(dt_list, function(x) x$creditability) # woe binning bins = woebin(dt_list$train, "creditability") # scorecard, prob cardprob = scorecard2(bins, dt = dt_list, y = 'creditability', return_prob = TRUE) # credit score score_list = lapply(dt_list, function(x) scorecard_ply(x, cardprob$card)) ###### perf_eva examples ###### # Example I, one datset ## predicted p1 perf_eva(pred = cardprob$prob$train, label=label_list$train, title = 'train') ## predicted score # perf_eva(pred = score_list$train, label=label_list$train, # title = 'train') # Example II, multiple datsets ## predicted p1 perf_eva(pred = cardprob$prob, label = label_list, show_plot = c('ks', 'lift', 'gain', 'roc', 'lz', 'pr', 'f1', 'density')) ## predicted score # perf_eva(score_list, label_list) perf_psi PSI Description perf_psi calculates population stability index (PSI) for total credit score and Characteristic Stabil- ity Index (CSI) for variables. It can also creates graphics to display score distribution and positive rate trends. perf_psi 11 Usage perf_psi(score, label = NULL, title = NULL, show_plot = TRUE, positive = "bad|1", threshold_variable = 20, var_skip = NULL, ...) Arguments score A list of credit score for actual and expected data samples. For example, score = list(expect = scoreE, actual = scoreA). label A list of label value for actual and expected data samples. For example, label = list(expect = labelE, actual = labelA). Defaults to NULL. title Title of plot, Defaults to NULL. show_plot Logical. Defaults to TRUE. positive Value of positive class, Defaults to "bad|1". threshold_variable Integer. Defaults to 20. If the number of unique values > threshold_variable, the provided score will be counted as total credit score, otherwise, it is variable score. var_skip Name of variables that are not score, such as id column. It should be the same with the var_kp in scorecard_ply function. Defaults to NULL. ... Additional parameters. Details The population stability index (PSI) formula is displayed below: X Actual% P SI = ((Actual% − Expected%) ∗ (ln( ))). Expected% The rule of thumb for the PSI is as follows: Less than 0.1 inference insignificant change, no ac- tion required; 0.1 - 0.25 inference some minor change, check other scorecard monitoring metrics; Greater than 0.25 inference major shift in population, need to delve deeper. Characteristic Stability Index (CSI) formula is displayed below: X CSI = ((Actual% − Expected%) ∗ score). Value A data frame of psi and graphics of credit score distribution See Also perf_eva gains_table 12 replace_na Examples # load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dtvf = var_filter(germancredit, "creditability") # breaking dt into train and test dt_list = split_df(dtvf, "creditability") label_list = lapply(dt_list, function(x) x$creditability) # binning bins = woebin(dt_list$train, "creditability") # scorecard card = scorecard2(bins, dt = dt_list$train, y = 'creditability') # credit score score_list = lapply(dt_list, function(x) scorecard_ply(x, card)) # credit score, only_total_score = FALSE score_list2 = lapply(dt_list, function(x) scorecard_ply(x, card, only_total_score=FALSE)) ###### perf_psi examples ###### # Example I # only total psi psi1 = perf_psi(score = score_list, label = label_list) psi1$psi # psi data frame psi1$pic # pic of score distribution # modify colors # perf_psi(score = score_list, label = label_list, # line_color='#FC8D59', bar_color=c('#FFFFBF', '#99D594')) # Example II # both total and variable psi psi2 = perf_psi(score = score_list2, label = label_list) # psi2$psi # psi data frame # psi2$pic # pic of score distribution replace_na Replace Missing Values Description Replace missing values with a specified value or mean/median value. Usage replace_na(dt, repl) report 13 Arguments dt A data frame or vector. repl Replace missing values with a specified value such as -1, or the mean/median value for numeric variable and mode value for categorical variable if repl is mean or median. Examples # load germancredit data data(germancredit) library(data.table) dat = rbind( setDT(germancredit)[, c(sample(20,3),21)], data.table(creditability=sample(c("good","bad"),10,replace=TRUE)), fill=TRUE) ## replace with -1 dat_repna1 = replace_na(dat, repl = -1) ## replace with median for numeric, and mode for categorical dat_repna2 = replace_na(dat, repl = 'median') ## replace with mean for numeric, and mode for categorical dat_repna3 = replace_na(dat, repl = 'mean') report Scorecard Modeling Report Description report creates a scorecard modeling report and save it as a xlsx file. Usage report(dt, y, x, breaks_list, x_name = NULL, special_values = NULL, seed = 618, save_report = "report", positive = "bad|1", ...) Arguments dt A data frame or a list of data frames that have both x (predictor/feature) and y (response/label) variables. If there are multiple data frames are provided, only the first data frame would be used for training, and the others would be used for testing/validation. y Name of y variable. x Name of x variables. Defaults to NULL. If x is NULL, then all columns except y are counted as x variables. 14 report breaks_list A list of break points. It can be extracted from woebin and woebin_adj via the argument save_breaks_list. x_name A vector of x variables’ name. special_values The values specified in special_values will be in separate bins. Defaults to NULL. seed A random seed to split input data frame. Defaults to 618. If it is NULL, input dt will not split into two datasets. save_report The name of xlsx file where the report is to be saved. Defaults to ’report’. positive Value of positive class, default "bad|1". ... Additional parameters. Examples ## Not run: data("germancredit") y = 'creditability' x = c( "status.of.existing.checking.account", "duration.in.month", "credit.history", "purpose", "credit.amount", "savings.account.and.bonds", "present.employment.since", "installment.rate.in.percentage.of.disposable.income", "personal.status.and.sex", "property", "age.in.years", "other.installment.plans", "housing" ) special_values=NULL breaks_list=list( status.of.existing.checking.account=c("... < 0 DM%,%0 <= ... < 200 DM", "... >= 200 DM / salary assignments for at least 1 year", "no checking account"), duration.in.month=c(8, 16, 34, 44), credit.history=c( "no credits taken/ all credits paid back duly%,%all credits at this bank paid back duly", "existing credits paid back duly till now", "delay in paying off in the past", "critical account/ other credits existing (not at this bank)"), purpose=c("retraining%,%car (used)", "radio/television", "furniture/equipment%,%domestic appliances%,%business%,%repairs", "car (new)%,%others%,%education"), credit.amount=c(1400, 1800, 4000, 9200), savings.account.and.bonds=c("... < 100 DM", "100 <= ... < 500 DM", "500 <= ... < 1000 DM%,%... >= 1000 DM%,%unknown/ no savings account"), present.employment.since=c("unemployed%,%... < 1 year", "1 <= ... < 4 years", "4 <= ... < 7 years", "... >= 7 years"), scorecard 15 installment.rate.in.percentage.of.disposable.income=c(2, 3), personal.status.and.sex=c("male : divorced/separated", "female : divorced/separated/married", "male : single", "male : married/widowed"), property=c("real estate", "building society savings agreement/ life insurance", "car or other, not in attribute Savings account/bonds", "unknown / no property"), age.in.years=c(26, 28, 35, 37), other.installment.plans=c("bank%,%stores", "none"), housing=c("rent", "own", "for free") ) # Example I # input dt is a data frame # split input data frame into two report(germancredit, y, x, breaks_list, special_values, seed=618, save_report='report1', show_plot = c('ks', 'lift', 'gain', 'roc', 'lz', 'pr', 'f1', 'density')) # donot split input data report(germancredit, y, x, breaks_list, special_values, seed=NULL, save_report='report2') # Example II # input dt is a list # only one dataset report(list(dt=germancredit), y, x, breaks_list, special_values, seed=NULL, save_report='report3') # multiple datasets report(list(dt1=germancredit[sample(1000,500)], dt2=germancredit[sample(1000,500)]), y, x, breaks_list, special_values, seed=NULL, save_report='report4') # multiple datasets report(list(dt1=germancredit[sample(1000,500)], dt2=germancredit[sample(1000,500)], dt3=germancredit[sample(1000,500)]), y, x, breaks_list, special_values, seed=NULL, save_report='report5') ## End(Not run) scorecard Creating a Scorecard Description scorecard creates a scorecard based on the results from woebin and glm. Usage scorecard(bins, model, points0 = 600, odds0 = 1/19, pdo = 50, basepoints_eq0 = FALSE, digits = 0) 16 scorecard Arguments bins Binning information generated from woebin function. model A glm model object. points0 Target points, default 600. odds0 Target odds, default 1/19. Odds = p/(1-p). pdo Points to Double the Odds, default 50. basepoints_eq0 Logical, Defaults to FALSE. If it is TRUE, the basepoints will equally distribute to each variable. digits The number of digits after the decimal point for points calculation. Default 0. Value A list of scorecard data frames See Also scorecard2 scorecard_ply Examples # load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dtvf = var_filter(germancredit, "creditability") # split into train and test dtlst = split_df(dtvf, y = 'creditability') # binning bins = woebin(dtlst$train, "creditability") # to woe dtlst_woe = lapply(dtlst, function(d) woebin_ply(d, bins)) # lr m = glm(creditability ~ ., family = binomial(), data = dtlst_woe$train) # scorecard card = scorecard(bins, m) prob = predict(m, dtlst_woe$train, type='response') # problst = lapply(dtlst_woe, function(x) predict(m, x, type='response')) scorecard2 17 scorecard2 Creating a Scorecard Description scorecard2 creates a scorecard based on the results from woebin. It has the same function of scorecard, but without model object input and provided adjustment for oversampling. Usage scorecard2(bins, dt, y, x = NULL, points0 = 600, odds0 = 1/19, pdo = 50, basepoints_eq0 = FALSE, digits = 0, return_prob = FALSE, posprob_pop = NULL, posprob_sample = NULL, positive = "bad|1", ...) Arguments bins Binning information generated from woebin function. dt A data frame with both x (predictor/feature) and y (response/label) variables. y Name of y variable. x Name of x variables. If it is NULL, then all variables in bins are used. Defaults to NULL. points0 Target points, default 600. odds0 Target odds, default 1/19. Odds = p/(1-p). pdo Points to Double the Odds, default 50. basepoints_eq0 Logical, defaults to FALSE. If it is TRUE, the basepoints will equally distribute to each variable. digits The number of digits after the decimal point for points calculation. Default 0. return_prob Logical, defaults to FALSE. If it is TRUE, the predict probability will also re- turn. posprob_pop Positive probability of population. Accepted range: 0-1, default to NULL. If it is not NULL, the model will adjust for oversampling. posprob_sample Positive probability of sample. Accepted range: 0-1, default to the positive probability of the input dt. positive Value of positive class, default "bad|1". ... Additional parameters. Value A list of scorecard data frames See Also scorecard scorecard_ply 18 scorecard_ply Examples # load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dtvf = var_filter(germancredit, "creditability") # split into train and test dtlst = split_df(dtvf, y = 'creditability') # binning bins = woebin(dtlst$train, "creditability") # train only ## create scorecard card1 = scorecard2(bins=bins, dt=dtlst$train, y='creditability') ## scorecard and predicted probability cardprob1 = scorecard2(bins=bins, dt=dtlst$train, y='creditability', return_prob = TRUE) # both train and test ## create scorecard card2 = scorecard2(bins=bins, dt=dtlst, y='creditability') ## scorecard and predicted probability cardprob2 = scorecard2(bins=bins, dt=dtlst, y='creditability', return_prob = TRUE) scorecard_ply Score Transformation Description scorecard_ply calculates credit score using the results from scorecard. Usage scorecard_ply(dt, card, only_total_score = TRUE, print_step = 0L, replace_blank_na = TRUE, var_kp = NULL) Arguments dt A data frame, which is the original dataset for training model. card A data frame or a list of data frames. It’s the scorecard generated from the function scorecard. only_total_score Logical, Defaults to TRUE. If it is TRUE, then the output includes only total credit score; Otherwise, if it is FALSE, the output includes both total and each variable’s credit score. print_step A non-negative integer. Defaults to 1. If print_step>0, print variable names by each print_step-th iteration. If print_step=0, no message is print. scorecard_pmml 19 replace_blank_na Logical. Replace blank values with NA. Defaults to TRUE. This argument should be the same with woebin’s. var_kp Name of force kept variables, such as id column. Defaults to NULL. Value A data frame in score values See Also scorecard scorecard2 Examples # load germancredit data data("germancredit") # filter variable via missing rate, iv, identical value rate dtvf = var_filter(germancredit, "creditability") # split into train and test dtlst = split_df(dtvf, y = 'creditability') # binning bins = woebin(dtlst$train, "creditability") # scorecard card = scorecard2(bins=bins, dt=dtlst$train, y='creditability') # credit score # Example I # only total score score1 = scorecard_ply(germancredit, card) # Example II # credit score for both total and each variable score2 = scorecard_ply(germancredit, card, only_total_score = FALSE) scorecard_pmml Scorecard to PMML Description scorecard_pmml converts scorecard into PMML format. Usage scorecard_pmml(card, save_name = NULL, model_name = "scorecard", model_version = NULL, description = "scorecard", copyright = NULL) 20 split_df Arguments card A data frame or a list of data frames. It’s a scorecard object generated from the function scorecard. save_name A string. The file name to save scorecard. Defaults to None. model_name A name to be given to the PMML model. model_version A string specifying the model version. description A descriptive text for the Header element of the PMML. copyright The copyright notice for the model. Examples data("germancredit") dtvf = var_filter(germancredit, y='creditability') bins = woebin(dtvf, y='creditability') card = scorecard2(bins, dtvf, y='creditability') # export scorecard into pmml cardpmml = scorecard_pmml(card) # save pmml # cardpmml = scorecard_pmml(card, save_name='scorecard', model_version='1.0') split_df Split a Data Frame Description Split a data frame into multiple data sets according to the specified ratios. Usage split_df(dt, y = NULL, ratios = c(0.7, 0.3), name_dfs = c("train", "test"), oot = list(order = NULL, start = NULL, ratio = NULL), seed = 618, ...) Arguments dt A data frame. y Name of y variable, Defaults to NULL. The input data will split based on the predictor y, if it is provide. ratios A numeric vector indicating the ratio of total rows contained in each split, de- faults to c(0.7, 0.3). name_dfs Name of returned data frames. Its length should equals to the ratios’. Defaults to train and test. oot out-of-time validation data set parameters. seed A random seed, Defaults to 618. ... Additional parameters. var_filter 21 Value A list of data frames Examples # load German credit data data(germancredit) # Example I dt_list = split_df(germancredit, y="creditability") # dimensions of each split data sets lapply(dt_list, dim) # Example II dt_list2 = split_df(germancredit, y="creditability", ratios = c(0.5, 0.3, 0.2), name_dfs = c('train', 'test', 'valid')) lapply(dt_list2, dim) var_filter Variable Filter Description This function filter variables base on specified conditions, such as missing rate, identical value rate, information value. Usage var_filter(dt, y, x = NULL, lims = list(missing_rate = 0.95, identical_rate = 0.95, info_value = 0.02), var_rm = NULL, var_kp = NULL, var_rm_reason = FALSE, positive = "bad|1", ...) Arguments dt A data frame with both x (predictor/feature) and y (response/label) variables. y Name of y variable. x Name of x variables. Defaults to NULL. If x is NULL, then all columns except y are counted as x variables. lims A list of variable filters’ thresholds. • missing_rate The missing rate of kept variables should <= 0.95 by de- faults. • identical_rate The identical value rate (excluding NAs) of kept variables should <= 0.95 by defaults. 22 var_scale • info_value The information value (iv) of kept variables should >= 0.02 by defaults. var_rm Name of force removed variables, Defaults to NULL. var_kp Name of force kept variables, Defaults to NULL. var_rm_reason Logical, Defaults to FALSE. positive Value of positive class, Defaults to "bad|1". ... Additional parameters. Value A data frame with columns for y and selected x variables, and a data frame with columns for remove reason if var_rm_reason is TRUE. Examples # Load German credit data data(germancredit) # variable filter dt_sel = var_filter(germancredit, y = "creditability") dim(dt_sel) # return the reason of varaible removed dt_sel2 = var_filter(germancredit, y = "creditability", var_rm_reason = TRUE) lapply(dt_sel2, dim) str(dt_sel2$dt) str(dt_sel2$rm) # keep columns manually, such as rowid germancredit$rowid = row.names(germancredit) dt_sel3 = var_filter(germancredit, y = "creditability", var_kp = 'rowid') # remove columns manually dt_sel4 = var_filter(germancredit, y = "creditability", var_rm = 'rowid') var_scale Variable Scaling Description scaling variables using standardization or normalization Usage var_scale(dt, var_skip = NULL, type = "standard", ...) vif 23 Arguments dt a data frame or vector var_skip Name of variables that will skip for scaling Defaults to NULL. type type of scaling method, including standard or minmax. ... Additional parameters. Examples data("germancredit") # standardization dts1 = var_scale(germancredit, type = 'standard') # normalization/minmax dts2 = var_scale(germancredit, type = 'minmax') dts2 = var_scale(germancredit, type = 'minmax', new_range = c(-1, 1)) vif Variance Inflation Factors Description vif calculates variance-inflation and generalized variance-inflation factors for linear, generalized linear to identify collinearity among explanatory variables. Usage vif(model, merge_coef = FALSE) Arguments model A model object. merge_coef Logical, whether to merge with coefficients of model summary matrix. Defaults to FALSE. Value A data frame with columns for variable and gvif, or additional columns for df and gvif^(1/(2*df)) if provided model uses factor variable. See Also https://cran.r-project.org/package=car 24 woebin Examples data(germancredit) # Example I fit1 = glm(creditability~ age.in.years + credit.amount + present.residence.since, family = binomial(), data = germancredit) vif(fit1) vif(fit1, merge_coef=TRUE) # Example II fit2 = glm(creditability~ status.of.existing.checking.account + credit.history + credit.amount, family = binomial(), data = germancredit) vif(fit2) vif(fit2, merge_coef=TRUE) woebin WOE Binning Description woebin generates optimal binning for numerical, factor and categorical variables using methods including tree-like segmentation or chi-square merge. woebin can also customizing breakpoints if the breaks_list was provided. The default woe is defined as ln(Pos_i/Neg_i). If you prefer ln(Neg_i/Pos_i), please set the argument positive as negative value, such as ’0’ or ’good’. If there is a zero frequency class when calculating woe, the zero will replaced by 0.99 to make the woe calculable. Usage woebin(dt, y, x = NULL, var_skip = NULL, breaks_list = NULL, special_values = NULL, missing_join = "left", stop_limit = 0.1, count_distr_limit = 0.05, bin_num_limit = 8, positive = "bad|1", no_cores = 2, print_step = 0L, method = "tree", save_breaks_list = NULL, ignore_const_cols = TRUE, ignore_datetime_cols = TRUE, check_cate_num = TRUE, replace_blank_inf = TRUE, ...) Arguments dt A data frame with both x (predictor/feature) and y (response/label) variables. y Name of y variable. x Name of x variables. Defaults to NULL. If x is NULL, then all columns except y and var_skip are counted as x variables. var_skip Name of variables that will skip for binning. Defaults to NULL. woebin 25 breaks_list List of break points, Defaults to NULL. If it is not NULL, variable binning will based on the provided breaks. special_values the values specified in special_values will be in separate bins. Defaults to NULL. missing_join missing values join with the left non-missing bin if its share is lower than thresh- old. Accepted values including left and right. Defaults to left. stop_limit Stop binning segmentation when information value gain ratio less than the ’stop_limit’ if using tree method; or stop binning merge when the chi-square of each neigh- bor bins are larger than the threshold under significance level of ’stop_limit’ and freedom degree of 1 if using chimerge method. Accepted range: 0-0.5; Defaults to 0.1. If it is ’N’, each x value is a bin. count_distr_limit The minimum count distribution percentage. Accepted range: 0.01-0.2; De- faults to 0.05. bin_num_limit Integer. The maximum number of binning. Defaults to 8. positive Value of positive class, defaults to "bad|1". no_cores Number of CPU cores for parallel computation. Defaults to 2, if it sets to NULL then 90 percent of total cpu cores will be used. print_step A non-negative integer. Defaults to 1. If print_step>0, print variable names by each print_step-th iteration. If print_step=0 or no_cores>1, no message is print. method Four methods are provided, "tree" and "chimerge" for optimal binning that sup- port both numerical and categorical variables, and ’width’ and ’freq’ for equal binning that support numerical variables only. Defaults to "tree". save_breaks_list A string. The file name to save breaks_list. Defaults to None. ignore_const_cols Logical. Ignore constant columns. Defaults to TRUE. ignore_datetime_cols Logical. Ignore datetime columns. Defaults to TRUE. check_cate_num Logical. Check whether the number of unique values in categorical columns larger than 50. It might make the binning process slow if there are too many unique categories. Defaults to TRUE. replace_blank_inf Logical. Replace blank values with NA and infinite with -1. Defaults to TRUE. ... Additional parameters. Value A list of data frames include binning information for each x variables. See Also woebin_ply, woebin_plot, woebin_adj 26 woebin Examples # load germancredit data data(germancredit) # Example I # binning of two variables in germancredit dataset # using tree method bins2_tree = woebin(germancredit, y="creditability", x=c("credit.amount","housing"), method="tree") bins2_tree ## Not run: # using chimerge method bins2_chi = woebin(germancredit, y="creditability", x=c("credit.amount","housing"), method="chimerge") # binning in equal freq/width # only supports numerical variables numeric_cols = c("duration.in.month", "credit.amount", "installment.rate.in.percentage.of.disposable.income", "present.residence.since", "age.in.years", "number.of.existing.credits.at.this.bank", "number.of.people.being.liable.to.provide.maintenance.for") bins_freq = woebin(germancredit, y="creditability", x=numeric_cols, method="freq") bins_width = woebin(germancredit, y="creditability", x=numeric_cols, method="width") # y can be NULL if no label column in dataset bins_freq_noy = woebin(germancredit, y=NULL, x=numeric_cols) # Example II # setting of stop_limit # stop_limit = 0.1 (by default) bins_x1 = woebin(germancredit, y = 'creditability', x = 'foreign.worker', stop_limit = 0.1) # stop_limit = 'N', each x value is a bin bins_x1_N = woebin(germancredit, y = 'creditability', x = 'foreign.worker', stop_limit = 'N') # Example III # binning of the germancredit dataset bins_germ = woebin(germancredit, y = "creditability") # converting bins_germ into a data frame # bins_germ_df = data.table::rbindlist(bins_germ) # Example IV # customizing the breakpoints of binning library(data.table) dat = rbind( setDT(germancredit), data.table(creditability=sample(c("good","bad"),10,replace=TRUE)), fill=TRUE) breaks_list = list( age.in.years = c(26, 35, 37, "Inf%,%missing"), housing = c("own", "for free%,%rent") ) woebin_adj 27 special_values = list( credit.amount = c(2600, 9960, "6850%,%missing"), purpose = c("education", "others%,%missing") ) bins_cus_brk = woebin(dat, y="creditability", x=c("age.in.years","credit.amount","housing","purpose"), breaks_list=breaks_list, special_values=special_values) # Example V # save breaks_list as a R file bins2 = woebin(germancredit, y="creditability", x=c("credit.amount","housing"), save_breaks_list='breaks_list') # Example VI # setting bin closed on the right options(scorecard.bin_close_right = TRUE) binsRight = woebin(germancredit, y = 'creditability', x = 'age.in.years') binsRight # setting bin close on the left, the default setting options(scorecard.bin_close_right = FALSE) ## End(Not run) woebin_adj WOE Binning Adjustment Description woebin_adj interactively adjust the binning breaks. Usage woebin_adj(dt, y, bins, breaks_list = NULL, save_breaks_list = NULL, adj_all_var = TRUE, special_values = NULL, method = "tree", count_distr_limit = 0.05, to = "breaks_list", ...) Arguments dt A data frame. y Name of y variable. bins A list of data frames. Binning information generated from woebin. breaks_list List of break points, Defaults to NULL. If it is not NULL, variable binning will based on the provided breaks. save_breaks_list A string. The file name to save breaks_list. Defaults to None. 28 woebin_plot adj_all_var Logical, whether to show variables have monotonic woe trends. Defaults to TRUE special_values The values specified in special_values will in separate bins. Defaults to NULL. method Optimal binning method, it should be "tree" or "chimerge". Defaults to "tree". count_distr_limit The minimum count distribution percentage. Accepted range: 0.01-0.2; De- faults to 0.05. This argument should be the same with woebin’s. to Adjusting bins into breaks_list or bins_list. Defaults to breaks_list. ... Additional parameters. Value A list of modified break points of each x variables. See Also woebin, woebin_ply, woebin_plot Examples ## Not run: # Load German credit data data(germancredit) # Example I dt = germancredit[, c("creditability", "age.in.years", "credit.amount")] bins = woebin(dt, y="creditability") breaks_adj = woebin_adj(dt, y="creditability", bins) bins_final = woebin(dt, y="creditability", breaks_list=breaks_adj) # Example II adjust two variables' breaks in brklst binsII = woebin(germancredit, y="creditability", save_breaks_list = 'breaks') brklst = source('breaks.R')$value # update break list file brklst_adj = woebin_adj(germancredit, "creditability", binsII[1:2], breaks_list = brklst, save_breaks_list = 'breaks') ## End(Not run) woebin_plot WOE Binning Visualization Description woebin_plot create plots of count distribution and positive probability for each bin. The binning informations are generates by woebin. woebin_plot 29 Usage woebin_plot(bins, x = NULL, title = NULL, show_iv = TRUE, line_value = "posprob", ...) Arguments bins A list of data frames. Binning information generated by woebin. x Name of x variables. Defaults to NULL. If x is NULL, then all columns except y are counted as x variables. title String added to the plot title. Defaults to NULL. show_iv Logical. Defaults to TRUE, which means show information value in the plot title. line_value The value displayed as line. Accepted values are ’posprob’ and ’woe’. Defaults to positive probability. ... Additional parameters Value A list of binning graphics. See Also woebin, woebin_ply, woebin_adj Examples # Load German credit data data(germancredit) # Example I bins1 = woebin(germancredit, y="creditability", x="credit.amount") p1 = woebin_plot(bins1) print(p1) # modify line value p1_w = woebin_plot(bins1, line_value = 'woe') print(p1_w) # modify colors p1_c = woebin_plot(bins1, line_color='#FC8D59', bar_color=c('#FFFFBF', '#99D594')) print(p1_c) # show iv, line value, bar value p1_iv = woebin_plot(bins1, show_iv = FALSE) print(p1_iv) p1_lineval = woebin_plot(bins1, show_lineval = FALSE) print(p1_lineval) p1_barval = woebin_plot(bins1, show_barval = FALSE) 30 woebin_ply print(p1_barval) # Example II bins = woebin(germancredit, y="creditability") plotlist = woebin_plot(bins) print(plotlist$credit.amount) # # save binning plot # for (i in 1:length(plotlist)) { # ggplot2::ggsave( # paste0(names(plotlist[i]), ".png"), plotlist[[i]], # width = 15, height = 9, units="cm" ) # } woebin_ply WOE/BIN Transformation Description woebin_ply converts original values of input data into woe or bin based on the binning information generated from woebin. Usage woebin_ply(dt, bins, to = "woe", no_cores = 2, print_step = 0L, replace_blank_inf = TRUE, ...) Arguments dt A data frame. bins Binning information generated from woebin. to Converting original values to woe or bin. Defaults to woe. no_cores Number of CPU cores for parallel computation. Defaults to 2, if it sets to NULL then 90 percent of total cpu cores will be used. print_step A non-negative integer. Defaults to 1. If print_step>0, print variable names by each print_step-th iteration. If print_step=0 or no_cores>1, no message is print. replace_blank_inf Logical. Replace blank values with NA and infinite with -1. Defaults to TRUE. This argument should be the same with woebin’s. ... Additional parameters. Value A data frame with columns for variables converted into woe values. woebin_ply 31 See Also woebin, woebin_plot, woebin_adj Examples # load germancredit data data(germancredit) # Example I dt = germancredit[, c("creditability", "credit.amount", "purpose")] # binning for dt bins = woebin(dt, y = "creditability") # converting to woe dt_woe = woebin_ply(dt, bins=bins) str(dt_woe) # converting to bin dt_bin = woebin_ply(dt, bins=bins, to = 'bin') str(dt_bin) # Example II # binning for germancredit dataset bins_germancredit = woebin(germancredit, y="creditability") # converting the values in germancredit to woe # bins is a list which generated from woebin() germancredit_woe = woebin_ply(germancredit, bins_germancredit) # bins is a data frame bins_df = data.table::rbindlist(bins_germancredit) germancredit_woe = woebin_ply(germancredit, bins_df) Index ∗ data germancredit, 4 describe, 2 gains_table, 3, 11 germancredit, 4 iv, 5 one_hot, 6 perf_cv, 7 perf_eva, 4, 9, 11 perf_psi, 4, 10, 10 replace_na, 12 report, 13 scorecard, 15, 17, 19 scorecard2, 16, 17, 19 scorecard_ply, 16, 17, 18 scorecard_pmml, 19 split_df, 20 var_filter, 21 var_scale, 22 vif, 23 woebin, 24, 28, 29, 31 woebin_adj, 25, 27, 29, 31 woebin_plot, 25, 28, 28, 31 woebin_ply, 25, 28, 29, 30 32

FishPhyloMaker

cran

Package ‘FishPhyloMaker’ October 12, 2022 Type Package Title Phylogenies for a List of Finned-Ray Fishes Version 0.2.0 Description Provides an alternative to facilitate the construction of a phy- logeny for fish species from a list of species or a community matrix using as a backbone the phylogenetic tree proposed by Ra- bosky et al. (2018) <doi:10.1038/s41586-018-0273-1>. License MIT + file LICENSE Encoding UTF-8 LazyData true RoxygenNote 7.1.1 Imports ape, fishtree, geiger, knitr, phytools, progress, rfishbase, rmarkdown, utils VignetteBuilder knitr, rmarkdown Depends R (>= 2.10) Suggests markdown, gh NeedsCompilation no Author Gabriel Nakamura [aut, cre] (<https://orcid.org/0000-0002-5144-5312>), Aline Richter [aut], Bruno Soares [aut] Maintainer Gabriel Nakamura <gabriel.nakamura.souza@gmail.com> Repository CRAN Date/Publication 2021-09-15 08:00:10 UTC R topics documented: FishPhyloMaker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 FishTaxaMaker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 neotropical_comm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 PD_defict . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1 2 FishPhyloMaker spp_afrotropic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 taxon_data_PhyloMaker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 whichFishAdd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Index 8 FishPhyloMaker Obtaining fish phylogeny according to a local pool of species Description Obtaining fish phylogeny according to a local pool of species Usage FishPhyloMaker( data, insert.base.node = FALSE, return.insertions = TRUE, progress.bar = TRUE ) Arguments data A data frame with three columns containing the name of species (s), the Fam- ily (f) and the Order (o). This data frame can be generated with tab_function function. insert.base.node Logical argument indicating if the species must be added automatically in the family and order (when needed) nodes. Default is FALSE return.insertions Logical, if TRUE (default) the output is a list of length two containing the phy- logeny and a dataframe with a column indicating at which level each species was inserted. progress.bar Logical argument. If TRUE (default) a progress bar will be shown in console. Value A newick object containing the phylogeny with the species in data object. If return.insertions = TRUE the output will be a list of length two containing the newick phylogeny and a data frame equal that provided in data plus a column indicating at which level each species was inserted in the tree. FishTaxaMaker 3 Examples data("taxon_data_PhyloMaker") res_phylo <- FishPhyloMaker(data = taxon_data_PhyloMaker, insert.base.node = TRUE, return.insertions = TRUE, progress.bar = TRUE) FishTaxaMaker Generate a list of species Auxiliary function to obtain taxonomic clas- sification and check the names of species present in species pool Description Generate a list of species Auxiliary function to obtain taxonomic classification and check the names of species present in species pool Usage FishTaxaMaker(data, allow.manual.insert = TRUE) Arguments data A character vector with species names or a community matrix with species names in columns allow.manual.insert Logical, if TRUE (default), the user must type the names of Family and Order of species not found in Fishbase Value List with three elements. - A data frame containing the taxonomic classification of valid species accordingy to Fishbase - A data frame with three columns containing the name of species (s), the Family (f) and Order (o) that ca FishPhyloMaker function - A character vector containing all names of species that was not find in Fishbase 4 neotropical_comm Examples ## Not run: data(neotropical_comm) data_comm <- neotropical_comm[, -c(1, 2)] taxon_data <- FishTaxaMaker(data_comm, allow.manual.insert = TRUE) Characidae Characiformes Characidae Characiformes Characidae Characiformes Loricariidae Siluriformes Characidae Characiformes Cichlidae Cichliformes Crenuchidae Characiformes Gymnotidae Gymnotiformes Loricariidae Siluriformes Loricariidae Siluriformes Loricariidae Siluriformes Loricariidae Siluriformes Heptapteridae Siluriformes Characidae Characiformes Loricariidae Siluriformes Characidae Characiformes ## End(Not run) neotropical_comm Abundance of stream fish species in Parana and Paraguay streams Description A dataset containing the abundance of stream fish species distributed in streams of Parana and Paraguay river Basins PD_defict 5 Usage neotropical_comm Format A data frame with 20 rows and 61 variables: Source Article published in Neotropical Ichthyology doi: 10.1590/1982022420200126 PD_defict Title Calculate the amount of phylogenetic deficit in assemblages Description Title Calculate the amount of phylogenetic deficit in assemblages Usage PD_defict(phylo, data, level = "Congeneric_insertion") Arguments phylo Phylogenetic tree in newick format, can be an object from FishPhyloMaker function data A data frame containing the classification informing the level of insertions. This can be obtained from FishPhyloMaker function level Character indicating which level must be considered in the calculation of PD deficit. default is "Congeneric_insertion" Value A scalar containing the value of PD deficit for the level chosen See Also FishPhyloMaker for phylogeny and data frame containing the classification of insertions 6 taxon_data_PhyloMaker spp_afrotropic List of fish species with occurrence in Afrotropical ecoregion Description A list of species that occur in basins of Afrotropical ecoregion Usage spp_afrotropic Format A character vector with 767 species names: References https://www.nature.com/articles/sdata2017141 taxon_data_PhyloMaker Data frame with species names needed to assemble the phylogenetic tree Description A data frame that can be directly used in FishPhyloMaker to obtain a phylogenetic tree Usage taxon_data_PhyloMaker Format A data frame with taxonomic classification (species, family and order) of 45 species References Species that make up the dataset in the paper published in Neotropical Ichthyology doi: 10.1590/ 1982022420200126 whichFishAdd 7 whichFishAdd Function to inform which species must be added to the mega-tree phy- logeny in the insertion process. Description Function to inform which species must be added to the mega-tree phylogeny in the insertion process. Usage whichFishAdd(data) Arguments data A data frame with three column containing the name of species (s), the Family (f) and Order (o). This can be generated with function FishTaxaMaker Details This function can be used in order to known which species that must be added in the insertion process made by FishPhyloMaker. Value A data frame containing a column informing at which level the species in data must be added. Examples data("taxon_data_PhyloMaker") res_test <- whichFishAdd(data = taxon_data_PhyloMaker) Index ∗ datasets neotropical_comm, 4 spp_afrotropic, 6 taxon_data_PhyloMaker, 6 FishPhyloMaker, 2, 5, 7 FishTaxaMaker, 3, 7 neotropical_comm, 4 PD_defict, 5 spp_afrotropic, 6 taxon_data_PhyloMaker, 6 whichFishAdd, 7 8

bingadsR

cran

Package ‘bingadsR’ October 29, 2022 Type Package Title Get Bing Ads Data via the 'Windsor.ai' API Version 0.1.0 Description Collect your data on digital marketing campaigns from bing Ads using the 'Wind- sor.ai' API <https://windsor.ai/api-fields/>. License GPL-3 URL https://windsor.ai/ Depends R (>= 3.5.0) Imports jsonlite (>= 1.7.2) Suggests knitr, rmarkdown, dplyr, ggplot2, tidyr, curl VignetteBuilder knitr Encoding UTF-8 Language en-US LazyData true RoxygenNote 7.2.1 NeedsCompilation no Author Pablo Sanchez [cre, aut], Windsor.ai [cph] Maintainer Pablo Sanchez <pablosama@outlook.es> Repository CRAN Date/Publication 2022-10-29 09:02:03 UTC R topics documented: fetch_bingads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 my_bingads_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Index 4 1 2 fetch_bingads fetch_bingads fetch_bingads A function to fetch bing Ads data from the windsor.ai API Description fetch_bingads A function to fetch bing Ads data from the windsor.ai API Usage fetch_bingads( api_key, date_from = NULL, date_to = NULL, fields = c("campaign", "clicks", "spend", "impressions", "date") ) Arguments api_key Your api key to access Windsor.ai API date_from The date from which to start getting data in format YYYY-MM-DD date_to The date until which to start getting data in format YYYY-MM-DD fields he fields fetched from the API for a given connector See https://windsor.ai/api- fields/ for details. Value A data frame with the desired data Examples ## Not run: my_bingads_data <- fetch_bingads(api_key = "your api key", date_from = "2022-10-01", date_to = "2022-10-02", fields = c("campaign", "clicks", "spend", "impressions", "date")) ## End(Not run) my_bingads_data 3 my_bingads_data Sample of digital marketing data from bing Ads downloaded by means of the Windsor.ai API. Description A dataset containing sample bing Ads data fetched from windsor.ai API. See more at: https://windsor.ai/ Usage my_bingads_data Format A data frame with 164 rows and 5 variables: campaign name of the campaign clicks number of clicks spend spend data impressions impressions data date date Source https://windsor.ai/ Index ∗ datasets my_bingads_data, 3 fetch_bingads, 2 my_bingads_data, 3 4

far

cran

Package ‘far’ October 13, 2022 Version 0.6-6 Date 2022-08-12 Title Modelization for Functional AutoRegressive Processes Author Damon Julien <julien.damon@gmail.com> Guillas Serge Maintainer Damon Julien <julien.damon@gmail.com> Depends R (>= 2.10.0), nlme, graphics, stats Description Modelizations and previsions functions for Functional AutoRegressive processes using nonparametric methods: functional kernel, estimation of the covariance operator in a subspace, ... License LGPL-2.1 URL https://github.com/Looping027/far NeedsCompilation yes Repository CRAN Date/Publication 2022-08-13 19:30:02 UTC R topics documented: base.simul.far . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 BaseK2BaseC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 coef.far . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 date.fdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 fapply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 far . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 far.cv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 fdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 interpol.matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 invgen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 is.na.fdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 kerfon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1 2 base.simul.far maxfdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 multplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 orthonormalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 plot.fdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 pred.persist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 predict.far . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 predict.kerfon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 select.fdata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 simul.far . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 simul.far.sde . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 simul.far.wiener . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 simul.farx . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 simul.wiener . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 Index 40 base.simul.far Creating functional basis Description Computation of a particular basis in a functional space. Usage base.simul.far(m=24, n=5) Arguments m Number of discretization points n Number of axis Details We consider a sinusoidal basis of the functional space C[0;1] of the continuous functions from [0;1] to R. We compute here the values of the n first (functional) axis at m equi-repartited discretization points in [0;1] (more precisely the point 0, m1 ,..., m−1 m ). Value A matrix of size m x n containing the m values of the n first axis of the basis. Note The chosen basis is orthogonal. The aim of this function is to provide an internal tool for the function simul.farx. BaseK2BaseC 3 Author(s) J. Damon See Also simul.farx Examples print(temp<-base.simul.far(10,3)) print(t(temp)%*%temp) matplot(base.simul.far(100,5),type='l') BaseK2BaseC Changing Basis Description Given the coordinates in the Karhunen-Loève expansion base of the Wiener, compute the coordi- nates in the canonical basis. Usage BaseK2BaseC(x, nb) Arguments x A matrix containing the coordinates in the Karhunen-Loève basis. One obser- vation per column. nb The dimension of the canonical basis consider. By default, the dimension is the same as the Karhunen-Loève one (i.e. number of row of x). Details The Karhunen-Loève expansion is a sum of an infinity of terms, but here the expansion is truncated to a finite number of terms. Empirically, we remark that using twice the dimension of the canonical basis desired for the number of terms in the expansion is a good compromise. Value A object of class fdata with nb discretization points and the same number of observations as x. Author(s) J. Damon 4 coef.far References Pumo, B. (1992). Estimation et Prévision de Processus Autoregressifs Fonctionnels. Applications aux Processus à Temps Continu. PhD Thesis, University Paris 6, Pierre et Marie Curie. See Also simul.wiener, simul.far.wiener Examples data1 <- BaseK2BaseC(x=matrix(rnorm(50),ncol=5,nrow=10), nb=5) multplot(data1,whole=TRUE) coef.far Extract Model Coefficients Description ’coef’ method to extract the linear operator of a FAR model. Usage ## S3 method for class 'far' coef(object, ...) Arguments object An object of type far. ... Other arguments (not used in this case). Details Give the matricial representation of the linear operator express in the canonical basis. See far for more details about the meaning of this operator. If the far model is used on a one dimensional variable or with the joined=TRUE option, then the matrix has a dimension equal to the subspace dimension. In the other case, the dimension of the matrix is equal to the sum of the dimensions of the various subspaces. In such a case, the order of the variables in the matrix is the same as in the vector c(y,x). For instance, if kn=c(3,2) with y="Var1" and x="Var3" then: • The first 3x3 first bloc of the matrix is the autocorrelation of “Var1”. • The 3x2 up right bloc of the matrix is the correlation of “Var3” on “Var1”. • The 2x3 down left bloc of the matrix is the correlation of “Var1” on “Var3”. • The 2x2 down right bloc of the matrix is the autocorrelation of “Var3”. date.fdata 5 Value A square matrix of size (raw and column) equal to the sum of the element of kn. Author(s) J. Damon, S. Guillas See Also far,coef Examples # Simulation of a FARX process data1 <- simul.farx(m=10,n=400,base=base.simul.far(20,5), base.exo=base.simul.far(20,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), cst1=0.0) # Modelization of the FARX process (joined and separate) model1 <- far(data1,kn=4,joined=TRUE) model2 <- far(data1,kn=c(3,1),joined=FALSE) # Calculation of the theoretical coefficients coef.theo <- theoretical.coef(m=10,base=base.simul.far(20,5), base.exo=base.simul.far(20,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), cst1=0.0) # Joined coefficient round(coef(model1),2) coef.theo$rho.T # Separate coefficient round(coef(model2),2) coef.theo$rho.X.Z date.fdata Extract the date of fdata 6 date.fdata Description Extract the date(s) of fdata objects Usage date.fdata(data) Arguments data A fdata object Details The dates are the labels of the functionals observations of the fdata object. fdata are not constructed as ts object so a specific function to obtain the date is useful. Value A vector giving the dates (as character). Author(s) J. Damon See Also fdata Examples # Reading the data library(stats) data(UKDriverDeaths) # Conversion of the data fUKDriverDeaths <- as.fdata(UKDriverDeaths,col=1,p=12,dates=1969:1984, name="UK Driver Deaths") date.fdata(fUKDriverDeaths) fapply 7 fapply Apply functions over a fdata object Description fapply returns a fdata object of the same length as data. Each element of which is the result of applying FUN to the corresponding element of data. Usage fapply(data, FUN, row.names, ...) Arguments data A fdata object FUN the function to be applied. In the case of functions like +, %*%, etc., the function name must be quoted. row.names a vector giving the names describing the results of FUN ... optional arguments to FUN. Details This function has to be used only with fdata objects, unless it stop, returning no value. Value The returned value is a fdata object too. Author(s) J. Damon See Also apply, lapply. Examples # Simulation of a FARX process data1 <- simul.farx(m=10,n=400,base=base.simul.far(20,5), base.exo=base.simul.far(20,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), cst1=0.0) 8 far fapply(data1,sum) multplot(fapply(fapply(data1,abs),cumsum)) far FARX(1) model estimation Description Estimates the parameters of FAR(1) and FARX(1) processes (mean and autocorrelation operator) Usage far(data, y, x, kn, center=TRUE, na.rm=TRUE, joined=FALSE) Arguments data A fdata object. y A vector giving the name(s) of the endogenous variable(s) of the model. x A vector giving the name(s) of the exogenous variable(s) of the model. kn A vector giving the values of the various kn (dimension of plug-in in the algo- rithm). If it not supplied, the default value is one. center Logical. Does the observation need to be centered. na.rm Logical. Does the n.a. need to be removed. joined Logical. If TRUE, the joined (whole) far model is computed, otherwise the model work with the separated variables. Details The models A Functional AutoRegressive of order 1 (FAR(1)) process is, in a general way, defined by the following equation: Tn = ρ (Tn−1 ) + n , n ∈ Z where Tn and n take their values in a functional space (for instance an Hilbertian one), and ρ is a linear operator. n is a strong white noise. Now, let us consider a vector of observations, for instance: (T1,n , ..., Ti,n , ..., Tm,n ) where each Ti,n lives in a one dimension functional space (not necessary the same). In the follow- ing, we will cut this list into two parts: the endogeneous variables Yn (the ones we are interested in), and the exogeneous variables Xn (which influence the endogeneous ones). far 9 Then an order 1 Functional AutoRegressive process with eXogeneous variables (FARX(1)) is de- fined by the equation: Yn = ρ (Yn−1 ) + a (Xn ) + n , n ∈ Z where ρ and a are linear operators in the adequate spaces. Estimation This function estimates the parameters of FAR and FARX models. First, if the mean of the data is not zero (which is required by the model), you can substance this mean using the center option. Moreover, if the data contains NA values, you can work with it using the na.rm option. FAR Estimation The estimation is mainly about estimating the ρ operator. This estimation is done in a appropriate subspace (computed from the variance of the observations). What is important to know is that the best dimension kn for this subspace is not determined by this function. So the user have to supply this dimension using the kn option. A way to chose this dimension is to first use the far.cv function on the history. FARX Estimation The FARX estimation can be realized by two methods: joined or not. The joined estimation is done by “joining” the variables into one and estimating a FAR model on the resulting variable. For instance, with the previous notations, the transformation is: Tn = (Yn , Xn+1 ) and Tn is then a peculiar FAR(1) process. In such a case, you have to use the joined=TRUE oto the interpretation of this operatorption and specify one value for kn (corresponding to the Tn variable). Alternatively, you can choose not to estimate the FARX model by the joined procedure, then kn need to be a vector with a length equal to the number of variables involved in the FARX model (endogeneous and exogeneous). In both procedures, the endogeneous and exogeneous variables are provided through the y and x options respectively. Results The function returns a far object. Use the print, coef and predict functions to get more infor- mations about the model. Value A far object, see details for more informations. Note This function could be used to estimate FAR and FARX with order higher than 1 as a change of variables can transform the process to an order 1 FAR or FARX. For instance, if Tn is a FAR(2) process then Yn = (Tn , Tn−1 ) is a FAR(1) process. However, this is not a basic use of this function and may require a hard work of the user to get the result. 10 far Author(s) J. Damon References Besse, P. and Cardot, H. (1996). Approximation spline de la prévision d’un processus fonctionnel autorégressif d’ordre 1. Revue Canadienne de Statistique/Canadian Journal of Statistics, 24, 467– 487. Bosq, D. (2000) Linear Processes in Function Spaces: Theory and Applications, (Lecture Notes in Statistics, Vol. 149). New York: Springer-Verlag. See Also predict.far, far.cv Examples # Simulation of a FARX process data1 <- simul.farx(m=10,n=400,base=base.simul.far(20,5), base.exo=base.simul.far(20,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), cst1=0.0) # Cross validation (joined and separate) model1.cv <- far.cv(data=data1, y="X", x="Z", kn=8, ncv=10, cvcrit="X", center=FALSE, na.rm=FALSE, joined=TRUE) model2.cv <- far.cv(data=data1, y="X", x="Z", kn=c(4,4), ncv=10, cvcrit="X", center=FALSE, na.rm=FALSE, joined=FALSE) print(model1.cv) print(model2.cv) k1 <- model1.cv$minL2[1] k2 <- model2.cv$minL2[1:2] # Modelization of the FARX process (joined and separate) model1 <- far(data=data1, y="X", x="Z", kn=k1, center=FALSE, na.rm=FALSE, joined=TRUE) model2 <- far(data=data1, y="X", x="Z", kn=k2, center=FALSE, na.rm=FALSE, joined=FALSE) print(model1) print(model2) far.cv 11 far.cv Cross Validation for FARX(1) model Description Cross Validation for FAR(1) and FARX(1) models Usage far.cv(data, y, x, kn, ncv, cvcrit, center=TRUE, na.rm=TRUE, joined=FALSE) Arguments data A fdata object. y A vector giving the name(s) of the endogenous variable(s) of the model. x A vector giving the name(s) of the exogenous variable(s) of the model. kn A vector giving the maximum values of the various kn (dimension of plug-in in the algorithm). If it not supplied, the number of discretization point is used. ncv Number of observations used to the cross validation cvcrit A vector of characters. Name of the variable used to measure the errors (y by default). center Logical. Does the observation need to be centered. na.rm Logical. Does the n.a. need to be removed. joined Logical. If TRUE, the joined (whole) far model is computed, otherwise the model work with the separated variables. Details In order to perform good forecasting with a FAR or FARX model, you need to determine the di- mensions kn of the subspace in which the linear operator is estimated (see far for more details). This function helps the user to do this choice by performing a cross validation on a test sample. The usage is close of the far function, so we will discuss about the options which differ. First, the kn option is used to restrict the values searched: this is a vector containing the maxima values. As in far, the dimension of this vector is function of the number of variables involved in the model and the type of estimation done (joined or not). ncv is the number of observation used to test the models. If it is not provided, the function use the last fifth of the observations in data. In such a case, the four first fifth are used to estimates the models. This is in general a good compromise. Finally, cvcrit list the variables used to test the models. If more than one variable is provided, the test is calculated as a mean of the errors over all the variables. The criteria used to test the (functional) errors are the norms L1, L2, L infinite, L1 on the maxima, L2 on the maxima, and L infinite on the maxima. 12 far.cv Value It is a LIST with the following elements cv Matrix giving the various errors (L1, L2, L infinite, L1 on the maxima, L2 on the maxima, L infinite on the maxima) for the tested values of kn minL1 A vector corresponding to the row of cv where the L1 error minima is obtained minL2 A vector corresponding to the row of cv where the L2 error minima is obtained minLinf A vector corresponding to the row of cv where the L infinite error minima is obtained minL1max A vector corresponding to the row of cv where the L1 maxima’s error minima is obtained minL2max A vector corresponding to the row of cv where the L2 maxima’s error minima is obtained minLinfmax A vector corresponding to the row of cv where the L infinite maxima’s error minima is obtained Author(s) J. Damon See Also far, fdata Examples # Simulation of a FARX process data1 <- simul.farx(m=10,n=400,base=base.simul.far(20,5), base.exo=base.simul.far(20,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), cst1=0.0) # Cross validation (joined and separate) model1.cv <- far.cv(data=data1, y="X", x="Z", kn=8, ncv=10, cvcrit="X", center=FALSE, na.rm=FALSE, joined=TRUE) model2.cv <- far.cv(data=data1, y="X", x="Z", kn=c(4,4), ncv=10, cvcrit="X", center=FALSE, na.rm=FALSE, joined=FALSE) print(model1.cv) print(model2.cv) k1 <- model1.cv$minL2[1] k2 <- model2.cv$minL2[1:2] # Modelization of the FARX process (joined and separate) model1 <- far(data=data1, y="X", x="Z", kn=k1, center=FALSE, na.rm=FALSE, joined=TRUE) fdata 13 model2 <- far(data=data1, y="X", x="Z", kn=k2, center=FALSE, na.rm=FALSE, joined=FALSE) print(model1) print(model2) fdata Functional Data class Description Object of class ’fdata’ and its methods. Usage as.fdata(object,...) as.fdata.matrix(object,..., col, p, dates, name) as.fdata.list(object,..., dates, name) Arguments object A matrix or a list. col A vector giving the names of the variables to include in the ’fdata’ object. p A real value giving the number of discretization point chosen. dates A vector of character containing the dates of the observations. name A vector of character containing the names of the variables (generated if not provided). ... Additional arguments. Details Fdata objects are mainly used to modelize functional data in the purpose of computing functional autoregressive model by the far and kerfon functions. An fdata is composed of one or several variables. Each ones is a functional time series. To be more precise, every variable got a functional data by element of the dates (explicitly given or implicitly deduced). So the number of functional observations is a common data. In the contrary, each variable can be expressed in a different functional space. For example, if you got two variables, Temperature and Wind, measured during 30 days. Choosing a daily rep- resentation, the fdata will contain a 30 elements long dates vector. Nevertheless, the variables measurement can be different. If Temperature is measured every hour and Wind every two hours, the fdata object can handle such a representation. The only constraint is to get a regular measure- ment: no changes in the methodology. Basically, the fdata objects are discrete measurements but the modelization which can be used on it will make it functional. Indeed, The first methods implemented as far and kerfon use a linear approximation, but more sophisticate modelization, as splines or wavelets approximations may come. 14 interpol.matrix Value An object of class fdata. Author(s) J. Damon See Also far, multplot, maxfdata, kerfon. Examples # Reading of the data library(stats) data(UKDriverDeaths) # Making the data of class 'fdata' fUKDriverDeaths <- as.fdata(UKDriverDeaths,col=1,p=12,dates=1969:1984, name="UK Driver Deaths") summary(fUKDriverDeaths) # ploting of the data : whole and 1 year par(mfrow=c(2,1)) plot(fUKDriverDeaths,xval=1969+(1:192)/12,whole=TRUE, name="Whole Evolution : ") plot(fUKDriverDeaths,date="1984",xval=1:12, name="Evolution during year 1984 : ") # Matrix conversion print(as.fdata(matrix(rnorm(50),10,5))) print(as.fdata(matrix(rnorm(500),100,5),col=1:2,p=5)) # List Conversions print(as.fdata(list("X"=matrix(rnorm(100),10,10), "Z"=matrix(rnorm(50),5,10)))) interpol.matrix Interpolation matrix Description Calculate the matrix giving the linear interpolation of regularly spaced points. Usage interpol.matrix(n = 12, m = 24, tol = sqrt(.Machine$double.eps)) invgen 15 Arguments n Number (integer) of points in output space m Number (integer) of points in the input function (or space) tol A relative tolerance to detect zero singular values. Details The general principle is, considering a function for which we know values at m equally spaced points (for instance 1/m, 2/m, ..., 1), to compute the matrix giving the linear approximation of n equally spaced points (for instance 1/n, 2/n, ..., 1). The function works whether n or m is the largest. The function is vectorized, so m and n can be vectors of integers. In this case, they have to be of the same size and the resulting matrix is block diagonal. Value A nxm matrix if they are integer, else a sum(n)xsum(m) matrix. Author(s) J. Damon See Also theoretical.coef, simul.far or simul.farx. Examples mat1 <- interpol.matrix(12,24) mat2 <- interpol.matrix(c(3,5),c(12,12)) print(mat1 %*% base.simul.far(24,5)) print(mat2 %*% base.simul.far(24,5)) invgen Generalized inverse of a Matrix Description Calculates the Moore-Penrose generalized inverse of a matrix X. Usage invgen(a, tol) 16 is.na.fdata Arguments a Matrix for which the Moore-Penrose inverse is required. tol A relative tolerance to detect zero singular values. Value A Moore-Penrose generalized inverse matrix for X. See Also solve,svd,eigen Examples mat1<-matrix(rnorm(100),ncol=10) print(invgen(mat1)) is.na.fdata Not Available / “Missing” Values Description The generic function is.na returns a logical vector of the same “form” as its argument x, containing TRUE for those elements marked NA or NaN (!) and FALSE otherwise. dim, dimnames and names attributes are preserved. Usage ## S3 method for class 'fdata' is.na(x) Arguments x A fdata object Details An observation is considered as NA if any of its values is NA. Value A matrix of Logical values giving as rows the variables of x and as columns the observation. Author(s) J. Damon kerfon 17 See Also NA Examples # Reading of the data library(stats) data(UKDriverDeaths) UKDriverDeaths[20]<-NA # Making the data of class 'fdata' fUKDriverDeaths <- as.fdata(UKDriverDeaths,col=1,p=12,dates=1969:1984, name="UK Driver Deaths") summary(fUKDriverDeaths) is.na(fUKDriverDeaths) kerfon Functional Kernel estimation Description Modelization of fdata using functional kernel. Usage kerfon(data, x, r, hmin, hmax, na.rm=TRUE) Arguments data A fdata object. x The name of the studied variable. r Number of observations used to cross validate the model. hmin Minimal value of the bandwidth. hmax Maximal value of the bandwidth. na.rm Logical. Does the n.a. need to be removed. Details This function constructs a functional kernel model and performs the estimation of it’s bandwidth. One nonparametric way to deal with the conditional expectation ρ(x) = IE [Xi |Xi−1 = x ], where (Xi ) is a $H$-valued process, is to consider a predictor inspired by the classical kernel regression, as in Nadaraja and Watson. This estimator is defined by : n−1   P kXi −xkH Xi+1 · K hn i=1 ρ̂hn (x) = n−1   ,x ∈ H P kXi −xkH K hn i=1 18 kerfon Where K is a kernel, k.kH is the norm in H, and hn is the bandwidth (∈ IR+ ∗ ). The function kerfon use the cross validation to determinate a value for hn . This method have been chosen because of the lack of theoretical results about this model. The parameters hmin and hmax are used, when provided, to control the permissible values of hn . By default, those parameters are respectively equals to σ/8 and 4 ∗ σ, where σ is the estimated squared root of the variance operator of X. To choose the value of hn , you need to provide the same value for both hmin and hmax. During the cross-validation, considering that the fdata object x contains n observations, the function use the first (n − r) observations as the past values, and compute the mean square norm of the errors on the last r observations. Of course, if the model created is then used to compute prediction through predict.kerfon, the whole set of observations (the n observations) are used as the past values. As fdata object may contains several variables, a way is provided to select the studied variable (the function only works with one variable for the moment). Value A kerfon object. A method for the print function is provided. For information, the object is a list with the following elements : call the call of the function. h the bandwidth (three values : optimal, minimum, maximum) x the name of the chosen variable xdata the past values for x ydata the associated values for x Author(s) J. Damon See Also predict.kerfon Examples # Simulation of a FARX process data1 <- simul.farx(m=10,n=400,base=base.simul.far(20,5), base.exo=base.simul.far(20,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), cst1=0.0) # Cross validation model1 <- kerfon(data=data1, x="X", r=10, na.rm=TRUE) print(model1) maxfdata 19 maxfdata Maxima of functional data Description Extract the maxima series from a functional data object. Usage maxfdata(data) Arguments data A fdata object Value A fdata object. Author(s) J. Damon See Also fapply Examples # Simulation of a FARX process data1 <- simul.farx(m=10,n=400,base=base.simul.far(20,5), base.exo=base.simul.far(20,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), cst1=0.0) print(data2 <- maxfdata(data1)) print(unclass(data2)) 20 multplot multplot Multivariate plots Description Multivariate plots of Functional Data (more precisely fdata objects). Usage multplot(object, ...) ## S3 method for class 'fdata' multplot(object, date = 1, xval = NULL, name = NULL, legend = FALSE, yleg, xlab = NULL, ylab = NULL, main = NULL, whole = FALSE, ...) Arguments object An fdata object for which a multplot is desired. date String vector. List of the dates to work with. xval Numerical vector. Values of the axis x. name String vector. The set of variables to plot. legend Boolean. Plot a legend ? yleg Numeric. Where to put the legend box (y value). xlab String. Title of the axis x. ylab String. Title of the axis y. main String. Title of the plot. whole Boolean. A global plot (TRUE) or a plot by day (FALSE) ... Additional arguments. Details This function facilitate the plotting of fdata objects. It is dedicated to multivariate plots, please take a look at plot.fdata if you need univariate plots in one graphic. The default behaviour is to produce one plot containing all the variables of the observation called "1". If you want less variables, use the name argument. If you need more observations, use the date argument. When provided, the xval argument allow you to change the labels of the x-axis. It is also possible to plot the complete series on the same plot using the whole argument. Moreover a legend facility is provided using the legend and yleg arguments. Author(s) J. Damon orthonormalization 21 See Also fdata, plot.fdata. Examples # Simulation of a FARX process data1 <- simul.farx(m=10,n=100,base=base.simul.far(20,5), base.exo=base.simul.far(20,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), cst1=0.0) # 2 variables : X et Z # number of points per curve : 10 # number of curves : 100 # corresponding dates date.fdata(data1) multplot(data1) # plot the date "1" of the variables "X" and "Z" multplot(data1,legend=TRUE) # Same thing with a legend multplot(data1,legend=TRUE,yleg=-0.5) # same thing with a legend misplaced multplot(data1,main="day 1",legend=TRUE,xlab="hour", ylab="object of study") par(mfrow=c(1,3)) multplot(data1,date=c("3","4","5")) # days "3", "4" and "5" are plotted par(mfrow=c(1,1)) # to plot the whole series, we used whole = TRUE # but we have to give the x values multplot(data1,xval=seq(from=0,to=99.9,by=0.1),whole=TRUE) # to plot a subset of the series, # it is recommended to create a subset object with select.fdata data2 <- select.fdata(data1,date=c("4","5","6")) multplot(data2,xval=seq(from=4,to=6.9,by=0.1),whole=TRUE) orthonormalization Orthonormalization of a set of a matrix Description Gram-Schmidt orthogonalization of a matrix considering its columns as vectors. Normalization is provided as will. 22 orthonormalization Usage orthonormalization(u, basis=TRUE, norm=TRUE) Arguments u a matrix (n x p) representing n different vectors in a n dimensional space basis does the returned matrix have to be a basis norm does the returned vectors have to be normed Details This is a simple application of the Gram-Schmidt algorithm of orthogonalization (please note that this process was presented first by Laplace). The user provides a set of vector (structured in a matrix) and the function calculate a orthogonal basis of the same space. If desired, the returned basis can be normed, or/and completed to cover the hole space. If the number of vectors in u is greater than the dimension of the space (that is if n > p), only the first p columns are taken into account to computed the result. A warning is also provided. The only assumption made on u is that the span space is of size min(n,p). In other words, there must be no colinearities in the initial set of vector. Value The orthogonalized matrix obtained from u where the vector are arranged in columns. If basis is set to TRUE, the returned matrix is squared. Author(s) J. Damon Examples mat1 <- matrix(c(1,0,1,1,1,0),nrow=3,ncol=2) orth1 <- orthonormalization(mat1, basis=FALSE, norm=FALSE) orth2 <- orthonormalization(mat1, basis=FALSE, norm=TRUE) orth3 <- orthonormalization(mat1, basis=TRUE, norm=TRUE) crossprod(orth1) crossprod(orth2) crossprod(orth3) plot.fdata 23 plot.fdata Plot Functional Data Description Plot Functional Data (more precisely fdata objects). Usage ## S3 method for class 'fdata' plot(x,...,date, xval, name, main, whole, separator) Arguments x A fdata object. date A vector of character giving the chosen dates. xval A numerical vector giving the values to appear on the x axis. name A vector of character giving the plotted variables. main an overall title for the plot. whole Logical. If TRUE all the observations are plot on the same graphic. separator Logical. It will be used when whole=TRUE. If TRUE then dashed lines are plotted to separated the observations. ... Additional arguments to the plot. Details This function facilitate the plotting of fdata objects. It is dedicated to univariate plots, please take a look at multplot if you need multivariate plots in one graphic. The default behaviour is to plot the observation called "1" of all the variables available in x (so it will produce as many plots as the number of variables). If you want less variables, use the name argument. If you need more observations, use the date argument. When provided, the xval argument allow you to change the labels of the x-axis. It is also possible to plot the complete series on the same plot using the whole argument. In this case, the separator allow you to draw line to distinguish the different observations of the functional data. Author(s) J. Damon See Also fdata, multplot. 24 pred.persist Examples # Reading of the data library(stats) data(UKDriverDeaths) # Making the data of class 'fdata' fUKDriverDeaths <- as.fdata(UKDriverDeaths, col=1, p=12, dates=1969:1984, name="UK Driver Deaths") summary(fUKDriverDeaths) # plotting of the data : whole and 1 year par(mfrow=c(2,1)) plot(fUKDriverDeaths, xval=1969+(1:192)/12, whole=TRUE, name="Whole Evolution : ", separator=TRUE) plot(fUKDriverDeaths, date="1984", xval=1:12, name="Evolution during year 1984 : ") pred.persist Forecasting using functional persistence Description Compute prediction of functional data using the persistence. Usage pred.persist(data, x, na.rm=TRUE, label, positive=FALSE) Arguments data A fdata object. x A vector of character giving the names of the variables predicted. na.rm Logical. Does the n.a. need to be removed. label A vector of character giving the dates to associate to the predicted observations. positive Logical. Does the result must be forced to positive values. Details The persistence model is a beautiful way to name the simplest model ever. This model just suppose that the next observation will be equal to the previous one, that is to say, noting X̂n the prediction for Xn that we "compute" : X̂n+1 = Xn Of course, the intrinsic purpose of this model is to be a comparison for more complicated models. predict.far 25 The x option is provided to select the variable to predict, using the label option value as the labels for the new observations. Notices that the output as the same length as the input as it is only a shift in time. In some special context, the user may need to suppress the na.rm observations with the na.rm option, or force the prediction to be positive with the positive option (in this case the maximum of 0 and the past value is computed). Value A fdata object. Note This has been more instinctive to call this function predict.persist but, due to the naming mechanism introduced by the object oriented programming, this would have reefer to the predict method for the persist objects. As it isn’t the meaning of this function, we preferred the name pred.persist. Author(s) J. Damon See Also predict.far,predict.kerfon. Examples # Simulation of a FARX process data1 <- simul.farx(m=10,n=40,base=base.simul.far(20,5), base.exo=base.simul.far(20,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), cst1=0.0) print(data2 <- pred.persist(data1,x="X",label="41")) print(unclass(select.fdata(data1,date=paste(38:40)))$X) print(unclass(select.fdata(data2,date=paste(39:41)))) predict.far Forecasting of FARX(1) model Description Forecasting using FAR(1) or FARX(1) model 26 predict.far Usage ## S3 method for class 'far' predict(object, ..., newdata=NULL, label, na.rm=TRUE, positive=FALSE) Arguments object A far object result of the far function. newdata A data matrix (one column for each observation) used to predict the FAR(1) model from the values in newdata, or NULL to predict one step forward with the data in object. label A vector of character giving the dates to associate to the predicted observations. na.rm Logical. Does the n.a. need to be removed. positive Logical. Does the result must be forced to positive values. ... Additional arguments. Details This function computes one step forward prediction for a far model. Use the newdata option to input the past values, and the label option value to define the labels for the new observations. Notices that the output as the same length as newdata in the case of a FAR model, and the length of newdata minus one in the case of a FARX model. This is due to the time shift of the exogeneous variable: Xt+1 and Yt are used in the computation of Ŷt+1 . In some special context, the user may need to suppress the na.rm observations with the na.rm option, or force the prediction to be positive with the positive option (in this case the result will be maximum of 0 and the predicted value). Value A fdata object. Author(s) J. Damon See Also far, pred.persist, predict.kerfon. Examples # Simulation of a FARX process data1 <- simul.farx(m=10,n=400,base=base.simul.far(20,5), base.exo=base.simul.far(20,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), predict.kerfon 27 cst1=0.0) # Cross validation (joined and separate) model1.cv <- far.cv(data=data1, y="X", x="Z", kn=8, ncv=10, cvcrit="X", center=FALSE, na.rm=FALSE, joined=TRUE) model2.cv <- far.cv(data=data1, y="X", x="Z", kn=c(4,4), ncv=10, cvcrit="X", center=FALSE, na.rm=FALSE, joined=FALSE) print(model1.cv) print(model2.cv) k1 <- model1.cv$minL2[1] k2 <- model2.cv$minL2[1:2] # Modelization of the FARX process (joined and separate) model1 <- far(data=data1, y="X", x="Z", kn=k1, center=FALSE, na.rm=FALSE, joined=TRUE) model2 <- far(data=data1, y="X", x="Z", kn=k2, center=FALSE, na.rm=FALSE, joined=FALSE) # Predicting values pred1 <- predict(model1,newdata=data1) pred2 <- predict(model2,newdata=data1) # Persistence persist1 <- pred.persist(select.fdata(data1,date=1:399),x="X") # Real values real1 <- select.fdata(data1,date=2:400) errors0 <- persist1[[1]]-real1[[1]] errors1 <- pred1[[1]]-real1[[1]] errors2 <- pred2[[1]]-real1[[1]] # Norm of observations summary(real1) # Persistence summary(as.fdata(errors0)) # FARX models summary(as.fdata(errors1)) summary(as.fdata(errors2)) predict.kerfon Forecasting of functional kernel model Description Computation of the prediction based on a functional kernel model Usage ## S3 method for class 'kerfon' predict(object, ..., newdata=NULL, label, na.rm=TRUE, positive=FALSE) 28 predict.kerfon Arguments object A kerfon object result of the kerfon function. newdata A fdata object used in the kerfon model to compute the prediction, or NULL to predict one step forward with the data in object. label A vector of character giving the dates to associate to the predicted observations. na.rm Logical. Does the n.a. need to be removed. positive Logical. Does the result must be forced to positive values. ... Additional arguments. Details This function computes one step forward prediction for a kerfon model. Use the newdata option to input the past values, and the label option value to define the labels for the new observations. Notices that the output as the same length as newdata. In some special context, the user may need to suppress the na.rm observations with the na.rm option, or force the prediction to be positive with the positive option (in this case the result will be maximum of 0 and the predicted value). Value A fdata object. Author(s) J. Damon See Also kerfon Examples # Simulation of a FARX process data1 <- simul.farx(m=10,n=400,base=base.simul.far(20,5), base.exo=base.simul.far(20,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), cst1=0.0) # Cross validation model1 <- kerfon(data=data1, x="X", r=10, na.rm=TRUE) print(model1) # Predicting values pred1 <- predict(model1,newdata=select.fdata(data1,date=1:399)) select.fdata 29 # Persistence persist1 <- pred.persist(select.fdata(data1,date=1:399),x="X") # Real values real1 <- select.fdata(data1,date=2:400) errors0 <- persist1[[1]]-real1[[1]] errors1 <- pred1[[1]]-real1[[1]] # Norm of observations summary(real1) # Persistence summary(as.fdata(errors0)) # kerfon model summary(as.fdata(errors1)) select.fdata Subscript of fdata Description Use this function to subscript some functional observations of a functional data. Usage select.fdata(data, date, name) Arguments data A fdata object. date A vector of character containing the chosen dates (could be NULL). name A vector giving the chosen name (could be NULL). Details This function select one or several variables from data and can also subset the dates. This is useful in order to study the endogenous variables of a FARX process. Value A fdata object. Author(s) J. Damon See Also fdata 30 simul.far Examples # Simulation of a FARX process data1 <- simul.farx(m=10,n=400,base=base.simul.far(20,5), base.exo=base.simul.far(20,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), cst1=0.0) print(data1) print(data1.X <- select.fdata(data1,name="X")) print(data2 <- select.fdata(data1,date=paste((1:5)*5))) date.fdata(data2) simul.far FAR(1) process simulation Description Simulation of a FAR process using a Gram-Schmidt basis. Usage simul.far(m=12, n=100, base=base.simul.far(24, 5), d.rho=diag(c(0.45, 0.9, 0.34, 0.45)), alpha=diag(c(0.5, 0.23, 0.018)), cst1=0.05) Arguments m Integer. Number of discretization points. n Integer. Number of observations. base A functional basis expressed as a matrix, as the matrix created by base.simul.far or with orthonormalization. d.rho Numerical matrix. Expression of the first bloc of the linear operator in the Gram- Schmidt basis. alpha Numerical matrix. Expression of the first bloc of the covariance operator in the Gram-Schmidt basis. cst1 Numeric. Perturbation coefficient on the linear operator. simul.far 31 Details This function simulate a FAR(1) process with a strong white noise. The simulation is realized in two steps. First step, the function compute a FAR(1) process Tn in a functional space (that we call in the sequel H) using a simple equation and the d.rho, alpha and cst parameters. Second step, the process Tn is projected in the canonical basis using the base linear projector. The base basis need to be a orthonormal basis wide enought. In the contrary, the function use the orthonormalization function to make it so. Notice that the size of this matrix corresponds to the dimension of the "modelization space" H (let’s call it m2 ). Of course, the larger m2 the better the functionnal approximation is. Whatever, keep in mind that m2=2m is a good compromise, in order to avoid the memory limits. In H, the linear operator ρ is expressed as: d.rho   0 0 eps.rho Where d.rho is the matrix provided in the call, the two 0 are in fact two blocks of 0, and eps.rho is a diagonal matrix having on his diagonal the terms: (εk+1 , εk+2 , . . . , εm2 ) where cst1 1 − cst1 εi = + i2 ei and k is the length of the d.rho diagonal. The d.rho matrix can be viewed as the information and the eps.rho matrix as a perturbation. In this logic, the norm of eps.rho need to be smaller than the one of d.rho. In H, C T , the covariance operator of Tn , is defined by: m2 ∗ alpha   0 0 eps.alpha Where alpha is the matrix provided in the call, the two 0 are in fact two blocks of 0, and eps.alpha is a diagonal matrix having on his diagonal the terms: (k+1 , k+2 , . . . , m2 ) where cst1 i = i Value A fdata object containing one variable ("var") which is a FAR(1) process of length n with p dis- cretization points. 32 simul.far.sde Note To simulate Tn , the function creates a white noise En having the following covariance operator: C T − ρ ∗ C T ∗ t(ρ) where t(.) is the transposition operator. Tn is the computed using the equation: Tn+1 = ρ ∗ Tn + En Author(s) J. Damon, S. Guillas See Also simul.far.sde, simul.far.wiener, simul.farx, simul.wiener, base.simul.far. Examples far1 <- simul.far(m=64,n=100) summary(far1) print(far(far1,kn=4)) par(mfrow=c(2,1)) plot(far1,date=1) plot(select.fdata(far1,date=1:5),whole=TRUE,separator=TRUE) simul.far.sde FAR-SDE process simulation Description Simulation of a FAR process following an Stochastic Differential Equation Usage simul.far.sde(coef=c(0.4, 0.8), n=80, p=32, sigma=1) Arguments coef Numerical vertor. It contains the two values of the coefficients (a1 and a2 , see details for more informations). n Integer. The number of observations generated. p Integer. The number of discretization points. sigma Numeric. The standard deviation (see details for more informations). simul.far.sde 33 Details This function implements the simulation proposed by Besse and Cardot (1996) to simulate a FAR process following the Stochastic Differential Equation: dX (2) + a2 .dX + a1 .X = sigma.dW Where dX (2) and dX stand respectively for the second and first derivate of the process X, and W is a brownian process. The coefficients a1 and a2 are the two first elements of coef. The simulation use a order one approximation inspired by the work of Milstein, as described in Besse and Cardot (1996). Value A fdata object containing one variable ("var") which is a FAR(1) process of length n with p dis- cretization points. Author(s) J. Damon References Besse, P. and Cardot, H. (1996). Approximation spline de la prévision d’un processus fonctionnel autorégressif d’ordre 1. Revue Canadienne de Statistique/Canadian Journal of Statistics, 24, 467– 487. See Also simul.far, simul.far.wiener, simul.farx, simul.wiener. Examples far1 <- simul.far.sde() summary(far1) print(far(far1,kn=2)) par(mfrow=c(2,1)) plot(far1,date=1) plot(select.fdata(far1,date=1:5),whole=TRUE,separator=TRUE) 34 simul.far.wiener simul.far.wiener FAR(1) process simulation with Wiener noise Description Simulation of a FAR(1) process using a Wiener noise. Usage simul.far.wiener(m=64, n=128, d.rho=diag(c(0.45, 0.9, 0.34, 0.45)), cst1=0.05, m2=NULL) Arguments m Integer. Number of discretization points. n Integer. Number of observations. d.rho Numerical matrix. Expression of the first bloc of the linear operator in the Karhunen-Loève basis. cst1 Numeric. Perturbation coefficient on the linear operator. m2 Integer. Length of the Karhunen-Loève expansion (2m by default). Details This function simulate a FAR(1) process with a Wiener noise. As for the simul.wiener, the func- tion use the Karhunen-Loève expansion of the noise. The FAR(1) process, defined by its linear operator (see far for more details), is computed in the Karhunen-Loève basis then projected in the natural basis. The parameters given in input (d.rho and cst1) are expressed in the Karhunen-Loève basis. The linear operator, expressed in the Karhunen-Loève basis, is of the form: d.rho   0 0 eps.rho Where d.rho is the matrix provided in ths call, the two 0 are in fact two blocks of 0, and eps.rho is a diagonal matrix having on his diagonal the terms: (εk+1 , εk+2 , . . . , εm2 ) where cst1 1 − cst1 εi = + i2 ei and k is the length of the d.rho diagonal. The d.rho matrix can be viewed as the information and the eps.rho matrix as a perturbation. In this logic, the norm of eps.rho need to be smaller than the one of d.rho. simul.farx 35 Value A fdata object containing one variable ("var") which is a FAR(1) process of length n with m dis- cretization points. Author(s) J. Damon References Pumo, B. (1992). Estimation et Prévision de Processus Autoregressifs Fonctionnels. Applications aux Processus à Temps Continu. PhD Thesis, University Paris 6, Pierre et Marie Curie. See Also fdata, far , simul.far.wiener. Examples far1 <- simul.far.wiener(m=64,n=100) summary(far1) print(far(far1,kn=4)) par(mfrow=c(2,1)) plot(far1,date=1) plot(select.fdata(far1,date=1:5),whole=TRUE,separator=TRUE) simul.farx FARX(1) process simulation Description Simulation of functional data with exogenous variables using a Gram-Schmidt basis. Usage simul.farx(m=12,n=100,base=base.simul.far(24,5), base.exo=base.simul.far(24,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), cst1=0.05) theoretical.coef(m=12,base=base.simul.far(24,5), base.exo=NULL, d.rho=diag(c(0.45,0.90,0.34,0.45)), d.a=NULL, d.rho.exo=NULL, 36 simul.farx alpha=diag(c(0.5,0.23,0.018)), alpha.conj=NULL, cst1=0.05) Arguments m Integer. Number of discretization points. n Integer. Number of observations. base A functional basis expressed as a matrix, as the matrix created by base.simul.far or with orthonormalization. base.exo A functional basis expressed as a matrix, as the matrix created by base.simul.far or with orthonormalization. d.rho Numerical matrix. Part of the linear operator in the Gram-Schmidt basis (see details for more informations). d.a Numerical matrix. Part of the linear operator in the Gram-Schmidt basis (see details for more informations). d.rho.exo Numerical matrix. Part of the linear operator in the Gram-Schmidt basis (see details for more informations). alpha Numerical matrix. Part of the linear operator in the Gram-Schmidt basis (see details for more informations). alpha.conj Numerical matrix. Part of the linear operator in the Gram-Schmidt basis (see details for more informations). cst1 Numeric. Perturbation coefficient on the linear operator. Details The simul.farx function simulates a FARX(1) process with one endogeneous variable, one exoge- neous variable and a strong white noise. To do so, the function uses the fact that a FARX(1) model can be seen as a FAR(1) model in a wider space. Therefore, the method is very similar to the one used by the function simul.far. The simulation is realized in two steps. First step, the function compute a FAR(1) process Tn in a functional space (that we call in the sequel H) using a simple equation and the given parameters. Tn is of the form (T1n , T2n ) where T1n and T2n are respectively the endogeneous and the exogeneous parts of the process. Second step, the process Tn is projected in the canonical basis using the base and base.exo linear projectors to give the endogeneous (Xn ) and the exogeneous (Zn ) variables respectively. Those two basis need to be orthonormal and wide enought. In the contrary, the function use the orthonormalization function to make it so. Notice that the size of this matrix corresponds to the dimension of the "modelization space" H (let’s call it m2 = m12 + m22 ). Of course, the larger m2 the better the functionnal approximation is. Whatever, keep in mind that m2=2m is a good compromise, in order to avoid the memory limits. In H, the linear operator ρ is expressed as: d.a   d.rho.mod 0 d.rho.exo.mod simul.farx 37 Where d.rho.mod and d.rho.exo.mod are modified version of the provided d.rho and d.rho.exo respectively to avoid 0 on their diagonal. More precisely, the 0 on their diaginals are replaced by: (εk+1 , εk+2 , . . . , εm2 ) where cst1 1 − cst1 εi = + i2 ei and k is the position in the d.rho or d.r.ho.exo diagonal. In H, C T , the covariance operator of Tn , is defined by:   alpha.mod alpha.conj.mod t(alpha.conj.mod) alpha.exo Where alpha.mod and alpha.exo.mod are modified versions of m12 ∗alpha and m22 ∗alpha.conj respectively to avoid 0 on their diagonal. More precisely, the 0 on their diaginals are replaced by:
k+1 , k+2 , . . . , m2b where cst1 i = i alpha.exo is a matrix representation of the covariance operator of T2n and is obtained by inverting the following relation: alpha.conj.mod = d.rho.exo.mod∗alpha.conj.mod∗t(d.rho.mod)+d.rho.exo.mod∗mod.alpha∗t(d.a) The theoretical.coef function is provided to help the user making comparison. Calling this function with the same parameters that where used in a simulation (realized with simul.farx or simul.far), we obtain the parameters used internaly by the function to make the simulation. Those values can therefore be compared to those obtained with the estimation function far (examples are provided below). Value A fdata object containing two variables ("X" the endogeous variable, and "Z" the exogeneous variable) which is a FARX(1) process of length n with p discretization points. Note To simulate Tn , the function creates a white noise En having the following covariance operator: C T − ρ ∗ C T ∗ t(ρ) where t(.) is the transposition operator. Tn is the computed using the equation: Tn+1 = ρ ∗ Tn + En 38 simul.wiener Author(s) J. Damon, S. Guillas See Also simul.far.sde, simul.far.wiener, simul.far, simul.wiener. Examples # Simulation of a FARX process data1 <- simul.farx(m=10,n=400,base=base.simul.far(20,5), base.exo=base.simul.far(20,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), cst1=0.0) # Modelisation of the FARX process (joined and separate) model1 <- far(data1,k=4,joined=TRUE) model2 <- far(data1,k=c(3,1),joined=FALSE) # Calculation of the theoretical coefficients coef.theo <- theoretical.coef(m=10,base=base.simul.far(20,5), base.exo=base.simul.far(20,5), d.a=matrix(c(0.5,0),nrow=1,ncol=2), alpha.conj=matrix(c(0.2,0),nrow=1,ncol=2), d.rho=diag(c(0.45,0.90,0.34,0.45)), alpha=diag(c(0.5,0.23,0.018)), d.rho.exo=diag(c(0.45,0.90,0.34,0.45)), cst1=0.0) # Joined coefficient round(coef(model1),2) coef.theo$rho.T # Separate coefficient round(coef(model2),2) coef.theo$rho.X.Z simul.wiener Wiener process simulation Description Simulation of Wiener processes. simul.wiener 39 Usage simul.wiener(m=64, n=1, m2=NULL) Arguments m Integer. Number of discretization points. n Integer. Number of observations. m2 Integer. Length of the Karhunen-Loève expansion (2m by default). Details This function use the known Karhunen-Loève expansion of Wiener processes to simulate observa- tions of such a process. The option m2 is internally used to set the length of the expansion. This expansion need to be larger than the number of discretization points, but a too important value may slow down the generation. The default value as been chosen as a compromise. Value A fdata object containing one variable ("var") which is a Wiener process of length n with m dis- cretization points. Author(s) J. Damon References Pumo, B. (1992). Estimation et Prévision de Processus Autoregressifs Fonctionnels. Applications aux Processus à Temps Continu. PhD Thesis, University Paris 6, Pierre et Marie Curie. See Also simul.far.sde, simul.far.wiener, simul.farx, simul.far. Examples noise <- simul.wiener(m=64,n=100,m2=512) summary(noise) par(mfrow=c(2,1)) plot(noise,date=1) plot(select.fdata(noise,date=1:5),whole=TRUE,separator=TRUE) Index ∗ NA pred.persist, 24 is.na.fdata, 16 predict.far, 25 ∗ algebra predict.kerfon, 27 base.simul.far, 2 simul.far, 30 BaseK2BaseC, 3 simul.far.sde, 32 coef.far, 4 simul.far.wiener, 34 interpol.matrix, 14 simul.farx, 35 invgen, 15 simul.wiener, 38 orthonormalization, 21 ∗ univar ∗ aplot fapply, 7 plot.fdata, 23 maxfdata, 19 ∗ hplot apply, 7 multplot, 20 as.fdata (fdata), 13 ∗ manip select.fdata, 29 base.simul.far, 2, 30, 32, 36 ∗ methods BaseK2BaseC, 3 predict.far, 25 ∗ misc coef, 5 date.fdata, 5 coef.far, 4 fdata, 13 pred.persist, 24 date.fdata, 5 simul.far, 30 eigen, 16 simul.far.sde, 32 simul.far.wiener, 34 fapply, 7, 19 simul.wiener, 38 far, 4, 5, 8, 11–14, 26, 34, 35 ∗ models far.cv, 9, 10, 11 far, 8 fdata, 3, 6, 12, 13, 21, 23, 28, 29, 35 far.cv, 11 predict.kerfon, 27 interpol.matrix, 14 ∗ nonlinear invgen, 15 kerfon, 17 is.na.fdata, 16 ∗ ts date.fdata, 5 kerfon, 13, 14, 17, 28 far, 8 lapply, 7 far.cv, 11 fdata, 13 maxfdata, 14, 19 kerfon, 17 multplot, 14, 20, 23 maxfdata, 19 plot.fdata, 23 NA, 17 40 INDEX 41 orthonormalization, 21, 30, 31, 36 plot.far (far), 8 plot.fdata, 20, 21, 23 pred.persist, 24, 26 predict.far, 10, 25, 25 predict.kerfon, 18, 25, 26, 27 print, 18 print.far (far), 8 print.fdata (fdata), 13 print.kerfon (kerfon), 17 print.summary.fdata (fdata), 13 select.fdata, 29 simul.far, 15, 30, 33, 36, 38, 39 simul.far.sde, 32, 32, 38, 39 simul.far.wiener, 4, 32, 33, 34, 35, 38, 39 simul.farx, 2, 3, 15, 32, 33, 35, 39 simul.wiener, 4, 32–34, 38, 38 solve, 16 summary.fdata (fdata), 13 svd, 16 theoretical.coef, 15 theoretical.coef (simul.farx), 35

SoftClustering

cran

Package ‘SoftClustering’ August 18, 2023 Type Package Title Soft Clustering Algorithms Description It contains soft clustering algorithms, in particular approaches derived from rough set the- ory: Lingras & West original rough k-means, Peters' refined rough k-means, and PI rough k- means. It also contains classic k-means and a corresponding illustrative demo. Version 2.1.3 Author G. Peters (Ed.) Maintainer G. Peters <peters.activities@gmail.com> Depends R (>= 4.1) License GPL-2 Encoding UTF-8 LazyData true RoxygenNote 7.2.3 NeedsCompilation no Repository CRAN Date/Publication 2023-08-18 07:52:35 UTC R topics documented: createLowerMShipMatrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 datatypeInteger . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 DemoDataC2D2a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 HardKMeans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 HardKMeansDemo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 initializeMeansMatrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 initMeansC2D2a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 initMeansC3D2a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 initMeansC4D2a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 initMeansC5D2a . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 normalizeMatrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 plotRoughKMeans . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1 2 datatypeInteger RoughKMeans_LW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 RoughKMeans_PE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 RoughKMeans_PI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 RoughKMeans_SHELL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 Index 16 createLowerMShipMatrix Create Lower Approximation Description Creates a lower approximation out of an upper approximation. Usage createLowerMShipMatrix(upperMShipMatrix) Arguments upperMShipMatrix An upper approximation matrix. Value Returns the corresponding lower approximation. Author(s) G. Peters. datatypeInteger Rough k-Means Plotting Description Checks for integer. Usage datatypeInteger(x) Arguments x As a replacement for is.integer(). is.integer() delivers FALSE when the variable is numeric (as superset for integer etc.) DemoDataC2D2a 3 Value TRUE if x is integer otherwise FALSE. Author(s) G. Peters. DemoDataC2D2a A small two-dimensional dataset with two clusters for demonstration purposes. See examples in the Help/Description of a function, e.g. for HardKMeansDemo(). Description A small two-dimensional dataset with two clusters for demonstration purposes. See examples in the Help/Description of a function, e.g. for HardKMeansDemo(). Usage data(DemoDataC2D2a) Format Rows: objects, columns: features Examples data(DemoDataC2D2a) HardKMeans Hard k-Means Description HardKMeans performs classic (hard) k-means. Usage HardKMeans(dataMatrix, meansMatrix, nClusters, maxIterations) 4 HardKMeansDemo Arguments dataMatrix Matrix with the objects to be clustered. Dimension: [nObjects x nFeatures]. meansMatrix Select means derived from 1 = random (unity interval), 2 = maximum distances, matrix [nClusters x nFeatures] = self-defined means. Default: 2 = maximum distances. nClusters Number of clusters: Integer in [2, nObjects). Note, nCluster must be set even when meansMatrix is a matrix. For transparency, nClusters will not be overrid- den by the number of clusters derived from meansMatrix. Default: nClusters=2. maxIterations Maximum number of iterations. Default: maxIterations=100. Value $upperApprox: Obtained upper approximations [nObjects x nClusters]. Note: Apply function createLowerMShipMatrix() to obtain lower approximations; and for the boundary: boundary = upperApprox - lowerApprox. $clusterMeans: Obtained means [nClusters x nFeatures]. $nIterations: Number of iterations. Author(s) M. Goetz, G. Peters, Y. Richter, D. Sacker, T. Wochinger. References Lloyd, S.P. (1982) Least squares quantization in PCM. IEEE Transactions on Information Theory 28, 128–137. <doi:10.1016/j.ijar.2012.10.003>. Peters, G.; Crespo, F.; Lingras, P. and Weber, R. (2013) Soft clustering – fuzzy and rough ap- proaches and their extensions and derivatives. International Journal of Approximate Reasoning 54, 307–322. <doi:10.1016/j.ijar.2012.10.003>. Examples # An illustrative example clustering the sample data set DemoDataC2D2a.txt HardKMeans(DemoDataC2D2a, 2, 2, 100) HardKMeansDemo Hard k-Means Demo Description HardKMeansDemo shows how hard k-means performs stepwise. The number of features is set to 2 and the maximum number of iterations is 100. Usage HardKMeansDemo(dataMatrix, meansMatrix, nClusters) initializeMeansMatrix 5 Arguments dataMatrix Matrix with the objects to be clustered. Dimension: [nObjects x nFeatures]. Default: no default set. meansMatrix Select means derived from 1 = random (unity interval), 2 = maximum distances, matrix [nClusters x nFeatures=2] = self-defined means. Default: meansMa- trix=1 (random). nClusters Number of clusters: Integer in [2, min(5, nObjects-1)]. Note, nCluster must be set even when meansMatrix is a matrix. For transparency, nClusters will not be overridden by the number of clusters derived from meansMatrix. Default: nClusters=2. Value None. Author(s) G. Peters. References Lloyd, S.P. (1982) Least squares quantization in PCM. IEEE Transactions on Information Theory 28, 128–137. <doi:10.1016/j.ijar.2012.10.003>. Peters, G.; Crespo, F.; Lingras, P. and Weber, R. (2013) Soft clustering – fuzzy and rough ap- proaches and their extensions and derivatives. International Journal of Approximate Reasoning 54, 307–322. <doi:10.1016/j.ijar.2012.10.003>. Examples # Clustering the data set DemoDataC2D2a.txt (nClusters=2, random initial means) HardKMeansDemo(DemoDataC2D2a,1,2) # Clustering the data set DemoDataC2D2a.txt (nClusters=2,3,4; initially set means) HardKMeansDemo(DemoDataC2D2a,initMeansC2D2a,2) HardKMeansDemo(DemoDataC2D2a,initMeansC3D2a,3) HardKMeansDemo(DemoDataC2D2a,initMeansC4D2a,4) # Clustering the data set DemoDataC2D2a.txt (nClusters=5, initially set means) # It leads to an empty cluster: a (rare) case for an abnormal termination of k-means. HardKMeansDemo(DemoDataC2D2a,initMeansC5D2a,5) initializeMeansMatrix Initialize Means Matrix Description initializeMeansMatrix delivers an initial means matrix. 6 initMeansC2D2a Usage initializeMeansMatrix(dataMatrix, nClusters, meansMatrix) Arguments dataMatrix Matrix with the objects as basis for the means matrix. nClusters Number of clusters. meansMatrix Select means derived from 1 = random (unity interval), 2 = maximum dis- tances, matrix [nClusters x nFeatures] = self-defined means (will be returned unchanged). Default: 2 = maximum distances. Value Initial means matrix [nClusters x nFeatures]. Author(s) M. Goetz, G. Peters, Y. Richter, D. Sacker, T. Wochinger. initMeansC2D2a Two-dimensional dataset with two initial cluster means for the dataset DemoDataC2D2a. See examples in the Help/Description of a func- tion, e.g. for HardKMeansDemo(). Description Two-dimensional dataset with two initial cluster means for the dataset DemoDataC2D2a. See ex- amples in the Help/Description of a function, e.g. for HardKMeansDemo(). Usage data(initMeansC2D2a) Format Rows: objects, columns: features Examples data(initMeansC2D2a) initMeansC3D2a 7 initMeansC3D2a Two-dimensional dataset with three initial cluster means for the dataset DemoDataC2D2a. See examples in the Help/Description of a function, e.g. for HardKMeansDemo(). Description Two-dimensional dataset with three initial cluster means for the dataset DemoDataC2D2a. See examples in the Help/Description of a function, e.g. for HardKMeansDemo(). Usage data(initMeansC3D2a) Format Rows: objects, columns: features Examples data(initMeansC3D2a) initMeansC4D2a Two-dimensional dataset with four initial cluster means for the dataset DemoDataC2D2a. See examples in the Help/Description of a func- tion, e.g. for HardKMeansDemo(). Description Two-dimensional dataset with four initial cluster means for the dataset DemoDataC2D2a. See examples in the Help/Description of a function, e.g. for HardKMeansDemo(). Usage data(initMeansC4D2a) Format Rows: objects, columns: features Examples data(initMeansC4D2a) 8 normalizeMatrix initMeansC5D2a Two-dimensional dataset with five initial cluster means for the dataset DemoDataC2D2a. See examples in the Help/Description of a func- tion, e.g. for HardKMeansDemo(). Description Two-dimensional dataset with five initial cluster means for the dataset DemoDataC2D2a. See ex- amples in the Help/Description of a function, e.g. for HardKMeansDemo(). Usage data(initMeansC5D2a) Format Rows: objects, columns: features Examples data(initMeansC5D2a) normalizeMatrix Matrix Normalization Description normalizeMatrix delivers a normalized matrix. Usage normalizeMatrix(dataMatrix, normMethod, bycol) Arguments dataMatrix Matrix with the objects to be normalized. normMethod 1 = unity interval, 2 = normal distribution (sample variance), 3 = normal dis- tribution (population variance). Any other value returns the matrix unchanged. Default: meansMatrix = 1 (unity interval). bycol TRUE = columns are normalized, i.e., each column is considered separately (e.g., in case of the unity interval and a column colA: max(colA)=1 and min(colA)=0). For bycol = FALSE rows are normalized. Default: bycol = TRUE (columns are normalized). plotRoughKMeans 9 Value Normalized matrix. Author(s) M. Goetz, G. Peters, Y. Richter, D. Sacker, T. Wochinger. plotRoughKMeans Rough k-Means Plotting Description plotRoughKMeans plots the rough clustering results in 2D. Note: Plotting is limited to a maximum of 5 clusters. Usage plotRoughKMeans(dataMatrix, upperMShipMatrix, meansMatrix, plotDimensions, colouredPlot) Arguments dataMatrix Matrix with the objects to be plotted. upperMShipMatrix Corresponding matrix with upper approximations. meansMatrix Corresponding means matrix. plotDimensions An integer vector of the length 2. Defines the to be plotted feature dimensions, i.e., max(plotDimensions = c(1:2)) <= nFeatures. Default: plotDimensions = c(1:2). colouredPlot Select TRUE = colouredPlot plot, FALSE = black/white plot. Value 2D-plot of clustering results. The boundary objects are represented by stars (*). Author(s) G. Peters. 10 RoughKMeans_LW RoughKMeans_LW Lingras & West’s Rough k-Means Description RoughKMeans_LW performs Lingras & West’s k-means clustering algorithm. The commonly ac- cepted relative threshold is applied. Usage RoughKMeans_LW(dataMatrix, meansMatrix, nClusters, maxIterations, threshold, weightLower) Arguments dataMatrix Matrix with the objects to be clustered. Dimension: [nObjects x nFeatures]. meansMatrix Select means derived from 1 = random (unity interval), 2 = maximum distances, matrix [nClusters x nFeatures] = self-defined means. Default: 2 = maximum distances. nClusters Number of clusters: Integer in [2, nObjects). Note, nCluster must be set even when meansMatrix is a matrix. For transparency, nClusters will not be overrid- den by the number of clusters derived from meansMatrix. Default: nClusters=2. maxIterations Maximum number of iterations. Default: maxIterations=100. threshold Relative threshold in rough k-means algorithms (threshold >= 1.0). Default: threshold = 1.5. weightLower Weight of the lower approximation in rough k-means algorithms (0.0 <= weight- Lower <= 1.0). Default: weightLower = 0.7. Value $upperApprox: Obtained upper approximations [nObjects x nClusters]. Note: Apply function createLowerMShipMatrix() to obtain lower approximations; and for the boundary: boundary = upperApprox - lowerApprox. $clusterMeans: Obtained means [nClusters x nFeatures]. $nIterations: Number of iterations. Author(s) M. Goetz, G. Peters, Y. Richter, D. Sacker, T. Wochinger. References Lingras, P. and West, C. (2004) Interval Set Clustering of web users with rough k-means. Journal of Intelligent Information Systems 23, 5–16. <doi:10.1023/b:jiis.0000029668.88665.1a>. Peters, G. (2006) Some refinements of rough k-means clustering. Pattern Recognition 39, 1481– 1491. <doi:10.1016/j.patcog.2006.02.002>. RoughKMeans_PE 11 Lingras, P. and Peters, G. (2011) Rough Clustering. WIREs Data Mining and Knowledge Discovery 1, 64–72. <doi:10.1002/widm.16>. Lingras, P. and Peters, G. (2012) Applying rough set concepts to clustering. In: Peters, G.; Lingras, P.; Slezak, D. and Yao, Y. Y. (Eds.) Rough Sets: Selected Methods and Applications in Management and Engineering, Springer, 23–37. <doi:10.1007/978-1-4471-2760-4_2>. Peters, G.; Crespo, F.; Lingras, P. and Weber, R. (2013) Soft clustering – fuzzy and rough ap- proaches and their extensions and derivatives. International Journal of Approximate Reasoning 54, 307–322. <doi:10.1016/j.ijar.2012.10.003>. Peters, G. (2014) Rough clustering utilizing the principle of indifference. Information Sciences 277, 358–374. <doi:10.1016/j.ins.2014.02.073>. Peters, G. (2015) Is there any need for rough clustering? Pattern Recognition Letters 53, 31–37. <doi:10.1016/j.patrec.2014.11.003>. Examples # An illustrative example clustering the sample data set DemoDataC2D2a.txt RoughKMeans_LW(DemoDataC2D2a, 2, 2, 100, 1.5, 0.7) RoughKMeans_PE Peters’ Rough k-Means Description RoughKMeans_PE performs Peters’ k-means clustering algorithm. Usage RoughKMeans_PE(dataMatrix, meansMatrix, nClusters, maxIterations, threshold, weightLower) Arguments dataMatrix Matrix with the objects to be clustered. Dimension: [nObjects x nFeatures]. meansMatrix Select means derived from 1 = random (unity interval), 2 = maximum distances, matrix [nClusters x nFeatures] = self-defined means. Default: 2 = maximum distances. nClusters Number of clusters: Integer in [2, nObjects). Note, nCluster must be set even when meansMatrix is a matrix. For transparency, nClusters will not be overrid- den by the number of clusters derived from meansMatrix. Default: nClusters=2. maxIterations Maximum number of iterations. Default: maxIterations=100. threshold Relative threshold in rough k-means algorithms (threshold >= 1.0). Default: threshold = 1.5. weightLower Weight of the lower approximation in rough k-means algorithms (0.0 <= weight- Lower <= 1.0). Default: weightLower = 0.7. 12 RoughKMeans_PI Value $upperApprox: Obtained upper approximations [nObjects x nClusters]. Note: Apply function createLowerMShipMatrix() to obtain lower approximations; and for the boundary: boundary = upperApprox - lowerApprox. $clusterMeans: Obtained means [nClusters x nFeatures]. $nIterations: Number of iterations. Author(s) M. Goetz, G. Peters, Y. Richter, D. Sacker, T. Wochinger. References Peters, G. (2006) Some refinements of rough k-means clustering. Pattern Recognition 39, 1481– 1491. <doi:10.1016/j.patcog.2006.02.002>. Peters, G.; Crespo, F.; Lingras, P. and Weber, R. (2013) Soft clustering – fuzzy and rough ap- proaches and their extensions and derivatives. International Journal of Approximate Reasoning 54, 307–322. <doi:10.1016/j.ijar.2012.10.003>. Peters, G. (2014) Rough clustering utilizing the principle of indifference. Information Sciences 277, 358–374. <doi:10.1016/j.ins.2014.02.073>. Peters, G. (2015) Is there any need for rough clustering? Pattern Recognition Letters 53, 31–37. <doi:10.1016/j.patrec.2014.11.003>. Examples # An illustrative example clustering the sample data set DemoDataC2D2a.txt RoughKMeans_PE(DemoDataC2D2a, 2, 2, 100, 1.5, 0.7) RoughKMeans_PI PI Rough k-Means Description RoughKMeans_PI performs pi k-means clustering algorithm in its standard case. Therefore, weights are not required. Usage RoughKMeans_PI(dataMatrix, meansMatrix, nClusters, maxIterations, threshold) RoughKMeans_PI 13 Arguments dataMatrix Matrix with the objects to be clustered. Dimension: [nObjects x nFeatures]. meansMatrix Select means derived from 1 = random (unity interval), 2 = maximum distances, matrix [nClusters x nFeatures] = self-defined means. Default: 2 = maximum distances. nClusters Number of clusters: Integer in [2, nObjects). Note, nCluster must be set even when meansMatrix is a matrix. For transparency, nClusters will not be overrid- den by the number of clusters derived from meansMatrix. Default: nClusters=2. maxIterations Maximum number of iterations. Default: maxIterations=100. threshold Relative threshold in rough k-means algorithms (threshold >= 1.0). Default: threshold = 1.5. Value $upperApprox: Obtained upper approximations [nObjects x nClusters]. Note: Apply function createLowerMShipMatrix() to obtain lower approximations; and for the boundary: boundary = upperApprox - lowerApprox. $clusterMeans: Obtained means [nClusters x nFeatures]. $nIterations: Number of iterations. Author(s) M. Goetz, G. Peters, Y. Richter, D. Sacker, T. Wochinger. References Peters, G. (2006) Some refinements of rough k-means clustering. Pattern Recognition 39, 1481– 1491. <doi:10.1016/j.patcog.2006.02.002>. Peters, G.; Crespo, F.; Lingras, P. and Weber, R. (2013) Soft clustering – fuzzy and rough ap- proaches and their extensions and derivatives. International Journal of Approximate Reasoning 54, 307–322. <doi:10.1016/j.ijar.2012.10.003>. Peters, G. (2014) Rough clustering utilizing the principle of indifference. Information Sciences 277, 358–374. <doi:10.1016/j.ins.2014.02.073>. Peters, G. (2015) Is there any need for rough clustering? Pattern Recognition Letters 53, 31–37. <doi:10.1016/j.patrec.2014.11.003>. Examples # An illustrative example clustering the sample data set DemoDataC2D2a.txt RoughKMeans_PI(DemoDataC2D2a, 2, 2, 100, 1.5) 14 RoughKMeans_SHELL RoughKMeans_SHELL Rough k-Means Shell Description RoughKMeans_SHELL performs rough k-means algorithms with options for normalization and a 2D-plot of the results. Usage RoughKMeans_SHELL(clusterAlgorithm, dataMatrix, meansMatrix, nClusters, normalizationMethod, maxIterations, plotDimensions, colouredPlot, threshold, weightLower) Arguments clusterAlgorithm Select 0 = classic k-means, 1 = Lingras & West’s rough k-means, 2 = Peters’ rough k-means, 3 = π rough k-means. Default: clusterAlgorithm = 3 (π rough k-means). dataMatrix Matrix with the objects to be clustered. Dimension: [nObjects x nFeatures]. meansMatrix Select means derived from 1 = random (unity interval), 2 = maximum distances, matrix [nClusters x nFeatures] = self-defined means. Default: 2 = maximum distances. nClusters Number of clusters: Integer in [2, nObjects). Note, nCluster must be set even when meansMatrix is a matrix. For transparency, nClusters will not be overrid- den by the number of clusters derived from meansMatrix. Default: nClusters=2. Note: Plotting is limited to a maximum of 5 clusters. normalizationMethod 1 = unity interval, 2 = normal distribution (sample variance), 3 = normal dis- tribution (population variance). Any other value returns the matrix unchanged. Default: meansMatrix = 1 (unity interval). maxIterations Maximum number of iterations. Default: maxIterations=100. plotDimensions An integer vector of the length 2. Defines the to be plotted feature dimensions, i.e., max(plotDimensions = c(1:2)) <= nFeatures. Default: plotDimensions = c(1:2). colouredPlot Select TRUE = colouredPlot plot, FALSE = black/white plot. threshold Relative threshold in rough k-means algorithms (threshold >= 1.0). Default: threshold = 1.5. Note: It can be ignored for classic k-means. weightLower Weight of the lower approximation in rough k-means algorithms (0.0 <= weight- Lower <= 1.0). Default: weightLower = 0.7. Note: It can be ignored for classic k-means and π rough k-means RoughKMeans_SHELL 15 Value 2D-plot of clustering results. The boundary objects are represented by stars (*). $upperApprox: Obtained upper approximations [nObjects x nClusters]. Note: Apply function createLowerMShipMatrix() to obtain lower approximations; and for the boundary: boundary = upperApprox - lowerApprox. $clusterMeans: Obtained means [nClusters x nFeatures]. $nIterations: Number of iterations. Author(s) M. Goetz, G. Peters, Y. Richter, D. Sacker, T. Wochinger. References Lloyd, S.P. (1982) Least squares quantization in PCM. IEEE Transactions on Information Theory 28, 128–137. <doi:10.1016/j.ijar.2012.10.003>. Lingras, P. and West, C. (2004) Interval Set Clustering of web users with rough k-means. Journal of Intelligent Information Systems 23, 5–16. <doi:10.1023/b:jiis.0000029668.88665.1a>. Peters, G. (2006) Some refinements of rough k-means clustering. Pattern Recognition 39, 1481– 1491. <doi:10.1016/j.patcog.2006.02.002>. Lingras, P. and Peters, G. (2011) Rough Clustering. WIREs Data Mining and Knowledge Discovery 1, 64–72. <doi:10.1002/widm.16>. Lingras, P. and Peters, G. (2012) Applying rough set concepts to clustering. In: Peters, G.; Lingras, P.; Slezak, D. and Yao, Y. Y. (Eds.) Rough Sets: Selected Methods and Applications in Management and Engineering, Springer, 23–37. <doi:10.1007/978-1-4471-2760-4_2>. Peters, G.; Crespo, F.; Lingras, P. and Weber, R. (2013) Soft clustering – fuzzy and rough ap- proaches and their extensions and derivatives. International Journal of Approximate Reasoning 54, 307–322. <doi:10.1016/j.ijar.2012.10.003>. Peters, G. (2014) Rough clustering utilizing the principle of indifference. Information Sciences 277, 358–374. <doi:10.1016/j.ins.2014.02.073>. Peters, G. (2015) Is there any need for rough clustering? Pattern Recognition Letters 53, 31–37. <doi:10.1016/j.patrec.2014.11.003>. Examples # An illustrative example clustering the sample data set DemoDataC2D2a.txt RoughKMeans_SHELL(3, DemoDataC2D2a, 2, 2, 1, 100, c(1:2), TRUE, 1.5, 0.7) Index ∗ datasets DemoDataC2D2a, 3 initMeansC2D2a, 6 initMeansC3D2a, 7 initMeansC4D2a, 7 initMeansC5D2a, 8 createLowerMShipMatrix, 2 datatypeInteger, 2 DemoDataC2D2a, 3 HardKMeans, 3 HardKMeansDemo, 4 initializeMeansMatrix, 5 initMeansC2D2a, 6 initMeansC3D2a, 7 initMeansC4D2a, 7 initMeansC5D2a, 8 normalizeMatrix, 8 plotRoughKMeans, 9 RoughKMeans_LW, 10 RoughKMeans_PE, 11 RoughKMeans_PI, 12 RoughKMeans_SHELL, 14 16

baseballr

cran

Package ‘baseballr’ March 21, 2023 Title Acquiring and Analyzing Baseball Data Version 1.5.0 Description Provides numerous utilities for acquiring and analyzing baseball data from online sources such as 'Baseball Reference' <https: //www.baseball-reference.com/>, 'FanGraphs' <https://www.fangraphs.com/>, and the 'MLB Stats' API <https: //www.mlb.com/>. License MIT + file LICENSE URL https://billpetti.github.io/baseballr/, https://github.com/BillPetti/baseballr BugReports https://github.com/BillPetti/baseballr/issues Depends R (>= 4.0.0) Imports cli (>= 3.4.1), data.table (>= 1.14.0), dplyr (>= 1.0.10), ggplot2, glue, httr (>= 0.5), janitor, jsonlite, lubridate, magrittr, purrr (>= 1.0.0), Rcpp, RcppParallel, rlang (>= 1.0.4), rvest (>= 1.0.0), stringr (>= 1.3.0), tibble (>= 3.0), tidyr (>= 1.0.0) Suggests crayon (>= 1.3.4), curl, DBI, furrr, future, ggrepel, knitr, pacman, progressr (>= 0.6.0), qs (>= 0.25.1), reshape2, rmarkdown, RSQLite, scales, stringi, stats, testthat, usethis (>= 1.6.0), xml2 (>= 1.3), zoo VignetteBuilder knitr Encoding UTF-8 LazyData true RoxygenNote 7.2.3 NeedsCompilation no Author Bill Petti [aut], Saiem Gilani [aut, cre], Ben Baumer [ctb], Ben Dilday [ctb], Robert Frey [ctb], Camden Kay [ctb] 1 2 R topics documented: Maintainer Saiem Gilani <saiem.gilani@gmail.com> Repository CRAN Date/Publication 2023-03-21 17:30:06 UTC R topics documented: batter_game_logs_fg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 bref . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 bref_daily_batter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 bref_daily_pitcher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 bref_standings_on_date . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 bref_team_results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 chadwick . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 chadwick_player_lu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 code_barrel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 column_structure_draft_mlb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 daily_batter_bref . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 daily_pitcher_bref . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 edge_code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 edge_frequency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 fangraphs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 fg_batter_game_logs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 fg_batter_leaders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 fg_bat_leaders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 fg_guts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 fg_milb_batter_game_logs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 fg_milb_pitcher_game_logs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 fg_park . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 fg_pitcher_game_logs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 fg_pitcher_leaders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 fg_pitch_leaders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 fg_team_batter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 fg_team_pitcher . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 fip_plus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 get_batting_orders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 get_draft_mlb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 get_game_info_mlb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 get_game_info_sup_petti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 get_game_pks_mlb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 get_ncaa_baseball_pbp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64 get_ncaa_game_logs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65 get_ncaa_lineups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 get_ncaa_park_factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 get_ncaa_schedule_info . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 get_pbp_mlb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67 get_probables_mlb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 get_retrosheet_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 R topics documented: 3 get_umpire_ids_petti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 ggspraychart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 label_statcast_imputed_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 linear_weights_savant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 load_game_info_sup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 load_ncaa_baseball_pbp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 load_ncaa_baseball_schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 load_ncaa_baseball_season_ids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 load_ncaa_baseball_teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 load_umpire_ids . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 milb_batter_game_logs_fg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 milb_pitcher_game_logs_fg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80 mlb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 mlb_all_star_ballots . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 mlb_all_star_final_vote . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83 mlb_all_star_write_ins . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85 mlb_attendance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86 mlb_award . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 mlb_awards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 mlb_awards_recipient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 mlb_baseball_stats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91 mlb_batting_orders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 mlb_conferences . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 mlb_divisions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 mlb_draft . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 mlb_draft_latest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96 mlb_draft_prospects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 mlb_event_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 mlb_fielder_detail_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 mlb_game_changes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103 mlb_game_content . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 mlb_game_context_metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 mlb_game_info . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 mlb_game_linescore . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 109 mlb_game_pace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111 mlb_game_pks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 mlb_game_status_codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115 mlb_game_timecodes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 mlb_game_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116 mlb_game_wp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117 mlb_high_low_stats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 mlb_high_low_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 mlb_hit_trajectories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 mlb_homerun_derby . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 mlb_homerun_derby_bracket . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 mlb_homerun_derby_players . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125 mlb_jobs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 4 R topics documented: mlb_jobs_datacasters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 mlb_jobs_official_scorers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 mlb_jobs_umpires . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130 mlb_job_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 mlb_languages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131 mlb_league . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 mlb_league_leader_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 132 mlb_logical_events . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 133 mlb_metrics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 mlb_pbp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 mlb_pbp_diff . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138 mlb_people . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 mlb_people_free_agents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 mlb_pitch_codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 mlb_pitch_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 mlb_player_game_stats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 mlb_player_game_stats_current . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148 mlb_player_status_codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150 mlb_positions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 151 mlb_probables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 mlb_review_reasons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152 mlb_rosters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153 mlb_roster_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154 mlb_runner_detail_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 mlb_schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155 mlb_schedule_event_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158 mlb_schedule_games_tied . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 159 mlb_schedule_postseason . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 mlb_schedule_postseason_series . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164 mlb_seasons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 mlb_seasons_all . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167 mlb_situation_codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 mlb_sky . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 mlb_sports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 170 mlb_sports_info . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171 mlb_sports_players . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172 mlb_standings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 mlb_standings_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 mlb_stats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176 mlb_stats_leaders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179 mlb_stat_groups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182 mlb_stat_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 mlb_teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183 mlb_teams_stats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 mlb_teams_stats_leaders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187 mlb_team_affiliates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 188 mlb_team_alumni . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190 mlb_team_coaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191 R topics documented: 5 mlb_team_history . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192 mlb_team_info . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193 mlb_team_leaders . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194 mlb_team_personnel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 196 mlb_team_stats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 197 mlb_venues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198 mlb_wind_direction_codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199 most_recent_mlb_season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 most_recent_ncaa_baseball_season . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200 ncaa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 ncaa_baseball_roster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 ncaa_game_logs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202 ncaa_lineups . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204 ncaa_park_factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205 ncaa_pbp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206 ncaa_roster . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207 ncaa_schedule_info . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 208 ncaa_school_id_lu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209 ncaa_scrape . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210 ncaa_teams . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211 ncaa_team_player_stats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212 pitcher_game_logs_fg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 playerid_lookup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 214 playername_lookup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 215 process_statcast_payload . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 216 retrosheet_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 217 run_expectancy_code . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 218 school_id_lu . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 scrape_savant_leaderboards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 221 scrape_statcast_savant . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 223 sptrc_league_payrolls . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 224 sptrc_team_active_payroll . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225 standings_on_date_bref . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 226 statcast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 227 statcast_impute . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 statcast_leaderboards . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 228 statcast_search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 230 statline_from_statcast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 238 stats_api_live_empty_df . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 teams_lu_table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 239 team_consistency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 team_results_bref . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 240 woba_plus . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241 Index 243 6 bref batter_game_logs_fg (legacy) Scrape Batter Game Logs from FanGraphs Description (legacy) Scrape Batter Game Logs from FanGraphs Usage batter_game_logs_fg(playerid, year = 2017) Arguments playerid This is the playerid used by FanGraphs for a given player year The season for which game logs should be returned (use the YYYY format) Value A data frame of batter game logs. bref Baseball Reference Functions Overview Description bref_daily_batter() Scrape Batter Performance Data Over a Custom Time Frame bref_daily_pitcher() Scrape Pitcher Performance Data Over a Custom Time Frame bref_standings_on_date() Scrape MLB Standings on a Given Date bref_team_results() Scrape Team Results Details Scrape Batter Performance Data Over a Custom Time Frame: bref_daily_batter("2015-05-10", "2015-06-20") Scrape Pitcher Performance Data Over a Custom Time Frame: bref_daily_batter("2015-05-10", "2015-06-20") Scrape MLB Standings on a Given Date: bref_standings_on_date(date = "2015-08-04", division = "AL East") Scrape Team Results: bref_daily_batter 7 bref_team_results("NYM", 2015) bref_team_results(Tm="TBR", year=2008) Team Level Consistency: Uses bref_team_results() to calculate team consistency metrics team_consistency(year=2015) bref_daily_batter Scrape Batter Performance Data Over a Custom Time Frame Description This function allows you to scrape basic batter statistics over a custom time frame. Data is sourced from Baseball-Reference.com. Usage bref_daily_batter(t1, t2) Arguments t1 First date data should be scraped from. Should take the form "YEAR-MONTH- DAY" t2 Last date data should be scraped from. Should take the form "YEAR-MONTH- DAY" Value Returns a tibble of batter performance with the following columns: col_name types bbref_id character season integer Name character Age numeric Level character Team character G numeric PA numeric AB numeric R numeric H numeric X1B numeric X2B numeric X3B numeric HR numeric RBI numeric 8 bref_daily_pitcher BB numeric IBB numeric uBB numeric SO numeric HBP numeric SH numeric SF numeric GDP numeric SB numeric CS numeric BA numeric OBP numeric SLG numeric OPS numeric Examples try(bref_daily_batter(t1="2015-05-10", t2="2015-06-20")) bref_daily_pitcher Scrape Pitcher Performance Data Over a Custom Time Frame Description This function allows you to scrape basic pitcher statistics over a custom time frame. Data is sourced from Baseball-Reference.com. Usage bref_daily_pitcher(t1, t2) Arguments t1 First date data should be scraped from. Should take the form "YEAR-MONTH- DAY" t2 Last date data should be scraped from. Should take the form "YEAR-MONTH- DAY" Value Returns a tibble of pitcher performance with the following columns: col_name types bbref_id character bref_daily_pitcher 9 season integer Name character Age numeric Level character Team character G numeric GS numeric W numeric L numeric SV numeric IP numeric H numeric R numeric ER numeric uBB numeric BB numeric SO numeric HR numeric HBP numeric ERA numeric AB numeric X1B numeric X2B numeric X3B numeric IBB numeric GDP numeric SF numeric SB numeric CS numeric PO numeric BF numeric Pit numeric Str numeric StL numeric StS numeric GB.FB numeric LD numeric PU numeric WHIP numeric BAbip numeric SO9 numeric SO.W numeric SO_perc numeric uBB_perc numeric SO_uBB numeric 10 bref_standings_on_date Examples try(bref_daily_pitcher("2015-05-10", "2015-06-20")) bref_standings_on_date Scrape MLB Standings on a Given Date Description This function allows you to scrape the standings from MLB for any date you choose. Usage bref_standings_on_date(date, division, from = FALSE) Arguments date a date object division One or more of AL East, AL Central, AL West, AL Overall, NL East, NL Cen- tral, NL West, and NL Overall from a logical indicating whether you want standings up to and including the date (FALSE, default) or rather standings for games played after the date Value Returns a tibble of MLB standings with the following columns: col_name types Tm character W integer L integer W-L% numeric GB character RS integer RA integer pythW-L% numeric Examples try(bref_standings_on_date(date = "2015-08-04", division = "AL East")) bref_team_results 11 bref_team_results Scrape Team Results Description This function allows you to scrape schedule and results for a major league team from Baseball- Reference.com Usage bref_team_results(Tm, year) Arguments Tm The abbreviation used by Baseball-Reference.com for the team whose results you want to scrape. year Season for which you want to scrape the park factors. Value Returns a tibble of MLB team results and the following columns: col_name types Gm numeric Date character Tm character H_A character Opp character Result character R numeric RA numeric Inn character Record character Rank numeric GB character Win character Loss character Save character Time character D/N character Attendance numeric cLI numeric Streak numeric Orig_Scheduled character Year numeric 12 chadwick_player_lu Examples try(bref_team_results("NYM", 2015)) try(bref_team_results(Tm="TBR", year=2008)) chadwick Chadwick Bureau Register Player Lookup Description chadwick_player_lu(): Directly download the Chadwick Bureau’s public register of baseball players and the various IDs associated with them in different systems of record. playername_lookup(): Look up Baseball Player Name. playerid_lookup(): Look up Baseball Player IDs. Details Directly download the Chadwick Bureau’s public register of baseball players.: chadwick_player_lu() Look up baseball player name by ID: playername_lookup(4885) playername_lookup("kaaihki01") Look up baseball player IDs by player name: playerid_lookup("Garcia", "Karim") chadwick_player_lu Download the Chadwick Bureau’s public register of baseball play- ers Description Download the Chadwick Bureau’s public register of baseball players Usage chadwick_player_lu() get_chadwick_lu() Value A data frame of baseball players and the various IDs associated with them in different systems of record and the following columns: chadwick_player_lu 13 col_name types key_person character key_uuid character key_mlbam integer key_retro character key_bbref character key_bbref_minors character key_fangraphs integer key_npb integer key_sr_nfl character key_sr_nba character key_sr_nhl character key_findagrave integer name_last character name_first character name_given character name_suffix character name_matrilineal character name_nick character birth_year integer birth_month integer birth_day integer death_year integer death_month integer death_day integer pro_played_first integer pro_played_last integer mlb_played_first integer mlb_played_last integer col_played_first integer col_played_last integer pro_managed_first integer pro_managed_last integer mlb_managed_first integer mlb_managed_last integer col_managed_first integer col_managed_last integer pro_umpired_first integer pro_umpired_last integer mlb_umpired_first integer mlb_umpired_last integer A data frame of baseball players and the various IDs associated with them in different systems of record. Examples 14 column_structure_draft_mlb try(chadwick_player_lu()) code_barrel Helper for determining whether a batted ball is a "barrel" Description This function allows you to code a batted ball as a barrel as defined by the Statcast research team using data as provided by baseballsavant.mlb.com. Usage code_barrel(df) Arguments df A dataframe generated by baseballsavant.mlb.com that must include the follow- ing variables: launch_angle and launch_speed. Value Returns a tibble with the additional column, barrel. column_structure_draft_mlb Column structure of the MLB Draft data Description A tibble giving column structure of MLB Draft data Usage column_structure_draft_mlb Format An object of class tbl_df (inherits from tbl, data.frame) with 0 rows and 72 columns. daily_batter_bref 15 daily_batter_bref (legacy) Scrape Batter Performance Data Over a Custom Time Frame Description (legacy) Scrape Batter Performance Data Over a Custom Time Frame Usage daily_batter_bref(t1, t2) Arguments t1 First date data should be scraped from. Should take the form "YEAR-MONTH- DAY" t2 Last date data should be scraped from. Should take the form "YEAR-MONTH- DAY" Value Returns a tibble of batter performance See Also bref_daily_batter() daily_pitcher_bref (legacy) Scrape Pitcher Performance Data Over a Custom Time Frame Description (legacy) Scrape Pitcher Performance Data Over a Custom Time Frame Usage daily_pitcher_bref(t1, t2) Arguments t1 First date data should be scraped from. Should take the form "YEAR-MONTH- DAY" t2 Last date data should be scraped from. Should take the form "YEAR-MONTH- DAY" 16 edge_code Value Returns a tibble of pitcher performance See Also bref_daily_pitcher() edge_code Edge Code Description This function allows you to classify individual pitches based on the various categories from the Edge% metric. The dataframe passed to the function must include the batter’s handedness, the px and pz coordinates from the PITCHf/x system, and the batter’s height. Usage edge_code(df, height_var_name = "b_height") Arguments df A dataframe that, at a minimum, includes the following columns: batter height (b_height), the batter’s handedness (stand), vertical location of the pitch (pz), and then horizontal location of the pitch (pz) height_var_name The name of the variable in the data set that includes the batter’s height. Defaults to b_height which assumes an height + inch format. If the variable name is "Height" it assumes the variable is already converted to inches (as is the case in some databases) Value Returns a tibble with the additional edge columns necessary for calculations. edge_frequency 17 edge_frequency Edge Percentage Frequency Description This function allows you to calculate the percent of pitches thrown to different edges of the strike zone for a pitch by pitch data set that has been coded using the edge_code() function. Usage edge_frequency(df, group = NULL) Arguments df A data frame of pitch by pitch data that has been coded using the edge_code() function. group Character string indicating what column to group the frequency by. For exam- ple, "pitcher" or "batter". Defaults to NULL, which calculates the frequencies across the entire data set. Value Returns a tibble with the additional edge columns necessary for frequency calculations. fangraphs FanGraphs Functions Overview Description fg_pitcher_game_logs(): Scrape Pitcher Game Logs from FanGraphs. fg_batter_game_logs(): Scrape Batter Game Logs from FanGraphs. fg_milb_pitcher_game_logs(): Scrape MiLB game logs for pitchers from Fangraphs, combin- ing ’standard’ and ’advanced’ tabs. fg_milb_batter_game_logs(): Scrape MiLB game logs for batters from Fangraphs, combining ’standard’ and ’advanced’ tabs. fg_batter_leaders(): Scrape Batter Leaderboards from FanGraphs. fg_pitcher_leaders(): Scrape Pitcher Leaderboards from FanGraphs. fg_guts(): Scrape FanGraphs.com Guts!. fg_park(): Scrape Park Factors from FanGraphs.com. fg_park_hand(): Scrape Park Factors by handedness from FanGraphs.com. 18 fg_batter_game_logs Details Scrape Pitcher Game Logs from FanGraphs: fg_pitcher_game_logs(playerid = 104, year = 2006) Scrape Batter Game Logs from FanGraphs: fg_batter_game_logs(playerid = 6184, year = 2017) Scrape MiLB game logs for pitchers from Fangraphs: fg_milb_pitcher_game_logs(playerid = "sa3004210", year=2017) Scrape MiLB game logs for batters from Fangraphs: fg_milb_batter_game_logs(playerid = "sa917940", year=2018) Scrape Batter Leaderboards from FanGraphs: fg_batter_leaders(x = 2015, y = 2015, qual = 400) Scrape Pitcher Leaderboards from FanGraphs: fg_pitcher_leaders(x = 2015, y = 2015, qual = 150) Scrape FanGraphs.com Guts!: fg_guts() Scrape Park Factors from FanGraphs.com: fg_park(2013) Scrape Park Factors by handedness from FanGraphs.com: fg_park_hand(2013) fg_batter_game_logs Scrape Batter Game Logs from FanGraphs Description This function allows you to scrape game logs by year for a batter from FanGraphs.com. Usage fg_batter_game_logs(playerid, year = 2017) Arguments playerid This is the playerid used by FanGraphs for a given player year The season for which game logs should be returned (use the YYYY format) fg_batter_game_logs 19 Value A data frame of batter game logs. |col_name |types | |:————-|:———| |PlayerName |charac- ter | |playerid |integer | |Date |character | |Team |character | |Opp |character | |season |integer | |Age |integer | |BatOrder |character | |Pos |character | |G |numeric | |AB |numeric | |PA |numeric | |H |nu- meric | |1B |numeric | |2B |numeric | |3B |numeric | |HR |numeric | |R |numeric | |RBI |numeric | |BB |numeric | |IBB |numeric | |SO |numeric | |HBP |numeric | |SF |numeric | |SH |numeric | |GDP |numeric | |SB |numeric | |CS |numeric | |AVG |numeric | |GB |numeric | |FB |numeric | |LD |numeric | |IFFB |numeric | |Pitches |numeric | |Balls |numeric | |Strikes |numeric | |IFH |numeric | |BU |nu- meric | |BUH |numeric | |BB% |numeric | |K% |numeric | |BB/K |numeric | |OBP |numeric | |SLG |numeric | |OPS |numeric | |ISO |numeric | |BABIP |numeric | |GB/FB |numeric | |LD% |numeric | |GB% |numeric | |FB% |numeric | |IFFB% |numeric | |HR/FB |numeric | |IFH% |numeric | |BUH% |numeric | |wOBA |numeric | |wRAA |numeric | |wRC |numeric | |Spd |numeric | |wRC+ |numeric | |wBSR |numeric | |WPA |numeric | |-WPA |numeric | |+WPA |numeric | |RE24 |numeric | |REW |nu- meric | |pLI |numeric | |phLI |numeric | |PH |numeric | |WPA/LI |numeric | |Clutch |numeric | |FB%1 |numeric | |FBv |numeric | |SL% |numeric | |SLv |numeric | |CT% |numeric | |CTv |numeric | |CB% |numeric | |CBv |numeric | |CH% |numeric | |CHv |numeric | |SF% |numeric | |SFv |numeric | |KN% |numeric | |KNv |numeric | |XX% |numeric | |wFB |numeric | |wSL |numeric | |wCT |numeric | |wCB |numeric | |wCH |numeric | |wSF |numeric | |wKN |numeric | |wFB/C |numeric | |wSL/C |numeric | |wCT/C |numeric | |wCB/C |numeric | |wCH/C |numeric | |wSF/C |numeric | |wKN/C |numeric | |O- Swing% |numeric | |Z-Swing% |numeric | |Swing% |numeric | |O-Contact% |numeric | |Z-Contact% |numeric | |Contact% |numeric | |Zone% |numeric | |F-Strike% |numeric | |SwStr% |numeric | |Pull |numeric | |Cent |numeric | |Oppo |numeric | |Soft |numeric | |Med |numeric | |Hard |numeric | |bip- Count |numeric | |Pull% |numeric | |Cent% |numeric | |Oppo% |numeric | |Soft% |numeric | |Med% |numeric | |Hard% |numeric | |pfxFA% |numeric | |pfxFT% |numeric | |pfxFC% |numeric | |pfxFS% |numeric | |pfxFO% |numeric | |pfxSI% |numeric | |pfxSL% |numeric | |pfxCU% |numeric | |pfxKC% |numeric | |pfxCH% |numeric | |pfxKN% |numeric | |pfxvFA |numeric | |pfxvFT |numeric | |pfxvFC |numeric | |pfxvFS |numeric | |pfxvFO |numeric | |pfxvSI |numeric | |pfxvSL |numeric | |pfxvCU |nu- meric | |pfxvKC |numeric | |pfxvCH |numeric | |pfxvKN |numeric | |pfxFA-X |numeric | |pfxFT-X |numeric | |pfxFC-X |numeric | |pfxFS-X |numeric | |pfxFO-X |numeric | |pfxSI-X |numeric | |pfxSL- X |numeric | |pfxCU-X |numeric | |pfxKC-X |numeric | |pfxCH-X |numeric | |pfxKN-X |numeric | |pfxFA-Z |numeric | |pfxFT-Z |numeric | |pfxFC-Z |numeric | |pfxFS-Z |numeric | |pfxFO-Z |numeric | |pfxSI-Z |numeric | |pfxSL-Z |numeric | |pfxCU-Z |numeric | |pfxKC-Z |numeric | |pfxCH-Z |nu- meric | |pfxKN-Z |numeric | |pfxwFA |numeric | |pfxwFT |numeric | |pfxwFC |numeric | |pfxwFS |numeric | |pfxwFO |numeric | |pfxwSI |numeric | |pfxwSL |numeric | |pfxwCU |numeric | |pfxwKC |numeric | |pfxwCH |numeric | |pfxwKN |numeric | |pfxwFA/C |numeric | |pfxwFT/C |numeric | |pfxwFC/C |numeric | |pfxwFS/C |numeric | |pfxwFO/C |numeric | |pfxwSI/C |numeric | |pfxwSL/C |numeric | |pfxwCU/C |numeric | |pfxwKC/C |numeric | |pfxwCH/C |numeric | |pfxwKN/C |numeric | |pfxO-Swing% |numeric | |pfxZ-Swing% |numeric | |pfxSwing% |numeric | |pfxO-Contact% |nu- meric | |pfxZ-Contact% |numeric | |pfxContact% |numeric | |pfxZone% |numeric | |pfxPace |numeric | |piCH% |numeric | |piCS% |numeric | |piCU% |numeric | |piFA% |numeric | |piFC% |numeric | |piFS% |numeric | |piKN% |numeric | |piSI% |numeric | |piSL% |numeric | |piXX% |numeric | |pivCH |numeric | |pivCS |numeric | |pivCU |numeric | |pivFA |numeric | |pivFC |numeric | |pivFS |numeric | |pivKN |numeric | |pivSI |numeric | |pivSL |numeric | |pivXX |numeric | |piCH-X |numeric | |piCS-X |numeric | |piCU-X |numeric | |piFA-X |numeric | |piFC-X |numeric | |piFS-X |numeric | |piKN-X |numeric | |piSI-X |numeric | |piSL-X |numeric | |piXX-X |numeric | |piCH-Z |numeric | |piCS-Z |numeric | |piCU-Z |numeric | |piFA-Z |numeric | |piFC-Z |numeric | |piFS-Z |numeric | |piKN-Z |nu- meric | |piSI-Z |numeric | |piSL-Z |numeric | |piXX-Z |numeric | |piwCH |numeric | |piwCS |numeric | |piwCU |numeric | |piwFA |numeric | |piwFC |numeric | |piwFS |numeric | |piwKN |numeric | |piwSI 20 fg_batter_leaders |numeric | |piwSL |numeric | |piwXX |numeric | |piwCH/C |numeric | |piwCS/C |numeric | |piwCU/C |numeric | |piwFA/C |numeric | |piwFC/C |numeric | |piwFS/C |numeric | |piwKN/C |numeric | |pi- wSI/C |numeric | |piwSL/C |numeric | |piwXX/C |numeric | |piO-Swing% |numeric | |piZ-Swing% |numeric | |piSwing% |numeric | |piO-Contact% |numeric | |piZ-Contact% |numeric | |piContact% |numeric | |piZone% |numeric | |Events |numeric | |EV |numeric | |LA |numeric | |Barrels |numeric | |Barrel% |numeric | |maxEV |numeric | |HardHit |numeric | |HardHit% |numeric | |gamedate |charac- ter | |dh |integer | Examples try(fg_batter_game_logs(playerid = 6184, year = 2017)) fg_batter_leaders Scrape Batter Leaderboards from FanGraphs Description This function allows you to scrape all leaderboard statistics (basic and advanced) from FanGraphs.com. Usage fg_batter_leaders(x, y, league = "all", qual = "y", ind = 1, exc_p = TRUE) Arguments x First season for which you want data. y Last season for which you want data. If multiple years selected, data returned will be aggregate data for the date range. If y = x, function will return single- season data. league Option for limiting results to different leagues or overall results. Options are "al", "nl", or "all". qual Whether you want only batters/pitchers that qualified in a given season, or the minimum number of plate appearances for inclusion. If you only want qualified hitters, use qual. If a minimum number of plate appearaces/innings pitched, use the number desired. Defaults to "y". ind Whether or not to break the seasons out individual, or roll them up together. 1 = split seasons, 0 = aggregate seasons. exc_p (logical) Whether or not to exclude pitchers from the batter leaderboards. TRUE = exclude pitchers, FALSE = retain pitchers. fg_batter_leaders 21 Value A data frame of batter data. col_name types playerid character # character Season character Name character Team character Age numeric G numeric AB numeric PA numeric H numeric 1B numeric 2B numeric 3B numeric HR numeric R numeric RBI numeric BB numeric IBB numeric SO numeric HBP numeric SF numeric SH numeric GDP numeric SB numeric CS numeric AVG numeric GB numeric FB numeric LD numeric IFFB numeric Pitches numeric Balls numeric Strikes numeric IFH numeric BU numeric BUH numeric BB_pct numeric K_pct numeric BB_K numeric OBP numeric SLG numeric OPS numeric ISO numeric BABIP numeric 22 fg_batter_leaders GB_FB numeric LD_pct numeric GB_pct numeric FB_pct numeric IFFB_pct numeric HR_FB numeric IFH_pct numeric BUH_pct numeric wOBA numeric wRAA numeric wRC numeric Bat numeric Fld numeric Rep numeric Pos numeric RAR numeric WAR numeric Dol numeric Spd numeric wRC_plus numeric WPA numeric WPA_minus numeric WPA_plus numeric RE24 numeric REW numeric pLI numeric phLI numeric PH numeric WPA_LI numeric Clutch numeric FBall_pct numeric FBv numeric SL_pct numeric SLv numeric CT_pct numeric CTv numeric CB_pct numeric CBv numeric CH_pct numeric CHv numeric SF_pct numeric SFv numeric KN_pct numeric KNv numeric XX_pct numeric PO_pct numeric wFB numeric wSL numeric fg_batter_leaders 23 wCT numeric wCB numeric wCH numeric wSF numeric wKN numeric wFB_C numeric wSL_C numeric wCT_C numeric wCB_C numeric wCH_C numeric wSF_C numeric wKN_C numeric O-Swing_pct numeric Z-Swing_pct numeric Swing_pct numeric O-Contact_pct numeric Z-Contact_pct numeric Contact_pct numeric Zone_pct numeric F-Strike_pct numeric SwStr_pct numeric BsR numeric FA_pct (sc) numeric FT_pct (sc) numeric FC_pct (sc) numeric FS_pct (sc) numeric FO_pct (sc) numeric SI_pct (sc) numeric SL_pct (sc) numeric CU_pct (sc) numeric KC_pct (sc) numeric EP_pct (sc) numeric CH_pct (sc) numeric SC_pct (sc) numeric KN_pct (sc) numeric UN_pct (sc) numeric vFA (sc) numeric vFT (sc) numeric vFC (sc) numeric vFS (sc) numeric vFO (sc) numeric vSI (sc) numeric vSL (sc) numeric vCU (sc) numeric vKC (sc) numeric vEP (sc) numeric vCH (sc) numeric vSC (sc) numeric 24 fg_batter_leaders vKN (sc) numeric FA-X (sc) numeric FT-X (sc) numeric FC-X (sc) numeric FS-X (sc) numeric FO-X (sc) numeric SI-X (sc) numeric SL-X (sc) numeric CU-X (sc) numeric KC-X (sc) numeric EP-X (sc) numeric CH-X (sc) numeric SC-X (sc) numeric KN-X (sc) numeric FA-Z (sc) numeric FT-Z (sc) numeric FC-Z (sc) numeric FS-Z (sc) numeric FO-Z (sc) numeric SI-Z (sc) numeric SL-Z (sc) numeric CU-Z (sc) numeric KC-Z (sc) numeric EP-Z (sc) numeric CH-Z (sc) numeric SC-Z (sc) numeric KN-Z (sc) numeric wFA (sc) numeric wFT (sc) numeric wFC (sc) numeric wFS (sc) numeric wFO (sc) numeric wSI (sc) numeric wSL (sc) numeric wCU (sc) numeric wKC (sc) numeric wEP (sc) numeric wCH (sc) numeric wSC (sc) numeric wKN (sc) numeric wFA_C (sc) numeric wFT_C (sc) numeric wFC_C (sc) numeric wFS_C (sc) numeric wFO_C (sc) numeric wSI_C (sc) numeric wSL_C (sc) numeric wCU_C (sc) numeric fg_batter_leaders 25 wKC_C (sc) numeric wEP_C (sc) numeric wCH_C (sc) numeric wSC_C (sc) numeric wKN_C (sc) numeric O-Swing_pct (sc) numeric Z-Swing_pct (sc) numeric Swing_pct (sc) numeric O-Contact_pct (sc) numeric Z-Contact_pct (sc) numeric Contact_pct (sc) numeric Zone_pct (sc) numeric Pace numeric Def numeric wSB numeric UBR numeric AgeRng numeric Off numeric Lg numeric wGDP numeric Pull_pct numeric Cent_pct numeric Oppo_pct numeric Soft_pct numeric Med_pct numeric Hard_pct numeric TTO_pct numeric CH_pct_pi numeric CS_pct_pi numeric CU_pct_pi numeric FA_pct_pi numeric FC_pct_pi numeric FS_pct_pi numeric KN_pct_pi numeric SB_pct_pi numeric SI_pct_pi numeric SL_pct_pi numeric XX_pct_pi numeric vCH_pi numeric vCS_pi numeric vCU_pi numeric vFA_pi numeric vFC_pi numeric vFS_pi numeric vKN_pi numeric vSB_pi numeric vSI_pi numeric vSL_pi numeric 26 fg_batter_leaders vXX_pi numeric CH-X_pi numeric CS-X_pi numeric CU-X_pi numeric FA-X_pi numeric FC-X_pi numeric FS-X_pi numeric KN-X_pi numeric SB-X_pi numeric SI-X_pi numeric SL-X_pi numeric XX-X_pi numeric CH-Z_pi numeric CS-Z_pi numeric CU-Z_pi numeric FA-Z_pi numeric FC-Z_pi numeric FS-Z_pi numeric KN-Z_pi numeric SB-Z_pi numeric SI-Z_pi numeric SL-Z_pi numeric XX-Z_pi numeric wCH_pi numeric wCS_pi numeric wCU_pi numeric wFA_pi numeric wFC_pi numeric wFS_pi numeric wKN_pi numeric wSB_pi numeric wSI_pi numeric wSL_pi numeric wXX_pi numeric wCH_C_pi numeric wCS_C_pi numeric wCU_C_pi numeric wFA_C_pi numeric wFC_C_pi numeric wFS_C_pi numeric wKN_C_pi numeric wSB_C_pi numeric wSI_C_pi numeric wSL_C_pi numeric wXX_C_pi numeric O-Swing_pct_pi numeric Z-Swing_pct_pi numeric Swing_pct_pi numeric fg_bat_leaders 27 O-Contact_pct_pi numeric Z-Contact_pct_pi numeric Contact_pct_pi numeric Zone_pct_pi numeric Pace_pi numeric Examples try(fg_batter_leaders(x = 2015, y = 2015, qual = 200)) fg_bat_leaders (legacy) Scrape Batter Leaderboards from FanGraphs Description (legacy) Scrape Batter Leaderboards from FanGraphs Usage fg_bat_leaders(x, y, league = "all", qual = "y", ind = 1, exc_p = TRUE) Arguments x First season for which you want data. y Last season for which you want data. If multiple years selected, data returned will be aggregate data for the date range. If y = x, function will return single- season data. league Option for limiting results to different leagues or overall results. Options are "al", "nl", or "all". qual Whether you want only batters/pitchers that qualified in a given season, or the minimum number of plate appearances for inclusion. If you only want qualified hitters, use qual. If a minimum number of plate appearaces/innings pitched, use the number desired. Defaults to "y". ind Whether or not to break the seasons out individual, or roll them up together. 1 = split seasons, 0 = aggregate seasons. exc_p (logical) Whether or not to exclude pitchers from the batter leaderboards. TRUE = exclude pitchers, FALSE = retain pitchers. Value A data frame of batter data. 28 fg_milb_batter_game_logs fg_guts Scrape FanGraphs.com Guts! Description Scrape historical FanGraphs Guts! table, wOBA, FIP coefficients and constants Usage fg_guts() Value Returns a tibble of seasonal constants from FanGraphs col_name types season integer lg_woba numeric woba_scale numeric wBB numeric wHBP numeric w1B numeric w2B numeric w3B numeric wHR numeric runSB numeric runCS numeric lg_r_pa numeric lg_r_w numeric cFIP numeric Examples try(fg_guts()) fg_milb_batter_game_logs Scrape MiLB game logs for batters from FanGraphs Description This function allows you to scrape MiLB game logs for individual batters from FanGraphs. fg_milb_batter_game_logs 29 Usage fg_milb_batter_game_logs(playerid, year) Arguments playerid The batter’s minor league ID from FanGraphs. year The season for which game logs should be returned. Value Returns a tibble of Minor League batter game logs with the following columns: col_name types player_name character minor_playerid character Date character Team character Level character Opp character G numeric AB numeric PA numeric H numeric 1B numeric 2B numeric 3B numeric HR numeric R numeric RBI numeric BB numeric IBB numeric SO numeric HBP numeric SF numeric SH numeric GDP numeric SB numeric CS numeric AVG numeric BB% numeric K% numeric BB/K numeric OBP numeric SLG numeric OPS numeric ISO numeric Spd numeric BABIP numeric 30 fg_milb_pitcher_game_logs wRC numeric wRAA numeric wOBA numeric wRC+ numeric wBsR numeric gamedate character dh integer UPId character MLBAMId character MinorMasterId character RRId character FirstName character LastName character firstLastName character Height character Weight character BirthDate character Bats character Throws character Position character BirthCity character College character Age character Examples try(fg_milb_batter_game_logs(playerid = "sa3010868", year=2021)) fg_milb_pitcher_game_logs Scrape MiLB game logs for pitchers from FanGraphs Description This function allows you to scrape MiLB game logs for individual batters from FanGraphs.com. Usage fg_milb_pitcher_game_logs(playerid, year) Arguments playerid The pitcher’s minor league ID from FanGraphs.com. year The season for which game logs should be returned. fg_milb_pitcher_game_logs 31 Value Returns a tibble of Minor League pitcher game logs. col_name types player_name character minor_playerid character Date character Team character Level character Opp character W numeric L numeric ERA numeric G numeric GS numeric CG numeric ShO numeric SV numeric IP numeric TBF numeric H numeric R numeric ER numeric HR numeric BB numeric IBB numeric HBP numeric WP numeric BK numeric SO numeric K/9 numeric BB/9 numeric K/BB numeric HR/9 numeric K% numeric K-BB% numeric BB% numeric AVG numeric WHIP numeric BABIP numeric LOB% numeric FIP numeric gamedate character dh integer UPId character MLBAMId character MinorMasterId character RRId character 32 fg_park FirstName character LastName character firstLastName character Height character Weight character BirthDate character Bats character Throws character Position character BirthCity character College character Age character Examples fg_milb_pitcher_game_logs(playerid = "sa3005315", year=2021) fg_park Scrape Park Factors from FanGraphs Description This function allows you to scrape park factors for a given season from FanGraphs.com. This function allows you to scrape park factors by handedness from FanGraphs.com for a given single year. Usage fg_park(yr) fg_park_hand(yr) Arguments yr Season for which you want to scrape the park factors. Value Returns a tibble of park factors. col_name types season integer home_team character basic_5yr integer fg_pitcher_game_logs 33 3yr integer 1yr integer single integer double integer triple integer hr integer so integer UIBB integer GB integer FB integer LD integer IFFB integer FIP integer Returns a tibble of park factors by handedness. col_name types season integer home_team character single_as_LHH integer single_as_RHH integer double_as_LHH integer double_as_RHH integer triple_as_LHH integer triple_as_RHH integer hr_as_LHH integer hr_as_RHH integer Examples try(fg_park(2013)) try(fg_park_hand(2013)) fg_pitcher_game_logs Scrape Pitcher Game Logs from FanGraphs Description This function allows you to scrape game logs by year for a pitcher from FanGraphs.com. 34 fg_pitcher_game_logs Usage fg_pitcher_game_logs(playerid, year = 2017) Arguments playerid This is the playerid used by FanGraphs for a given player year The season for which game logs should be returned (use the YYYY format) Value Returns a tibble of pitcher game logs with the following columns: col_name types PlayerName character playerid integer Date character Opp character teamid integer season integer Team character HomeAway character Age integer W numeric L numeric ERA numeric G numeric GS numeric CG numeric ShO numeric SV numeric HLD numeric BS numeric IP numeric TBF numeric H numeric R numeric ER numeric HR numeric BB numeric IBB numeric HBP numeric WP numeric BK numeric SO numeric K/9 numeric BB/9 numeric H/9 numeric K/BB numeric fg_pitcher_game_logs 35 IFH% numeric BUH% numeric GB numeric FB numeric LD numeric IFFB numeric IFH numeric BU numeric BUH numeric K% numeric BB% numeric K-BB% numeric SIERA numeric HR/9 numeric AVG numeric WHIP numeric BABIP numeric LOB% numeric FIP numeric E-F numeric xFIP numeric ERA- numeric FIP- numeric xFIP- numeric GB/FB numeric LD% numeric GB% numeric FB% numeric IFFB% numeric HR/FB numeric RS numeric RS/9 numeric Balls numeric Strikes numeric Pitches numeric WPA numeric -WPA numeric +WPA numeric RE24 numeric REW numeric pLI numeric inLI numeric gmLI numeric exLI numeric Pulls numeric Games numeric WPA/LI numeric Clutch numeric 36 fg_pitcher_game_logs SD numeric MD numeric FB%1 numeric FBv numeric SL% numeric SLv numeric CT% numeric CTv numeric CB% numeric CBv numeric CH% numeric CHv numeric XX% numeric PO% numeric wFB numeric wSL numeric wCT numeric wCB numeric wCH numeric wFB/C numeric wSL/C numeric wCT/C numeric wCB/C numeric wCH/C numeric O-Swing% numeric Z-Swing% numeric Swing% numeric O-Contact% numeric Z-Contact% numeric Contact% numeric Zone% numeric F-Strike% numeric SwStr% numeric Pull numeric Cent numeric Oppo numeric Soft numeric Med numeric Hard numeric bipCount numeric Pull% numeric Cent% numeric Oppo% numeric Soft% numeric Med% numeric Hard% numeric tERA numeric GSv2 numeric fg_pitcher_leaders 37 Events numeric gamedate character dh integer Examples try(fg_pitcher_game_logs(playerid = 104, year = 2006)) fg_pitcher_leaders Scrape Pitcher Leaderboards from FanGraphs Description Scrape Pitcher Leaderboards from FanGraphs Usage fg_pitcher_leaders( x, y, league = "all", qual = "y", pitcher_type = "pit", ind = 1 ) Arguments x First season for which you want data. y Last season for which you want data. If multiple years selected, data returned will be aggregate data for the date range. If y = x, function will return single- season data. league Option for limiting results to different leagues or overall results. Options are "al", "nl", or "all". qual Whether you want only batters/pitchers that qualified in a given season, or the minimum number of plate appearances for inclusion. If you only want qualified hitters, use qual. If a minimum number of plate appearaces/innings pitched, use the number desired. Defaults to "y". pitcher_type Whether you want only starting pitchers, relievers, or all pitchers that meet the criteria specified in the qual argument. Options include "pit", "sta", "rel". ind Whether or not to break the seasons out individual, or roll them up together. 1 = split seasons, 0 = aggregate seasons. 38 fg_pitcher_leaders Value A data frame of pitcher data. fg_pitcher_leaders 39 col_name types playerid character # character Season character Name character Team character Age numeric W numeric L numeric ERA numeric G numeric GS numeric CG numeric ShO numeric SV numeric BS numeric IP numeric TBF numeric H numeric R numeric ER numeric HR numeric BB numeric IBB numeric HBP numeric WP numeric BK numeric SO numeric GB numeric FB numeric LD numeric IFFB numeric Balls numeric Strikes numeric Pitches numeric RS numeric IFH numeric BU numeric BUH numeric K_9 numeric BB_9 numeric K_BB numeric H_9 numeric HR_9 numeric AVG numeric WHIP numeric BABIP numeric LOB_pct numeric 40 fg_pitcher_leaders FIP numeric GB_FB numeric LD_pct numeric GB_pct numeric FB_pct numeric IFFB_pct numeric HR_FB numeric IFH_pct numeric BUH_pct numeric Starting numeric Start_IP numeric Relieving numeric Relief_IP numeric RAR numeric WAR numeric Dollars numeric tERA numeric xFIP numeric WPA numeric WPA_minus numeric WPA_plus numeric RE24 numeric REW numeric pLI numeric inLI numeric gmLI numeric exLI numeric Pulls numeric WPA_LI numeric Clutch numeric FBall_pct numeric FBv numeric SL_pct numeric SLv numeric CT_pct numeric CTv numeric CB_pct numeric CBv numeric CH_pct numeric CHv numeric SF_pct numeric SFv numeric KN_pct numeric KNv numeric XX_pct numeric PO_pct numeric wFB numeric wSL numeric fg_pitcher_leaders 41 wCT numeric wCB numeric wCH numeric wSF numeric wKN numeric wFB_C numeric wSL_C numeric wCT_C numeric wCB_C numeric wCH_C numeric wSF_C numeric wKN_C numeric O-Swing_pct numeric Z-Swing_pct numeric Swing_pct numeric O-Contact_pct numeric Z-Contact_pct numeric Contact_pct numeric Zone_pct numeric F-Strike_pct numeric SwStr_pct numeric HLD numeric SD numeric MD numeric ERA- numeric FIP- numeric xFIP- numeric K_pct numeric BB_pct numeric SIERA numeric RS_9 numeric E-F numeric FA_pct (sc) numeric FT_pct (sc) numeric FC_pct (sc) numeric FS_pct (sc) numeric FO_pct (sc) numeric SI_pct (sc) numeric SL_pct (sc) numeric CU_pct (sc) numeric KC_pct (sc) numeric EP_pct (sc) numeric CH_pct (sc) numeric SC_pct (sc) numeric KN_pct (sc) numeric UN_pct (sc) numeric vFA (sc) numeric vFT (sc) numeric 42 fg_pitcher_leaders vFC (sc) numeric vFS (sc) numeric vFO (sc) numeric vSI (sc) numeric vSL (sc) numeric vCU (sc) numeric vKC (sc) numeric vEP (sc) numeric vCH (sc) numeric vSC (sc) numeric vKN (sc) numeric FA-X (sc) numeric FT-X (sc) numeric FC-X (sc) numeric FS-X (sc) numeric FO-X (sc) numeric SI-X (sc) numeric SL-X (sc) numeric CU-X (sc) numeric KC-X (sc) numeric EP-X (sc) numeric CH-X (sc) numeric SC-X (sc) numeric KN-X (sc) numeric FA-Z (sc) numeric FT-Z (sc) numeric FC-Z (sc) numeric FS-Z (sc) numeric FO-Z (sc) numeric SI-Z (sc) numeric SL-Z (sc) numeric CU-Z (sc) numeric KC-Z (sc) numeric EP-Z (sc) numeric CH-Z (sc) numeric SC-Z (sc) numeric KN-Z (sc) numeric wFA (sc) numeric wFT (sc) numeric wFC (sc) numeric wFS (sc) numeric wFO (sc) numeric wSI (sc) numeric wSL (sc) numeric wCU (sc) numeric wKC (sc) numeric wEP (sc) numeric wCH (sc) numeric fg_pitcher_leaders 43 wSC (sc) numeric wKN (sc) numeric wFA_C (sc) numeric wFT_C (sc) numeric wFC_C (sc) numeric wFS_C (sc) numeric wFO_C (sc) numeric wSI_C (sc) numeric wSL_C (sc) numeric wCU_C (sc) numeric wKC_C (sc) numeric wEP_C (sc) numeric wCH_C (sc) numeric wSC_C (sc) numeric wKN_C (sc) numeric O-Swing_pct (sc) numeric Z-Swing_pct (sc) numeric Swing_pct (sc) numeric O-Contact_pct (sc) numeric Z-Contact_pct (sc) numeric Contact_pct (sc) numeric Zone_pct (sc) numeric Pace numeric RA9-WAR numeric BIP-Wins numeric LOB-Wins numeric FDP-Wins numeric AgeRng numeric K-BB_pct numeric Pull_pct numeric Cent_pct numeric Oppo_pct numeric Soft_pct numeric Med_pct numeric Hard_pct numeric kwERA numeric TTO_pct numeric CH_pct_pi numeric CS_pct_pi numeric CU_pct_pi numeric FA_pct_pi numeric FC_pct_pi numeric FS_pct_pi numeric KN_pct_pi numeric SB_pct_pi numeric SI_pct_pi numeric SL_pct_pi numeric XX_pct_pi numeric 44 fg_pitcher_leaders vCH_pi numeric vCS_pi numeric vCU_pi numeric vFA_pi numeric vFC_pi numeric vFS_pi numeric vKN_pi numeric vSB_pi numeric vSI_pi numeric vSL_pi numeric vXX_pi numeric CH-X_pi numeric CS-X_pi numeric CU-X_pi numeric FA-X_pi numeric FC-X_pi numeric FS-X_pi numeric KN-X_pi numeric SB-X_pi numeric SI-X_pi numeric SL-X_pi numeric XX-X_pi numeric CH-Z_pi numeric CS-Z_pi numeric CU-Z_pi numeric FA-Z_pi numeric FC-Z_pi numeric FS-Z_pi numeric KN-Z_pi numeric SB-Z_pi numeric SI-Z_pi numeric SL-Z_pi numeric XX-Z_pi numeric wCH_pi numeric wCS_pi numeric wCU_pi numeric wFA_pi numeric wFC_pi numeric wFS_pi numeric wKN_pi numeric wSB_pi numeric wSI_pi numeric wSL_pi numeric wXX_pi numeric wCH_C_pi numeric wCS_C_pi numeric wCU_C_pi numeric wFA_C_pi numeric fg_pitch_leaders 45 wFC_C_pi numeric wFS_C_pi numeric wKN_C_pi numeric wSB_C_pi numeric wSI_C_pi numeric wSL_C_pi numeric wXX_C_pi numeric O-Swing_pct_pi numeric Z-Swing_pct_pi numeric Swing_pct_pi numeric O-Contact_pct_pi numeric Z-Contact_pct_pi numeric Contact_pct_pi numeric Zone_pct_pi numeric Pace_pi numeric Dol numeric Examples fg_pitcher_leaders(x = 2015, y = 2015, qual = 150) fg_pitch_leaders (legacy) Scrape Pitcher Leaderboards from FanGraphs Description (legacy) Scrape Pitcher Leaderboards from FanGraphs Usage fg_pitch_leaders( x, y, league = "all", qual = "y", pitcher_type = "pit", ind = 1 ) Arguments x First season for which you want data. y Last season for which you want data. If multiple years selected, data returned will be aggregate data for the date range. If y = x, function will return single- season data. 46 fg_team_batter league Option for limiting results to different leagues or overall results. Options are "al", "nl", or "all". qual Whether you want only batters/pitchers that qualified in a given season, or the minimum number of plate appearances for inclusion. If you only want qualified hitters, use qual. If a minimum number of plate appearaces/innings pitched, use the number desired. Defaults to "y". pitcher_type Whether you want only starting pitchers, relievers, or all pitchers that meet the criteria specified in the qual argument. Options include "pit", "sta", "rel". ind Whether or not to break the seasons out individual, or roll them up together. 1 = split seasons, 0 = aggregate seasons. Value A data frame of pitcher data. fg_team_batter Scrape Team Batter Leaderboards from FanGraphs Description This function allows you to scrape all leaderboard statistics (basic and advanced) from FanGraphs.com. Usage fg_team_batter(x, y, league = "all", qual = "y", ind = 1, exc_p = TRUE) Arguments x First season for which you want data. y Last season for which you want data. If multiple years selected, data returned will be aggregate data for the date range. If y = x, function will return single- season data. league Option for limiting results to different leagues or overall results. Options are "al", "nl", or "all". qual Whether you want only batters/pitchers that qualified in a given season, or the minimum number of plate appearances for inclusion. If you only want qualified hitters, use qual. If a minimum number of plate appearaces/innings pitched, use the number desired. Defaults to "y". ind Whether or not to break the seasons out individual, or roll them up together. 1 = split seasons, 0 = aggregate seasons. exc_p (logical) Whether or not to exclude pitchers from the batter leaderboards. TRUE = exclude pitchers, FALSE = retain pitchers. Value A data frame of batter data. fg_team_batter 47 col_name types Season character # character Team character G numeric AB numeric PA numeric H numeric 1B numeric 2B numeric 3B numeric HR numeric R numeric RBI numeric BB numeric IBB numeric SO numeric HBP numeric SF numeric SH numeric GDP numeric SB numeric CS numeric AVG numeric GB numeric FB numeric LD numeric IFFB numeric Pitches numeric Balls numeric Strikes numeric IFH numeric BU numeric BUH numeric BB_pct numeric K_pct numeric BB_K numeric OBP numeric SLG numeric OPS numeric ISO numeric BABIP numeric GB_FB numeric LD_pct numeric GB_pct numeric FB_pct numeric IFFB_pct numeric HR_FB numeric 48 fg_team_batter IFH_pct numeric BUH_pct numeric wOBA numeric wRAA numeric wRC numeric Bat numeric Fld numeric Rep numeric Pos numeric RAR numeric WAR numeric Dol numeric Spd numeric wRC_plus numeric WPA numeric WPA_minus numeric WPA_plus numeric RE24 numeric REW numeric pLI numeric phLI numeric PH numeric WPA_LI numeric Clutch numeric FBall_pct numeric FBv numeric SL_pct numeric SLv numeric CT_pct numeric CTv numeric CB_pct numeric CBv numeric CH_pct numeric CHv numeric SF_pct numeric SFv numeric KN_pct numeric KNv numeric XX_pct numeric PO_pct numeric wFB numeric wSL numeric wCT numeric wCB numeric wCH numeric wSF numeric wKN numeric wFB_C numeric fg_team_batter 49 wSL_C numeric wCT_C numeric wCB_C numeric wCH_C numeric wSF_C numeric wKN_C numeric O-Swing_pct numeric Z-Swing_pct numeric Swing_pct numeric O-Contact_pct numeric Z-Contact_pct numeric Contact_pct numeric Zone_pct numeric F-Strike_pct numeric SwStr_pct numeric BsR numeric FA_pct (sc) numeric FT_pct (sc) numeric FC_pct (sc) numeric FS_pct (sc) numeric FO_pct (sc) numeric SI_pct (sc) numeric SL_pct (sc) numeric CU_pct (sc) numeric KC_pct (sc) numeric EP_pct (sc) numeric CH_pct (sc) numeric SC_pct (sc) numeric KN_pct (sc) numeric UN_pct (sc) numeric vFA (sc) numeric vFT (sc) numeric vFC (sc) numeric vFS (sc) numeric vFO (sc) numeric vSI (sc) numeric vSL (sc) numeric vCU (sc) numeric vKC (sc) numeric vEP (sc) numeric vCH (sc) numeric vSC (sc) numeric vKN (sc) numeric FA-X (sc) numeric FT-X (sc) numeric FC-X (sc) numeric FS-X (sc) numeric FO-X (sc) numeric 50 fg_team_batter SI-X (sc) numeric SL-X (sc) numeric CU-X (sc) numeric KC-X (sc) numeric EP-X (sc) numeric CH-X (sc) numeric SC-X (sc) numeric KN-X (sc) numeric FA-Z (sc) numeric FT-Z (sc) numeric FC-Z (sc) numeric FS-Z (sc) numeric FO-Z (sc) numeric SI-Z (sc) numeric SL-Z (sc) numeric CU-Z (sc) numeric KC-Z (sc) numeric EP-Z (sc) numeric CH-Z (sc) numeric SC-Z (sc) numeric KN-Z (sc) numeric wFA (sc) numeric wFT (sc) numeric wFC (sc) numeric wFS (sc) numeric wFO (sc) numeric wSI (sc) numeric wSL (sc) numeric wCU (sc) numeric wKC (sc) numeric wEP (sc) numeric wCH (sc) numeric wSC (sc) numeric wKN (sc) numeric wFA_C (sc) numeric wFT_C (sc) numeric wFC_C (sc) numeric wFS_C (sc) numeric wFO_C (sc) numeric wSI_C (sc) numeric wSL_C (sc) numeric wCU_C (sc) numeric wKC_C (sc) numeric wEP_C (sc) numeric wCH_C (sc) numeric wSC_C (sc) numeric wKN_C (sc) numeric O-Swing_pct (sc) numeric fg_team_batter 51 Z-Swing_pct (sc) numeric Swing_pct (sc) numeric O-Contact_pct (sc) numeric Z-Contact_pct (sc) numeric Contact_pct (sc) numeric Zone_pct (sc) numeric Pace numeric Def numeric wSB numeric UBR numeric AgeRng numeric Off numeric Lg numeric wGDP numeric Pull_pct numeric Cent_pct numeric Oppo_pct numeric Soft_pct numeric Med_pct numeric Hard_pct numeric TTO_pct numeric CH_pct_pi numeric CS_pct_pi numeric CU_pct_pi numeric FA_pct_pi numeric FC_pct_pi numeric FS_pct_pi numeric KN_pct_pi numeric SB_pct_pi numeric SI_pct_pi numeric SL_pct_pi numeric XX_pct_pi numeric vCH_pi numeric vCS_pi numeric vCU_pi numeric vFA_pi numeric vFC_pi numeric vFS_pi numeric vKN_pi numeric vSB_pi numeric vSI_pi numeric vSL_pi numeric vXX_pi numeric CH-X_pi numeric CS-X_pi numeric CU-X_pi numeric FA-X_pi numeric FC-X_pi numeric 52 fg_team_batter FS-X_pi numeric KN-X_pi numeric SB-X_pi numeric SI-X_pi numeric SL-X_pi numeric XX-X_pi numeric CH-Z_pi numeric CS-Z_pi numeric CU-Z_pi numeric FA-Z_pi numeric FC-Z_pi numeric FS-Z_pi numeric KN-Z_pi numeric SB-Z_pi numeric SI-Z_pi numeric SL-Z_pi numeric XX-Z_pi numeric wCH_pi numeric wCS_pi numeric wCU_pi numeric wFA_pi numeric wFC_pi numeric wFS_pi numeric wKN_pi numeric wSB_pi numeric wSI_pi numeric wSL_pi numeric wXX_pi numeric wCH_C_pi numeric wCS_C_pi numeric wCU_C_pi numeric wFA_C_pi numeric wFC_C_pi numeric wFS_C_pi numeric wKN_C_pi numeric wSB_C_pi numeric wSI_C_pi numeric wSL_C_pi numeric wXX_C_pi numeric O-Swing_pct_pi numeric Z-Swing_pct_pi numeric Swing_pct_pi numeric O-Contact_pct_pi numeric Z-Contact_pct_pi numeric Contact_pct_pi numeric Zone_pct_pi numeric Pace_pi numeric fg_team_pitcher 53 Examples try(fg_team_batter(x = 2015, y = 2015, qual = 200)) fg_team_pitcher Scrape Team Pitcher Leaderboards from FanGraphs Description Scrape Team Pitcher Leaderboards from FanGraphs Usage fg_team_pitcher(x, y, league = "all", qual = 0, pitcher_type = "pit", ind = 1) Arguments x First season for which you want data. y Last season for which you want data. If multiple years selected, data returned will be aggregate data for the date range. If y = x, function will return single- season data. league Option for limiting results to different leagues or overall results. Options are "al", "nl", or "all". qual Whether you want only batters/pitchers that qualified in a given season, or the minimum number of plate appearances for inclusion. If you only want qualified hitters, use qual. If a minimum number of plate appearaces/innings pitched, use the number desired. Defaults to "y". pitcher_type Whether you want only starting pitchers, relievers, or all pitchers that meet the criteria specified in the qual argument. Options include "pit", "sta", "rel". ind Whether or not to break the seasons out individual, or roll them up together. 1 = split seasons, 0 = aggregate seasons. Value A data frame of pitcher data. col_name types Season character # character Team character W numeric L numeric ERA numeric G numeric GS numeric 54 fg_team_pitcher CG numeric ShO numeric SV numeric BS numeric IP numeric TBF numeric H numeric R numeric ER numeric HR numeric BB numeric IBB numeric HBP numeric WP numeric BK numeric SO numeric GB numeric FB numeric LD numeric IFFB numeric Balls numeric Strikes numeric Pitches numeric RS numeric IFH numeric BU numeric BUH numeric K_9 numeric BB_9 numeric K_BB numeric H_9 numeric HR_9 numeric AVG numeric WHIP numeric BABIP numeric LOB_pct numeric FIP numeric GB_FB numeric LD_pct numeric GB_pct numeric FB_pct numeric IFFB_pct numeric HR_FB numeric IFH_pct numeric BUH_pct numeric Starting numeric Start_IP numeric Relieving numeric fg_team_pitcher 55 Relief_IP numeric RAR numeric WAR numeric Dollars numeric tERA numeric xFIP numeric WPA numeric WPA_minus numeric WPA_plus numeric RE24 numeric REW numeric pLI numeric inLI numeric gmLI numeric exLI numeric Pulls numeric WPA_LI numeric Clutch numeric FBall_pct numeric FBv numeric SL_pct numeric SLv numeric CT_pct numeric CTv numeric CB_pct numeric CBv numeric CH_pct numeric CHv numeric SF_pct numeric SFv numeric KN_pct numeric KNv numeric XX_pct numeric PO_pct numeric wFB numeric wSL numeric wCT numeric wCB numeric wCH numeric wSF numeric wKN numeric wFB_C numeric wSL_C numeric wCT_C numeric wCB_C numeric wCH_C numeric wSF_C numeric wKN_C numeric 56 fg_team_pitcher O-Swing_pct numeric Z-Swing_pct numeric Swing_pct numeric O-Contact_pct numeric Z-Contact_pct numeric Contact_pct numeric Zone_pct numeric F-Strike_pct numeric SwStr_pct numeric HLD numeric SD numeric MD numeric ERA- numeric FIP- numeric xFIP- numeric K_pct numeric BB_pct numeric SIERA numeric RS_9 numeric E-F numeric FA_pct (sc) numeric FT_pct (sc) numeric FC_pct (sc) numeric FS_pct (sc) numeric FO_pct (sc) numeric SI_pct (sc) numeric SL_pct (sc) numeric CU_pct (sc) numeric KC_pct (sc) numeric EP_pct (sc) numeric CH_pct (sc) numeric SC_pct (sc) numeric KN_pct (sc) numeric UN_pct (sc) numeric vFA (sc) numeric vFT (sc) numeric vFC (sc) numeric vFS (sc) numeric vFO (sc) numeric vSI (sc) numeric vSL (sc) numeric vCU (sc) numeric vKC (sc) numeric vEP (sc) numeric vCH (sc) numeric vSC (sc) numeric vKN (sc) numeric FA-X (sc) numeric fg_team_pitcher 57 FT-X (sc) numeric FC-X (sc) numeric FS-X (sc) numeric FO-X (sc) numeric SI-X (sc) numeric SL-X (sc) numeric CU-X (sc) numeric KC-X (sc) numeric EP-X (sc) numeric CH-X (sc) numeric SC-X (sc) numeric KN-X (sc) numeric FA-Z (sc) numeric FT-Z (sc) numeric FC-Z (sc) numeric FS-Z (sc) numeric FO-Z (sc) numeric SI-Z (sc) numeric SL-Z (sc) numeric CU-Z (sc) numeric KC-Z (sc) numeric EP-Z (sc) numeric CH-Z (sc) numeric SC-Z (sc) numeric KN-Z (sc) numeric wFA (sc) numeric wFT (sc) numeric wFC (sc) numeric wFS (sc) numeric wFO (sc) numeric wSI (sc) numeric wSL (sc) numeric wCU (sc) numeric wKC (sc) numeric wEP (sc) numeric wCH (sc) numeric wSC (sc) numeric wKN (sc) numeric wFA_C (sc) numeric wFT_C (sc) numeric wFC_C (sc) numeric wFS_C (sc) numeric wFO_C (sc) numeric wSI_C (sc) numeric wSL_C (sc) numeric wCU_C (sc) numeric wKC_C (sc) numeric wEP_C (sc) numeric 58 fg_team_pitcher wCH_C (sc) numeric wSC_C (sc) numeric wKN_C (sc) numeric O-Swing_pct (sc) numeric Z-Swing_pct (sc) numeric Swing_pct (sc) numeric O-Contact_pct (sc) numeric Z-Contact_pct (sc) numeric Contact_pct (sc) numeric Zone_pct (sc) numeric Pace numeric RA9-WAR numeric BIP-Wins numeric LOB-Wins numeric FDP-Wins numeric AgeRng numeric K-BB_pct numeric Pull_pct numeric Cent_pct numeric Oppo_pct numeric Soft_pct numeric Med_pct numeric Hard_pct numeric kwERA numeric TTO_pct numeric CH_pct_pi numeric CS_pct_pi numeric CU_pct_pi numeric FA_pct_pi numeric FC_pct_pi numeric FS_pct_pi numeric KN_pct_pi numeric SB_pct_pi numeric SI_pct_pi numeric SL_pct_pi numeric XX_pct_pi numeric vCH_pi numeric vCS_pi numeric vCU_pi numeric vFA_pi numeric vFC_pi numeric vFS_pi numeric vKN_pi numeric vSB_pi numeric vSI_pi numeric vSL_pi numeric vXX_pi numeric CH-X_pi numeric fg_team_pitcher 59 CS-X_pi numeric CU-X_pi numeric FA-X_pi numeric FC-X_pi numeric FS-X_pi numeric KN-X_pi numeric SB-X_pi numeric SI-X_pi numeric SL-X_pi numeric XX-X_pi numeric CH-Z_pi numeric CS-Z_pi numeric CU-Z_pi numeric FA-Z_pi numeric FC-Z_pi numeric FS-Z_pi numeric KN-Z_pi numeric SB-Z_pi numeric SI-Z_pi numeric SL-Z_pi numeric XX-Z_pi numeric wCH_pi numeric wCS_pi numeric wCU_pi numeric wFA_pi numeric wFC_pi numeric wFS_pi numeric wKN_pi numeric wSB_pi numeric wSI_pi numeric wSL_pi numeric wXX_pi numeric wCH_C_pi numeric wCS_C_pi numeric wCU_C_pi numeric wFA_C_pi numeric wFC_C_pi numeric wFS_C_pi numeric wKN_C_pi numeric wSB_C_pi numeric wSI_C_pi numeric wSL_C_pi numeric wXX_C_pi numeric O-Swing_pct_pi numeric Z-Swing_pct_pi numeric Swing_pct_pi numeric O-Contact_pct_pi numeric Z-Contact_pct_pi numeric 60 fip_plus Contact_pct_pi numeric Zone_pct_pi numeric Pace_pi numeric Dol numeric Examples fg_team_pitcher(x = 2015, y = 2015, qual = 150) fip_plus Calculate FIP and related metrics for any set of data Description This function allows you to calculate FIP and related metrics for any given set of data, provided the right variables are in the data set. The function currently returns both FIP per inning pitched, wOBA against (based on batters faced), and wOBA against per instance of fair contact. Usage fip_plus(df) Arguments df A data frame of statistics that includes, at a minimum, the following columns: IP (innings pitched), BF (batters faced), uBB (unintentional walks), HBP (Hit By Pitch), x1B (singles), x2B (doubles), x3B (triples), HR (home runs), AB (at-bats), SH (sacrifice hits), SO (strike outs), and season. Value Returns a tibble with the following columns: col_name types bbref_id character season integer Name character Age numeric Level character Team character G numeric GS numeric W numeric L numeric SV numeric fip_plus 61 IP numeric H numeric R numeric ER numeric uBB numeric BB numeric SO numeric HR numeric HBP numeric ERA numeric AB numeric X1B numeric X2B numeric X3B numeric IBB numeric GDP numeric SF numeric SB numeric CS numeric PO numeric BF numeric Pit numeric Str numeric StL numeric StS numeric GB.FB numeric LD numeric PU numeric WHIP numeric BAbip numeric SO9 numeric SO.W numeric SO_perc numeric uBB_perc numeric SO_uBB numeric FIP numeric wOBA_against numeric wOBA_CON_against numeric Examples df <- bref_daily_pitcher("2015-04-05", "2015-04-30") try(fip_plus(df)) 62 get_draft_mlb get_batting_orders (legacy) Retrieve batting orders for a given MLB game Description (legacy) Retrieve batting orders for a given MLB game Usage get_batting_orders(game_pk, type = "starting") Arguments game_pk The unique game_pk identifier for the game type Whether to just return the starting lineup (’starting’) or all batters that appeared (’all’) Value Returns a tibble that includes probable starting pitchers and the home plate umpire for the game_pk requested get_draft_mlb (legacy) Retrieve draft pick information by year Description (legacy) Retrieve draft pick information by year Usage get_draft_mlb(year) Arguments year The year for which to return data Value Returns a tibble with information for every draft pick in every round for the year requested get_game_info_mlb 63 get_game_info_mlb (legacy) Retrieve additional game information for major and mi- nor league games Description (legacy) Retrieve additional game information for major and minor league games Usage get_game_info_mlb(game_pk) Arguments game_pk The unique game_pk identifier for the game Value Returns a tibble that includes supplemental information, such as weather, official scorer, attendance, etc., for the game_pk provided get_game_info_sup_petti (legacy) Download a data frame of supplemental data about MLB games since 2008. Description (legacy) Download a data frame of supplemental data about MLB games since 2008. Usage get_game_info_sup_petti() Value Function returns a tibble with various columns, including: • game_pk • game_date • venue id • attendance • game temperature • wind speed • direction • start time • end time 64 get_ncaa_baseball_pbp get_game_pks_mlb (legacy) Get MLB Game Info by Date and Level Description (legacy) Get MLB Game Info by Date and Level Usage get_game_pks_mlb(date, level_ids = c(1)) Arguments date The date for which you want to find game_pk values for MLB games level_ids A numeric vector with ids for each level where game_pks are desired. See below for a reference of level ids. Value Returns a tibble that includes game_pk values and additional information for games scheduled or played get_ncaa_baseball_pbp (legacy) Get Play-By-Play Data for NCAA Baseball Games Description (legacy) Get Play-By-Play Data for NCAA Baseball Games (legacy) Get Play-By-Play Data for NCAA Baseball Games Usage get_ncaa_baseball_pbp( game_info_url = NA_character_, game_pbp_url = NA_character_, raw_html_to_disk = FALSE, raw_html_path = "/", read_from_file = FALSE, file = NA_character_, ... ) ncaa_baseball_pbp( game_info_url = NA_character_, game_pbp_url = NA_character_, get_ncaa_game_logs 65 raw_html_to_disk = FALSE, raw_html_path = "/", read_from_file = FALSE, file = NA_character_, ... ) Arguments game_info_url The url for the game’s boxscore data. This can be found using the ncaa_schedule_info function. game_pbp_url The url for the game’s play-by-play data. This can be found using the ncaa_schedule_info function. raw_html_to_disk Write raw html to disk (saves as game_pbp_id.html in raw_html_path direc- tory) raw_html_path Directory path to write raw html read_from_file Read from raw html on disk file File with full path to read raw html ... Additional arguments passed to an underlying function like httr. Value A data frame with play-by-play data for an individual game. A data frame with play-by-play data for an individual game. get_ncaa_game_logs (legacy) Get NCAA Baseball Game Logs Description (legacy) Get NCAA Baseball Game Logs Usage get_ncaa_game_logs(player_id, year, type = "batting", span = "game", ...) Arguments player_id A player’s unique id. Can be found using the get_ncaa_baseball_roster function. year The year of interest. type The kind of statistics you want to return. Current options are ’batting’ or ’pitch- ing’. span The span of time; can either be ’game’ for game logs in a season, or ’career’ which returns seasonal stats for a player’s career. ... Additional arguments passed to an underlying function like httr. 66 get_ncaa_park_factor Value A data frame containing player and school information as well as game by game statistics get_ncaa_lineups (legacy) Retrieve lineups for a given NCAA game via its game_info_url Description (legacy) Retrieve lineups for a given NCAA game via its game_info_url Usage get_ncaa_lineups(game_info_url = NULL, ...) Arguments game_info_url The unique game info url ... Additional arguments passed to an underlying function like httr. Value Returns a tibble of each school’s starting lineup and starting pitcher get_ncaa_park_factor (legacy) Get Park Effects for NCAA Baseball Teams Description (legacy) Get Park Effects for NCAA Baseball Teams Usage get_ncaa_park_factor(team_id, years, type = "conference", ...) Arguments team_id The team’s unique NCAA id. years The season or seasons (i.e. use 2016 for the 2015-2016 season, etc., limited to just 2013-2020 seasons). type default is conference. the conference parameter adjusts for the conference the school plays in, the division parameter calculates based on the division the school plays in 1,2,or 3. Defaults to ’conference’. ... Additional arguments passed to an underlying function like httr. get_ncaa_schedule_info 67 Value A data frame with the following fields: school, home_game, away_game, runs_scored_home, runs_allowed_home, run_scored_away, runs_allowed_away, base_pf (base park factor), home_game_adj (an adjustment for the percentage of home games played) final_pf (park factor after adjustments) get_ncaa_schedule_info (legacy) Get Schedule and Results for NCAA Baseball Teams Description (legacy) Get Schedule and Results for NCAA Baseball Teams Usage get_ncaa_schedule_info(team_id = NULL, year = NULL, pbp_links = FALSE, ...) Arguments team_id The team’s unique NCAA id. year The season (i.e. use 2016 for the 2015-2016 season, etc.) pbp_links Logical parameter to run process for scraping play_by_play urls for each game ... Additional arguments passed to an underlying function like httr. Value A data frame with the following fields: date, opponent, result, score, innings (if more than regula- tion), and the url for the game itself. get_pbp_mlb (legacy) Acquire pitch-by-pitch data for Major and Minor League games Description (legacy) Acquire pitch-by-pitch data for Major and Minor League games (legacy) Acquire pitch-by-pitch data for Major and Minor League games Usage get_pbp_mlb(game_pk) get_pbp_mlb(game_pk) 68 get_retrosheet_data Arguments game_pk The date for which you want to find game_pk values for MLB games Value Returns a tibble that includes over 100 columns of data provided by the MLB Stats API at a pitch level. Returns a tibble that includes over 100 columns of data provided by the MLB Stats API at a pitch level. get_probables_mlb (legacy) Retrieve probable starters for a given MLB game Description (legacy) Retrieve probable starters for a given MLB game Usage get_probables_mlb(game_pk) Arguments game_pk The unique game_pk identifier for the game Value Returns a tibble that includes probable starting pitchers and the home plate umpire for the game_pk requested get_retrosheet_data (legacy) Get, Parse, and Format Retrosheet Event and Roster Files Description (legacy) Get, Parse, and Format Retrosheet Event and Roster Files Usage get_retrosheet_data( path_to_directory = NULL, years_to_acquire = most_recent_mlb_season() - 1, sequence_years = FALSE ) get_umpire_ids_petti 69 Arguments path_to_directory (default: NULL) A file path that if set, either: 1. creates a new directory, or 2. uses the path to an existing directory years_to_acquire (format: YYYY) The seasons to collect. Single, multiple, and sequential years can be passed. If passing multiple years, enclose in a vector (i.e. c(2017,2018)). Defaults to most_recent_mlb_season(). sequence_years (logical, default: FALSE): If the seasons passed in the years_to_acquire param- eter should be sequenced so that the function returns all years including and between the vector passed, set the argument to TRUE. Defaults to FALSE. Value If path_to_directory is not set (default), the process will return a named list of tibbles: ’events’ and ’rosters’ for each season provided to years_to_acquire If path_to_directory is set, will also write two csv files to the unzipped directory: 1) a combined csv of the event data for a given year and 2) a combined csv of each team’s roster for each year provided to years_to_acquire get_umpire_ids_petti (legacy) Download a data frame of all umpires and their MLBAM IDs for games since 2008 Description (legacy) Download a data frame of all umpires and their MLBAM IDs for games since 2008 Usage get_umpire_ids_petti() Value Function returns a tibble with the following columns: • id • position, • name • game_pk • game_date 70 ggspraychart ggspraychart Generate spray charts with ggplot2 Description This function allows you to create spray charts with ggplots given a data frame with batted ball location coordinates. Usage ggspraychart( data, x_value = "hc_x", y_value = "-hc_y", fill_value = NULL, fill_palette = NULL, fill_legend_title = NULL, density = FALSE, bin_size = 15, point_alpha = 0.75, point_size = 2, frame = NULL ) Arguments data A data frame that includes batted ball coordinates. Typically, this coordinates will come from the GameDay xml feed or downloads from baseballsavant.com x_value The x coordindate. Typically hc_x. y_value The y coordinate. Typically hc_y. You generally need the inverse or negative of the hc_y values, so it is recommended you calculate before plotting. fill_value The categorical variable that you want the geom_points to base the fill on. Pass as a string. If left blank, defaults to blue. fill_palette An object containing a customer palette to be used with ggplot2::scale_fill_manual. fill_legend_title A string containing a custom legend title to be used with ggplot2::scale_fill_manual. density Chooses between a 2d density plot or a point plot. Defaults to FALSE. bin_size Size of bins used when building a density plot. Defaults to 15. point_alpha Alpha value whenever geom_point is used. Defaults to .75. Recommend .3 for density plots. To remove points on density points set use point_alpha = 0. point_size Set the size of geom_point if used. frame Variable to use as the frame argument if using gganimate to create animated plots. For density plots be sure your variable is a factor. label_statcast_imputed_data 71 Details ggspraychart(df, x_value = "hc_x", y_value = "-hc_y", fill_value = "events") Value A plot of the spraychart for the supplied dataset label_statcast_imputed_data Label Statcast data as imputed Description Based on a series of heuristics, this function attempts to label Statcast data for which the launch angle and speed have been imputed. Usage label_statcast_imputed_data( statcast_df, impute_file = NULL, inverse_precision = 10000 ) Arguments statcast_df A dataframe containing Statcast batted ball data impute_file A CSV file giving the launch angle, launch speed, bb_type, events fields to label as imputed. if NULL then it’s read from the extdata folder of the package. inverse_precision inverse of how many digits to truncate the launch angle and speed to for com- parison. Default is 10000, i.e. keep 4 digits of precision. Value A copy of the input dataframe with a new column imputed appended. imputed is 1 if launch angle and launch speed are likely imputed, 0 otherwise. Returns a tibble with the following columns: col_name types pitch_type character game_date Date release_speed numeric release_pos_x numeric release_pos_z numeric player_name character batter numeric pitcher numeric 72 label_statcast_imputed_data events character description character spin_dir logical spin_rate_deprecated logical break_angle_deprecated logical break_length_deprecated logical zone numeric des character game_type character stand character p_throws character home_team character away_team character type character hit_location integer bb_type character balls integer strikes integer game_year integer pfx_x numeric pfx_z numeric plate_x numeric plate_z numeric on_3b numeric on_2b numeric on_1b numeric outs_when_up integer inning numeric inning_topbot character hc_x numeric hc_y numeric tfs_deprecated logical tfs_zulu_deprecated logical fielder_2 numeric umpire logical sv_id logical vx0 numeric vy0 numeric vz0 numeric ax numeric ay numeric az numeric sz_top numeric sz_bot numeric hit_distance_sc numeric launch_speed numeric launch_angle numeric effective_speed numeric label_statcast_imputed_data 73 release_spin_rate numeric release_extension numeric game_pk numeric pitcher_1 numeric fielder_2_1 numeric fielder_3 numeric fielder_4 numeric fielder_5 numeric fielder_6 numeric fielder_7 numeric fielder_8 numeric fielder_9 numeric release_pos_y numeric estimated_ba_using_speedangle numeric estimated_woba_using_speedangle numeric woba_value numeric woba_denom integer babip_value integer iso_value integer launch_speed_angle integer at_bat_number numeric pitch_number numeric pitch_name character home_score numeric away_score numeric bat_score numeric fld_score numeric post_away_score numeric post_home_score numeric post_bat_score numeric post_fld_score numeric if_fielding_alignment character of_fielding_alignment character spin_axis numeric delta_home_win_exp numeric delta_run_exp numeric ila integer ils integer imputed numeric Examples statcast_df <- statcast_search("2017-05-01", "2017-05-02") sc_df <- label_statcast_imputed_data(statcast_df) mean(sc_df$imputed) 74 load_game_info_sup linear_weights_savant Generate linear weight values for events using Baseball Savant data Description This function allows a user to generate linear weight values for events using Baseball Savant data. Output includes both linear weights above average and linear weights above outs for home runs, triples, doubles, singles, walks, hit by pitches, and outs. Usage linear_weights_savant(df, level = "plate appearance") Arguments df A data frame generated from Baseball Savant that has been run through the run_expectancy_code() function. level Whether to calculate linear weights the plate appearance or pitch level. Defaults to ’plate appearance’. Value Returns a tibble with the following columns: col_name types events character linear_weights_above_average numeric linear_weights_above_outs numeric Examples df <- statcast_search(start_date = "2016-04-06", end_date = "2016-04-15", playerid = 621043, player_type = 'batter') df <- run_expectancy_code(df, level = "plate appearances") try(linear_weights_savant(df, level = "plate appearance")) load_game_info_sup Download a data frame of supplemental data about MLB games since 2008. Description Download a data frame of supplemental data about MLB games since 2008. load_ncaa_baseball_pbp 75 Usage load_game_info_sup() Value Function returns a tibble with various columns, including: • game_pk • game_date • venue id • attendance • game temperature • wind speed • direction • start time • end time Examples try(load_game_info_sup()) load_ncaa_baseball_pbp Load cleaned NCAA baseball play-by-play data from the base- ballr data repo Description helper that loads multiple seasons from the data repo either into memory or writes it into a db using some forwarded arguments in the dots Usage load_ncaa_baseball_pbp( seasons = most_recent_ncaa_baseball_season(), ..., dbConnection = NULL, tablename = NULL ) 76 load_ncaa_baseball_schedule Arguments seasons A vector of 4-digit years associated with given NCAA college baseball seasons. (Min: 2022) ... Additional arguments passed to an underlying function that writes the season data into a database. dbConnection A DBIConnection object, as returned by tablename The name of the schedule data table within the database Value Returns a tibble Examples load_ncaa_baseball_pbp(seasons = 2021) load_ncaa_baseball_schedule Load cleaned NCAA baseball schedule from the baseballr data repo Description helper that loads multiple seasons from the data repo either into memory or writes it into a db using some forwarded arguments in the dots Usage load_ncaa_baseball_schedule( seasons = most_recent_ncaa_baseball_season(), ..., dbConnection = NULL, tablename = NULL ) Arguments seasons A vector of 4-digit years associated with given NCAA college baseball seasons. (Min: 2012) ... Additional arguments passed to an underlying function that writes the season data into a database. dbConnection A DBIConnection object, as returned by tablename The name of the schedule data table within the database load_ncaa_baseball_season_ids 77 Value Returns a tibble Examples load_ncaa_baseball_schedule(seasons = 2022) load_ncaa_baseball_season_ids Load cleaned NCAA men’s college baseball season IDs from the baseballr data repo Description helper that loads multiple seasons of season IDs from the data repo either into memory or writes it into a db using some forwarded arguments in the dots Usage load_ncaa_baseball_season_ids(..., dbConnection = NULL, tablename = NULL) Arguments ... Additional arguments passed to an underlying function that writes the season data into a database. dbConnection A DBIConnection object, as returned by tablename The name of the data table within the database Value Returns a tibble Examples load_ncaa_baseball_season_ids() 78 load_umpire_ids load_ncaa_baseball_teams Load cleaned NCAA men’s college baseball teams from the base- ballr data repo Description helper that loads multiple seasons of teams from the data repo either into memory or writes it into a db using some forwarded arguments in the dots Usage load_ncaa_baseball_teams(..., dbConnection = NULL, tablename = NULL) Arguments ... Additional arguments passed to an underlying function that writes the season data into a database. dbConnection A DBIConnection object, as returned by tablename The name of the data table within the database Value Returns a tibble Examples load_ncaa_baseball_teams() load_umpire_ids Download a data frame of all umpires and their mlbamids for games since 2008 Description Download a data frame of all umpires and their mlbamids for games since 2008 Usage load_umpire_ids() metrics 79 Value Function returns a tibble with the following columns: • id • position • name • game_pk • game_date Examples try(load_umpire_ids()) metrics Metrics Functions Overview Description fip_plus(): Calculate FIP and related metrics for any set of data. woba_plus() Calculate wOBA and related metrics for any set of data. team_consistency() Calculate Team-level Consistency. label_statcast_imputed_data() Label Statcast data as imputed. run_expectancy_code() Generate run expectancy and related measures from Baseball Savant data. linear_weights_savant() Generate linear weight values for events using Baseball Savant data. Details Calculate Team-level Consistency: team_consistency(year=2015) Calculate FIP and related metrics for any set of data: fips_plus(df) Calculate wOBA and related metrics for any set of data: woba_plus(df) Label Statcast data as imputed: statcast_df <- scrape_statcast_savant("2017-05-01", "2017-05-02") sc_df <- label_statcast_imputed_data(statcast_df) mean(sc_df$imputed) 80 milb_pitcher_game_logs_fg Generate run expectancy and related measures from Baseball Savant data: df <- statcast_search(start_date = "2016-04-06", end_date = "2016-04-15", playerid = 621043, player_type = 'batter') run_expectancy_code(df, level = "plate appearances") Generate linear weight values for events using Baseball Savant data: df <- statcast_search(start_date = "2016-04-06", end_date = "2016-04-15", playerid = 621043, player_type = 'batter') df <- run_expectancy_code(df, level = "plate appearances") linear_weights_savant(df, level = "plate appearance") milb_batter_game_logs_fg (legacy) Scrape MiLB game logs for batters from FanGraphs Description (legacy) Scrape MiLB game logs for batters from FanGraphs Usage milb_batter_game_logs_fg(playerid, year) Arguments playerid The batter’s minor league ID from FanGraphs. year The season for which game logs should be returned. Value Returns a tibble of Minor League batter game logs. milb_pitcher_game_logs_fg (legacy) Scrape MiLB game logs for pitchers from FanGraphs Description (legacy) Scrape MiLB game logs for pitchers from FanGraphs Usage milb_pitcher_game_logs_fg(playerid, year) mlb 81 Arguments playerid The pitcher’s minor league ID from FanGraphs.com. year The season for which game logs should be returned. Value Returns a tibble of Minor League pitcher game logs. mlb MLB Functions Overview Description mlb_batting_orders(): Retrieve batting orders for a given MLB game. mlb_draft(): Retrieve draft pick information by year. mlb_pbp(): Acquire pitch-by-pitch data for Major and Minor League games. mlb_game_info(): Retrieve additional game information for major and minor league games. mlb_game_pks(): Get MLB Game Info by Date and Level. mlb_schedule(): Find game_pk values for professional baseball games (major and minor leagues). mlb_probables(): Retrieve probable starters for a given MLB game. Details Retrieve batting orders for a given MLB game: mlb_batting_orders(game_pk=566001) Retrieve draft pick information by year: mlb_draft(year= 2018) Acquire pitch-by-pitch data for Major and Minor League games: mlb_pbp(game_pk = 575156) Retrieve additional game information for major and minor league games: mlb_game_info(game_pk = 566001) Get MLB Game Info by Date and Level: mlb_game_pks("2019-04-29") Find game_pk values for professional baseball games (major and minor leagues): mlb_schedule(season = "2019") Retrieve probable starters for a given MLB game: mlb_probables(566001) 82 mlb_all_star_ballots mlb_all_star_ballots Find MLB All-Star Ballots Description Find MLB All-Star Ballots Usage mlb_all_star_ballots(league_id = NULL, season = NULL) Arguments league_id League ID for league all-star ballot of interest. season The season of the all-star ballot. Value Returns a tibble with the following columns: col_name types player_id integer full_name character link character first_name character last_name character primary_number character birth_date character current_age integer birth_city character birth_state_province character birth_country character height character weight integer active logical use_name character middle_name character boxscore_name character nick_name character gender character is_player logical is_verified logical draft_year integer mlb_debut_date character name_first_last character name_slug character first_last_name character mlb_all_star_final_vote 83 last_first_name character last_init_name character init_last_name character full_fml_name character full_lfm_name character strike_zone_top numeric strike_zone_bottom numeric pronunciation character name_matrilineal character name_title character primary_position_code character primary_position_name character primary_position_type character primary_position_abbreviation character bat_side_code character bat_side_description character pitch_hand_code character pitch_hand_description character league_id numeric season numeric Examples try(mlb_all_star_ballots(league_id = 103, season = 2021)) mlb_all_star_final_vote Find MLB All-Star Final Vote Description Find MLB All-Star Final Vote Usage mlb_all_star_final_vote(league_id = NULL, season = NULL) Arguments league_id League ID for league all-star ballot of interest. season The season of the all-star ballot. Value Returns a tibble with the following columns: 84 mlb_all_star_final_vote col_name types player_id integer full_name character link character first_name character last_name character primary_number character birth_date character current_age integer birth_city character birth_state_province character birth_country character height character weight integer active logical use_name character middle_name character boxscore_name character nick_name character gender character is_player logical is_verified logical draft_year integer mlb_debut_date character name_first_last character name_slug character first_last_name character last_first_name character last_init_name character init_last_name character full_fml_name character full_lfm_name character strike_zone_top numeric strike_zone_bottom numeric pronunciation character name_matrilineal character name_title character primary_position_code character primary_position_name character primary_position_type character primary_position_abbreviation character bat_side_code character bat_side_description character pitch_hand_code character pitch_hand_description character league_id numeric season numeric mlb_all_star_write_ins 85 Examples try(mlb_all_star_final_vote(league_id = 103, season = 2021)) mlb_all_star_write_ins Find MLB All-Star Write-ins Description Find MLB All-Star Write-ins Usage mlb_all_star_write_ins(league_id = NULL, season = NULL) Arguments league_id League ID for league all-star ballot of interest. season The season of the all-star ballot. Value Returns a tibble with the following columns: col_name types player_id integer full_name character link character first_name character last_name character primary_number character birth_date character current_age integer birth_city character birth_state_province character birth_country character height character weight integer active logical use_name character middle_name character boxscore_name character nick_name character gender character is_player logical 86 mlb_attendance is_verified logical draft_year integer mlb_debut_date character name_first_last character name_slug character first_last_name character last_first_name character last_init_name character init_last_name character full_fml_name character full_lfm_name character strike_zone_top numeric strike_zone_bottom numeric pronunciation character name_matrilineal character name_title character primary_position_code character primary_position_name character primary_position_type character primary_position_abbreviation character bat_side_code character bat_side_description character pitch_hand_code character pitch_hand_description character league_id numeric season numeric Examples try(mlb_all_star_write_ins(league_id = 103, season = 2021)) mlb_attendance MLB Attendance Description MLB Attendance Usage mlb_attendance( team_id = NULL, league_id = NULL, season = NULL, mlb_attendance 87 date = NULL, league_list_id = NULL ) Arguments team_id Return attendance information for a particular team_id(s). league_id Return attendance information for a particular league_id(s). Format: ’103,104’ season Return attendance information for particular year(s). date Return attendance information on a particular date. Format: MM/DD/YYYY league_list_id Unique league list identifier to return a directory of attendance for a specific league list_id Valid values include: • milb_full • milb_short • milb_complex • milb_all • milb_all_nomex • milb_all_domestic • milb_noncomp • milb_noncomp_nomex • milb_domcomp • milb_intcomp • win_noabl • win_caribbean • win_all • abl • mlb • mlb_hist • mlb_milb • mlb_milb_hist • mlb_milb_win • baseball_all Value Returns a tibble with the following columns col_name types openings_total integer openings_total_away integer openings_total_home integer openings_total_lost integer games_total integer games_away_total integer games_home_total integer 88 mlb_award year character attendance_average_away integer attendance_average_home integer attendance_average_ytd integer attendance_high integer attendance_high_date character attendance_low integer attendance_low_date character attendance_opening_average integer attendance_total integer attendance_total_away integer attendance_total_home integer attendance_high_game_game_pk integer attendance_high_game_link character attendance_high_game_day_night character attendance_high_game_content_link character attendance_low_game_game_pk integer attendance_low_game_link character attendance_low_game_day_night character attendance_low_game_content_link character game_type_id character game_type_description character team_id integer team_name character team_link character Examples try(mlb_attendance(team_id = 109, season = 2021)) mlb_award MLB All-Star, Awards, Home Run Derby Functions Description mlb_all_star_ballots(): Find MLB All-Star Ballots. mlb_all_star_final_vote(): Find MLB All-Star Final Vote. mlb_all_star_write_ins(): Find MLB All-Star Write-ins. mlb_awards(): Find MLB Awards. mlb_awards_recipient(): Find MLB Award Recipients. mlb_homerun_derby(): Retrieve MLB Home Run Derby Data. mlb_homerun_derby_bracket(): Retrieve MLB Home Run Derby Bracket. mlb_homerun_derby_players(): Retrieve MLB Home Run Derby Players. mlb_awards 89 Details Find MLB All-Star Ballots: try(mlb_all_star_ballots(league_id = 103, season = 2021)) Find MLB All-Star Final Vote: try(mlb_all_star_final_vote(league_id = 103, season = 2021)) Find MLB All-Star Write-ins: try(mlb_all_star_write_ins(league_id = 103, season = 2021)) Find MLB Awards: try(mlb_awards()) Find MLB Award Recipients: try(mlb_awards_recipient(award_id = 'MLBHOF', season = 2020)) Retrieve MLB Home Run Derby Data: try(mlb_homerun_derby(game_pk = 511101)) Retrieve MLB Home Run Derby Bracket: try(mlb_homerun_derby_bracket(game_pk = 511101)) Retrieve MLB Home Run Derby Players: try(mlb_homerun_derby_players(game_pk = 511101)) mlb_awards MLB Awards Description MLB Awards Usage mlb_awards() 90 mlb_awards_recipient Value Returns a tibble with the following columns col_name types award_id character award_name character award_description character sort_order integer notes character sport_id integer sport_link character league_id integer league_link character Examples try(mlb_awards()) mlb_awards_recipient MLB Award Recipients Description MLB Award Recipients Usage mlb_awards_recipient( award_id = NULL, sport_id = NULL, league_id = NULL, season = NULL ) Arguments award_id award_id to return a directory of players for a given award. sport_id sport_id to return a directory of players for a given aware in a specific sport. league_id league_id(s) to return a directory of players for a given award in a specific league. Format ’103,104’ season Year(s) to return a directory of players for a given award in a given season. Value Returns a tibble with the following columns mlb_baseball_stats 91 col_name types award_id character award_name character date character season character votes integer notes character player_id integer player_link character player_name_first_last character player_primary_position_code character player_primary_position_name character player_primary_position_type character player_primary_position_abbreviation character team_id integer team_link character Examples try(mlb_awards_recipient(award_id = 'MLBHOF', season = 2020)) mlb_baseball_stats MLB Baseball Stats Description MLB Baseball Stats Usage mlb_baseball_stats() Value Returns a tibble with the following columns: col_name types stat_name character stat_lookup_param character is_counting logical stat_label character stat_group character 92 mlb_batting_orders Examples try(mlb_baseball_stats()) mlb_batting_orders Retrieve batting orders for a given MLB game Description Retrieve batting orders for a given MLB game Usage mlb_batting_orders(game_pk, type = "starting") Arguments game_pk The unique game_pk identifier for the game type Whether to just return the starting lineup (’starting’) or all batters that appeared (’all’) Value Returns a tibble that includes probable starting pitchers and the home plate umpire for the game_pk requested col_name types id integer fullName character abbreviation character batting_order character batting_position_num character team character teamName character teamID integer Examples try(mlb_batting_orders(game_pk=566001)) mlb_divisions 93 mlb_conferences View all PCL conferences Description View all PCL conferences Usage mlb_conferences(conference_id = NULL, season = NULL) Arguments conference_id Conference ID to return information for. season Year to return to return conference information for. Value Returns a tibble with the following columns col_name types conference_id integer conference_name character link character conference_abbreviation character has_wildcard logical name_short character league_id integer league_link character sport_id integer sport_link character Examples try(mlb_conferences()) try(mlb_conferences(conference_id = 301, season = 2020)) mlb_divisions MLB Divisions Description MLB Divisions 94 mlb_draft Usage mlb_divisions(division_id = NULL, league_id = NULL, sport_id = NULL) Arguments division_id Return division(s) data for a specific division league_id Return division(s) data for all divisions in a specific league sport_id Return division(s) for all divisions in a specific sport. Value Returns a tibble with the following columns col_name types division_id integer division_name character season character division_name_short character division_link character division_abbreviation character has_wildcard logical sort_order integer num_playoff_teams integer active logical league_id integer league_link character sport_id integer sport_link character Examples try(mlb_divisions(sport_id = 1)) mlb_draft Retrieve draft pick information by year Description Retrieve draft pick information by year Usage mlb_draft(year) mlb_draft 95 Arguments year The year for which to return data Value Returns a tibble with information for every draft pick in every round for the year requested col_name types bis_player_id integer pick_round character pick_number integer round_pick_number integer rank integer pick_value character signing_bonus character scouting_report character blurb character headshot_link character is_drafted logical is_pass logical year character home_city character home_state character home_country character school_name character school_school_class character school_country character school_state character person_id integer person_full_name character person_link character person_first_name character person_last_name character person_primary_number character person_birth_date character person_current_age integer person_birth_city character person_birth_state_province character person_birth_country character person_height character person_weight integer person_active logical person_use_name character person_middle_name character person_boxscore_name character person_gender character person_is_player logical person_is_verified logical 96 mlb_draft_latest person_draft_year integer person_name_first_last character person_name_slug character person_first_last_name character person_last_first_name character person_last_init_name character person_init_last_name character person_full_fml_name character person_full_lfm_name character person_strike_zone_top numeric person_strike_zone_bottom numeric person_pronunciation character person_name_title character person_mlb_debut_date character person_name_matrilineal character person_primary_position_code character person_primary_position_name character person_primary_position_type character person_primary_position_abbreviation character person_bat_side_code character person_bat_side_description character person_pitch_hand_code character person_pitch_hand_description character team_id integer team_name character team_link character team_all_star_status character team_spring_league_id integer team_spring_league_name character team_spring_league_link character team_spring_league_abbreviation character draft_type_code character draft_type_description character Examples try(mlb_draft(year = 2020)) mlb_draft_latest Retrieve latest draft information by year Description Retrieve latest draft information by year mlb_draft_latest 97 Usage mlb_draft_latest(year) Arguments year The year for which to return data Value Returns a tibble with the latest draft information for the year requested: col_name types bis_player_id integer pick_round character pick_number integer round_pick_number integer rank integer pick_value character signing_bonus character home_city character home_state character home_country character scouting_report character school_name character school_school_class character school_country character school_state character blurb character headshot_link character person_id integer person_full_name character person_link character person_first_name character person_last_name character person_primary_number character person_birth_date character person_current_age integer person_birth_city character person_birth_state_province character person_birth_country character person_height character person_weight integer person_active logical person_primary_position_code character person_primary_position_name character person_primary_position_type character person_primary_position_abbreviation character person_use_name character person_middle_name character 98 mlb_draft_latest person_boxscore_name character person_gender character person_is_player logical person_is_verified logical person_draft_year integer person_bat_side_code character person_bat_side_description character person_pitch_hand_code character person_pitch_hand_description character person_name_first_last character person_name_slug character person_first_last_name character person_last_first_name character person_last_init_name character person_init_last_name character person_full_fml_name character person_full_lfm_name character person_strike_zone_top numeric person_strike_zone_bottom numeric team_id integer team_name character team_link character team_season integer team_venue_id integer team_venue_name character team_venue_link character team_spring_venue_id integer team_spring_venue_link character team_team_code character team_file_code character team_abbreviation character team_team_name character team_location_name character team_first_year_of_play character team_league_id integer team_league_name character team_league_link character team_division_id integer team_division_name character team_division_link character team_sport_id integer team_sport_link character team_sport_name character team_short_name character team_franchise_name character team_club_name character team_spring_league_id integer team_spring_league_name character mlb_draft_prospects 99 team_spring_league_link character team_spring_league_abbreviation character team_all_star_status character team_active logical draft_type_code character draft_type_description character is_drafted logical is_pass logical year character Examples try(mlb_draft_latest(year = 2020)) mlb_draft_prospects Retrieve draft prospect information by year Description Retrieve draft prospect information by year Usage mlb_draft_prospects(year) Arguments year The year for which to return data Value Returns a tibble with information for every draft prospect for the year requested: col_name types bis_player_id integer pick_round character pick_number integer rank integer scouting_report character blurb character headshot_link character is_drafted logical year character home_city character home_state character 100 mlb_draft_prospects home_country character school_name character school_school_class character school_country character school_state character person_id integer person_full_name character person_link character person_first_name character person_last_name character person_birth_date character person_current_age integer person_birth_city character person_birth_state_province character person_birth_country character person_height character person_weight integer person_active logical person_use_name character person_middle_name character person_boxscore_name character person_gender character person_is_player logical person_is_verified logical person_draft_year integer person_name_first_last character person_name_slug character person_first_last_name character person_last_first_name character person_last_init_name character person_init_last_name character person_full_fml_name character person_full_lfm_name character person_strike_zone_top numeric person_strike_zone_bottom numeric person_primary_number character person_pronunciation character person_name_title character person_mlb_debut_date character person_name_matrilineal character person_nick_name character person_death_date character person_death_city character person_death_state_province character person_death_country character person_primary_position_code character person_primary_position_name character person_primary_position_type character mlb_draft_prospects 101 person_primary_position_abbreviation character person_bat_side_code character person_bat_side_description character person_pitch_hand_code character person_pitch_hand_description character team_id integer team_name character team_link character team_season integer team_team_code character team_file_code character team_abbreviation character team_team_name character team_location_name character team_first_year_of_play character team_short_name character team_franchise_name character team_club_name character team_all_star_status character team_active logical team_venue_id integer team_venue_name character team_venue_link character team_spring_venue_id integer team_spring_venue_link character team_league_id integer team_league_name character team_league_link character team_division_id integer team_division_name character team_division_link character team_sport_id integer team_sport_link character team_sport_name character team_spring_league_id integer team_spring_league_name character team_spring_league_link character team_spring_league_abbreviation character draft_type_code character draft_type_description character Examples try(mlb_draft_prospects(year = 2020)) 102 mlb_fielder_detail_types mlb_event_types MLB Event Types Description MLB Event Types Usage mlb_event_types() Value Returns a tibble with the following columns col_name types plate_appearance logical hit logical event_code character base_running_event logical event_description character Examples try(mlb_event_types()) mlb_fielder_detail_types MLB Fielder Detail Types Description MLB Fielder Detail Types Usage mlb_fielder_detail_types() Value Returns a tibble with the following columns col_name types mlb_game_changes 103 stat_name character code character names character chance logical error logical Examples try(mlb_fielder_detail_types()) mlb_game_changes Acquire time codes for Major and Minor League games Description Acquire time codes for Major and Minor League games Usage mlb_game_changes(updated_since, sport_id) Arguments updated_since Updated since date time sport_id Return division(s) for all divisions in a specific sport. Value Returns a tibble that includes time codes from the game_pk requested col_name types date character total_items integer total_events integer total_games integer total_games_in_progress integer game_pk integer link character game_type character season character game_date character official_date character is_tie logical game_number integer 104 mlb_game_changes public_facing logical double_header character gameday_type character tiebreaker character calendar_event_id character season_display character day_night character description character scheduled_innings integer reverse_home_away_status logical inning_break_length integer games_in_series integer series_game_number integer series_description character record_source character if_necessary character if_necessary_description character status_abstract_game_state character status_coded_game_state character status_detailed_state character status_status_code character status_start_time_tbd logical status_abstract_game_code character teams_away_score integer teams_away_is_winner logical teams_away_split_squad logical teams_away_series_number integer teams_away_league_record_wins integer teams_away_league_record_losses integer teams_away_league_record_pct character teams_away_team_id integer teams_away_team_name character teams_away_team_link character teams_home_score integer teams_home_is_winner logical teams_home_split_squad logical teams_home_series_number integer teams_home_league_record_wins integer teams_home_league_record_losses integer teams_home_league_record_pct character teams_home_team_id integer teams_home_team_name character teams_home_team_link character venue_id integer venue_name character venue_link character content_link character status_reason character mlb_game_content 105 rescheduled_from character rescheduled_from_date character resumed_from character resumed_from_date character events list Examples try(mlb_game_changes(updated_since = "2021-08-10T19:08:24.000004Z", sport_id = 1)) mlb_game_content Retrieve additional game content for major and minor league games Description Retrieve additional game content for major and minor league games Usage mlb_game_content(game_pk) Arguments game_pk The unique game_pk identifier for the game Value Returns a tibble of game content data with the following columns: col_name types title character epg_id integer content_id character media_id character media_state character media_feed_type character media_feed_sub_type character call_letters character fox_auth_required logical tbs_auth_required logical espn_auth_required logical fs1auth_required logical mlbn_auth_required logical free_game logical 106 mlb_game_context_metrics type character description character rendition_name character language character Examples try(mlb_game_content(game_pk = 566001)) mlb_game_context_metrics Acquire game context metrics for Major and Minor League games Description Acquire game context metrics for Major and Minor League games Usage mlb_game_context_metrics(game_pk, timecode) Arguments game_pk The game_pk for the game requested timecode The time code for the MLB game (format: MMDDYYYY_HHMMSS) Value Returns a tibble that includes time codes from the game_pk requested col_name types game_pk integer link character game_type character season character game_date character official_date character status_abstract_game_state character status_coded_game_state character status_detailed_state character status_status_code character status_start_time_tbd logical status_abstract_game_code character teams_away_league_record_wins integer mlb_game_context_metrics 107 teams_away_league_record_losses integer teams_away_league_record_pct character teams_away_score integer teams_away_team_id integer teams_away_team_name character teams_away_team_link character teams_away_is_winner logical teams_away_probable_pitcher_id integer teams_away_probable_pitcher_full_name character teams_away_probable_pitcher_link character teams_away_split_squad logical teams_away_series_number integer teams_home_league_record_wins integer teams_home_league_record_losses integer teams_home_league_record_pct character teams_home_score integer teams_home_team_id integer teams_home_team_name character teams_home_team_link character teams_home_is_winner logical teams_home_probable_pitcher_id integer teams_home_probable_pitcher_full_name character teams_home_probable_pitcher_link character teams_home_split_squad logical teams_home_series_number integer venue_id integer venue_name character venue_link character link_1 character is_tie logical game_number integer public_facing logical double_header character gameday_type character tiebreaker character calendar_event_id character season_display character day_night character scheduled_innings integer reverse_home_away_status logical inning_break_length integer games_in_series integer series_game_number integer series_description character record_source character if_necessary character if_necessary_description character game_id character 108 mlb_game_info home_win_probability numeric away_win_probability numeric Examples try(mlb_game_context_metrics(game_pk = 531060, timecode = "20180803_182458")) mlb_game_info Retrieve additional game information for major and minor league games Description Retrieve additional game information for major and minor league games Usage mlb_game_info(game_pk) Arguments game_pk The unique game_pk identifier for the game Value Returns a tibble that includes supplemental information, such as weather, official scorer, attendance, etc., for the game_pk provided col_name types game_date character game_pk numeric venue_name character venue_id integer temperature character other_weather character wind character attendance character start_time character elapsed_time character game_id character game_type character home_sport_code character official_scorer character date character status_ind character mlb_game_linescore 109 home_league_id integer gameday_sw character Examples try(mlb_game_info(game_pk = 566001)) mlb_game_linescore Retrieve game linescores for major and minor league games Description Retrieve game linescores for major and minor league games Usage mlb_game_linescore(game_pk) Arguments game_pk The unique game_pk identifier for the game Value Returns a tibble with the following columns col_name types game_pk numeric home_team_id character home_team_name character away_team_id character away_team_name character num integer ordinal_num character home_runs integer home_hits integer home_errors integer home_left_on_base integer away_runs integer away_hits integer away_errors integer away_left_on_base integer home_team_link character home_team_season character home_team_venue_id character 110 mlb_game_linescore home_team_venue_name character home_team_venue_link character home_team_team_code character home_team_file_code character home_team_abbreviation character home_team_team_name character home_team_location_name character home_team_first_year_of_play character home_team_league_id character home_team_league_name character home_team_league_link character home_team_division_id character home_team_division_name character home_team_division_link character home_team_sport_id character home_team_sport_link character home_team_sport_name character home_team_short_name character home_team_record_games_played character home_team_record_wild_card_games_back character home_team_record_league_games_back character home_team_record_spring_league_games_back character home_team_record_sport_games_back character home_team_record_division_games_back character home_team_record_conference_games_back character home_team_record_league_record_wins character home_team_record_league_record_losses character home_team_record_league_record_pct character home_team_record_division_leader character home_team_record_wins character home_team_record_losses character home_team_record_winning_percentage character home_team_franchise_name character home_team_club_name character home_team_all_star_status character home_team_active character away_team_link character away_team_season character away_team_venue_id character away_team_venue_name character away_team_venue_link character away_team_team_code character away_team_file_code character away_team_abbreviation character away_team_team_name character away_team_location_name character away_team_first_year_of_play character away_team_league_id character mlb_game_pace 111 away_team_league_name character away_team_league_link character away_team_division_id character away_team_division_name character away_team_division_link character away_team_sport_id character away_team_sport_link character away_team_sport_name character away_team_short_name character away_team_record_games_played character away_team_record_wild_card_games_back character away_team_record_league_games_back character away_team_record_spring_league_games_back character away_team_record_sport_games_back character away_team_record_division_games_back character away_team_record_conference_games_back character away_team_record_league_record_wins character away_team_record_league_record_losses character away_team_record_league_record_pct character away_team_record_division_leader character away_team_record_wins character away_team_record_losses character away_team_record_winning_percentage character away_team_franchise_name character away_team_club_name character away_team_all_star_status character away_team_active character Examples try(mlb_game_linescore(game_pk = 566001)) mlb_game_pace Retrieve game pace metrics for major and minor league Description Retrieve game pace metrics for major and minor league Usage mlb_game_pace( season, league_ids = NULL, 112 mlb_game_pace sport_ids = NULL, team_ids = NULL, game_type = NULL, venue_ids = NULL, org_type = NULL, start_date = NULL, end_date = NULL ) Arguments season Year for which to return information (Required). league_ids The league_id(s) for which to return information. sport_ids The sport_id(s) for which to return information. team_ids The team_id(s) for which to return information. game_type The game_type for which to return information. venue_ids Venue directorial information based venue_id. org_type pace of game metrics based on team (’T’), league (’L’) or sport(’S’) start_date Date of first game for which you want data. Format must be in MM/DD/YYYY format. end_date Date of last game for which you want data. Format must be in MM/DD/YYYY format. Value Returns a tibble with the following columns col_name types hits_per9inn numeric runs_per9inn numeric pitches_per9inn numeric plate_appearances_per9inn numeric hits_per_game numeric runs_per_game numeric innings_played_per_game numeric pitches_per_game numeric pitchers_per_game numeric plate_appearances_per_game numeric total_game_time character total_innings_played integer total_hits integer total_runs integer total_plate_appearances integer total_pitchers integer total_pitches integer total_games integer total7inn_games integer mlb_game_pks 113 total9inn_games integer total_extra_inn_games integer time_per_game character time_per_pitch character time_per_hit character time_per_run character time_per_plate_appearance character time_per9inn character time_per77plate_appearances character total_extra_inn_time character time_per7inn_game character time_per7inn_game_without_extra_inn character total7inn_games_scheduled integer total7inn_games_without_extra_inn integer total9inn_games_completed_early integer total9inn_games_without_extra_inn integer total9inn_games_scheduled integer hits_per_run numeric pitches_per_pitcher numeric season character sport_id integer sport_code character sport_link character pr_portal_calculated_fields_total7inn_games integer pr_portal_calculated_fields_total9inn_games integer pr_portal_calculated_fields_total_extra_inn_games integer pr_portal_calculated_fields_time_per7inn_game character pr_portal_calculated_fields_time_per9inn_game character pr_portal_calculated_fields_time_per_extra_inn_game character Examples try(mlb_game_pace(season = 2021, start_date = "09/14/2021", end_date = "09/16/2021")) mlb_game_pks Get MLB Game Info by Date and Level Description Find game_pk values for professional baseball games (major and minor leagues) via the MLB api https://www.mlb.com/ Usage mlb_game_pks(date, level_ids = c(1)) 114 mlb_game_pks Arguments date The date for which you want to find game_pk values for MLB games level_ids A numeric vector with ids for each level where game_pks are desired. See below for a reference of level ids. Details Level IDs: The following IDs can be passed to the level_ids argument: 1 = MLB 11 = Triple-A 12 = Doubl-A 13 = Class A Advanced 14 = Class A 15 = Class A Short Season 5442 = Rookie Advanced 16 = Rookie 17 = Winter League Value Returns a tibble that includes game_pk values and additional information for games scheduled or played with the following columns: col_name types game_pk integer link character gameType character season character gameDate character officialDate character isTie logical gameNumber integer publicFacing logical doubleHeader character gamedayType character tiebreaker character calendarEventID character seasonDisplay character dayNight character scheduledInnings integer reverseHomeAwayStatus logical inningBreakLength integer gamesInSeries integer seriesGameNumber integer seriesDescription character recordSource character ifNecessary character ifNecessaryDescription character status.abstractGameState character status.codedGameState character status.detailedState character status.statusCode character status.startTimeTBD logical status.abstractGameCode character mlb_game_status_codes 115 teams.away.score integer teams.away.isWinner logical teams.away.splitSquad logical teams.away.seriesNumber integer teams.away.leagueRecord.wins integer teams.away.leagueRecord.losses integer teams.away.leagueRecord.pct character teams.away.team.id integer teams.away.team.name character teams.away.team.link character teams.home.score integer teams.home.isWinner logical teams.home.splitSquad logical teams.home.seriesNumber integer teams.home.leagueRecord.wins integer teams.home.leagueRecord.losses integer teams.home.leagueRecord.pct character teams.home.team.id integer teams.home.team.name character teams.home.team.link character venue.id integer venue.name character venue.link character content.link character Examples try(mlb_game_pks("2019-04-29")) mlb_game_status_codes MLB Game Status Codes Description MLB Game Status Codes Usage mlb_game_status_codes() Value Returns a tibble with the following columns col_name types 116 mlb_game_types abstract_game_state character coded_game_state character detailed_state character status_code character reason character abstract_game_code character Examples try(mlb_game_status_codes()) mlb_game_timecodes Acquire time codes for Major and Minor League games Description Acquire time codes for Major and Minor League games Usage mlb_game_timecodes(game_pk) Arguments game_pk The game_pk for the game requested Value Returns a tibble that includes time codes from the game_pk requested col_name types timecodes (MMDDYYYY_HHMMSS) numeric Examples try(mlb_game_timecodes(game_pk = 632970)) mlb_game_types MLB Game Types mlb_game_wp 117 Description MLB Game Types Usage mlb_game_types() Value Returns a tibble with the following columns col_name types game_type_id character game_type_description character Examples try(mlb_game_types()) mlb_game_wp Acquire win probability for Major and Minor League games Description Acquire win probability for Major and Minor League games Usage mlb_game_wp(game_pk, timecode = NULL) Arguments game_pk The game_pk for the game requested timecode The time code for the MLB game (format: MMDDYYYY_HHMMSS) Value Returns a tibble that includes time codes from the game_pk requested col_name types home_team_win_probability numeric away_team_win_probability numeric home_team_win_probability_added numeric at_bat_index integer leverage_index numeric 118 mlb_high_low_stats Examples try(mlb_game_wp(game_pk = 531060)) mlb_high_low_stats Acquire high/low stats for Major and Minor Leagues Description Acquire high/low stats for Major and Minor Leagues Usage mlb_high_low_stats( org_type, season, sort_stat, team_ids = NULL, league_ids = NULL, sport_ids = NULL, game_type = NULL, stat_group = NULL, limit = NULL ) Arguments org_type The organization type for return information (Required). Valid values include: • player • team • division • league • sport season The season for which you want to return information (Required). sort_stat The stat to sort the return (Required). Valid values can be found from ’stat_lookup_param’ below stat_name stat_lookup_param is_counting stat_label stat_groups at_bats atBats TRUE At bats hitting , pitching total_plate_appearances plateAppearances TRUE Total plate appearances hitting runs runs TRUE Runs hitting runs_batted_in rbi TRUE Runs batted in hitting home_team_runs runs TRUE Home team runs hitting away_team_runs runs TRUE Away team runs hitting hits hits TRUE Hits hitting mlb_high_low_stats 119 hits_risp hitsRisp TRUE Hits risp hitting home_team_hits hits TRUE Home team hits hitting away_team_hits hits TRUE Away team hits hitting total_bases totalBases TRUE Total bases hitting , pitching doubles doubles TRUE Doubles hitting , pitching triples triples TRUE Triples hitting home_runs homeRuns TRUE Home runs hitting , pitching extra_base_hits extraBaseHits TRUE Extra base hits hitting walks baseOnBalls TRUE Walks hitting , pitching strikeouts strikeouts TRUE Strikeouts hitting , pitching stolen_bases stolenBases TRUE Stolen bases hitting caught_stealing caughtStealing TRUE Caught stealing hitting , pitching, fielding sacrifice_flies sacFlies TRUE Sacrifice flies hitting sacrifice_bunts sacBunts TRUE Sacrifice bunts hitting hit_by_pitches hitByPitch TRUE Hit by pitches hitting , pitching left_on_base leftOnBase TRUE Left on base hitting ground_into_double_plays groundIntoDoublePlay TRUE Ground into double plays hitting , pitching strikes strikes TRUE Strikes pitching pitches pitchesThrown TRUE Pitches pitching balks balks TRUE Balks pitching innings_pitched inningsPitched TRUE Innings pitched pitching errors errors TRUE Errors fielding home_team_errors errors TRUE Home team errors fielding away_team_errors errors TRUE Away team errors fielding chances chances TRUE Chances fielding put_outs putOuts TRUE Put outs fielding assists assists TRUE Assists fielding double_plays doublePlays TRUE Double plays fielding attendance attendance TRUE Attendance game game_time gameDuration TRUE Game time game delay_time gameDuration TRUE Delay time game longest gameDuration TRUE Longest game shortest gameDuration TRUE Shortest game inning innings TRUE Inning game win_streak winStreak TRUE Win streak streak loss_streak lossStreak TRUE Loss streak streak team_ids The team_id(s) for which to return information. league_ids The league_id(s) for which to return information. sport_ids The sport_id(s) for which to return information. game_type The game_type for which to return information. stat_group Stat group for which to return information. Valid values include: stat_group hitting pitching 120 mlb_high_low_stats fielding catching running game team streak limit Number of records as the limit of the return. Value Returns a tibble with the following columns col_name types total_splits integer season integer date character is_home logical rank integer game_innings integer stat_at_bats integer team_id integer team_name character team_link character opponent_id integer opponent_name character opponent_link character game_pk integer game_link character game_number integer game_content_link character home_team_id integer home_team_name character home_team_link character away_team_id integer away_team_name character away_team_link character combined_stats logical group_display_name character game_type_id character game_type_description character sort_stat_name character sort_stat_lookup_param character sort_stat_is_counting logical sort_stat_label character mlb_hit_trajectories 121 Examples try(mlb_high_low_stats(org_type = 'Team', season = 2020, sort_stat = 'atBats')) mlb_high_low_types MLB Stat High/Low Types Description MLB Stat High/Low Types Usage mlb_high_low_types() Value Returns a tibble with the following columns col_name types stat_name character stat_lookup_param character is_counting logical stat_label character stat_groups list org_types list high_low_types list Examples try(mlb_high_low_types()) mlb_hit_trajectories MLB Hit Trajectories Description MLB Hit Trajectories Usage mlb_hit_trajectories() 122 mlb_homerun_derby Value Returns a tibble with the following columns col_name types hit_trajectory_code character hit_trajectory_description character Examples try(mlb_hit_trajectories()) mlb_homerun_derby Retrieve Homerun Derby data Description Retrieve Homerun Derby data Usage mlb_homerun_derby(game_pk) Arguments game_pk The game_pk for which you want to return data Value Returns a tibble with the following columns col_name types game_pk integer event_name character event_date character event_type_code character event_type_name character venue_id integer venue_name character round integer num_batters integer batter character batter_id integer batter_link character top_seed_started logical mlb_homerun_derby 123 top_seed_complete logical top_seed_winner logical bonus_time logical home_run logical tie_breaker logical is_home_run logical time_remaining character is_bonus_time logical is_tie_breaker logical hit_data_launch_speed integer hit_data_launch_angle integer hit_data_total_distance integer hit_data_coordinates_coord_x numeric hit_data_coordinates_coord_y numeric hit_data_coordinates_landing_pos_x numeric hit_data_coordinates_landing_pos_y numeric hit_data_trajectory_data_trajectory_polynomial_x list hit_data_trajectory_data_trajectory_polynomial_y list hit_data_trajectory_data_trajectory_polynomial_z list hit_data_trajectory_data_valid_time_interval list top_seed_seed integer top_seed_is_winner logical top_seed_is_complete logical top_seed_is_started logical top_seed_num_home_runs integer top_seed_player_id integer top_seed_player_full_name character top_seed_player_link character top_seed_top_derby_hit_data_launch_speed integer top_seed_top_derby_hit_data_total_distance integer bottom_seed_started logical bottom_seed_complete logical bottom_seed_winner logical bottom_seed_seed integer bottom_seed_is_winner logical bottom_seed_is_complete logical bottom_seed_is_started logical bottom_seed_num_home_runs integer bottom_seed_player_id integer bottom_seed_player_full_name character bottom_seed_player_link character bottom_seed_top_derby_hit_data_launch_speed integer bottom_seed_top_derby_hit_data_total_distance integer venue_link character is_multi_day logical is_primary_calendar logical file_code character event_number integer 124 mlb_homerun_derby_bracket public_facing logical Examples try(mlb_homerun_derby(game_pk = 511101)) mlb_homerun_derby_bracket Retrieve Homerun Derby Bracket Description Retrieve Homerun Derby Bracket Usage mlb_homerun_derby_bracket(game_pk) Arguments game_pk The game_pk for which you want to return data Value Returns a tibble with the following columns col_name types game_pk integer event_name character event_type_code character event_type_name character event_date character venue_id integer venue_name character venue_link character is_multi_day logical is_primary_calendar logical file_code character event_number integer public_facing logical round integer num_batters integer top_seed_complete logical top_seed_started logical top_seed_winner logical mlb_homerun_derby_players 125 top_seed_seed integer top_seed_is_winner logical top_seed_is_complete logical top_seed_is_started logical top_seed_num_home_runs integer top_seed_player_id integer top_seed_player_full_name character top_seed_player_link character top_seed_top_derby_hit_data_launch_speed integer top_seed_top_derby_hit_data_total_distance integer bottom_seed_complete logical bottom_seed_started logical bottom_seed_winner logical bottom_seed_seed integer bottom_seed_is_winner logical bottom_seed_is_complete logical bottom_seed_is_started logical bottom_seed_num_home_runs integer bottom_seed_player_id integer bottom_seed_player_full_name character bottom_seed_player_link character bottom_seed_top_derby_hit_data_launch_speed integer bottom_seed_top_derby_hit_data_total_distance integer Examples try(mlb_homerun_derby_bracket(game_pk = 511101)) mlb_homerun_derby_players Retrieve Homerun Derby Players Description Retrieve Homerun Derby Players Usage mlb_homerun_derby_players(game_pk) Arguments game_pk The game_pk for which you want to return data 126 mlb_homerun_derby_players Value Returns a tibble with the following columns col_name types game_pk integer event_name character event_date character event_type_code character event_type_name character venue_id integer venue_name character player_id integer player_full_name character player_link character player_first_name character player_last_name character player_primary_number character player_birth_date character player_current_age integer player_birth_city character player_birth_state_province character player_birth_country character player_height character player_weight integer player_active logical player_use_name character player_middle_name character player_boxscore_name character player_nick_name character player_gender character player_is_player logical player_is_verified logical player_draft_year integer player_pronunciation character player_mlb_debut_date character player_name_first_last character player_name_slug character player_first_last_name character player_last_first_name character player_last_init_name character player_init_last_name character player_full_fml_name character player_full_lfm_name character player_strike_zone_top numeric player_strike_zone_bottom numeric player_name_matrilineal character player_current_team_id integer player_current_team_name character mlb_homerun_derby_players 127 player_current_team_link character player_current_team_season integer player_current_team_team_code character player_current_team_file_code character player_current_team_abbreviation character player_current_team_team_name character player_current_team_location_name character player_current_team_first_year_of_play character player_current_team_short_name character player_current_team_franchise_name character player_current_team_club_name character player_current_team_all_star_status character player_current_team_active logical player_current_team_parent_org_name character player_current_team_parent_org_id integer player_current_team_venue_id integer player_current_team_venue_name character player_current_team_venue_link character player_current_team_spring_venue_id integer player_current_team_spring_venue_link character player_current_team_league_id integer player_current_team_league_name character player_current_team_league_link character player_current_team_division_id integer player_current_team_division_name character player_current_team_division_link character player_current_team_sport_id integer player_current_team_sport_link character player_current_team_sport_name character player_current_team_spring_league_id integer player_current_team_spring_league_name character player_current_team_spring_league_link character player_current_team_spring_league_abbreviation character player_primary_position_code character player_primary_position_name character player_primary_position_type character player_primary_position_abbreviation character player_bat_side_code character player_bat_side_description character player_pitch_hand_code character player_pitch_hand_description character venue_link character is_multi_day logical is_primary_calendar logical file_code character event_number integer public_facing logical 128 mlb_jobs_datacasters Examples try(mlb_homerun_derby_players(game_pk = 511101)) mlb_jobs MLB Jobs Description MLB Jobs Usage mlb_jobs(job_type = "UMPR", sport_id = NULL, date = NULL) Arguments job_type Return information for a given job_type. See mlb_job_types() sport_id Return information for a given sport_id. date Return information for a given date. Value Returns a tibble with the following columns col_name types jersey_number character job character job_code character title character person_id integer person_full_name character person_link character Examples try(mlb_jobs(job_type='UMPR')) mlb_jobs_datacasters MLB Jobs Datacasters mlb_jobs_official_scorers 129 Description MLB Jobs Datacasters Usage mlb_jobs_datacasters(sport_id = NULL, date = NULL) Arguments sport_id Return information for a given sport_id. date Return information for a given date. Value Returns a tibble with the following columns col_name types jersey_number character job character job_code character title character person_id integer person_full_name character person_link character Examples try(mlb_jobs_datacasters(sport_id=1)) mlb_jobs_official_scorers MLB Jobs Official Scorers Description MLB Jobs Official Scorers Usage mlb_jobs_official_scorers(sport_id = NULL, date = NULL) Arguments sport_id Return information for a given sport_id. date Return information for a given date. 130 mlb_jobs_umpires Value Returns a tibble with the following columns col_name types jersey_number character job character job_code character title character person_id integer person_full_name character person_link character Examples try(mlb_jobs_official_scorers(sport_id=1)) mlb_jobs_umpires MLB Jobs Umpires Description MLB Jobs Umpires Usage mlb_jobs_umpires(sport_id = NULL, date = NULL) Arguments sport_id Return information for a given sport_id. date Return information for a given date. Value Returns a tibble with the following columns col_name types jersey_number character job character job_code character title character person_id integer person_full_name character person_link character mlb_languages 131 Examples try(mlb_jobs_umpires(sport_id=1)) mlb_job_types MLB Job Types Description MLB Job Types Usage mlb_job_types() Value Returns a tibble with the following columns col_name types job_code character job character sort_order integer Examples try(mlb_job_types()) mlb_languages MLB API Language Options Description MLB API Language Options Usage mlb_languages() Value Returns a tibble with the following columns 132 mlb_league_leader_types col_name types language_name character language_code character locale character Examples try(mlb_languages()) mlb_league MLB Leagues Description MLB Leagues Usage mlb_league(seasons = NULL, sport_id = NULL, league_id = NULL) Arguments seasons Year(s) to return to return league information for. sport_id The sport_id to return league information for. league_id The league_id(s) to return league information for. Value Returns a tibble with the following columns col_name types leader_type character Examples try(mlb_league(seasons = 2021, sport_id = 1)) mlb_league_leader_types MLB League Leader Types mlb_logical_events 133 Description MLB League Leader Types Usage mlb_league_leader_types() Value Returns a tibble with the following columns col_name types leader_type character Examples try(mlb_league_leader_types()) mlb_logical_events MLB Logical Events Description MLB Logical Events Usage mlb_logical_events() Value Returns a tibble with the following columns col_name types event_code character Examples try(mlb_logical_events()) 134 mlb_pbp mlb_metrics MLB Metrics Description MLB Metrics Usage mlb_metrics() Value Returns a tibble with the following columns col_name types metric_name character metric_id integer stat_group character metric_unit character Examples try(mlb_metrics()) mlb_pbp Acquire pitch-by-pitch data for Major and Minor League games Description Acquire pitch-by-pitch data for Major and Minor League games Usage mlb_pbp(game_pk) Arguments game_pk The date for which you want to find game_pk values for MLB games mlb_pbp 135 Value Returns a tibble that includes over 100 columns of data provided by the MLB Stats API at a pitch level. Some data will vary depending on the park and the league level, as most sensor data is not available in minor league parks via this API. Note that the column names have mostly been left as-is and there are likely duplicate columns in terms of the information they provide. I plan to clean the output up down the road, but for now I am leaving the majority as-is. Both major and minor league pitch-by-pitch data can be pulled with this function. col_name types game_pk numeric game_date character index integer startTime character endTime character isPitch logical type character playId character pitchNumber integer details.description character details.event character details.awayScore integer details.homeScore integer details.isScoringPlay logical details.hasReview logical details.code character details.ballColor character details.isInPlay logical details.isStrike logical details.isBall logical details.call.code character details.call.description character count.balls.start integer count.strikes.start integer count.outs.start integer player.id integer player.link character pitchData.strikeZoneTop numeric pitchData.strikeZoneBottom numeric details.fromCatcher logical pitchData.coordinates.x numeric pitchData.coordinates.y numeric hitData.trajectory character hitData.hardness character hitData.location character hitData.coordinates.coordX numeric hitData.coordinates.coordY numeric actionPlayId character 136 mlb_pbp details.eventType character details.runnerGoing logical position.code character position.name character position.type character position.abbreviation character battingOrder character atBatIndex character result.type character result.event character result.eventType character result.description character result.rbi integer result.awayScore integer result.homeScore integer about.atBatIndex integer about.halfInning character about.inning integer about.startTime character about.endTime character about.isComplete logical about.isScoringPlay logical about.hasReview logical about.hasOut logical about.captivatingIndex integer count.balls.end integer count.strikes.end integer count.outs.end integer matchup.batter.id integer matchup.batter.fullName character matchup.batter.link character matchup.batSide.code character matchup.batSide.description character matchup.pitcher.id integer matchup.pitcher.fullName character matchup.pitcher.link character matchup.pitchHand.code character matchup.pitchHand.description character matchup.splits.batter character matchup.splits.pitcher character matchup.splits.menOnBase character batted.ball.result factor home_team character home_level_id integer home_level_name character home_parentOrg_id integer home_parentOrg_name character home_league_id integer mlb_pbp 137 home_league_name character away_team character away_level_id integer away_level_name character away_parentOrg_id integer away_parentOrg_name character away_league_id integer away_league_name character batting_team character fielding_team character last.pitch.of.ab character pfxId character details.trailColor character details.type.code character details.type.description character pitchData.startSpeed numeric pitchData.endSpeed numeric pitchData.zone integer pitchData.typeConfidence numeric pitchData.plateTime numeric pitchData.extension numeric pitchData.coordinates.aY numeric pitchData.coordinates.aZ numeric pitchData.coordinates.pfxX numeric pitchData.coordinates.pfxZ numeric pitchData.coordinates.pX numeric pitchData.coordinates.pZ numeric pitchData.coordinates.vX0 numeric pitchData.coordinates.vY0 numeric pitchData.coordinates.vZ0 numeric pitchData.coordinates.x0 numeric pitchData.coordinates.y0 numeric pitchData.coordinates.z0 numeric pitchData.coordinates.aX numeric pitchData.breaks.breakAngle numeric pitchData.breaks.breakLength numeric pitchData.breaks.breakY numeric pitchData.breaks.spinRate integer pitchData.breaks.spinDirection integer hitData.launchSpeed numeric hitData.launchAngle numeric hitData.totalDistance numeric injuryType character umpire.id integer umpire.link character isBaseRunningPlay logical isSubstitution logical about.isTopInning logical 138 mlb_pbp_diff matchup.postOnFirst.id integer matchup.postOnFirst.fullName character matchup.postOnFirst.link character matchup.postOnSecond.id integer matchup.postOnSecond.fullName character matchup.postOnSecond.link character matchup.postOnThird.id integer matchup.postOnThird.fullName character matchup.postOnThird.link character Examples try(mlb_pbp(game_pk = 632970)) mlb_pbp_diff Acquire pitch-by-pitch data between two timecodes for Major and Minor League games Description Acquire pitch-by-pitch data between two timecodes for Major and Minor League games Usage mlb_pbp_diff(game_pk, start_timecode, end_timecode) Arguments game_pk The date for which you want to find game_pk values for MLB games start_timecode The start time code for the MLB game (format: MMDDYYYY_HHMMSS) end_timecode The end time code for the MLB game (format: MMDDYYYY_HHMMSS) Value Returns a tibble that includes over 100 columns of data provided by the MLB Stats API at a pitch level between the start_timecode and end_timecode col_name types game_pk numeric game_date character index integer startTime character endTime character isPitch logical mlb_pbp_diff 139 type character playId character pitchNumber integer details.description character details.event character details.awayScore integer details.homeScore integer details.isScoringPlay logical details.hasReview logical details.code character details.ballColor character details.isInPlay logical details.isStrike logical details.isBall logical details.call.code character details.call.description character count.balls.start integer count.strikes.start integer count.outs.start integer player.id integer player.link character pitchData.strikeZoneTop numeric pitchData.strikeZoneBottom numeric details.fromCatcher logical pitchData.coordinates.x numeric pitchData.coordinates.y numeric hitData.trajectory character hitData.hardness character hitData.location character hitData.coordinates.coordX numeric hitData.coordinates.coordY numeric actionPlayId character details.eventType character details.runnerGoing logical position.code character position.name character position.type character position.abbreviation character battingOrder character atBatIndex character result.type character result.event character result.eventType character result.description character result.rbi integer result.awayScore integer result.homeScore integer about.atBatIndex integer 140 mlb_pbp_diff about.halfInning character about.inning integer about.startTime character about.endTime character about.isComplete logical about.isScoringPlay logical about.hasReview logical about.hasOut logical about.captivatingIndex integer count.balls.end integer count.strikes.end integer count.outs.end integer matchup.batter.id integer matchup.batter.fullName character matchup.batter.link character matchup.batSide.code character matchup.batSide.description character matchup.pitcher.id integer matchup.pitcher.fullName character matchup.pitcher.link character matchup.pitchHand.code character matchup.pitchHand.description character matchup.splits.batter character matchup.splits.pitcher character matchup.splits.menOnBase character batted.ball.result factor home_team character home_level_id integer home_level_name character home_parentOrg_id integer home_parentOrg_name character home_league_id integer home_league_name character away_team character away_level_id integer away_level_name character away_parentOrg_id integer away_parentOrg_name character away_league_id integer away_league_name character batting_team character fielding_team character last.pitch.of.ab character pfxId character details.trailColor character details.type.code character details.type.description character pitchData.startSpeed numeric mlb_people 141 pitchData.endSpeed numeric pitchData.zone integer pitchData.typeConfidence numeric pitchData.plateTime numeric pitchData.extension numeric pitchData.coordinates.aY numeric pitchData.coordinates.aZ numeric pitchData.coordinates.pfxX numeric pitchData.coordinates.pfxZ numeric pitchData.coordinates.pX numeric pitchData.coordinates.pZ numeric pitchData.coordinates.vX0 numeric pitchData.coordinates.vY0 numeric pitchData.coordinates.vZ0 numeric pitchData.coordinates.x0 numeric pitchData.coordinates.y0 numeric pitchData.coordinates.z0 numeric pitchData.coordinates.aX numeric pitchData.breaks.breakAngle numeric pitchData.breaks.breakLength numeric pitchData.breaks.breakY numeric pitchData.breaks.spinRate integer pitchData.breaks.spinDirection integer hitData.launchSpeed numeric hitData.launchAngle numeric hitData.totalDistance numeric injuryType character umpire.id integer umpire.link character about.isTopInning logical matchup.postOnFirst.id integer matchup.postOnFirst.fullName character matchup.postOnFirst.link character Examples try(mlb_pbp_diff(game_pk = 632970, start_timecode = "20210808_231704", end_timecode = "20210808_233711")) mlb_people Find Biographical Information for MLB Players 142 mlb_people Description Find Biographical Information for MLB Players Usage mlb_people(person_ids = NULL) Arguments person_ids MLBAMIDs for players of interest. Multiple IDs should be provided in a vector separated by a comma. Value Returns a tibble with the following columns: col_name types id integer full_name character link character first_name character last_name character primary_number character birth_date character current_age integer birth_city character birth_state_province character birth_country character height character weight integer active logical use_name character middle_name character boxscore_name character nick_name character gender character is_player logical is_verified logical draft_year integer mlb_debut_date character name_first_last character name_slug character first_last_name character last_first_name character last_init_name character init_last_name character full_fml_name character full_lfm_name character strike_zone_top numeric mlb_people_free_agents 143 strike_zone_bottom numeric pronunciation character primary_position_code character primary_position_name character primary_position_type character primary_position_abbreviation character bat_side_code character bat_side_description character pitch_hand_code character pitch_hand_description character Examples try(mlb_people(person_ids = 502671)) try(mlb_people(person_ids = c(502671,605151))) mlb_people_free_agents Find Information About MLB Free Agents Description Find Information About MLB Free Agents Usage mlb_people_free_agents(season = NULL) Arguments season Season preceding free agency Value Returns a tibble with the following columns: col_name types date_declared character notes character date_signed character sort_order integer player_id integer player_full_name character player_link character original_team_id integer 144 mlb_pitch_types original_team_name character original_team_link character position_code character position_name character position_type character position_abbreviation character new_team_id integer new_team_name character new_team_link character Examples try(mlb_people_free_agents(season = 2018)) mlb_pitch_codes MLB Pitch Codes Description MLB Pitch Codes Usage mlb_pitch_codes() Value Returns a tibble with the following columns col_name types pitch_code character pitch_description character Examples try(mlb_pitch_codes()) mlb_pitch_types MLB Pitch Types mlb_player_game_stats 145 Description MLB Pitch Types Usage mlb_pitch_types() Value Returns a tibble with the following columns col_name types pitch_type_code character pitch_type_description character Examples try(mlb_pitch_types()) mlb_player_game_stats Find MLB Player Game Stats Description Find MLB Player Game Stats Usage mlb_player_game_stats(person_id = NULL, game_pk = NULL) Arguments person_id MLBAMIDs for player of interest. game_pk The game_pk to return game_log statistics for a specific player in a specific game and to complete the call. Value Returns a tibble with the following columns: col_name types type character group character assists integer 146 mlb_player_game_stats put_outs integer errors integer chances integer fielding character caught_stealing integer passed_ball integer stolen_bases integer stolen_base_percentage character pickoffs integer games_played integer games_started integer fly_outs integer ground_outs integer air_outs integer runs integer doubles integer triples integer home_runs integer strike_outs integer base_on_balls integer intentional_walks integer hits integer hit_by_pitch integer at_bats integer number_of_pitches integer innings_pitched character wins integer losses integer saves integer save_opportunities integer holds integer blown_saves integer earned_runs integer batters_faced integer outs integer games_pitched integer complete_games integer shutouts integer pitches_thrown integer balls integer strikes integer strike_percentage character hit_batsmen integer balks integer wild_pitches integer rbi integer games_finished integer runs_scored_per9 character mlb_player_game_stats 147 home_runs_per9 character inherited_runners integer inherited_runners_scored integer catchers_interference integer sac_bunts integer sac_flies integer ground_into_double_play integer ground_into_triple_play integer plate_appearances integer total_bases integer left_on_base integer at_bats_per_home_run character game_type character num_teams integer avg character obp character slg character ops character outs_pitched integer whip character ground_outs_to_airouts character pitches_per_inning character strikeout_walk_ratio character strikeouts_per9inn character walks_per9inn character hits_per9inn character team_id integer team_name character team_link character opponent_id integer opponent_name character opponent_link character pitcher_id integer pitcher_full_name character pitcher_link character pitcher_first_name character pitcher_last_name character batter_id integer batter_full_name character batter_link character batter_first_name character batter_last_name character total_splits integer type_display_name character group_display_name character player_id numeric game_pk numeric 148 mlb_player_game_stats_current Examples try(mlb_player_game_stats(person_id = 605151, game_pk = 531368)) mlb_player_game_stats_current Find MLB Player Game Stats - Current Game Description Find MLB Player Game Stats - Current Game Usage mlb_player_game_stats_current(person_id = NULL) Arguments person_id MLBAMIDs for player of interest. Value Returns a tibble with the following columns: col_name types type character group character stat_assists integer stat_put_outs integer stat_errors integer stat_chances integer stat_fielding character stat_caught_stealing integer stat_passed_ball integer stat_stolen_bases integer stat_stolen_base_percentage character stat_pickoffs integer stat_games_played integer stat_games_started integer stat_fly_outs integer stat_ground_outs integer stat_air_outs integer stat_runs integer stat_doubles integer stat_triples integer stat_home_runs integer mlb_player_game_stats_current 149 stat_strike_outs integer stat_base_on_balls integer stat_intentional_walks integer stat_hits integer stat_hit_by_pitch integer stat_at_bats integer stat_number_of_pitches integer stat_innings_pitched character stat_wins integer stat_losses integer stat_saves integer stat_save_opportunities integer stat_holds integer stat_blown_saves integer stat_earned_runs integer stat_batters_faced integer stat_outs integer stat_games_pitched integer stat_complete_games integer stat_shutouts integer stat_pitches_thrown integer stat_balls integer stat_strikes integer stat_strike_percentage character stat_hit_batsmen integer stat_balks integer stat_wild_pitches integer stat_rbi integer stat_games_finished integer stat_runs_scored_per9 character stat_home_runs_per9 character stat_inherited_runners integer stat_inherited_runners_scored integer stat_catchers_interference integer stat_sac_bunts integer stat_sac_flies integer stat_ground_into_double_play integer stat_ground_into_triple_play integer stat_plate_appearances integer stat_total_bases integer stat_left_on_base integer stat_at_bats_per_home_run character game_type character num_teams integer stat_avg character stat_obp character stat_slg character stat_ops character 150 mlb_player_status_codes stat_outs_pitched integer stat_whip character stat_ground_outs_to_airouts character stat_pitches_per_inning character stat_strikeout_walk_ratio character stat_strikeouts_per9inn character stat_walks_per9inn character stat_hits_per9inn character team_id integer team_name character team_link character opponent_id integer opponent_name character opponent_link character pitcher_id integer pitcher_full_name character pitcher_link character pitcher_first_name character pitcher_last_name character batter_id integer batter_full_name character batter_link character batter_first_name character batter_last_name character total_splits integer type_display_name character group_display_name character player_id numeric game_pk numeric Examples try(mlb_player_game_stats_current(person_id = 660271)) mlb_player_status_codes MLB Player Status Codes Description MLB Player Status Codes Usage mlb_player_status_codes() mlb_positions 151 Value Returns a tibble with the following columns col_name types player_status_code character player_status_description character Examples mlb_player_status_codes() mlb_positions MLB Positions Description MLB Positions Usage mlb_positions() Value Returns a tibble with the following columns col_name types position_short_name character position_full_name character position_abbreviation character position_code character position_type character position_formal_name character game_position logical pitcher logical fielder logical outfield logical position_display_name character Examples try(mlb_positions()) 152 mlb_review_reasons mlb_probables Retrieve probable starters for a given MLB game Description Retrieve probable starters for a given MLB game Usage mlb_probables(game_pk) Arguments game_pk The unique game_pk identifier for the game Value Returns a tiible that includes probable starting pitchers and the home plate umpire for the game_pk requested including the following columns: col_name types game_pk integer game_date character fullName character id integer team character team_id integer home_plate_full_name character home_plate_id integer Examples try(mlb_probables(566001)) mlb_review_reasons MLB Review Reasons Description MLB Review Reasons mlb_rosters 153 Usage mlb_review_reasons() Value Returns a tibble with the following columns col_name types review_reason_code character review_reason_description character Examples try(mlb_review_reasons()) mlb_rosters Find MLB Rosters by Roster Type Description Find MLB Rosters by Roster Type Usage mlb_rosters(team_id = NULL, season = NULL, date = NULL, roster_type = NULL) Arguments team_id team_id to return team roster information for a particular club. season Year to return team roster information for a particular club in a specific season. date Date to return team roster and their coaching staff directorial information for a particular team. roster_type roster_type to return team directorial information for. See mlb_roster_types() for more options. Valid options include: ’40Man’, ’fullSeason’, ’fullRoster’, ’nonRosterInvitees’, ’active’, ’allTime’, ’depthChart’, ’gameday’, ’coach’ Value Returns a tibble with the following columns: col_name types jersey_number character person_id integer person_full_name character 154 mlb_roster_types person_link character position_code character position_name character position_type character position_abbreviation character status_code character status_description character link character team_id integer roster_type character season numeric date character Examples try(mlb_rosters(team_id = 109, season = 2018, roster_type = 'active')) try(mlb_rosters(team_id = 109, season = 2018, roster_type = 'coach')) mlb_roster_types MLB Roster Types Description MLB Roster Types Usage mlb_roster_types() Value Returns a tibble with the following columns col_name types roster_type_description character roster_type_lookup_name character roster_type_parameter character Examples try(mlb_roster_types()) mlb_schedule 155 mlb_runner_detail_types MLB Runner Detail Types Description MLB Runner Detail Types Usage mlb_runner_detail_types() Value Returns a tibble with the following columns col_name types stat_name character Examples try(mlb_runner_detail_types()) mlb_schedule Find game_pk values for professional baseball games (major and minor leagues) Description Find game_pk values for professional baseball games (major and minor leagues) Usage mlb_schedule(season = 2019, level_ids = "1") Arguments season The season for which you want to find game_pk values for MLB games level_ids A numeric vector with ids for each level where game_pks are desired. See below for a reference of level ids. sport_id sport_code sport_link sport_name sport_abbreviation sort_order active 1 mlb /api/v1/sports/1 Major League Baseball MLB 11 TRU 156 mlb_schedule 11 aaa /api/v1/sports/11 Triple-A AAA 101 TRU 12 aax /api/v1/sports/12 Double-A AA 201 TRU 13 afa /api/v1/sports/13 High-A A+ 301 TRU 14 afx /api/v1/sports/14 Low-A A 401 TRU 16 rok /api/v1/sports/16 Rookie ROK 701 TRU 17 win /api/v1/sports/17 Winter Leagues WIN 1301 TRU 8 bbl /api/v1/sports/8 Organized Baseball Pros 1401 TRU 21 min /api/v1/sports/21 Minor League Baseball Minors 1402 TRU 23 ind /api/v1/sports/23 Independent Leagues IND 2101 TRU 51 int /api/v1/sports/51 International Baseball INT 3501 TRU 508 nat /api/v1/sports/508 International Baseball (Collegiate) INTC 3502 TRU 509 nae /api/v1/sports/509 International Baseball (18 and under) 18U 3503 TRU 510 nas /api/v1/sports/510 International Baseball (16 and under) 16U 3505 TRU 22 bbc /api/v1/sports/22 College Baseball College 5101 TRU 586 hsb /api/v1/sports/586 High School Baseball H.S. 6201 TRU Value Returns a tibble which includes game_pk values and additional information for games scheduled or played with the following columns: col_name types date character total_items integer total_events integer total_games integer total_games_in_progress integer game_pk integer link character game_type character season character game_date character official_date character game_number integer public_facing logical double_header character gameday_type character tiebreaker character calendar_event_id character season_display character day_night character scheduled_innings integer reverse_home_away_status logical inning_break_length integer games_in_series integer series_game_number integer series_description character record_source character mlb_schedule 157 if_necessary character if_necessary_description character status_abstract_game_state character status_coded_game_state character status_detailed_state character status_status_code character status_start_time_tbd logical status_reason character status_abstract_game_code character teams_away_split_squad logical teams_away_series_number integer teams_away_league_record_wins integer teams_away_league_record_losses integer teams_away_league_record_pct character teams_away_team_id integer teams_away_team_name character teams_away_team_link character teams_home_split_squad logical teams_home_series_number integer teams_home_league_record_wins integer teams_home_league_record_losses integer teams_home_league_record_pct character teams_home_team_id integer teams_home_team_name character teams_home_team_link character venue_id integer venue_name character venue_link character content_link character is_tie logical description character teams_away_score integer teams_away_is_winner logical teams_home_score integer teams_home_is_winner logical reschedule_date character reschedule_game_date character rescheduled_from character rescheduled_from_date character resume_date character resume_game_date character resumed_from character resumed_from_date character events list Level IDs The following IDs can be passed to the level_ids argument: 158 mlb_schedule_event_types 1 = MLB 11 = Triple-A 12 = Doubl-A 13 = Class A Advanced 14 = Class A 15 = Class A Short Season 5442 = Rookie Advanced 16 = Rookie 17 = Winter League Examples try(mlb_schedule(season = "2019")) mlb_schedule_event_types MLB Schedule Event Types Description MLB Schedule Event Types Usage mlb_schedule_event_types() Value Returns a tibble with the following columns col_name types schedule_event_type_code character schedule_event_type_name character Examples try(mlb_schedule_event_types()) mlb_schedule_games_tied 159 mlb_schedule_games_tied Find game_pk values for professional baseball games (major and minor leagues) that are tied Description Find game_pk values for professional baseball games (major and minor leagues) that are tied Usage mlb_schedule_games_tied(season = 2021, game_type = "S") Arguments season The season for which you want to find game_pk values for MLB games game_type game_type to return schedule information for all tied games in a particular game_type game_type_id game_type_description S Spring Training R Regular Season F Wild Card Game D Division Series L League Championship Series W World Series C Championship N Nineteenth Century Series P Playoffs A All-Star Game I Intrasquad E Exhibition Value Returns a tibble that includes game_pk values and additional information for games scheduled or played col_name types date character total_items integer total_events integer total_games integer total_games_in_progress integer game_pk integer link character 160 mlb_schedule_games_tied game_type character season character game_date character official_date character game_number integer public_facing logical double_header character gameday_type character tiebreaker character calendar_event_id character season_display character day_night character scheduled_innings integer reverse_home_away_status logical inning_break_length integer games_in_series integer series_game_number integer series_description character record_source character if_necessary character if_necessary_description character status_abstract_game_state character status_coded_game_state character status_detailed_state character status_status_code character status_start_time_tbd logical status_reason character status_abstract_game_code character teams_away_split_squad logical teams_away_series_number integer teams_away_league_record_wins integer teams_away_league_record_losses integer teams_away_league_record_pct character teams_away_team_id integer teams_away_team_name character teams_away_team_link character teams_home_split_squad logical teams_home_series_number integer teams_home_league_record_wins integer teams_home_league_record_losses integer teams_home_league_record_pct character teams_home_team_id integer teams_home_team_name character teams_home_team_link character venue_id integer venue_name character venue_link character content_link character mlb_schedule_postseason 161 is_tie logical description character teams_away_score integer teams_away_is_winner logical teams_home_score integer teams_home_is_winner logical reschedule_date character reschedule_game_date character rescheduled_from character rescheduled_from_date character resume_date character resume_game_date character resumed_from character resumed_from_date character events list Examples try(mlb_schedule_games_tied(season = 2021)) mlb_schedule_postseason Find game_pk values for professional baseball postseason games (major and minor leagues) Description Find game_pk values for professional baseball postseason games (major and minor leagues) Usage mlb_schedule_postseason( season = 2021, game_type = NULL, series_number = NULL, sport_id = 1, team_id = NULL ) Arguments season The season for which you want to find game_pk values for MLB games game_type game_type to return schedule information for all tied games in a particular game_type 162 mlb_schedule_postseason series_number The Series number to return schedule information for all tied games in a partic- ular series number sport_id The sport_id to return schedule information for. team_id The team_id to return schedule information for. game_type_id game_type_description S Spring Training R Regular Season F Wild Card Game D Division Series L League Championship Series W World Series C Championship N Nineteenth Century Series P Playoffs A All-Star Game I Intrasquad E Exhibition Value Returns a tibble that includes game_pk values and additional information for games scheduled or played col_name types date character total_items integer total_events integer total_games integer total_games_in_progress integer game_pk integer link character game_type character season character game_date character official_date character is_tie logical is_featured_game logical game_number integer public_facing logical double_header character gameday_type character tiebreaker character calendar_event_id character season_display character day_night character description character mlb_schedule_postseason 163 scheduled_innings integer reverse_home_away_status logical games_in_series integer series_game_number integer series_description character record_source character if_necessary character if_necessary_description character status_abstract_game_state character status_coded_game_state character status_detailed_state character status_status_code character status_start_time_tbd logical status_abstract_game_code character teams_away_score integer teams_away_is_winner logical teams_away_split_squad logical teams_away_series_number integer teams_away_league_record_wins integer teams_away_league_record_losses integer teams_away_league_record_pct character teams_away_team_id integer teams_away_team_name character teams_away_team_link character teams_home_score integer teams_home_is_winner logical teams_home_split_squad logical teams_home_series_number integer teams_home_league_record_wins integer teams_home_league_record_losses integer teams_home_league_record_pct character teams_home_team_id integer teams_home_team_name character teams_home_team_link character venue_id integer venue_name character venue_link character content_link character inning_break_length integer reschedule_date character reschedule_game_date character status_reason character rescheduled_from character rescheduled_from_date character is_default_game logical events list 164 mlb_schedule_postseason_series Examples try(mlb_schedule_postseason(season = 2021)) mlb_schedule_postseason_series Find game_pk values for professional baseball postseason series games (major and minor leagues) Description Find game_pk values for professional baseball postseason series games (major and minor leagues) Usage mlb_schedule_postseason_series( season = 2021, game_type = NULL, series_number = NULL, sport_id = 1, team_id = NULL ) Arguments season The season for which you want to find game_pk values for MLB games game_type game_type to return schedule information for all tied games in a particular game_type series_number The Series number to return schedule information for all tied games in a partic- ular series number sport_id The sport_id to return schedule information for. team_id The team_id to return schedule information for. game_type_id game_type_description S Spring Training R Regular Season F Wild Card Game D Division Series L League Championship Series W World Series C Championship N Nineteenth Century Series P Playoffs A All-Star Game mlb_schedule_postseason_series 165 I Intrasquad E Exhibition Value Returns a tibble that includes game_pk values and additional information for games scheduled or played col_name types total_items integer total_games integer total_games_in_progress integer game_pk integer link character game_type character season character game_date character official_date character is_tie logical is_featured_game logical game_number integer public_facing logical double_header character gameday_type character tiebreaker character calendar_event_id character season_display character day_night character description character scheduled_innings integer reverse_home_away_status logical inning_break_length integer games_in_series integer series_game_number integer series_description character record_source character if_necessary character if_necessary_description character is_default_game logical status_abstract_game_state character status_coded_game_state character status_detailed_state character status_status_code character status_start_time_tbd logical status_abstract_game_code character teams_away_score integer teams_away_is_winner logical teams_away_split_squad logical 166 mlb_seasons teams_away_series_number integer teams_away_league_record_wins integer teams_away_league_record_losses integer teams_away_league_record_pct character teams_away_team_id integer teams_away_team_name character teams_away_team_link character teams_home_score integer teams_home_is_winner logical teams_home_split_squad logical teams_home_series_number integer teams_home_league_record_wins integer teams_home_league_record_losses integer teams_home_league_record_pct character teams_home_team_id integer teams_home_team_name character teams_home_team_link character venue_id integer venue_name character venue_link character content_link character reschedule_date character reschedule_game_date character rescheduled_from character rescheduled_from_date character status_reason character sort_order integer series_id character series_sort_number integer series_is_default logical series_game_type character Examples try(mlb_schedule_postseason_series(season = 2021, sport_id = 1)) mlb_seasons Find MLB Seasons Description Find MLB Seasons mlb_seasons_all 167 Usage mlb_seasons(sport_id = 1, with_game_type_dates = TRUE) Arguments sport_id The sport_id to return season information for. with_game_type_dates with_game_type_dates to return season information Value Returns a tibble with the following columns: col_name types season_id character has_wildcard logical pre_season_start_date character pre_season_end_date character season_start_date character spring_start_date character spring_end_date character regular_season_start_date character last_date1st_half character all_star_date character first_date2nd_half character regular_season_end_date character post_season_start_date character post_season_end_date character season_end_date character offseason_start_date character off_season_end_date character season_level_gameday_type character game_level_gameday_type character qualifier_plate_appearances numeric qualifier_outs_pitched integer Examples mlb_seasons(sport_id = 1) mlb_seasons_all Find MLB Seasons all 168 mlb_seasons_all Description Find MLB Seasons all Usage mlb_seasons_all( sport_id = 1, division_id = NULL, league_id = NULL, with_game_type_dates = TRUE ) Arguments sport_id The sport_id to return season information for. division_id The division_id to return season information for. league_id The league_id to return season information for. with_game_type_dates with_game_type_dates to return season information for. Value Returns a tibble with the following columns: col_name types season_id character has_wildcard logical pre_season_start_date character season_start_date character regular_season_start_date character regular_season_end_date character season_end_date character offseason_start_date character off_season_end_date character season_level_gameday_type character game_level_gameday_type character qualifier_plate_appearances numeric qualifier_outs_pitched integer post_season_start_date character post_season_end_date character last_date1st_half character all_star_date character first_date2nd_half character pre_season_end_date character spring_start_date character spring_end_date character mlb_sky 169 Examples mlb_seasons_all(sport_id = 1) mlb_situation_codes MLB Situation Codes Description MLB Situation Codes Usage mlb_situation_codes() Value Returns a tibble with the following columns col_name types situation_code character sort_order integer navigation_menu character situation_code_description character team logical batting logical fielding logical pitching logical Examples try(mlb_situation_codes()) mlb_sky MLB Sky (Weather) Codes Description MLB Sky (Weather) Codes Usage mlb_sky() 170 mlb_sports Value Returns a tibble with the following columns col_name types sky_code character sky_description character Examples try(mlb_sky()) mlb_sports MLB Sport IDs Description MLB Sport IDs Usage mlb_sports(sport_id = NULL) Arguments sport_id The sport_id to return information for. Value Returns a tibble with the following columns col_name types sport_id integer sport_code character sport_link character sport_name character sport_abbreviation character sort_order integer active_status logical and the following values: sport_id sport_code sport_link sport_name sport_abbreviation sort_order active 1 mlb /api/v1/sports/1 Major League Baseball MLB 11 TRU mlb_sports_info 171 11 aaa /api/v1/sports/11 Triple-A AAA 101 TRU 12 aax /api/v1/sports/12 Double-A AA 201 TRU 13 afa /api/v1/sports/13 High-A A+ 301 TRU 14 afx /api/v1/sports/14 Low-A A 401 TRU 16 rok /api/v1/sports/16 Rookie ROK 701 TRU 17 win /api/v1/sports/17 Winter Leagues WIN 1301 TRU 8 bbl /api/v1/sports/8 Organized Baseball Pros 1401 TRU 21 min /api/v1/sports/21 Minor League Baseball Minors 1402 TRU 23 ind /api/v1/sports/23 Independent Leagues IND 2101 TRU 51 int /api/v1/sports/51 International Baseball INT 3501 TRU 508 nat /api/v1/sports/508 International Baseball (Collegiate) INTC 3502 TRU 509 nae /api/v1/sports/509 International Baseball (18 and under) 18U 3503 TRU 510 nas /api/v1/sports/510 International Baseball (16 and under) 16U 3505 TRU 22 bbc /api/v1/sports/22 College Baseball College 5101 TRU 586 hsb /api/v1/sports/586 High School Baseball H.S. 6201 TRU Examples try(mlb_sports()) mlb_sports_info MLB Sport IDs Information Description MLB Sport IDs Information Usage mlb_sports_info(sport_id = 1) Arguments sport_id The sport_id to return information for. Value Returns a tibble with the following columns col_name types sport_id integer sport_code character sport_link character sport_name character sport_abbreviation character 172 mlb_sports_players sort_order integer active_status logical Examples try(mlb_sports_info(sport_id = 1)) mlb_sports_players MLB Sport Players Description MLB Sport Players Usage mlb_sports_players(sport_id = 1, season = 2021) Arguments sport_id The sport_id to return information for. season The season to return information for. Value Returns a tibble with the following columns: col_name types player_id integer full_name character link character first_name character last_name character primary_number character birth_date character current_age integer birth_city character birth_country character height character weight integer active logical use_name character middle_name character boxscore_name character mlb_standings 173 nick_name character gender character is_player logical is_verified logical pronunciation character mlb_debut_date character name_first_last character name_slug character first_last_name character last_first_name character last_init_name character init_last_name character full_fml_name character full_lfm_name character strike_zone_top numeric strike_zone_bottom numeric birth_state_province character draft_year integer name_matrilineal character name_title character last_played_date character current_team_id integer current_team_name character current_team_link character primary_position_code character primary_position_name character primary_position_type character primary_position_abbreviation character bat_side_code character bat_side_description character pitch_hand_code character pitch_hand_description character Examples try(mlb_sports_players(sport_id = 1, season = 2021)) mlb_standings MLB Standings Description MLB Standings 174 mlb_standings Usage mlb_standings( season = NULL, date = NULL, standings_type = NULL, league_id = NULL ) Arguments season Year(s) to return to return standings information for. date Date to return to return standings information for. standings_type The standings_type(s) to return standings information for. Description of all standings_types 1. regularSeason - Regular Season Standings 2. wildCard - Wild card standings 3. divisionLeaders - Division Leader standings 4. wildCardWithLeaders - Wild card standings with Division Leaders 5. firstHalf - First half standings. Only valid for leagues with a split season (Mexican League). 6. secondHalf - Second half standings. Only valid for leagues with a split season (Mexican League). 7. springTraining - Spring Training Standings 8. postseason - Postseason Standings 9. byDivision - Standings by Division 10. byConference - Standings by Conference 11. byLeague - Standings by League league_id The league_id(s) to return standings information for. Value Returns a tibble with the following columns col_name types standings_type character last_updated character team_records_season character team_records_clinch_indicator character team_records_division_rank character team_records_league_rank character team_records_sport_rank character team_records_games_played integer team_records_games_back character team_records_wild_card_games_back character team_records_league_games_back character team_records_spring_league_games_back character mlb_standings 175 team_records_sport_games_back character team_records_division_games_back character team_records_conference_games_back character team_records_last_updated character team_records_runs_allowed integer team_records_runs_scored integer team_records_division_champ logical team_records_division_leader logical team_records_has_wildcard logical team_records_clinched logical team_records_elimination_number character team_records_wild_card_elimination_number character team_records_magic_number character team_records_wins integer team_records_losses integer team_records_run_differential integer team_records_winning_percentage character team_records_wild_card_rank character team_records_wild_card_leader logical team_records_team_id integer team_records_team_name character team_records_team_link character team_records_streak_streak_type character team_records_streak_streak_number integer team_records_streak_streak_code character team_records_league_record_wins integer team_records_league_record_losses integer team_records_league_record_ties integer team_records_league_record_pct character team_records_records_split_records list team_records_records_division_records list team_records_records_overall_records list team_records_records_league_records list team_records_records_expected_records list league_id integer league_link character division_id integer division_link character sport_id integer sport_link character Examples try(mlb_standings(season = 2021, league_id = 103)) 176 mlb_stats mlb_standings_types MLB Standings Types Description MLB Standings Types Usage mlb_standings_types() Value Returns a tibble with the following columns col_name types standings_type_name character standings_type_description character Examples try(mlb_standings_types()) mlb_stats MLB Stats Description MLB Stats Usage mlb_stats( stat_type = NULL, player_pool = NULL, game_type = NULL, team_id = NULL, position = NULL, stat_group = NULL, season = NULL, league_id = NULL, sport_ids = NULL, sort_stat = NULL, mlb_stats 177 order = NULL, limit = 1000, offset = NULL ) Arguments stat_type Stat type to return statistics for. player_pool There are 4 different types of player pools to return statistics for a particular player pool across a sport. Acceptable values include: All, Qualified, Rookies, or Qualified_rookies game_type Game type to return information for a particular statistic in a particular game type. team_id Team ID to return information and ranking for a particular statistic for a partic- ular team. position Position to return statistics for a given position. Default to "Qualified" player pool Acceptable values include: • P • C • 1B • 2B • 3B • SS • LF • CF • RF • DH • PH • PR • BR • OF • IF • SP • RP • CP • UT • UI • UO • RHP • LHP • RHS • LHS • LHR 178 mlb_stats • RHR • B • X stat_group Stat group to return information and ranking for a particular statistic in a partic- ular group. season Year to return information and ranking for a particular statistic in a given year. league_id League ID to return statistics for a given league. Default to "Qualified" player pool. sport_ids The sport_id(s) to return information and ranking information for. sort_stat Sort return based on stat. order Order return based on either desc or asc. limit A limit to limit return to a particular number of records. offset An offset to returns i+1 as the first record in the set of players. Value Returns a tibble with the following columns col_name types total_splits integer season character num_teams integer rank integer games_played integer ground_outs integer air_outs integer runs integer doubles integer triples integer home_runs integer strike_outs integer base_on_balls integer intentional_walks integer hits integer hit_by_pitch integer avg character at_bats integer obp character slg character ops character caught_stealing integer stolen_bases integer stolen_base_percentage character ground_into_double_play integer number_of_pitches integer plate_appearances integer mlb_stats_leaders 179 total_bases integer rbi integer left_on_base integer sac_bunts integer sac_flies integer babip character ground_outs_to_airouts character catchers_interference integer at_bats_per_home_run character team_id integer team_name character team_link character player_id integer player_full_name character player_link character player_first_name character player_last_name character league_id integer league_name character league_link character sport_id integer sport_link character sport_abbreviation character position_code character position_name character position_type character position_abbreviation character splits_tied_with_offset list splits_tied_with_limit list player_pool character type_display_name character group_display_name character Examples try(mlb_stats(stat_type = 'season', stat_group = 'hitting', season = 2021)) mlb_stats_leaders MLB Stats Leaders Description MLB Stats Leaders 180 mlb_stats_leaders Usage mlb_stats_leaders( leader_categories = NULL, player_pool = NULL, leader_game_types = NULL, sit_codes = NULL, position = NULL, stat_group = NULL, season = NULL, league_id = NULL, sport_id = NULL, start_date = NULL, end_date = NULL, stat_type = NULL, limit = 1000 ) Arguments leader_categories League leader category to return information and ranking for a particular statis- tic. player_pool There are 4 different types of player pools to return statistics for a particular player pool across a sport. Acceptable values include: All, Qualified, Rookies, or Qualified_rookies leader_game_types Game type to return information and ranking for a particular statistic in a partic- ular game type. sit_codes Situation code to return information and ranking for a particular statistic in a particular game type. position Position to return statistics for a given position. Default to "Qualified" player pool Acceptable values include: • P • C • 1B • 2B • 3B • SS • LF • CF • RF • DH • PH • PR • BR mlb_stats_leaders 181 • OF • IF • SP • RP • CP • UT • UI • UO • RHP • LHP • RHS • LHS • LHR • RHR • B • X stat_group Stat group to return information and ranking for a particular statistic in a partic- ular group. season Year to return information and ranking for a particular statistic in a given year. league_id League ID to return statistics for a given league. Default to "Qualified" player pool. sport_id The sport_id to return information and ranking information for. start_date Start date to return information and ranking for a particular statistic for a par- ticular date range. Format: MM/DD/YYYY start_date must be coupled with end_date and byDateRange stat_type end_date End date to return information and ranking for a particular statistic for a par- ticular date range. Format: MM/DD/YYYY end_date must be coupled with start_date and byDateRange stat_type stat_type The stat_type to return information and ranking for a particular statistic for a particular stat type. limit A limit to limit return to a particular number of records. Value Returns a tibble with the following columns col_name types leader_category character rank integer value character season character num_teams integer team_id integer team_name character 182 mlb_stat_groups team_link character league_id integer league_name character league_link character person_id integer person_full_name character person_link character person_first_name character person_last_name character sport_id integer sport_link character sport_abbreviation character stat_group character total_splits integer game_type_id character game_type_description character Examples try(mlb_stats_leaders(leader_categories='homeRuns',sport_id=1, season = 2021)) mlb_stat_groups MLB Stat Groups Description MLB Stat Groups Usage mlb_stat_groups() Value Returns a tibble with the following columns col_name types stat_group_name character Examples try(mlb_stat_groups()) mlb_teams 183 mlb_stat_types MLB Stat Types Description MLB Stat Types Usage mlb_stat_types() Value Returns a tibble with the following columns col_name types stat_type_name character Examples try(mlb_stat_types()) mlb_teams MLB Teams Description MLB Teams Usage mlb_teams( season = NULL, active_status = NULL, all_star_statuses = NULL, league_ids = NULL, sport_ids = NULL, game_type = NULL ) 184 mlb_teams Arguments season Year to return to return team information for. active_status The active statuses to populate teams for a given season. all_star_statuses The all-star statuses to populate teams for a given season. league_ids The league_id(s) to return team information for. sport_ids The sport_id(s) to return team information for. game_type The game_type to return team information for. Value Returns a tibble with the following columns col_name types team_id integer team_full_name character link character season integer team_code character file_code character team_abbreviation character team_name character location_name character first_year_of_play character short_name character franchise_name character club_name character all_star_status character active logical venue_id integer venue_name character venue_link character spring_venue_id integer spring_venue_link character league_id integer league_name character league_link character division_id integer division_name character division_link character sport_id integer sport_link character sport_name character spring_league_id integer spring_league_name character spring_league_link character spring_league_abbreviation character mlb_teams_stats 185 Examples try(mlb_teams(season = 2021, sport_ids = c(1))) mlb_teams_stats MLB Teams Stats Description MLB Teams Stats Usage mlb_teams_stats( stat_type = NULL, game_type = NULL, stat_group = NULL, season = NULL, sport_ids = NULL, sort_stat = NULL, order = NULL ) Arguments stat_type Stat type to return statistics for. game_type Game type to return information for a particular statistic in a particular game type. stat_group Stat group to return information and ranking for a particular statistic in a partic- ular group. season Year to return information and ranking for a particular statistic in a given year. sport_ids The sport_id(s) to return information and ranking information for. sort_stat Sort return based on stat. order Order return based on either desc or asc. Value Returns a tibble with the following columns col_name types total_splits integer season character rank integer games_played integer 186 mlb_teams_stats ground_outs integer air_outs integer runs integer doubles integer triples integer home_runs integer strike_outs integer base_on_balls integer intentional_walks integer hits integer hit_by_pitch integer avg character at_bats integer obp character slg character ops character caught_stealing integer stolen_bases integer stolen_base_percentage character ground_into_double_play integer number_of_pitches integer plate_appearances integer total_bases integer rbi integer left_on_base integer sac_bunts integer sac_flies integer babip character ground_outs_to_airouts character catchers_interference integer at_bats_per_home_run character team_id integer team_name character team_link character splits_tied_with_offset list splits_tied_with_limit list type_display_name character group_display_name character Examples try(mlb_teams_stats(stat_type = 'season', stat_group = 'hitting', season = 2021)) mlb_teams_stats_leaders 187 mlb_teams_stats_leaders MLB Teams Stats Leaders Description MLB Teams Stats Leaders Usage mlb_teams_stats_leaders( leader_categories = NULL, leader_game_types = NULL, sit_codes = NULL, stat_group = NULL, season = NULL, league_id = NULL, sport_id = NULL, start_date = NULL, end_date = NULL, stat_type = NULL, limit = 1000 ) Arguments leader_categories League leader category to return information and ranking for a particular statis- tic. leader_game_types Game type to return information and ranking for a particular statistic in a partic- ular game type. sit_codes Situation code to return information and ranking for a particular statistic in a particular game type. stat_group Stat group to return information and ranking for a particular statistic in a partic- ular group. season Year to return information and ranking for a particular statistic in a given year. league_id League ID to return statistics for a given league. Default to "Qualified" player pool. sport_id The sport_id to return information and ranking information for. start_date Start date to return information and ranking for a particular statistic for a par- ticular date range. Format: MM/DD/YYYY start_date must be coupled with end_date and byDateRange stat_type end_date End date to return information and ranking for a particular statistic for a par- ticular date range. Format: MM/DD/YYYY end_date must be coupled with start_date and byDateRange stat_type 188 mlb_team_affiliates stat_type The stat_type to return information and ranking for a particular statistic for a particular stat type. limit A limit to limit return to a particular number of records. Value Returns a tibble with the following columns col_name types leader_category character rank integer value character season character num_teams integer team_id integer team_name character team_link character league_id integer league_name character league_link character person_id integer person_full_name character person_link character person_first_name character person_last_name character sport_id integer sport_link character sport_abbreviation character stat_group character total_splits integer game_type_id character game_type_description character Examples try(mlb_teams_stats_leaders(leader_categories='homeRuns',sport_id=1, season = 2021)) mlb_team_affiliates MLB Team Affiliates Description MLB Team Affiliates mlb_team_affiliates 189 Usage mlb_team_affiliates(team_ids = NULL, sport_ids = NULL, season = NULL) Arguments team_ids The team_id(s) to return affiliates data for. sport_ids The sport_id to return team affiliates information for. season The season to return team affiliates data for the particular season. Value Returns a tibble with the following columns col_name types all_star_status character team_id integer team_full_name character link character season integer team_code character file_code character team_abbreviation character team_name character location_name character first_year_of_play character short_name character franchise_name character club_name character active logical parent_org_name character parent_org_id integer spring_league_id integer spring_league_name character spring_league_link character spring_league_abbreviation character venue_id integer venue_name character venue_link character spring_venue_id integer spring_venue_link character league_id integer league_name character league_link character division_id integer division_name character division_link character sport_id integer sport_link character 190 mlb_team_alumni sport_name character Examples try(mlb_team_affiliates(team_ids = 147)) mlb_team_alumni MLB Team Alumni Description MLB Team Alumni Usage mlb_team_alumni(team_id = NULL, stat_group = NULL, season = NULL) Arguments team_id Team ID to return information and ranking for a particular statistic for a partic- ular team. stat_group Stat group to return information and ranking for a particular statistic in a partic- ular group. season Year to return information and ranking for a particular statistic in a given year. Value Returns a tibble with the following columns col_name types player_id integer player_full_name character link character first_name character last_name character primary_number character birth_date character current_age integer birth_city character birth_country character height character weight integer active logical use_name character mlb_team_coaches 191 middle_name character boxscore_name character nick_name character gender character name_matrilineal character is_player logical is_verified logical pronunciation character mlb_debut_date character name_first_last character name_slug character first_last_name character last_first_name character last_init_name character init_last_name character full_fml_name character full_lfm_name character strike_zone_top numeric strike_zone_bottom numeric alumni_last_season character birth_state_province character draft_year integer primary_position_code character primary_position_name character primary_position_type character primary_position_abbreviation character bat_side_code character bat_side_description character pitch_hand_code character pitch_hand_description character Examples try(mlb_team_alumni(team_id = 137, stat_group = 'hitting', season = 2021)) mlb_team_coaches MLB Team Coaches Description MLB Team Coaches Usage mlb_team_coaches(team_id = NULL, date = NULL, season = NULL) 192 mlb_team_history Arguments team_id Team ID to return team coach information for. date Date to return team coach information for. season Year to return team coach information for. Value Returns a tibble with the following columns col_name types jersey_number character job character job_id character title character person_id integer person_full_name character person_link character Examples try(mlb_team_coaches(team_id = 137, season = 2021)) mlb_team_history MLB Teams History Description MLB Teams History Usage mlb_team_history(team_ids = NULL, start_season = NULL, end_season = NULL) Arguments team_ids The team_id(s) to return historical data for. start_season The start_season to return historical data for from the given year to present. end_season The end_season to return historical data for from the the creation to the given year. Value Returns a tibble with the following columns mlb_team_info 193 col_name types all_star_status character team_id integer team_full_name character link character season integer team_code character file_code character team_abbreviation character team_name character location_name character first_year_of_play character short_name character franchise_name character club_name character active logical venue_id integer venue_name character venue_link character spring_venue_id integer spring_venue_link character league_id integer league_name character league_link character sport_id integer sport_link character sport_name character Examples try(mlb_team_history(team_ids = 147)) mlb_team_info MLB Team Info Description MLB Team Info Usage mlb_team_info(team_id = NULL, season = NULL, sport_id = NULL) 194 mlb_team_leaders Arguments team_id The team_id to return team data for. season The season to return team data for the given year. sport_id The sport_id to return a directory of team data for a particular club in a sport. Value Returns a tibble with the following columns col_name types all_star_status character team_id integer team_full_name character link character season integer team_code character file_code character team_abbreviation character team_name character location_name character first_year_of_play character short_name character franchise_name character club_name character active logical venue_id integer venue_name character venue_link character spring_venue_id integer spring_venue_link character league_id integer league_name character league_link character sport_id integer sport_link character sport_name character Examples try(mlb_team_info(team_id = 147)) mlb_team_leaders MLB Team Leaders mlb_team_leaders 195 Description MLB Team Leaders Usage mlb_team_leaders( team_id = NULL, leader_categories = NULL, leader_game_types = NULL, season = NULL, limit = 1000 ) Arguments team_id Team ID to return team leader information for. leader_categories Team leader category to return information and ranking for a particular statistic. leader_game_types Game type to return information and ranking for a particular statistic in a partic- ular game type. season Season to return team leader information for. limit A limit to limit return to a particular number of records. Value Returns a tibble with the following columns col_name types leader_category character rank integer value character season character team_id integer team_name character team_link character league_id integer league_name character league_link character person_id integer person_full_name character person_link character person_first_name character person_last_name character sport_id integer sport_link character sport_abbreviation character stat_group character 196 mlb_team_personnel total_splits integer game_type_id character game_type_description character Examples try(mlb_team_leaders(team_id = 137, leader_categories = "homeRuns", season = 2021)) mlb_team_personnel MLB Team Personnel Description MLB Team Personnel Usage mlb_team_personnel(team_id = NULL, date = NULL) Arguments team_id Team ID to return team coach information for. date Date to return team coach information for. Value Returns a tibble with the following columns col_name types jersey_number character job character job_id character title character person_id integer person_full_name character person_link character Examples try(mlb_team_personnel(team_id = 137, date = "08/28/2016")) mlb_team_stats 197 mlb_team_stats MLB Team Individual Stats Description MLB Team Individual Stats Usage mlb_team_stats( team_id = NULL, stat_type = NULL, game_type = NULL, stat_group = NULL, season = NULL, sport_ids = NULL ) Arguments team_id Team ID to return information and ranking for a particular statistic for a partic- ular team. stat_type Stat type to return statistics for. game_type Game type to return information for a particular statistic in a particular game type. stat_group Stat group to return information and ranking for a particular statistic in a partic- ular group. season Year to return information and ranking for a particular statistic in a given year. sport_ids The sport_id(s) to return information and ranking information for. Value Returns a tibble with the following columns col_name types season character games_played integer ground_outs integer air_outs integer runs integer doubles integer triples integer home_runs integer strike_outs integer base_on_balls integer intentional_walks integer 198 mlb_venues hits integer hit_by_pitch integer avg character at_bats integer obp character slg character ops character caught_stealing integer stolen_bases integer stolen_base_percentage character ground_into_double_play integer number_of_pitches integer plate_appearances integer total_bases integer rbi integer left_on_base integer sac_bunts integer sac_flies integer babip character ground_outs_to_airouts character catchers_interference integer at_bats_per_home_run character team_id integer team_name character team_link character type_display_name character group_display_name character Examples try(mlb_team_stats(team_id = 137, stat_type = 'season', stat_group = 'hitting', season = 2021)) mlb_venues Find MLB Venues Description Find MLB Venues Usage mlb_venues(venue_ids = NULL, sport_ids = NULL, season = NULL) mlb_wind_direction_codes 199 Arguments venue_ids Venue directorial information based venue_id. sport_ids The sport_id(s) for which to return venue directorial information. season Year for which to return venue directorial information for a given season. Value Returns a tibble with the following columns: col_name types venue_id integer venue_name character venue_link character active logical season logical Examples try(mlb_venues()) try(mlb_venues(venue_ids = 4781)) try(mlb_venues(sport_ids = 1)) mlb_wind_direction_codes MLB Wind Direction Codes Description MLB Wind Direction Codes Usage mlb_wind_direction_codes() Value Returns a tibble with the following columns col_name types wind_direction_code character wind_direction_description character 200 most_recent_ncaa_baseball_season Examples try(mlb_wind_direction_codes()) most_recent_mlb_season Most Recent MLB Season Description Most Recent MLB Season Usage most_recent_mlb_season() Value An integer indicating the year of the most recent season of Major League Baseball most_recent_ncaa_baseball_season Most Recent NCAA Baseball Season Description Most Recent NCAA Baseball Season Usage most_recent_ncaa_baseball_season() Value An integer indicating the year of the most recent season of NCAA baseball ncaa 201 ncaa NCAA Functions Overview Description ncaa_team_player_stats(): This function allows the user to obtain batting or pitching statistics for any school affiliated with the NCAA at the division I, II, or III levels. The function acquires data from the NCAA’s website (stats.ncaa.org) and returns a tibble. ncaa_pbp(): Get Play-By-Play Data for NCAA Baseball Games. ncaa_roster(): Get NCAA Baseball Rosters. ncaa_game_logs(): Get NCAA Baseball Game Logs. ncaa_lineups(): Get NCAA Baseball Game Lineups. ncaa_park_factor(): Get Park Effects for NCAA Baseball Teams. ncaa_schedule_info(): Get Schedule and Results for NCAA Baseball Teams. ncaa_school_id_lu(): Lookup NCAA School IDs (Division I, II, and III) ncaa_teams(): Lookup NCAA Teams by Division (I, II, and III) and Season Details Scrape NCAA baseball data (Division I, II, and III): ncaa_team_player_stats(team_id = 255, year = 2013, type = "batting") Get Play-By-Play Data for NCAA Baseball Games: x <- ncaa_schedule_info(736, 2021)$game_info_url[2] ncaa_pbp(game_info_url = x) Get NCAA Baseball Rosters: ncaa_roster(team_id = 104, year = 2021) Get NCAA Baseball Game Logs: ncaa_game_logs(player_id = 2113782, year = 2021, type = "pitching", span = "game") Get NCAA Baseball Game Lineups: ncaa_lineups(game_info_url="https://stats.ncaa.org/game/index/4587474?org_id=528",year=2018) Get Park Effects for NCAA Baseball Teams: ncaa_park_factor(team_id = 736, years = c(2017:2019), type = "conference") Get Schedule and Results for NCAA Baseball Teams: ncaa_schedule_info(team_id = 736, year = 2021) 202 ncaa_game_logs Lookup NCAA School IDs (Division I, II, and III): ncaa_school_id_lu("VAN") Scrape NCAA baseball Teams (Division I, II, and III): ncaa_teams(year = 2023, division = 1) ncaa_baseball_roster (legacy) Get NCAA Baseball Rosters Description (legacy) Get NCAA Baseball Rosters (legacy) Get NCAA Baseball Rosters Usage ncaa_baseball_roster(team_id = NULL, year, ...) get_ncaa_baseball_roster(team_id = NULL, year, ...) Arguments team_id NCAA id for a school year The year of interest ... Additional arguments passed to an underlying function like httr. Value A data frame containing roster information, including IDs and urls for each player (if available) A data frame containing roster information, including IDs and urls for each player (if available) ncaa_game_logs Get NCAA Baseball Game Logs Description Get NCAA Baseball Game Logs Usage ncaa_game_logs(player_id, year, type = "batting", span = "game", ...) ncaa_game_logs 203 Arguments player_id A player’s unique id. Can be found using the get_ncaa_baseball_roster function. year The year of interest. type The kind of statistics you want to return. Current options are ’batting’ or ’pitch- ing’. span The span of time; can either be ’game’ for game logs in a season, or ’career’ which returns seasonal stats for a player’s career. ... Additional arguments passed to an underlying function like httr. Value A data frame containing player and school information as well as game by game statistics col_name types player_id numeric player_name character Date character Opponent character Result character App numeric G numeric GS numeric IP numeric CG numeric H numeric R numeric ER numeric BB numeric SO numeric SHO numeric BF numeric P-OAB numeric 2B-A numeric 3B-A numeric Bk numeric HR-A numeric WP numeric HB numeric IBB numeric Inh Run numeric Inh Run Score numeric SHA numeric SFA numeric Pitches numeric GO numeric FO numeric W numeric 204 ncaa_lineups L numeric SV numeric OrdAppeared numeric KL numeric pickoffs character Examples try(ncaa_game_logs(player_id = 2113782, year = 2021, type = "pitching", span = "game")) try(ncaa_game_logs(player_id = 2113782, year = 2021, type = "pitching", span = "career")) try(ncaa_game_logs(player_id = 1879650, year = 2019, type = "batting", span = "game")) try(ncaa_game_logs(player_id = 1879650, year = 2019, type = "batting", span = "career")) ncaa_lineups Retrieve lineups for a given NCAA game via its game_info_url Description Retrieve lineups for a given NCAA game via its game_info_url Usage ncaa_lineups(game_info_url = NULL, ...) Arguments game_info_url The unique game info url ... Additional arguments passed to an underlying function like httr. Value Returns a tibble of each school’s starting lineup and starting pitcher col_name types year numeric player_name character position character slug character batting_order character team_name character sub numeric attendance character game_date character location character player_id integer ncaa_park_factor 205 team_id numeric team_url character conference_id numeric conference character division numeric season_id numeric Examples try(ncaa_lineups(game_info_url="https://stats.ncaa.org/contests/2167178/box_score")) try(ncaa_lineups(game_info_url="https://stats.ncaa.org/game/index/4587474?org_id=528")) ncaa_park_factor Get Park Effects for NCAA Baseball Teams Description Get Park Effects for NCAA Baseball Teams Usage ncaa_park_factor(team_id, years, type = "conference", ...) Arguments team_id The team’s unique NCAA id. years The season or seasons (i.e. use 2016 for the 2015-2016 season, etc., limited to just 2013-2020 seasons). type default is conference. the conference parameter adjusts for the conference the school plays in, the division parameter calculates based on the division the school plays in 1,2,or 3. Defaults to ’conference’. ... Additional arguments passed to an underlying function like httr. Details try(ncaa_park_factor(team_id = 736, years = c(2018:2019), type = "conference")) Value A data frame with the following fields: school, home_game, away_game, runs_scored_home, runs_allowed_home, run_scored_away, runs_allowed_away, base_pf (base park factor), home_game_adj (an adjustment for the percentage of home games played) final_pf (park factor after adjustments) col_name types 206 ncaa_pbp school character home_game numeric away_game numeric runs_scored_home numeric runs_allowed_home numeric runs_scored_away numeric runs_allowed_away numeric base_pf numeric home_game_adj numeric final_pf numeric ncaa_pbp Get Play-By-Play Data for NCAA Baseball Games Description Get Play-By-Play Data for NCAA Baseball Games Usage ncaa_pbp( game_info_url = NA_character_, game_pbp_url = NA_character_, raw_html_to_disk = FALSE, raw_html_path = "/", read_from_file = FALSE, file = NA_character_, ... ) Arguments game_info_url The url for the game’s boxscore data. This can be found using the ncaa_schedule_info function. game_pbp_url The url for the game’s play-by-play data. This can be found using the ncaa_schedule_info function. raw_html_to_disk Write raw html to disk (saves as game_pbp_id.html in raw_html_path direc- tory) raw_html_path Directory path to write raw html read_from_file Read from raw html on disk file File with full path to read raw html ... Additional arguments passed to an underlying function like httr. ncaa_roster 207 Value A data frame with play-by-play data for an individual game. col_name types game_date character location character attendance logical inning character inning_top_bot character score character batting character fielding character description character game_pbp_url character game_pbp_id integer Examples try(ncaa_pbp(game_info_url = "https://stats.ncaa.org/contests/2167178/box_score")) ncaa_roster Get NCAA Baseball Rosters Description Get NCAA Baseball Rosters Usage ncaa_roster(team_id = NULL, year, ...) Arguments team_id NCAA id for a school year The year of interest ... Additional arguments passed to an underlying function like httr. Value A data frame containing roster information, including IDs and urls for each player (if available) col_name types player_name character class character 208 ncaa_schedule_info player_id character season numeric number character position character player_url character team_name character conference character team_id numeric division numeric conference_id numeric Examples try(ncaa_roster(team_id = 104, year = 2021)) ncaa_schedule_info Get Schedule and Results for NCAA Baseball Teams Description Get Schedule and Results for NCAA Baseball Teams Usage ncaa_schedule_info(team_id = NULL, year = NULL, pbp_links = FALSE, ...) Arguments team_id The team’s unique NCAA id. year The season (i.e. use 2016 for the 2015-2016 season, etc.) pbp_links Logical parameter to run process for scraping play_by_play urls for each game ... Additional arguments passed to an underlying function like httr. Details try(ncaa_schedule_info(team_id = 736, year = 2019)) Value A data frame with the following fields: date, opponent, result, score, innings (if more than regula- tion), and the url for the game itself. col_name types year integer ncaa_school_id_lu 209 season_id integer date character home_team character home_team_id integer home_team_conference character home_team_conference_id integer home_team_slug character home_team_division integer away_team character away_team_id integer away_team_conference character away_team_conference_id integer away_team_slug character away_team_division integer neutral_site character result character score character innings character slug character game_info_url character contest_id integer ncaa_school_id_lu Lookup NCAA baseball school IDs (Division I, II, and III) Description This function allows the user to look up the team_id needed for the ncaa_team_player_stats() function. Usage ncaa_school_id_lu(team_name = NULL) Arguments team_name A string that will be searched for in the names of the teams. Value Returns a tibble with school identification data: team_id, team_name, team_url, conference, con- ference_id, division, year, and season_id col_name types team_id numeric team_name character 210 ncaa_scrape team_url character conference_id numeric conference character division numeric year numeric season_id numeric Examples try(ncaa_school_id_lu("Van")) ncaa_scrape (legacy) Scrape NCAA baseball Team Player Stats (Division I, II, and III) Description (legacy) Scrape NCAA baseball Team Player Stats (Division I, II, and III) Usage ncaa_scrape( team_id, year = most_recent_ncaa_baseball_season(), type = "batting", ... ) Arguments team_id The numerical ID that the NCAA website uses to identify a team year The season for which data should be returned, in the form of "YYYY". Years currently available: 2013-2017. type A string indicating whether to return "batting" or "pitching" statistics ... Additional arguments passed to an underlying function like httr. Value A data frame with the following variables col_name types year integer team_name character team_id numeric ncaa_teams 211 conference_id integer conference character division numeric player_id integer player_url character player_name character Yr character Pos character Jersey character GP numeric GS numeric BA numeric OBPct numeric SlgPct numeric R numeric AB numeric H numeric 2B numeric 3B numeric TB numeric HR numeric RBI numeric BB numeric HBP numeric SF numeric SH numeric K numeric DP numeric CS numeric Picked numeric SB numeric RBI2out numeric ncaa_teams Scrape NCAA baseball Teams (Division I, II, and III) Description This function allows the user to obtain NCAA teams by year and division Usage ncaa_teams(year = most_recent_ncaa_baseball_season(), division = 1, ...) 212 ncaa_team_player_stats Arguments year The season for which data should be returned, in the form of "YYYY". Years currently available: 2002 onward. division Division - 1, 2, 3 ... Additional arguments passed to an underlying function like httr. Details ncaa_teams(2023, 1) Value A data frame with the following variables col_name types team_id character team_name character team_url character conference_id character conference character division numeric year numeric season_id character ncaa_team_player_stats Scrape NCAA baseball Team Player Stats (Division I, II, and III) Description This function allows the user to obtain batting or pitching statistics for any school affiliated with the NCAA at the division I, II, or III levels. The function acquires data from the NCAA’s website (stats.ncaa.org) and returns a tibble. Usage ncaa_team_player_stats( team_id, year = most_recent_ncaa_baseball_season(), type = "batting", ... ) ncaa_team_player_stats 213 Arguments team_id The numerical ID that the NCAA website uses to identify a team year The season for which data should be returned, in the form of "YYYY". Years currently available: 2013-2017. type A string indicating whether to return "batting" or "pitching" statistics ... Additional arguments passed to an underlying function like httr. Value A data frame with the following variables col_name types year integer team_name character team_id numeric conference_id integer conference character division numeric player_id integer player_url character player_name character Yr character Pos character Jersey character GP numeric GS numeric BA numeric OBPct numeric SlgPct numeric R numeric AB numeric H numeric 2B numeric 3B numeric TB numeric HR numeric RBI numeric BB numeric HBP numeric SF numeric SH numeric K numeric DP numeric CS numeric Picked numeric SB numeric RBI2out numeric 214 playerid_lookup Examples try(ncaa_team_player_stats(team_id = 234, year = 2023, type = "batting")) pitcher_game_logs_fg (legacy) Scrape Pitcher Game Logs from FanGraphs Description (legacy) Scrape Pitcher Game Logs from FanGraphs Usage pitcher_game_logs_fg(playerid, year = 2017) Arguments playerid This is the playerid used by FanGraphs for a given player year The season for which game logs should be returned (use the YYYY format) Value A data frame of pitcher game logs. playerid_lookup Look up Baseball Player IDs by Player Name Description This function allows you to query the Chadwick Bureau’s public register of baseball players and the various IDs associated with them in different systems of record. Usage playerid_lookup(last_name = NULL, first_name = NULL) Arguments last_name A text string used to return results for players with that string in their last name. first_name A text string used to return results for players with that string in their first name. Value A data frame of baseball players and the various IDs associated with them in different systems of record. playername_lookup 215 col_name types first_name character last_name character given_name character name_suffix character nick_name character birth_year integer mlb_played_first integer mlbam_id integer retrosheet_id character bbref_id character fangraphs_id integer Examples try(playerid_lookup("Garcia", "Karim")) playername_lookup Look up Baseball Player Name by ID Description This function allows you to query the Chadwick Bureau’s public register of baseball players and the various IDs associated with them in different systems of record. Usage playername_lookup(id) Arguments id An integer or character string representing a player ID in a baseball database, cross-referenced through the Chadwick Bureau’s public register of baseball play- ers. Value A data frame of baseball players and the various IDs associated with them in different systems of record. col_name types name_first character name_last character name_given character name_suffix character 216 process_statcast_payload name_nick character birth_year integer mlb_played_first integer key_mlbam integer key_retro character key_bbref character key_fangraphs integer Examples try(playername_lookup(4885)) try(playername_lookup("kaaihki01")) process_statcast_payload Process Baseball Savant CSV payload Description This is a helper function for all statcast_search() functions. The function processes the initial csv payload acquired from Baseball Savant to ensure consistency in formatting across downloads Usage process_statcast_payload(payload) Arguments payload payload from a Baseball Savant request Value A tibble with the processed Statcast data coerced to the correct types. retrosheet_data 217 retrosheet_data Get, Parse, and Format Retrosheet Event and Roster Files Description This function requires the use of the Chadwick CLI. Follow the directions at the repository for installation of the CLI release for your platform. Specifically from the Chadwick CLI tools, this function requires the cwevent application to be available from the command line. For unix platform users: the retrosheet_data() function uses the system() interface under the hood. For Windows and other platform users: the retrosheet_data() function interacts with the cwevent application using the shell() interface under the hood. Usage retrosheet_data( path_to_directory = NULL, years_to_acquire = most_recent_mlb_season() - 1, sequence_years = FALSE ) Arguments path_to_directory (default: NULL) A file path that if set, either: 1. creates a new directory, or 2. uses the path to an existing directory years_to_acquire (format: YYYY) The seasons to collect. Single, multiple, and sequential years can be passed. If passing multiple years, enclose in a vector (i.e. c(2017,2018)). Defaults to most_recent_mlb_season(). sequence_years (logical, default: FALSE): If the seasons passed in the years_to_acquire param- eter should be sequenced so that the function returns all years including and between the vector passed, set the argument to TRUE. Defaults to FALSE. Details retrosheet_data(path_to_directory = NULL, years_to_acquire = most_recent_mlb_season()-1, sequence_years = FALSE) Value If path_to_directory is not set (default), the process will return a named list of tibbles: ’events’ and ’rosters’ for each season provided to years_to_acquire If path_to_directory is set, will also write two csv files to the unzipped directory: 1) a combined csv of the event data for a given year and 2) a combined csv of each team’s roster for each year provided to years_to_acquire 218 run_expectancy_code run_expectancy_code Generate run expectancy and related measures from Baseball Sa- vant data Description These functions allow a user to generate run expectancy and related measures and variables from Baseball Savant data. Measures and variables will be added to the data frame. Usage run_expectancy_code(df, level = "plate appearance") Arguments df A data frame generated from Baseball Savant. level Whether you want run expectancy calculated at the plate appearance or pitch level. Defaults to plate appearance. Value Returns a tibble with the following columns: col_name types pitch_type character game_date Date release_speed numeric release_pos_x numeric release_pos_z numeric player_name character batter numeric pitcher numeric events character description character spin_dir logical spin_rate_deprecated logical break_angle_deprecated logical break_length_deprecated logical zone numeric des character game_type character stand character p_throws character home_team character away_team character type character hit_location integer run_expectancy_code 219 bb_type character balls integer strikes integer game_year integer pfx_x numeric pfx_z numeric plate_x numeric plate_z numeric on_3b numeric on_2b numeric on_1b numeric outs_when_up integer inning numeric inning_topbot character hc_x numeric hc_y numeric tfs_deprecated logical tfs_zulu_deprecated logical fielder_2 numeric umpire logical sv_id character vx0 numeric vy0 numeric vz0 numeric ax numeric ay numeric az numeric sz_top numeric sz_bot numeric hit_distance_sc numeric launch_speed numeric launch_angle numeric effective_speed numeric release_spin_rate numeric release_extension numeric game_pk numeric pitcher_1 numeric fielder_2_1 numeric fielder_3 numeric fielder_4 numeric fielder_5 numeric fielder_6 numeric fielder_7 numeric fielder_8 numeric fielder_9 numeric release_pos_y numeric estimated_ba_using_speedangle numeric estimated_woba_using_speedangle numeric 220 run_expectancy_code woba_value numeric woba_denom integer babip_value integer iso_value integer launch_speed_angle integer at_bat_number numeric pitch_number numeric pitch_name character home_score numeric away_score numeric bat_score numeric fld_score numeric post_away_score numeric post_home_score numeric post_bat_score numeric post_fld_score numeric if_fielding_alignment character of_fielding_alignment character spin_axis numeric delta_home_win_exp numeric delta_run_exp numeric final_pitch_game numeric final_pitch_at_bat numeric runs_scored_on_pitch numeric bat_score_after numeric final_pitch_inning numeric bat_score_start_inning numeric bat_score_end_inning numeric cum_runs_in_inning numeric runs_to_end_inning numeric count_base_out_state character avg_re numeric next_count_base_out_state character next_avg_re numeric change_re numeric re24 numeric Examples df <- statcast_search(start_date = "2016-04-06", end_date = "2016-04-15", playerid = 621043, player_type = 'batter') try(run_expectancy_code(df, level = "plate appearances")) school_id_lu 221 school_id_lu (legacy) Lookup NCAA baseball school IDs (Division I, II, and III) Description (legacy) Lookup NCAA baseball school IDs (Division I, II, and III) Usage school_id_lu(team_name = NULL) Arguments team_name A string that will be searched for in the names of the teams. Value Returns a tibble with school identification data: team_id, team_name, team_url, conference, con- ference_id, division, year, and season_id scrape_savant_leaderboards (legacy) Query Baseball Savant Leaderboards Description (legacy) Query Baseball Savant Leaderboards Usage scrape_savant_leaderboards( leaderboard = "exit_velocity_barrels", year = 2020, abs = 50, min_pa = "q", min_pitches = 100, min_throws = 100, min_field = "q", min_run = 0, player_type = "batter", fielding_type = "player", oaa_position = "", oaa_roles = "", team = "", arsenal_type = "n_", 222 scrape_savant_leaderboards run_type = "raw", min2b = 5, min3b = 0, position = "", bats = "", hand = "" ) Arguments leaderboard The type of leaderboard to retrieve, input as a string. Current options include exit_velocity_barrels, expected_statistics, pitch_arsenal, outs_above_average, di- rectional_oaa, catch_probability, pop_time, sprint_speed, and running_splits_90_ft, arm_strength. year The season for which you want data. abs The minimum number of batted balls. Applies only to exit_velocity_barrels leaderboards. min_pa Minimum number of plate appearances. Can be a number or ’q’ for qualified batters. min_pitches Minimum number of pitches thrown. min_throws Minimum number of throwing opportunities. min_field Minimum number of fieding opportunities. min_run Minimum number of running opportunities. player_type One of either ’batter’ or pitcher. For the expected_statistics leaderboard, ’batter- team’ and ’pitcher-team’ are also available. fielding_type One of either ’player’ or ’team’. oaa_position Can be either the number position of a player or ’if’ or ’of’ for position cate- gories. oaa_roles Can be either the number position of a player or ’if’ or ’of’ for position cate- gories. team An abbreviation for a team. Can be left blank. arsenal_type One of either ’n_’, ’avg_spin’, or ’avg_speed’. run_type One of either ’percent’ or ’raw’. min2b The minimum number of throwing attempts to second base. min3b The minimum number of throwing attempts to third base. position The numeric position of the player. For DH use 10. Can be left blank. bats The handedness of the batter. One of ’R’ or ’L’. Can be left blank. hand The handedness of the pitcher. One of ’R’ or ’L’. Can be left blank. Value Returns a tibble of Statcast leaderboard data. scrape_statcast_savant 223 scrape_statcast_savant (legacy) Query Statcast by Date Range and Players Description (legacy) Query Statcast by Date Range and Players Usage scrape_statcast_savant( start_date = Sys.Date() - 1, end_date = Sys.Date(), playerid = NULL, player_type = "batter", ... ) scrape_statcast_savant.Date( start_date = Sys.Date() - 1, end_date = Sys.Date(), playerid = NULL, player_type = "batter", ... ) scrape_statcast_savant.default( start_date = Sys.Date() - 1, end_date = Sys.Date(), playerid = NULL, player_type = "batter", ... ) scrape_statcast_savant_batter(start_date, end_date, batterid = NULL, ...) scrape_statcast_savant_batter_all(start_date, end_date, batterid = NULL, ...) scrape_statcast_savant_pitcher(start_date, end_date, pitcherid = NULL, ...) scrape_statcast_savant_pitcher_all(start_date, end_date, pitcherid = NULL, ...) Arguments start_date Date of first game for which you want data. Format must be in YYYY-MM-DD format. 224 sptrc_league_payrolls end_date Date of last game for which you want data. Format must be in YYYY-MM-DD format. playerid The MLBAM ID for the player whose data you want to query. player_type The player type. Can be batter or pitcher. Default is batter ... currently ignored batterid The MLBAM ID for the batter whose data you want to query. pitcherid The MLBAM ID for the pitcher whose data you want to query. Value Returns a tibble with Statcast data. Returns a tibble with Statcast data. Returns a tibble with Statcast data. Returns a tibble with Statcast data. Returns a tibble with Statcast data. Returns a tibble with Statcast data. Returns a tibble with Statcast data. sptrc_league_payrolls Scrape League Payroll Breakdowns from Spotrac Description This function allows you to scrape each team’s payroll from Spotrac. Usage sptrc_league_payrolls(year = most_recent_mlb_season()) Arguments year Year to load Value A data frame of contract data. col_name types year character team character team_abbr character rank numeric win_percent numeric roster numeric sptrc_team_active_payroll 225 active_man_payroll numeric injured_reserve numeric retained numeric buried numeric suspended numeric yearly_total_payroll numeric Examples try(sptrc_league_payrolls(year = most_recent_mlb_season())) sptrc_team_active_payroll Scrape Team Active Payroll Breakdown from Spotrac Description This function allows you to scrape a team’s active payroll from Spotrac. Usage sptrc_team_active_payroll(team_abbr, year = most_recent_mlb_season()) Arguments team_abbr Team abbreviation year Year to load Value A data frame of contract data. col_name types year numeric team character player_name character roster_status character age numeric pos numeric status numeric waiver_options numeric base_salary numeric signing_bonus numeric payroll_salary numeric 226 standings_on_date_bref adj_salary numeric payroll_percent numeric lux_tax_salary numeric total_salary numeric Examples try(sptrc_team_active_payroll(team_abbr = "BAL", year = most_recent_mlb_season())) standings_on_date_bref (legacy) Scrape MLB Standings on a Given Date Description (legacy) Scrape MLB Standings on a Given Date Usage standings_on_date_bref(date, division, from = FALSE) Arguments date a date object division One or more of AL East, AL Central, AL West, AL Overall, NL East, NL Cen- tral, NL West, and NL Overall from a logical indicating whether you want standings up to and including the date (FALSE, default) or rather standings for games played after the date Value Returns a tibble of MLB standings statcast 227 statcast Statcast Functions Overview Description statcast_search(): Query Statcast by Date Range and Players. statcast_search_batters(): Query Statcast Batters by Date Range and Player. statcast_search_pitchers(): Query Statcast Pitchers by Date Range and Player. statcast_leaderboards(): Query Baseball Savant Leaderboards. Details Query Statcast Batters by Date Range: statcast_search(start_date = "2016-04-06", end_date = "2016-04-15", player_type = 'batter') ## The above is equivalent to: statcast_search_batters(start_date = "2016-04-06", end_date = "2016-04-15", batterid = NULL) Query Statcast Pitchers by Date Range: statcast_search(start_date = "2016-04-06", end_date = "2016-04-15", player_type = 'pitcher') ## The above is equivalent to: statcast_search_pitchers(start_date = "2016-04-06", end_date = "2016-04-15", pitcherid = NULL) Query Statcast Batters by Date Range and Player ID: correa <- statcast_search(start_date = "2016-04-06", end_date = "2016-04-15", playerid = 621043, player_type = 'batter') ## The above is equivalent to: correa <- statcast_search_batters(start_date = "2016-04-06", end_date = "2016-04-15", batterid = 621043) Query Statcast Pitchers by Date Range and Player ID: 228 statcast_leaderboards noah <- statcast_search(start_date = "2016-04-06", end_date = "2016-04-15", playerid = 592789, player_type = 'pitcher') ## The above is equivalent to: noah <- statcast_search_pitchers(start_date = "2016-04-06", end_date = "2016-04-15", pitcherid = 592789) Query Baseball Savant Leaderboards: statcast_leaderboards(leaderboard = "exit_velocity_barrels", year = 2021) statcast_impute Statcast Label Imputation Description Statcast Label Imputation Usage statcast_impute Format An object of class data.frame with 44 rows and 4 columns. statcast_leaderboards Query Baseball Savant Leaderboards Description This function allows you to read leaderboard data from BaseballSavant directly into R as data frame. Usage statcast_leaderboards( leaderboard = "exit_velocity_barrels", year = 2020, abs = 50, min_pa = "q", min_pitches = 100, min_throws = 100, min_field = "q", statcast_leaderboards 229 min_run = 0, player_type = "batter", fielding_type = "player", oaa_position = "", oaa_roles = "", team = "", arsenal_type = "n_", run_type = "raw", min2b = 5, min3b = 0, position = "", bats = "", hand = "" ) Arguments leaderboard The type of leaderboard to retrieve, input as a string. Current options include exit_velocity_barrels, expected_statistics, pitch_arsenal, outs_above_average, di- rectional_oaa, catch_probability, pop_time, sprint_speed, and running_splits_90_ft, arm_strength. year The season for which you want data. abs The minimum number of batted balls. Applies only to exit_velocity_barrels leaderboards. min_pa Minimum number of plate appearances. Can be a number or ’q’ for qualified batters. min_pitches Minimum number of pitches thrown. min_throws Minimum number of throwing opportunities. min_field Minimum number of fieding opportunities. min_run Minimum number of running opportunities. player_type One of either ’batter’ or pitcher. For the expected_statistics leaderboard, ’batter- team’ and ’pitcher-team’ are also available. fielding_type One of either ’player’ or ’team’. oaa_position Can be either the number position of a player or ’if’ or ’of’ for position cate- gories. oaa_roles Can be either the number position of a player or ’if’ or ’of’ for position cate- gories. team An abbreviation for a team. Can be left blank. arsenal_type One of either ’n_’, ’avg_spin’, or ’avg_speed’. run_type One of either ’percent’ or ’raw’. min2b The minimum number of throwing attempts to second base. min3b The minimum number of throwing attempts to third base. position The numeric position of the player. For DH use 10. Can be left blank. bats The handedness of the batter. One of ’R’ or ’L’. Can be left blank. hand The handedness of the pitcher. One of ’R’ or ’L’. Can be left blank. 230 statcast_search Details oaa_roles argument: 30 = 1B - Straight Up 31 = 1B - Towards 1B/2B Hole 32 = 1B - Close to Line 40 = 2B - Straight Up 41 = 2B - Shaded Towards 2B Bag 42 = 2B - Towards 1B/2B Hole 43 = 2B - Behind First Basemen 46 = 2B - Up the Middle 60 = SS - Straight Up 61 = SS - Towards 3B/SS Hole 62 = SS - Shaded Towards 2B Bag 64 = SS - Up the Middle 50 = 3B - Straight Up 51 = 3B - Close to Line 52 = 3B - Towards 3B/SS Hole 77 = LF - Close to Line 71 = LF - Leaning Left 70 = LF - Straight Up 72 = LF - Leaning Right 78 = LF - LF Gap 87 = CF - LF Gap 81 = CF - Leaning Left 82 = CF - Leaning Right 89 = CF - RF Gap 98 = RF - RF Gap 91 = RF - Leaning Left 90 = RF - Straight Up 92 = RF - Leaning Right 99 = RF - Close to Line Value Returns a tibble of Statcast leaderboard data with the following columns (for leaderboard: ’exit_velocity_barrels’): col_name types year numeric last_name character first_name character player_id integer attempts integer avg_hit_angle numeric anglesweetspotpercent numeric max_hit_speed numeric avg_hit_speed numeric fbld numeric gb numeric max_distance integer avg_distance integer avg_hr_distance integer ev95plus integer ev95per-swing numeric ev95percent numeric barrels integer brl_percent numeric brl_pa numeric Examples try(statcast_leaderboards(leaderboard = "expected_statistics", year = 2018)) try(statcast_leaderboards(leaderboard = "arm_strength", year = 2020)) statcast_search Query Statcast by Date Range and Players statcast_search 231 Description This function allows you to query Statcast data as provided on https://baseballsavant.mlb.com Usage statcast_search( start_date = Sys.Date() - 1, end_date = Sys.Date(), playerid = NULL, player_type = "batter", ... ) statcast_search.default( start_date = Sys.Date() - 1, end_date = Sys.Date(), playerid = NULL, player_type = "batter", ... ) statcast_search_batters(start_date, end_date, batterid = NULL, ...) statcast_search_pitchers(start_date, end_date, pitcherid = NULL, ...) Arguments start_date Date of first game for which you want data. Format must be in YYYY-MM-DD format. end_date Date of last game for which you want data. Format must be in YYYY-MM-DD format. playerid The MLBAM ID for the player whose data you want to query. player_type The player type. Can be batter or pitcher. Default is batter ... currently ignored batterid The MLBAM ID for the batter whose data you want to query. pitcherid The MLBAM ID for the pitcher whose data you want to query. Value Returns a tibble with Statcast data with the following columns: col_name types pitch_type character game_date Date release_speed numeric release_pos_x numeric release_pos_z numeric 232 statcast_search player_name character batter numeric pitcher numeric events character description character spin_dir logical spin_rate_deprecated logical break_angle_deprecated logical break_length_deprecated logical zone numeric des character game_type character stand character p_throws character home_team character away_team character type character hit_location integer bb_type character balls integer strikes integer game_year integer pfx_x numeric pfx_z numeric plate_x numeric plate_z numeric on_3b numeric on_2b numeric on_1b numeric outs_when_up integer inning numeric inning_topbot character hc_x numeric hc_y numeric tfs_deprecated logical tfs_zulu_deprecated logical fielder_2 numeric umpire logical sv_id character vx0 numeric vy0 numeric vz0 numeric ax numeric ay numeric az numeric sz_top numeric sz_bot numeric hit_distance_sc numeric statcast_search 233 launch_speed numeric launch_angle numeric effective_speed numeric release_spin_rate numeric release_extension numeric game_pk numeric pitcher_1 numeric fielder_2_1 numeric fielder_3 numeric fielder_4 numeric fielder_5 numeric fielder_6 numeric fielder_7 numeric fielder_8 numeric fielder_9 numeric release_pos_y numeric estimated_ba_using_speedangle numeric estimated_woba_using_speedangle numeric woba_value numeric woba_denom integer babip_value integer iso_value integer launch_speed_angle integer at_bat_number numeric pitch_number numeric pitch_name character home_score numeric away_score numeric bat_score numeric fld_score numeric post_away_score numeric post_home_score numeric post_bat_score numeric post_fld_score numeric if_fielding_alignment character of_fielding_alignment character spin_axis numeric delta_home_win_exp numeric delta_run_exp numeric Returns a tibble with Statcast data. Returns a tibble with Statcast data with the following columns: col_name types pitch_type character game_date Date release_speed numeric 234 statcast_search release_pos_x numeric release_pos_z numeric player_name character batter numeric pitcher numeric events character description character spin_dir logical spin_rate_deprecated logical break_angle_deprecated logical break_length_deprecated logical zone numeric des character game_type character stand character p_throws character home_team character away_team character type character hit_location integer bb_type character balls integer strikes integer game_year integer pfx_x numeric pfx_z numeric plate_x numeric plate_z numeric on_3b numeric on_2b numeric on_1b numeric outs_when_up integer inning numeric inning_topbot character hc_x numeric hc_y numeric tfs_deprecated logical tfs_zulu_deprecated logical fielder_2 numeric umpire logical sv_id character vx0 numeric vy0 numeric vz0 numeric ax numeric ay numeric az numeric sz_top numeric statcast_search 235 sz_bot numeric hit_distance_sc numeric launch_speed numeric launch_angle numeric effective_speed numeric release_spin_rate numeric release_extension numeric game_pk numeric pitcher_1 numeric fielder_2_1 numeric fielder_3 numeric fielder_4 numeric fielder_5 numeric fielder_6 numeric fielder_7 numeric fielder_8 numeric fielder_9 numeric release_pos_y numeric estimated_ba_using_speedangle numeric estimated_woba_using_speedangle numeric woba_value numeric woba_denom integer babip_value integer iso_value integer launch_speed_angle integer at_bat_number numeric pitch_number numeric pitch_name character home_score numeric away_score numeric bat_score numeric fld_score numeric post_away_score numeric post_home_score numeric post_bat_score numeric post_fld_score numeric if_fielding_alignment character of_fielding_alignment character spin_axis numeric delta_home_win_exp numeric delta_run_exp numeric Returns a tibble with Statcast data with the following columns: col_name types pitch_type character game_date Date 236 statcast_search release_speed numeric release_pos_x numeric release_pos_z numeric player_name character batter numeric pitcher numeric events character description character spin_dir logical spin_rate_deprecated logical break_angle_deprecated logical break_length_deprecated logical zone numeric des character game_type character stand character p_throws character home_team character away_team character type character hit_location integer bb_type character balls integer strikes integer game_year integer pfx_x numeric pfx_z numeric plate_x numeric plate_z numeric on_3b numeric on_2b numeric on_1b numeric outs_when_up integer inning numeric inning_topbot character hc_x numeric hc_y numeric tfs_deprecated logical tfs_zulu_deprecated logical fielder_2 numeric umpire logical sv_id character vx0 numeric vy0 numeric vz0 numeric ax numeric ay numeric az numeric statcast_search 237 sz_top numeric sz_bot numeric hit_distance_sc numeric launch_speed numeric launch_angle numeric effective_speed numeric release_spin_rate numeric release_extension numeric game_pk numeric pitcher_1 numeric fielder_2_1 numeric fielder_3 numeric fielder_4 numeric fielder_5 numeric fielder_6 numeric fielder_7 numeric fielder_8 numeric fielder_9 numeric release_pos_y numeric estimated_ba_using_speedangle numeric estimated_woba_using_speedangle numeric woba_value numeric woba_denom integer babip_value integer iso_value integer launch_speed_angle integer at_bat_number numeric pitch_number numeric pitch_name character home_score numeric away_score numeric bat_score numeric fld_score numeric post_away_score numeric post_home_score numeric post_bat_score numeric post_fld_score numeric if_fielding_alignment character of_fielding_alignment character spin_axis numeric delta_home_win_exp numeric delta_run_exp numeric Examples ### Harper try(statcast_search(start_date = "2022-10-06", 238 statline_from_statcast end_date = "2022-10-16", playerid = 547180, player_type = 'batter')) ### Framber try(statcast_search(start_date = "2022-10-06", end_date = "2022-10-16", playerid = 664285, player_type = 'pitcher')) ### Daily try(statcast_search(start_date = "2022-11-04", end_date = "2022-11-06")) correa <- statcast_search_batters(start_date = "2016-04-06", end_date = "2016-04-15", batterid = 621043) daily <- statcast_search_batters(start_date = "2016-04-06", end_date = "2016-04-06", batterid = NULL) x <- statcast_search_pitchers(start_date = "2016-04-06", end_date = "2016-04-15", pitcherid = 592789) daily <- statcast_search_pitchers(start_date = "2016-04-06", end_date = "2016-04-06", pitcherid = NULL) statline_from_statcast Create stat lines from Statcast data Description This function allows you to create stat lines of statistics for players or groups of players from raw Statcast. When calculating wOBA, the most recent year in the data frame is used for weighting. Usage statline_from_statcast(df, base = "pa") Arguments df A data frame of statistics that includes, at a minimum, the following columns: events, description, game_date, and type. base Tells the function what to use as the population of pitches to use for the stat line. Options include "swings", "contact", or "pa". Defaults to "pa". Details statline_from_statcast(df, base = "contact") stats_api_live_empty_df 239 Value A tibble with the additional columns calculated using the Statcast data. stats_api_live_empty_df Column structure of MLB Stats Live Game API data frame Description An empty tibble Usage stats_api_live_empty_df Format An object of class tbl_df (inherits from tbl, data.frame) with 0 rows and 131 columns. teams_lu_table A Team Lookup Table Description A Team Lookup Table Usage teams_lu_table Format An object of class data.frame with 797 rows and 31 columns. 240 team_results_bref team_consistency Calculate Team-level Consistency Description This function allows you to calculate team-level consistency in run scoring and run prevention over the course of an entire season. Usage team_consistency(year) Arguments year Season consistency should be run for. Details try(team_consistency(year=2021)) Value Returns a tibble with the following columns col_name types Team character Con_R numeric Con_RA numeric Con_R_Ptile numeric Con_RA_Ptile numeric team_results_bref (legacy) Scrape Team Results Description (legacy) Scrape Team Results Usage team_results_bref(Tm, year) woba_plus 241 Arguments Tm The abbreviation used by Baseball-Reference.com for the team whose results you want to scrape. year Season for which you want to scrape the park factors. Value Returns a tibble of MLB team results woba_plus Calculate wOBA and related metrics for any set of data Description This function allows you to calculate wOBA for any given set of data, provided the right variables are in the data set. The function currently returns both wOBA per plate appearance on wOBA per instance of fair contact. Usage woba_plus(df) Arguments df A data frame of statistics that includes, at a minimum, the following columns: uBB (unintentional walks), HBP (Hit By Pitch), X1B (singles), X2B (doubles), X3B (triples), HR (home runs), AB (at-bats), SH (sacrifice hits), SO (strike outs), and season. Value Returns a tibble with the wOBA factors calculated and the following columns: col_name types bbref_id character season integer Name character Age numeric Level character Team character G numeric PA numeric AB numeric R numeric H numeric X1B numeric X2B numeric 242 woba_plus X3B numeric HR numeric RBI numeric BB numeric IBB numeric uBB numeric SO numeric HBP numeric SH numeric SF numeric GDP numeric SB numeric CS numeric BA numeric OBP numeric SLG numeric OPS numeric wOBA numeric wOBA_CON numeric Examples df <- bref_daily_batter("2015-08-01", "2015-10-03") try(woba_plus(df)) Index ∗ datasets bref_standings_on_date, 10 column_structure_draft_mlb, 14 bref_team_results, 11 statcast_impute, 228 stats_api_live_empty_df, 239 chadwick, 12 teams_lu_table, 239 chadwick_player_lu, 12 ∗ legacy code_barrel, 14 batter_game_logs_fg, 6 column_structure_draft_mlb, 14 daily_batter_bref, 15 daily_batter_bref, 15 daily_pitcher_bref, 15 daily_pitcher_bref, 15 fg_bat_leaders, 27 fg_pitch_leaders, 45 edge_code, 16 get_batting_orders, 62 edge_frequency, 17 get_draft_mlb, 62 get_game_info_mlb, 63 fangraphs, 17 get_game_info_sup_petti, 63 fg_bat_leaders, 27 get_game_pks_mlb, 64 fg_batter_game_logs, 18 get_ncaa_baseball_pbp, 64 fg_batter_leaders, 20 get_ncaa_game_logs, 65 fg_guts, 28 get_ncaa_lineups, 66 fg_milb_batter_game_logs, 28 get_ncaa_park_factor, 66 fg_milb_pitcher_game_logs, 30 get_ncaa_schedule_info, 67 fg_park, 32 get_pbp_mlb, 67 fg_park_hand (fg_park), 32 get_probables_mlb, 68 fg_pitch_leaders, 45 get_retrosheet_data, 68 fg_pitcher_game_logs, 33 get_umpire_ids_petti, 69 fg_pitcher_leaders, 37 milb_batter_game_logs_fg, 80 fg_team_batter, 46 milb_pitcher_game_logs_fg, 80 fg_team_pitcher, 53 ncaa_baseball_roster, 202 fip_plus, 60 ncaa_scrape, 210 pitcher_game_logs_fg, 214 get_batting_orders, 62 school_id_lu, 221 get_chadwick_lu (chadwick_player_lu), 12 scrape_savant_leaderboards, 221 get_draft_mlb, 62 scrape_statcast_savant, 223 get_game_info_mlb, 63 standings_on_date_bref, 226 get_game_info_sup_petti, 63 team_results_bref, 240 get_game_pks_mlb, 64 get_ncaa_baseball_pbp, 64 batter_game_logs_fg, 6 get_ncaa_baseball_roster bref, 6 (ncaa_baseball_roster), 202 bref_daily_batter, 7 get_ncaa_game_logs, 65 bref_daily_pitcher, 8 get_ncaa_lineups, 66 243 244 INDEX get_ncaa_park_factor, 66 mlb_high_low_types, 121 get_ncaa_schedule_info, 67 mlb_hit_trajectories, 121 get_pbp_mlb, 67 mlb_homerun_derby, 122 get_probables_mlb, 68 mlb_homerun_derby_bracket, 124 get_retrosheet_data, 68 mlb_homerun_derby_players, 125 get_umpire_ids_petti, 69 mlb_job_types, 131 ggspraychart, 70 mlb_jobs, 128 mlb_jobs_datacasters, 128 label_statcast_imputed_data, 71 mlb_jobs_official_scorers, 129 linear_weights_savant, 74 mlb_jobs_umpires, 130 load_game_info_sup, 74 mlb_languages, 131 load_ncaa_baseball_pbp, 75 mlb_league, 132 load_ncaa_baseball_schedule, 76 mlb_league_leader_types, 132 load_ncaa_baseball_season_ids, 77 mlb_logical_events, 133 load_ncaa_baseball_teams, 78 mlb_metrics, 134 load_umpire_ids, 78 mlb_pbp, 134 mlb_pbp_diff, 138 metrics, 79 mlb_people, 141 milb_batter_game_logs_fg, 80 mlb_people_free_agents, 143 milb_pitcher_game_logs_fg, 80 mlb_pitch_codes, 144 mlb, 81 mlb_pitch_types, 144 mlb_all_star_ballots, 82 mlb_all_star_final_vote, 83 mlb_player_game_stats, 145 mlb_all_star_write_ins, 85 mlb_player_game_stats_current, 148 mlb_attendance, 86 mlb_player_status_codes, 150 mlb_award, 88 mlb_positions, 151 mlb_awards, 89 mlb_probables, 152 mlb_awards_recipient, 90 mlb_review_reasons, 152 mlb_baseball_stats, 91 mlb_roster_types, 154 mlb_batting_orders, 92 mlb_rosters, 153 mlb_conferences, 93 mlb_runner_detail_types, 155 mlb_divisions, 93 mlb_schedule, 155 mlb_draft, 94 mlb_schedule_event_types, 158 mlb_draft_latest, 96 mlb_schedule_games_tied, 159 mlb_draft_prospects, 99 mlb_schedule_postseason, 161 mlb_event_types, 102 mlb_schedule_postseason_series, 164 mlb_fielder_detail_types, 102 mlb_seasons, 166 mlb_game_changes, 103 mlb_seasons_all, 167 mlb_game_content, 105 mlb_situation_codes, 169 mlb_game_context_metrics, 106 mlb_sky, 169 mlb_game_info, 108 mlb_sports, 170 mlb_game_linescore, 109 mlb_sports_info, 171 mlb_game_pace, 111 mlb_sports_players, 172 mlb_game_pks, 113 mlb_standings, 173 mlb_game_status_codes, 115 mlb_standings_types, 176 mlb_game_timecodes, 116 mlb_stat_groups, 182 mlb_game_types, 116 mlb_stat_types, 183 mlb_game_wp, 117 mlb_stats, 176 mlb_high_low_stats, 118 mlb_stats_leaders, 179 INDEX 245 mlb_team_affiliates, 188 sptrc_league_payrolls, 224 mlb_team_alumni, 190 sptrc_team_active_payroll, 225 mlb_team_coaches, 191 standings_on_date_bref, 226 mlb_team_history, 192 statcast, 227 mlb_team_info, 193 statcast_impute, 228 mlb_team_leaders, 194 statcast_leaderboards, 228 mlb_team_personnel, 196 statcast_search, 230 mlb_team_stats, 197 statcast_search_batters mlb_teams, 183 (statcast_search), 230 mlb_teams_stats, 185 statcast_search_pitchers mlb_teams_stats_leaders, 187 (statcast_search), 230 mlb_venues, 198 statline_from_statcast, 238 mlb_wind_direction_codes, 199 stats_api_live_empty_df, 239 most_recent_mlb_season, 200 most_recent_ncaa_baseball_season, 200 team_consistency, 240 team_results_bref, 240 ncaa, 201 teams_lu_table, 239 ncaa_baseball_pbp (get_ncaa_baseball_pbp), 64 woba_plus, 241 ncaa_baseball_roster, 202 ncaa_game_logs, 202 ncaa_lineups, 204 ncaa_park_factor, 205 ncaa_pbp, 206 ncaa_roster, 207 ncaa_schedule_info, 208 ncaa_school_id_lu, 209 ncaa_scrape, 210 ncaa_team_player_stats, 212 ncaa_teams, 211 pitcher_game_logs_fg, 214 playerid_lookup, 214 playername_lookup, 215 process_statcast_payload, 216 retrosheet_data, 217 run_expectancy_code, 218 school_id_lu, 221 scrape_savant_leaderboards, 221 scrape_statcast_savant, 223 scrape_statcast_savant_batter (scrape_statcast_savant), 223 scrape_statcast_savant_batter_all (scrape_statcast_savant), 223 scrape_statcast_savant_pitcher (scrape_statcast_savant), 223 scrape_statcast_savant_pitcher_all (scrape_statcast_savant), 223

sensobol

cran

Package ‘sensobol’ April 6, 2023 Title Computation of Variance-Based Sensitivity Indices Version 1.1.4 Maintainer Arnald Puy <arnald.puy@pm.me> Description It allows to rapidly compute, bootstrap and plot up to fourth-order Sobol'-based sensitivity in- dices using several state-of-the-art first and total-order estimators. Sobol' indices can be com- puted either for models that yield a scalar as a model output or for systems of differential equa- tions. The package also provides a suit of benchmark tests functions and several options to ob- tain publication-ready figures of the model output uncertainty and sensitivity-related analy- sis. An overview of the package can be found in Puy et al. (2022) <doi:10.18637/jss.v102.i05>. License GPL-3 Encoding UTF-8 Imports boot (>= 1.3.20), data.table (>= 1.12.0), ggplot2 (>= 3.1.0), lhs (>= 1.0.2), magrittr (>= 1.5), matrixStats (>= 0.54.0), randtoolbox (>= 1.17.1), deSolve (>= 1.27.1), Rdpack (>= 2.1.2), Rfast (>= 2.0.1), rlang (>= 0.3.1), scales (>= 1.0.0), stats, stringr (>= 1.4.0), utils, Rcpp RdMacros Rdpack Depends R (>= 3.5.0) RoxygenNote 7.2.2 Suggests knitr, rmarkdown, testthat (>= 2.1.0), covr VignetteBuilder knitr URL https://github.com/arnaldpuy/sensobol BugReports https://github.com/arnaldpuy/sensobol/issues LinkingTo Rfast, Rcpp, RcppArmadillo, NeedsCompilation yes Author Arnald Puy [aut, cre] (<https://orcid.org/0000-0001-9469-2156>), Bertrand Ioos [ctb] (Author of included 'sensitivity' fragments), Gilles Pujol [ctb] (Author of included 'sensitivity' fragments), RStudio [cph] (Copyright holder of included 'sensitivity' fragments) Repository CRAN Date/Publication 2023-04-06 11:40:07 UTC 1 2 sensobol-package R topics documented: sensobol-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 bratley1988_Fun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 bratley1992_Fun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 discrepancy_ersatz . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 ishigami_Fun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 load_packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 metafunction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 oakley_Fun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 plot.sensobol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 plot_multiscatter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 plot_scatter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 plot_uncertainty . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 print.sensobol . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 print.vars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 sobol_convergence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 sobol_dummy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 sobol_Fun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 sobol_indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 sobol_matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 sobol_ode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 vars_matrices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 vars_to . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Index 28 sensobol-package sensobol: Computation of Variance-Based Sensitivity Indices Description It allows to rapidly compute, bootstrap and plot up to third-order Sobol’-based sensitivity indices using several state-of-the-art first and total-order estimators. Sobol’ indices can be computed either for models that yield a scalar as a model output or for systems of differential equations. The package also provides a suit of benchmark tests functions and several options to obtain publication-ready figures of the model output uncertainty and sensitivity-related analysis. Details A comprehensive empirical study of several total-order estimators included in sensobol can be found in Puy et al. (2021). Author(s) Arnald Puy (<arnald.puy@pm.me>) Maintainer: Arnald Puy (<arnald.puy@pm.me>) bratley1988_Fun 3 References Puy A, Becker W, Lo Piano S, Saltelli A (2021). “A Comprehensive Comparison of Total-Order Estimators for Global Sensitivity Analysis.” International Journal for Uncertainty Quantification. doi:10.1615/Int.J.UncertaintyQuantification.2021038133. bratley1988_Fun Bratley and Fox (1988) function Description It implements the Bratley and Fox (1988) function. Usage bratley1988_Fun(X) Arguments X A data frame or numeric matrix where each column is a model input and each row a sample point. Details The function requires k model inputs and reads as follows: Yk y= |4xi − 2| , i=1 where xi ∼ U(0, 1). Value A numeric vector with the model output. Examples # Define settings (test with k = 10) N <- 100; params <- paste("X", 1:10, sep = "") # Create sample matrix mat <- sobol_matrices(N = N, params = params) # Compute Bratley and Fox (1988) function Y <- bratley1988_Fun(mat) 4 bratley1992_Fun bratley1992_Fun Bratley, Fox and Niederreiter (1992) function. Description It implements the Bratley et al. (1992) function. Usage bratley1992_Fun(X) Arguments X A data frame or numeric matrix where each column is a model input and each row a sample point. Details The function requires k model inputs and reads as: Xk Yi y= (−1)i xj , i=1 j=1 where xi ∼ U(0, 1). Value A numeric vector with the model output. References Bratley P, Fox BL, Niederreiter H (1992). “Implementation and tests of low-discrepancy sequences.” ACM Transactions on Modeling and Computer Simulation (TOMACS), 2(3), 195–213. Examples # Define settings (test with k = 10) N <- 100; params <- paste("X", 1:10, sep = "") # Create sample matrix mat <- sobol_matrices(N = N, params = params) # Compute Bratley et al. (1992) function Y <- bratley1992_Fun(mat) discrepancy_ersatz 5 discrepancy_ersatz Computation of the S-ersatz discrepancy. Description It allows to use the S-ersatz discrepancy measure by Puy et al. (2023) as a sensitivity measure. Usage discrepancy_ersatz(mat, Y, params) Arguments mat A numeric matrix created with sobol_matrices and matrices = "A", where each column represents an uncertain model input and each row a model simula- tion. Y A numeric vector with the model output obtained from the matrix created with sobol_matrices. The numeric vector should not contain any NA or NaN val- ues. params A character vector with the name of the model inputs. Details It is recommended to define mat using a power of 2 as a sample size. Value A data.table object. References Puy A, Roy PT, Saltelli A (2023). “Discrepancy measures for sensitivity analysis.” arXiv. 2206.13470, https://arxiv.org/abs/2206.13470. Examples # Define settings N <- 2^9; params <- paste("X", 1:8, sep = "") # Create sample matrix mat <- sobol_matrices(N = N, params = params, matrices = "A") # Compute the Sobol' G function Y <- sobol_Fun(mat) # Compute the S-ersatz discrepancy values ind <- discrepancy_ersatz(mat = mat, Y = Y, params = params) 6 ishigami_Fun ishigami_Fun Ishigami function Description It implements the Ishigami and Homma (1990) function. Usage ishigami_Fun(X) Arguments X A data frame or numeric matrix where each column is a model input and each row a sample point. Details The function requires 3 model inputs and reads as y = sin(x1 ) + a sin(x2 )2 + bx43 sin(x1 ) , where a = 2, b = 1 and (x1 , x2 , x3 ) ∼ U(−π, +π). The transformation of the distribution of the model inputs from U (0, 1) to U (−π, +π)) is conducted internally. Value A numeric vector with the model output. References Ishigami T, Homma T (1990). “An importance quantification technique in uncertainty analysis for computer models.” Proceedings. First International Symposium on Uncertainty Modeling and Analysis, 12, 398–403. Examples # Define settings N <- 100; params <- paste("X", 1:3, sep = "") # Create sample matrix mat <- sobol_matrices(N = N, params = params) # Compute Ishigami function Y <- ishigami_Fun(mat) load_packages 7 load_packages Load (and install) R packages. Description The function loads R packages. If the packages are not already in the local system, the function also downloads, installs and loads them. Usage load_packages(x) Arguments x A character vector with the name of the packages to load. Examples # Load packages: ## Not run: load_packages(c("tidyverse", "data.table")) metafunction Random metafunction based on Becker (2020)’s metafunction. Description Random metafunction based on Becker (2020)’s metafunction. Usage metafunction(data, k_2 = 0.5, k_3 = 0.2, epsilon = NULL) Arguments data A numeric matrix where each column is a model input and each row a sampling point. k_2 Numeric value indicating the fraction of active pairwise interactions (between 0 and 1). Default is k_2 = 0.5. k_3 Numeric value indicating the fraction of active three-wise interactions (between 0 and 1). Default is k_2 = 0.2. epsilon Integer value. It fixes the seed for the random number generator. The default is epsilon = NULL. 8 metafunction Details The metafunction randomly combines the following functions in a metafunction of dimension k: • f (x) = x3 (cubic). • f (x) = 1 if(x > 0.5), 0 otherwise (discontinuous). ex • f (x) = e−1 (exponential). 10−1 −1 • f (x) = 1.1 (x + 0.1)−1 (inverse). • f (x) = x (linear) • f (x) = 0 (no effect). • f (x) = 4(x − 0.5)2 (non-monotonic). sin(2πx) • f (x) = 2 (periodic). 2 • f (x) = x (quadratic). • f (x) = cos(x) (trigonometric). It is constructed as follows: Xk X k2 X k3 y= αi f ui (xi )+ βi f uVi,1 (xVi,1 )f uVi,2 (xVi,2 )+ γi f uWi,1 (xWi,1 )f uWi,2 (xWi,2 )f uWi,3 (xWi,3 ) i=1 i=1 i=1 where k is the model dimensionality, u is a k-length vector formed by randomly sampling with replacement the ten functions mentioned above, V and W are two matrices specifying the number of pairwise and three-wise interactions given the model dimensionality, and α, β, γ are three vectors of length k generated by sampling from a mixture of two normal distributions Ψ = 0.3N (0, 5) + 0.7N (0, 0.5). See Puy et al. (2020) and Becker (2020) for a full mathematical description of the metafunction approach. Value A numeric vector with the function output. References Becker W (2020). “Metafunctions for benchmarking in sensitivity analysis.” Reliability Engineer- ing and System Safety, 204, 107189. doi:10.1016/j.ress.2020.107189. Puy A, Becker W, Piano SL, Saltelli A (2020). “The battle of total-order sensitivity estimators.” arXiv. 2009.01147, https://arxiv.org/abs/2009.01147. Examples # Define settings (number of model inputs = 86) N <- 100; params <- paste("X", 1:86, sep = "") # Create sample matrix mat <- sobol_matrices(N = N, params = params) oakley_Fun 9 # Compute metafunction Y <- metafunction(mat) oakley_Fun Oakley & O’Hagan (2004) function Description It implements the Oakley and O’Hagan (2004) function. Usage oakley_Fun(X) Arguments X A data frame or numeric matrix where each column is a model input and each row a sample point. Details The function requires 15 model inputs and reads as y = aT1 x + aT2 sin(x) + aT3 cos(x) + xT Mx , where x = x1 , x2 , ..., xk , k = 15, and values for aTi , i = 1, 2, 3 and M are defined by Oakley and O’Hagan (2004). The transformation of the distribution of the model inputs from U (0, 1) to N (0, 1)) is conducted internally. Value A numeric vector with the model output. References Oakley JE, O’Hagan A (2004). “Probabilistic sensitivity analysis of complex models: a Bayesian approach.” Journal of the Royal Statistical Society B, 66(3), 751–769. doi:10.1111/j.14679868.2004.05304.x. Examples # Define settings N <- 100; params <- paste("X", 1:15, sep = "") # Create sample matrix mat <- sobol_matrices(N = N, params = params) # Compute Oakley and O'Hagan (2004) function Y <- oakley_Fun(mat) 10 plot.sensobol plot.sensobol Visualization of first, total, second, third and fourth-order Sobol’ in- dices. Description It plots first, total, second, third and fourth-order Sobol’ indices. Usage ## S3 method for class 'sensobol' plot(x, order = "first", dummy = NULL, ...) Arguments x The output of sobol_indices. order If order = "first", it plots first and total-order effects. If order = "second", it plots second-order effects. If order = "third", it plots third-order effects. If order = "fourth", it plots third-order effects. Default is order = "first". dummy The output of sobol_dummy. Default is NULL. ... Other graphical parameters to plot. Value A ggplot object. Examples # Define settings N <- 1000; params <- paste("X", 1:3, sep = ""); R <- 10 # Create sample matrix mat <- sobol_matrices(N = N, params = params) # Compute Ishigami function Y <- ishigami_Fun(mat) # Compute and bootstrap Sobol' indices ind <- sobol_indices(Y = Y, N = N, params = params, boot = TRUE, R = R) # Plot Sobol' indices plot(ind) plot_multiscatter 11 plot_multiscatter Pairwise combinations of model inputs with the colour proportional the model output value. Description It plots all pairwise combinations of model inputs with the colour proportional the model output value. Usage plot_multiscatter(data, N, Y, params, smpl = NULL) Arguments data The matrix created with sobol_matrices. N Positive integer, the initial sample size of the base sample matrix created with sobol_matrices. Y A numeric vector with the model output obtained from the matrix created with sobol_matrices. params Character vector with the name of the model inputs. smpl The number of simulations to plot. The default is NULL. Value A ggplot2 object. Examples # Define settings N <- 1000; params <- paste("X", 1:3, sep = ""); R <- 10 # Create sample matrix mat <- sobol_matrices(N = N, params = params) # Compute Ishigami function Y <- ishigami_Fun(mat) # Plot scatterplot matrix plot_multiscatter(data = mat, N = N, Y = Y, params = params) 12 plot_scatter plot_scatter Scatter plots of the model output against the model inputs. Description It creates scatter plots of the model output against the model inputs. Usage plot_scatter(data, N, Y, params, method = "point", size = 0.7, alpha = 0.2) Arguments data The matrix created with sobol_matrices. N Positive integer, the initial sample size of the base sample matrix created with sobol_matrices. Y A numeric vector with the model output obtained from the matrix created with sobol_matrices. params Character vector with the name of the model inputs. method The type of plot. If method = "point" (the default), each simulation is a point. If method = "bin", bins are used to aggregate simulations. size Number between 0 and 1, argument of geom_point(). Default is 0.7. alpha Number between 0 and 1, transparency scale of geom_point(). Default is 0.2. Value A ggplot2 object. Examples # Define settings N <- 1000; params <- paste("X", 1:3, sep = ""); R <- 10 # Create sample matrix mat <- sobol_matrices(N = N, params = params) # Compute Ishigami function Y <- ishigami_Fun(mat) # Plot scatter plot_scatter(data = mat, Y = Y, N = N, params = params) plot_uncertainty 13 plot_uncertainty Visualization of the model output uncertainty Description It creates an histogram with the model output distribution. Usage plot_uncertainty(Y, N = NULL) Arguments Y A numeric vector with the model output obtained from the matrix created with sobol_matrices. N Positive integer, the initial sample size of the base sample matrix created with sobol_matrices. Value A ggplot2 object. Examples # Define settings N <- 1000; params <- paste("X", 1:3, sep = ""); R <- 10 # Create sample matrix mat <- sobol_matrices(N = N, params = params) # Compute Ishigami function Y <- ishigami_Fun(mat) # Plot uncertainty plot_uncertainty(Y = Y, N = N) print.sensobol Display the results obtained with the sobol_indices function. Description Display the results obtained with the sobol_indices function. Usage ## S3 method for class 'sensobol' print(x, ...) 14 sobol_convergence Arguments x A sensobol object produced by sobol_indices. ... Further arguments passed to or from other methods. Value The function print.sensobol informs on the first and total-order estimators used in the computa- tions, the total number of model runs and the sum of first-order index. It also plots the estimated results. print.vars Display the results obtained with the vars_to function. Description Display the results obtained with the vars_to function. Usage ## S3 method for class 'vars' print(x, ...) Arguments x A vars object produced by vars_to. ... Further arguments passed to or from other methods. Value The function print.vars informs on the number of star centers, the value of h used and the total number of model runs.. It also plots the VARS-TO indices. sobol_convergence Check convergence of Sobol’ indices. Description It checks the convergence of Sobol’ indices on different sub-samples of the model output-. sobol_convergence 15 Usage sobol_convergence( matrices, Y, N, sub.sample, params, first, total, order = order, seed = 666, plot.order, ... ) Arguments matrices Character vector with the required matrices. The default is matrices = c("A", "B", "AB"). See sobol_matrices. Y Numeric vector with the model output obtained from the matrix created with sobol_matrices. N Positive integer, the initial sample size of the base sample matrix created with sobol_matrices. sub.sample Numeric vector with the sub-samples of the model output at which to check convergence. params Character vector with the name of the model inputs. first Estimator to compute first-order indices. Check options in sobol_indices. total Estimator to compute total-order indices. Check options in sobol_indices. order Whether to plot convergence for "second" or "third" order indices. seed Whether to compute "first", "second", or "third" -order Sobol’ indices. Default is order = "first". plot.order Whether to plot convergence for "second" or "third"-order indices. ... Further arguments in sobol_indices. Value A list with the results and the plots Examples # Define settings matrices <- c("A", "B", "AB") params <- paste("X", 1:3, sep = "") N <- 2^10 first <- "saltelli" total <- "jansen" 16 sobol_dummy order <- "second" # Create sample matrix mat <- sobol_matrices(N = N, params = params, order = order) # Compute Ishigami function Y <- ishigami_Fun(mat) # Check convergence at specific sample sizes sub.sample <- seq(100, N, 500) # Define sub-samples sobol_convergence(matrices = matrices, Y = Y, N = N, sub.sample = sub.sample, params = params, first = first, total = total, order = order, plot.order = order) sobol_dummy Computation of Sobol’ indices for a dummy parameter Description This function computes first and total-order Sobol’ indices for a dummy parameter following the formulae shown in Khorashadi Zadeh et al. (2017). Usage sobol_dummy( Y, N, params, boot = FALSE, R = NULL, parallel = "no", ncpus = 1, conf = 0.95, type = "norm" ) Arguments Y A numeric vector with the model output obtained from the matrix created with sobol_matrices. The numeric vector should not contain any NA or NaN val- ues. N Positive integer, the initial sample size of the base sample matrix created with sobol_matrices. params A character vector with the name of the model inputs. boot Logical. If TRUE, the function bootstraps the Sobol’ indices. If FALSE, it provides point estimates. Default is boot = FALSE. R Positive integer, number of bootstrap replicas. sobol_Fun 17 parallel The type of parallel operation to be used (if any). If missing, the default is taken from the option "boot.parallel" (and if that is not set, "no"). For more informa- tion, check the parallel option in the boot function of the boot package. ncpus Positive integer: number of processes to be used in parallel operation: typically one would chose this to the number of available CPUs. Check the ncpus option in the boot function of the boot package. conf Confidence intervals, number between 0 and 1. Default is conf = 0.95. type Method to compute the confidence intervals. Default is type = "norm". Check the type option in the boot function of the boot package. Value A data.table object. References Khorashadi Zadeh F, Nossent J, Sarrazin F, Pianosi F, van Griensven A, Wagener T, Bauwens W (2017). “Comparison of variance-based and moment-independent global sensitivity analysis ap- proaches by application to the SWAT model.” Environmental Modelling and Software, 91, 210–222. doi:10.1016/j.envsoft.2017.02.001. Examples # Define settings N <- 100; params <- paste("X", 1:3, sep = ""); R <- 10 # Create sample matrix mat <- sobol_matrices(N = N, params = params) # Compute Ishigami function Y <- ishigami_Fun(mat) # Compute and bootstrap Sobol' indices for dummy parameter ind.dummy <- sobol_dummy(Y = Y, N = N, params = params, boot = TRUE, R = R) sobol_Fun Sobol’ G function Description It implements the Sobol’ (1998) G function. Usage sobol_Fun(X) Arguments X A data frame or numeric matrix. 18 sobol_indices Details The function requires eight model inputs and reads as k Y |4xi − 2| + ai y= , i=1 1 + ai where k = 8, xi ∼ U(0, 1) and a = (0, 1, 4.5, 9, 99, 99, 99, 99). Value A numeric vector with the model output. References Sobol’ IM (1998). “On quasi-Monte Carlo integrations.” Mathematics and Computers in Simula- tion, 47(2-5), 103–112. doi:10.1016/S03784754(98)000962. Examples # Define settings N <- 100; params <- paste("X", 1:8, sep = "") # Create sample matrix mat <- sobol_matrices(N = N, params = params) # Compute Sobol' G Y <- sobol_Fun(mat) sobol_indices Computation of Sobol’ indices Description It allows to compute Sobol’ indices up to the fourth-order using state-of-the-art estimators. Usage sobol_indices( matrices = c("A", "B", "AB"), Y, N, params, first = "saltelli", total = "jansen", order = "first", boot = FALSE, R = NULL, parallel = "no", sobol_indices 19 ncpus = 1, conf = 0.95, type = "norm" ) Arguments matrices Character vector with the required matrices. The default is matrices = c("A", "B", "AB"). See sobol_matrices. Y Numeric vector with the model output obtained from the matrix created with sobol_matrices. The numeric vector should not contain any NA or NaN val- ues. N Positive integer, the initial sample size of the base sample matrix created with sobol_matrices. params Character vector with the name of the model inputs. first Estimator to compute first-order indices. Options are: • first = "saltelli" (Saltelli et al. 2010). • first = "jansen" (Jansen 1999). • first = "sobol" (Sobol’ 1993). • first = "azzini" (Azzini et al. 2020). total Estimator to compute total-order indices. Options are: • total = "jansen" (Jansen 1999). • total = "sobol" (Sobol’ 2001). • total = "homma" (Homma and Saltelli 1996). • total = "janon" (Janon et al. 2014). • total = "glen" (Glen and Isaacs 2012). • total = "azzini" (Azzini et al. 2020). • total = "saltelli" (Saltelli et al. 2008). order Whether to compute "first", "second", "third" or fourth-order Sobol’ indices. Default is order = "first". boot Logical. If TRUE, the function bootstraps the Sobol’ indices. If FALSE, it provides point estimates. Default is boot = FALSE. R Positive integer, number of bootstrap replicas. Default is NULL. parallel The type of parallel operation to be used (if any). If missing, the default is taken from the option "boot.parallel" (and if that is not set, "no"). For more informa- tion, check the parallel option in the boot function of the boot package. ncpus Positive integer: number of processes to be used in parallel operation: typically one would chose this to the number of available CPUs. Check the ncpus option in the boot function of the boot package. conf Confidence interval if boot = TRUE. Number between 0 and 1. Default is conf = 0.95. type Method to compute the confidence interval if boot = TRUE. Default is "norm". Check the type option in the boot function of the boot package. 20 sobol_indices Details Any first and total-order estimator can be combined with the appropriate sampling design. Check Table 3 of the vignette for a summary of all possible combinations, and Tables 1 and 2 for a math- ematical description of the estimators. If the analyst mismatches estimators and sampling designs, the function will generate an error and urge to redefine the sample matrices or the estimators. For all estimators except Azzini et al. (2020)’s and Janon et al. (2014)’s, sobol_indices() calcu- lates the sample mean as N 1 X fˆ0 = (f (A)v + f (B)v ) , 2N v=1 where N is the row dimension of the base sample matrix, and the unconditional sample variance as 1 X V̂ (y) = v = 1N ((f (A)v − fˆ)2 + (f (B)v − fˆ)2 ) , 2N − 1 where f (A)v (f (B)v ) indicates the model output y obtained after running the model f in the v-th row of the A (B) matrix. For the Azzini estimator, N (i) (i) X V̂ (y) = (f (A)v − f (B)v )2 + (f (BA )v − f (AB )v )2 v=1 and for the Janon estimator, N (i) 1 X f (A)2v + f (AB )2v V̂ (y) = − f02 N v=1 2 (i) (i) where f (AB )v (f (BA )v ) is the model output obtained after running the model f in the v-th row (i) (i) of an AB )v (BA )v ) matrix, where all columns come from A (B) except the i-th, which comes from B (A). Value A sensobol object. References Azzini I, Mara T, Rosati R (2020). “Monte Carlo estimators of first-and total-orders Sobol’ indices.” arXiv. 2006.08232, https://arxiv.org/abs/2006.08232. Glen G, Isaacs K (2012). “Estimating Sobol sensitivity indices using correlations.” Environmental Modelling and Software, 37, 157–166. doi:10.1016/j.envsoft.2012.03.014. Homma T, Saltelli A (1996). “Importance measures in global sensitivity analysis of nonlinear models.” Reliability Engineering and System Safety, 52, 1–17. doi:10.1016/09518320(96)000026. Janon A, Klein T, Lagnoux A, Nodet M, Prieur C (2014). “Asymptotic normality and efficiency sobol_matrices 21 of two Sobol index estimators.” ESAIM: Probability and Statistics, 18(3), 342–364. doi:10.1051/ ps/2013040. Jansen M (1999). “Analysis of variance designs for model output.” Computer Physics Commu- nications, 117(1), 35–43. doi:10.1016/S00104655(98)001544. Saltelli A, Annoni P, Azzini I, Campolongo F, Ratto M, Tarantola S (2010). “Variance based sen- sitivity analysis of model output. Design and estimator for the total sensitivity index.” Computer Physics Communications, 181(2), 259–270. doi:10.1016/j.cpc.2009.09.018. Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008). Global Sensitivity Analysis. The Primer. John Wiley and Sons, Ltd, Chichester, UK. doi:10.1002/ 9780470725184. Sobol’ IM (1993). “Sensitivity analysis for nonlinear mathematical models.” Mathematical Model- ing and Computational Experiment, 1(4), 407–414. Sobol’ IM (2001). “Global sensitivity indices for nonlinear mathematical models and their Monte Carlo estimates.” Mathematics and Computers in Simulation, 55(1-3), 271–280. doi:10.1016/ S03784754(00)002706. See Also Check the function boot for further details on the bootstrapping with regards to the methods avail- able for the computation of confidence intervals in the type argument. Examples # Define settings N <- 1000; params <- paste("X", 1:3, sep = ""); R <- 10 # Create sample matrix mat <- sobol_matrices(N = N, params = params) # Compute Ishigami function Y <- ishigami_Fun(mat) # Compute and bootstrap Sobol' indices ind <- sobol_indices(Y = Y, N = N, params = params, boot = TRUE, R = R) sobol_matrices Creation of the sample matrices Description It creates the sample matrices to compute Sobol’ first and total-order indices. If needed, it also creates the sample matrices required to compute second, third and fourth-order indices. 22 sobol_matrices Usage sobol_matrices( matrices = c("A", "B", "AB"), N, params, order = "first", type = "QRN", ... ) Arguments matrices Character vector with the required matrices. The default is matrices = c("A", "B", "AB"). N Positive integer, initial sample size of the base sample matrix. params Character vector with the name of the model inputs. order One of "first", "second", "third" or "fourth" to create a matrix to compute first, second, third or up to fourth-order Sobol’ indices. The default is order = "first". type Approach to construct the sample matrix. Options are: • type = "QRN" (default): It uses Sobol’ (1967) Quasi-Random Numbers. through a call to the function sobol of the randtoolbox package. • type = "LHS": It uses a Latin Hypercube Sampling Design (McKay et al. 1979) through a call to the function randomLHS of the lhs package. • type = "R": It uses random numbers. ... Further arguments in sobol. Details Before calling sobol_matrices, the user must decide which estimators will be used to compute first and total-order indices, for this option conditions the design of the sample matrix and therefore the argument matrices. See Table 3 in the vignette for further details on the specific sampling designs required by the estimators. The user can select one of the following sampling designs: (i) • A, B, AB . (i) • A, B, BA . (i) (i) • A, B, AB , BA . If order = "first", the function creates an (N, 2k) matrix according to the approach defined by type, where the leftmost and the rightmost k columns are respectively allocated to the A and the B (i) (i) matrix. Depending on the sampling design, it also creates k AB (BA ) matrices, where all columns come from A (B) except the i-th, which comes from B (A). All matrices are returned row-binded. (ij) (ij) If order = "second", 2!(k−2)! k! extra (N, k) AB (BA ) matrices are created, where all columns come from A (B) except the i-th and j-th, which come from B (A). These matrices allow the sobol_ode 23 computation of second-order effects, and are row-bound to those created for first and total-order indices. (ijl) (ijl) If order = "third", 3!(k−3)! k! extra (N, k) AB (BA ) matrices are bound below those created for the computation of second-order effects. In these matrices, all columns come from A (B) except the i-th, the j-th and the l-th, which come from B (A). These matrices are needed to compute third-order effects, and are row-bound below those created for second-order effects. The same process applies to create the matrices to compute fourth-order effects. All columns are distributed in (0,1). If the uncertainty in some parameter(s) is better described with another distribution, the user should apply the required quantile inverse transformation to the column of interest once the sample matrix is produced. Value A numeric matrix where each column is a model input distributed in (0,1) and each row a sampling point. References McKay MD, Beckman RJ, Conover WJ (1979). “Comparison of three methods for selecting values of input variables in the analysis of output from a computer code.” Technometrics, 21(2), 239–245. doi:10.1080/00401706.1979.10489755. Sobol’ IM (1967). “On the distribution of points in a cube and the approximate evaluation of inte- grals.” USSR Computational Mathematics and Mathematical Physics, 7(4), 86–112. doi:10.1016/ 00415553(67)901449. Examples # Define settings N <- 100; params <- paste("X", 1:10, sep = ""); order <- "third" # Create sample matrix using Sobol' Quasi Random Numbers. mat <- sobol_matrices(N = N, params = params, order = order) # Let's assume that the uncertainty in X3 is better described # with a normal distribution with mean 0 and standard deviation 1: mat[, 3] <- qnorm(mat[, 3], 0, 1) sobol_ode Wrapper around deSolve ode. Description It solves a system of ordinary differential equations and extracts the model output at the selected times. 24 sobol_ode Usage sobol_ode(d, times, timeOutput, state, func, ...) Arguments d Character vector with the name of the model inputs. times Time sequence as defined by ode. timeOutput Numeric vector determining the time steps at which the output is wanted. state Initial values of the state variables. func An R function as defined by ode. ... Additional arguments passed to ode. Value A matrix with the output values. Examples # Define the model: the Lotka-Volterra system of equations lotka_volterra_fun <- function(t, state, parameters) { with(as.list(c(state, parameters)), { dX <- r * X * (1 - X / K) - alpha * X * Y dY <- -m * Y + theta * X * Y list(c(dX, dY)) }) } # Define the settings of the sensitivity analysis N <- 2 ^ 5 # Sample size of sample matrix params <- c("r", "alpha", "m", "theta", "K", "X", "Y") # Parameters # Define the times times <- seq(5, 20, 1) # Define the times at which the output is wanted timeOutput <- c(10, 15) # Construct the sample matrix mat <- sobol_matrices(N = N, params = params) # Transform to appropriate distributions mat[, "r"] <- qunif(mat[, "r"], 0.8, 1.8) mat[, "alpha"] <- qunif(mat[, "alpha"], 0.2, 1) mat[, "m"] <- qunif(mat[, "m"], 0.6, 1) mat[, "theta"] <- qunif(mat[, "theta"], 0.05, 0.15) mat[, "K"] <- qunif(mat[, "K"], 47, 53) mat[, "X"] <- floor(mat[, "X"] * (15 - 8 + 1) + 8) mat[, "Y"] <- floor(mat[, "Y"] * (2 - 6 + 1) + 6) # Run the model vars_matrices 25 y <- list() for (i in 1:nrow(mat)) { y[[i]] <- sobol_ode(d = mat[i, ], times = times, timeOutput = timeOutput, state = c(X = mat[[i, "X"]], Y = mat[[i, "Y"]]), func = lotka_volterra_fun) } vars_matrices STAR-VARS sampling strategy Description It creates the STAR-VARS matrix needed to compute VARS-TO following Razavi and Gupta (2016). Usage vars_matrices(star.centers, params, h = 0.1, type = "QRN", ...) Arguments star.centers Positive integer, number of star centers. params Character vector with the name of the model inputs. h Distance between pairs. The user should select between 0.001, 0.002, 0.005, 0.01, 0.02, 0.05, 0.1, 0.2. Default is h = 0.1. type Approach to construct the STAR-VARS. Options are: • type = "QRN": It uses Sobol’ (1967) Quasi-Random Numbers through a call to the function sobol of the randtoolbox package. • type = "R": It uses random numbers. ... Further arguments in sobol. Details The user randomly selects Nstar points across the factor space using either Sobol’ Quasi Random Numbers (type = "QRN") or random numbers (type = "R"). These are the star centres and their location can be denoted as sv = sv1 , ..., svi , ..., svk , where v = 1, 2, ..., Nstar . Then, for each star centre, the function generates a cross section of equally spaced points ∆h apart for each of the k model inputs, including and passing through the star centre. The cross section is produced by fixing sv∼i and varying si . Finally, for each factor all pairs of points with h values of ∆h, 2∆h, 3∆h and 1 so on are extracted. The total computational cost of this design is Nt = Nstar (k( ∆h − 1) + 1). Value A matrix where each column is a model input and each row a sampling point. 26 vars_to References Razavi S, Gupta HV (2016). “A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application.” Water Resources Research, 52(1), 440–455. doi:10.1002/ 2015WR017558, 2014WR016527. Sobol’ IM (1967). “On the distribution of points in a cube and the approximate evaluation of inte- grals.” USSR Computational Mathematics and Mathematical Physics, 7(4), 86–112. doi:10.1016/ 00415553(67)901449. Examples # Define settings star.centers <- 10; params <- paste("X", 1:5, sep = ""); h <- 0.1 # Create STAR-VARS mat <- vars_matrices(star.centers = star.centers, params = params, h = h) vars_to Computation of VARS Total order index (VARS-TO) Description It computes VARS-TO following Razavi and Gupta (2016). Usage vars_to(Y, star.centers, params, h, method = "all.step") Arguments Y A numeric vector with the model output obtained from the matrix created with vars_matrices. star.centers Positive integer, number of star centers. params Character vector with the name of the model inputs. h Distance between pairs. method Type of computation. If method = "all.step", all pairs of points with values ∆h, 2∆h, 3∆h, ... are used in each dimension. If method = "one.step", only the pairs ∆h away are used. The default is method = "all.step". Details VARS is based on variogram analysis to characterize the spatial structure and variability of a given model output across the input space (Razavi and Gupta 2016). Variance- based total-order effects can be computed as by-products of the VARS framework. The total-order index is related to the variogram γ(.) and co-variogram C(.) functions by the following equation: vars_to 27 γ(hi ) + E [Cx∼i (hi )] Ti = V̂ (y) where x∗∼i is a vector of all k factors except xi . Value A data.table with the VARS-TO indices of each parameter. References Razavi S, Gupta HV (2016). “A new framework for comprehensive, robust, and efficient global sensitivity analysis: 2. Application.” Water Resources Research, 52(1), 440–455. doi:10.1002/ 2015WR017558, 2014WR016527. Examples # Define settings star.centers <- 10; params <- paste("X", 1:3, sep = ""); h <- 0.1 # Create STAR-VARS mat <- vars_matrices(star.centers = star.centers, params = params, h = h) # Run model y <- sensobol::ishigami_Fun(mat) # Compute VARS-TO ind <- vars_to(Y = y, star.centers = star.centers, params = params, h = h) ind Index ∗ modeling sensobol-package, 2 ∗ sensitivity sensobol-package, 2 ∗ uncertainty sensobol-package, 2 boot, 17, 19, 21 bratley1988_Fun, 3 bratley1992_Fun, 4 discrepancy_ersatz, 5 ishigami_Fun, 6 load_packages, 7 metafunction, 7 oakley_Fun, 9 ode, 23, 24 plot.sensobol, 10 plot_multiscatter, 11 plot_scatter, 12 plot_uncertainty, 13 print.sensobol, 13 print.vars, 14 randomLHS, 22 sensobol (sensobol-package), 2 sensobol-package, 2 sobol, 22, 25 sobol_convergence, 14 sobol_dummy, 10, 16 sobol_Fun, 17 sobol_indices, 10, 15, 18 sobol_matrices, 5, 11–13, 15, 16, 19, 21 sobol_ode, 23 vars_matrices, 25, 26 vars_to, 26 28

LIM

cran

Package ‘LIM’ October 12, 2022 Version 1.4.7 Title Linear Inverse Model Examples and Solution Methods Author Karline Soetaert <karline.soetaert@nioz.nl>, Dick van Oevelen<dick.vanoevelen@nioz.nl> Maintainer Karline Soetaert <karline.soetaert@nioz.nl> Depends R (>= 2.01), limSolve, diagram Imports graphics Description Functions that read and solve linear inverse problems (food web problems, linear pro- gramming problems). These problems find solutions to linear or quadratic functions: min or max (f(x)), where f(x) = ||Ax-b||^2 or f(x) = sum(ai*xi) subject to equality constraints Ex=f and inequality constraints Gx>=h. License GPL (>= 2) LazyData yes NeedsCompilation no Repository CRAN Date/Publication 2022-05-11 10:10:02 UTC R topics documented: LIM-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 FILERigaAutumn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Flowmatrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Ldei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 LIMBlending . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 LIMBrouageMudflat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 LIMCaliforniaSediment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 LIMCoralRockall . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 LIMEcoli . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 LIMEverglades . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 LIMRigaAutumn . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 LIMRigaSpring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1 2 LIM-package LIMRigaSummer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 LIMScheldtIntertidal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 LIMTakapoto . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Linp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Lsei . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 Plotranges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 PrintMat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 Read . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 Varranges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 Xranges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Xsample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 Index 39 LIM-package Linear Inverse Model examples and solution methods Description functions that read and solve linear inverse problems (food web problems, linear programming problems, flux balance analysis). These problems find solutions to linear or quadratic functions: min or max (f(x)), where f (x) = ||Ax − b||2 or f (x) = sum(ai ∗ xi) subject to equality constraints Ex = f and inequality constraints Gx >= h. Uses package limSolve. Details Package: LIM Type: Package Version: 1.4.3 Date: 2011-09-05 License: GNU Public License 2 or above The model problem is formulated in text files in a way that is natural and comprehensible. Functions in LIM then converts this input into the required linear equality and inequality conditions, which can be solved either by least squares or by linear programming techniques. By letting an algorithm formulate the mathematics, it is simple to reformulate the model in case a parameter value changes, or a component is added or removed. Three different types of problems are supported: flow networks, reaction networks (e.g. flux balance analysis). LIM-package 3 and other (operations research) problems. The first two cases are based on mass balances of the components. The package includes many examples Author(s) Karline Soetaert (Maintainer), Dick van Oevelen References Description of the software: van Oevelen D, Van den Meersche K, Meysman FJR Soetaert K, Middelburg JJ, Vezina AF., 2009. Quantifying Food Web Flows Using Linear Inverse Models. Ecosystems 13: 32-45 DOI: 10.1007/s10021-009-9297-6. http://www.springerlink.com/content/4q6h4011511731m5/fulltext.pdf (please use the above citation when using the software) About food web modelling: Soetaert, K., van Oevelen, D., 2009. Modeling food web interactions in benthic deep-sea ecosys- tems: a practical guide. Oceanography (22) 1: 130-145. Application of deep-water food web: van Oevelen, Dick, Gerard Duineveld, Marc Lavaleye, Furu Mienis, Karline Soetaert, and Carlo H. R. Heip, 2009. The cold-water coral community as hotspot of carbon cycling on continental mar- gins: A food web analysis from Rockall Bank (northeast Atlantic). Limnology and Oceangraphy 54:1829-1844. http://www.aslo.org/lo/toc/vol_54/issue_6/1829.pdf A flux balance analysis application: Karline Soetaert. Escherichia coli Core Metabolism Model in LIM. LIM package vignette (see also below). See Also Read, Setup for reading files and creating the model Ldei, Lsei,Linp, Flowmatrix, Plotranges, Variables, Varranges, Xranges, Xsample. Examples ## Not run: ## show examples (see respective help pages for details) example(Lsei) example(LIMRigaSpring) example(Ldei) example(Xsample) 4 FILERigaAutumn example(Varranges) ## run demos demo("LIMexamples") ## open the directory with R sourcecode examples browseURL(paste(system.file(package="LIM"), "/doc/examples/Foodweb", sep="")) browseURL(paste(system.file(package="LIM"), "/doc/examples/LinearProg", sep="")) browseURL(paste(system.file(package="LIM"), "/doc/examples/Reactions", sep="")) ## the deep-water coral food -web browseURL(paste(system.file(package="LIM"), "/doc/examples/Foodweb/coral", sep="")) ## show package vignette with tutorial about how to create input files vignette("LIM") ## E.coli example vignette - flux balance analysis vignette("LIMecoli") browseURL(paste(system.file(package="LIM"), "/doc", sep="")) ## End(Not run) FILERigaAutumn Input text "file" for gulf of Riga autumn planktonic food web Description Input text "file" for the Carbon flux Gulf of Riga planktonic food web in autumn as described in Donali et al. (1999). The Gulf of Riga is a highly eutrophic system in the Baltic Sea The foodweb comprises 7 functional compartments and two external compartments, connected with 26 flows. Units of the flows are mg C/m3/day The "dataset" RigaAutumnFile is included to demonstrate the use of a text input file for food web models. The original file, RigaAutumn.input can be found in subdirectory ‘web’ of the packages directory In this subdirectory you will find many foodweb example input files • They can be read using Read(file) • Or they can be directly solved using Setup(file) Usage data(FILERigaAutumn) Flowmatrix 5 Format vector of character strings as present in the original file Author(s) Karline Soetaert <karline.soetaert@nioz.nl> References Donali, E., Olli, K., Heiskanen, A.S., Andersen, T., 1999. Carbon flow patterns in the planktonic food web of the Gulf of Riga, the Baltic Sea: a reconstruction by the inverse method. Journal of Marine Systems 23, 251..268. See Also LIMRigaAutumn a list containing the linear inverse model specification, generated from file ‘RigaAutumn.input’ Examples print(FILERigaAutumn) # RigaAutumnInput is a vector of text strings - # here it is first converted to a "File" # When using the example files in the LIM directory, # this first statement is not necessary ## Not run: File <- textConnection(FILERigaAutumn) RigaAutumn.input <- Read(File) ## End(Not run) Flowmatrix Generates a flow matrix for an inverse (foodweb) problem Description Given a linear inverse model food web input list, generates a flow matrix, that contains the values of flows Usage Flowmatrix(lim, web = NULL) Arguments lim a list that contains the linear inverse model specification, as generated by func- tion setup.limfile. web the solved (food) web problem, i.e. the values of the unknowns; if not specified, the model is solved first, using Lsei 6 Ldei Value the flow matrix, containing the magnitude of the flows. The value on row i and column j is the flow *from* i and *to* j Author(s) Karline Soetaert <karline.soetaert@nioz.nl> See Also plotweb from package diagram, which takes as input the flow matrix and plots the food web Examples Flowmatrix(LIMRigaAutumn) Ldei Solves a linear inverse model using least distance programming Description Solves a linear inverse model using least distance programming, i.e. minimizes the sum of squared unknowns. Input presented either: • as matrices E, F, A, B, G, H (Ldei.double) • as a list (Ldei.lim) or • as a lim input file (Ldei.limfile) Usage Ldei(...) ## S3 method for class 'lim' Ldei(lim, ...) ## S3 method for class 'limfile' Ldei(file, verbose = TRUE, ...) ## S3 method for class 'character' Ldei(...) ## S3 method for class 'double' Ldei(...) Arguments lim a list that contains the linear inverse model specification, as generated by func- tion setup.limfile. file name of the inverse input file. verbose if TRUE: prints warnings and messages to the screen. ... other arguments passed to function ldei from packagelimSolve. Ldei 7 Details Solves the following inverse problem: X min( Costi ∗ xi 2 ) subject to Ax = B Gx >= H Value a list containing: X vector containing the solution of the least distance problem. unconstrained.Solution vector containing the unconstrained solution of the least distance problem. residualNorm scalar, the sum of residuals of equalities and violated inequalities. solutionNorm scalar, the value of the quadratic function at the solution. IsError logical, TRUE, if an error occurred. Error ldei error text. type ldei. Author(s) Karline Soetaert <karline.soetaert@nioz.nl> References Lawson C.L.and Hanson R.J. 1974. SOLVING LEAST SQUARES PROBLEMS, Prentice-Hall Lawson C.L.and Hanson R.J. 1995. Solving Least Squares Problems. SIAM classics in applied mathematics, Philadelphia. (reprint of book) See Also ldei, the more general function from package limSolve. Linp, to solve the linear inverse problem by linear programming. Lsei, to solve the linear inverse problem by lsei (least squares with equality and inequality con- straints). function ldei from packagelimSolve. Examples Ldei(LIMRigaAutumn) 8 LIMBlending LIMBlending A blending problem specification Description A manufacturer produces a feeding mix for pet animals. The feed mix contains two nutritive ingredients and one ingredient (filler) to provide bulk. One kg of feed mix must contain a minimum quantity of each of four nutrients as below: Nutrient A B C D gram 80 50 25 5 The ingredients have the following nutrient values and cost (gram/kg) A B C D Cost/kg Ingredient 1 100 50 40 10 40 Ingredient 2 200 150 10 - 60 Filler - - - - 0 The linear inverse models LIMBlending and LIMinputBlending are generated from the file Blend- ing.input which can be found in subdirectory /examples/LinearProg of the package directory LIMBlending is generated by function Setup LIMinputBlending is generated by function Read The problem is to find the composition of the feeding mix that minimises the production costs subject to the constraints above. Stated otherwise: what is the optimal amount of ingredients in one kg of feeding mix? Mathematically this can be estimated by solving a linear programming problem: X min( Costi ∗ xi ) subject to xi >= 0 Ex = f Gx >= h Where the Cost (to be minimised) is given by: x1 ∗ 40 + x2 ∗ 60 The equality ensures that the sum of the three fractions equals 1: 1 = x1 + x2 + x3 LIMBlending 9 And the inequalities enforce the nutritional constraints: 100 ∗ x1 + 200 ∗ x2 > 80 50 ∗ x1 + 150 ∗ x2 > 50 and so on The solution is Ingredient1 (x1) = 0.5909, Ingredient2 (x2)=0.1364 and Filler (x3)=0.2727. Usage LIMBlending LIMinputBlending Format LIMBlending is of type lim, which is a list of matrices, vectors, names and values that specify the linear inverse model problem. see the return value of Setup for more information about this list LIMinputBlending is of type liminput, see the return value of Read for more information. A more complete description of these structures is in vignette("LIM") Author(s) Karline Soetaert <karline.soetaert@nioz.nl> See Also browseURL(paste(system.file(package="LIM"), "/doc/examples/LinearProg/", sep="")) contains "blending.input", the input file; read this with Setup LIMTakapoto, LIMEcoli and many others Examples # 1. Solve the model with linear programming res <- Linp(LIMBlending, ispos = TRUE) # show results print(c(res$X, Cost = res$solutionNorm)) # 2. Possible ranges of the three ingredients (xr <- Xranges(LIMBlending, ispos = TRUE)) Nx <- LIMBlending$NUnknowns # plot dotchart(x = as.vector(res$X), xlim = range(xr), labels = LIMBlending$Unknowns, main = "Optimal blending with ranges", 10 LIMBrouageMudflat sub = "using linp and xranges", pch = 16) segments(xr[ ,1], 1:Nx, xr[ ,2], 1:Nx) legend ("topright", pch = c(16, NA), lty = c(NA, 1), legend = c("Minimal cost", "range")) # 3. Random sample of the three ingredients # The inequality that all x > 0 has to be added! blend <- LIMBlending blend$G <- rbind(blend$G, diag(3)) blend$H <- c(blend$H, rep(0, 3)) xs <- Xsample(blend) pairs(xs, main = "Blending, 3000 solutions with xsample") LIMBrouageMudflat Linear inverse model specification for the Intertidal mudflat food web on the Atlantic coast of France Description Linear inverse model specification for the Intertidal mudflat food web on the Atlantic coast of France as in Leguerrier et al., 2003. The foodweb comprises 16 functional compartments and 3 external compartments, connected with 95 flows. Units of the flows are g C/m2/year The linear inverse model LIMBrouageMudflat is generated from the file BrouageMudflat.input which can be found in subdirectory /examples/FoodWeb of the package directory In this subdirectory you will find many foodweb example input files These files can be read using Read and their output processed by Setup which will produce a linear inverse problem specification similar to LIMBrouageMudflat Usage data(LIMBrouageMudflat) Format a list of matrices, vectors, names and values that specify the linear inverse model problem. see the return value of Setup for more information about this list A more complete description of this structures is in vignette("LIM") Author(s) Karline Soetaert <karline.soetaert@nioz.nl> Dick van Oevelen <dick.vanoevelen@nioz.nl> LIMCaliforniaSediment 11 References Leguerrier, D., Niquil, N., Boileau, N., Rzeznik, J., Sauriau, P.G., Le Moine, O., Bacher, C., 2003. Numerical analysis of the food web of an intertidal mudflat ecosystem on the Atlantic coast of France. Marine Ecology Progress Series 246, 17-37. See Also browseURL(paste(system.file(package="LIM"), "/doc/examples/Foodweb/", sep="")) contains "BrouageMudflat.input", the input file; read this with Setup LIMTakapoto, LIMRigaSummer and many others Examples Brouage <- Flowmatrix(LIMBrouageMudflat) plotweb(Brouage, main = "Brouage mudflat food web", sub = "gC/m2/yr") # Some ranges are infinite ->marked with "* Plotranges(LIMBrouageMudflat, lab.cex = 0.7, sub = "*=unbounded", xlab = "gC/m2/year", main = "Brouage mudflat, Flowranges") Plotranges(LIMBrouageMudflat, type = "V", lab.cex = 0.7, sub = "*=unbounded", xlab = "gC/m2/year",main="Brouage mudflat, Variable ranges") LIMCaliforniaSediment Linear inverse model specification for the Santa Monica Basin sedi- ment food web Description Linear inverse model specification for the Santa Monica Basin (California) sediment food web as in Eldridge and Jackson (1993). The Santa Monica Basin is a hypoxic-anoxic basin located near California. The model contains both chemical and biological species. The foodweb comprises 7 functional compartments and five external compartments, connected with 32 flows. Units of the flows are mg /m2/day The linear inverse model LIMCaliforniaSediment is generated from the file ‘CaliforniaSediment.input’ which can be found in subdirectory /examples/FoodWeb of the package directory In this subdirectory you will find many foodweb example input files These files can be read using Read and their output processed by Setup which will produce a linear inverse problem specification similar to LIMCaliforniaSediment Usage data(LIMCaliforniaSediment) 12 LIMCoralRockall Format a list of matrices, vectors, names and values that specify the linear inverse model problem. see the return value of Setup for more information about this list A more complete description of this structures is in vignette("LIM") Author(s) Karline Soetaert <karline.soetaert@nioz.nl> Dick van Oevelen <dick.vanoevelen@nioz.nl> References Eldridge, P.M., Jackson, G.A., 1993. Benthic trophic dynamics in California coastal basin and continental slope communities inferred using inverse analysis. Marine Ecology Progress Series 99, 115-135. See Also browseURL(paste(system.file(package="LIM"), "/doc/examples/Foodweb/", sep="")) contains "CaliforniaSediment.input", the input file; read this with Setup LIMTakapoto, LIMRigaSummer and many others Examples CaliforniaSediment <- Flowmatrix(LIMCaliforniaSediment) plotweb(CaliforniaSediment, main = "Santa Monica Basin Benthic web", sub = "mgN/m2/day", lab.size = 0.8) ## Not run: xr <- LIMCaliforniaSediment$NUnknowns i1 <- 1:(xr/2) i2 <- (xr/2+1):xr Plotranges(LIMCaliforniaSediment, index = i1, lab.cex = 0.7, sub = "*=unbounded", main = "Santa Monica Basin Benthic web, Flowranges - part1") Plotranges(LIMCaliforniaSediment, index = i2, lab.cex = 0.7, sub = "*=unbounded", main = "Santa Monica Basin Benthic web, Flowranges - part2") ## End(Not run) LIMCoralRockall Linear inverse model specification for the Deep-water Coral food web at Rockall Bank LIMCoralRockall 13 Description Linear inverse model specification for the deep-water coral ecosystem at Rockall Bank, North-East Atlantic. See van Oevelen et al. (2009) Units of the flows are mmol C/m2/day The linear inverse model LIMCoralRockall is generated from the file ‘CWCRockall.input’ which can be found in subdirectory /examples/FoodWeb of the package directory Usage data(LIMCoralRockall) Format a list of matrices, vectors, names and values that specify the linear inverse model problem. see the return value of Setup for more information about this list A more complete description of this structure is in vignette("LIM") Author(s) Dick van Oevelen <dick.vanoevelen@nioz.nl> Karline Soetaert <karline.soetaert@nioz.nl> References van Oevelen, Dick, Gerard Duineveld, Marc Lavaleye, Furu Mienis, Karline Soetaert, and Carlo H. R. Heip, 2009. The cold-water coral community as hotspot of carbon cycling on continental margins: A food web analysis from Rockall Bank (northeast Atlantic). Limnology and Oceangraphy 54 : 1829 – 1844. http://www.aslo.org/lo/toc/vol_54/issue_6/1829.pdf See Also browseURL(paste(system.file(package="LIM"), "/examples/Foodweb/", sep="")) contains "CWCRockall.input", the input file; read this with Setup LIMTakapoto, LIMRigaSummer and many others Examples Coral <- Flowmatrix(LIMCoralRockall) plotweb(Coral, main = "Deep Water Coral Foodweb, Rockall Bank", sub = "mmolC/m2/day", lab.size = 0.8) ## Not run: xr <- LIMCoralRockall$NUnknowns i1 <- 1:(xr/2) 14 LIMEcoli i2 <- (xr/2+1):xr pm <- par(mfrow = c(1, 1)) Simplest <- Ldei(LIMCoralRockall)$X Ranges <- Xranges(LIMCoralRockall) Plotranges(Ranges[i1, 1], Ranges[i1, 2], Simplest[i1], lab.cex = 0.7, main = "Deep Water Coral - ranges part 1") Plotranges(Ranges[i2, 1], Ranges[i2, 2], Simplest[i2], lab.cex = 0.7, main = "Deep Water Coral - ranges part 2") par(mfrow = pm) ## End(Not run) LIMEcoli The Escherichia Coli Core Metabolism: Reaction network model specificiation Description Linear inverse model specification for performing Flux Balance Analysis of the E.coli metabolism (as from http://gcrg.ucsd.edu/Downloads/Flux_Balance_Analysis). The original input file can be found in the package subdirectory /examples/Reactions/E_coli.lim There are 53 substances: GLC, G6P, F6P, FDP, T3P2, T3P1, 13PDG, 3PG, 2PG, PEP, PYR, ACCOA, CIT, ICIT, AKG, SUCCOA, SUCC, FUM, MAL, OA, ACTP, ETH, AC, LAC, FOR, D6PGL, D6PGC, RL5P, X5P, R5P, S7P, E4P, RIB, GLX, NAD, NADH, NADP, NADPH, HEXT, Q, FAD, FADH, AMP, ADP, ATP, GL3P, CO2, PI, PPI, O2, COA, GL, QH2 and 13 externals: Biomass, GLCxt, GLxt, RIBxt, ACxt, LACxt, FORxt, ETHxt, SUCCxt, PYRxt, PIxt, O2xt, CO2xt There are 70 unknown reactions (named by the gene encoding for it): GLK1, PGI1, PFKA, FBP, FBA, TPIA, GAPA, PGK, GPMA, ENO, PPSA, PYKA, ACEE, ZWF, PGL, GND, RPIA, RPE, TKTA1, TKTA2, TALA, GLTA, ACNA, ICDA, SUCA, SUCC1, SDHA1, FRDA, FUMA, MDH, DLD1, ADHE2, PFLA, PTA, ACKA, ACS, PCKA, PPC, MAEB, SFCA, ACEA, ACEB, PPA, GLPK, GPSA1, RBSK, NUOA, FDOH, GLPD, CYOA, SDHA2, PNT1A, PNT2A, ATPA, GLCUP, GLCPTS, GLUP, RIBUP, ACUP, LACUP, FORUP, ETHUP, SUCCUP, PYRUP, PIUP, O2TX, CO2TX, ATPM, ADK, Growth The model contains: • 54 equalities (Ax=B): the 53 mass balances (one for each substance) and one equation that sets the ATP drain flux for constant maintenance requirements to a fixed value (5.87) • 70 unknowns (x), the reaction rates • 62 inequalities (Gx>h). The first 28 inequalities impose bounds on some reactions. The last 34 inequalities impose that the reaction rates have to be positive (for unidirectional reactions only). • 2 functions that have to be maximised, the biomass production (growth). LIMEcoli 15 Usage LIMEcoli Format LIMEcoli is of type lim, which is a list of matrices, vectors, names and values that specify the linear inverse model problem. see the return value of Setup for more information about this list A more complete description of this structures is in vignette("LIM") Author(s) Karline Soetaert <karline.soetaert@nioz.nl> References Edwards,J.S., Covert, M., and Palsson, B.., (2002) Metabolic Modeling of Microbes: the Flux Balance Approach, Environmental Microbiology, 4(3): pp. 133-140. See Also browseURL(paste(system.file(package="LIM"), "/doc/examples/Reactions/", sep="")) contains "E\_coli.lim", the input file; read this with Setup Examples # 1. parsimonious (simplest) solution pars <- Ldei(LIMEcoli) # 2. the ranges of each reaction xr <- Xranges(LIMEcoli, central = TRUE, full = TRUE) # 3. the optimal solution - solved with linear programming LP <- Linp(LIMEcoli) Optimal <- t(LP$X) # show the results data.frame(pars = pars$X, Optimal, xr[ ,1:3]) # The central value of linear programming problem is a valid solution # the central point is a valid solution: X <- xr[ ,"central"] max(abs(LIMEcoli$A%*%X - LIMEcoli$B)) min(LIMEcoli$G%*%X - LIMEcoli$H) # 4. Sample solution space - this takes a while - note that iter is not enough print(system.time( xs <- Xsample(LIMEcoli, iter = 200, type = "mirror", test = TRUE) )) 16 LIMEverglades pairs(xs[ ,1:10], pch = ".", cex = 2) # Print results: data.frame(pars = pars$X, Optimal = Optimal, xr[ ,1:2], Mean = colMeans(xs), sd = apply(xs,2,sd)) # Plot results par(mfrow = c(1, 2)) nr <- LIMEcoli$NUnknowns ii <- 1:(nr/2) dotchart(Optimal[ii, 1], xlim = range(xr), pch = 16, cex = 0.8) segments(xr[ii, 1], 1:nr, xr[ii, 2], 1:nr) ii <- (nr/2+1):nr dotchart(Optimal[ii, 1], xlim = range(xr), pch = 16, cex = 0.8) segments(xr[ii, 1], 1:nr, xr[ii, 2], 1:nr) mtext(side = 3, cex = 1.5, outer = TRUE, line = -1.5, "E coli Core Metabolism, optimal solution and ranges") LIMEverglades Linear inverse model specification for the herpetological food web of the Everglades Description Linear inverse model specification for the herpetological wet prairie example from the everglades. as described in Diffendorfer et al., 2001 The everglades are a freshwater wetland in Florida, USA. The model contains 9 functional compartments and 3 external compartments, connected with 402 flows. Units of the flows are gram wet weight / Ha / year The linear inverse model LIMEverglades is generated from the file Everglades.input which can be found in subdirectory /examples/FoodWeb of the package directory In this subdirectory you will find many foodweb example input files These files can be read using Read and their output processed by Setup which will produce a linear inverse problem specification similar to LIMEverglades Usage data(LIMEverglades) Format a list of matrices, vectors, names and values that specify the linear inverse model problem. see the return value of Setup for more information about this list A more complete description of this structures is in vignette("LIM") LIMRigaAutumn 17 Author(s) Karline Soetaert <karline.soetaert@nioz.nl> Dick van Oevelen <dick.vanoevelen@nioz.nl> References Diffendorfer, J.E., Richards, P.M., Dalrymple, G.H., DeAngelis, D.L., 2001. Applying Linear Pro- gramming to estimate fluxes in ecosystems or food webs: an example from the herpetological assemblage of the freshwater Everglades. Ecol. Model. 144, 99-120. See Also browseURL(paste(system.file(package="LIM"), "/doc/examples/Foodweb/", sep="")) contains "Everglades.input", the input file; read this with Setup LIMTakapoto, LIMRigaSummer and many others Examples # Cannot be solved, but the least squares solution is found Flows <- Lsei(LIMEverglades, parsimonious = TRUE) Everglades <- Flowmatrix(LIMEverglades) plotweb(Everglades, main = "Everglades Herpetological Wet Prairie model", sub = "g WW/Ha/Yr", lab.size = 0.8) LIMRigaAutumn Linear inverse model specification for the Gulf of Riga *autumn* planktonic food web Description Linear inverse model specification for the Gulf of Riga planktonic food web in *autumn* as in Donali et al. (1999). The Gulf of Riga is a highly eutrophic system in the Baltic Sea. The foodweb comprises 7 functional compartments and two external compartments, connected with 26 flows. Units of the flows are mg C/m3/day The linear inverse model LIMRigaAutumn is generated from the file RigaAutumn.input which can be found in subdirectory /examples/FoodWeb of the package directory In this subdirectory you will find many foodweb example input files These files can be read using Read and their output processed by Setup which will produce a linear inverse problem specification similar to LIMRigaAutumn Usage data(LIMRigaAutumn) 18 LIMRigaSpring Format a list of matrices, vectors, names and values that specify the linear inverse model problem. see the return value of Setup for more information about this list A more complete description of this structures is in vignette("LIM") Author(s) Karline Soetaert <karline.soetaert@nioz.nl> Dick van Oevelen<dick.vanoevelen@nioz.nl> References Donali, E., Olli, K., Heiskanen, A.S., Andersen, T., 1999. Carbon flow patterns in the planktonic food web of the Gulf of Riga, the Baltic Sea: a reconstruction by the inverse method. Journal of Marine Systems 23, 251..268. See Also browseURL(paste(system.file(package="LIM"), "/doc/examples/Foodweb/", sep="")) contains "RigaAutumn.input", the input file; read this with Setup LIMTakapoto, LIMRigaSummer, LIMRigaSpring and many others Examples rigaAutumn <- Flowmatrix(LIMRigaAutumn) plotweb(rigaAutumn, main = "Gulf of Riga planktonic food web, autumn", sub = "mgC/m3/day") # ranges of flows Plotranges(LIMRigaAutumn, lab.cex = 0.7, xlab = "mgC/m3/d", main = "Gulf of Riga planktonic food web, autumn, Flowranges") # ranges of variables Plotranges(LIMRigaAutumn, type="V", lab.cex = 0.7, xlab = "mgC/m3/d", main = "Gulf of Riga planktonic food web, autumn, variables") LIMRigaSpring Linear inverse model specification for the Gulf of Riga *spring* plank- tonic food web. Description Linear inverse model specification for the Gulf of Riga planktonic food web in *spring* as in Donali et al. (1999). The Gulf of Riga is a highly eutrophic system in the Baltic Sea. The foodweb comprises 7 functional compartments and two external compartments, connected with 26 flows. LIMRigaSpring 19 Units of the flows are mg C/m3/day The linear inverse model LIMRigaSpring is generated from the file RigaSpring.input which can be found in subdirectory /examples/FoodWeb of the package directory In this subdirectory you will find many foodweb example input files These files can be read using Read and their output processed by Setup which will produce a linear inverse problem specification similar to LIMRigaSpring. Usage data(LIMRigaSpring) Format a list of matrices, vectors, names and values that specify the linear inverse model problem. see the return value of Setup for more information about this list A more complete description of this structures is in vignette("LIM") Author(s) Karline Soetaert <karline.soetaert@nioz.nl> Dick van Oevelen<dick.vanoevelen@nioz.nl> References Donali, E., Olli, K., Heiskanen, A.S., Andersen, T., 1999. Carbon flow patterns in the planktonic food web of the Gulf of Riga, the Baltic Sea: a reconstruction by the inverse method. Journal of Marine Systems 23, 251..268. See Also browseURL(paste(system.file(package="LIM"), "/doc/examples/Foodweb/", sep="")) contains "RigaSpring.input", the input file; read this with Setup LIMTakapoto,LIMRigaAutumn, LIMRigaAutumn and many others Examples rigaSpring <- Flowmatrix(LIMRigaSpring) plotweb(rigaSpring, main = "Gulf of Riga planktonic food web, spring", sub = "mgC/m3/day") Plotranges(LIMRigaSpring, lab.cex = 0.7, main = "Gulf of Riga planktonic food web, spring, Flowranges") Plotranges(LIMRigaSpring, type = "V", lab.cex = 0.7, main = "Gulf of Riga planktonic food web, spring, Variable ranges") 20 LIMRigaSummer LIMRigaSummer Linear inverse model specification for the Gulf of Riga *summer* planktonic food web. Description Linear inverse model specification for the Gulf of Riga planktonic food web in *summer* as in Donali et al. (1999). The Gulf of Riga is a highly eutrophic system in the Baltic Sea. The foodweb comprises 7 functional compartments and two external compartments, connected with 26 flows. Units of the flows are mg C/m3/day The linear inverse model LIMRigaSummer is generated from the file RigaSummer.input which can be found in subdirectory /examples/FoodWeb of the package directory In this subdirectory you will find many foodweb example input files These files can be read using Read and their output processed by Setup which will produce a linear inverse problem specification similar to LIMRigaSummer Usage data(LIMRigaSummer) Format a list of matrices, vectors, names and values that specify the linear inverse model problem. see the return value of Setup for more information about this list. A more complete description of this structures is in vignette("LIM") Author(s) Karline Soetaert <karline.soetaert@nioz.nl> Dick van Oevelen <dick.vanoevelen@nioz.nl> References Donali, E., Olli, K., Heiskanen, A.S., Andersen, T., 1999. Carbon flow patterns in the planktonic food web of the Gulf of Riga, the Baltic Sea: a reconstruction by the inverse method. Journal of Marine Systems 23, 251..268. See Also browseURL(paste(system.file(package="LIM"), "/doc/examples/Foodweb/", sep="")) contains "RigaSummer.input", the input file; read this with Setup LIMTakapoto,LIMRigaAutumn, LIMRigaSpring and many others LIMScheldtIntertidal 21 Examples rigaSummer <- Flowmatrix(LIMRigaSummer) plotweb(rigaSummer, sub = "mgC/m3/day", main = "Gulf of Riga planktonic food web, summer") Plotranges(LIMRigaSummer, type = "V", lab.cex = 0.7, main = "Gulf of Riga planktonic food web, summer, Variable ranges") LIMScheldtIntertidal Linear inverse model specification for the Schelde Intertidal flat food web Description Linear inverse model specification for the Westerschelde Intertidal flat food web in June as in Van Oevelen et al. (2006). The Westerschelde is a highly eutrophic estuary in the Netherlands. The food web model was created for the intertidal flat called the "Molenplaat", site 2. It is the basic input model. The foodweb comprises 7 functional compartments and five external compartments, connected with 32 flows. Units of the flows are mg C/m2/day The linear inverse model LIMScheldtIntertidal is generated from the file ScheldtIntertidal.input which can be found in subdirectory /examples/FoodWeb of the package directory In this subdirectory you will find many foodweb example input files These files can be read using Read and their output processed by Setup which will produce a linear inverse problem specification similar to LIMScheldtIntertidal Usage data(LIMScheldtIntertidal) Format a list of matrices, vectors, names and values that specify the linear inverse model problem. see the return value of Setup for more information about this list A more complete description of this structures is in vignette("LIM") Author(s) Karline Soetaert <karline.soetaert@nioz.nl> Dick van Oevelen<dick.vanoevelen@nioz.nl> 22 LIMTakapoto References Van Oevelen, D., Soetaert, K., Middelburg, J.J., Herman, P.M.J., Moodley, L., Hamels, I., Moens, T., Heip, C.H.R., 2006b. Carbon flows through a benthic food web: Integrating biomass, isotope and tracer data. J. Mar. Res. 64, 1-30. See Also browseURL(paste(system.file(package="LIM"), "/doc/examples/Foodweb/", sep="")) contains "ScheldtIntertidal.input", the input file; read this with Setup LIMTakapoto, LIMRigaSummer and many others Examples ScheldtIntertidal <- Flowmatrix(LIMScheldtIntertidal) plotweb(ScheldtIntertidal, main = "Scheldt intertidal flat food web", sub = "mgC/m2/day") Plotranges(LIMScheldtIntertidal, lab.cex = 0.7, main = "Scheldt intertidal flat food web, Flowranges") Plotranges(LIMScheldtIntertidal, type = "V", lab.cex = 0.7, main = "Scheldt intertidal flat food web, Variable ranges") LIMTakapoto Linear inverse model specification for the Takapoto atoll planktonic food web. Description Linear inverse model specification for the Carbon flux model of the Takapoto atoll planktonic food web as reconstructed by inverse modelling by Niquil et al. (1998). The Takapoto Atoll lagoon is located in the French Polynesia of the South Pacific The food web comprises 7 functional compartments and three external compartments/sinks con- nected with 32 flows. Units of the flows are mg C/m2/day The linear inverse model LIMTakapoto is generated from the file Takapoto.input which can be found in subdirectory /examples/FoodWeb of the package directory In this subdirectory you will find many foodweb example input files These files can be read using Read and their output processed by Setup which will produce a linear inverse problem specification similar to LIMTakapoto Usage data(LIMTakapoto) Linp 23 Format a list of matrices, vectors, names and values that specify the linear inverse model problem. see the return value of Setup for more information about this list A more complete description of this structures is in vignette("LIM") Author(s) Karline Soetaert <karline.soetaert@nioz.nl> Dick van Oevelen<dick.vanoevelen@nioz.nl> References Niquil, N., Jackson, G.A., Legendre, L., Delesalle, B., 1998. Inverse model analysis of the plank- tonic food web of Takapoto Atoll (French Polynesia). Marine Ecology Progress Series 165, 17..29. See Also browseURL(paste(system.file(package="LIM"), "/doc/examples/Foodweb/", sep="")) contains "Takapoto.input", the input file; read this with Setup LIMRigaAutumn and many others Examples Takapoto <- Flowmatrix(LIMTakapoto) plotweb(Takapoto, main="Takapoto atoll planktonic food web", sub = "mgC/m2/day", lab.size = 1) # some ranges extend to infinity - they are marked with "*" Plotranges(LIMTakapoto, lab.cex = 0.7, sub = "*=unbounded", xlab = "mgC/m2/d", main = "Takapoto atoll planktonic food web, Flowranges") # ranges of variables, exclude first Plotranges(LIMTakapoto, type = "V", lab.cex = 0.7, index = 2:23, xlab = "mgC/m2/d", main = "Takapoto atoll planktonic food web, Variable ranges") Linp Solves a linear inverse model using linear programming. Description Solves a linear inverse model using linear programming Input presented either as: • matrices E, F, A, B, G, H (Linp.double) or • as a list (Linp.lim) or • as a lim input file (Linp.limfile) 24 Linp Usage Linp(...) ## S3 method for class 'lim' Linp(lim, cost = NULL, ispos = lim$ispos, ...) ## S3 method for class 'limfile' Linp(file, verbose = TRUE,...) ## S3 method for class 'character' Linp(...) ## S3 method for class 'double' Linp(...) Arguments lim a list that contains the linear inverse model specification, as generated by func- tion setup.limfile. file name of the inverse input file. verbose if TRUE: when reading the file, prints warnings and messages to the screen. cost if not NULL, a vector with the coefficients of the cost function (to be minimised). ispos if TRUE: all x-values have to be positive. ... other arguments passed to function linp from packagelimSolve. Details Solves the following inverse problem: X min( Costi ∗ xi ) or X max( P rof iti ∗ xi ) subject to xi >= 0 Ax = B Gx >= H and where Costi or P rof iti are weighting coefficients Value a list containing: X vector containing the solution of the linear programming problem. unconstrained.solution vector containing the unconstrained solution of the linear programming prob- lem. residualNorm scalar, the sum of residuals of equalities and violated inequalities. solutionNorm scalar, the value of the quadratic function at the solution. Lsei 25 IsError logical, TRUE if an error occurred. Error linp error text. type linp. Author(s) Karline Soetaert <karline.soetaert@nioz.nl> References Michel Berkelaar and others (2005). lpSolve: Interface to Lpsolve v. 5 to solve linear/integer programs. R package version 1.1.9. See Also linp, the more general function from package lpSolve Ldei, to solve the linear inverse problem by least distance programming Lsei, to solve the linear inverse problem by lsei (least squares with equality and inequality con- straints) function linp from packagelimSolve Examples # the Blending example Linp(LIMBlending) # the E coli example: two functions to maximimise Linp(LIMEcoli) # E coli example, but only first function optimised.. Linp(LIMEcoli, cost = -LIMEcoli$Profit[1,]) # a foodweb example: need to specify the cost function # here just sum of absolute values of flows... Linp(LIMRigaAutumn, cost = (rep(1, LIMRigaAutumn$NUnknowns))) Lsei Solves a linear inverse model using the least squares method. Description Solves a linear inverse model using the least squares method Input presented as: • matrices E, F, A, B, G, H (Lsei.double) or • a list (Lsei.lim) or • as a lim input file (Lsei.limfile) Useful for solving overdetermined lims. 26 Lsei Usage Lsei(...) ## S3 method for class 'double' Lsei(...) ## S3 method for class 'lim' Lsei(lim, exact = NULL, parsimonious = FALSE, ...) ## S3 method for class 'limfile' Lsei(file, exact = NULL, parsimonious = FALSE, verbose = TRUE, ...) ## S3 method for class 'character' Lsei(...) Arguments lim a list that contains the linear inverse model specification, as generated by func- tion setup.limfile. exact if not NULL, a vector containing the numbers of the equations to be solved ex- actly; if NULL, all equations are considered exact. parsimonious if TRUE, also minimises the sum of squared unknowns. file name of the inverse input file. verbose if TRUE: when reading the file, prints warnings and messages to the screen. ... other arguments passed to function lsei from packagelimSolve. Details Solves the following inverse problem: min(||AAx − BB||2 ) , the approximate equations subject to Ex = F , the mass balances Gx >= H , the constraints. and where E and F make up the equations from A and B, as specified by vector exact. AA and BB are the equations from A and B, NOT in vector exact. in case exact = NULL, there are no approximate equations. in case parsimonious = TRUE, then the sum of squared unknowns is also minimised. This means that AA is augmented with the unity matrix (of size Nunknowns) and BB contains Nunknowns addi- tional zeros. For overdetermined lim problems, for instance, the inverse equations may be split up in the mass balance equations which have to be exactly met and the other equations which have to be approxi- mated. This is, it is assumed that the first *NComponents* equations, the mass balances, should be met exactly and the call to the function is: Lsei(lim,exact = 1:lim$NComponents,...) Lsei 27 If the lim is underdetermined, an alternative is to use Ldei instead. This will return the parsimonious solution. The results should be similar with Lsei(...,parsimonious=TRUE). In theory both Lsei.lim and Ldei should return the same value for underdetermined systems. Value a list containing: X vector containing the solution of the least squares problem. residualNorm scalar, the sum of residuals of equalities and violated inequalities. solutionNorm scalar, the value of the minimised quadratic function at the solution. IsError TRUE if an error occurred. Error error text. type lsei. Author(s) Karline Soetaert <karline.soetaert@nioz.nl> References K. H. Haskell and R. J. Hanson, An algorithm for linear least squares problems with equality and nonnegativity constraints, Report SAND77-0552, Sandia Laboratories, June 1978. K. H. Haskell and R. J. Hanson, Selected algorithms for the linearly constrained least squares prob- lem - a users guide, Report SAND78-1290, Sandia Laboratories,August 1979. K. H. Haskell and R. J. Hanson, An algorithm for linear least squares problems with equality and nonnegativity constraints, Mathematical Programming 21 (1981), pp. 98-118. R. J. Hanson and K. H. Haskell, Two algorithms for the linearly constrained least squares problem, ACM Transactions on Mathematical Software, September 1982. See Also lsei, the more general function from package limSolve Linp, to solve the linear inverse problem by linear programming Ldei, to solve the linear inverse problem by least distance programming function lsei from packagelimSolve Examples Lsei(LIMRigaAutumn, parsimonious = TRUE) 28 Plotranges Plotranges Plots the minimum and maximum and central values Description Plots minimum and maximum ranges. Takes as input either a lim list, as generated by Setup or a set of vectors specifying the minimum, maximum and the central value, or a data.frame that contains min, max and central values. Usage Plotranges(...) ## S3 method for class 'double' Plotranges(min, max, value = NULL, labels = NULL, log = "", pch = 16, pch.col = "black", line.col = "gray", seg.col = "black", xlim = NULL, main = NULL, xlab = NULL, ylab = NULL, lab.cex = 1.0, mark = NULL,...) ## S3 method for class 'lim' Plotranges(lim = NULL, labels = NULL, type = "X", log = "", pch = 16, pch.col = "black", line.col = "gray", seg.col = "black", xlim = NULL, main = NULL, xlab = NULL, ylab = NULL, lab.cex = 1.0, index = NULL, ...) ## S3 method for class 'character' Plotranges(file, ...) Arguments min minimum value. max maximum value. value median or mean value. lim a list that contains the linear inverse model specification, as generated by func- tion setup.limfile. file name of the inverse input file. labels names of each value. type one of "X" or "V" for plotting of unknowns (X) or variables. log if = x: logarithmic scale for x-axis. pch pch symbol used for mean value. pch.col pch color for mean value. line.col color for each variable, spanning x-axis. seg.col color for variable range. PrintMat 29 xlim limits on x-axis. main main title. xlab x-axis label. ylab y-axis label. lab.cex label expansion value. index list of elements to be plotted, a vector of integers; default = all elements. mark list of elements to be marked with a "*", i.e. when range is unbounded. ... arguments passed to R-function "text" when writing labels. Value Only when a lim list was inputted. A data frame with min the minimum. max the maximum. values the central value. Author(s) Karline Soetaert <karline.soetaert@nioz.nl> Examples # The Takapoto food web. # some ranges extend to infinity - they are marked with "*" Plotranges(LIMTakapoto, lab.cex = 0.7, sub = "*=unbounded", xlab = "mgC/m2/d", main = "Takapoto atoll planktonic food web, Flowranges") # ranges of variables, exclude first variable Plotranges(LIMTakapoto, type = "V", lab.cex = 0.7, index = 2:23, xlab = "mgC/m2/d", main = "Takapoto atoll planktonic food web, Variable ranges") PrintMat Writes linear inverse matrices to the screen Description Prints the linear inverse problem: • inverse matrices and vectors A, b of the equalities Ax = b • inverse matrices and vectors G, h of the inequalities Gx >= h • if present, also writes the cost/profit function 30 Read Usage PrintMat(lim) Arguments lim a list that contains the linear inverse model specification, as generated by func- tion setup.limfile. Value returns nothing. Author(s) Karline Soetaert <karline.soetaert@nioz.nl> Examples PrintMat(LIMBlending) Read Reads an inverse input file Description Reads an inverse input file and creates the inverse problem as a list, of type "liminput" Usage Read(file, verbose = FALSE, checkLinear = TRUE, remtabs = TRUE) Arguments file name of inverse input file. verbose if TRUE: prints warnings and messages to the screen. checkLinear if FALSE: does not check for linearity remtabs remove tabs. Details The structure of an inverse input file is explained in vignette("LIM") which should be consulted. In short the inverse input file contains the declaration sections enclosed inbetween two lines starting with a \#\#. For instance, the following section declares two components \# COMP State1 Read 31 State2 \# END COMP Only the first 4 characters of the section names are read The following sections are allowed: • Parameters - \#\# PARAMETERS • Components - \#\# STOCKS or \#\# DECISION VARIABLES or \#\# STATES or \#\# UN- KNOWNS • Externals - \#\# EXTERNALS • Rates - \#\# RATES • Flows - \#\# FLOWS • Variables - \#\# VARIABLES • Cost - \#\# COST or \#\# MINIMISE • Profit - \#\# PROFIT or \#\# MAXIMISE • Equalities - \#\# EQUALITIES • InEqualities - \#\# INEQUALITIES or \#\# CONSTRAINTS Any (part of a) line starting with a "!" is considered a comment. Input is NOT case sensitive The output of this function is used as input in function Setup which creates the inverse matrices By default, only linear problems can be solved, and the function checks whether the input is linear. To toggle off this check, set checkLinear to FALSE. Some input files contain tabs, which are converted to spaces, unless this logical is set to FALSE. Value a list containing : file name of the inverse input file. pars a data.frame with parameter declarations. comp a data.frame with compartments (or states, stocks). rate a data.frame with rate declarations. extern a data.frame with external declarations. flows a data.frame with flow declarations. vars a data.frame with variable declarations. cost a data.frame with cost declarations. profit a data.frame with profit declarations. equations a data.frame with equality declarations. constraints a data.frame with constraint declarations. reactions a data.frame with reaction declarations. 32 Setup posreac a vector with TRUE values if reaction or flow is unidirectional (and the unknown x is thus positive), FALSE if it is two-way reaction or flow, and x can be positive or negative. marker a data.frame with marker declarations - see vignette("LIM"). parnames a vector with parameter names. varnames a vector with variable names. compnames a vector with compartment names. externnames a vector with names of externals. Type a string; one of "web" (flows are unknowns), "reaction" (reaction rates unknown) and "simple" (compartments are unknowns). Author(s) Karline Soetaert <karline.soetaert@nioz.nl> See Also Setup the function to create inverse matrices, based on output of Read. Examples # this input has been created with function Read: LIMinputBlending ## Not run: wd <- getwd() setwd(paste(system.file(package = "LIM"), "/doc/examples/Foodweb", sep = "")) Read("RigaAutumn.input") setwd(wd) ## End(Not run) Setup Creates linear inverse matrices Description Creates the linear problem with equality and inequality equations. Takes as input either a liminput list, as generated by Read or a filename with the linear inverse model specifications. Creates: • inverse matrices and vectors A, b, G, h of the equalities/inequalities: Ax = b Gx >= h Setup 33 • if present, also generates the cost/profit function which is used as: min(cost) or max(prof it) • if the input was a flow network, Setup will also create the flow matrix (see details). Usage Setup(...) ## S3 method for class 'limfile' Setup(file, verbose = TRUE, ...) ## S3 method for class 'character' Setup(...) ## S3 method for class 'liminput' Setup(liminput,...) Arguments file name of the inverse input file. verbose if TRUE: prints warnings and messages to the screen. liminput list of elements, as returned by Read. ... extra parameters allowing this to be a generic function. Value a list containing: file name of the inverse input file. NUnknowns number of unknowns. NEquations number of equations. NConstraints number of constraints. NComponents number of components. NExternal number of externals. NVariables number of variables. A matrix A of equalities Ax=B. B vector B of equalities Ax=B. G matrix G of inequalities Gx>h. H vector H of inequalities Gx=h. Cost cost vector (to minimise), the weight of each unknown; if not specified; 1 for all unknowns. Profit profit vector (to maximise). Flowmatrix matrix where element ij denotes flow from compartment i to j. 34 Variables VarA matrix VarA of variable equation VarA*x=VarB. VarB vector VarB of variable equation VarA*x=VarB. Flows a vector with flow names. Parameters a data.frame with parameter names and values. Components a data.frame with state names and values. Externals a data.frame with external names and values. rates a data.frame with rate names and values. markers a data.frame with marker names and values. Variables a vector with variable names. Unknowns a vector with names of unknowns (either states or flows). Weight a vector with the weights of unknowns- default is 1. Author(s) Karline Soetaert <karline.soetaert@nioz.nl> See Also Read function that reads inverse input files and produces the input list used by Setup Lsei, Ldei, Linp functions to solve inverse problem, based on output generated by setup.limfile Examples LIMinputBlending Setup(LIMinputBlending ) Variables Generates the values of variables for a linear inverse (foodweb) prob- lem Description Given an linear inverse model input list, generates the values of the inverse variables Usage Variables (lim, res, ...) Arguments lim a list that contains the linear inverse model specification, as generated by func- tion setup.limfile. res the solved linear inverse problem; if not specified, the model is solved first, using Lsei.lim<. ... extra parameters passed to function Lsei.lim. Varranges 35 Value the variables, a one-column data.frame Author(s) Karline Soetaert <karline.soetaert@nioz.nl> See Also Varranges which estimates the ranges of variables. Examples Variables(LIMRigaAutumn) Varranges Generates ranges of the variables for a linear inverse problem Description Given an inverse input list, generates the minimal and maximal values of the variables (linear com- binations of unknowns). Usage Varranges(lim, ...) Arguments lim a list that contains the linear inverse model specification, as generated by func- tion setup.limfile. ... extra arguments passed to function varranges. Value a 2-columned vector containing the minimum (column 1) and maximum (column 2) of each vari- able. Author(s) Karline Soetaert <karline.soetaert@nioz.nl> See Also Xranges which estimates the ranges of unknowns Plotranges to plot the ranges 36 Xranges Examples Varranges(LIMRigaAutumn) Xranges Generates ranges of the unknowns of a linear inverse problem Description Given an inverse input list, generates the minimal and maximal values of the unknowns Usage Xranges (lim, ...) Arguments lim a list that contains the linear inverse model specification, as generated by func- tion setup.limfile. ... extra arguments passed to function xranges from packagelimSolve. Value a 2-columned vector containing the minimum (column 1) and maximum (column 2) of each un- known. Author(s) Karline Soetaert <karline.soetaert@nioz.nl> See Also Varranges which estimates the ranges of inverse variables Plotranges to plot the ranges function xranges from packagelimSolve Examples # ranges xr <- Xranges(LIMRigaAutumn) xlim <- range(xr) # parsimonious pars <- Lsei(LIMRigaAutumn)$X # plot dotchart(x = pars, labels = rownames(xr), xlim = xlim, main = "Riga Autumn ", sub = "ranges and parsimonious solution", pch = 16) Xsample 37 cc <- 1:nrow(xr) segments(xr[ ,1], cc, xr[ ,2], cc) Xsample Generates a random sample of the unknowns for a linear inverse prob- lem Description Given an inverse input list, randomly samples the unknowns, using an MCMC method Usage Xsample(lim, exact = NULL, ...) Arguments lim a list that contains the linear inverse model specification, as generated by func- tion setup.limfile. exact if not NULL, a vector containing the numbers of the equations to be solved ex- actly; if NULL, all equations are considered exact. ... extra parameters passed to function xsample from packagelimSolve. Details For overdetermined LIM problems, the inverse equations may be split up in equations which have to be exactly met and other equations which have to be approximated. exact is a vector with the exact equations The default settings of xsample will often not do. For instance, the default consists of 3000 itera- tions (iter) and a jump length of jmp of 0.1. You may need to increase one of those to ensure that the entire solution space has been adequately sampled. Value a 2-columned vector containing the minimum (column 1) and maximum (column 2) of each un- known. Author(s) Karline Soetaert <karline.soetaert@nioz.nl> References Van den Meersche K, Soetaert K, Van Oevelen D (2009). xsample(): An R Function for Sampling Linear Inverse Problems. Journal of Statistical Software, Code Snippets, 30(1), 1-15. http://www.jstatsoft.org/v30/c01/ 38 Xsample See Also Varranges which estimates the ranges of inverse variables Plotranges to plot the ranges function xsample from packagelimSolve Examples # sample solution space xs <- Xsample(LIMRigaAutumn, iter = 500, jmp = 5) # remove flows that are invariable (sd=0) xs <- xs[ ,-which(apply(xs, 2, sd) == 0 )] #pairs plot pairs(xs, gap = 0, pch = ".", upper.panel = NULL) Index ∗ IO Setup, 32 PrintMat, 29 Variables, 34 Read, 30 Varranges, 35 Setup, 32 Xranges, 36 ∗ algebra Xsample, 37 Ldei, 6 Linp, 23 FILERigaAutumn, 4 Lsei, 25 Flowmatrix, 3, 5 ∗ array Ldei, 6 Ldei, 3, 6, 25, 27, 34 Linp, 23 ldei, 6, 7 Lsei, 25 LIM (LIM-package), 2 ∗ datasets LIM-package, 2 FILERigaAutumn, 4 LIMBlending, 8 LIMBlending, 8 LIMBrouageMudflat, 10 LIMBrouageMudflat, 10 LIMCaliforniaSediment, 11 LIMCaliforniaSediment, 11 LIMCoralRockall, 12 LIMCoralRockall, 12 LIMEcoli, 9, 14 LIMEverglades, 16 LIMEcoli, 14 LIMinputBlending (LIMBlending), 8 LIMEverglades, 16 LIMRigaAutumn, 5, 17, 19, 20, 23 LIMRigaAutumn, 17 LIMRigaSpring, 18, 18, 20 LIMRigaSpring, 18 LIMRigaSummer, 11–13, 17, 18, 20, 22 LIMRigaSummer, 20 LIMScheldtIntertidal, 21 LIMScheldtIntertidal, 21 LIMTakapoto, 9, 11–13, 17–20, 22, 22 LIMTakapoto, 22 Linp, 3, 7, 23, 27, 34 ∗ hplot linp, 24, 25 Plotranges, 28 Lsei, 3, 7, 25, 25, 34 ∗ optimize lsei, 26, 27 Ldei, 6 Linp, 23 Plotranges, 3, 28, 35, 36, 38 Lsei, 25 plotweb, 6 Varranges, 35 PrintMat, 29 Xranges, 36 Xsample, 37 Read, 3, 4, 9–11, 16, 17, 19–22, 30, 34 ∗ package LIM-package, 2 Setup, 3, 4, 9–13, 15–23, 31, 32, 32 ∗ utilities Flowmatrix, 5 Variables, 3, 34 PrintMat, 29 Varranges, 3, 35, 35, 36, 38 39 40 INDEX Xranges, 3, 35, 36 xranges, 36 Xsample, 3, 37 xsample, 37, 38

CoClust

cran

Package ‘CoClust’ October 12, 2022 Title Copula Based Cluster Analysis Date 2017-12-15 Version 0.3-2 Author Francesca Marta Lilja Di Lascio, Simone Giannerini Depends R (>= 2.15.1), methods, copula Imports gtools Description A copula based clustering algorithm that finds clusters according to the complex multi- variate dependence structure of the data generating process. The updated version of the algo- rithm is described in Di Lascio, F.M.L. and Giannerini, S. (2016). ``Clustering dependent obser- vations with copula functions''. Statistical Papers, p.1-17. <doi:10.1007/s00362-016-0822-3>. Maintainer Francesca Marta Lilja Di Lascio <marta.dilascio@unibz.it> License GPL (>= 2) NeedsCompilation no Repository CRAN Date/Publication 2017-12-17 16:14:20 UTC R topics documented: CoClust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 CoClust-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Index 7 CoClust Copula-Based Clustering Algorithm Description Cluster analysis based on copula functions 1 2 CoClust Usage CoClust(m, dimset = 2:5, noc = 4, copula = "frank", fun = median, method.ma = c("empirical", "pseudo"), method.c = c("ml", "mpl", "irho", "itau"), dfree = NULL, writeout = 5, penalty = c("BICk", "AICk", "LL"), ...) Arguments m a data matrix. dimset the set of dimensions for which the function tries the clustering. noc sample size of the set for selecting the number of clusters. copula a copula model. This should be one of "normal", "t", "frank", "clayton" and "gumbel". See the Details section. fun combination function of the pairwise Spearman’s rho used to select the k-plets. The default is median method.ma estimation method for margins. See the Details section. method.c estimation method for copula. See fitCopula. dfree degrees of freedom for the t copula. writeout writes a message on the number of allocated observations every writeout obser- vations. penalty Specifies the likelihood criterion used for selecting the number of clusters. ... further parameters for fitCopula. Details Usage for Frank copula: CoClust(m, nmaxmarg = 2:5, noc = 4, copula = "frank", fun = median, method.ma=c("gaussian","empirical"), method.c = "mpl", penalty ="BICk", ...) CoClust is a clustering algorithm that, being based on copula functions, allows to group obser- vations according to the multivariate dependence structure of the generating process without any assumptions on the margins. For each k in dimset the algorithm builds a sample of noc observations (rows of the data matrix m) by using the matrix of Spearman’s rho correlation coefficients which are combined by means of the function fun (median by default). The number of clusters K is selected by means of a criterion based on the likelihood of the copula fit. The switch penalty allows to select 3 different criteria; The choice LL corresponds to using the likelihood without penalty terms. Then, the remaining observations are allocated to the clusters as follows: 1. selects a K-plet of observations on the basis of fun applied to the pairwise Spearman’s rho; 2. allocates or discards the K-plet on the basis of the likelihood of the copula fit. The estimation approach for the copula fit is semiparametric: a range of nonparametric margins and parametric copula models can be selected by the user. The CoClust algorithm does not require to set a priori the number of clusters nor it needs a starting classification. Notice that the dependence structure for the Gaussian and the t copula is set to exchangeable. Non structured dependence structures will be allowed in a future version. CoClust 3 Value An object of S4 class "CoClust", which is a list with the following elements: Number.of.Clusters the number K of identified clusters. Index.Matrix a n.obs by (K+1) matrix where n.obs is the number of observations put in each cluster. The matrix contains the row indexes of the observations of the data matrix m. The last column contains the log-likelihood of the copula fit. Data.Clusters the matrix of the final clustering. Dependence a list containing: Model the copula model used for the clustering. Param the estimated dependence parameter between clusters. Std.Err the standard error of Param. P.val the p-value associated to the null hypothesis H_0: theta=0. LogLik the maximized log-likelihood copula fit. Est.Method the estimation method used for the copula fit. Opt.Method the optimization method used for the copula fit. LLC the value of the LogLikelihood Criterion for each k in dimset. Index.dimset a list that, for each k in dimset, contains the index matrix of the initial set of nk observations used for selecting the number of clusters, together with the associated loglikelihood. Note The final clustering is composed of K groups in which observations of the same group are indepen- dent whereas the observations that belong to different groups and that form a K-plet are dependent. Author(s) Francesca Marta Lilja Di Lascio <marta.dilascio@unibz.it>, Simone Giannerini <simone.giannerini@unibo.it> References Di Lascio, F.M.L. (201x). "CoClust: An R Package for Copula-based Cluster Analysis". To be submitted. Di Lascio, F.M.L., Durante, F. and Pappada’, R. (2017). "Copula-based clustering methods", Cop- ulas and Dependence Models with Applications, p.49-67. Eds Ubeda-Flores, M., de Amo, E., Du- rante, F. and Fernandez Sanchez, J., Springer International Publishing. ISBN: 978-3-319-64220-8. Di Lascio, F.M.L. and Disegna, M. (2017). "A copula-based clustering algorithm to analyse EU country diets". Knowledge-Based Systems, 132, p.72-84. DOI: 10.1016/j.knosys.2017.06.004. Di Lascio, F.M.L. and Giannerini, S. (2016). "Clustering dependent observations with copula func- tions". Statistical Papers, p.1-17. DOI 10.1007/s00362-016-0822-3. 4 CoClust Di Lascio, F.M.L. and Giannerini, S. (2012). "A Copula-Based Algorithm for Discovering Patterns of Dependent Observations", Journal of Classification, 29(1), p.50-75. Di Lascio, F.M.L. (2008). "Analyzing the dependence structure of microarray data: a copula-based approach". PhD thesis, Dipartimento di Scienze Statistiche, Universita’ di Bologna, Italy. Examples ## ****************************************************************** ## 1. builds a 3-variate copula with different margins ## (Gaussian, Gamma, Beta) ## ## 2. generates a data matrix xm with 15 rows and 21 columns and ## builds the matrix of the true cluster indexes ## ## 3. applies the CoClust to the rows of xm and recovers the ## multivariate dependence structure of the data ## ****************************************************************** ## Step 1. ********************************************************** n <- 105 # total number of observations n.col <- 21 # number of columns of the data matrix m n.marg <- 3 # dimension of the copula n.row <- n*n.marg/n.col # number of rows of the data matrix m theta <- 10 copula <- frankCopula(theta, dim = n.marg) mymvdc <- mvdc(copula, c("norm", "gamma", "beta"),list(list(mean=7, sd=2), list(shape=3, rate=4), list(shape1=2, shape2=1))) ## Step 2. ********************************************************** set.seed(11) x.samp <- rMvdc(n, mymvdc) xm <- matrix(x.samp, nrow = n.row, ncol = n.col, byrow=TRUE) index.true <- matrix(1:15,5,3) colnames(index.true) <- c("Cluster 1","Cluster 2", "Cluster 3") ## Step 3. ********************************************************** clust <- CoClust(xm, dimset = 2:4, noc=2, copula="frank", method.ma="empirical", method.c="ml",writeout=1) clust clust@"Number.of.Clusters" clust@"Dependence"$Param clust@"Data.Clusters" index.clust <- clust@"Index.Matrix" ## compare with index.true index.clust index.true ## CoClust-class 5 CoClust-class Class "CoClust" Description A class for CoClust and its extensions Objects from the Class Objects can be created by calls of the form new("CoClust", ...). Slots Number.of.Clusters: Object of class "integer". The number K of identified clusters. Index.Matrix: Object of class "matrix". A n.obs by (K+1) matrix where n.obs is the number of observations put in each cluster. The matrix contains the row indexes of the observations of the data matrix m. The last column contains the log-likelihood of the copula fit. Data.Clusters: Object of class "matrix". The matrix of the final clustering. Dependence: Object of class "list". The list contains: Model the copula model used for the clustering. Param the estimated dependence parameter between clusters. Std.Err the standard error of Param. P.val the p-value associated to the null hypothesis H_0: theta=0. LogLik: Object of class "numeric". The maximized log-likelihood copula fit. Est.Method Object of class "character". The estimation method used for the copula fit. Opt.Method Object of class "character". The optimization method used for the copula fit. LLC Object of class "numeric". The value of the LogLikelihood Criterion for each k in dimset. Index.dimset Object of class "list". A list that, for each k in dimset, contains the index matrix of the initial set of nk observations used for selecting the number of clusters, together with the associated loglikelihood. Methods No methods defined with class "CoClust" in the signature. Author(s) Francesca Marta Lilja Di Lascio <marta.dilascio@unibz.it>, Simone Giannerini <simone.giannerini@unibo.it> 6 CoClust-class References Di Lascio, F.M.L. (201x). "CoClust: An R Package for Copula-based Cluster Analysis". To be submitted. Di Lascio, F.M.L., Durante, F. and Pappada’, R. (2017). "Copula-based clustering methods", Cop- ulas and Dependence Models with Applications, p.49-67. Eds Ubeda-Flores, M., de Amo, E., Du- rante, F. and Fernandez Sanchez, J., Springer International Publishing. ISBN: 978-3-319-64220-8. Di Lascio, F.M.L. and Disegna, M. (2017). "A copula-based clustering algorithm to analyse EU country diets". Knowledge-Based Systems, 132, p.72-84. DOI: 10.1016/j.knosys.2017.06.004. Di Lascio, F.M.L. and Giannerini, S. (2016). "Clustering dependent observations with copula func- tions". Statistical Papers, p.1-17. DOI 10.1007/s00362-016-0822-3. Di Lascio, F.M.L. and Giannerini, S. (2012). "A Copula-Based Algorithm for Discovering Patterns of Dependent Observations", Journal of Classification, 29(1), p.50-75. Di Lascio, F.M.L. (2008). "Analyzing the dependence structure of microarray data: a copula-based approach". PhD thesis, Dipartimento di Scienze Statistiche, Universita’ di Bologna, Italy. See Also See Also CoClust and copula. Examples showClass("CoClust") Index ∗ classes CoClust-class, 5 ∗ cluster CoClust, 1 ∗ multivariate CoClust, 1 CoClust, 1, 6 CoClust-class, 5 copula, 6 fitCopula, 2 7

gifti

cran

Package ‘gifti’ October 13, 2022 Type Package Title Reads in 'Neuroimaging' 'GIFTI' Files with Geometry Information Version 0.8.0 Date 2020-11-10 Author John Muschelli Maintainer John Muschelli <muschellij2@gmail.com> Description Functions to read in the geometry format under the 'Neuroimaging' 'Informatics' Technology Initiative ('NIfTI'), called 'GIFTI' <https://www.nitrc.org/projects/gifti/>. These files contain surfaces of brain imaging data. License GPL-2 Imports xml2 (>= 1.1.1), base64enc, R.utils, tools, utils Suggests rgl, grDevices, testthat, knitr, rmarkdown, covr BugReports https://github.com/muschellij2/gifti/issues Encoding UTF-8 LazyData true RoxygenNote 7.1.1 VignetteBuilder knitr NeedsCompilation no Repository CRAN Date/Publication 2020-11-11 22:40:02 UTC R topics documented: convert_binary_datatype . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 convert_endian . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 convert_intent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 create_data_matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 data_array_attributes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 data_decoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1 2 convert_binary_datatype data_encoder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 decompress_gii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 download_gifti_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 gifti_list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 gifti_map_value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 have_gifti_test_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 is.gifti . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 readgii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 surf_triangles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 writegii . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Index 13 convert_binary_datatype Convert Binary Data Type Description Converts a data type to the size and what for readBin, necessary for Base64Binary and GZipBase64Binary formats Usage convert_binary_datatype( datatype = c("NIFTI_TYPE_UINT8", "NIFTI_TYPE_INT32", "NIFTI_TYPE_UINT32", "NIFTI_TYPE_FLOAT32") ) Arguments datatype data type from GIFTI image Value List of length 2: with elements of size and what Examples convert_binary_datatype() convert_binary_datatype('NIFTI_TYPE_INT32') testthat::expect_error(convert_binary_datatype('NIFTI_TYPE_BLAH')) convert_endian 3 convert_endian Convert Endian from GIFTI Description Converts Endian format from GIFTI Usage convert_endian(endian) Arguments endian character passed from GIFTI XML Value Character string convert_intent Convert Intent Description Converts the intent field from a GIFTI image to a more standard naming Usage convert_intent(intent) Arguments intent (character) string of intent type Value A character string 4 data_array_attributes create_data_matrix Create Data Matrix with ordering respected Description Create Data Matrix with ordering respected Usage create_data_matrix( data, dims, ordering = c("RowMajorOrder", "ColumnMajorOrder") ) Arguments data Data output from data_decoder dims Dimensions of output ordering Ordering of the data Value Matrix of Values data_array_attributes Data Array Attributes Description Parses a list of XML data to get the attributes Usage data_array_attributes(darray) Arguments darray List of xml_nodes from GIFTI data array Value data.frame of attributes data_decoder 5 data_decoder Array Data Decoder Description Decodes values from a GIFTI image Usage data_decoder( values, encoding = c("ASCII", "Base64Binary", "GZipBase64Binary", "ExternalFileBinary"), datatype = NULL, endian = c("little", "big", "LittleEndian", "BigEndian"), ext_filename = NULL, n = NULL ) Arguments values text from XML of GIFTI image encoding encoding of GIFTI values datatype Passed to convert_binary_datatype endian Endian to pass in readBin ext_filename if encoding = "ExternalFileBinary", then this is the external filename n number of values to read. Relevant if encoding = "ExternalFileBinary" Value Vector of values Examples if (have_gifti_test_data(outdir = NULL)) { gii_files = download_gifti_data(outdir = NULL) L = gifti_list(gii_files[1]) orig = L$DataArray$Data[[1]] encoding = attributes(L$DataArray)$Encoding datatype = attributes(L$DataArray)$DataType endian = attributes(L$DataArray)$Endian vals = data_decoder(orig, encoding = encoding, datatype = datatype, endian = endian) enc = data_encoder(vals, encoding = encoding, datatype = datatype, endian = endian) enc == orig } 6 data_encoder data_encoder Array Data Encoder Description Encodes values for a GIFTI image Usage data_encoder( values, encoding = c("ASCII", "Base64Binary", "GZipBase64Binary"), datatype = NULL, endian = c("little", "big", "LittleEndian", "BigEndian") ) Arguments values values to be encoded encoding encoding of GIFTI values datatype Passed to convert_binary_datatype endian Endian to pass in readBin Value Single character vector Examples if (have_gifti_test_data(outdir = NULL)) { gii_files = download_gifti_data(outdir = NULL) L = gifti_list(gii_files[1]) orig = L$DataArray$Data[[1]] encoding = attributes(L$DataArray)$Encoding datatype = attributes(L$DataArray)$DataType endian = attributes(L$DataArray)$Endian vals = data_decoder(orig, encoding = encoding, datatype = datatype, endian = endian) enc = data_encoder(vals, encoding = encoding, datatype = datatype, endian = endian) enc == orig } decompress_gii 7 decompress_gii Decompress Gzipped GIFTI (with extension .gz) Description If a GIFTI file is compressed, as in .gii.gz, this will decompress the file. This has nothing to do with the encoding WITHIN the file Usage decompress_gii(file) Arguments file file name of GIFTI file Value Filename of decompressed GIFTI Examples if (have_gifti_test_data(outdir = NULL)) { gii_files = download_gifti_data(outdir = NULL) outfile = decompress_gii(gii_files[1]) print(outfile) } download_gifti_data Download GIFTI Test Data Description Downloads GIFTI test data from https://www.nitrc.org/frs/download.php/411/BV_GIFTI_ 1.3.tar.gz Usage download_gifti_data( outdir = system.file(package = "gifti"), overwrite = FALSE, ... ) 8 gifti_list Arguments outdir Output directory for test file directory overwrite Should files be overwritten if already exist? ... additional arguments to download.file Value Vector of file names gifti_list Convert GIFTI to List Description Reads in a GIFTI file and coerces it to a list Usage gifti_list(file) Arguments file file name of GIFTI file Value List of elements Examples if (have_gifti_test_data(outdir = NULL)) { gii_files = download_gifti_data(outdir = NULL) L = gifti_list(gii_files[1]) orig = L$DataArray$Data[[1]] encoding = attributes(L$DataArray)$Encoding datatype = attributes(L$DataArray)$DataType endian = attributes(L$DataArray)$Endian vals = data_decoder(orig, encoding = encoding, datatype = datatype, endian = endian) enc = data_encoder(vals, encoding = encoding, datatype = datatype, endian = endian) enc == orig } gifti_map_value 9 gifti_map_value Map Values to Triangles from GIFTI Description Takes values and maps them to the correct triangles in space. Usage gifti_map_value( pointset, triangle, values, indices = seq(nrow(pointset)), add_one = TRUE ) Arguments pointset pointset from GIFTI triangle triangles from GIFTI values Values to map to the triangles. Same length as indices indices indices to place the values, must be in the range of 1 and the number of rows of pointset add_one Should 1 be added to the indices for the triangle? Value A list of coordinates (in triangles) and the corresponding value mapped to those triangles have_gifti_test_data Check Presence of GIFTI Test Data Description Checks if GIFTI test data is downloaded Usage have_gifti_test_data(outdir = system.file(package = "gifti")) Arguments outdir Output directory for test file directory 10 readgii Value Logical indicator Examples have_gifti_test_data(outdir = NULL) is.gifti Test if GIFTI Description Simple wrapper to determine if class is GIFTI Usage is.gifti(x) is_gifti(x) Arguments x object to test Value Logical if x is GIFTI readgii Read GIFTI File Description Reads a GIFTI File and parses the output Usage readgii(file) readGIfTI(file) read_gifti(file) Arguments file Name of file to read surf_triangles 11 Value List of values Examples if (have_gifti_test_data(outdir = NULL)) { gii_files = download_gifti_data(outdir = NULL) gii_list = lapply(gii_files, readgii) surf_files = grep("white[.]surf[.]gii", gii_files, value = TRUE) surfs = lapply(surf_files, surf_triangles) col_file = grep("white[.]shape[.]gii", gii_files, value = TRUE) cdata = readgii(col_file) cdata = cdata$data$shape mypal = grDevices::colorRampPalette(colors = c("blue", "black", "red")) n = 4 breaks = quantile(cdata) ints = cut(cdata, include.lowest = TRUE, breaks = breaks) ints = as.integer(ints) stopifnot(!any(is.na(ints))) cols = mypal(n)[ints] cols = cols[surfs[[1]]$triangle] } ## Not run: if (have_gifti_test_data(outdir = NULL)) { if (requireNamespace("rgl", quietly = TRUE)) { rgl::rgl.open() rgl::rgl.triangles(surfs[[1]]$pointset, color = cols) rgl::play3d(rgl::spin3d(), duration = 5) } } ## End(Not run) surf_triangles Make Triangles from GIfTI Image Description Creates Triangles for plotting in RGL from a GIfTI image Usage surf_triangles(file) Arguments file File name of GIfTI image, usually surf.gii 12 writegii Value List of values corresponding to the data element from readgii writegii Write .gii xml from "gifti" object Description Writes a "gifti" object to a GIFTI file (ends in *.gii). Usage writegii(gii, out_file, use_parsed_transformations = FALSE) writeGIfTI(gii, out_file, use_parsed_transformations = FALSE) write_gifti(gii, out_file, use_parsed_transformations = FALSE) Arguments gii The "gifti" object out_file Where to write the new GIFTI file use_parsed_transformations Should the $parsed_transformations be written instead of the transformations? Use if the XML pointers in transformations might be invalid. Default: FALSE Index convert_binary_datatype, 2, 5, 6 convert_endian, 3 convert_intent, 3 create_data_matrix, 4 data_array_attributes, 4 data_decoder, 4, 5 data_encoder, 6 decompress_gii, 7 download.file, 8 download_gifti_data, 7 gifti_list, 8 gifti_map_value, 9 have_gifti_test_data, 9 is.gifti, 10 is_gifti (is.gifti), 10 read_gifti (readgii), 10 readBin, 2, 5, 6 readGIfTI (readgii), 10 readgii, 10, 12 surf_triangles, 11 write_gifti (writegii), 12 writeGIfTI (writegii), 12 writegii, 12 13

viridisLite

cran

Package ‘viridisLite’ May 3, 2023 Type Package Title Colorblind-Friendly Color Maps (Lite Version) Version 0.4.2 Date 2023-05-02 Maintainer Simon Garnier <garnier@njit.edu> Description Color maps designed to improve graph readability for readers with common forms of color blindness and/or color vision deficiency. The color maps are also perceptually-uniform, both in regular form and also when converted to black-and-white for printing. This is the 'lite' version of the 'viridis' package that also contains 'ggplot2' bindings for discrete and continuous color and fill scales and can be found at <https://cran.r-project.org/package=viridis>. License MIT + file LICENSE Encoding UTF-8 Depends R (>= 2.10) Suggests hexbin (>= 1.27.0), ggplot2 (>= 1.0.1), testthat, covr URL https://sjmgarnier.github.io/viridisLite/, https://github.com/sjmgarnier/viridisLite/ BugReports https://github.com/sjmgarnier/viridisLite/issues/ RoxygenNote 7.2.3 NeedsCompilation no Author Simon Garnier [aut, cre], Noam Ross [ctb, cph], Bob Rudis [ctb, cph], Marco Sciaini [ctb, cph], Antônio Pedro Camargo [ctb, cph], Cédric Scherer [ctb, cph] Repository CRAN Date/Publication 2023-05-02 23:50:02 UTC 1 2 viridis R topics documented: viridis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 viridis.map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Index 6 viridis Viridis Color Palettes Description This function creates a vector of n equally spaced colors along the selected color map. Usage viridis(n, alpha = 1, begin = 0, end = 1, direction = 1, option = "D") viridisMap(n = 256, alpha = 1, begin = 0, end = 1, direction = 1, option = "D") magma(n, alpha = 1, begin = 0, end = 1, direction = 1) inferno(n, alpha = 1, begin = 0, end = 1, direction = 1) plasma(n, alpha = 1, begin = 0, end = 1, direction = 1) cividis(n, alpha = 1, begin = 0, end = 1, direction = 1) rocket(n, alpha = 1, begin = 0, end = 1, direction = 1) mako(n, alpha = 1, begin = 0, end = 1, direction = 1) turbo(n, alpha = 1, begin = 0, end = 1, direction = 1) Arguments n The number of colors (≥ 1) to be in the palette. alpha The alpha transparency, a number in [0,1], see argument alpha in hsv. begin The (corrected) hue in [0,1] at which the color map begins. end The (corrected) hue in [0,1] at which the color map ends. direction Sets the order of colors in the scale. If 1, the default, colors are ordered from darkest to lightest. If -1, the order of colors is reversed. option A character string indicating the color map option to use. Eight options are available: • "magma" (or "A") • "inferno" (or "B") viridis 3 • "plasma" (or "C") • "viridis" (or "D") • "cividis" (or "E") • "rocket" (or "F") • "mako" (or "G") • "turbo" (or "H") Details Here are the color scales: magma(), plasma(), inferno(), cividis(), rocket(), mako(), and turbo() are convenience functions for the other color map options, which are useful when the scale must be passed as a function name. Semi-transparent colors (0 < alpha < 1) are supported only on some devices: see rgb. Value viridis returns a character vector, cv, of color hex codes. This can be used either to create a user- defined color palette for subsequent graphics by palette(cv), a col = specification in graphics functions or in par. viridisMap returns a n lines data frame containing the red (R), green (G), blue (B) and alpha (alpha) channels of n equally spaced colors along the selected color map. n = 256 by default. 4 viridis.map Author(s) Simon Garnier: <garnier@njit.edu> / @sjmgarnier Examples library(ggplot2) library(hexbin) dat <- data.frame(x = rnorm(10000), y = rnorm(10000)) ggplot(dat, aes(x = x, y = y)) + geom_hex() + coord_fixed() + scale_fill_gradientn(colours = viridis(256, option = "D")) # using code from RColorBrewer to demo the palette n = 200 image( 1:n, 1, as.matrix(1:n), col = viridis(n, option = "D"), xlab = "viridis n", ylab = "", xaxt = "n", yaxt = "n", bty = "n" ) viridis.map Color Map Data Description A data set containing the RGB values of the color maps included in the package. These are: • ’magma’, ’inferno’, ’plasma’, and ’viridis’ as defined in Matplotlib for Python. These color maps are designed in such a way that they will analytically be perfectly perceptually-uniform, both in regular form and also when converted to black-and-white. They are also designed to be perceived by readers with the most common form of color blindness. They were created by Stéfan van der Walt and Nathaniel Smith; • ’cividis’, a corrected version of ’viridis’, ’cividis’, developed by Jamie R. Nuñez, Christopher R. Anderton, and Ryan S. Renslow, and originally ported to R by Marco Sciaini. It is designed to be perceived by readers with all forms of color blindness; • ’rocket’ and ’mako’ as defined in Seaborn for Python; • ’turbo’, an improved Jet rainbow color map for reducing false detail, banding and color blind- ness ambiguity. Usage viridis.map viridis.map 5 Format A data frame with 2048 rows and 4 variables: • R: Red value; • G: Green value; • B: Blue value; • opt: The colormap "option" (A: magma; B: inferno; C: plasma; D: viridis; E: cividis; F: rocket; G: mako; H: turbo). Author(s) Simon Garnier: <garnier@njit.edu> / @sjmgarnier References • ’magma’, ’inferno’, ’plasma’, and ’viridis’: https://bids.github.io/colormap/ • ’cividis’: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0199239 • ’rocket’ and ’mako’: https://seaborn.pydata.org/index.html • ’turbo’: https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html Index ∗ datasets viridis.map, 4 cividis (viridis), 2 hsv, 2 inferno (viridis), 2 magma (viridis), 2 mako (viridis), 2 plasma (viridis), 2 rgb, 3 rocket (viridis), 2 turbo (viridis), 2 viridis, 2 viridis.map, 4 viridisMap (viridis), 2 6

shinyjqui

cran

Package ‘shinyjqui’ October 14, 2022 Type Package Title 'jQuery UI' Interactions and Effects for Shiny Version 0.4.1 Maintainer Yang Tang <tang_yang@outlook.com> Description An extension to shiny that brings interactions and animation effects from 'jQuery UI' library. License MIT + file LICENSE Encoding UTF-8 Depends R (>= 3.2.0) Imports shiny (>= 1.5.0), htmltools, htmlwidgets, jsonlite, rlang Suggests ggplot2, highcharter, knitr, markdown, rmarkdown, plotly URL https://github.com/yang-tang/shinyjqui, https://yang-tang.github.io/shinyjqui/ BugReports https://github.com/yang-tang/shinyjqui/issues RoxygenNote 7.1.2 VignetteBuilder knitr NeedsCompilation no Author Yang Tang [aut, cre] Repository CRAN Date/Publication 2022-02-03 07:00:02 UTC R topics documented: Animation_effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Class_effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 draggableModalDialog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 get_jqui_effects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 jqui_bookmarking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1 2 Animation_effects jqui_icon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 jqui_position . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 orderInput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 selectableTableOutput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 sortableCheckboxGroupInput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 sortableRadioButtons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 sortableTableOutput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 sortableTabsetPanel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 updateOrderInput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Index 21 Animation_effects Animation effects. Description Allow element(s) to show animation effects. • jqui_effect(): Apply an animation effect to matched element(s). • jqui_hide(): Hide the matched element(s) with animation effect. • jqui_show(): Display the matched element(s) with animation effect. • jqui_toggle(): Display or hide the matched element(s) with animation effect. Usage jqui_effect(ui, effect, options = NULL, duration = 400, complete = NULL) jqui_show(ui, effect, options = NULL, duration = 400, complete = NULL) jqui_hide(ui, effect, options = NULL, duration = 400, complete = NULL) jqui_toggle(ui, effect, options = NULL, duration = 400, complete = NULL) Arguments ui The target ui element(s) to be manipulated. Can be • A string of jQuery_selector • A JS() wrapped javascript expression that returns a jQuery object. effect A string indicating which animation effect to use for the transition. options A list of effect-specific properties and easing. duration A string or number determining how long the animation will run. complete A function to call once the animation is complete, called once per matched ele- ment. Class_effects 3 Details These functions are R wrappers of effect(), hide(), show() and toggle() from jQuery UI li- brary. They should be used in server of a shiny document. Examples ## Not run: # in shiny ui create a plot plotOutput('foo') # in shiny server apply a 'bounce' effect to the plot jqui_effect('#foo', 'bounce') # in shiny server hide the plot with a 'fold' effect jqui_hide('#foo', 'fold') # in shiny server show the plot with a 'blind' effect jqui_show('#foo', 'blind') ## End(Not run) Class_effects Class effects. Description Manipulate specified class(es) to matched elements while animating all style changes. • jqui_add_class(): Add class(es). • jqui_remove_class(): Remove class(es). • jqui_switch_class(): Switch class(es). Usage jqui_add_class( ui, className, duration = 400, easing = "swing", complete = NULL ) jqui_remove_class( ui, className, duration = 400, easing = "swing", 4 Class_effects complete = NULL ) jqui_switch_class( ui, removeClassName, addClassName, duration = 400, easing = "swing", complete = NULL ) Arguments ui The target ui element(s) to be manipulated. Can be • A string of jQuery_selector • A JS() wrapped javascript expression that returns a jQuery object. className One or more class names (space separated) to be added to or removed from the class attribute of each matched element. duration A string or number determining how long the animation will run. easing A string indicating which easing function to use for the transition. complete A js function to call once the animation is complete, called once per matched element. removeClassName One or more class names (space separated) to be removed from the class at- tribute of each matched element. addClassName One or more class names (space separated) to be added to the class attribute of each matched element. Details These functions are the R wrappers of addClass(), removeClass() and switchClass() from jQuery UI library. They should be used in server of a shiny app. Examples ## Not run: # in shiny ui create a span tags$span(id = 'foo', 'class animation demo') # in shiny server add class 'lead' to the span jqui_add_class('#foo', className = 'lead') ## End(Not run) draggableModalDialog 5 draggableModalDialog Create a draggable modal dialog UI Description This creates the UI for a modal dialog similar to shiny::modalDialog except its content is draggable. Usage draggableModalDialog( ..., title = NULL, footer = shiny::modalButton("Dismiss"), size = c("m", "s", "l"), easyClose = FALSE, fade = TRUE ) Arguments ... UI elements for the body of the modal dialog box. title An optional title for the dialog. footer UI for footer. Use NULL for no footer. size One of "s" for small, "m" (the default) for medium, or "l" for large. easyClose If TRUE, the modal dialog can be dismissed by clicking outside the dialog box, or be pressing the Escape key. If FALSE (the default), the modal dialog can’t be dismissed in those ways; instead it must be dismissed by clicking on a modalButton(), or from a call to removeModal() on the server. fade If FALSE, the modal dialog will have no fade-in animation (it will simply appear rather than fade in to view). Value A modified shiny modal dialog UI with its content draggable. get_jqui_effects Get available animation effects. Description Use this function to get all animation effects in jQuery UI. Usage get_jqui_effects() 6 Interactions Value A character vector of effect names Interactions Mouse interactions Description Attach mouse-based interactions to shiny html tags, shiny input/output widgets or static htmlwidgets and provide ways to manipulate them. The interactions include: • draggable: Allow elements to be moved using the mouse. • droppable: Create targets for draggable elements. • resizable: Change the size of an element using the mouse. • selectable: Use the mouse to select elements, individually or in a group. • sortable: Reorder elements in a list or grid using the mouse. Usage jqui_draggable( ui, operation = c("enable", "disable", "destroy", "save", "load"), options = NULL ) jqui_droppable( ui, operation = c("enable", "disable", "destroy", "save", "load"), options = NULL ) jqui_resizable( ui, operation = c("enable", "disable", "destroy", "save", "load"), options = NULL ) jqui_selectable( ui, operation = c("enable", "disable", "destroy", "save", "load"), options = NULL ) jqui_sortable( ui, operation = c("enable", "disable", "destroy", "save", "load"), options = NULL ) Interactions 7 Arguments ui The target ui element(s) to be manipulated. Can be • A shiny.tag or shiny.tag.list object • A static htmlwidget object • A string of jQuery_selector • A JS() wrapped javascript expression that returns a jQuery object. operation A string to determine how to manipulate the mouse interaction. Can be one of enable, disable, destroy, save and load. Ignored when ui is a shiny.tag or shiny.tag.list object. See Details. options A list of interaction_specific_options. Ignored when operation is set as destroy. This parameter also accept a shiny option that controls the shiny input value re- turned from the element. See Details. Details The first parameter ui determines the target ui and working mode. If the target ui is a shiny.tag (e.g., shiny inputs/outputs or ui created by tags) or a shiny.tag.list (by tagList()) object or a static htmlwidget, the functions return the a modified ui object with interaction effects attached. When a jQuery_selector or a javascript expression is provided as the ui parameter, the functions first use it to locate the target ui element(s) in the shiny app, and then attach or manipulate the interactions. Therefore, you can use the first way in the ui of a shiny app to create elements with interaction effects (the ui mode), or use the second way in the server to manipulate the interactions (the server mode). The operation parameter is valid only in server mode. It determines how to manipulate the inter- action, which includes: • enable: Attach the corresponding mouse interaction to the target(s). • disable: Attach the interaction if not and disable it at once (only set the options). • destroy: Destroy the interaction. • save: Attach the interaction if not and save the current interaction state. • load: Attach the interaction if not and restore the target(s) to the last saved interaction state. With mouse interactions attached, the corresponding interaction states, e.g. position of draggable, size of resizable, selected of selectable and order of sortable, will be sent to server side in the form of input$<id>_<state>. The default values can be overridden by setting the shiny option in the options parameter. Please see the vignette Introduction to shinyjqui for more details. Value The same object passed in the ui parameter Examples library(shiny) library(highcharter) ## used in ui 8 Interactions jqui_resizable(actionButton('btn', 'Button')) jqui_draggable(plotOutput('plot', width = '400px', height = '400px'), options = list(axis = 'x')) jqui_selectable( div( id = 'sel_plots', highchartOutput('highchart', width = '300px'), plotOutput('ggplot', width = '300px') ), options = list( classes = list(`ui-selected` = 'ui-state-highlight') ) ) jqui_sortable(tags$ul( id = 'lst', tags$li('A'), tags$li('B'), tags$li('C') )) ## used in server ## Not run: jqui_draggable('#foo', options = list(grid = c(80, 80))) jqui_droppable('.foo', operation = "enable") ## End(Not run) ## use shiny input if (interactive()) { shinyApp( server = function(input, output) { output$foo <- renderHighchart({ hchart(mtcars, "scatter", hcaes(x = cyl, y = mpg)) }) output$position <- renderPrint({ print(input$foo_position) }) }, ui = fluidPage( verbatimTextOutput('position'), jqui_draggable(highchartOutput('foo', width = '200px', height = '200px')) ) ) } ## custom shiny input func <- JS('function(event, ui){return $(event.target).offset();}') options <- list( shiny = list( abs_position = list( dragcreate = func, # send returned value back to shiny when interaction is created. drag = func # send returned value to shiny when dragging. ) jqui_bookmarking 9 ) ) jqui_draggable(highchartOutput('foo', width = '200px', height = '200px'), options = options) jqui_bookmarking Enable bookmarking state of mouse interactions Description Enable shiny bookmarking_state of mouse interactions. By calling this function in server, the elements’ position, size, selection state and sorting state changed by mouse operations can be saved and restored through an URL. Usage jqui_bookmarking() jqui_icon Create a jQuery UI icon Description Create an jQuery UI pre-defined icon. For lists of available icons, see https://api.jqueryui. com/theming/icons/. Usage jqui_icon(name) Arguments name Class name of icon. The "ui-icon-" prefix can be omitted (i.e. use "ui-icon-flag" or "flag" to display a flag icon) Value An icon element 10 jqui_position Examples jqui_icon('caret-1-n') library(shiny) # add an icon to an actionButton actionButton('button', 'Button', icon = jqui_icon('refresh')) # add an icon to a tabPanel tabPanel('Help', icon = jqui_icon('help')) jqui_position Position an element relative to another Description Wrapper of the jQuery UI .position() method, allows you to position an element relative to the window, document, another element, or the cursor/mouse, without worrying about offset parents. Usage jqui_position( ui, my = "center", at = "center", of, collision = "flip", within = JS("$(window)") ) Arguments ui Which element to be positioned. Can be a string of jQuery_selector or a JS() wrapped javascript expression that returns a jQuery object. Only the first match- ing element will be used. my String. Defines which position on the element being positioned to align with the target element: "horizontal vertical" alignment. A single value such as "right" will be normalized to "right center", "top" will be normalized to "center top" (following CSS convention). Acceptable horizontal values: "left", "center", "right". Acceptable vertical values: "top", "center", "bottom". Example: "left top" or "center center". Each dimension can also contain offsets, in pixels or percent, e.g., "right+10 top-25%". Percentage offsets are relative to the element being positioned. at String. Defines which position on the target element to align the positioned el- ement against: "horizontal vertical" alignment. See the my option for full details on possible values. Percentage offsets are relative to the target element. orderInput 11 of Which element to position against. Can be a string of jQuery_selector or a JS() wrapped javascript expression that returns a jQuery object. Only the first matching element will be used. collision String. When the positioned element overflows the window in some direction, move it to an alternative position. Similar to my and at, this accepts a single value or a pair for horizontal/vertical, e.g., "flip", "fit", "fit flip", "fit none". • "flip": Flips the element to the opposite side of the target and the collision detection is run again to see if it will fit. Whichever side allows more of the element to be visible will be used. • "fit": Shift the element away from the edge of the window. • "flipfit": First applies the flip logic, placing the element on whichever side allows more of the element to be visible. Then the fit logic is applied to ensure as much of the element is visible as possible. • "none": Does not apply any collision detection. within Element to position within, affecting collision detection. Can be a string of jQuery_selector or a JS() wrapped javascript expression that returns a jQuery object. Only the first matching element will be used. orderInput Create a shiny input control to show the order of a set of items Description Display a set of items whose order can be changed by drag and drop inside or between orderInput(s). The item order is send back to server in the from of input$inputId. Usage orderInput( inputId, label, items, as_source = FALSE, connect = NULL, item_class = c("default", "primary", "success", "info", "warning", "danger"), placeholder = NULL, width = "500px", legacy = FALSE, ... ) Arguments inputId The input slot that will be used to access the current order of items. label Display label for the control, or NULL for no label. 12 orderInput items Items to display, can be a list, an atomic vector or a factor. For list or atomic vector, if named, the names are displayed and the order is given in values. For factor, values are displayed and the order is given in levels as_source A boolean value to determine whether the orderInput is set as source mode. Only works if the connect argument was set. connect Optional. Allow items to be dragged between orderInputs. Should be a vec- tor of inputId(s) of other orderInput(s) that the items from this orderInput should be connected to. item_class One of the Bootstrap color utility classes to apply to each item. placeholder A character string to show when there is no item left in the orderInput. width The width of the input, e.g. ’400px’, or ’100\ shiny::validateCssUnit. legacy A boolean value. Whether to use the old version of the orderInput function. ... Arguments passed to shiny::tags$div which is used to build the container of the orderInput. Details orderInputs can work in either connected mode or stand-alone mode. In stand-alone mode, items can only be drag and drop inside the input control. In connected mode, items to be dragged between orderInputs, which is controlled by the connect parameter. This is a one-way relationship. To connect items in both directions, the connect parameter must be set in both orderInputs. When in connected mode, orderInput can be set as source-only through the as_source parameter. The items in a "source" orderInput can only be copied, instead of moved, to other connected non- source orderInput(s). From shinyjqui v0.4.0, A "source" orderInput will become a "recycle bin" for items from other orderInputs as well. This means, if you want to delete an item, you can drag and drop it into a "source" orderInput. This feature can be disabled by setting the options of non-source orderInput(s) as list(helper = "clone"). From shinyjqui v0.4.0 and above, the orderInput function was implemented in the similar way as other classical shiny inputs, which brought two changes: 1. The input value was changed from input$inputId_order to input$inputId; 2. The new version supports updateOrderInput function which works in the same way as other shiny input updater functions. To keep the backward compatibility, a legacy argument was provided if user wanted to use the old version. Value An orderInput control that can be added to a UI definition. Examples orderInput('items1', 'Items1', items = month.abb, item_class = 'info') ## build connections between orderInputs orderInput('items2', 'Items2 (can be moved to Items1 and Items4)', items = month.abb, connect = c('items1', 'items4'), item_class = 'primary') selectableTableOutput 13 ## build connections in source mode orderInput('items3', 'Items3 (can be copied to Items2 and Items4)', items = month.abb, as_source = TRUE, connect = c('items2', 'items4'), item_class = 'success') ## show placeholder orderInput('items4', 'Items4 (can be moved to Items2)', items = NULL, connect = 'items2', placeholder = 'Drag items here...') selectableTableOutput Create a table output element with selectable rows or cells Description Render a standard HTML table with its rows or cells selectable. The server will receive the index of selected rows or cells stored in input$<outputId>_selected. Usage selectableTableOutput(outputId, selection_mode = c("row", "cell")) Arguments outputId output variable to read the table from selection_mode one of "row" or "cell" to define either entire row or individual cell can be selected. Details Use mouse click to select single target, lasso (mouse dragging) to select multiple targets, and Ctrl + click to add or remove selection. In row selection mode, input$<outputId>_selected will receive the selected row index in the form of numeric vector. In cell selection mode, input$<outputId>_selected will receive a dataframe with rows and columns index of each selected cells. Value A table output element that can be included in a panel See Also shiny::tableOutput, sortableTableOutput Examples ## Only run this example in interactive R sessions if (interactive()) { shinyApp( ui = fluidPage( verbatimTextOutput("selected"), selectableTableOutput("tbl") 14 sortableCheckboxGroupInput ), server = function(input, output) { output$selected <- renderPrint({input$tbl_selected}) output$tbl <- renderTable(mtcars, rownames = TRUE) } ) } sortableCheckboxGroupInput Create a Checkbox Group Input Control with Sortable Choices Description Render a group of checkboxes with multiple choices toggleable. The choices are also sortable by drag and drop. In addition to the selected values stored in input$<inputId>, the server will also receive the order of choices in input$<inputId>_order. Usage sortableCheckboxGroupInput( inputId, label, choices = NULL, selected = NULL, inline = FALSE, width = NULL, choiceNames = NULL, choiceValues = NULL ) Arguments inputId The input slot that will be used to access the value. label Display label for the control, or NULL for no label. choices List of values to show checkboxes for. If elements of the list are named then that name rather than the value is displayed to the user. If this argument is provided, then choiceNames and choiceValues must not be provided, and vice-versa. The values should be strings; other types (such as logicals and numbers) will be coerced to strings. selected The values that should be initially selected, if any. inline If TRUE, render the choices inline (i.e. horizontally) width The width of the input, e.g. '400px', or '100%'; see validateCssUnit(). sortableRadioButtons 15 choiceNames List of names and values, respectively, that are displayed to the user in the app and correspond to the each choice (for this reason, choiceNames and choiceValues must have the same length). If either of these arguments is provided, then the other must be provided and choices must not be provided. The advantage of using both of these over a named list for choices is that choiceNames allows any type of UI object to be passed through (tag objects, icons, HTML code, ...), instead of just simple text. See Examples. choiceValues List of names and values, respectively, that are displayed to the user in the app and correspond to the each choice (for this reason, choiceNames and choiceValues must have the same length). If either of these arguments is provided, then the other must be provided and choices must not be provided. The advantage of using both of these over a named list for choices is that choiceNames allows any type of UI object to be passed through (tag objects, icons, HTML code, ...), instead of just simple text. See Examples. Value A list of HTML elements that can be added to a UI definition See Also shiny::checkboxGroupInput, sortableRadioButtons(), sortableTableOutput(), sortableTabsetPanel() Examples ## Only run this example in interactive R sessions if (interactive()) { shinyApp( ui = fluidPage( sortableCheckboxGroupInput("foo", "SortableCheckboxGroupInput", choices = month.abb), verbatimTextOutput("order") ), server = function(input, output) { output$order <- renderPrint({input$foo_order}) } ) } sortableRadioButtons Create radio buttons with sortable choices Description Create a set of radio buttons used to select an item from a list. The choices are sortable by drag and drop. In addition to the selected values stored in input$<inputId>, the server will also receive the order of choices in input$<inputId>_order. 16 sortableRadioButtons Usage sortableRadioButtons( inputId, label, choices = NULL, selected = NULL, inline = FALSE, width = NULL, choiceNames = NULL, choiceValues = NULL ) Arguments inputId The input slot that will be used to access the value. label Display label for the control, or NULL for no label. choices List of values to select from (if elements of the list are named then that name rather than the value is displayed to the user). If this argument is provided, then choiceNames and choiceValues must not be provided, and vice-versa. The values should be strings; other types (such as logicals and numbers) will be coerced to strings. selected The initially selected value. If not specified, then it defaults to the first item in choices. To start with no items selected, use character(0). inline If TRUE, render the choices inline (i.e. horizontally) width The width of the input, e.g. '400px', or '100%'; see validateCssUnit(). choiceNames List of names and values, respectively, that are displayed to the user in the app and correspond to the each choice (for this reason, choiceNames and choiceValues must have the same length). If either of these arguments is provided, then the other must be provided and choices must not be provided. The advantage of using both of these over a named list for choices is that choiceNames allows any type of UI object to be passed through (tag objects, icons, HTML code, ...), instead of just simple text. See Examples. choiceValues List of names and values, respectively, that are displayed to the user in the app and correspond to the each choice (for this reason, choiceNames and choiceValues must have the same length). If either of these arguments is provided, then the other must be provided and choices must not be provided. The advantage of using both of these over a named list for choices is that choiceNames allows any type of UI object to be passed through (tag objects, icons, HTML code, ...), instead of just simple text. See Examples. Value A set of radio buttons that can be added to a UI definition. See Also shiny::radioButtons, sortableCheckboxGroupInput, sortableTableOutput, sortableTabsetPanel sortableTableOutput 17 Examples ## Only run this example in interactive R sessions if (interactive()) { shinyApp( ui = fluidPage( sortableRadioButtons("foo", "SortableRadioButtons", choices = month.abb), verbatimTextOutput("order") ), server = function(input, output) { output$order <- renderPrint({input$foo_order}) } ) } sortableTableOutput Create a table output element with sortable rows Description Render a standard HTML table with table rows sortable by drag and drop. The order of table rows is recorded in input$<outputId>_order. Usage sortableTableOutput(outputId) Arguments outputId output variable to read the table from Value A table output element that can be included in a panel See Also shiny::tableOutput, sortableRadioButtons, sortableCheckboxGroupInput, sortableTabsetPanel, se- lectableTableOutput Examples ## Only run this example in interactive R sessions if (interactive()) { shinyApp( ui = fluidPage( verbatimTextOutput("rows"), sortableTableOutput("tbl") 18 sortableTabsetPanel ), server = function(input, output) { output$rows <- renderPrint({input$tbl_row_index}) output$tbl <- renderTable(mtcars, rownames = TRUE) } ) } sortableTabsetPanel Create a tabset panel with sortable tabs Description Create a tabset that contains shiny::tabPanel elements. The tabs are sortable by drag and drop. In addition to the activated tab title stored in input$<id>, the server will also receive the order of tabs in input$<id>_order. Usage sortableTabsetPanel( ..., id = NULL, selected = NULL, type = c("tabs", "pills", "hidden"), header = NULL, footer = NULL ) Arguments ... tabPanel() elements to include in the tabset id If provided, you can use input$id in your server logic to determine which of the current tabs is active. The value will correspond to the value argument that is passed to tabPanel(). selected The value (or, if none was supplied, the title) of the tab that should be selected by default. If NULL, the first tab will be selected. type "tabs" Standard tab look "pills" Selected tabs use the background fill color "hidden" Hides the selectable tabs. Use type = "hidden" in conjunction with tabPanelBody() and updateTabsetPanel() to control the active tab via other input controls. (See example below) header Tag or list of tags to display as a common header above all tabPanels. footer Tag or list of tags to display as a common footer below all tabPanels updateOrderInput 19 Value A tabset that can be passed to shiny::mainPanel See Also shiny::tabsetPanel, sortableRadioButtons, sortableCheckboxGroupInput, sortableTableOutput Examples ## Only run this example in interactive R sessions if (interactive()) { shinyApp( ui = fluidPage( sortableTabsetPanel( id = "tabs", tabPanel(title = "A", "AAA"), tabPanel(title = "B", "BBB"), tabPanel(title = "C", "CCC") ), verbatimTextOutput("order") ), server = function(input, output) { output$order <- renderPrint({input$tabs_order}) } ) } updateOrderInput Change the value of an orderInput on the client Description Similar to the input updater functions of shiny package, this function send a message to the client, telling it to change the settings of an orderInput object. Any arguments with NULL values will be ignored; they will not result in any changes to the input object on the client. The function can’t update the "source" orderInputs. Usage updateOrderInput( session, inputId, label = NULL, items = NULL, connect = NULL, item_class = NULL ) 20 updateOrderInput Arguments session The session object passed to function given to shinyServer. inputId The input slot that will be used to access the current order of items. label Display label for the control, or NULL for no label. items Items to display, can be a list, an atomic vector or a factor. For list or atomic vector, if named, the names are displayed and the order is given in values. For factor, values are displayed and the order is given in levels connect Optional. Allow items to be dragged between orderInputs. Should be a vec- tor of inputId(s) of other orderInput(s) that the items from this orderInput should be connected to. item_class One of the Bootstrap color utility classes to apply to each item. Examples library(shiny) if (interactive()) { ui <- fluidPage( orderInput("foo", "foo", items = month.abb[1:3], item_class = 'info'), verbatimTextOutput("order"), actionButton("update", "update") ) server <- function(input, output, session) { output$order <- renderPrint({input$foo}) observeEvent(input$update, { updateOrderInput(session, "foo", items = month.abb[1:6], item_class = "success") }) } shinyApp(ui, server) } Index Animation_effects, 2 sortableCheckboxGroupInput, 14, 16, 17, 19 Class_effects, 3 sortableRadioButtons, 15, 17, 19 sortableRadioButtons(), 15 draggableModalDialog, 5 sortableTableOutput, 13, 16, 17, 19 sortableTableOutput(), 15 get_jqui_effects, 5 sortableTabsetPanel, 16, 17, 18 sortableTabsetPanel(), 15 Interactions, 6 tabPanel(), 18 jqui_add_class (Class_effects), 3 tabPanelBody(), 18 jqui_bookmarking, 9 tagList(), 7 jqui_draggable (Interactions), 6 tags, 7 jqui_droppable (Interactions), 6 jqui_effect (Animation_effects), 2 updateOrderInput, 12, 19 jqui_hide (Animation_effects), 2 updateTabsetPanel(), 18 jqui_icon, 9 jqui_position, 10 validateCssUnit(), 14, 16 jqui_remove_class (Class_effects), 3 jqui_resizable (Interactions), 6 jqui_selectable (Interactions), 6 jqui_show (Animation_effects), 2 jqui_sortable (Interactions), 6 jqui_switch_class (Class_effects), 3 jqui_toggle (Animation_effects), 2 JS(), 2, 4, 7, 10, 11 orderInput, 11, 19 removeModal(), 5 selectableTableOutput, 13, 17 shiny::checkboxGroupInput, 15 shiny::mainPanel, 19 shiny::modalDialog, 5 shiny::radioButtons, 16 shiny::tableOutput, 13, 17 shiny::tabPanel, 18 shiny::tabsetPanel, 19 shiny::validateCssUnit, 12 21

svyweight

cran

Package ‘svyweight’ October 14, 2022 Title Quick and Flexible Survey Weighting Version 0.1.0 Description Quickly and flexibly calculates weights for survey data, in order to correct for survey non-response or other sampling issues. Uses rake weighting, a common technique also know as rim weighting or iterative proportional fitting. This technique allows for weighting on multiple variables, even when the interlocked distribution of the two variables is not known. Interacts with Thomas Lumley's 'survey' package, as described in Lumley, Thomas (2011, ISBN:978-1-118-21093- 2). Adds additional functionality, more adaptable syntax, and error-checking to the base weighting functionality in 'survey.' License GPL-3 Encoding UTF-8 LazyData true Imports survey, gdata, stats Depends R (>= 3.5.00) Suggests dplyr, srvyr, testthat, mice RoxygenNote 7.1.2 NeedsCompilation no Author Ben Mainwaring [aut, cre] Maintainer Ben Mainwaring <mainwaringb@gmail.com> Repository CRAN Date/Publication 2022-05-03 10:00:02 UTC R topics documented: as.w8margin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 eff_n . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 gles17 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 impute_w8margin . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 rakesvy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 svyweight . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 w8margin_matched . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 1 2 as.w8margin Index 14 as.w8margin Weight Margin Objects Description Creates an object of class w8margin. Represents the desired target distribution of a categorical vari- able, after weighting (as a counts, not percentage). w8margin objects are in the format required by the ’survey’ package’s survey::rake()and survey::postStratify(), and are intended mostly for use with these functions. Methods exist for numeric vectors, matrices, and data frames (see details). Usage as.w8margin( target, varname, levels = NULL, samplesize = NULL, na.allow = FALSE, rebase.tol = 0.01, ... ) ## S3 method for class 'data.frame' as.w8margin( target, varname, levels = NULL, samplesize = NULL, na.allow = FALSE, rebase.tol = 0.01, ... ) ## S3 method for class 'numeric' as.w8margin( target, varname, levels = NULL, samplesize = NULL, na.allow = FALSE, rebase.tol = 0.01, ... ) ## S3 method for class 'matrix' as.w8margin 3 as.w8margin( target, varname, levels = NULL, samplesize = NULL, na.allow = FALSE, rebase.tol = 0.01, byrow = TRUE, ... ) Arguments target Numbers specifying the desired target distribution of a categorical variable, after rake weighting. Can be a numeric vector, numeric matrix, or data frame with one (and only one) numeric column. Unless levels is specified, vectors and matrices must be named, and data frames must have a character or factor column specifying names. See details. varname Character vector specifying the name of the observed variable that the w8margin object should match. Can take a NULL value for data frames, in which case the original column name is used. levels Optional character vector, specifying which numeric elements of target match with each factor level in the observed data. Overrides names specified in target. samplesize Numeric with the desired target sample size for the w8margin object. Defaults to the sum of target. na.allow Logical specifying whether NA values should be allowed in w8margin objects. If TRUE, w8margin objects must be imputed (such as with impute_w8margin() before they can be used for weighting. rebase.tol Numeric between 0 and 1. If targets are rebased, and the rebased sample sizes differs from the original sample size by more than this percentage, generates a warning. ... Other method-specific arguments, currently not used byrow Logical, specifying how matrix targets should be converted to vectors. If FALSE, the elements within columns will be adjacent in the resulting w8margin object, otherwise elements within rows will be adjacent. Details w8margin objects are inputs to the survey::rake()and survey::postStratify(). These func- tions require a specific, highly-structured input format. For flexibility, as.w8margin() can be used to convert a variety of common inputs into the format needed by these functions. as.w8margin() has methods for numeric vectors, numeric matrices, and data frames. Each method has multiple ways of determining how to match numeric elements of target with factor levels in the observed data. For numeric vector and matrix inputs, the default is to match based on the name of each element (for vectors) or the interaction of row and column names of each element (for matrices). These names can be overridden by specifying the levels parameter. 4 as.w8margin Data frame inputs must have either one or two columns. Two-column data frames must have one numeric column and one character column. The numeric column specifies the target distribution, while the character column specifies how to match numeric elements with factor levels in the ob- served data. If varname is NULL, a default value will be taken from the name of the non-numeric column. One-column data frames must have a numeric column. Row names are converted to a character column in order to match numeric elements with factor levels in the observed data. One-column data frames must specify a varname parameter, and (unless levels is specified) must have non- default row names. The levels parameter can be used with both one- and two-column data frames. Technically, w8margin objects are data frames with two columns. The first column specifies levels in the observed factor variable, and the name of the first column indicates the name of the observed factor variable. The second column is named "Freq" and indicates the desired post-raking frequency of each category (as a count rather than percentage). The structure is designed for compatibility with the ’survey’ package. Because frequency is specified as a count, rakesvy() and rakew8() re-call as.w8margin() whenever weighting a data set to a new observed sample size. Weight margins must be manually re-calculated for new sample sizes when using survey::postStratify() or rake. Value An object of class w8margin, with specified variable name and sample size. Examples # Convert vector of percentages to w8margin turnout2013_w8margin <- as.w8margin( c(voted = .715, `did not vote` = .285, ineligible = NA), varname = "turnout2013", na.allow = TRUE, samplesize = 1500) # Convert matrix of percentages to w8margin gender_educ_mat <- matrix( c(.15, .17, .17, .01, .19, .16, .14, .01), nrow = 2, byrow = TRUE, dimnames = list(c("Male", "Female"), c("Low", "Medium", "High", NA))) gender_educ_w8margin <- as.w8margin(gender_educ_mat, varname = "gender_educ", samplesize = 2179) # Convert data frame of counts to w8margin # Keep default values for samplesize and varname region_df <- data.frame( eastwest = c("east", "west"), Freq = c(425, 1754)) region_w8margin <- as.w8margin(region_df, levels = c("East Germany", "West Germany"), varname = NULL) # Calculate rake weights using w8margin objects (without NAs) require(survey) gles17_dweighted <- svydesign(ids = gles17$vpoint, weights = gles17$dweight, eff_n 5 strata = gles17$eastwest, data = gles17, nest = TRUE) rake(design = gles17_dweighted, sample.margins = list(~gender_educ, ~eastwest), population.margins = list(gender_educ_w8margin, region_w8margin)) eff_n Effective Sample Size and Weighting Efficiency Description Computes Kish’s effective sample size or weighting efficiency for a survey.design object. Usage eff_n(design) weight_eff(design) Arguments design An svydesign object, presumably with design or post-stratification weights. Details Kish’s effective sample size is a frequently-used, general metric to indicate how much uncertainty and error increase due to weighting. Effective sample size is calculated as sum(weights(design))^2 / sum(weights(design)^2). Weighting efficiency is eff_n(design) / sum(weights(design)). While weighting efficency and effective sample size are frequently use, they are less valid than the standard errors produced by survey::svymean() and related functions from the survey package. In particular, they ignore clustering and stratification in sample designs, and covariance between weighting variables and outcome variables. As such, these metrics should be used with caution Value A numeric value, indicating effective sample size (for eff_n()) or weighting efficiency (for weight_eff()) References Kish, Leslie. 1965. Survey Sampling New York: Wiley. 6 gles17 Examples gles17_weighted <- rakesvy(design = gles17, gender ~ c("Male" = .495, "Female" = .505), eastwest ~ c("East Germany" = .195, "West Germany" = .805) ) eff_n(gles17_weighted) weight_eff(gles17_weighted) gles17 Partial Data from the 2017 German Election Survey Description Partial data from the pre-election 2017 wave of the German Longitudinal Election Study (GLES). Includes variables for vote in the 2013 German federal election to the Bundestag (lower house of parliament) - specifically the ’second vote’. Also includes other demographics that might be used for weighting, such as gender, birth year, and state. Each row in the dataset is a unique respondent who completed the survey. Usage gles17 Format A data frame with 2179 rows and 11 columns: gender gender educ educational attainment, based on kind of secondary school from which respondent graduated gender_educ interaction of gender and education attainment birthyear four-digit birth year votingage eligibility to vote in the (upcoming) 2017 German federal elections agecat approximate age category in 2017, estimated from birth year state state the respondent lives in eastwest whether the respondent lives in East or West Germany vote2013 respondent’s reported vote in 2013 (specifically the ’second vote’) turnout2013 whether the respondent actually voted in 2013 votecurrent party the respondent plans to vote for in the upcoming (2017) election intnum unique code for the interviewer who conducted an interview vpoint unique code anonymously identifying census block where an interview was conducted hhsize number of people in the household dweight nationally-representative design weight supplied by the GLES study authors ... impute_w8margin 7 Source GLES data and documentation is available at https://gles-en.eu/download-data/vor-und-nachwahlquerschnitt-20 Data is taken from the pre-election wave, file ZA6800, for a limited number of variables. Note that most documentation is available in English, but some may be in German only. impute_w8margin Impute NAs in w8margin Object Description Imputes NA values in a weight target (in w8margin form), based on the observed distribution of the variable in a dataset. Usage impute_w8margin(w8margin, observed, weights = NULL, rebase = TRUE) Arguments w8margin w8margin object, with NA values that should be imputed based on observed data. observed factor or character vector, containing observed data used for imputing targets. weights numeric vector of weights, the same length as observed, to be used when com- puting the distribution of the observed variable. NULL is equivalent to a vector where all elements are 1, and indicates the data is unweighted. rebase logical, indicating whether non-NA weight targets should be adjusted so that the total target sample size is unchanged (rebase = TRUE), or whether non-NA weight targets should remain the same and total target sample size increases. Details Any NA target frequencies in w8margin are imputed using the percentage distribution in observed, from svytable(~observed, Ntotal = 1, ...). The percentage is multiplied by the desired target sample size. For example, if has a target of NA and a desired total sample of 1500, and the observed frequency of the weighting variable is 0%, the imputed target will be (10% * 1500). If a weights argument is provided, then weighted percentage distributions are used; this may be useful when design weights are present, or when first raking on variables with complete targets. If rebase == TRUE (the default), targets for non-NA categories are scaled down so that the total target frequency (sum(w8margin$Freq, na.rm = TRUE)) remains constant, after imputing new cat- egory targets. If rebase == FALSE, targets for non-NA categories remain constant, and the total target frequency will increase. There is an important theoretical distinction between missing targets for conceptually valid cate- gories, versus missing observed data due to non-response or refusal. It is only conceptually ap- propriate to impute targets if the targets themselves are missing. When handling missing observed data, multiple imputation techniques (such as mice::mice()) will often produce better results, ex- cept when missingness is closely related to weighting variable (technically referred to as "missing not at random"). 8 rakesvy Value A w8margin object, where NA target frequencies have been replaced using the observed distribution of the weighting variable. Examples turnout_w8margin <- as.w8margin( c(voted = .715, `did not vote` = .285, ineligible = NA), varname = "turnout2013", na.allow = TRUE, samplesize = 1500) impute_w8margin(turnout_w8margin, observed = gles17$turnout2013) rakesvy Flexibly Calculate Rake Weights Description Calculate rake weights on a data frame, or on a survey.design object from survey::svydesign(). Targets may be counts or percentages, in vector, matrix, data frame, or w8margin form. Before weighting, targets are converted to w8margins, checked for validity, and matched to variables in observed data, rakesvy returns a weighted svydesign object, while rakew8 returns a vector of weights. Usage rakesvy( design, ..., samplesize = "from.data", match.levels.by = "name", na.targets = "fail", rebase.tol = 0.01, control = list(maxit = 10, epsilon = 1, verbose = FALSE) ) rakew8( design, ..., samplesize = "from.data", match.levels.by = "name", na.targets = "fail", rebase.tol = 0.01, control = list(maxit = 10, epsilon = 1, verbose = FALSE) ) rakesvy 9 Arguments design A survey.design object from survey::svydesign(), or a data frame that can be coerced to one. When a data frame is coerced, the coercion assuming no clustering or design weighting. ... Formulas specifying weight targets, with an object that can be coerced to class w8margin (see as.w8margin()) on the right-hand side, and (optionally) a match- ing variable or transformation of it on the left-hand side. Objects that can be coerced to w8margin include named numeric vectors and matrices, and data frames in the format accepted by rake. samplesize Either a number specifying the desired post-raking sample size, or a charac- ter string "from.data" or "from.targets" specifying how to calculate the desired sample size (see details). match.levels.by A character string that specifies how to match levels in the target with the ob- served data, either "name" (the default) or "order" (see details). na.targets A characters string that specifies how to handle NAs in targets, either "fail" (the default) or "observed" (see details). rebase.tol Numeric between 0 and 1. If targets are rebased, and the rebased sample sizes differs from the original sample size by more than this percentage, generates a warning. control Parameters passed to the control argument of survey::rake(), to control the details of convergence in weighting. Details rakesvy and rakew8 wrangles observed data and targets into compatible formats, before using survey::rake() to make underlying weighting calculations. The function matches weight tar- gets to observed variables, cleans both targets and observed variables, and then checks the validity of weight targets (partially by calling w8margin_matched()) before raking. It also allows a weight target of zero, and assigns an automatic weight of zero to cases on this target level. Weight target levels can be matched with observed variable levels in two ways, specified via the match.levels.by parameter. "name" (the default) matches based on name, disregarding order (so a "male" level in the weight target will be matched with a "male" level in the observed data). "order" matches based on order, disregarding name (so the first level or row of the target will match with the first level of the observed factor variable). By default, with parameter na.targets = "fail"), an NA in weight targets will cause an error. With na.targets = "observed", rakesvy() and rakew8() will instead compute a target that matches the observed data. The category with an NA target will therefore have a similar incidence rate in the pre-raking and post-raking dataset. This is accomplished by calling impute_w8margin() before raking; see the impute_w8margin documentation for more details. Note that NAs in observed data (as opposed to targets) will always cause failure, and are not affected by this parameter. The desired sample size (in other words, the desired sum of weights after raking) is specified via the samplesize parameter. This can be a numeric value. Alternatively, "from.data" specifies that the observed sample size before weighting (taken from sum(weights(design)) if applicable, or nrow() if not); "from.targets" specifies that the total sample sizes in target objects should be fol- lowed, and should only be used if all targets specify the same sample size. 10 svyweight Value rakesvy() returns a survey.design object with rake weights applied. Any changes made to the variables in design in order to call rake, such as dropping empty factor levels, are temporary and not returned in the output object. rakew8() returns a vector of weights. This avoids creating duplicated survey.design objects, which can be useful when calculating multiple sets of weights for the same data. Examples # Computing only rake weights # EG, for a survey conducted with simple random sampling gles17$simple_weight <- rakew8(design = gles17, gender ~ c("Male" = .495, "Female" = .505), eastwest ~ c("East Germany" = .195, "West Germany" = .805) ) # Specifying a recode of variable in observed dataset require(dplyr) gles17_raked <- rakesvy(design = gles17, gender ~ c("Male" = .495, "Female" = .505), dplyr::recode(agecat, `<=29` = "<=39", `30-39` = "<=39") ~ c("<=39" = .31, "40-49" = .15, "50-59" = .19, "60-69" = .15, ">=70" = .21) ) # Computing rake weights after design weights # EG, for a survey with complex sampling design require(survey) gles17_dweighted <- svydesign(ids = gles17$vpoint, weights = gles17$dweight, strata = gles17$eastwest, data = gles17, nest = TRUE) gles17_raked <- rakesvy(design = gles17_dweighted, gender ~ c("Male" = .495, "Female" = .505), agecat ~ c("<=29" = .16, "30-39" = .15, "40-49" = .15, "50-59" = .19, "60-69" = .15, ">=70" = .21) ) # With unnamed target levels, using match.levels.by = "order" rakew8(design = gles17, gender ~ c(.495, .505), eastwest ~ c(.195, .805), match.levels.by = "order" ) svyweight svyweight: Quick and Flexible Rake Weighting Description svyweight is a package for quickly and flexibly calculating rake weights (also know as rim weights). It is designed to interact with survey.design objects generated via survey::svydesign(), and other to otherwise build on functionalities from Thomas Lumley’s ’survey’ package. svyweight 11 Rake weighting concepts Post-stratification weights are commonly used in survey research to ensure that sample is repre- sentative of the population it is drawn from, in cases where some people selected for inclusion in a sample might decline to participate. To calculate post-stratification weights, observed categori- cal variables in a survey dataset (usually demographic variables) must be matched with "targets" (typically known population demographics from census data). Survey respondents from underrep- resented categories are upweighted, while respondents from overrepresented categories are down- weighted. svyweight implements "rake" or "rim" weighting (sometimes known as iterative proportional fit- ting). This is a widely-used method for simultaneously calculating weights on multiple variables, when no join distribution for these variables is known. For example, population data on past vote (from election results) and age (from the census) are generally known. However, as the joint dis- tribution of past vote and age is not generally known, a technique such as rake weighting must be used to apply weights on both variables simultaneously. Package features The core function in svyweight is rakesvy() (and the related rakew8(). This takes calculates post- stratification weights given A) data frame or a survey.design object generated by svydesign(), and B) a set of weighting targets The command is designed to make weighting as simple as possible, with the following features: • Weighting to either counts or percentage targets • Allowing specification of targets as vectors, matrices, or data frames • Accepting targets of 0 (equivalent to dropping cases from analysis) • Allowing targets to be quickly rebased a specified sample size • Flexibly matching targets to the correct variables in a dataset • Dynamically specifying weight targets based on recodes of variables in observed data The package does this in part by introducing the w8margin object class. A w8margin is a desired raw count of categories for a variable, in the format required for actually computing weights. How- ever, this format is somewhat cumbersome to specify manually. The package includes methods for converting named vectors, matrices, and data frames to w8margin object; [rakesvy()] and rakew8() call these methods automatically. At present, the core weighting calculations are actually performed via the ’survey’ package’s survey::rake() function. This might change with future releases, although the basic approach to iterative weighting is not expected to change. The package is under development. Contributions to the package, or suggestions for additional features, are gratefully accepted via email or GitHub. Author(s) Ben Mainwaring (<mainwaringb@gmail.com>, https://www.linkedin.com/in/mainwaringb) References Lumley, Thomas. 2011. Complex Surveys: A Guide to Analysis Using R. New York: Wiley. 12 w8margin_matched See Also Package GitHub repository: https://github.com/mainwaringb/svyweight w8margin_matched Check if w8margin Matches Observed Data Description Checks whether specified w8margin object and variable in observed data are compatible, and are expected to produce valid call to rake. Returns a logical true/false, and generates warning messages to specify likely issues. Intended to help quickly diagnose incompatibilities between w8margins and observed data. Usage w8margin_matched(w8margin, observed, refactor = FALSE, na.targets.allow = FALSE, zero.targets.allow = FALSE) Arguments w8margin w8margin object, or other object type that can be coerced to w8margin with a temporary variable name. observed factor vector (or, if refactor = TRUE, a vector that can be coerced to factor). refactor logical, specifying whether to factor observed variable before checking match. na.targets.allow logical, indicating whether NA values in target should produce error (FALSE, the default) or be allowed. NA values are never allowed in observed data. zero.targets.allow logical, indicating whether zero values in target should produce error (FALSE, the default) or be allowed. Details With default parameters (na.targets.allow = FALSE, zero.targets.allow = FALSE, and refactor = FALSE), the function checks whether a w8margin object is in the strict format required by rake; this format will also be accepted by rakesvy() and rakew8(). Changing the default parameters relaxes some checks. With the parameters altered, the function will only assess whether w8margin objects are usable by rakesvy() and rakew8(), which accept a more flexible range of target for- mats. It should not generally be necessary to call w8margin_matched() manually when using rakesvy() and rakew8() to compute weights. However, may be useful to call directly, when manually calling underlying weighting functions from the survey package, or for diagnostic purposes. Value A logical, indicating whether w8margin is compatible with observed. w8margin_matched 13 Examples gender_w8margin <- as.w8margin( c(Male = .49, Female = .51), varname = "gender", samplesize = 2179) # Returns TRUE w8margin_matched(gender_w8margin, gles17$gender) gender_w8margin_alt <- as.w8margin( c(man = .49, woman = .51), varname = "gender", samplesize = 2179) # Returns FALSE - level names in gles17$gender do not match level names in gender_w8margin_alt w8margin_matched(gender_w8margin_alt, gles17$gender) agecat_50plus_w8margin <- as.w8margin( c("50-59" = .35, "60-69" = .27, ">=70" = .38), varname = "educ", samplesize = 2179 ) gles17_50plus <- gles17[gles17$agecat %in% c("50-59", "60-69", ">=70"),] # Returns FALSE - gles17$agecat has empty factor levels for <=29, 30-39, 40-49 w8margin_matched(agecat_50plus_w8margin, gles17_50plus$agecat) # Returns TRUE - gles17$agecat is refactored to drop empty levels w8margin_matched(agecat_50plus_w8margin, gles17_50plus$agecat, refactor = TRUE) Index ∗ datasets gles17, 6 as.w8margin, 2 as.w8margin(), 9 eff_n, 5 gles17, 6 impute_w8margin, 7 impute_w8margin(), 3, 9 mice::mice(), 7 rake, 4, 12 rakesvy, 8 rakesvy(), 4, 11, 12 rakew8 (rakesvy), 8 rakew8(), 4, 11, 12 survey::postStratify(), 2–4 survey::rake(), 2, 3, 9, 11 survey::svydesign(), 8–10 survey::svymean(), 5 svydesign, 5 svyweight, 10 w8margin, 7, 11, 12 w8margin (as.w8margin), 2 w8margin_matched, 12 w8margin_matched(), 9 weight_eff (eff_n), 5 14

hydroToolkit

cran

Package ‘hydroToolkit’ October 13, 2022 Type Package Title Hydrological Tools for Handling Hydro-Meteorological Data from Argentina and Chile Version 0.1.0 Date 2020-05-07 Author Ezequiel Toum <etoum@mendoza-conicet.gob.ar> Maintainer Ezequiel Toum <etoum@mendoza-conicet.gob.ar> Description Read, plot, manipulate and process hydro-meteorological data from Argentina and Chile. Depends R (>= 2.10) License GPL (>= 3) Imports ggplot2, plotly, lubridate, utils, methods, readxl, reshape2 Encoding UTF-8 LazyData true RoxygenNote 7.1.0 NeedsCompilation no Repository CRAN Date/Publication 2020-05-16 10:00:02 UTC R topics documented: agg_hydroMet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 agg_serie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 build_hydroMet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 create_hydroMet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 fill_serie . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 fill_value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 get_hydroMet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 hydroMet-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 hydroMet_BDHI-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 hydroMet_compact-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 hydroMet_CR2-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 1 2 agg_hydroMet hydroMet_DGI-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 hydroMet_IANIGLA-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 hydro_year_DGI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 interpolate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 modify_hydroMet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 movAvg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 plot_hydroMet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 precip_cumsum . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 precip_hydroMet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Qmm_to_Dm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 read_BDHI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 read_CR2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 read_DGI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 read_IANIGLA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 report_hydroMet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 report_miss_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 rm_spikes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 set_hydroMet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 set_threshold . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 subset_hydroMet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 swe_to_melt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 swe_to_precip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 Index 44 agg_hydroMet Aggregate slot data Description This method provides common functions to aggregate the data inside a slot. Usage agg_hydroMet( obj, slot_name, col_name, fun, period, out_name = NULL, start_month = NULL, end_month = NULL, allow_NA = NULL ) ## S4 method for signature 'hydroMet_BDHI' agg_hydroMet( agg_hydroMet 3 obj, slot_name, col_name, fun, period, out_name = NULL, start_month = NULL, end_month = NULL, allow_NA = NULL ) ## S4 method for signature 'hydroMet_DGI' agg_hydroMet( obj, slot_name, col_name, fun, period, out_name = NULL, start_month = NULL, end_month = NULL, allow_NA = NULL ) ## S4 method for signature 'hydroMet_CR2' agg_hydroMet( obj, slot_name, col_name, fun, period, out_name = NULL, start_month = NULL, end_month = NULL, allow_NA = NULL ) ## S4 method for signature 'hydroMet_IANIGLA' agg_hydroMet( obj, slot_name, col_name, fun, period, out_name = NULL, start_month = NULL, end_month = NULL, allow_NA = NULL 4 agg_hydroMet ) Arguments obj an hydroMet_XXX class object. This method is not allowed for hydroMet_compact class. This is because this class was thought as ready to use, so when building this class you should have already aggregated your data. slot_name a single or vector string containing the slot(s) to aggregate. col_name a single or vector string with the name of the column to aggregate in slot_name. fun a single or vector string containing one of the following functions: ‘mean’, ‘min’, ‘max’ or ‘sum’. period a single or vector string with the period of aggregation: ‘hourly’, ‘daily’, ‘monthly’, ‘annual’ or ‘climatic’. NOTE_1: the ’climatic’ option returns the all series annual statistics (’fun’). NOTE_2: if the object is of class hydroMet_IANIGLA you must provide a single period value. out_name optional. Single or vector string with the output column name of the variable to aggregate. start_month optional. Numeric (or numeric vector) value of the first month. It only makes sense if the ‘period’ is ‘annual’. NOTE: as an example, in case you have just two slots (out of five) that you want to aggregate annually you must provide a vector of length two. Default value is January. NOTE*: if the object is of class hydroMet_IANIGLA you must provide a single start_month value. end_month optional. Numeric (or numeric vector) value of the last month. It only makes sense if the ‘period’ is ‘annual’. NOTE: as an example, in case you have just two slots (out of five) that you want to aggregate annually you must provide a vector of length two. Default value es December. NOTE*: if the object is of class hydroMet_IANIGLA you must provide a single end_month value. allow_NA optional. Numeric (or numeric vector) value with the maximum allowed number of NA_real_ values. By default the function will not tolerate any NA_real_ in an aggregation period (and will return NA_real_ instead). Value An hydroMet_XXX class object with the required slot(s) aggregated. Functions • agg_hydroMet,hydroMet_BDHI-method: aggregation method for BDHI data • agg_hydroMet,hydroMet_DGI-method: aggregation method for DGI data • agg_hydroMet,hydroMet_CR2-method: aggregation method for CR2 data • agg_hydroMet,hydroMet_IANIGLA-method: aggregation method for IANIGLA data agg_serie 5 Examples # Create BDHI hydro-met station guido <- create_hydroMet(class_name = 'BDHI') # List with meteorological variables (slots in BDHI's object) cargar <- list('precip', 'Qmd', 'Qmm') # Assign as names the files hydro_files <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Guido' ) names(cargar) <- hydro_files # Build the object with the met records guido <- build_hydroMet(obj = guido, slot_list = cargar, path = system.file('extdata', package = "hydroToolkit") ) # Aggregrate precipitation serie guido <- agg_hydroMet(obj = guido, slot_name = 'precip', col_name = 'precip', fun = 'sum', period = 'monthly', out_name = 'P_month', allow_NA = 3) agg_serie Aggregates a data frame to a larger time period Description This is a useful function to easily aggregate your data. Usage agg_serie( df, fun, period, out_name, start_month = NULL, end_month = NULL, allow_NA = NULL ) Arguments df data frame with class Date or POSIXct in the first column. The function always aggregates the second column. fun string containing one of the following functions: ‘mean’, ‘min’, ‘max’ or ‘sum’. period string with the period of aggregation: ‘hourly’, ‘daily’, ‘monthly’, ‘annual’ or ‘climatic’. NOTE: the ’climatic’ option returns the all series annual statis- tics (’fun’). 6 build_hydroMet out_name string with the output column name of the variable to aggregate. start_month optional. Numeric value of the first month. It only makes sense if the ‘period’ is ‘annual’. end_month optional. Numeric value of the last month. It only makes sense if the ‘period’ is ‘annual’. allow_NA optional. Numeric value with the maximum allowed number of NA_real_ val- ues. By default the function will not tolerate any NA_real_ in an aggregation period (and will return NA_real_ instead). Value A data frame with to columns: the date and the aggregated variable. Examples # Path to file dgi_path <- system.file('extdata', package = "hydroToolkit") toscas <- read_DGI(file = 'Toscas.xlsx', sheet = 'tmean', path = dgi_path) # Monthly mean temperature m_toscas <- agg_serie(df = toscas, fun = 'mean', period = 'monthly', out_name = 'T_month') build_hydroMet Automatically load native data files Description This method is the recommended one for loading your data-sets (as provided by the agency). Usage build_hydroMet( obj, slot_list, path = NULL, col_names = NULL, start_date = NULL, end_date = NULL ) ## S4 method for signature 'hydroMet_BDHI' build_hydroMet(obj, slot_list, path = NULL) ## S4 method for signature 'hydroMet_CR2' build_hydroMet(obj, slot_list, path = NULL) build_hydroMet 7 ## S4 method for signature 'hydroMet_DGI' build_hydroMet(obj, slot_list, path = NULL) ## S4 method for signature 'hydroMet_IANIGLA' build_hydroMet(obj, slot_list, path = NULL) ## S4 method for signature 'hydroMet_compact' build_hydroMet( obj, slot_list, col_names = NULL, start_date = NULL, end_date = NULL ) Arguments obj an hydroMet_XXX class object (see create_hydroMet). slot_list a list containing (in each element) a vector string with the slot names. The name of the list elements are the native file names (e.g.: Qmd_Guido_BDHI.txt). NOTE: when the obj argument is of class hydroMet_compact, slot_list al- lows to build from multiple objects. So, in this case you have to provide a list of list: the top list contains as names the objects names (as you read them from Global Environment); then every object (top level) contains another list with slot names as names and the column(s) number(s) to extract as nu- meric value. E.g.: list(bdhi_obj = list(Qmd = 2, Qmm = c(2, 5)), cr2_obj = list(precip = 4) ). path string with the files directory. If not provided, the method will use the cur- rent working directory. NOTE: this argument is harmless for an object of class hydroMet_compact. col_names it just make sense if 'obj' argument is of hydroMet_compact class. String vector with the names of the column output. Default value (NULL) will return expressive column names. start_date it just make sense if 'obj' argument is of hydroMet_compact class. String or POSIXct with the starting date to extract. You can use start_date without end_date. In this case you will subset your data from start_date till the end. end_date it just make sense if 'obj' argument is of hydroMet_compact class. String or POSIXct with the last date to extract. You can use end_date without start_date. In this case you will subset your data from the beginning till end_date. Value An S4 object of class hydroMet_XXX with the data loaded in each slot. Functions • build_hydroMet,hydroMet_BDHI-method: build up method for BDHI class 8 create_hydroMet • build_hydroMet,hydroMet_CR2-method: build up method for CR2 class • build_hydroMet,hydroMet_DGI-method: build up method for DGI class • build_hydroMet,hydroMet_IANIGLA-method: build up method for IANIGLA class • build_hydroMet,hydroMet_compact-method: build up method for compact class Examples # Path to file dgi_path <- system.file('extdata', package = "hydroToolkit") file_name <- list.files(path = dgi_path, pattern = 'Toscas') # Read Toscas var_nom <- list(slotNames(x = 'hydroMet_DGI')[2:7]) names(var_nom) <- file_name # Load Toscas meteo station data toscas_dgi <- create_hydroMet(class_name = 'DGI') toscas_dgi <- build_hydroMet(obj = toscas_dgi, slot_list = var_nom, path = dgi_path) create_hydroMet Creates an hydroMet class or subclass. Description This function is the constructor of hydroMet class and its subclasses. Usage create_hydroMet(class_name = "hydroMet") Arguments class_name string with the name of the class. Valid arguments are: hydroMet, BDHI, CR2, DGI, IANIGLA or compact. Value an S4 object of class hydroMet Examples # Create class 'hydroMet' met_station <- create_hydroMet(class_name = 'hydroMet') # Subclass 'BDHI' bdhi_station <- create_hydroMet(class_name = 'BDHI') fill_serie 9 # Subclass 'DGI' dgi_station <- create_hydroMet(class_name = 'DGI') # Subclass 'CR2' cr2_station <- create_hydroMet(class_name = 'CR2') # Subclass 'IANIGLA' ianigla_station <- create_hydroMet(class_name = 'IANIGLA') fill_serie Find non-reported dates and fill them with NA_real_ Description This function complete non-reported dates and assign NA_real_ as their value. Usage fill_serie(df, colName, timeStep) Arguments df data frame with date and numeric vector as first and second column respectively. colName output colname of the numeric variable, e.g.: ’Qmd(m3/s)’. timeStep character with a valid time step: ’day’, ’month’, ’4h’, ’day/3’, ’hour’. Value A data frame with missing time steps filled with NA’s. Examples # Create a data frame dates <- seq.Date(from = as.Date('1990-01-01'), to = as.Date('1990-12-01'), by = 'm') met_var <- runif(n = 12, 0, 10) met_serie <- data.frame(dates, met_var) # Fill serie met_fill <- fill_serie(df = met_serie, colName = 'Temp', timeStep = 'day') 10 fill_value fill_value Fill a time interval in a data frame with a specific numeric value Description Assign specific values to a time interval. Usage fill_value(df, col, value, from, to) Arguments df data frame with the first column being the date and the others numeric variables. col numeric vector with column(s) number(s) to be filled. value numeric or NA_real_. This numeric vector contains the elements to be fill in. from character, Date or POSIXct with the first date to be filled. to character, Date or POSIXct with the last date to be filled. Value A data frame filled with the ‘value’ in the specified time period. Examples # Create a data frame dates <- seq.Date(from = as.Date('1990-01-01'), to = as.Date('1990-12-01'), by = 'm') met_var <- runif(n = 12, 0, 10) met_serie <- data.frame(dates, met_var) # Fill serie met_fill <- fill_serie(df = met_serie, colName = 'Temp', timeStep = 'day') # Now fill value met_fill <- fill_value(df = met_fill, col = 2, value = 10, from = '1990-02-01', to = '1990-02-15') get_hydroMet 11 get_hydroMet Get the slot(s) content(s) Description Extract the slots that you want from an hydroMet or hydroMet_XXX class. Usage get_hydroMet(obj, name = NA_character_) ## S4 method for signature 'hydroMet' get_hydroMet(obj, name = NA_character_) ## S4 method for signature 'hydroMet_BDHI' get_hydroMet(obj, name = NA_character_) ## S4 method for signature 'hydroMet_DGI' get_hydroMet(obj, name = NA_character_) ## S4 method for signature 'hydroMet_IANIGLA' get_hydroMet(obj, name = NA_character_) ## S4 method for signature 'hydroMet_CR2' get_hydroMet(obj, name = NA_character_) ## S4 method for signature 'hydroMet_compact' get_hydroMet(obj, name = NA_character_) Arguments obj an hydroMet or hydroMet_XXX class object. name a valid single string or vector string with the required slot name(s). Value A list with the slot’s data. Functions • get_hydroMet,hydroMet-method: get method for generic hydroMet object • get_hydroMet,hydroMet_BDHI-method: get method for BDHI class • get_hydroMet,hydroMet_DGI-method: get method for DGI class • get_hydroMet,hydroMet_IANIGLA-method: get method for IANIGLA class • get_hydroMet,hydroMet_CR2-method: get method for CR2 class • get_hydroMet,hydroMet_compact-method: get method for compact class 12 hydroMet-class Examples # Create an IANIGLA object cuevas <- create_hydroMet(class_name = 'IANIGLA') # Extract one of its slots tair <- get_hydroMet(obj = cuevas, name = 'tair') hydroMet-class hydroMet superclass object Description A suitable object for store basic information about an hydro-meteorological station. Value A basic hydroMet class object. Slots id numeric. This is the ID assigned by the agency. agency character. The name of the agency (or institution) that provides the data of the station. station character. The name of the (hydro)-meteorological station. lat numeric. Latitude of the station. long numeric. Longitude of the station alt numeric. Altitude of the station. country character. Country where the station is located. Argentina is set as default value. province character. Name of the province where the station is located. Mendoza is set as default value. river character. Basin river’s name. active logical. It indicates whether or not the station is currently operated. Default value is TRUE. hydroMet_BDHI-class 13 hydroMet_BDHI-class hydroMet subclass for BDHI (Base de Datos Hidrologica Integrada) data Description An suitable object for store hydro-meteorological data from BDHI. Value A hydroMet_BDHI class object. Slots Qmd data.frame from read_BDHI containing daily mean river discharge [m3/s]. Qmm data.frame from read_BDHI containing monthly mean river discharge [m3/s]. precip data.frame from read_BDHI containing daily liquid precipitation [mm]. tdb data.frame from read_BDHI containing subdaily dry bulb temperature [ºC]. tmax data.frame from read_BDHI containing daily maximum air temperature [ºC]. tmin data.frame from read_BDHI containing daily minimum air temperature [ºC]. swe data.frame from read_BDHI containing daily snow water equivalent [mm]. hr data.frame from read_BDHI containing subdaily relative humidity [%]. wspd data.frame from read_BDHI containing subdaily wind speed [km/hr]. wdir data.frame from read_BDHI containing subdaily wind direction [º]. evap data.frame from read_BDHI containing daily pan-evaporation [mm]. anem data.frame from read_BDHI containing daily wind speed above the evap tank [km/hr]. patm data.frame from read_BDHI containing subdaily atmospheric pressure [mbar]. hydroMet_compact-class hydroMet subclass for compact data Description This subclass is useful for storing in a single data frame ready to use hydro-meteorological series or many variables of the same kind (e.g. lets say precipitacion series). Value A hydroMet_compact class object. 14 hydroMet_DGI-class Slots compact data.frame with Date as first column (class ’Date’ or ’POSIXct’). All other columns are the numeric hydro-meteorological variables (double). This subclass was though to join in a single table ready to use data (e.g. in modelling). You can also use it to put together variables of the same kind (e.g. precipitation records) to make some regional analysis. hydroMet_CR2-class hydroMet subclass for CR2 (Explorador Climático) data Description A suitable object for store hydro-meteorological data from CR2. Value A hydroMet_CR2 class object. Slots precip data.frame from read_CR2 containing daily precipitation [mm]. tmean data.frame from read_CR2 containing daily mean air temperature [ºC]. tmax data.frame from read_CR2 containing daily maximum air temperature [ºC]. tmin data.frame from read_CR2 containing daily minimum air temperature [ºC]. hydroMet_DGI-class hydroMet subclass for DGI (Departamento General de Irrigación) data Description A suitable object for store hydro-meteorological data from DGI. Value A hydroMet_DGI class object. Slots hsnow data.frame from read_DGI containing daily snow height [m]. swe data.frame from read_DGI containing daily snow water equivalent [mm]. tmean data.frame from read_DGI containing daily mean air temperature [ºC]. tmax data.frame from read_DGI containing daily max. air temperature [ºC]. tmin data.frame from read_DGI containing daily min. air temperature [ºC]. hr data.frame from read_DGI containing daily mean relative humidity [%]. patm data.frame from read_DGI containing daily mean atmospheric pressure [hPa]. hydroMet_IANIGLA-class 15 hydroMet_IANIGLA-class hydroMet subclass for IANIGLA (Instituto Argentino de Nivología, Glaciología y Ciencias Ambientales) data Description A suitable object for store hydro-meteorological data provided by IANIGLA. Value A hydroMet_IANIGLA class object. Slots date time serie of dates (class POSIXct or Date). tair numeric matrix with air temperature. hr numeric matrix with relative humidity. patm numeric matrix with atmospheric pressure. precip numeric matrix with precipitacion. wspd numeric matrix with wind speed. wdir numeric matrix with wind direction. kin numeric matrix with incoming short-wave radiation. hsnow numeric matrix with snow height. tsoil numeric matrix with soil temperature. hwat numeric matrix with stream water level. hydro_year_DGI Hydrological year classification Description This function allows you to get the hydrological year. The criteria is consistent with the one of Departamento General de Irrigacion (Mendoza - Argentina). Usage hydro_year_DGI(df) Arguments df a data frame with total annual volumes discharges created with agg_serie func- tion. 16 interpolate Value A data frame containing the hydrological classification for each year. Examples # Create BDHI hydro-met station guido <- create_hydroMet(class_name = 'BDHI') # List with meteorological variables (slots in BDHI's object) cargar <- list('precip', 'Qmd', 'Qmm') # Now assign as names the files hydro_files <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Guido' ) names(cargar) <- hydro_files # Build the object with the met records guido <- build_hydroMet(obj = guido, slot_list = cargar, path = system.file('extdata', package = "hydroToolkit") ) # Now get mean monthly discharge Qmm <- get_hydroMet(obj = guido, name = 'Qmm')[[1]] # Get the monthly water volume Qmm_vol <- Qmm_to_Dm(df = Qmm) # Aggregate data frame to get total annual discharges AD <- agg_serie(df = Qmm_vol, fun = 'sum', period = 'annual', out_name = 'Ann_vol', start_month = 7, end_month = 6, allow_NA = 2) # Get hydrological year classification AD_class <- hydro_year_DGI(df = AD) interpolate Interpolation Description This functions applies interpolation to fill in missing (or non-recorded) values. Usage interpolate(df, miss_table, threshold, method = "linear") Arguments df data frame with two columns: ’Date’ or ’POSIXct’ class in the first column and a numeric variable in the second one. modify_hydroMet 17 miss_table data frame with three columns: first and last date of interpolation (first and sec- ond column respectively). The last and third column, is a numeric with the number of steps to interpolate. See report_miss_data. threshold numeric variable with the maximum number of dates in which to apply the in- terpolation. method string with the interpolation method. In this version only ’linear’ method is allowed. Value A data frame with date and the interpolated numeric variable. Examples # Create BDHI hydro-met station guido <- create_hydroMet(class_name = 'BDHI') # List with meteorological variables (slots in BDHI's object) cargar <- list('precip', 'Qmd', 'Qmm') # Now assign as names the files hydro_files <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Guido' ) names(cargar) <- hydro_files # Build the object with the met records guido <- build_hydroMet(obj = guido, slot_list = cargar, path = system.file('extdata', package = "hydroToolkit") ) # Get mean daily discharge and report miss data Qmd <- get_hydroMet(obj = guido, name = 'Qmd')[[1]] miss <- report_miss_data(df = Qmd) # Now interpolate miss values Qmd_fill <- interpolate(df = Qmd, miss_table = miss, threshold = 5, method = "linear") modify_hydroMet Modify data inside a specific slot Description Apply a pre-defined (e.g.: movAvg, fill_value or Qmm_to_Dm) or user defined function to an existing series inside a slot. 18 modify_hydroMet Usage modify_hydroMet( obj, name = NA_character_, colName = NA_character_, colNum = 2, FUN = NULL, ... ) ## S4 method for signature 'hydroMet_BDHI' modify_hydroMet( obj, name = NA_character_, colName = NA_character_, colNum = 2, FUN = NULL, ... ) ## S4 method for signature 'hydroMet_CR2' modify_hydroMet( obj, name = NA_character_, colName = NA_character_, colNum = 2, FUN = NULL, ... ) ## S4 method for signature 'hydroMet_DGI' modify_hydroMet( obj, name = NA_character_, colName = NA_character_, colNum = 2, FUN = NULL, ... ) ## S4 method for signature 'hydroMet_IANIGLA' modify_hydroMet( obj, name = NA_character_, colName = NA_character_, colNum = 1, FUN = NULL, ... modify_hydroMet 19 ) Arguments obj hydroMet_XXX subclass object. See hydroMet_BDHI, hydroMet_DGI, hy- droMet_IANIGLA or hydroMet_CR2. name string with the slot name of the data frame. colName string with the new column name (from FUN). colNum numeric value with the data frame column where to apply FUN. It must be > 1 (except in ’IANIGLA’ subclass). FUN the function name. ... FUN arguments to pass. Value The same hydroMet subclass provided in obj with an extra column. Functions • modify_hydroMet,hydroMet_BDHI-method: modify method for BDHI class • modify_hydroMet,hydroMet_CR2-method: modify method for CR2 class • modify_hydroMet,hydroMet_DGI-method: modify method for DGI class • modify_hydroMet,hydroMet_IANIGLA-method: modify method for IANIGLA class Examples # Create BDHI hydro-met station guido <- create_hydroMet(class_name = 'BDHI') # List with meteorological variables (slots in BDHI's object) cargar <- list('precip', 'Qmd', 'Qmm') # Now assign as names the files hydro_files <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Guido' ) names(cargar) <- hydro_files # Build the object with the met records guido <- build_hydroMet(obj = guido, slot_list = cargar, path = system.file('extdata', package = "hydroToolkit") ) 20 movAvg movAvg Moving average windows Description Smooth a numeric serie with a moving average windows Usage movAvg(df, k, pos) Arguments df data frame with the serie that you want to smooth. By default, t he function uses column 2. k numeric value with windows size., e.g.: 5 pos string with the position of the window: • ’izq’: left aligned. The output value is on the left, so the function weights the (k - 1) values at the right side. • ’der’: right aligned. The output value is on the right, so the function weights the (k - 1) values at the left side. • ’cen’: center. The output value is in the middle of the window. Value data frame with the smooth serie. Examples # Relative path to raw data full_path <- system.file('extdata', package = "hydroToolkit") # Apply function cuevas <- read_IANIGLA(file = 'Cuevas.csv', path = full_path) # Get air temperature cuevas_tair <- cuevas[ , 1:2] # Create a moving average serie of Tair Tair_mov <- movAvg(df = cuevas_tair, k = 10, pos = 'izq') plot_hydroMet 21 plot_hydroMet Methods to easily use ggplot2 or plotly (interactive) Description This method allows you to make plots (using simple and expressive arguments) of the variables contained inside an hydroMet_XXX object. The plot outputs can be static (ggplot2) or interactive (plotly). Usage plot_hydroMet( obj, slot_name, col_number, interactive = FALSE, line_type = NULL, line_color = "dodgerblue", x_lab = "Date", y_lab = "y", title_lab = NULL, legend_lab = NULL, double_yaxis = NULL, list_extra = NULL, from = NULL, to = NULL, scatter = NULL ) ## S4 method for signature 'hydroMet_BDHI' plot_hydroMet( obj, slot_name, col_number, interactive = FALSE, line_type = NULL, line_color = "dodgerblue", x_lab = "Date", y_lab = "y", title_lab = NULL, legend_lab = NULL, double_yaxis = NULL, list_extra = NULL, from = NULL, to = NULL ) 22 plot_hydroMet ## S4 method for signature 'hydroMet_CR2' plot_hydroMet( obj, slot_name, col_number, interactive = FALSE, line_type = NULL, line_color = "dodgerblue", x_lab = "Date", y_lab = "y", title_lab = NULL, legend_lab = NULL, double_yaxis = NULL, list_extra = NULL, from = NULL, to = NULL ) ## S4 method for signature 'hydroMet_DGI' plot_hydroMet( obj, slot_name, col_number, interactive = FALSE, line_type = NULL, line_color = "dodgerblue", x_lab = "Date", y_lab = "y", title_lab = NULL, legend_lab = NULL, double_yaxis = NULL, list_extra = NULL, from = NULL, to = NULL ) ## S4 method for signature 'hydroMet_IANIGLA' plot_hydroMet( obj, slot_name, col_number, interactive = FALSE, line_type = NULL, line_color = "dodgerblue", x_lab = "Date", y_lab = "y", title_lab = NULL, legend_lab = NULL, plot_hydroMet 23 double_yaxis = NULL, list_extra = NULL, from = NULL, to = NULL ) ## S4 method for signature 'hydroMet_compact' plot_hydroMet( obj, slot_name, col_number, interactive = FALSE, line_type = NULL, line_color = "dodgerblue", x_lab = "x", y_lab = "y", title_lab = NULL, legend_lab = NULL, double_yaxis = NULL, list_extra = NULL, from = NULL, to = NULL, scatter = NULL ) Arguments obj a valid hydroMet_XXX object. slot_name string(s) with the name of the slot(s) to use in plotting. col_number numeric (vector) with the column’s variable to plot. In case you decide to merge slots you must provide a list in which each element contains the column numbers of the variable to plot. interactive logical. Default value, FALSE, will return a ggplot2 class object. Otherwise you will get a plotly one. line_type string with line dash type (ggplot2) or mode in plotly case. ggplot2: 'solid' (default value), 'twodash', 'longdash', 'dotted', 'dotdash', 'dashed' or 'blank'. plotly: 'lines' (default value), 'lines+markers' or 'markers'. line_color string with a valid color. See ’colors()’ or Rcolor document. x_lab string with x axis label. y_lab string with y axis label. In case you use double_yaxis argument you must supply both c('ylab', 'y2lab'). title_lab string with the title of the plot. Default is a plot without title. legend_lab string with plot label(s) name(s). NOTE: ggplot2 double_yaxis does not sup- port legend_lab in this package version, so giving values to this argument will be harmfulness. 24 plot_hydroMet double_yaxis numeric vector with either 1 (= main axis - left) or 2 (= secondary axis - right) indicating whether the variable should be plotted in either left or right axis. NOTE: in this package version ggplot2 supports just one line plot for each ’y’ axis. list_extra list with the ggplot2 argument to pass. This argument was design to allow the user to modify ggplot2 arguments (you can find nice examples in ggplot2 - Essentials) NOTE: in this package version this argument doesn’t make sense for plotly (except for scatter plot in hydroMet_compact class). from string (or POSIXct - valid only in ’BDHI’ and ’IANIGLA’) with the starting Date. You can use 'from' without 'to'. In this case you will subset your data ’from’ till the end. to string (or POSIXct - valid only in ’BDHI’ and ’IANIGLA’) with the ending Date. You can use 'to' without 'from'. In this case you will subset your data from the beginning till ’to’. scatter numeric vector of length two with the column number to plot as scatter. The first variable (column number) will be the 'x' variable and the second one the 'y' variable. This argument will work just for class hydroMet_compact. Value A ggplot2 or plotly objects to analyze your data. Functions • plot_hydroMet,hydroMet_BDHI-method: plot method for BDHI class • plot_hydroMet,hydroMet_CR2-method: plot method for CR2 class • plot_hydroMet,hydroMet_DGI-method: plot method for DGI class • plot_hydroMet,hydroMet_IANIGLA-method: plot method for IANIGLA class • plot_hydroMet,hydroMet_compact-method: plot method for compact class Examples # Path to file dgi_path <- system.file('extdata', package = "hydroToolkit") file_name <- list.files(path = dgi_path, pattern = 'Toscas') # Read Toscas var_nom <- list(slotNames(x = 'hydroMet_DGI')[2:7]) names(var_nom) <- file_name # Load Toscas meteo station data toscas_dgi <- create_hydroMet(class_name = 'DGI') toscas_dgi <- build_hydroMet(obj = toscas_dgi, slot_list = var_nom, path = dgi_path) # Plot mean air temperature plot_hydroMet(obj = toscas_dgi, col_number = 2, slot_name = 'tmean', legend_lab = 'Tmean(ºC)' ) precip_cumsum 25 # Now let's plot an interactive graph plot_hydroMet(obj = toscas_dgi, col_number = 2, slot_name = 'tmean', interactive = TRUE, y_lab = 'Tmean(ºC)' ) precip_cumsum Cumulative sum of precipitation series Description Returns a data frame with two columns: the date and the cumulative sum of the chosen col_number. This function can deal with NA_real_. Usage precip_cumsum(df, col_number = 2, out_name = NULL) Arguments df data frame with Date (or POSIXct) in the first column and numeric variables on the others. col_number numeric. The column number of the series where to apply the cumulative sum. out_name optional. String value with the column output name. Default is ’cumsum_’ plus the original name. Value A data frame with two columns: date and the cumulative sum of the series. Examples # Load daily precipitation data-set from BDHI load( paste0(system.file('extdata', package = "hydroToolkit"), '/bdhi_p.rda') ) # Get compact slot p_bdhi <- get_hydroMet(obj = bdhi_p, name = 'compact')[[1]] # Apply cumulative precipitation function p_cum <- precip_cumsum(df = p_bdhi, col_number = 2, out_name = 'cum_guido') 26 precip_hydroMet precip_hydroMet Make homogeneity test or fill gaps in a series Description This method can do both: test homogeneity in precipitation series or fill data gaps using regional analysis. Usage precip_hydroMet( obj, col_target = 2, fill = FALSE, method = "spearman", min_value = 0.2 ) ## S4 method for signature 'hydroMet_compact' precip_hydroMet( obj, col_target = 2, fill = FALSE, method = "spearman", min_value = 0.2 ) Arguments obj an hydroMet_compact class object. col_target numeric. The column number of the target series (either to test homogeneity or to fill gaps) in compact slot. fill logical. By default value (FALSE) you will make an homogeneity test to your target series. method string (default is spearman - possible values are: spearman, pearson or kendall). When creating the regional (or master series) the method uses a weighted mean. The weighted values are the correlations coefficients. min_value numeric. Series with a correlation value less than min_value are thrown away. Value If fill = FALSE the method will return a list with three elements: a data frame with all necessary values to correct your target serie, a plot with p-values and the correlation matrix. When fill = TRUE the list will contain: the data frame with the target series gaps filled and the correlation matrix. Qmm_to_Dm 27 Functions • precip_hydroMet,hydroMet_compact-method: homogeneity test applied to precipitation data stored in compact class. Examples # Load daily precipitation data-set from BDHI load( paste0(system.file('extdata', package = "hydroToolkit"), '/bdhi_p.rda') ) # Fill gaps in Tupungato station relleno <- precip_hydroMet(obj = bdhi_p, col_target = 5, fill = TRUE) Qmm_to_Dm River discharge [m3/s] to volume [hm3] Description Converts mean monthly river discharge [m3/s] to total volume discharge [hm3]. Usage Qmm_to_Dm(df) Arguments df data frame with class Date in the first column. By default the function converts the second column only. If you have daily or hourly data see agg_serie. Value A data frame with two columns: Date and total volume discharge. Examples # Create BDHI hydro-met station guido <- create_hydroMet(class_name = 'BDHI') # List with meteorological variables (slots in BDHI's object) cargar <- list('precip', 'Qmd', 'Qmm') # Now assign as names the files hydro_files <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Guido' ) names(cargar) <- hydro_files # Build the object with the met records guido <- build_hydroMet(obj = guido, slot_list = cargar, path = system.file('extdata', package = "hydroToolkit") ) 28 read_BDHI # Now get mean monthly discharge Qmm <- get_hydroMet(obj = guido, name = 'Qmm')[[1]] # Get the monthly water volume Qmm_vol <- Qmm_to_Dm(df = Qmm) read_BDHI Reads data from Base de Datos Hidrológica Integrada (BDHI) - Ar- gentina Description Reads files downloaded from the Base de Datos Hidrológica Integrada (BDHI) as a data frame. Usage read_BDHI(file, colName, timeStep, is.Wdir = FALSE) Arguments file string with the name (including extension) of the file. colName string with variable name. E.g.: Qmd(m3/s) timeStep string with time step: ’month’, ’day’, ’day/3’, ’4h’ or ’hour’. • ’day’: data recorded once a day • ’month’: data recorded monthly • ’4h’: applies to atmospheric pressure time series only • ’day/3’: applies to wind related variables, relative humidity, and dry bulb temperature’ • ’hour’: in case you have to deal with hourly data. is.Wdir a logical value indicating if the variable is wind direction. Default value is set to FALSE. Value A data frame with two columns: date and variable. Gaps between dates are filled with NA_real_ and duplicated rows are eliminated automatically. Examples # Relative path to raw data full_path <- system.file('extdata', package = "hydroToolkit") # Apply function guido_Qmd <- read_BDHI(file = paste0(full_path, '/Qmd_Mendoza_Guido'), colName = 'Q(m3/s)', timeStep = 'day') read_CR2 29 read_CR2 Reads data from Explorador Climático de Chile Description Reads data downloaded from Explorador Climatico de Chile (CR2) as a data frame. Usage read_CR2(file, colName, path = NULL) Arguments file string with the file name (include extension). The only accepted format is ’.csv’. colName string with the name of the variable. path string with the files directory. If not provided, the function will use the current working directory. Value A two column data frame with date and variable. Gaps between dates are filled with NA_real_ and duplicated rows are eliminated automatically. Examples # Relative path to raw data full_path <- system.file('extdata', package = "hydroToolkit") # Apply function yeso_tmed <- read_CR2(file = 'Tmed_Yeso_Embalse.csv', colName = 'T(ºC)', path = full_path) read_DGI Reads data from Departamento General de Irrigación (Mendoza - Ar- gentina) Description Reads the Departamento General de Irrigacion(Mendoza - Argentina) excel sheet. Usage read_DGI(file, sheet = NULL, colName = NULL, range = NULL, path = NULL) 30 read_IANIGLA Arguments file string with the file name (’xlsx’ excel files). sheet sheet to read. Either a string (the name of a sheet), or an integer (the position of the sheet). Default value is sheet one. colName string with the name of the second column (as default first column is Date). If ignored first row excel names are used. range string providing cell range to read. E.g.: ’A1:B75’. path string with the files directory. If not provided, the function will use the current working directory. Value A data frame with two columns: date and variable. Gaps between dates are filled with NA_real_ and duplicated rows are eliminated automatically. Examples # Relative path to raw data full_path <- system.file('extdata', package = "hydroToolkit") # Apply function toscas_hr <- read_DGI(file = 'Toscas.xlsx', sheet = 'hr', colName = 'RH(%)', path = full_path) read_IANIGLA Reads data provided by IANIGLA Description Reads the data provided by IANIGLA (Instituto Argentino de Nivologia, Glaciologia y Ciencias Ambientales). Usage read_IANIGLA(file, all = FALSE, path = NULL) Arguments file string with the name of the ’.csv’ file downloaded from the meteo-stations web page. all logical value indicating whether the returned data frame contain all the original columns or just the date and data. path string with the files directory. If not provided, the function will use the current working directory. report_hydroMet 31 Value A data frame containing the hourly data measured by the automatic weather stations. Gaps between dates are filled with NA_real_ and duplicated rows are eliminated automatically. Note In this package version we only provide functionality for a specific data-set generated in the insti- tute. Examples # Relative path to raw data full_path <- system.file('extdata', package = "hydroToolkit") # Apply function cuevas <- read_IANIGLA(file = 'Cuevas.csv', path = full_path) report_hydroMet Object summaries Description This method returns a list with two elements: the first one is a data frame with miss data (see also report_miss_data) and the second one is also a data frame with the mean, sd, max and min values. Usage report_hydroMet( obj, slot_name, col_name, start_date = NULL, end_date = NULL, Lang = "spanish" ) ## S4 method for signature 'hydroMet_BDHI' report_hydroMet( obj, slot_name, col_name, start_date = NULL, end_date = NULL, Lang = "spanish" ) 32 report_hydroMet ## S4 method for signature 'hydroMet_CR2' report_hydroMet( obj, slot_name, col_name, start_date = NULL, end_date = NULL, Lang = "spanish" ) ## S4 method for signature 'hydroMet_DGI' report_hydroMet( obj, slot_name, col_name, start_date = NULL, end_date = NULL, Lang = "spanish" ) ## S4 method for signature 'hydroMet_IANIGLA' report_hydroMet( obj, slot_name, col_name, start_date = NULL, end_date = NULL, Lang = "spanish" ) Arguments obj an hydroMet_XXX object. slot_name a single or vector string containing the slot(s) to report. col_name a single or vector string with the name of the column to report in slot_name. start_date optional (default is the first Date). Single string or POSIXct with the starting Date to report. end_date optional (default is the last Date). Single string or POSIXct with the last Date to report. Lang optional (default value is spanish). Single string with the language to report results: spanish or english. Value A list containing two data frames: the first one with miss data and the second with the mean, sd, max and min values of the series. report_miss_data 33 Functions • report_hydroMet,hydroMet_BDHI-method: report method for BDHI class • report_hydroMet,hydroMet_CR2-method: report method for CR2 class • report_hydroMet,hydroMet_DGI-method: report method for DGI class • report_hydroMet,hydroMet_IANIGLA-method: report method for IANIGLA class Examples # Create IANIGLA class cuevas <- create_hydroMet(class_name = 'IANIGLA') # List with meteorological variables (slots in BDHI's object) cargar <- list( slotNames(x = 'hydroMet_IANIGLA')[2:11] ) # Assign as names the files hydro_files <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Cuevas' ) names(cargar) <- hydro_files # Build met-station cuevas <- build_hydroMet(obj = cuevas, slot_list = cargar, path = system.file('extdata', package = "hydroToolkit") ) # Get report report_hydroMet(obj = cuevas, slot_name = 'kin', col_name = 'kin_1') report_miss_data Report NA_real_ values Description Creates a data frame with reported dates and number of times-step of missing or not recorded data. Usage report_miss_data(df, Lang = "spanish") Arguments df data frame with hydro-meteo data. First column is date and the second the numeric vector to be reported. Lang string with output column name language: ’spanish’ (default) or ’english’. Value A data frame with three columns: start-date, end-date and number of missing time steps. 34 rm_spikes Examples # Create BDHI hydro-met station guido <- create_hydroMet(class_name = 'BDHI') # List with meteorological variables (slots in BDHI's object) cargar <- list('precip', 'Qmd', 'Qmm') # Now assign as names the files hydro_files <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Guido' ) names(cargar) <- hydro_files # Build the object with the met records guido <- build_hydroMet(obj = guido, slot_list = cargar, path = system.file('extdata', package = "hydroToolkit") ) # Get mean daily discharge and report miss data Qmd <- get_hydroMet(obj = guido, name = 'Qmd')[[1]] miss <- report_miss_data(df = Qmd) rm_spikes Remove spikes Description Removes spikes, and sets their value to NA_real_. Usage rm_spikes(df, tolerance) Arguments df data frame with date and numeric variable in the first and second column respec- tively (from read_XXX functions). tolerance numeric with maximum tolerance between a number and its successor. Value The same data frame but without peaks. Examples # Relative path to raw data full_path <- system.file('extdata', package = "hydroToolkit") # Read IANIGLA file cuevas <- read_IANIGLA(file = 'Cuevas.csv', path = full_path) set_hydroMet 35 # Remove spikes from air temperature series tair_rm_spikes <- rm_spikes(df = cuevas, tolerance = 10) set_hydroMet Set the data of an hydroMet object or its subclasses Description With this method you can set (or change) an specific slot value. Usage set_hydroMet( obj = NULL, id = NULL, agency = NULL, station = NULL, lat = NULL, long = NULL, alt = NULL, country = NULL, province = NULL, river = NULL, active = NULL, ... ) ## S4 method for signature 'hydroMet' set_hydroMet( obj = NULL, id = NULL, agency = NULL, station = NULL, lat = NULL, long = NULL, alt = NULL, country = NULL, province = NULL, river = NULL, active = NULL ) ## S4 method for signature 'hydroMet_BDHI' set_hydroMet( obj = NULL, id = NULL, 36 set_hydroMet agency = NULL, station = NULL, lat = NULL, long = NULL, alt = NULL, country = NULL, province = NULL, river = NULL, active = NULL, Qmd = NULL, Qmm = NULL, precip = NULL, tdb = NULL, tmax = NULL, tmin = NULL, swe = NULL, hr = NULL, wspd = NULL, wdir = NULL, evap = NULL, anem = NULL, patm = NULL ) ## S4 method for signature 'hydroMet_DGI' set_hydroMet( obj = NULL, id = NULL, agency = NULL, station = NULL, lat = NULL, long = NULL, alt = NULL, country = NULL, province = NULL, river = NULL, active = NULL, swe = NULL, tmean = NULL, tmax = NULL, tmin = NULL, hr = NULL, patm = NULL, hsnow = NULL ) ## S4 method for signature 'hydroMet_IANIGLA' set_hydroMet( set_hydroMet 37 obj = NULL, id = NULL, agency = NULL, station = NULL, lat = NULL, long = NULL, alt = NULL, country = NULL, province = NULL, river = NULL, active = NULL, date = NULL, tair = NULL, hr = NULL, patm = NULL, precip = NULL, wspd = NULL, wdir = NULL, kin = NULL, hsnow = NULL, tsoil = NULL, hwat = NULL ) ## S4 method for signature 'hydroMet_CR2' set_hydroMet( obj = NULL, id = NULL, agency = NULL, station = NULL, lat = NULL, long = NULL, alt = NULL, country = NULL, province = NULL, river = NULL, active = NULL, precip = NULL, tmean = NULL, tmax = NULL, tmin = NULL ) ## S4 method for signature 'hydroMet_compact' set_hydroMet( obj = NULL, id = NULL, agency = NULL, 38 set_hydroMet station = NULL, lat = NULL, long = NULL, alt = NULL, country = NULL, province = NULL, river = NULL, active = NULL, compact = NULL ) Arguments obj an hydroMet or hydroMet_XXX class object. id numeric. This is the ID assigned by the agency. agency character. The name of the agency (or institution) that provides the data of the station. station character. The name of the (hydro)-meteorological station. lat numeric. Latitude of the station. long numeric. Longitude of the station alt numeric. Altitute of the station. country character. Country where the station is located. Argentina is set as default value. province character. Name of the province where the station is located. Mendoza is set as default value. river character. Basin river’s name. active logical. It indicates whether or not the station is currently operated. Default value is TRUE. ... arguments to be passed to methods. They rely on the slots of the obj subclass. Qmd daily mean river discharge. Qmm monthly mean river discharge. precip precipitation. tdb dry bulb temperature. tmax daily maximum air temperature. tmin daily minimum air temperature. swe snow water equivalent. hr relative humidity. wspd wind speed. wdir wind direction. evap evaporation. anem wind speed above the pan-evaporation. patm atmospheric pressure. set_threshold 39 tmean daily mean air temperature. hsnow snow height. date time serie with dates. tair air temperature. kin incoming shortwave radiation. tsoil soil temperature. hwat stream water level. compact data frame with Date as first column. All other columns are hydro-meteorological variables. Value The hydroMet object with the slots setted. Functions • set_hydroMet,hydroMet-method: set method for generic object • set_hydroMet,hydroMet_BDHI-method: set method for BDHI object • set_hydroMet,hydroMet_DGI-method: set method for DGI object • set_hydroMet,hydroMet_IANIGLA-method: set method for IANIGLA object • set_hydroMet,hydroMet_CR2-method: set method for CR2 object • set_hydroMet,hydroMet_compact-method: set method for compact object Examples # Create BDHI hydro-met station guido <- create_hydroMet(class_name = 'BDHI') # Assign altitude guido <- set_hydroMet(obj = guido, alt = 2480) set_threshold Set a threshold Description Set tolerable extreme values (maximum or minimum). Records greater or equal than (’>=’) or lesser or equal than (’<=’) ’threshold’ argument are set to NA_real_. Usage set_threshold(x, threshold, case = ">=") 40 subset_hydroMet Arguments x numeric vector or data frame with a numeric series in the second column. threshold numeric value with threshold. case string with either ’>=’ (greater or equal than) or ’<=’ (lesser or equal than) sym- bol. Value Numeric vector or data frame with values greater (or lesser) or equal than ’threshold’ set as NA_real_. Examples # Relative path to raw data full_path <- system.file('extdata', package = "hydroToolkit") # Read IANIGLA file cuevas <- read_IANIGLA(file = 'Cuevas.csv', path = full_path) # Set threshold from air temperature series tair_thres <- set_threshold(x = cuevas, threshold = 40) subset_hydroMet Subset your data Description This method allows you to easily cut the data stored in an hydroMet_XXX class object by dates. Usage subset_hydroMet(obj, slot_name, from = NULL, to = NULL) ## S4 method for signature 'hydroMet_BDHI' subset_hydroMet(obj, slot_name, from = NULL, to = NULL) ## S4 method for signature 'hydroMet_DGI' subset_hydroMet(obj, slot_name, from = NULL, to = NULL) ## S4 method for signature 'hydroMet_CR2' subset_hydroMet(obj, slot_name, from = NULL, to = NULL) ## S4 method for signature 'hydroMet_IANIGLA' subset_hydroMet(obj, slot_name, from = NULL, to = NULL) ## S4 method for signature 'hydroMet_compact' subset_hydroMet(obj, slot_name, from = NULL, to = NULL) subset_hydroMet 41 Arguments obj an hydroMet_XXX class object. slot_name string vector with the slot(s) name(s) to subset. NOTE: in case you want to subset a hydroMet_IANIGLA object is recommended to consider all the slots with data. from string (or POSIXct - valid only in ’BDHI’ and ’IANIGLA’) with the starting Date. You can use from without to. In this case you will subset your data ’from’ till the end. to string (or POSIXct - valid only in ’BDHI’ and ’IANIGLA’) with the ending Date. You can use to without from. In this case you will subset your data from the beginning till ’to’. Value The same hydroMet_XXX class provided in obj but subsetted. Functions • subset_hydroMet,hydroMet_BDHI-method: subset method for BDHI data • subset_hydroMet,hydroMet_DGI-method: subset method for DGI data • subset_hydroMet,hydroMet_CR2-method: subset method for CR2 data • subset_hydroMet,hydroMet_IANIGLA-method: subset method for IANIGLA data • subset_hydroMet,hydroMet_compact-method: subset method for compact data Examples # Create BDHI hydro-met station guido <- create_hydroMet(class_name = 'BDHI') # List with meteorological variables (slots in BDHI's object) cargar <- list('precip', 'Qmd', 'Qmm') # Assign as names the files hydro_files <- list.files( system.file('extdata', package = "hydroToolkit"), pattern = 'Guido' ) names(cargar) <- hydro_files # Build the object with the met records guido <- build_hydroMet(obj = guido, slot_list = cargar, path = system.file('extdata', package = "hydroToolkit") ) # Subset daily mean discharge guido <- subset_hydroMet(obj = guido, slot_name = 'Qmd', from = '2005-01-01', to = '2010-12-31') 42 swe_to_precip swe_to_melt Snow water equivalent to melt Description Converts a snow water equivalent series (from snow pillow) into a melt series. Usage swe_to_melt(df) Arguments df data frame with ’swe’ serie in the second column. See 'read_XXX' functions. Value Data frame containing the numeric vector with melted snow. Examples # Relative path to raw data full_path <- system.file('extdata', package = "hydroToolkit") # Read swe sheet toscas_swe <- read_DGI(file = 'Toscas.xlsx', sheet = 'swe', colName = 'swe(mm)', path = full_path) # swe to melt toscas_melt <- swe_to_melt(df = toscas_swe) swe_to_precip Snow water equivalent to snowfall Description Converts a snow water equivalent series (from snow pillow) to a snowfall series. Usage swe_to_precip(df) Arguments df data frame with ’swe’ series in the second column. See 'read_XXX' functions. swe_to_precip 43 Value Data frame containing the numeric vector with inferred snowfall. Examples # Relative path to raw data full_path <- system.file('extdata', package = "hydroToolkit") # Read swe sheet toscas_swe <- read_DGI(file = 'Toscas.xlsx', sheet = 'swe', colName = 'swe(mm)', path = full_path) # swe to snowfall toscas_snfall <- swe_to_precip(df = toscas_swe) Index agg_hydroMet, 2 hydro_year_DGI, 15 agg_hydroMet,hydroMet_BDHI-method hydroMet (hydroMet-class), 12 (agg_hydroMet), 2 hydroMet-class, 12 agg_hydroMet,hydroMet_CR2-method hydroMet_BDHI, 19 (agg_hydroMet), 2 hydroMet_BDHI (hydroMet_BDHI-class), 13 agg_hydroMet,hydroMet_DGI-method hydroMet_BDHI-class, 13 (agg_hydroMet), 2 hydroMet_compact agg_hydroMet,hydroMet_IANIGLA-method (hydroMet_compact-class), 13 (agg_hydroMet), 2 hydroMet_compact-class, 13 agg_serie, 5, 15, 27 hydroMet_CR2, 19 hydroMet_CR2 (hydroMet_CR2-class), 14 build_hydroMet, 6 hydroMet_CR2-class, 14 build_hydroMet,hydroMet_BDHI-method hydroMet_DGI, 19 (build_hydroMet), 6 hydroMet_DGI (hydroMet_DGI-class), 14 build_hydroMet,hydroMet_compact-method hydroMet_DGI-class, 14 (build_hydroMet), 6 hydroMet_IANIGLA, 19 build_hydroMet,hydroMet_CR2-method hydroMet_IANIGLA (build_hydroMet), 6 (hydroMet_IANIGLA-class), 15 build_hydroMet,hydroMet_DGI-method hydroMet_IANIGLA-class, 15 (build_hydroMet), 6 build_hydroMet,hydroMet_IANIGLA-method interpolate, 16 (build_hydroMet), 6 modify_hydroMet, 17 create_hydroMet, 7, 8 modify_hydroMet,hydroMet_BDHI-method fill_serie, 9 (modify_hydroMet), 17 fill_value, 10, 17 modify_hydroMet,hydroMet_CR2-method (modify_hydroMet), 17 get_hydroMet, 11 modify_hydroMet,hydroMet_DGI-method get_hydroMet,hydroMet-method (modify_hydroMet), 17 (get_hydroMet), 11 modify_hydroMet,hydroMet_IANIGLA-method get_hydroMet,hydroMet_BDHI-method (modify_hydroMet), 17 (get_hydroMet), 11 movAvg, 17, 20 get_hydroMet,hydroMet_compact-method (get_hydroMet), 11 plot_hydroMet, 21 get_hydroMet,hydroMet_CR2-method plot_hydroMet,hydroMet_BDHI-method (get_hydroMet), 11 (plot_hydroMet), 21 get_hydroMet,hydroMet_DGI-method plot_hydroMet,hydroMet_compact-method (get_hydroMet), 11 (plot_hydroMet), 21 get_hydroMet,hydroMet_IANIGLA-method plot_hydroMet,hydroMet_CR2-method (get_hydroMet), 11 (plot_hydroMet), 21 44 INDEX 45 plot_hydroMet,hydroMet_DGI-method subset_hydroMet,hydroMet_DGI-method (plot_hydroMet), 21 (subset_hydroMet), 40 plot_hydroMet,hydroMet_IANIGLA-method subset_hydroMet,hydroMet_IANIGLA-method (plot_hydroMet), 21 (subset_hydroMet), 40 precip_cumsum, 25 swe_to_melt, 42 precip_hydroMet, 26 swe_to_precip, 42 precip_hydroMet,hydroMet_compact-method (precip_hydroMet), 26 Qmm_to_Dm, 17, 27 read_BDHI, 13, 28 read_CR2, 14, 29 read_DGI, 14, 29 read_IANIGLA, 30 report_hydroMet, 31 report_hydroMet,hydroMet_BDHI-method (report_hydroMet), 31 report_hydroMet,hydroMet_CR2-method (report_hydroMet), 31 report_hydroMet,hydroMet_DGI-method (report_hydroMet), 31 report_hydroMet,hydroMet_IANIGLA-method (report_hydroMet), 31 report_miss_data, 17, 31, 33 rm_spikes, 34 set_hydroMet, 35 set_hydroMet,hydroMet-method (set_hydroMet), 35 set_hydroMet,hydroMet_BDHI-method (set_hydroMet), 35 set_hydroMet,hydroMet_compact-method (set_hydroMet), 35 set_hydroMet,hydroMet_CR2-method (set_hydroMet), 35 set_hydroMet,hydroMet_DGI-method (set_hydroMet), 35 set_hydroMet,hydroMet_IANIGLA-method (set_hydroMet), 35 set_threshold, 39 subset_hydroMet, 40 subset_hydroMet,hydroMet_BDHI-method (subset_hydroMet), 40 subset_hydroMet,hydroMet_compact-method (subset_hydroMet), 40 subset_hydroMet,hydroMet_CR2-method (subset_hydroMet), 40

VariableScreening

cran

Package ‘VariableScreening’ October 12, 2022 Type Package Title High-Dimensional Screening for Semiparametric Longitudinal Regression Version 0.2.1 Depends R (>= 3.2.1) Description Implements variable screening techniques for ultra-high dimensional regression settings. Techniques for independent (iid) data, varying-coefficient models, and longitudinal data are implemented. The package currently contains three screen functions: screenIID(), screenLD() and screenVCM(), and six methods for simulating dataset: simulateDCSIS(), simulateLD, simulateMVSIS(), simulateMVSISNY(), simulateSIRS() and simulateVCM(). The package is based on the work of Li-Ping ZHU, Lexin LI, Runze LI, and Li-Xing ZHU (2011) <DOI:10.1198/jasa.2011.tm10563>, Runze LI, Wei ZHONG, & Liping ZHU (2012) <DOI:10.1080/01621459.2012.695654>, Jingyuan LIU, Runze LI, & Rongling WU (2014) <DOI:10.1080/01621459.2013.850086> Hengjian CUI, Runze LI, & Wei ZHONG (2015) <DOI:10.1080/01621459.2014.920256>, and Wanghuan CHU, Runze LI and Matthew REIMHERR (2016) <DOI:10.1214/16-AOAS912>. Copyright (c) 2022 by Runze LI Encoding UTF-8 Imports gee, expm, splines, MASS, energy License GPL (>= 2) RoxygenNote 7.2.0 NeedsCompilation no Author Runze Li [aut], Liying Huang [aut], John Dziak [aut, cre] Maintainer John Dziak <dziakj1@gmail.com> Repository CRAN Date/Publication 2022-06-23 22:20:02 UTC 1 2 screenIID R topics documented: screenIID . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 screenLD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 screenVCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 simulateDCSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 simulateLD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 simulateMVSIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 simulateMVSISNY . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 simulateSIRS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 simulateVCM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Index 15 screenIID Feature Selection for Ultrahigh-Dimensional Datasets with Indepen- dent Subjects, Description Implements one of three screening procedures: Sure Independent Ranking and Screening (SIRS), Distance Correlation Sure Independence Screening (DC-SIS), or MV Sure Independence Screening (MV-SIS). In general they are extensions of the sure independence screening concept proposed by Fan and Lv (2008), but without a parametric assumption (e.g., linear or logistic) on the relationship between the predictor variables X and outcome Y. Screening methods each rank the predictors based on some measure of their estimated strength of relationship with Y. The assumption is that only a few among the top-ranked variables are likely to be truly significant predictors. The original version of SIS involved ranking the predictors by their correlation with Y, implying a linear relationship. The SIRS method is an extension proposed by Zhu, Li, Li, & Zhu (2011), which involved ranking the predictors by their correlation with the rank-ordered Y instead, thereby not assuming a linear correlation, and potentially outperforming SIS. DC-SIS was then proposed by Li, Zhong and Zhu (2012) and its relationship measure is the dis- tance correlation (DC) between a covariate and the outcome, a nonparametric generalization of the correlation coefficient (Szekely, Rizzo, & Bakirov, 2007). The function uses the dcor function from the R package energy in order to calculate this correlation. Simulations showed that DC-SIS could sometimes provide a further advantage over SIRS. The above measures were primarily intended for a numerical Y. Cui, Li, and Zhong (2015) proposed MV-SIS, which was developed for categorical Y (including binary Y) as in discriminant analysis, and which is also robust to heavy-tailed predictor distributions. The measure used by MV-SIS for the association strength between a particular Xk and Y is a mean conditional variance measure called MV for short, namely the expectation in X of the variance in Y of the conditional cumulative distribution function F(x|Y)=P(X<=x|Y); note that like the correlation or distance correlation, this is zero if X and Y are independent because F(x) does not depend on Y in that case. Cui, Li, and Zhong (2015) also point out that the MV-SIS can alternatively be used with categorical X variables and numerical Y, instead of numerical X and categorical Y. This function supports that option as "MV-SIS-NY." screenIID 3 Whichever option is chosen, the function returns the ranking of the predictors according to the appropriate association measure. The function code is adapted from the relevant authors’ code. Special thanks are due to Wei Zhong for providing some of the code upon which this function is based. Usage screenIID(X, Y, method = "DC-SIS") Arguments X Matrix of predictors to be screened. There should be one row for each observa- tion. Y Vector of responses. It should have the same length as the number of rows of X. The responses should be numerical if SIRS or DC-SIS is used. The responses should be integers representing response categories if MV-SIS is used. Binary responses can be used for any method. method Screening method. The options are "SIRS", "DC-SIS", "MV-SIS" and "MV- SIS-NY", as described above. Value A list with following components: measurement A vector of length equal to the number of columns in the input matrix X. It contains estimated strength of relationship with Y rank The rank of the error measures. This will have length equal to the number of columns in the input matrix X, and will consist of a permutation of the integers 1 through that length. A rank of 1 indicates the feature which appears to have the best predictive performance, 2 represents the second best and so on. References Cui, H., Li, R., & Zhong, W. (2015). Model-free feature screening for ultrahigh dimensional dis- criminant analysis. Journal of the American Statistical Association, 110: 630-641. <DOI:10.1080/01621459.2014.920256> Fan, J., & Lv, J. (2008). Sure independence screening for ultrahigh dimensional feature space. Journal of the Royal Statistical Society, B, 70: 849-911. <DOI:10.1111/j.1467-9868.2008.00674.x> Li, R., zhong, W., & Zhu, L. (2012). Feature screening via distance correlation learning. Journal of the American Statistical Association, 107: 1129-1139. <DOI:10.1080/01621459.2012.695654> Szekely, G. J., Rizzo, M. L., & Bakirov, N. K. (2007). Measuring and Testing Dependence by Cor- relation of Distances. Annals of Statistics, 35, 2769-2794. <DOI: 10.1214/009053607000000505> Zhu, L.-P., Li, L., Li, R., & Zhu, L.-X. (2011) Model-free feature screening for ultrahigh-dimensional data. Journal of the American Statistical Association, 106: 1464-1475. <DOI:10.1198/jasa.2011.tm10563> Examples set.seed(12345678) results <- simulateDCSIS(n=100,p=500) rank<- screenIID(X = results$X, Y = results$Y, method="DC-SIS") 4 screenLD screenLD Perform high-dimensional screening for semiparametric longitudinal regression Description Implements a screening procedure proposed by Chu, Li, and Reimherr (2016) <DOI:10.1214/16- AOAS912> for varying coefficient longitudinal models with ultra-high dimensional predictors. The effect of each predictor is allowed to vary over time, approximated by a low-dimensional B-spline. Within-subject correlation is handled using a generalized estimation equation approach with struc- ture specified by the user. Variance is allowed to change over time, also approximated by a B-spline. Usage screenLD( X, Y, z, id, subset = 1:ncol(X), time, degree = 3, df = 4, corstr = "stat_M_dep", M = NULL ) Arguments X Matrix of features (for example, SNP’s). There should be one row for each observation. Y Vector of responses. It should have the same length as the number of rows of X. z Optional matrix of covariates to be included in all models. They may include demographic covariates such as gender or ethnic background, or some other theoretically important constructs. It should have the same number of rows as the number of rows of X. We suggest a fairly low dimensional z. If the model is intended to include an intercept function (which is recommended), then z should include a column of 1’s representing the constant term. id Vector of integers identifying the subject to which each observation belongs. It should have the same length as the number of rows of X. subset Vector of integers identifying a subset of the features of X to be screened, the default is 1:ncol(X), i.e., to screen all columns of X. time Vector of real numbers identifying observation times. It should have the same length as the number of rows of X. We suggest using the convention of scaling time to the interval [0,1]. screenLD 5 degree Degree of the piecewise polynomial for the B-spline basis for the varying co- efficient functions; see the documentation for the bs() function in the splines library. df Degrees of freedom of the B-spline basis for the varying coefficient functions; see the documentation for the bs() function in the splines library. corstr Working correlation structure for the generalized estimation equations model used to estimate the coefficient functions; see the documentation for the gee() function in the gee library. Options provided by the gee() function include "in- dependence", "fixed", "stat_M_dep", "non_stat_M_dep", "exchangeable", "AR- M" and "unstructured". M An integer indexing the M value (complexity) of the dependence structure, if corstr is M-dependent or AR-M; see the documentation for the gee() function in the gee library. This will be ignored if the correlation structure does not require an M parameter. The default value is set to be 1. Value A list with following components: error A vector of length equal to the number of columns in the input matrix X. It contains sum squared error values for regression models which include the time- varying effects of the z covariates (if any) as well as each X covariate by itself. The lower this error is, the more desirable it is to retain the corresponding X covariate in a later predictive model. rank The rank of the error measures. This will have length equal to the number of columns in the input matrix X, and will consist of a permutation of the integers 1 through that length. A rank of 1 indicates the feature which appears to have the best predictive performance, 2 represents the second best and so on. Examples set.seed(12345678) results <- simulateLD(p=500) subset1 <- seq(1,5,2) subset2 <- seq(100,200,2) subset3 <- seq(202,400,2) subset4 <- seq(401,499,2) set <-c(subset1,subset2,subset3,subset4) Jmin <- min(table(results$id)) - 1 screenResults <- screenLD(X = results$X, Y = results$Y, z = results$z, id = results$id, subset = set, time = results$time, degree = 3, df = 4, corstr = "stat_M_dep", M = Jmin ) rank <- screenResults$rank 6 screenVCM unlist(rank) trueIdx <- c(5,100,200,400) rank[which(set %in% trueIdx)] screenVCM Perform screening for ultrahigh-dimensional varying coefficient model Description Implements a screening procedure proposed by Liu, Li and Wu(2014) for varying coefficient models with ultra-high dimensional predictors. The function code is adapted from the relevant authors’ code. Special thanks are due to Jingyuan Liu for providing some of the code upon which this function is based. Usage screenVCM(X, Y, U) Arguments X Matrix of predictors to be screened. There should be one row for each observa- tion. Y Vector of responses. It should have the same length as the number of rows of X. U Covariate, with which coefficient functions vary. Value A list with following components: CORR_sq A vector of the unconditioned squared correlation with length equal to the number of columns in the input matrix X. The hgh the unconditioned squared correlation is, the more desirable it is to retain the corresponding X covariate in a later predictive model. rank Vector for the rank of the predictors in terms of the conditional correlation ˆ j in the paper). This will have length equal to the number of columns in the input matrix ( rho∗ X, and will consist of a permutation of the integers 1 through that length. A rank of 1 indicates the feature which appears to have the best marginal predictive performance with largest rho∗ˆ j, 2 represents the second best and so forth. References Liu, J., Li, R., & Wu, R. (2014). Feature selection for varying coefficient models with ultrahigh- dimensional covariates. Journal of the American Statistical Association, 109: 266-274. <DOI:10.1080/01621459.2013.85008 simulateDCSIS 7 Examples set.seed(12345678) results <- simulateVCM(p=400, trueIdx = c(2, 100, 300), betaFun = function(U) { beta2 <- 2*I(U>0.4) beta100 <- 1+U beta300 <- (2-3*U)^2 return(c(beta2, beta100, beta300)) }) screenResults<- screenVCM(X = results$X, Y = results$Y, U = results$U) rank <- screenResults$rank unlist(rank) trueIdx <- c(2,100,400, 600, 1000) rank[trueIdx] simulateDCSIS Simulate a dataset for demonstrating the performance of screenIID with the DC-SIS method Description Simulates a dataset that can be used to demonstrate variable screening for ultrahigh-dimensional regression with the DC-SIS option in screenIID. The simulated dataset has p numerical predictors X and a categorical Y-response. The data-generating scenario is a simplified version of Example 3.1a (homoskedastic) or 3.1d (heteroskedastic) of Li, Zhong & Zhu (2012). Specifically, the X covariates are normally distributed with mean zero and variance one, and may be correlated if the argument rho is set to a nonzero value. The response Y is generated as either Y = 6*X1 + 1.5*X2 + 9*1X12 < 0 + exp(2*X22)*e if heteroskedastic=TRUE, or Y = 6*X1 + 1.5*X2 + 9*1X12 < 0 + 6*X22 + e if heteroskedastic=FALSE, where e is a standard normal error term and 1 is a zero-one indicator function for the truth of the statement contained. Special thanks are due to Wei Zhong for providing some of the code upon which this function is based. Usage simulateDCSIS(n = 200, p = 5000, rho = 0, heteroskedastic = TRUE) Arguments n Number of subjects in the dataset to be simulated. It will also equal to the number of rows in the dataset to be simulated, because it is assumed that each row represents a different independent and identically distributed subject. p Number of predictor variables (covariates) in the simulated dataset. These co- variates will be the features screened by DC-SIS. 8 simulateLD rho The correlation between adjacent covariates in the simulated matrix X. The within-subject covariance matrix of X is assumed to has the same form as an AR(1) autoregressive covariance matrix, although this is not meant to imply that the X covariates for each subject are in fact a time series. Instead, it is just used as an example of a parsimonious but nontrivial covariance structure. If rho is left at the default of zero, the X covariates will be independent and the simulation will run faster. heteroskedastic Whether the error variance should be allowed to depend on one of the predictor variables. Value A list with following components: X Matrix of predictors to be screened. It will have n rows and p columns. Y Vector of responses. It will have length n. References Li, R., Zhong, W., & Zhu, L. (2012) Feature screening via distance correlation learning. Journal of the American Statistical Association, 107: 1129-1139. <DOI:10.1080/01621459.2012.695654> Examples set.seed(12345678) results <- simulateDCSIS() simulateLD Simulate a dataset for testing the performance of screenlong Description Simulates a dataset that can be used to test the screenlong function, and to test the performance of the proposed method under different scenarios. The simulated dataset has two z-covariates and p x-covariates, only a few of which have nonzero effect. There are n subjects in the simulated dataset, each having J observations, which are not necessarily evenly timed, we randomly draw a subset to create an unbalanced dataset. The within-subject correlation is assumed to be AR-1. Usage simulateLD( n = 100, J = 10, rho = 0.6, p = 500, trueIdx = c(5, 100, 200, 400), beta0Fun = NULL, betaFun = NULL, gammaFun = NULL, simulateLD 9 varFun = NULL ) Arguments n Number of subjects in the simulated dataset J Number of observations per subject rho The correlation parameter for the AR-1 correlation structure. p The total number of features to be screened from trueIdx The indexes for the active features in the simulated x matrix. This should be a vector, and the values should be a subset of 1:p. beta0Fun The time-varying intercept for the data-generating model, as a function of time. If left as null, it will default to f(t) 2 * t^2 - 1. Time is assumed to be scaled to the interval [0,1]. betaFun The time-varying coefficients for z in the data-generating model, as a function of time. If left as null, it will be specified as two functions. The first is f(t) exp(t + 1)/2. The second is f(t) t^2 + 0.5. Time is assumed to be scaled to the interval [0,1]. gammaFun A list of functions of time, one function for each entry in trueIdx, giving the time-varying effects of each active feature in the simulated x matrix. If left as null, it will be specified as four functions. The first is a step function f(t)=(t > 0.4). The second is f(t)=- cos(2 * pi * t). the third is f(t)=(2 - 3 * t)^2/2 - 1. The fourth is f(t)=sin(2 * pi * t). varFun A function of time telling the marginal variance of the error function at a given time. If left as null, it will be specified as function(t) 0.5 + 3 * t^3. Value A list with following components: x Matrix of features to be screened. It will have n*J rows and p columns. y Vector of responses. It will have length of n*J. z A matrix representing covariates to be included in each of the screening models. The first column will be all ones, representing the intercept. The second will consist of random ones and zeros, representing simulated genders. id Vector of integers identifying the subject to which each observation belongs. time Vector of real numbers identifying observation times. It should have the same length as the number of rows of x. Examples set.seed(12345678) results <- simulateLD(p=1000) 10 simulateMVSIS simulateMVSIS Simulate a dataset for demonstrating the performance of screenIID with the MV-SIS option with categorical outcome variable Description Simulates a dataset that can be used to test screenIID for ultrahigh-dimensional discriminant anal- ysis with the MV-SIS option. The simulation is based on the balanced scenarios in Example 3.1 of Cui, Li & Zhong (2015). The simulated dataset has p numerical X-predictors and a categorical Y-response. Special thanks are due to Wei Zhong for providing some of the code upon which this function is based. Usage simulateMVSIS(R = 2, n = 40, p = 2000, mu = 3, heavyTailedCovariates = FALSE) Arguments R a positive integer, number of outcome categories for multinomial (categorical) outcome Y. n Number of subjects in the dataset to be simulated. It will also equal to the number of rows in the dataset to be simulated, because it is assumed that each row represents a different independent and identically distributed subject. p Number of predictor variables (covariates) in the simulated dataset. These co- variates will be the features screened by DC-SIS. mu Signal strength; the larger mu is, the easier the active covariates will be to dis- cover. # Specifically, mu is added to the rth predictor for r=1,...,R, so that the probability that Y equals r will be higher if the rth predictor is higher. It is as- sumed that p»r so that most predictors will be inactive. In real data there is no reason why, say, the first two columns in the matrix should be the important ones, but this is convenient in a simulation and the choice of permutation of the columns involves no loss of generality. heavyTailedCovariates If TRUE, the covariates will be generated as independent t variates, plus covariate- specific constants. If FALSE, they will be generated as independent standard normal variates. Value A list with following components: X Matrix of predictors to be screened. It will have n rows and p columns. Y Vector of responses. It will have length n. References Cui, H., Li, R., & Zhong, W. (2015). Model-free feature screening for ultrahigh dimensional dis- criminant analysis. Journal of the American Statistical Association, 110: 630-641. <DOI:10.1080/01621459.2014.920256> simulateMVSISNY 11 Examples set.seed(12345678) results <- simulateMVSIS() simulateMVSISNY Simulate a dataset for demonstrating the performance of screenIID with the MV-SIS method with numeric outcome Y Description Simulates a dataset that can be used to demonstrate variable screening for ultrahigh-dimensional regression with categorical predictors and numerical outcome variable using the MV-SIS-NY option in screenIID. The simulated dataset has p numerical predictors X and a categorical response Y. The X covariates are generated as binary with success probability 0.5 each. The response Y is generated as Y = 5*X1 + 5*X2 + 5*X12 + 5*X22 + e if heteroskedastic=FALSE, where e is a standard normal error term and 1 is a zero-one indicator function for the truth of the statement contained. Special thanks are due to Wei Zhong for providing some of the code upon which this function is based. Usage simulateMVSISNY(n = 500, p = 1000) Arguments n Number of subjects in the dataset to be simulated. It will also equal to the number of rows in the dataset to be simulated, because it is assumed that each row represents a different independent and identically distributed subject. p Number of predictor variables (covariates) in the simulated dataset. These co- variates will be the features screened by DC-SIS. Value A list with following components: X Matrix of predictors to be screened. It will have n rows and p columns. Y Vector of responses. It will have length n. References Cui, H., Li, R., & Zhong, W. (2015). Model-free feature screening for ultrahigh dimensional dis- criminant analysis. Journal of the American Statistical Association, 110: 630-641. <DOI:10.1080/01621459.2014.920256> Examples set.seed(12345678) results <- simulateMVSISNY() 12 simulateSIRS simulateSIRS Simulate a dataset for demonstrating the performance of screenIID with the SIRS method Description Simulates a dataset that can be used to demonstrate variable screening for ultrahigh-dimensional regression with the SIRS option in screenIID. The simulated dataset has p numerical predictors X and a categorical Y-response. The data-generating scenario is a simplified version of Exam- ple 1 of Zhu, Li, Li and Zhu (2011). Specifically, the X covariates are normally distributed with mean zero and variance one, and may be correlated if the argument rho is set to a nonzero value. The response Y is generated as Y = c*X1 + 0.8*c*X2 + 0.6*c*X3 + 0.4*c*X4 + 0.5*c*X5 + sigma*e. where c is the argument SignalStrength, e is either a standard normal distribution (if HeavyTailedResponse==FALSE) or t distribution with 1 degree of freedom (if HeavyTaile- dResponse==TRUE). sigma is either sqrt(6.83) if heteroskedastic==FALSE, or else exp(X20+X21+X22) if heteroskedastic=TRUE. Usage simulateSIRS( n = 200, p = 5000, rho = 0, HeavyTailedResponse = TRUE, heteroskedastic = TRUE, SignalStrength = 1 ) Arguments n Number of subjects in the dataset to be simulated. It will also equal to the number of rows in the dataset to be simulated, because it is assumed that each row represents a different independent and identically distributed subject. p Number of predictor variables (covariates) in the simulated dataset. These co- variates will be the features screened by DC-SIS. rho The correlation between adjacent covariates in the simulated matrix X. The within-subject covariance matrix of X is assumed to has the same form as an AR(1) autoregressive covariance matrix, although this is not meant to imply that the X covariates for each subject are in fact a time series. Instead, it is just used as an example of a parsimonious but nontrivial covariance structure. If rho is left at the default of zero, the X covariates will be independent and the simulation will run faster. HeavyTailedResponse If this is true, Y residuals will be generated to have much heavier tails (more unusually high or low values) then a normal distribution would have. simulateVCM 13 heteroskedastic Whether the error variance should be allowed to depend on one of the predictor variables. SignalStrength A constant used in the simulation to increase or decrease the signal-to-noise ratio; it was set to 0.5, 1, or 2 for weaker, medium or stronger signal. Value A list with following components: X Matrix of predictors to be screened. It will have n rows and p columns. Y Vector of responses. It will have length n. References Zhu, L.-P., Li, L., Li, R., & Zhu, L.-X. (2011). Model-free feature screening for ultrahigh-dimensional data. Journal of the American Statistical Association, 106, 1464-1475. <DOI:10.1198/jasa.2011.tm10563> Examples set.seed(12345678) results <- simulateSIRS() simulateVCM Simulate a dataset for testing the performance of screenVCM Description Simulates a dataset that can be used to test the screenVCM function, and to test the performance of the proposed method under different scenarios. The simulated dataset has a single U-covariate and p X-predictors, only a few of which have nonzero effect. Jingyuan Liu for providing some of the code upon which this function is based. Usage simulateVCM( n = 200, rho = 0.4, p = 1000, trueIdx = c(2, 100, 400, 600, 1000), betaFun = NULL ) Arguments n Number of subjects in the simulated dataset rho The correlation matrix of columns of X. p The total number of features to be screened from 14 simulateVCM trueIdx The indexes for the active features in the simulated X matrix. This should be a vector, and the values should be a subset of 1:p. betaFun A list of functions of U, one function for each entry in trueIdx, giving the varying effects of each active predictor in the simulated X matrix. Value A list with following components: X Matrix of predictors to be screened. It will have n rows and p columns. Y Vector of responses. It will have length of n. U A vector representing a covariate with which the coefficient functions vary. Examples set.seed(12345678) results <- simulateVCM(p=1000) Index ∗ analysis simulateLD, 8 screenIID, 2 simulateMVSIS, 10 ∗ dimensional simulateMVSISNY, 11 screenVCM, 6 simulateSIRS, 12 ∗ discriminant simulateVCM, 13 screenIID, 2 ∗ feature screenIID, 2 screenLD, 4 screenVCM, 6 ∗ high-dimensional screenIID, 2 screenLD, 4 ∗ models screenVCM, 6 ∗ regression screenIID, 2 screenLD, 4 screenVCM, 6 ∗ screening screenIID, 2 screenLD, 4 screenVCM, 6 ∗ selection screenIID, 2 screenLD, 4 screenVCM, 6 ∗ ultra-high screenVCM, 6 ∗ variable screenIID, 2 screenLD, 4 screenVCM, 6 ∗ varying-coefficient screenVCM, 6 screenIID, 2 screenLD, 4 screenVCM, 6 simulateDCSIS, 7 15

COLP

cran

Package ‘COLP’ October 12, 2022 Type Package Title Causal Discovery for Categorical Data with Label Permutation Version 1.0.0 Date 2022-09-23 Description Discover causality for bivariate categorical data. This package aims to enable users to dis- cover causality for bivariate observational categori- cal data. See Ni, Y. (2022) <arXiv:2209.08579> ``Bivariate Causal Discovery for Categori- cal Data via Classification with Optimal Label Permutation. Advances in Neural Informa- tion Processing Systems 35 (in press)''. License MIT + file LICENSE Encoding UTF-8 LazyData true RoxygenNote 7.1.2 Imports MASS, combinat, stats URL https://github.com/nySTAT/COLP BugReports https://github.com/nySTAT/COLP/issues NeedsCompilation no Author Yang Ni [aut, cre] (<https://orcid.org/0000-0003-0636-2363>) Maintainer Yang Ni <yni@stat.tamu.edu> Depends R (>= 3.5.0) Repository CRAN Date/Publication 2022-09-29 08:40:12 UTC R topics documented: CatPairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 COLP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Index 3 1 2 COLP CatPairs Categorical Cause-Effect Pairs Description Cause-effect pairs extracted from R packages MASS and datasets for which the pairwise causal relationships are clear from the context, and at least one of the variables in each pair is categorical. For non-categorical variable, we discretized it at 5 evenly spaced quantiles.The current version contains 33 categorical cause-effect pairs. Usage data(CatPairs) Format A list of length 2. The first element is a list of 33 cause-effect pairs as data frames with the first column being the cause and the second column being the effect. The second element is a list of sources of each pair. COLP Causal Discovery for Bivariate Cateogrical Data Description Estimate a causal directed acyclic graph (DAG) for ordinal cateogrical data with greedy or exhaus- tive search. Usage COLP(y, x, algo = "E") Arguments y factor, a potential effect variable x factor, a potential cause variable algo exhaustive search (algo="E") of category ordering or greedy search (algo="G") Value A list of length 3. cd = 1 if x causes y; cd = 0 otherwise. P is the optimal odering of the effect variable. epsilon is the difference in log-likelihood favoring x causes y. Examples fit = COLP(CatPairs[[1]][[1]]$Diffwt,CatPairs[[1]][[1]]$Treat,algo="E") fit$cd Index ∗ datasets CatPairs, 2 CatPairs, 2 COLP, 2 3

rD3plot

cran

Package ‘rD3plot’ March 22, 2023 Type Package Version 1.0.68 Date 2023-03-21 Title Interactive Networks, Timelines, Barplots, Galleries with 'D3.js' Description Creates interactive analytic graphs with 'R'. It joins the data analysis power of R and the visual- ization libraries of JavaScript in one package. The package provides interactive networks, time- lines, barplots, image galleries and evolving networks. Graphs are repre- sented as 'D3.js' graphs embedded in a web page ready for its interactive analysis and exploration. License GPL-2 | GPL-3 Depends R (>= 3.5.0) Imports igraph (>= 1.0.1) Suggests shiny, evolMap NeedsCompilation no Maintainer Modesto Escobar <modesto@usal.es> Encoding UTF-8 Author Modesto Escobar [aut, cph, cre] (<https://orcid.org/0000-0003-2072-6071>), Carlos Prieto [aut] (<https://orcid.org/0000-0001-8178-9768>), David Barrios [aut] Repository CRAN Date/Publication 2023-03-22 16:50:10 UTC R topics documented: rD3plot-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 add_tutorial_rd3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 barplot_rd3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 evolNetwork_rd3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 finches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1 2 add_tutorial_rd3 galapagos . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 gallery_rd3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 miserables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 network_rd3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 pie_rd3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 rd3_addDescription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 rd3_addImage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 rd3_fromIgraph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 rd3_layoutCircle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 rd3_layoutGrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 rd3_multigraph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 rd3_multiPages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 rd3_toIgraph . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 shiny_rd3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 sociologists . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 timeline_rd3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Index 25 rD3plot-package The rD3plot package. Description Creates interactive analytic graphs with ’R’. It joins the data analysis power of R and the visualiza- tion libraries of JavaScript in one package. The package provides interactive networks, timelines, barplots, image galleries and evolving networks. Graphs are represented as D3 graphs embedded in a web page ready for its interactive analysis and exploration add_tutorial_rd3 Adds a tutorial for the gallery. Description add_tutorial_rd3 adds a tutorial for a gallery. Usage add_tutorial_rd3(x, image = NULL, description = NULL) Arguments x object of class gallery_rd3. image character vector indicating the image path, header for the tutorial. description a character string indicating a desription text to insert in the tutorial. barplot_rd3 3 Value Object of class gallery_rd3. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. Examples data("finches") finches$species <- system.file("extdata", finches$species, package="rD3plot") # copy path to the species field gallery <- gallery_rd3(finches, image="species", main="Species in Galapagos Islands", note="Data source: Sanderson (2000)") gallery <- add_tutorial_rd3(gallery, description="Here you can see different finches species in Galapagos islands.") ## Not run: plot(gallery) ## End(Not run) barplot_rd3 Networked barplot. Description barplot_rd3 produces an interactive barplot of coincidences between events. Usage barplot_rd3(events, links, name = NULL, select = NULL, source = NULL, target = NULL, label = NULL, text = NULL, color = NULL, incidences = NULL, coincidences = NULL, expected = NULL, confidence = NULL, level = .95, significance = NULL, sort = NULL, decreasing = FALSE, scalebar = FALSE, defaultColor = "#1f77b4", note = NULL, cex = 1, language = c("en","es","ca"), dir = NULL) Arguments events a data frame with at least two columns of event names (by default 1st column) and incidences (2nd column). Columns for each variable can be specified at name and incidences parameters. 4 barplot_rd3 links a data frame with at least three columns indicating source event, target event and number of coincidences (in that order). Columns assigned to each variable can be specified at source, target and concidences parameters. name column name with event names in the events data frame. source column name with source names in the links data frame. target column name with target names in the links data frame. select event name to start the visualization. label column name with labels in the events data frame. text column name with html text in the events data frame. color column name with color variable in the events data frame. coincidences column nane with coincidences in the links data frame. incidences column name with incidences in the events data frame. expected column name with expected coincidences in the links data frame. confidence column name with confidence interval in the links data frame. level confidence level significance column name with significance in the links data frame. sort column name in the events data frame to order the bars in the graph. decreasing order the events in a decreasing order. scalebar bars are represented filling all the screen height. defaultColor a string giving a valid html color. note the lower title of the graph. cex a number giving the amount by which plotting text should be scaled relative to the default. language a character string indicating the language of the graph (en=english (default); es=spanish; ca=catalan). dir a character string representing the directory where the web files will be saved. Value Object of class barplot_rd3. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. Examples data(finches) data(galapagos) barplot <- barplot_rd3(finches, galapagos, select="Certhidea olivacea", note="Data source: Sanderson (2000)") ## Not run: plot(barplot) ## End(Not run) evolNetwork_rd3 5 evolNetwork_rd3 Create evolving networks. Description evolNetwork_rd3 produce an evolving network. Usage evolNetwork_rd3(..., frame = 0, speed = 50, loop = FALSE, lineplots = NULL, dir = NULL) Arguments ... network_rd3 objects that will be integrated as temporal frames in the evolving network. frame a frame ordinal position where the playback will start. speed a percentage value for the playback speed of network frames. loop allowing continuous repetition. lineplots a character vector giving the node attributes to show as lineplots. dir a "character" string representing the directory where the graph will be saved. Value This function returns a network_rd3 object. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. Examples nets <- list() N <- data.frame(name=paste0("node",1:2)) E <- data.frame(Source="node1",Target="node2") nets[["net1"]] <- network_rd3(N, E, repulsion=98, label=FALSE) for(i in 3:100){ N <- rbind(N,data.frame(name=paste0("node",i))) E <- rbind(E,data.frame(Source=paste0("node",i-1),Target=paste0("node",i))) nets[[paste0("net",i-1)]] <- network_rd3(N, E, repulsion=100-i, label=FALSE) } nets$speed=100 net <- do.call(evolNetwork_rd3,nets) 6 finches ## Not run: plot(net) ## End(Not run) finches Data: Finches’ attributes in Galapagos islands. Description Data frame with events as result. Usage data("finches") Format A data frame with 13 observations (pinches) and 4 variables (name and characteristics): name : Genus and species of the finche frequency : number of islands where the finche can be found type : Genus of the finche species : name of the file containing the picture of the finche References Sanderson, James (2000). Testing Ecological Patterns: A Well-known Algorithm from Computer Science Aids the Evaluation of Species Distributions. American Scientist, 88, pp. 332-339. Examples data(finches) head(finches,10) galapagos 7 galapagos Data: Finches’ presence in Galapagos Islands. Description Data frame containing data of finches coapperance in the Galagos Islands. Usage data("galapagos") Format This links data set consists of three variables of length 60: Source : Finche 1 Target : Finche 2 coincidences : number of islands they share References Sanderson, James (2000). Testing Ecological Patterns: A Well-known Algorithm from Computer Science Aids the Evaluation of Species Distributions. American Scientist, 88, pp. 332-339. Examples data(galapagos) head(galapagos,10) gallery_rd3 Images in a grid gallery. Description gallery_rd3 produces an interactive image gallery. Usage gallery_rd3(nodes, tree = NULL, name = NULL, label = NULL, color = NULL, border = NULL, ntext = NULL, info = NULL, infoFrame = c("right","left"), image = NULL, zoom = 1, itemsPerRow = NULL, main = NULL, note = NULL, showLegend = TRUE, frequencies = FALSE, help = NULL, helpOn = FALSE, tutorial = FALSE, description = NULL, descriptionWidth = NULL, roundedItems = FALSE, controls = 1:5, cex = 1, defaultColor = "#1f77b4", language = c("en", "es", "ca"), dir = NULL) 8 gallery_rd3 Arguments nodes a data frame with at least three columns of names, start and end. tree a data frame with two columns: source and target, describing relationships be- tween nodes. It indicates a hierarchy between nodes which can be dynamically explored. name column name with image names in the nodes data frame. label column name with image labels in the nodes data frame. color column name with image background color variable in the nodes data frame. border column name with image border width variable in the nodes data frame or a numeric vector. ntext column name with html text in the nodes data frame. info column name with information to display in a panel in the nodes data frame. infoFrame In which panel should the information be displayed? The left panel is only avail- able if the description argument is provided and frequencies are not showing. image column name which indicates the image paths in the nodes data frame. zoom a number between 0.1 and 10 as initial displaying zoom. itemsPerRow number of items in each row. main upper title of the graph. note lower title of the graph. frequencies a logical value true if barplots representing node attributes frequencies will be added to the final graph. showLegend a logical value true if the legend is to be shown. help a character string indicating a help text of the graph. helpOn Should the help be shown at the beginning? tutorial Should tutorial be displayed? description a character string indicating a desription text for the graph. descriptionWidth a percentage indicating a width for the description panel (25 by default). roundedItems Display items with rounded borders. controls a numeric vector indicating which controls will be shown. 1 = topbar, 2 = pdf exportation, 3 = xlsx exportation, 4 = table, 5 = netCoin logo. NULL hide all controls, negative values deny each control and 0 deny all. cex number indicating the amount by which plotting text should be scaled relative to the default. defaultColor a character vector giving a valid html color for node representation. language a character string indicating the language of the graph (en=english (default); es=spanish; ca=catalan). dir a character string representing the directory where the web files will be saved. miserables 9 Value Object of class gallery_rd3. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. Examples data("finches") finches$species <- system.file("extdata", finches$species, package="rD3plot") # copy path to the species field gallery <- gallery_rd3(finches, image="species", main="Species in Galapagos Islands", note="Data source: Sanderson (2000)") ## Not run: plot(gallery) ## End(Not run) miserables Coappearance network of characters in Les Miserables (undirected) Description A list of two datasets, vertices and edges, containing data on characters and their coapperance in chapters in Victor Hugo’s Les Miserables. Usage data("miserables") Format A list of two data frames: • the links data set consists of three variables of length 254: – Source: Character 1 – Target: Character 2 – value: number of times they appear together in a chapter of Les Miserables • the nodes data set consists of two variables with information on 77 characters: – name: Character name – group: Character group References D. E. Knuth, The Stanford GraphBase: A Platform for Combinatorial Computing, Addison-Wesley, Reading, MA (1993). 10 network_rd3 Examples data(miserables) head(miserables,10) network_rd3 Interactive network. Description network_rd3 produces a network_rd3 object ready for its representation as an interactive network in a web browser. Its input has to be two data.frames: one of attributes of events or nodes, and the other of attributes of the edges or links. Usage network_rd3(nodes = NULL, links = NULL, tree = NULL, community = NULL, layout = NULL, name = NULL, label = NULL, group = NULL, groupText = FALSE, labelSize = NULL, size = NULL, color = NULL, shape = NULL, border = NULL, legend = NULL, sort = NULL, decreasing = FALSE, ntext = NULL, info = NULL, image = NULL, imageNames = NULL, nodeBipolar = FALSE, nodeFilter = NULL, degreeFilter = NULL, source = NULL, target = NULL, lwidth = NULL, lweight = NULL, lcolor = NULL, ltext = NULL, intensity = NULL, linkBipolar = FALSE, linkFilter = NULL, repulsion = 25, distance = 10, zoom = 1, fixed = showCoordinates, limits = NULL, main = NULL, note = NULL, showCoordinates = FALSE, showArrows = FALSE, showLegend = TRUE, frequencies = FALSE, showAxes = FALSE, axesLabels = NULL, scenarios = NULL, help = NULL, helpOn = FALSE, mode = c("network","heatmap"), roundedItems = FALSE, controls = 1:4, cex = 1, background = NULL, defaultColor = "#1f77b4", language = c("en","es","ca"), dir = NULL) Arguments nodes a data frame with at least one column of node names. links a data frame with at least two columns with source and target node names. tree a data frame with two columns: source and target, describing relationships be- tween nodes. It indicates a hierarchy between nodes which can be dynamically explored. name name of the column with names in the nodes data frame. source name of the column with source names in the links data frame. target name of the column with target names in the links data frame. label name of the column with labels in the nodes data frame. network_rd3 11 group name of the column with groups in the nodes data frame. groupText show names of the groups. community algorithm to make communities: edge_betweenness("ed"), fast_greedy("fa"), label_prop("la"), leiden_eigen("le"), louvain("lo"), optimal("op"), spinglass("sp"), walktrap("wa") labelSize name of the column with label size in the nodes data frame. size name of the column with size in the nodes data frame. color name of the column with color variable in the nodes data frame. shape name of the column with shape variable in the nodes data frame. legend name of the column with the variable to represent as a legend in the nodes data frame. ntext name of the column with html text in the nodes data frame. info name of the column with information to display in a panel in the nodes data frame. border name of the column with border width in the nodes data frame. sort name of the column with node order in the nodes data frame (only for heatmap). decreasing decreasing or increasing sort of the nodes (only for heatmap). intensity name of the column with intensity variable in the links data frame (only for heatmap). lwidth name of the column with width variable in the links data frame. lweight name of the column with weight variable in the links data frame. lcolor name of the column with color variable in the links data frame. ltext name of the column with labels in the links data frame. nodeFilter a character string with a condition for filtering nodes. linkFilter a character string with a condition for filtering links. degreeFilter numeric vector to filter the resulting network by degree. Input can be a number which specifies the minimum degree or two numbers which specify the lower and upper limits of the filter. nodeBipolar a logical value that polarizes negative and positive node values in the graphical representation. Indicates whether the color key should be made symmetric about 0. linkBipolar a logical value that polarizes negative and positive link values in the graphical representation. Indicates whether the color key should be made symmetric about 0. defaultColor a character vector giving a valid html color for node representation. repulsion a percentage for repulsion between nodes. distance a percentage for distance of links. zoom a number between 0.1 and 10 to start displaying zoom. fixed prevent nodes from being dragged. scenarios a note showing number of scenarios. 12 network_rd3 main upper title of the graph. note lower title of the graph. frequencies a logical value true if barplots representing node attributes frequencies will be added to the final graph. help help text of the graph. helpOn Should the help be shown at the beginning? background background color or image path of the graph. layout a matrix with two columns with x/y coordinates or an algorithm to calculate the static layout of the network: davidson.harel drl("da"), circle("ci"), Force-Atlas- 2("fo"), fruchterman.reingold("fr"), gem("ge"), grid("gr"), kamada.kawai("ka"), lgl("lg"), mds("md"), random("ra"), reingold.tilford("re"), star("sta"), sugiyama("sug") limits vector indicating the layout limits, must be a numeric vector of length 4 on this order: x_min, y_min, x_max, y_max. cex number indicating the amount by which plotting text should be scaled relative to the default. roundedItems Display images with rounded borders. controls a numeric vector indicating which controls will be shown. 1 = sidebar, 2 = selection buttons, 3 = export buttons, 4 = nodes table, 5 = links table. NULL hide all controls, negative values deny each control and 0 deny all. mode a character vector indicating the graph mode allowed: network, heatmap or both (both by default). showCoordinates a logical value true if the coordinates are to be shown in tables and axes. Default = FALSE. showArrows a logical value true if the directional arrows are to be shown. Default = FALSE. showLegend a logical value true if the legend is to be shown. showAxes a logical value true if the axes are to be shown. axesLabels a character vector giving the axes names. language a character string indicating the language of the graph (en=english (default); es=spanish; ca=catalan). image name of the column with the path to node image files in the nodes data frame. imageNames name of the column with names for image files in the nodes data frame which will be shown in the legend. dir a "character" string representing the directory where the resulting web files will be saved. Value This function returns a network_rd3 object. If the ’dir’ attribute is specified, the function creates a folder in the computer with an HTML document named index.html which contains the produced graph. This file can be directly opened with your browser and sent to a web server to work properly. pie_rd3 13 Note nodes and links arguments can be substituted by a network_rd3 object to add or change options to it. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. Examples data(miserables) net <- network_rd3(miserables$nodes, miserables$links, size="degree", color="group", lwidth="value") ## Not run: plot(net) ## End(Not run) pie_rd3 Networked barplot. Description pie_rd3 produces an interactive barplot of coincidences between events. Usage pie_rd3(v, w = NULL, labels = NULL, colors = NULL, nodes = NULL, links = NULL, name = NULL, source = NULL, target = NULL, lcolor = NULL, ablineX = NULL, ablineY = NULL, hideUpper = FALSE, main = NULL, note = NULL, showLegend = TRUE, help = NULL, helpOn = FALSE, cex = 1, language = c("en", "es", "ca"), dir = NULL) Arguments v a vector or array of non-negative numerical quantities. The values are displayed as the areas of pie slices. w an array of non-negative numerical quantities. The first value is displayed as a pie slice bordered red. labels character strings giving names for the slices. colors a vector of colors to be used in filling the slices. nodes a data frame with information for rows and columns. links a data frame with information for each pie. name name of the column with rownames and colnames in the nodes data frame. source name of the column with rownames in the links data frame. 14 rd3_addDescription target name of the column with colnames in the links data frame. lcolor name of the column with color variable in the links data frame. ablineX adds one or more straight vertical lines between pies. ablineY adds one or more straight horizontal lines between pies. hideUpper should hide the upper triangle? main upper title of the graph. note lower title of the graph. showLegend a logical value true if the legend is to be shown. help a character string indicating a help text of the graph. helpOn Should the help be shown at the beginning? cex number indicating the amount by which plotting text should be scaled relative to the default. language a character vector (es=spanish; en=english; ca=catalan). dir a character string representing the directory where the web files will be saved. Value Object of class pie_rd3. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. Examples pie <- pie_rd3(1:4, labels = c("XY","X","","Y"), colors = c("black","cadetblue2", "white","cadetblue3")) ## Not run: plot(pie) ## End(Not run) rd3_addDescription Adds a description to a ’rD3plot’ object. Description rd3_addDescription adds a description to a ’rD3plot’ object. Usage rd3_addDescription(x, description) rd3_addImage 15 Arguments x A ’rD3plot’ object. description the description text. Value A ’rD3plot’ object. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. Examples data(finches) data(galapagos) bar <- barplot_rd3(finches, galapagos, select="Certhidea olivacea") img <- system.file("extdata", "p.Crassirostris.png", package="rD3plot") bar <- rd3_addDescription(bar,"Species coincidences in Galapagos Islands") multi <- rd3_multigraph(barplot=bar) ## Not run: rd3_multiPages(multi,"Graph image example",show=TRUE) ## End(Not run) rd3_addImage Adds an image to a ’rD3plot’ object. Description rd3_addImage adds an image to a ’rD3plot’ object. Usage rd3_addImage(x, img) Arguments x A ’rD3plot’ object. img character vector indicating the image path. Value A ’rD3plot’ object. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. 16 rd3_fromIgraph Examples data(finches) data(galapagos) bar <- barplot_rd3(finches, galapagos, select="Certhidea olivacea") img <- system.file("extdata", "p.Crassirostris.png", package="rD3plot") bar <- rd3_addImage(bar,img) multi <- rd3_multigraph(barplot=bar) ## Not run: rd3_multiPages(multi,"Graph image example",show=TRUE) ## End(Not run) rd3_fromIgraph Produce interactive networks from ’igraph’ objects. Description rd3_fromIgraph produce an interactive network from an ’igraph’ object. Usage rd3_fromIgraph(G, ...) Arguments G an igraph object. ... Any network_rd3 argument. Value This function returns a network_rd3 object. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. Examples g <- igraph::make_ring(10) rd3_fromIgraph(g) rd3_layoutCircle 17 rd3_layoutCircle Produce a circle layout of any number of nodes. Description rd3_layoutCircle produces a circle layout of any number of nodes. Usage rd3_layoutCircle(N,nodes=seq_len(nrow(N)),deg=0,name=NULL) Arguments N a data frame of nodes. nodes a vector specifing the node names inclued in the layout calculation. deg rotation degrees. name column name with node names in the N data frame. Value ‘rd3_layoutCircle’ produces a circle layout of any number of nodes. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. Examples A <- data.frame(name=letters) L <- rd3_layoutCircle(A,name="name") net <- network_rd3(A,layout=L) ## Not run: plot(net) ## End(Not run) 18 rd3_layoutGrid rd3_layoutGrid Produce a grid layout of any number of nodes. Description rd3_layoutGrid produces a grid layout of any number of nodes. Usage rd3_layoutGrid(N,string,name=NULL,byrow=FALSE) Arguments N a data frame of nodes. string a character vector specifing grouped nodes. name column name with node names in the N data frame. byrow order nodes by row (default) or by columns (FALSE) Value ‘rd3_layoutGrid’ produces a grid layout of any number of nodes. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. Examples A <- data.frame(name=letters) L <- rd3_layoutGrid(A,"a,b,c,d,e.f,g,h,i,j.k,l,m,n,o,p.q,r,s,t,u.v,w,x,y,z","name") net <- network_rd3(A,layout=L) ## Not run: plot(net) ## End(Not run) rd3_multigraph 19 rd3_multigraph Integrates interactive ’rD3plot’ graphs. Description rd3_multigraph produce an interactive multi graph with the integration of ’rD3plot’ graphs in the final result. Usage rd3_multigraph(..., mfrow = NULL, dir = NULL) Arguments ... rD3plot graphs (network_rd3, barplot_rd3, timeplot_rd3) objects or string paths to html "directories". mfrow a vector of the form ’c(nr, nc)’. Subsequent graphs will be drawn in an ’nr’-by- ’nc’ array on the device by rows. dir a "character" string representing the directory where the graph will be saved. Value This function returns a multi_rd3 object. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. Examples data(miserables) net <- network_rd3(miserables$nodes, miserables$links, size="degree", color="group", lwidth="value") data(finches) data(galapagos) bar <- barplot_rd3(finches, galapagos, select="Certhidea olivacea") data(sociologists) time <- timeline_rd3(sociologists,"name","birth","death","birthcountry") multi <- rd3_multigraph(network=net, barplot=bar, timeline=time) ## Not run: plot(multi) ## End(Not run) 20 rd3_multiPages rd3_multiPages Produces a gallery of ’rD3plot’ graphs. Description rd3_multiPages produces a gallery page to explore multiple ’rD3plot’ graphs. Usage rd3_multiPages(x, title = NULL, columns = NULL, imageSize = NULL, description = NULL, note = NULL, cex = 1, dir = tempDir(), show = FALSE) Arguments x is a multi_rd3 object. See rd3_multigraph title the text for a main title. columns a numeric vector giving the number of columns to display items in gallery. De- fault = 3. imageSize a numeric vector giving the size of images in gallery. Default = 75. description a description text for the gallery. note a footer text for the gallery. cex number indicating the amount by which plotting text should be scaled relative to the default. Default = 1. dir a "character" string representing the directory where the graph will be saved. show a logical value true if the graph is to be shown. Default = FALSE. Value The function creates a folder in your computer with an HTML document named index.html which contains the graph. This file can be directly opened with your browser. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. Examples data(miserables) net <- network_rd3(miserables$nodes, miserables$links, size="degree", color="group", lwidth="value") data(finches) data(galapagos) bar <- barplot_rd3(finches, galapagos, select="Certhidea olivacea") rd3_toIgraph 21 data(sociologists) time <- timeline_rd3(sociologists,"name","birth","death","birthcountry") multi <- rd3_multigraph(network=net, barplot=bar, timeline=time) ## Not run: rd3_multiPages(multi,"Some graphs",show=TRUE) ## End(Not run) rd3_toIgraph ’igraph’ object. Description creates an igraph object from a network_rd3 object. Usage rd3_toIgraph(net) Arguments net is a network_rd3 object. See network_rd3 Value An igraph object. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. Examples # A character column (with separator) data(miserables) net <- network_rd3(miserables$nodes, miserables$links, size="degree", color="group", lwidth="value") rd3_toIgraph(net) # conversion into a igraph object 22 sociologists shiny_rd3 Include rD3plot graphs in ’Shiny’. Description Load a rD3plot graph to display in ’Shiny’. Usage shiny_rd3(x) Arguments x is a network_rd3, barplot_rd3 or timeplot_rd3 object. Value This function returns a shiny.tag object. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. sociologists Data: Sociologists born in the 19th century. Description Data frame with names, birth and death year data, birth country and movement. Usage data("sociologists") Format A data frame with 33 observations and the following 4 variables (events) to study coincidences in time: name : name and last name of the sociologist birth : birth year death : death year birthcountry : birth country movements : movement or school of thought timeline_rd3 23 Source Own elaboration from manuals of sociology. Examples data(sociologists) head(sociologists, 10) tail(sociologists, 10) timeline_rd3 Interactive time-bar plot. Description timeline_rd3 produces a timeline_rd3 object ready for its representation as an interactive time line in a web browser. Usage timeline_rd3(periods, name = "name", start = "start", end = "end", group = NULL, text = NULL, main = NULL, note = NULL, info = NULL, events = NULL, eventNames = "name", eventPeriod = "period", eventTime = "date", eventColor = NULL, eventShape = NULL, cex = 1, language = c("en","es","ca"), dir = NULL) Arguments periods a data frame with at least three columns describing period names, start and end. name name of the column with names in the periods data frame. start name of the column with starts in the periods data frame. end name of the column with ends in the periods data frame. group name of the column with a grouping criteria in the periods data frame. text name of the column with a descriptive text of periods (html format) in the peri- ods data frame. main upper title of the graph. note lower title of the graph. info name of the column in the periods data frame with information to display on the information panel. events a data frame of events related to periods (shown as dots) with three columns: interval name, event name and event date eventNames name of the column with event identifiers in the events data frame. eventPeriod name of the column with interval identifiers in the events data frame. eventTime name of the column with time points in the events data frame. 24 timeline_rd3 eventColor name of the column with the color criteria in the events data frame. eventShape name of the column with the shape criteria in the events data frame. cex number indicating the amount by which plotting text should be scaled relative to the default. language a character string indicating the language of the graph (en=english (default); es=spanish; ca=catalan). dir a "character" string representing the directory where the web files will be saved. Value Object of class timeline_rd3. Author(s) Modesto Escobar, Department of Sociology and Communication, University of Salamanca. Examples # Database of 19th century sociologists data(sociologists) timeline <- timeline_rd3(sociologists,"name","birth","death","birthcountry") ## Not run: plot(timeline) ## End(Not run) Index ∗ datasets finches, 6 galapagos, 7 miserables, 9 sociologists, 22 add_tutorial_rd3, 2 barplot_rd3, 3 evolNetwork_rd3, 5 finches, 6 galapagos, 7 gallery_rd3, 7 miserables, 9 network_rd3, 10, 16, 21 pie_rd3, 13 rd3_addDescription, 14 rd3_addImage, 15 rd3_fromIgraph, 16 rd3_layoutCircle, 17 rd3_layoutGrid, 18 rd3_multigraph, 19, 20 rd3_multiPages, 20 rd3_toIgraph, 21 rD3plot-package, 2 shiny_rd3, 22 sociologists, 22 timeline_rd3, 23 25

EIAapi

cran

Package ‘EIAapi’ August 13, 2023 Type Package Title Query Data from the 'EIA' API Version 0.1.2 Maintainer Rami Krispin <rami.krispin@gmail.com> Description Provides a function to query and extract data from the 'US Energy Information Adminis- tration' ('EIA') API V2 <https://www.eia.gov/opendata/>. The 'EIA' API provides a vari- ety of information, in a time series format, about the energy sec- tor in the US. The API is open, free, and requires an access key and registra- tion at <https://www.eia.gov/opendata/>. License MIT + file LICENSE Encoding UTF-8 RoxygenNote 7.2.1 Imports data.table (>= 1.14.2), dplyr (>= 1.0.9), jsonlite (>= 1.8.2), lubridate (>= 1.8.0) Suggests knitr, plotly (>= 4.10.0), rmarkdown SystemRequirements The package required the jq command line library. Please check https://stedolan.github.io/jq/download/ for download instructions. URL https://github.com/RamiKrispin/EIAapi BugReports https://github.com/RamiKrispin/EIAapi/issues NeedsCompilation no Author Rami Krispin [aut, cre] Repository CRAN Date/Publication 2023-08-13 07:10:06 UTC R topics documented: eia_backfill . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 eia_get . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 eia_metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 Index 6 1 2 eia_backfill eia_backfill Pull a Large Number of Observations with a Sequential Query Description This function allows users to overcome the API’s observation limit per query by breaking down the query into smaller sequential sub-queries and appending back the results. The main use case of this function is for backfilling hourly series. Usage eia_backfill(start, end, offset, api_key, api_path, facets) Arguments start defines the start time of the series, should use a POSIXt class for hourly series or Date format for non-hourly series (daily, monthly, etc.) end defines the end time of the series, should use a POSIXt class for hourly series or Date format for non-hourly series (daily, monthly, etc.) offset An integer, defines the number of observations limitation per query. In line with the API limitation of up to 5000 observations per query, the offset argument’s upper limit is 5000 observations. api_key A string, EIA API key, see https://www.eia.gov/opendata/ for registration to the API service api_path A string, the API path to follow the API endpoint https://api.eia.gov/v2/. The path can be found on the EIA API dashboard, for more details see https://www.eia.gov/opendata/browser/ facets A list, optional, set the filtering argument (defined as ’facets’ on the API header), following the structure of list(facet_name_1 = value_1, facet_name_2 = value_2) Details The function use start, end, and offset arguments to define a sequence of queries. Value A time series Examples ## Not run: start <- as.POSIXlt("2018-06-19T00", tz = "UTC") end <- lubridate::floor_date(Sys.time()- lubridate::days(2), unit = "day") attr(end, "tzone") <- "UTC" offset <- 2000 api_key <- Sys.getenv("eia_key") api_path <- "electricity/rto/region-sub-ba-data/data/" eia_get 3 facets = list(parent = "NYIS", subba = "ZONA") df <- eia_backfill(start = start, end = end, offset = offset, api_key = api_key, api_path = api_path, facets = facets) at_y <- pretty(df$value)[c(2, 4, 6)] at_x <- seq.POSIXt(from = start, to = end, by = "2 years") plot(df$time, df$value, col = "#1f77b4", type = "l", frame.plot = FALSE, axes = FALSE, panel.first = abline(h = at_y, col = "grey80"), main = "NY Independent System Operator (West) - Hourly Generation of Electricity", xlab = "Source: https://www.eia.gov/", ylab = "MegaWatt/Hours") mtext(side =1, text = format(at_x, format = "%Y"), at = at_x, col = "grey20", line = 1, cex = 0.8) mtext(side =2, text = format(at_y, scientific = FALSE), at = at_y, col = "grey20", line = 1, cex = 0.8) ## End(Not run) eia_get Query the EIA API Description Function to query and extract data from the EIA API v2 Usage eia_get( api_key, api_path, data = "value", facets = NULL, start = NULL, end = NULL, length = NULL, 4 eia_get offset = NULL, frequency = NULL, format = "data.frame" ) Arguments api_key A string, EIA API key, see https://www.eia.gov/opendata/ for registration to the API service api_path A string, the API path to follow the API endpoint https://api.eia.gov/v2/. The path can be found on the EIA API dashboard, for more details see https://www.eia.gov/opendata/browser/ data A string, the metric type, by default uses ’value’ (defined as ’data’ on the API header) facets A list, optional, set the filtering argument (defined as ’facets’ on the API header), following the structure of list(facet_name_1 = value_1, facet_name_2 = value_2) start A string, optional, set the starting date or time of the series using "YYYY-MM- DD" format for date and "YYYY-MM-DDTHH" format for hourly time series end A string, optional, set the ending date or time of the series using "YYYY-MM- DD" format for date and "YYYY-MM-DDTHH" format for hourly time series length An integer, optional, defines the length of the series, if set to NULL (default), will default to the API default value of 5000 observations per pull. The API enables a pull of up to 100K observations per call. If needed to pull more than the API limit per call, recommend to iterate the call with the use of the start, end and/or offset arguments offset An integer, optional, set the number of observations to offset from the default starting point of the series. If set to NULL (default), will default to the API default value of 0 frequency A string, optional, define the API frequency argument (e.g., hourly, monthly, annual, etc.). If set to NULL (default), will default to the API default value format A string, defines the output of the return object to either "data.frame" (default) or "data.table" Value data.table/data.frame object Examples ## Not run: # Required an EIA API key to send a query api_key <- "YOUR_API_KEY" df <- eia_get( api_key = api_key, api_path = "electricity/rto/fuel-type-data/data/", data = "value" ) eia_metadata 5 ## End(Not run) eia_metadata Pull Metadata from EIA API Description Get data descriptions and metadata from the EIA API Usage eia_metadata(api_path = NULL, api_key) Arguments api_path A string, the API category/route path following the API endpoint (i.e., ’https://api.eia.gov/v2/’) If set to NULL (default) or as empty string "" it returns the main categories avail- able on the API. The path can be found on the EIA API dashboard, for more details see https://www.eia.gov/opendata/browser/ api_key A string, EIA API key, see https://www.eia.gov/opendata/ for registration to the API service Details The function enables to explore the different data categories and available routes inline with the API dashboard (https://www.eia.gov/opendata/browser/) Value a list object with the series description and metadata Examples ## Not run: electricity_metadata <- eia_metadata(api_key = Sys.getenv("eia_key"), api_path = "electricity") electricity_metadata$response$description electricity_metadata$response$id electricity_metadata$response$name electricity_metadata$response$routes ## End(Not run) Index eia_backfill, 2 eia_get, 3 eia_metadata, 5 6

eatRep

cran

Package ‘eatRep’ March 27, 2023 Type Package Title Educational Assessment Tools for Replication Methods Version 0.14.7 Depends R (>= 4.1), survey (>= 4.1-1), BIFIEsurvey, progress, lavaan (>= 0.6-7) Imports Hmisc, fmsb, mice (>= 2.46), boot, car, reshape2, plyr, combinat, miceadds, tidyr, EffectLiteR, estimatr, eatTools (>= 0.7.4), eatGADS (>= 0.20.0), janitor, msm, lme4, utils, methods Description Replication methods to compute some basic statistic operations (means, standard deviations, frequency tables, percentiles and generalized linear models) in complex survey designs compris- ing multiple imputed variables and/or a clustered sampling structure which both deserve special proce- dures at least in estimating standard errors. See the package documentation for a more detailed descrip- tion along with references. License GPL (>= 2) Encoding UTF-8 URL https://github.com/weirichs/eatRep LazyLoad yes LazyData yes NeedsCompilation no Suggests testthat, knitr, rmarkdown VignetteBuilder knitr Author Sebastian Weirich [aut, cre], Martin Hecht [aut], Karoline Sachse [aut], Benjamin Becker [aut] Maintainer Sebastian Weirich <sebastian.weirich@iqb.hu-berlin.de> Repository CRAN Date/Publication 2023-03-26 22:30:10 UTC 1 2 eatRep-package R topics documented: eatRep-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 checkLEs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 generateRandomJk1Zones . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 lsa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 repGlm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 repLmer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 repMean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 repQuantile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 repTable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Index 34 eatRep-package Statistical analyses in complex survey designs with multiple imputed data and trend estimation. Description The package provide functions to computes some basic statistic operations—(adjusted) means, stan- dard deviations, frequency tables, percentiles and generalized linear models—in complex survey designs comprising multiple imputed variables and/or a clustered sampling structure which both deserve special procedures at least in estimating standard errors. In large-scale assessments, stan- dard errors are comprised of three components: the measurement error, the sampling error, and (if trend estimation of at least two times of measurement are involved) the linking error. Measurement error: In complex surveys or large-scale assessments, measurement errors are taken into account by the mean of multiple imputed variables. The computation of standard errors for the mean of a multiple imputed variable (e.g. plausible values) involves the formulas provided by Rubin (1987). Computing standard errors for the mean of a nested imputed variable involves the formulas provided by Rubin (2003). Both methods are implemented in the package. The estimation of R2 and adjusted R2 in linear and generalized linear regression models with multiple imputed data sets is realized using the methods provided in Harel (2009). Sampling error: Computation of sampling errors of variables which stem from a clustered design may involve replication methods like balanced repeated replicate (BRR), bootstrap or Jackknife methods. See Westat (2000), Foy, Galia & Li (2008), Rust and Rao (1996), and Wolter (1985) for details. To date, the Jackknife-1 (JK1), Jackknife-2 (JK2) and the Balanced Repeated Replicates (BRR; optionally with Fay’s method) procedures are supported. Linking error: Lastly, standard errors for trend estimates may involve incorporating linking er- rors to account for potential differential item functioning or item parameter drift. eatRep allows to account for linking error when computing standard errors for trend estimates. Standard error estimation is conducted according to the operational practice in PISA, see equation 5 in Sachse & Haag (2017). The package eatRep is designed to combine one or several error types which is necessary, for example, if (nested) multiple imputed data are used in clustered designs. Considering the structure eatRep-package 3 is relevant especially for the estimation of standard errors. The estimation of national trends requires a sequential analysis for both measurements and a comparison of estimates between them. Technically, eatRep is a wrapper for the survey package (Lumley, 2004). Each function in eatRep corresponds to a specific function in survey which is called repeatedly during the analysis. Hence, a nested loop is used. We use “trend replicates” in the outer loop, “imputation replicates” in the middle loop to account for multiple imputed data, and “cluster replicates” in the inner loop to account for the clustered sampling structure. While the functional principle of survey is based on replication of standard analyses, eatRep is based on replication of survey analyses to take multiple imputed data into account. More recent versions of the package additionally allow estimations using the BIFIEsurvey package instead of survey which provide substantial advantages in terms of speed. For each imputed data set in each measurement, i.e. in the inner loop, the eatRep function first creates replicate weights based on the primary sampling unit (PSU) variable and the replication indicator variable. In the jackknife procedure, the first one is often referred to as “jackknife zone”, whereas the second one is often referred to as “jackknife replicate”. The number of distinct units in the PSU variable defines the number of replications which are necessary due to the clustered structure. A design object is created and the appropriate survey function is called. The process is repeated for each imputed dataset and the results of the analyses are pooled. The pooling procedure varies in relation to the type of variable to be pooled. For examples, means or regression coefficients are pooled according to Rubin (1987) or Rubin (2003). R2 is pooled according to Harel (2009), using a Fisher z-transformation. Chi-square distributed values are pooled according to Thomas and Rao (1990) for clustered data and according to Enders (2010) and Allison (2002) for multiple imputed data. For trend analyses, the whole process is repeated two times (according to the two measurements) and the difference of the estimates are computed along with their pooled standard errors. Without trend estimation, the outer loop has only one cycle (instead of two). Without multiple imputations, the middle loop has only one cycle. Without a clustered sampling structure (i.e, in a random sample), the inner loop has only one cycle. Without trend, imputation and clustered struc- ture, no replication is performed at all. To compute simple mean estimates, for example, eatRep then simply calls mean instead of svymean from the survey package. A special case occurs with nested multiple imputation. We then have four loops in a nested structure. Hence, the corresponding analyses may take considerably computational effort. Important note: Starting with version 0.10.0, several methods for the standard error estimation of cross level differences are implemented. Prior to version 0.10.0, the standard error for the difference between one single group (e.g., Belgium) and the total population (which is comprised of several states including Belgium) was estimated as if both groups would have been independent from each other. The standard errors, however, are biased then. Two new methods are now applicable using the argument crossDiffSE in repMean and provide unbiased standard errors—weighted effect coding (wec) and replication methods (rep); see, for example te Grotenhuis et al. (2017) and Weirich et al. (2021). The old method is still available by using crossDiffSE = "old". Note that the default method now is weighted effect coding. Second important note: Starting with version 0.13.0, function names have been changed due to inconsistent former denomination: Function jk2.mean now goes under the name of repMean, jk2.table was renamed to repTable, jk2.quantile was renamed to repQuantile, and jk2.glm now goes under the name of repGlm. The old functions are deprecated and will be removed in fur- ther package publications. Renaming was driven by the fact that the corresponding functions now have broader range of methods than only jackknife-2. 4 eatRep-package Details Package: eatRep Type: Package Version: 0.14.7 Date: 2023-03-24 License: GPL(>=2) Author(s) Authors: Sebastian Weirich <sebastian.weirich@iqb.hu-berlin.de>, Martin Hecht <martin.hecht@hu- berlin.de>, Benjamin Becker <b.becker@iqb.hu-berlin.de> References Allison, P. D. (2002). Missing data. Newbury Park, CA: Sage. Enders, C. K. (2010). Applied missing data analysis. Guilford Press. Foy, P., Galia , J. & Li, I. (2008). Scaling the data from the TIMSS 2007 mathematics and science assessment. In J. F. Olson, M. O. Martin & I. V. S. Mullis (ed.), TIMSS 2007 Technical Report (S. 225–280). Chestnut Hill, MA: TIMSS & PIRLS International Study Center, Lynch School of Education, Boston College. Harel, O. (2009): The estimation of R2 and adjusted R2 in incomplete data sets using multiple imputation. Journal of Applied Statistics. 36, 10, 1109–1118. Lumley, T. (2004). Analysis of complex survey samples. Journal of Statistical Software 9(1): 1–19 Rubin, D. B. (1987). Multiple imputation for nonresponse in surveys. New York: Wiley. Rubin, D.B. (2003): Nested multiple imputation of NMES via partially incompatible MCMC. Sta- tistica Neerlandica 57, 1, 3–18. Rust, K., & Rao, JNK. (1996): Variance estimation for complex surveys using replication tech- niques. Statistical Methods in Medical Research 5, 283–310. Sachse, K. A. & Haag, N. (2017). Standard errors for national trends in international large-scale assessments in the case of cross-national differential item functioning. Applied Measurement in Education, 30, (2), 102-116. http://dx.doi.org/10.1080/08957347.2017.1283315 Satorra, A., & Bentler, P. M. (1994). Corrections to test statistics and standard errors in covariance structure analysis. te Grotenhuis, M., Pelzer, B., Eisinga, R., Nieuwenhuis, R., Schmidt-Catran, A., & Konig, R. (2017). When size matters: advantages of weighted effect coding in observational studies. Interna- tional Journal of Public Health. 62, 163–167. Thomas, D. R. & Rao, JNK (1990): Small-sample comparison of level and power for simple goodness-of- fit statistics under cluster sampling. JASA 82:630-636 Weirich, S., Hecht, M., Becker, B. et al. (2021). Comparing group means with the total mean in ran- dom samples, surveys, and large-scale assessments: A tutorial and software illustration. Behavior Research Methods. https://doi.org/10.3758/s13428-021-01553-1 checkLEs 5 Westat (2000). WesVar. Rockville, MD: Westat. Wolter, K. M. (1985). Introduction to variance estimation. New York: Springer. checkLEs Checks compatibility of linking errors with GADS data bases. Description This function checks if a linking error data.frame is compatible with multiple trend eatGADS data bases. Usage checkLEs(filePaths, leDF) Arguments filePaths Character vectors with at least two paths to the eatGADS db files. leDF Linking error data.frame. Details This function inspects whether all linking error variables correspond to variables in the eatGADS data base and if the key variables also correspond to existing variables in the trend eatGADS data bases. Value Returns a report list. Examples # define eatGADs data bases trenddat1 <- system.file("extdata", "trend_gads_2010.db", package = "eatGADS") trenddat2 <- system.file("extdata", "trend_gads_2015.db", package = "eatGADS") trenddat3 <- system.file("extdata", "trend_gads_2020.db", package = "eatGADS") # use template linking Error Object load(system.file("extdata", "linking_error.rda", package = "eatRep")) check1 <- checkLEs(c(trenddat1, trenddat2, trenddat3), lErr) check2 <- checkLEs(c(trenddat1, trenddat2, trenddat3), lErr[1:14,]) 6 lsa generateRandomJk1Zones Generates random jackknife-1 zones based on sampling units in the data set. Description Function adds randomly generated jackknife-1 zones to the data. Usage generateRandomJk1Zones (datL, unit, nZones, name = "randomCluster") Arguments datL Data frame containing at least the primary sampling unit variable unit Variable name or column number of the primary sampling unit (i.e. student or class identifier nZones integer: number of jackknife zones. Note: The umber of jackknife zones must not exceed the number of distinct sampling units name New name of the jackknife-zone variable in the data set Value The original data with an additional column of the jackknife-zone variable Examples data(lsa) ### We only consider year 2010 lsa10<- lsa[which(lsa[,"year"] == 2010),] lsa10<- generateRandomJk1Zones(datL = lsa10, unit="idclass", nZones = 50) lsa Achievement data from two large-scale assessments of 2010 and 2015. Description This example data set contains fictional achievement scores of 11637 students from three countries and two times of measurement in two domains (reading and listening comprehension) in the long format. The data set contains nested multiple imputed plausible values of achievement scores as well as some demographic variables. Illustrating trend analyses, data from two fictional time points (2010 and 2015) are included. lsa 7 The data set can be used for several illustration purposes. For example, if only multiple imputation should be considered (without nesting), simply use only cases from the first nest (by subsetting). If only one time of measurement should be considered (i.e., without any trend analyses), simply choose only cases from 2010 or 2015. If only reading or listening should be considered, choose the desired domain by subsetting according to the domain column. Usage data(lsa) Format ’data.frame’: 77322 obs. of 25 variables year Year of evaluation idstud individual student identification idclass class identifier wgt Total case weight L2wgt School weight (level 2 weight) L1wgt Student weight (level 1 weight) jkzone jackknifing zone (jk2) jkrep jackknife replicate imp Number of imputation nest Number of nest (for nested imputation only) country The country an examinee stems from sex student’s sex ses student’s socio-economical status mig student’s migration background domain The domain the corresponding score belongs to score student’s achievement score (corresponding to the domain reading or listening, and to the imputation 1, 2, or 3) comp student’s competence level failMin dichotomous indicator whether the student fails to fulfill the minimal standard passReg dichotomous indicator whether the student fulfills at least the regular standard passOpt dichotomous indicator whether the student fulfills the optimal standard leSore linking error of each student’s achievement score leComp linking error of each student’s competence level leFailMin linking error of each student’s indicator of failing to fulfill the minimal standard lePassReg linking error of each student’s indicator of fulfilling the regular standard lePassOpt linking error of each student’s indicator of fulfilling the optimal standard Source Simulated data 8 repGlm repGlm Replication methods (JK1, JK2 and BRR) for linear regression models and trend estimation. Description Compute generalized linear models for complex cluster designs with multiple imputed variables based on the Jackknife (JK1, JK2) or balanced repeated replicates (BRR) procedure. Conceptually, the function combines replication methods and methods for multiple imputed data. Technically, this is a wrapper for the svyglm function of the survey package. Usage repGlm(datL, ID, wgt = NULL, type = c("none", "JK2", "JK1", "BRR", "Fay"), PSU = NULL, repInd = NULL, repWgt = NULL, nest=NULL, imp=NULL, groups = NULL, group.splits = length(groups), group.delimiter = "_", cross.differences = FALSE, trend = NULL, linkErr = NULL, formula, family=gaussian, forceSingularityTreatment = FALSE, glmTransformation = c("none", "sdY"), doCheck = TRUE, na.rm = FALSE, poolMethod = c("mice", "scalar"), useWec = FALSE, scale = 1, rscales = 1, mse=TRUE, rho=NULL, hetero=TRUE, se_type = c("HC3", "HC0", "HC1", "HC2", "CR0", "CR2"), clusters = NULL, crossDiffSE.engine= c("lavaan", "lm"), stochasticGroupSizes = FALSE, verbose = TRUE, progress = TRUE) Arguments datL Data frame in the long format (i.e. each line represents one ID unit in one imputation of one nest) containing all variables for analysis. ID Variable name or column number of student identifier (ID) variable. ID variable must not contain any missing values. wgt Optional: Variable name or column number of weighting variable. If no weight- ing variable is specified, all cases will be equally weighted. type Defines the replication method for cluster replicates which is to be applied. Depending on type, additional arguments must be specified (e.g., PSU and/or repInd or repWgt). PSU Variable name or column number of variable indicating the primary sampling unit (PSU). When a jackknife procedure is applied, the PSU is the jackknife zone variable. If NULL, no cluster structure is assumed and standard errors are computed according to a random sample. repInd Variable name or column number of variable indicating replicate ID. In a jack- knife procedure, this is the jackknife replicate variable. If NULL, no cluster struc- ture is assumed and standard errors are computed according to a random sample. repWgt Normally, replicate weights are created by repGlm directly from PSU and repInd variables. Alternatively, if replicate weights are included in the data.frame, spec- ify the variable names or column number in the repWgt argument. repGlm 9 nest Optional: name or column number of the nesting variable. Only applies in nested multiple imputed data sets. imp Optional: name or column number of the imputation variable. Only applies in multiple imputed data sets. groups Optional: vector of names or column numbers of one or more grouping vari- ables. group.splits Optional: If groups are defined, group.splits optionally specifies whether analysis should be done also in the whole group or overlying groups. See exam- ples for more details. group.delimiter Character string which separates the group names in the output frame. cross.differences Either a list of vectors, specifying the pairs of levels for which cross-level differ- ences should be computed. Alternatively, if TRUE, cross-level differences for all pairs of levels are computed. If FALSE, no cross-level differences are computed. (see examples 2a, 3, and 4 in the help file of the repMean function) trend Optional: name or column number of the trend variable. Note: Trend variable must have exact two levels. Levels for grouping variables must be equal in both ’sub populations’ partitioned by the trend variable. linkErr Optional: name or column number of the linking error variable. If NULL, a linking error of 0 will be assumed in trend estimation. formula Model formula, see help page of glm for details. family A description of the error distribution and link function to be used in the model. See help page of glm for details. forceSingularityTreatment Logical: Forces the function to use the workaround to handle singularities in regression models. glmTransformation Optional: Allows for transformation of parameters from linear regression and logistic regression before pooling. Useful to compare parameters from dif- ferent glm models, see Mood (2010). Note: This argument applies only if forceSingularityTreatment is set to ’TRUE’. doCheck Logical: Check the data for consistency before analysis? If TRUE groups with in- sufficient data are excluded from analysis to prevent subsequent functions from crashing. na.rm Logical: Should cases with missing values be dropped? poolMethod Which pooling method should be used? The “mice” method is recommended. useWec Logical: use weighted effect coding? scale scaling constant for variance, for details, see help page of svrepdesign from the survey package rscales scaling constant for variance, for details, see help page of svrepdesign from the survey package 10 repGlm mse Logical: If TRUE, compute variances based on sum of squares around the point estimate, rather than the mean of the replicates. See help page of svrepdesign from the survey package for further details. rho Shrinkage factor for weights in Fay’s method. See help page of svrepdesign from the survey package for further details. hetero Logical: Assume heteroscedastic variance for weighted effect coding? Only applies for random samples, i.e. if no replication analyses are executed. se_type The sort of standard error sought for cross level differences. Only applies if crossDiffSE == "wec" and hetero == TRUE and crossDiffSE.engine == "lm". See the help page of lm_robust from the estimatr package for further details. clusters Optional: Variable name or column number of cluster variable. Only necessary if weighted effecting coding should be performed using heteroscedastic vari- ances. See the help page of lm_robust from the estimatr package for further details. crossDiffSE.engine Optional: Sort of estimator which should be used for standard error estimation in weighted effect coding regression. Only applies if useWec == TRUE. To date, only lavaan allows for stochastic group sizes. stochasticGroupSizes Logical: Assume stochastic group sizes for using weighted effect coding regres- sion with categorical predictors? Note: To date, only lavaan allows for stochastic group sizes. Stochastic group sizes cannot be assumed if any replication method (jackknife, BRR) is applied. verbose Logical: Show analysis information on console? progress Logical: Show progress bar on console? Details Function first creates replicate weights based on PSU and repInd variables according to JK2 or BRR procedure. According to multiple imputed data sets, a workbook with several analyses is created. The function afterwards serves as a wrapper for svyglm implemented in the survey package. The results of the several analyses are then pooled according to Rubin’s rule, which is adapted for nested imputations if the nest argument implies a nested structure. Value A list of data frames in the long format. The output can be summarized using the report function. The first element of the list is a list with either one (no trend analyses) or two (trend analyses) data frames with at least six columns each. For each subpopulation denoted by the groups statement, each dependent variable, each parameter and each coefficient the corresponding value is given. group Denotes the group an analysis belongs to. If no groups were specified and/or analysis for the whole sample were requested, the value of ‘group’ is ‘whole- Group’. depVar Denotes the name of the dependent variable in the analysis. repGlm 11 modus Denotes the mode of the analysis. For example, if a JK2 analysis without sam- pling weights was conducted, ‘modus’ takes the value ‘jk2.unweighted’. If a analysis without any replicates but with sampling weights was conducted, ‘modus’ takes the value ‘weighted’. parameter Denotes the parameter of the regression model for which the corresponding value is given further. Amongst others, the ‘parameter’ column takes the val- ues ‘(Intercept)’ and ‘gendermale’ if ‘gender’ was the dependent variable, for instance. See example 1 for further details. coefficient Denotes the coefficient for which the corresponding value is given further. Takes the values ‘est’ (estimate) and ‘se’ (standard error of the estimate). value The value of the parameter estimate in the corresponding group. If groups were specified, further columns which are denoted by the group names are added to the data frame. References te Grotenhuis, M., Pelzer, B., Eisinga, R., Nieuwenhuis, R., Schmidt-Catran, A., & Konig, R. (2017). When size matters: advantages of weighted effect coding in observational studies. Interna- tional Journal of Public Health. 62, 163–167. Examples ### load example data (long format) data(lsa) ### use only the first nest bt <- lsa[which(lsa[,"nest"] == 1),] ### use only data from 2010 bt2010 <- bt[which(bt[,"year"] == 2010),] ## use only reading data bt2010read <- bt2010[which(bt2010[,"domain"] == "reading"),] ### Example 1: Computes linear regression from reading score on gender separately ### for each country. Assume no nested structure. mod1 <- repGlm(datL = bt2010read, ID = "idstud", wgt = "wgt", type = "jk2", PSU = "jkzone", repInd = "jkrep", imp = "imp", groups = "country", formula = score~sex, family ="gaussian") res1 <- report(mod1, printGlm = TRUE) ### Example 2: Computes log linear regression from pass/fail on ses and gender ### separately for each country in a nested structure. Assuming equally weighted ### cases by omitting "wgt" argument dat <- lsa[intersect(which(lsa[,"year"] == 2010), which(lsa[,"domain"] == "reading")),] mod2 <- repGlm(datL = dat, ID = "idstud", type = "JK2", PSU = "jkzone", repInd = "jkrep", imp = "imp", nest="nest", groups = "country", formula = passReg~sex*ses, family = quasibinomial(link="logit")) res2 <- report(mod2, printGlm = TRUE) ### Example 3: Like example 1, but without any replication methods ### trend estimation (without linking error) and nested imputation 12 repLmer dat <- lsa[which(lsa[,"domain"] == "reading"),] mod3 <- repGlm(datL = dat, ID = "idstud", wgt = "wgt", imp = "imp", nest = "nest", groups = "country", formula = score~sex, trend = "year") res3 <- report(mod3, printGlm = TRUE) ### Example 4: weighted effect coding to estimate whether a specific country's mean ### differs from the overall mean (whereas the overall population is a composite of ### all countries). The procedure adapts the weighted effect coding procedures ### described in te Grotenhuis (2017) for multiple imputation and replication methods. mod4 <- repGlm(datL = bt2010read, ID = "idstud", wgt = "wgt", type = "jk2", PSU = "jkzone", repInd = "jkrep", imp = "imp", formula = score~country, useWec=TRUE) res4 <- report(mod4, printGlm = FALSE) repLmer Replication methods (JK1 and JK2) for multilevel linear regression models and trend estimation. Description Compute multilevel linear models for complex cluster designs with multiple imputed variables based on the Jackknife (JK1, JK2) procedure. Conceptually, the function combines replication methods and methods for multiple imputed data. Technically, this is a wrapper for the BIFIE.twolevelreg function of the BIFIEsurvey package. repLmer only adds functionality for trend estimation. Please note that the function is not suitable for logistic logit/probit models. Usage repLmer(datL, ID, wgt = NULL, L1wgt=NULL, L2wgt=NULL, type = c("JK2", "JK1"), PSU = NULL, repInd = NULL, jkfac = NULL, rho = NULL, imp=NULL, group = NULL, trend = NULL, dependent, formula.fixed, formula.random, doCheck = TRUE, na.rm = FALSE, clusters, verbose = TRUE) Arguments datL Data frame in the long format (i.e. each line represents one ID unit in one imputation of one nest) containing all variables for analysis. ID Variable name or column number of student identifier (ID) variable. ID variable must not contain any missing values. wgt Optional: Variable name or column number of case weighting variable. If no weighting variable is specified, all cases will be equally weighted. L1wgt Name of Level 1 weight variable. This is optional. If it is not provided, L1wgt is calculated from the total weight (i.e., wgt) and L2wgt. L2wgt Name of Level 2 weight variable type Defines the replication method for cluster replicates which is to be applied. Depending on type, additional arguments must be specified (e.g., PSU and/or repInd or repWgt). repLmer 13 PSU Variable name or column number of variable indicating the primary sampling unit (PSU). When a jackknife procedure is applied, the PSU is the jackknife zone variable. If NULL, no cluster structure is assumed and standard errors are computed according to a random sample. repInd Variable name or column number of variable indicating replicate ID. In a jack- knife procedure, this is the jackknife replicate variable. If NULL, no cluster struc- ture is assumed and standard errors are computed according to a random sample. jkfac Argument is passed to BIFIE.data.jack and specifies the factor for multiply- ing jackknife replicate weights. rho Fay factor for statistical inference. The argument is passed to the fayfac argu- ment of the BIFIE.data.jack function from the BIFIEsurvey package. See the corresponding help page for further details. For convenience, if rho = NULL (the default) and type = "JK1", BIFIE.data.jack is called with jktype="JK_GROUP" −1 and fayfac = rho, where ρ = (Ncluster − 1) × Ncluster imp Name or column number of the imputation variable. group Optional: column number or name of one grouping variable. Note: in contrast to repMean, only one grouping variable can be specified. trend Optional: name or column number of the trend variable. Note: Trend variable must have exact two levels. Levels for grouping variables must be equal in both ’sub populations’ partitioned by the trend variable. dependent Name or column number of the dependent variable formula.fixed An R formula for fixed effects formula.random An R formula for random effects doCheck Logical: Check the data for consistency before analysis? If TRUE groups with in- sufficient data are excluded from analysis to prevent subsequent functions from crashing. na.rm Logical: Should cases with missing values be dropped? clusters Variable name or column number of cluster variable. verbose Logical: Show analysis information on console? Value A list of data frames in the long format. The output can be summarized using the report function. The first element of the list is a list with either one (no trend analyses) or two (trend analyses) data frames with at least six columns each. For each subpopulation denoted by the groups statement, each dependent variable, each parameter and each coefficient the corresponding value is given. group Denotes the group an analysis belongs to. If no groups were specified and/or analysis for the whole sample were requested, the value of ‘group’ is ‘whole- Group’. depVar Denotes the name of the dependent variable in the analysis. modus Denotes the mode of the analysis. For example, if a JK2 analysis without sam- pling weights was conducted, ‘modus’ takes the value ‘jk2.unweighted’. If a analysis without any replicates but with sampling weights was conducted, ‘modus’ takes the value ‘weighted’. 14 repMean parameter Denotes the parameter of the regression model for which the corresponding value is given further. Amongst others, the ‘parameter’ column takes the val- ues ‘(Intercept)’ and ‘gendermale’ if ‘gender’ was the dependent variable, for instance. See example 1 for further details. coefficient Denotes the coefficient for which the corresponding value is given further. Takes the values ‘est’ (estimate) and ‘se’ (standard error of the estimate). value The value of the parameter estimate in the corresponding group. If groups were specified, further columns which are denoted by the group names are added to the data frame. Examples ### load example data (long format) data(lsa) ### use only the first nest, use only reading btRead <- subset(lsa, nest==1 & domain=="reading") ### random intercept model with groups mod1 <- repLmer(datL = btRead, ID = "idstud", wgt = "wgt", L1wgt="L1wgt", L2wgt="L2wgt", type = "jk2", PSU = "jkzone", repInd = "jkrep", imp = "imp",trend="year", group="country", dependent="score", formula.fixed = ~as.factor(sex)+mig, formula.random=~1, clusters="idclass") res1 <- report(mod1) ### random slope without groups and without trend mod2 <- repLmer(datL = subset(btRead, country=="countryA" & year== 2010), ID = "idstud", wgt = "wgt", L1wgt="L1wgt", L2wgt="L2wgt", type = "jk2", PSU = "jkzone", repInd = "jkrep", imp = "imp", dependent="score", formula.fixed = ~as.factor(sex)*mig, formula.random=~mig, clusters="idclass") res2 <- report(mod2) repMean Replication methods (JK1, JK2 and BRR) for descriptive statistics. Description Compute totals, means, adjusted means, mean differences, variances and standard deviations with standard errors in random or clustered or complex samples. Variance estimation in complex cluster designs based on Jackknife (JK1, JK2) or Balanced Repeated Replicates (BRR) procedure. More- over, analyses can be customized for multiple or nested imputed variables, applying the combination rules of Rubin (1987) for imputed data and Rubin (2003) for nested imputed data. Conceptually, the function combines replication methods and methods for multiple imputed data. Trend estimation as usual in large-scale assessments is supported as well. Technically, this is a wrapper for the svymean and svyvar functions of the survey package. repMean 15 Usage repMean (datL, ID, wgt = NULL, type = c("none", "JK2", "JK1", "BRR", "Fay"), PSU = NULL, repInd = NULL, jkfac=NULL, repWgt = NULL, nest=NULL, imp=NULL, groups = NULL, group.splits = length(groups), group.differences.by = NULL, cross.differences = FALSE, crossDiffSE = c("wec", "rep","old"), adjust = NULL, useEffectLiteR = FALSE, nBoot = 100, group.delimiter = "_", trend = NULL, linkErr = NULL, dependent, na.rm = FALSE, doCheck = TRUE, engine = c("survey", "BIFIEsurvey"), scale = 1, rscales = 1, mse=TRUE, rho=NULL, hetero=TRUE, se_type = c("HC3", "HC0", "HC1", "HC2", "CR0", "CR2"), clusters = NULL, crossDiffSE.engine= c("lavaan", "lm"), stochasticGroupSizes = FALSE, verbose = TRUE, progress = TRUE) Arguments datL Data frame in the long format (i.e. each line represents one ID unit in one imputation of one nest) containing all variables for analysis. ID Variable name or column number of student identifier (ID) variable. ID variable must not contain any missing values. wgt Optional: Variable name or column number of weighting variable. If no weight- ing variable is specified, all cases will be equally weighted. type Defines the replication method for cluster replicates which is to be applied. Depending on type, additional arguments must be specified (e.g., PSU and/or repInd or repWgt). PSU Variable name or column number of variable indicating the primary sampling unit (PSU). When a jackknife procedure is applied, the PSU is the jackknife zone variable. If NULL, no cluster structure is assumed and standard errors are computed according to a random sample. repInd Variable name or column number of variable indicating replicate ID. In a jack- knife procedure, this is the jackknife replicate variable. If NULL, no cluster struc- ture is assumed and standard errors are computed according to a random sample. jkfac Only applies if engine = "BIFIEsurvey". Argument is passed to BIFIE.data.jack and specifies the factor for multiplying jackknife replicate weights. repWgt Normally, replicate weights are created by repMean directly from PSU and repInd variables. Alternatively, if replicate weights are included in the data.frame, spec- ify the variable names or column number in the repWgt argument. nest Optional: name or column number of the nesting variable. Only applies in nested multiple imputed data sets. imp Optional: name or column number of the imputation variable. Only applies in multiple imputed data sets. groups Optional: vector of names or column numbers of one or more grouping vari- ables. group.splits Optional: If groups are defined, group.splits optionally specifies whether analysis should be done also in the whole group or overlying groups. See exam- ples for more details. 16 repMean group.differences.by Optional: Specifies variable group differences should be computed for. The corresponding variable must be included in the groups statement. Exception: choose ’wholePop’ if you want to estimate each’s group difference from the overall sample mean. See examples for further details. cross.differences Either a list of vectors, specifying the pairs of levels for which cross-level differ- ences should be computed. Alternatively, if TRUE, cross-level differences for all pairs of levels are computed. If FALSE, no cross-level differences are computed. (see example 2a, 3, and 4) crossDiffSE Method for standard error estimation for cross level differences, where groups are dependent. wec uses weighted effect coding, rep uses replication methods (bootstrap or jackknife) to estimate the standard error between the total mean and group-specific means. old does not account for dependent groups and treat the groups as if they were independent from each other. adjust Variable name or column number of variable(s) for which adjusted means should be computed. Non-numeric variables (factors) will be converted to 0/1 dichoto- mous variables. useEffectLiteR Logical: use the lavaan-wrapper EffectLiteR to compute adjusted means? Al- ternatively, adjusted means are computed by applying a simple linear regression model in each group, using the variables in adjust as independent variables. Afterwards, the coefficients are weighted with the (weighted) means of the inde- pendent variables. Standard errors for this procedure are received using the delta method by applying an augmented variance-covariance matrix which assumes zero covariances between independent variable means and regression coeffi- cients. We recommend to set useEffectLiteR = TRUE if no replication meth- ods are applied. When replication methods are used (jackknife-1, jackknife-2, BRR), we recommend to set useEffectLiteR = FALSE, because otherwise the estimation is very slow. nBoot Without replicates (i.e., for completely random samples), the rep method for standard error estimation for cross level differences needs a bootstrap. nBoot therefore specifies the number of bootstrap samples. This argument is only nec- essary, if crossDiffSE = "rep" and none of the replicate methods (JK1, JK2, or BRR) is applied. Otherwise, nBoot will be ignored. group.delimiter Character string which separates the group names in the output frame. trend Optional: name or column number of the trend variable. Note: Trend variable must have exact two levels. Levels for grouping variables must be equal in both ’sub populations’ partitioned by the trend variable. linkErr Optional: Either the name or column number of the linking error variable. If NULL, a linking error of 0 will be assumed in trend estimation. Alternatively, linking errors may be given as data.frame with following specifications: Two columns, named trendLevel1 and trendLevel2 which contain the levels of the trend variable. The contrasts between both values indicates which trend is meant. For only two measurement occasions, i.e. 2010 and 2015, trendLevel1 should be 2010, and trendLevel2 should be 2015. For three measurement occasions, i.e. 2010, 2015, and 2020, additional lines are necessary where repMean 17 trendLevel1 should be 2010, and trendLevel2 should be 2020, to mark the contrast between 2010 and 2020, and further additional lines are necessary where trendLevel1 should be 2015, and trendLevel2 should be 2020. The column depVar must include the name of the dependent variable. This string must correspond to the name of the dependent variable in the data. The col- umn parameter indicates the parameter the linking error belongs to. Column linkingError includes the linking error value. Providing linking error in a data.frame is necessary for more than two measurement occasions. See the ex- ample 3a for further details. dependent Variable name or column number of the dependent variable. na.rm Logical: Should cases with missing values be dropped? doCheck Logical: Check the data for consistency before analysis? If TRUE groups with in- sufficient data are excluded from analysis to prevent subsequent functions from crashing. engine Which package should be used for estimation? scale scaling constant for variance, for details, see help page of svrepdesign from the survey package rscales scaling constant for variance, for details, see help page of svrepdesign from the survey package mse Logical: If TRUE, compute variances based on sum of squares around the point estimate, rather than the mean of the replicates. See help page of svrepdesign from the survey package for further details. rho Shrinkage factor for weights in Fay’s method. If engine = "survey", argument is passed to the rho argument of the svrepdesign function from the survey package. See the corresponding help page for further details. If engine = "BIFIEsurvey", argument is passed to the fayfac argument of the BIFIE.data.jack function from the BIFIEsurvey package. See the corresponding help page for further details. For convenience, if rho = NULL (the default) and engine = "BIFIEsurvey" and type = "JK1", BIFIE.data.jack is called with jktype="JK_GROUP" −1 and fayfac = rho, where ρ = (Ncluster − 1) × Ncluster hetero Logical: Assume heteroscedastic variance for weighted effect coding? se_type The sort of standard error sought for cross level differences. Only applies if crossDiffSE == "wec" and hetero == TRUE and crossDiffSE.engine == "lm". See the help page of lm_robust from the estimatr package for further details. clusters Optional: Variable name or column number of cluster variable. Only necessary if weighted effecting coding should be performed using heteroscedastic vari- ances. See the help page of lm_robust from the estimatr package for further details. crossDiffSE.engine Software implementation used for estimating cross-level differences. Choices are either "lavaan" (required if stochasticGroupSites == "TRUE") or R func- tion lm. "lavaan" is the default. stochasticGroupSizes Logical: Assume stochastic group sizes for using weighted effect coding in cross-level differences? Note: To date, only crossDiffSE.engine = "lavaan" 18 repMean allows for stochastic group sizes. Stochastic group sizes are not yet implemented for any replication method (jackknife, BRR). verbose Logical: Show analysis information on console? progress Logical: Show progress bar on console? Details Function first creates replicate weights based on PSU and repInd variables (if defined) according to JK2 or BRR procedure as implemented in WesVar. According to multiple imputed data sets, a workbook with several analyses is created. The function afterwards serves as a wrapper for svymean called by svyby implemented in the ‘survey’ package. The results of the several analyses are then pooled according to Rubin’s rule. Value A list of data frames in the long format. The output can be summarized using the report function. The first element of the list is a list with either one (no trend analyses) or two (trend analyses) data frames with at least six columns each. For each subpopulation denoted by the groups statement, each parameter (i.e., mean, variance, or group differences) and each coefficient (i.e., the estimate and the corresponding standard error) the corresponding value is given. group Denotes the group an analysis belongs to. If no groups were specified and/or analysis for the whole sample were requested, the value of ‘group’ is ‘whole- Group’. depVar Denotes the name of the dependent variable in the analysis. modus Denotes the mode of the analysis. For example, if a JK2 analysis without sam- pling weights was conducted, ‘modus’ takes the value ‘jk2.unweighted’. If a analysis without any replicates but with sampling weights was conducted, ‘modus’ takes the value ‘weighted’. parameter Denotes the parameter of the regression model for which the corresponding value is given further. Amongst others, the ‘parameter’ column takes the values ‘mean’, ‘sd’, ‘var’ and ‘meanGroupDiff’ if group differences were requested. coefficient Denotes the coefficient for which the corresponding value is given further. Takes the values ‘est’ (estimate) and ‘se’ (standard error of the estimate). value The value of the parameter estimate in the corresponding group. If groups were specified, further columns which are denoted by the group names are added to the data frame. References te Grotenhuis, M., Pelzer, B., Eisinga, R., Nieuwenhuis, R., Schmidt-Catran, A., & Konig, R. (2017). When size matters: advantages of weighted effect coding in observational studies. Interna- tional Journal of Public Health. 62, 163–167. Sachse, K. A. & Haag, N. (2017). Standard errors for national trends in international large-scale assessments in the case of cross-national differential item functioning. Applied Measurement in Education, 30, (2), 102-116. http://dx.doi.org/10.1080/08957347.2017.1283315 repMean 19 Weirich, S., Hecht, M., Becker, B. et al. Comparing group means with the total mean in random samples, surveys, and large-scale assessments: A tutorial and software illustration. Behav Res (2021). https://doi.org/10.3758/s13428-021-01553-1 Examples data(lsa) ### Example 1: only means, SD and variances for each country ### We only consider domain 'reading' rd <- lsa[which(lsa[,"domain"] == "reading"),] ### We only consider the first "nest". rdN1 <- rd[which(rd[,"nest"] == 1),] ### First, we only consider year 2010 rdN1y10<- rdN1[which(rdN1[,"year"] == 2010),] ### mean estimation means1 <- repMean(datL = rdN1y10, ID="idstud", wgt="wgt", type = "JK2", PSU = "jkzone", repInd = "jkrep", imp="imp", groups = "country", dependent = "score", na.rm=FALSE, doCheck=TRUE, engine = "BIFIEsurvey") ### reporting function: the function does not know which content domain is being considered, ### so the user may add new columns in the output using the 'add' argument res1 <- report(means1, add = list(domain = "reading")) ### Example 1a: Additionally to example 1, we decide to estimate whether ### each country's mean differ significantly from the overall mean as well ### as from the individual means of the other contries means1a<- repMean(datL = rdN1y10, ID="idstud", wgt="wgt", type = "JK2", PSU = "jkzone", repInd = "jkrep", imp="imp", groups = "country", group.splits = 0:1, group.differences.by = "country", cross.differences = TRUE, dependent = "score", na.rm=FALSE, doCheck=TRUE, hetero=FALSE) res1a <- report(means1a, add = list(domain = "reading")) ### See that the means of 'countryA' and 'countryB' significantly differ from the overall mean. print(res1a[intersect(which(res1a[,"comparison"] == "crossDiff"), which(res1a[,"parameter"] == "mean")),], digits = 3) ### Example 2: Sex differences by country. Assume equally weighted cases by omitting ### 'wgt' argument. means2 <- repMean(datL = rdN1y10, ID="idstud", type = "JK2", PSU = "jkzone", repInd = "jkrep", imp="imp", groups = c("country", "sex"), group.splits = 0:2, group.differences.by="sex", dependent = "score", na.rm=FALSE, doCheck=TRUE, cross.differences =TRUE, crossDiffSE.engine= "lm") res2 <- report(means2,add = list(domain = "reading")) ### Example 2a: Additionally to example 2, we decide to estimate whether ### each country's mean differ significantly from the overall mean. (Note: by default, ### such cross level differences are estimated using 'weighted effect coding'. Use the ### 'crossDiffSE' argument to choose alternative methods.) Moreover, we estimate whether ### each country's sex difference differ significantly from the sex difference in the 20 repMean ### whole population. means2a<- repMean(datL = rdN1y10, ID="idstud", wgt="wgt", type = "JK2", PSU = "jkzone", repInd = "jkrep", imp="imp", groups = c("country", "sex"), group.splits = 0:2, group.differences.by="sex", cross.differences = list(c(0,1), c(0,2)), dependent = "score", na.rm=FALSE, doCheck=TRUE, crossDiffSE.engine= "lm", clusters = "idclass") res2a <- report(means2a,add = list(domain = "reading"), trendDiffs = TRUE) ### Third example: like example 2a, but using nested imputations of dependent variable, ### and additionally estimating trend: use 'rd' instead of 'rdN1y10' ### assume equally weighted cases by omitting 'wgt' argument ### ignoring jackknife by omitting 'type', 'PSU' and 'repInd' argument means3T<- repMean(datL = rd, ID="idstud", imp="imp", nest="nest", groups = c("country", "sex"), group.splits = 0:2, group.differences.by="sex", cross.differences = list(c(0,1), c(0,2)), dependent = "score", na.rm=FALSE, doCheck=TRUE, trend = "year", linkErr = "leScore", crossDiffSE = "wec", crossDiffSE.engine= "lavaan") res3T <- report(means3T, add = list(domain = "reading")) ### Example 3a: like example 3, but providing linking errors in an additional data.frame ### This is optional for two measurement occasions but mandatory if the analysis contains ### more than two measurement occasions linkErr<- data.frame ( trendLevel1 = 2010, trendLevel2 = 2015, depVar = "score", parameter = "mean", unique(lsa[,c("domain", "leScore")]), stringsAsFactors = FALSE) colnames(linkErr) <- car::recode(colnames(linkErr), "'leScore'='linkingError'") ### note that the linking errors for the specified domain have to be chosen via ### subsetting means3a<- repMean(datL = rd, ID="idstud", imp="imp", nest="nest", groups = c("country", "sex"), group.splits = 0:2, group.differences.by="sex", cross.differences = list(c(0,1), c(0,2)), dependent = "score", na.rm=FALSE, doCheck=TRUE, trend = "year", linkErr = linkErr[which(linkErr[,"domain"] == "reading"),], crossDiffSE = "wec", crossDiffSE.engine= "lavaan") res3a <- report(means3a, add = list(domain = "reading")) ### Fourth example: using a loop do analyse 'reading' and 'listening' comprehension ### in one function call. Again with group and cross differences and trends, and ### trend differences ### we use weights but omit jackknife analysis by omitting 'type', 'PSU' and 'repInd' ### argument means4T<- by ( data = lsa, INDICES = lsa[,"domain"], FUN = function (sub.dat) { repMean(datL = sub.dat, ID="idstud", wgt="wgt", imp="imp", nest="nest", groups = c("country", "sex"), group.splits = 0:2, group.differences.by="sex", cross.differences = list(c(0,1), c(0,2)), dependent = "score", na.rm=FALSE, doCheck=TRUE, trend = "year", linkErr = "leScore", crossDiffSE.engine= "lm") }) ret4T <- do.call("rbind", lapply(names(means4T), FUN = function ( domain ) { report(means4T[[domain]], trendDiffs = TRUE, add = list(domain = domain))})) ### Fifth example: compute adjusted means, also with trend estimation repMean 21 ### Note: all covariates must be numeric or 0/1 dichotomous rdN1[,"mignum"] <- as.numeric(rdN1[,"mig"]) rdN1[,"sexnum"] <- car::recode(rdN1[,"sex"], "'male'=0; 'female'=1", as.numeric=TRUE, as.factor=FALSE) means5 <- repMean(datL = rdN1, ID="idstud", wgt="wgt", type = "JK2", PSU = "jkzone", repInd = "jkrep", imp="imp", groups = "country", adjust = c("sexnum", "ses", "mignum"), useEffectLiteR = FALSE, dependent = "score", na.rm=FALSE, doCheck=TRUE, trend = "year", linkErr = "leScore") res5 <- report(means5, add = list(domain = "reading")) ## Not run: ############################################################################################ # Example 6: R code for running the PISA 2015 science example to compare group means # # with the total mean using weighted effect coding # ############################################################################################ # Warning: large PISA data set requires at least 16 GB free working memory (RAM): ### define necessary directories (note: writing permissions required) folder <- tempdir() ### download PISA 2015 zipped student questionnaire data (420 MB) to a folder with ### writing permissions download.file(url = "https://webfs.oecd.org/pisa/PUF_SPSS_COMBINED_CMB_STU_QQQ.zip", destfile = file.path(folder, "pisa2015.zip")) ### unzip PISA 2015 student questionnaire data (1.5 GB) to temporary folder zip::unzip(zipfile = file.path(folder, "pisa2015.zip"), files= "CY6_MS_CMB_STU_QQQ.sav", exdir=folder) ### read data pisa <- foreign::read.spss(file.path (folder, "CY6_MS_CMB_STU_QQQ.sav"), to.data.frame=TRUE, use.value.labels = FALSE, use.missings = TRUE) # dependent variables measure.vars <- paste0("PV", 1:10, "SCIE") ### choose desired variables and reshape into the long format # 'CNTSTUID' = individual student identifier # 'CNT' = country identifier # 'SENWT' = senate weight (assume a population of 5000 in each country) # 'W_FSTUWT' = final student weight # 'OECD' = dummy variable indicating which country is part of the OECD # 'W_FSTURWT' (1 to 80) = balanced repeated replicate weights # 'PV1SCIE' to 'PV10SCIE' = 10 plausible values of (latent) science performance pisaLong <- reshape2::melt(pisa, id.vars = c("CNTSTUID", "CNT", "SENWT", "W_FSTUWT", "OECD", paste0("W_FSTURWT", 1:80)), measure.vars = measure.vars, value.name = "value", variable.name="imp", na.rm=TRUE) ### choose OECD countries oecd <- pisaLong[which(pisaLong[,"OECD"] == 1),] 22 report ### analyze data ### analysis takes approximately 30 minutes on an Intel i5-6500 machine with 32 GB RAM means <- repMean( datL = oecd, # data.frame in the long format ID = "CNTSTUID", # student identifier dependent = "value", # the dependent variable in the data groups = "CNT", # the grouping variable wgt = "SENWT", # (optional) weighting variable. We use senate # weights (assume a population of 5000 in each # country) type = "Fay", # type of replication method. Corresponding to # the PISA sampling method, we use "Fay" rho = 0.5, # shrinkage factor for weights in Fay's method scale = NULL, # scaling constant for variance, set to NULL # according to PISA's sampling method rscales = NULL, # scaling constant for variance, set to NULL # according to PISA's sampling method repWgt = paste0("W_FSTURWT", 1:80), # the replicate weights, # provided by the OECD imp = "imp", # the imputation variable mse = FALSE, # if TRUE, compute variances based on sum of # squares around the point estimate, rather # than the mean of the replicates. group.splits = 0:1, # defining the 'levels' for which means should # be computed. 0:1 implies that means for the # whole sample (level 0) as well as for groups # (level 1) are computed cross.differences = TRUE, # defines whether (and which) cross level mean # differences should be computed. TRUE means # that all cross level mean differences are # computed crossDiffSE = "wec", # method for standard errors of mean # differences crossDiffSE.engine = "lm", # software implementation for standard # errors of mean differences hetero = TRUE, # assume heteroscedastic group variances stochasticGroupSizes = FALSE # assume fixed group sizes ) ### call a reporting function to generate user-friendly output results <- report(means, exclude = c("Ncases", "NcasesValid", "var", "sd")) ## End(Not run) report Reporting function for repMean, repTable, repQuantile, and repGlm report 23 Description Summarizes the output of the four main functions repMean,repTable, repQuantile, and repGlm, and provides a single data.frame with all results. Usage report(repFunOut, trendDiffs = FALSE, add = list(), exclude = c("NcasesValid", "var", "sampleSize"), printGlm = FALSE, round = TRUE, digits = 3, printDeviance = FALSE) Arguments repFunOut output of one of the four eatRep-functions. trendDiffs Logical: compute differences of trends? add Optional: additional columns for output. See examples of the jk2-functions exclude Which parameters should be excluded from reporting? printGlm Only relevant for repGlm: print summary on console? round Logical: should the results be rounded to a limited number of digits? digits How many digits should be used for rounding? printDeviance Only relevant for repGlm when other than the identical function is used as link function, and if printGlm is TRUE. Should the deviance information printed additionally? Note: To print deviance information, the argument poolMethod of the repGlm function must be set to "scalar". Value A data frame with at least nine columns. group Denotes the group an analysis belongs to. If no groups were specified and/or analysis for the whole sample were requested, the value of ‘group’ is ‘whole- Group’. depVar Denotes the name of the dependent variable in the analysis. modus Denotes the mode of the analysis. For example, if a JK2 regression analysis was conducted, ‘modus’ takes the value ‘JK2.glm’. If a mean analysis without any replicates was conducted, ‘modus’ takes the value ‘CONV.mean’. comparison Denotes whether group mean comparisons or cross-level comparisons were con- ducted. Without any comparisons, ‘comparison’ takes the value ‘NA’ parameter Denotes the parameter of the corresponding analysis. If regression analysis was applied, the regression parameter is given. Amongst others, the ‘parameter’ column takes the values ‘(Intercept)’ and ‘gendermale’ if ‘gender’ was the in- dependent variable, for instance. If mean analysis was applied, the ‘parameter’ column takes the values ‘mean’, ‘sd’, ‘var’, or ‘Nvalid’. See the examples of repMean,repTable, repQuantile, or repGlm for further details. depVar Denotes the name of the dependent variable (only if repGlm was called before) est Denotes the estimate of the corresponding analysis. se Denotes the standard error of the corresponding estimate. p Denotes the p value of the estimate. 24 repQuantile Author(s) Benjamin Becker, Sebastian Weirich Examples ### see examples of the eatRep main functions. repQuantile Replication methods (JK1, JK2 and BRR) for quantiles and trend esti- mation. Description Compute quantiles with standard errors for complex cluster designs with multiple imputed variables (e.g. plausible values) based on Jackknife (JK1, JK2) or balanced repeated replicates (BRR) proce- dure. Conceptually, the function combines replication methods and methods for multiple imputed data. Technically, this is a wrapper for the svyquantile() function of the survey package. Usage repQuantile(datL, ID, wgt = NULL, type = c("none", "JK2", "JK1", "BRR", "Fay"), PSU = NULL, repInd = NULL, repWgt = NULL, nest=NULL, imp=NULL, groups = NULL, group.splits = length(groups), cross.differences = FALSE, group.delimiter = "_", trend = NULL, linkErr = NULL, dependent, probs = c(0.25, 0.50, 0.75), na.rm = FALSE, nBoot = NULL, bootMethod = c("wSampling","wQuantiles") , doCheck = TRUE, scale = 1, rscales = 1, mse=TRUE, rho=NULL, verbose = TRUE, progress = TRUE) Arguments datL Data frame in the long format (i.e. each line represents one ID unit in one imputation of one nest) containing all variables for analysis. ID Variable name or column number of student identifier (ID) variable. ID variable must not contain any missing values. wgt Optional: Variable name or column number of weighting variable. If no weight- ing variable is specified, all cases will be equally weighted. type Defines the replication method for cluster replicates which is to be applied. Depending on type, additional arguments must be specified (e.g., PSU and/or repInd or repWgt). PSU Variable name or column number of variable indicating the primary sampling unit (PSU). When a jackknife procedure is applied, the PSU is the jackknife zone variable. If NULL, no cluster structure is assumed and standard errors are computed according to a random sample. repQuantile 25 repInd Variable name or column number of variable indicating replicate ID. In a jack- knife procedure, this is the jackknife replicate variable. If NULL, no cluster struc- ture is assumed and standard errors are computed according to a random sample. repWgt Normally, replicate weights are created by repQuantile directly from PSU and repInd variables. Alternatively, if replicate weights are included in the data.frame, specify the variable names or column number in the repWgt argument. nest Optional: name or column number of the nesting variable. Only applies in nested multiple imputed data sets. imp Optional: name or column number of the imputation variable. Only applies in multiple imputed data sets. groups Optional: vector of names or column numbers of one or more grouping vari- ables. group.splits Optional: If groups are defined, group.splits optionally specifies whether analysis should be done also in the whole group or overlying groups. See exam- ples for more details. cross.differences Either a list of vectors, specifying the pairs of levels for which cross-level dif- ferences should be computed. Alternatively, if TRUE, cross-level differences for all pairs of levels are computed. If FALSE, no cross-level differences are computed. (see examples 2a, 3, and 4 in the help file of the repMean function) group.delimiter Character string which separates the group names in the output frame. trend Optional: name or column number of the trend variable. Note: Trend variable must have exact two levels. Levels for grouping variables must be equal in both ’sub populations’ partitioned by the trend variable. linkErr Optional: name or column number of the linking error variable. If ’NULL’, a linking error of 0 will be assumed in trend estimation. Alternatively, the linking error may be given as a single scalar value (i.e. ’linkErr = 1.225’). dependent Variable name or column number of the dependent variable. probs Numeric vector with probabilities for which to compute quantiles. na.rm Logical: Should cases with missing values be dropped? nBoot Optional: Without replicates, standard error cannot be computed in a weighted sample. Alternatively, standard errors may be computed using the boot package. nBoot therefore specifies the number of bootstrap samples. If not specified, no standard errors will be given. In analyses containing replicates or samples without specifying person weights, nBoot will be ignored. bootMethod Optional: If standard error are computed in a bootstrap, two possible meth- ods may be applied. wSampling requests the function to draw nBoot weighted bootstrap samples for which unweighted quantiles are computed. wQuantiles requests the function to draw nBoot unweighted bootstrap samples for which weighted quantiles are computed. doCheck Logical: Check the data for consistency before analysis? If TRUE groups with in- sufficient data are excluded from analysis to prevent subsequent functions from crashing. 26 repQuantile scale scaling constant for variance, for details, see help page of svrepdesign from the survey package rscales scaling constant for variance, for details, see help page of svrepdesign from the survey package mse Logical: If TRUE, compute variances based on sum of squares around the point estimate, rather than the mean of the replicates. See help page of svrepdesign from the survey package for further details. rho Shrinkage factor for weights in Fay’s method. See help page of svrepdesign from the survey package for further details. verbose Logical: Show analysis information on console? progress Logical: Show progress bar on console? Details Function first creates replicate weights based on PSU and repInd variables according to JK2 or BRR procedure implemented in WesVar. According to multiple imputed data sets, a workbook with several analyses is created. The function afterwards serves as a wrapper for svyquantile called by svyby implemented in the survey package. The results of the several analyses are then pooled according to Rubins rule, which is adapted for nested imputations if the dependent argument implies a nested structure. Value A list of data frames in the long format. The output can be summarized using the report function. The first element of the list is a list with either one (no trend analyses) or two (trend analyses) data frames with at least six columns each. For each subpopulation denoted by the groups statement, each dependent variable, each parameter (i.e., the values of the corresponding categories of the dependent variable) and each coefficient (i.e., the estimate and the corresponding standard error) the corresponding value is given. group Denotes the group an analysis belongs to. If no groups were specified and/or analysis for the whole sample were requested, the value of ‘group’ is ‘whole- Group’. depVar Denotes the name of the dependent variable in the analysis. modus Denotes the mode of the analysis. For example, if a JK2 analysis without sam- pling weights was conducted, ‘modus’ takes the value ‘jk2.unweighted’. If a analysis without any replicates but with sampling weights was conducted, ‘modus’ takes the value ‘weighted’. parameter Denotes the parameter of the regression model for which the corresponding value is given further. For frequency tables, this is the value of the category of the dependent variable which relative frequency is given further. coefficient Denotes the coefficient for which the corresponding value is given further. Takes the values ‘est’ (estimate) and ‘se’ (standard error of the estimate). value The value of the parameter, i.e. the relative frequency or its standard error. If groups were specified, further columns which are denoted by the group names are added to the data frame. repTable 27 Examples data(lsa) ### Example 1: only means, SD and variances for each country ### We only consider domain 'reading' rd <- lsa[which(lsa[,"domain"] == "reading"),] ### We only consider the first "nest". rdN1 <- rd[which(rd[,"nest"] == 1),] ### First, we only consider year 2010 rdN1y10<- rdN1[which(rdN1[,"year"] == 2010),] ### First example: Computes percentile in a nested data structure for reading ### scores conditionally on country and for the whole group perzent <- repQuantile(datL = rd, ID = "idstud", wgt = "wgt", type = "JK2", PSU = "jkzone", repInd = "jkrep", imp = "imp", nest="nest", groups = "country", group.splits = c(0:1), dependent = "score", probs = seq(0.1,0.9,0.2) ) res <- report(perzent, add = list(domain = "reading")) ### Second example: Computes percentile for reading scores conditionally on country, ### use 100 bootstrap samples, assume no nested structure perzent <- repQuantile(datL = rdN1y10, ID = "idstud", wgt = "wgt", imp = "imp", groups = "country", dependent = "score", probs = seq(0.1,0.9,0.2), nBoot = 100 ) res <- report(perzent, add = list(domain = "reading")) repTable JK1, JK2 and BRR for frequency tables and trend estimation. Description Compute frequency tables for categorical variables (e.g. factors: dichotomous or polytomous) in complex cluster designs. Estimation of standard errors optionally takes the clustered structure and multiple imputed variables into account. To date, Jackknife-1 (JK1), Jackknife-2 (JK2) and Bal- anced repeated replicate (BRR) methods are implemented to account for clustered designs. Proce- dures of Rubin (1987) and Rubin (2003) are implemented to account for multiple imputed data and nested imputed data, if necessary. Conceptually, the function combines replication and imputation methods. Technically, this is a wrapper for the svymean function of the survey package. Usage repTable(datL, ID, wgt = NULL, type = c("none", "JK2", "JK1", "BRR", "Fay"), PSU = NULL, repInd = NULL, jkfac=NULL, repWgt = NULL, nest=NULL, imp=NULL, groups = NULL, group.splits = length(groups), group.differences.by = NULL, cross.differences = FALSE, crossDiffSE = c("wec", "rep","old"), nBoot = 100, chiSquare = FALSE, correct = TRUE, group.delimiter = "_", trend = NULL, linkErr = NULL, dependent, separate.missing.indicator = FALSE, 28 repTable na.rm=FALSE, expected.values = NULL, doCheck = TRUE, forceTable = FALSE, engine = c("survey", "BIFIEsurvey"), scale = 1, rscales = 1, mse=TRUE, rho=NULL, verbose = TRUE, progress = TRUE ) Arguments datL Data frame in the long format (i.e. each line represents one ID unit in one imputation of one nest) containing all variables for analysis. ID Variable name or column number of student identifier (ID) variable. ID variable must not contain any missing values. wgt Optional: Variable name or column number of weighting variable. If no weight- ing variable is specified, all cases will be equally weighted. type Defines the replication method for cluster replicates which is to be applied. Depending on type, additional arguments must be specified (e.g., PSU and/or repInd or repWgt). PSU Variable name or column number of variable indicating the primary sampling unit (PSU). When a jackknife procedure is applied, the PSU is the jackknife zone variable. If NULL, no cluster structure is assumed and standard errors are computed according to a random sample. repInd Variable name or column number of variable indicating replicate ID. In a jack- knife procedure, this is the jackknife replicate variable. If NULL, no cluster struc- ture is assumed and standard errors are computed according to a random sample. jkfac Only applies if engine = "BIFIEsurvey". Argument is passed to BIFIE.data.jack and specifies the factor for multiplying jackknife replicate weights. repWgt Normally, replicate weights are created by repTable directly from PSU and repInd variables. Alternatively, if replicate weights are included in the data.frame, specify the variable names or column number in the repWgt argument. nest Optional: name or column number of the nesting variable. Only applies in nested multiple imputed data sets. imp Optional: name or column number of the imputation variable. Only applies in multiple imputed data sets. groups Optional: vector of names or column numbers of one or more grouping vari- ables. group.splits Optional: If groups are defined, group.splits optionally specifies whether analysis should be done also in the whole group or overlying groups. See exam- ples for more details. group.differences.by Optional: Specifies one grouping variable for which a chi-square test should be applied. The corresponding variable must be included in the groups statement. If specified, the distribution of the dependent variable is compared between the groups. See examples for further details. cross.differences Either a list of vectors, specifying the pairs of levels for which cross-level dif- ferences should be computed. Alternatively, if TRUE, cross-level differences for all pairs of levels are computed. If FALSE, no cross-level differences are computed. (see examples 2a, 3, and 4 in the help file of the repMean function) repTable 29 crossDiffSE Method for standard error estimation for cross level differences, where groups are dependent. wec uses weighted effect coding, rep uses replication methods (bootstrap or jackknife) to estimate the standard error between the total mean and group-specific means. old does not account for dependent groups and treat the groups as if they were independent from each other. nBoot Without replicates (i.e., for completely random samples), the rep method for standard error estimation for cross level differences needs a bootstrap. nBoot therefore specifies the number of bootstrap samples. This argument is only nec- essary, if crossDiffSE = "rep" and none of the replicate methods (JK1, JK2, or BRR) is applied. Otherwise, nBoot will be ignored. chiSquare Logical. Applies only if group.differences.by was specified. Defines whether group differences should be represented in a chi square test or in (mean) differ- ences of each group’s relative frequency. Note: To date, chi square test is not available for engine = "BIFIEsurvey". correct Logical. Applies only if ’group.differences.by’ is requested without cluster replicates. A logical indicating whether to apply continuity correction when computing the test statistic for 2 by 2 tables. See help page of ’chisq.test’ for further details. group.delimiter Character string which separates the group names in the output frame. trend Optional: name or column number of the trend variable. Note: Trend variable must have exact two levels. Levels for grouping variables must be equal in both ’sub populations’ partitioned by the trend variable. linkErr Optional: Either the name or column number of the linking error variable. If NULL, a linking error of 0 will be assumed in trend estimation. Alterna- tively, linking errors may be given as data.frame with following specifications: Two columns, named trendLevel1 and trendLevel2 which contain the lev- els of the trend variable. The contrasts between both values indicates which trend is meant. For only two measurement occasions, i.e. 2010 and 2015, trendLevel1 should be 2010, and trendLevel2 should be 2015. For three measurement occasions, i.e. 2010, 2015, and 2020, additional lines are neces- sary where trendLevel1 should be 2010, and trendLevel2 should be 2020, to mark the contrast between 2010 and 2020, and further additional lines are nec- essary where trendLevel1 should be 2015, and trendLevel2 should be 2020. The column depVar must include the name of the dependent variable. This string must correspond to the name of the dependent variable in the data. The column parameter indicates the parameter the linking error belongs to. Col- umn linkingError includes the linking error value. Providing linking error in a data.frame is necessary for more than two measurement occasions. See the fourth example below for further details. dependent Variable name or column number of the dependent variable. separate.missing.indicator Logical. Should frequencies of missings in dependent variable be integrated? Note: That is only useful if missing occur as NA. If the dependent variable is coded as character, for example 'male', 'female', 'missing', separate miss- ing indicator is not necessary. 30 repTable na.rm Logical: Should cases with missing values be dropped? expected.values Optional. A vector of values expected in dependent variable. Recommend to left this argument empty. doCheck Logical: Check the data for consistency before analysis? If TRUE groups with in- sufficient data are excluded from analysis to prevent subsequent functions from crashing. forceTable Logical: Function decides internally whether the table or the mean function of survey is called. If the mean function is called, the polytomous dependent vari- able is converted to dichotomous indicator variables. If mean is called, group differences for each category of the polytomous dependent variable can be com- puted. If table is called, a chi square statistic may be computed. The argument allows to force the function either to call mean or table. engine Which package should be used for estimation? scale scaling constant for variance, for details, see help page of svrepdesign from the survey package rscales scaling constant for variance, for details, see help page of svrepdesign from the survey package mse Logical: If TRUE, compute variances based on sum of squares around the point estimate, rather than the mean of the replicates. See help page of svrepdesign from the survey package for further details. rho Shrinkage factor for weights in Fay’s method. If engine = "survey", argument is passed to the rho argument of the svrepdesign function from the survey package. See the corresponding help page for further details. If engine = "BIFIEsurvey", argument is passed to the fayfac argument of the BIFIE.data.jack function from the BIFIEsurvey package. See the corresponding help page for further details. For convenience, if rho = NULL (the default) and engine = "BIFIEsurvey" and type = "JK1", BIFIE.data.jack is called with jktype="JK_GROUP" −1 and fayfac = rho, where ρ = (Ncluster − 1) × Ncluster verbose Logical: Show analysis information on console? progress Logical: Show progress bar on console? Details Function first creates replicate weights based on PSU and repInd variables according to JK2 pro- cedure implemented in WesVar. According to multiple imputed data sets, a workbook with several analyses is created. The function afterwards serves as a wrapper for svymean called by svyby im- plemented in the survey package. Relative frequencies of the categories of the dependent variable are computed by the means of the dichotomous indicators (e.g. dummy variables) of each category. The results of the several analyses are then pooled according to Rubin’s rule, which is adapted for nested imputations if the dependent argument implies a nested structure. Value A list of data frames in the long format. The output can be summarized using the report function. The first element of the list is a list with either one (no trend analyses) or two (trend analyses) data repTable 31 frames with at least six columns each. For each subpopulation denoted by the groups statement, each dependent variable, each parameter (i.e., the values of the corresponding categories of the dependent variable) and each coefficient (i.e., the estimate and the corresponding standard error) the corresponding value is given. group Denotes the group an analysis belongs to. If no groups were specified and/or analysis for the whole sample were requested, the value of ‘group’ is ‘whole- Group’. depVar Denotes the name of the dependent variable in the analysis. modus Denotes the mode of the analysis. For example, if a JK2 analysis without sam- pling weights was conducted, ‘modus’ takes the value ‘jk2.unweighted’. If a analysis without any replicates but with sampling weights was conducted, ‘modus’ takes the value ‘weighted’. parameter Denotes the parameter of the regression model for which the corresponding value is given further. For frequency tables, this is the value of the category of the dependent variable which relative frequency is given further. coefficient Denotes the coefficient for which the corresponding value is given further. Takes the values ‘est’ (estimate) and ‘se’ (standard error of the estimate). value The value of the parameter, i.e. the relative frequency or its standard error. If groups were specified, further columns which are denoted by the group names are added to the data frame. References Rubin, D.B. (2003): Nested multiple imputation of NMES via partially incompatible MCMC. Sta- tistica Neerlandica 57, 1, 3–18. Examples data(lsa) ### Example 1: only means, SD and variances for each country ### subsetting: We only consider domain 'reading' rd <- lsa[which(lsa[,"domain"] == "reading"),] ### We only consider the first "nest". rdN1 <- rd[which(rd[,"nest"] == 1),] ### First, we only consider year 2010 rdN1y10<- rdN1[which(rdN1[,"year"] == 2010),] ### First example: Computes frequencies of polytomous competence levels (1, 2, 3, 4, 5) ### conditionally on country, using a chi-square test to decide whether the distribution ### varies between countries (it's an overall test, i.e. with three groups, df1=8). freq.tab1 <- repTable(datL = rdN1y10, ID = "idstud", wgt = "wgt", imp="imp", type = "JK2", PSU = "jkzone", repInd = "jkrep", groups = "country", group.differences.by = "country", dependent = "comp", chiSquare = TRUE) res1 <- report(freq.tab1, add = list ( domain = "reading" )) 32 repTable ### Second example: Computes frequencies of polytomous competence levels (1, 2, 3, 4, 5) ### conditionally on country. Now we test whether the frequency of each single category ### differs between pairs of countries (it's not an overall test ... repTable now ### calls repMean internally, using dummy variables freq.tab2 <- repTable(datL = rdN1y10, ID = "idstud", wgt = "wgt", imp="imp", type = "JK2", PSU = "jkzone", repInd = "jkrep", groups = "country", group.differences.by = "country", dependent = "comp", chiSquare = FALSE) res2 <- report(freq.tab2, add = list ( domain = "reading" )) ### Third example: trend estimation and nested imputation and 'by' loop ### (to date, only crossDiffSE = "old" works) freq.tab3 <- by ( data = lsa, INDICES = lsa[,"domain"], FUN = function (subdat) { repTable(datL = subdat, ID = "idstud", wgt = "wgt", imp="imp", nest = "nest", type = "JK2", PSU = "jkzone", repInd = "jkrep", groups = "country", group.differences.by = "country", group.splits = 0:1, cross.differences = TRUE, crossDiffSE = "old", dependent = "comp", chiSquare = FALSE, trend = "year", linkErr = "leComp") }) res3 <- do.call("rbind", lapply(names(freq.tab3), FUN = function (domain) { report(freq.tab3[[domain]], trendDiffs = TRUE, add = list ( domain = domain )) })) ### Fourth example: similar to example 3. trend estimation using a linking ### error data.frame linkErrs <- data.frame ( trendLevel1 = 2010, trendLevel2 = 2015, depVar = "comp", unique(lsa[,c("domain", "comp", "leComp")]), stringsAsFactors = FALSE) colnames(linkErrs) <- car::recode(colnames(linkErrs), "'comp'='parameter'; 'leComp'='linkingError'") freq.tab4 <- by ( data = lsa, INDICES = lsa[,"domain"], FUN = function (subdat) { repTable(datL = subdat, ID = "idstud", wgt = "wgt", type="none", imp="imp", nest = "nest", groups = "country", group.differences.by = "country", group.splits = 0:1, cross.differences = FALSE, dependent = "comp", chiSquare = FALSE, trend = "year", linkErr = linkErrs[which(linkErrs[,"domain"] == subdat[1,"domain"]),]) }) res4 <- do.call("rbind", lapply(names(freq.tab4), FUN = function (domain) { report(freq.tab4[[domain]], trendDiffs = TRUE, add = list ( domain = domain )) })) ### Fifth example: minimal example for three measurement occasions ### borrow data from the eatGADS package trenddat1 <- system.file("extdata", "trend_gads_2010.db", package = "eatGADS") trenddat2 <- system.file("extdata", "trend_gads_2015.db", package = "eatGADS") trenddat3 <- system.file("extdata", "trend_gads_2020.db", package = "eatGADS") trenddat <- eatGADS::getTrendGADS(filePaths = c(trenddat1, trenddat2, trenddat3), years = c(2010, 2015, 2020), fast=FALSE) dat <- eatGADS::extractData(trenddat) ### use template linking Error Object load(system.file("extdata", "linking_error.rda", package = "eatRep")) repTable 33 ### check consistency of data and linking error object check1 <- checkLEs(c(trenddat1, trenddat2, trenddat3), lErr) ### Analysis for reading comprehension freq.tab5 <- repTable(datL = dat[which(dat[,"dimension"] == "reading"),], ID = "idstud", type="none", imp="imp", dependent = "traitLevel", chiSquare = FALSE, trend = "year", linkErr = lErr[which(lErr[,"domain"] == "reading"),]) res5 <- report(freq.tab5, trendDiffs = TRUE, add = list ( domain = "reading" )) Index ∗ datasets lsa, 6 ∗ package eatRep-package, 2 BIFIE.data.jack, 13, 15, 17, 28, 30 BIFIE.twolevelreg, 12 checkLEs, 5 eatRep-package, 2 generateRandomJk1Zones, 6 jk2.glm (repGlm), 8 jk2.mean (repMean), 14 jk2.quantile (repQuantile), 24 jk2.table (repTable), 27 lm, 17 lm_robust, 10, 17 lsa, 6 repGlm, 3, 8, 22, 23 repLmer, 12 repMean, 3, 14, 22, 23, 25 report, 22 repQuantile, 3, 22, 23, 24 repTable, 3, 22, 23, 27 svrepdesign, 10, 17, 26, 30 svyby, 18, 26, 30 svyglm, 8, 10 svymean, 14, 18, 27, 30 svyquantile, 26 svyvar, 14 34

PSinference

cran

Package ‘PSinference’ July 19, 2023 Type Package Title Inference for Released Plug-in Sampling Single Synthetic Dataset Version 0.1.0 Maintainer Ricardo Moura <rp.moura@fct.unl.pt> Description Considering the singly imputed synthetic data generated via plug-in sampling un- der the multivariate normal model, draws inference procedures including the generalized vari- ance, the sphericity test, the test for independence between two subsets of vari- ables, and the test for the regression of one set of variables on the other. For more de- tails see Klein et al. (2021) <doi:10.1007/s13571-019-00215-9>. License GPL (>= 2) URL https://github.com/ricardomourarpm/PSinference Imports MASS, stats Encoding UTF-8 RoxygenNote 7.2.3 NeedsCompilation no Author Ricardo Moura [aut, cre] (<https://orcid.org/0000-0002-3003-9235>), Mina Norouzirad [aut] (<https://orcid.org/0000-0003-0311-6888>), Danial Mazarei [aut] (<https://orcid.org/0000-0002-3633-9298>), FCT, I.P. [fnd] (under the scope of the projects UIDB/00297/2020 and UIDP/00297/2020 (NovaMath)) Repository CRAN Date/Publication 2023-07-19 11:00:08 UTC R topics documented: canodist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 GVdist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Inddist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 partition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 simSynthData . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Sphdist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Index 11 1 2 canodist canodist Canonical Empirical Distribution Description This function calculates the empirical distribution of the pivotal random variable that can be used to perform inferential procedures for the regression of one subset of variables on the other based on the released Single Synthetic data generated under Plug-in Sampling, assuming that the original dataset is normally distributed. Usage canodist(part, nsample, pvariates, iterations) Arguments part Number of partitions. nsample Sample size. pvariates Number of variables. iterations Number of iterations for simulating values from the distribution and finding the quantiles. Default is 10000. Details We define (|S ?12 (S ?22 )−1 − ∆)S ?22 (S ?12 )(S ?22 )−1 − ∆)> | T4? |∆ = |S ?11.2 | Pn where S ? = i=1 (vi − v̄)(vi − v̄)> , vi is the ith observation of the synthetic dataset, considering ? S partitioned as S 11 S ?12  ?  S? = . S ?21 S ?22 For ∆ = Σ12 Σ−1 ? 22 , where Σ is partitioned the same way as S its distribution is stochastic equiva- lent to |Ω12 Ω−1 22 Ω21 | |Ω11 − Ω12 Ω−1 22 Ω21 | where Ω ∼ Wp (n − 1, n−1 W ), W ∼ Wp (n − 1, Ip ) and Ω partitioned in the same way as S ? . To test H0 : ∆ = ∆0 , compute the value of T4? , Tf ? 4 , with the observed values and reject the null hypothesis if Tf? > t? 4 4,1−α for α-significance level, where t? is the γth percentile of T ? . 4,γ 4 Value a vector of length iterations that recorded the empirical distribution’s values. canodist 3 References Klein, M., Moura, R. and Sinha, B. (2021). Multivariate Normal Inference based on Singly Imputed Synthetic Data under Plug-in Sampling. Sankhya B 83, 273–287. Examples # generate original data library(MASS) n_sample = 100 p = 4 mu <- c(1,2,3,4) Sigma = matrix(c(1, 0.5, 0.1, 0.7, 0.5, 2, 0.4, 0.9, 0.1, 0.4, 3, 0.2, 0.7, 0.9, 0.2, 4), nr = 4, nc = 4, byrow = TRUE) df = mvrnorm(n_sample, mu = mu, Sigma = Sigma) # generate synthetic data df_s = simSynthData(df) #Decompose Sigma and Sstar part = 2 Sigma_12 = partition(Sigma,nrows = part, ncol = part)[[2]] Sigma_22 = partition(Sigma,nrows = part, ncol = part)[[4]] Delta0 = Sigma_12 %*% solve(Sigma_22) Sstar = cov(df_s) Sstar_11 = partition(Sstar,nrows = part, ncol = part)[[1]] Sstar_12 = partition(Sstar,nrows = part, ncol = part)[[2]] Sstar_21 = partition(Sstar,nrows = part, ncol = part)[[3]] Sstar_22 = partition(Sstar,nrows = part, ncol = part)[[4]] DeltaEst = Sstar_12 %*% solve(Sstar_22) Sstar11_2 = Sstar_11 - Sstar_12 %*% solve(Sstar_22) %*% Sstar_21 T4_obs = det((DeltaEst-Delta0)%*%Sstar_22%*%t(DeltaEst-Delta0))/det(Sstar11_2) T4 <- canodist(part = part, nsample = n_sample, pvariates = p, iterations = 10000) q95 <- quantile(T4, 0.95) T4_obs > q95 #False means that we don't have statistical evidences to reject Delta0 print(T4_obs) print(q95) # When the observed value is smaller than the 95% quantile, # we don't have statistical evidences to reject the Sphericity property. # # Note that the value is very close to zero 4 GVdist GVdist Generalized Variance Empirical Distribution Description This function calculates the empirical distribution of the pivotal random variable that can be used to perform inferential procedures for the Generalized Variance of the released Single Synthetic dataset generated under Plug-in Sampling, assuming that the original distribution is normally distributed. Usage GVdist(nsample, pvariates, iterations = 10000) Arguments nsample Sample size. pvariates Number of variables. iterations Number of iterations for simulating values from the distribution and finding the quantiles. Default is 10000. Details We define |S ∗ | T1? = (n − 1) , |Σ| Pn where S ? = > i=1 (vi − v̄)(vi − v̄) , Σ is the population covariance matrix and vi is the ith observation of the synthetic dataset. Its distribution is stochastic equivalent to Yn Yp χ2n−i χ2n−i i=1 i=1 where χ2n−i are all independent chi-square random variables. The (1 − α) level confidence interval for |Σ| is given by ? ? ! (n − 1)p |S̃ | (n − 1)p |S̃ | , t?1,1−α/2 t?1,α/2 ? where S̃ is the observed value of S ? and t?1,γ is the γth percentile of T1 . Value a vector of length iterations that recorded the empirical distribution’s values. References Klein, M., Moura, R. and Sinha, B. (2021). Multivariate Normal Inference based on Singly Imputed Synthetic Data under Plug-in Sampling. Sankhya B 83, 273–287. Inddist 5 Examples # Original data creation library(MASS) mu <- c(1,2,3,4) Sigma <- matrix(c(1, 0.5, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 1, 0.5, 0.5, 0.5, 0.5, 1), nrow = 4, ncol = 4, byrow = TRUE) seed = 1 n_sample = 100 # Create original simulated dataset df = mvrnorm(n_sample, mu = mu, Sigma = Sigma) # Synthetic data created df_s = simSynthData(df) # Gather the 0.025 and 0.975 quantiles and construct confident interval for sigma^2 # Check that sigma^2 is inside in both cases p = dim(df_s)[2] T <- GVdist(100, p, 10000) q975 <- quantile(T, 0.975) q025 <- quantile(T, 0.025) left <- (n_sample-1)^p * det(cov(df_s)*(n_sample-1))/q975 right <- (n_sample-1)^p * det(cov(df_s)*(n_sample-1))/q025 cat(left,right,'\n') print(det(Sigma)) # The synthetic value is inside the confidence interval of GV Inddist Independence Empirical Distribution Description This function calculates the empirical distribution of the pivotal random variable that can be used to perform inferential procedures and test the independence of two subsets of variables based on the released Single Synthetic data generated under Plug-in Sampling, assuming that the original dataset is normally distributed. Usage Inddist(part, nsample, pvariates, iterations) 6 Inddist Arguments part Number of partitions. nsample Sample size. pvariates Number of variables. iterations Number of iterations for simulating values from the distribution and finding the quantiles. Default is 10000. Details We define |S ? | T3? = |S ?11 ||S ?22 | Pn where S ? = i=1 (vi − v̄)(vi − v̄)> , vi is the ith observation of the synthetic dataset, considering S ? partitioned as S 11 S ?12  ?  S? = . S ?21 S ?22 Under the assumption that Σ12 = 0, its distribution is stochastic equivalent to |Ω| |Ω11 ||Ω22 | where Ω ∼ Wp (n − 1, n−1 W ), W ∼ Wp (n − 1, Ip ) and Ω partitioned in the same way as S ? . To test H0 : Σ12 = 0, compute the value of T3? , Tf ? 3 , with the observed values and reject the null hypothesis if Tf? < t? for α-significance level, where t? is the γth percentile of T ? . 3 3,α 3,γ 3 Value a vector of length iterations that recorded the empirical distribution’s values. References Klein, M., Moura, R. and Sinha, B. (2021). Multivariate Normal Inference based on Singly Imputed Synthetic Data under Plug-in Sampling. Sankhya B 83, 273–287. Examples #generate original data with two independent subsets of variables library(MASS) n_sample = 100 p = 4 mu <- c(1,2,3,4) Sigma = matrix(c(1, 0.5, 0, 0, 0.5, 2, 0, 0, 0, 0, 3, 0.2, 0, 0, 0.2, 4), nr = 4, nc = 4, byrow = TRUE) df = mvrnorm(n_sample, mu = mu, Sigma = Sigma) # generate synthetic data df_s = simSynthData(df) partition 7 #Decompose Sstar in 4 parts part = 2 Sstar = cov(df_s) Sstar_11 = partition(Sstar,nrows = part, ncol = part)[[1]] Sstar_12 = partition(Sstar,nrows = part, ncol = part)[[2]] Sstar_21 = partition(Sstar,nrows = part, ncol = part)[[3]] Sstar_22 = partition(Sstar,nrows = part, ncol = part)[[4]] #Compute observed T3_star T3_obs = det(Sstar)/(det(Sstar_11)*det(Sstar_22)) alpha = 0.05 # colect the quantile from the distribution assuming independence between the two subsets T3 <- Inddist(part = part, nsample = n_sample, pvariates = p, iterations = 10000) q5 <- quantile(T3, alpha) T3_obs < q5 #False means that we don't have statistical evidences to reject independence print(T3_obs) print(q5) # Note that the value of the observed T3_obs is close to one as expected partition Split a matrix into blocks Description This function Split a matrix into a list of blocks (either by rows and columns). Usage partition(Matrix, nrows, ncols) Arguments Matrix a matrix to split . nrows positive integer indicating the number of rows blocks. ncols positive integer indicating the number of columns blocks. Value a list of partitioned submatrices 8 simSynthData Examples df = matrix(c(1,0.5,0,0, 0.5,2,0,0, 0,0,3,0.2, 0, 0, 0.2,4), nr = 4, nc = 4, byrow = TRUE) partition(df,2,2) simSynthData Plug-in Sampling Single Synthetic Dataset Generation Description This function is used to generate a single synthetic version of the original data via Plug-in Sampling. Usage simSynthData(X, n_imp = dim(X)[1]) Arguments X matrix or dataframe n_imp sample size Details Assume that X = (x1 , . . . , xn ) is the original data, assumed to be normally distributed, we compute x̄ as the sample mean and Σ̂ = S/(n − 1) as the sample covariance matrix, where S is the sample Wishart matrix. We generate V = (v1 , . . . , vn ), by drawing i.i.d. vi ∼ Np (x̄, Σ̂). Value a matrix of generated dataset References Klein, M., Moura, R. and Sinha, B. (2021). Multivariate Normal Inference based on Singly Imputed Synthetic Data under Plug-in Sampling. Sankhya B 83, 273–287. Sphdist 9 Examples library(MASS) n_sample = 1000 mu=c(0,0,0,0) Sigma=diag(1,4,4) # Create original simulated dataset df_o = mvrnorm(n_sample, mu, Sigma) # Create singly imputed synthetic dataset df_s = simSynthData(df_o) #Estimators synthetic mean_s <- colMeans(df_s) S_s <- (t(df_s)- mean_s) %*% t(t(df_s)- mean_s) # careful about this computation # mean_o is a column vector and if you are thinking as n X p matrices and # row vectors you should be aware of this. print(mean_s) print(S_s/(dim(df_s)[1]-1)) Sphdist Spherical Empirical Distribution Description This function calculates the empirical distribution of the pivotal random variable that can be used to perform the Sphericity test of the population covariance matrix Σ that is Σ = σ 2 Ip , based on the released Single Synthetic data generated under Plug-in Sampling, assuming that the original dataset is normally distributed. Usage Sphdist(nsample, pvariates, iterations) Arguments nsample Sample size. pvariates Number of variables. iterations Number of iterations for simulating values from the distribution and finding the quantiles. Default is 10000. Details We define 1 |S ? | p T2? = tr(S ? )/p Pn where S ? = i=1 (vi −v̄)(vi −v̄)> , vi is the ith observation of the synthetic dataset. For Σ = σ 2 Ip , its distribution is stochastic equivalent to 1 |Ω1 Ω2 | p tr(Ω1 Ω2 )/p 10 Sphdist Ip where Ω1 and Ω2 are Wishart random variables, Ω1 ∼ Wp (n − 1, n−1 ) is independent of Ω2 ∼ Wp (n − 1, Ip ). To test H0 : Σ = σ Ip , compute the observed value of T2? , Tf 2 ? 2 , with the observed values and reject the null hypothesis if T2 > t2,α for α-significance level, where t?2,γ is the γth f? ? percentile of T2? . Value a vector of length iterations that recorded the empirical distribution’s values. References Klein, M., Moura, R. and Sinha, B. (2021). Multivariate Normal Inference based on Singly Imputed Synthetic Data under Plug-in Sampling. Sankhya B 83, 273–287. Examples # Original data created library(MASS) mu <- c(1,2,3,4) Sigma <- matrix(c(1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1), nrow = 4, ncol = 4, byrow = TRUE) seed = 1 n_sample = 100 # Create original simulated dataset df = mvrnorm(n_sample, mu = mu, Sigma = Sigma) # Sinthetic data created df_s = simSynthData(df) # Gather the 0.95 quantile p = dim(df_s)[2] T_sph <- Sphdist(nsample = n_sample, pvariates = p, iterations = 10000) q95 <- quantile(T_sph, 0.95) # Compute the observed value of T from the synthetic dataset S_star = cov(df_s*(n_sample-1)) T_obs = (det(S_star)^(1/p))/(sum(diag(S_star))/p) print(q95) print(T_obs) #Since the observed value is bigger than the 95% quantile, #we don't have statistical evidences to reject the Sphericity property. # #Note that the value is very close to one Index canodist, 2 GVdist, 4 Inddist, 5 partition, 7 simSynthData, 8 Sphdist, 9 11

deckgl

cran

Package ‘deckgl’ February 19, 2023 Title An R Interface to 'deck.gl' Version 0.3.0 Date 2023-02-19 Maintainer Stefan Kuethe <crazycapivara@gmail.com> Description Makes 'deck.gl' <https://deck.gl/>, a WebGL-powered open-source JavaScript framework for visual exploratory data analysis of large datasets, available within R via the 'htmlwid- gets' package. Furthermore, it supports basemaps from 'mapbox' <https://www.mapbox.com/> via 'mapbox-gl-js' <https://github.com/mapbox/mapbox-gl-js>. URL https://github.com/crazycapivara/deckgl/, https://crazycapivara.github.io/deckgl/ BugReports https://github.com/crazycapivara/deckgl/issues/ Depends R (>= 3.3) Imports htmlwidgets, htmltools, magrittr, base64enc, yaml, jsonlite, readr, tibble License MIT + file LICENSE Encoding UTF-8 LazyData true RoxygenNote 7.2.0 Suggests knitr, rmarkdown, testthat, rprojroot, sf, scales, RColorBrewer, shiny VignetteBuilder knitr NeedsCompilation no Author Stefan Kuethe [aut, cre] Repository CRAN Date/Publication 2023-02-19 20:30:02 UTC 1 2 R topics documented: R topics documented: add_arc_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 add_basemap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 add_bitmap_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 add_column_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 add_contour_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 add_control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 add_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 add_geojson_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 add_great_circle_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 add_grid_cell_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 add_grid_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 add_h3_cluster_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 add_h3_hexagon_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 add_heatmap_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 add_hexagon_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 add_icon_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 add_json_editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 add_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 add_legend . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 add_legend_pal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 add_line_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 add_mapbox_basemap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 add_path_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 add_point_cloud_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 add_polygon_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 add_raster_tile_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 add_scatterplot_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 add_screen_grid_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 add_source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 add_source_as_dep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 add_text_layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 bart_segments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 bart_stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 deckgl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 deckgl-shiny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 deckgl_proxy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 does_it_work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 encode_icon_atlas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 get_color_to_rgb_array . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 get_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 get_first_element . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 get_last_element . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 get_position . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 get_property . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 set_view_state . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 sf_bike_parking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 add_arc_layer 3 update_deckgl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 use_carto_style . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 use_contour_definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 use_default_icon_properties . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 use_icon_definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 use_tooltip . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Index 47 add_arc_layer Add an arc layer to the deckgl widget Description The ArcLayer renders raised arcs joining pairs of source and target points, specified as latitude/longitude coordinates. Usage add_arc_layer(deckgl, data = NULL, properties = list(), ..., id = "arc-layer") Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/arc-layer Examples data("bart_segments") properties <- list( getWidth = 12, getSourcePosition = ~from_lng + from_lat, getTargetPosition = ~to_lng + to_lat, getSourceColor = "@=[Math.sqrt(inbound), 140, 0]", getTargetColor = "@=[Math.sqrt(outbound), 140, 0]", tooltip = use_tooltip( 4 add_bitmap_layer html = "{{from_name}} to {{to_name}}", style = "background: steelBlue; border-radius: 5px;" ) ) deck <- deckgl(zoom = 10, pitch = 35) %>% add_arc_layer(data = bart_segments, properties = properties) %>% add_control("Arc Layer", "top-left") %>% add_basemap() if (interactive()) deck add_basemap Add a basemap to the deckgl widget Description Add a basemap to the deckgl widget Usage add_basemap(deckgl, style = use_carto_style(), ...) Arguments deckgl deckgl widget style The style definition of the map conforming to the Mapbox Style Specification. ... not used add_bitmap_layer Add a bitmap layer to the deckgl widget Description Add a bitmap layer to the deckgl widget Usage add_bitmap_layer( deckgl, image = NULL, properties = list(), ..., id = "h3-hexagon-layer" ) add_column_layer 5 Arguments deckgl A deckgl widget object. image image properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. Examples image <- paste0( "https://raw.githubusercontent.com/", "uber-common/deck.gl-data/master/", "website/sf-districts.png" ) bounds <- c(-122.5190, 37.7045, -122.355, 37.829) deck <- deckgl() %>% add_bitmap_layer(image = image, bounds = bounds) %>% add_basemap() if (interactive()) deck add_column_layer Add a column layer to the deckgl widget Description The ColumnLayer can be used to render a heatmap of vertical cylinders. It renders a tesselated regular polygon centered at each given position (a "disk"), and extrude it in 3d. Usage add_column_layer( deckgl, data = NULL, properties = list(), ..., id = "column-layer" ) 6 add_contour_layer Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/column-layer Examples hexagon_centroids <- system.file("sample-data/centroids.csv", package = "deckgl") %>% read.csv() deck <- deckgl(zoom = 11, pitch = 35) %>% add_column_layer( data = hexagon_centroids, diskResolution = 12, getPosition = ~lng + lat, getElevation = ~value, getFillColor = "@=[48, 128, value * 255, 255]", elevationScale = 5000, radius = 250, extruded = TRUE, tooltip = "Value: {{value}}" ) %>% add_control("Column Layer", "bottom-left") %>% add_basemap() if (interactive()) deck add_contour_layer Add a contour layer to the deckgl widget Description The ContourLayer renders contour lines for a given threshold and cell size. Internally it implements Marching Squares algorithm to generate contour line segments and feeds them into LineLayer to render lines. add_contour_layer 7 Usage add_contour_layer( deckgl, data = NULL, properties = list(), ..., id = "contour-layer" ) Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/contour-layer Examples ## @knitr contour-layer data("sf_bike_parking") contours <- list( use_contour_definition( threshold = 1, color = c(255, 0, 0), stroke_width = 2 ), use_contour_definition( threshold = 5, color = c(0, 255, 0), stroke_width = 3 ), use_contour_definition( threshold = 15, color = c(0, 0, 255), stroke_width = 5 ) ) properties <- list( 8 add_control contours = contours, cellSize = 200, elevationScale = 4, getPosition = ~lng + lat ) deck <- deckgl(zoom = 10.5, pitch = 30) %>% add_contour_layer(data = sf_bike_parking, properties = properties) %>% add_control("Contour Layer") %>% add_basemap() if (interactive()) deck add_control Add a control to the widget Description Add a control to the widget Usage add_control(deckgl, html, pos = "top-right", style = NULL) Arguments deckgl A deckgl widget object. html The innerHTML of the element. pos The position of the control. Possible values are top-left, top-right, bottom-right and bottom-left. style A cssText string that will modefiy the default style of the element. Examples deck <- deckgl() %>% add_basemap() %>% add_control( "<h1>Blank Base Map</h1>", pos = "top-right", style = "background: #004080; color: white;" ) if (interactive()) deck add_data 9 add_data Add JavaScript data file Description EXPERIMENTAL Usage add_data(deckgl, data, var_name = "thanksForAllTheFish") Arguments deckgl deckgl widget data data object var_name JavaScript variable name used to make the data available add_geojson_layer Add a geojson layer to the deckgl widget Description The GeoJsonLayer takes in GeoJson formatted data and renders it as interactive polygons, lines and points. Usage add_geojson_layer( deckgl, data = NULL, properties = list(), ..., id = "geojson-layer" ) Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. 10 add_great_circle_layer See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/geojson-layer Examples geojson <- paste0( "https://raw.githubusercontent.com/", "uber-common/deck.gl-data/", "master/website/bart.geo.json" ) deck <- deckgl(zoom = 10, pickingRadius = 5) %>% add_geojson_layer( data = geojson, filled = TRUE, extruded = TRUE, getRadius = 100, lineWidthScale = 20, lineWidthMinPixels = 2, getLineWidth = 1, getLineColor = "@=properties.color || 'green'", getFillColor = c(160, 160, 180, 200), getElevation = 30, tooltip = JS("object => object.properties.name || object.properties.station") ) %>% add_basemap() if (interactive()) deck add_great_circle_layer Add a great circle layer to the deckgl widget Description The GreatCircleLayer is a variation of the ArcLayer. It renders flat arcs along the great circle joining pairs of source and target points, specified as latitude/longitude coordinates. Usage add_great_circle_layer( deckgl, data = NULL, properties = list(), ..., id = "great-circle-layer" ) add_grid_cell_layer 11 Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/great-circle-layer Examples ## @knitr great-circle-layer data("bart_segments") properties <- list( pickable = TRUE, getWidth = 12, getSourcePosition = ~from_lng + from_lat, getTargetPosition = ~to_lng + to_lat, getSourceColor = JS("d => [Math.sqrt(d.inbound), 140, 0]"), getTargetColor = JS("d => [Math.sqrt(d.outbound), 140, 0]"), getTooltip = "{{from_name}} to {{to_name}}" ) deck <- deckgl(zoom = 10, pitch = 35) %>% add_great_circle_layer(data = bart_segments, properties = properties) %>% add_control("Great Circle Layer") %>% add_basemap() if (interactive()) deck add_grid_cell_layer Add a grid cell layer to the deckgl widget Description The GridCellLayer can render a grid-based heatmap. It is a variation of the ColumnLayer. It takes the constant width / height of all cells and top-left coordinate of each cell. The grid cells can be given a height using the getElevation accessor. 12 add_grid_cell_layer Usage add_grid_cell_layer( deckgl, data = NULL, properties = list(), ..., id = "grid-cell-layer" ) Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/grid-cell-layer Examples hexagon_centroids <- system.file("sample-data/centroids.csv", package = "deckgl") %>% read.csv() deck <- deckgl(zoom = 11, pitch = 35) %>% add_grid_cell_layer( data = hexagon_centroids, getPosition = ~lng + lat, getElevation = ~value, getFillColor = "@=[48, 128, value * 255, 255]", elevationScale = 5000, cellSize = 250, extruded = TRUE, tooltip = "{{value}}" ) %>% add_mapbox_basemap() if (interactive()) deck add_grid_layer 13 add_grid_layer Add a grid layer to the deckgl widget Description The GridLayer renders a grid heatmap based on an array of points. It takes the constant size all each cell, projects points into cells. The color and height of the cell is scaled by number of points it contains. Usage add_grid_layer( deckgl, data = NULL, properties = list(), ..., id = "grid-layer" ) Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/grid-layer Examples data("sf_bike_parking") properties <- list( filter = "spaces > 4", visible = TRUE, extruded = TRUE, cellSize = 200, elevationScale = 4, getPosition = "@=[lng, lat]", #~lng + lat, colorRange = RColorBrewer::brewer.pal(6, "YlOrRd"), 14 add_h3_cluster_layer tooltip = "{{position.0}}, {{position.1}}<br/>Count: {{count}}" ) deck <- deckgl(zoom = 11, pitch = 45, bearing = 35, element_id = "grid-layer") %>% add_source("sf-bike-parking", sf_bike_parking) %>% add_grid_layer( source = "sf-bike-parking", properties = properties ) %>% add_control("Grid Layer") %>% add_basemap() %>% add_json_editor(wrap = 50, maxLines = 23) if (interactive()) deck add_h3_cluster_layer Add a h3 cluster layer to the deckgl widget Description Add a h3 cluster layer to the deckgl widget Usage add_h3_cluster_layer( deckgl, data = NULL, properties = list(), ..., id = "h3-cluster-layer" ) Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/h3-cluster-layer add_h3_hexagon_layer 15 Examples ## @knitr h3-cluster-layer data_url <- paste0( "https://raw.githubusercontent.com/uber-common/deck.gl-data/", "master/website/sf.h3clusters.json" ) # sample_data <- jsonlite::fromJSON(data_url, simplifyDataFrame = FALSE) sample_data <- data_url properties <- list( stroked = TRUE, filled = TRUE, extruded = FALSE, getHexagons = ~hexIds, getFillColor = JS("d => [255, (1 - d.mean / 500) * 255, 0]"), getLineColor = c(255, 255, 255), lineWidthMinPixels = 2, getTooltip = ~mean ) deck <- deckgl(zoom = 10.5, pitch = 20) %>% add_h3_cluster_layer(data = sample_data, properties = properties) %>% add_basemap() if (interactive()) deck add_h3_hexagon_layer Add a h3 hexagon layer to the deckgl widget Description Add a h3 hexagon layer to the deckgl widget Usage add_h3_hexagon_layer( deckgl, data = NULL, properties = list(), ..., id = "h3-hexagon-layer" ) Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. 16 add_heatmap_layer properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/h3-hexagon-layer Examples ## @knitr h3-hexagon-layer-layer h3_cells <- system.file("sample-data/h3-cells.csv", package = "deckgl") %>% read.csv() properties <- list( getHexagon = ~h3_index, getFillColor =JS("d => [255, (1 - d.count / 500) * 255, 0]"), getElevation = ~count, elevationScale = 20, getTooltip = "{{h3_index}}: {{count}}" ) deck <- deckgl(zoom = 11, pitch = 35) %>% add_h3_hexagon_layer(data = h3_cells, properties = properties) %>% add_control("H3 Hexagon Layer") %>% add_basemap() if (interactive()) deck add_heatmap_layer Add a heatmap layer to the deckgl widget Description The HeatmapLayer can be used to visualize spatial distribution of data. It internally implements Gaussian Kernel Density Estimation to render heatmaps. Usage add_heatmap_layer( deckgl, id = "heatmap-layer", data = NULL, properties = list(), ... ) add_hexagon_layer 17 Arguments deckgl A deckgl widget object. id The unique id of the layer. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/heatmap-layer Examples ## @knitr heatmap-layer data("sf_bike_parking") map <- deckgl() %>% add_heatmap_layer( data = sf_bike_parking, getPosition = ~lng + lat, getWeight = ~spaces ) %>% add_basemap() if (interactive()) map add_hexagon_layer Add a hexagon layer to the deckgl widget Description The HexagonLayer renders a hexagon heatmap based on an array of points. It takes the radius of hexagon bin, projects points into hexagon bins. The color and height of the hexagon is scaled by number of points it contains. Usage add_hexagon_layer( deckgl, data = NULL, properties = list(), ..., id = "hexagon-layer" ) 18 add_icon_layer Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/hexagon-layer Examples ## @knitr hexagon-layer data("sf_bike_parking") properties <- list( extruded = TRUE, radius = 200, elevationScale = 4, getPosition = ~lng + lat, colorRange = RColorBrewer::brewer.pal(6, "Oranges"), tooltip = " <p>{{position.0}}, {{position.1}}<p> <p>Count: {{points.length}}</p> <p>{{#points}}<div>{{address}}</div>{{/points}}</p> ", onClick = JS("obj => console.log(obj)"), autoHighlight = TRUE ) deck <- deckgl(zoom = 11, pitch = 45, bearing = 35) %>% add_hexagon_layer(data = sf_bike_parking, properties = properties) %>% add_control("Hexagon Layer", "top-left") %>% add_basemap() if (interactive()) deck add_icon_layer Add an icon layer to the deckgl widget Description The IconLayer renders raster icons at given coordinates. add_icon_layer 19 Usage add_icon_layer( deckgl, data = NULL, properties = use_default_icon_properties(), ..., id = "icon-layer" ) Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/icon-layer Examples ## @knitr icon-layer data("bart_stations") properties <- list( iconAtlas = encode_icon_atlas(), iconMapping = list(marker = use_icon_definition()), sizeScale = 10, getPosition = ~lng + lat, getIcon = JS("d => 'marker'"), getSize = 5, getColor = JS("d => [Math.sqrt(d.exits), 140, 0]"), getTooltip = "{{name}}<br/>{{address}}" ) deck <- deckgl(zoom = 10, pitch = 45) %>% add_icon_layer(data = bart_stations, properties = properties) %>% add_control("Icon Layer") %>% add_basemap() if (interactive()) deck 20 add_layer add_json_editor Add a JSON-editor to the deckgl widget Description Adds a Ace-editor in JSON mode to the map to interact with the layers of your deck instance. Usage add_json_editor(deckgl, ..., style = "width: 40%;", theme = "idle_fingers") Arguments deckgl A deckgl widget object. ... Optional args that are passed to the editor. See https://github.com/ajaxorg/ ace/wiki/Configuring-Ace for a list of available options. style A cssText string that will modefiy the default style of the container that holds the editor. theme The name of the theme used by the editor. add_layer Add any kind of layer to the deckgl widget Description Generic function to add any kind of layer to the deckgl widget. Usually you will not use this one but any of the add_*_layer functions instead. Usage add_layer( deckgl, class_name, data = NULL, properties = list(), ..., id = "hopeful-hopper", tooltip = NULL, source = NULL, filter = NULL ) add_legend 21 Arguments deckgl A deckgl widget object. class_name The name of the JavaScript layer class, e. g. ScatterplotLayer. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. tooltip A tooltip template that defines what should be displayed when the mouse enters an object. You can also pass a list with the properties html and style. See also use_tooltip. source The ID of the data source. See add_source. filter A filter expression that is applied to the data object. Value A deckgl widget object. add_legend Add a legend to the deckgl widget Description Add a legend to the deckgl widget Usage add_legend( deckgl, colors, labels, title = NULL, pos = "top-right", style = NULL, ... ) 22 add_legend_pal Arguments deckgl A deckgl widget object. colors The colors of the legend items. labels The labels corresponding to the colors of the legend items. title The title of the legend. pos The position of the control. Possible values are top-left, top-right, bottom-right and bottom-left. style A cssText string that will modefiy the default style of the element. ... not used add_legend_pal Add a legend to the deckgl widget using a palette func Description Add a legend to the deckgl widget using a palette func Usage add_legend_pal(deckgl, pal, ...) Arguments deckgl A deckgl widget object. pal A palette function that is used to create the legend elements (colors and labels) automatically. ... Parameters that are passed to add_legend. See Also col_numeric et cetera for how to create a palette function. add_line_layer 23 add_line_layer Add a line layer to the deckgl widget Description The LineLayer renders flat lines joining pairs of source and target points, specified as latitude/longitude coordinates. Usage add_line_layer( deckgl, data = NULL, properties = list(), ..., id = "line-layer" ) Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/line-layer Examples ## @knitr line-layer data("bart_segments") properties <- list( pickable = TRUE, getWidth = 12, getSourcePosition = ~from_lng + from_lat, getTargetPosition = ~to_lng + to_lat, getColor = JS("d => [Math.sqrt(d.inbound + d.outbound), 140, 0]"), tooltip = "{{from_name}}} to {{to_name}}" ) 24 add_path_layer deck <- deckgl(zoom = 10, pitch = 20) %>% add_line_layer(data = bart_segments, properties = properties) %>% add_basemap() %>% add_control("Line Layer") if (interactive()) deck add_mapbox_basemap Add a basemap from mapbox to the deckgl widget Description Add a basemap from mapbox to the deckgl widget Usage add_mapbox_basemap( deckgl, style = "mapbox://styles/mapbox/light-v9", token = Sys.getenv("MAPBOX_API_TOKEN") ) Arguments deckgl deckgl widget style map style token mapbox API access token Value deckgl widget add_path_layer Add a path layer to the deckgl widget Description The PathLayer takes in lists of coordinate points and renders them as extruded lines with mitering. Usage add_path_layer( deckgl, data = NULL, properties = list(), ..., id = "path-layer" ) add_point_cloud_layer 25 Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/path-layer Examples sample_data <- paste0( "https://raw.githubusercontent.com/", "uber-common/deck.gl-data/", "master/website/bart-lines.json" ) properties <- list( pickable = TRUE, widthScale = 20, widthMinPixels = 2, getPath = ~path, getColor = ~color, getWidth = 5, tooltip = ~name ) deck <- deckgl(pitch = 25, zoom = 10.5) %>% add_path_layer(data = sample_data, properties = properties) %>% add_basemap() %>% add_control("Path Layer") if (interactive()) deck add_point_cloud_layer Add a point cloud layer to the deckgl widget Description The PointCloudLayer takes in points with 3d positions, normals and colors and renders them as spheres with a certain radius. 26 add_point_cloud_layer Usage add_point_cloud_layer( deckgl, data = NULL, properties = list(), ..., id = "point-cloud-layer" ) Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/point-cloud-layer Examples ## @knitr point-cloud-layer sample_data <- paste0( "https://raw.githubusercontent.com/", "uber-common/deck.gl-data/", "master/website/pointcloud.json" ) properties <- list( pickable = TRUE, coordinateSystem = JS("deck.COORDINATE_SYSTEM.METER_OFFSETS"), coordinateOrigin = c(-122.4, 37.74), pointSize = 4, getPosition = ~position, getNormal = ~normal, getColor = ~color, lightSettings = list(), tooltip = "{{position.0}}, {{position.1}}" ) deck <- deckgl(pitch = 45, zoom = 10.5) %>% add_point_cloud_layer(data = sample_data, properties = properties) %>% add_basemap() %>% add_polygon_layer 27 add_control("Point Cloud Layer") if (interactive()) deck add_polygon_layer Add a polygon layer to the deckgl widget Description The PolygonLayer renders filled and/or stroked polygons. Usage add_polygon_layer( deckgl, data = NULL, properties = list(), ..., id = "polygon-layer" ) Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/polygon-layer Examples ## @knitr polygon-layer sample_data <- paste0( "https://raw.githubusercontent.com/", "uber-common/deck.gl-data/", "master/website/sf-zipcodes.json" ) properties <- list( 28 add_raster_tile_layer pickable = TRUE, stroked = TRUE, filled = TRUE, wireframe = TRUE, lineWidthMinPixels = 1, getPolygon = ~contour, getElevation = JS("d => d.population / d.area / 10"), getFillColor = JS("d => [d.population / d.area / 60, 140, 0]"), getLineColor = c(80, 80, 80), getLineWidth = 1, tooltip = "{{zipcode}}<br/>Population: {{population}}" ) deck <- deckgl(zoom = 11, pitch = 25) %>% add_polygon_layer(data = sample_data, properties = properties) %>% add_basemap() %>% add_control("Polygon Layer") if (interactive()) deck add_raster_tile_layer Add a raster tile layer to the deckgl widget Description EXPERIMENTAL, see https://deck.gl/#/examples/core-layers/tile-layer Usage add_raster_tile_layer( deckgl, id = "raster-tiles", tileServer = "https://c.tile.openstreetmap.org/", properties = list(), ... ) Arguments deckgl A deckgl widget object. id The unique id of the layer. tileServer base url of the tile server properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. add_scatterplot_layer 29 Examples ## @knitr raster-tile-layer tile_servers <- list( osm = "https://a.tile.openstreetmap.org/", carto_light = "https://cartodb-basemaps-a.global.ssl.fastly.net/light_all/", carto_dark = "https://cartodb-basemaps-a.global.ssl.fastly.net/dark_all/", stamen_toner = "http://a.tile.stamen.com/toner/" ) deck <- deckgl() %>% add_raster_tile_layer( tileServer = tile_servers$osm, pickable = TRUE, autoHighlight = TRUE, highlightColor = c(60, 60, 60, 40) ) if (interactive()) deck add_scatterplot_layer Add a scatterplot layer to the deckgl widget Description The ScatterplotLayer takes in paired latitude and longitude coordinated points and renders them as circles with a certain radius. Usage add_scatterplot_layer( deckgl, data = NULL, properties = list(), ..., id = "scatterplot-layer" ) Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. 30 add_screen_grid_layer See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/scatterplot-layer Examples data("bart_stations") properties <- list( getPosition = ~lng + lat, getRadius = "@=Math.sqrt(exits)", #JS("data => Math.sqrt(data.exits)"), radiusScale = 6, getFillColor = "@=code === 'LF' ? 'white': 'red'", #c(255, 140, 20), tooltip = "{{name}}" ) deck <- deckgl(zoom = 10.5, pitch = 35) %>% add_scatterplot_layer(data = bart_stations, properties = properties) %>% add_basemap() %>% add_control("Scatterplot Layer") if (interactive()) deck add_screen_grid_layer Add a screen grid layer to the deckgl widget Description The ScreenGridLayer takes in an array of latitude and longitude coordinated points, aggregates them into histogram bins and renders as a grid. Usage add_screen_grid_layer( deckgl, data = NULL, properties = list(), ..., id = "screen-grid-layer" ) Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. add_source 31 ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/screen-grid-layer Examples ## @knitr screen-grid-layer data("sf_bike_parking") properties <- list( opacity = 0.8, cellSizePixels = 50, colorRange = RColorBrewer::brewer.pal(6, "Blues"), getPosition = ~lng + lat, getWeight = ~spaces ) deck <- deckgl() %>% add_screen_grid_layer(data = sf_bike_parking, properties = properties) %>% add_basemap() %>% add_control("Screen Grid Layer") if (interactive()) deck add_source Add a data source to the deckgl widget Description Add a data source to the deckgl widget Usage add_source(deckgl, id, data) Arguments deckgl A deckgl widget object. id The unique id of the source. data The url to fetch data from or a data object. 32 add_source_as_dep Examples data("bart_stations") deckgl() %>% add_source("bart-stations", bart_stations) %>% add_scatterplot_layer( source = "bart-stations", getPosition = ~lng + lat, getFillColor = "steelblue", getRadius = 50, radiusScale = 6 ) %>% add_text_layer( source = "bart-stations", getPosition = ~lng + lat, getText = ~name, getSize = 15, sizeScale = 1.5, getColor = "white" ) %>% add_basemap() add_source_as_dep Add source as JavaScript dep Description Add source as JavaScript dep Usage add_source_as_dep(deckgl, id, data) Arguments deckgl A deckgl widget object. id The unique id of the source. data The url to fetch data from or a data object. add_text_layer 33 add_text_layer Add a text layer to the deckgl widget Description The TextLayer renders text labels on the map using texture mapping. Usage add_text_layer( deckgl, data = NULL, properties = list(), ..., id = "text-layer" ) Arguments deckgl A deckgl widget object. data The url to fetch data from or a data object. properties A named list of properties with names corresponding to the properties defined in the deckgl-api-reference for the given layer class. The properties param- eter can also be an empty list. In this case all props must be passed as named arguments. ... Named arguments that will be added to the properties object. Identical pa- rameters are overwritten. id The unique id of the layer. See Also https://deck.gl/#/documentation/deckgl-api-reference/layers/text-layer Examples ## @knitr text-layer data("bart_stations") deck <- deckgl(zoom = 10, pitch = 35) %>% add_text_layer( data = bart_stations, pickable = TRUE, getPosition = ~lng + lat, getText = ~name, getSize = 15, getAngle = 0, getTextAnchor = "middle", 34 bart_segments getAlignmentBaseline = "center", tooltip = "{{name}}<br/>{{address}}" ) %>% add_basemap(use_carto_style("voyager")) if (interactive()) deck bart_segments bart segments Description bart segments Usage bart_segments Format tibble with 45 rows and 8 variables: inbound number of inbound trips outbound number of outbound trips from_name name of source station from_lng longitude of source station from_lat latitude of source station to_name name of target station to_lng longitude of target station to_lat latitude of target station Source https://raw.githubusercontent.com/uber-common/deck.gl-data/master/website/bart-segments. json bart_stations 35 bart_stations bart stations Description bart stations Usage bart_stations Format tibble with 44 rows and 7 variables: name station name code two-letter station code address address entries number of entries exits number of exits lng longitude lat latitude Source https://raw.githubusercontent.com/uber-common/deck.gl-data/master/website/bart-stations. json deckgl Create a deckgl widget Description Create a deckgl widget Usage deckgl( latitude = 37.8, longitude = -122.45, zoom = 12, pitch = 0, bearing = 0, initial_view_state = NULL, 36 deckgl-shiny views = NULL, width = NULL, height = NULL, element_id = NULL, ... ) Arguments latitude The latitude of the initial view state. longitude The longitude of the initial view state. zoom The zoom level of the initial view state. pitch The pitch of the initial view state. bearing The bearing of the initial view state. initial_view_state The initial view state. If set, other view state arguments (longitude, latidude et cetera) are ignored. views A single View, or an array of View instances. If not supplied, a single MapView will be created. width The width of the widget. height The height of the widget. element_id The explicit id of the widget (usually not needed). ... Optional properties that are passed to the deck instance. Value deckgl widget See Also https://deck.gl/#/documentation/deckgl-api-reference/deck for optional properties that can be passed to the deck instance. deckgl-shiny Shiny bindings for deckgl Description Output and render functions for using deckgl within Shiny applications and interactive Rmd docu- ments. Usage deckglOutput(outputId, width = "100%", height = "400px") renderDeckgl(expr, env = parent.frame(), quoted = FALSE) deckgl_proxy 37 Arguments outputId output variable to read from width, height Must be a valid CSS unit (like '100%', '400px', 'auto') or a number, which will be coerced to a string and have 'px' appended. expr An expression that generates a deckgl env The environment in which to evaluate expr. quoted Is expr a quoted expression (with quote())? This is useful if you want to save an expression in a variable. deckgl_proxy Create a deckgl proxy object Description Creates a deckgl-like object that can be used to update a deckgl object that has already been ren- dered. Usage deckgl_proxy(shinyId, session = shiny::getDefaultReactiveDomain()) Arguments shinyId single-element character vector indicating the output ID of the deck to modify session the Shiny session object to which the deckgl widget belongs; usually the default value will suffice. does_it_work Check if everything works fine Description Check if everything works fine Usage does_it_work(token = NULL) Arguments token mapbox API access token 38 get_color_to_rgb_array encode_icon_atlas Encode atlas image to base64 Description Encode atlas image to base64 Usage encode_icon_atlas(filename = NULL) Arguments filename The filename of the atlas image. Value base64 encoded atlas image get_color_to_rgb_array Create a getColor data accessor Description Creates a JS method to retrieve the color of each object. The method parses the HEX color property of the data object to an rgb color array. Usage get_color_to_rgb_array(color_property) Arguments color_property property name of data object containing the HEX color Value JavaScript code evaluated on the client-side get_data 39 get_data Get data Description EXPERIMENTAL, usually used in conjunction with add_data Usage get_data(var_name = "thanksForAllTheFish") Arguments var_name JavaScript variable name get_first_element Create a data accessor retrieving the first element of an array Description Create a data accessor retrieving the first element of an array Usage get_first_element(property_name) Arguments property_name property name of data object Value JavaScript code evaluated on the client-side 40 get_position get_last_element Create a data accessor retrieving the last element of an array Description Create a data accessor retrieving the last element of an array Usage get_last_element(property_name) Arguments property_name property name of data object Value JavaScript code evaluated on the client-side get_position Create a getPosition data accessor Description Creates a JS method to retrieve the position of each object. Usage get_position(latitude = NULL, longitude = NULL, coordinates = NULL) Arguments latitude latitude property of data object longitude longitude property of data object coordinates coordinates property of data object (in this case latitude and longitude pa- rameters are ignored) Value JavaScript code evaluated on the client-side get_property 41 get_property Create a data accessor Description Creates a JS method to retrieve a given property of each object. Usage get_property(property_name) Arguments property_name property name of data object Value JavaScript code evaluated on the client-side set_view_state Set the view state of the map Description Set the view state of the map Usage set_view_state( deckgl, latitude = 37.8, longitude = -122.45, zoom = 12, pitch = 0, bearing = 0 ) Arguments deckgl A deckgl widget object. latitude The latitude of the view state. longitude The longitude of the view state. zoom The zoom level of the view state. pitch The pitch of the view state. bearing The bearing of the view state. 42 update_deckgl sf_bike_parking sf bike parking Description sf bike parking Usage sf_bike_parking Format tibble with 2520 rows and 5 variables: address address racks number of racks spaces number of spaces lng longitude lat latidude Source https://raw.githubusercontent.com/uber-common/deck.gl-data/master/website/sf-bike-parking. json update_deckgl Send commands to a deckgl instance in a Shiny app Description Send commands to a deckgl instance in a Shiny app Usage update_deckgl(proxy, ...) Arguments proxy deckgl proxy object ... unused See Also deckgl_proxy use_carto_style 43 use_carto_style Use a Carto style Description Use a Carto style Usage use_carto_style(theme = "dark-matter") Arguments theme The theme of the style, dark-matter, positron or voyager. use_contour_definition Create a contour definition Description Create a contour definition Usage use_contour_definition( threshold = 1, color = c(255, 255, 255), stroke_width = 1 ) Arguments threshold The threshold value used in contour generation. color The RGB color array used to render contour lines. stroke_width The width of the contour lines in pixels. 44 use_icon_definition use_default_icon_properties Use default icon properties Description Returns icon properties with default values for iconAtlas, iconMapping and getIcon, so that the default icon is used. Usage use_default_icon_properties( sizeScale = 15, getSize = 5, getColor = c(240, 140, 0) ) Arguments sizeScale icon size multiplier getSize height of each object (in pixels), if a number is provided, it is used as the size for all objects, if a function is provided, it is called on each object to retrieve its size getColor rgba color of each object, if an array is provided, it is used as the color for all objects if a function is provided, it is called on each object to retrieve its color use_icon_definition Create an icon definition on an atlas image Description Create an icon definition on an atlas image Usage use_icon_definition( x = 0, y = 0, width = 128, height = 128, anchor_x = (width/2), anchor_y = 128, mask = TRUE ) use_tooltip 45 Arguments x The x position of the icon on the atlas image. y The y position of the icon on the atlas image. width The width of the icon on the atlas image. height The height of the icon on the atlas image. anchor_x The horizontal position of the icon anchor. anchor_y the vertical position of the icon anchor. mask whether icon is treated as a transparency mask, if TRUE, user defined color is applied, if FALSE, pixel color from the image is applied use_tooltip Create a tooltip property Description Create a tooltip property Usage use_tooltip(html, style, ...) Arguments html The innerHTML of the element. style A cssText string that will modefiy the default style of the element. ... not used Tooltip template Syntax The tooltip string is a mustache template in which variable names are identified by the double curly brackets (mustache tags) that surround them. The variable names available to the template are given by deck.gl’s pickingInfo.object and vary by layer. See Also mustache for a complete syntax overwiew. 46 use_tooltip Examples data("bart_segments") props <- list( tooltip = use_tooltip( html = "{{from_name}} to {{to_name}}", style = "background: steelBlue; border-radius: 5px;" ) ) # The picking object of the hexagon layer offers # a property that contains the list of points of the hexagon. # You can iterate over this list as shown below. data("sf_bike_parking") html = " <p>{{position.0}}, {{position.1}}<p> <p>Count: {{points.length}}</p> <p>{{#points}}<div>{{address}}</div>{{/points}}</p> " Index ∗ datasets col_numeric, 22 bart_segments, 34 bart_stations, 35 deckgl, 35 sf_bike_parking, 42 deckgl-shiny, 36 deckgl_proxy, 37, 42 add_arc_layer, 3 deckglOutput (deckgl-shiny), 36 add_basemap, 4 does_it_work, 37 add_bitmap_layer, 4 encode_icon_atlas, 38 add_column_layer, 5 add_contour_layer, 6 get_color_to_rgb_array, 38 add_control, 8 get_data, 39 add_data, 9, 39 get_first_element, 39 add_geojson_layer, 9 get_last_element, 40 add_great_circle_layer, 10 get_position, 40 add_grid_cell_layer, 11 get_property, 41 add_grid_layer, 13 add_h3_cluster_layer, 14 renderDeckgl (deckgl-shiny), 36 add_h3_hexagon_layer, 15 add_heatmap_layer, 16 set_view_state, 41 add_hexagon_layer, 17 sf_bike_parking, 42 add_icon_layer, 18 update_deckgl, 42 add_json_editor, 20 use_carto_style, 43 add_layer, 20 use_contour_definition, 43 add_legend, 21, 22 use_default_icon_properties, 44 add_legend_pal, 22 use_icon_definition, 44 add_line_layer, 23 use_tooltip, 21, 45 add_mapbox_basemap, 24 add_path_layer, 24 add_point_cloud_layer, 25 add_polygon_layer, 27 add_raster_tile_layer, 28 add_scatterplot_layer, 29 add_screen_grid_layer, 30 add_source, 21, 31 add_source_as_dep, 32 add_text_layer, 33 bart_segments, 34 bart_stations, 35 47

tuneR

cran

Package ‘tuneR’ August 14, 2023 Version 1.4.5 Date 2023-08-14 Title Analysis of Music and Speech Depends R (>= 3.0.0) Encoding UTF-8 Suggests pastecs Imports signal, methods Description Analyze music and speech, extract features like MFCCs, handle wave files and their rep- resentation in various ways, read mp3, read midi, perform steps of a transcription, ... Also contains functions ported from the 'rastamat' 'Matlab' package. License GPL-2 | GPL-3 NeedsCompilation yes Author Uwe Ligges [aut, cre, cph] (<https://orcid.org/0000-0001-5875-6167>), Sebastian Krey [aut, cph], Olaf Mersmann [aut, cph], Sarah Schnackenberg [aut, cph], Guillaume Guénard [aut, cph] (for the 'pulse' functionality), Daniel P. W. Ellis [aut, cph] (functions ported from 'rastamat'), Underbit Technologies [aut, cph] (for the included 'libmad MPEG audio decoder library'), Andrea Preusser [ctb], Anita Thieler [ctb], Johanna Mielke [ctb], Claus Weihs [ctb], Brian D. Ripley [ctb], Matthias Heymann [ctb] (for ideas from the former 'sound' package) Maintainer Uwe Ligges <ligges@statistik.tu-dortmund.de> Repository CRAN Date/Publication 2023-08-14 15:50:02 UTC 1 2 R topics documented: R topics documented: Arith-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 audspec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 bind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 channel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 deltas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 dolpc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 downsample . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 equalWave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 extractWave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 FF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 freqconv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 getMidiNotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 length . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 lifter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 lilyinput . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 lpc2cep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 MCnames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 melfcc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 melodyplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Mono-Stereo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 nchannel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 normalize-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 noSilence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 noteFromFF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 notenames . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 panorama . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 periodogram-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 play-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 plot-Wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 plot-Wspec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 plot-WspecMat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 postaud . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 powspec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 prepComb . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 quantize . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 quantplot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 readMidi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 readMP3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 readWave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 show-WaveWspec-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 smoother . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 spec2cep . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 summary-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 tuneR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 updateWave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 Wave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 Arith-methods 3 Wave-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 Waveforms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 WaveMC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 WaveMC-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 WavPlayer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 writeWave . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Wspec-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 WspecMat-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 [-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61 Index 62 Arith-methods Arithmetics on Waves Description Methods for arithmetics on Wave and WaveMC objects Methods object = "Wave" An object of class Wave. object = "WaveMC" An object of class WaveMC. object = "numeric" For, e.g., adding a number to the whole Wave, e.g. useful for demeaning. object = "missing" For unary Wave operations. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also For the S3 generic: groupGeneric, Wave-class, Wave, WaveMC-class, WaveMC audspec Frequency band conversion Description Perform critical band analysis (see PLP), which means the reduction of the fourier frequencies of a signal’s powerspectrum to a reduced number of frequency bands in an auditory frequency scale. Usage audspec(pspectrum, sr = 16000, nfilts = ceiling(hz2bark(sr/2)) + 1, fbtype = c("bark", "mel", "htkmel", "fcmel"), minfreq = 0, maxfreq = sr/2, sumpower = TRUE, bwidth = 1) 4 audspec Arguments pspectrum Output of powspec, matrix with the powerspectrum of each time frame in its columns. sr Sample rate of the original recording. nfilts Number of filters/frequency bins in the auditory frequency scale. fbtype Used auditory frequency scale. minfreq Lowest frequency. maxfreq Highest frequency. sumpower If sumpower = TRUE, the frequency scale transformation is based on the power- spectrum, if sumpower = FALSE, it is based on its squareroot (absolute value of the spectrum) and squared afterwards. bwidth Modify the width of the frequency bands. Value aspectrum Matrix with the auditory spectrum of each time frame in its columns. wts Weight matrix for the frequency band conversion. Author(s) Sebastian Krey <krey@statistik.tu-dortmund.de> References Daniel P. W. Ellis: https://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/ See Also fft2melmx, fft2barkmx Examples testsound <- normalize(sine(400) + sine(1000) + square(250), "16") pspectrum <- powspec(testsound@left, testsound@samp.rate) aspectrum <- audspec(pspectrum, testsound@samp.rate) bind 5 bind Concatenating Wave objects Description Generic function for concatenating objects of class Wave or WaveMC. Usage bind(object, ...) ## S4 method for signature 'Wave' bind(object, ...) ## S4 method for signature 'WaveMC' bind(object, ...) Arguments object, ... Objects of class Wave or class WaveMC, each of the same class and of the same kind (checked by equalWave), i.e. identical sampling rate, resolution (bit), and number of channels (for WaveMC, resp. stereo/mono for Wave). Value An object of class Wave or class WaveMC that corresponds to the class of the input. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de>, Sarah Schnackenberg See Also prepComb for preparing the concatenation, Wave-class, Wave, WaveMC-class, WaveMC, extractWave, stereo channel Channel conversion for Wave objects Description Convenient wrapper to extract one or more channels (or mirror channels) from an object of class Wave. Usage channel(object, which = c("both", "left", "right", "mirror")) 6 deltas Arguments object Object of class Wave. which Character indicating which channel(s) should be returned. Details For objects of WaveMC-class, channel selection can be performed by simple matrix indexing, e.g. WaveMCobject[,2] selects the second channel. Value Wave object including channels specified by which. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also Wave, Wave-class, mono, extractWave deltas Calculate delta features Description Calculate the deltas (derivatives) of a sequence of features using a w-point window with a simple linear slope. Usage deltas(x, w = 9) Arguments x Matrix of features. Every column represents one time frame. Each row is filtered separately. w Window width (usually odd). Details This function mirrors the delta calculation performed in HTKs ‘feacalc’. Value Returns a matrix of the delta features (one column per frame). dolpc 7 Author(s) Sebastian Krey <krey@statistik.tu-dortmund.de> References Daniel P. W. Ellis: https://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/ Examples testsound <- normalize(sine(400) + sine(1000) + square(250), "16") m <- melfcc(testsound, frames_in_rows=FALSE) d <- deltas(m) dolpc (Perceptive) Linear Prediction Description Compute autoregressive model from spectral magnitude samples via Levinson-Durbin recursion. Usage dolpc(x, modelorder = 8) Arguments x Matrix of spectral magnitude samples (each sample/time frame in one column). modelorder Lag of the AR model. Value Returns a matrix of the normalized AR coefficients (depending on the input spectrum: LPC or PLP coefficients). Every column represents one time frame. Author(s) Sebastian Krey <krey@statistik.tu-dortmund.de> References Daniel P. W. Ellis: https://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/ See Also levinson 8 downsample Examples testsound <- normalize(sine(400) + sine(1000) + square(250), "16") pspectrum <- powspec(testsound@left, testsound@samp.rate) aspectrum <- audspec(pspectrum, testsound@samp.rate)$aspectrum lpcas <- dolpc(aspectrum, 10) downsample Downsampling a Wave or WaveMC object Description Downsampling an object of class Wave or class WaveMC. Usage downsample(object, samp.rate) Arguments object Object of class Wave or class WaveMC. samp.rate Sampling rate the object is to be downsampled to. samp.rate must be in [2000, 192000]; typical values are 11025, 22050, and 44100 for CD quality. If the object’s sampling rate is already equal or smaller than samp.rate, the object will be returned unchanged. Value An object of class Wave or class WaveMC. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also Wave-class, Wave, WaveMC-class, WaveMC equalWave 9 equalWave Checking Wave objects Description Internal S4 generic function that checks for some kind of equality of objects of class Wave or class WaveMC. Usage equalWave(object1, object2) Arguments object1, object2 Object(s) of class Wave or class WaveMC (both of the same class). Value Does not return anything. It stops code execution with an error message indicating the problem if the objects are not of the same class (either Wave oder WaveMC) or if the two objects don’t have the same properties, i.e. identical sampling rate, resolution (bit), and number of channels (for WaveMC, resp. stereo/mono for Wave). Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de>, Sarah Schnackenberg See Also Wave-class, Wave, WaveMC-class, WaveMC extractWave Extractor for Wave and WaveMC objects Description Extractor function that allows to extract inner parts for Wave or WaveMC objects (interactively). Usage extractWave(object, from = 1, to = length(object), interact = interactive(), xunit = c("samples", "time"), ...) 10 extractWave Arguments object Object of class Wave or class WaveMC. from Sample number or time in seconds (see xunit) at which to start extraction. to Sample number or time in seconds (see xunit) at which to stop extraction. If to < from, object will be returned as is. interact Logical indicating whether to choose the range to be extracted interactively (if TRUE). See Section Details. xunit Character indicating which units are used to specify the range to be extracted (both in arguments from and to, and in the plot, if interact = TRUE). If xunit = "time", the unit is time in seconds, otherwise the number of samples. ... Parameters to be passed to the underlying plot function (plot-methods) if interact = TRUE. Details This function allows interactive selection of a range to be extracted from an object of class Wave or class WaveMC. The default is to use interactive selection if the current R session is interactive. In case of interactive selection, plot-methods plot the Wave or WaveMC object, and the user may click on the starting and ending points of his selection (given neither from nor to have been specified, see below). The cut-points are drawn and the corresponding selection will be returned in form of a Wave or WaveMC object. Setting interact = TRUE in a non-interactive session does not work. Setting arguments from or to explicitly means that the specified one does not need to be selected interactively, hence only the non-specified one will be selected interactively. Moreover, setting both from or to implies interact = FALSE. Value An object of class Wave or class WaveMC. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de>, Sarah Schnackenberg See Also Wave-class, Wave, WaveMC-class, WaveMC, bind, channel, mono Examples Wobj <- sine(440) # extracting the middle 0.5 seconds of that 1 sec. sound: Wobj2 <- extractWave(Wobj, from = 0.25, to = 0.75, xunit = "time") Wobj2 ## Not run: # or interactively: FF 11 Wobj2 <- extractWave(Wobj) ## End(Not run) FF Estimation of Fundamental Frequencies from a Wspec object Description Estimation of Fundamental Frequencies from an object of class Wspec. Additionally, some heuris- tics are used to distinguish silence, noise (and breathing for singers) from real tones. Usage FF(object, peakheight = 0.01, silence = 0.2, minpeak = 9, diapason = 440, notes = NULL, interest.frqs = seq(along = object@freq), search.par = c(0.8, 10, 1.3, 1.7)) FFpure(object, peakheight = 0.01, diapason = 440, notes = NULL, interest.frqs = seq(along = object@freq), search.par = c(0.8, 10, 1.3, 1.7)) Arguments object An object of class Wspec. peakheight The peak’s proportion of the maximal peak height to be considered for funda- mental frequency detection. The default (0.01) means peaks smaller than 0.02 times the maximal peak height are omitted. silence The maximum proportion of periodograms to be considered as silence or noise (such as breathing). The default (0.2) means that less than 20 out of 100 peri- odograms represent silence or noise. minpeak If more than minpeak peaks are considered for detection and passed argument peakheight, such periodograms are detected to be silence or noise (if silence > 0). diapason Frequency of diapason a, default is 440 (Hertz). notes Optional, a vector of integers indicating the notes (in halftones from diapason a) that are expected. By applying this restriction, the “detection error” might be reduced in some cases. interest.frqs Optional, either a vector of integers indicating the indices of (fundamental) fre- quencies in object that are expected, or one of the character strings "bass", "tenor", "alto" or "soprano". For these voice types, only typical frequency ranges are considered for detection. By applying this restriction, the “detection error” might be reduced in some cases. search.par Parameters to look for peaks: 12 freqconv 1. The first peak larger than peakheight * 'largest_peak' is taken. 2. Its frequency is multiplied by 1+search.par[1] Now, any larger peak be- tween the old peak and that value is taken, if (a) it exists and if (b) it is above the search.par[2]-th Fourier-Frequency. 3. Within the interval of frequencies 'current peak' * search.par[3:4], another high peak is looked for. If any high peak exists in that interval, it can be assumed we got the wrong partial and the ‘real’ fundamental frequency can be re-estimated from the next two partials. Details FFpure just estimates the fundamental frequencies for all periodograms contained in the object (of class Wspec). FF additionally uses some heuristics to distinguish silence, noise (and breathing for singers) from real tones. It is recommended to use the wrapper function FF rather than FFpure. If silence detecion can be omitted by specifying silence = 0. Value Vector of estimated fundamental frequencies (in Hertz) for each periodogram conatined in object. Note These functions are still in development and may be changed in due course. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also Wspec, periodogram (including an example), noteFromFF, and tuneR for a very complete example. freqconv Frequency scale conversion Description Perform frequency scale conversions between Hertz, Bark- and different variants von the Melscale. Usage bark2hz(z) hz2bark(f) hz2mel(f, htk = FALSE) mel2hz(z, htk = FALSE) getMidiNotes 13 Arguments f Frequency in Hertz z Frequency in the auditory frequency scale htk Use the HTK-Melscale (htk = TRUE) or Slaney’s Melscale from the Auditory Toolbox (htk = FALSE) Value The value of the input in the target frequency scale. Author(s) Sebastian Krey <krey@statistik.tu-dortmund.de> References Daniel P. W. Ellis: https://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/, Mal- colm Slaney: Auditory Toolbox Examples hz2bark(440) bark2hz(hz2bark(440)) hz2mel(440, htk = TRUE) mel2hz(hz2mel(440, htk = TRUE), htk = TRUE) hz2mel(440, htk = FALSE) mel2hz(hz2mel(440, htk = FALSE), htk = FALSE) getMidiNotes Extract note events from objects returned by readMidi Description Extract only note events from an object returned by the readMidi function. Usage getMidiNotes(x, ...) Arguments x A data.frame returned by the readMidi function. ... Further arguments are passed to the notenames function for extracting the hu- man readable note names rather than their integer representations. 14 length Value A data frame with columns time start time length length track track number channel channel number note note notename notename velocity note velocity Author(s) Uwe Ligges and Johanna Mielke See Also readMidi Examples content <- readMidi(system.file("example_files", "Bass_sample.mid", package="tuneR")) getMidiNotes(content) length S4 generic for length Description S4 generic for length. Methods x = "Wave" The length of the left channel (in samples) of this object of class Wave will be returned. x = "WaveMC" The length for each of the time series in the WaveMC will be returned. object = "ANY" For compatibility. See Also For the primitive: length lifter 15 lifter Liftering of cepstra Description Apply liftering to a matrix of cepstra. Usage lifter(x, lift = 0.6, inv = FALSE, htk = FALSE) Arguments x Matrix of cepstra, one sample/time frame per column. lift Liftering exponent/length. inv Invert the liftering (undo a previous liftering). htk Switch liftering type. Details If htk = FALSE, then perform xil if t, i = 1, . . . , nrow(x) liftering. If htk = TRUE, then perform HTK-style sin-curve liftering with length lift. Value Matrix of the liftered cepstra. Author(s) Sebastian Krey <krey@statistik.tu-dortmund.de> References Daniel P. W. Ellis: https://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/ Examples testsound <- normalize(sine(400) + sine(1000) + square(250), "16") m <- melfcc(testsound, frames_in_rows=FALSE) unlm <- lifter(m, inv=TRUE) 16 lilyinput lilyinput Providing LilyPond compatible input Description A function (in development!) that writes a file to be processed by LilyPond by extracting the relevant information (e.g. pitch, length, ...) from columns of a data frame. The music notation software LilyPond can “transcribe” such an input file into sheet music. Usage lilyinput(X, file = "Rsong.ly", Major = TRUE, key = "c", clef = c("treble", "bass", "alto", "tenor"), time = "4/4", endbar = TRUE, midi = TRUE, tempo = "2 = 60", textheight = 220, linewidth = 150, indent = 0, fontsize = 14) Arguments X A data frame containing 4 named components (columns): • note: Integer - the notes’ pitch in halftones from diapason (a), i.e. 0 for diapason a, 3 for c’, ... • duration: Integer - denominator of lengths of the notes, e.g. 8 for a quaver. • punctate: Logical - whether to punctate a note. • slur: Logical - TRUE indicates to start a slur, or to end it. That means that the first, third, ... occurences of TRUE start slurps, while the second, fourth, ... occurences end slurps. Note that it is only possible to draw one slur at a time. file The file to be written for LilyPond’s input. Major Logical indicating major key (if TRUE) or minor key. key Keynote, necessary to set sharps/flats. clef Integer indicating the kind of clef, supported are "treble" (default), "bass", "alto", and "tenor". time Character indicating which meter to use, examples are: "3/4", "4/4". endbar Logical indicating whether to set an ending bar at the end of the sheet music. midi Logical indicating whether Midi output (by LilyPond) is desirable. tempo Character specifying the tempo to be used for the Midi file if midi = TRUE. The default, "2 = 60" indicates: 60 half notes per minute, whereas "4 = 90" indicates 90 quarters per minute. textheight Textheight of the sheet music to be written by LilyPond. linewidth Linewidth of the sheet music to be written by LilyPond. indent Indentation of the sheet music to be written by LilyPond. fontsize Fontsize of the sheet music to be written by LilyPond. lpc2cep 17 Details Details will be given when development has reached a stable stage ...! Value Nothing is returned, but a file is written. Note This function is in development!!! Everything (and in particular its user interface) is subject to change!!! Author(s) Andrea Preußer and Uwe Ligges <ligges@statistik.tu-dortmund.de> References The LilyPond development team (2005): LilyPond - The music typesetter. https://lilypond. org/, Version 2.7.20. Preußer, A., Ligges, U. und Weihs, C. (2002): Ein R Exportfilter für das Notations- und Midi- Programm LilyPond. Arbeitsbericht 35. Fachbereich Statistik, Universität Dortmund. (german) See Also quantMerge prepares the data to be written into the LilyPond format; quantize and quantplot generate another kind of plot; and exhaustive example is given in tuneR. lpc2cep LPC to cepstra conversion Description Convert the LPC coefficients in each column of a into frames of cepstra. Usage lpc2cep(a, nout = nrow(a)) Arguments a Matrix of LPC coefficients. nout Number of cepstra to produce. Value Matrix of cepstra (one column per time frame). 18 MCnames Author(s) Sebastian Krey <krey@statistik.tu-dortmund.de> References Daniel P. W. Ellis: https://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/ See Also spec2cep Examples testsound <- normalize(sine(400) + sine(1000) + square(250), "16") pspectrum <- powspec(testsound@left, testsound@samp.rate) aspectrum <- audspec(pspectrum, testsound@samp.rate) lpcas <- dolpc(aspectrum$aspectrum, 8) cepstra <- lpc2cep(lpcas) MCnames Default channel ordering for multi channel wave files Description A data frame representing the default channel ordering with id, descriptive label, and abbreviated name for multi channel wave files. Format A data frame with 18 observations on the following 3 variables: id id of the channel label full label for the channel name abbreviated name for the channel Source Data derived from the technical documentation given at https://docs.microsoft.com/en-us/ windows-hardware/drivers/ddi/content/ksmedia/ns-ksmedia-waveformatextensible. References Microsoft Corporation (2018): WAVEFORMATEXTENSIBLE structure, https://docs.microsoft. com/en-us/windows-hardware/drivers/ddi/content/ksmedia/ns-ksmedia-waveformatextensible. Examples MCnames # the 18 predefined channels in a multi channel Wave file (WaveMC object) melfcc 19 melfcc MFCC Calculation Description Calculate Mel-frequency cepstral coefficients. Usage melfcc(samples, sr = samples@samp.rate, wintime = 0.025, hoptime = 0.01, numcep = 12, lifterexp = 0.6, htklifter = FALSE, sumpower = TRUE, preemph = 0.97, dither = FALSE, minfreq = 0, maxfreq = sr/2, nbands = 40, bwidth = 1, dcttype = c("t2", "t1", "t3", "t4"), fbtype = c("mel", "htkmel", "fcmel", "bark"), usecmp = FALSE, modelorder = NULL, spec_out = FALSE, frames_in_rows = TRUE) Arguments samples Object of Wave-class or WaveMC-class. Only the first channel will be used. sr Sampling rate of the signal. wintime Window length in sec. hoptime Step between successive windows in sec. numcep Number of cepstra to return. lifterexp Exponent for liftering; 0 = none. htklifter Use HTK sin lifter. sumpower If sumpower = TRUE the frequency scale transformation is based on the power- spectrum, if sumpower = FALSE it is based on its squareroot (absolute value of the spectrum) and squared afterwards. preemph Apply pre-emphasis filter [1 -preemph] (0 = none). dither Add offset to spectrum as if dither noise. minfreq Lowest band edge of mel filters (Hz). maxfreq Highest band edge of mel filters (Hz). nbands Number of warped spectral bands to use. bwidth Width of spectral bands in Bark/Mel. dcttype Type of DCT used - 1 or 2 (or 3 for HTK or 4 for feacalc). fbtype Auditory frequency scale to use: "mel", "bark", "htkmel", "fcmel". usecmp Apply equal-loudness weighting and cube-root compression (PLP instead of LPC). modelorder If modelorder > 0, fit a linear prediction (autoregressive-) model of this order and calculation of cepstra out of lpcas. spec_out Should matrices of the power- and the auditory-spectrum be returned. frames_in_rows Return time frames in rows instead of columns (original Matlab code). 20 melfcc Details Calculation of the MFCCs imlcudes the following steps: 1. Preemphasis filtering 2. Take the absolute value of the STFT (usage of Hamming window) 3. Warp to auditory frequency scale (Mel/Bark) 4. Take the DCT of the log-auditory-spectrum 5. Return the first ‘ncep’ components Value cepstra Cepstral coefficients of the input signal (one time frame per row/column) aspectrum Auditory spectrum (spectrum after transformation to Mel/Bark scale) of the sig- nal pspectrum Power spectrum of the input signal. lpcas If modelorder > 0, the linear prediction coefficients (LPC/PLP). Note The following non-default values nearly duplicate Malcolm Slaney’s mfcc (i.e. melfcc(d, 16000, wintime=0.016, lifterexp=0, minfreq=133.33, maxfreq=6855.6, sumpower=FALSE) =~= log(10) * 2 * mfcc(d, 16000) in the Auditory toolbox for Matlab). The following non-default values nearly duplicate HTK’s MFCC (i.e. melfcc(d, 16000, lifterexp=22, htklifter=TRUE, nbands=20, maxfreq=8000, sumpower=FALSE, fbtype="htkmel", dcttype="t3") =~= 2 * htkmelfcc(:,[13,[1:12]]) where HTK config has ‘PREEMCOEF = 0.97’, ‘NUM- CHANS = 20’, ‘CEPLIFTER = 22’, ‘NUMCEPS = 12’, ‘WINDOWSIZE = 250000.0’, ‘USE- HAMMING = T’, ‘TARGETKIND = MFCC_0’). For more detail on reproducing other programs’ outputs, see https://www.ee.columbia.edu/ ~dpwe/resources/matlab/rastamat/mfccs.html Author(s) Sebastian Krey <krey@statistik.tu-dortmund.de> References Daniel P. W. Ellis: https://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/ melodyplot 21 Examples testsound <- normalize(sine(400) + sine(1000) + square(250), "16") m1 <- melfcc(testsound) #Use PLP features to calculate cepstra and output the matrices like the #original Matlab code (note: modelorder limits the number of cepstra) m2 <- melfcc(testsound, numcep=9, usecmp=TRUE, modelorder=8, spec_out=TRUE, frames_in_rows=FALSE) melodyplot Plotting a melody Description Plot a observed melody and (optional) an expected melody, as well as corresponding energy values (corresponding to the loudness of the sound). Usage melodyplot(object, observed, expected = NULL, bars = NULL, main = NULL, xlab = NULL, ylab = "note", xlim = NULL, ylim = NULL, observedtype = "l", observedcol = "red", expectedcol = "grey", gridcol = "grey", lwd = 2, las = 1, cex.axis = 0.9, mar = c(5, 4, 4, 4) + 0.1, notenames = NULL, thin = 1, silence = "silence", plotenergy = TRUE, ..., axispar = list(ax1 = list(side=1), ax2 = list(side=2), ax4 = list(side=4)), boxpar = list(), energylabel = list(text="energy", side=4, line=2.5, at=rg.s-0.25, las=3), energypar = list(), expectedpar = list(), gridpar = list(col=gridcol), observedpar = list(col=observedcol, type=observedtype, lwd=2, pch=15)) Arguments object An object of class Wspec. observed Observed notes, probably as a result from noteFromFF (or a smoothed ver- sion). This should correspond to the Wspec object. It can also be a matrix of k columns where those k notes in the same row are displayed at the same timepoint. expected Expected notes (optional; in order to compare results), same format as observed. bars Number of bars to be plotted (a virtual static segmentation takes place). If NULL (default), time rather than bars are used. main Main title of the plot. 22 melodyplot xlab, ylab Annotation of -/y-axes. xlim, ylim Range of x-/y-axis, where ylim must be an integer that represents the range of note heights that should be displayed. observedtype Type (either "p" for points or "l" for lines) used for representing observed notes. "l" (the default) is not sensible for polyphonic representations. observedcol Colour for the observed melody. expectedcol Colour for the expected melody. gridcol Colour of the grid. lwd Line width, see par for details. las Orientation of axis labels, see par for details. cex.axis Size of tick mark labels, see par for details. mar Margins of the plot, see par for details. notenames Optionally specify other notenames (character) for the y axis. thin Amount of thinning of notenames, i.e. only each thinth notename is displayed on the y-axis. silence Character string for label of the ‘silence’ (default) axis. plotenergy Logical (default: TRUE), whether to plot energy values in the bottom part of the plot. ... Additional graphical parameters to be passed to underlying plot function. axispar A named list of three other lists (ax1, ax2, and ax4) containing parameters passed to the corresponding axis calls for the three axis time (ax1), notes (ax2), and energy (ax4). boxpar A list of parameters to be passed to the box generating functions. energylabel A list of parameters to be passed to the energy-label generating mtext call. energypar A list of parameters to be passed to the lines function that draws the energy curve. expectedpar A list of parameters to be passed to the rect function that draws the rectangles for expected values. gridpar A list of parameters to be passed to the abline function that draws the grid lines. observedpar A list of parameters to be passed to the lines function that draws the observed values. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also noteFromFF, FF, quantplot; for an example, see the help in tuneR. Mono-Stereo 23 Mono-Stereo Converting (extracting, joining) stereo to mono and vice versa Description Functions to extract a channel from a stereo Wave object, and to join channels of two monophonic Wave objects to a stereophonic one. Usage mono(object, which = c("left", "right", "both")) stereo(left, right) Arguments object Object of class Wave. which Character, indicating whether the “left” or “right” channel should be extracted, or whether “both” channels should be averaged. left Object of class Wave containing monophonic sound, to be used for the left chan- nel. right Object of class Wave containing monophonic sound, to be used for the right chan- nel (if missing, the left channel is duplicated). If right is missing, stereo returns whether left is stereo (TRUE) or mono (FALSE). Details For objects of WaveMC-class, a mono channel can be created by simple matrix indexing, e.g. WaveMCobject[,2] selects the second channel. Value An object of class Wave. If argument right is missing in stereo, a logical values is returned that indicates whether left is stereo (TRUE) or mono (FALSE). Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also Wave-class, Wave 24 normalize-methods Examples Wobj <- sine(440) Wobj Wobj2 <- stereo(Wobj, Wobj) Wobj2 mono(Wobj2, "right") nchannel Number of channels Description Get the number of channels from a Wave or WaveMC object Usage nchannel(object) ## S4 method for signature 'Wave' nchannel(object) ## S4 method for signature 'WaveMC' nchannel(object) Arguments object Object of class Wave or class WaveMC. Value An integer, the number of channels given in the object. See Also Wave-class, WaveMC-class normalize-methods Rescale the range of values Description Centering and rescaling the waveform of a Wave or WaveMC object to a canonical interval corre- sponding to the Wave format (e.g. [-1, 1], [0, 254], [-32767, 32767], [-8388607, 8388607], or [-2147483647, 2147483647]). Usage normalize(object, unit = c("1", "8", "16", "24", "32", "64", "0"), center = TRUE, level = 1, rescale = TRUE, pcm = object@pcm) noSilence 25 Arguments object Object of class Wave or WaveMC. unit Unit to rescale to. "1" (default) for rescaling to numeric values in [-1, 1], "8" (i.e. 8-bit) for rescaling to integers in [0, 254], "16" (i.e. 16-bit) for rescaling to integers in [-32767, 32767], "24" (i.e. 24-bit) for rescaling to integers in [-8388607, 8388607], "32" (i.e. 32-bit) for rescaling either to integers in [-2147483647, 2147483647] (PCM Wave format if pcm=TRUE) or to numeric values in [-1, 1] (FLOAT_IEEE Wave format if pcm = FALSE), "64" (i.e. 64-bit) for rescaling to real values in [-1, 1] (FLOAT_IEEE Wave format), and "0" for not rescaling (hence only centering if center = TRUE). center If TRUE (default), values are centered around 0 (or 127 if unit = "8"). level Maximal percentage of the amplitude used for normalizing (default is 1). rescale Logical, whether to rescale to the maximal possible dynamic range. pcm Logical. By default, the pcm information from the object is kept. Otherwise, if TRUE, the object is coerced to the PCM Wave format. If FALSE, the object is coerced to the FLOAT_IEEE format, i.e. numeric values in [-1, 1]. Value An object containing the normalized data of the same class as the input object, i.e. either Wave or WaveMC. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de>, Sarah Schnackenberg, based on code from Matthias Heymann’s former package ‘sound’. See Also writeWave, Wave-class, Wave, WaveMC-class, WaveMC noSilence Cut off silence from a Wave or WaveMC object Description Generic function to cut off silence or low noise at the beginning and/or at the end of an object of class Wave or class WaveMC. Usage noSilence(object, zero = 0, level = 0, where = c("both", "start", "end")) 26 noteFromFF Arguments object Object of class Wave or class WaveMC. zero The zero level (default: 0) at which ideal cut points are determined (see Details). A typical alternative would be 127 for 8 bit Wave or WaveMC objects. If zero = NA, the mean of the left Wave channel (for Wave, resp. the mean of the first channel for WaveMC) is taken as zero level. level Values in the interval between zero and zero - level/zero + level are con- sidered as silence. where One of "both" (default), "start", or "end" indicating at where to prepare the Wave or WaveMC object for concatenation. Details Silcence is removed at the locations given by where of the Wave or WaveMC object, where silence is defined such that (in both channels if stereo, in all channels if multichannel for WaveMC) all values are in the interval between zero - level and zero + level. All values before (or after, respectively) the first non-silent value are removed from the object. Value An object of class Wave or WaveMC. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de>, Sarah Schnackenberg, based on code from Matthias Heymann’s former package ‘sound’. See Also silence, Wave-class, Wave, WaveMC-class, WaveMC, extractWave noteFromFF Deriving notes from frequencies Description Deriving notes from given (fundamental) frequencies. Usage noteFromFF(x, diapason = 440, roundshift = 0) notenames 27 Arguments x Fundamental frequency. diapason Frequency of diapason a, default is 440 (Hertz). roundshift Shift that indicates from here to round to the next integer (note). The default (0) is “classical” rounding as described in round. A higher value means that roundshift is added to the calculated real note value before rounding to an integer. This is useful if it is unclear that some instruments really shift the note in the center between two theoretical frequencies. Example: if x = 452 and diapason = 440, the internally calculated real value of 0.46583 is rounded to 0, but for roundshift = 0.1 we get 0.56583 and it is rounded to note 1. Details The formula used is simply round(12 * log(x / diapason, 2) + roundshift). Value An integer representing the (rounded) difference in halftones from diapason a, i.e. indicating the note that corresponds to fundamental frequency x given the value of diapason. For example: 0 indicates diapason a, 3: c’, 12: a’, . . . Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also FF, periodogram, and tuneR for a very complete example. notenames Generating note names from numbers Description A function that generates note names from numbers Usage notenames(notes, language = c("english", "german")) Arguments notes An interger values vector, where 0 corresponds to a’, notes below and above have to be specified in the corresponding halftone distance. language Language of the note names. Currently only english and german are supported. 28 panorama Value A character vector of note names. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> Examples notenames(c(-24, -12, 0, 12)) # octaves of a notenames(3:15) # chromaticism ## same in german: notenames(3:15, language = "german") panorama Narrow the Panorama of a Stereo Sample Description Generic function to narrow the panorama of a stereo Wave or WaveMC object. Usage panorama(object, pan = 1) Arguments object Object of class Wave or class WaveMC. pan Value in [-1,1] to narrow the panorama, see the Details below. The default (1) does not change anything. Details If abs(pan) < 1, mixtures of the two channels of the Wave or WaveMC objects are used for the left and the right channel of the returned Sample object if the object is of class Wave, resp. for the first and second channel of the returned Sample object if the object is of class WaveMC, so that they appear closer to the center. For pan = 0, both sounds are completely in the center (i.e. averaged). If pan < 0, the left and the right channel (for Wave objects, the first and the second channel for WaveMC objects) are interchanged. Value An object of class Wave or class WaveMC with the transformed panorama. periodogram-methods 29 Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de>, Sarah Schnackenberg, based on code by Matthias Heymann See Also Wave-class, Wave, WaveMC-class, WaveMC periodogram-methods Periodogram (Spectral Density) Estimation on Wave objects Description This function estimates one or more periodograms (spectral densities) of the time series contained in an object of class Wave or WaveMC (or directly in a Wave file) using a window running through the time series (possibly with overlapping). It returns an object of class Wspec. Usage periodogram(object, ...) ## S4 method for signature 'WaveGeneral' periodogram(object, width = length(object), overlap = 0, starts = NULL, ends = NULL, taper = 0, normalize = TRUE, frqRange = c(-Inf, Inf), ...) ## S4 method for signature 'character' periodogram(object, width, overlap = 0, from = 1, to = Inf, units = c("samples", "seconds", "minutes", "hours"), downsample = NA, channel = c("left", "right"), pieces = 1, ...) Arguments object An object of class Wave, WaveMC, or a character string pointing to a Wave file. width A window of width ‘width’ running through the time series selects the samples from which the periodograms are to be calculated. overlap The window can be applied by each overlapping overlap samples. starts Start number (in samples) for a window. If not given, this value is derived from argument ends, or will be derived width and overlap. ends End number (in samples) for a window. If not given, this value is derived from argument starts, or will be derived from width and overlap. taper proportion of data to taper. See spec.pgram for details. normalize Logical; if TRUE (default), two steps will be applied: (i) the input signal will be normalized to amplitude max(abs(amplitude)) == 1, (ii) the resulting spec values will be normalized to sum up to one for each periodogram. 30 periodogram-methods frqRange Numeric vector of two elements indicating minimum and maximum of the fre- quency range that is to be stored in the resulting object. This is useful to reduce memory consumption. from Where to start reading in the Wave file, in units. to Where to stop reading in the Wave file, in units. units Units in which from and to is given, the default is “samples”, but can be set to time intervals such as “seconds”, see the Usage Section above. downsample Sampling rate the object is to be downsampled to. If NA, the default, no changes are applied. Otherwise downsample must be in [2000, 192000]; typical values are 11025, 22050, and 44100 for CD quality. See also downsample. channel Character, indicating whether the “left” or “right” channel should be extracted (see mono for details) - stereo processing is not yet implemented. pieces The Wave file will be read in in pieces steps in order to reduce the amount of required memory. ... Further arguments to be passed to the underlying function spec.pgram. Value An object of class Wspec is returned containing the following slots. freq Vector of frequencies at which the spectral density is estimated. See spectrum for details. (1) spec List of vectors or matrices of the spec values returned by spec.pgram at fre- quencies corresponding to freq. Each element of the list corresponds to one periodogram estimated from samples of the window beginning at start of the Wave or WaveMC object. kernel The kernel argument, or the kernel constructed from spans returned by spec.pgram. (1) df The distribution of the spectral density estimate can be approximated by a chi square distribution with df degrees of freedom. (1) taper The value of the taper argument. (1) width The value of the width argument. (1) overlap The value of the overlap argument. (1) normalize The value of the normalize argument. (1) starts If the argument starts was given in the call, its value. If the argument ends was given in the call, ‘ends - width’. If neither starts nor ends was given, the start points of all periodograms. In the latter case the start points are calculated from the arguments width and overlap. stereo Always FALSE (for back compatibility). (1) samp.rate Sampling rate of the underlying Wave or WaveMC object. (1) variance The variance of samples in each window, corresponding to amplitude / loudness of sound. periodogram-methods 31 energy The “energy” E, also an indicator for the amplitude / loudness of sound: X E(xI ) := 20 ∗ log10 |xj |, j∈I where I indicates the interval I := start[i]:end[i] for all i := 1, . . . , length(starts). Those slots marked with “(1)” contain the information once, because it is unique for all peri- odograms of estimated by the function call. Note Support for processing more than one channel of Wave or WaveMC objects has not yet been imple- mented. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also • for the resulting objects’ class: Wspec, • for plotting: plot-Wspec, • for the underlying periodogram calculations: spec.pgram, • for the input data class: Wave-class, Wave, WaveMC-class, WaveMC. Examples # constructing a Wave object (1 sec.) containing sinus sound with 440Hz: Wobj <- sine(440) Wobj # Calculate periodograms in windows of 4096 samples each - without # any overlap - resulting in an Wspec object that is printed: Wspecobj <- periodogram(Wobj, width = 4096) Wspecobj # Plot the first periodogram from Wspecobj: plot(Wspecobj) # Plot the third one and choose a reasonable xlim: plot(Wspecobj, which = 3, xlim = c(0, 1000)) # Mark frequency that has been generated before: abline(v = 440, col="red") # plot the spectrogram image(Wspecobj, ylim=c(0, 2000)) # same again with normalize = FALSE and with logarithmic y-axis plotted: Wspecobj2 <- periodogram(Wobj, width = 4096, normalize = FALSE) Wspecobj2 32 play-methods plot(Wspecobj2, which = 3, xlim = c(0, 1000), log="y") abline(v = 440, col="red") image(Wspecobj2, ylim=c(0, 2000), log="z") FF(Wspecobj) # all ~ 440 Hertz noteFromFF(FF(Wspecobj)) # all diapason a play-methods Playing Waves Description Plays wave files and objects of class Wave. Usage play(object, player, ...) Arguments object Either a filename pointing to a Wave file, or an object of class Wave or WaveMC. If the latter, it is written to a temporary file by writeWave, played by the chosen player, and deleted afterwards. player (Path to) a program capable of playing a wave file by invocation from the com- mand line. If under Windows and no player is given, “mplay32.exe” or “wm- player.exe” (if the former does not exists as under Windows 7) will be chosen as the default. ... Further arguments passed to the Wave file player. If no player and no further arguments are given under Windows, the default is: "/play /close". Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also Wave-class, WaveMC-class, Wave, WaveMC, writeWave, setWavPlayer plot-Wave 33 plot-Wave Plotting Wave objects Description Plotting objects of class Wave. Usage ## S4 method for signature 'Wave,missing' plot(x, info = FALSE, xunit = c("time", "samples"), ylim = NULL, main = NULL, sub = NULL, xlab = NULL, ylab = NULL, simplify = TRUE, nr = 2500, axes = TRUE, yaxt = par("yaxt"), las = 1, center = TRUE, ...) ## S4 method for signature 'WaveMC,missing' plot(x, info = FALSE, xunit = c("time", "samples"), ylim = NULL, main = NULL, sub = NULL, xlab = NULL, ylab = colnames(x), simplify = TRUE, nr = 2500, axes = TRUE, yaxt = par("yaxt"), las = 1, center = TRUE, mfrow = NULL, ...) plot_Wave_channel(x, xunit, ylim, xlab, ylab, main, nr, simplify, axes = TRUE, yaxt = par("yaxt"), las = 1, center = TRUE, ...) Arguments x Object of class Wave or WaveMC, respectively. info Logical, whether to include (written) information on the Wave or WaveMC object within the plot. xunit Character indicating which units are used for setting up user coordinates (see par) and x-axis labeling. If xunit = "time", the unit is time in seconds, other- wise the number of samples. ylim The y (amplitude) limits of the plot. main, sub A title / subtitle for the plot. xlab Label for x-axis. ylab Label for y-axis (on the right side of the plot). For WaveMC objects, this can be the default colnames(x) (i.e. channel names of the WaveMC object), NULL for “channel 1”, . . . , “channel nc” where nc is ncol(x), NA for no labels, or a character vector of labels (one element for each channel). For Wave objects, this can be de default “left channel” (for mono) or “left channel” and “right channel” (for stereo), NA for no labels, or a character vector of labels (one element for each channel). simplify Logical, whether the plot should be “simplified”. If TRUE (default), not all (thou- sand/millions/billions) of points (samples) of the Wave or WaveMC object are 34 plot-Wspec drawn, but the nr (see below) ranges (in form of segments) within nr windows of the time series. Plotting with simplify = FALSE may take several minutes (depending on the number of samples in the Wave or WaveMC) and output in any vector format may be really huge. nr Number of windows (segments) to be used approximately (an appropriate num- ber close to nr is selected) to simplify (see above) the plot. Only used if simplify = TRUE and the number of samples of the Wave or WaveMC object x is larger. axes Whether to plot axes, default is TRUE. yaxt How to plot the y-axis ("n" for no y-axis). las The style of the axis labels, default is las = 1 (always horizontal), see par for details. center Whether to plot with y-axes centered around 0 (or 127 if 8-bit), default is TRUE. mfrow A vector indicating the arrangement of the figures, see par for details. ... Further arguments to be passed to the underlying plot functions. Details Function plot_Wave_channel is a helper function to plot a single channel (left for a Wave object, first channel / first column of data slot of a WaveMC object); in particular it is not intended to be called by the user directly. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de>, Sarah Schnackenberg See Also Wave-class, Wave, WaveMC-class, WaveMC and tuneR plot-Wspec Plotting Wspec objects Description Plotting a periodogram contained in an object of class Wspec. Usage ## S4 method for signature 'Wspec,missing' plot(x, which = 1, type = "h", xlab = "Frequency", ylab = NULL, log = "", ...) plot-WspecMat 35 Arguments x Object of class Wspec. which Integer indicating which of the periodograms contained in object x to plot. De- fault is to plot the first one. type The default is to plot horizontal lines, rather than points. See plot.default for details. xlab, ylab Label for x-/y-axis. log Character - "x" if the x-axis is to be logarithmic, "y" if the y-axis is to be logarithmic (quite typical for some visualizations of periodograms), and "xy" or "yx" if both axes are to be logarithmic. ... Further arguments to be passed to the underlying plot functions. See plot.default for details. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also see Wspec, periodogram and tuneR for the constructor function and some examples. plot-WspecMat Plotting WspecMat objects Description Plotting a spectogram (image) of an object of class Wspec or WspecMat. Usage ## S4 method for signature 'WspecMat,missing' plot(x, xlab = "time", ylab = "Frequency", xunit = c("samples", "time"), log = "", ...) ## S4 method for signature 'Wspec' image(x, xlab = "time", ylab = "Frequency", xunit = c("samples", "time"), log = "", ...) Arguments x Object of class WspecMat (for plot) or Wspec (for image). xlab, ylab Label for x-/y-axis. xunit Character indicating which units are used to annotate the x-axis. If xunit = "time", the unit is time in seconds, otherwise the number of samples. log Character - "z" if the z values are to be logarithmic. ... Further arguments to be passed to the underlying image function. See image for details. 36 postaud Details Calling image on a Wspec object converts it to class WspecMat and calls the corresponding plot function. Calling plot on a WspecMat object generates an image with correct annotated axes. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also see image, Wspec, WspecMat, periodogram and tuneR for the constructor function and some ex- amples. postaud Equal loudness compression Description Do loudness equalization and cube root compression Usage postaud(x, fmax, fbtype = c("bark", "mel", "htkmel", "fcmel"), broaden = FALSE) Arguments x Matrix of spectra (output of audspec). fmax Maximum frequency im Hertz. fbtype Auditory frequency scale. broaden Use two additional frequency bands for calculation. Value x Matrix of the per sample/frame (columns) spectra after applying the frequency dependant loudness equalization and compression. eql Vector of the equal loudness curve. Author(s) Sebastian Krey <krey@statistik.tu-dortmund.de> References Daniel P. W. Ellis https://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/, Hynek Hermansky powspec 37 See Also audspec, dolpc Examples testsound <- normalize(sine(400) + sine(1000) + square(250), "16") pspectrum <- powspec(testsound@left, testsound@samp.rate) aspectrum <- audspec(pspectrum, testsound@samp.rate) paspectrum <- postaud(x = aspectrum$aspectrum, fmax = 5000, fbtype = "mel") powspec Powerspectrum Description Compute the powerspectrum of the input signal. Basically output a power spectrogram using a Hamming window. Usage powspec(x, sr = 8000, wintime = 0.025, steptime = 0.01, dither = FALSE) Arguments x Vector of samples. sr Sampling rate of the signal. wintime Window length in sec. steptime Step between successive windows in sec. dither Add offset to spectrum as if dither noise. Value Matrix, where each column represents a power spectrum for a given frame and each row represents a frequency. Author(s) Sebastian Krey <krey@statistik.tu-dortmund.de> References Daniel P. W. Ellis: https://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/ See Also specgram 38 prepComb Examples testsound <- normalize(sine(400) + sine(1000) + square(250), "16") pspectrum <- powspec(testsound@left, testsound@samp.rate) prepComb Preparing the combination/concatenation of Wave or WaveMC objects Description Preparing objects of class Wave or class WaveMC for binding/combination/concatenation by removing small amounts at the beginning/end of the Wave or WaveMC in order to make the transition smooth by avoiding clicks. Usage prepComb(object, zero = 0, where = c("both", "start", "end")) Arguments object Object of class Wave or class WaveMC. zero The zero level (default: 0) at which ideal cut points are determined (see Details). A typical alternative would be 127 for 8 bit Wave or WaveMC objects. If zero = NA, the mean of the left Wave channel (for a Wave object) or the mean of the first channel (for a WaveMC object) is taken as zero level. where One of "both" (default), "start", or "end" indicating at where to prepare the Wave or WaveMC object for concatenation. Details This function is useful to prepare objects of class Wave or class WaveMC for binding/combination/concatenation. At the side(s) indicated by where small amounts of the Wave or WaveMC are removed in order to make the transition between two Waves or WaveMCs smooth (avoiding clicks). This is done by dropping all values at the beginning of a Wave or WaveMC before the first positive point after the zero level is crossed from negative to positive. Analogously, at the end of a Wave or WaveMC all points are cut after the last negative value before the last zero level crossing from negative to positive. Value An object of class Wave or class WaveMC. Note If stereo (for Wave), only the left channel is analyzed while the right channel will simply be cut at the same locations. If multi channel (for WaveMC), only the first channel is analyzed while all other channels will simply be cut at the same locations. quantize 39 Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de>, Sarah Schnackenberg, based on code from Matthias Heymann’s former package ‘sound’. See Also bind, Wave-class, Wave, WaveMC-class, WaveMC, extractWave, and noSilence to cut off silence Examples Wobj1 <- sine(440, duration = 520) Wobj2 <- extractWave(sine(330, duration = 500), from = 110, to = 500) par(mfrow = c(2,1)) plot(bind(Wobj1, Wobj2), xunit = "samples") abline(v = 520, col = "red") # here is a "click"! # now remove the "click" by deleting a minimal amount of information: Wobj1 <- prepComb(Wobj1, where = "end") Wobj2 <- prepComb(Wobj2, where = "start") plot(bind(Wobj1, Wobj2), xunit = "samples") quantize Functions for the quantization of notes Description These functions apply (static) quantization of notes in order to produce sheet music by pressing the notes into bars. Usage quantize(notes, energy, parts) quantMerge(notes, minlength, barsize, bars) Arguments notes Series of notes, a vector of integers such as returned by noteFromFF. At least one argument (notes and/or energy) must be specified. energy Series of energy values, a vector of numerics such as corresponding components of a Wspec object. parts Number of outcoming parts. The notes vector is divided into parts bins, the outcome is a vector of the modes of all bins. minlength 1/(length of the shortest note). Example: if the shortest note is a quaver (1/8), set minlength = 8. barsize One bar contains barsize number of notes of length minlength. bars We expect bars number of bars. 40 quantplot Value quantize returns a list with components: notes Vector of length parts corresponding to the input data The data is binned and modes corresponding to the data in those bins are returned. energy Same as notes, but for the energy argument. quantMerge returns a data.frame with components: note integer representation of a note (see Arguments). duration 1/duration of a note (see minlength in Section Arguments), if punctuation = FALSE. punctuation Whether the note should be punctuated. If TRUE, the real duration is 1.5 times the duration given in duration. slur currently always FALSE, sensible processing is not yet implemented. It is supposed to indicate the beginning and ending positions of slurs. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also to get the input: noteFromFF, for plotting: quantplot, for further processing: lilyinput, to get notenames: notenames; for an example, see the help in tuneR. quantplot Plotting the quantization of a melody Description Plot an observed melody and (optional) an expected melody, as well as corresponding energy values (corresponding to the loudness of the sound) within a quantization grid. Usage quantplot(observed, energy = NULL, expected = NULL, bars, barseg = round(length(observed) / bars), main = NULL, xlab = NULL, ylab = "note", xlim = NULL, ylim = NULL, observedcol = "red", expectedcol = "grey", gridcol = "grey", lwd = 2, las = 1, cex.axis = 0.9, mar = c(5, 4, 4, 4) + 0.1, notenames = NULL, silence = "silence", plotenergy = TRUE, ..., axispar = list(ax1 = list(side=1), ax2 = list(side=2), ax4 = list(side=4)), boxpar = list(), energylabel = list(text="energy", side=4, line=2.5, at=rg.s-0.25, las=3), energypar = list(pch=20), quantplot 41 expectedpar = list(), gridpar = list(gridbar = list(col = 1), gridinner = list(col=gridcol)), observedpar = list(col=observedcol, pch=15)) Arguments observed Either a vector of observed notes resulting from some quantization, or a list with components notes (observed notes) and energy (corresponding energy values), e.g. the result from a call to quantize. energy A vector of energy values with same quantization as observed (overwrites any given energy values if observed is a list). expected Expected notes (optional; in order to compare results). bars Number of bars to be plotted (e.g. corresponding to quantize arguments). barseg Number of segments (minimal length notes) in each bar. main Main title of the plot. xlab, ylab Annotation of x-/y-axes. xlim, ylim Range of x-/y-axis. observedcol Colour for the observed notes. expectedcol Colour for the expected notes. gridcol Colour of the inner-bar grid. lwd Line width, see par for details. las Orientation of axis labels, see par for details. cex.axis Size of tick mark labels, see par for details. mar Margins of the plot, see par for details. notenames Optionally specify other notenames (character) for the y-axis. silence Character string for label of the ‘silence’ (default) axis. plotenergy Logical indicating whether to plot energy values in the bottom part of the plot (default is TRUE) if energy values are specified, and FALSE otherwise. ... Additional graphical parameters to be passed to underlying plot function. axispar A named list of three other lists (ax1, ax2, and ax4) containing parameters passed to the corresponding axis calls for the three axis time (ax1), notes (ax2), and energy (ax4). boxpar A list of parameters to be passed to the box generating functions. energylabel A list of parameters to be passed to the energy-label generating mtext call. energypar A list of parameters to be passed to the points function that draws the energy values. expectedpar A list of parameters to be passed to the rect function that draws the rectangles for expected values. gridpar A named list of two other lists (gridbar and gridinner) containing parameters passed to the abline functions that draw the grid lines (for bar separators and inner bar (note) separators). observedpar A list of parameters to be passed to the lines function that draws the observed values. 42 readMidi Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also noteFromFF, FF, melodyplot, quantize; for an example, see the help in tuneR. readMidi Read a MIDI file Description A MIDI file is read and returned in form of a structured data frame containing most event infor- mation (minus some meta events and minus all system events). For details about the represented information see the reference given below. Usage readMidi(file) Arguments file Filename of MIDI file. Value A data frame consisting of columns time Time or delta-time of the events, depending on the MIDI format. event A factor indicating the event. type An integer indicating the type of a “meta event”, otherwise NA. channel The channel number or NA if not applicable. parameter1 First parameter of an event, e.g. a representation for a note in a “note event”. parameter2 Second parameter of an event. parameterMetaSystem Information in a “meta event”, currently all meta events are converted to a char- acter representation (of hex, if all fails), but future versions may have more appropriate representations. track The track number. Please see the given reference about the MIDI file format about details. Note The data structure may be changed or extended in future versions. readMP3 43 Author(s) Uwe Ligges and Johanna Mielke References A good reference about the Midi file format can be found at http://www.music.mcgill.ca/~ich/ classes/mumt306/StandardMIDIfileformat.html. See Also The function getMidiNotes extracts a more readable representation of note events only. You may also want to read Wave (readWave) or MP3 (readMP3). Examples content <- readMidi(system.file("example_files", "Bass_sample.mid", package="tuneR")) str(content) content readMP3 Read an MPEG-2 layer 3 file into a Wave object Description A bare bones MPEG-2 layer 3 (MP3) file reader that returns the results as 16bit PCM data stored in a Wave object. Usage readMP3(filename) Arguments filename Filename of MP3 file. Value A Wave object. Note The decoder can currently only handle files which are either mono or stereo. This is a limitation of the Wave object and the underlying MAD decoder. Author(s) Olaf Mersmann <olafm@statistik.tu-dortmund.de> 44 readWave References The decoder source code is taken from the MAD library, see http://www.underbit.com/products/ mad/. See Also Wave Examples ## Not run: ## Requires an mp3 file named sample.mp3 in the current directory. mpt <- readMP3("sample.mp3") summary(mpt) ## End(Not run) readWave Reading Wave files Description Reading Wave files. Usage readWave(filename, from = 1, to = Inf, units = c("samples", "seconds", "minutes", "hours"), header = FALSE, toWaveMC = NULL) Arguments filename Filename of the file to be read. from Where to start reading (in order to save memory by reading wave file piecewise), in units. to Where to stop reading (in order to save memory by reading wave file piecewise), in units. units Units in which from and to is given, the default is "samples", but can be set to time intervals such as "seconds", see the Usage Section above. header If TRUE, just header information of the Wave file are returned, otherwise (the default) the whole Wave object. toWaveMC If TRUE, a WaveMC-class object is returned. If NULL (default) or FALSE and a non-extensible Wave file or an extensible Wave file with no other than the “FL” and “FR” channels is found, a Wave-class object is returned, otherwise a WaveMC-class object. show-WaveWspec-methods 45 Value An object of class Wave or WaveMC or a list containing just the header information if header = TRUE. If the latter, some experimental support for reading bext chunks in Broadcast Wave Format files is implemented, and the content is returned as an unprocessed string (character). Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de>, Sarah Schnackenberg See Also Wave-class, Wave, WaveMC-class, WaveMC, writeWave Examples Wobj <- sine(440) tdir <- tempdir() tfile <- file.path(tdir, "myWave.wav") writeWave(Wobj, filename = tfile) list.files(tdir, pattern = "\\.wav$") newWobj <- readWave(tfile) newWobj file.remove(tfile) show-WaveWspec-methods Showing objects Description Showing Wave, Wspec, and WspecMat objects. Methods object = "Wave" The Wave object is being shown. The number of samples, duration in seconds, Samplingrate (Hertz), Stereo / Mono, PCM / IEEE, and the resolution in bits are printed. Note that it does not make sense to print the whole channels containing several thousands or millions of samples. object = "WaveMC" The WaveMC object is being shown. The number of samples, duration in seconds, Samplingrate (Hertz), number of channels, PCM / IEEE, and the resolution in bits are printed. Note that it does not make sense to print the whole channels containing several thousands or millions of samples. object = "Wspec" The number of periodograms, Fourier frequencies, window width (used amount of data), amount of overlap of neighboring windows, and whether the periodogram(s) has/have been normalized will be printed. object = "WspecMat" The number of periodograms, Fourier frequencies, window width (used amount of data), amount of overlap of neighboring windows, and whether the periodogram(s) has/have been normalized will be printed. 46 smoother Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also Wave-class, Wave, WaveMC-class, WaveMC, Wspec, WspecMat, plot-methods, summary-methods, and periodogram for the constructor function and some examples smoother Meta Function for Smoothers Description Apply a smoother to estimated notes. Currently, only a running median (using decmedian in pack- age pastecs) is available. Usage smoother(notes, method = "median", order = 4, times = 2) Arguments notes Series of notes, a vector of integers such as returned by noteFromFF. method Currently, only a running 'median' (using decmedian in package pastecs) is available. order The window used for the running median corresponds to 2*order + 1. times The number of times the running median is applied (default: 2). Value The smoothed series of notes. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> spec2cep 47 spec2cep Spectra to Cepstra Conversion Description Calculate cepstra from spectral samples (in columns of spec) through Discrete Cosine Transforma- tion. Usage spec2cep(spec, ncep = 12, type = c("t2", "t1", "t3", "t4")) Arguments spec Input spectra (samples/time frames in columns). ncep Number of cepstra to return. type DCT Type. Value cep Matrix of resulting cepstra. dctm Returns the DCT matrix that spec was multiplied by to give cep. Author(s) Sebastian Krey <krey@statistik.tu-dortmund.de> References Daniel P. W. Ellis: https://www.ee.columbia.edu/~dpwe/resources/matlab/rastamat/ See Also lpc2cep Examples testsound <- normalize(sine(400) + sine(1000) + square(250), "16") pspectrum <- powspec(testsound@left, testsound@samp.rate) aspectrum <- audspec(pspectrum, testsound@samp.rate) cepstra <- spec2cep(aspectrum$aspectrum) 48 tuneR summary-methods Object Summaries Description summary is a generic function used to produce result summaries of the results of various model fitting functions. The function invokes particular methods which depend on the class of the first argument. Methods object = "ANY" Any object for which a summary is desired, dispatches to the S3 generic. object = "Wave" The Wave object is being shown and an additional summary of the Wave-object’s (one or two) channels is given. object = "WaveMC" The WaveMC object is being shown and an additional summary of the WaveMC- object’s channels is given. object = "Wspec" The Wspec object is being shown and as an additional output is given: df, taper (see spectrum) and for the underlying Wave object the number of channels and its sampling rate. object = "WspecMat" The WspecMat object is being shown and as an additional output is given: df, taper (see spectrum) and for the underlying Wave object the number of channels and its sampling rate. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also For the S3 generic: summary.default, plot-methods, Wave-class, Wave, WaveMC-class, WaveMC, Wspec, WspecMat, show tuneR tuneR Description tuneR, a collection of examples tuneR 49 Functions in tuneR tuneR consists of several functions to work with and to analyze Wave files. In the following ex- amples, some of the functions to generate some data (such as sine), to read and write Wave files (readWave, writeWave), to represent or construct (multi channel) Wave files (Wave, WaveMC), to transform Wave objects (bind, channel, downsample, extractWave, mono, stereo), and to play Wave objects are used. Other functions and classes are available to calculate several periodograms of a signal (periodogram, Wspec), to estimate the corresponding fundamental frequencies (FF, FFpure), to derive the cor- responding notes (noteFromFF), and to apply a smoother. Now, the melody and corresponding energy values can be plotted using the function melodyplot. A next step is the quantization (quantize) and a corresponding plot (quantplot) showing the note values for binned data. Moreover, a function called lilyinput (and a data-preprocessing function quantMerge) can prepare a data frame to be presented as sheet music by postprocessing with the music typesetting software LilyPond. Of course, print (show), plot and summary methods are available for most classes. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> with contributions from Sebastian Krey, Olaf Mers- mann, Sarah Schnackenberg, Andrea Preusser, Anita Thieler, and Claus Weihs, as well as code fragments and ideas from the former package sound by Matthias Heymann and functions from ‘rastamat’ by Daniel P. W. Ellis. The included parts of the libmad MPEG audio decoder library are authored by Underbit Technologies. Examples library("tuneR") # in a regular session, we are loading tuneR # constructing a mono Wave object (2 sec.) containing sinus # sound with 440Hz and folled by 220Hz: Wobj <- bind(sine(440), sine(220)) show(Wobj) plot(Wobj) # it does not make sense to plot the whole stuff plot(extractWave(Wobj, from = 1, to = 500)) ## Not run: play(Wobj) # listen to the sound ## End(Not run) tmpfile <- file.path(tempdir(), "testfile.wav") # write the Wave object into a Wave file (can be played with any player): writeWave(Wobj, tmpfile) # reading it in again: Wobj2 <- readWave(tmpfile) Wobjm <- mono(Wobj, "left") # extract the left channel # and downsample to 11025 samples/sec.: Wobjm11 <- downsample(Wobjm, 11025) # extract a part of the signal interactively (click for left/right limits): 50 updateWave ## Not run: Wobjm11s <- extractWave(Wobjm11) ## End(Not run) # or extract some values reproducibly Wobjm11s <- extractWave(Wobjm11, from=1000, to=17000) # calculating periodograms of sections each consisting of 1024 observations, # overlapping by 512 observations: WspecObject <- periodogram(Wobjm11s, normalize = TRUE, width = 1024, overlap = 512) # Let's look at the first periodogram: plot(WspecObject, xlim = c(0, 2000), which = 1) # or a spectrogram image(WspecObject, ylim = c(0, 1000)) # calculate the fundamental frequency: ff <- FF(WspecObject) print(ff) # derive note from FF given diapason a'=440 notes <- noteFromFF(ff, 440) # smooth the notes: snotes <- smoother(notes) # outcome should be 0 for diapason "a'" and -12 (12 halftones lower) for "a" print(snotes) # plot melody and energy of the sound: melodyplot(WspecObject, snotes) # apply some quantization (into 8 parts): qnotes <- quantize(snotes, WspecObject@energy, parts = 8) # an plot it, 4 parts a bar (including expected values): quantplot(qnotes, expected = rep(c(0, -12), each = 4), bars = 2) # now prepare for LilyPond qlily <- quantMerge(snotes, 4, 4, 2) qlily updateWave Update old Wave objects for use with new versions of tuneR Description Update old Wave objects generated with tuneR < 1.0.0 to the new class definition for use with new versions of the package. Usage updateWave(object) Arguments object An object of Wave-class. Wave 51 Details This function is only needed to convert Wave-class objects that have been saved with tuneR versions prior to 1.0-0 to match the new class definition. Value An object of Wave-class as implemented in tuneR versions >= 1.0-0. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de>, Sarah Schnackenberg See Also Wave-class, Wave Examples x <- sine(440) updateWave(x) Wave Constructors and coercion for class Wave objects Description Constructors and coercion for class Wave objects Usage Wave(left, ...) ## S4 method for signature 'numeric' Wave(left, right = numeric(0), samp.rate = 44100, bit = 16, pcm = TRUE, ...) Arguments left, right, samp.rate, bit, pcm See Section “Slots” on the help page Wave-class. Except for numeric, the argu- ment left can also be a matrix (1 or 2 columns), data.frame (1 or 2 columns), list (1 or 2 elements), or WaveMC (1 or 2 channels) object representing the chan- nels. ... Further arguments to be passed to the numeric method. Details The class definition has been extended in tuneR version 1.0-0. Saved objects of class Wave gener- ated with former versions can be updated with updateWave to match the new definition. 52 Wave-class Value An object of Wave-class. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also Wave-class, WaveMC-class, writeWave, readWave, updateWave Examples # constructing a Wave object (1 sec.) containing sinus sound with 440Hz: x <- seq(0, 2*pi, length = 44100) channel <- round(32000 * sin(440 * x)) Wobj <- Wave(left = channel) Wobj # or more easily: Wobj <- sine(440) Wave-class Class Wave Description Class “Wave”. Details The class definition has been extended in tuneR version 1.0-0. Saved objects of class Wave gener- ated with former versions can be updated with updateWave to match the new definition. Objects from the Class Objects can be created by calls of the form new("Wave", ...), or more conveniently using the function Wave. Slots left: Object of class "numeric" representing the left channel. right: Object of class "numeric" representing the right channel, NULL if mono. stereo: Object of class "logical" indicating whether this is a stereo (two channels) or mono representation. samp.rate: Object of class "numeric" - the sampling rate, e.g. 44100 for CD quality. bit: Object of class "numeric", common is 16 for CD quality, or 8 for a rather rough representa- tion. pcm: Object of class "logical" indicating whether this is a PCM or IEEE_FLOAT Wave format. Waveforms 53 Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also Wave, updateWave, and for multi channel Wave files see WaveMC-class Waveforms Create Wave Objects of Special Waveforms Description Create a Wave object of special waveform such as silcence, power law (white, red, pink, ...) noise, sawtooth, sine, square, and pulse. Usage noise(kind = c("white", "pink", "power", "red"), duration = samp.rate, samp.rate = 44100, bit = 1, stereo = FALSE, xunit = c("samples", "time"), alpha = 1, ...) pulse(freq, duration = samp.rate, from = 0, samp.rate = 44100, bit = 1, stereo = FALSE, xunit = c("samples", "time"), width = 0.1, plateau = 0.2, interval = 0.5, ...) sawtooth(freq, duration = samp.rate, from = 0, samp.rate = 44100, bit = 1, stereo = FALSE, xunit = c("samples", "time"), reverse = FALSE, ...) silence(duration = samp.rate, from = 0, samp.rate = 44100, bit = 1, stereo = FALSE, xunit = c("samples", "time"), ...) sine(freq, duration = samp.rate, from = 0, samp.rate = 44100, bit = 1, stereo = FALSE, xunit = c("samples", "time"), ...) square(freq, duration = samp.rate, from = 0, samp.rate = 44100, bit = 1, stereo = FALSE, xunit = c("samples", "time"), up = 0.5, ...) Arguments kind The kind of noise, “white”, “pink”, “power”, or “red” (these are not dB adjusted (!) but all except for “white” are linear decreasing on a log-log scale). Algorithm for generating power law noise is taken from Timmer and König (1995). freq The frequency (in Hertz) to be generated. duration Duration of the Wave in xunit. 54 Waveforms from Starting value of the Wave in xunit. samp.rate Sampling rate of the Wave. bit Resolution of the Wave and rescaling unit. This may be 1 (default) for rescaling to numeric values in [-1,1], 8 (i.e. 8-bit) for rescaling to integers in [0, 254], 16 (i.e. 16-bit) for rescaling to integers in [-32767, 32767], 24 (i.e. 24-bit) for rescaling to integers in [-8388607, 8388607], 32 (i.e. 32-bit) for rescaling either to integers in [-2147483647, 2147483647] (PCM Wave format if pcm = TRUE) or to numeric values in [-1, 1] (FLOAT_IEEE Wave format if pcm = FALSE), 64 (i.e. 64-bit) for rescaling to numeric values in [-1, 1] (FLOAT_IEEE Wave format), and 0 for not rescaling at all. These numbers are internally passed to normalize. The Wave slot bit will be set to 32 if bit = 0, bit = 1 or bit = 32. stereo Logical, if TRUE, a stereo sample will be generated. The right channel is identical to the left one for sawtooth, silence, sine, and square. For noise, both channel are independent. xunit Character indicating which units are used (both in arguments duration and from). If xunit = "time", the unit is time in seconds, otherwise the number of samples. alpha The power for the power law noise (defaults are 1 for pink and 1.5 for red noise) 1/f α . reverse Logical, if TRUE, the waveform will be mirrored vertically. up A number between 0 and 1 giving the percentage of the waveform at max value (= 1 - percentage of min value). width Relative pulses width: the proportion of time the amplitude is non-zero. plateau Relative plateau width: the proportion of the pulse width where amplitude is ±1. interval Relative interval between the up-going and down-going pulses with respect to the center of the wave period (0: immediatly after up-going, 1: center of the wave period). ... Further arguments to be passed to Wave through the internal function postWaveform. Value A Wave object. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de>, partly based on code from Matthias Hey- mann’s former package ‘sound’, Anita Thieler, Guillaume Guénard References J. Timmer and M. König (1995): On generating power law noise. Astron. Astrophys. 300, 707-710. WaveMC 55 See Also Wave-class, Wave, normalize, noSilence Examples Wobj <- sine(440, duration = 1000) Wobj2 <- noise(duration = 1000) Wobj3 <- pulse(220, duration = 1000) plot(Wobj) plot(Wobj2) plot(Wobj3) WaveMC Constructors and coercion for class WaveMC objects Description Constructors and coercion for class WaveMC objects Usage WaveMC(data, ...) ## S4 method for signature 'matrix' WaveMC(data = matrix(numeric(0), 0, 0), samp.rate = 44100, bit = 16, pcm = TRUE, ...) Arguments data Except for a numeric matrix, the argument data can also be a numeric vector (for one channel), data.frame (columns representing channels), list (elements containing numeric vectors that represent the channels), or Wave object. samp.rate, bit, pcm See Section “Slots” on the help page WaveMC-class. ... Further arguments to be passed to the matrix method. Value An object of WaveMC-class. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de>, Sarah Schnackenberg See Also WaveMC-class, Wave-class, writeWave, readWave 56 WaveMC-class Examples # constructing a WaveMC object (1 sec.) containing sinus sound with 440Hz: x <- seq(0, 2*pi, length = 44100) channel <- round(32000 * sin(440 * x)) WMCobj <- WaveMC(data = channel) WMCobj WaveMC-class Class WaveMC Description Class “WaveMC”. Details This class has been added in tuneR version 1.0-0 for representation and construction of multi chan- nel Wave files. Objects of class Wave can be transformed to the new class definition by calls of the form as(..., "WaveMC"). Coercion from the WaveMC class to the Wave-class works via as(..., "Wave") if there are no more than 2 channels. Coercing back to the Wave-class can be useful since some (very few) functions cannot yet deal with multi channel Wave objects. Note that also the Wave-class definition has been extended in tuneR version 1.0-0. For more details see Wave-class. Objects from the Class Objects can be created by calls of the form new("WaveMC", ...), or more conveniently using the function WaveMC. Slots .Data: Object of class "matrix" containing numeric data, where each column is representing one channel. Column names are the appropriate way to name different channels. The data object MCnames contains a data frame of standard names for channels in multi channel Wave files. samp.rate: Object of class "numeric" - the sampling rate, e.g. 44100 for CD quality. bit: Object of class "numeric", common is 16 for CD quality, or 8 for a rather rough representa- tion. pcm: Object of class "logical" indicating whether this is a PCM or IEEE_FLOAT Wave format. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de>, Sarah Schnackenberg See Also WaveMC, Wave-class, MCnames WavPlayer 57 WavPlayer Getting and setting the default player for Wave files Description Getting and setting the default player for Wave files Usage setWavPlayer(player) getWavPlayer() Arguments player Set the character string to call a Wave file player (including optional arguments) using options. Value getWavPlayer returns the character string that has been set by setWavPlayer. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also Wave-class, Wave, play writeWave Writing Wave files Description Writing Wave files. Usage writeWave(object, filename, extensible = TRUE) Arguments object Object of class Wave or WaveMC to be written to a Wave file. filename Filename of the file to be written. extensible If TRUE (default), an extensible Wave format file is written. If FALSE, a non- extensible Wave file is written. 58 writeWave Details It is only possible to write a non-extensible Wave format file for objects of class Wave or for objects of class WaveMC with one or two channels (mono or stereo). If the argument object is a Wave-class object, the channels are automatically chosen to be “FL” (for mono) or “FL” and “FR” (for stereo). The channel mask used to arrange the channel ordering in multi channel Wave files is written ac- cording to Microsoft standards as given in the data frame MCnames containing the first 18 stan- dard channels. In the case of writing a multi channel Wave file, the column names of the object object (colnames(object)) must be specified and must uniquely identify the channel ordering for WaveMC objects. The column names of the object of class WaveMC have to be a subset of the 18 standard channels and have to match the corresponding abbreviated names. (See MCnames for pos- sible channels and the abbreviated names: “FL”, “FR”, “FC”, “LF”, “BL”, “BR”, “FLC”, “FRC”, “BC”, “SL”, “SR”, “TC”, “TFL”, “TFC”, “TFR”, “TBL”, “TBC” and “TBR”). The function normalize can be used to transform and rescale data to an appropriate amplitude range for various Wave file formats (either pcm with 8-, 16-, 24- or 32-bit or IEEE_FLOAT with 32- or 64-bit). Value writeWave creates a Wave file, but returns nothing. Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de>, Sarah Schnackenberg See Also Wave-class, Wave, WaveMC-class, WaveMC, normalize, MCnames, readWave Examples Wobj <- sine(440) tdir <- tempdir() tfile <- file.path(tdir, "myWave.wav") writeWave(Wobj, filename = tfile) list.files(tdir, pattern = "\\.wav$") newWobj <- readWave(tfile) newWobj file.remove(tfile) Wspec-class 59 Wspec-class Class Wspec Description Class “Wspec” (Wave spectrums). Objects of this class represent a bunch of periodograms (see periodogram, each generated by spectrum) corresponding to one or several windows of one Wave or WaveMC object. Redundancy (e.g. same frequencies in each of the periodograms) will be omitted, hence reducing memory consumption. Details The subset function “[” extracts the selected elements of slots spec, starts, variance and energy and returns the other slots unchanged. Objects from the Class Objects can be created by calls of the form new("Wspec", ...), but regularly they will be created by calls to the function periodogram. Slots The following slots are defined. For details see the constructor function periodogram. freq: Object of class "numeric". spec: Object of class "list". kernel: Object of class "ANY". df: Object of class "numeric". taper: Object of class "numeric". width: Object of class "numeric". overlap: Object of class "numeric". normalize: Object of class "logical". starts: Object of class "numeric". stereo: Object of class "logical". samp.rate: Object of class "numeric". variance: Object of class "numeric". energy: Object of class "numeric". Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> 60 WspecMat-class See Also • the show, plot and summary methods, • for the constructor function and some examples: periodogram (and hence also spec.pgram, Wave-class, Wave, WaveMC-class, and WaveMC) • WspecMat for a similar class that represents the spectrum in form of a matrix. WspecMat-class Class WspecMat Description Class “WspecMat” (Wave spectrums as Matrix). Objects of this class represent a bunch of peri- odograms (see periodogram, each generated by spectrum) corresponding to one or several win- dows of one Wave or WaveMC object. Redundancy (e.g. same frequencies in each of the peri- odograms) will be omitted, hence reducing memory consumption. Details The subset function “[” extracts the selected elements of slots spec, starts, variance and energy and returns the other slots unchanged. Objects from the Class Objects can be created by calls of the form new("WspecMat", ...), but regularly they will be created from a Wspec object by calls such as as(Wspec_Object, "WspecMat"). Slots The following slots are defined. For details see the constructor function periodogram. freq: Object of class "numeric". spec: Object of class "matrix". kernel: Object of class "ANY". df: Object of class "numeric". taper: Object of class "numeric". width: Object of class "numeric". overlap: Object of class "numeric". normalize: Object of class "logical". starts: Object of class "numeric". stereo: Object of class "logical". samp.rate: Object of class "numeric". variance: Object of class "numeric". energy: Object of class "numeric". [-methods 61 Author(s) Uwe Ligges <ligges@statistik.tu-dortmund.de> See Also the show, plot and summary methods [-methods Extract or Replace Parts of an Object Description Operators act on objects to extract or replace subsets. See Also Extract for the S3 generic. Index ∗ CD quality readWave, 44 downsample, 8 show-WaveWspec-methods, 45 ∗ IO tuneR, 48 play-methods, 32 Wave, 51 readMidi, 42 Wave-class, 52 readMP3, 43 Waveforms, 53 readWave, 44 WaveMC, 55 writeWave, 57 WavPlayer, 57 ∗ LilyPond writeWave, 57 lilyinput, 16 Wspec-class, 59 ∗ MIDI WspecMat-class, 60 readMidi, 42 ∗ aplot ∗ MP3 plot-Wave, 33 readMP3, 43 ∗ arith ∗ WaveMC Arith-methods, 3 downsample, 8 ∗ bark noSilence, 25 audspec, 3 panorama, 28 freqconv, 12 prepComb, 38 ∗ bar WaveMC, 55 quantize, 39 WaveMC-class, 56 ∗ bin ∗ Wave quantize, 39 bind, 5 ∗ bit channel, 5 Wave, 51 downsample, 8 Wave-class, 52 equalWave, 9 WaveMC, 55 extractWave, 9 WaveMC-class, 56 FF, 11 ∗ cepstra Mono-Stereo, 23 lpc2cep, 17 normalize-methods, 24 spec2cep, 47 noSilence, 25 ∗ cepstrum noteFromFF, 26 melfcc, 19 panorama, 28 ∗ channel periodogram-methods, 29 channel, 5 play-methods, 32 Mono-Stereo, 23 plot-Wave, 33 panorama, 28 plot-Wspec, 34 Wave, 51 plot-WspecMat, 35 Wave-class, 52 prepComb, 38 WaveMC, 55 62 INDEX 63 WaveMC-class, 56 ∗ hplot ∗ classes melodyplot, 21 Wave-class, 52 plot-Wave, 33 WaveMC-class, 56 plot-Wspec, 34 Wspec-class, 59 plot-WspecMat, 35 WspecMat-class, 60 quantplot, 40 ∗ compression ∗ interface postaud, 36 lilyinput, 16 ∗ conversion play-methods, 32 audspec, 3 ∗ iplot freqconv, 12 extractWave, 9 lpc2cep, 17 ∗ levinson spec2cep, 47 dolpc, 7 ∗ cut ∗ liftering noSilence, 25 lifter, 15 ∗ datagen ∗ loudness Waveforms, 53 postaud, 36 ∗ datasets ∗ lpc MCnames, 18 dolpc, 7 ∗ declick lpc2cep, 17 prepComb, 38 melfcc, 19 ∗ deltas ∗ manip deltas, 6 bind, 5 ∗ documentation channel, 5 tuneR, 48 downsample, 8 ∗ durbin extractWave, 9 dolpc, 7 Mono-Stereo, 23 ∗ error normalize-methods, 24 equalWave, 9 noSilence, 25 ∗ f0 panorama, 28 FF, 11 prepComb, 38 noteFromFF, 26 ∗ median ∗ file smoother, 46 lilyinput, 16 ∗ melody readMidi, 42 melodyplot, 21 readMP3, 43 quantplot, 40 readWave, 44 ∗ mel writeWave, 57 audspec, 3 ∗ frequency freqconv, 12 audspec, 3 melfcc, 19 FF, 11 ∗ methods freqconv, 12 [-methods, 61 noteFromFF, 26 Arith-methods, 3 ∗ fundamental length, 14 FF, 11 play-methods, 32 noteFromFF, 26 plot-Wave, 33 ∗ hertz plot-Wspec, 34 freqconv, 12 plot-WspecMat, 35 64 INDEX show-WaveWspec-methods, 45 melodyplot, 21 summary-methods, 48 noteFromFF, 26 Wave, 51 quantplot, 40 WaveMC, 55 ∗ player ∗ mfcc play-methods, 32 melfcc, 19 WavPlayer, 57 ∗ misc ∗ plp smoother, 46 dolpc, 7 ∗ mono melfcc, 19 Mono-Stereo, 23 postaud, 36 Wave, 51 ∗ powerspectrum Wave-class, 52 powspec, 37 WaveMC, 55 ∗ print WaveMC-class, 56 show-WaveWspec-methods, 45 ∗ music summary-methods, 48 play-methods, 32 ∗ quantization plot-Wave, 33 quantize, 39 readMP3, 43 ∗ recursion readWave, 44 dolpc, 7 tuneR, 48 ∗ running Wave, 51 smoother, 46 Wave-class, 52 ∗ sample WaveMC, 55 Waveforms, 53 WaveMC-class, 56 ∗ sampling rate WavPlayer, 57 downsample, 8 writeWave, 57 Wave, 51 Wspec-class, 59 Wave-class, 52 WspecMat-class, 60 WaveMC, 55 ∗ noise WaveMC-class, 56 noSilence, 25 ∗ sampling ∗ note downsample, 8 lilyinput, 16 Wave, 51 melodyplot, 21 Wave-class, 52 noteFromFF, 26 WaveMC, 55 notenames, 27 WaveMC-class, 56 quantize, 39 ∗ silcence quantplot, 40 Waveforms, 53 ∗ periodogram ∗ silence FF, 11 noSilence, 25 noteFromFF, 26 ∗ smooth plot-Wspec, 34 smoother, 46 plot-WspecMat, 35 ∗ sound show-WaveWspec-methods, 45 play-methods, 32 tuneR, 48 readMP3, 43 Wspec-class, 59 readWave, 44 WspecMat-class, 60 Waveforms, 53 ∗ pitch WavPlayer, 57 FF, 11 writeWave, 57 INDEX 65 ∗ spectogram equalWave, 9 plot-WspecMat, 35 extractWave, 9 ∗ spectra Mono-Stereo, 23 spec2cep, 47 noSilence, 25 ∗ spectrum noteFromFF, 26 periodogram-methods, 29 notenames, 27 Wspec-class, 59 play-methods, 32 WspecMat-class, 60 prepComb, 38 ∗ speech quantize, 39 play-methods, 32 WavPlayer, 57 plot-Wave, 33 ∗ waveform readMP3, 43 Waveforms, 53 readWave, 44 [,ANY-method ([-methods), 61 Wave, 51 [,Wave-method (Wave), 51 Wave-class, 52 [,WaveMC-method (WaveMC), 55 WaveMC, 55 [,Wspec-method (Wspec-class), 59 WaveMC-class, 56 [,WspecMat-method (WspecMat-class), 60 WavPlayer, 57 [-methods, 61 writeWave, 57 Wspec-class, 59 abline, 22, 41 WspecMat-class, 60 Arith,numeric,Wave-method ∗ stereo (Arith-methods), 3 Arith,numeric,WaveMC-method Mono-Stereo, 23 (Arith-methods), 3 panorama, 28 Arith,Wave,missing-method Wave, 51 (Arith-methods), 3 Wave-class, 52 Arith,Wave,numeric-method WaveMC, 55 (Arith-methods), 3 WaveMC-class, 56 Arith,Wave,Wave-method (Arith-methods), ∗ tracking 3 FF, 11 Arith,WaveMC,numeric-method melodyplot, 21 (Arith-methods), 3 noteFromFF, 26 Arith,WaveMC,WaveMC-method quantplot, 40 (Arith-methods), 3 ∗ transcribe Arith-methods, 3 lilyinput, 16 audspec, 3, 36, 37 ∗ transcription axis, 22, 41 lilyinput, 16 melodyplot, 21 bark2hz (freqconv), 12 quantplot, 40 bind, 5, 10, 39, 49 ∗ ts bind,Wave-method (bind), 5 FF, 11 bind,WaveMC-method (bind), 5 melfcc, 19 periodogram-methods, 29 channel, 5, 10, 49 smoother, 46 coerce,data.frame,Wave-method (Wave), 51 ∗ utilities coerce,data.frame,WaveMC-method bind, 5 (WaveMC), 55 channel, 5 coerce,list,Wave-method (Wave), 51 downsample, 8 coerce,list,WaveMC-method (WaveMC), 55 66 INDEX coerce,matrix,Wave-method (Wave), 51 levinson, 7 coerce,matrix,WaveMC-method (WaveMC), 55 lifter, 15 coerce,numeric,Wave-method (Wave), 51 lilyinput, 16, 40, 49 coerce,numeric,WaveMC-method (WaveMC), lines, 22, 41 55 lpc2cep, 17, 47 coerce,Wave,data.frame-method (Wave), 51 coerce,Wave,matrix-method (Wave), 51 MCnames, 18, 56, 58 coerce,Wave,WaveMC-method (Wave), 51 mel2hz (freqconv), 12 coerce,WaveGeneral,list-method (Wave), melfcc, 19 51 melodyplot, 21, 42, 49 coerce,WaveMC,data.frame-method mono, 6, 10, 30, 49 (WaveMC), 55 mono (Mono-Stereo), 23 coerce,WaveMC,matrix-method (WaveMC), 55 Mono-Stereo, 23 coerce,WaveMC,Wave-method (WaveMC), 55 mtext, 22, 41 coerce,Wspec,WspecMat-method nchannel, 24 (WspecMat-class), 60 nchannel,Wave-method (nchannel), 24 decmedian, 46 nchannel,WaveMC-method (nchannel), 24 deltas, 6 noise (Waveforms), 53 dolpc, 7, 37 normalize, 54, 55, 58 downsample, 8, 30, 49 normalize (normalize-methods), 24 normalize,Wave-method equalWave, 5, 9 (normalize-methods), 24 Extract, 61 normalize,WaveMC-method extractWave, 5, 6, 9, 26, 39, 49 (normalize-methods), 24 normalize-methods, 24 FF, 11, 22, 27, 42, 49 noSilence, 25, 39, 55 FFpure, 49 noSilence,Wave-method (noSilence), 25 FFpure (FF), 11 noSilence,WaveMC-method (noSilence), 25 fft2barkmx, 4 noteFromFF, 12, 21, 22, 26, 39, 40, 42, 46, 49 fft2melmx, 4 notenames, 13, 27, 40 freqconv, 12 options, 57 getMidiNotes, 13, 43 panorama, 28 getWavPlayer (WavPlayer), 57 panorama,Wave-method (panorama), 28 groupGeneric, 3 panorama,WaveMC-method (panorama), 28 hz2bark (freqconv), 12 par, 22, 33, 34, 41 hz2mel (freqconv), 12 periodogram, 12, 27, 35, 36, 46, 49, 59, 60 periodogram (periodogram-methods), 29 image, 35, 36 periodogram,character-method image,ANY-method (plot-WspecMat), 35 (periodogram-methods), 29 image,Wspec-method (plot-WspecMat), 35 periodogram,WaveGeneral-method image-Wspec (plot-WspecMat), 35 (periodogram-methods), 29 interactive, 10 periodogram-methods, 29 play, 49, 57 length, 14, 14 play (play-methods), 32 length,ANY-method (length), 14 play,character-method (play-methods), 32 length,Wave-method (length), 14 play,WaveGeneral-method (play-methods), length,WaveMC-method (length), 14 32 INDEX 67 play-methods, 32 spec2cep, 18, 47 plot,Wave,missing-method (plot-Wave), 33 specgram, 37 plot,WaveMC,missing-method (plot-Wave), spectrum, 30, 48, 59, 60 33 square (Waveforms), 53 plot,Wspec,missing-method (plot-Wspec), stereo, 5, 49 34 stereo (Mono-Stereo), 23 plot,WspecMat,missing-method stop, 9 (plot-WspecMat), 35 summary,ANY-method (summary-methods), 48 plot-Wave, 33 summary,Wave-method (summary-methods), plot-Wspec, 34 48 plot-WspecMat, 35 summary,WaveMC-method plot.default, 35 (summary-methods), 48 plot_Wave_channel (plot-Wave), 33 summary,Wspec-method (summary-methods), points, 41 48 postaud, 36 summary,WspecMat-method powspec, 4, 37 (summary-methods), 48 prepComb, 5, 38 summary-methods, 48 pulse (Waveforms), 53 summary.default, 48 quantize, 17, 39, 41, 42, 49 tuneR, 12, 17, 22, 27, 34–36, 40, 42, 48 quantMerge, 17, 49 tuneR-package (tuneR), 48 quantMerge (quantize), 39 quantplot, 17, 22, 40, 40, 49 updateWave, 50, 51–53 readMidi, 13, 14, 42 Wave, 3, 5, 6, 8–10, 14, 23–26, 28–34, 38, 39, readMP3, 43, 43 43–46, 48, 49, 51, 51, 52–55, 57–60 readWave, 43, 44, 49, 52, 55, 58 Wave,ANY-method (Wave), 51 rect, 22, 41 Wave,data.frame-method (Wave), 51 round, 27 Wave,list-method (Wave), 51 Wave,matrix-method (Wave), 51 sawtooth (Waveforms), 53 Wave,numeric-method (Wave), 51 setWavPlayer, 32 Wave,WaveMC-method (Wave), 51 setWavPlayer (WavPlayer), 57 Wave-class, 3, 5, 6, 8–10, 19, 23–26, 29, 31, show, 48 32, 34, 39, 44–46, 48, 50–52, 52, show,Wave-method 55–58, 60 (show-WaveWspec-methods), 45 Waveforms, 53 show,WaveMC-method WaveMC, 3, 5, 8–10, 14, 24–26, 28–34, 38, 39, (show-WaveWspec-methods), 45 45, 46, 48, 49, 55, 56–60 show,Wspec-method WaveMC,ANY-method (WaveMC), 55 (show-WaveWspec-methods), 45 WaveMC,data.frame-method (WaveMC), 55 show,WspecMat-method WaveMC,list-method (WaveMC), 55 (show-WaveWspec-methods), 45 WaveMC,matrix-method (WaveMC), 55 show-WaveWspec-methods, 45 WaveMC,numeric-method (WaveMC), 55 silence, 26 WaveMC,Wave-method (WaveMC), 55 silence (Waveforms), 53 WaveMC-class, 3, 5, 6, 8–10, 19, 23–26, 29, sine, 49 31, 32, 34, 39, 44–46, 48, 52, 53, 55, sine (Waveforms), 53 56, 58, 60 smoother, 46, 49 WavPlayer, 57 spec.pgram, 29–31, 60 writeWave, 25, 32, 45, 49, 52, 55, 57 68 INDEX Wspec, 11, 12, 21, 30, 31, 35, 36, 39, 46, 48, 49, 60 Wspec (Wspec-class), 59 Wspec-class, 59 WspecMat, 35, 36, 46, 48, 60 WspecMat (WspecMat-class), 60 WspecMat-class, 60

KGode

cran

Package ‘KGode’ October 12, 2022 Title Kernel Based Gradient Matching for Parameter Inference in Ordinary Differential Equations Version 1.0.4 Author Mu Niu [aut, cre] Maintainer Mu Niu <mu.niu@glasgow.ac.uk> Description The kernel ridge regression and the gradient matching algorithm pro- posed in Niu et al. (2016) <https://proceedings.mlr.press/v48/niu16.html> and the warp- ing algorithm proposed in Niu et al. (2017) <DOI:10.1007/s00180-017-0753-z> are imple- mented for parameter inference in differential equations. Four schemes are provided for improv- ing parameter estimation in odes by using the odes regularisation and warping. Depends R (>= 3.2.0) License GPL (>= 2) Imports R6,pracma,pspline,mvtnorm,graphics Encoding UTF-8 RoxygenNote 7.2.1 NeedsCompilation no Repository CRAN Date/Publication 2022-08-19 14:00:08 UTC R topics documented: bootstrap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 crossv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 diagnostic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Kernel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 MLP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 ode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 RBF . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 rkg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 rkg3 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 rkhs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 third . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 1 2 bootstrap Warp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 warpfun . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 warpInitLen . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Index 34 bootstrap The ’bootstrap’ function Description This function is used to perform bootstrap procedure to estimate parameter uncertainty. Usage bootstrap(kkk, y_no, ktype, K, ode_par, intp_data, www = NULL) Arguments kkk ode class object. y_no matrix(of size n_s*n_o) containing noisy observations. The row(of length n_s) represent the ode states and the column(of length n_o) represents the time points. ktype character containing kernel type. User can choose ’rbf’ or ’mlp’ kernel. K the number of bootstrap replicates to collect. ode_par a vector of ode parameters estimated using gradient matching. intp_data a list of interpolations produced by gradient matching for each ode state. www an optional warping object (if warping has been performed using warpfun). Details Arguments of the ’bootstrap’ function are ’ode’ class, noisy observation, kernel type, the set of parameters that have been estimated before using gradient matching, a list of interpolations for each of the ode state from gradient matching, and the warping object (if warping has been performed). It returns a vector of the median absolute standard deviations for each ode state, computed from the bootstrap replicates. Value return a vector of the median absolute deviation (MAD) for each ode state. Author(s) Mu Niu <mu.niu@glasgow.ac.uk> bootstrap 3 Examples ## Not run: require(mvtnorm) noise = 0.1 ## set the variance of noise SEED = 19537 set.seed(SEED) ## Define ode function, we use lotka-volterra model in this example. ## we have two ode states x[1], x[2] and four ode parameters alpha, beta, gamma and delta. LV_fun = function(t,x,par_ode){ alpha=par_ode[1] beta=par_ode[2] gamma=par_ode[3] delta=par_ode[4] as.matrix( c( alpha*x[1]-beta*x[2]*x[1] , -gamma*x[2]+delta*x[1]*x[2] ) ) } ## Define the gradient of ode function against ode parameters ## df/dalpha, df/dbeta, df/dgamma, df/ddelta where f is the differential equation. LV_grlNODE= function(par,grad_ode,y_p,z_p) { alpha = par[1]; beta= par[2]; gamma = par[3]; delta = par[4] dres= c(0) dres[1] = sum( -2*( z_p[1,]-grad_ode[1,])*y_p[1,]*alpha ) dres[2] = sum( 2*( z_p[1,]-grad_ode[1,])*y_p[2,]*y_p[1,]*beta) dres[3] = sum( 2*( z_p[2,]-grad_ode[2,])*gamma*y_p[2,] ) dres[4] = sum( -2*( z_p[2,]-grad_ode[2,])*y_p[2,]*y_p[1,]*delta) dres } ## create a ode class object kkk0 = ode$new(2,fun=LV_fun,grfun=LV_grlNODE) ## set the initial values for each state at time zero. xinit = as.matrix(c(0.5,1)) ## set the time interval for the ode numerical solver. tinterv = c(0,6) ## solve the ode numerically using predefined ode parameters. alpha=1, beta=1, gamma=4, delta=1. kkk0$solve_ode(c(1,1,4,1),xinit,tinterv) ## Add noise to the numerical solution of the ode model and use it as the noisy observation. n_o = max( dim( kkk0$y_ode) ) t_no = kkk0$t y_no = t(kkk0$y_ode) + rmvnorm(n_o,c(0,0),noise*diag(2)) ## Create a ode class object by using the simulation data we created from the ode numerical solver. ## If users have experiment data, they can replace the simulation data with the experiment data. ## Set initial value of ode parameters. init_par = rep(c(0.1),4) init_yode = t(y_no) init_t = t_no kkk = ode$new(1,fun=LV_fun,grfun=LV_grlNODE,t=init_t,ode_par= init_par, y_ode=init_yode ) ## The following examples with CPU or elapsed time > 10s 4 bootstrap ##Use function 'rkg' to estimate the ode parameters. The standard gradient matching method is coded ##in the the 'rkg' function. The parameter estimations are stored in the returned vector of 'rkg'. ## Choose a kernel type for 'rkhs' interpolation. Two options are provided 'rbf' and 'mlp'. ktype ='rbf' rkgres = rkg(kkk,y_no,ktype) ## show the results of ode parameter estimation using the standard gradient matching kkk$ode_par ## Perform bootstrap procedure to estimate the median absolute deviations of ode parameters # here we get the resulting interpolation from gradient matching using 'rkg' for each ode state bbb = rkgres$bbb nst = length(bbb) intp_data = list() for( i in 1:nst) { intp_data[[i]] = bbb[[i]]$predictT(bbb[[i]]$t)$pred } K = 12 # the number of bootstrap replicates mads = bootstrap(kkk, y_no, ktype, K, ode_par, intp_data) ## show the results of ode parameter estimation and its uncertainty ## using the standard gradient matching ode_par mads ############# gradient matching + ODE regularisation crtype='i' lam=c(10,1,1e-1,1e-2,1e-4) lamil1 = crossv(lam,kkk,bbb,crtype,y_no) lambdai1=lamil1[[1]] res = third(lambdai1,kkk,bbb,crtype) oppar = res$oppar ### do bootstrap here for gradient matching + ODE regularisation ode_par = oppar K = 12 intp_data = list() for( i in 1:nst) { intp_data[[i]] = res$rk3$rk[[i]]$predictT(bbb[[i]]$t)$pred } mads = bootstrap(kkk, y_no, ktype, K, ode_par, intp_data) ode_par mads ############# gradient matching + ODE regularisation + warping ###### warp state peod = c(6,5.3) #8#9.7 ## the guessing period eps= 1 ## the standard deviation of period fixlens=warpInitLen(peod,eps,rkgres) kkkrkg = kkk$clone() www = warpfun(kkkrkg,bbb,peod,eps,fixlens,y_no,kkkrkg$t) ### do bootstrap here for gradient matching + ODE regularisation + warping nst = length(bbb) crossv 5 K = 12 ode_par = www$wkkk$ode_par intp_data = list() for( i in 1:nst) { intp_data[[i]] = www$bbbw[[i]]$predictT(www$wtime[i, ])$pred } mads = bootstrap(kkk, y_no, ktype, K, ode_par, intp_data,www) ode_par mads ## End(Not run) crossv The ’crossv’ function Description This function is used to estimate the weighting parameter for ode regularisation using cross valida- tion. Usage crossv(lam, kkk, bbb, crtype, y_no, woption, resmtest, dtilda, fold) Arguments lam vector containing different choices of the weighting parameter of ode regulari- sation. kkk ’ode’ class object containing all information about the odes. bbb list of ’rkhs’ class object containing the interpolation for all ode states. crtype character containing the optimisation scheme type. User can choose ’i’ or ’3’. ’i’ is for fast iterative scheme and ’3’ for optimising the ode parameters and interpolation coefficients simultaneously. y_no matrix(of size n_s*n_o) containing noisy observations. The row(of length n_s) represent the ode states and the column(of length n_o) represents the time points. woption character containing the indication of using warping. If the warping scheme is done before using the ode regularisation, user can choose ’w’ otherwise just leave this option empty. resmtest vector(of length n_o) containing the warped time points. This variable is only used if user want to combine warping and the ode regularisation. dtilda vector(of length n_o) containing the gradient of warping function. This variable is only used if user want to combine warping and the ode regularisation. fold scalar indicating the folds of cross validation. 6 crossv Details Arguments of the ’crossv’ function are list of weighting parameter for ode regularisation, ’ode’ class objects, ’rkhs’ class objects, noisy observation, type of regularisation scheme, option of warping and the gradient of warping function. It return the interpolation for each of the ode states. The ode parameters are estimated using gradient matching, and the results are stored in the ’ode’ class as the ode_par attribute. Value return list containing : • lam - scalar containing the optimised weighting parameter. • ress -vector containing the cross validation error for all choices of weighting parameter. Author(s) Mu Niu <mu.niu@glasgow.ac.uk> Examples ## Not run: require(mvtnorm) noise = 0.1 SEED = 19537 set.seed(SEED) ## Define ode function, we use lotka-volterra model in this example. ## we have two ode states x[1], x[2] and four ode parameters alpha, beta, gamma and delta. LV_fun = function(t,x,par_ode){ alpha=par_ode[1] beta=par_ode[2] gamma=par_ode[3] delta=par_ode[4] as.matrix( c( alpha*x[1]-beta*x[2]*x[1] , -gamma*x[2]+delta*x[1]*x[2] ) ) } ## Define the gradient of ode function against ode parameters ## df/dalpha, df/dbeta, df/dgamma, df/ddelta where f is the differential equation. LV_grlNODE= function(par,grad_ode,y_p,z_p) { alpha = par[1]; beta= par[2]; gamma = par[3]; delta = par[4] dres= c(0) dres[1] = sum( -2*( z_p[1,]-grad_ode[1,])*y_p[1,]*alpha ) dres[2] = sum( 2*( z_p[1,]-grad_ode[1,])*y_p[2,]*y_p[1,]*beta) dres[3] = sum( 2*( z_p[2,]-grad_ode[2,])*gamma*y_p[2,] ) dres[4] = sum( -2*( z_p[2,]-grad_ode[2,])*y_p[2,]*y_p[1,]*delta) dres } ## create a ode class object kkk0 = ode$new(2,fun=LV_fun,grfun=LV_grlNODE) ## set the initial values for each state at time zero. xinit = as.matrix(c(0.5,1)) diagnostic 7 ## set the time interval for the ode numerical solver. tinterv = c(0,6) ## solve the ode numerically using predefined ode parameters. alpha=1, beta=1, gamma=4, delta=1. kkk0$solve_ode(c(1,1,4,1),xinit,tinterv) ## Add noise to the numerical solution of the ode model and use it as the noisy observation. n_o = max( dim( kkk0$y_ode) ) t_no = kkk0$t y_no = t(kkk0$y_ode) + rmvnorm(n_o,c(0,0),noise*diag(2)) ## create a ode class object by using the simulation data we created from the Ode numerical solver. ## If users have experiment data, they can replace the simulation data with the experiment data. ## set initial value of Ode parameters. init_par = rep(c(0.1),4) init_yode = t(y_no) init_t = t_no kkk = ode$new(1,fun=LV_fun,grfun=LV_grlNODE,t=init_t,ode_par= init_par, y_ode=init_yode ) ## The following examples with CPU or elapsed time > 10s ## Use function 'rkg' to estimate the Ode parameters. ktype ='rbf' rkgres = rkg(kkk,y_no,ktype) bbb = rkgres$bbb ############# gradient matching + third step crtype='i' ## using cross validation to estimate the weighting parameters of the ode regularisation lam=c(1e-4,1e-5) lamil1 = crossv(lam,kkk,bbb,crtype,y_no) lambdai1=lamil1[[1]] ## End(Not run) diagnostic The ’diagnostic’ function Description This function is used to perform diagnostic procedure to compute the residual and make diagnostic plots. Usage diagnostic(infer_list, index, type, qq_plot) Arguments infer_list a list of inference results including ode objects and inference objects. index the index of the ode states which the user want to do the diagnostic analysis. 8 diagnostic type character containing the type of inference methods. User can choose ’rkg’, ’third’, or ’warp’. qq_plot boolean variable, enable or disable the plotting function. Details Arguments of the ’diagnostic’ function are inference list , inference type, a list of interpolations for each of the ode state from gradient matching, and . It returns a vector of the median absolute standard deviations for each ode state. Value return list containing : • residual - vector containing residual. • interp - vector containing interpolation. Author(s) Mu Niu <mu.niu@glasgow.ac.uk> Examples ## Not run: require(mvtnorm) set.seed(SEED); SEED = 19537 FN_fun <- function(t, x, par_ode) { a = par_ode[1] b = par_ode[2] c = par_ode[3] as.matrix(c(c*(x[1]-x[1]^3/3 + x[2]),-1/c*(x[1]-a+b*x[2]))) } solveOde = ode$new(sample=2,fun=FN_fun) xinit = as.matrix(c(-1,-1)) tinterv = c(0,10) solveOde$solve_ode(par_ode=c(0.2,0.2,3),xinit,tinterv) n_o = max(dim(solveOde$y_ode)) noise = 0.01 y_no = t(solveOde$y_ode)+rmvnorm(n_o,c(0,0),noise*diag(2)) t_no = solveOde$t odem = ode$new(fun=FN_fun,grfun=NULL,t=t_no,ode_par=rep(c(0.1),3),y_ode=t(y_no)) ktype = 'rbf' rkgres = rkg(odem,y_no,ktype) rkgdiag = diagnostic( rkgres,1,'rkg',qq_plot=FALSE ) ## End(Not run) Kernel 9 Kernel The ’Kernel’ class object Description This a abstract class provide the kernel function and the 1st order derivative of rbf kernel function. Format R6Class object. Value an R6Class object which can be used for the rkhs interpolation. Methods kern(t1,t2) This method is used to calculate the kernel function given two one dimensional real inputs. dkd_kpar(t1,t2) This method is used to calculate the gradient of kernel function against the kernel hyper parameters given two one dimensional real inputs. dkdt(t1,t2) This method is used to calculate the 1st order derivative of kernel function given two one dimensional real inputs. Public fields k_par vector(of length n_hy) containing the hyper-parameter of kernel. n_hy is the length of kernel hyper parameters. Methods Public methods: • Kernel$new() • Kernel$greet() • Kernel$kern() • Kernel$dkd_kpar() • Kernel$dkdt() • Kernel$clone() Method new(): Usage: Kernel$new(k_par = NULL) Method greet(): Usage: 10 MLP Kernel$greet() Method kern(): Usage: Kernel$kern(t1, t2) Method dkd_kpar(): Usage: Kernel$dkd_kpar(t1, t2) Method dkdt(): Usage: Kernel$dkdt(t1, t2) Method clone(): The objects of this class are cloneable with this method. Usage: Kernel$clone(deep = FALSE) Arguments: deep Whether to make a deep clone. Author(s) Mu Niu, <mu.niu@glasgow.ac.uk> MLP The ’MLP’ class object Description This a R6 class. It inherits from ’kernel’ class. It provides the mlp kernel function and the 1st order derivative of mlp kernel function. Format R6Class object. Value an R6Class object which can be used for the rkhs interpolation. Super class KGode::Kernel -> MLP MLP 11 Methods Public methods: • MLP$greet() • MLP$set_k_par() • MLP$kern() • MLP$dkd_kpar() • MLP$dkdt() • MLP$clone() Method greet(): Usage: MLP$greet() Method set_k_par(): Usage: MLP$set_k_par(val) Method kern(): Usage: MLP$kern(t1, t2) Method dkd_kpar(): Usage: MLP$dkd_kpar(t1, t2) Method dkdt(): Usage: MLP$dkdt(t1, t2) Method clone(): The objects of this class are cloneable with this method. Usage: MLP$clone(deep = FALSE) Arguments: deep Whether to make a deep clone. Author(s) Mu Niu, <mu.niu@glasgow.ac.uk> 12 ode ode The ’ode’ class object Description This class provide all information about odes and methods for numerically solving odes. Format R6Class object. Value an R6Class object which can be used for gradient matching. Methods solve_ode(par_ode,xinit,tinterv) This method is used to solve ode numerically. optim_par(par,y_p,z_p) This method is used to estimate ode parameters by standard gradient matching. lossNODE(par,y_p,z_p) This method is used to calculate the mismatching between gradient of interpolation and gradient from ode. Public fields ode_par vector(of length n_p) containing ode parameters. n_p is the number of ode parameters. ode_fun function containing the ode function. t vector(of length n_o) containing time points of observations. n_o is the length of time points. Methods Public methods: • ode$new() • ode$greet() • ode$solve_ode() • ode$rmsfun() • ode$gradient() • ode$lossNODE() • ode$grlNODE() • ode$loss32NODE() • ode$grl32NODE() • ode$optim_par() • ode$clone() Method new(): ode 13 Usage: ode$new( sample = NULL, fun = NULL, grfun = NULL, t = NULL, ode_par = NULL, y_ode = NULL ) Method greet(): Usage: ode$greet() Method solve_ode(): Usage: ode$solve_ode(par_ode, xinit, tinterv) Method rmsfun(): Usage: ode$rmsfun(par_ode, state, M1, true_par) Method gradient(): Usage: ode$gradient(y_p, par_ode) Method lossNODE(): Usage: ode$lossNODE(par, y_p, z_p) Method grlNODE(): Usage: ode$grlNODE(par, y_p, z_p) Method loss32NODE(): Usage: ode$loss32NODE(par, y_p, z_p) Method grl32NODE(): Usage: ode$grl32NODE(par, y_p, z_p) Method optim_par(): Usage: ode$optim_par(par, y_p, z_p) Method clone(): The objects of this class are cloneable with this method. Usage: ode$clone(deep = FALSE) Arguments: deep Whether to make a deep clone. 14 RBF Author(s) Mu Niu, < mu.niu@glasgow.ac.uk> Examples noise = 0.1 ## set the variance of noise SEED = 19537 set.seed(SEED) ## Define ode function, we use lotka-volterra model in this example. ## we have two ode states x[1], x[2] and four ode parameters alpha, beta, gamma and delta. LV_fun = function(t,x,par_ode){ alpha=par_ode[1] beta=par_ode[2] gamma=par_ode[3] delta=par_ode[4] as.matrix( c( alpha*x[1]-beta*x[2]*x[1] , -gamma*x[2]+delta*x[1]*x[2] ) ) } ## Define the gradient of ode function against ode parameters ## df/dalpha, df/dbeta, df/dgamma, df/ddelta where f is the differential equation. LV_grlNODE= function(par,grad_ode,y_p,z_p) { alpha = par[1]; beta= par[2]; gamma = par[3]; delta = par[4] dres= c(0) dres[1] = sum( -2*( z_p[1,]-grad_ode[1,])*y_p[1,]*alpha ) dres[2] = sum( 2*( z_p[1,]-grad_ode[1,])*y_p[2,]*y_p[1,]*beta) dres[3] = sum( 2*( z_p[2,]-grad_ode[2,])*gamma*y_p[2,] ) dres[4] = sum( -2*( z_p[2,]-grad_ode[2,])*y_p[2,]*y_p[1,]*delta) dres } ## create a ode class object kkk0 = ode$new(2,fun=LV_fun,grfun=LV_grlNODE) ## set the initial values for each state at time zero. xinit = as.matrix(c(0.5,1)) ## set the time interval for the ode numerical solver. tinterv = c(0,6) ## solve the ode numerically using predefined ode parameters. alpha=1, beta=1, gamma=4, delta=1. kkk0$solve_ode(c(1,1,4,1),xinit,tinterv) ## Create another ode class object by using the simulation data from the ode numerical solver. ## If users have experiment data, they can replace the simulation data with the experiment data. ## set initial values for ode parameters. init_par = rep(c(0.1),4) init_yode = kkk0$y_ode init_t = kkk0$t kkk = ode$new(1,fun=LV_fun,grfun=LV_grlNODE,t=init_t,ode_par= init_par, y_ode=init_yode ) RBF The ’RBF’ class object RBF 15 Description This a R6 class. It inherits from ’kernel’ class. It provides the rbf kernel function and the 1st order derivative of rbf kernel function. Format R6Class object. Value an R6Class object which can be used for the rkhs interpolation. Super class KGode::Kernel -> RBF Methods Public methods: • RBF$greet() • RBF$set_k_par() • RBF$kern() • RBF$dkd_kpar() • RBF$dkdt() • RBF$clone() Method greet(): Usage: RBF$greet() Method set_k_par(): Usage: RBF$set_k_par(val) Method kern(): Usage: RBF$kern(t1, t2) Method dkd_kpar(): Usage: RBF$dkd_kpar(t1, t2) Method dkdt(): Usage: RBF$dkdt(t1, t2) Method clone(): The objects of this class are cloneable with this method. 16 rkg Usage: RBF$clone(deep = FALSE) Arguments: deep Whether to make a deep clone. Author(s) Mu Niu, <mu.niu@glasgow.ac.uk> rkg The ’rkg’ function Description This function is used to create ’rkhs’ class object and estimate ode parameters using standard gra- dient matching. Usage rkg(kkk, y_no, ktype) Arguments kkk ode class object. y_no matrix(of size n_s*n_o) containing noisy observations. The row(of length n_s) represent the ode states and the column(of length n_o) represents the time points. ktype character containing kernel type. User can choose ’rbf’ or ’mlp’ kernel. Details Arguments of the ’rkg’ function are ’ode’ class, noisy observation, and kernel type. It return the interpolation for each of the ode states. The Ode parameters are estimated using gradient matching, and the results are stored in the ’ode’ class as the ode_par attribute. Value return list containing : • intp - list containing interpolation for each ode state. • bbb - rkhs class objects for each ode state. Author(s) Mu Niu <mu.niu@glasgow.ac.uk> rkg 17 Examples ## Not run: require(mvtnorm) noise = 0.1 ## set the variance of noise SEED = 19537 set.seed(SEED) ## Define ode function, we use lotka-volterra model in this example. ## we have two ode states x[1], x[2] and four ode parameters alpha, beta, gamma and delta. LV_fun = function(t,x,par_ode){ alpha=par_ode[1] beta=par_ode[2] gamma=par_ode[3] delta=par_ode[4] as.matrix( c( alpha*x[1]-beta*x[2]*x[1] , -gamma*x[2]+delta*x[1]*x[2] ) ) } ## Define the gradient of ode function against ode parameters ## df/dalpha, df/dbeta, df/dgamma, df/ddelta where f is the differential equation. LV_grlNODE= function(par,grad_ode,y_p,z_p) { alpha = par[1]; beta= par[2]; gamma = par[3]; delta = par[4] dres= c(0) dres[1] = sum( -2*( z_p[1,]-grad_ode[1,])*y_p[1,]*alpha ) dres[2] = sum( 2*( z_p[1,]-grad_ode[1,])*y_p[2,]*y_p[1,]*beta) dres[3] = sum( 2*( z_p[2,]-grad_ode[2,])*gamma*y_p[2,] ) dres[4] = sum( -2*( z_p[2,]-grad_ode[2,])*y_p[2,]*y_p[1,]*delta) dres } ## create a ode class object kkk0 = ode$new(2,fun=LV_fun,grfun=LV_grlNODE) ## set the initial values for each state at time zero. xinit = as.matrix(c(0.5,1)) ## set the time interval for the ode numerical solver. tinterv = c(0,6) ## solve the ode numerically using predefined ode parameters. alpha=1, beta=1, gamma=4, delta=1. kkk0$solve_ode(c(1,1,4,1),xinit,tinterv) ## Add noise to the numerical solution of the ode model and use it as the noisy observation. n_o = max( dim( kkk0$y_ode) ) t_no = kkk0$t y_no = t(kkk0$y_ode) + rmvnorm(n_o,c(0,0),noise*diag(2)) ## Create a ode class object by using the simulation data we created from the ode numerical solver. ## If users have experiment data, they can replace the simulation data with the experiment data. ## Set initial value of ode parameters. init_par = rep(c(0.1),4) init_yode = t(y_no) init_t = t_no kkk = ode$new(1,fun=LV_fun,grfun=LV_grlNODE,t=init_t,ode_par= init_par, y_ode=init_yode ) ## The following examples with CPU or elapsed time > 10s 18 rkg3 ##Use function 'rkg' to estimate the ode parameters. The standard gradient matching method is coded ##in the the 'rkg' function. The parameter estimations are stored in the returned vector of 'rkg'. ## Choose a kernel type for 'rkhs' interpolation. Two options are provided 'rbf' and 'mlp'. ktype ='rbf' rkgres = rkg(kkk,y_no,ktype) ## show the results of ode parameter estimation using the standard gradient matching kkk$ode_par ## End(Not run) rkg3 The ’rkg3’ class object Description This class provides advanced gradient matching method by using the ode as a regularizer. Format R6Class object. Value an R6Class object which can be used for improving ode parameters estimation by using ode as a regularizer. Methods iterate(iter,innerloop,lamb) Iteratively updating ode parameters and interpolation regres- sion coefficients. witerate(iter,innerloop,dtilda,lamb) Iteratively updating ode parameters and the warped interpolation regression coefficients. full(par,lam) Updating ode parameters and rkhs interpolation regression coefficients simultane- ously. This method is slow but guarantee convergence. Public fields rk the ’rkhs’ class object containing the interpolation information for each state of the ode. ode_m the ’ode’ class object containing the information about the odes. Active bindings ode_m the ’ode’ class object containing the information about the odes. rkg3 19 Methods Public methods: • rkg3$new() • rkg3$greet() • rkg3$add() • rkg3$iterate() • rkg3$witerate() • rkg3$full() • rkg3$wfull() • rkg3$opfull() • rkg3$wopfull() • rkg3$cross() • rkg3$fullos() • rkg3$clone() Method new(): Usage: rkg3$new(rk = NULL, odem = NULL) Method greet(): Usage: rkg3$greet() Method add(): Usage: rkg3$add(x) Method iterate(): Usage: rkg3$iterate(iter, innerloop, lamb) Method witerate(): Usage: rkg3$witerate(iter, innerloop, dtilda, lamb) Method full(): Usage: rkg3$full(par, lam) Method wfull(): Usage: rkg3$wfull(par, lam, dtilda) Method opfull(): 20 rkhs Usage: rkg3$opfull(lam) Method wopfull(): Usage: rkg3$wopfull(lam, dtilda) Method cross(): Usage: rkg3$cross(lam, testX, testY) Method fullos(): Usage: rkg3$fullos(par) Method clone(): The objects of this class are cloneable with this method. Usage: rkg3$clone(deep = FALSE) Arguments: deep Whether to make a deep clone. Author(s) Mu Niu, <mu.niu@glasgow.ac.uk> rkhs The ’rkhs’ class object Description This class provide the interpolation methods using reproducing kernel Hilbert space. Format R6Class object. Value an R6Class object which can be used for doing interpolation using reproducing kernel Hilbert space. Methods predict() This method is used to make prediction on given time points skcross() This method is used to do cross-validation to estimate the weighting parameter lambda of L^2 norm. rkhs 21 Public fields y matrix(of size n_s*n_o) containing observation. t vector(of length n_o) containing time points for observation. b vector(of length n_o) containing coefficients of kernel or basis functions. lambda scalar containing the weighting parameter for L2 norm of the reproducing kernel Hilbert space. ker kernel class object containing kernel. Methods Public methods: • rkhs$new() • rkhs$greet() • rkhs$showker() • rkhs$predict() • rkhs$predictT() • rkhs$lossRK() • rkhs$grlossRK() • rkhs$numgrad() • rkhs$skcross() • rkhs$mkcross() • rkhs$loss11() • rkhs$grloss11() • rkhs$clone() Method new(): Usage: rkhs$new(y = NULL, t = NULL, b = NULL, lambda = NULL, ker = NULL) Method greet(): Usage: rkhs$greet() Method showker(): Usage: rkhs$showker() Method predict(): Usage: rkhs$predict() Method predictT(): Usage: 22 rkhs rkhs$predictT(testT) Method lossRK(): Usage: rkhs$lossRK(par, tl1, y_d, jitter) Method grlossRK(): Usage: rkhs$grlossRK(par, tl1, y_d, jitter) Method numgrad(): Usage: rkhs$numgrad(par, tl1, y_d, jitter) Method skcross(): Usage: rkhs$skcross(init, bounded) Method mkcross(): Usage: rkhs$mkcross(init) Method loss11(): Usage: rkhs$loss11(par, tl1, y_d, jitter) Method grloss11(): Usage: rkhs$grloss11(par, tl1, y_d, jitter) Method clone(): The objects of this class are cloneable with this method. Usage: rkhs$clone(deep = FALSE) Arguments: deep Whether to make a deep clone. Author(s) Mu Niu, <mu.niu@glasgow.ac.uk> rkhs 23 Examples ## Not run: require(mvtnorm) noise = 0.1 ## set the variance of noise SEED = 19537 set.seed(SEED) ## Define ode function, we use lotka-volterra model in this example. ## we have two ode states x[1], x[2] and four ode parameters alpha, beta, gamma and delta. LV_fun = function(t,x,par_ode){ alpha=par_ode[1] beta=par_ode[2] gamma=par_ode[3] delta=par_ode[4] as.matrix( c( alpha*x[1]-beta*x[2]*x[1] , -gamma*x[2]+delta*x[1]*x[2] ) ) } ## Define the gradient of ode function against ode parameters ## df/dalpha, df/dbeta, df/dgamma, df/ddelta where f is the differential equation. LV_grlNODE= function(par,grad_ode,y_p,z_p) { alpha = par[1]; beta= par[2]; gamma = par[3]; delta = par[4] dres= c(0) dres[1] = sum( -2*( z_p[1,]-grad_ode[1,])*y_p[1,]*alpha ) dres[2] = sum( 2*( z_p[1,]-grad_ode[1,])*y_p[2,]*y_p[1,]*beta) dres[3] = sum( 2*( z_p[2,]-grad_ode[2,])*gamma*y_p[2,] ) dres[4] = sum( -2*( z_p[2,]-grad_ode[2,])*y_p[2,]*y_p[1,]*delta) dres } ## create a ode class object kkk0 = ode$new(2,fun=LV_fun,grfun=LV_grlNODE) ## set the initial values for each state at time zero. xinit = as.matrix(c(0.5,1)) ## set the time interval for the ode numerical solver. tinterv = c(0,6) ## solve the ode numerically using predefined ode parameters. alpha=1, beta=1, gamma=4, delta=1. kkk0$solve_ode(c(1,1,4,1),xinit,tinterv) ## Add noise to the numerical solution of the ode model and use it as the noisy observation. n_o = max( dim( kkk0$y_ode) ) t_no = kkk0$t y_no = t(kkk0$y_ode) + rmvnorm(n_o,c(0,0),noise*diag(2)) ## Create a ode class object by using the simulation data we created from the ode numerical solver. ## If users have experiment data, they can replace the simulation data with the experiment data. ## Set initial value of ode parameters. init_par = rep(c(0.1),4) init_yode = t(y_no) init_t = t_no kkk = ode$new(1,fun=LV_fun,grfun=LV_grlNODE,t=init_t,ode_par= init_par, y_ode=init_yode ) ## The following examples with CPU or elapsed time > 5s ####### rkhs interpolation for the 1st state of ode using 'rbf' kernel 24 third ### set initial value of length scale of rbf kernel initlen = 1 aker = RBF$new(initlen) bbb = rkhs$new(t(y_no)[1,],t_no,rep(1,n_o),1,aker) ## optimise lambda by cross-validation ## initial value of lambda initlam = 2 bbb$skcross( initlam ) ## make prediction using the 'predict()' method of 'rkhs' class and plot against the time. plot(t_no,bbb$predict()$pred) ## End(Not run) third The ’third’ function Description This function is used to create ’rk3g’ class objects and estimate ode parameters using ode regularised gradient matching. Usage third(lam, kkk, bbb, crtype, woption, dtilda) Arguments lam scalar containing the weighting parameter of ode regularisation. kkk ’ode’ class object containing all information about the odes. bbb list of ’rkhs’ class object containing the interpolation for all ode states. crtype character containing the optimisation scheme type. User can choose ’i’ or ’3’. ’i’ is for fast iterative scheme and ’3’ for optimising the ode parameters and interpolation coefficients simultaneously. woption character containing the indication of using warping. If the warping scheme is done before using the ode regularisation, user can choose ’w’ otherwise just leave this option empty. dtilda vector(of length n_o) containing the gradient of warping function. This variable is only used if user want to combine warping and the ode regularisation. Details Arguments of the ’third’ function are ode regularisation weighting parameter, ’ode’ class objects, ’rkhs’ class objects, noisy observation, type of regularisation scheme, option of warping and the gradient of warping function. It return the interpolation for each of the ode states. The ode param- eters are estimated using gradient matching, and the results are stored in the ode_par attribute of ’ode’ class. third 25 Value return list containing : • oppar - vector(of length n_p) containing the ode parameters estimation. n_p is the length of ode parameters. • rk3 - list of ’rkhs’ class object containing the updated interpolation results. Author(s) Mu Niu <mu.niu@glasgow.ac.uk> Examples ## Not run: require(mvtnorm) noise = 0.1 SEED = 19537 set.seed(SEED) ## Define ode function, we use lotka-volterra model in this example. ## we have two ode states x[1], x[2] and four ode parameters alpha, beta, gamma and delta. LV_fun = function(t,x,par_ode){ alpha=par_ode[1] beta=par_ode[2] gamma=par_ode[3] delta=par_ode[4] as.matrix( c( alpha*x[1]-beta*x[2]*x[1] , -gamma*x[2]+delta*x[1]*x[2] ) ) } ## Define the gradient of ode function against ode parameters ## df/dalpha, df/dbeta, df/dgamma, df/ddelta where f is the differential equation. LV_grlNODE= function(par,grad_ode,y_p,z_p) { alpha = par[1]; beta= par[2]; gamma = par[3]; delta = par[4] dres= c(0) dres[1] = sum( -2*( z_p[1,]-grad_ode[1,])*y_p[1,]*alpha ) dres[2] = sum( 2*( z_p[1,]-grad_ode[1,])*y_p[2,]*y_p[1,]*beta) dres[3] = sum( 2*( z_p[2,]-grad_ode[2,])*gamma*y_p[2,] ) dres[4] = sum( -2*( z_p[2,]-grad_ode[2,])*y_p[2,]*y_p[1,]*delta) dres } ## create a ode class object kkk0 = ode$new(2,fun=LV_fun,grfun=LV_grlNODE) ## set the initial values for each state at time zero. xinit = as.matrix(c(0.5,1)) ## set the time interval for the ode numerical solver. tinterv = c(0,6) ## solve the ode numerically using predefined ode parameters. alpha=1, beta=1, gamma=4, delta=1. kkk0$solve_ode(c(1,1,4,1),xinit,tinterv) ## Add noise to the numerical solution of the ode model and use it as the noisy observation. n_o = max( dim( kkk0$y_ode) ) t_no = kkk0$t 26 Warp y_no = t(kkk0$y_ode) + rmvnorm(n_o,c(0,0),noise*diag(2)) ## create a ode class object by using the simulation data we created from the ode numerical solver. ## If users have experiment data, they can replace the simulation data with the experiment data. ## set initial value of Ode parameters. init_par = rep(c(0.1),4) init_yode = t(y_no) init_t = t_no kkk = ode$new(1,fun=LV_fun,grfun=LV_grlNODE,t=init_t,ode_par= init_par, y_ode=init_yode ) ## The following examples with CPU or elapsed time > 10s ## Use function 'rkg' to estimate the ode parameters. ktype ='rbf' rkgres = rkg(kkk,y_no,ktype) bbb = rkgres$bbb ############# gradient matching + ode regularisation crtype='i' ## using cross validation to estimate the weighting parameters of the ode regularisation lam=c(1e-4,1e-5) lamil1 = crossv(lam,kkk,bbb,crtype,y_no) lambdai1=lamil1[[1]] ## estimate ode parameters using gradient matching and ode regularisation res = third(lambdai1,kkk,bbb,crtype) ## display the ode parameter estimation. res$oppar ## End(Not run) Warp The ’Warp’ class object Description This class provide the warping method which can be used to warp the original signal to sinusoidal like signal. Format R6Class object. Value an R6Class object which can be used for doing interpolation using reproducing kernel Hilbert space. Warp 27 Methods warpsin(len ,lop,p0,eps) This method is used to warp the initial interpolation into a sinusoidal shape. slowWarp(lens,peod,eps) This method is used to find the optimised initial hyper parameters for the sigmoid basis function for each ode states. warpLossLen(par,lam,p0,eps) This method is used to implement the loss function for warping. It is called by the ’warpSin’ function. Public fields y matrix(of size n_s*n_o) containing observation. t vector(of length n_o) containing time points for observation. b vector(of length n_o) containing coefficients of kernel or basis functions. lambda scalar containing the weighting parameter for penalising the length of warped time span. ker kernel class object containing sigmoid basis function. Methods Public methods: • Warp$new() • Warp$greet() • Warp$showker() • Warp$warpLoss() • Warp$warpLossLen() • Warp$warpSin() • Warp$slowWarp() • Warp$clone() Method new(): Usage: Warp$new(y = NULL, t = NULL, b = NULL, lambda = NULL, ker = NULL) Method greet(): Usage: Warp$greet() Method showker(): Usage: Warp$showker() Method warpLoss(): Usage: Warp$warpLoss(par, len, p0, eps) 28 warpfun Method warpLossLen(): Usage: Warp$warpLossLen(par, lam, p0, eps) Method warpSin(): Usage: Warp$warpSin(len, lop, p0, eps) Method slowWarp(): Usage: Warp$slowWarp(lens, p0, eps) Method clone(): The objects of this class are cloneable with this method. Usage: Warp$clone(deep = FALSE) Arguments: deep Whether to make a deep clone. Author(s) Mu Niu, <mu.niu@glasgow.ac.uk> warpfun The ’warpfun’ function Description This function is used to produce the warping function and learning the interpolation in the warped time domain. Usage warpfun(kkkrkg, bbb, peod, eps, fixlens, y_no, testData, witer) Arguments kkkrkg ’ode’ class object. bbb list of ’rkhs’ class object. peod vector(of length n_s) containing the period of warped signal. n_s is the length of the ode states. eps vector(of length n_s) containing the uncertainty level of the period. n_s is the length of the ode states. fixlens vector(of length n_s) containing the initial values of the hyper parameters of sigmoid basis function. warpfun 29 y_no matrix(of size n_s*n_o) containing noisy observations. The row(of length n_s) represent the ode states and the column(of length n_o) represents the time points. testData vector(of size n_x) containing user defined time points which will be warped by the warping function. witer scale containing the number of iterations for optimising the hyper parameters of warping. Details Arguments of the ’warpfun’ function are ’ode’ class, ’rkhs’ class, period of warped signal, uncer- tainty level of the period, initial values of the hyper parameters for sigmoid basis function, noisy observations and the time points that user want to warped. It return the interpolation for each of the ode states. The ode parameters are estimated using gradient matching, and the results are stored in the ’ode’ class as the ode_par attribute. Value return list containing : • dtilda - vector(of length n_x) containing the gradients of warping function at user defined time points. • bbbw - list of ’rkhs’ class object containing the interpolation in warped time domain. • wtime - vector(of length n_x) containing the warped time points. • wfun - list of ’rkhs’ class object containing information about warping function. • wkkk - ’ode’ class object containing the result of parameter estimation using the warped signal and gradient matching. Author(s) Mu Niu <mu.niu@glasgow.ac.uk> Examples ## Not run: require(mvtnorm) noise = 0.1 SEED = 19537 set.seed(SEED) ## Define ode function, we use lotka-volterra model in this example. ## we have two ode states x[1], x[2] and four ode parameters alpha, beta, gamma and delta. LV_fun = function(t,x,par_ode){ alpha=par_ode[1] beta=par_ode[2] gamma=par_ode[3] delta=par_ode[4] as.matrix( c( alpha*x[1]-beta*x[2]*x[1] , -gamma*x[2]+delta*x[1]*x[2] ) ) } ## Define the gradient of ode function against ode parameters 30 warpfun ## df/dalpha, df/dbeta, df/dgamma, df/ddelta where f is the differential equation. LV_grlNODE= function(par,grad_ode,y_p,z_p) { alpha = par[1]; beta= par[2]; gamma = par[3]; delta = par[4] dres= c(0) dres[1] = sum( -2*( z_p[1,]-grad_ode[1,])*y_p[1,]*alpha ) dres[2] = sum( 2*( z_p[1,]-grad_ode[1,])*y_p[2,]*y_p[1,]*beta) dres[3] = sum( 2*( z_p[2,]-grad_ode[2,])*gamma*y_p[2,] ) dres[4] = sum( -2*( z_p[2,]-grad_ode[2,])*y_p[2,]*y_p[1,]*delta) dres } ## create a ode class object kkk0 = ode$new(2,fun=LV_fun,grfun=LV_grlNODE) ## set the initial values for each state at time zero. xinit = as.matrix(c(0.5,1)) ## set the time interval for the ode numerical solver. tinterv = c(0,6) ## solve the ode numerically using predefined ode parameters. alpha=1, beta=1, gamma=4, delta=1. kkk0$solve_ode(c(1,1,4,1),xinit,tinterv) ## Add noise to the numerical solution of the ode model and use it as the noisy observation. n_o = max( dim( kkk0$y_ode) ) t_no = kkk0$t y_no = t(kkk0$y_ode) + rmvnorm(n_o,c(0,0),noise*diag(2)) ## create a ode class object by using the simulation data we created from the Ode numerical solver. ## If users have experiment data, they can replace the simulation data with the experiment data. ## set initial value of Ode parameters. init_par = rep(c(0.1),4) init_yode = t(y_no) init_t = t_no kkk = ode$new(1,fun=LV_fun,grfun=LV_grlNODE,t=init_t,ode_par= init_par, y_ode=init_yode ) ## The following examples with CPU or elapsed time > 10s ## Use function 'rkg' to estimate the Ode parameters. ktype ='rbf' rkgres = rkg(kkk,y_no,ktype) bbb = rkgres$bbb ###### warp all ode states peod = c(6,5.3) ## the guessing period eps= 1 ## the uncertainty level of period ###### learn the initial value of the hyper parameters of the warping basis function fixlens=warpInitLen(peod,eps,rkgres) kkkrkg = kkk$clone() ## make a copy of ode class objects ##learn the warping function, warp data points and do gradient matching in the warped time domain. www = warpfun(kkkrkg,bbb,peod,eps,fixlens,y_no,kkkrkg$t) dtilda= www$dtilda ## gradient of warping function bbbw = www$bbbw ## interpolation in warped time domain warpInitLen 31 resmtest = www$wtime ## warped time points ##display the results of parameter estimation using gradient matching in the warped time domain. www$wkkk$ode_par ## End(Not run) warpInitLen The ’warpInitLen’ function Description This function is used to find the optmised initial value of the hyper parameter for the sigmoid basis function which is used for warping. Usage warpInitLen(peod, eps, rkgres, lens) Arguments peod vector(of length n_s) containing the period of warped signal. n_s is the length of the ode states. eps vector(of length n_s) containing the uncertainty level of the period. n_s is the length of the ode states. rkgres list containing interpolation and ’rkhs’ class objects for all ode states. lens vector(of length n_l) containing a list of hyper parameters of sigmoid basis func- tion. n_l is the length of user defined hyper parameters of the sigmoid basis functino. Details Arguments of the ’warpfun’ function are ’ode’ class, ’rkhs’ class, period of warped signal, uncer- tainty level of the period, initial values of the hyper parameters for sigmoid basis function, noisy observations and the time points that user want to warped. It return the interpolation for each of the ode states. The ode parameters are estimated using gradient matching, and the results are stored in the ’ode’ class as the ode_par attribute. Value return list containing : • wres- vector(of length n_s) contaning the optimised initial hyper parameters of sigmoid basis function for each ode states. Author(s) Mu Niu <mu.niu@glasgow.ac.uk> 32 warpInitLen Examples ## Not run: require(mvtnorm) noise = 0.1 SEED = 19537 set.seed(SEED) ## Define ode function, we use lotka-volterra model in this example. ## we have two ode states x[1], x[2] and four ode parameters alpha, beta, gamma and delta. LV_fun = function(t,x,par_ode){ alpha=par_ode[1] beta=par_ode[2] gamma=par_ode[3] delta=par_ode[4] as.matrix( c( alpha*x[1]-beta*x[2]*x[1] , -gamma*x[2]+delta*x[1]*x[2] ) ) } ## Define the gradient of ode function against ode parameters ## df/dalpha, df/dbeta, df/dgamma, df/ddelta where f is the differential equation. LV_grlNODE= function(par,grad_ode,y_p,z_p) { alpha = par[1]; beta= par[2]; gamma = par[3]; delta = par[4] dres= c(0) dres[1] = sum( -2*( z_p[1,]-grad_ode[1,])*y_p[1,]*alpha ) dres[2] = sum( 2*( z_p[1,]-grad_ode[1,])*y_p[2,]*y_p[1,]*beta) dres[3] = sum( 2*( z_p[2,]-grad_ode[2,])*gamma*y_p[2,] ) dres[4] = sum( -2*( z_p[2,]-grad_ode[2,])*y_p[2,]*y_p[1,]*delta) dres } ## create a ode class object kkk0 = ode$new(2,fun=LV_fun,grfun=LV_grlNODE) ## set the initial values for each state at time zero. xinit = as.matrix(c(0.5,1)) ## set the time interval for the ode numerical solver. tinterv = c(0,6) ## solve the ode numerically using predefined ode parameters. alpha=1, beta=1, gamma=4, delta=1. kkk0$solve_ode(c(1,1,4,1),xinit,tinterv) ## Add noise to the numerical solution of the ode model and use it as the noisy observation. n_o = max( dim( kkk0$y_ode) ) t_no = kkk0$t y_no = t(kkk0$y_ode) + rmvnorm(n_o,c(0,0),noise*diag(2)) ## create a ode class object by using the simulation data we created from the Ode numerical solver. ## If users have experiment data, they can replace the simulation data with the experiment data. ## set initial value of Ode parameters. init_par = rep(c(0.1),4) init_yode = t(y_no) init_t = t_no kkk = ode$new(1,fun=LV_fun,grfun=LV_grlNODE,t=init_t,ode_par= init_par, y_ode=init_yode ) ## The following examples with CPU or elapsed time > 10s warpInitLen 33 ## Use function 'rkg' to estimate the Ode parameters. ktype ='rbf' rkgres = rkg(kkk,y_no,ktype) bbb = rkgres$bbb ###### warp all ode states peod = c(6,5.3) ## the guessing period eps= 1 ## the uncertainty level of period ###### learn the initial value of the hyper parameters of the warping basis function fixlens=warpInitLen(peod,eps,rkgres) ## End(Not run) Index ∗ data Kernel, 9 MLP, 10 ode, 12 RBF, 14 rkg3, 18 rkhs, 20 Warp, 26 bootstrap, 2 crossv, 5 diagnostic, 7 Kernel, 9 KGode::Kernel, 10, 15 MLP, 10 ode, 12 R6Class, 9, 10, 12, 15, 18, 20, 26 RBF, 14 rkg, 16 rkg3, 18 rkhs, 20 third, 24 Warp, 26 warpfun, 28 warpInitLen, 31 34

bigalgebra

cran

Package ‘bigalgebra’ October 12, 2022 Type Package Title 'BLAS' and 'LAPACK' Routines for Native R Matrices and 'big.matrix' Objects Version 1.1.0 Date 2022-04-07 Depends bigmemory (>= 4.0.0) Imports methods LinkingTo bigmemory, BH, Rcpp Author Frederic Bertrand [cre, ctb] (<https://orcid.org/0000-0002-0837-8281>), Michael J. Kane [aut], Bryan Lewis [aut], John W. Emerson [aut] Maintainer Frederic Bertrand <frederic.bertrand@utt.fr> Description Provides arithmetic functions for R matrix and 'big.matrix' objects as well as func- tions for QR factorization, Cholesky factorization, General eigenvalue, and Singular value de- composition (SVD). A method matrix multiplication and an arithmetic method -for matrix addi- tion, matrix difference- allows for mixed type operation -a matrix class ob- ject and a big.matrix class object- and pure type operation for two big.matrix class objects. License LGPL-3 | Apache License 2.0 Encoding UTF-8 Copyright (C) 2014 Michael J. Kane, Bryan Lewis, and John W. Emerson LazyLoad yes NeedsCompilation yes RoxygenNote 7.1.1 URL https://fbertran.github.io/bigalgebra/, https://github.com/fbertran/bigalgebra/ BugReports https://github.com/fbertran/bigalgebra/issues/ Repository CRAN Date/Publication 2022-04-08 06:42:37 UTC 1 2 bigalgebra-package R topics documented: bigalgebra-package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 balgebra-methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 daxpy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 dcopy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 dgeev . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 dgemm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 dgeqrf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 dgesdd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 dpotrf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 dscal . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Index 17 bigalgebra-package Arithmetic routines for native R matrices and big.matrix objects. Description This package provides arithmetic functions for native R matrices and big.matrix objects. Details This package provides arithmetic functions for native R matrices and big.matrix objects. The package defines a number of global options that begin with bigalgebra. They include: Option Default value bigalgebra.temp_pattern "matrix_" bigalgebra.tempdir tempdir bigalgebra.mixed_arithmetic_returns_R_matrix TRUE bigalgebra.DEBUG FALSE The bigalgebra.tempdir option must be a function that returns a temporary directory path used to big matrix results of BLAS and LAPACK operations. The deault value is simply the default R tempdir function. The bigalgebra.temp_pattern is a name prefix for file names of generated big matrix objects output as a result of BLAS and LAPACK operations. The bigalgebra.mixed_arithmetic_returns_R_matrix option determines whether arithmetic operations involving an R matrix or vector and a big.matrix matrix or vector return a big matrix (when the option is FALSE), or return a normal R matrix (TRUE). The package is built, by default, with R’s native BLAS libraries, which use 32-bit signed integer indexing. The default build is limited to vectors of at most 2**31 - 1 entries and matrices with at most 2**31 - 1 rows and 2**31 - 1 columns (note that standard R matrices are limtied to 2**31 - 1 total entries). bigalgebra-package 3 The package includes a reference BLAS implementation that supports 64-bit integer indexing, re- laxing the limitation on vector lengths and matrix row and column limits. Installation of this pack- age with the 64-bit reference BLAS implementation may be performed from the command-line install: REFBLAS=1 R CMD INSTALL bigalgebra where "bigalgebra" is the source package (for example, bigalgebra_0.8.4.tar.gz). The package may also be build with user-supplied external BLAS and LAPACK libraries, in either 32- or 64-bit varieties. This is an advanced topic that requires additional Makevars modification, and may include adjustment of the low-level calling syntax depending on the library used. Feel free to contact us for help installing and running the package. Author(s) Frédéric Bertrand, Michael J. Kane, Bryan Lewis, John W. Emerson Maintainer: Frédéric Bertrand <frederic.bertrand@utt.fr> References https://www.netlib.org/blas/ https://www.netlib.org/lapack/ See Also bigmemory, big.matrix Examples # Testing the development of the user-friendly operators: # if you have any problems, please email us! - Jay & Mike 4/29/2010 library("bigmemory") A <- big.matrix(5,4, type="double", init=0, dimnames=list(NULL, c("alpha", "beta"))) B <- big.matrix(4,4, type="double", init=0, dimnames=list(NULL, c("alpha", "beta"))) C <- A D <- A[] print(C - D) # Compare the results (subtraction of an R matrix from a # big.matrix) # The next example illustrates mixing R and big.matrix objects. It returns by # default (see # options("bigalgebra.mixed_arithmetic_returns_R_matrix") D <- matrix(rnorm(16),4) E <- A 4 daxpy balgebra-methods Class "big.matrix" arithmetic methods Description Arithmetic operations for big.matrices Methods %*% signature{x="big.matrix", y="big.matrix"}: ... %*% signature{x="matrix", y="big.matrix"}: ... %*% signature{x="big.matrix", y="matrix"}: ... Arith signature{x="big.matrix", y="big.matrix"}: ... Arith signature{x="big.matrix", y="matrix"}: ... Arith signature{x="matrix", y="big.matrix"}: ... Arith signature{x="big.matrix", y="numeric"}: ... Arith signature{x="numeric", y="big.matrix"}: ... Notes Miscellaneous arithmetic methods for matrices and big.matrices. See also options("bigalgebra.mixed_arithmetic_retu Author(s) B. W. Lewis <blewis@illposed.net> daxpy BLAS daxpy functionality Description This function implements the function Y := A * X + Y where X and Y may be either native double- precision valued R matrices or numeric vectors, or double-precision valued big.matrix objects, and A is a scalar. Usage daxpy(A = 1, X, Y) Arguments A Optional numeric scalar value to scale the matrix X by, with a default value of 1. X Requried to be either a native R matrix or numeric vector, or a big.matrix object Y Optional native R matrix or numeric vector, or a big.matrix object daxpy 5 Details At least one of either X or Y must be a big.matrix. All values must be of type double (the only type presently supported by the bigalgebra package). This function is rarely necessary to use directly since the bigalgebra package defines standard arith- metic operations and scalar multiplication. It is more efficient to use daxpy directly when both scaling and matrix addition are required, in which case both operations are performed in one step. Value The output value depends on the classes of input values X and Y and on the value of the global option bigalgebra.mixed_arithmetic_returns_R_matrix. If X and Y are both big matrices, or Y is missing, options("bigalgebra.mixed_arithmetic_returns_R_matrix") is FALSE, then a big.matrix is returned. The returned big.matrix is backed by a temporary file mapping that will be deleted when the returned result is garbage collected by R (see the examples). Otherwise, a standard R matrix is returned. The dimensional shape of the output is taken from X. If input X is dimensionless (that is, lacks a dimension attribute), then the output is a column vector. Author(s) Michael J. Kane References https://www.netlib.org/blas/daxpy.f See Also bigmemory Examples require(bigmemory) A = matrix(1, nrow=3, ncol=2) B <- big.matrix(3, 2, type="double", init=0, dimnames=list(NULL, c("alpha", "beta")), shared=FALSE) C = B + B # C is a new big matrix D = A + B # D defaults to a regular R matrix, to change this, set the option: # options(bigalgebra.mixed_arithmetic_returns_R_matrix=FALSE) E = daxpy(A=1.0, X=B, Y=B) # Same kind of result as C print(C[]) print(D) print(E[]) # The C and E big.matrix file backings will be deleted when garbage collected: # (We enable debugging to see this explicitly) options(bigalgebra.DEBUG=TRUE) rm(C,E) gc() 6 dcopy dcopy Copy a vector. Description Copy double precision DX to double precision DY. For I = 0 to N-1, copy DX(LX+I*INCX) to DY(LY+I*INCY), where LX = 1 if INCX .GE. 0, else LX = 1+(1-N)*INCX, and LY is defined in a similar way using INCY. Usage dcopy(N = NULL, X, INCX = 1, Y, INCY = 1) Arguments N number of elements in input vector(s) X double precision vector with N elements INCX storage spacing between elements of DX Y double precision vector with N elements INCY storage spacing between elements of DY Value DY copy of vector DX (unchanged if N .LE. 0) References C. L. Lawson, R. J. Hanson, D. R. Kincaid and F. T. Krogh, Basic linear algebra subprograms for Fortran usage, Algorithm No. 539, Transactions on Mathematical Software 5, 3 (September 1979), pp. 308-323. Examples ## Not run: set.seed(4669) A = big.matrix(3, 2, type="double", init=1, dimnames=list(NULL, c("alpha", "beta")), shared=FALSE) B = big.matrix(3, 2, type="double", init=0, dimnames=list(NULL, c("alpha", "beta")), shared=FALSE) dcopy(X=A,Y=B) A[,]-B[,] # The big.matrix file backings will be deleted when garbage collected. rm(A,B) gc() ## End(Not run) dgeev 7 dgeev DGEEV computes eigenvalues and eigenvectors. Description DGEEV computes the eigenvalues and, optionally, the left and/or right eigenvectors for GE matri- ces. DGEEV computes for an N-by-N real nonsymmetric matrix A, the eigenvalues and, optionally, the left and/or right eigenvectors. The right eigenvector v(j) of A satisfies A * v(j) = lambda(j) * v(j) where lambda(j) is its eigenvalue. The left eigenvector u(j) of A satisfies u(j)**H * A = lambda(j) * u(j)**H where u(j)**H denotes the conjugate-transpose of u(j). The computed eigenvectors are normalized to have Euclidean norm equal to 1 and largest compo- nent real. Usage dgeev( JOBVL = "V", JOBVR = "V", N = NULL, A, LDA = NULL, WR, WI, VL, LDVL = NULL, VR = NULL, LDVR = NULL, WORK = NULL, LWORK = NULL ) Arguments JOBVL a character. = ’N’: left eigenvectors of A are not computed; = ’V’: left eigenvectors of A are computed. JOBVR a character. = ’N’: right eigenvectors of A are not computed; = ’V’: right eigenvectors of A are computed. N an integer. The order of the matrix A. N >= 0. A a matrix of dimension (LDA,N), the N-by-N matrix A. LDA an integer. The leading dimension of the matrix A. LDA >= max(1,N). 8 dgeev WR a vector of dimension (N). WR contain the real part of the computed eigen- values. Complex conjugate pairs of eigenvalues appear consecutively with the eigenvalue having the positive imaginary part first. WI a vector of dimension (N). WI contain the imaginary part of the computed eigen- values. Complex conjugate pairs of eigenvalues appear consecutively with the eigenvalue having the positive imaginary part first. VL a matrx of dimension (LDVL,N) If JOBVL = ’V’, the left eigenvectors u(j) are stored one after another in the columns of VL, in the same order as their eigenvalues. If JOBVL = ’N’, VL is not referenced. If the j-th eigenvalue is real, then u(j) = VL(:,j), the j-th column of VL. If the j-th and (j+1)-st eigenvalues form a complex conjugate pair, then u(j) = VL(:,j) + i*VL(:,j+1) and u(j+1) = VL(:,j) - i*VL(:,j+1). LDVL an integer. The leading dimension of the array VL. LDVL >= 1; if JOBVL = ’V’, LDVL >= N. VR a matrix of dimension (LDVR,N). If JOBVR = ’V’, the right eigenvectors v(j) are stored one after another in the columns of VR, in the same order as their eigenvalues. If JOBVR = ’N’, VR is not referenced. If the j-th eigenvalue is real, then v(j) = VR(:,j), the j-th column of VR. If the j-th and (j+1)-st eigenvalues form a complex conjugate pair, then v(j) = VR(:,j) + i*VR(:,j+1) and v(j+1) = VR(:,j) - i*VR(:,j+1). LDVR an integer. The leading dimension of the array VR. LDVR >= 1; if JOBVR = ’V’, LDVR >= N. WORK a matrix of dimension (MAX(1,LWORK)) LWORK an integer. The dimension of the array WORK.LWORK >= max(1,3*N), and if JOBVL = ’V’ or JOBVR = ’V’, LWORK >= 4*N. For good performance, LWORK must generally be larger. If LWORK = -1, then a workspace query is assumed; the routine only calculates the optimal size of the WORK array, returns this value as the first entry of the WORK array, and no error message related to LWORK is issued by XERBLA. Value WR, WI, VR, VL and Work. On exit, A has been overwritten. Examples ## Not run: set.seed(4669) A = matrix(rnorm(16),4) WR= matrix(0,nrow=4,ncol=1) WI= matrix(0,nrow=4,ncol=1) VL = matrix(0,ncol=4,nrow=4) eigen(A) dgeev(A=A,WR=WR,WI=WI,VL=VL) dgemm 9 VL WR WI rm(A,WR,WI,VL) A = as.big.matrix(matrix(rnorm(16),4)) WR= matrix(0,nrow=4,ncol=1) WI= matrix(0,nrow=4,ncol=1) VL = as.big.matrix(matrix(0,ncol=4,nrow=4)) eigen(A[,]) dgeev(A=A,WR=WR,WI=WI,VL=VL) VL[,] WR[,] WI[,] rm(A,WR,WI,VL) gc() ## End(Not run) dgemm Matrix Multiply Description This is function provides dgemm functionality, which DGEMM performs one of the matrix-matrix operations. C := ALPHA * op(A) * op(B) + BETA * C. Usage dgemm( TRANSA = "N", TRANSB = "N", M = NULL, N = NULL, K = NULL, ALPHA = 1, A, LDA = NULL, B, LDB = NULL, BETA = 0, C, LDC = NULL, COFF = 0 ) 10 dgemm Arguments TRANSA a character. TRANSA specifies the form of op( A ) to be used in the matrix multiplication as follows: TRANSA = ’N’ or ’n’, op( A ) = A. TRANSA = ’T’ or ’t’, op( A ) = A**T. TRANSA = ’C’ or ’c’, op( A ) = A**T. TRANSB a character. TRANSB specifies the form of op( B ) to be used in the matrix multiplication as follows: #’ TRANSA = ’N’ or ’n’, op( B ) = B. TRANSA = ’T’ or ’t’, op( B ) = B**T. TRANSA = ’C’ or ’c’, op( B ) = B**T. M an integer. M specifies the number of rows of the matrix op( A ) and of the matrix C. M must be at least zero. N an integer. N specifies the number of columns of the matrix op( B ) and of the matrix C. N must be at least zero. K an integer. K specifies the number of columns of the matrix op( A ) and the number of rows of the matrix op( B ). K must be at least zero. ALPHA a real number. Specifies the scalar alpha. A a matrix of dimension (LDA, ka), where ka is k when TRANSA = ’N’ or ’n’, and is m otherwise. Before entry with TRANSA = ’N’ or ’n’, the leading m by k part of the array A must contain the matrix A, otherwise the leading k by m part of the array A must contain the matrix A. LDA an integer. B a matrix of dimension ( LDB, kb ), where kb is n when TRANSB = ’N’ or ’n’, and is k otherwise. Before entry with TRANSB = ’N’ or ’n’, the leading k by n part of the array B must contain the matrix B, otherwise the leading n by k part of the array B must contain the matrix B. LDB an integer. BETA a real number. Specifies the scalar beta C a matrix of dimension ( LDC, N ). Before entry, the leading m by n part of the array C must contain the matrix C, except when beta is zero, in which case C need not be set on entry. On exit, the array C is overwritten by the m by n matrix ( alpha*op( A )*op( B ) + beta*C ). LDC an integer. COFF offset for C. Value Update C with the result. dgeqrf 11 Examples require(bigmemory) A = as.big.matrix(matrix(1, nrow=3, ncol=2)) B <- big.matrix(2, 3, type="double", init=-1, dimnames=list(NULL, c("alpha", "beta")), shared=FALSE) C = big.matrix(3, 3, type="double", init=1, dimnames=list(NULL, c("alpha", "beta", "gamma")), shared=FALSE) 2*A[,]%*%B[,]+0.5*C[,] E = dgemm(ALPHA=2.0, A=A, B=B, BETA=0.5, C=C) E[,] # Same result # The big.matrix file backings will be deleted when garbage collected. rm(A,B,C,E) gc() dgeqrf QR factorization Description DGEQRF computes a QR factorization of a real M-by-N matrix A: A = Q * R. Usage dgeqrf( M = NULL, N = NULL, A, LDA = NULL, TAU = NULL, WORK = NULL, LWORK = NULL ) Arguments M an integer. The number of rows of the matrix A. M >= 0. N an integer. The number of columns of the matrix A. N >= 0. A the M-by-N big matrix A. LDA an integer. The leading dimension of the array A. LDA >= max(1,M). TAU a min(M,N) matrix. The scalar factors of the elementary reflectors. WORK a (MAX(1,LWORK)) matrix. On exit, if INFO = 0, WORK(1) returns the opti- mal LWORK. LWORK an integer. The dimension of th array WORK. 12 dgesdd Value M-by-N big matrix A. The elements on and above the diagonal of the array contain the min(M,N)- by-N upper trapezoidal matrix R (R is upper triangular if m >= n); the elements below the diagonal, with the array TAU, represent the orthogonal matrix Q as a product of min(m,n) elementary reflec- tors. Examples ## Not run: #' hilbert <- function(n) { i <- 1:n; 1 / outer(i - 1, i, "+") } h9 <- hilbert(9); h9 qr(h9)$rank #--> only 7 qrh9 <- qr(h9, tol = 1e-10) qrh9$rank C <- as.big.matrix(h9) dgeqrf(A=C) # The big.matrix file backings will be deleted when garbage collected. rm(C) gc() ## End(Not run) dgesdd DGESDD computes the singular value decomposition (SVD) of a real matrix. Description DGESDD computes the singular value decomposition (SVD) of a real M-by-N matrix A, optionally computing the left and right singular vectors. If singular vectors are desired, it uses a divide-and- conquer algorithm. The SVD is written A = U * SIGMA * transpose(V) where SIGMA is an M-by-N matrix which is zero except for its min(m,n) diagonal elements, U is an M-by-M orthogonal matrix, and V is an N-by-N orthogonal matrix. The diagonal elements of SIGMA are the singular values of A; they are real and non-negative, and are returned in descending order. The first min(m,n) columns of U and V are the left and right singular vectors of A. Note that the routine returns VT = V**T, not V. Usage dgesdd( JOBZ = "A", M = NULL, N = NULL, A, dgesdd 13 LDA = NULL, S, U, LDU = NULL, VT, LDVT = NULL, WORK = NULL, LWORK = NULL ) Arguments JOBZ a character. Specifies options for computing all or part of the matrix U: = ’A’: all M columns of U and all N rows of V**T are returned in the arrays U and VT; = ’S’: the first min(M,N) columns of U and the first min(M,N) rows of V**T are returned in the arrays U and VT; = ’O’: If M >= N, the first N columns of U are overwritten on the array A and all rows of V**T are returned in the array VT; otherwise, all columns of U are returned in the array U and the first M rows of V**T are overwritten in the array A; = ’N’: no columns of U or rows of V**T are computed. M an integer. The number of rows of the input matrix A. M >= 0. N an integer. The number of columns of the input matrix A. N >= 0. A the M-by-N matrix A. LDA an integer. The leading dimension of the matrix A. LDA >= max(1,M). S a matrix of dimension (min(M,N)). The singular values of A, sorted so that S(i) >= S(i+1). U U is a matrx of dimension (LDU,UCOL) UCOL = M if JOBZ = ’A’ or JOBZ = ’O’ and M < N; UCOL = min(M,N) if JOBZ = ’S’. If JOBZ = ’A’ or JOBZ = ’O’ and M < N, U contains the M-by-M orthogonal matrix U; if JOBZ = ’S’, U contains the first min(M,N) columns of U (the left singular vectors, stored columnwise); if JOBZ = ’O’ and M >= N, or JOBZ = ’N’, U is not referenced. LDU an integer. The leading dimension of the matrix U. LDU >= 1; if JOBZ = ’S’ or ’A’ or JOBZ = ’O’ and M < N, LDU >= M. VT VT is matrix of dimension (LDVT,N) If JOBZ = ’A’ or JOBZ = ’O’ and M >= N, VT contains the N-by-N orthogonal matrix V**T; if JOBZ = ’S’, VT contains the first min(M,N) rows of V**T (the right singular vectors, stored rowwise); if JOBZ = ’O’ and M < N, or JOBZ = ’N’, VT is not referenced. 14 dgesdd LDVT an integer. The leading dimension of the matrix VT. LDVT >= 1; if JOBZ = ’A’ or JOBZ = ’O’ and M >= N, LDVT >= N; if JOBZ = ’S’, LDVT >= min(M,N). WORK a matrix of dimension (MAX(1,LWORK)) LWORK an integer. The dimension of the array WORK. LWORK >= 1. If LWORK = -1, a workspace query is assumed. The optimal size for the WORK array is calculated and stored in WORK(1), and no other work except argument checking is performed. Let mx = max(M,N) and mn = min(M,N). If JOBZ = ’N’, LWORK >= 3*mn + max( mx, 7*mn ). If JOBZ = ’O’, LWORK >= 3*mn + max( mx, 5*mn*mn + 4*mn ). If JOBZ = ’S’, LWORK >= 4*mn*mn + 7*mn. If JOBZ = ’A’, LWORK >= 4*mn*mn + 6*mn + mx. These are not tight minimums in all cases; see comments inside code. For good performance, LWORK should generally be larger; a query is recommended. Value IWORK an integer matrix dimension of (8*min(M,N)) A is updated. if JOBZ = ’O’, A is overwritten with the first N columns of U (the left singular vectors, stored columnwise) if M >= N; A is overwritten with the first M rows of V**T (the right singular vectors, stored rowwise) otherwise. if JOBZ .ne. ’O’, the contents of A are destroyed. INFO an integer = 0: successful exit. < 0: if INFO = -i, the i-th argument had an illegal value. > 0: DBDSDC did not converge, updating process failed. Examples ## Not run: set.seed(4669) A = matrix(rnorm(12),4,3) S = matrix(0,nrow=3,ncol=1) U = matrix(0,nrow=4,ncol=4) VT = matrix(0,ncol=3,nrow=3) dgesdd(A=A,S=S,U=U,VT=VT) S U VT rm(A,S,U,VT) A = as.big.matrix(matrix(rnorm(12),4,3)) S = as.big.matrix(matrix(0,nrow=3,ncol=1)) U = as.big.matrix(matrix(0,nrow=4,ncol=4)) VT = as.big.matrix(matrix(0,ncol=3,nrow=3)) dpotrf 15 dgesdd(A=A,S=S,U=U,VT=VT) S[,] U[,] VT[,] rm(A,S,U,VT) gc() ## End(Not run) dpotrf Cholesky factorization Description DPOTRF computes the Cholesky factorization of a real symmetric positive definite matrix A. The factorization has the form A = U**T * U, if UPLO = ’U’, or A = L * L**T, if UPLO = ’L’, where U is an upper triangular matrix and L is lower triangular. This is the block version of the algorithm, calling Level 3 BLAS. Usage dpotrf(UPLO = "U", N = NULL, A, LDA = NULL) Arguments UPLO a character. ’U’: Upper triangle of A is stored; ’L’: Lower triangle of A is stored. N an integer. The order of the matrix A. N >= 0. A a big.matrix, dimension (LDA,N). LDA an integer. Dimension of the array A. LDA >= max(1,N). Value updates the big matrix A with the result, INFO is an integer = 0: successful exit < 0: if INFO = -i, the i-th argument had an illegal value > 0: if INFO = i, the leading minor of order i is not positive definite, and the factorization could not be completed. Terms laying out of the computed triangle should be discarded. 16 dscal Examples set.seed(4669) A = matrix(rnorm(16),4) B = as.big.matrix(A %*% t(A)) C = A %*% t(A) chol(C) dpotrf(UPLO='U', N=4, A=B, LDA=4) D <- B[,] D[lower.tri(D)]<-0 D D-chol(C) t(D)%*%D-C #' # The big.matrix file backings will be deleted when garbage collected. rm(A,B,C,D) gc() dscal Scales a vector by a constant. Description Scales a vector by a constant. Usage dscal(N = NULL, ALPHA, Y, INCY = 1) Arguments N an integer. Number of elements in input vector(s) ALPHA a real number. The scalar alpha Y a big matrix to scale by ALPHA INCY an integer. Storage spacing between elements of Y. Value Update Y. Examples set.seed(4669) A = big.matrix(3, 2, type="double", init=1, dimnames=list(NULL, c("alpha", "beta")), shared=FALSE) dscal(ALPHA=2,Y=A) A[,] # The big.matrix file backings will be deleted when garbage collected. rm(A) gc() Index ∗ package bigalgebra-package, 2 %*%,big.matrix,big.matrix-method (balgebra-methods), 4 %*%,big.matrix,matrix-method (balgebra-methods), 4 %*%,matrix,big.matrix-method (balgebra-methods), 4 Arith,big.matrix,big.matrix-method (balgebra-methods), 4 Arith,big.matrix,matrix-method (balgebra-methods), 4 Arith,big.matrix,numeric-method (balgebra-methods), 4 Arith,matrix,big.matrix-method (balgebra-methods), 4 Arith,numeric,big.matrix-method (balgebra-methods), 4 balgebra-methods, 4 big.matrix, 2–4 bigalgebra (bigalgebra-package), 2 bigalgebra-package, 2 bigmemory, 3, 5 daxpy, 4 dcopy, 6 dgeev, 7 dgemm, 9 dgeqrf, 11 dgesdd, 12 dpotrf, 15 dscal, 16 matrix, 4 17

cosso

cran

Package ‘cosso’ March 8, 2023 Version 2.1-2 Date 2023-03-07 Title Fit Regularized Nonparametric Regression Models Using COSSO Penalty Description The COSSO regularization method automatically estimates and selects important function components by a soft-thresholding penalty in the context of smoothing spline ANOVA models. Implemented models include mean regression, quantile regression, logistic regression and the Cox regression models. License GPL (>= 2) Depends quadprog, Rglpk, parallel, glmnet NeedsCompilation no URL https://arxiv.org/abs/math/0702659 Author Hao Helen Zhang [aut, cph], Chen-Yen Lin [aut, cph], Isaac Ray [cre, ctb] Maintainer Isaac Ray <null@stat.tamu.edu> Repository CRAN Date/Publication 2023-03-08 09:30:09 UTC R topics documented: BUPA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 cosso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 ozone . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 plot.cosso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 predict.cosso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 SSANOVAwt . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 tune.cosso . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 veteran . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Index 14 1 2 cosso BUPA BUPA Liver Disorder Data Description 345 male patients’ blood test result and liver disorder status. Usage data(BUPA) Format CLASS 0: no liver disorder 1: liver disorder MCV mean corpuscular volume. minimum 65 and maximum 103 in original scale. ALKPHOS alkaline phosphotase. minimum 23 and maximum 138 in original scale. SGPT alamine aminotransferase. minimum 4 and maximum 155 in original scale. SGOT aspartate aminotransferase. minimum 5 and maximum 82 in original scale. GAMMAGT gamma-glutamyl transpeptidase. minimum 5 and maximum 297 in original scale. DRINKS number of alcoholic beverages drunk per day. minimum 0 and maximum 20 in original scale. Details All the variables, except for the response, have been scaled to [0,1] interval. To transform back to the original scale, use the formula: x = min + (max − min) ∗ z. Source Richard S. Forsyth at BUPA Medical Research Ltd. cosso Fit a generalized nonparametric model with cosso penalty Description A comprehensive method for fitting various type of regularized nonparametric regression models using cosso penalty. Fits mean, logistic, Cox and quantile regression. cosso 3 Usage cosso(x,y,tau,family=c("Gaussian","Binomial","Cox","Quantile"),wt=rep(1,ncol(x)), scale=FALSE,nbasis,basis.id,cpus) Arguments x input matrix; the number of rows is sample size, the number of columns is the data dimension. The range of input variables is scaled to [0,1] for continuous variables. Variables with less than 7 unique values will be considered as discrete variable. y response vector. Quantitative for family="Gaussian" or family="Quantile". For family="Binomial" should be a vector with two levels. For family="Cox", y should be a two-column matrix (or data frame) with columns named ’time’ and ’status’ tau the quantile to be estimated, a number strictly between 0 and 1. Argument re- quired when family="Quantile". family response type. Abbreviations are allowed. wt weights for predictors. Default is rep(1,ncol(x)) scale if TRUE, continuous predictors will be rescaled to [0,1] interval. Default is FALSE. nbasis number of "knots" to be selected. Ignored when basis.id is provided. basis.id index designating selected "knots". Argument is not valid for family="Quantile". cpus number of available processor units. Default is 1. If cpus>=2, parallelize task using "parallel" package. Recommended when either sample size or number of covariates is large. Argument is only valid for family="Cox" or family="Quantile". Details In the SS-ANOVA model framework, the regression function is assumed to have an additive form X p η(x) = b + ηj (x(j) ), j=1 where b denotes intercept and ηj denotes the main effect of the j-th covariate. For "Gaussian" response, the mean function is estimated by minimizing the objective function: X Xp Xp (yi − η(xi ))2 /nobs + λ0 θj−1 wj2 ||ηj ||2 , s.t. θj ≤ M. i j=1 j=1 For "Binomial" response, the log-odd function is estimated by minimizing the objective function: X p Xp −log − likelihood/nobs + λ0 θj−1 wj2 ||ηj ||2 , s.t. θj ≤ M. j=1 j=1 For "Quantile" regression model, the quantile function, is estimated by minimizing the objective function: X X p Xp ρτ (yi − η(xi ))/nobs + λ0 θj−1 wj2 ||ηj ||2 , s.t. θj ≤ M. i j=1 j=1 4 cosso For "Cox" regression model, the log-relative hazard function is estimated by minimizing the objec- tive function: Xp Xp −log − P artialLikelihood/nobs + λ0 θj−1 wj2 ||ηj ||2 , s.t. θj ≤ M. j=1 j=1 For identifiability sake, the intercept term in Cox model is absorbed into basline hazard, or equiva- lently set b = 0. For large data sets, we can reduce the computational load of the optimization problem by selecting a subset of the observations of size nbais as "knots", which reduces the dimension of the kernel matrices from nobs to nbasis. Unless specified via basis.id or nbasis, the default number of "knots" is max(40,12*nobs^(2/9)) for "Gaussian" and "Binomial" and max(35,11 * nobs^(2/9)) for "Cox". The weights can be specified based on either user’s own discretion or adaptively computed from initial function estimates. See Storlie et al. (2011) for more discussions. One possible choice is to specify the weights as the inverse L2 norm of initial function estimator, see SSANOVAwt. Value An object with S3 class "cosso". y the response vector. x the input matrix. Kmat a three-dimensional array containing kernel matrices for each input variables. wt weights for predictors. family type of regression model. basis.id indices of observations used as "knots". cpus number of cpu units used. Will be returned if family="Cox" or family="Quantile". tau the quantile to be estimated. Will be returned if family="Quantile". tune a list containing preliminary tuning result and L2-norm. Author(s) Hao Helen Zhang and Chen-Yen Lin References Lin, Y. and Zhang, H. H. (2006) "Component Selection and Smoothing in Smoothing Spline Anal- ysis of Variance Models", Annals of Statistics, 34, 2272–2297. Leng, C. and Zhang, H. H. (2006) "Model selection in nonparametric hazard regression", Nonpara- metric Statistics, 18, 417–429. Zhang, H. H. and Lin, Y. (2006) "Component Selection and Smoothing for Nonparametric Regres- sion in Exponential Families", Statistica Sinica, 16, 1021–1041. Storlie, C. B., Bondell, H. D., Reich, B. J. and Zhang, H. H. (2011) "Surface Estimation, Variable Selection, and the Nonparametric Oracle Property", Statistica Sinica, 21, 679–705. cosso 5 See Also plot.cosso, predict.cosso, tune.cosso Examples ## Gaussian set.seed(20130310) x=cbind(rbinom(200,1,.7),matrix(runif(200*9,0,1),nc=9)) y=x[,1]+sin(2*pi*x[,2])+5*(x[,4]-0.4)^2+rnorm(200,0,1) G.Obj=cosso(x,y,family="Gaussian") plot.cosso(G.Obj,plottype="Path") ## Not run: ## Use all observations as knots set.seed(20130310) x=cbind(rbinom(200,1,.7),matrix(runif(200*9,0,1),nc=9)) y=x[,1]+sin(2*pi*x[,2])+5*(x[,4]-0.4)^2+rnorm(200,0,1) G.Obj=cosso(x,y,family="Gaussian",nbasis=200) ## Clean up rm(list=ls()) ## Binomial set.seed(20130310) x=cbind(rbinom(200,1,.7),matrix(runif(200*9,0,1),nc=9)) trueProb=1/(1+exp(-x[,1]-sin(2*pi*x[,2])-5*(x[,4]-0.4)^2)) y=rbinom(200,1,trueProb) B.Obj=cosso(x,y,family="Bin") ## Clean up rm(list=ls()) ## Cox set.seed(20130310) x=cbind(rbinom(200,1,.7),matrix(runif(200*9,0,1),nc=9)) hazard=x[,1]+sin(2*pi*x[,2])+5*(x[,4]-0.4)^2 surTime=rexp(200,exp(hazard)) cenTime=rexp(200,exp(-hazard)*runif(1,4,6)) y=cbind(time=apply(cbind(surTime,cenTime),1,min),status=1*(surTime<cenTime)) C.obj=cosso(x,y,family="Cox",cpus=1) ## Try parallel computing C.obj=cosso(x,y,family="Cox",cpus=4) ## Clean up rm(list=ls()) ## Quantile set.seed(20130310) x=cbind(rbinom(200,1,.7),matrix(runif(200*7,0,1),nc=7)) y=x[,1]+sin(2*pi*x[,2])+5*(x[,4]-0.4)^2+rt(200,3) Q.obj=cosso(x,y,0.3,family="Quan",cpus=1) 6 ozone ## Try parallel computing Q.obj=cosso(x,y,0.3,family="Quan",cpus=4) ## End(Not run) ozone Ozone pollution data in Los Angels, 1976 Description This is the ozone data used in Breiman and Friedman (1985). This dataset contains 330 observa- tions, and each observation is a daily measurement. Usage data(ozone) Format ozone Ozone reading temp Temperature (degree C). minimum 25 and maximum 93 in original scale. invHt Inversion base height (feet). minimum 111 and maximum 5000 in original scale. press Pressure gradient (mm Hg). minimum -69 and maximum 107 in original scale. vis Visibility (miles). minimum 0 and maximum 350 in original scale. milPress 500 millibar pressure height (m). minimum 5320 and maximum 5950 in original scale. hum Humidity (percent). minimum 19 and maximum 93. invTemp Inversion base temperature (degrees F). minimum -25 and maximum 332 in original scale. wind Wind speed (mph). minimum 0 and maximum 21 in original scale. Details All the variables, except for the response, have been scaled to [0,1] interval. To transform back to the original scale, use the formula: x = min + (max − min) ∗ z. Source Breiman, L. and Friedman, J. (1985), "Estimating Optimal Transformations for Multiple Regression and Correlation", Journal of the American Statistical Association, 80, 580–598. plot.cosso 7 plot.cosso Plot method for COSSO object Description Plot L2 norm solution path or main effects of selected functional components Usage ## S3 method for class 'cosso' plot(x,M,plottype =c("Path","Functionals"),eps=1e-7,...) Arguments x a cosso object. M a smoothing parameter value. Argument required when plottype="Functionals". plottype either Path (default) or Functionals. The Path plot shows the L2 norm path for each functional component as a function of smoothing parameter M. The Func- tional plot shows the estimated functional components for each input variable at a particular smoothing parameter M. Abbreviations are allowed. eps an effective zero, default is 1e-7. ... additional arguments for plot generic. Value NULL Author(s) Hao Helen Zhang and Chen-Yen Lin See Also predict.cosso Examples set.seed(20130310) x=cbind(rbinom(200,1,.7),matrix(runif(200*7,0,1),nc=7)) trueProb=1/(1+exp(-x[,1]-sin(2*pi*x[,2])-5*(x[,4]-0.4)^2)) y=rbinom(200,1,trueProb) B.Obj=cosso(x,y,family="Bin") plot.cosso(B.Obj,plottype="Path") plot.cosso(B.Obj,M=2,plottype="Func") 8 predict.cosso predict.cosso Make predictions or extract coefficients from a cosso model Description Make prediction for future observations or extract the model parameters at a particular smoothing parameter. Usage ## S3 method for class 'cosso' predict(object,xnew,M,type=c("fit","coefficients","nonzero"),eps=1e-7,...) Arguments object a cosso object. xnew matrix of new values for x at which predictions are to be made. Object must be a matrix and have the same dimension as the training design points. Continuous variable will also have to be scaled to [0,1] interval. M a smoothing parameter value. M should be taken between 0 and p. If not pro- vided, a cross-validation procedure will be carried out to select an appropriate value. type if type="fit" (default), fitted values will be returned. If type="coefficients", model coefficients will be returned. Abbreviations are allowed. eps an effective zero, default is 1e-7 ... additional arguments for predict function. Value The object returned depends on type. When type="fit", predicted eta function value will be given at the new design points xnew. When type="coefficients", three sets of coefficients will be returned. Intercept the estimated intercept. If family="Cox", the intercept is zero. coefs the estimated coefficients for kernel representers. theta the estimated scale parameters for each functional component. When type="nonzero", a list of the indices of the nonconstant functional components will be returned. SSANOVAwt 9 Author(s) Hao Helen Zhang and Chen-Yen Lin See Also plot.cosso Examples ## Gaussian set.seed(20130310) x=cbind(rbinom(200,1,.7),matrix(runif(200*7,0,1),nc=7)) y=x[,1]+sin(2*pi*x[,2])+5*(x[,4]-0.4)^2+rnorm(200,0,1) G.Obj=cosso(x,y,family="Gaussian") predict.cosso(G.Obj,M=2,type="nonzero") predict.cosso(G.Obj,xnew=x[1:3,],M=2,type="fit") ## Clean up rm(list=ls()) ## Not run: ## Binomial set.seed(20130310) x=cbind(rbinom(200,1,.7),matrix(runif(200*9,0,1),nc=9)) trueProb=1/(1+exp(-x[,1]-sin(2*pi*x[,2])-5*(x[,4]-0.4)^2)) y=rbinom(200,1,trueProb) B.Obj=cosso(x,y,family="Bin") f.hat=predict.cosso(B.Obj,xnew=x,M=2,type="fit") prob.hat=1/(1+exp(-f.hat)) ## Clean up rm(list=ls()) ## End(Not run) SSANOVAwt Compute adaptive weights by fitting a SS-ANOVA model Description A preliminary estimate η̃ is first obtained by fitting a smoothing spline ANOVA model, and then use the inverse L2 -norm, ||η̃j ||−γ , as the initial weight for the j-th functional component. Usage SSANOVAwt(x,y,tau,family=c("Gaussian","Binomial","Cox","Quantile"),mscale=rep(1,ncol(x)), gamma=1,scale=FALSE,nbasis,basis.id,cpus) 10 SSANOVAwt Arguments x input matrix; the number of rows is sample size, the number of columns is the data dimension. The range of input variables is scaled to [0,1] for continuous variables. y response vector. Quantitative for family="Gaussian" or family="Quantile". For family="Binomial" should be a vector with two levels. For family="Cox", y should be a two-column matrix (data frame) with columns named ’time’ and ’status’ tau the quantile to be estimated, a number strictly between 0 and 1. Argument re- quired when family="Quantile". family response type. Abbreviations are allowed. mscale scale parameter for the Gram matrix associated with each function component. Default is rep(1,ncol(x)) gamma power of inverse L2 -norm. Default is 1. scale if TRUE, continuous predictors will be rescaled to [0,1] interval. Default is FALSE. nbasis number of "knots" to be selected. Ignored when basis.id is provided. basis.id index designating selected "knots". Argument is not valid if family="Quantile". cpus number of available processor units. Default is 1. If cpus>=2, parallelize task using "parallel" package. Recommended when either sample size or num- ber of covariates is large. Argument is not valid if family="Gaussian" or family="Binomial". Details The initial mean function is estimated via a smooothing spline objective function. In the SS- ANOVA model framework, the regression function is assumed to have an additive form Xp η(x) = b + ηj (x(j) ), j=1 where b denotes intercept and ηj denotes the main effect of the j-th covariate. For "Gaussian" response, the mean regression function is estimated by minimizing the objective function: X Xp (yi − η(xi ))2 /nobs + λ0 αj ||ηj ||2 . i j=1 where RSS is residual sum of squares. For "Binomial" response, the regression function is estimated by minimizing the objective func- tion: X p −log − likelihood/nobs + λ0 αj ||ηj ||2 j=1 For "Quantile" regression model, the quantile function, is estimated by minimizing the objective function: X X p ρ(yi − η(xi ))/nobs + λ0 αj ||ηj ||2 . i j=1 SSANOVAwt 11 For "Cox" regression model, the log-hazard function, is estimated by minimizing the objective function: X p −log − P artialLikelihood/nobs + λ0 αj ||ηj ||2 . j=1 The smoothing parameter λ0 is tuned by 5-fold Cross-Validation, if family="Gaussian", "Binomial" or "Quantile", and Approximate Cross-Validation, if family="Cox". But the smoothing parame- ters αj are given in the argument mscale. The adaptive weights are then fiven by ||η̃j ||−γ . Value wt The adaptive weights. Author(s) Hao Helen Zhang and Chen-Yen Lin References Storlie, C. B., Bondell, H. D., Reich, B. J. and Zhang, H. H. (2011) "Surface Estimation, Variable Selection, and the Nonparametric Oracle Property", Statistica Sinica, 21, 679–705. Examples ## Adaptive COSSO Model ## Binomial set.seed(20130310) x=cbind(rbinom(200,1,.7),matrix(runif(200*7,0,1),nc=7)) trueProb=1/(1+exp(-x[,1]-sin(2*pi*x[,2])-5*(x[,4]-0.4)^2)) y=rbinom(200,1,trueProb) Binomial.wt=SSANOVAwt(x,y,family="Bin") ada.B.Obj=cosso(x,y,wt=Binomial.wt,family="Bin") ## Not run: ## Gaussian set.seed(20130310) x=cbind(rbinom(200,1,.7),matrix(runif(200*7,0,1),nc=7)) y=x[,1]+sin(2*pi*x[,2])+5*(x[,4]-0.4)^2+rnorm(200,0,1) Gaussian.wt=SSANOVAwt(designx,response,family="Gau") ada.G.Obj=cosso(x,y,wt=Gaussian.wt,family="Gaussian") ## End(Not run) 12 tune.cosso tune.cosso Tuning procedure for cosso Description Compute K-fold cross-validated score and plot cross-validated score against a grid values of smooth parameter M. Usage tune.cosso(object,folds=5,plot.it=TRUE) Arguments object a cosso object. folds number of folds for corss-validation. Default is 5. It is not recommended to use folds less than 4. plot.it if TRUE, plot the cross-validated score against a sequence values of M. Value OptM the optimal smoothing parameter for M. M used tuning grid points. cvm the mean cross-validated error/minus log-likelihood. cvsd estimate of standard error of cvm. Author(s) Hao Helen Zhang and Chen-Yen Lin See Also cosso, predict.cosso Examples ## Binomial set.seed(20130310) x=cbind(rbinom(150,1,.7),matrix(runif(150*5,0,1),nc=5)) trueProb=1/(1+exp(-x[,1]-sin(2*pi*x[,2])-5*(x[,4]-0.4)^2)) y=rbinom(150,1,trueProb) B.Obj=cosso(x,y,family="Bin",nbasis=30) tune.cosso(B.Obj,4,TRUE) veteran 13 veteran Veterans’ Administration Lung Cancer study Description Randomized trial of two treatment regimens for lung cancer. Usage data(veteran) Format time survival time status censoring status trt 0=standard 1=test celltype 1=squamous, 2=smallcell, 3=adeno, 4=large. karno Karnofsky performance score. minimum 10 and maximum 99 in original scale. diagtime months from diagnosis to randomization. minimum 1 and maximum 87 in original scale. age in years. minimum 34 and maximum 81 in original scale. prior prior therapy 0=no, 1=yes. Details All the variables, except for the response, have been scaled to [0,1] interval. To transform back to the original scale, use the formula: x = min + (max − min) ∗ z. Source Kalbfleisch, J. and Prentice, R.L. (2002), The Statistical Analysis of Failure Time Data (Second Edition) Wiley: New Jersey. Index BUPA, 2 cosso, 2, 12 ozone, 6 plot.cosso, 5, 7, 9 predict.cosso, 5, 7, 8, 12 SSANOVAwt, 4, 9 tune.cosso, 5, 12 veteran, 13 14

GB2

cran

Package ‘GB2’ October 12, 2022 Version 2.1.1 Date 2015-05-01 Title Generalized Beta Distribution of the Second Kind: Properties, Likelihood, Estimation Author Monique Graf <monique.p.n.graf@bluewin.ch>, De- sislava Nedyalkova <desislava.nedyalkova@gmail.com>. Maintainer Desislava Nedyalkova <desislava.nedyalkova@gmail.com> Depends R (>= 3.1.0) Imports cubature, hypergeo, laeken, numDeriv, stats, survey Suggests simFrame Description Package GB2 explores the Generalized Beta distribution of the second kind. Density, cu- mulative distribution function, quantiles and moments of the distributions are given. Func- tions for the full log-likelihood, the profile log-likelihood and the scores are provided. Formu- las for various indicators of inequality and poverty under the GB2 are imple- mented. The GB2 is fitted by the methods of maximum pseudo-likelihood estimation us- ing the full and profile log-likelihood, and non-linear least squares estimation of the model pa- rameters. Various plots for the visualization and analysis of the results are provided. Variance es- timation of the parameters is provided for the method of maximum pseudo-likelihood estima- tion. A mixture distribution based on the compounding property of the GB2 is presented (de- noted as ``compound'' in the documentation). This mixture distribution is based on the discretiza- tion of the distribution of the underlying random scale parameter. The discretiza- tion can be left or right tail. Density, cumulative distribution function, moments and quan- tiles for the mixture distribution are provided. The compound mixture distribution is fitted us- ing the method of maximum pseudo-likelihood estimation. The fit can also incorpo- rate the use of auxiliary information. In this new version of the package, the mixture case is com- plemented with new functions for variance estimation by linearization and comparative den- sity plots. License GPL (>= 2) NeedsCompilation no Repository CRAN Date/Publication 2022-06-22 05:53:16 UTC 1 2 Compound R topics documented: Compound . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 CompoundAuxDensPlot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 CompoundAuxFit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 CompoundAuxVarest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 CompoundDensPlot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 CompoundFit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 CompoundIndicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 CompoundMoments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 CompoundQuantiles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 CompoundVarest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Contindic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Contprof . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Fisk . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 gb2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Gini . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 Indicators . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 LogDensity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 LogLikelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 MLfitGB2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 MLfullGB2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 MLprofGB2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 Moments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 NonlinearFit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 PlotsML . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 ProfLogLikelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 RobustWeights . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 Thomae . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 Varest . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 Index 51 Compound Compound Distribution based on the Generalized Beta Distribution of the Second Kind Description Mixture distribution based on the compounding property of the GB2, in short "compound GB2". Decomposition of the GB2 distribution with respect to the left and right tail of the distribution. Calculation of the component densities and cumulative distribution functions. Calculation of the compound density function and the compound cumulative distribution function. Compound 3 Usage fg.cgb2(x, shape1, scale, shape2, shape3, pl0, decomp="r") dl.cgb2(x, shape1, scale, shape2, shape3, pl0, decomp="r") pl.cgb2(y, shape1, scale, shape2, shape3, pl0, decomp="r", tol=1e-05) dcgb2(x, shape1, scale, shape2, shape3, pl0, pl, decomp="r") pcgb2(y, shape1, scale, shape2, shape3, pl0, pl, decomp="r") prcgb2(y1, y2, shape1, scale, shape2, shape3, pl0, pl, decomp="r", tol=1e-08, debug=FALSE) Arguments x numeric; can be a vector. The value(s) at which the compound density and the component densities are calculated, x is positive. y numeric; can be a vector. The value(s) at which the compound distribution function and the component distribution functions are calculated. y1, y2 numeric values. shape1, scale ,shape2, shape3 numeric; positive parameters of the GB2 distribution. pl0 numeric; a vector of initial proportions defining the number of components and the weight of each component density in the decomposition. Sums to one. pl numeric; a vector of fitted proportions. Sums to one. If pl is equal to pl0, we obtain the GB2 distribution. decomp string; specifying if the decomposition of the GB2 is done with respect to the right tail ("r") or the left tail ("l") of the distribution. By default, decomp = "r" - right tail decomposition. debug logical; By default, debug = FALSE. tol numeric; tolerance with default 0, meaning to iterate until additional terms do not change the partial sum. Details The number of components L is given by the length of the vector pl0. In our examples L = 3. Let N denote the length of the vector x. Function fg.cgb2 calculates the L gamma factors which multiply the GB2 density in order to obtain the component density f` . These component densities are calculated using the function dl.cgb2. Function pl.cgb2 calculates the corresponding L cumulative component distribution functions. Function dcgb2 calculates the resulting compound density function. Function pcgb2 calculates the compound cumulative distribution function for a vector of values y and function prcgb2, given 2 arguments y1 and y2, calculates the probability P (min(y1, y2) < Y < max(y1, y2)), where the random variable Y follows a compound GB2 distribution. Value fg.cgb2 returns a matrix of size N × L of the Gamma factors, dl.cgb2 returns a matrix of size N × L of component densities, pl.cgb2 returns a matrix containing the L component cdfs, dcgb2 returns a matrix of size N × 1 of the GB2 compound density function, pcgb2 returns a matrix of 4 Compound size N × 1 of the GB2 compound distribution function and prcgb2 returns a probability between 0 and 1. Author(s) Monique Graf and Desislava Nedyalkova References Graf, M., Nedyalkova, D., Muennich, R., Seger, J. and Zins, S. (2011) AMELI Deliverable 2.1: Parametric Estimation of Income Distributions and Indicators of Poverty and Social Exclusion. Technical report, AMELI-Project. Examples #\dontrun{ #\library{cubature} # GB2 parameters af <- 5 bf <- 20000 pf <- 0.45 qf <- 0.75 p0 <- rep(1/3,3) p1 <- c(0.37,0.43,0.2) # a vector of values x <- rep(20000*seq(1,2,length.out=9)) #Gamma components fg.cgb2(20000,af,bf,pf,qf,p0) fg.cgb2(Inf,af,bf,pf,qf,p0,"l") #Component densities dl.cgb2(x,af,bf,pf,qf,p0) dl.cgb2(20000,af,bf,pf,qf,p0,"l") #Component cdf pl.cgb2(25000,af,bf,pf,qf,p0) #Compound cdf pcgb2(x,af,bf,pf,qf,p0,p1) prcgb2(37000,38000,af,bf,pf,qf,p0,p1,"l") #} CompoundAuxDensPlot 5 CompoundAuxDensPlot Comparison of the compound GB2 and kernel densities by group Description Function dplot.cavgb2 produces a plot in which the compound and kernel (Epanechnikov) densi- ties are plotted by group. Usage dplot.cavgb2(group, x, shape1, scale, shape2, shape3, pl0, pl, w=rep(1,length(x)), xmax = max(x)*(2/3), ymax=2e-05, decomp="r", choicecol=1:length(levels(group)), xlab="") Arguments group numeric; a factor variable giving the group membership of each sampled unit. x numeric; can be a vector. The value(s) at which the density is calculated, used for the kernel estimate only. x is positive. shape1, scale, shape2, shape3 numeric; positive parameters of the GB2 distribution. On the plot they are de- notes as a, b, p, q and pl0 respectively. pl0 numeric; a vector of initial proportions defining the number of components and the weight of each component density in the decomposition. Sums to one. pl numeric; a vector of fitted proportions (output of pkl.cavgb2). Sums to one. If pl is equal to pl0, we obtain the GB2 distribution. w numeric; weights. xmax numeric; scale on the horizontal axis. By default is equal to max(x) ∗ (2/3). ymax numeric; scale on the vertical axis. By default is equal to 2e-05. decomp string; specifying if the decomposition of the GB2 is done with respect to the right tail ("r") or the left tail ("l") of the distribution. By default, decomp = "r" - right tail decomposition. choicecol numeric vector of length the number of groups; defines the color with which the density curves will be plotted for each group. xlab string; label for x. The default is " ". Details The legend is placed interactively. Value dplot.cavgb2 plots a graph with two curves - the GB2 density, the compound GB2 per group and the corresponding kernel estimate. 6 CompoundAuxFit Author(s) Monique Graf and Desislava Nedyalkova CompoundAuxFit Fitting the Compound Distribution based on the GB2 by the Method of Pseudo Maximum Likelihood Estimation using Auxiliary Information Description Calculates the log-likelihood, the score functions of the log-likelihood and fits the compound dis- tribution based on the GB2 and using auxiliary information. Usage pkl.cavgb2(z, lambda) lambda0.cavgb2(pl0, z, w=rep(1, dim(z)[1])) logl.cavgb2(fac, z, lambda, w=rep(1, dim(fac)[1])) scores.cavgb2(fac, z, lambda, w=rep(1, dim(fac)[1])) ml.cavgb2(fac, z, lambda0, w = rep(1, dim(fac)[1]), maxiter = 100, fnscale=length(w)) Arguments z numeric; a matrix of auxiliary variables. lambda numeric; a matrix of parameters. pl0 numeric; a vector of initial proportions defining the number of components and the weight of each component density in the decomposition. Sums to one. w numeric; vector of weights of length the number of rows of the matrix fac. By default w is a vector of 1. fac numeric; a matrix of Gamma factors. lambda0 numeric; a matrix of initial parameters. maxiter numeric; maximum number of iterations to perform. By default maxiter = 100. fnscale numeric; parameter of the optim function. By default fnscale is equal to the lenth of the vector of weights (value of fnscale in the preceding version of the package). Permits to solve some convergence problems (see optim). Details We model the probabilities p` with auxiliary variables. Let zk denote the vector of auxiliary infor- mation for unit k. This auxiliary information modifies the probabilities p` at the unit level. Denote by pk,` the weight of the density f` for unit k. For ` = 1, ..., L − 1, we pose a linear model for the log-ratio vk,` : XI log(pk,` /pk,L ) = vk,` = λ`i zki = zTk λ` . i=1 CompoundAuxFit 7 Function pkl.cavgb2 calculates the pk,` . Function lambda0.cavgb2 calculates the initial values λ`i , i = 1, ..., I, ` = 1, ..., L − 1 . Let X X z̄i = wk zki / wk k k be the mean value of the i-th explanatory variable. Writing I (0) (0) (0) (0) X log(p̂` /p̂L ) = v` = λ`i z̄i , i=1 (0) (0) we can choose λ`i = v` /(I z̄i ). Analogically to the ordinary fit of the compound distribution based Pon the GB2 CompoundFit, we express the log-likelihood as a weighted mean of log(f ) = log( (pk,` f` (xk )), evaluated at the data points, where f is the GB2 compound density. The scores are obtained as the weighted sums of the first derivatives of the log-likelihood, with respect to the parameters λ` , ` = 1, ..., L − 1, evaluated at the data points. Function ml.cavgb2 performs maxi- mum likelihood estimation through the general-purpose optimization function optim from package stats. The considered method of optimization is "BFGS" (optim). Once we have the fitted param- eters λ̂ we can deduce the fitted parameters vk` ˆ and pˆk` in function of z̄ and λ̂` . Value pkl.cavgb2 returns a matrix of probabilities. lambda0.cavgb2 returns a matrix of size I × L − 1. logl.cavgb2 returns the value of the pseudo log-likelihood. scores.cavgb2 returns the weighted sum of the scores of the log-likelihood. ml.cavgb2 returns a list containing two objects - the vector of fitted coefficients λˆ` and the output of the "BFGS" fit. Author(s) Monique Graf and Desislava Nedyalkova See Also optim Examples ## Not run: library(simFrame) data(eusilcP) # Stratified cluster sampling set.seed(12345) srss <- SampleControl(design = "region", grouping = "hid", size = c(200*3, 1095*3, 1390*3, 425*3, 820*3, 990*3, 400*3, 450*3, 230*3), k = 1) # Draw a sample s1 <- draw(eusilcP,srss) #names(s1) 8 CompoundAuxFit # Creation of auxiliary variables ind <- order(s1[["hid"]]) ss1 <- data.frame(hid=s1[["hid"]], region=s1[["region"]], hsize=s1[["hsize"]], peqInc=s1[["eqIncome"]], age=s1[["age"]], pw=s1[[".weight"]])[ind,] ss1[["child"]] <- as.numeric((ss1[["age"]]<=14)) ss1[["adult"]] <- as.numeric((ss1[["age"]]>=20)) sa <- aggregate(ss1[,c("child","adult")],list(ss1[["hid"]]),sum) names(sa)[1] <- "hid" sa[["children"]] <- as.numeric((sa[["child"]]>0)) sa[["single_a"]] <- as.numeric((sa[["adult"]]==1)) sa[["sa.ch"]] <- sa[["single_a"]]*sa[["children"]] sa[["ma.ch"]] <- (1-sa[["single_a"]])*sa[["children"]] sa[["nochild"]] <- 1-sa[["children"]] # New data set ns <- merge(ss1[,c("hid","region","hsize","peqInc","pw")], sa[,c("hid","nochild","sa.ch","ma.ch")], by="hid") # Ordering the data set ns <- ns[!is.na(ns$peqInc),] index <- order(ns$peqInc) ns <- ns[index,] # Truncate at 0 ns <- ns[ns$peqInc>0,] # income peqInc <- ns$peqInc # weights pw <- ns$pw # Adding the weight adjustment c1 <- 0.1 pwa <- robwts(peqInc,pw,c1,0.001)[[1]] corr <- mean(pw)/mean(pwa) pwa <- pwa*corr ns <- data.frame(ns, aw=pwa) # Empirical indicators with original weights emp.ind <- c(main.emp(peqInc, pw), main.emp(peqInc[ns[["nochild"]]==1], pw[ns[["nochild"]]==1]), main.emp(peqInc[ns[["sa.ch"]]==1], pw[ns[["sa.ch"]]==1]), main.emp(peqInc[ns[["ma.ch"]]==1], pw[ns[["ma.ch"]]==1])) # Matrix of auxiliary variables z <- ns[,c("nochild","sa.ch","ma.ch")] #unique(z) z <- as.matrix(z) # global GB2 fit, ML profile log-likelihood gl.fit <- profml.gb2(peqInc,pwa)$opt1 agl.fit <- gl.fit$par[1] bgl.fit <- gl.fit$par[2] CompoundAuxVarest 9 pgl.fit <- prof.gb2(peqInc,agl.fit,bgl.fit,pwa)[3] qgl.fit <- prof.gb2(peqInc,agl.fit,bgl.fit,pwa)[4] # Likelihood and convergence proflikgl <- -gl.fit$value convgl <- gl.fit$convergence # Fitted GB2 parameters and indicators profgb2.par <- c(agl.fit, bgl.fit, pgl.fit, qgl.fit) profgb2.ind <- main.gb2(0.6, agl.fit, bgl.fit, pgl.fit, qgl.fit) # Initial lambda and pl pl0 <- c(0.2,0.6,0.2) lambda0 <- lambda0.cavgb2(pl0, z, pwa) # left decomposition decomp <- "l" facgl <- fg.cgb2(peqInc, agl.fit, bgl.fit, pgl.fit, qgl.fit, pl0 ,decomp) fitcml <- ml.cavgb2(facgl, z, lambda0, pwa, maxiter=500) fitcml convcl <- fitcml[[2]]$convergence convcl lambdafitl <- fitcml[[1]] pglfitl <- pkl.cavgb2(diag(rep(1,3),lambdafitl) row.names(pglfitl) <- colnames(z) ## End(Not run) CompoundAuxVarest Variance Estimation under the Compound GB2 Distribution Using Auxiliary Information Description Calculation of variance estimates of the parameters of the compound GB2 distribution and of the estimated compound GB2 indicators under a complex survey design (see package survey). Usage scoreU.cavgb2(fac, z, lambda) scorez.cavgb2(U,z) varscore.cavgb2(SC, w=rep(1,dim(SC)[1])) desvar.cavgb2(data=data, SC=SC, ids=NULL, probs=NULL, strata = NULL, variables = NULL, fpc=NULL, nest = FALSE, check.strata = !nest, weights=NULL, pps=FALSE, variance=c("HT","YG")) hess.cavgb2(U, P, z, w=rep(1, dim(z)[1])) vepar.cavgb2(ml, Vsc, hess) veind.cavgb2(group, vepar, shape1, scale, shape2, shape3, pl0, P, decomp="r") 10 CompoundAuxVarest Arguments fac numeric; a matrix of Gamma factors. z numeric; a matrix of auxiliary variables. lambda numeric; a matrix of parameters. U numeric; a matrix of scores Uk,` (output of the scoreU.cavgb2 function). SC numeric; scores, output of scorez.cavgb2. w numeric; vector of extrapolation weights. By default w is a vector of 1. data dataset containing the design information per unit ids, probs, strata, variables, fpc, nest, check.strata, weights, pps, variance parameters of svydesign. P numeric; matrix of mixture probabilities (output of pkl.cavgb2). ml numeric; estimated values of the vector of v’s. Output of the ml.cavgb2 function (the second element in the list). Vsc numeric; 4 by 4 matrix. Variance of the scores SC, computed in varscore.cavgb2 or with the design information in desvar.cavgb2. hess numeric; Hessian (bread) for the sandwich variance estimate (output of hess.cavgb2). group numeric; a factor variable of the same length as the sample size giving the group membership in the special case when the auxiliary information defines group membership. vepar numeric; output of vepar.cavgb2. shape1, scale ,shape2, shape3 numeric; positive parameters of the GB2 distribution. pl0 numeric; a vector of initial proportions defining the number of components and the weight of each component density in the decomposition. Sums to one. decomp string; specifying if the decomposition of the GB2 is done with respect to the right tail ("r") or the left tail ("l") of the distribution. Details The N × L matrix of fitted mixture probabilities P= (pk,` ) depends on the N × I matrix z of auxiliary variables. P has as many distinct rows as there are distinct rows in z. The N × L matrix of gamma factors fac= F , output of fg.cgb2 depends on the vector of initial probabilities p0,` only. The N × (L − 1) matrix of scores U is defined as ! F (k, `) U (k, `) = pk,` PL −1 . j=1 pk,j F (k, j) The linearized scores are the columns of a N × I(L − 1) matrix SC(k, I(` − 1) + i) = U (k, `) z(k, i). Function varscore.cavgb2 calculates the middle term of the sandwich variance estimator, that is the (I(L−1)×I(L−1)) estimated variance-covariance matrix of the I(L−1) weighted sums of the CompoundAuxVarest 11 columns of SC, without design information. desvar.cavgb2 calculates the design-based variance- covariance matrix of the I(L − 1) weighted sums of the columns of SC, invoking svydesign and svytotal of package survey. hess.cavgb2 calculates the Hessian (I(L − 1) × I(L − 1) matrix of second derivatives of the pseudo-log-likelihood with respect to the parameters). It should be nega- tive definite. If not, the maximum likelihood estimates are spurious. vepar.cavgb2 calculates the sandwich variance estimate of the vectorized matrix of parameters lambda. veind.cavgb2 calcu- lates estimates, std error, covariance and correlation matrices of the indicators under the compound GB2 with auxiliary variables in the particular case where the unique combinations of the auxiliary variables define a small number of groups. Group membership is specified by the vector group of length N . Value scoreU.cavgb2 returns a N ×(L−1) matrix of scores U. scorez.cavgb2 returns a N ×I(L−1) ma- trix whose columns are the linearized scores SC. varscore.cavgb2 returns the variance-covariance estimate of the weighted sums of scores SC, given by weighted cross products. desvar.cavgb2 returns a list of two elements. The first is the output of svytotal and the second is the design-based variance-covariance matrix of the weighted sums of the scores SC. hess.cavgb2 returns the ma- trix of second derivatives of the likelihood with respect to the parameters (bread for the sandwich variance estimate). vepar.cavgb2 returns a list of five elements - [["type"]] with value "param- eter", [["estimate"]] estimated parameters, [["stderr"]] corresponding standard errors, [["Vcov"]] variance -covariance matrix and [["Vcor"]] - correlation matrix. veind.cavgb2 returns a list of five elements: [["type"]] with value "indicator", followed by a list with as many arguments as length(levels(group)). Each argument is itself a list with 5 arguments: [["group"]] group name, [["estimate"]] estimated indicators under the compound GB2, [["stderr"]] corresponding standard errors, [["Vcov"]] variance -covariance matrix and [["Vcor"]] - correlation matrix. Author(s) Monique Graf and Desislava Nedyalkova References Davison, A. (2003), Statistical Models. Cambridge University Press. Freedman, D. A. (2006), On The So-Called "Huber Sandwich Estimator" and "Robust Standard Errors". The American Statistician, 60, 299–302. Graf, M., Nedyalkova, D., Muennich, R., Seger, J. and Zins, S. (2011) AMELI Deliverable 2.1: Parametric Estimation of Income Distributions and Indicators of Poverty and Social Exclusion. Technical report, AMELI-Project. Pfeffermann, D. and Sverchkov, M. Yu. (2003), Fitting Generalized Linear Models under Informa- tive Sampling. In, Skinner, C.J. and Chambers, R.L. (eds.). Analysis of Survey Data, chapter 12, 175–195. Wiley, New York. Examples ## Not run: # Example (following of example in CompoundAuxFit) # Scores U 12 CompoundDensPlot U <- scoreU.cavgb2(facgl, z, lambdafitl) # Scores multiplied by z SC <- scorez.cavgb2(U,z) # Naive variance estimate of sum of scores (Vsc <- varscore.cavgb2(SC,w=pwa)) # Design based variance of sum of scores (desv <- desvar.cavgb2(data=ns,SC=SC,id=~hid,strata=~region,weights=~pwa)) # Hessian hess <- hess.cavgb2(U,pglfitl,z,w=pwa) # 1. Sandwich variance-covariance matrix estimate of parameters using Vsc: Param1 <- vepar.cavgb2(fitcml,Vsc, hess) Param1 # 2. Sandwich variance-covariance matrix estimate of parameters using # the design variance: Param2 <- vepar.cavgb2(fitcml,desv$Vtheta, hess) Param2 # 3. Indicators and conditional variances : takes a long time! (Indic <- veind.cavgb2(group,Param2 ,agl.fit,bgl.fit,pgl.fit,qgl.fit, pl0, pglfitl, decomp="l") ) ## End(Not run) CompoundDensPlot Comparison of the GB2, compound GB2 and kernel densities Description Function dplot.cgb2 produces a plot in which the three densities are plotted. Usage dplot.cgb2(x,shape1, scale, shape2, shape3, pl0, pl, w=rep(1,length(x)), decomp="r", xmax = max(x)*(2/3), choicecol=1:3, kernel="epanechnikov", adjust=1, title=NULL, ylim=NULL) Arguments x numeric; can be a vector. The value(s) at which the density is calculated, used for the kernel estimate only. x is positive. shape1, scale ,shape2, shape3 numeric; positive parameters of the GB2 distribution. On the plot they are de- notes as a, codeb, p, q and pl0 respectively. CompoundFit 13 pl0 numeric; a vector of initial proportions defining the number of components and the weight of each component density in the decomposition. Sums to one. pl numeric; a vector of mixture probabilities (output of ml.cgb2). Sums to one. If pl is equal to pl0, we obtain the GB2 distribution. w numeric; weights. decomp string; specifying if the decomposition of the GB2 is done with respect to the right tail ("r") or the left tail ("l") of the distribution. By default, decomp = "r" - right tail decomposition. xmax numeric; maximum x value to be plotted. choicecol numeric vector of length 3; defines the color with which the density curves will be plotted. adjust numeric; graphical parameter of the generic function density. title string; title of the plot. By default is equall to NULL (no title). ylim string; scaling of parameters. By default is equall to NULL (automatic scaling). kernel string; the kernel used for the kernel density estimate. The default value is "Epanechnikov" (see plot.density). Details The legend is placed interactively. Value dplot.cgb2 plots a graph with three curves - the GB2 density, the compound GB2 density and the corresponding kernel estimate Author(s) Monique Graf and Desislava Nedyalkova CompoundFit Fitting the Compound Distribution based on the GB2 by the Method of Maximum Likelihood Estimation Description Calculates the log-likelihood, the score functions of the log-likelihood, the weighted mean of scores, and fits the parameters of the Compound Distribution based on the GB2. Usage vofp.cgb2(pl) pofv.cgb2(vl) logl.cgb2(fac, pl, w=rep(1, dim(fac)[1])) scores.cgb2(fac, pl, w=rep(1, dim(fac)[1])) ml.cgb2(fac, pl0, w=rep(1, dim(fac)[1]), maxiter=100, fnscale=length(w)) 14 CompoundFit Arguments pl0 numeric; vector of initial proportions defining the number of components and the weight of each component density in the decomposition. Sums to one. pl numeric; vector of fitted proportions. Sums to one. If pl is equal to pl0, we obtain the GB2 distribution. fac numeric; matrix of Gamma factors (output of fac.cgb2. vl numeric; vector of parameters. Its length is equal to the length of pl - 1. w numeric; vector of weights of length the number of rows of the matrix fac. By default w is a vector of 1. maxiter numeric; maximum number of iterations to perform. By default maxiter = 100. fnscale numeric; an overall scaling parameter used in the function optim. By default it is equal to the length of the vector of weights w. Details There are only L − 1 parameters to estimate, because the probabilities p` sum to 1 (L is the di- mension of the vector of probailities p` ). Knowing this, we change the parameters p` to v` = log(p` /pL ), ` = 1, ..., L − 1. This calculation is done through the function vofp.cgb2. pofv.cgb2 calculates the p` inPfunction of the given v` . We express the log-likelihood as a weighted mean of log(f ) = log( (p` f` ), evaluated at the data points, where f is the GB2 compound density. If the weights are not available, then we suppose that w = 1. Analogically, the scores are ob- tained as weighted sums of the first derivatives of the log-likelihood, with respect to the parameters v` , ` = 1, ..., L − 1, evaluated at the data points. Function ml.cgb2 performs maximum like- lihood estimation through the general-purpose optimization function optim from package stats. The considered method of optimization is BFGS. Value vofp.cgb2 returns a vector of length L − 1, where L is the length of the vector p` . pofv.cgb2 returns a vector of length `. logl.cgb2 returns the value of the pseudo log-likelihood. scores.cgb2 returns a vector of the weighted mean of the scores of length L−1. ml.cgb2 returns a list containing two objects - the vector of fitted proportions pˆ` and the output of the BFGS fit. Author(s) Monique Graf and Desislava Nedyalkova See Also optim Examples ## Not run: # GB2 parameters: a <- 4 b <- 1950 p <- 0.8 CompoundIndicators 15 q <- 0.6 # Proportions defining the component densities: pl0 <- rep(1/3,3) # Mixture probabilities pl <- c(0.1,0.8,0.1) # Random generation: n <- 10000 set.seed(12345) x <- rcgb2(n,a,b,p,q,pl0,pl,decomp="l") # Factors in component densities fac <- fg.cgb2(x,a,b,p,q, pl0,decomp="l") # Estimate the mixture probabilities: estim <- ml.cgb2(fac,pl0) # estimated mixture probabilities: estim[[1]] #[1] 0.09724319 0.78415797 0.11859883 ## End(Not run) CompoundIndicators Indicators of Poverty and Social Exclusion under the Compound Dis- tribution based on the GB2 Description Functions to calculate four primary social welfare indicators under the compound GB2 distribu- tion, i.e. the at-risk-of-poverty threshold, the at-risk-of-poverty rate, the relative median at-risk-of- poverty gap, and the income quintile share ratio. Usage arpt.cgb2(prop, shape1, scale, shape2, shape3, pl0, pl, decomp="r") arpr.cgb2(prop, shape1, shape2, shape3, pl0, pl, decomp="r") rmpg.cgb2(arpr, shape1, shape2, shape3, pl0, pl, decomp="r") qsr.cgb2(shape1, shape2, shape3, pl0, pl, decomp="r") main.cgb2(prop, shape1, scale, shape2, shape3, pl0, pl, decomp="r") Arguments prop numeric; proportion (in general is set to 0.6). arpr numeric; the value of the at-risk-of-poverty rate. shape1,scale,shape2,shape3 numeric; positive parameters of the GB2 distribution. 16 CompoundIndicators pl0 numeric; a vector of initial proportions defining the number of components and the weight of each component density in the decomposition. Sums to one. pl numeric; a vector of mixture probabilities. Sums to one. If pl = pl0 we obtain the GB2 distribution. decomp string; specifying if the decomposition of the GB2 is done with respect to the right tail ("r") or the left tail ("l") of the distribution. By default, decomp = "r" - right tail decomposition. Details The four indicators are described in details in the case of the GB2. The difference here is that we need to give an initial vector of proportions, fitted proportions and define for which decomposition (left or right) the indicators should be calculated. Value arpt.cgb2 gives the ARPT, arpr.cgb2 the ARPR, rmpg.cgb2 the RMPG, qsr.cgb2 gives the QSR and main.cgb2 calculates the median, the mean, the ARPR, the RMPG and the QSR under the compound GB2. Author(s) Monique Graf References Graf, M., Nedyalkova, D., Muennich, R., Seger, J. and Zins, S. (2011) AMELI Deliverable 2.1: Parametric Estimation of Income Distributions and Indicators of Poverty and Social Exclusion. Technical report, AMELI-Project. See Also arpr.gb2 for details on the welfare indicators under the GB2. Examples # GB2 parameters a <- 3.9 b <- 18873 p <- 0.97 q <- 1.03 # Proportions defining the component densities p0 <- rep(1/3,3) # Mixture probabilities pl <- c(0.39,0.26,0.35) # for the right discretization arpt <- arpt.cgb2(0.6, a, b, p, q, p0, pl) CompoundMoments 17 arpr <- arpr.cgb2(0.6, a, p, q, p0, pl) rmpg <- rmpg.cgb2(arpr, a, p, q, p0, pl) qsr <- qsr.cgb2(a, p, q, p0, pl) # for the left discretization arptleft <- arpt.cgb2(0.6, a, b, p, q, p0, pl, "l") CompoundMoments Moments of the Compound Distribution based on the GB2 Description These functions calculate the moment of order k and incomplete moment of order k of a GB2 compound random variable X as well as the moment of order k for each component density. Usage mkl.cgb2(k, shape1, scale, shape2, shape3, pl0, decomp="r") moment.cgb2(k, shape1, scale, shape2 ,shape3, pl0, pl, decomp="r") incompl.cgb2(x, k, shape1, scale, shape2, shape3, pl0, pl, decomp="r") Arguments x numeric; vector of quantiles. k numeric; order of the moment. shape1,scale,shape2,shape3 numeric; positive parameters of the GB2 distribution. pl0 numeric; a vector of initial proportions defining the number of components and the weight of each component density in the decomposition. Sums to one. pl numeric; a vector of mixture probabilities. Sums to one. If pl = pl0 we obtain the GB2 distribution. decomp string; specifying if the decomposition of the GB2 is done with respect to the right tail ("r") or the left tail ("l") of the distribution. By default, decomp = "r" - right tail decomposition. Value mkl.cgb2 returns a vector of the moments of the component densities, moment.cgb2 returns the moment of order k and incompl.cgb2 - the incomplete moment of order k. Author(s) Monique Graf 18 CompoundQuantiles References Graf, M., Nedyalkova, D., Muennich, R., Seger, J. and Zins, S. (2011) AMELI Deliverable 2.1: Parametric Estimation of Income Distributions and Indicators of Poverty and Social Exclusion. Technical report, AMELI-Project. Examples #\dontrun{ #\library{cubature} # GB2 parameters af <- 5 bf <- 20000 pf <- 0.45 qf <- 0.75 p0 <- rep(1/3,3) p1 <- c(0.37,0.43,0.2) # moments for the component densities mkl.cgb2(1,af,bf,pf,qf,p0) mkl.cgb2(-1,af,bf,pf,qf,p0,"l") #Moment of order k moment.cgb2(0.5,af,bf,pf,qf,p0,p1) moment.cgb2(0.5,af,bf,pf,qf,p0,p1,"l") #Incomplete moment of order k incompl.cgb2(20000,1,af,bf,pf,qf,p0,p1) incompl.cgb2(20000,1,af,bf,pf,qf,p0,p1,"l") #} CompoundQuantiles Quantiles and random generation of the Compound Distribution based on the GB2 Description Calculation of the quantiles of a compound GB2 random variable. Random generation of compound GB2 variables. Usage qcgb2(prob, shape1, scale, shape2, shape3, pl0, pl, decomp="r", tol=1e-08, ff=1.5, debug=FALSE, maxiter=50) rcgb2(n, shape1, scale, shape2, shape3, pl0, pl, decomp="r", tol=1e-02, maxiter=100, debug = FALSE) CompoundQuantiles 19 Arguments prob numeric; vector of probabilities between 0 and 1. shape1,scale,shape2,shape3 numeric; positive parameters of the GB2 distribution. n numeric; number of observations. If length(n) > 1, the length is taken to be the number required. pl0 numeric; a vector of initial proportions defining the number of components and the weight of each component density in the decomposition. Sums to one. pl numeric; a vector of mixture probabilities. Sums to one. If pl = pl0 we obtain the GB2 distribution. decomp string; specifying if the decomposition of the GB2 is done with respect to the right tail ("r") or the left tail ("l") of the distribution. By default, decomp = "r" - right tail decomposition. ff numeric; a tuning parameter. debug logical; By default, debug = FALSE. maxiter numeric; maximum number of iterations to perform. tol numeric; tolerance with default 0, meaning to iterate until additional terms do not change the partial sum. Value qcgb2 returns a vector of quantiles and rcgb2 return a vector of size n of GB2 compound random deviates. Author(s) Monique Graf and Desislava Nedyalkova References Graf, M., Nedyalkova, D., Muennich, R., Seger, J. and Zins, S. (2011) AMELI Deliverable 2.1: Parametric Estimation of Income Distributions and Indicators of Poverty and Social Exclusion. Technical report, AMELI-Project. Examples #\dontrun{ #\library{cubature} # GB2 parameters af <- 5 bf <- 20000 pf <- 0.45 qf <- 0.75 p0 <- rep(1/3,3) 20 CompoundVarest p1 <- c(0.37,0.43,0.2) #Quantiles qcgb2(0.5,af,bf,pf,qf,p0,p1) qcgb2(1,af,bf,pf,qf,p0,p1) qcgb2(c(0.5,0.8),af,bf,pf,qf,p0,p1) #Random generation rcgb2(10,af,bf,pf,qf,p0,p1) #} CompoundVarest Variance Estimation of the Compound GB2 Distribution Description Calculation of variance estimates of the parameters of the compound GB2 distribution and of the estimated compound GB2 indicators under cluster sampling. Usage scoreU.cgb2(fac, pl) varscore.cgb2(U, w=rep(1,dim(U)[1])) desvar.cgb2(data=data, U=U, ids=NULL, probs=NULL, strata = NULL, variables = NULL, fpc=NULL, nest = FALSE, check.strata = !nest, weights=NULL, pps=FALSE, variance=c("HT","YG")) hess.cgb2(U, pl, w=rep(1,dim(U)[1])) vepar.cgb2(ml, Vsc, hess) derivind.cgb2(shape1, scale, shape2, shape3, pl0, pl, prop=0.6, decomp="r") veind.cgb2(Vpar, shape1, scale, shape2, shape3, pl0, pl, decomp="r") Arguments fac numeric; matrix of Gamma factors (output of fac.cgb2. pl numeric; a vector of fitted mixture probabilities. Sums to one. If pl is equal to pl0, we obtain the GB2 distribution. U numeric; vector of scores. Output of the scoreU.cgb2 function. w numeric; vector of some extrapolation weights. By default w is a vector of 1. data dataset containing the design information per unit. ids, probs, strata, variables, fpc, nest, check.strata, weights, pps, variance parameters of svydesign. ml numeric; output of the ml.cgb2 function. A list with two components. First component: estimated mixture probabilities. Second component: list containing the output of optim. CompoundVarest 21 Vsc numeric; 4 by 4 matrix. hess numeric; Hessian (bread) for the sandwich variance estimate. shape1, scale ,shape2, shape3 numeric; positive parameters of the GB2 distribution. pl0 numeric; a vector of initial proportions defining the number of components and the weight of each component density in the decomposition. Sums to one. prop numeric; proportion (in general is set to 0.6). decomp string; specifying if the decomposition of the GB2 is done with respect to the right tail ("r") or the left tail ("l") of the distribution. Vpar numeric; 4 by 4 matrix. Output of the function vepar.cgb2. Details Function scoreU.cgb2 calculates the N × (L − 1) matrix of scores U is defined as ! F (k, `) U (k, `) = p` PL −1 , j=1 pj F (k, j) where p` , ` = 1, .., L is the vector of fitted mixture probabilities and F is the N ×L matrix of gamma factors, output of fg.cgb2. The linearized scores are the columns of U. They serve to compute the linearization approximation of the covariance matrix of the parameters v` = log(p` /pL ), ` = 1, ..., L − 1. Function varscore.cgb2 calculates the middle term of the sandwich variance estima- tor, that is the ((L − 1) × (L − 1)) estimated variance-covariance matrix of the (L − 1) weighted sums of the columns of U, without design information. desvar.cgb2 calculates the design-based variance-covariance matrix of the (L − 1) weighted sums of the columns of U, invoking svydesign and svytotal of package survey. hess.cgb2 calculates the Hessian ((L − 1) × (L − 1)) matrix of second derivatives of the pseudo-log-likelihood with respect to the parameters v` ). It should be negative definite. If not, the maximum likelihood estimates are spurious. vepar.cgb2 calculates the sandwich covariance matrix estimate of the vector of parameters v. veind.cgb2 calculates es- timates, standard error, covariance and correlation matrices of the indicators under the compound GB2. Value scoreU.cgb2 returns a N × (L − 1) matrix of scores <codeU. varscore.cgb2 returns the variance- covariance estimate of the weighted sums of scores U, given by weighted cross products. desvar.cgb2 returns a list of two elements. The first is the output of svytotal and the second is the design-based variance-covariance matrix of the weighted sums of the scores. hess.cgb2 returns the matrix of second derivatives of the likelihood with respect to the parameters (bread for the sandwich vari- ance estimate). vepar.cgb2 returns a list of five elements - [["type"]] with value "parameter", [["estimate"]] estimated parameters, [["stderr"]] corresponding standard errors, [["Vcov"]] variance -covariance matrix and [["Vcor"]] - correlation matrix. veind.cgb2 returns a list of five elements: [["type"]] with value "indicator", [["estimate"]] estimated indicators under the compound GB2, [["stderr"]] corresponding standard errors, [["Vcov"]] variance -covariance matrix and [["Vcor"]] - correlation matrix. Author(s) Monique Graf and Desislava Nedyalkova 22 Contindic References Davison, A. (2003), Statistical Models. Cambridge University Press. Freedman, D. A. (2006), On The So-Called "Huber Sandwich Estimator" and "Robust Standard Errors". The American Statistician, 60, 299–302. Graf, M., Nedyalkova, D., Muennich, R., Seger, J. and Zins, S. (2011) AMELI Deliverable 2.1: Parametric Estimation of Income Distributions and Indicators of Poverty and Social Exclusion. Technical report, AMELI-Project. Pfeffermann, D. and Sverchkov, M. Yu. (2003), Fitting Generalized Linear Models under Informa- tive Sampling. In, Skinner, C.J. and Chambers, R.L. (eds.). Analysis of Survey Data, chapter 12, 175–195. Wiley, New York. Examples ## Not run: # Example (following of example in CompoundFit) # Estimated mixture probabilities: (pl.hat <- estim[[1]]) # scores per unit U <- scoreU.cgb2(fac, pl.hat) # Conditional variances given a,b,p,q: # 1. Variance of sum of scores: (Vsc <- t(U) (Vsc <- varscore.cgb2(U)) # 2. sandwich variance-covariance matrix estimate of (v_1,v_2): (hess <- hess.cgb2(U,pl.hat)) (Parameters <- vepar.cgb2(estim, Vsc, hess)) # 3. Theoretical indicators (with mixture prob pl) decomp <- "r" (theoretical <- main.cgb2( 0.6,a,b,p,q,pl0, pl,decomp=decomp)) # Estimated indicators and conditional variances : takes a long time! (Indic <- veind.cgb2(Parameters,a,b,p,q, pl0, pl.hat, decomp="r") ) ## End(Not run) Contindic Sensitivity Analysis of Laeken Indicators on GB2 Parameters Description Produces a contour plot of an indicator for a given shape1. Contindic 23 Usage contindic.gb2(resol, shape1, shape21, shape22, shape31, shape32, fn, title, table=FALSE) Arguments resol numeric; number of grid points horizontally and vertically. shape1 numeric; positive parameter, first shape parameter of the GB2 distribution. shape21, shape22, shape31, shape32 numeric; limits on the positive parameters of the Beta distribution. fn string; the name of the function to be used for the calculation of the values to be plotted. title string; title of the plot. table boolean; if TRUE, a table containing the values of the function fn at the different grid points is printed. Details An indicator is defined as a function of three parameters. The shape parameter, shape1, is held fixed. The shape parameters shape2 and shape3 vary between shape21 and shape22, and shape31 and shape32, respectively. Value A contour plot of a given indicator for a fixed value of the shape parameter shape1. Author(s) Monique Graf See Also contour (package graphics) for more details on contour plots. Examples par(mfrow=c(2,2)) shape21 <- 0.3 shape31 <- 0.36 shape22 <- 1.5 shape32 <- 1.5 shape11 <- 2.7 shape12 <- 9.2 resol <- 11 rangea <- round(seq(shape11, shape12 ,length.out=4),digits=1) arpr <- function(shape1, shape2, shape3) 100*arpr.gb2(0.6, shape1, shape2, shape3) fonc <- "arpr" for (shape1 in rangea){ contindic.gb2(resol, shape1, shape21, shape22, shape31, shape32, arpr, "At-risk-of-poverty rate", table=TRUE) } 24 Contprof Contprof Contour Plot of the Profile Log-likelihood of the GB2 Distribution Description Produces a contour plot of the profile log-likelihood, which is a function of two parameters only. Usage contprof.gb2(z, w=rep(1,length(z)), resol, low=0.1, high=20) Arguments z numeric; vector of data values. w numeric; vector of weights. Must have the same length as z. By default w is a vector of 1. resol numeric; number of grid points horizontally and vertically. For better graph quality, we recommend a value of 100. low, high numeric; lower and upper factors for scale. Details The matrix containing the values to be plotted (NAs are allowed) is of size resol × resol. The locations of the grid lines at which the values of the profile log-likelihood are measured are equally- spaced values between low and high multiplied by the initial parameters. Value A contour plot of the profile log-likelihood. The initial Fisk estimate is added as point "F". Author(s) Monique Graf See Also fisk for the Fisk estimate, ProfLogLikelihood for the profile log-likelihood and contour (pack- age graphics) for more details on contour plots. Fisk 25 Fisk Parameters of the Fisk Distribution Description Calculation of the parameters a and b of the Fisk distribution, which is a GB2 distribution √ with p = q = 1. If m and v denote, respectively, the mean and variance of log(z), then â = π/ 3 ∗ v and b̂ = exp(m). Usage fisk(z, w=rep(1, length(z))) fiskh(z, w=rep(1, length(z)), hs=rep(1, length(z))) Arguments z numeric; vector of data values. w numeric; vector of weights. Must have the same length as z. By default w is a vector of 1. hs numeric; vector of household sizes. Must have the same length as z. By default hs is a vector of 1. Details Function fisk first calculates the mean and variance of log(z) and next the values of a and b under the Fisk distribution. Function fiskh first calculates the mean and variance of log(z), assuming a sample of households, and next the values of a and b under the Fisk distribution. Value fisk and fiskh return vectors of length 4 containing the estimated parameters a, eqnb, as well as p = 1 and q = 1. Author(s) Monique Graf References Graf, M., Nedyalkova, D., Muennich, R., Seger, J. and Zins, S. (2011) AMELI Deliverable 2.1: Parametric Estimation of Income Distributions and Indicators of Poverty and Social Exclusion. Technical report, AMELI-Project. See Also optim for the general-purpose optimization 26 gb2 Examples library(laeken) data(eusilc) # Income inc <- as.vector(eusilc$eqIncome) # Weights w <- eusilc$rb050 #Fisk parameters fpar <- fisk(inc, w) gb2 The Generalized Beta Distribution of the Second Kind Description Density, distribution function, quantile function and random generation for the Generalized beta distribution of the second kind with parameters a, b, p and q. Usage dgb2(x, shape1, scale, shape2, shape3) pgb2(x, shape1, scale, shape2, shape3) qgb2(prob, shape1, scale, shape2, shape3) rgb2(n, shape1, scale, shape2, shape3) Arguments x numeric; vector of quantiles. shape1 numeric; positive parameter. scale numeric; positive parameter. shape2, shape3 numeric; positive parameters of the Beta distribution. prob numeric; vector of probabilities. n numeric; number of observations. If length(n) > 1, the length is taken to be the number required. Details The Generalized Beta distribution of the second kind with parameters shape1 = a, scale = b, shape2 = p and shape3 = q has density a(x/b)ap−1 f (x) = bB(p, q)(1 + (x/b)a )p+q Gini 27 for a > 0, b > 0, p > 0 and q > 0, where B(p, q) is the Beta function (beta). If Z follows a Beta distribution with parameters p and q and z y= , 1−z then x = b ∗ y 1/a follows the GB2 distribution. Value dgb2 gives the density, pgb2 the distribution function, qgb2 the quantile function, and rgb2 gener- ates random deviates. Author(s) Monique Graf References Kleiber, C. and Kotz, S. (2003) Statistical Size Distributions in Economics and Actuarial Sciences, chapter 6. Wiley, Ney York. McDonald, J. B. (1984) Some generalized functions for the size distribution of income. Economet- rica, 52, 647–663. See Also beta for the Beta function and dbeta for the Beta distribution. Examples a <- 3.9 b <- 18873 p <- 0.97 q <- 1.03 x <- qgb2(0.6, a, b, p, q) y <- dgb2(x, a, b, p, q) Gini Computation of the Gini Coefficient for the GB2 Distribution and its Particular Cases. Description Computes the Gini coefficient for the GB2 distribution using the function gb2.gini. Computes the Gini coefficient for the Beta Distribution of the Second Kind, Dagum and Singh-Maddala distribu- tions. 28 Indicators Usage gini.gb2(shape1, shape2, shape3) gini.b2(shape2, shape3) gini.dag(shape1, shape2) gini.sm(shape1, shape3) Arguments shape1 numeric; positive parameter. shape2, shape3 numeric; positive parameters of the Beta distribution. Value The Gini coefficient. Author(s) Monique Graf References Kleiber, C. and Kotz, S. (2003) Statistical Size Distributions in Economics and Actuarial Sciences, chapter 6. Wiley, Ney York. McDonald, J. B. (1984) Some generalized functions for the size distribution of income. Economet- rica, 52, 647–663. See Also gb2.gini Indicators Monetary Laeken Indicators under the GB2 Description Functions to calculate four primary social welfare indicators under the GB2, i.e. the at-risk-of- poverty threshold, the at-risk-of-poverty rate, the relative median at-risk-of-poverty gap, and the income quintile share ratio. Usage arpt.gb2(prop, shape1, scale, shape2, shape3) arpr.gb2(prop, shape1, shape2, shape3) rmpg.gb2(arpr, shape1, shape2, shape3) qsr.gb2(shape1, shape2, shape3) main.gb2(prop, shape1, scale, shape2, shape3) main2.gb2(prop, shape1, scale, shape12, shape13) Indicators 29 Arguments prop numeric; proportion (in general is set to 0.6). arpr numeric; the value of the at-risk-of-poverty rate. shape1 numeric; positive parameter. scale numeric; positive parameter. shape2, shape3 numeric; positive parameters of the Beta distribution. shape12 numeric; the product of the two parameters shape1 and shape2. shape13 numeric; the product of the two parameters shape1 and shape3. Details In June 2006, the Social Protection Committee, which is a group of officials of the European Com- misiion, adopts a set of common indicators for the social protection and social inclusion process. It consists of a portfolio of 14 overarching indicators (+11 context indicators) meant to reflect the overarching objectives (a) "social cohesion" and (b) "interaction with the Lisbon strategy for growth and jobs (launched in 2000) objectives"; and of three strand portfolios for social inclusion, pensions, and health and long-term care. The at-risk-of-poverty threshold (or ARPT) is defined as 60% of the median national equivalized income. The at-risk-of-poverty rate (or ARPR) is defined as the share of persons with an equivalised dispos- able income below the ARPT. The relative median at-risk-of-poverty gap (or RMPG) is defined as the difference between the me- dian equivalised income of persons below the ARPT and the ARPT itself, expressed as a percentage of the ARPT. The income quintile share ratio (or QSR) is defined as the ratio of total income received by the 20% of the country’s population with the highest income (top quintile) to that received by the 20% of the country’s population with the lowest income (lowest quintile). Let x0.5 be the median of a GB2 with parameters shape1 = a, scale = b, shape2 = p and shape3 = q. Then, ARP T (a, b, p, q) = 0.6x0.5 The ARPR being scale-free, b can be chosen arbitrarily and can be fixed to 1. The QSR is calculated with the help of the incomplete moments of order 1. main.gb2 and main2.gb2 return a vector containing the following set of GB2 indicators: the me- dian, the mean, the ARPR, the RMPG, the QSR and the Gini coefficient. The only difference is in the input parameters. Value arpt.gb2 gives the ARPT, arpr.gb2 the ARPR, rmpg.gb2 the RMPG, and qsr.gb2 calculates the QSR. main.gb2 returns a vector containing the median of the distribution, the mean of the distribution, the ARPR, the RMPG, the QSR and the Gini coefficient. main2.gb2 produces the same output as main.gb2. 30 LogDensity Author(s) Monique Graf References https://ec.europa.eu/social/main.jsp?langId=en&catId=750 See Also qgb2, incompl.gb2 Examples a <- 3.9 b <- 18873 p <- 0.97 q <- 1.03 ap <- a*p aq <- a*q arpt <- arpt.gb2(0.6, a, b, p, q) arpr <- arpr.gb2(0.6, a, p, q) rmpg <- rmpg.gb2(arpr, a, p, q) qsr <- qsr.gb2(a, p, q) ind1 <- main.gb2(0.6, a, b, p, q) ind2 <- main2.gb2(0.6, a, b, ap, aq) LogDensity Log Density of the GB2 Distribution Description Calculates the log density of the GB2 distribution for a single value or a vector of values. Calculates the first- and second-order partial derivatives of the log density evaluated at a single value. Usage logf.gb2(x, shape1, scale, shape2, shape3) dlogf.gb2(xi, shape1, scale, shape2, shape3) d2logf.gb2(xi, shape1, scale, shape2, shape3) Arguments xi numeric; a data value. x numeric; a vector of data values. shape1 numeric; positive parameter. scale numeric; positive parameter. shape2, shape3 numeric; positive parameters of the Beta distribution. LogLikelihood 31 Details We calculate log(f (x, θ)), where f is the GB2 density with parameters shape1 = a, scale = b, shape2 = p and shape3 = q, θ is the parameter vector. We calculate the first- and second-order partial derivatives of log(f (x, θ)) with respect to the parameter vector θ. Value Depending on the input logf.gb2 gives the log density for a single value or a vector of val- ues. dlogf.gb2 gives the vector of the four first-order partial derivatives of the log density and d2logf.gb2 gives the 4 × 4 matrix of second-order partial derivatives of the log density. Author(s) Desislava Nedyalkova References Brazauskas, V. (2002) Fisher information matrix for the Feller-Pareto distribution. Statistics & Probability Letters, 59, 159–167. LogLikelihood Full Log-likelihood of the GB2 Distribution Description Calculates the log-likelihood, the score functions of the log-likelihood and the Fisher information matrix based on all four parameters of the GB2 distribution. Usage loglp.gb2(x, shape1, scale, shape2, shape3, w=rep(1, length(x))) loglh.gb2(x, shape1, scale, shape2, shape3, w=rep(1, length(x)), hs=rep(1, length(x))) scoresp.gb2(x, shape1, scale, shape2, shape3, w=rep(1, length(x))) scoresh.gb2(x, shape1, scale, shape2, shape3, w=rep(1, length(x)), hs=rep(1, length(x))) info.gb2(shape1, scale, shape2, shape3) Arguments x numeric; vector of data values. shape1 numeric; positive parameter. scale numeric; positive parameter. shape2, shape3 numeric; positive parameters of the Beta distribution. w numeric; vector of weights. Must have the same length as x. By default w is a vector of 1. hs numeric; vector of household sizes. Must have the same length as x. By default hs is a vector of 1. 32 MLfitGB2 Details We express the log-likelihood as a weighted mean of log(f ), evaluated at the data points, where f is the GB2 density with parameters shape1 = a, scale = b, shape2 = p and shape3 = q. If the weights are not available, then we suppose that w = 1. loglp.gb2 calculates the log-likelihood in the case where the data is a sample of persons and loglh.gb2 is adapted for a sample of households. Idem for the scores, which are obtained as weighted sums of the first derivatives of log(f ) with respect to the GB2 parameters, evaluated at the data points. The Fisher information matrix of the GB2 was obtained by Brazauskas (2002) and is expressed in terms of the second derivatives of the log-likelihood with respect to the GB2 parameters. Author(s) Monique Graf References Brazauskas, V. (2002) Fisher information matrix for the Feller-Pareto distribution. Statistics & Probability Letters, 59, 159–167. Kleiber, C. and Kotz, S. (2003) Statistical Size Distributions in Economics and Actuarial Sciences, chapter 6. Wiley, Ney York. MLfitGB2 Fitting the GB2 by the Method of Maximum Likelihood Estimation and Comparison of the Fitted Indicators with the Empirical Indicators Description The function mlfit.gb2 makes a call to ml.gb2 and profml.gb2. Estimates of the GB2 parameters are calculated using maximum likelihood estimation based on the full and profile log-likelihoods. Empirical estimates of the set of primary indicators of poverty and social inclusion are calculated using the function main.emp (see package laeken) and these estimates are compared with the indicators calculated with the GB2 fitted parameters using the function main.gb2. Usage main.emp(z, w=rep(1, length(z))) mlfit.gb2(z, w=rep(1, length(z))) Arguments z numeric; vector of data values. w numeric; vector of weights. Must have the same length as z. By default w is a vector of 1. MLfitGB2 33 Value A list containing three different objects. The first is a data frame with the values of the fitted pa- rameters for the full log-likelihood and the profile log-likelihood, the values of the two likelihoods, the values of the GB2 estimates of the indicators and the values of the empirical estimates of the indicators. The second and third objects are, respectively, the fit using the full log-likelihood and the fit using the profile log-likelihood. Author(s) Monique Graf and Desislava Nedyalkova See Also optim , ml.gb2, profml.gb2 Examples # An example of using the function mlfit.gb2 # See also the examples of ml.gb2 and mlprof.gb2 ## Not run: library(laeken) data(eusilc) # Income inc <- as.vector(eusilc$eqIncome) # Weights w <- eusilc$rb050 # Data set d <- data.frame(inc, w) d <- d[!is.na(d$inc),] # Truncate at 0 inc <- d$inc[d$inc > 0] w <- d$w[d$inc > 0] # ML fit m1 <- mlfit.gb2(inc,w) # GB2 fitted parameters and indicators through maximum likelihood estimation m1[[1]] # The fit using the full log-likelihood m1[[2]] # The fit using the profile log-likelihood m1[[3]] # ML fit, when no weights are avalable m2 <- mlfit.gb2(inc) # Results 34 MLfullGB2 m2[[1]] ## End(Not run) MLfullGB2 Maximum Likelihood Estimation of the GB2 Based on the Full Log- likelihood Description Performs maximum pseudo-likelihood estimation through the general-purpose optimisation func- tion optim from package stats. Two methods of optimization are considered: BFGS and L-BFGS- B (see optim documentation for more details). Initial values of the parameters to be optimized over (a, b, p and q) are given from the Fisk distribution and p = q = 1. The function to be maximized by optim is the negative of the full log-likelihood and the gradient is equal to the negative of the scores, respectively for the case of a sample of persons and a sample of households. Usage ml.gb2(z, w=rep(1, length(z)), method=1, hess=FALSE) mlh.gb2(z, w=rep(1, length(z)), hs=rep(1, length(z)), method=1, hess = FALSE) Arguments z numeric; vector of data values. w numeric; vector of weights. Must have the same length as z. By default w is a vector of 1. hs numeric; vector of household sizes. Must have the same length as z. By default hs is a vector of 1. method numeric; the method to be used by optim. By default, codemethod = 1 and the used method is BFGS. If method = 2, method L-BFGS-B is used. hess logical; By default, hess = FALSE, the hessian matrix is not calculated. Details Function ml.gb2 performs maximum likelihood estimation through the general-purpose optimiza- tion function optim from package stats, based on the full log-likelihood calculated in a sample of persons. Function mlh.gb2 performs maximum likelihood estimation through the general-purpose optimization function optim from package stats, based on the full log-likelihood calculated in a sample of households. Value ml.gb2 and mlh.gb2 return a list with 1 argument: opt1 for the output of the BFGS fit or opt2 for the output of the L-BFGS fit. Further values are given by the values of optim. MLfullGB2 35 Author(s) Monique Graf References Graf, M., Nedyalkova, D., Muennich, R., Seger, J. and Zins, S. (2011) AMELI Deliverable 2.1: Parametric Estimation of Income Distributions and Indicators of Poverty and Social Exclusion. Technical report, AMELI-Project. See Also optim for the general-purpose optimization and fisk for the Fisk distribution. Examples ## Not run: library(laeken) data(eusilc) # Income inc <- as.vector(eusilc$eqIncome) # Weights w <- eusilc$rb050 # Data set d <- data.frame(inc, w) d <- d[!is.na(d$inc),] # Truncate at 0 inc <- d$inc[d$inc > 0] w <- d$w[d$inc > 0] # Fit using the full log-likelihood fitf <- ml.gb2(inc, w) # Fitted GB2 parameters af <- fitf$par[1] bf <- fitf$par[2] pf <- fitf$par[3] qf <- fitf$par[4] # Likelihood flik <- fitf$value # If we want to compare the indicators # GB2 indicators indicf <- round(main.gb2(0.6,af,bf,pf,qf), digits=3) # Empirical indicators indice <- round(main.emp(inc,w), digits=3) 36 MLprofGB2 # Plots plotsML.gb2(inc,af,bf,pf,qf,w) ## End(Not run) MLprofGB2 Maximum Likelihood Estimation of the GB2 Based on the Profile Log- likelihood Description profml.gb2 performs maximum likelihood estimation based on the profile log-likelihood through the general-purpose optimisation function optim from package stats. Usage profml.gb2(z, w=rep(1, length(z)), method=1, hess = FALSE) Arguments z numeric; vector of data values. w numeric; vector of weights. Must have the same length as z. By default w is a vector of 1. method numeric; the method to be used by optim. By default, codemethod = 1 and the used method is BFGS. If method = 2, method L-BFGS-B is used. hess logical; By default, hess = FALSE, the hessian matrix is not calculated. Details Two methods are considered: BFGS and L-BFGS-B (see optim documentation for more details). Initial values of the parameters to be optimized over (a and b) are given from the Fisk distribution. The function to be maximized by optim is the negative of the profile log-likelihood (proflogl.gb2) and the gradient is equal to the negative of the scores (profscores.gb2). Value A list with 1 argument: opt1 for the output of the BFGS fit or opt2 for the output of the L-BFGS fit. Further values are given by the values of optim. Author(s) Monique Graf References Graf, M., Nedyalkova, D., Muennich, R., Seger, J. and Zins, S. (2011) AMELI Deliverable 2.1: Parametric Estimation of Income Distributions and Indicators of Poverty and Social Exclusion. Technical report, AMELI-Project. Moments 37 See Also optim for the general-purpose optimization, link{ml.gb2} for the fit using the full log-likelihood and fisk for the Fisk distribution. Examples library(laeken) data(eusilc) # Income inc <- as.vector(eusilc$eqIncome) # Weights w <- eusilc$rb050 # Data set d <- data.frame(inc,w) d <- d[!is.na(d$inc),] # Truncate at 0 inc <- d$inc[d$inc > 0] w <- d$w[d$inc > 0] # Fit using the profile log-likelihood fitp <- profml.gb2(inc, w)$opt1 # Fitted GB2 parameters ap <- fitp$par[1] bp <- fitp$par[2] pp <- prof.gb2(inc, ap, bp, w)[3] qp <- prof.gb2(inc, ap, bp, w)[4] # Profile log-likelihood proflik <- fitp$value # If we want to compare the indicators ## Not run: # GB2 indicators indicp <- round(main.gb2(0.6,ap,bp,pp,qp), digits=3) # Empirical indicators indice <- round(main.emp(inc,w), digits=3) ## End(Not run) # Plots ## Not run: plotsML.gb2(inc,ap,bp,pp,qp,w) Moments Moments and Other Properties of a GB2 Random Variable 38 Moments Description These functions calculate the moments of order k and incomplete moments of order k of a GB2 random variable X as well as the expectation, the variance, the kurtosis and the skewness of log(X). Usage moment.gb2(k, shape1, scale, shape2, shape3) incompl.gb2(x, k, shape1, scale, shape2, shape3) el.gb2(shape1, scale, shape2, shape3) vl.gb2(shape1, shape2, shape3) sl.gb2(shape2, shape3) kl.gb2(shape2, shape3) Arguments x numeric; vector of quantiles. k numeric; order of the moment. shape1 numeric; positive parameter. scale numeric; positive parameter. shape2, shape3 numeric; positive parameters of the Beta distribution. Details Let X be a random variable following a GB2 distribution with parameters shape1 = a, scale = b, shape2 = p and shape3 = q. Moments and incomplete moments of X exist only for −ap ≤ k ≤ aq. Moments are given by Γ(p + k/a)Γ(q − k/a) E(X k ) = bk Γ(p)Γ(q) This expression, when considered a function of k, can be viewed as the moment-generating function of Y = log(X). Thus, it is useful to compute the moments of log(X), which are needed for deriving, for instance, the Fisher information matrix of the GB2 distribution. Moments of log(X) exist for all k. Value moment.gb2 gives the moment of order k, incompl.gb2 gives the incomplete moment of order k, El.gb2 gives the expectation of log(X), vl.gb2 gives the variance of log(X), sl.gb2 gives the skewness of log(X), kl.gb2 gives the kurtosis of log(X). Author(s) Monique Graf References Kleiber, C. and Kotz, S. (2003) Statistical Size Distributions in Economics and Actuarial Sciences, chapter 6. Wiley, Ney York. NonlinearFit 39 See Also gamma for the Gamma function and related functions (digamma, trigamma and psigamma). Examples a <- 3.9 b <- 18873 p <- 0.97 q <- 1.03 k <- 2 x <- qgb2(0.6, a, b, p, q) moment.gb2(k, a, b, p, q) incompl.gb2(x, k, a, b, p, q) vl.gb2(a, p, q) kl.gb2(p, q) NonlinearFit Fitting the GB2 by Minimizing the Distance Between a Set of Empiri- cal Indicators and Their GB2 Expressions Description Fitting the parameters of the GB2 distribution by optimizing the squared weighted distance between a set of empirical indicators, i.e. the median, the ARPR, the RMPG, the QSR and the Gini coeffi- cient, and the GB2 indicators using nonlinear least squares (function nls from package stats). Usage nlsfit.gb2(med, ei4, par0=c(1/ei4[4],med,1,1), cva=1, bound1=par0[1]*max(0.2,1-2*cva), bound2=par0[1]*min(2,1+2*cva), ei4w=1/ei4) Arguments med numeric; the empirical median. ei4 numeric; the values of the empirical indicators. par0 numeric; vector of initial values for the GB2 parameters a, b, p and q. The de- fault is to take a equal to the inverse of the empirical Gini coefficient, b equal to the empirical median and p = q = 1. cva numeric; the coefficient of variation of the ML estimate of the parameter a. The default value is 1. bound1, bound2 numeric; the lower and upper bounds for the parameter a in the algorithm. The default values are 0.2∗a0 and 2∗a0 , where a0 is the initial value of the parameter a. ei4w numeric; vector of weights of to be passed to the nls function. The default values are the inverse of the empirical indicators. 40 NonlinearFit Details We consider the following set of indicators A = (median, ARP R, RM P G, QSR, Gini) and their corresponding GB2 expressions AGB2 . We fit the parameters of the GB2 in two consecutive steps. In the first step, we use the set of indicators, excluding the median, and their corresponding expressions in function of a, ap and aq. The bounds for a are defined in function of the coefficient of variation of the fitted parameter ˆ(a). The nonlinear model that is passed to nls is given by: X4 ci (Aempir,i − AGB2,i (a, ap, aq))2 , i=1 where the weights ci take the differing scales into account and are given by the vector ei4w. ap and aq are bounded so that the constraints for the existence of the moments of the GB2 distribution and the excess for calculating the Gini coefficient are fulfilled, i.e. ap ≥ 1 and aq ≥ 2. In the second step, only the the parameter b is estimated, optimizing the weighted square difference between the empirical median and the GB2 median in function of the already obtained NLS parameters a, p and q. Value nlsfit.gb2 returns a list of three values: the fitted GB2 parameters, the first fitted object and the second fitted object. Author(s) Monique Graf and Desislava Nedyalkova See Also nls, Thomae, moment.gb2 Examples # Takes long time to run, as it makes a call to the function ml.gb2 ## Not run: library(laeken) data(eusilc) # Personal income inc <- as.vector(eusilc$eqIncome) # Sampling weights w <- eusilc$rb050 # Data set d <- data.frame(inc, w) d <- d[!is.na(d$inc),] # Truncate at 0 d <- d[d$inc > 0,] PlotsML 41 inc <- d$inc w <- d$w # ML fit, full log-likelihood fitf <- ml.gb2(inc, w)$opt1 # Estimated parameters af <- fitf$par[1] bf <- fitf$par[2] pf <- fitf$par[3] qf <- fitf$par[4] gb2.par <- c(af, bf, pf, qf) # Empirical indicators indicEMP <- main.emp(inc, w) indicEMP <- c(indicEMP[1],indicEMP[3:6]) indicE <- round(indicEMP, digits=3) # Nonlinear fit nn <- nlsfit.gb2(indicEMP[1,3:6],indicEMP[3:6]) an <- nn[[1]][1] bn <- nn[[1]][2] pn <- nn[[1]][3] qn <- nn[[1]][4] # GB2 indicators indicNLS <- c(main.gb2(0.6, an, bn, pn, qn)[1], main.gb2(0.6, an, bn, pn, qn)[3:6]) indicML <- c(main.gb2(0.6, af, bf, pf, qf)[1], main.gb2(0.6, af, bf, pf, qf)[3:6]) indicN <- round(indicNLS, digits=3) indicM <- round(indicML, digits=3) # Likelihoods nlik <- loglp.gb2(inc, an, bn, pn, qn, w) mlik <- loglp.gb2(inc, af, bf, pf, qf, w) # Results type=c("Emp. est", "NLS", "ML full") results <- data.frame(type=type, median=c(indicE[1], indicN[1], indicM[1]), ARPR=c(indicE[2], indicN[2], indicM[2]), RMPG=c(indicE[3], indicN[3], indicM[3]), QSR =c(indicE[4], indicN[4], indicM[4]), GINI=c(indicE[5], indicN[5], indicM[5]), likelihood=c(NA, nlik, mlik), a=c(NA, an, af), b=c(NA, bn, bf) ,p=c(NA, pn, pf), q=c(NA, qn, qf)) ## End(Not run) 42 ProfLogLikelihood PlotsML Cumulative Distribution Plot and Kernel Density Plot for the Fitted GB2 Description Function plotsML.gb2 produces two plots. The first is a plot of the empirical cumulative distribu- tion function versus the fitted cumulative distibution function. The second is a plot of the kernel density versus the fitted GB2 density. Function saveplot saves locally the produced plot. Usage plotsML.gb2(z, shape1, scale, shape2, shape3, w=rep(1,length(z))) saveplot(name, pathout) Arguments z numeric; vector of data values. w numeric; vector of weights. Must have the same length as z. By default w is a vector of 1. shape1 numeric; positive parameter. scale numeric; positive parameter. shape2, shape3 numeric; positive parameters of the Beta distribution. name string; the name of the plot. pathout string; the path of the folder or device where the plot will be saved. Details The used kernel is "Epanechnikov" (see plot). Author(s) Monique Graf and Desislava Nedyalkova ProfLogLikelihood Profile Log-likelihood of the GB2 Distribution Description Expression of the parameters shape2 = p and shape3 = q of the GB2 distribution as functions of shape1 = a and scale = b, profile log-likelihood of the GB2 distribution, scores of the profile log-likelihood. RobustWeights 43 Usage prof.gb2(x, shape1, scale, w=rep(1, length(x))) proflogl.gb2(x, shape1, scale, w=rep(1, length(x))) profscores.gb2(x, shape1, scale, w=rep(1, length(x))) Arguments x numeric; vector of data values. shape1 numeric; positive parameter. scale numeric; positive parameter. w numeric; vector of weights. Must have the same length as x. By default w is a vector of 1. Details Using the full log-likelihood equations for the GB2 distribution, the parameters p and q can be estimated as functions of a and b. These functions are plugged into the log-likelihood expression, which becomes a function of a and b only. This is obtained by reparametrizing the GB2, i.e. we set p r = p+q and s = p + q. More details can be found in Graf (2009). Value prof returns a vector containing the values of r, s, p, q as well as two other parameters used in the calculation of the profile log-likelihood and its first derivatives. proflogl.gb2 returns the value of the profile log-likelihood and profscores.gb2 returns the vector of the first derivatives of the profile log-likelihhod with respect to a and b. Author(s) Monique Graf and Desislava Nedyalkova References Graf, M. (2009) The Log-Likelihood of the Generalized Beta Distribution of the Second Kind. working paper, SFSO. RobustWeights Robustification of the sampling weights Description Calculation of a Huber-type correction factor by which the vector of weights is multiplied. Usage robwts(x, w=rep(1,length(x)), c=0.01, alpha=0.001) 44 Thomae Arguments x numeric; vector of data values. w numeric; vector of weights. Must have the same length as x. By default w is a vector of 1. c numeric; a constant which can take different values, e.g. 0.01,0.02. By default c = 0.1. alpha numeric; a probability in the interval (0, 1). By default alpha = 0.001. Details If x denotes the observed value and xα the α-th qiantile of the Fisk distribution, then we define our scale as: x1−α xα d= − b b . Next, the correction factor is calculated as follows:    d d corr = max c, min 1, , |b/x − 1| |x/b − 1| Value robwts returns a list of two elements: the vector of correction factors by which the weights are multiplied and the vector of corrected (robustified) weights. Author(s) Monique Graf References Graf, M., Nedyalkova, D., Muennich, R., Seger, J. and Zins, S. (2011) AMELI Deliverable 2.1: Parametric Estimation of Income Distributions and Indicators of Poverty and Social Exclusion. Technical report, AMELI-Project. Thomae Maximum Excess Representation of a Generalized Hypergeometric Function Using Thomae’s Theorem Description Defines Thomae’s arguments from the upper (U) and lower (L) parameters of a 3 F2 (U, L; 1). Com- putes the optimal combination leading to the maximum excess. Computes the optimal combination of Thomae’s arguments and calculates the optimal representation of the 3 F2 (U, L; 1) using the genhypergeo_series function from package hypergeo. Computes the Gini coefficient for the GB2 distribution, using Thomae’s theorem. Thomae 45 Usage ULg(U, L) combiopt(g) Thomae(U, L, lB, tol, maxiter, debug) gb2.gini(shape1, shape2, shape3, tol=1e-08, maxiter=10000, debug=FALSE) Arguments U numeric; vector of length 3 giving the upper arguments of the generalized hy- pergeometric function 3 F2 . L numeric; vector of length 2 giving the lower arguments of the generalized hy- pergeometric function 3 F2 . g numeric; vector of Thomae’s permuting arguments. lB numeric; ratio of beta functions (a common factor in the expression of the Gini coefficient under the GB2). shape1 numeric; positive parameter. shape2, shape3 numeric; positive parameters of the Beta distribution. tol numeric; tolerance with default 0, meaning to iterate until additional terms do not change the partial sum. maxiter numeric; maximum number of iterations to perform. debug boolean; if TRUE, returns the list of changes to the partial sum. Details Internal use only. More details can be found in Graf (2009). Value ULg returns a list containing Thomae’s arguments and the excess, combiopt gives the optimal com- bination of Thomae’s arguments, Thomae returns the optimal representation of the 3 F2 (U, L; 1), gb2.gini returns the value of the Gini coefficient under the GB2. Author(s) Monique Graf References Graf, M. (2009) An Efficient Algorithm for the Computation of the Gini coefficient of the Gen- eralized Beta Distribution of the Second Kind. ASA Proceedings of the Joint Statistical Meetings, 4835–4843. American Statistical Association (Alexandria, VA). McDonald, J. B. (1984) Some generalized functions for the size distribution of income. Economet- rica, 52, 647–663. See Also genhypergeo_series, gini.gb2 46 Varest Varest Variance Estimation of the Parameters of the GB2 Distribution Description Calculation of variance estimates of the estimated GB2 parameters and the estimated GB2 indicators under cluster sampling. Usage varscore.gb2(x, shape1, scale, shape2, shape3, w=rep(1,length(x)), hs=rep(1,length(x))) vepar.gb2(x, Vsc, shape1, scale, shape2, shape3, w=rep(1,length(x)), hs=rep(1,length(x))) derivind.gb2(shape1, scale, shape2, shape3) veind.gb2(Vpar, shape1, scale, shape2, shape3) Arguments x numeric; vector of data values. Vsc numeric; 4 by 4 matrix. shape1 numeric; positive parameter. scale numeric; positive parameter. shape2, shape3 numeric; positive parameters of the Beta distribution. w numeric; vector of weights. Must have the same length as x. By default w is a vector of 1. hs numeric; vector of household sizes. Must have the same length as x. By default w is a vector of 1. Vpar numeric; 4 by 4 matrix. Details Knowing the first and second derivatives of log(f ), and using the sandwich variance estimator (see Freedman (2006)), the calculation of the variance estimates of the GB2 parameters is straightfor- ward. Vsc is a square matrix of size the number of parameters, e.g. the estimated design variance- covariance matrix of the estimated parameters. We know that the GB2 estimates of the Laeken indicators are functions of the GB2 parameters. In this case, the variance estimates of the fitted indicators are obtained using the delta method. The function veind.gb2 uses Vpar, the sandwich variance estimator of the vector of parameters, in order to obtain the sandwich variance estimator of the indicators. More details can be found in Graf and Nedyalkova (2011). Value varscore.gb2 calculates the middle term of the sandwich variance estimator under simple ran- dom cluster sampling. vepar.gb2 returns a list of two elements: the estimated variance-covariance matrix of the estimated GB2 parameters and the second-order partial derivative of the pseudo log- likelihood function. The function veind.gb2 returns the estimated variance-covariance matrix of the estimated GB2 indicators. derivind.gb2 calculates the numerical derivatives of the GB2 indi- cators and is for internal use only. Varest 47 Author(s) Monique Graf and Desislava Nedyalkova References Davison, A. (2003), Statistical Models. Cambridge University Press. Freedman, D. A. (2006), On The So-Called "Huber Sandwich Estimator" and "Robust Standard Errors". The American Statistician, 60, 299–302. Graf, M., Nedyalkova, D., Muennich, R., Seger, J. and Zins, S. (2011) AMELI Deliverable 2.1: Parametric Estimation of Income Distributions and Indicators of Poverty and Social Exclusion. Technical report, AMELI-Project. Pfeffermann, D. and Sverchkov, M. Yu. (2003), Fitting Generalized Linear Models under Informa- tive Sampling. In, Skinner, C.J. and Chambers, R.L. (eds.). Analysis of Survey Data, chapter 12, 175–195. Wiley, New York. Examples # An example of variance estimation of the GB2 parameters, # using the dataset "eusilcP" from the R package simFrame. # Takes long time to run ## Not run: library(survey) library(simFrame) data(eusilcP) # Draw a sample from eusilcP # 1-stage simple random cluster sampling of size 6000 (cluster = household) # directly, #s <- draw(eusilcP[, c("hid", "hsize", "eqIncome")], grouping = "hid", size = 6000) # or setting up 250 samples, and drawing the first one. # This sample setup can be used for running a simulation. set.seed(12345) scs <- setup(eusilcP, grouping = "hid", size = 6000, k = 250) s <- draw(eusilcP[, c("region", "hid", "hsize", "eqIncome")], scs, i=1) # The number of observations (persons) in eusilcP (58654 persons) \dontrun{N <- dim(eusilcP)[1]} # The number of households in eusilcP (25000 households) Nh <- length(unique(eusilcP$hid)) # Survey design corresponding to the drawn sample sdo = svydesign(id=~hid, fpc=rep(Nh,nrow(s)), data=s) \dontrun{summary(sdo)} # Truncated sample (truncate at 0) s <- s[!is.na(s$eqIncome),] str <- s[s$eqIncome > 0, ] eqInc <- str$eqIncome 48 Varest w <- str$.weight # Designs for the truncated sample sdotr <- subset(sdo, eqIncome >0) sddtr = svydesign(id=~hid, strata=~region, fpc=NULL, weights=~.weight, data=str) \dontrun{summary(sdotr)} \dontrun{summary(sddtr)} # Fit by maximum likelihood fit <- ml.gb2(eqInc,w)$opt1 af <- fit$par[1] bf <- fit$par[2] pf <- fit$par[3] qf <- fit$par[4] mlik <- -fit$value # Estimated parameters and indicators, empirical indicators gb2.par <- round(c(af, bf, pf, qf), digits=3) emp.ind <- main.emp(eqInc, w) gb2.ind <- main.gb2(0.6, af, bf, pf, qf) # Scores scores <- matrix(nrow=length(eqInc), ncol=4) for (i in 1:length(eqInc)){ scores[i,] <- dlogf.gb2(eqInc[i], af, bf, pf, qf) } # Data on households only sh <- unique(str) heqInc <- sh$eqIncome hw <- sh$.weight hhs <- sh$hsize hs <- as.numeric(as.vector(hhs)) # Variance of the scores VSC <- varscore.gb2(heqInc, af, bf, pf, qf, hw, hs) # Variance of the scores using the explicit designs, and package survey DV1 <- vcov(svytotal(~scores[,1]+scores[,2]+scores[,3]+scores[,4], design=sdotr)) DV2 <- vcov(svytotal(~scores[,1]+scores[,2]+scores[,3]+scores[,4], design=sddtr)) # Estimated variance-covariance matrix of the parameters af, bf, pf and qf VCMP <- vepar.gb2(heqInc, VSC, af, bf, pf, qf, hw, hs)[[1]] DVCMP1 <- vepar.gb2(heqInc, DV1, af, bf, pf, qf, hw, hs)[[1]] DVCMP2 <- vepar.gb2(heqInc, DV2, af, bf, pf, qf, hw, hs)[[1]] \dontrun{diag(DVCMP1)/diag(VCMP)} \dontrun{diag(DVCMP2)/diag(VCMP)} \dontrun{diag(DV1)/diag(VSC)} \dontrun{diag(DV2)/diag(VSC)} # Standard errors of af, bf, pf and qf se.par <- sqrt(diag(VCMP)) Varest 49 sed1.par <- sqrt(diag(DVCMP1)) sed2.par <- sqrt(diag(DVCMP2)) # Estimated variance-covariance matrix of the indicators (VCMI) VCMI <- veind.gb2(VCMP, af, bf, pf, qf) DVCMI1 <- veind.gb2(DVCMP1, af, bf, pf, qf) DVCMI2 <- veind.gb2(DVCMP2, af, bf, pf, qf) # Standard errors and confidence intervals varest.ind <- diag(VCMI) se.ind <- sqrt(varest.ind) lci.ind <- gb2.ind - 1.96*se.ind uci.ind <- gb2.ind + 1.96*se.ind inCI <- as.numeric(lci.ind <= emp.ind & emp.ind <= uci.ind) # under the sampling design sdotr varestd1.ind <- diag(DVCMI1) sed1.ind <- sqrt(varestd1.ind) lcid1.ind <- gb2.ind - 1.96*sed1.ind ucid1.ind <- gb2.ind + 1.96*sed1.ind inCId1 <- as.numeric(lcid1.ind <= emp.ind & emp.ind <= ucid1.ind) #under the sampling design sddtr varestd2.ind <- diag(DVCMI2) sed2.ind <- sqrt(varestd2.ind) lcid2.ind <- gb2.ind - 1.96*sed2.ind ucid2.ind <- gb2.ind + 1.96*sed2.ind inCId2 <- as.numeric(lcid2.ind <= emp.ind & emp.ind <= ucid2.ind) #coefficients of variation .par (parameters), .ind (indicators) cv.par <- se.par/gb2.par names(cv.par) <- c("am","bm","pm","qm") cvd1.par <- sed1.par/gb2.par names(cvd1.par) <- c("am","bm","pm","qm") cvd2.par <- sed2.par/gb2.par names(cvd2.par) <- c("am","bm","pm","qm") cv.ind <- se.ind/gb2.ind cvd1.ind <- sed1.ind/gb2.ind cvd2.ind <- sed2.ind/gb2.ind #results res <- data.frame(am = af, bm = bf, pm = pf, qm = qf, lik = mlik, median = gb2.ind[[1]], mean = gb2.ind[[2]], ARPR = gb2.ind[[3]], RMPG = gb2.ind[[4]], QSR = gb2.ind[[5]], Gini = gb2.ind[[6]], emedian = emp.ind[[1]], emean = emp.ind[[2]], eARPR = emp.ind[[3]], eRMPG = emp.ind[[4]], eQSR = emp.ind[[5]], eGini = emp.ind[[6]], cva = cv.par[1], cvb = cv.par[2], cvp= cv.par[3], cvq = cv.par[4], cvd1a = cvd1.par[1], cvd1b = cvd1.par[2], cvd1p= cvd1.par[3], cvd1q = cvd1.par[4], cvd2a = cvd2.par[1], cvd2b = cvd2.par[2], cvd2p= cvd2.par[3], cvd2q = cvd2.par[4], 50 Varest cvmed = cv.ind[[1]], cvmean = cv.ind[[2]], cvARPR = cv.ind[[3]], cvRMPG = cv.ind[[4]], cvQSR = cv.ind[[5]], cvGini = cv.ind[[6]], cvd1med = cvd1.ind[[1]], cvd1mean = cvd1.ind[[2]], cvd1ARPR = cvd1.ind[[3]], cvd1RMPG = cvd1.ind[[4]], cvd1QSR = cvd1.ind[[5]], cvd1Gini = cvd1.ind[[6]], cvd2med = cvd2.ind[[1]], cvd2mean = cvd2.ind[[2]], cvd2ARPR = cvd2.ind[[3]], cvd2RMPG = cvd2.ind[[4]], cvd2QSR = cvd2.ind[[5]], cvd2Gini = cvd2.ind[[6]]) res <- list(parameters = data.frame(am = af, bm = bf, pm = pf, qm = qf, lik = mlik), cv.parameters.naive = cv.par, cv.parameters.design1 = cvd1.par, cv.parameters.design2 = cvd2.par, GB2.indicators = gb2.ind, emp.indicators = emp.ind, cv.indicators.naive = cv.ind, cv.indicators.design1 = cvd1.ind, cv.indicators.design2 = cvd2.ind) res \dontrun{inCI} ## End(Not run) Index ∗ distribution CompoundAuxVarest, 9 Compound, 2 CompoundDensPlot, 12 CompoundAuxFit, 6 CompoundFit, 7, 13 CompoundAuxVarest, 9 CompoundIndicators, 15 CompoundFit, 13 CompoundMoments, 17 CompoundIndicators, 15 CompoundQuantiles, 18 CompoundMoments, 17 CompoundVarest, 20 CompoundQuantiles, 18 Contindic, 22 CompoundVarest, 20 contindic.gb2 (Contindic), 22 Contprof, 24 contour, 23, 24 Fisk, 25 Contprof, 24 gb2, 26 contprof.gb2 (Contprof), 24 Gini, 27 Indicators, 28 d2logf.gb2 (LogDensity), 30 LogDensity, 30 dbeta, 27 LogLikelihood, 31 dcgb2 (Compound), 2 MLfitGB2, 32 density, 13 MLfullGB2, 34 derivind.cgb2 (CompoundVarest), 20 MLprofGB2, 36 derivind.gb2 (Varest), 46 Moments, 37 desvar.cavgb2, 10 NonlinearFit, 39 desvar.cavgb2 (CompoundAuxVarest), 9 PlotsML, 42 desvar.cgb2 (CompoundVarest), 20 ProfLogLikelihood, 42 dgb2 (gb2), 26 Thomae, 44 dl.cgb2 (Compound), 2 Varest, 46 dlogf.gb2 (LogDensity), 30 ∗ dplot.cavgb2 (CompoundAuxDensPlot), 5 Compound, 2 dplot.cgb2 (CompoundDensPlot), 12 NonlinearFit, 39 el.gb2 (Moments), 37 arpr.cgb2 (CompoundIndicators), 15 arpr.gb2 (Indicators), 28 fg.cgb2 (Compound), 2 arpt.cgb2 (CompoundIndicators), 15 Fisk, 25 arpt.gb2 (Indicators), 28 fisk, 24, 35, 37 fisk (Fisk), 25 beta, 27 fiskh (Fisk), 25 combiopt (Thomae), 44 gamma, 39 Compound, 2 gb2, 26 CompoundAuxDensPlot, 5 gb2.gini, 27, 28 CompoundAuxFit, 6 gb2.gini (Thomae), 44 51 52 INDEX genhypergeo_series, 45 nlsfit.gb2 (NonlinearFit), 39 Gini, 27 NonlinearFit, 39 gini.b2 (Gini), 27 gini.dag (Gini), 27 optim, 6, 7, 14, 25, 33, 35, 37 gini.gb2, 45 gini.gb2 (Gini), 27 pcgb2 (Compound), 2 gini.sm (Gini), 27 pgb2 (gb2), 26 pkl.cavgb2, 5 hess.cavgb2 (CompoundAuxVarest), 9 pkl.cavgb2 (CompoundAuxFit), 6 hess.cgb2 (CompoundVarest), 20 pl.cgb2 (Compound), 2 plot, 42 incompl.cgb2 (CompoundMoments), 17 plot.density, 13 incompl.gb2, 30 PlotsML, 41 incompl.gb2 (Moments), 37 plotsML.gb2 (PlotsML), 42 Indicators, 28 pofv.cgb2 (CompoundFit), 13 info.gb2 (LogLikelihood), 31 prcgb2 (Compound), 2 prof.gb2 (ProfLogLikelihood), 42 kl.gb2 (Moments), 37 proflogl.gb2, 36 lambda0.cavgb2 (CompoundAuxFit), 6 proflogl.gb2 (ProfLogLikelihood), 42 LogDensity, 30 ProfLogLikelihood, 24, 42 logf.gb2 (LogDensity), 30 profml.gb2, 33 logl.cavgb2 (CompoundAuxFit), 6 profml.gb2 (MLprofGB2), 36 logl.cgb2 (CompoundFit), 13 profscores.gb2, 36 loglh.gb2 (LogLikelihood), 31 profscores.gb2 (ProfLogLikelihood), 42 LogLikelihood, 31 loglp.gb2 (LogLikelihood), 31 qcgb2 (CompoundQuantiles), 18 qgb2, 30 main.cgb2 (CompoundIndicators), 15 qgb2 (gb2), 26 main.emp (MLfitGB2), 32 qsr.cgb2 (CompoundIndicators), 15 main.gb2, 32 qsr.gb2 (Indicators), 28 main.gb2 (Indicators), 28 main2.gb2 (Indicators), 28 rcgb2 (CompoundQuantiles), 18 mkl.cgb2 (CompoundMoments), 17 rgb2 (gb2), 26 ml.cavgb2 (CompoundAuxFit), 6 rmpg.cgb2 (CompoundIndicators), 15 ml.cgb2, 13 rmpg.gb2 (Indicators), 28 ml.cgb2 (CompoundFit), 13 RobustWeights, 43 ml.gb2, 33 robwts (RobustWeights), 43 ml.gb2 (MLfullGB2), 34 mlfit.gb2 (MLfitGB2), 32 saveplot (PlotsML), 42 MLfitGB2, 32 scores.cavgb2 (CompoundAuxFit), 6 MLfullGB2, 34 scores.cgb2 (CompoundFit), 13 mlh.gb2 (MLfullGB2), 34 scoresh.gb2 (LogLikelihood), 31 MLprofGB2, 36 scoresp.gb2 (LogLikelihood), 31 moment.cgb2 (CompoundMoments), 17 scoreU.cavgb2 (CompoundAuxVarest), 9 moment.gb2, 40 scoreU.cgb2 (CompoundVarest), 20 moment.gb2 (Moments), 37 scorez.cavgb2 (CompoundAuxVarest), 9 Moments, 37 sl.gb2 (Moments), 37 survey, 9, 11, 21 nls, 40 svydesign, 10, 20 INDEX 53 Thomae, 40, 44 ULg (Thomae), 44 Varest, 46 varscore.cavgb2 (CompoundAuxVarest), 9 varscore.cgb2 (CompoundVarest), 20 varscore.gb2 (Varest), 46 veind.cavgb2 (CompoundAuxVarest), 9 veind.cgb2 (CompoundVarest), 20 veind.gb2 (Varest), 46 vepar.cavgb2 (CompoundAuxVarest), 9 vepar.cgb2 (CompoundVarest), 20 vepar.gb2 (Varest), 46 vl.gb2 (Moments), 37 vofp.cgb2 (CompoundFit), 13

SpatialML

cran

Package ‘SpatialML’ October 12, 2022 Version 0.1.5 Date 2022-09-02 Type Package Title Spatial Machine Learning Author Stamatis Kalogirou [aut, cre], Stefanos Georganos [aut, ctb] Maintainer Stamatis Kalogirou <stamatis@lctools.science> Depends R (>= 4.1.0), ranger (>= 0.13.1), caret Description Implements a spatial extension of the random forest algorithm (Georganos et al. (2019) <doi:10.1080/10106049.2019.1595177>). Allows for a geographically weighted random forest regression including a function to find the optical bandwidth. (Georganos and Kalogirou (2022) <https://www.mdpi.com/2220-9964/11/9/471>). License GPL (>= 2) Encoding UTF-8 LazyData true URL http://lctools.science/ NeedsCompilation no Repository CRAN Date/Publication 2022-09-02 07:20:15 UTC R topics documented: grf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 grf.bw . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 Income . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 predict.grf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 random.test.data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 rf.mtry.optim . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Index 13 1 2 grf grf Geographically Weighted Random Forest Description This function refers to a local version of the popular Random Forest algorithm. Usage grf(formula, dframe, bw, kernel, coords, ntree=500, mtry=NULL, importance="impurity", nthreads = NULL, forests = TRUE, weighted = TRUE, print.results=TRUE, ...) Arguments formula the local model to be fitted using the same syntax used in the ranger function of the R package ranger. This is a string that is passed to the sub-models’ ranger function. For more details look at the class formula. dframe a numeric data frame of at least two suitable variables (one dependent and one independent) bw a positive number that may be an integer in the case of an "adaptive kernel" or a real in the case of a "fixed kernel". In the first case, the integer denotes the number of nearest neighbours, whereas in the latter case the real number refers to the bandwidth (in meters if the coordinates provided are Cartesian). kernel the kernel to be used in the regression. Options are "adaptive" or "fixed". coords a numeric matrix or data frame of two columns giving the X,Y coordinates of the observations ntree an integer referring to the number of trees to grow for each of the local random forests. mtry Number of variables randomly sampled as candidates at each split. Note that the default values is p/3, where p is number of variables in the formula importance Feature importance of the dependent variables used as input at the random forest. Default value is "impurity" which refers to the Gini index for classification and the variance of the responses for regression. nthreads Number of threads. Default is number of CPUs available. The argument passes to both rnager and predict functions. forests a option to save and export (TRUE) or not (FALSE) all the local forests weighted if TRUE the algorithm calculates Geographically Weighted Random Forest us- ing the case.weights option of the packare ranger. If FALSE it will calculate local random forests without weighting each observation in the local data set. print.results a option to print in the console (TRUE) or not (FALSE) the summary of the analysis ... further arguments passed to the ranger function grf 3 Details Geographically Weighted Random Forest (GRF) is a spatial analysis method using a local version of the famous Machine Learning algorithm. It allows for the investigation of the existence of spatial non-stationarity, in the relationship between a dependent and a set of independent variables. The latter is possible by fitting a sub-model for each observation in space, taking into account the neigh- bouring observations. This technique adopts the idea of the Geographically Weighted Regression, Kalogirou (2003). The main difference between a tradition (linear) GWR and GRF is that we can model non-stationarity coupled with a flexible non-linear model which is very hard to overfit due to its bootstrapping nature, thus relaxing the assumptions of traditional Gaussian statistics. Essen- tially, it was designed to be a bridge between machine learning and geographical models, combining inferential and explanatory power. Additionally, it is suited for datasets with numerous predictors, due to the robust nature of the random forest algorithm in high dimensionality. Value Global.Model A ranger object of the global random forest model Locations a numeric matrix or data frame of two columns giving the X,Y coordinates of the observations Local.Variable.Importance a numeric data frame with the local feature importance for each predictor in each local random forest model LGofFit a numeric data frame with residuals and local goodness of fit statistics. Forests all local forests. lModelSummary Local Model Summary and goodness of fit statistics. Warning Large datasets may take long to calibrate. A high number of observations may result in a volumi- nous forests output. Note This function is under development. There should be improvements in future versions of the pack- age SpatialML. Any suggestion is welcome! Author(s) Stamatis Kalogirou <stamatis@lctools.science>, Stefanos Georganos <sgeorgan@ulb.ac.be> References Stefanos Georganos, Tais Grippa, Assane Niang Gadiaga, Catherine Linard, Moritz Lennert, Sabine Vanhuysse, Nicholus Odhiambo Mboga, Eléonore Wolff & Stamatis Kalogirou (2019) Geographi- cal Random Forests: A Spatial Extension of the Random Forest Algorithm to Address Spatial Het- erogeneity in Remote Sensing and Population Modelling, Geocarto International, DOI: 10.1080/10106049.2019.1595177 Georganos, S. and Kalogirou, S. (2022) A Forest of Forests: A Spatially Weighted and Computa- tionally Efficient Formulation of Geographical Random Forests. ISPRS, International Journal of Geo-Information, 2022, 11, 471. <https://www.mdpi.com/2220-9964/11/9/471> 4 grf.bw See Also predict.grf Examples ## Not run: RDF <- random.test.data(10,10,3) Coords<-RDF[ ,4:5] grf <- grf(dep ~ X1 + X2, dframe=RDF, bw=10, kernel="adaptive", coords=Coords) ## End(Not run) data(Income) Coords<-Income[ ,1:2] grf <- grf(Income01 ~ UnemrT01 + PrSect01, dframe=Income, bw=60, kernel="adaptive", coords=Coords) grf.bw Geographically Weighted Random Forest optimal bandwidth selection Description This function finds the optimal bandwidth for the Geographically Weighted Random Forest algo- rithm using an exhaustive approach. Usage grf.bw(formula, dataset, kernel="adaptive", coords, bw.min = NULL, bw.max = NULL, step = 1, trees=500, mtry=NULL, importance="impurity", nthreads = 1, forests = FALSE, weighted = TRUE, ...) Arguments formula the local model to be fitted using the same syntax used in the ranger function of the R package ranger. This is a string that is passed to the sub-models’ ranger function. For more details look at the class formula. dataset a numeric data frame of at least two suitable variables (one dependent and one independent) kernel the kernel to be used in the regression. Options are "adaptive" (default) or "fixed". coords a numeric matrix or data frame of two columns giving the X,Y coordinates of the observations bw.min an integer referring to the minimum bandwidth that evaluation starts. bw.max an integer referring to the maximum bandwidth that evaluation ends. grf.bw 5 step an integer referring to the step for each iteration of the evaluation between the min and the max bandwidth. Default value is 1. trees an integer referring to the number of trees to grow for each of the local random forests. mtry the number of variables randomly sampled as candidates at each split. Note that the default values is p/3, where p is number of variables in the formula importance feature importance of the dependent variables used as input at the random forest. Default value is "impurity" which refers to the Gini index for classification and the variance of the responses for regression. nthreads Number of threads. Default is number of CPUs available. The argument passes to both ranger and predict functions. forests a option to save and export (TRUE) or not (FALSE) all the local forests. Default value is FALSE. weighted if TRUE the algorithm calculates Geographically Weighted Random Forest us- ing the case.weights option of the package ranger. If FALSE it will calculate local random forests without weighting each observation in the local data set. ... further arguments passed to the grf and ranger functions Details Geographically Weighted Random Forest (GRF) is a spatial analysis method using a local version of the famous Machine Learning algorithm. It allows for the investigation of the existence of spatial non-stationarity, in the relationship between a dependent and a set of independent variables. The latter is possible by fitting a sub-model for each observation in space, taking into account the neigh- bouring observations. This technique adopts the idea of the Geographically Weighted Regression, Kalogirou (2003). The main difference between a tradition (linear) GWR and GRF is that we can model non-stationarity coupled with a flexible non-linear model which is very hard to over-fit due to its bootstrapping nature, thus relaxing the assumptions of traditional Gaussian statistics. Essen- tially, it was designed to be a bridge between machine learning and geographical models, combining inferential and explanatory power. Additionally, it is suited for datasets with numerous predictors, due to the robust nature of the random forest algorithm in high dimensionality. This function is a first attempt to find the optimal bandwidth for the grf. It uses an exhaustive approach, i.e. it tests sequential nearest neighbour bandwidths within a range and with a user defined step, and returns a list of goodness of fit statistics. It chooses the best bandwidth based on the maximum R2 value of the local model. Future versions of this function will include heuristic methods to find the optimal bandwidth using algorithms such as optim. Value tested.bandwidths A table with the tested bandwidths and the corresponding R2 of three model configurations: Local that refers to predictions based on the local (grf) model only; Mixed that refers to predictions that equally combine local (grf) and global (rf) model predictors; and Low.Local that refers to a prediction based on the combination of the local model predictors with a weight of 0.25 and the global model predictors with a weight of 0.75). best.bw Best bandwidth based on the local model predictions. 6 grf.bw Warning Large datasets may take long time to evaluate the optimal bandwidth. Note This function is under development. There should be improvements in future versions of the pack- age SpatialML. Any suggestion is welcome! Author(s) Stamatis Kalogirou <stamatis@lctools.science>, Stefanos Georganos <sgeorgan@ulb.ac.be> References Stefanos Georganos, Tais Grippa, Assane Niang Gadiaga, Catherine Linard, Moritz Lennert, Sabine Vanhuysse, Nicholus Odhiambo Mboga, Eléonore Wolff and Stamatis Kalogirou (2019) Geograph- ical Random Forests: A Spatial Extension of the Random Forest Algorithm to Address Spatial Het- erogeneity in Remote Sensing and Population Modelling, Geocarto International, DOI: 10.1080/10106049.2019.1595177 Georganos, S. and Kalogirou, S. (2022) A Forest of Forests: A Spatially Weighted and Computa- tionally Efficient Formulation of Geographical Random Forests. ISPRS, International Journal of Geo-Information, 2022, 11, 471. <https://www.mdpi.com/2220-9964/11/9/471> See Also grf Examples ## Not run: RDF <- random.test.data(8,8,3) Coords<-RDF[ ,4:5] bw.test <- grf.bw(dep ~ X1 + X2, RDF, kernel="adaptive", coords=Coords, bw.min = 20, bw.max = 23, step = 1, forests = FALSE, weighted = TRUE) ## End(Not run) data(Income) Coords<-Income[ ,1:2] bwe <-grf.bw(Income01 ~ UnemrT01 + PrSect01, Income, kernel="adaptive", coords=Coords, bw.min = 30, bw.max = 80, step = 1, forests = FALSE, weighted = TRUE) grf <- grf(Income01 ~ UnemrT01 + PrSect01, dframe=Income, bw=bwe$Best.BW, kernel="adaptive", coords=Coords) Income 7 Income Mean household income at lcoal authorities in Greece in 2011 Description Municipality centroids and socioeconomic variables aggregated to the new local authority geogra- phy in Greece (Programme Kallikratis). Usage data(Income) Format A data frame with 325 observations on the following 5 variables. X a numeric vector of x coordinates Y a numeric vector of y coordinates UnemrT01 a numeric vector of total unemployment rate in 2001 (Census) PrSect01 a numeric vector of the proportion of economically active working in the primary finan- cial sector (mainly agriculture; fishery; and forestry in 2001 (Census)) Foreig01 a numeric vector of proportion of people who do not have the Greek citizenship in 2001 (Census) Income01 a numeric vector of mean recorded household income (in Euros) earned in 2001 and declared in 2002 tax forms Details The X,Y coordinates refer to the geometric centroids of the new 325 Municipalities in Greece (Programme Kallikratis) in 2011. Source The original shapefile of the corresponding polygons is available from the Hellenic Statistical Au- thority (EL.STAT.) at http://www.statistics.gr/el/digital-cartographical-data. The population, employment, citizenship and employment sector data is available from the Hellenic Statistical Authority (EL.STAT.) at http://www.statistics.gr/en/home but were aggregated to the new municipalities by the author. The income data are available from the General Secretariat of Information Systems in Greece at http://www.gsis.gr/ at the postcode level of geography and were aggregated to the new municipalities by the author. 8 predict.grf References Kalogirou, S., and Hatzichristos, T. (2007). A spatial modelling framework for income estimation. Spatial Economic Analysis, 2(3), 297-316. https://www.tandfonline.com/doi/abs/10.1080/ 17421770701576921 Kalogirou, S. (2010). Spatial inequalities in income and post-graduate educational attainment in Greece. Journal of Maps, 6(1), 393-400.https://www.tandfonline.com/doi/abs/10.4113/ jom.2010.1095 Kalogirou, S. (2013) Testing geographically weighted multicollinearity diagnostics, GISRUK 2013, Department of Geography and Planning, School of Environmental Sciences, University of Liver- pool, Liverpool, UK, 3-5 April 2013. http://gisc.gr/?mdocs-file=1140&mdocs-url=false Examples data(Income) boxplot(Income$Income01) hist(Income$PrSect01) predict.grf Predict Method for Geographical Random Forest Description Prediction of test data using the geographical random forest. Usage ## S3 method for class 'grf' predict(object, new.data, x.var.name, y.var.name, local.w=1, global.w=0,...) Arguments object an object that created by the function grf that includes all local forests. new.data a data frame containing new data. x.var.name the name of the variable with X coordinates. y.var.name the name of the variable with Y coordinates. local.w weight of the local model predictor allowing semi-local predictions. Default value is 1. global.w weight of the global model predictor allowing semi-local predictions. Default value is 0. ... for other arguments passed to the generic predict functions. For example you may pass here the number of threats Details A Geographical Random Forest prediction on unknown data. The nearest local random forest model in coordinate space is used to predict in each unknown y-variable location. predict.grf 9 Value vector of predicted values Note This function is under development. There should be improvements in future versions of the pack- age SpatialML. Any suggestion is welcome! Author(s) Stamatis Kalogirou <stamatis@lctools.science>, Stefanos Georganos <sgeorgan@ulb.ac.be> References Stefanos Georganos, Tais Grippa, Assane Niang Gadiaga, Catherine Linard, Moritz Lennert, Sabine Vanhuysse, Nicholus Odhiambo Mboga, Eléonore Wolff & Stamatis Kalogirou (2019) Geographi- cal Random Forests: A Spatial Extension of the Random Forest Algorithm to Address Spatial Het- erogeneity in Remote Sensing and Population Modelling, Geocarto International, DOI: 10.1080/10106049.2019.1595177 See Also grf Examples ## Not run: RDF <- random.test.data(10,10,3) Coords<-RDF[ ,4:5] grf <- grf(dep ~ X1 + X2, dframe=RDF, bw=10, kernel="adaptive", coords=Coords) RDF.Test <- random.test.data(2,2,3) predict.grf(grf, RDF.Test, x.var.name="X", y.var.name="Y", local.w=1, global.w=0) ## End(Not run) #Load the sample data data(Income) #Create the vector of XY coordinates Coords<-Income[,1:2] #Fit local model grf <- grf(Income01 ~ UnemrT01 + PrSect01, dframe=Income, bw=60, kernel="adaptive", coords=Coords) #Create New Random Data - XY coordinates inside the sample data map extend x<-runif(20, min = 142498, max = 1001578) y<-runif(20, min = 3855768, max = 4606754) u<-runif(20, min = 5, max = 50) 10 random.test.data p<-runif(20, min = 0, max = 100) f<-runif(20, min = 2, max = 30) df2<-data.frame(X=x, Y= y, UnemrT01=u, PrSect01=p, Foreig01=f) #Make predictions using the local model predict.grf(grf, df2, x.var.name="X", y.var.name="Y", local.w=1, global.w=0) random.test.data Radmom data generator Description Generates datasets with random data for modelling including a dependent variable, independent variables and X,Y coordinates. Usage random.test.data(nrows = 10, ncols = 10, vars.no = 3, dep.var.dis = "normal", xycoords = TRUE) Arguments nrows an integer referring to the number of rows for a regular grid ncols an integer referring to the number of columns for a regular grid vars.no an integer referring to the number of independent variables dep.var.dis a character referring to the distribution of the dependent variable. Options are "normal" (default) and "poisson" xycoords a logical value indicating whether X,Y coordinates will be created (default) or not. Details The creation of a random dataset was necessary here to provide examples to some functions. How- ever, random datasets may be used in simulation studies. Value a dataframe Author(s) Stamatis Kalogirou <stamatis@lctools.science> Examples RDF <- random.test.data(12,12,3) rf.mtry.optim 11 rf.mtry.optim Optimal mtry Description This function calculates the optimal mtry for a given Random Forest (RF) model in a specified range of values. The optimal mtry value can then be used in the grf model. Usage rf.mtry.optim(formula, dataset, min.mtry=NULL, max.mtry=NULL, mtry.step, cv.method="repeatedcv", cv.folds=10, ...) Arguments formula the model to be fitted using the function train of the R package caret. dataset a numeric data frame of at least two suitable variables (one dependent and one independent) min.mtry the minimum mtry value for its optimisation (function expand.grid) max.mtry the maximum mtry value for its optimisation (function expand.grid) mtry.step the step in the sequence of mtry values for its optimisation (function expand.grid) cv.method the resampling method in the function trainControl of the R package caret. Default option is "repeatedcv" and alternative option is "cv". cv.folds the number of folds (argument "number" in the function trainControl). De- fault value is 10) ... additional arguments affecting the function trainControl) Details Based on the train function of the caret package, this function sets up a grid of tuning param- eters for a number of random forest routines, fits each model and calculates a resampling based performance measure to choose the best mtry value. Value A list is returned of class train as in the function train in the caret package. Note This function is under development. Author(s) Stamatis Kalogirou <stamatis@lctools.science>, Stefanos Georganos <sgeorgan@ulb.ac.be> 12 rf.mtry.optim References Kuhn, M. (2008). Building Predictive Models in R Using the caret Package. Journal of Statistical Software, 28(5), 1 - 26. doi: <http://dx.doi.org/10.18637/jss.v028.i05> Georganos, S. and Kalogirou, S. (2022) A Forest of Forests: A Spatially Weighted and Computa- tionally Efficient Formulation of Geographical Random Forests. ISPRS, International Journal of Geo-Information, 2022, 11, 471. <https://www.mdpi.com/2220-9964/11/9/471> Examples data(Income) Coords <- Income[ ,1:2] results <- rf.mtry.optim(Income01 ~ UnemrT01 + PrSect01, Income) Index ∗ Greek Municipalities Income, 7 ∗ Income Income, 7 ∗ datasets Income, 7 ∗ local random forest predict.grf, 8 ∗ predictive analytics grf, 2 grf.bw, 4 ∗ random data random.test.data, 10 ∗ spatial random forest grf, 2 grf.bw, 4 formula, 2, 4 grf, 2, 6, 9 grf.bw, 4 Income, 7 predict.grf, 4, 8 random.test.data, 10 ranger, 2, 4 rf.mtry.optim, 11 13

jtdm

cran

Package ‘jtdm’ September 25, 2023 Type Package Title Joint Modelling of Functional Traits Version 0.1-1 Description Fitting and analyzing a Joint Trait Distribution Model. The Joint Trait Distribu- tion Model is implemented in the Bayesian framework using conjugate priors and posteri- ors, thus guaranteeing fast inference. In particular the package computes joint probabili- ties and multivariate confidence intervals, and enables the investigation of how they de- pend on the environment through partial response curves. The method implemented by the pack- age is described in Poggiato et al. (2023) <doi:10.1111/geb.13706>. License GPL-3 Encoding UTF-8 LazyData true Imports ggforce, mniw, mvtnorm, parallel, stats, utils, ggplot2, gridExtra, reshape2 RoxygenNote 7.2.3 Depends R (>= 3.5.0) URL https://github.com/giopogg/jtdm, https://giopogg.github.io/jtdm/ VignetteBuilder knitr BugReports https://github.com/giopogg/jtdm/issues Suggests knitr, rmarkdown, devtools NeedsCompilation no Author Giovanni Poggiato [aut, cre, cph] (<https://orcid.org/0000-0003-1957-9764>) Maintainer Giovanni Poggiato <giov.poggiato@gmail.com> Repository CRAN Date/Publication 2023-09-25 09:50:06 UTC 1 2 ellipse_plot R topics documented: ellipse_plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 getB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 get_sigma . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 global . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 joint_trait_prob . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 joint_trait_prob_gradient . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 jtdmCV . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 jtdm_fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 jtdm_predict . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 partial_response . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 plot.jtdm_fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 summary.jtdm_fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 X . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 Y . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Index 17 ellipse_plot Partial response curve of the pairwise most suitable community-level strategy and of the pairwise envelop of possible community-level strat- egy Description Partial response curve of the pairwise most suitable community-level strategy and of the pairwise envelop of possible community-level strategy. In order to build the response curve, the function builds a dataframe where the focal variable varies along a gradient and the other (non-focal) vari- ables are fixed to their mean (but see FixX parameter for fixing non-focal variables to user-defined values). The chosen traits are specified in indexTrait. Then uses the jtdm_predict function to com- pute the most suitable community-level strategy and the residual covariance matrix to build the envelop of possible CWM combinations. Usage ellipse_plot( m, indexGradient, indexTrait, FullPost = FALSE, grid.length = 20, FixX = NULL, confL = 0.95 ) getB 3 Arguments m a model fitted with jtdm_fit indexGradient The name (as specified in the column names of X) of the focal variable. indexTrait A vector of the two names (as specified in the column names of Y) containing the two (or more!) traits we want to compute the community level strategy of. FullPost If FullPost = TRUE, the function returns samples from the predictive distribution of joint probabilities. If FullPost= FALSE, joint probabilities are computed only using the posterior mean of the parameters. grid.length The number of points along the gradient of the focal variable. Default to 20 (which ensures a fair visualization). FixX Optional. A parameter to specify the value to which non-focal variables are fixed. This can be useful for example if we have some categorical variables (e.g. forest vs meadows) and we want to obtain the partial response curve for a given value of the variable. It has to be a list of the length and names of the columns of X. For example, if the columns of X are "MAT","MAP","Habitat" and we want to fix "Habitat" to 1, then FixX=list(MAT=NULL,MAP=NULL,Habitat=1.). De- fault to NULL. confL The confidence level of the confidence ellipse (i.e. of the envelop of possible community-level strategies). Default is 0.95. Value Plot of the partial response curve of the pairwise most suitable community-level strategy and of the pairwise envelop of possible community-level strategy Examples data(Y) data(X) # Short MCMC to obtain a fast example: results are unreliable ! m = jtdm_fit(Y=Y, X=X, formula=as.formula("~GDD+FDD+forest"), sample = 1000) # plot the pairwise SLA-LNC partial response curve along the GDD gradient ellipse_plot(m,indexTrait = c("SLA","LNC"),indexGradient="GDD") # plot the pairwise SLA-LNC partial response curve along the GDD gradient # in forest (i.e. when forest=1) ellipse_plot(m,indexTrait = c("SLA","LNC"),indexGradient="GDD", FixX=list(GDD=NULL,FDD=NULL,forest=1)) getB Get the inferred regression coefficients 4 get_sigma Description Get the samples from the posterior distribution of the regression coefficient matrix B, together with the posterior mean and quantiles. The regression coefficient matrix B is a matrix where the number of rows is defined by the number of traits that are modeled, and the number of columns is the number of columns of the matrix m$X (the number of explanatory variables after transformation via formula) Usage getB(m) Arguments m a model fitted with jtdm_fit Value A list containing: Bsamples Sample from the posterior distribution of the regression coefficient matrix. It is an array where the first dimension is the number of traits, the second the number of columns in m$X (the number of variables after transformation via formula) and the third the number of MCMC samples. Bmean Posterior mean of the regression coefficient matrix. Bq975,Bq025 97.5% and 0.25% posterior quantiles of the regression coefficient matrix. Examples data(Y) data(X) m = jtdm_fit(Y=Y, X=X, formula=as.formula("~GDD+FDD+forest"), sample = 1000) # get the inferred regression coefficients B=getB(m) get_sigma Get the inferred residual covariance matrix Description Get the samples from the posterior distribution of the residual covariance matrix, together with the posterior mean and quantiles. Usage get_sigma(m) global 5 Arguments m a model fitted with jtdm_fit Value A list containing: Ssamples Sample from the posterior distribution of the residual covariance matrix. It is an array where the first two dimensions are the rows and columns of the matrix, and the third dimensions are the samples from the posterior distribution Smean Posterior mean of the residual covariance matrix. Sq975,Sq025 97.5% and 0.25% posterior quantiles of the residual covariance matrix. Examples data(Y) data(X) # Short MCMC to obtain a fast example: results are unreliable ! m = jtdm_fit(Y=Y, X=X, formula=as.formula("~GDD+FDD+forest"), sample = 1000) # get the inferred residual covariance Sigma =get_sigma(m) global Global Description Declare global variables joint_trait_prob Computes joint probabilities. Description Computes the joint probability of CWM traits in regions in the community-trait space specified by bounds and in sites specified in Xnew. Usage joint_trait_prob( m, indexTrait, bounds, Xnew = NULL, FullPost = FALSE, samples = NULL, parallel = FALSE ) 6 joint_trait_prob Arguments m a model fitted with jtdm_fit indexTrait A vector of the names (as specified in the column names of Y) of the two (or more!) traits we want to compute the joint probabilities of. bounds The parameter to specify a region in the community-trait space where the func- tion computes the joint probabilities of traits. It is a list of the length of "index- Trait", each element of the list is a vector of length two. The vector represents the inferior and superior bounds of the region for the specified trait. For exam- ple, if we consider two traits, bounds=list(c(10,Inf),c(10,Inf)) corresponds to the region in the community-trait space where both traits both take values greater than 10. Xnew Optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used. FullPost If FullPost = TRUE, the function returns samples from the predictive distribu- tion of joint probabilities, thus allowing the computation of credible intervals. If FullPost= FALSE, joint probabilities are computed only using the posterior mean of the parameters. FullPost cannot be equal to "mean" here. samples Optional, default to NULL, only works when FullPost=FALSE. Defines the number of posterior samples to compute the posterior distribution of joint prob- abilities. Needs to be between 1 the total number of samples drawn from the posterior distribution. parallel Optional, only works when FullPost = TRUE. When parallel = TRUE, the func- tion uses mclapply to parallelise the calculation of the posterior distribution joint probabilities. Details This function is time consuming when FullPost = TRUE. Consider setting parallel = TRUE and/or to set samples to a value smaller than the total number of posterior samples . Value A list containing: PROBsamples Samples from the posterior distribution of the joint probability.NULL if Full- Post=FALSE. PROBmean Posterior mean of the joint probability. PROBq975,PROBq025 97.5% and 0.25% posterior quantiles of the joint probability. NULL if Full- Post=FALSE. Examples data(Y) data(X) #We sample only few samples from the posterior in order to reduce # the computational time of the examples. joint_trait_prob_gradient 7 #Increase the number of samples to obtain robust results m = jtdm_fit(Y = Y, X = X, formula = as.formula("~GDD+FDD+forest"), sample = 10) # Compute probability of SLA and LNC to be joint-high at sites in the studies joint = joint_trait_prob(m, indexTrait = c("SLA","LNC"), bounds = list(c(mean(Y[,"SLA"]),Inf), c(mean(Y[,"SLA"]),Inf)), FullPost = TRUE) joint_trait_prob_gradient Computes partial response curves of joint probabilities Description Computes the partial responses curves of joint probability of CWM traits as a function of a focal variable. The regions in which joint probabilities are computed are specified by bounds. In order to build the response curve, the function builds a dataframe where the focal variable varies along a gradient and the other (non-focal) variables are fixed to their mean (but see FixX parameter for fixing non-focal variables to user-defined values). Then, uses joint_trait_prob to compute the joint probability in these dataset. Usage joint_trait_prob_gradient( m, indexTrait, indexGradient, bounds, grid.length = 200, XFocal = NULL, FixX = NULL, FullPost = FALSE, samples = NULL, parallel = FALSE ) Arguments m A model fitted with jtdm_fit indexTrait A vector of the names (as specified in the column names of Y) of the two (or more!) traits we want to compute the joint probabilities of. indexGradient The name (as specified in the column names of X) of the focal variable. bounds The parameter to specify a region in the community-trait space where the func- tion computes the joint probabilities of traits. It is a list of the length of "index- Trait", each element of the list is a vector of length two. The vector represents the inferior and superior bounds of the region for the specified trait. For exam- ple, if we consider two traits, bounds=list(c(10,Inf),c(10,Inf)) corresponds to the region in the community-trait space where both traits both take values greater than 10. 8 joint_trait_prob_gradient grid.length The number of points along the gradient of the focal variable. Default to 200. XFocal Optional. A gradient of the focal variable provided by the user. If provided, the function will used this gradient instead of building a regular one. Default to NULL. FixX Optional. A parameter to specify the value to which non-focal variables are fixed. This can be useful for example if we have some categorical variables (e.g. forest vs meadows) and we want to obtain the partial response curve for a given value of the variable. It has to be a list of the length and names of the columns of X. For example, if the columns of X are "MAT","MAP","Habitat" and we want to fix "Habitat" to 1, then FixX=list(MAT=NULL,MAP=NULL,Habitat=1.). De- fault to NULL. FullPost If FullPost = TRUE, the function returns samples from the predictive distribu- tion of joint probabilities, thus allowing the computation of credible intervals. If FullPost= FALSE, joint probabilities are computed only using the posterior mean of the parameters. FullPost cannot be equal to "mean" here. samples Optional, default to NULL, only works when FullPost=FALSE. Defines the number of samples to compute the posterior distribution of joint probabilities. Needs to be between 1 the total number of samples drawn from the posterior distribution. parallel Optional, only works when FullPost = TRUE. When TRUE, the function uses mclapply to parallelise the calculation of the posterior distribution joint proba- bilities. Details This function is time consuming when FullPost = TRUE. Consider setting parallel = TRUE and/or to set samples to a value smaller than the total number of posterior samples. Value A list containing: GradProbssamples Sample from the posterior distribution of the joint probability along the gradi- ent. It is a vector whose length is the number of posterior samples. NULL if FullPost=FALSE. GradProbsmean Posterior mean of the joint probability along the gradient. GradProbsq975,GradProbsq025 97.5% and 0.25% posterior quantiles of the joint probability along the gradient. NULL if FullPost=FALSE. gradient The gradient of the focal variable built by the function. Examples data(Y) data(X) # We sample only few samples from the posterior in order to reduce # the computational time of the examples. jtdmCV 9 # Increase the number of samples to obtain robust results m = jtdm_fit(Y = Y, X = X, formula = as.formula("~GDD+FDD+forest"), sample = 10) # Compute probability of SLA and LNC to be joint-high at sites in the studies # Compute the joint probability of SLA and LNC # to be joint-high along the GDD gradient joint = joint_trait_prob_gradient(m,indexTrait = c("SLA","LNC"), indexGradient = "GDD", bounds = list(c(mean(Y[,"SLA"]),Inf),c(mean(Y[,"SLA"]),Inf)), FullPost = TRUE) # Compute the joint probability of SLA and LNC to be joint-high along the # GDD gradient when forest = 1 (i.e. in forests) joint = joint_trait_prob_gradient(m, indexTrait = c("SLA","LNC"), indexGradient = "GDD", bounds = list(c(mean(Y[,"SLA"]),Inf), c(mean(Y[,"SLA"]),Inf)), FixX = list(GDD = NULL, FDD = NULL, forest = 1), FullPost = TRUE) jtdmCV K-fold cross validation predictions and goodness of fit metrics Description Run K-fold cross validation predictions of the model m on a specified dataset. Usage jtdmCV(m, K = 5, sample = 1000, partition = NULL) Arguments m a model fitted with jtdm_fit K The number of folds of the K-fold cross validation sample Number of samples from the posterior distribution. Since we sample from the exact posterior distribution, the number of samples is relative lower than MCMC samplers. As a rule of thumb, 1000 samples should provide correct inference. partition A partition of the dataset specified by the user. It is a vector (whose length are the number of sites), where each element specifies the fold index of the site. Value A list containing: Pred Sample from the posterior predictive distribution in cross validation. It is an array where the first dimension is the number of sites in Xnew, the second is the number of traits modeled and the third the number of MCMC samples. NULL if FullPost=FALSE. 10 jtdm_fit PredMean Posterior mean of posterior predictive distribution in cross validation. Predq975,Predq025 97.5% and 0.25% posterior quantiles of the posterior predictive distribution in cross validation. NULL if FullPost=FALSE. R2 R squared of predictions in cross validation. RMSE Root square mean error between squared of predictions in cross validation. Examples data(Y) data(X) m = jtdm_fit(Y=Y, X=X, formula=as.formula("~GDD+FDD+forest"), sample = 1000) # Run 3-fold cross validation on m pred = jtdmCV(m, K = 5, sample = 1000) jtdm_fit Fitting joint trait distribution models Description jtdm_fit is used to fit a Joint trait distribution model. Requires the response variable Y (the sites x traits matrix) and the explanatory variables X.This function samples from the posterior distribution of the parameters, which has been analytically determined. Therefore, there is no need for classical MCMC convergence checks. Usage jtdm_fit(Y, X, formula, sample = 1000) Arguments Y The sites x traits matrix containing community (weighted) means of each trait at each site. X The design matrix, i.e. sites x predictor matrix containing the value of each explanatory variable (e.g. the environmental conditions) at each site. formula An object of class "formula" (or one that can be coerced to that class): a sym- bolic description of the model to be fitted. The details of model specification are given under ’Details’. sample Number of samples from the posterior distribution. Since we sample from the exact posterior distribution, the number of samples is relative lower than MCMC samplers. As a rule of thumb, 1000 samples should provide correct inference. Details A formula has an implied intercept term. To remove this use either y ~ x - 1 or y ~ 0 + x. See formula for more details of allowed formulae. jtdm_predict 11 Value A list containing: model An object of class "jtdm_fit", containing the samples from the posterior distri- bution of the regression coefficients (B) and residual covariance matrix (Sigma), together with the likelihood of the model. Y A numeric vector of standard errors on parameters X_raw The design matrix specified as input X The design matrix transformed as specified in formula formula The formula specified as input Examples data(Y) data(X) m = jtdm_fit(Y = Y, X = X, formula = as.formula("~GDD+FDD+forest"), sample = 1000) jtdm_predict Predict method for joint trait distribution model Description Obtains predictions from a fitted joint trait distribution model and optionally computes their R squared and root mean square error (RMSE) Usage jtdm_predict( m = m, Xnew = NULL, Ynew = NULL, validation = FALSE, FullPost = "mean" ) Arguments m a model fitted with jtdm_fit Xnew optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used Ynew Optional. The observed response variables at sites specified in Xnew. It is used to compute goodness of fit metrics when validation= T. validation boolean parameter to decide whether we want to compute goodness of fit mea- sures. If true, then Ynew is needed. 12 partial_response FullPost The type of predictions to be obtain. If FullPost = TRUE, the function returns samples from the predictive distribution, the credible intervals are thus the pre- dictive credible interval. If FullPost="mean", the function computes the poste- rior distribution of the regression term BXnew), i.e., classical credible inter- vals. If FullPost=FALSE, the function only returns the posterior mean of the regression term (BmeanXnew), i.e., no credible intervals. Details To obtain a full assessment of the posterior distribution, the function should be ran with Full- Post=TRUE, although this can be time consuming. FullPost="mean" is used to compute partial response curves, while FullPost=FALSE is used to compute goodness of fit metrics. Value A list containing: Pred Sample from the posterior distribution of the posterior predictive distribution. It is an array where the first dimension is the number of sites in Xnew, the second is the number of traits modelled and the third the number of MCMC samples. NULL if FullPost=FALSE. PredMean Posterior mean of posterior predictive distribution Predq975,Predq025 97.5% and 0.25% posterior quantiles of the posterior predictive distribution. NULL if FullPost=FALSE. R2 R squared of predictions (squared Pearson correlation between Ynew and the predictions). NULL if validation=FALSE. RMSE Root square mean error between squared of predictions. NULL if validation=FALSE. Examples data(Y) data(X) m = jtdm_fit(Y = Y, X = X, formula=as.formula("~GDD+FDD+forest"), sample = 1000) # marginal predictions of traits in the sites of X pred = jtdm_predict(m) partial_response Computes and plots the trait-environment relationship of a given CWM trait and a given environmental variable Description Computes and plots the trait-environment relationship of a given CWM trait and a focal environ- mental variable. In order to build the response curve, the function builds a dataframe where the focal environmental variable varies along a gradient and the other (non-focal) variables are fixed to their mean (but see FixX parameter for fixing non-focal variables to user-defined values). partial_response 13 Usage partial_response( m, indexGradient, indexTrait, XFocal = NULL, grid.length = 200, FixX = NULL, FullPost = "mean" ) Arguments m a model fitted with jtdm_fit indexGradient The name (as specified in the column names of X) of the focal variable. indexTrait The name (as specified in the column names of Y) of the focal trait. XFocal Optional. A gradient of the focal variable provided by the user. If provided, the function will used this gradient instead of building a regular one. Default to NULL. grid.length The number of points along the gradient of the focal variable. Default to 200. FixX Optional. A parameter to specify the value to which non-focal variables are fixed. This can be useful for example if we have some categorical variables (e.g. forest vs meadows) and we want to obtain the partial response curve for a given value of the variable. It has to be a list of the length and names of the columns of X. For example, if the columns of X are "MAT","MAP","Habitat" and we want to fix "Habitat" to 1, then FixX=list(MAT=NULL,MAP=NULL,Habitat=1.). De- fault to NULL. FullPost The type of predictions to be obtain. If FullPost = TRUE, the function re- turns samples from the predictive distribution. If FullPost="mean", the function computes the posterior distribution of the regression term B%*%X). Default to "mean", here FullPost cannot be FALSE. Value A list containing: p A plot of the trait-environment relationship. predictions A data frame containing the predicted trait-environmental relationships includ- ing the gradient of the focal environmental variable, mean trait predictions and quantiles (can be useful to code customized plot). Examples data(Y) data(X) # Short MCMC to obtain a fast example: results are unreliable ! m = jtdm_fit(Y=Y, X=X, formula=as.formula("~GDD+FDD+forest"), sample = 1000) 14 plot.jtdm_fit # SLA-GDD relationship plot = partial_response(m,indexGradient="GDD",indexTrait="SLA") plot$p # SLA-GDD relationship in forest (i.e. when forest=1) plot = partial_response(m,indexGradient="GDD",indexTrait="SLA", FixX=list(GDD=NULL,FDD=NULL,forest=1)) plot$p plot.jtdm_fit Plots the parameters of a fitted jtdm Description Plots the regression coefficients and covariance matrix of a fitted jtdm Usage ## S3 method for class 'jtdm_fit' plot(x, ...) Arguments x a model fitted with jtdm_fit ... additional arguments Value A plot of the regression coefficients and covariance matrix of the fitted model Author(s) Giovanni Poggiato Examples data(Y) data(X) m = jtdm_fit(Y=Y, X=X, formula=as.formula("~GDD+FDD+forest"), sample = 1000) plot(m) summary.jtdm_fit 15 summary.jtdm_fit Prints the summary of a fitted jtdm Description Prints the summary of a fitted jtdm Usage ## S3 method for class 'jtdm_fit' summary(object, ...) Arguments object a model fitted with jtdm_fit ... additional arguments Value A printed summary of the fitted jtdm Author(s) Giovanni Poggiato Examples data(Y) data(X) m = jtdm_fit(Y=Y, X=X, formula=as.formula("~GDD+FDD+forest"), sample = 1000) summary(m) X Site x environmental covariates dataset Description Includes the Growing Degree Days (GDD) during the growing season and Freezing Degree Days (FDD) during the growing season averaged over the period 1989-2019 Usage data(X) data(X) 16 Y Format A matrix Author(s) Orchamp consortium Examples data(X) Y Site x CWM traits dataset Description A site x CWM traits dataset computed using pinpoint abundances of plants and species mean Usage data(Y) Format A matrix Author(s) Orchamp Consortium Examples data(Y) Index ∗ datasets X, 15 Y, 16 ellipse_plot, 2 get_sigma, 4 getB, 3 global, 5 joint_trait_prob, 5 joint_trait_prob_gradient, 7 jtdm_fit, 10 jtdm_predict, 11 jtdmCV, 9 partial_response, 12 plot.jtdm_fit, 14 summary.jtdm_fit, 15 X, 15 Y, 16 17

spinyReg

cran

Package ‘spinyReg’ October 14, 2022 Type Package Title Sparse Generative Model and Its EM Algorithm Version 0.1-0 Date 2015-09-05 Author Charles Bouveyron, Julien Chiquet, Pierre Latouche, Pierre-Alexandre Mattei Maintainer Julien Chiquet <julien.chiquet@gmail.com> Description Implements a generative model that uses a spike-and-slab like prior distribution obtained by multiplying a deterministic binary vector. Such a model allows an EM algorithm, optimizing a type-II log-likelihood. License GPL (>= 2) Imports methods Repository CRAN Repository/R-Forge/Project spinyreg Repository/R-Forge/Revision 11 Repository/R-Forge/DateTimeStamp 2015-09-07 10:50:53 Date/Publication 2015-09-07 18:18:03 NeedsCompilation no R topics documented: spinyReg . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 spinyreg-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Index 4 1 2 spinyReg spinyReg spinyReg Description Computethe path of solution of a spinyReg fit. Usage spinyreg(X, Y, alpha = 0.1, gamma = 1, z = rep(1, ncol(X)), intercept = TRUE, normalize = TRUE, verbose = 1, recovery = TRUE, maxit = 1000, eps = 1e-10) Arguments X matrix of features. Do NOT include intercept. Y matrix of responses. alpha numeric scalar; prior value for the alpha parameter (see the model’s details). Default is 0.1. gamma numeric scalar; prior value for the gamma parameter (see the model’s details). Default is 1. z numeric vector; prior support of active variable. Default is rep(1,p), meaning all variable activated intercept logical; indicates if a vector of intercepts should be included in the model. De- fault is TRUE. normalize logical; indicates if predictor variables should be normalized to have unit L2 norm before fitting. Default is TRUE. verbose integer; activate verbose mode from ’0’ (nothing) to ’2’ (detailed output). should be included in the model. Default is TRUE. recovery logical; indicates if the full path of models should be inspected for model selec- tion. Default is TRUE. maxit integer; the maximal number of iteration (i.e. number of alternated optimization between each parameter) in the Expectation/Maximization algorithm. eps a threshold for convergence. Default is 1e-10. Value an object with class spinyreg, see the documentation page spinyreg for details. See Also See also spinyreg. spinyreg-class 3 Examples ## Not run: data <- read.table(file="http://statweb.stanford.edu/~tibs/ElemStatLearn/datasets/prostate.data") x <- data[, 1:8] y <- data[, 9] out <- spinyreg(x,y,verbose=2) ## End(Not run) spinyreg-class Class "spinyreg" Description Class of object returned by the spinyreg function. Slots coefficients: numeric vector of coefficients with respect to the original input. Contains the intercept if the model owns any. alpha: numeric scalar. gamma: numeric scalar. normx: Vector (class "numeric") containing the square root of the sum of squares of each column of the design matrix. residuals: Vector of residuals. r.squared: scalar giving the coefficient of determination. fitted: Vector of fitted values. monitoring: List (class "list") which contains various indicators dealing with the optimization process. intercept: Logical which indicates if a intercept is included in the model. Methods This class comes with the usual predict(object, newx, ...), fitted(object, ...), residuals(object, ...), coefficients(object, ...), print(object, ...) and show(object) generic (undocu- mented) methods. Index ∗ class spinyreg-class, 3 ∗ models, spinyReg, 2 ∗ regression spinyReg, 2 coefficients,spinyreg-method (spinyreg-class), 3 fitted,spinyreg-method (spinyreg-class), 3 predict,spinyreg-method (spinyreg-class), 3 print,spinyreg-method (spinyreg-class), 3 residuals,spinyreg-method (spinyreg-class), 3 show,spinyreg-method (spinyreg-class), 3 spinyReg, 2 spinyreg, 2 spinyreg (spinyReg), 2 spinyreg-class, 3 4

RProbSup

cran

Package ‘RProbSup’ October 12, 2022 Type Package Title Calculates Probability of Superiority Version 3.0 Author John Ruscio Maintainer John Ruscio <ruscio@tcnj.edu> Description The A() function calculates the A statistic, a nonparametric measure of effect size for two independent groups that’s also known as the probability of superiority (Ruscio, 2008), along with its standard error and a confidence interval constructed using bootstrap methods (Ruscio & Mullen, 2012). Optional arguments can be specified to calculate variants of the A statistic developed for other research designs (e.g., related samples, more than two independent groups or related samples; Ruscio & Gera, 2013). <DOI:10.1037/1082-989X.13.1.19>. <DOI:10.1080/00273171.2012.658329>. <DOI:10.1080/00273171.2012.738184>. License MIT + file LICENSE Encoding UTF-8 LazyData true NeedsCompilation no Repository CRAN Date/Publication 2020-10-19 04:40:06 UTC R topics documented: A . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 A1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 A2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 AAD1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 AAD2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 AAPD1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 AAPD2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1 2 A CalcA1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 CalcA2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 CalcAAD1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 CalcAAD2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 CalcAAPD1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 CalcAAPD2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 CalcIK1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 CalcIK2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 CalcOrd1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 CalcOrd2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 IK1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 IK2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 Ord1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 Ord2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 RemoveMissing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 Index 24 A A Description Calculates probability of superiority (A), its standard error, and a confidence interval. Usage A(data, design = 1, statistic = 1, weights = FALSE, w = 0, w1 = 0, w2 = 0, increase = FALSE, ref = 1, r = 0, n.bootstrap = 1999, conf.level = .95, ci.method = 1, seed = 1) Arguments data For a between subjects design, a matrix of cases (rows) by scores (column 1) and group codes (column 2). For a within subjects design, a matrix of scores with each sample in its own column (matrix). design Design of experiment (scalar, default = 1 (for between subjects design), user can also call 2 (for within subjects design)). statistic Statistic to be calculated (scalar, default = 1 (A), user can also call 2 (A.AAD), 3 (A.AAPD), 4 (A.IK), or 5 (A.Ord)). weights Whether to assign weights to cases (default = FALSE); if set to TRUE, data contains case weights in final column. w Weights for cases (vector; default = 0). w1 Weights for cases in group 1 (vector; default = 0). w2 Weights for cases in group 2 (vector; default = 0). A1 3 increase Set to TRUE if scores are predicted to increase with group codes (default = FALSE). ref Reference group (to compare to all others) (scalar, default = 1). r Vector of proportions (vector, default = 0, represents equal proportions). n.bootstrap Number of bootstrap samples (scalar, default = 1999). conf.level Confidence level (default = .95). ci.method Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile)). seed Random number seed (scalar, default = 1). Value Returns list object with the following elements: A : A statistic (scalar). SE : Standard error of A (scalar). ci.lower : Lower bound of confidence interval (scalar). ci.upper : Upper bound of confi- dence interval (scalar). conf.level : Confidence level (scalar). n.bootstrap : Number of bootstrap samples (scalar). boot.method : Bootstrap method ("BCA" or "percentile"). n : Sample size (after missing data removed; scalar). n.missing : Number of cases of missing data, removed listewise (scalar). Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) data <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25))) A(data, 1, 2) A1 A1 Description Calculates the standard error and constructs a confidence interval for the A statistic using bootstrap methods. Usage A1(y1, y2, weights = FALSE, w1 = 0, w2 = 0, n.bootstrap = 1999, conf.level = .95, ci.method = 1, seed = 1) 4 A2 Arguments y1 Scores for group 1 (vector). y2 Scores for group 2 (vector). weights Whether to weight cases (default = FALSE). w1 Weights for cases in group 1 (optional) (vector, default is 0). w2 Weights for cases in group 2 (optional) (vector, default is 0). n.bootstrap Number of bootstrap samples (scalar, default = 1999). conf.level Confidence level (scalar, default = .95). ci.method Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile). seed Random number seed (scalar, default = 1). Value A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples #Example used in Ruscio and Mullen (2012) y1 <- c(6, 7, 8, 7, 9, 6, 5, 4, 7, 8, 7, 6, 9, 5, 4) y2 <- c(4, 3, 5, 3, 6, 2, 2, 1, 6, 7, 4, 3, 2, 4, 3) A1(y1, y2) A2 A2 Description Calculates the standard error and constructs a confidence interval for the A statistic for two corre- lated samples using bootstrap methods. Usage A2(y1, y2, weights = FALSE, w = 0, n.bootstrap = 1999, conf.level = .95, ci.method = 1, seed = 1) AAD1 5 Arguments y1 Scores for group 1 (vector). y2 Scores for group 2 (vector). weights Whether to weight cases (default = FALSE). w Weights for cases in group 1 (optional) (vector, default is 0). n.bootstrap Number of bootstrap samples (scalar, default = 1999). conf.level Confidence level (scalar, default = .95). ci.method Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile). seed Random number seed (scalar, default = 1). Value A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples y1 <- c(6, 7, 8, 7, 9, 6, 5, 4, 7, 8, 7, 6, 9, 5, 4) y2 <- c(7, 5, 6, 7, 6, 4, 3, 5, 4, 5, 4, 5, 7, 4, 5) A2(y1, y2) AAD1 AAD1 Description Calculates the confidence interval for the A statistic for the average absolute deviation for two or more groups. Usage AAD1(y, r = 0, weights = FALSE, n.bootstrap = 1999, conf.level = .95, ci.method = 1, seed = 1) 6 AAD2 Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). r Vector of proportions (default = 0, represents equal proportions) (vector). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). n.bootstrap Number of bootstrap samples (scalar, default = 1999). conf.level Confidence level (scalar, default = .95). ci.method Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile). seed Random number seed (scalar, default = 1). Value A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25))) AAD1(y) AAD2 AAD2 Description Calculates the confidence interval for the A statistic for the average absolute deviation for two or more correlated samples. Usage AAD2(y, r = 0, weights = FALSE, n.bootstrap = 1999, conf.level = .95, ci.method = 1, seed = 1) AAPD1 7 Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). r Vector of proportions (default = 0, represents equal proportions) (vector). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). n.bootstrap Number of bootstrap samples (scalar, default = 1999). conf.level Confidence level (scalar, default = .95). ci.method Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile). seed Random number seed (scalar, default = 1). Value A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(x1, x2, x3) AAD2(y) AAPD1 AAPD1 Description Calculates the confidence interval for the A statistic for the average absolute paired deviation for two or more groups. Usage AAPD1(y, weights = FALSE, n.bootstrap = 1999, conf.level = .95, ci.method = 1, seed = 1) 8 AAPD2 Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). n.bootstrap Number of bootstrap samples (scalar, default = 1999). conf.level Confidence level (scalar, default = .95). ci.method Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile). seed Random number seed (scalar, default = 1). Value A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25))) AAPD1(y) AAPD2 AAPD2 Description Calculates the confidence interval for the A statistic for the average absolute paired deviation for two or more correlated samples. Usage AAPD2(y, weights = FALSE, n.bootstrap = 1999, conf.level = .95, ci.method = 1, seed = 1) CalcA1 9 Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). n.bootstrap Number of bootstrap samples (scalar, default = 1999). conf.level Confidence level (scalar, default = .95). ci.method Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile). seed Random number seed (scalar, default = 1). Value A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(x1, x2, x3) AAPD2(y) CalcA1 CalcA1 Description Calculates the A statistic for 2 groups. Usage CalcA1(y1, y2, weights = FALSE, w1 = 0, w2 = 0) 10 CalcA2 Arguments y1 Scores for group 1 (vector). y2 Scores for group 2 (vector). weights Whether to weight cases (default = FALSE). w1 Weights for cases in group 1 (optional) (vector, default is 0). w2 Weights for cases in group 2 (optional) (vector, default is 0). Value a The A statistic. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples #Example used in Ruscio and Mullen (2012) y1 <- c(6, 7, 8, 7, 9, 6, 5, 4, 7, 8, 7, 6, 9, 5, 4) y2 <- c(4, 3, 5, 3, 6, 2, 2, 1, 6, 7, 4, 3, 2, 4, 3) CalcA1(y1, y2) CalcA2 CalcA2 Description Calculates the A statistic for 2 correlated samples. Usage CalcA2(y1, y2, weights = FALSE, w = 0) Arguments y1 Scores for variable 1 (vector). y2 Scores for variable 2 (vector). weights Whether to weight cases (default = FALSE). w Weights (optional) (vector, default is 0). Value a The A statistic. CalcAAD1 11 Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples y1 <- c(6, 7, 8, 7, 9, 6, 5, 4, 7, 8, 7, 6, 9, 5, 4) y2 <- c(7, 5, 6, 7, 6, 4, 3, 5, 4, 5, 4, 5, 7, 4, 5) CalcA2(y1, y2) CalcAAD1 CalcAAD1 Description Calculates the A statistic for the average absolute deviation for two or more groups. Note: This function is not meant to be called by the user, but it is called by AAD1. Usage CalcAAD1(y, r = 0, weights = FALSE) Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). r Vector of proportions (default = 0, represents equal proportions) (vector). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). Value a The A statistic. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) 12 CalcAAD2 Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25))) CalcAAD1(y) CalcAAD2 CalcAAD2 Description Calculates the A statistic for the average absolute deviation for two or more correlated samples. Note: This function is not meant to be called by the user, but it is called by AAD2. Usage CalcAAD2(y, r = 0, weights = FALSE) Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). r Vector of proportions (default = 0, represents equal proportions) (vector). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). Value a The A statistic. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(x1, x2, x3) CalcAAD2(y) CalcAAPD1 13 CalcAAPD1 CalcAAPD1 Description Calculates the A statistic for the average absolute paired deviation for two or more groups. Note: This function is not meant to be called by the user, but it is called by AAPD1. Usage CalcAAPD1(y, weights = FALSE) Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). Value a The A statistic. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25))) AAPD1(y) 14 CalcAAPD2 CalcAAPD2 CalcAAPD2 Description Calculates the A statistic for the average absolute paired deviation for two or more correlated sam- ples. Note: This function is not meant to be called by the user, but it is called by AAPD2. Usage CalcAAPD2(y, weights = FALSE) Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). Value a The A statistic. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(x1, x2, x3) AAPD2(y) CalcIK1 15 CalcIK1 CalcIK1 Description Calculates the A statistic while singling out one group for two or more groups. Note: This function is not meant to be called by the user, but it is called by IK1. Usage CalcIK1(y, ref = 1, weights = FALSE) Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). ref Reference group (to compare to all others) (scalar, default = 1). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). Value a The A statistic. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25))) CalcIK1(y) 16 CalcIK2 CalcIK2 CalcIK2 Description Calculates the A statistic while singling out one group for two or more correlated samples. Note: This function is not meant to be called by the user, but it is called by IK2. Usage CalcIK2(y, ref = 1, weights = FALSE) Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). ref Reference group (to compare to all others) (scalar, default = 1). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). Value a The A statistic. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(x1, x2, x3) CalcIK2(y) CalcOrd1 17 CalcOrd1 CalcOrd1 Description Calculates the ordinal comparison of the A statistic for two or more groups. Note: This function is not meant to be called by the user, but it is called by AOrd1. Usage CalcOrd1(y, weights = FALSE, increase = FALSE) Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). increase Set to TRUE if scores are predicted to increase with group codes (default = FALSE). Value a The A statistic. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25))) CalcOrd1(y) 18 CalcOrd2 CalcOrd2 CalcOrd2 Description Calculates the ordinal comparison of the A statistic for two or more correlated samples. Note: This function is not meant to be called by the user, but it is called by AOrd2. Usage CalcOrd2(y, weights = FALSE, increase = FALSE) Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). increase Set to TRUE if scores are predicted to increase with group codes (default = FALSE). Value a The A statistic. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(x1, x2, x3) CalcOrd2(y) IK1 19 IK1 IK1 Description Calculates the confidence interval for the A statistic while singling out one group for two or more groups. Usage IK1(y, ref = 1, weights = FALSE, n.bootstrap = 1999, conf.level = .95, ci.method = 1, seed = 1) Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). ref Reference group (to compare to all others) (scalar, default = 1). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). n.bootstrap Number of bootstrap samples (scalar, default = 1999). conf.level Confidence level (scalar, default = .95). ci.method Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile). seed Random number seed (scalar, default = 1). Value A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25))) IK1(y) 20 IK2 IK2 IK2 Description Calculates the confidence interval for the A statistic while singling out one group for two or more correlated samples. Usage IK2(y, ref = 1, weights = FALSE, n.bootstrap = 1999, conf.level = .95, ci.method = 1, seed = 1) Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). ref Reference group (to compare to all others) (scalar, default = 1). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). n.bootstrap Number of bootstrap samples (scalar, default = 1999). conf.level Confidence level (scalar, default = .95). ci.method Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile). seed Random number seed (scalar, default = 1). Value A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(x1, x2, x3) IK2(y) Ord1 21 Ord1 Ord1 Description Calculates the confidence interval for the ordinal comparison of the A statistic for two or more groups. Usage Ord1(y, weights = FALSE, increase = FALSE, n.bootstrap = 1999, conf.level = .95, ci.method = 1, seed = 1) Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). increase Set to TRUE if scores are predicted to increase with group codes (default = FALSE). n.bootstrap Number of bootstrap samples (scalar, default = 1999). conf.level Confidence level (scalar, default = .95). ci.method Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile). seed Random number seed (scalar, default = 1). Value A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(c(x1, x2, x3), c(rep(1, 25), rep(2, 25), rep(3, 25))) Ord1(y) 22 Ord2 Ord2 Ord2 Description Calculates the confidence interval for the ordinal comparison of the A statistic for two or more correlated samples. Usage Ord2(y, weights = FALSE, increase = FALSE, n.bootstrap = 1999, conf.level = .95, ci.method = 1, seed = 1) Arguments y Matrix of cases (rows) by scores (column 1) and group codes (column 2) (ma- trix). weights Weight of each case. Set to TRUE to weight cases; if so, column 3 contains case weights (default = FALSE). increase Set to TRUE if scores are predicted to increase with group codes (default = FALSE). n.bootstrap Number of bootstrap samples (scalar, default = 1999). conf.level Confidence level (scalar, default = .95). ci.method Method used to construct confidence interval (scalar, default = 1 (for BCA), user can also call 2 (for percentile). seed Random number seed (scalar, default = 1). Value A vector containing the A statistic, its estimated standard error, and the upper and lower bounds of the confidence interval. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- rnorm(25) x2 <- x1 - rnorm(25, mean = 1) x3 <- x2 - rnorm(25, mean = 1) y <- cbind(x1, x2, x3) Ord2(y) RemoveMissing 23 RemoveMissing RemoveMissing Description Checks for missing data and performs listwise deletion if any is detected. Usage RemoveMissing(data) Arguments data For a between subjects design, a matrix of cases (rows) by scores (column 1) and group codes (column 2). For a within subjects design, a matrix of scores with each sample in its own column (matrix). Value Data matrix with any missing data removed using listwise deletion of cases. Author(s) John Ruscio References Ruscio (2008) & Ruscio and Mullen (2012) & Ruscio and Gera (2013) Examples x1 <- c(rnorm(25), NA) x2 <- x1 - rnorm(26, mean = 1) x3 <- x2 - rnorm(26, mean = 1) data <- cbind(c(x1, x2, x3), c(rep(1, 26), rep(2, 26), rep(3, 26))) A(data, 1, 2) Index A, 2 A1, 3 A2, 4 AAD1, 5 AAD2, 6 AAPD1, 7 AAPD2, 8 CalcA1, 9 CalcA2, 10 CalcAAD1, 11 CalcAAD2, 12 CalcAAPD1, 13 CalcAAPD2, 14 CalcIK1, 15 CalcIK2, 16 CalcOrd1, 17 CalcOrd2, 18 IK1, 19 IK2, 20 Ord1, 21 Ord2, 22 RemoveMissing, 23 24

hint

cran

Package ‘hint’ October 13, 2022 Type Package Title Tools for Hypothesis Testing Based on Hypergeometric Intersection Distributions Version 0.1-3 Date 2022-02-01 Author Alex T. Kalinka Maintainer Alex T. Kalinka <alex.t.kalinka@gmail.com> Description Hypergeometric Intersection distributions are a broad group of distributions that describe the probability of picking intersections when drawing independently from two (or more) urns containing variable numbers of balls belonging to the same n categories. <arXiv:1305.0717>. License GPL (>= 2) URL https://github.com/alextkalinka/hint Imports graphics, grDevices Encoding UTF-8 LazyLoad yes NeedsCompilation yes Repository CRAN RoxygenNote 7.1.2 Suggests testthat (>= 3.0.0) Config/testthat/edition 3 Date/Publication 2022-02-02 14:40:02 UTC R topics documented: add.distr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 Binomialintersection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 hint.dist.test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 hint.test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 1 2 Binomialintersection Hyperdistinct . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Hyperintersection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 plot.hint.test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 plotDistr . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 print.hint.test . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 Index 12 add.distr add.distr Description This function will add one or more distributions or hypothesis tests to an existing plot. Usage add.distr(..., cols = "blue", test.cols = "red") Arguments ... One or more distributions or objects of class hint.test. cols A character string vector naming the colours of the distributions. If length(cols) is less than the number of distributions, the colours will be recycled. Defaults to "blue". test.cols A character string vector naming the colours to use for the regions in which the cumulative probability of the hypothesis test was derived (if it exists). If length(test.cols) is less than the number of distributions, the colours will be re- cycled. Defaults to "red". Value Plots to the current device. Binomialintersection The Binomial Intersection Distribution Description Density, distribution function, quantile function and random generation for the binomial intersection distribution. Binomialintersection 3 Usage dbint(n, A, range = NULL, log = FALSE) pbint(n, A, vals, upper.tail = TRUE, log.p = FALSE) qbint(p, n, A, upper.tail = TRUE, log.p = FALSE) rbint(num = 5, n, A) Arguments n An integer specifying the number of categories in the urns. A A vector of integers specifying the numbers of balls drawn from each urn. The length of the vector equals the number of urns. range A vector of integers specifying the intersection sizes for which probabilities (dhint) or cumulative probabilites (phint) should be computed (can be a single number). If range is NULL (default) then probabilities will be returned over the entire range of possible values. log Logical. If TRUE, probabilities p are given as log(p). Defaults to FALSE. vals A vector of integers specifying the intersection sizes for which probabilities (dhint) or cumulative probabilites (phint) should be computed (can be a single number). If range is NULL (default) then probabilities will be returned over the entire range of possible values. upper.tail Logical. If TRUE, probabilities are P(X >= v), else P(X <= v). Defaults to TRUE. log.p Logical. If TRUE, probabilities p are given as log(p). Defaults to FALSE. p A probability between 0 and 1. num An integer specifying the number of random numbers to generate. Defaults to 5. Details The binomial intersection distribution is given by   N −1 !v N −1 !b−v b Y Y P (X = v|N ) = pi 1− pi v i=1 i=1 where b gives the sample size which is smallest. This is an approximation for the hypergeometric intersection distribution when n is large and b is small relative to the samples taken from the N − 1 other urns. Examples ## Generate the distribution of intersections sizes: dd <- dbint(20, c(10, 12, 11, 14)) ## Restrict the range of intersections. 4 hint.dist.test dd <- dbint(20, c(10, 12), range = 0:5) ## Generate cumulative probabilities. pp <- pbint(29, c(15, 8), vals = 5) pp <- pbint(29, c(15, 8), vals = 2, upper.tail = FALSE) ## Extract quantiles: qq <- qbint(0.15, 23, c(12, 10)) ## Generate random samples from Binomial intersection distributions. rr <- rbint(num = 10, 18, c(9, 14)) hint.dist.test hint.dist.test Description Tests whether the absolute distance between two intersection sizes would be expected by chance, i.e. whether they fall into opposite tails of their respective Hypergeometric Intersection distributions. Usage hint.dist.test(d, n1, A1, n2, A2, q1 = 0, q2 = 0, alternative = "greater") Arguments d A positive integer specifying the observed distance to be tested. n1 An integer specifying the number of categories in the urns for the first distribu- tion. A1 An integer vector specifying the number of balls drawn from urns for the first distribution. n2 An integer specifying the number of categories in the urns for the second distri- bution. A2 An integer vector specifying the number of balls drawn from the urns for the second distribution. q1 An integer specifying the number of categories with duplicates in the second urn of the first distribution. If 0 then the symmetric, singleton case is computed, oth- erwise the asymmetric, duplicates case is computed (see Hyperintersection). q2 An integer specifying the number of categories with duplicates in the second urn of the second distribution. If 0 then the symmetric, singleton case is computed, otherwise the asymmetric, duplicates case is computed (see Hyperintersection). alternative A characer string specifying the hypothesis to be tested. Can be one of "greater", "less", or "two.sided". Details The distribution of absolute distances between two hypergeometric intersection sizes is given by |Dd | X P (X = d) = P (v1i |n1 , a1 , b1 , ...) · P (v2i |n2 , a2 , b2 , ...) {v1 ,v2 }i ∈Dd where Dd is the set of pairs of intersection sizes, {v1 , v2 }, with absolute differences of size d. hint.test 5 Value An object of class hint.dist.test, which is a list containing the following components: • parameters An integer vector giving the parameter values. • p.value A numerical value giving the p-value associated with the test. • alternative A character string naming the hypothesis that was tested. hint.test hint.test Description Apply the hypergeometric intersection test to categorical data to test for enrichment or depletion of intersections between two samples. Usage hint.test(cats, draw1, draw2, alternative = "greater") Arguments cats A data frame or matrix with 3 columns; the first gives the category identifier, and the second and third give the number of balls belonging to this category in the first and second urns respectively. draw1 A vector of objects corresponding to the categories given in cats drawn from the first urn. draw2 A vector of objects corresponding to the categories given in cats drawn from the second urn. alternative A characer string specifying the hypothesis to be tested. Can be one of "greater", "less", or "two.sided". Details The hypergeometric intersection distributions describe the distribution of intersection sizes when sampling without replacement from two separate urns in which reside balls belonging to the same n object categories (see Hyperintersection). Value An object of class hint.test, which is a list containing the following components: • parameters An integer vector giving the parameter values. • p.value A numerical value giving the p-value associated with the test. • alternative A character string naming the hypothesis that was tested. 6 Hyperdistinct References Kalinka, A. T. (2013). The probability of drawing intersections: extending the hypergeometric distribution. arXiv.1305.0717 Hyperdistinct Drawing Distinct Categories from a Single Urn Description Density, distribution function, quantile function and random generation for the distribution of dis- tinct categories drawn from a single urn in which there are duplicates in q of the categories. Usage dhydist(n, a, q, range = NULL, log = FALSE) phydist(n, a, q, vals, upper.tail = TRUE, log.p = FALSE) qhydist(p, n, a, q, upper.tail = TRUE, log.p = FALSE) rhydist(num = 5, n, a, q) Arguments n An integer specifying the number of categories in the urn. a An integer specifying the number of balls drawn from the urn. q An integer specifying the number of categories in the urn which have duplicate members. range A vector of integers specifying the intersection sizes for which probabilities (dhydist) or cumulative probabilites (phydist) should be computed (can be a single number). If range is NULL (default) then probabilities will be returned over the entire range of possible values. log Logical. If TRUE, probabilities p are given as log(p). Defaults to FALSE. vals A vector of integers specifying the intersection sizes for which probabilities (dhydist) or cumulative probabilites (phydist) should be computed (can be a single number). If range is NULL (default) then probabilities will be returned over the entire range of possible values. upper.tail Logical. If TRUE, probabilities are P(X >= c), else P(X <= c). Defaults to TRUE. log.p Logical. If TRUE, probabilities p are given as log(p). Defaults to FALSE. p A probability between 0 and 1. num An integer specifying the number of random numbers to generate. Defaults to 5. Hyperintersection 7 Examples ## Generate the distribution of distinct categories drawn from a single urn. dd <- dhydist(20, 10, 12) ## Restrict the range of intersections. dd <- dhydist(20, 10, 12, range = 5:10) ## Generate cumulative probabilities. pp <- phydist(29, 15, 8, vals = 5) pp <- phydist(29, 15, 8, vals = 2, upper.tail = FALSE) ## Extract quantiles: qq <- qhydist(0.15, 23, 12, 10) ## Generate random samples based on this distribution. rr <- rhydist(num = 10, 18, 9, 12) Hyperintersection The Hypergeometric Intersection Family of Distributions Description The Hypergeometric Intersection Family of Distributions Usage dhint(n, A, q = 0, range = NULL, approx = FALSE, log = FALSE, verbose = TRUE) phint(n, A, q = 0, vals, upper.tail = TRUE, log.p = FALSE) qhint(p, n, A, q = 0, upper.tail = TRUE, log.p = FALSE) rhint(num = 5, n, A, q = 0) Arguments n An integer specifying the number of categories in the urns. A A vector of integers specifying the numbers of balls drawn from each urn. The length of the vector equals the number of urns. q An integer specifying the number of categories in the second urn which have duplicate members. If q is 0 (default) then the symmetrical, singleton case is computed, otherwise the asymmetrical, duplicates case is computed (see De- tails). range A vector of integers specifying the intersection sizes for which probabilities (dhint) or cumulative probabilites (phint) should be computed (can be a single number). If range is NULL (default) then probabilities will be returned over the entire range of possible values. approx Logical. If TRUE, a binomial approximation will be used to generate the distri- bution. log Logical. If TRUE, probabilities p are given as log(p). Defaults to FALSE. 8 Hyperintersection verbose Logical. If TRUE, progress of calculation in the asymmetric, duplicates case is printed to the screen. vals A vector of integers specifying the intersection sizes for which probabilities (dhint) or cumulative probabilites (phint) should be computed (can be a single number). If range is NULL (default) then probabilities will be returned over the entire range of possible values. upper.tail Logical. If TRUE, probabilities are P(X >= c), else P(X <= c). Defaults to TRUE. log.p Logical. If TRUE, probabilities p are given as log(p). Defaults to FALSE. p A probability between 0 and 1. num An integer specifying the number of random numbers to generate. Defaults to 5. Details The hypergeometric intersection distributions describe the distribution of intersection sizes when sampling without replacement from two separate urns in which reside balls belonging to the same n object categories. In the simplest case when there is exactly one ball in each category in each urn (symmetrical, singleton case), then the distribution is hypergeometric: a n−a

v b−v P (X = v) = n
b When there are three urns, the distribution is given by a a−v n−a n−v−i
P


v i i P (X = v) =
b−v−i n n
c−v b c If, however, we allow duplicates in q ≤ n of the categories in the second urn, then the distribution of intersection sizes is described by the following variant of the hypergeometric: α X β X l           X n−q q q−l n−v−q+l l n+q−a−m−j n n+q P (X = v) = / m=0 j=0 v−l l m a−v−m j b−v a b l=0 Value ‘dhint‘, ‘phint‘, and ‘qhint‘ return a data frame with two columns: v, the intersection size, and p, the associated p-values. ‘rhint‘ returns an integer vector of random samples based on the hyperge- ometric intersection distribution. References Kalinka, A. T. (2013). The probability of drawing intersections: extending the hypergeometric distribution. arXiv.1305.0717 plot.hint.test 9 Examples ## Generate the distribution of intersections sizes without duplicates: dd <- dhint(20, c(10, 12)) ## Restrict the range of intersections. dd <- dhint(20, c(10, 12), range = 0:5) ## Allow duplicates in q of the categories in the second urn: dd <- dhint(35, c(15, 11), 22, verbose = FALSE) ## Generate cumulative probabilities. pp <- phint(29, c(15, 8), vals = 5) pp <- phint(29, c(15, 8), vals = 2, upper.tail = FALSE) pp <- phint(29, c(15, 8), 23, vals = 2) ## Extract quantiles: qq <- qhint(0.15, 23, c(12, 10)) qq <- qhint(0.15, 23, c(12, 10), 18) ## Generate random samples from Hypergeometric intersection distributions. rr <- rhint(num = 10, 18, c(9, 14)) rr <- rhint(num = 10, 22, c(11, 17), 12) plot.hint.test plot.hint.test Description This function visualises the results of a Hypergeometric Intersection test. Usage ## S3 method for class 'hint.test' plot(x, ...) Arguments x An object of class ‘hint.test‘. ... Additional arguments to be passed to ‘plot‘. Details Plots the relevant Hypergeometric Intersection distribution as a segment plot, and highlights the region where the observed statistic falls, i.e. the region from which the probability is computed (two.sided tests are visualised in one tail, the one with the smallest density). This can be especially useful for pedagogical purposes. Value Plots to the current device. 10 plotDistr plotDistr plotDistr Description Plot a distribution or visualise the result of a hypothesis test. Usage plotDistr( distr, col = "black", test.col = "red", xlim = NULL, ylim = NULL, xlab = "Intersection size (v)", ylab = "Probability", add = FALSE, ... ) Arguments distr A data frame or matrix in which the first column gives random variable values, and the second gives probabilities. Can also be a vector (in which case random variables of 0:length(distr) will be automatically assigned, or an object of class hint.test. col A character string naming the colour to use for the distribution. Defaults to "black". test.col A character string naming the colour to use for the region in which the cumu- lative probability of the hypothesis test was derived (if it exists). Defaults to "red". xlim A vector of two numbers giving the range for the x-axis. If NULL (default), then this is determined by the maximum and minimum values in distr. ylim A vector of two numbers giving the range for the y-axis. If NULL (default), then this is determined by the maximum and minimum values in distr. xlab A character string giving a label for the x-axis. Deafults to "Intersection size (v)". ylab A character string giving a label for the y-axis. Deafults to "Probability". add Logical. Whether the plot will be added to an existing plot or not. Defaults to FALSE. ... Additional arguments to be passed to plot. print.hint.test 11 Details Visualising the results of a hypothesis test may often be of interest, but can be especially useful for pedagogical purposes. Value Plots to the current device. print.hint.test print.hint.test Description Prints the resuls of ‘hint.test‘. Usage ## S3 method for class 'hint.test' print(x, ...) Arguments x An object of class ‘hint.test‘. ... Additional arguments to be passed to ‘print‘. Value Prints output to the console. Index add.distr, 2 Binomialintersection, 2 dbint (Binomialintersection), 2 dhint (Hyperintersection), 7 dhydist (Hyperdistinct), 6 hint.dist.test, 4 hint.test, 5 Hyperdistinct, 6 Hyperintersection, 4, 5, 7 pbint (Binomialintersection), 2 phint (Hyperintersection), 7 phydist (Hyperdistinct), 6 plot.hint.test, 9 plotDistr, 10 print.hint.test, 11 qbint (Binomialintersection), 2 qhint (Hyperintersection), 7 qhydist (Hyperdistinct), 6 rbint (Binomialintersection), 2 rhint (Hyperintersection), 7 rhydist (Hyperdistinct), 6 12

qkerntool

cran

Package ‘qkerntool’ October 13, 2022 Title Q-Kernel-Based and Conditionally Negative Definite Kernel-Based Machine Learning Tools Version 1.19 Description Nonlinear machine learning tool for classification, clustering and dimensionality reduction. It integrates 12 q-kernel functions and 15 conditional negative definite kernel functions and includes the q-kernel and conditional negative definite kernel version of density-based spatial clustering of applications with noise, spectral clustering, generalized discriminant analysis, principal component analysis, multidimensional scaling, locally linear embedding, sammon's mapping and t-Distributed stochastic neighbor embedding. Depends R (>= 3.0.1) Imports stats, class, graphics, methods License GPL (>= 2) Encoding UTF-8 LazyData true Maintainer Yusen Zhang <yusenzhang@126.com> RoxygenNote 6.1.0 NeedsCompilation no Author Yusen Zhang [aut, cre] (<https://orcid.org/0000-0003-3842-1153>), Daolin Pang [ctb], Jinghao Wang [ctb], Jialin Zhang [ctb] Repository CRAN Date/Publication 2019-04-13 23:02:44 UTC R topics documented: as.cndkernmatrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 as.qkernmatrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1 2 as.cndkernmatrix blkdiag . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 cndkernel-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 cndkernmatrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 cnds . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Eucdist . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 mfeat_pix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 qkdbscan . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 qkdbscan-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 qkernel-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 qkernmatrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 qkgda . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 qkgda-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 qkIsomap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 qkIsomap-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 qkLLE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 qkLLE-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 qkMDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 qkMDS-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 qkpca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 qkpca-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 qkprc-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 qkspecc . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 qkspecc-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 qkspeclust . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 qsammon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 qsammon-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 qtSNE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 qtSNE-class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 Index 59 as.cndkernmatrix Assing cndkernmatrix class to matrix objects Description as.cndkernmatrix in package qkerntool can be used to create the cndkernmatrix class to matrix objects representing a CND kernel matrix. These matrices can then be used with the cndkernmatrix interfaces which most of the functions in qkerntool support. Usage ## S4 method for signature 'matrix' as.cndkernmatrix(x, center = FALSE) Arguments x matrix to be assigned the cndkernmatrix class center center the cndkernel matrix in feature space (default: FALSE) as.qkernmatrix 3 Author(s) Yusen Zhang <yusenzhang@126.com> See Also cndkernmatrix,qkernmatrix Examples ## Create the data x <- rbind(matrix(rnorm(10),,2),matrix(rnorm(10,mean=3),,2)) y <- matrix(c(rep(1,5),rep(-1,5))) ### Use as.cndkernmatrix to label the cov. matrix as a CND kernel matrix ### which is eq. to using a linear kernel K <- as.cndkernmatrix(crossprod(t(x))) K as.qkernmatrix Assing qkernmatrix class to matrix objects Description as.qkernmatrix in package qkerntool can be used to create the qkernmatrix class to matrix objects representing a q kernel matrix. These matrices can then be used with the qkernmatrix interfaces which most of the functions in qkerntool support. Usage ## S4 method for signature 'matrix' as.qkernmatrix(x, center = FALSE) Arguments x matrix to be assigned the qkernmatrix class center center the kernel matrix in feature space (default: FALSE) Author(s) Yusen Zhang <yusenzhang@126.com> 4 bases See Also qkernmatrix,cndkernmatrix Examples ## Create the data x <- rbind(matrix(rnorm(10),,2),matrix(rnorm(10,mean=3),,2)) y <- matrix(c(rep(1,5),rep(-1,5))) ### Use as.qkernmatrix to label the cov. matrix as a qkernel matrix ### which is eq. to using a linear kernel K <- as.qkernmatrix(crossprod(t(x))) K bases qKernel Functions Description The kernel generating functions provided in qkerntool. 2 2 0 The Non Linear Kernel k(x, y) = 2(1−q) 1 (q −α||x|| + q −α||y|| − 2q −αx y ). 2 1 The Gaussian kernel k(x, y) = 1−q (1 − q (||x−y|| /σ) ). 1 The Laplacian Kernel k(x, y) = 1−q (1 − q (||x−y||/σ) ). ||x−y||2 1 1−q (1√− q The Rational Quadratic Kernel k(x, y) = ||x−y||2 +c ). 1 c ||x−y||2 +c The Multiquadric Kernel k(x, y) = 1−q (q −q ). 1 −√ 1 − 1c The Inverse Multiquadric Kernel k(x, y) = 1−q (q −q ||x−y||2 +c ). θ ||x−y|| − ||x−y|| sin The Wave Kernel k(x, y) = 1−q 1 (q −1 − q θ ). 1 ( d The d Kernel k(x, y) = 1−q [1 − q ||x − y|| )]. 1 The Log Kernel k(x, y) = 1−q [1 − q l n(||x − y||d + 1)]. 1 1 −1 − The Cauchy Kernel k(x, y) = 1−q (q − q 1+||x−y||2 /σ ). P 2 The Chi-Square Kernel k(x, y) = 1−q 1 (1 − q 2(x−y) /(x+y)γ ). 1 − The Generalized T-Student Kernel k(x, y) = 1−q 1 (q −1 − q 1+||x−y||d ). Usage rbfbase(sigma=1,q=0.8) nonlbase(alpha = 1,q = 0.8) bases 5 laplbase(sigma = 1, q = 0.8) ratibase(c = 1, q = 0.8) multbase(c = 1, q = 0.8) invbase(c = 1, q = 0.8) wavbase(theta = 1,q = 0.8) powbase(d = 2, q = 0.8) logbase(d = 2, q = 0.8) caubase(sigma = 1, q = 0.8) chibase(gamma = 1, q = 0.8) studbase(d = 2, q = 0.8) Arguments q for all the qkernel function. sigma for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase". alpha for the Non Linear qkernel function "nonlbase". c for the Rational Quadratic qkernel function "ratibase" , the Multiquadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase". theta for the Wave qkernel function "wavbase". d for the d qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase". gamma for the Chi-Square qkernel function "chibase". Details The kernel generating functions are used to initialize a kernel function which calculates the kernel function value between two feature vectors in a Hilbert Space. These functions can be passed as a qkernel argument on almost all functions in qkerntool(e.g., qkgda, qkpca etc). Value Return an S4 object of class qkernel which extents the function class. The resulting function implements the given kernel calculating the kernel function value between two vectors. qpar a list containing the kernel parameters (hyperparameters) used. The kernel parameters can be accessed by the qpar function. Author(s) Yusen Zhang <yusenzhang@126.com> See Also qkernmatrix, cndkernmatrix 6 blkdiag Examples qkfunc <- rbfbase(sigma=1,q=0.8) qkfunc qpar(qkfunc) ## create two vectors x <- rnorm(10) y <- rnorm(10) ## calculate dot product qkfunc(x,y) blkdiag Block diagonal concatenation of matrix Description Y = BLKDIAG(A,B,...) produces diag(A,B,...) Usage blkdiag(x) Arguments x a list of matrix Value E - Block diagonal concatenation of matrix Author(s) Yusen Zhang <yusenzhang@126.com> cndkernel-class 7 cndkernel-class Class "cndkernel" "nonlkernel" "polykernel" "rbfkernel" "laplkernel" Description The built-in kernel classes in qkerntool Objects from the Class Objects can be created by calls of the form new("nonlkernel"), new{"polykernel"}, new{"rbfkernel"}, new{"laplkernel"}, new{"anokernel"}, new{"ratikernel"}, new{"multkernel"}, new{"invkernel"}, new{"wavkernel"}, new{"powkernel"}, new{"logkernel"}, new{"caukernel"}, new{"chikernel"}, new{"studkernel"},new{"norkernel"} or by calling the nonlcnd,polycnd, rbfcnd, laplcnd, anocnd, raticnd, multcnd, invcnd, wavcnd, powcnd, logcnd, caucnd, chicnd, studcnd, norcnd functions etc.. Slots .Data: Object of class "function" containing the kernel function qpar: Object of class "list" containing the kernel parameters Methods cndkernmatrix signature(kernel = "rbfkernel", x ="matrix"): computes the kernel matrix Author(s) Yusen Zhang <yusenzhang@126.com> See Also qkernmatrix,cndkernmatrix Examples cndkfunc <- rbfcnd(gamma = 1) cndkfunc qpar(cndkfunc) ## create two vectors x <- rnorm(10) y <- rnorm(10) cndkfunc(x,y) 8 cndkernmatrix cndkernmatrix CND Kernel Matrix functions Description cndkernmatrix calculates the kernel matrix Kij = k(xi , xj ) or Kij = k(xi , yj ). Usage ## S4 method for signature 'cndkernel' cndkernmatrix(cndkernel, x, y = NULL) Arguments cndkernel the cndkernel function to be used to calculate the CND kernel matrix. This has to be a function of class cndkernel, i.e. which can be generated either one of the build in kernel generating functions (e.g., rbfcnd nonlcnd etc.) or a user defined function of class cndkernel taking two vector arguments and returning a scalar. x a data matrix to be used to calculate the kernel matrix. y second data matrix to calculate the kernel matrix. Details Common functions used during kernel based computations. The cndkernel parameter can be set to any function, of class cndkernel, which computes the kernel function value in feature space between two vector arguments. qkerntool provides more than 10 CND kernel functions which can be initialized by using the following functions: • nonlcnd Non Linear cndkernel function • polycnd Polynomial cndkernel function • rbfcnd Gaussian cndkernel function • laplcnd Laplacian cndkernel function • anocnd ANOVA cndkernel function • raticnd Rational Quadratic cndkernel function • multcnd Multiquadric cndkernel function • invcnd Inverse Multiquadric cndkernel function • wavcnd Wave cndkernel function • powcnd d cndkernel function • logcnd Log cndkernel function • caucnd Cauchy cndkernel function • chicnd Chi-Square cndkernel function • studcnd Generalized T-Student cndkernel function (see example.) cnds 9 Value cndkernmatrix returns a conditionally negative definite matrix with a zero diagonal element. Author(s) Yusen Zhang <yusenzhang@126.com> See Also nonlbase, rbfbase, laplbase, ratibase, multbase, invbase, wavbase, powbase, logbase, caubase, chibase, studbase Examples ## use the iris data data(iris) dt <- as.matrix(iris[ ,-5]) ## initialize cndkernel function lapl <- laplcnd(gamma = 1) lapl ## calculate cndkernel matrix cndkernmatrix(lapl, dt) cnds CND Kernel Functions Description The kernel generating functions provided in qkerntool. The Non Linear Kernel k(x, y) = [exp(α||x||2 ) + exp(α||y||2 ) − 2exp(αx0 y)]/2. The Polynomial kernel k(x, y) = [(α||x||2 + c)d + (α||y||2 + c)d − 2(αx0 y + c)d ]/2. The Gaussian kernel k(x, y) = 1 − exp(−||x − y||2 /γ). The Laplacian Kernel k(x, y) = 1 −P exp(−||x − y||/γ). The ANOVA Kernel k(x, y) = n − exp(−σ(x − y)2 )d . The Rational Quadratic Kernel k(x, p y) = ||x − y||2 /(||x − y||2 + c). The Multiquadric Kernel k(x, y) = (||x − y||2 + cp 2 ) − c. The Inverse Multiquadric Kernel k(x, y) = 1/c − 1/ ||x − y||2 + c2 . θ The Wave Kernel k(x, y) = 1 − ||x−y|| sin ||x−y|| θ . d The d Kernel k(x, y) = ||x − y|| . The Log Kernel k(x, y) = log(||x − y||d + 1). The Cauchy Kernel k(x, y) = 1 −P 1/(1 + ||x − y||2 /γ). The Chi-Square Kernel k(x, y) = 2(x − y)2 /(x + y). The Generalized T-Student Kernel k(x, y) = 1 − 1/(1 + ||x − y||d ). The normal Kernel k(x, y) = ||x − y||2 . 10 cnds Usage nonlcnd(alpha = 1) polycnd(d = 2, alpha = 1, c = 1) rbfcnd(gamma = 1) laplcnd(gamma = 1) anocnd(d = 2, sigma = 1) raticnd(c = 1) multcnd(c = 1) invcnd(c = 1) wavcnd(theta = 1) powcnd(d = 2) logcnd(d = 2) caucnd(gamma = 1) chicnd( ) studcnd(d = 2) norcnd() Arguments alpha for the Non Linear cndkernel function "nonlcnd" and the Polynomial cndkernel function "polycnd". gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd". sigma for the ANOVA cndkernel function "anocnd". theta for the Wave cndkernel function "wavcnd". c for the Rational Quadratic cndkernel function "raticnd", the Polynomial cndker- nel function "polycnd", the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel function "invcnd". d for the Polynomial cndkernel function "polycnd", the ANOVA cndkernel func- tion "anocnd", the cndkernel function "powcnd", the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd". Details The kernel generating functions are used to initialize a kernel function which calculates the kernel function value between two feature vectors in a Hilbert Space. These functions can be passed as a qkernel argument on almost all functions in qkerntool. Value Return an S4 object of class cndkernel which extents the function class. The resulting function implements the given kernel calculating the kernel function value between two vectors. qpar a list containing the kernel parameters (hyperparameters) used. The kernel parameters can be accessed by the qpar function. Eucdist 11 Author(s) Yusen Zhang <yusenzhang@126.com> See Also cndkernmatrix, qkernmatrix Examples cndkfunc <- rbfcnd(gamma = 1) cndkfunc qpar(cndkfunc) ## create two vectors x <- rnorm(10) y <- rnorm(10) ## calculate dot product cndkfunc(x,y) Eucdist Computes the Euclidean(square Euclidean) distance matrix Description Eucdist Computes the Euclidean(square Euclidean) distance matrix. Arguments x (NxD) matrix (N samples, D features) y (MxD) matrix (M samples, D features) sEuclidean can be TRUE or FALSE, FALSE to Compute the Euclidean distance matrix. Value E - (MxN) Euclidean (square Euclidean) distances between vectors in x and y Author(s) Yusen Zhang <yusenzhang@126.com> 12 mfeat_pix Examples ### data(iris) testset <- sample(1:150,20) x <- as.matrix(iris[-testset,-5]) y <- as.matrix(iris[testset,-5]) ## res0 <- Eucdist(x) res1 <- Eucdist(x, x, sEuclidean = FALSE) res2 <- Eucdist(x, y = NULL, sEuclidean = FALSE) res3 <- Eucdist(x, x, sEuclidean = TRUE) res4 <- Eucdist(x, y = NULL) res5 <- Eucdist(x, sEuclidean = FALSE) mfeat_pix mfeat_pix dataset Description This dataset consists of features of handwritten numerals (‘0’–‘9’) extracted from a collection of Dutch utility maps. 200 patterns per class (for a total of 2,000 patterns) have been digitized in binary images. This dataset is about 240 pixel averages in 2 x 3 windows Usage data("mfeat_pix") Format A data frame with 2000 observations on the following 240 variables. Source https://archive.ics.uci.edu/ml/datasets/Multiple+Features Examples data(mfeat_pix) qkdbscan 13 qkdbscan qKernel-DBSCAN density reachability and connectivity clustering Description Similiar to the Density-Based Spatial Clustering of Applications with Noise(or DBSCAN) algo- rithm, qKernel-DBSCAN is a density-based clustering algorithm that can be applied under both linear and non-linear situations. Usage ## S4 method for signature 'matrix' qkdbscan(x, kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9), eps = 0.25, MinPts = 5, hybrid = TRUE, seeds = TRUE, showplot = FALSE, countmode = NULL, na.action = na.omit, ...) ## S4 method for signature 'cndkernmatrix' qkdbscan(x, eps = 0.25, MinPts = 5, seeds = TRUE, showplot = FALSE, countmode = NULL, ...) ## S4 method for signature 'qkernmatrix' qkdbscan(x, eps = 0.25, MinPts = 5, seeds = TRUE, showplot = FALSE, countmode = NULL, ...) ## S4 method for signature 'qkdbscan' predict(object, data, newdata = NULL, predict.max = 1000, ...) Arguments x the data matrix indexed by row, or a kernel matrix of cndkernmatrix or qkernmatrix. kernel the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings: • rbfbase Radial Basis qkernel function "Gaussian" • nonlbase Non Linear qkernel function • laplbase Laplbase qkernel function • ratibase Rational Quadratic qkernel function • multbase Multiquadric qkernel function • invbase Inverse Multiquadric qkernel function • wavbase Wave qkernel function • powbase Power qkernel function • logbase Log qkernel function • caubase Cauchy qkernel function 14 qkdbscan • chibase Chi-Square qkernel function • studbase Generalized T-Student qkernel function • nonlcnd Non Linear cndkernel function • polycnd Polynomial cndkernel function • rbfcnd Radial Basis cndkernel function "Gaussian" • laplcnd Laplacian cndkernel function • anocnd ANOVA cndkernel function • raticnd Rational Quadratic cndkernel function • multcnd Multiquadric cndkernel function • invcnd Inverse Multiquadric cndkernel function • wavcnd Wave cndkernel function • powcnd Power cndkernel function • logcnd Log cndkernel function • caucnd Cauchy cndkernel function • chicnd Chi-Square cndkernel function • studcnd Generalized T-Student cndkernel function The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument. qpar the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are : • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase". • alpha, q for the Non Linear qkernel function "nonlbase". • c, q for the Rational Quadratic qkernel function "ratibase" , the Multi- quadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase". • theta, q for the Wave qkernel function "wavbase". • d, q for the Power qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase". • alpha for the Non Linear cndkernel function "nonlcnd". • power, alpha, c for the Polynomial cndkernel function "polycnd". • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd". • power, sigma for the ANOVA cndkernel function "anocnd". • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel func- tion "invcnd". • theta for the Wave cndkernel function "wavcnd". • power for the Power cndkernel function "powcnd" , the Log cndkernel func- tion "logcnd" and the Generalized T-Student cndkernel function "studcnd". Hyper-parameters for user defined kernels can be passed through the qpar pa- rameter as well. qkdbscan 15 eps reachability distance, see Ester et al. (1996). (default:0.25) MinPts reachability minimum number of points, see Ester et al.(1996).(default : 5) hybrid whether the algothrim expects raw data but calculates partial distance matrices, can be TRUE or FALSE seeds can be TRUE or FALSE, FALSE to not include the isseed-vector in the dbscan- object. showplot whether to show the plot or not, can be TRUE or FALSE na.action a function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.) countmode NULL or vector of point numbers at which to report progress. object object of class dbscan. data matrix or data.frame. newdata matrix or data.frame with raw data to predict. predict.max max. batch size for predictions. ... Further arguments transferred to plot methods. Details The data can be passed to the qkdbscan function in a matrix, in addition qkdbscan also supports input in the form of a kernel matrix of class qkernmatrix or class cndkernmatrix. Value predict(qkdbscan-method) gives out a vector of predicted clusters for the points in newdata. qkdbscan gives out an S4 object which is a LIST with components clust integer vector coding cluster membership with noise observations (singletons) coded as 0 eps parameter eps MinPts parameter MinPts kcall the function call cndkernf the kernel function used xmatrix the original data matrix all the slots of the object can be accessed by accessor functions. Note The predict function can be used to embed new data on the new space. Author(s) Yusen Zhang <yusenzhang@126.com> 16 qkdbscan-class References Martin Ester, Hans-Peter Kriegel, Joerg Sander, Xiaowei Xu(1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise Institute for Computer Science, University of Munich. Proceedings of 2nd International Conference on Knowledge Discovery and Data Mining (KDD-96) See Also qkernmatrix, cndkernmatrix Examples # a simple example using the iris data(iris) test <- sample(1:150,20) x<- as.matrix(iris[-test,-5]) ds <- qkdbscan (x,kernel="laplbase",qpar=list(sigma=3.5,q=0.8),eps=0.15, MinPts=5,hybrid = FALSE) plot(ds,x) emb <- predict(ds, x, as.matrix(iris[test,-5])) points(iris[test,], col= as.integer(1+emb)) qkdbscan-class Class "qkdbscan" Description The qkernel-DBSCAN class. Objects of class "qkdbscan" Objects can be created by calls of the form new("qkdbscan", ...). or by calling the qkdbscan function. Slots clust: Object of class "vector" containing the cluster membership of the samples eps: Object of class "numeric" containing the reachability distance MinPts: Object of class "numeric" containing the reachability minimum number of points isseed: Object of class "logical" containing the logical vector indicating whether a point is a seed (not border, not noise) qkernel-class 17 Methods clust signature(object = "qkdbscan"): returns the cluster membership kcall signature(object = "qkdbscan"): returns the performed call cndkernf signature(object = "qkdbscan"): returns the used kernel function eps signature(object = "qkdbscan"): returns the reachability distance MinPts signature(object = "qkdbscan"): returns the reachability minimum number of points predict signature(object = "qkdbscan"): embeds new data xmatrix signature(object = "qkdbscan"): returns the used data matrix Author(s) Yusen Zhang <yusenzhang@126.com> See Also qkernel-class, cndkernel-class Examples # a simple example using the iris data x<- as.matrix(iris[,-5]) ds <- qkdbscan (x,kernel="laplbase",qpar=list(sigma=3.5,q=0.8),eps=0.15, MinPts=5,hybrid = FALSE) # print the results clust(ds) eps(ds) MinPts(ds) cndkernf(ds) xmatrix(ds) kcall(ds) qkernel-class Class "qkernel" "rbfqkernel" "nonlqkernel" "laplqkernel" "ratiqker- nel" Description The built-in kernel classes in qkerntool 18 qkernmatrix Objects from the Class Objects can be created by calls of the form new("rbfqkernel"), new{"nonlqkernel"}, new{"laplqkernel"}, new{"ratiqkernel"}, new{"multqkernel"}, new{"invqkernel"}, new{"wavqkernel"}, new{"powqkernel"}, new{"logqkernel"}, new{"cauqkernel"}, new{"chiqkernel"}, new{"studqkernel"} or by calling the rbfbase, nonlbase, laplbase, ratibase, multbase, invbase, wavbase, powbase, logbase, caubase, chibase, studbase functions etc.. Slots .Data: Object of class "function" containing the kernel function qpar: Object of class "list" containing the kernel parameters Methods qkernmatrix signature(kernel = "rbfqkernel", x = "matrix"): computes the qkernel matrix Author(s) Yusen Zhang <yusenzhang@126.com> See Also qkernmatrix,cndkernmatrix Examples qkfunc <- rbfbase(sigma=1,q=0.8) qkfunc qpar(qkfunc) ## create two vectors x <- rnorm(10) y <- rnorm(10) ## calculate dot product qkfunc(x,y) qkernmatrix qKernel Matrix functions Description qkernmatrix calculates the qkernel matrix Kij = k(xi , xj ) or Kij = k(xi , yj ). qkernmatrix 19 Usage ## S4 method for signature 'qkernel' qkernmatrix(qkernel, x, y = NULL) Arguments qkernel the kernel function to be used to calculate the qkernel matrix. This has to be a function of class qkernel, i.e. which can be generated either one of the build in kernel generating functions (e.g., rbfbase etc.) or a user defined function of class qkernel taking two vector arguments and returning a scalar. x a data matrix to be used to calculate the kernel matrix y second data matrix to calculate the kernel matrix Details Common functions used during kernel based computations. The qkernel parameter can be set to any function, of class qkernel, which computes the kernel function value in feature space between two vector arguments. qkerntool provides more than 10 qkernel functions which can be initialized by using the following functions: • nonlbase Non Linear qkernel function • rbfbase Gaussian qkernel function • laplbase Laplacian qkernel function • ratibase Rational Quadratic qkernel function • multbase Multiquadric qkernel function • invbase Inverse Multiquadric qkernel function • wavbase Wave qkernel function • powbase d qkernel function • logbase Log qkernel function • caubase Cauchy qkernel function • chibase Chi-Square qkernel function • studbase Generalized T-Student qkernel function (see example.) Value qkernmatrix returns a conditionally negative definite matrix with a zero diagonal element. Author(s) Yusen Zhang <yusenzhang@126.com> 20 qkgda See Also nonlcnd, rbfcnd,polycnd,laplcnd, anocnd, raticnd, multcnd, invcnd, wavcnd, powcnd, logcnd, caucnd, chicnd, studcnd Examples data(iris) dt <- as.matrix(iris[ ,-5]) ## initialize kernel function rbf <- rbfbase(sigma = 1.4, q=0.8) rbf ## calculate qkernel matrix qkernmatrix(rbf, dt) qkgda qKernel Generalized Discriminant Analysis Description The qkernel Generalized Discriminant Analysis is a method that deals with nonlinear discriminant analysis using kernel function operator. Usage ## S4 method for signature 'matrix' qkgda(x, label, kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9), features = 0, th = 1e-4, na.action = na.omit, ...) ## S4 method for signature 'cndkernmatrix' qkgda(x, label, features = 0, th = 1e-4, na.action = na.omit, ...) ## S4 method for signature 'qkernmatrix' qkgda(x, label, features = 0, th = 1e-4, ...) Arguments x the data matrix indexed by row, or a kernel matrix of cndkernmatrix or qkernmatrix. label The original labels of the samples. kernel the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings: • rbfbase Radial Basis qkernel function "Gaussian" qkgda 21 • nonlbase Non Linear qkernel function • laplbase Laplbase qkernel function • ratibase Rational Quadratic qkernel function • multbase Multiquadric qkernel function • invbase Inverse Multiquadric qkernel function • wavbase Wave qkernel function • powbase Power qkernel function • logbase Log qkernel function • caubase Cauchy qkernel function • chibase Chi-Square qkernel function • studbase Generalized T-Student qkernel function • nonlcnd Non Linear cndkernel function • polycnd Polynomial cndkernel function • rbfcnd Radial Basis cndkernel function "Gaussian" • laplcnd Laplacian cndkernel function • anocnd ANOVA cndkernel function • raticnd Rational Quadratic cndkernel function • multcnd Multiquadric cndkernel function • invcnd Inverse Multiquadric cndkernel function • wavcnd Wave cndkernel function • powcnd Power cndkernel function • logcnd Log cndkernel function • caucnd Cauchy cndkernel function • chicnd Chi-Square cndkernel function • studcnd Generalized T-Student cndkernel function The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument. qpar the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are : • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase". • alpha, q for the Non Linear qkernel function "nonlbase". • c, q for the Rational Quadratic qkernel function "ratibase" , the Multi- quadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase". • theta, q for the Wave qkernel function "wavbase". • d, q for the Power qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase". • alpha for the Non Linear cndkernel function "nonlcnd". • d, alpha, c for the Polynomial cndkernel function "polycnd". • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd". 22 qkgda • d, sigma for the ANOVA cndkernel function "anocnd". • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel func- tion "invcnd". • theta for the Wave cndkernel function "wavcnd". • d for the Power cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd". Hyper-parameters for user defined kernels can be passed through the qpar pa- rameter as well. features Number of features (principal components) to return. (default: 0 , all) th the value of the eigenvalue under which principal components are ignored (only valid when features = 0). (default : 0.0001) na.action A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.) ... additional parameters Details The qkernel Generalized Discriminant Analysis method provides a mapping of the input vectors into high dimensional feature space, generalizing the classical Linear Discriminant Analysis to non-linear discriminant analysis. The data can be passed to the qkgda function in a matrix, in addition qkgda also supports input in the form of a kernel matrix of class qkernmatrix or class cndkernmatrix. Value An S4 object containing the eigenvectors and their normalized projections, along with the corre- sponding eigenvalues and the original function. prj The normalized projections on eigenvectors) eVal The corresponding eigenvalues eVec The corresponding eigenvectors kcall The formula of the function called cndkernf The kernel function used xmatrix The original data matrix all the slots of the object can be accessed by accessor functions. Note The predict function can be used to embed new data on the new space Author(s) Yusen Zhang <yusenzhang@126.com> qkgda-class 23 References 1.Baudat, G, and F. Anouar: Generalized discriminant analysis using a kernel approach Neural Computation 12.10(2000),2385 2.Deng Cai, Xiaofei He, and Jiawei Han: Speed Up Kernel Discriminant Analysis The VLDB Journal,January,2011,vol.20, no.1,21-33. See Also qkernmatrix, cndkernmatrix Examples Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), Sp = rep(c("1","2","3"), rep(50,3))) testset <- sample(1:150,20) train <- as.matrix(iris[-testset,-5]) test <- as.matrix(iris[testset,-5]) Sp = rep(c("1","2","3"), rep(50,3)) labels <-as.numeric(Sp) trainlabel <- labels[-testset] testlabel <- labels[testset] kgda1 <- qkgda(train, label=trainlabel, kernel = "ratibase", qpar = list(c=1,q=0.9),features = 2) prj(kgda1) eVal(kgda1) eVec(kgda1) kcall(kgda1) # xmatrix(kgda1) #print the principal component vectors prj(kgda1) #plot the data projection on the components plot(kgda1@prj,col=as.integer(train), xlab="1st Principal Component",ylab="2nd Principal Component") qkgda-class Class "qkgda" Description The qkernel Generalized Discriminant Analysis class Objects of class "qkgda" Objects can be created by calls of the form new("qkgda", ...). or by calling the qkgda function. 24 qkgda-class Slots prj: Object of class "matrix" containing the normalized projections on eigenvectors eVal: Object of class "matrix" containing the corresponding eigenvalues eVec: Object of class "matrix" containing the corresponding eigenvectors label: Object of class "matrix" containing the categorical variables that the categorical data be assigned to one of the categories Methods prj signature(object = "qkgda"): returns the normalized projections eVal signature(object = "qkgda"): returns the eigenvalues eVec signature(object = "qkgda"): returns the eigenvectors kcall signature(object = "qkgda"): returns the performed call cndkernf signature(object = "qkgda"): returns the used kernel function predict signature(object = "qkgda"): embeds new data xmatrix signature(object = "qkgda"): returns the used data matrix Author(s) Yusen Zhang <yusenzhang@126.com> See Also qkernel-class, cndkernel-class Examples Iris <- data.frame(rbind(iris3[,,1], iris3[,,2], iris3[,,3]), Sp = rep(c("1","2","3"), rep(50,3))) testset <- sample(1:150,20) train <- as.matrix(iris[-testset,-5]) test <- as.matrix(iris[testset,-5]) Sp = rep(c("1","2","3"), rep(50,3)) labels <-as.numeric(Sp) trainlabel <- labels[-testset] testlabel <- labels[testset] kgda1 <- qkgda(train, label=trainlabel, kernel = "ratibase", qpar = list(c=1,q=0.9),features = 2) prj(kgda1) eVal(kgda1) eVec(kgda1) cndkernf(kgda1) kcall(kgda1) qkIsomap 25 qkIsomap qKernel Isometric Feature Mapping Description Computes the Isomap embedding as introduced in 2000 by Tenenbaum, de Silva and Langford. Usage ## S4 method for signature 'matrix' qkIsomap(x, kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9), dims = 2, k, mod = FALSE, plotResiduals = FALSE, verbose = TRUE, na.action = na.omit, ...) ## S4 method for signature 'cndkernmatrix' qkIsomap(x, dims = 2, k, mod = FALSE, plotResiduals = FALSE, verbose = TRUE, na.action = na.omit, ...) ## S4 method for signature 'qkernmatrix' qkIsomap(x, dims = 2, k, mod = FALSE, plotResiduals = FALSE, verbose = TRUE, na.action = na.omit, ...) Arguments x N x D matrix (N samples, D features) or a kernel matrix of cndkernmatrix or qkernmatrix. kernel the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings: • rbfbase Radial Basis qkernel function "Gaussian" • nonlbase Non Linear qkernel function • laplbase Laplbase qkernel function • ratibase Rational Quadratic qkernel function • multbase Multiquadric qkernel function • invbase Inverse Multiquadric qkernel function • wavbase Wave qkernel function • powbase Power qkernel function • logbase Log qkernel function • caubase Cauchy qkernel function • chibase Chi-Square qkernel function • studbase Generalized T-Student qkernel function • nonlcnd Non Linear cndkernel function • polycnd Polynomial cndkernel function • rbfcnd Radial Basis cndkernel function "Gaussian" 26 qkIsomap • laplcnd Laplacian cndkernel function • anocnd ANOVA cndkernel function • raticnd Rational Quadratic cndkernel function • multcnd Multiquadric cndkernel function • invcnd Inverse Multiquadric cndkernel function • wavcnd Wave cndkernel function • powcnd Power cndkernel function • logcnd Log cndkernel function • caucnd Cauchy cndkernel function • chicnd Chi-Square cndkernel function • studcnd Generalized T-Student cndkernel function The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument. qpar the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are : • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase". • alpha, q for the Non Linear qkernel function "nonlbase". • c, q for the Rational Quadratic qkernel function "ratibase" , the Multi- quadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase". • theta, q for the Wave qkernel function "wavbase". • d, q for the Power qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase". • alpha for the Non Linear cndkernel function "nonlcnd". • d, alpha, c for the Polynomial cndkernel function "polycnd". • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd". • d, sigma for the ANOVA cndkernel function "anocnd". • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel func- tion "invcnd". • theta for the Wave cndkernel function "wavcnd". • d for the Power cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd". Hyper-parameters for user defined kernels can be passed through the qpar pa- rameter as well. dims vector containing the target space dimension(s) k number of neighbours mod use modified Isomap algorithm plotResiduals show a plot with the residuals between the high and the low dimensional data verbose show a summary of the embedding procedure at the end qkIsomap 27 na.action A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.) ... additional parameters Details The qkIsomap is a nonlinear dimension reduction technique, that preserves global properties of the data. That means, that geodesic distances between all samples are captured best in the low dimensional embedding. This R version is based on the Matlab implementation by Tenenbaum and uses Floyd’s Algorithm to compute the neighbourhood graph of shortest distances, when calculating the geodesic distances. A modified version of the original Isomap algorithm is included. It respects nearest and farthest neighbours. To estimate the intrinsic dimension of the data, the function can plot the residuals between the high and the low dimensional data for a given range of dimensions. Value qkIsomap gives out an S4 object which is a LIST with components prj a N x dim matrix (N samples, dim features) with the reduced input data (list of several matrices if more than one dimension was specified). dims the dimension of the target space. Residuals the residual variances for all dimensions. eVal the corresponding eigenvalues. eVec the corresponding eigenvectors. cndkernf the kernel function used. kcall The formula of the function called all the slots of the object can be accessed by accessor functions. Author(s) Yusen Zhang <yusenzhang@126.com> References Tenenbaum, J. B. and de Silva, V. and Langford, J. C., "A global geometric framework for nonlinear dimensionality reduction.", 2000; Matlab code is available at http://waldron.stanford.edu/~isomap/ 28 qkIsomap-class Examples # another example using the iris data(iris) testset <- sample(1:150,20) train <- as.matrix(iris[-testset,-5]) labeltrain<- as.integer(iris[-testset,5]) test <- as.matrix(iris[testset,-5]) # ratibase(c=1,q=0.8) d_low = qkIsomap(train, kernel = "ratibase", qpar = list(c=1,q=0.8), dims=2, k=5, plotResiduals = TRUE) #plot the data projection on the components plot(prj(d_low),col=labeltrain, xlab="1st Principal Component",ylab="2nd Principal Component") prj(d_low) dims(d_low) Residuals(d_low) eVal(d_low) eVec(d_low) kcall(d_low) cndkernf(d_low) qkIsomap-class qKernel Isomap embedding Description The qKernel Isometric Feature Mapping class Objects of class "qkIsomap" Objects can be created by calls of the form new("qkIsomap", ...). or by calling the qkIsomap function. Slots prj: Object of class "matrix" containing the Nxdim matrix (N samples, dim features) with the reduced input data (list of several matrices if more than one dimension specified) dims: Object of class "numeric" containing the dimension of the target space (default 2) connum: Object of class "numeric" containing the number of connected components in graph Residuals: Object of class "vector" containing the residual variances for all dimensions eVal: Object of class "vector" containing the corresponding eigenvalues eVec: Object of class "vector" containing the corresponding eigenvectors qkIsomap-class 29 Methods prj signature(object = "qkIsomap"): returns the Nxdim matrix (N samples, dim features) dims signature(object = "qkIsomap"): returns the dimension Residuals signature(object = "qkIsomap"): returns the residual variances eVal signature(object = "qkIsomap"): returns the eigenvalues eVec signature(object = "qkIsomap"): returns the eigenvectors xmatrix signature(object = "qkIsomap"): returns the used data matrix kcall signature(object = "qkIsomap"): returns the performed call cndkernf signature(object = "qkIsomapa"): returns the used kernel function Author(s) Yusen Zhang <yusenzhang@126.com> See Also qkernel-class, cndkernel-class, qkIsomap Examples # another example using the iris data data(iris) testset <- sample(1:150,20) train <- as.matrix(iris[-testset,-5]) labeltrain<- as.integer(iris[-testset,5]) test <- as.matrix(iris[testset,-5]) # ratibase(c=1,q=0.8) d_low = qkIsomap(train, kernel = "ratibase", qpar = list(c=1,q=0.8), dims=2, k=5, plotResiduals = TRUE) #plot the data projection on the components plot(prj(d_low),col=labeltrain, xlab="1st Principal Component",ylab="2nd Principal Component") prj(d_low) dims(d_low) Residuals(d_low) eVal(d_low) eVec(d_low) kcall(d_low) cndkernf(d_low) 30 qkLLE qkLLE qKernel Locally Linear Embedding Description Computes the qkernel Locally Linear Embedding Usage ## S4 method for signature 'matrix' qkLLE(x, kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9), dims = 2, k, na.action = na.omit, ...) ## S4 method for signature 'cndkernmatrix' qkLLE(x, dims = 2, k, na.action = na.omit, ...) ## S4 method for signature 'qkernmatrix' qkLLE(x, dims = 2, k, na.action = na.omit,...) Arguments x N x D matrix (N samples, D features) or a kernel matrix of cndkernmatrix or qkernmatrix. kernel the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings: • rbfbase Radial Basis qkernel function "Gaussian" • nonlbase Non Linear qkernel function • laplbase Laplbase qkernel function • ratibase Rational Quadratic qkernel function • multbase Multiquadric qkernel function • invbase Inverse Multiquadric qkernel function • wavbase Wave qkernel function • powbase Power qkernel function • logbase Log qkernel function • caubase Cauchy qkernel function • chibase Chi-Square qkernel function • studbase Generalized T-Student qkernel function • nonlcnd Non Linear cndkernel function • polycnd Polynomial cndkernel function • rbfcnd Radial Basis cndkernel function "Gaussian" • laplcnd Laplacian cndkernel function • anocnd ANOVA cndkernel function • raticnd Rational Quadratic cndkernel function qkLLE 31 • multcnd Multiquadric cndkernel function • invcnd Inverse Multiquadric cndkernel function • wavcnd Wave cndkernel function • powcnd Power cndkernel function • logcnd Log cndkernel function • caucnd Cauchy cndkernel function • chicnd Chi-Square cndkernel function • studcnd Generalized T-Student cndkernel function The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument. qpar the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are : • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase". • alpha, q for the Non Linear qkernel function "nonlbase". • c, q for the Rational Quadratic qkernel function "ratibase" , the Multi- quadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase". • theta, q for the Wave qkernel function "wavbase". • d, q for the Power qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase". • alpha for the Non Linear cndkernel function "nonlcnd". • power, alpha, c for the Polynomial cndkernel function "polycnd". • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd". • power, sigma for the ANOVA cndkernel function "anocnd". • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel func- tion "invcnd". • theta for the Wave cndkernel function "wavcnd". • power for the Power cndkernel function "powcnd" , the Log cndkernel func- tion "logcnd" and the Generalized T-Student cndkernel function "studcnd". Hyper-parameters for user defined kernels can be passed through the qpar pa- rameter as well. dims dimension of the target space k the number of nearest neighbours. na.action A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.) ... additional parameters 32 qkLLE Details The qkernel Locally Linear Embedding (qkLLE) preserves local properties of the data by represent- ing each sample in the data by a linear combination of its k nearest neighbours with each neighbour weighted independently. qkLLE finally chooses the low-dimensional representation that best pre- serves the weights in the target space. It is an extension of Locally Linear Embedding (LLE) with qkernel method. Value It returns an S4 object containing the principal component vectors along with the corresponding eigenvalues. prj a matrix with the reduced input data dims dimension of the target space eVal The corresponding eigenvalues eVec The corresponding eigenvectors cndkernf the kernel function used all the slots of the object can be accessed by accessor functions. Author(s) Yusen Zhang <yusenzhang@126.com> References Roweis, Sam T. and Saul, Lawrence K., "Nonlinear Dimensionality Reduction by Locally Linear Embedding",2000; Examples ## S4 method for signature 'matrix' data(iris) testset <- sample(1:150,20) train <- as.matrix(iris[-testset,-5]) labeltrain<- as.integer(iris[-testset,5]) test <- as.matrix(iris[testset,-5]) plot(train ,col=labeltrain, xlab="1st Principal Component",ylab="2nd Principal Component") # ratibase(c=1,q=0.8) d_low <- qkLLE(train, kernel = "ratibase", qpar = list(c=1,q=0.8), dims=2, k=5) #plot the data projection on the components plot(prj(d_low),col=labeltrain, xlab="1st Principal Component",ylab="2nd Principal Component") ## S4 method for signature 'qkernmatrix' # ratibase(c=0.1,q=0.8) qkfunc <- ratibase(c=0.1,q=0.8) ktrain1 <- qkernmatrix(qkfunc,train) d_low <- qkLLE(ktrain1, dims = 2, k=5) qkLLE-class 33 #plot the data projection on the components plot(prj(d_low),col=labeltrain, xlab="1st Principal Component",ylab="2nd Principal Component") qkLLE-class Class "qkLLE" Description The qKernel Locally Linear Embedding class Objects of class "qkLLE" Objects can be created by calls of the form new("qkLLE", ...). or by calling the qkLLE function. Slots prj: Object of class "matrix" containing the reduced input data dims: Object of class "numeric" containing the dimension of the target space (default 2) eVal: Object of class "vector" containing the corresponding eigenvalues eVec: Object of class "matrix" containing the corresponding eigenvectors Methods prj signature(object = "qkLLE"): returns the reduced input data dims signature(object = "qkLLE"): returns the dimension eVal signature(object = "qkLLE"): returns the eigenvalues eVec signature(object = "qkLLE"): returns the eigenvectors xmatrix signature(object = "qkLLE"): returns the used data matrix kcall signature(object = "qkLLE"): returns the performed call cndkernf signature(object = "qkLLE"): returns the used kernel function Author(s) Yusen Zhang <yusenzhang@126.com> See Also qkernel-class, cndkernel-class 34 qkMDS Examples ## S4 method for signature 'matrix' data(iris) testset <- sample(1:150,20) train <- as.matrix(iris[-testset,-5]) labeltrain<- as.integer(iris[-testset,5]) test <- as.matrix(iris[testset,-5]) plot(train ,col=labeltrain, xlab="1st Principal Component",ylab="2nd Principal Component") # ratibase(c=1,q=0.8) d_low <- qkLLE(train, kernel = "ratibase", qpar = list(c=1,q=0.8), dims=2, k=5) #plot the data projection on the components plot(prj(d_low),col=labeltrain,xlab="1st Principal Component",ylab="2nd Principal Component") ## S4 method for signature 'qkernmatrix' # ratibase(c=0.1,q=0.8) qkfunc <- ratibase(c=0.1,q=0.8) ktrain1 <- qkernmatrix(qkfunc,train) d_low <- qkLLE(ktrain1, dims = 2, k=5) #plot the data projection on the components plot(prj(d_low),col=labeltrain,xlab="1st Principal Component",ylab="2nd Principal Component") qkMDS qKernel Metric Multi-Dimensional Scaling Description The qkernel Metric Multi-Dimensional Scaling is a nonlinear form of Metric Multi-Dimensional Scaling Usage ## S4 method for signature 'matrix' qkMDS(x, kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9), dims = 2, plotResiduals = FALSE, verbose = TRUE, na.action = na.omit, ...) ## S4 method for signature 'cndkernmatrix' qkMDS(x, dims = 2,plotResiduals = FALSE, verbose = TRUE, na.action = na.omit, ...) ## S4 method for signature 'qkernmatrix' qkMDS(x, dims = 2,plotResiduals = FALSE, verbose = TRUE, na.action = na.omit, ...) Arguments x N x D matrix (N samples, D features) or a kernel matrix of cndkernmatrix or qkernmatrix. qkMDS 35 kernel the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings: • rbfbase Radial Basis qkernel function "Gaussian" • nonlbase Non Linear qkernel function • laplbase Laplbase qkernel function • ratibase Rational Quadratic qkernel function • multbase Multiquadric qkernel function • invbase Inverse Multiquadric qkernel function • wavbase Wave qkernel function • powbase Power qkernel function • logbase Log qkernel function • caubase Cauchy qkernel function • chibase Chi-Square qkernel function • studbase Generalized T-Student qkernel function • nonlcnd Non Linear cndkernel function • polycnd Polynomial cndkernel function • rbfcnd Radial Basis cndkernel function "Gaussian" • laplcnd Laplacian cndkernel function • anocnd ANOVA cndkernel function • raticnd Rational Quadratic cndkernel function • multcnd Multiquadric cndkernel function • invcnd Inverse Multiquadric cndkernel function • wavcnd Wave cndkernel function • powcnd Power cndkernel function • logcnd Log cndkernel function • caucnd Cauchy cndkernel function • chicnd Chi-Square cndkernel function • studcnd Generalized T-Student cndkernel function The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument. qpar the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are : • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase". • alpha, q for the Non Linear qkernel function "nonlbase". • c, q for the Rational Quadratic qkernel function "ratibase" , the Multi- quadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase". • theta, q for the Wave qkernel function "wavbase". 36 qkMDS • d, q for the Power qkernel function "powbase" , the Log qkernel function "logbase" and the Generalized T-Student qkernel function "studbase". • alpha for the Non Linear cndkernel function "nonlcnd". • d, alpha, c for the Polynomial cndkernel function "polycnd". • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd". • d, sigma for the ANOVA cndkernel function "anocnd". • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel func- tion "invcnd". • theta for the Wave cndkernel function "wavcnd". • d for the Power cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd". Hyper-parameters for user defined kernels can be passed through the qpar pa- rameter as well. dims vector containing the target space dimension(s) plotResiduals show a plot with the residuals between the high and the low dimensional data verbose show a summary of the embedding procedure at the end na.action A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.) ... additional parameters Details There are several versions of non-metric multidimensional scaling in R, but qkerntool offers the following unique combination of using qKernel methods Value qkMDS gives out an S4 object which is a LIST with components prj a N x dim matrix (N samples, dim features) with the reduced input data (list of several matrices if more than one dimension was specified). dims the dimension of the target space. Residuals the residual variances for all dimensions. eVal the corresponding eigenvalues. eVec the corresponding eigenvectors. cndkernf the kernel function used. kcall The formula of the function called all the slots of the object can be accessed by accessor functions. qkMDS-class 37 Author(s) Yusen Zhang <yusenzhang@126.com> References Kruskal, J.B. 1964a. Multidimensional scaling by optimizing goodness-of-fit to a nonmetric hy- pothesis. Psychometrika 29, 1–28. Examples # another example using the iris data(iris) testset <- sample(1:150,20) train <- as.matrix(iris[-testset,-5]) labeltrain<- as.integer(iris[-testset,5]) test <- as.matrix(iris[testset,-5]) # ratibase(c=1,q=0.8) d_low = qkMDS(train, kernel = "ratibase", qpar = list(c=1,q=0.9),dims = 2, plotResiduals = TRUE) #plot the data projection on the components plot(prj(d_low),col=labeltrain, xlab="1st Principal Component",ylab="2nd Principal Component") prj(d_low) dims(d_low) Residuals(d_low) eVal(d_low) eVec(d_low) kcall(d_low) cndkernf(d_low) qkMDS-class qKernel Metric Multi-Dimensional Scaling Description The qkernel Metric Multi-Dimensional Scaling class Objects of class "qkMDS" Objects can be created by calls of the form new("qkMDS", ...). or by calling the qkMDS function. Slots prj: Object of class "matrix" containing the Nxdim matrix (N samples, dim features) with the reduced input data (list of several matrices if more than one dimension specified) dims: Object of class "numeric" containing the dimension of the target space (default 2) connum: Object of class "numeric" containing the number of connected components in graph 38 qkMDS-class Residuals: Object of class "vector" containing the residual variances for all dimensions eVal: Object of class "vector" containing the corresponding eigenvalues eVec: Object of class "vector" containing the corresponding eigenvectors Methods prj signature(object = "qkMDS"): returns the Nxdim matrix (N samples, dim features) dims signature(object = "qkMDS"): returns the dimension Residuals signature(object = "qkMDS"): returns the residual variances eVal signature(object = "qkMDS"): returns the eigenvalues eVec signature(object = "qkMDS"): returns the eigenvectors xmatrix signature(object = "qkMDS"): returns the used data matrix kcall signature(object = "qkMDS"): returns the performed call cndkernf signature(object = "qkMDS"): returns the used kernel function Author(s) Yusen Zhang <yusenzhang@126.com> See Also qkernel-class, cndkernel-class, qkMDS Examples # another example using the iris data(iris) testset <- sample(1:150,20) train <- as.matrix(iris[-testset,-5]) labeltrain<- as.integer(iris[-testset,5]) test <- as.matrix(iris[testset,-5]) # ratibase(c=1,q=0.8) d_low = qkMDS(train, kernel = "ratibase", qpar = list(c=1,q=0.8), dims=2, plotResiduals = TRUE) #plot the data projection on the components plot(prj(d_low),col=labeltrain, xlab="1st Principal Component",ylab="2nd Principal Component") prj(d_low) dims(d_low) Residuals(d_low) eVal(d_low) eVec(d_low) kcall(d_low) cndkernf(d_low) qkpca 39 qkpca qKernel Principal Components Analysis Description The qkernel Principal Components Analysis is a nonlinear form of principal component analysis. Usage ## S4 method for signature 'formula' qkpca(x, data = NULL, na.action, ...) ## S4 method for signature 'matrix' qkpca(x, kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9), features = 0, th = 1e-4, na.action = na.omit, ...) ## S4 method for signature 'cndkernmatrix' qkpca(x, features = 0, th = 1e-4, ...) ## S4 method for signature 'qkernmatrix' qkpca(x, features = 0, th = 1e-4, ...) Arguments x the data matrix indexed by row, a formula describing the model or a kernel matrix of cndkernmatrix or qkernmatrix. data an optional data frame containing the variables in the model (when using a for- mula). kernel the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings: • rbfbase Radial Basis qkernel function "Gaussian" • nonlbase Non Linear qkernel function • laplbase Laplbase qkernel function • ratibase Rational Quadratic qkernel function • multbase Multiquadric qkernel function • invbase Inverse Multiquadric qkernel function • wavbase Wave qkernel function • powbase d qkernel function • logbase Log qkernel function • caubase Cauchy qkernel function • chibase Chi-Square qkernel function • studbase Generalized T-Student qkernel function • nonlcnd Non Linear cndkernel function • polycnd Polynomial cndkernel function • rbfcnd Radial Basis cndkernel function "Gaussian" 40 qkpca • laplcnd Laplacian cndkernel function • anocnd ANOVA cndkernel function • raticnd Rational Quadratic cndkernel function • multcnd Multiquadric cndkernel function • invcnd Inverse Multiquadric cndkernel function • wavcnd Wave cndkernel function • powcnd power cndkernel function • logcnd Log cndkernel function • caucnd Cauchy cndkernel function • chicnd Chi-Square cndkernel function • studcnd Generalized T-Student cndkernel function The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument. qpar the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are : • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase". • alpha, q for the Non Linear qkernel function "nonlbase". • c, q for the Rational Quadratic qkernel function "ratibase" , the Multi- quadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase". • theta, q for the Wave qkernel function "wavbase". • d, q for the d qkernel function "powbase" , the Log qkernel function "log- base" and the Generalized T-Student qkernel function "studbase". • alpha for the Non Linear cndkernel function "nonlcnd". • d, alpha, c for the Polynomial cndkernel function "polycnd". • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd". • d, sigma for the ANOVA cndkernel function "anocnd". • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel func- tion "invcnd". • theta for the Wave cndkernel function "wavcnd". • d for the power cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd". Hyper-parameters for user defined kernels can be passed through the qpar pa- rameter as well. features Number of features (principal components) to return. (default: 0 , all) th the value of the eigenvalue under which principal components are ignored (only valid when features = 0). (default : 0.0001) na.action A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.) qkpca 41 ... additional parameters Details Using kernel functions one can efficiently compute principal components in high-dimensional fea- ture spaces, related to input space by some non-linear map. The data can be passed to the qkpca function in a matrix, in addition qkpca also supports input in the form of a kernel matrix of class qkernmatrix or class cndkernmatrix. Value An S4 object containing the principal component vectors along with the corresponding eigenvalues. pcv a matrix containing the principal component vectors (column wise) eVal The corresponding eigenvalues rotated The original data projected (rotated) on the principal components cndkernf the kernel function used xmatrix The original data matrix all the slots of the object can be accessed by accessor functions. Note The predict function can be used to embed new data on the new space Author(s) Yusen Zhang <yusenzhang@126.com> References Schoelkopf B., A. Smola, K.-R. Mueller : Nonlinear component analysis as a kernel eigenvalue problem Neural Computation 10, 1299-1319 http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.29.1366 See Also qkernmatrix, cndkernmatrix Examples # another example using the iris data data(iris) test <- sample(1:150,20) qkpc <- qkpca(~.,data=iris[-test,-5],kernel="rbfbase", qpar=list(sigma=50,q=0.8),features=2) # print the principal component vectors 42 qkpca-class pcv(qkpc) #plot the data projection on the components plot(rotated(qkpc),col=as.integer(iris[-test,5]), xlab="1st Principal Component",ylab="2nd Principal Component") # embed remaining points emb <- predict(qkpc,iris[test,-5]) points(emb,col=as.integer(iris[test,5])) qkpca-class Class "qkpca" Description The qkernel Principal Components Analysis class Objects of class "qkpca" Objects can be created by calls of the form new("qkpca", ...). or by calling the qkpca function. Slots pcv: Object of class "matrix" containing the principal component vectors eVal: Object of class "vector" containing the corresponding eigenvalues rotated: Object of class "matrix" containing the projection of the data on the principal compo- nents Methods eVal signature(object = "qkpca"): returns the eigenvalues pcv signature(object = "qkpca"): returns the principal component vectors predict signature(object = "qkpca"): embeds new data rotated signature(object = "qkpca"): returns the projected data xmatrix signature(object = "qkpca"): returns the used data matrix kcall signature(object = "qkpca"): returns the performed call cndkernf signature(object = "qkpca"): returns the used kernel function Author(s) Yusen Zhang <yusenzhang@126.com> See Also qkernel-class, cndkernel-class qkprc-class 43 Examples # another example using the iris data data(iris) test <- sample(1:150,20) qkpc <- qkpca(~.,iris[-test,-5], kernel = "rbfbase", qpar = list(sigma = 50, q = 0.8), features = 2) # print the principal component vectors pcv(qkpc) rotated(qkpc) cndkernf(qkpc) eVal(qkpc) xmatrix(qkpc) names(eVal(qkpc)) qkprc-class Class "qkprc" Description The qKernel Prehead class Objects of class "qkprc" Objects from the class cannot be created directly but only contained in other classes. Slots cndkernf: Object of class "kfunction" containing the kernel function used qpar: Object of class "list" containing the kernel parameters used xmatrix: Object of class "input" containing the data matrix used ymatrix: Object of class "input" containing the data matrix used kcall: Object of class "ANY" containing the function call terms: Object of class "ANY" containing the function terms n.action: Object of class "ANY" containing the action performed on NA Methods cndkernf signature(object = "qkprc"): returns the used kernel function xmatrix signature(object = "qkprc"): returns the used data matrix ymatrix signature(object = "qkprc"): returns the used data matrix kcall signature(object = "qkprc"): returns the performed call Author(s) Yusen Zhang <yusenzhang@126.com> 44 qkspecc See Also qkernel-class, cndkernel-class qkspecc qkernel spectral Clustering Description A qkernel spectral clustering algorithm. Clustering is performed by embedding the data into the subspace of the eigenvectors of a graph Laplacian matrix. Usage ## S4 method for signature 'matrix' qkspecc(x,kernel = "rbfbase", qpar = list(sigma = 2, q = 0.9), Nocent=NA, normalize="symmetric", maxk=20, iterations=200, na.action = na.omit, ...) ## S4 method for signature 'cndkernmatrix' qkspecc(x, Nocent=NA, normalize="symmetric", maxk=20,iterations=200, ...) ## S4 method for signature 'qkernmatrix' qkspecc(x, Nocent=NA, normalize="symmetric", maxk=20,iterations=200, ...) Arguments x the matrix of data to be clustered or a kernel Matrix of class qkernmatrix or cndkernmatrix. kernel the kernel function used in computing the affinity matrix. This parameter can be set to any function, of class kernel, which computes a kernel function value be- tween two vector arguments. kernlab provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings: • rbfbase Radial Basis qkernel function "Gaussian" • nonlbase Non Linear qkernel function • laplbase Laplbase qkernel function • ratibase Rational Quadratic qkernel function • multbase Multiquadric qkernel function • invbase Inverse Multiquadric qkernel function • wavbase Wave qkernel function • powbase d qkernel function • logbase Log qkernel function • caubase Cauchy qkernel function qkspecc 45 • chibase Chi-Square qkernel function • studbase Generalized T-Student qkernel function • nonlcnd Non Linear cndkernel function • polycnd Polynomial cndkernel function • rbfcnd Radial Basis cndkernel function "Gaussian" • laplcnd Laplacian cndkernel function • anocnd ANOVA cndkernel function • raticnd Rational Quadratic cndkernel function • multcnd Multiquadric cndkernel function • invcnd Inverse Multiquadric cndkernel function • wavcnd Wave cndkernel function • powcnd d cndkernel function • logcnd Log cndkernel function • caucnd Cauchy cndkernel function • chicnd Chi-Square cndkernel function • studcnd Generalized T-Student cndkernel function The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument. qpar a character string or the list of hyper-parameters (kernel parameters). The de- fault character string list(sigma = 2, q = 0.9) uses a heuristic to determine a suitable value for the width parameter of the RBF kernel. The second option "local" (local scaling) uses a more advanced heuristic and sets a width param- eter for every point in the data set. This is particularly useful when the data incorporates multiple scales. A list can also be used containing the parameters to be used with the kernel function. Valid parameters for existing kernels are : • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase". • alpha, q for the Non Linear qkernel function "nonlbase". • c, q for the Rational Quadratic qkernel function "ratibase" , the Multi- quadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase". • theta, q for the Wave qkernel function "wavbase". • d, q for the d qkernel function "powbase" , the Log qkernel function "log- base" and the Generalized T-Student qkernel function "studbase". • alpha for the Non Linear cndkernel function "nonlcnd". • d, alpha, c for the Polynomial cndkernel function "polycnd". • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd". • d, sigma for the ANOVA cndkernel function "anocnd". • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel func- tion "invcnd". • theta for the Wave cndkernel function "wavcnd". 46 qkspecc • d for the d cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd". where length is the length of the strings considered, lambda the decay factor and nor- malized a logical parameter determining if the kernel evaluations should be normalized. Hyper-parameters for user defined kernels can be passed through the qpar pa- rameter as well. Nocent the number of clusters. normalize Normalisation of the Laplacian ("none", "symmetric" or "random-walk"). maxk If k is NA, an upper bound for the automatic estimation. Defaults to 20. iterations the maximum number of iterations allowed. na.action the action to perform on NA. ... additional parameters. Details The qkernel spectral clustering works by embedding the data points of the partitioning problem into the subspace of the eigenvectors corresponding to the k smallest eigenvalues of the graph Laplacian matrix. Using a simple clustering method like kmeans on the embedded points usually leads to good performance. It can be shown that qkernel spectral clustering methods boil down to graph partitioning. The data can be passed to the qkspecc function in a matrix, in addition qkspecc also supports input in the form of a kernel matrix of class qkernmatrix or cndkernmatrix. Value An S4 object of class qkspecc which extends the class vector containing integers indicating the cluster to which each point is allocated. The following slots contain useful information clust The cluster assignments eVec The corresponding eigenvector eVal The corresponding eigenvalues ymatrix The eigenvectors corresponding to the k smallest eigenvalues of the graph Lapla- cian matrix. Author(s) Yusen Zhang <yusenzhang@126.com> References Andrew Y. Ng, Michael I. Jordan, Yair Weiss On Spectral Clustering: Analysis and an Algorithm Neural Information Processing Symposium 2001 qkspecc-class 47 See Also qkernmatrix, cndkernmatrix, qkpca Examples data("iris") x=as.matrix(iris[,-5]) qspe <- qkspecc(x,kernel = "rbfbase", qpar = list(sigma = 10, q = 0.9), Nocent=3, normalize="symmetric", maxk=15, iterations=1200) plot(x, col = clust(qspe)) qkfunc <- nonlbase(alpha=1/15,q=0.8) Ktrain <- qkernmatrix(qkfunc, x) qspe <- qkspecc(Ktrain, Nocent=3, normalize="symmetric", maxk=20) plot(x, col = clust(qspe)) qkspecc-class Class "qkspecc" Description The qKernel Spectral Clustering Class Objects from the Class Objects can be created by calls of the form new("qkspecc", ...). or by calling the function qkspecc. Slots clust: Object of class "vector" containing the cluster assignments eVec: Object of class "matrix" containing the corresponding eigenvector in each cluster eVal: Object of class "vector" containing the corresponding eigenvalue for each cluster withinss: Object of class "vector" containing the within-cluster sum of squares for each cluster Methods clust signature(object = "qkspecc"): returns the cluster assignments eVec signature(object = "qkspecc"): returns the corresponding eigenvector in each cluster eVal signature(object = "qkspecc"): returns the corresponding eigenvalue for each cluster xmatrix signature(object = "qkspecc"): returns the original data matrix or a kernel Matrix ymatrix signature(object = "qkspecc"): returns The eigenvectors corresponding to the k small- est eigenvalues of the graph Laplacian matrix. cndkernf signature(object = "qkspecc"): returns the used kernel function kcall signature(object = "qkspecc"): returns the performed call 48 qkspeclust Author(s) Yusen Zhang <yusenzhang@126.com> See Also qkspecc, qkernel-class, cndkernel-class Examples ## Cluster the iris data set. data("iris") x=as.matrix(iris[,-5]) qspe <- qkspecc(x,kernel = "rbfbase", qpar = list(sigma = 10, q = 0.9), Nocent=3, normalize="symmetric", maxk=15, iterations=1200) clust(qspe) eVec(qspe) eVal(qspe) xmatrix(qspe) ymatrix(qspe) cndkernf(qspe) qkspeclust qkernel spectral Clustering Description This is also a qkernel spectral clustering algorithm which uses three ways to assign labels after the laplacian embedding: kmeans, hclust and dbscan. Usage ## S4 method for signature 'qkspecc' qkspeclust(x, clustmethod = "kmeans", Nocent=NULL,iterations=NULL, hmethod=NULL,eps = NULL, MinPts = NULL) Arguments x object of class qkspecc. clustmethod the strategy to use to assign labels in the embedding space. There are three ways to assign labels after the laplacian embedding: kmeans, hclust and dbscan. Nocent the number of clusters iterations the maximum number of iterations allowed for "kmeans". hmethod the agglomeration method for "hclust". This should be (an unambiguous ab- breviation of) one of "ward.D", "ward.D2", "single", "complete", "average" (= UPGMA), "mcquitty" (= WPGMA), "median" (= WPGMC) or "centroid" (= UPGMC). qkspeclust 49 eps Reachability distance for "dbscan". MinPts Reachability minimum no. of points for "dbscan". Details The qkernel spectral clustering works by embedding the data points of the partitioning problem into the subspace of the eigenvectors corresponding to the k smallest eigenvalues of the graph Laplacian matrix. Using the simple clustering methods like kmeans, hclust and dbscan on the embedded points usually leads to good performance. It can be shown that qkernel spectral clustering methods boil down to graph partitioning. Value An S4 object of class qkspecc which extends the class vector containing integers indicating the cluster to which each point is allocated. The following slots contain useful information clust The cluster assignments eVec The corresponding eigenvector eVal The corresponding eigenvalues xmatrix The original data matrix ymatrix The real valued matrix of eigenvectors corresponding to the k smallest eigenval- ues of the graph Laplacian matrix cndkernf The kernel function used Author(s) Yusen Zhang <yusenzhang@126.com> References Andrew Y. Ng, Michael I. Jordan, Yair Weiss On Spectral Clustering: Analysis and an Algorithm Neural Information Processing Symposium 2001 See Also qkernmatrix, cndkernmatrix, qkspecc-class, qkspecc Examples data("iris") x=as.matrix(iris[ ,-5]) qspe <- qkspecc(x,kernel = "rbfbase", qpar = list(sigma = 90, q = 0.9), Nocent=3, normalize="symmetric", maxk=15,iterations=1200) plot(x, col = clust(qspe)) 50 qsammon qspec <- qkspeclust(qspe,clustmethod = "hclust", Nocent=3, hmethod="ward.D2") plot(x, col = clust(qspec)) plot(qspec) qsammon qKernel Sammon Mapping Description The qkernel Sammon Mapping is an implementation for Sammon mapping, one of the earliest dimension reduction techniques that aims to find low-dimensional embedding that preserves pair- wise distance structure in high-dimensional data space. qsammon is a nonlinear form of Sammon Mapping. Usage ## S4 method for signature 'matrix' qsammon(x, kernel = "rbfbase", qpar = list(sigma = 0.5, q = 0.9), dims = 2, Initialisation = 'random', MaxHalves = 20, MaxIter = 500, TolFun = 1e-7, na.action = na.omit, ...) ## S4 method for signature 'cndkernmatrix' qsammon(cndkernel, x, k, dims = 2, Initialisation = 'random', MaxHalves = 20,MaxIter = 500, TolFun = 1e-7, ...) ## S4 method for signature 'qkernmatrix' qsammon(qkernel, x, k, dims = 2, Initialisation = 'random', MaxHalves = 20, MaxIter = 500, TolFun = 1e-7, ...) Arguments x the data matrix indexed by row or a kernel matrix of cndkernmatrix or qkernmatrix. kernel the kernel function used in training and predicting. This parameter can be set to any function, of class kernel, which computes a kernel function value between two vector arguments. qkerntool provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings: • rbfbase Radial Basis qkernel function "Gaussian" • nonlbase Non Linear qkernel function • laplbase Laplbase qkernel function • ratibase Rational Quadratic qkernel function • multbase Multiquadric qkernel function • invbase Inverse Multiquadric qkernel function qsammon 51 • wavbase Wave qkernel function • powbase d qkernel function • logbase Log qkernel function • caubase Cauchy qkernel function • chibase Chi-Square qkernel function • studbase Generalized T-Student qkernel function • nonlcnd Non Linear cndkernel function • polycnd Polynomial cndkernel function • rbfcnd Radial Basis cndkernel function "Gaussian" • laplcnd Laplacian cndkernel function • anocnd ANOVA cndkernel function • raticnd Rational Quadratic cndkernel function • multcnd Multiquadric cndkernel function • invcnd Inverse Multiquadric cndkernel function • wavcnd Wave cndkernel function • powcnd d cndkernel function • logcnd Log cndkernel function • caucnd Cauchy cndkernel function • chicnd Chi-Square cndkernel function • studcnd Generalized T-Student cndkernel function The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument. qpar the list of hyper-parameters (kernel parameters). This is a list which contains the parameters to be used with the kernel function. Valid parameters for existing kernels are : • sigma, q for the Radial Basis qkernel function "rbfbase" , the Laplacian qkernel function "laplbase" and the Cauchy qkernel function "caubase". • alpha, q for the Non Linear qkernel function "nonlbase". • c, q for the Rational Quadratic qkernel function "ratibase" , the Multi- quadric qkernel function "multbase" and the Inverse Multiquadric qkernel function "invbase". • theta, q for the Wave qkernel function "wavbase". • d, q for the d qkernel function "powbase" , the Log qkernel function "log- base" and the Generalized T-Student qkernel function "studbase". • alpha for the Non Linear cndkernel function "nonlcnd". • d, alpha, c for the Polynomial cndkernel function "polycnd". • gamma for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd". • d, sigma for the ANOVA cndkernel function "anocnd". • c for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel function "multcnd" and the Inverse Multiquadric cndkernel func- tion "invcnd". • theta for the Wave cndkernel function "wavcnd". 52 qsammon • d for the d cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd". Hyper-parameters for user defined kernels can be passed through the qpar pa- rameter as well. qkernel the kernel function to be used to calculate the qkernel matrix. cndkernel the cndkernel function to be used to calculate the CND kernel matrix. k the dimension of the original data. dims Number of features to return. (default: 2) Initialisation "random" or "pca"; the former performs fast random projection and the latter performs standard PCA (default : "random") MaxHalves maximum number of step halvings. (default : 20) MaxIter the maximum number of iterations allowed. (default : 500) TolFun relative tolerance on objective function. (default : 1e-7) na.action A function to specify the action to be taken if NAs are found. The default action is na.omit, which leads to rejection of cases with missing values on any required variable. An alternative is na.fail, which causes an error if NA cases are found. (NOTE: If given, this argument must be named.) ... additional parameters Details Using kernel functions one can efficiently compute principal components in high-dimensional fea- ture spaces, related to input space by some non-linear map. The data can be passed to the qsammon function in a matrix, in addition qsammon also supports input in the form of a kernel matrix of class qkernmatrix or class cndkernmatrix. Value dimRed The matrix whose rows are embedded observations. kcall The function call contained cndkernf The kernel function used all the slots of the object can be accessed by accessor functions. Author(s) Yusen Zhang <yusenzhang@126.com> References Sammon, J.W. (1969) A Nonlinear Mapping for Data Structure Analysis. IEEE Transactions on Computers, C-18 5:401-409. See Also qkernmatrix, cndkernmatrix qsammon-class 53 Examples data(iris) train <- as.matrix(iris[,1:4]) labeltrain<- as.integer(iris[,5]) ## S4 method for signature 'matrix' kpc2 <- qsammon(train, kernel = "rbfbase", qpar = list(sigma = 2, q = 0.9), dims = 2, Initialisation = 'pca', TolFun = 1e-5) plot(dimRed(kpc2), col = as.integer(labeltrain)) cndkernf(kpc2) qsammon-class Class "qsammon" Description The qKernel Sammon Mapping class Objects of class "qsammon" Objects can be created by calls of the form new("qsammon", ...). or by calling the qsammon function. Slots dimRed: Object of class "matrix" containing the matrix whose rows are embedded observations cndkernf: Object of class "function" containing the kernel function used kcall: Object of class "ANY" containing the function call Methods dimRed signature(object = "qsammon"): returns the matrix whose rows are embedded obser- vations kcall signature(object = "qsammon"): returns the performed call cndkernf signature(object = "qsammon"): returns the used kernel function Author(s) Yusen Zhang <yusenzhang@126.com> See Also qsammon 54 qtSNE Examples data(iris) train <- as.matrix(iris[,1:4]) labeltrain<- as.integer(iris[,5]) ## S4 method for signature 'matrix' qkpc <- qsammon(train, kernel = "rbfbase", qpar = list(sigma = 0.5, q = 0.9), dims = 2, Initialisation = 'pca', MaxHalves = 50) cndkernf(qkpc) dimRed(qkpc) kcall(qkpc) qtSNE qKernel t-Distributed Stochastic Neighbor Embedding Description Wrapper for the qkernel t-distributed stochastic neighbor embeddingg. qtSNE is a method for constructing a low dimensional embedding of high-dimensional data, distances or similarities. Usage ## S4 method for signature 'matrix' qtSNE(x,kernel = "rbfbase", qpar = list(sigma = 0.1, q = 0.9), initial_config = NULL, no_dims=2, initial_dims=30, perplexity=30, max_iter= 1300, min_cost=0, epoch_callback=NULL, epoch=100, na.action = na.omit, ...) ## S4 method for signature 'cndkernmatrix' qtSNE(x,initial_config = NULL, no_dims=2, initial_dims=30, perplexity=30, max_iter = 1000, min_cost=0, epoch_callback=NULL,epoch=100) ## S4 method for signature 'qkernmatrix' qtSNE(x,initial_config = NULL, no_dims=2, initial_dims=30, perplexity=30, max_iter = 1000, min_cost=0, epoch_callback=NULL,epoch=100) Arguments x the matrix of data to be clustered or a kernel Matrix of class qkernmatrix or cndkernmatrix. kernel the kernel function used in computing the affinity matrix. This parameter can be set to any function, of class kernel, which computes a kernel function value be- tween two vector arguments. kernlab provides the most popular kernel functions which can be used by setting the kernel parameter to the following strings: • rbfbase Radial Basis qkernel function "Gaussian" • nonlbase Non Linear qkernel function • laplbase Laplbase qkernel function • ratibase Rational Quadratic qkernel function qtSNE 55 • multbase Multiquadric qkernel function • invbase Inverse Multiquadric qkernel function • wavbase Wave qkernel function • powbase Power qkernel function • logbase Log qkernel function • caubase Cauchy qkernel function • chibase Chi-Square qkernel function • studbase Generalized T-Student qkernel function • nonlcnd Non Linear cndkernel function • polycnd Polynomial cndkernel function • rbfcnd Radial Basis cndkernel function "Gaussian" • laplcnd Laplacian cndkernel function • anocnd ANOVA cndkernel function • raticnd Rational Quadratic cndkernel function • multcnd Multiquadric cndkernel function • invcnd Inverse Multiquadric cndkernel function • wavcnd Wave cndkernel function • powcnd Power cndkernel function • logcnd Log cndkernel function • caucnd Cauchy cndkernel function • chicnd Chi-Square cndkernel function • studcnd Generalized T-Student cndkernel function The kernel parameter can also be set to a user defined function of class kernel by passing the function name as an argument. qpar a character string or the list of hyper-parameters (kernel parameters). The de- fault character string list(sigma = 2, q = 0.9) uses a heuristic to determine a suitable value for the width parameter of the RBF kernel. The second option "local" (local scaling) uses a more advanced heuristic and sets a width param- eter for every point in the data set. This is particularly useful when the data incorporates multiple scales. A list can also be used containing the parameters to be used with the kernel function. Valid parameters for existing kernels are : • sigma for the Radial Basis qkernel function "rbfbase" , the Laplacian qker- nel function "laplbase" the Cauchy qkernel function "caubase" and for the ANOVA cndkernel function "anocnd". • alpha for the Non Linear qkernel function "nonlbase",for the Non Linear cndkernel function "nonlcnd",and for the Polynomial cndkernel function "polycnd". • c for the Rational Quadratic qkernel function "ratibase" , the Multiquadric qkernel function "multbase", the Inverse Multiquadric qkernel function "in- vbase",for the Polynomial cndkernel function "polycnd",for the Rational Quadratic cndkernel function "raticnd" , the Multiquadric cndkernel func- tion "multcnd" and the Inverse Multiquadric cndkernel function "invcnd". 56 qtSNE • d for qkernel function "powbase" , the Log qkernel function "logbase", the Generalized T-Student qkernel function "studbase", for the Polynomial cnd- kernel function "polycnd", for the ANOVA cndkernel function "anocnd",for the d cndkernel function "powcnd" , the Log cndkernel function "logcnd" and the Generalized T-Student cndkernel function "studcnd". • theta for the Wave qkernel function "wavbase" and for the Wave cndkernel function "wavcnd". • gamma for the Chi-Square qkernel function "chibase",for the Radial Basis cndkernel function "rbfcnd" and the Laplacian cndkernel function "laplcnd" and the Cauchy cndkernel function "caucnd". • q For all qkernel Function. where length is the length of the strings con- sidered, lambda the decay factor and normalized a logical parameter deter- mining if the kernel evaluations should be normalized. Hyper-parameters for user defined kernels can be passed through the qkpar pa- rameter as well. initial_config An intitial configure about x (default: NULL) no_dims the dimension of the resulting embedding. (default: 2) initial_dims The number of dimensions to use in reduction method. (default: 30) perplexity Perplexity parameter max_iter Number of iterations (default: 1300) min_cost The minimum cost for every object after the final iteration epoch_callback A callback function used after each epoch (an epoch here means a set number of iterations) epoch The interval of the number of iterations displayed (default: 100) na.action the action to perform on NA ... Other arguments that can be passed to qtSNE Details When the initial_config argument is specified, the algorithm will automatically enter the final mo- mentum stage. This stage has less large scale adjustment to the embedding, and is intended for small scale tweaking of positioning. This can greatly speed up the generation of embeddings for various similar X datasets, while also preserving overall embedding orientation. Value qtSNE gives out an S4 object which is a LIST with components dimRed Matrix containing the new representations for the objects after qtSNE cndkernf The kernel function used Author(s) Yusen Zhang <yusenzhang@126.com> qtSNE-class 57 References Maaten, L. Van Der, 2014. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research, 15, p.3221-3245. van der Maaten, L.J.P. & Hinton, G.E., 2008. Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research, 9, pp.2579-2605. Examples ## Not run: #use iris data set data(iris) testset <- sample(1:150,20) train <- as.matrix(iris[,1:4]) colors = rainbow(length(unique(iris$Species))) names(colors) = unique(iris$Species) #for matrix ecb = function(x,y){ plot(x,t='n'); text(x,labels=iris$Species, col=colors[iris$Species]) } kpc2 <- qtSNE(train, kernel = "rbfbase", qpar = list(sigma=1,q=0.8), epoch_callback = ecb, perplexity=10, max_iter = 500) ## End(Not run) qtSNE-class Class "qtSNE" Description An S4 Class for qtSNE. Details The qtSNE is a method that uses Qkernel t-Distributed Stochastic Neighborhood Embedding be- tween the distance matrices in high and low-dimensional space to embed the data. The method is very well suited to visualize complex structures in low dimensions. Objects from the Class Objects can be created by calls of the form new("qtSNE", ...). or by calling the function qtSNE. Slots dimRed Matrix containing the new representations for the objects after qtSNE cndkernf The kernel function used 58 qtSNE-class Method dimRed signature(object="qtSNE"): return a new representation matrix cndkernf signature(object="qtSNE"): return the kernel used Author(s) Yusen Zhang <yusenzhang@126.com> References Maaten, L. van der, 2014. Accelerating t-SNE using Tree-Based Algorithms. Journal of Machine Learning Research 15, 3221-3245. van der Maaten, L., Hinton, G., 2008. Visualizing Data using t-SNE. J. Mach. Learn. Res. 9, 2579-2605. See Also qtSNE Examples ## Not run: #use iris data set data(iris) testset <- sample(1:150,20) train <- as.matrix(iris[,1:4]) colors = rainbow(length(unique(iris$Species))) names(colors) = unique(iris$Species) #for matrix ecb = function(x,y){ plot(x,t='n'); text(x,labels=iris$Species, col=colors[iris$Species]) } kpc2 <- qtSNE(train, kernel = "rbfbase", qpar = list(sigma=1,q=0.8), epoch_callback = ecb, perplexity=10, max_iter = 500) #cndernf cndkernf(kpc2) #dimRed plot(dimRed(kpc2),col=train) ## End(Not run) Index ∗ algebra as.cndkernmatrix,matrix-method cndkernmatrix, 8 (as.cndkernmatrix), 2 qkernmatrix, 18 as.cndkernmatrix-methods ∗ array (as.cndkernmatrix), 2 cndkernmatrix, 8 as.qkernmatrix, 3 qkernmatrix, 18 as.qkernmatrix,matrix-method ∗ classes (as.qkernmatrix), 3 cndkernel-class, 7 as.qkernmatrix-methods qkdbscan-class, 16 (as.qkernmatrix), 3 qkernel-class, 17 qkgda-class, 23 bases, 4 qkIsomap-class, 28 blkdiag, 6 qkLLE-class, 33 caubase, 9 qkMDS-class, 37 caubase (bases), 4 qkpca-class, 42 caucnd, 20 qkprc-class, 43 caucnd (cnds), 9 qkspecc-class, 47 caukernel-class (cndkernel-class), 7 qsammon-class, 53 cauqkernel-class (qkernel-class), 17 ∗ classif chibase, 9 qkgda, 20 chibase (bases), 4 ∗ cluster chicnd, 20 qkdbscan, 13 chicnd (cnds), 9 qkpca, 39 chikernel-class (cndkernel-class), 7 qkspecc, 44 chiqkernel-class (qkernel-class), 17 qkspeclust, 48 clust (qkdbscan-class), 16 qsammon, 50 clust,qkdbscan-method (qkdbscan-class), ∗ datasets 16 mfeat_pix, 12 clust,qkspecc-method (qkspecc-class), 47 ∗ methods clust<- (qkdbscan-class), 16 as.cndkernmatrix, 2 clust<-,qkdbscan-method as.qkernmatrix, 3 (qkdbscan-class), 16 ∗ symbolmath clust<-,qkspecc-method (qkspecc-class), bases, 4 47 cnds, 9 cndkernel-class, 7 cndkernf (qkprc-class), 43 anocnd, 20 cndkernf,qkprc-method (qkprc-class), 43 anocnd (cnds), 9 cndkernf<- (qkprc-class), 43 anokernel-class (cndkernel-class), 7 cndkernf<-,qkprc-method (qkprc-class), as.cndkernmatrix, 2 43 59 60 INDEX cndkernmatrix, 3–5, 7, 8, 11, 18, 23, 41, 47, cndkernmatrix.multkernel 49, 52 (cndkernmatrix), 8 cndkernmatrix,anokernel-method cndkernmatrix.nonlkernel (cndkernmatrix), 8 (cndkernmatrix), 8 cndkernmatrix,caukernel-method cndkernmatrix.norkernel (cndkernmatrix), 8 (cndkernmatrix), 8 cndkernmatrix,chikernel-method cndkernmatrix.polykernel (cndkernmatrix), 8 (cndkernmatrix), 8 cndkernmatrix,cndkernel-method cndkernmatrix.powkernel (cndkernmatrix), 8 (cndkernmatrix), 8 cndkernmatrix,invkernel-method cndkernmatrix.ratikernel (cndkernmatrix), 8 (cndkernmatrix), 8 cndkernmatrix,laplkernel-method cndkernmatrix.rbfkernel (cndkernmatrix), 8 (cndkernmatrix), 8 cndkernmatrix,logkernel-method cndkernmatrix.studkernel (cndkernmatrix), 8 (cndkernmatrix), 8 cndkernmatrix,multkernel-method cndkernmatrix.wavkernel (cndkernmatrix), 8 (cndkernmatrix), 8 cndkernmatrix,nonlkernel-method cnds, 9 (cndkernmatrix), 8 connum (qkIsomap-class), 28 cndkernmatrix,norkernel-method connum,qkIsomap-method (cndkernmatrix), 8 (qkIsomap-class), 28 cndkernmatrix,polykernel-method connum,qkMDS-method (qkMDS-class), 37 (cndkernmatrix), 8 connum<- (qkIsomap-class), 28 cndkernmatrix,powkernel-method connum<-,qkIsomap-method (cndkernmatrix), 8 (qkIsomap-class), 28 connum<-,qkMDS-method (qkMDS-class), 37 cndkernmatrix,ratikernel-method (cndkernmatrix), 8 dimRed (qsammon-class), 53 cndkernmatrix,rbfkernel-method dimRed,qsammon-method (qsammon-class), (cndkernmatrix), 8 53 cndkernmatrix,studkernel-method dimRed,qtSNE-method (qtSNE-class), 57 (cndkernmatrix), 8 dimRed<- (qsammon-class), 53 cndkernmatrix,wavkernel-method dimRed<-,qsammon-method (cndkernmatrix), 8 (qsammon-class), 53 cndkernmatrix-class (as.cndkernmatrix), dimRed<-,qtSNE-method (qtSNE-class), 57 2 dims (qkIsomap-class), 28 cndkernmatrix.anokernel dims,qkIsomap-method (qkIsomap-class), (cndkernmatrix), 8 28 cndkernmatrix.caukernel dims,qkLLE-method (qkLLE-class), 33 (cndkernmatrix), 8 dims,qkMDS-method (qkMDS-class), 37 cndkernmatrix.chikernel dims<- (qkIsomap-class), 28 (cndkernmatrix), 8 dims<-,qkIsomap-method cndkernmatrix.invkernel (qkIsomap-class), 28 (cndkernmatrix), 8 dims<-,qkLLE-method (qkLLE-class), 33 cndkernmatrix.laplkernel dims<-,qkMDS-method (qkMDS-class), 37 (cndkernmatrix), 8 cndkernmatrix.logkernel eps (qkdbscan-class), 16 (cndkernmatrix), 8 eps,qkdbscan-method (qkdbscan-class), 16 INDEX 61 eps<- (qkdbscan-class), 16 isseed,qkdbscan-method eps<-,qkdbscan-method (qkdbscan-class), (qkdbscan-class), 16 16 isseed<- (qkdbscan-class), 16 Eucdist, 11 isseed<-,qkdbscan-method Eucdist,matrix-method (Eucdist), 11 (qkdbscan-class), 16 eVal (qkgda-class), 23 eVal,qkgda-method (qkgda-class), 23 kcall (qkprc-class), 43 eVal,qkIsomap-method (qkIsomap-class), kcall,qkprc-method (qkprc-class), 43 28 kcall<- (qkprc-class), 43 eVal,qkLLE-method (qkLLE-class), 33 kcall<-,qkprc-method (qkprc-class), 43 eVal,qkMDS-method (qkMDS-class), 37 kfunction-class (qkernel-class), 17 eVal,qkpca-method (qkpca-class), 42 eVal,qkspecc-method (qkspecc-class), 47 label (qkgda-class), 23 eVal<- (qkgda-class), 23 label,qkgda-method (qkgda-class), 23 eVal<-,qkgda-method (qkgda-class), 23 label<- (qkgda-class), 23 eVal<-,qkIsomap-method label<-,qkgda-method (qkgda-class), 23 (qkIsomap-class), 28 laplbase, 9 eVal<-,qkLLE-method (qkLLE-class), 33 laplbase (bases), 4 eVal<-,qkMDS-method (qkMDS-class), 37 laplcnd, 20 eVal<-,qkpca-method (qkpca-class), 42 laplcnd (cnds), 9 eVal<-,qkspecc-method (qkspecc-class), laplkernel-class (cndkernel-class), 7 47 laplqkernel-class (qkernel-class), 17 eVec (qkgda-class), 23 logbase, 9 eVec,qkgda-method (qkgda-class), 23 logbase (bases), 4 eVec,qkIsomap-method (qkIsomap-class), logcnd, 20 28 logcnd (cnds), 9 eVec,qkLLE-method (qkLLE-class), 33 logkernel-class (cndkernel-class), 7 logqkernel-class (qkernel-class), 17 eVec,qkMDS-method (qkMDS-class), 37 eVec,qkspecc-method (qkspecc-class), 47 mfeat_pix, 12 eVec<- (qkgda-class), 23 MinPts (qkdbscan-class), 16 eVec<-,qkgda-method (qkgda-class), 23 MinPts,qkdbscan-method eVec<-,qkIsomap-method (qkdbscan-class), 16 (qkIsomap-class), 28 MinPts<- (qkdbscan-class), 16 eVec<-,qkLLE-method (qkLLE-class), 33 MinPts<-,qkdbscan-method eVec<-,qkMDS-method (qkMDS-class), 37 (qkdbscan-class), 16 eVec<-,qkspecc-method (qkspecc-class), multbase, 9 47 multbase (bases), 4 multcnd, 20 fun (qsammon-class), 53 multcnd (cnds), 9 multkernel-class (cndkernel-class), 7 input-class (qkernel-class), 17 multqkernel-class (qkernel-class), 17 invbase, 9 invbase (bases), 4 n.action (qkprc-class), 43 invcnd, 20 n.action,qkprc-method (qkprc-class), 43 invcnd (cnds), 9 n.action<- (qkprc-class), 43 invkernel-class (cndkernel-class), 7 n.action<-,qkprc-method (qkprc-class), invqkernel-class (qkernel-class), 17 43 isseed (qkdbscan-class), 16 nonlbase, 9 62 INDEX nonlbase (bases), 4 qkernmatrix, 3–5, 7, 11, 16, 18, 18, 23, 41, nonlcnd, 20 47, 49, 52 nonlcnd (cnds), 9 qkernmatrix,cauqkernel-method nonlkernel-class (cndkernel-class), 7 (qkernmatrix), 18 nonlqkernel-class (qkernel-class), 17 qkernmatrix,chiqkernel-method norcnd (cnds), 9 (qkernmatrix), 18 norkernel-class (cndkernel-class), 7 qkernmatrix,invqkernel-method (qkernmatrix), 18 pcv (qkpca-class), 42 qkernmatrix,laplqkernel-method pcv,qkpca-method (qkpca-class), 42 (qkernmatrix), 18 pcv<- (qkpca-class), 42 qkernmatrix,logqkernel-method pcv<-,qkpca-method (qkpca-class), 42 (qkernmatrix), 18 plot (qkdbscan-class), 16 qkernmatrix,multqkernel-method plot,qkdbscan-method (qkdbscan-class), (qkernmatrix), 18 16 qkernmatrix,nonlqkernel-method plot,qkspecc-method (qkspecc-class), 47 (qkernmatrix), 18 polycnd, 20 qkernmatrix,powqkernel-method polycnd (cnds), 9 (qkernmatrix), 18 polykernel-class (cndkernel-class), 7 qkernmatrix,qkernel-method powbase, 9 (qkernmatrix), 18 powbase (bases), 4 qkernmatrix,ratiqkernel-method powcnd, 20 (qkernmatrix), 18 powcnd (cnds), 9 qkernmatrix,rbfqkernel-method powkernel-class (cndkernel-class), 7 (qkernmatrix), 18 powqkernel-class (qkernel-class), 17 qkernmatrix,studqkernel-method predict,qkdbscan-method (qkdbscan), 13 (qkernmatrix), 18 predict,qkgda-method (qkgda), 20 qkernmatrix,wavqkernel-method predict,qkpca-method (qkpca), 39 (qkernmatrix), 18 print,qkdbscan-method (qkdbscan), 13 qkernmatrix-class (as.qkernmatrix), 3 prj (qkgda-class), 23 qkernmatrix.cauqkernel (qkernmatrix), 18 prj,qkgda-method (qkgda-class), 23 qkernmatrix.chiqkernel (qkernmatrix), 18 prj,qkIsomap-method (qkIsomap-class), 28 qkernmatrix.invqkernel (qkernmatrix), 18 prj,qkLLE-method (qkLLE-class), 33 prj,qkMDS-method (qkMDS-class), 37 qkernmatrix.laplqkernel (qkernmatrix), prj<- (qkgda-class), 23 18 prj<-,qkgda-method (qkgda-class), 23 qkernmatrix.logqkernel (qkernmatrix), 18 prj<-,qkIsomap-method (qkIsomap-class), qkernmatrix.multqkernel (qkernmatrix), 28 18 prj<-,qkLLE-method (qkLLE-class), 33 qkernmatrix.nonlqkernel (qkernmatrix), prj<-,qkMDS-method (qkMDS-class), 37 18 qkernmatrix.powqkernel (qkernmatrix), 18 qkdbscan, 13 qkernmatrix.ratiqkernel (qkernmatrix), qkdbscan,cndkernmatrix-method 18 (qkdbscan), 13 qkernmatrix.rbfqkernel (qkernmatrix), 18 qkdbscan,matrix-method (qkdbscan), 13 qkernmatrix.studqkernel (qkernmatrix), qkdbscan,qkernmatrix-method (qkdbscan), 18 13 qkernmatrix.wavqkernel (qkernmatrix), 18 qkdbscan-class, 16 qkgda, 20 qkernel-class, 17 qkgda,cndkernmatrix-method (qkgda), 20 INDEX 63 qkgda,matrix-method (qkgda), 20 qsammon-class, 53 qkgda,qkernmatrix-method (qkgda), 20 qtSNE, 54, 58 qkgda-class, 23 qtSNE,cndkernmatrix-method (qtSNE), 54 qkIsomap, 25, 29 qtSNE,matrix-method (qtSNE), 54 qkIsomap,cndkernmatrix-method qtSNE,qkernmatrix-method (qtSNE), 54 (qkIsomap), 25 qtSNE-class, 57 qkIsomap,matrix-method (qkIsomap), 25 qkIsomap,qkernmatrix-method (qkIsomap), ratibase, 9 25 ratibase (bases), 4 raticnd, 20 qkIsomap-class, 28 raticnd (cnds), 9 qkLLE, 30 ratikernel-class (cndkernel-class), 7 qkLLE,cndkernmatrix-method (qkLLE), 30 ratiqkernel-class (qkernel-class), 17 qkLLE,matrix-method (qkLLE), 30 rbfbase, 9 qkLLE,qkernmatrix-method (qkLLE), 30 rbfbase (bases), 4 qkLLE-class, 33 rbfcnd, 20 qkMDS, 34, 38 rbfcnd (cnds), 9 qkMDS,cndkernmatrix-method (qkMDS), 34 rbfkernel-class (cndkernel-class), 7 qkMDS,matrix-method (qkMDS), 34 rbfqkernel-class (qkernel-class), 17 qkMDS,qkernmatrix-method (qkMDS), 34 Residuals (qkIsomap-class), 28 qkMDS-class, 37 Residuals,qkIsomap-method qkpca, 39, 47 (qkIsomap-class), 28 qkpca,cndkernmatrix-method (qkpca), 39 Residuals,qkMDS-method (qkMDS-class), 37 qkpca,formula-method (qkpca), 39 Residuals<- (qkIsomap-class), 28 qkpca,matrix-method (qkpca), 39 Residuals<-,qkIsomap-method qkpca,qkernmatrix-method (qkpca), 39 (qkIsomap-class), 28 qkpca-class, 42 Residuals<-,qkMDS-method (qkMDS-class), qkprc-class, 43 37 qkspecc, 44, 48, 49 rotated (qkpca-class), 42 qkspecc,cndkernmatrix-method (qkspecc), rotated,qkpca-method (qkpca-class), 42 44 rotated<- (qkpca-class), 42 qkspecc,matrix-method (qkspecc), 44 rotated<-,qkpca-method (qkpca-class), 42 qkspecc,qkernmatrix-method (qkspecc), 44 qkspecc-class, 47 show,cndkernel-method qkspeclust, 48 (cndkernel-class), 7 qkspeclust,qkspecc-method (qkspeclust), show,qkernel-method (qkernel-class), 17 48 show,qkspecc-method (qkspecc), 44 qpar (qkprc-class), 43 studbase, 9 qpar,cndkernel-method studbase (bases), 4 (cndkernel-class), 7 studcnd, 20 qpar,qkernel-method (qkernel-class), 17 studcnd (cnds), 9 qpar,qkprc-method (qkprc-class), 43 studkernel-class (cndkernel-class), 7 qpar<- (qkprc-class), 43 studqkernel-class (qkernel-class), 17 qpar<-,qkprc-method (qkprc-class), 43 terms (qkprc-class), 43 qsammon, 50, 53 terms,qkprc-method (qkprc-class), 43 qsammon,cndkernmatrix-method (qsammon), terms<- (qkprc-class), 43 50 terms<-,qkprc-method (qkprc-class), 43 qsammon,matrix-method (qsammon), 50 qsammon,qkernmatrix-method (qsammon), 50 wavbase, 9 64 INDEX wavbase (bases), 4 wavcnd, 20 wavcnd (cnds), 9 wavkernel-class (cndkernel-class), 7 wavqkernel-class (qkernel-class), 17 withinss (qkspecc-class), 47 withinss,qkspecc-method (qkspecc-class), 47 withinss<- (qkspecc-class), 47 withinss<-,qkspecc-method (qkspecc-class), 47 xmatrix (qkprc-class), 43 xmatrix,qkprc-method (qkprc-class), 43 xmatrix<- (qkprc-class), 43 xmatrix<-,qkprc-method (qkprc-class), 43 ymatrix (qkprc-class), 43 ymatrix,qkprc-method (qkprc-class), 43 ymatrix<- (qkprc-class), 43 ymatrix<-,qkprc-method (qkprc-class), 43

tidyAML

cran

Package ‘tidyAML’ April 20, 2023 Title Automatic Machine Learning with 'tidymodels' Version 0.0.2 Description The goal of this package will be to provide a simple interface for automatic ma- chine learning that fits the 'tidymodels' framework. The intention is to work for regres- sion and classification problems with a simple verb framework. License MIT + file LICENSE Encoding UTF-8 RoxygenNote 7.2.3 URL https://github.com/spsanderson/tidyAML BugReports https://github.com/spsanderson/tidyAML/issues Depends parsnip, R (>= 3.4.0) Suggests knitr, rmarkdown, roxygen2, stats, tibble, stringr, utils, recipes VignetteBuilder knitr Imports magrittr, rlang (>= 0.4.11), purrr (>= 0.3.5), dplyr (>= 1.0.10), rsample (>= 1.1.0), workflows (>= 1.1.2), forcats, workflowsets NeedsCompilation no Author Steven Sanderson [aut, cre, cph] Maintainer Steven Sanderson <spsanderson@gmail.com> Repository CRAN Date/Publication 2023-04-20 13:40:02 UTC R topics documented: core_packages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 create_model_spec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 create_splits . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 create_workflow_set . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 extract_model_spec . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 1 2 core_packages extract_wflw . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 extract_wflw_fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 extract_wflw_pred . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 fast_classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 fast_classification_parsnip_spec_tbl . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 fast_regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 fast_regression_parsnip_spec_tbl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 get_model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 install_deps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 internal_make_fitted_wflw . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 internal_make_spec_tbl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 internal_make_wflw . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 internal_make_wflw_predictions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 internal_set_args_to_tune . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 load_deps . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 make_classification_base_tbl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 make_regression_base_tbl . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 match_args . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 Index 26 core_packages Functions to Install all Core Libraries Description Lists the core packages necessary to run all potential modeling algorithms. Usage core_packages() Details Lists the core packages necessary to run all potential modeling algorithms. Value A character vector Author(s) Steven P. Sanderson II, MPH See Also Other Utility: create_splits(), create_workflow_set(), fast_classification_parsnip_spec_tbl(), fast_regression_parsnip_spec_tbl(), install_deps(), load_deps(), match_args() create_model_spec 3 Examples core_packages() create_model_spec Generate Model Specification calls to parsnip Description Creates a list/tibble of parsnip model specifications. Usage create_model_spec( .parsnip_eng = list("lm"), .mode = list("regression"), .parsnip_fns = list("linear_reg"), .return_tibble = TRUE ) Arguments .parsnip_eng The input must be a list. The default for this is set to all. This means that all of the parsnip linear regression engines will be used, for example lm, or glm. You can also choose to pass a c() vector like c('lm', 'glm') .mode The input must be a list. The default is ’regression’ .parsnip_fns The input must be a list. The default for this is set to all. This means that all of the parsnip linear regression functions will be used, for example linear_reg(), or cubist_rules. You can also choose to pass a c() vector like c("linear_reg","cubist_rules") .return_tibble The default is TRUE. FALSE will return a list object. Details Creates a list/tibble of parsnip model specifications. With this function you can generate a list/tibble output of any model specification and engine you choose that is supported by the parsnip ecosys- tem. Value A list or a tibble. Author(s) Steven P. Sanderson II, MPH 4 create_splits See Also Other Model_Generator: fast_classification(), fast_regression() Examples create_model_spec( .parsnip_eng = list("lm","glm","glmnet","cubist"), .parsnip_fns = list( rep( "linear_reg", 3), "cubist_rules" ) ) create_model_spec( .parsnip_eng = list("lm","glm","glmnet","cubist"), .parsnip_fns = list( rep( "linear_reg", 3), "cubist_rules" ), .return_tibble = FALSE ) create_splits Utility Create Splits Object Description Create a splits object. Usage create_splits(.data, .split_type = "initial_split", .split_args = NULL) Arguments .data The data being passed to make a split on .split_type The default is "initial_split", you can pass any other split type from the rsample library. .split_args The default is NULL in order to use the default split arguments. If you want to pass other arguments then must pass a list with the parameter name and the argument. Details Create a splits object that returns a list object of both the splits object itself and the splits type. This function supports all splits types from the rsample package. create_workflow_set 5 Value A list object Author(s) Steven P. Sanderson II, MPH See Also Other Utility: core_packages(), create_workflow_set(), fast_classification_parsnip_spec_tbl(), fast_regression_parsnip_spec_tbl(), install_deps(), load_deps(), match_args() Examples create_splits(mtcars, .split_type = "vfold_cv") create_workflow_set Create a Workflow Set Object Description Create a workflow set object tibble from a model spec tibble. Usage create_workflow_set(.model_tbl = NULL, .recipe_list = list(), .cross = TRUE) Arguments .model_tbl The model table that is generated from a function like fast_regression_parsnip_spec_tbl(). The model spec column will be grabbed automatically as the class of the object must be tidyaml_base_tbl .recipe_list Provide a list of recipes here that will get added to the workflow set object. .cross The default is TRUE, can be set to FALSE. This is passed to the cross parameter as an argument to the workflow_set() function. Details Create a workflow set object/tibble from a model spec tibble where the object class type is tidyaml_base_tbl. This function will take in a list of recipes and will grab the model specifi- cations from the base tibble to create the workflow sets object. You can also supply the logical of TRUE/FALSe the .cross parameter which gets passed to the corresponding parameter as an argumnt to the workflowsets::workflow_set() function. Value A list object of workflows. 6 extract_model_spec Author(s) Steven P. Sanderson II, MPH See Also https://workflowsets.tidymodels.org/ Other Utility: core_packages(), create_splits(), fast_classification_parsnip_spec_tbl(), fast_regression_parsnip_spec_tbl(), install_deps(), load_deps(), match_args() Examples library(recipes) rec_obj <- recipe(mpg ~ ., data = mtcars) spec_tbl <- fast_regression_parsnip_spec_tbl( .parsnip_fns = "linear_reg", .parsnip_eng = c("lm","glm") ) create_workflow_set( spec_tbl, list(rec_obj) ) extract_model_spec Extract A Model Specification Description Extract a model specification from a tidyAML model tibble. Usage extract_model_spec(.data, .model_id = NULL) Arguments .data The model table that must have the class tidyaml_mod_spec_tbl. .model_id The model number that you want to select, Must be an integer or sequence of integers, ie. 1 or c(1,3,5) or 1:2 Details This function allows you to get a model specification or more from a tibble with a class of "tidyaml_mod_spec_tbl". It allows you to select the model by the .model_id column. You can call the model id’s by an inte- ger or a sequence of integers. extract_wflw 7 Value A tibble with the chosen model specification(s). Author(s) Steven P. Sanderson II, MPH See Also Other Extractor: extract_wflw_fit(), extract_wflw_pred(), extract_wflw(), get_model() Examples library(recipes) rec_obj <- recipe(mpg ~ ., data = mtcars) spec_tbl <- fast_regression_parsnip_spec_tbl( .parsnip_fns = "linear_reg", .parsnip_eng = c("lm","glm") ) extract_model_spec(spec_tbl, 1) extract_model_spec(spec_tbl, 1:2) extract_wflw Extract A Model Workflow Description Extract a model workflow from a tidyAML model tibble. Usage extract_wflw(.data, .model_id = NULL) Arguments .data The model table that must have the class tidyaml_mod_spec_tbl. .model_id The model number that you want to select, Must be an integer or sequence of integers, ie. 1 or c(1,3,5) or 1:2 Details This function allows you to get a model workflow or more from a tibble with a class of "tidyaml_mod_spec_tbl". It allows you to select the model by the .model_id column. You can call the model id’s by an inte- ger or a sequence of integers. 8 extract_wflw_fit Value A tibble with the chosen model workflow(s). Author(s) Steven P. Sanderson II, MPH See Also Other Extractor: extract_model_spec(), extract_wflw_fit(), extract_wflw_pred(), get_model() Examples library(recipes) rec_obj <- recipe(mpg ~ ., data = mtcars) frt_tbl <- fast_regression(mtcars, rec_obj, .parsnip_eng = c("lm","glm"), .parsnip_fns = "linear_reg") extract_wflw(frt_tbl, 1) extract_wflw(frt_tbl, 1:2) extract_wflw_fit Extract A Model Fitted Workflow Description Extract a model fitted workflow from a tidyAML model tibble. Usage extract_wflw_fit(.data, .model_id = NULL) Arguments .data The model table that must have the class tidyaml_mod_spec_tbl. .model_id The model number that you want to select, Must be an integer or sequence of integers, ie. 1 or c(1,3,5) or 1:2 Details This function allows you to get a model fitted workflow or more from a tibble with a class of "tidyaml_mod_spec_tbl". It allows you to select the model by the .model_id column. You can call the model id’s by an integer or a sequence of integers. Value A tibble with the chosen model workflow(s). extract_wflw_pred 9 Author(s) Steven P. Sanderson II, MPH See Also Other Extractor: extract_model_spec(), extract_wflw_pred(), extract_wflw(), get_model() Examples library(recipes) rec_obj <- recipe(mpg ~ ., data = mtcars) frt_tbl <- fast_regression(mtcars, rec_obj, .parsnip_eng = c("lm","glm"), .parsnip_fns = "linear_reg") extract_wflw_fit(frt_tbl, 1) extract_wflw_fit(frt_tbl, 1:2) extract_wflw_pred Extract A Model Workflow Predictions Description Extract a model workflow predictions from a tidyAML model tibble. Usage extract_wflw_pred(.data, .model_id = NULL) Arguments .data The model table that must have the class tidyaml_mod_spec_tbl. .model_id The model number that you want to select, Must be an integer or sequence of integers, ie. 1 or c(1,3,5) or 1:2 Details This function allows you to get a model workflow predictions or more from a tibble with a class of "tidyaml_mod_spec_tbl". It allows you to select the model by the .model_id column. You can call the model id’s by an integer or a sequence of integers. Value A tibble with the chosen model workflow(s). Author(s) Steven P. Sanderson II, MPH 10 fast_classification See Also Other Extractor: extract_model_spec(), extract_wflw_fit(), extract_wflw(), get_model() Examples library(recipes) rec_obj <- recipe(mpg ~ ., data = mtcars) frt_tbl <- fast_regression(mtcars, rec_obj, .parsnip_eng = c("lm","glm"), .parsnip_fns = "linear_reg") extract_wflw_pred(frt_tbl, 1) extract_wflw_pred(frt_tbl, 1:2) fast_classification Generate Model Specification calls to parsnip Description Creates a list/tibble of parsnip model specifications. Usage fast_classification( .data, .rec_obj, .parsnip_fns = "all", .parsnip_eng = "all", .split_type = "initial_split", .split_args = NULL ) Arguments .data The data being passed to the function for the classification problem .rec_obj The recipe object being passed. .parsnip_fns The default is ’all’ which will create all possible classification model specifica- tions supported. .parsnip_eng the default is ’all’ which will create all possible classification model specifica- tions supported. .split_type The default is ’initial_split’, you can pass any type of split supported by rsample .split_args The default is NULL, when NULL then the default parameters of the split type will be executed for the rsample split type. fast_classification_parsnip_spec_tbl 11 Details With this function you can generate a tibble output of any classification model specification and it’s fitted workflow object. Per recipes documentation explicitly with step_string2factor() it is encouraged to mutate your predictor into a factor before you create your recipe. Value A list or a tibble. Author(s) Steven P. Sanderson II, MPH See Also Other Model_Generator: create_model_spec(), fast_regression() Examples library(recipes, quietly = TRUE) library(dplyr, quietly = TRUE) df <- mtcars %>% mutate(cyl = as.factor(cyl)) rec_obj <- recipe(cyl ~ ., data = df) fct_tbl <- fast_classification( .data = df, .rec_obj = rec_obj, .parsnip_eng = c("glm","LiblineaR")) glimpse(fct_tbl) fast_classification_parsnip_spec_tbl Utility Classification call to parsnip Description Creates a tibble of parsnip classification model specifications. Usage fast_classification_parsnip_spec_tbl( .parsnip_fns = "all", .parsnip_eng = "all" ) 12 fast_regression Arguments .parsnip_fns The default for this is set to all. This means that all of the parsnip classifica- tion functions will be used, for example bag_mars(), or bart(). You can also choose to pass a c() vector like c("barg_mars","bart") .parsnip_eng The default for this is set to all. This means that all of the parsnip classification engines will be used, for example earth, or dbarts. You can also choose to pass a c() vector like c('earth', 'dbarts') Details Creates a tibble of parsnip classification model specifications. This will create a tibble of 32 dif- ferent classification model specifications which can be filtered. The model specs are created first and then filtered out. This will only create models for classification problems. To find all of the supported models in this package you can visit https://www.tidymodels.org/find/parsnip/ Value A tibble with an added class of ’fst_class_spec_tbl’ Author(s) Steven P. Sanderson II, MPH See Also Other Utility: core_packages(), create_splits(), create_workflow_set(), fast_regression_parsnip_spec_tbl() install_deps(), load_deps(), match_args() Examples fast_classification_parsnip_spec_tbl(.parsnip_fns = "logistic_reg") fast_classification_parsnip_spec_tbl(.parsnip_eng = c("earth","dbarts")) fast_regression Generate Model Specification calls to parsnip Description Creates a list/tibble of parsnip model specifications. fast_regression 13 Usage fast_regression( .data, .rec_obj, .parsnip_fns = "all", .parsnip_eng = "all", .split_type = "initial_split", .split_args = NULL ) Arguments .data The data being passed to the function for the regression problem .rec_obj The recipe object being passed. .parsnip_fns The default is ’all’ which will create all possible regression model specifications supported. .parsnip_eng the default is ’all’ which will create all possible regression model specifications supported. .split_type The default is ’initial_split’, you can pass any type of split supported by rsample .split_args The default is NULL, when NULL then the default parameters of the split type will be executed for the rsample split type. Details With this function you can generate a tibble output of any regression model specification and it’s fitted workflow object. Value A list or a tibble. Author(s) Steven P. Sanderson II, MPH See Also Other Model_Generator: create_model_spec(), fast_classification() Examples library(recipes, quietly = TRUE) library(dplyr, quietly = TRUE) rec_obj <- recipe(mpg ~ ., data = mtcars) frt_tbl <- fast_regression(mtcars, rec_obj, .parsnip_eng = c("lm","glm"), .parsnip_fns = "linear_reg") glimpse(frt_tbl) 14 fast_regression_parsnip_spec_tbl fast_regression_parsnip_spec_tbl Utility Regression call to parsnip Description Creates a tibble of parsnip regression model specifications. Usage fast_regression_parsnip_spec_tbl(.parsnip_fns = "all", .parsnip_eng = "all") Arguments .parsnip_fns The default for this is set to all. This means that all of the parsnip linear regres- sion functions will be used, for example linear_reg(), or cubist_rules. You can also choose to pass a c() vector like c("linear_reg","cubist_rules") .parsnip_eng The default for this is set to all. This means that all of the parsnip linear regression engines will be used, for example lm, or glm. You can also choose to pass a c() vector like c('lm', 'glm') Details Creates a tibble of parsnip regression model specifications. This will create a tibble of 46 different regression model specifications which can be filtered. The model specs are created first and then filtered out. This will only create models for regression problems. To find all of the supported models in this package you can visit https://www.tidymodels.org/find/parsnip/ Value A tibble with an added class of ’fst_reg_spec_tbl’ Author(s) Steven P. Sanderson II, MPH See Also Other Utility: core_packages(), create_splits(), create_workflow_set(), fast_classification_parsnip_spec_t install_deps(), load_deps(), match_args() Examples fast_regression_parsnip_spec_tbl(.parsnip_fns = "linear_reg") fast_regression_parsnip_spec_tbl(.parsnip_eng = c("lm","glm")) get_model 15 get_model Get a Model Description Get a model from a tidyAML model tibble. Usage get_model(.data, .model_id = NULL) Arguments .data The model table that must have the class tidyaml_mod_spec_tbl. .model_id The model number that you want to select, Must be an integer or sequence of integers, ie. 1 or c(1,3,5) or 1:2 Details This function allows you to get a model or models from a tibble with a class of "tidyaml_mod_spec_tbl". It allows you to select the model by the .model_id column. You can call the model id’s by an inte- ger or a sequence of integers. Value A tibble with the chosen models. Author(s) Steven P. Sanderson II, MPH See Also Other Extractor: extract_model_spec(), extract_wflw_fit(), extract_wflw_pred(), extract_wflw() Examples library(recipes) rec_obj <- recipe(mpg ~ ., data = mtcars) spec_tbl <- fast_regression_parsnip_spec_tbl( .parsnip_fns = "linear_reg", .parsnip_eng = c("lm","glm") ) get_model(spec_tbl, 1) get_model(spec_tbl, 1:2) 16 internal_make_fitted_wflw install_deps Functions to Install all Core Libraries Description Installs all dependencies in the core_packages() function. Usage install_deps() Details Installs all dependencies in the core_packages() function. Value No return value, called for side effects Author(s) Steven P. Sanderson II, MPH See Also Other Utility: core_packages(), create_splits(), create_workflow_set(), fast_classification_parsnip_spec_t fast_regression_parsnip_spec_tbl(), load_deps(), match_args() Examples ## Not run: install_deps() ## End(Not run) internal_make_fitted_wflw Internals Safely Make a Fitted Workflow from Model Spec tibble Description Safely Make a fitted workflow from a model spec tibble. Usage internal_make_fitted_wflw(.model_tbl, .splits_obj) internal_make_fitted_wflw 17 Arguments .model_tbl The model table that is generated from a function like fast_regression_parsnip_spec_tbl(), must have a class of "tidyaml_mod_spec_tbl". This is meant to be used after the function internal_make_wflw() has been run and the tibble has been saved. .splits_obj The splits object from the auto_ml function. It is internal to the auto_ml_ func- tion. Details Create a fitted parnsip model from a workflow object. Value A list object of workflows. Author(s) Steven P. Sanderson II, MPH See Also Other Internals: internal_make_spec_tbl(), internal_make_wflw_predictions(), internal_make_wflw(), internal_set_args_to_tune(), make_classification_base_tbl(), make_regression_base_tbl() Examples library(recipes, quietly = TRUE) library(dplyr, quietly = TRUE) mod_spec_tbl <- fast_regression_parsnip_spec_tbl( .parsnip_eng = c("lm","glm","gee"), .parsnip_fns = "linear_reg" ) rec_obj <- recipe(mpg ~ ., data = mtcars) splits_obj <- create_splits(mtcars, "initial_split") mod_tbl <- mod_spec_tbl %>% mutate(wflw = internal_make_wflw(mod_spec_tbl, rec_obj)) internal_make_fitted_wflw(mod_tbl, splits_obj) 18 internal_make_spec_tbl internal_make_spec_tbl Internals Make a Model Spec tibble Description Make a Model Spec tibble. Usage internal_make_spec_tbl(.data) Arguments .data This is the data that should be coming from inside of the regression/classification to parsnip spec functions. Details Make a Model Spec tibble. Value A model spec tbl. Author(s) Steven P. Sanderson II, MPH See Also Other Internals: internal_make_fitted_wflw(), internal_make_wflw_predictions(), internal_make_wflw(), internal_set_args_to_tune(), make_classification_base_tbl(), make_regression_base_tbl() Examples make_regression_base_tbl() %>% internal_make_spec_tbl() make_classification_base_tbl() %>% internal_make_spec_tbl() internal_make_wflw 19 internal_make_wflw Internals Safely Make Workflow from Model Spec tibble Description Safely Make a workflow from a model spec tibble. Usage internal_make_wflw(.model_tbl, .rec_obj) Arguments .model_tbl The model table that is generated from a function like fast_regression_parsnip_spec_tbl(), must have a class of "tidyaml_mod_spec_tbl". .rec_obj The recipe object that is going to be used to make the workflow object. Details Create a model specification tibble that has a workflows::workflow() list column. Value A list object of workflows. Author(s) Steven P. Sanderson II, MPH See Also Other Internals: internal_make_fitted_wflw(), internal_make_spec_tbl(), internal_make_wflw_predictions(), internal_set_args_to_tune(), make_classification_base_tbl(), make_regression_base_tbl() Examples library(recipes, quietly = TRUE) library(dplyr, quietly = TRUE) mod_spec_tbl <- fast_regression_parsnip_spec_tbl( .parsnip_eng = c("lm","glm","gee"), .parsnip_fns = "linear_reg" ) rec_obj <- recipe(mpg ~ ., data = mtcars) internal_make_wflw(mod_spec_tbl, rec_obj) 20 internal_make_wflw_predictions internal_make_wflw_predictions Internals Safely Make Predictions on a Fitted Workflow from Model Spec tibble Description Safely Make predictions on a fitted workflow from a model spec tibble. Usage internal_make_wflw_predictions(.model_tbl, .splits_obj) Arguments .model_tbl The model table that is generated from a function like fast_regression_parsnip_spec_tbl(), must have a class of "tidyaml_mod_spec_tbl". This is meant to be used after the function internal_make_fitted_wflw() has been run and the tibble has been saved. .splits_obj The splits object from the auto_ml function. It is internal to the auto_ml_ func- tion. Details Create predictions on a fitted parnsip model from a workflow object. Value A list object of workflows. Author(s) Steven P. Sanderson II, MPH See Also Other Internals: internal_make_fitted_wflw(), internal_make_spec_tbl(), internal_make_wflw(), internal_set_args_to_tune(), make_classification_base_tbl(), make_regression_base_tbl() Examples library(recipes, quietly = TRUE) library(dplyr, quietly = TRUE) mod_spec_tbl <- fast_regression_parsnip_spec_tbl( .parsnip_eng = c("lm","glm","gee"), .parsnip_fns = "linear_reg" ) internal_set_args_to_tune 21 rec_obj <- recipe(mpg ~ ., data = mtcars) splits_obj <- create_splits(mtcars, "initial_split") mod_tbl <- mod_spec_tbl %>% mutate(wflw = internal_make_wflw(mod_spec_tbl, rec_obj)) mod_fitted_tbl <- mod_tbl %>% mutate(fitted_wflw = internal_make_fitted_wflw(mod_tbl, splits_obj)) internal_make_wflw_predictions(mod_fitted_tbl, splits_obj) internal_set_args_to_tune Internals Make a Tunable Model Specification Description Make a tuned model specification object. Usage internal_set_args_to_tune(.model_tbl) Arguments .model_tbl The model table that is generated from a function like fast_regression_parsnip_spec_tbl(), must have a class of "tidyaml_mod_spec_tbl". Details This will take a model specification that is created from a function like fast_regression_parsnip_spec_tbl() and update the model_spec args to tune::tune(). This is done dynamically, meaning you do not need to know the names of the parameters inside of the model specification. Value A list object of workflows. Author(s) Steven P. Sanderson II, MPH See Also Other Internals: internal_make_fitted_wflw(), internal_make_spec_tbl(), internal_make_wflw_predictions(), internal_make_wflw(), make_classification_base_tbl(), make_regression_base_tbl() 22 load_deps Examples library(dplyr) mod_tbl <- fast_regression_parsnip_spec_tbl() mod_tbl$model_spec[[1]] updated_mod_tbl <- mod_tbl %>% mutate(model_spec = internal_set_args_to_tune(mod_tbl)) updated_mod_tbl$model_spec[[1]] load_deps Functions to Install all Core Libraries Description Load all the core packages necessary to run all potential modeling algorithms. Usage load_deps() Details Load all the core packages necessary to run all potential modeling algorithms. Value No return value, called for side effects Author(s) Steven P. Sanderson II, MPH See Also Other Utility: core_packages(), create_splits(), create_workflow_set(), fast_classification_parsnip_spec_t fast_regression_parsnip_spec_tbl(), install_deps(), match_args() Examples ## Not run: load_deps() ## End(Not run) make_classification_base_tbl 23 make_classification_base_tbl Internals Make Base Classification Tibble Description Creates a base tibble to create parsnip classification model specifications. Usage make_classification_base_tbl() Details Creates a base tibble to create parsnip classification model specifications. Value A tibble Author(s) Steven P. Sanderson II, MPH See Also Other Internals: internal_make_fitted_wflw(), internal_make_spec_tbl(), internal_make_wflw_predictions(), internal_make_wflw(), internal_set_args_to_tune(), make_regression_base_tbl() Examples make_classification_base_tbl() make_regression_base_tbl Internals Make Base Regression Tibble Description Creates a base tibble to create parsnip regression model specifications. Usage make_regression_base_tbl() 24 match_args Details Creates a base tibble to create parsnip regression model specifications. Value A tibble Author(s) Steven P. Sanderson II, MPH See Also Other Internals: internal_make_fitted_wflw(), internal_make_spec_tbl(), internal_make_wflw_predictions(), internal_make_wflw(), internal_set_args_to_tune(), make_classification_base_tbl() Examples make_regression_base_tbl() match_args Match function arguments Description Match a functions arguments. Usage match_args(f, args) Arguments f The parsnip function such as "linear_reg" as a string and without the paren- theses. args The arguments you want to supply to f Details Match a functions arguments, the bad ones passed will be rejected but the remaining passing ones will be returned. Value A list of matched arguments. match_args 25 Author(s) Steven P. Sanderson II, MPH See Also Other Utility: core_packages(), create_splits(), create_workflow_set(), fast_classification_parsnip_spec_t fast_regression_parsnip_spec_tbl(), install_deps(), load_deps() Examples library(parsnip) match_args( f = "linear_reg", args = list( mode = "regression", engine = "lm", trees = 1, mtry = 1 ) ) Index ∗ Extractor fast_classification, 4, 10, 13 extract_model_spec, 6 fast_classification_parsnip_spec_tbl, extract_wflw, 7 2, 5, 6, 11, 14, 16, 22, 25 extract_wflw_fit, 8 fast_regression, 4, 11, 12 extract_wflw_pred, 9 fast_regression_parsnip_spec_tbl, 2, 5, get_model, 15 6, 12, 14, 16, 22, 25 ∗ Internals fast_regression_parsnip_spec_tbl(), 21 internal_make_fitted_wflw, 16 internal_make_spec_tbl, 18 get_model, 7–10, 15 internal_make_wflw, 19 internal_make_wflw_predictions, 20 install_deps, 2, 5, 6, 12, 14, 16, 22, 25 internal_set_args_to_tune, 21 internal_make_fitted_wflw, 16, 18–21, 23, make_classification_base_tbl, 23 24 make_regression_base_tbl, 23 internal_make_spec_tbl, 17, 18, 19–21, 23, ∗ Model_Generator 24 create_model_spec, 3 internal_make_wflw, 17, 18, 19, 20, 21, 23, fast_classification, 10 24 fast_regression, 12 internal_make_wflw_predictions, 17–19, ∗ Utility 20, 21, 23, 24 core_packages, 2 internal_set_args_to_tune, 17–20, 21, 23, create_splits, 4 24 create_workflow_set, 5 load_deps, 2, 5, 6, 12, 14, 16, 22, 25 fast_classification_parsnip_spec_tbl, 11 make_classification_base_tbl, 17–21, 23, fast_regression_parsnip_spec_tbl, 24 14 make_regression_base_tbl, 17–21, 23, 23 install_deps, 16 match_args, 2, 5, 6, 12, 14, 16, 22, 24 load_deps, 22 match_args, 24 workflows::workflow(), 19 workflowsets::workflow_set(), 5 core_packages, 2, 5, 6, 12, 14, 16, 22, 25 create_model_spec, 3, 11, 13 create_splits, 2, 4, 6, 12, 14, 16, 22, 25 create_workflow_set, 2, 5, 5, 12, 14, 16, 22, 25 extract_model_spec, 6, 8–10, 15 extract_wflw, 7, 7, 9, 10, 15 extract_wflw_fit, 7, 8, 8, 10, 15 extract_wflw_pred, 7–9, 9, 15 26

Rtropical

cran

Package ‘Rtropical’ October 12, 2022 Title Data Analysis Tools over Space of Phylogenetic Trees Using Tropical Geometry Version 1.2.1 Maintainer Houjie Wang <wanghoujie6688@gmail.com> Description Process phylogenetic trees with tropical support vector machine and principal compo- nent analysis defined with tropical geometry. Details about tropical support vector ma- chine are available in : Tang, X., Wang, H. & Yoshida, R. (2020) <arXiv:2003.00677>. De- tails about tropical principle component analysis are avail- able in : Page, R., Yoshida, R. & Zhang L. (2020) <doi:10.1093/bioinformatics/btaa564> and Yoshida, R., Zhang, L. & Zhan 018-0493-4>. License GPL-3 Encoding UTF-8 LazyData true RoxygenNote 7.1.1 Imports ape, lpSolve, lpSolveAPI, parallel, Rfast, RcppAlgos, caret Depends R (>= 3.5.0) Suggests rmarkdown, knitr, e1071, testthat (>= 3.0.0) URL https://github.com/HoujieWang/Rtropical VignetteBuilder knitr Config/testthat/edition 3 NeedsCompilation no Author Houjie Wang [aut, cre], Kaizhang Wang [aut], Grady Weyenberg [aut], Xiaoxian Tang [aut], Ruriko Yoshida [aut] Repository CRAN Date/Publication 2021-11-09 18:50:08 UTC 1 2 all_trees R topics documented: all_trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 apicomplexa . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 as.matrix.multiPhylo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 as.vector.phylo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 coef.cv.tropsvm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 coef.tropsvm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 cv.tropsvm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 lungfish . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 plot.troppca . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 predict.cv.tropsvm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 predict.tropsvm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 read.nexus.to.data.matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 read.tree.to.data.matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 sim_trees . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 summary.cv.tropsvm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 tropdet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 tropFW . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 troppca.linsp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 troppca.linsp2poly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 troppca.poly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 tropproj.linsp . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 tropproj.poly . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 tropsvm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Index 21 all_trees Simulated Tree Data with Different Proximity Parameter Value Description Simulated Tree Data with Different Proximity Parameter Value Usage data(all_trees) Format A list of length 12 with each element a sublist containing an ape multiPhylo object with 300 rooted trees on 5 tips and the tree categories. apicomplexa 3 apicomplexa Apicomplexa gene trees sample data set. Description Apicomplexa gene trees sample data set. Usage apicomplexa Format An ape multiPhylo object with 268 rooted trees on 8 tips. Source Chih-Horng Kuo, John P. Wares, Jessica C. Kissinger. The Apicomplexan Whole-Genome Phy- logeny: An Analysis of Incongruence among Gene Trees Molecular Biology and Evolution, Volume 25, Issue 12, December 2008, Pages 2689–2698. as.matrix.multiPhylo Vectorize a Set of Phylognetic Trees Description Unifies tip labels of all phylogenetic trees in multiPhylo object the same as the first tree and returns the cophenetic distance of their corresponding chronogram. Usage ## S3 method for class 'multiPhylo' as.matrix(x, tipOrder = x[[1]]$tip.label, parallel = FALSE, ncores = 2, ...) Arguments x an object of class multiPhylo tipOrder a numeric vector of order of leaf names to which all trees in the multiPhylo object will unified. If not specified on purpose, the tip order of the first tree will be used. parallel a logical value indicating if parallel computing should be used. (default: FALSE) ncores a numeric value indicating the number of threads utilized for multi-cored CPUs. (default: 2) ... Not used. Other arguments to as.vector 4 as.vector.phylo Value A data matrix with each row a vector representation of a chronogram. Each element of the vector is the distance between two leaves. Examples data(apicomplexa) data <- as.matrix(apicomplexa[1: 10]) # matrixize first ten trees as.vector.phylo Vectorize a Phylogenetic Tree Description Computes the cophenetic distance and outputs them in a vector of a phylogenetic tree in phylo object Usage ## S3 method for class 'phylo' as.vector(x, mode = "any") Arguments x A object of class phylo mode The same as base::as.vector. But only numeric output in vector form is accepted for other functions in Rtropical Value A vector with its elements the distance between two leaves of the tree. Examples library(ape) tree <- rcoal(5) tree_vec <- as.vector(tree) coef.cv.tropsvm 5 coef.cv.tropsvm Extract Optimal Tropical Hyperplane from a cv.tropsvm object Description Obtain the optimal tropical hyperplane in the form of vectors from a cv.tropsvm object. Usage ## S3 method for class 'cv.tropsvm' coef(object, ...) Arguments object a fitted "cv.tropsvm" object. ... Not used. Other arguments. Value An output of the apex of the fitted optimal tropical hyperplane. coef.tropsvm Extract Optimal Tropical Hyperplane from a tropsvm object Description Obtain the optimal tropical hyperplane in the form of vectors from a tropsvm object. Usage ## S3 method for class 'tropsvm' coef(object, ...) Arguments object a fitted "tropsvm" object. ... Not used. Other arguments. Value An output of the apex of the fitted optimal tropical hyperplane. 6 cv.tropsvm cv.tropsvm Cross-Validation for Tropical Support Vector Machines Description Conduct k-fold cross validation for tropsvm and return an object "cv.tropsvm". Usage cv.tropsvm(x, y, parallel = FALSE, nfold = 10, nassignment = 10, ncores = 2) Arguments x a data matrix, of dimension nobs x nvars; each row is an observation vector. y a response vector with one label for each row/component of x. parallel a logical value indicating if parallel computing should be used. (default: FALSE) nfold a numeric value of the number of data folds for cross-validation. (default: 10) nassignment a numeric value indicating the size of the parameter grid of assignments. (de- fault: 10) ncores a numeric value indicating the number of threads utilized for multi-cored CPUs. (default: 2) Value object with S3 class cv.tropsvm containing the fitted model, including: apex The negative apex of the fitted optimal tropical hyperplane. assignment The best assignment tuned by cross-validation. index The best classification method tuned by cross-validation. levels The name of each category, consistent with categories in y. accuracy The validation accuracy for each fold. nfold The number of folds used in cross-validation. See Also summary, predict, coef and the tropsvm function. Examples # data generation library(Rfast) set.seed(101) e <- 20 n <- 10 lungfish 7 N <- 10 s <- 5 x <- rbind( rmvnorm(n, mu = c(5, -5, rep(0, e - 2)), sigma = diag(s, e)), rmvnorm(n, mu = c(-5, 5, rep(0, e - 2)), sigma = diag(s, e)) ) y <- as.factor(c(rep(1, n), rep(2, n))) newx <- rbind( rmvnorm(N, mu = c(5, -5, rep(0, e - 2)), sigma = diag(s, e)), rmvnorm(N, mu = c(-5, 5, rep(0, e - 2)), sigma = diag(s, e)) ) newy <- as.factor(rep(c(1, 2), each = N)) # train the tropical svm cv_tropsvm_fit <- cv.tropsvm(x, y, parallel = FALSE) summary(cv_tropsvm_fit) coef(cv_tropsvm_fit) # test with new data pred <- predict(cv_tropsvm_fit, newx) # check with accuracy table(pred, newy) # compute testing accuracy sum(pred == newy) / length(newy) lungfish Coelacanths genome and transcriptome data Description Coelacanths genome and transcriptome data Usage lungfish Format An ape multiPhylo object with 1193 rooted trees on 10 tips. Source Tom M. W. Nye, Xiaoxian Tang, Grady Weyenberg and Ruriko Yoshida. Principal component analysis and the locus of the Fréchet mean in the space of phylogenetic trees, Biometrika, Volume 104, Issue 4, December 2017, Pages 901–922. 8 predict.cv.tropsvm plot.troppca Plot the Tropical Principal Components with Data Projections Description Visualize the second order tropical principle components in troppca as a tropical triangle with projections on a two-dimensional plot via tropical isometry. Usage ## S3 method for class 'troppca' plot(x, plab = NULL, fw = FALSE, ...) Arguments x a fitted troppca object. plab a vector of labels of all points in the given data matrix. Not needed for unlabeled data. (default: NULL) fw a logical variable to determine if to add Fermat-Weber point of the data projec- tion. (default: FALSE) ... Not used. Other arguments to plot Value plot.troppca does not return anything other than the plot. predict.cv.tropsvm Predict Method for Tropical Support Vector Machines based on Cross- Validation Description Predicts values based upon a model trained by cv.tropsvm. Usage ## S3 method for class 'cv.tropsvm' predict(object, newx, ...) Arguments object a fitted "cv.tropsvm" object. newx a data matrix, of dimension nobs x nvars used as testing data. ... Not used. Other arguments to predict. predict.tropsvm 9 Value A vector of predicted values of a vector of labels. See Also summary, coef and the cv.tropsvm function. Examples # data generation library(Rfast) e <- 20 n <- 10 N <- 10 s <- 5 x <- rbind( rmvnorm(n, mu = c(5, -5, rep(0, e - 2)), sigma = diag(s, e)), rmvnorm(n, mu = c(-5, 5, rep(0, e - 2)), sigma = diag(s, e)) ) y <- as.factor(c(rep(1, n), rep(2, n))) newx <- rbind( rmvnorm(N, mu = c(5, -5, rep(0, e - 2)), sigma = diag(s, e)), rmvnorm(N, mu = c(-5, 5, rep(0, e - 2)), sigma = diag(s, e)) ) newy <- as.factor(rep(c(1, 2), each = N)) # train the tropical svm cv_tropsvm_fit <- cv.tropsvm(x, y, parallel = FALSE) # test with new data pred <- predict(cv_tropsvm_fit, newx) # check with accuracy table(pred, newy) # compute testing accuracy sum(pred == newy) / length(newy) predict.tropsvm Predict Method for Tropical Support Vector Machines Description Predicts values based upon a model trained by tropsvm. Usage ## S3 method for class 'tropsvm' predict(object, newx, ...) 10 predict.tropsvm Arguments object a fitted tropsvm object. newx a data matrix, of dimension nobs x nvars used as testing data. ... Not used. Other arguments to predict. Value A vector of predicted values of a vector of labels. See Also summary, coef and the tropsvm function. Examples # data generation library(Rfast) e <- 100 n <- 10 N <- 10 s <- 5 x <- rbind( rmvnorm(n, mu = c(5, -5, rep(0, e - 2)), sigma = diag(s, e)), rmvnorm(n, mu = c(-5, 5, rep(0, e - 2)), sigma = diag(s, e)) ) y <- as.factor(c(rep(1, n), rep(2, n))) newx <- rbind( rmvnorm(N, mu = c(5, -5, rep(0, e - 2)), sigma = diag(s, e)), rmvnorm(N, mu = c(-5, 5, rep(0, e - 2)), sigma = diag(s, e)) ) newy <- as.factor(rep(c(1, 2), each = N)) # train the tropical svm tropsvm_fit <- tropsvm(x, y, auto.assignment = TRUE, ind = 1) # test with new data pred <- predict(tropsvm_fit, newx) # check with accuracy table(pred, newy) # compute testing accuracy sum(pred == newy) / length(newy) read.nexus.to.data.matrix 11 read.nexus.to.data.matrix Read NEXUS-formatted trees from two categories into a data matrix Description Read NEXUS-formatted trees from two categories into a data matrix Usage read.nexus.to.data.matrix(data.file1, data.file2) Arguments data.file1 A data set with trees from one category. data.file2 A data set with trees from the other category. Value A data matrix with the first x rows corresponding the x trees in the first file and the last y rows are the trees from the second file. read.tree.to.data.matrix Read Newick-formatted trees in two categories into a data matrix Description Read Newick-formatted trees in two categories into a data matrix Usage read.tree.to.data.matrix(data.file1, data.file2) Arguments data.file1 A file containing trees in Newick form in a category. data.file2 A file containing trees in Newick form in an assumed different category. Value read.tree.to.data.matrix has the same return as read.nexus.to.data.matrix. 12 summary.cv.tropsvm sim_trees Simulated Tree Data Description Simulated Tree Data Usage data(sim_trees) Format An ape multiPhylo object with 300 rooted trees on 5 tips. summary.cv.tropsvm Summarize an Analysis of Cross-Validated Tropical Support Vector Machine Description Return a summary with a more detailed explanation of the object "cv.tropsvm". Usage ## S3 method for class 'cv.tropsvm' summary(object, ...) Arguments object a fitted "cv.tropsvm" object. ... Not used. Other arguments to summary. Value A summary of the crucial information of a tropical support vector machine is printed, including the selected best assignment and classification methods and the validation accuracy of each data fold. The summary section of classification methods specifies the sectors and their intersections used to classify points of two different categories. See Also predict, coef and the cv.tropsvm function. tropdet 13 Examples # data generation library(Rfast) e <- 20 n <- 10 N <- 10 s <- 5 x <- rbind( rmvnorm(n, mu = c(5, -5, rep(0, e - 2)), sigma = diag(s, e)), rmvnorm(n, mu = c(-5, 5, rep(0, e - 2)), sigma = diag(s, e)) ) y <- as.factor(c(rep(1, n), rep(2, n))) newx <- rbind( rmvnorm(N, mu = c(5, -5, rep(0, e - 2)), sigma = diag(s, e)), rmvnorm(N, mu = c(-5, 5, rep(0, e - 2)), sigma = diag(s, e)) ) newy <- as.factor(rep(c(1, 2), each = N)) # train the tropical svm with cross-validation cv_tropsvm_fit <- cv.tropsvm(x, y, parallel = FALSE) summary(cv_tropsvm_fit) tropdet Tropical Determinant of a Matrix Description Compute the tropical determinant for a given matrix. This is equivalent to solving an assignment problem. Usage tropdet(x) Arguments x a square matrix Value The determinant of the given matrix, Examples R <- matrix(sample(1:9, 9), nrow = 3) tropdet(R) 14 troppca.linsp tropFW Tropical Fermat-Weber Point Description Compute the tropical Fermat-Weber (FW) point for a given data matrix. The FW point minimizes the summed tropical distance to the trees described in the data matrix. Usage tropFW(x) Arguments x a data matrix, of dimension nobs x nvars; each row is an observation vector. Value A list containing: fw The fermat-weber point. distsum The sum of distance from each observation to the fermat-weber point. Examples x <- matrix(rnorm(100), ncol = 10) tropFW(x) troppca.linsp Tropical Principal Component Analysis by Tropical Linear Space Description Approximate the principal component as a tropical linear space for a given data matrix and returns the results as an object of class troppca. Usage troppca.linsp(x, pcs = 2, iteration = list(), ncores = 2) troppca.linsp2poly 15 Arguments x a data matrix, of size n x e, with each row an observation vector. e is the dimen- sion of the tropical space pcs a numeric value indicating the order of principal component. (default: 2) iteration a list with arguments controlling the iteration of the algorithm. exhaust a logical variable indicating if to iterate over all possible combinations of the linear space based on the given data matrix x. If FALSE, please input a number of iteration for niter. If TRUE, please enter 0 for niter and this function will iterate over all possible combinations of linear space. This could be time consuming when x is large. (default: FALSE) niter a numeric variable indicating the number of iterations. (default: 100) ncores a numeric value indicating the number of threads utilized for multi-cored CPUs. (default: 2) Value A list of S3 class "troppca", including: pc The principal component as a tropical linear space obj The tropical PCA objective, the sum of tropical distance from each point to the projection. projection The projections of all data points. type The geometry of principal component. Examples library(Rfast) n <- 100 e <- 10 sig2 <- 1 x <- rbind(rmvnorm(n, mu = c(5, -5, rep(0, e - 2)), sigma = diag(sig2, e))) troppca_fit <- troppca.linsp(x) troppca.linsp2poly Tropical Principal Component Analysis by Polytope Converted from Linear Space Description Approximate the principal component as a tropical polytope converted from tropical linear space for a given data matrix via MCMC and return the results as an object of class troppca. 16 troppca.poly Usage troppca.linsp2poly(x, pcs = 2, nsample = 1000, ncores = 2) Arguments x a data matrix, of size n x e, with each row an observation vector. e is the dimen- sion of the tropical space#’ pcs a numeric value indicating the order of principal component. (default: 2) nsample a numeric value indicating the number of samples of MCMC. (default: 1000) ncores a numeric value indicating the number of threads utilized for multi-cored CPUs. (default: 2) Value A list of S3 class "troppca", including: pc The principal component as a tropical linear space obj The tropical PCA objective, the sum of tropical distance from each point to the projection. projection The projections of all data points. type The geometry of principal component. Examples library(Rfast) n <- 50 e <- 50 s <- 5 x <- rbind( rmvnorm(n, mu = c(5, -5, rep(0, e - 2)), sigma = diag(s, e)), rmvnorm(n, mu = c(-5, 5, rep(0, e - 2)), sigma = diag(s, e)) ) troppca_fit <- troppca.linsp2poly(x) troppca.poly Tropical Principal Component Analysis by Tropical Polytope Description Approximates the principal component as a tropical polytope for a given data matrix via MCMC and return the results as an object of class troppca. tropproj.linsp 17 Usage troppca.poly(x, pcs = 2, nsample = 1000, ncores = 2) Arguments x a data matrix, of size n x e, with each row an observation vector. e is the dimen- sion of the tropical space#’ pcs a numeric value indicating the order of principal component. (default: 2) nsample a numeric value indicating the number of samples of MCMC. (default: 1000) ncores a numeric value indicating the number of threads utilized for multi-cored CPUs. (default: 2) Value A list of S3 class "troppca", including: pc The principal component as a tropical linear space obj The tropical PCA objective, the sum of tropical distance from each point to the projection. projection The projections of all data points. type The geometry of principal component. Examples library(Rfast) n <- 50 e <- 50 s <- 5 x <- rbind( rmvnorm(n, mu = c(5, -5, rep(0, e - 2)), sigma = diag(s, e)), rmvnorm(n, mu = c(-5, 5, rep(0, e - 2)), sigma = diag(s, e)) ) troppca_fit <- troppca.poly(x) plot(troppca_fit) tropproj.linsp Projection on Tropical Linear Space Description Compute projection of data points on a given tropical linear space. 18 tropproj.poly Usage tropproj.linsp(x, V) Arguments x a data matrix, of size n x e, with each row an observation. V a data matrix, of dimension s x e, with each row a basis of tropical linear space. e is the dimension of the tropical space and s is the dimension of the linear space. Value A matrix of projections of all data points. Examples library(Rfast) n <- 100 e <- 10 sig2 <- 1 s <- 3 x <- rbind(rmvnorm(n, mu = c(5, -5, rep(0, e - 2)), sigma = diag(sig2, e))) V <- matrix(runif(s * e, -10, 10), nrow = s, ncol = e) x_proj <- tropproj.linsp(x, V) head(x_proj) tropproj.poly Projection on Tropical Polytope Description Project a point onto a given tropical polytope. Usage tropproj.poly(x, tconv) Arguments x a data vector, of length e. tconv a data matrix, of size e x s, with each column a vertex of the tropical polytope. e is the dimension of the tropical space and s is the number of vertices of the polytope Value A projected vector on the given tropical polytope. tropsvm 19 Examples # Generate a tropical polytope consisting of three trees each with 5 leaves library(ape) pltp <- sapply(1:3, function(i) { as.vector(rcoal(5)) }) # Generate an observation and vectorize it tree <- rcoal(5) tree_vec <- as.vector(tree) tropproj.poly(tree_vec, pltp) tropsvm Tropical Support Vector Machines Description Fit a discriminative two-class classifier via linear programming defined by the tropical hyperplane which maximizes the minimum tropical distance from data points to itself in order to separate the data points into sectors (half-spaces) in the tropical projective torus. Usage tropsvm(x, y, auto.assignment = FALSE, assignment = NULL, ind = 1) Arguments x a data matrix, of dimension nobs x nvars; each row is an observation vector. y a response vector with one label for each row/component of x. auto.assignment a logical value indicating if to provide an assignment manually. If FALSE, an input is required, otherwise the function automatically finds a good assign- ment.(default: FALSE) assignment a numeric vector of length 4 indicating the sectors of tropical hyperplane that the data will be assigned to. The first and third elements in the assignment are the coordinates of an observed point in data matrix x believed from the first category where the maximum and second maximum of the vector addition between the fitted optimal tropical hyperplane and the point itself are achieved. The mean- ings for the second and the fourth element in the assignment are the same but for the points in the second category. Namely, the first and second values in the assignment are the indices of sectors where the two point clouds are assigned. Not needed when auto.assignment = TRUE. (default: NULL) ind a numeric value or a numeric vector ranging from 1 to 70 indicating which clas- sification method to be used. There are 70 different classification methods. De- tails of a given method can be retrieved by summary. The different classification methods are proposed to resolve the issue when points fall on the intersections of sectors. Users can have personal choices if better knowledge is assumed. (default: 1) 20 tropsvm Value An object with S3 class tropsvm containing the fitted model, including: apex The negative apex of the fitted optimal tropical hyperplane. assignment The user-input or auto-found assignment. index The user-input classification method. levels The name of each category, consistent with categories in y. See Also predict, coef and the cv.tropsvm function. Examples # data generation library(Rfast) e <- 100 n <- 10 N <- 100 s <- 10 x <- rbind( rmvnorm(n, mu = c(5, -5, rep(0, e - 2)), sigma = diag(s, e)), rmvnorm(n, mu = c(-5, 5, rep(0, e - 2)), sigma = diag(s, e)) ) y <- as.factor(c(rep(1, n), rep(2, n))) newx <- rbind( rmvnorm(N, mu = c(5, -5, rep(0, e - 2)), sigma = diag(s, e)), rmvnorm(N, mu = c(-5, 5, rep(0, e - 2)), sigma = diag(s, e)) ) newy <- as.factor(rep(c(1, 2), each = N)) # train the tropical svm tropsvm_fit <- tropsvm(x, y, auto.assignment = TRUE, ind = 1) coef(tropsvm_fit) # test with new data pred <- predict(tropsvm_fit, newx) # check with accuracy table(pred, newy) # compute testing accuracy sum(pred == newy) / length(newy) Index ∗ datasets all_trees, 2 apicomplexa, 3 lungfish, 7 sim_trees, 12 all_trees, 2 apicomplexa, 3 as.matrix.multiPhylo, 3 as.vector.phylo, 4 coef.cv.tropsvm, 5 coef.tropsvm, 5 cv.tropsvm, 6 lungfish, 7 plot.troppca, 8 predict.cv.tropsvm, 8 predict.tropsvm, 9 read.nexus.to.data.matrix, 11 read.tree.to.data.matrix, 11 sim_trees, 12 summary.cv.tropsvm, 12 tropdet, 13 tropFW, 14 troppca.linsp, 14 troppca.linsp2poly, 15 troppca.poly, 16 tropproj.linsp, 17 tropproj.poly, 18 tropsvm, 19 21

cspp

cran

Package ‘cspp’ December 17, 2022 Type Package Title A Tool for the Correlates of State Policy Project Data Version 0.3.3 Author Caleb Lucas (https://caleblucas.com/) and Joshua McCrain (http://joshuamccrain.com/) Maintainer Caleb Lucas <calebjlucas@gmail.com> Description A tool that imports, subsets, visualizes, and exports the Correlates of State Pol- icy Project dataset assembled by Marty P. Jordan and Matt Grossmann (2020) <http: //ippsr.msu.edu/public-policy/correlates-state-policy>. The Correlates data con- tains over 2000 variables across more than 100 years that pertain to state politics and pol- icy in the United States. Users with only a basic understanding of R can sub- set this data across multiple dimensions, export their search results, create map visualiza- tions, export the citations associated with their searches, and more. License GPL (>= 3) Encoding UTF-8 LazyData true Language en-US Depends R (>= 2.10), dplyr(>= 1.0.0) Imports stringr, readr, tidyselect, ggplot2, mapproj, rlang, haven, purrr, csppData, ggcorrplot RoxygenNote 7.2.3 Suggests knitr, rmarkdown, testthat, ggraph, igraph VignetteBuilder knitr NeedsCompilation no Repository CRAN Date/Publication 2022-12-17 00:20:02 UTC R topics documented: corr_plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 generate_map . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1 2 corr_plot get_cites . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 get_cspp_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 get_network_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 get_var_info . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 map_example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 network_data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 network_vars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 plot_panel . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 var_names_db . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Index 14 corr_plot Create correlation plots of CSPP data Description corr_plot takes CSPP data from get_cspp_data and returns either a correlation matrix or corre- lation plot. Usage corr_plot( data = NULL, vars = NULL, summarize = TRUE, labels = TRUE, label_size = 3, colors = c("#6D9EC1", "#FFFFFF", "#E46726"), cor_matrix = FALSE ) Arguments data A dataframe. If data is generated by get_cspp_data function, the function can automatically parse the dataframe. Otherwise, this function will attempt to make a correlation plot or matrix from all numeric variables within the passed dataframe. vars Default is NULL. If left NULL, uses all variables within the passed dataframe. Otherwise, must be a character vector. The dataframe is subset based on vari- ables listed. summarize Default is TRUE. If TRUE, and if the variable st is present, the function will create state specific averages for each variable in the dataframe. If FALSE, the function will generate the correlation matrix and plot for all values in the dataset. labels Default is TRUE. If TRUE, the correlation plot will include labels for the corre- lation value. If FALSE, no labels will be present. label_size Default is 3. Controls the size of the font for labels. generate_map 3 colors Specify the colors to be used in the correlation plot. Must include three values in a character vector format. The default values are ‘c("#6D9EC1", "#FFFFFF", "#E46726")‘. cor_matrix Default is FALSE. If set to TRUE, instead of returning a ggplot object that is a correlation plot, returns a correlation matrix. This is particularly useful if you want to customize the output with ggcorrplot. Details This function is a wrapper that passes a dataframe to the ggcorrplot::ggcorrplot function which generates correlation heat plots. Value ggplot2 object or correlation matrix See Also ggcorrplot Examples corr_plot(data = get_cspp_data(), vars = c("pollib_median", "innovatescore_boehmkeskinner", "citi6013", "ranney4_control", "h_diffs"), cor_matrix = FALSE) generate_map Generate map visualizations (choropleths) of CSPP data Description generate_map takes CSPP data from get_cspp_data and plots the values of numeric variables on the map of the U.S. It can also plot individual states or sets of states. Arguments cspp_data Dataframe generated by get_cspp_data which must include the variable state. If there are multiple years of data per state, by default the most recent year is used in creating the map unless average_years is set to TRUE. Default is NULL and returns the most recent year’s poptotal data as an example map. var_name Specify the variable from the dataset passed to cspp_data to plot on the map. If left blank, the first variable that is not "year", "st", "state", "state_fips", or "state_icspr" is used. Default is NULL. average_years Default is FALSE. If TRUE, averages over all of the years per state in the dataframe to produce a value to plot on the map. If the type of the variable in var_name is not numeric, will reset this parameter to FALSE. 4 generate_map drop_NA_states Choose whether to drop states at the map generating stage which have NA val- ues. Default is FALSE and states with missing data will be filled grey. If set to TRUE, states will have no fill in the plot. If you’re passing a dataframe subset to certain states, set this to TRUE. poly_args Default is list(color = "#666666", size = .5). Changes the aesthetics of how the states look when plotted. The fill of each state can be manually changed through ggplot’s scale_fill_ (see examples). See geom_polygon for other options to pass to this argument. Details Note: due to complications with plotting Alaska and Hawaii, this package currently does not support plotting these two states. This function is general in the sense that it will produce a ggplot-style map for any dataframe passed to it with the proper formatting. Any dataframe that has at least three columns, with the first two a numeric ‘year‘ column and a state name as a string, and the final column the value to be plotted, will work with this function. Value Returns a ggplot object. See examples for how to work with this object. See Also get_cspp_data, get_cites, get_var_info Examples ## default map with total population generate_map() ## pass specific variables # returns average over all non NA years in the data generate_map(get_cspp_data(var_category = "demographics"), var_name = "pctpopover65") ## add additional ggplot options generate_map(get_cspp_data(var_category = "demographics"), var_name = "pctpopover65", poly_args = list(color = "black"), drop_NA_states = FALSE) + ggplot2::scale_fill_gradient(low = "white", high = "red") + ggplot2::theme(legend.position = "none") + ggplot2::ggtitle("% Population Over 65") ## plot specific states # drop_NA_states set to TRUE plots only those states library(dplyr) generate_map(get_cspp_data(var_category = "demographics") %>% get_cites 5 dplyr::filter(st %in% c("NC", "VA", "SC")), var_name = "pctpopover65", poly_args = list(color = "black"), drop_NA_states = TRUE) + ggplot2::scale_fill_gradient(low = "white", high = "red") + ggplot2::theme(legend.position = "none") + ggplot2::ggtitle("% Population Over 65") ## pass specific variables and years # returns average over set of years provided library(dplyr) generate_map(get_cspp_data(var_category = "demographics") %>% dplyr::filter(year %in% seq(2001, 2010))) # returns average over set of years provided library(dplyr) generate_map(get_cspp_data(var_category = "demographics") %>% dplyr::filter(year %in% seq(2001, 2010))) get_cites Get citations for CSPP variables Description get_cites retrieves citations for variables in the CSPP dataset. Users can print the citations to the console, save them as dataframes, and write them to multiple file types (csv, txt). Citations can be written in one of multiple formats (plaintext, bib). Supply variable names that need to be cited with the var_names argument. The function prints user-supplied variable names that do not match any in the CSPP dataset by default (print_nomatch). The function also returns the citation for the cspp package and the CSPP dataset as a whole. We request you cite both if you use this package for your research. Usage get_cites( var_names, write_out = FALSE, file_path = NULL, format = "bib", print_cites = FALSE, print_nomatch = TRUE ) Arguments var_names Default is NULL. Takes a character string. Should be one or more variables from the CSPP dataset. A citation for each variable is returned. 6 get_cspp_data write_out Default is FALSE. Takes a logical. If FALSE the function does not write the citations out to a file. file_path Default is NULL. Takes a character string. If write_out = T then the file will be saved to this filepath. format Default is bib. Takes a character string. If write_out = T then the resulting file will be in this format. User must supply "bib", "csv", or "txt". print_cites Default is FALSE. Takes a logical value. If TRUE then the function prints the citations to the console. print_nomatch Default is TRUE. Takes a logical value. If FALSE then the function does not print variables the user supplied that had no match in CSPP. See Also get_cspp_data, get_var_info, generate_map Examples get_cites("poptotal") ## Not run: get_cites(var_names = "poptotal", write_out = TRUE, file_path = "~/path/to/file.csv", format = "csv") ## End(Not run) get_cspp_data Load CSPP data into the R environment Description get_cspp_data loads either a full or subsetted version of the full CSPP dataset into the R environ- ment as a dataframe. Usage get_cspp_data( vars = NULL, var_category = NULL, states = NULL, years = NULL, core = FALSE, output = NULL, path = "" ) get_cspp_data 7 Arguments vars Default is NULL. If left blank, returns all variables within the dataset. Takes a string or vector of strings. See get_var_info for pulling variable names and get_cites for citations of specific variables and datasets. Names of variables must be exact matches to variables in the dataset. var_category Default is NA. If left blank, returns all datasets. Takes a string or vector of strings. Options are one of, or a combination of: "demographics", "economic-fiscal", "government", "elections", "policy_ideology", "criminal justice", "education", "healthcare", "welfare", "rights", "environment", "drug-alcohol", "gun control", "labor", "transportation", "misc. regulation" states Default is NULL. If left blank, returns all states. Takes a string or vector of strings of state abbreviations. Use state.abb to load state abbreviations into the R environment. years Default is NULL. If left blank, returns all years. Coverage begins at 1900 and runs to 2019. However, coverage depends on the specific variable – see get_var_info. Input can be a vector of years (or a singular year), such as c(2000, 2001, 2002, 2012) or seq(2000, 2012). core Default is FALSE. If TRUE, merge the core CSPP data (approximately 70 com- mon and important variables) with the search result. output Default is NULL. One of "csv", "dta", "rdata". Optional parameter for writing the resulting dataframe to a file. path The directory to write the file to. Default is blank, so writes to working directory. Exclude final slash: e.g., path = "dir1/dir2" See Also get_var_info, get_cites, generate_map Examples ## returns full dataset data <- get_cspp_data() ## use variable names from get_var_info data <- get_cspp_data(vars = get_var_info(var_names="pctpop")$variable) ## return subsets # note: this returns the specific variables listed as well as those in the # var_category argument data <- get_cspp_data(vars = c("sess_length", "hou_majority", "term_length"), var_category = "demographics", states = c("NC", "VA", "GA"), years = seq(1995, 2004)) 8 get_network_data get_network_data Get state networks data Description network_data returns a dataframe of the state networks data compiled by the Correlates of State Policy Project. The dataframe is in an edge list format, with each row a state dyad combination. The merge argument allows the direct merging of a dataframe generated by the get_cspp_data function. Usage get_network_data(category = NULL, merge_data = NULL) Arguments category A category within the networks data. Default is NULL. If left blank, returns the full state networks data. Category options are "Distance Travel Migration", "Economic", "Political", "Policy", "Demographic". merge_data Default is NULL. Takes a dataframe object in the format generated by get_cspp_data. The function merges this dataframe into the network data by state. If the merge dataframe has multiple observations per state, this function averages over all values per state as long as the variables are numeric. If the dataframe passed has multiple values per state and some are not numeric, only numeric variables are merged. Details The network dataframe that results is directed, with variables directed towards the state in the State1 column. For instance, the IncomingFlights variable is the number of flights from State2 with a destination in State1. Value A dataframe formatted as an edge list. See Also For more information on the construction of the network data as well as a full codebook see http: //ippsr.msu.edu/public-policy/state-networks. Examples # Load full network data: network.df <- get_network_data() # Network data for subset of categories: get_var_info 9 network.df <- get_network_data(category = c("Economic", "Political")) # Merge in data from get_cspp_data() network.df <- get_network_data(category = "Distance Travel Migration", merge_data = get_cspp_data(vars = c("sess_length", "hou_majority"), years = seq(1999, 2000))) get_var_info Get information regarding the CSPP variables Description get_var_info retrieves information regarding variables in the CSPP dataset. The information available includes: the years each variable is observed in the data; a short and long description of each variable; the source and citation for each variable; and a general category that describes each variable. Usage get_var_info( var_names = NULL, categories = NULL, related_to = NULL, exact = FALSE ) Arguments var_names Default is NULL. Takes a character string. If left blank the function does not subset by variable name. categories Default is NULL. Takes a character string. If left blank the function does not subset by category. related_to Default is NULL. Takes a character string. If the user supplies a character string, the function searches the other relevant fields (variable name, short/long descrip- tion, and source) for string and returns either exact or partial matches depending on the value of the exact argument. exact Default is FALSE. If true, exact matches for the other supplied arguments are used. If TRUE, then partial matches are also returned. Details Users can request this information regarding specific variables or all the variables within a specific category. Users can request exact matches of their supplied arguments or allow partial matches with the exact argument. Users can also search all these relevant fields (variable name, short/long description, source) for a keyword/s with the supply a string related_to argument to identify variables related to a topic of interest. Specifying no arguments returns all the information for all the variables in the CSPP dataset. 10 map_example See Also get_cspp_data, get_cites, generate_map Examples # returns all variable information get_var_info() # searches all columns for non-exact matches of "pop" and "fem" get_var_info(related_to = c("pop","femal")) get_var_info(categories = "demographics") # returns non-exact matches for variables with "pop" and that have "femal" anywhere in the row get_var_info(var_names = "pop", related_to = "femal") map_example Sample Dataset for Working with generate_map() Description A dataframe to create a sample map using the generate_map function. The variable plotted is population. Usage map_example Format An object of class tbl_df (inherits from tbl, data.frame) with 51 rows and 3 columns. Details @name map_example @docType data @usage data(map_example) @keywords datasets network_data 11 network_data State Network data (IPPSR) Description The State Networks dataset is a compilation of many state-to-state relational variables, including measures of shared borders, travel and trade between states, and demographic characteristics of state populations collected by Shayla Olson (2020) and Marty P. Jordan and Matt Grossmann (2020) <http://ippsr.msu.edu/public-policy/state-networks>. Usage network_data Format An object of class tbl_df (inherits from tbl, data.frame) with 2550 rows and 120 columns. Details @name network_data @docType data @usage data(network_data) @keywords datasets network_vars State Network (IPPSR) Dataset Variable Names Description A dataset of the the names of the variables in the IPPSR state networks data. Usage network_vars Format An object of class data.frame with 118 rows and 2 columns. Details @name network_vars @docType data @usage data(network_vars) @keywords datasets 12 plot_panel plot_panel Generate time series plots of CSPP data Description plot_panel takes CSPP data from get_cspp_data and plots the values of the passed variable name in a time series (grid or line) format. Usage plot_panel( cspp_data = NULL, var_name = NULL, years = NULL, colors = c("#b3a4a4", "#8f3838", "#dbdbdb") ) Arguments cspp_data Dataframe generated by get_cspp_data which must include the variable st. var_name Specific variable within the dataframe passed to ‘cspp_data‘ to plot. If left NULL, will automatically plot the first variable after state identifiers. years Specify years within the passed dataframe to plot. If left NULL, will plot all years for which not all observations have missing values. Takes a vector of years. colors Specify the colors to be used in a grid plot. Must include three values in a charac- ter vector format. The default values are ‘c("#b3a4a4", "#8f3838", "#dbdbdb")‘. If the variable plotted is dichotomous, the first color is the non-treated value and the second color is the treated value. The third color is the value for NA. If plot- ting a continuous variable, the first color is the low end of the gradient and the second value is the high end of the gradient. See scale_fill_gradient. Details This function will take any dataframe consisting of the variables ‘year‘ and ‘st‘ plus one other variable. Value ggplot2 object See Also get_var_info, get_cites, generate_map var_names_db 13 Examples # dichotomous variable cspp <- get_cspp_data(vars = c("drugs_medical_marijuana")) plot_panel(cspp) # change colors and years plot_panel(cspp, colors = c("white", "blue", "black"), years = seq(1980, 2000)) # continuous variable with missing data: continuous_data <- get_cspp_data(vars = c("h_diffs")) plot_panel(continuous_data, colors = c("white", "dodgerblue", "#eeeeee")) # add ggplot2 features library(ggplot2) plot_panel(continuous_data, colors = c("white", "dodgerblue", "#eeeeee")) + theme(legend.position = "none") + ggplot2::ggtitle("Continuous variable") var_names_db Correlates of State Policy Project Dataset (IPPSR) Variable Names Description A dataframe of all variable names, their descriptions, and sources in the Correlates of State Policy Project Dataset. Usage var_names_db Format An object of class data.frame with 2179 rows and 3 columns. Details @name var_names_db @docType data @usage data(var_names_db) @keywords datasets Index ∗ datasets map_example, 10 network_data, 11 network_vars, 11 var_names_db, 13 corr_plot, 2 generate_map, 3, 6, 7, 10, 12 geom_polygon, 4 get_cites, 4, 5, 7, 10, 12 get_cspp_data, 2–4, 6, 6, 8, 10, 12 get_network_data, 8 get_var_info, 4, 6, 7, 9, 12 map_example, 10 network_data, 11 network_vars, 11 plot_panel, 12 scale_fill_gradient, 12 var_names_db, 13 14

tidyhydat

cran

Package ‘tidyhydat’ April 4, 2023 Title Extract and Tidy Canadian 'Hydrometric' Data Version 0.6.0 Description Provides functions to access historical and real-time national 'hydrometric' data from Water Survey of Canada data sources (<https: //dd.weather.gc.ca/hydrometric/csv/> and <https://collaboration.cmc.ec.gc.ca/cmc/hydrometrics/ www/>) and then applies tidy data principles. License Apache License (== 2.0) | file LICENSE URL https://docs.ropensci.org/tidyhydat/, https://github.com/ropensci/tidyhydat/ BugReports https://github.com/ropensci/tidyhydat/issues/ Depends R (>= 3.4.0) Imports cli (>= 1.0.0), crayon (>= 1.3.4), DBI (>= 0.7), dbplyr (>= 1.1.0), dplyr (>= 0.7.4), httr (>= 1.3.1), lubridate (>= 1.6.0), rappdirs (>= 0.3.1), readr (>= 1.1.1), rlang (>= 0.1.2), RSQLite (>= 2.0), tidyr (>= 0.7.1) Suggests ggplot2, knitr, rmarkdown, testthat (>= 3.0.0), covr Config/testthat/edition 3 VignetteBuilder knitr Encoding UTF-8 LazyData true RoxygenNote 7.2.3 NeedsCompilation no Author Sam Albers [aut, cre] (<https://orcid.org/0000-0002-9270-7884>), David Hutchinson [ctb], Dewey Dunnington [ctb], Ryan Whaley [ctb], Province of British Columbia [cph], Government of Canada [dtc], Luke Winslow [rev] (Reviewed for rOpenSci), Laura DeCicco [rev] (Reviewed for rOpenSci) 1 2 R topics documented: Maintainer Sam Albers <sam.albers@gmail.com> Repository CRAN Date/Publication 2023-04-04 16:00:06 UTC R topics documented: allstations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 download_hydat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 hy_agency_list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 hy_annual_instant_peaks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 hy_annual_stats . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 hy_daily . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 hy_daily_flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 hy_daily_levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11 hy_data_symbols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 hy_data_types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 hy_datum_list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 hy_dir . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 hy_monthly_flows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 hy_monthly_levels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 hy_plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 hy_reg_office_list . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 hy_remote . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 hy_sed_daily_loads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 hy_sed_daily_suscon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 hy_sed_monthly_loads . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24 hy_sed_monthly_suscon . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 hy_sed_samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 hy_sed_samples_psd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 hy_set_default_db . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 hy_src . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 hy_stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 hy_stn_data_coll . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 hy_stn_data_range . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 hy_stn_datum_conv . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 hy_stn_datum_unrelated . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 hy_stn_op_schedule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 hy_stn_regulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 hy_stn_remarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42 hy_test_db . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 hy_version . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 param_id . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 pull_station_number . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 realtime_add_local_datetime . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 realtime_daily_mean . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 realtime_dd . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 allstations 3 realtime_plot . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 realtime_stations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 realtime_ws . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 search_stn_name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 Index 55 allstations All Canadian stations Description A shorthand to avoid having always call hy_stations or realtime_stations. Populated by both realtime and historical data from HYDAT. Usage allstations Format A tibble with 5 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number STATION_NAME Official name for station identification PROV_TERR_STATE_LOC The province, territory or state in which the station is located HYD_STATUS Current status of discharge or level monitoring in the hydrometric network REAL_TIME Logical. Indicates if a station has the capacity to deliver data in real-time or near real-time LATITUDE North-South Coordinates of the gauging station in decimal degrees LONGITUDE East-West Coordinates of the gauging station in decimal degrees station_tz Timezone of station calculated using the lutz package based on LAT/LONG of stations standard_offset Offset from UTC of local standard time Source HYDAT, Meteorological Service of Canada datamart 4 hy_agency_list download_hydat Download and set the path to HYDAT Description Download the HYDAT sqlite database. This database contains all the historical hydrometric data for Canada’s integrated hydrometric network. The function will check for a existing sqlite file and won’t download the file if the same version is already present. Usage download_hydat(dl_hydat_here = NULL, ask = TRUE) Arguments dl_hydat_here Directory to the HYDAT database. The path is chosen by the rappdirs package and is OS specific and can be view by hy_dir(). This path is also supplied automatically to any function that uses the HYDAT database. A user specified path can be set though this is not the advised approach. It also downloads the database to a directory specified by hy_dir(). ask Whether to ask (as TRUE/FALSE) if HYDAT should be downloaded. If FALSE the keypress question is skipped. Examples ## Not run: download_hydat() ## End(Not run) hy_agency_list hy_agency_list function Description AGENCY look-up Table Usage hy_agency_list(hydat_path = NULL) Arguments hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. hy_annual_instant_peaks 5 Value A tibble of agencies Source HYDAT See Also Other HYDAT functions: hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_agency_list() ## End(Not run) hy_annual_instant_peaks Extract annual max/min instantaneous flows and water levels from HY- DAT database Description Provides wrapper to turn the ANNUAL_INSTANT_PEAKS table in HYDAT into a tidy data frame of instantaneous flows and water levels. station_number and prov_terr_state_loc can both be supplied. Usage hy_annual_instant_peaks( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL, start_year = NULL, end_year = NULL ) 6 hy_annual_instant_peaks Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. start_year First year of the returned record end_year Last year of the returned record Value A tibble of hy_annual_instant_peaks. Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: ## Multiple stations province not specified hy_annual_instant_peaks(station_number = c("08NM083", "08NE102")) ## Multiple province, station number not specified hy_annual_instant_peaks(prov_terr_state_loc = c("AB", "YT")) ## End(Not run) hy_annual_stats 7 hy_annual_stats Extract annual statistics information from the HYDAT database Description Provides wrapper to turn the ANNUAL_STATISTICS table in HYDAT into a tidy data frame of annual statistics. Statistics provided include MEAN, MAX and MIN on an annual basis. Usage hy_annual_stats( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL, start_year = "ALL", end_year = "ALL" ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. start_year First year of the returned record end_year Last year of the returned record Format A tibble with 8 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number Parameter Parameter being measured. Only possible values are FLOW and LEVEL Year Year of record. Sum_stat Summary statistic being used. Value Value of the measurement. If Parameter equals FLOW the units are m^3/s. If Parameter equals LEVEL the units are metres. Date Observation date. Formatted as a Date class. MEAN is a annual summary and therefore has an NA value for Date. Symbol Measurement/river conditions 8 hy_daily Value A tibble of hy_annual_stats. Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: ## Multiple stations province not specified hy_annual_stats(station_number = c("08NM083", "05AE027")) ## Multiple province, station number not specified hy_annual_stats(prov_terr_state_loc = c("AB", "SK")) ## End(Not run) hy_daily Extract all daily water level and flow measurements Description A thin wrapper around hy_daily_flows and ‘hy_daily_levels“ that returns a data frames that con- tains both parameters. All arguments are passed directly to these functions. Usage hy_daily( station_number = NULL, prov_terr_state_loc = NULL, hydat_path = NULL, ... ) hy_daily 9 Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. ... See hy_daily_flows() arguments Format A tibble with 5 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number Date Observation date. Formatted as a Date class. Parameter Parameter being measured. Value Discharge value. The units are m^3/s. Symbol Measurement/river conditions Value A tibble of daily flows and levels Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_daily(station_number = c("02JE013", "08MF005")) ## End(Not run) 10 hy_daily_flows hy_daily_flows Extract daily flows information from the HYDAT database Description Provides wrapper to turn the DLY_FLOWS table in HYDAT into a tidy data frame of daily flows. station_number and prov_terr_state_loc can both be supplied. If both are omitted all values from the hy_stations table are returned. That is a large tibble for hy_daily_flows. Usage hy_daily_flows( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL, start_date = NULL, end_date = NULL, symbol_output = "code" ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. start_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. end_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. symbol_output Set whether the raw code, or the english or the french translations are out- putted. Default value is code. Format A tibble with 5 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number Date Observation date. Formatted as a Date class. Parameter Parameter being measured. Only possible value is Flow Value Discharge value. The units are m^3/s. Symbol Measurement/river conditions hy_daily_levels 11 Value A tibble of daily flows Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: # download_hydat() hy_daily_flows( station_number = c("08MF005"), start_date = "1996-01-01", end_date = "2000-01-01" ) hy_daily_flows(prov_terr_state_loc = "PE") ## End(Not run) hy_daily_levels Extract daily levels information from the HYDAT database Description Provides wrapper to turn the DLY_LEVELS table in HYDAT into a tidy data frame. The primary value returned by this function is discharge. station_number and prov_terr_state_loc can both be supplied. If both are omitted all values from the hy_stations table are returned. That is a large vector for hy_daily_levels. Usage hy_daily_levels( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL, start_date = NULL, end_date = NULL, 12 hy_daily_levels symbol_output = "code" ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. start_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. end_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. symbol_output Set whether the raw code, or the english or the french translations are out- putted. Default value is code. Format A tibble with 5 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number Date Observation date. Formatted as a Date class. Parameter Parameter being measured. Only possible value is Level Value Level value. The units are metres. Symbol Measurement/river conditions Value A tibble of daily levels Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() hy_data_symbols 13 Examples ## Not run: hy_daily_levels( station_number = c("02JE013", "08MF005"), start_date = "1996-01-01", end_date = "2000-01-01" ) hy_daily_levels(prov_terr_state_loc = "PE") ## End(Not run) hy_data_symbols DATA SYMBOLS look-up table Description A look table for data symbols Usage hy_data_symbols Format A tibble with 5 rows and 3 variables: SYMBOL_ID Symbol code SYMBOL_EN Description of Symbol (English) SYMBOL_FR Description of Symbol (French) Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() 14 hy_datum_list hy_data_types DATA TYPES look-up table Description A look table for data types Usage hy_data_types Format A tibble with 5 rows and 3 variables: DATA_TYPE Data type code DATA_TYPE_EN Descriptive data type (English) DATA_TYPE_FR Descriptive data type (French) Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() hy_datum_list Extract datum list from HYDAT database Description DATUM look-up Table Usage hy_datum_list(hydat_path = NULL) hy_dir 15 Arguments hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. Value A tibble of DATUMS Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_datum_list() ## End(Not run) hy_dir Output OS-independent path to the HYDAT sqlite database Description Provides the download location for download_hydat in an OS independent manner. Usage hy_dir(...) Arguments ... arguments potentially passed to rappdirs::user_data_dir 16 hy_monthly_flows Examples ## Not run: hy_dir() ## End(Not run) hy_monthly_flows Extract monthly flows information from the HYDAT database Description Tidy data of monthly flows information from the monthly_flows HYDAT table. station_number and prov_terr_state_loc can both be supplied. If both are omitted all values from the hy_stations table are returned. That is a large vector for hy_monthly_flows. Usage hy_monthly_flows( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL, start_date = NULL, end_date = NULL ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. start_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. end_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. hy_monthly_flows 17 Format A tibble with 8 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number Year Year of record. Month Numeric month value Full_Month Logical value is there is full record from Month No_days Number of days in that month Sum_stat Summary statistic being used. Value Value of the measurement in m^3/s. Date_occurred Observation date. Formatted as a Date class. MEAN is a annual summary and therefore has an NA value for Date. Value A tibble of monthly flows. Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_monthly_flows( station_number = c("02JE013", "08MF005"), start_date = "1996-01-01", end_date = "2000-01-01" ) hy_monthly_flows(prov_terr_state_loc = "PE") ## End(Not run) 18 hy_monthly_levels hy_monthly_levels Extract monthly levels information from the HYDAT database Description Tidy data of monthly river or lake levels information from the DLY_LEVELS HYDAT table. station_number and prov_terr_state_loc can both be supplied. If both are omitted all val- ues from the hy_stations table are returned. That is a large vector for hy_monthly_levels. Usage hy_monthly_levels( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL, start_date = NULL, end_date = NULL ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. start_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. end_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. Format A tibble with 8 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number Year Year of record. Month Numeric month value Full_month Logical value is there is full record from Month No_days Number of days in that month Sum_stat Summary statistic being used. hy_plot 19 Value Value of the measurement in metres. Date_occurred Observation date. Formatted as a Date class. MEAN is a annual summary and therefore has an NA value for Date. Value A tibble of monthly levels. Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_monthly_levels( station_number = c("02JE013", "08MF005"), start_date = "1996-01-01", end_date = "2000-01-01" ) hy_monthly_levels(prov_terr_state_loc = "PE") ## End(Not run) hy_plot This function is deprecated in favour of generic plot methods Description This is an easy way to visualize a single station using base R graphics. More complicated plotting needs should consider using ggplot2. Inputting more 5 stations will result in very busy plots and longer load time. Legend position will sometimes overlap plotted points. Usage hy_plot( station_number = NULL, Parameter = c("Flow", "Level", "Suscon", "Load") ) 20 hy_reg_office_list Arguments station_number A (or several) seven digit Water Survey of Canada station number. Parameter Parameter of interest. Either "Flow" or "Level". hy_reg_office_list Extract regional office list from HYDAT database Description OFFICE look-up Table Usage hy_reg_office_list(hydat_path = NULL) Arguments hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. Value A tibble of offices Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_reg_office_list() ## End(Not run) hy_remote 21 hy_remote Get the version date of HYDAT that is current on the ECCC website Description Retrieve the date of the HYDAT version available for download. Usage hy_remote() hy_sed_daily_loads Extract daily sediment load information from the HYDAT database Description Provides wrapper to turn the SED_DLY_LOADS table in HYDAT into a tidy data frame of daily sediment load information. station_number and prov_terr_state_loc can both be supplied. If both are omitted all values from the hy_stations table are returned. That is a large vector for hy_sed_daily_loads. Usage hy_sed_daily_loads( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL, start_date = NULL, end_date = NULL ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. start_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. end_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. 22 hy_sed_daily_suscon Format A tibble with 4 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number Date Observation date. Formatted as a Date class. Parameter Parameter being measured. Only possible value is Load Value Discharge value. The units are tonnes. Value A tibble of daily suspended sediment loads Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_sed_daily_loads(prov_terr_state_loc = "PE") ## End(Not run) hy_sed_daily_suscon Extract daily suspended sediment concentration information from the HYDAT database Description Provides wrapper to turn the SED_DLY_SUSCON table in HYDAT into a tidy data frame of daily suspended sediment concentration information. station_number and prov_terr_state_loc can both be supplied. If both are omitted all values from the hy_stations table are returned. That is a large vector for hy_sed_daily_suscon. hy_sed_daily_suscon 23 Usage hy_sed_daily_suscon( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL, start_date = NULL, end_date = NULL, symbol_output = "code" ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. start_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. end_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. symbol_output Set whether the raw code, or the english or the french translations are out- putted. Default value is code. Format A tibble with 5 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number Date Observation date. Formatted as a Date class. Parameter Parameter being measured. Only possible value is Suscon Value Discharge value. The units are mg/l. Symbol Measurement/river conditions Value A tibble of daily suspended sediment concentration Source HYDAT 24 hy_sed_monthly_loads See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_sed_daily_suscon(station_number = "01CE003") ## End(Not run) hy_sed_monthly_loads Extract monthly flows information from the HYDAT database Description Tidy data of monthly loads information from the SED_DLY_LOADS HYDAT table. station_number and prov_terr_state_loc can both be supplied. If both are omitted all values from the hy_stations table are returned. That is a large vector for hy_sed_monthly_loads. Usage hy_sed_monthly_loads( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL, start_date = NULL, end_date = NULL ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. hy_sed_monthly_loads 25 start_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. end_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. Format A tibble with 8 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number Year Year of record. Month Numeric month value Full_Month Logical value is there is full record from Month No_days Number of days in that month Sum_stat Summary statistic being used. Value Value of the measurement in tonnes. Date_occurred Observation date. Formatted as a Date class. MEAN is a annual summary and therefore has an NA value for Date. Value A tibble of monthly sediment loads. Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_sed_monthly_loads(station_number = "01CE003") ## End(Not run) 26 hy_sed_monthly_suscon hy_sed_monthly_suscon Extract monthly flows information from the HYDAT database Description Tidy data of monthly suspended sediment concentration information from the SED_DLY_SUSCON HYDAT table. station_number and prov_terr_state_loc can both be supplied. If both are omitted all values from the hy_stations table are returned. That is a large vector for hy_sed_monthly_suscon. Usage hy_sed_monthly_suscon( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL, start_date = NULL, end_date = NULL ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. start_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. end_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. Format A tibble with 8 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number Year Year of record. Month Numeric month value Full_Month Logical value is there is full record from Month No_days Number of days in that month Sum_stat Summary statistic being used. hy_sed_samples 27 Value Value of the measurement in mg/l. Date_occurred Observation date. Formatted as a Date class. MEAN is a annual summary and therefore has an NA value for Date. Value A tibble of monthly suspended sediment concentrations. Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_sed_monthly_suscon(station_number = "08MF005") ## End(Not run) hy_sed_samples Extract instantaneous sediment sample information from the HYDAT database Description Provides wrapper to turn the hy_sed_samples table in HYDAT into a tidy data frame of instan- taneous sediment sample information. station_number and prov_terr_state_loc can both be supplied. If both are omitted all values from the hy_stations table are returned. That is a large vector for hy_sed_samples. Usage hy_sed_samples( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL, start_date = NULL, end_date = NULL ) 28 hy_sed_samples Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. start_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. end_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. Format A tibble with 19 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number SED_DATA_TYPE Contains the type of sampling method used in collecting sediment for a station Date Contains the time to the nearest minute of when the sample was taken SAMPLE_REMARK_CODE Descriptive Sediment Sample Remark in English TIME_SYMBOL An "E" symbol means the time is an estimate only FLOW Contains the instantaneous discharge in cubic metres per second at the time the sample was taken SYMBOL_EN Indicates a condition where the daily mean has a larger than expected error SAMPLER_TYPE Contains the type of measurement device used to take the sample SAMPLING_VERTICAL_LOCATION The location on the cross-section of the river at which the single sediment samples are collected. If one of the standard locations is not used the distance in meters will be shown SAMPLING_VERTICAL_EN Indicates sample location relative to the regular measurement cross- section or the regular sampling site TEMPERATURE Contains the instantaneous water temperature in Celsius at the time the sample was taken CONCENTRATION_EN Contains the instantaneous concentration sampled in milligrams per litre SV_DEPTH2 Depth 2 for split vertical depth integrating (m) Value A tibble of instantaneous sediment samples data hy_sed_samples_psd 29 Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_sed_samples(station_number = "01CA004") ## End(Not run) hy_sed_samples_psd Extract instantaneous sediment sample particle size distribution infor- mation from the HYDAT database Description Provides wrapper to turn the hy_sed_samples_psd table in HYDAT into a tidy data frame of instan- taneous sediment sample particle size distribution. station_number and prov_terr_state_loc can both be supplied. If both are omitted all values from the hy_stations() table are returned. That is a large vector for hy_sed_samples_psd. Usage hy_sed_samples_psd( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL, start_date = NULL, end_date = NULL ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. 30 hy_sed_samples_psd prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. start_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. end_date Leave blank if all dates are required. Date format needs to be in YYYY-MM- DD. Date is inclusive. Format A tibble with 5 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number SED_DATA_TYPE Contains the type of sampling method used in collecting sediment for a station Date Contains the time to the nearest minute of when the sample was taken PARTICLE_SIZE Particle size (mm) PERCENT Contains the percentage values for indicated particle sizes for samples collected Value A tibble of sediment sample particle size data Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_sed_samples_psd(station_number = "01CA004") ## End(Not run) hy_set_default_db 31 hy_set_default_db Set the default database path Description For many reasons, it may be convenient to set the default database location to somewhere other than the global default. Users may wish to use a previously downloaded version of the database for reproducibility purposes, store hydat somewhere other than hy_dir(). Usage hy_set_default_db(hydat_path = NULL) Arguments hydat_path The path to the a HYDAT sqlite3 database file (e.g., hy_test_db) Value returns the previous value of hy_default_db. Examples ## Not run: # set default to the test database hy_set_default_db(hy_test_db()) # get the default value hy_default_db() # set back to the default db location hy_set_default_db(NULL) ## End(Not run) hy_src Open a connection to the HYDAT database Description This function gives low-level access to the underlying HYDAT database used by other functions. Many of these tables are too large to load into memory, so it is best to use dplyr to dplyr::filter() them before using dplyr::collect() to read them into memory. 32 hy_src Usage hy_src(hydat_path = NULL) hy_src_disconnect(src) Arguments hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. src A as returned by hy_src(). Value A SQLite DBIConnection See Also download_hydat() Examples ## Not run: library(dplyr) # src is a src_sqlite src <- hy_src(hydat_path = hy_test_db()) src_tbls(src) # to get a table, use dplyr::tbl() tbl(src, "STATIONS") # one you're sure the results are what you want # get a data.frame using collect() tbl(src, "STATIONS") %>% filter(PROV_TERR_STATE_LOC == "BC") %>% collect() # close the connection to the database hy_src_disconnect(src) ## End(Not run) hy_stations 33 hy_stations Extract station information from the HYDAT database Description Provides wrapper to turn the hy_stations table in HYDAT into a tidy data frame of station infor- mation. station_number and prov_terr_state_loc can both be supplied. If both are omitted all values from the hy_stations table are returned. This is the entry point for most analyses is tidyhydat as establish the stations for consideration is likely the first step in many instances. Usage hy_stations( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. Format A tibble with 15 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number STATION_NAME Official name for station identification PROV_TERR_STATE_LOC The province, territory or state in which the station is located REGIONAL_OFFICE_ID The identifier of the regional office responsible for the station. Links to hy_reg_office_list HYD_STATUS Current status of discharge or level monitoring in the hydrometric network SED_STATUS Current status of sediment monitoring in the hydrometric network LATITUDE North-South Coordinates of the gauging station in decimal degrees LONGITUDE East-West Coordinates of the gauging station in decimal degrees DRAINAGE_AREA_GROSS The total surface area that drains to the gauge site (km^2) 34 hy_stations DRAINAGE_AREA_EFFECT The portion of the drainage basin that contributes runoff to the gauge site, calculated by subtracting any noncontributing portion from the gross drainage area (km^2) RHBN Logical. Reference Hydrometric Basin Network station. The Reference Hydrometric Basin Network (RHBN) is a sub-set of the national network that has been identified for use in the detection, monitoring, and assessment of climate change. REAL_TIME Logical. Indicates if a station has the capacity to deliver data in real-time or near real-time CONTRIBUTOR_ID Unique ID of an agency that contributes data to the HYDAT database. The agency is non-WSC and non WSC funded OPERATOR_ID Unique ID of an agency that operates a hydrometric station DATUM_ID Unique ID for a datum Value A tibble of stations and associated metadata Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: ## Multiple stations province not specified hy_stations(station_number = c("08NM083", "08NE102")) ## Multiple province, station number not specified hy_stations(prov_terr_state_loc = c("AB", "YT")) ## End(Not run) hy_stn_data_coll 35 hy_stn_data_coll Extract station data collection from HYDAT database Description hy_stn_data_coll look-up Table Usage hy_stn_data_coll( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. Format A tibble with 6 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number DATA_TYPE The type of data Year_from First year of use Year_to Last year of use MEASUREMENT The sampling method used in the collection of sediment data or the type of the gauge used in the collection of the hydrometric data OPERATION The schedule of station operation for the collection of sediment or hydrometric data Value A tibble of hy_stn_data_coll Source HYDAT 36 hy_stn_data_range See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_stn_data_coll(station_number = c("02JE013", "08MF005")) ## End(Not run) hy_stn_data_range Extract station data range from HYDAT database Description hy_stn_data_range look-up Table Usage hy_stn_data_range( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. hy_stn_datum_conv 37 Format A tibble with 6 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number DATA_TYPE Code for the type of data SED_DATA_TYPE Code for the type of instantaneous sediment data Year_from First year of use Year_to Last year of use RECORD_LENGTH Number of years of data available in the HYDAT database Value A tibble of hy_stn_data_range Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_op_schedule(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_stn_data_range(station_number = c("02JE013", "08MF005")) ## End(Not run) hy_stn_datum_conv Extract station datum conversions from HYDAT database Description hy_stn_datum_conv look-up Table 38 hy_stn_datum_conv Usage hy_stn_datum_conv( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. Format A tibble with 4 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number DATUM_FROM Identifying a datum from which water level is being converted DATUM_TO Identifying a datum to which water level is being converted CONVERSTION_FACTOR The conversion factor applied to water levels referred to one datum to obtain water levels referred to another datum Value A tibble of hy_stn_datum_conv Examples ## Not run: hy_stn_datum_conv(station_number = c("02JE013", "08MF005")) ## End(Not run) hy_stn_datum_unrelated 39 hy_stn_datum_unrelated Extract station datum unrelated from HYDAT database Description hy_stn_datum_unrelated look-up Table Usage hy_stn_datum_unrelated( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. Format A tibble with 4 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number DATUM_ID Unique code identifying a datum Year_from First year of use Year_to Last year of use Value A tibble of hy_stn_datum_unrelated Examples ## Not run: hy_stn_datum_unrelated() ## End(Not run) 40 hy_stn_op_schedule hy_stn_op_schedule Extract station operation schedule from HYDAT database Description hy_stn_op_schedule look-up Table Usage hy_stn_op_schedule( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. Format A tibble with 6 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number DATA_TYPE The type of data Year Year of operation schedule Month_from First month of use Month_to Last month of use Value A tibble of hy_stn_op_schedule Source HYDAT hy_stn_regulation 41 See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_regulation(), hy_version() Examples ## Not run: hy_stn_op_schedule(station_number = c("02JE013")) ## End(Not run) hy_stn_regulation Extract station regulation from the HYDAT database Description Provides wrapper to turn the hy_stn_regulation table in HYDAT into a tidy data frame of station regulation. station_number and prov_terr_state_loc can both be supplied. If both are omitted all values from the hy_stations table are returned. Usage hy_stn_regulation( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. 42 hy_stn_remarks Format A tibble with 4 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number Year_from First year of use Year_to Last year of use REGULATED logical Value A tibble of stations, years of regulation and the regulation status Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_version() Examples ## Not run: ## Multiple stations province not specified hy_stn_regulation(station_number = c("08NM083", "08NE102")) ## Multiple province, station number not specified hy_stn_regulation(prov_terr_state_loc = c("AB", "YT")) ## End(Not run) hy_stn_remarks Extract station remarks from HYDAT database Description hy_stn_remarks look-up Table hy_stn_remarks 43 Usage hy_stn_remarks( station_number = NULL, hydat_path = NULL, prov_terr_state_loc = NULL ) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. Format A tibble with 4 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number REMARK_TYPE Type of Remark Year Year of the remark REMARK Remark Value A tibble of hy_stn_remarks Examples ## Not run: hy_stn_remarks(station_number = c("02JE013", "08MF005")) ## End(Not run) 44 hy_version hy_test_db Get the location of the HYDAT database Description The full HYDAT database needs to be downloaded from download_hydat, but for testing purposes, a small test database is included in this package. Use hydat_path = hy_test_db() in hy_* functions to explicitly use the test database; use hydat_path = hy_downloaded_db() to explicitly use the full, most recent downloaded database (this is also the path returned by hy_default_db()). Usage hy_test_db() hy_downloaded_db() hy_default_db() Value The file location of a HYDAT database. See Also hy_src, hy_set_default_db. Examples ## Not run: hy_test_db() hy_downloaded_db() hy_default_db() ## End(Not run) hy_version Extract version number from HYDAT database Description A function to get version number of hydat Usage hy_version(hydat_path = NULL) param_id 45 Arguments hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. Value version number and release date Source HYDAT See Also Other HYDAT functions: hy_agency_list(), hy_annual_instant_peaks(), hy_annual_stats(), hy_daily_flows(), hy_daily_levels(), hy_daily(), hy_data_symbols, hy_data_types, hy_datum_list(), hy_monthly_flows(), hy_monthly_levels(), hy_reg_office_list(), hy_sed_daily_loads(), hy_sed_daily_suscon(), hy_sed_monthly_loads(), hy_sed_monthly_suscon(), hy_sed_samples_psd(), hy_sed_samples(), hy_stations(), hy_stn_data_coll(), hy_stn_data_range(), hy_stn_op_schedule(), hy_stn_regulation() Examples ## Not run: hy_version() ## End(Not run) param_id Parameter ID Description A tibble of parameter id codes and their corresponding explanation/description specific to the ECCC webservice Usage param_id 46 plot Format A tibble with 8 rows and 7 variables: Parameter Numeric parameter code Code Letter parameter code Name_En Code name in English Name_En Code name in French Unit Parameter units plot Plot historical and realtime data Description This method plots either daily time series data from HYDAT or realtime data from the datamart. These plots are intended to be convenient and quick methods to visualize hydrometric data. Usage ## S3 method for class 'hy' plot(x = NULL, ...) ## S3 method for class 'realtime' plot(x = NULL, Parameter = c("Flow", "Level"), ...) Arguments x Object created by either a hy_daily_* or realtime_dd data retrieval function ... passed to plot() Parameter Parameter of interest. Either "Flow" or "Level". Defaults to "Flow". Methods (by class) • plot(realtime): plot.realtime Examples ## Not run: # One station fraser <- hy_daily_flows("08MF005") plot(fraser) ## End(Not run) ## Not run: # One station pull_station_number 47 fraser_realtime <- realtime_dd("08MF005") plot(fraser_realtime) ## End(Not run) pull_station_number Convenience function to pull station number from tidyhydat functions Description This function mimics dplyr::pull to avoid having to always type dplyr::pull(STATION_NUMBER). Instead we can now take advantage of autocomplete. This can be used with realtime_ and hy_ functions. Usage pull_station_number(.data) Arguments .data A table of data Value A vector of station_numbers Examples ## Not run: hy_stations(prov_terr_state_loc = "PE") %>% pull_station_number() %>% hy_annual_instant_peaks() ## End(Not run) 48 realtime_daily_mean realtime_add_local_datetime Add local datetime column to realtime tibble Description Adds local_datetime and tz_used columns based on either the most common timezone in the original data or a user supplied timezone. This function is meant to used in a pipe with the realtime_dd() function. Usage realtime_add_local_datetime(.data, set_tz = NULL) Arguments .data Tibble created by realtime_dd set_tz A timezone string in the format of OlsonNames() Details Date from realtime_dd is supplied in UTC which is the easiest format to work with across timezones. This function does not change Date from UTC. Rather station_tz specifies the lo- cal timezone name and is useful in instances where realtime_add_local_datetime adjusts lo- cal_datetime to a common timezone that is not the station_tz. This function is most useful when all stations exist within the same timezone. Examples ## Not run: realtime_dd(c("08MF005", "02LA004")) %>% realtime_add_local_datetime() ## End(Not run) realtime_daily_mean Calculate daily means from higher resolution realtime data Description This function is meant to be used within a pipe as a means of easily moving from higher resolution data to daily means. realtime_dd 49 Usage realtime_daily_mean(.data, na.rm = FALSE) Arguments .data A data argument that is designed to take only the output of realtime_dd na.rm a logical value indicating whether NA values should be stripped before the com- putation proceeds. Examples ## Not run: realtime_dd("08MF005") %>% realtime_daily_mean() ## End(Not run) realtime_dd Download a tibble of realtime river data from the last 30 days from the Meteorological Service of Canada datamart Description Download realtime river data from the last 30 days from the Meteorological Service of Canada (MSC) datamart. The function will prioritize downloading data collected at the highest resolution. In instances where data is not available at high (hourly or higher) resolution daily averages are used. Currently, if a station does not exist or is not found, no data is returned. Usage realtime_dd(station_number = NULL, prov_terr_state_loc = NULL) Arguments station_number A seven digit Water Survey of Canada station number. If this argument is omit- ted, the value of prov_terr_state_loc is returned. prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. 50 realtime_dd Format A tibble with 8 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number PROV_TERR_STATE_LOC The province, territory or state in which the station is located Date Observation date and time for last thirty days. Formatted as a POSIXct class in UTC for consistency. Parameter Parameter being measured. Only possible values are Flow and Level Value Value of the measurement. If Parameter equals Flow the units are m^3/s. If Parameter equals Level the units are metres. Grade reserved for future use Symbol reserved for future use Code quality assurance/quality control flag for the discharge station_tz Station timezone based on tidyhydat::allstations$station_tz Value A tibble of water flow and level values. The date and time of the query (in UTC) is also stored as an attribute. See Also Other realtime functions: realtime_stations(), realtime_ws() Examples ## Not run: ## Download from multiple provinces realtime_dd(station_number = c("01CD005", "08MF005")) ## To download all stations in Prince Edward Island: pei <- realtime_dd(prov_terr_state_loc = "PE") ## Access the time of query attributes(pei)$query_time ## End(Not run) realtime_plot 51 realtime_plot Convenience function to plot realtime data Description This is an easy way to visualize a single station using base R graphics. More complicated plotting needs should consider using ggplot2. Inputting more 5 stations will result in very busy plots and longer load time. Legend position will sometimes overlap plotted points. Usage realtime_plot(station_number = NULL, Parameter = c("Flow", "Level")) Arguments station_number A seven digit Water Survey of Canada station number. Can only be one value. Parameter Parameter of interest. Either "Flow" or "Level". Defaults to "Flow". Value A plot of recent realtime values Examples ## Not run: ## One station realtime_plot("08MF005") ## Multiple stations realtime_plot(c("07EC002", "01AD003")) ## End(Not run) realtime_stations Download a tibble of active realtime stations Description An up to date dataframe of all stations in the Realtime Water Survey of Canada hydrometric network operated by Environment and Climate Change Canada Usage realtime_stations(prov_terr_state_loc = NULL) 52 realtime_ws Arguments prov_terr_state_loc Province, state or territory. If this argument is omitted, the value of station_number is returned. See unique(allstations$prov_terr_state_loc). Will also ac- cept CA to return only Canadian stations. Format A tibble with 6 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number STATION_NAME Official name for station identification LATITUDE North-South Coordinates of the gauging station in decimal degrees LONGITUDE East-West Coordinates of the gauging station in decimal degrees PROV_TERR_STATE_LOC The province, territory or state in which the station is located TIMEZONE Timezone of the station See Also Other realtime functions: realtime_dd(), realtime_ws() Examples ## Not run: ## Available inputs for prov_terr_state_loc argument: unique(realtime_stations()$prov_terr_state_loc) realtime_stations(prov_terr_state_loc = "BC") realtime_stations(prov_terr_state_loc = c("QC", "PE")) ## End(Not run) realtime_ws Download realtime data from the ECCC web service Description Function to actually retrieve data from ECCC web service. The maximum number of days that can be queried depends on other parameters being requested. If one station is requested, 18 months of data can be requested. If you continually receiving errors when invoking this function, reduce the number of observations (via station_number, parameters or dates) being requested. realtime_ws 53 Usage realtime_ws( station_number, parameters = NULL, start_date = Sys.Date() - 30, end_date = Sys.Date() ) Arguments station_number Water Survey of Canada station number. parameters parameter ID. Can take multiple entries. Parameter is a numeric code. See param_id for some options though undocumented parameters may be imple- mented. Defaults to Water level provisional, Secondary water level, Tertiary water level, Discharge Provisional, Discharge, sensor, Water temperature, Sec- ondary water temperature, Accumulated precipitation start_date Accepts either YYYY-MM-DD or YYYY-MM-DD HH:MM:SS. If only start date is supplied (i.e. YYYY-MM-DD) values are returned from the start of that day. Defaults to 30 days before current date. Time is supplied in UTC. end_date Accepts either YYYY-MM-DD or YYYY-MM-DD HH:MM:SS. If only a date is supplied (i.e. YYYY-MM-DD) values are returned from the end of that day. Defaults to current date. Time is supplied in UTC. Format A tibble with 6 variables: STATION_NUMBER Unique 7 digit Water Survey of Canada station number Date Observation date and time. Formatted as a POSIXct class as UTC for consistency. Name_En Code name in English Value Value of the measurement. Unit Value units Grade future use Symbol future use Approval future use Parameter Numeric parameter code Code Letter parameter code See Also Other realtime functions: realtime_dd(), realtime_stations() 54 search_stn_name Examples ## Not run: ws_08 <- realtime_ws( station_number = c("08NL071", "08NM174"), parameters = c(47, 5) ) fivedays <- realtime_ws( station_number = c("08NL071", "08NM174"), parameters = c(47, 5), end_date = Sys.Date(), # today start_date = Sys.Date() - 5 # five days ago ) ## End(Not run) search_stn_name A search function for hydrometric station name or number Description Use this search function when you only know the partial station name or want to search. Usage search_stn_name(search_term, hydat_path = NULL) search_stn_number(search_term, hydat_path = NULL) Arguments search_term Only accepts one word. hydat_path The path to the hydat database or NULL to use the default location used by download_hydat. It is also possible to pass in an existing src_sqlite such that the database only needs to be opened once per user-level call. Value A tibble of stations that match the search_term Examples ## Not run: search_stn_name("Cowichan") search_stn_number("08HF") ## End(Not run) Index ∗ HYDAT functions download_hydat(), 32 hy_agency_list, 4 dplyr::collect(), 31 hy_annual_instant_peaks, 5 dplyr::filter(), 31 hy_annual_stats, 7 hy_daily, 8 hy_agency_list, 4, 6, 8, 9, 11–15, 17, 19, 20, hy_daily_flows, 10 22, 24, 25, 27, 29, 30, 34, 36, 37, 41, hy_daily_levels, 11 42, 45 hy_data_symbols, 13 hy_annual_instant_peaks, 5, 5, 8, 9, 11–15, hy_data_types, 14 17, 19, 20, 22, 24, 25, 27, 29, 30, 34, hy_datum_list, 14 36, 37, 41, 42, 45 hy_monthly_flows, 16 hy_annual_stats, 5, 6, 7, 9, 11–15, 17, 19, hy_monthly_levels, 18 20, 22, 24, 25, 27, 29, 30, 34, 36, 37, hy_reg_office_list, 20 41, 42, 45 hy_sed_daily_loads, 21 hy_daily, 5, 6, 8, 8, 11–15, 17, 19, 20, 22, 24, hy_sed_daily_suscon, 22 25, 27, 29, 30, 34, 36, 37, 41, 42, 45 hy_sed_monthly_loads, 24 hy_daily_flows, 5, 6, 8, 9, 10, 12–15, 17, 19, hy_sed_monthly_suscon, 26 20, 22, 24, 25, 27, 29, 30, 34, 36, 37, hy_sed_samples, 27 41, 42, 45 hy_sed_samples_psd, 29 hy_daily_flows(), 9 hy_stations, 33 hy_daily_levels, 5, 6, 8, 9, 11, 11, 13–15, hy_stn_data_coll, 35 17, 19, 20, 22, 24, 25, 27, 29, 30, 34, hy_stn_data_range, 36 36, 37, 41, 42, 45 hy_stn_op_schedule, 40 hy_data_symbols, 5, 6, 8, 9, 11, 12, 13, 14, hy_stn_regulation, 41 15, 17, 19, 20, 22, 24, 25, 27, 29, 30, hy_version, 44 34, 36, 37, 41, 42, 45 ∗ datasets hy_data_types, 5, 6, 8, 9, 11–13, 14, 15, 17, allstations, 3 19, 20, 22, 24, 25, 27, 29, 30, 34, 36, hy_data_symbols, 13 37, 41, 42, 45 hy_data_types, 14 hy_datum_list, 5, 6, 8, 9, 11–14, 14, 17, 19, param_id, 45 20, 22, 24, 25, 27, 29, 30, 34, 36, 37, ∗ realtime functions 41, 42, 45 realtime_dd, 49 hy_default_db, 31 realtime_stations, 51 hy_default_db (hy_test_db), 44 realtime_ws, 52 hy_dir, 15 hy_dir(), 4 allstations, 3 hy_downloaded_db (hy_test_db), 44 hy_monthly_flows, 5, 6, 8, 9, 11–15, 16, 19, download_hydat, 4, 4, 6, 7, 9, 10, 12, 15, 16, 20, 22, 24, 25, 27, 29, 30, 34, 36, 37, 18, 20, 21, 23, 24, 26, 28, 29, 32, 33, 41, 42, 45 35, 36, 38–41, 43–45, 54 hy_monthly_levels, 5, 6, 8, 9, 11–15, 17, 18, 55 56 INDEX 20, 22, 24, 25, 27, 29, 30, 34, 36, 37, hy_test_db, 31, 44 41, 42, 45 hy_version, 5, 6, 8, 9, 11–15, 17, 19, 20, 22, hy_plot, 19 24, 25, 27, 29, 30, 34, 36, 37, 41, 42, hy_reg_office_list, 5, 6, 8, 9, 11–15, 17, 44 19, 20, 22, 24, 25, 27, 29, 30, 33, 34, 36, 37, 41, 42, 45 param_id, 45 hy_remote, 21 plot, 46 hy_sed_daily_loads, 5, 6, 8, 9, 11–15, 17, plot(), 46 19, 20, 21, 24, 25, 27, 29, 30, 34, 36, pull_station_number, 47 37, 41, 42, 45 realtime_add_local_datetime, 48 hy_sed_daily_suscon, 5, 6, 8, 9, 11–15, 17, realtime_daily_mean, 48 19, 20, 22, 22, 25, 27, 29, 30, 34, 36, realtime_dd, 49, 52, 53 37, 41, 42, 45 realtime_plot, 51 hy_sed_monthly_loads, 5, 6, 8, 9, 11–15, 17, realtime_stations, 50, 51, 53 19, 20, 22, 24, 24, 27, 29, 30, 34, 36, realtime_ws, 50, 52, 52 37, 41, 42, 45 hy_sed_monthly_suscon, 5, 6, 8, 9, 11–15, search_stn_name, 54 17, 19, 20, 22, 24, 25, 26, 29, 30, 34, search_stn_number (search_stn_name), 54 36, 37, 41, 42, 45 src_sqlite, 4, 6, 7, 9, 10, 12, 15, 16, 18, 20, hy_sed_samples, 5, 6, 8, 9, 11–15, 17, 19, 20, 21, 23, 24, 26, 28, 29, 32, 33, 35, 36, 22, 24, 25, 27, 27, 30, 34, 36, 37, 41, 38–41, 43, 45, 54 42, 45 hy_sed_samples_psd, 5, 6, 8, 9, 11–15, 17, 19, 20, 22, 24, 25, 27, 29, 29, 34, 36, 37, 41, 42, 45 hy_set_default_db, 31, 44 hy_src, 31, 44 hy_src(), 32 hy_src_disconnect (hy_src), 31 hy_stations, 5, 6, 8, 9, 11–15, 17, 19, 20, 22, 24, 25, 27, 29, 30, 33, 36, 37, 41, 42, 45 hy_stations(), 29 hy_stn_data_coll, 5, 6, 8, 9, 11–15, 17, 19, 20, 22, 24, 25, 27, 29, 30, 34, 35, 37, 41, 42, 45 hy_stn_data_range, 5, 6, 8, 9, 11–15, 17, 19, 20, 22, 24, 25, 27, 29, 30, 34, 36, 36, 41, 42, 45 hy_stn_datum_conv, 37 hy_stn_datum_unrelated, 39 hy_stn_op_schedule, 5, 6, 8, 9, 11–15, 17, 19, 20, 22, 24, 25, 27, 29, 30, 34, 36, 37, 40, 42, 45 hy_stn_regulation, 5, 6, 8, 9, 11–15, 17, 19, 20, 22, 24, 25, 27, 29, 30, 34, 36, 37, 41, 41, 45 hy_stn_remarks, 42

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beam 0.7.0 documentation [beam](index.html#document-index) stable * [About BEAM](index.html#document-about) + [Overview](index.html#overview) + [MATSim Integration](index.html#matsim-integration) - [BeamMobSim](index.html#beammobsim) - [AgentSim](index.html#agentsim) - [PhysSim](index.html#physsim) - [R5 Router](index.html#r5-router) - [MATSim Events](index.html#matsim-events) + [Resource Markets](index.html#resource-markets) + [Dynamic Within-Day Planning](index.html#dynamic-within-day-planning) + [Rich Modal Choice](index.html#rich-modal-choice) + [Plug-in Electric Vehicle Modeling with BEAM](index.html#plug-in-electric-vehicle-modeling-with-beam) + [Contact Information](index.html#contact-information) + [Reports and Papers](index.html#reports-and-papers) + [References](index.html#references) * [User’s Guide](index.html#document-users) + [Getting Started](index.html#getting-started) - [System Requirements](index.html#system-requirements) - [Prerequisites :](index.html#prerequisites) - [GIT-LFS Configuration](index.html#git-lfs-configuration) - [Installing BEAM](index.html#installing-beam) - [Running BEAM](index.html#running-beam) - [Running BEAM with Intellij IDE](index.html#running-beam-with-intellij-ide) - [Scenarios](index.html#scenarios) - [Inputs](index.html#inputs) - [Outputs](index.html#outputs) - [Model Config](index.html#model-config) + [Experiment Manager](index.html#experiment-manager) + [Calibration](index.html#calibration) - [Optimization-based Calibration Principles](index.html#optimization-based-calibration-principles) - [SigOpt Setup](index.html#sigopt-setup) - [Configuration](index.html#configuration) - [Execution](index.html#execution) - [Manage Experiment](index.html#manage-experiment) + [Timezones and GTFS](index.html#timezones-and-gtfs) + [Converting a MATSim Scenario to Run with BEAM](index.html#converting-a-matsim-scenario-to-run-with-beam) - [Conversion Instructions](index.html#conversion-instructions) * [Developer’s Guide](index.html#document-developers) + [Repositories](index.html#repositories) + [Configuration](index.html#configuration) + [Environment Variables](index.html#environment-variables) + [GIT-LFS timeout - how to proceed](index.html#git-lfs-timeout-how-to-proceed) + [Keeping Production Data out of Master Branch](index.html#keeping-production-data-out-of-master-branch) + [Automated Cloud Deployment](index.html#automated-cloud-deployment) - [AWS EC2 Start](index.html#aws-ec2-start) - [AWS EC2 Stop](index.html#aws-ec2-stop) + [Performance Monitoring](index.html#performance-monitoring) - [Beam Metrics Utility (MetricsSupport)](index.html#beam-metrics-utility-metricssupport) - [Beam Metrics Configuration](index.html#beam-metrics-configuration) - [Setup Docker as Metric Backend](index.html#setup-docker-as-metric-backend) + [Tagging Tests for Periodic CI](index.html#tagging-tests-for-periodic-ci) + [Instructions for forking BEAM](index.html#instructions-for-forking-beam) + [Scala tips](index.html#scala-tips) - [Scala Collection](index.html#scala-collection) - [Use lazy logging](index.html#use-lazy-logging) * [BeamAgents](index.html#document-agents) + [Person Agents](index.html#person-agents) + [Ride Hail Agents](index.html#ride-hail-agents) + [Transit Driver Agents](index.html#transit-driver-agents) * [Behaviors](index.html#document-behaviors) + [Mode Choice](index.html#mode-choice) - [Multinomial Logit Mode Choice](index.html#multinomial-logit-mode-choice) - [Latent Class Mode Choice](index.html#latent-class-mode-choice) + [Parking](index.html#parking) + [Refueling](index.html#refueling) * [Event Specifications](index.html#document-events) + [MATSim Events](index.html#matsim-events) - [ActivityStartEvent](index.html#activitystartevent) - [ActivityEndEvent](index.html#activityendevent) - [PersonDepartureEvent](index.html#persondepartureevent) - [PersonArrivalEvent](index.html#personarrivalevent) - [PersonEntersVehicleEvent](index.html#personentersvehicleevent) - [PersonLeavesVehicleEvent](index.html#personleavesvehicleevent) + [BEAM Events](index.html#beam-events) - [ModeChoiceEvent](index.html#modechoiceevent) - [PathTraversalEvent](index.html#pathtraversalevent) * [Model Inputs](index.html#document-inputs) + [Configuration file](index.html#configuration-file) - [Config Options](index.html#config-options) * [Model Outputs](index.html#document-outputs) + [File: /modeChoice.csv](index.html#file-modechoice-csv) + [File: /referenceModeChoice.csv](index.html#file-referencemodechoice-csv) + [File: /realizedMode.csv](index.html#file-realizedmode-csv) + [File: /rideHailRevenue.csv](index.html#file-ridehailrevenue-csv) + [File: /ITERS/it.0/0.averageTravelTimes.csv](index.html#file-iters-it-0-0-averagetraveltimes-csv) + [File: /ITERS/it.0/0.energyUse.png.csv](index.html#file-iters-it-0-0-energyuse-png-csv) + [File: /ITERS/it.0/0.physsimLinkAverageSpeedPercentage.csv](index.html#file-iters-it-0-0-physsimlinkaveragespeedpercentage-csv) + [File: /ITERS/it.0/0.physsimFreeFlowSpeedDistribution.csv](index.html#file-iters-it-0-0-physsimfreeflowspeeddistribution-csv) + [File: /ITERS/it.0/0.rideHailWaitingStats.csv](index.html#file-iters-it-0-0-ridehailwaitingstats-csv) + [File: /ITERS/it.0/0.rideHailIndividualWaitingTimes.csv](index.html#file-iters-it-0-0-ridehailindividualwaitingtimes-csv) + [File: /ITERS/it.0/0.rideHailSurgePriceLevel.csv](index.html#file-iters-it-0-0-ridehailsurgepricelevel-csv) + [File: /ITERS/it.0/0.rideHailRevenue.csv](index.html#file-iters-it-0-0-ridehailrevenue-csv) + [File: /ITERS/it.0/0.tazRideHailSurgePriceLevel.csv](index.html#file-iters-it-0-0-tazridehailsurgepricelevel-csv) + [File: /ITERS/it.0/0.rideHailWaitingSingleStats.csv](index.html#file-iters-it-0-0-ridehailwaitingsinglestats-csv) + [File: /ITERS/it.0/0.rideHailInitialLocation.csv](index.html#file-iters-it-0-0-ridehailinitiallocation-csv) + [File: /stopwatch.txt](index.html#file-stopwatch-txt) + [File: /scorestats.txt](index.html#file-scorestats-txt) + [File: /summaryStats.txt](index.html#file-summarystats-txt) + [File: /ITERS/it.0/0.countsCompare.txt](index.html#file-iters-it-0-0-countscompare-txt) + [File: /ITERS/it.0/0.events.csv](index.html#file-iters-it-0-0-events-csv) + [File: /ITERS/it.0/0.legHistogram.txt](index.html#file-iters-it-0-0-leghistogram-txt) + [File: /ITERS/it.0/0.rideHailTripDistance.csv](index.html#file-iters-it-0-0-ridehailtripdistance-csv) + [File: /ITERS/it.0/0.tripDuration.txt](index.html#file-iters-it-0-0-tripduration-txt) + [File: /ITERS/it.0/0.biasErrorGraphData.txt](index.html#file-iters-it-0-0-biaserrorgraphdata-txt) + [File: /ITERS/it.0/0.biasNormalizedErrorGraphData.txt](index.html#file-iters-it-0-0-biasnormalizederrorgraphdata-txt) * [Protocols](index.html#document-protocols) + [Trip Planning](index.html#trip-planning) - [RoutingRequests](index.html#routingrequests) - [ChoosesMode](index.html#choosesmode) + [Traveling](index.html#traveling) - [Driver](index.html#driver) - [Traveler](index.html#traveler) + [Household](index.html#household) - [Escort](index.html#escort) + [RideHailing](index.html#ridehailing) - [Inquiry](index.html#inquiry) - [Reserve](index.html#reserve) + [Transit](index.html#transit) + [Refueling](index.html#refueling) + [Modify Passenger Schedule Manager](index.html#modify-passenger-schedule-manager) * [DevOps Guide](index.html#document-devops) + [Git LFS](index.html#git-lfs) - [Setup git-lfs Server](index.html#setup-git-lfs-server) + [Jenkins](index.html#jenkins) - [Setup Jenkins Server](index.html#setup-jenkins-server) - [Setup Jenkins Slave](index.html#setup-jenkins-slave) - [Configure Jenkins Master](index.html#configure-jenkins-master) - [Configure Jenkins Jobs](index.html#configure-jenkins-jobs) - [Configure Periodic Jobs](index.html#configure-periodic-jobs) - [References](index.html#references) + [Automated Cloud Deployment](index.html#automated-cloud-deployment) - [Automatic Image (AMI) Update](index.html#automatic-image-ami-update) [beam](index.html#document-index) * [Docs](index.html#document-index) » * beam 0.7.0 documentation * [Edit on GitHub](https://github.com/colinsheppard/beam/blob/7255d94749e4556bab8ee7b604d2500de36a7150/docs/index.rst) --- Welcome to BEAM docs[¶](#welcome-to-beam-docs "Permalink to this headline") =========================================================================== BEAM extends the [Multi-Agent Transportation Simulation Framework (MATSim)](<http://www.matsim.org/>) to enable powerful and scalable analysis of urban transportation systems. Contents: About BEAM[¶](#about-beam "Permalink to this headline") ------------------------------------------------------- ### Overview[¶](#overview "Permalink to this headline") BEAM stands for Behavior, Energy, Autonomy, and Mobility. The model is being developed as a framework for a series of research studies in sustainable transportation at Lawrence Berkeley National Laboratory and the UC Berkeley Institute for Transportation Studies. BEAM is an extension to the MATSim (Multi-Agent Transportation Simulation) model, where agents employ reinforcement learning across successive simulated days to maximize their personal utility through plan mutation (exploration) and selecting between previously executed plans (exploitation). The BEAM model shifts some of the behavioral emphasis in MATSim from across-day planning to within-day planning, where agents dynamically respond to the state of the system during the mobility simulation. In BEAM, agents can plan across all major modes of travel including driving, walking, biking, transit, and transportation network companies (TNCs). These key features are summarized here and described in further detail below: * **MATSim Integration** BEAM leverages the MATSim modeling framework[1], an open source simulation tool with a vibrant community of global developers. MATSim is extensible (BEAM is one of those extensions) which allows modelers to utilize a large suite of tools and plug-ins to serve their research and analytical interests. * **Resource Markets** While BEAM can be used as a tool for modeling and analyzing the detailed operations of a transportation system, it is designed primarily as an approach to modeling resource markets in the transportation sector. The transportation system is composed of several sets of mobility resources that are in limited supply (e.g. road capacities, vehicle seating, TNC fleet availability, refueling infrastructure). By adopting the MATSim utility maximization approach to achieving user equilibrium for traffic modeling, BEAM is able to find the corresponding equilibrium point across all resource markets of interest. * **Dynamic Within-Day Planning** Because BEAM places a heavy emphasis on within-day planning, it is possible to simulate modern mobility services in a manner that reflects the emerging transportation system. For example, a virtual TNC in BEAM responds to customer inquiries by reporting the wait time for a ride, which the BEAM agents consider in their decision on what service or mode to use. * **Rich Modal Choice** BEAM’s mode choice model is structured so that agents select modal strategies (e.g. “car” versus “walk to transit” versus “TNC”) for each tour prior to the simulation day, but resolve the outcome of these strategies within the day (e.g. route selection, standard TNC versus pooled, etc.). BEAM currently supports a simple multinomial logit choice model and a more advanced model is under development and will be fully supported by Spring 2018. * **Transportation Network Companies** TNCs are already changing the mobility landscape and as driverless vehicles come online, the economics of these services will improve substantially. In BEAM, TNCs are modeled as a fleet of taxis controlled by a centralized manager that responds to requests from customers and dispatches vehicles accordingly. In 2018, BEAM will be extended to simulate the behavioral processes of TNC drivers as well as implement control algorithms designed to optimize fleets of fully automated vehicles. * **Designed for Scale** BEAM is written primarily in Scala and leverages the [Akka](https://akka.io/) library for currency which implements the [Actor Model of Computation](<https://en.wikipedia.org/wiki/Actor_model>). This approach simplifies the process of deploying transportation simulations at full scale and utilizing high performance computing resources. BEAM has been designed to integrate with Amazon Web Services including a framework to automatically deploy simulation runs to the cloud. ### MATSim Integration[¶](#matsim-integration "Permalink to this headline") [MATSim](http://www.matsim.org/) is a well established agent-based transportation simulation framework with hundreds of users and developers worldwide. BEAM leverages much of the overall structure and conventions of MATSim, but replaces several facilities with new software. The most important of these are the Mobility Simulation and Router. #### BeamMobSim[¶](#beammobsim "Permalink to this headline") When BEAM is executed, the MATSim engine manages loading of most data (population, households, vehicles, and the network used by PhysSim) as well as executing the MobSim -> Scoring -> Replanning iterative loop. BEAM takes over the MobSim, replacing the legacy MobSim engines (i.e. QSim) with the BeamMobSim.  The BeamMobSim is composed of two simulators, the **AgentSim** and the **PhysSim**. These simulators are related to each other and to the router as illustrated in the following diagram:  #### AgentSim[¶](#agentsim "Permalink to this headline") The AgentSim is designed to execute the daily plans of the population, allowing the agents to dynamically resolve how limited transportation resources are allocated (see [Resource Markets](#resource-markets)). All movements in the AgentSim occur via “teleportation” as discrete events. In other words, given a route and a travel time, an agent simply schedules herself to “arrive” at the destination accordingly. When this occurs, a PathTraversal Event is thrown – one for each vehicle movement, not necessarily each passenger movement – which is used by the PhysSim to simulate traffic flow, resolve congestion, and update travel times in the router so that in future iterations, agents will teleport according to travel times that are consistent with network congestion. #### PhysSim[¶](#physsim "Permalink to this headline") The PhysSim simulates traffic on the road network. The underlying simulation engine is based on the Java Discrete Event Queue Simulator ([JDEQSim](https://www.researchgate.net/publication/239925133_Performance_Improvements_for_Large_Scale_Traffic_Simula-_tion_in_MATSim)) from the MATSim framework. The JDEQSim then simulates traffic flow through the system and updated the Router with new network travel times for use in subsequent iterations. JDEQSim was designed as a MobSim engine for MATSim, so it is capable of simulating activities and movements through the network. In BEAM, we use JDEQSim within PhysSim as purely a **vehicle** movement simulator. As PathTraversalEvents are received by the PhysSim, a set of MATSim Plans are created, with one plan for each vehicle in the AgentSim. These plans include “Activities” but they are just dummy activities that bracket the movement of each vehicle. Currently, PhySim and AgentSim run serially, one after another. This is due to the fact that the PhySim is substantially faster to run than the AgentSim, because the PhysSim does not need to do any routing calculations. As improvements to AgentSim reduce run times, future versions of BEAM will likely allow AgentSim and PhysSim to run concurrently, or even be run in a tightly coupled manner where each teleportation in AgentSim is replaced with a direct simulation of the propagation of vehicles through the network by the PhysSim. #### R5 Router[¶](#r5-router "Permalink to this headline") BEAM uses the [R5 routing engine](https://github.com/conveyal/r5) to accomplish multi-modal routing. Agents from BEAM make request of the router, and the results of the routing calculation are then transformed into objects that are directly usable by the BEAM agents to choose between alternative routes and move throughout the system. #### MATSim Events[¶](#matsim-events "Permalink to this headline") BEAM adopts the MATSim convention of throwing events that correspond to key moments in the agent’s day. But in BEAM, there are two separate event managers, one for the ActorSim and another for the PhySim. The standard events output file (e.g. 0.events.csv) comes from the AgentSim, but in the outputs directory, you will also find an events file from the PhysSim (e.g. 0.physSimEvents.xml.gz). The events from AgentSim pertain to agents while the events in PhysSim pertain to vehicles. This is an important distinction. The following MATSim events are thrown within the AgentSim: * ActivityEndEvent * PersonDepartureEvent * PersonEntersVehicleEvent * PersonLeavesVehicleEvent * PersonArrivalEvent * ActivityStartEvent The following MATSim events are thrown within the PhysSim: * ActivityEndEvent - these are dummy activities that bracket every vehicle movement * PersonDepartureEvent - should be interpreted as **vehicle** departure * LinkEnterEvent * Wait2LinkEvent / VehicleEntersTraffic * LinkLeaveEvent * PersonArrivalEvent - should be interpreted as **vehicle** arrival * ActivityStartEvent - these are dummy activities that bracket every vehicle movement Extensions and modules written to observe the above MATSim events can be seamlessly integrated with BEAM in a read-only manner (i.e. for analysis, summary, visualization purposes). However, extensions that are designed to accomplish “within-day” replanning in MATSim will not be directly compatible with BEAM. This is because BEAM already does extensive “within-day” replanning in a manner that is substantially different from QSim. In addition to the standard MATSim events described above, BEAM throws additional events that correspond to the act of choosing a Mode (ModeChoiceEvent) and of vehicle movements through the network (PathTraversalEvent). All events (both MATSim and BEAM-specific) and their field descriptions are described in further detail in [Event Specifications](index.html#event-specifications). ### Resource Markets[¶](#resource-markets "Permalink to this headline")  While BEAM can be used as a tool for modeling and analyzing the detailed operations of a transportation system, it is designed primarily as an approach to modeling resource markets in the transportation sector. The transportation system is composed of several sets of mobility resources that are in limited supply (e.g. road capacities, vehicle seating, TNC fleet availability, refueling infrastructure). With the exception of road capacities, all resources in BEAM are explicitly modeled. For example, there are a finite number of seats available on transit vehicles and there are a finite number of TNC drivers. As resources are utilized by travelers, they become unavailable to other travelers. This resource competition is resolved dynamically within the AgentSim, making it impossible for multiple agents to simultaneously utilize the same resource. The degree to which agents use resources is determined both by resource availability and traveler behavior. As the supply of TNC drivers becomes limited, the wait times for hailing a ride increase, which leads to lower utility scores in the mode choice process and therefore reduced consumption of that resource. By adopting the MATSim utility maximization approach to achieving user equilibrium for traffic modeling, BEAM is able to find the corresponding equilibrium point across all resource markets of interest. Each agent maximizes her utility through the replanning process (which occurs outside the simulation day) as well as within the day through dynamic choice processes (e.g. choosing mode based on with-in day evaluation of modal alternatives). Ultimately, the combined outcome of running BEAM over successive iterations is a system equilibrium that balances the trade-offs between all resources in the system. In the figure above, the resource markets that are functioning in BEAM v0.5 are boxed in blue. Future versions of BEAM (planned for 2018) will include the additional resources boxed in red. ### Dynamic Within-Day Planning[¶](#dynamic-within-day-planning "Permalink to this headline") Because BEAM places a heavy emphasis on within-day planning, it is possible to simulate modern mobility services in a manner that reflects the emerging transportation system. For example, a virtual TNC in BEAM responds to customer inquiries by reporting the wait time for a ride, which the BEAM agents consider in their decision on what service or mode to use. ### Rich Modal Choice[¶](#rich-modal-choice "Permalink to this headline") BEAM’s mode choice model is structured so that agents select modal strategies (e.g. “car” versus “walk to transit” versus “TNC”) for each tour prior to the simulation day, but resolve the outcome of these strategies within the day (e.g. route selection, standard TNC versus pooled, etc.). BEAM currently supports a simple multinomial logit choice model and a more advanced model is under development and will be fully supported by Spring 2018. ### Plug-in Electric Vehicle Modeling with BEAM[¶](#plug-in-electric-vehicle-modeling-with-beam "Permalink to this headline") In 2016, BEAM was originally developed to simulate personally-owned plug-in electric vehicles (PEVs), with an emphasis on detailed representation of charging infrastructure and driver behavior around charging. In 2017, BEAM underwent a major revision, designed to simulate all modes of travel and to prepare the software for scalability and extensibility. We therefore no longer support the “PEV Only” version of BEAM, though the codebase is still available on the BEAM Github repository under the branch [pev-only](https://github.com/LBNL-UCB-STI/beam/tree/pev-only). In 2018, PEVs will be re-implemented in BEAM following the new framework. In addition, BEAM will support modeling the refueling of fleets of electrified TNCs. The key features of the “PEV Only” version of BEAM are summarized here and described in further detail in reports linked below. * **Detailed Representation of Charging Infrastructure** In BEAM, individual chargers are explicitly represented in the region of interest. Chargers are organized as sites that can have multiple charging points which can have multiple plugs of any plug type. The plug types are defined by their technical characteristics (i.e. power capacity, price, etc.) and their compatibility with vehicles types (e.g. Tesla chargers vs. CHAdeMO vs. SAE). Physical access to chargers is also represented explicitly, i.e., charging points can only be accessed by a limited number of parking spaces. Chargers are modeled as queues, which can be served in an automated fashion (vehicle B begins charging as soon as vehicle A ends) or manually by sending notifications to agents that it is their turn to begin a charging session. * **Robust Behavioral Modeling** The operational decisions made by PEV drivers are modeled using discrete choice models, which can be parameterized based on the outcomes of stated preference surveys or reveled preference analyses. For example, the decision of whether and where to charge is currently modeled in BEAM as a nested logit choice that considers a variety of factors including the location, capacity, and price of all chargers within a search radius in addition to the state of charge of the PEV and features of the agent’s future mobility needs for the day. The utility functions for the model are in part based on empirical work by Wen et al.[2] who surveyed PEV drivers and analyzed the factors that influence their charging decisions. ### Contact Information[¶](#contact-information "Permalink to this headline") Primary Technical Contacts: Colin Sheppard [colin.sheppard@lbl.gov](mailto:colin.sheppard%40lbl.gov) Rashid Waraich [rwaraich@lbl.gov](mailto:rwaraich%40lbl.gov) ### Reports and Papers[¶](#reports-and-papers "Permalink to this headline") “Modeling Plug-in Electric Vehicle Trips, Charging Demand and Infrastructure”. ### References[¶](#references "Permalink to this headline") 1. Horni, A., Nagel, K. and Axhausen, K.W. (eds.) 2016 [The Multi-Agent Transport Simulation MATSim](http://www.matsim.org/the-book). London: Ubiquity Press. DOI: <http://dx.doi.org/10.5334/baw>. License: CC-BY 4.0. 2. Wen, Y., MacKenzie, D. & Keith, D. Modeling the Charging Choices of Battery Electric Vehicle Drivers Using Stated Preference Data. TRB Proc. Pap. No 16-5618 User’s Guide[¶](#user-s-guide "Permalink to this headline") ----------------------------------------------------------- ### Getting Started[¶](#getting-started "Permalink to this headline") The following guide is designed as a demonstration of using BEAM and involves running the model on a scaled population and transportation system. This is the ideal place to familiarize yourself with the basics of configuring and running BEAM as well as doing small scale tests and analysis. For more advanced utilization or to contribute to the BEAM project, see the [Developer’s Guide](index.html#developers-guide). #### System Requirements[¶](#system-requirements "Permalink to this headline") * At least 8GB RAM * Windows, Mac OSX, Linux * Java Runtime Environment or Java Development Kit 1.8 * To verify your JRE: <https://www.java.com/en/download/help/version_manual.xml> * To download JRE 1.8 (AKA JRE 8): <http://www.oracle.com/technetwork/java/javase/downloads/jre8-downloads-2133155.html> * We also recommend downloading the VIA vizualization app and obtaining a free or paid license: <https://simunto.com/via/> * Git and Git-LFS #### Prerequisites :[¶](#prerequisites "Permalink to this headline") **Install Java** BEAM requires Java 1.8 JDK / JRE to be installed. If a different version of java is already installed on your system, please upgrade the version to 1.8. See this [link](https://www.java.com/en/download/help/version_manual.xml) for steps to check the current version of your JRE. If java is not already installed on your system , please follow this [download manual](https://www.java.com/en/download/manual.jsp) to install java on your system. Please note that BEAM is currently compatible only with Java 1.8 and is not compatible with any of the older or recent versions. **Install Gradle** BEAM uses [gradle](https://gradle.org) as its build tool. If gradle is not already installed, check this [gradle installation guide](https://gradle.org/install) for steps on how to download and install gradle. Once gradle is successfully installed , verify the installation by running the command ``` gradle ``` #### GIT-LFS Configuration[¶](#git-lfs-configuration "Permalink to this headline") The installation process for git-lfs client([v2.3.4](https://github.com/git-lfs/git-lfs/releases/tag/v2.3.4), latest installer has some issue with node-git-lfs) is very simple. For detailed documentation please consult [github guide](https://help.github.com/articles/installing-git-large-file-storage/) for Mac, windows and Linux. To verify successful installation, run following command: ``` $ git lfs install Git LFS initialized. ``` To confirm that you have installed the correct version of client run the following command: ``` $ git lfs env ``` It will print out the installed version, and please make sure it is git-lfs/2.3.4. To update the text pointers with the actual contents of files, run the following command (if it requests credentials, use any username and leave the password empty): ``` $ git lfs pull Git LFS: (98 of 123 files) 343.22 MB / 542.18 MB ``` **Installing git lfs on windows :** With Git LFS windows installation, it is common to have the wrong version of git-lfs installed, because in these latest git client version on windows, git lfs comes packaged with the git client. When installing the git client one needs to uncheck git lfs installation. If mistakenly you installed git lfs with the git client, the following steps are needed to correct it (uninstalling git lfs and installing the required version does not work…): > > * Uninstall git > * Install the git client (uncheck lfs installation) > * Install git lfs version 2.3.4 separately > > > Another alternative to above is to get the old git-lfs.exe from the release archives and replace the executable found in [INSTALL PATH]\mingw6\bin and [INSTALL PATH]\cmd, where the default installation path usually is C:\Program Files\Git #### Installing BEAM[¶](#installing-beam "Permalink to this headline") Clone the beam repository: ``` git clone git@github.com:LBNL-UCB-STI/beam.git ``` Change directories into that repository: ``` cd beam ``` Now checkout the latest stable version of BEAM, v0.7.0: ``` git checkout v0.7.0 ``` Run the gradle command to compile BEAM, this will also downloaded all required dependencies automatically: ``` gradle classes ``` Now you’re ready to run BEAM! #### Running BEAM[¶](#running-beam "Permalink to this headline") Inside of the respository is a folder ‘test/input’ containing several scenarios and configurations you can experiment with. The simplest, smallest, and fastest is the beamville scenario (described below). Try to run beamville with this command: ``` ./gradlew :run -PappArgs="['--config', 'test/input/beamville/beam.conf']" ``` The BEAM application by default sets max RAM allocation to 140g (see **maxRAM** setting in gradle.properties). This needs to be adjusted based on the available memory on your system. The max allocatable RAM value can be overriden by setting the environment variable **MAXRAM** to the required value. On Ubuntu , the environment variable can be set using the below command : ``` export MAXRAM=10g ``` where 10g = 10GB Similarly on windows it can be set using the below command : ``` setx MAXRAM="10g" ``` The outputs are written to the ‘output’ directory, should see results appear in a sub-folder called “beamville\_%DATE\_TIME%”. Optionally you can also run BEAM from your favourite IDE . Check the below section on how to configure and run BEAM using Intellij IDEA. #### Running BEAM with Intellij IDE[¶](#running-beam-with-intellij-ide "Permalink to this headline") IntelliJ IDEA community edition is an open source IDE available for free. It can be downloaded from [here](https://www.jetbrains.com/idea/download/#section=windows) After successful download , run the executable and follow the installation wizard to install Intellij IDEA. When running the IDE for the first time , it asks to import previous settings (if any) from a local path, if no previous settings to choose , select “Do not import settings” and click Ok. **Importing BEAM project into IDE** Once the IDE is successfully installed , proceed with the below steps to import BEAM into the IDE. 1. Open the IDE and agree to the privacy policy and continue (Optional) IDEA walks you through some default configurations set up here . In case you want to skip these steps , choose “Skip and install defaults” and go to step 2 * Select a UI theme of choice and go to Next: Default Plugins * Select only the required plugins (gradle , java are mandatory) and disable the others and go to Next:Feature plugins * Install scala and click “Start using Intellij IDEA” 2. In the welcome menu , select “Import Project” and provide the location of the locally cloned BEAM project 3. Inside the import project screen, select “Import project from external model” and choose “Gradle” from the available and click Next 4. Click Finish. The project should now be successfully imported into the IDE and a build should be initiated automatically. If no build is triggered automatically , you can manually trigger one by going to Build > Build Project. **Installing scala plugin** If optional configuration in step 1 of above section was skipped , scala plugin will not be added automatically . To manually enable scala plugin go to File > Settings > Plugins. Search for scala plugin and click Install. **Setting up scala SDK** Since BEAM is built with java/scala . A scala sdk module needs to be configured to run BEAM. Check the below steps on how to add a scala module to IDEA \* Go to File > Project Settings > Global Libraries \* Click + and select Scala SDK \* Select the required scala SDK from the list , if no SDK found click Create. \* Click “Browse” and select the scala home path or click “Download” (choose 2.12.x version) **Running BEAM from IDE** BEAM requires some arguments to be specified during run-time like the scenario configuration. These configuration settings can be added as a run configuration inside the IDE. Steps to add a new configuration : * Go to Run > Edit Configurations * Click + and from the templates list and select “Application” * Fill in the following values + Main Class : beam.sim.RunBeam + VM options : -Xmx8g + Program Arguments : –config test/input/beamville/beam.conf (this runs beaamville scenario, changes the folder path to run a different scenario) + Working Directory : /home/beam/BEAM + Environment Variables : PWD=/home/beam/BEAM + use submodule of path : beam.beam.main * Click Ok to save the configuration. To add a configuration for a different scenario , follow the above steps and change the folder path to point to the required scenario in program arguments #### Scenarios[¶](#scenarios "Permalink to this headline") We have provided two scenarios for you to explore under the test/input directory. The beamville test scenario is a toy network consisting of a 4 x 4 block gridded road network, a light rail transit agency, a bus transit agency, and a population of ~50 agents.  The sf-light scenario is based on the City of San Francisco, including the SF Muni public transit service and a range of sample populations from 1000 to 25,000 agents.  #### Inputs[¶](#inputs "Permalink to this headline") For more detailed inputs documentation, see [Model Inputs](index.html#model-inputs). BEAM follows the [MATSim convention](http://archive.matsim.org/docs) for most of the inputs required to run a simulation, though some inputs files can alternatively be provided in CSV instead of XML format. Also, the road network and transit system inputs are based on the [R5 requirements](https://github.com/conveyal/r5). The following is a brief overview of the minimum requirements needed to conduct a BEAM run. * A configuration file (e.g. beam.conf) * The person population and corresponding attributes files (e.g. population.xml and populationAttributes.xml) * The household population and corresponding attributes files (e.g. households.xml and householdAttributes.xml) * The personal vehicle fleet (e.g. vehicles.csv) * The definition of vehicle types including for personal vehicles and the public transit fleet (e.g. vehicleTypes.csv) * A directory containing network and transit data used by R5 (e.g. r5/) * The open street map network (e.g. r5/beamville.osm) * GTFS archives, one for each transit agency (e.g. r5/bus.zip) #### Outputs[¶](#outputs "Permalink to this headline") At the conclusion of a BEAM run using the default beamville scenario, the output files in the should look like this when the run is complete:  Each iteration of the run produces a sub-folder under the ITERS directory. Within these, several automatically generated outputs are written including plots of modal usage, TNC dead heading, and energy consumption by mode. In addition, raw outputs are available in the two events file (one from the AgentSim and one from the PhysSim, see [MATSim Events](index.html#matsim-events) for more details), titled %ITER%.events.csv and %ITER%.physSimEvents.xml.gz respectively. #### Model Config[¶](#model-config "Permalink to this headline") To get started, we will focus your attention on a few of the most commonly used and useful configuration parameters that control beam: ``` # Ride Hailing Params beam.agentsim.agents.rideHail.initialization.procedural.numDriversAsFractionOfPopulation=0.05 beam.agentsim.agents.rideHail.defaultCostPerMile=1.25 beam.agentsim.agents.rideHail.defaultCostPerMinute=0.75 # Scaling and Tuning Params; 1.0 results in no scaling beam.agentsim.tuning.transitCapacity = 0.2 beam.agentsim.tuning.transitPrice = 1.0 beam.agentsim.tuning.tollPrice = 1.0 beam.agentsim.tuning.rideHailPrice = 1.0 ``` * numDriversAsFractionOfPopulation - Defines the # of ride hailing drivers to create. Drivers begin the simulation located at or near the homes of existing agents, uniformly distributed. * defaultCostPerMile - One component of the 2 part price of ride hail calculation. * defaultCostPerMinute - One component of the 2 part price of ride hail calculation. * transitCapacity - Scale the number of seats per transit vehicle… actual seats are rounded to nearest whole number. Applies uniformly to all transit vehilces. * transitPrice - Scale the price of riding on transit. Applies uniformly to all transit trips. * tollPrice - Scale the price to cross tolls. * rideHailPrice - Scale the price of ride hailing. Applies uniformly to all trips and is independent of defaultCostPerMile and defaultCostPerMinute described above. I.e. price = (costPerMile + costPerMinute)\*rideHailPrice ### Experiment Manager[¶](#experiment-manager "Permalink to this headline") BEAM features a flexible experiment manager which allows users to conduct multi-factorial experiments with minimal configuration. The tool is powered by Jinja templates ( see more <http://jinja.pocoo.org/docs/2.10/>). We have created two example experiments to demonstrate how to use the experiment manager. The first is a simple 2-factorial experiment that varies some parameters of scientific interest. The second involves varying parameters of the mode choice model as one might do in a calibration exercise. In any experiment, we seek to vary the parameters of BEAM systematically and producing results in an organized, predicable location to facilitate post-processing. For the two factor experiment example, we only need to vary the contents of the BEAM config file (beam.conf) in order to achieve the desired anlaysis. Lets start from building your experiment definitions in experiment.yml ( see example in test/input/beamville/example-experiment/experiment.yml). experiment.yml is a YAML config file which consists of 3 sections: header, defaultParams, and factors. The Header defines the basic properties of the experiment, the title, author, and a path to the configuration file (paths should be relative to the project root): ``` title: Example-Experiment author: MyName beamTemplateConfPath: test/input/beamville/beam.conf ``` The Default Params are used to override any parameters from the BEAM config file for the whole experiment. These values can, in turn, be overridden by factor levels if specified. This section is mostly a convenient way to ensure certain parameters take on specific values without modifying the BEAM config file in use. Experiments consist of ‘factors’, which are a dimension along which you want to vary parameters. Each instance of the factor is a level. In our example, one factor is “transitCapacity” consisting of two levels, “Low” and “High”. You can think about factors as of main influencers (or features) of simulation model while levels are discrete values of each factor. Factors can be designed however you choose, including adding as many factors or levels within those factors as you want. E.g. to create a 3 x 3 experimental design, you would set three levels per factor as in the example below: ``` factors: - title: transitCapacity levels: - name: Low params: beam.agentsim.tuning.transitCapacity: 0.01 - name: Base params: beam.agentsim.tuning.transitCapacity: 0.05 - name: High params: beam.agentsim.tuning.transitCapacity: 0.1 - title: ridehailNumber levels: - name: Low params: beam.agentsim.agents.rideHail.numDriversAsFractionOfPopulation: 0.001 - name: Base params: beam.agentsim.agents.rideHail.numDriversAsFractionOfPopulation: 0.01 - name: High params: beam.agentsim.agents.rideHail.numDriversAsFractionOfPopulation: 0.1 ``` Each level and the baseScenario defines params, or a set of key,value pairs. Those keys are either property names from beam.conf or placeholders from any template config files (see below for an example of this). Param names across factors and template files must be unique, otherwise they will overwrite each other. In our second example (see directory test/input/beamville/example-calibration/), we have added a template file modeChoiceParameters.xml.tpl that allows us to change the values of parameters in BEAM input file modeChoiceParameters.xml. In the experiment.yml file, we have defined 3 factors with two levels each. One level contains the property mnl\_ride\_hail\_intercept, which appears in modeChoiceParameters.xml.tpl as {{ mnl\_ride\_hail\_intercept }}. This placeholder will be replaced during template processing. The same is true for all properties in the defaultParams and under the facts. Placeholders for template files must NOT contain the dot symbol due to special behaviour of Jinja. However it is possible to use the full names of properties from beam.conf (which *do* include dots) if they need to be overridden within this experiment run. Also note that mnl\_ride\_hail\_intercept appears both in the level specification and in the baseScenario. When using a template file (versus a BEAM Config file), each level can only override properties from Default Params section of experiment.yml. Experiment generation can be run using following command: ``` gradle -PmainClass=beam.experiment.ExperimentGenerator -PappArgs="['--experiments', 'test/input/beamville/example-experiment/experiment.yml']" execute ``` It’s better to create a new sub-folder folder (e.g. ‘calibration’ or ‘experiment-1’) in your data input directory and put both templates and the experiment.yml there. The ExperimentGenerator will create a sub-folder next to experiment.yml named runs which will include all of the data needed to run the experiment along with a shell script to execute a local run. The generator also creates an experiments.csv file next to experiment.yml with a mapping between experimental group name, the level name and the value of the params associated with each level. Within each run sub-folder you will find the generated BEAM config file (based on beamTemplateConfPath), any files from the template engine (e.g. modeChoiceParameters.xml) with all placeholders properly substituted, and a runBeam.sh executable which can be used to execute an individual simulation. The outputs of each simulation will appear in the output subfolder next to runBeam.sh ### Calibration[¶](#calibration "Permalink to this headline") This section describes calibrating BEAM simulation outputs to achieve real-world targets (e.g., volumetric traffic counts, mode splits, transit boarding/alighting, etc.). A large number of parameters affect simulation behavior in complex ways such that grid-search tuning methods would be extremely time-consuming. Instead, BEAM uses [SigOpt](http://sigopt.com), which uses Bayesian optimization to rapidly tune scenarios as well as analyze the sensitivity of target metrics to parameters. #### Optimization-based Calibration Principles[¶](#optimization-based-calibration-principles "Permalink to this headline") At a high level, the SigOpt service seeks to find the *optimal value*, \(p^\*\) of an *objective*, \(f\_0: \mathbb{R}^n\rightarrow\mathbb{R}\), which is a function of a vector of *decision variables* \(x\in\mathbb{R}^n\) subject to *constraints*, \(f\_i: \mathbb{R}^n\rightarrow\mathbb{R}, i=1,\ldots,m\). In our calibration problem, \(p^\*\) represents the value of a *metric* representing an aggregate measure of some deviation of simulated values from real-world values. Decision variables are hyperparameters defined in the .conf file used to configure a BEAM simulation. The constraints in this problem are the bounds within which it is believed that the SigOpt optimization algorithm should search. The calibration problem is solved by selecting values of the hyperparameters that minimize the output of the objective function. Operationally, for each calibration attempt, BEAM creates an Experiment using specified Parameter variables, their Bounds`s, and the number of workers (applicable only when using parallel calibration execution) using the SigOpt API. The experiment is assigned a unique ID and then receives a `Suggestion (parameter values to simulate) from the SigOpt API, which assigns a value for each Parameter. Once the simulation has completed, the metric (an implementation of the beam.calibration.api.ObjectiveFunction interface) is evaluated, providing an Observation to the SigOpt API. This completes one iteration of the calibration cycle. At the start of the next iteration new Suggestion is returned by SigOpt and the simulation is re-run with the new parameter values. This process continues for the number of iterations specified in a command-line argument. > > Note: that this is a different type of iteration from the number of iterations of a run of BEAM itself. > Users may wish to run BEAM for several iterations of the co-evolutionary plan modification loop prior to > evaluating the metric. #### SigOpt Setup[¶](#sigopt-setup "Permalink to this headline") Complete the following steps in order to prepare your simulation scenarios for calibration with SigOpt: 1. [Sign up](http://sigopt.com/pricing) for a SigOpt account (note that students and academic researchers may be able to take advantage of [educational pricing](http://sigopt.com/edu) options). 2. [Log-in](http://app.sigopt.com/login) to the SigOpt web interface. 3. Under the [API Tokens](http://app.sigopt.com/tokens/info) menu, retrieve the **API Token** and **Development Token** add the tokens as environmental variables in your execution environment with the keys SIGOPT\_API\_TOKEN and SIGOPT\_DEV\_API\_TOKEN. #### Configuration[¶](#configuration "Permalink to this headline") ##### Prepare YML File[¶](#prepare-yml-file "Permalink to this headline") Configuring a BEAM scenario for calibration proceeds in much the same way as it does for an experiment using the [Experiment Manager](#experiment-manager). In fact, with some minor adjustments, the YAML text file used to define experiments has the same general structure as the one used to specify tuning hyperparameters and ranges for calibration (see example file beam/test/input/beamville/example-calibration/experiment.yml): ``` title: this is the name of the SigOpt experiment beamTemplateConfPath: the config file to be used for the experiments modeChoiceTemplate: mode choice template file numWorkers: this defines for a remote run, how many parallel runs should be executed (number of machines to be started) params: ### ---- run template env variables ---#### EXPERIMENT\_MAX\_RAM: 16g (might be removed in future) S3\_OUTPUT\_PATH\_SUFFIX: "sf-light" (might be removed in future) DROP\_OUTPUT\_ONCOMPLETE: "true" (might be removed in future) IS\_PARALLEL: "false" (might be removed in future) runName: instance name for remote run beamBranch: branch name beamCommit: commit hash deployMode: "execute" executeClass: "beam.calibration.RunCalibration" beamBatch: "false" shutdownWait: "15" shutdownBehavior: "stop" s3Backup: "true" maxRAM: "140g" region: "us-west-2" instanceType: "m4.16xlarge" ``` The major exceptions are the following: * Factors may have only a single numeric parameter, which may (at the moment) only take two levels (High and Low). These act as bounds on the values that SigOpt will try for a particular decision variable. * The level of parallelism is controlled by a new parameter in the header called numberOfWorkers. Setting its value above 1 permits running calibrations in parallel in response to multiple concurrent open Suggestions. ##### Create Experiment[¶](#create-experiment "Permalink to this headline") Use beam.calibration.utils.CreateExperiment to create a new SigOpt experiment. Two inputs are needed for this: a YAML file and a benchmark.csv file (this second parameter might be removed in the near future, as not needed). After running the script you should be able to see the newly created experiment in the SigOpt web interface and the experiment id is also printed out in the console. ##### Set in Config[¶](#set-in-config "Permalink to this headline") One must also select the appropriate implementation of the ObjectiveFunction interface in the .conf file pointed to in the YAML, which implicitly defines the metric and input files. Several example implementations are provided such as ModeChoiceObjectiveFunction. This implementation compares modes used at the output of the simulation with benchmark values. To optimize this objective, it is necessary to have a set of comparison benchmark values, which are placed in the same directory as other calibration files: ``` beam.calibration.objectiveFunction = "ModeChoiceObjectiveFunction_AbsolutErrorWithPreferrenceForModeDiversity" beam.calibration.mode.benchmarkFileLoc=${beam.inputDirectory}"/calibration/benchmark.csv" ``` (Needed for scoring funtions which try to match mode share). #### Execution[¶](#execution "Permalink to this headline") Execution of a calibration experiment requires running the beam.calibration.RunCalibration class using the following arguments: | `--experiments` | production/application-sfbay/calibration/experiment\_counts\_calibration.yml | | `--benchmark` | Location of the benchmark file (production/applicaion-sfbay/calibration/benchmark.csv) | | `--num\_iters` | Number of SigOpt iterations to be conducted (in series). | | `--experiment\_id` | | | If an experimentID has already been defined, add it here to continue an experiment or put | “None” to start a new experiment. | `--run\_type` | Can be local or remote | #### Manage Experiment[¶](#manage-experiment "Permalink to this headline") As the number of open suggestions for an experiment is limited (10 in our case), we sometimes might need to cleanup suggestions maually using beam.calibration.utils.DeleteSuggestion script to both delete specific and all open suggestions (e.g. if there was an exception during all runs and need to restart). ### Timezones and GTFS[¶](#timezones-and-gtfs "Permalink to this headline") There is a subtle requirement in BEAM related to timezones that is easy to miss and cause problems. BEAM uses the R5 router, which was designed as a stand-alone service either for doing accessibility analysis or as a point to point trip planner. R5 was designed with public transit at the top of the developers’ minds, so they infer the time zone of the region being modeled from the “timezone” field in the “agency.txt” file in the first GTFS data archive that is parsed during the network building process. Therefore, if no GTFS data is provided to R5, it cannot infer the locate timezone and it then assumes UTC. Meanwhile, there is a parameter in beam, “beam.routing.baseDate” that is used to ensure that routing requests to R5 are send with the appropriate timestamp. This allows you to run BEAM using any sub-schedule in your GTFS archive. I.e. if your base date is a weekday, R5 will use the weekday schedules for transit, if it’s a weekend day, then the weekend schedules will be used. The time zone in the baseDate parameter (e.g. for PST one might use “2016-10-17T00:00:00-07:00”) must match the time zone in the GTFS archive(s) provided to R5. As a default, we provide a “dummy” GTFS data archive that is literally empty of any transit schedules, but is still a valid GTFS archive. This archive happens to have a time zone of Los Angeles. You can download a copy of this archive here: <https://www.dropbox.com/s/2tfbhxuvmep7wf7/dummy.zip?dl=1> But in general, if you use your own GTFS data for your region, then you may need to change this baseDate parameter to reflect the local time zone there. Look for the “timezone” field in the “agency.txt” data file in the GTFS archive. The date specified by the baseDate parameter must fall within the schedule of all GTFS archives included in the R5 sub-directory. See the “calendar.txt” data file in the GTFS archive and make sure your baseDate is within the “start\_date” and “end\_date” fields folder across all GTFS inputs. If this is not the case, you can either change baseDate or you can change the GTFS data, expanding the date ranges… the particular dates chosen are arbitrary and will have no other impact on the simulation results. One more word of caution. If you make changes to GTFS data, then make sure your properly zip the data back into an archive. You do this by selecting all of the individual text files and then right-click-compress. Do not compress the folder containing the GTFS files, if you do this, R5 will fail to read your data and will do so without any warning or errors. Finally, any time you make a changes to either the GTFS inputs or the OSM network inputs, then you need to delete the file “network.dat” under the “r5” sub-directory. This will signal to the R5 library to re-build the network. ### Converting a MATSim Scenario to Run with BEAM[¶](#converting-a-matsim-scenario-to-run-with-beam "Permalink to this headline") The following MATSim input data are required to complete the conversion process: * Matsim network file: (e.g. network.xml) * Matsim plans (or population) file: (e.g. population.xml) * A download of OpenStreetMap data for a region that includes your region of interest. Should be in pbf format. For North American downloads: <http://download.geofabrik.de/north-america.html> The following inputs are optional and only recommended if your MATSim scenario has a constrained vehicle stock (i.e. not every person owns a vehicle): * Matsim vehicle definition (e.g. vehicles.xml) * Travel Analysis Zone shapefile for the region, (e.g. as can be downloaded from <https://www.census.gov/geo/maps-data/data/cbf/cbf_taz.html>) Finally, this conversion can only be done with a clone of the full BEAM repository. Gradle commands will **not** work with releases: <https://github.com/LBNL-UCB-STI/beam/releases> #### Conversion Instructions[¶](#conversion-instructions "Permalink to this headline") Note that we use the MATSim Sioux Falls scenario as an example. The data for this scenario are already in the BEAM repository under “test/input/siouxfalls”. We recommend that you follow the steps in this guide with that data to produce a working BEAM Sioux Falls scenario and then attempt to do the process with your own data. 1. Create a folder for your scenario in project directory under test/input (e.g: test/input/siouxfalls) 2. Create a sub-directory to your scenario directory and name it “conversion-input” (exact name required) 3. Create a another sub-directory and name it “r5”. 4. Copy the MATSim input data to the conversion-input directory. 5. Copy the BEAM config file from test/input/beamville/beam.conf into the scenario directory and rename to your scenario (e.g. test/input/siouxfalls/siouxfalls.conf) 6. Make the following edits to siouxfalls.conf (or your scenario name, replace Sioux Falls names below with appropriate names from your case): * Do a global search/replace and search for “beamville” and replace with your scenario name (e.g. “siouxfalls”). * matsim.conversion.scenarioDirectory = “test/input/siouxfalls” * matsim.conversion.populationFile = “Siouxfalls\_population.xml” (just the file name, assumed to be under conversion-input) * matsim.conversion.matsimNetworkFile = “Siouxfalls\_network\_PT.xml” (just the file name, assumed to be under conversion-input) * matsim.conversion.generateVehicles = true (If true – common – the conversion will use the population data to generate default vehicles, one per agent) * matsim.conversion.vehiclesFile = “Siouxfalls\_vehicles.xml” (optional, if generateVehicles is false, specify the matsim vehicles file name, assumed to be under conversion-input) * matsim.conversion.defaultHouseholdIncome (an integer to be used for default household incomes of all agents) * matsim.conversion.osmFile = “south-dakota-latest.osm.pbf” (the Open Street Map source data file that should be clipped to the scenario network, assumed to be under conversion-input) * matsim.conversion.shapeConfig.shapeFile (file name shape file package, e.g: for shape file name tz46\_d00, there should be following files: tz46\_d00.shp, tz46\_d00.dbf, tz46\_d00.shx) * matsim.conversion.shapeConfig.tazIdFieldName (e.g. “TZ46\_D00\_I”, the field name of the TAZ ID in the shape file) * beam.spatial.localCRS = “epsg:26914” (the local EPSG CRS used for distance calculations, should be in units of meters and should be the CRS used in the network, population and shape files) * beam.routing.r5.mNetBuilder.toCRS = “epsg:26914” (same as above) * beam.spatial.boundingBoxBuffer = 10000 (meters to pad bounding box around the MATSim network when clipping the OSM network) * The BEAM parameter beam.routing.baseDate has a time zone (e.g. for PST one might use “2016-10-17T00:00:00-07:00”). This time zone must match the time zone in the GTFS data provided to the R5 router. As a default, we provide the latest GTFS data from the City of Sioux Falls (“siouxareametro-sd-us.zip”. downloaded from transitland.org) with a timezone of America/Central. But in general, if you use your own GTFS data for your region, then you may need to change this baseDate parameter to reflect the local time zone there. Look for the “timezone” field in the “agency.txt” data file in the GTFS archive. Finally, the date specified by the baseDate parameter must fall within the schedule of all GTFS archives included in the R5 sub-directory. See the “calendar.txt” data file in the GTFS archive and make sure your baseDate is within the “start\_date” and “end\_date” fields folder across all GTFS inputs. If this is not the case, you can either change baseDate or you can change the GTFS data, expanding the date ranges… the particular dates chosen are arbitrary and will have no other impact on the simulation results. 8. Run the conversion tool * Open command line in beam root directory and run the following command, replace [/path/to/conf/file] with the path to your config file: gradlew matsimConversion -PconfPath=[/path/to/conf/file] The tool should produce the following outputs: * householdAttributes.xml * households.xml * population.xml * populationAttributes.xml * taz-centers.csv * transitVehicles.xml * vehicles.xml 9. Run OSMOSIS The console output should contain a command for the osmosis tool, a command line utility that allows you manipulate OSM data. If you don’t have osmosis installed, download and install from: <https://wiki.openstreetmap.org/wiki/Osmosis> Copy the osmosis command generated by conversion tool and run from the command line from within the BEAM project directory: > > osmosis –read-pbf file=/path/to/osm/file/south-dakota-latest.osm.pbf –bounding-box top=43.61080226522504 left=-96.78138443934351 bottom=43.51447260628691 right=-96.6915507011093 completeWays=yes completeRelations=yes clipIncompleteEntities=true –write-pbf file=/path/to/dest-osm.pbf 10. Run BEAM * Main class to execute: beam.sim.RunBeam * VM Options: -Xmx2g (or more if a large scenario) * Program arguments, path to beam config file from above, (e.g. –config “test/input/siouxfalls/siouxfalls.conf”) * Environment variables: PWD=/path/to/beam/folder Developer’s Guide[¶](#developer-s-guide "Permalink to this headline") --------------------------------------------------------------------- ### Repositories[¶](#repositories "Permalink to this headline") The beam repository on github [is here.](https://github.com/LBNL-UCB-STI/beam) The convention for merging into the master branch is that master needs to be pass all tests and at least one other active BEAM developer needs to review your changes before merging. Please do this by creating a pull request from any new feature branches into master. We also encourage you to create pull requests early in your development cycle which gives other’s an opportunity to observe and/or provide feedback in real time. When you are ready for a review, invite one or more through the pull request. Please use the following naming convention for feature branches, “<initials-or-username>/<descriptive-feature-branch-name>”. Adding the issue number is also helpful, e.g.: cjrs/issue112-update-docs An example workflow for contributing a new feature beam might look like this: * create a new branch off of master (e.g. cjrs/issue112-update-docs) * push and create a pull request right away * work in cjrs/issue112-update-docs * get it to compile, pass tests * request reviews from pull request * after reviews and any subsequent iterations, merge into master and close pull request * delete feature branch unless continued work to happy imminently on same feature branch The pev-only and related feature branches hold a previous version of BEAM (v0.1.X) which is incompatible with master but is still used for modeling and analysis work. ### Configuration[¶](#configuration "Permalink to this headline") We use [typesafe config](https://github.com/typesafehub/config) for our configuration parser and we use the [tscfg](https://github.com/carueda/tscfg) utility for generating typesafe container class for our config that we can browse with auto-complete while developing. Then you can make a copy of the config template under: ``` src/main/resources/config-template.conf ``` and start customizing the configurations to your use case. To add new parameters or change the structure of the configuration class itself, simply edit the config-template.conf file and run the gradle task: ``` gradle generateConfig ``` This will generate a new class src/main/scala/beam/metasim/config/BeamConfig.scala which will reflect the new structure and parameters. ### Environment Variables[¶](#environment-variables "Permalink to this headline") BEAM supports using an environment variable to optionally specify a directory to write outputs. This is not required. Depending on your operating system, the manner in which you want to run the BEAM application or gradle tasks, the specific place where you set these variables will differ. To run from the command line, add these statements to your .bash\_profile file: ``` export BEAM_OUTPUT=/path/to/your/preferred/output/destination/` ``` To run from IntelliJ as an “Application”, edit the “Environment Variables” field in your Run Configuration to look like this: ``` BEAM\_OUTPUT="/path/to/your/preferred/output/destination/" ``` Finally, if you want to run the gradle tasks from IntelliJ in OS X, you need to configure your variables as launch tasks by creating a plist file for each. The files should be located under `~/Library/LaunchAgents/` and look like the following. Note that after creating the files you need to log out / log in to OS X and you can’t Launch IntelliJ automatically on log-in because the LaunchAgents might not complete in time. File: `~/Library/LaunchAgents/setenv.BEAM\_OUTPUT.plist`: ``` <?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd"> <plist version="1.0"> <dict> <key>Label</key> <string>setenv.BEAM_OUTPUT</string> <key>ProgramArguments</key> <array> <string>/bin/launchctl</string> <string>setenv</string> <string>BEAM_OUTPUT</string> <string>/path/to/your/preferred/output/destination/</string> </array> <key>RunAtLoad</key> <true/> <key>ServiceIPC</key> <false/> </dict> </plist> ``` ### GIT-LFS timeout - how to proceed[¶](#git-lfs-timeout-how-to-proceed "Permalink to this headline") Sometimes it is possible to face a timeout issue when trying to push huge files. The steps below can be followed: 1. Connect to some EC2 server inside the same Amazon S3 region: us-east-2 2. Copy the file to the server using scp: ``` $ scp -i mykey.pem somefile.txt remote_username@machine.us-east-2.compute.amazonaws.com:/tmp ``` 3. Clone the repository as usual (make sure git and git-lfs are properly installed) 4. Just push the files as usual ### Keeping Production Data out of Master Branch[¶](#keeping-production-data-out-of-master-branch "Permalink to this headline") Production versus test data. Any branch beginning with “production” or “application” will contain data in the “production/” subfolder. This data should stay in that branch and not be merged into master. To keep the data out, the easiest practice is to simply keep merges one-way from master into the production branch and not vice versa. However, sometimes troubleshooting / debugging / development happens on a production branch. The cleanest way to get changes to source code or other non-production files back into master is the following. Checkout your production branch: ``` git checkout production-branch ``` Bring branch even with master: ``` git merge master ``` Resolve conflicts if needed Capture the files that are different now between production and master: ``` git diff --name-only HEAD master > diff-with-master.txt ``` You have created a file “diff-with-master.txt” containing a listing of every file that is different. IMPORTANT!!!! – Edit the file diff-with-master.txt and remove all production-related data (this typically will be all files underneath “production” sub-directory. Checkout master: ``` git checkout master ``` Create a new branch off of master, this is where you will stage the files to then merge back into master: ``` git checkout -b new-branch-with-changes-4ci ``` Do a file by file checkout of all differing files from production branch onto master: ``` cat diff-with-master.txt | xargs git checkout production-branch -- ``` Note, if any of our diffs include the deletion of a file on your production branch, then you will need to remove (i.e. with “git remove” these before you do the above “checkout” step and you should also remove them from the diff-with-master.txt”). If you don’t do this, you will see an error message (“did not match any file(s) known to git.”) and the checkout command will not be completed. Finally, commit the files that were checked out of the production branch, push, and go create your pull request! ### Automated Cloud Deployment[¶](#automated-cloud-deployment "Permalink to this headline") > > This functionality is available for core BEAM development team with Amazon Web Services access privileges. Please contact [Colin](mailto:colin.sheppard%40lbl.gov) for access to capability. To run a BEAM simulation or experiment on amazon ec2, use following command with some optional parameters: ``` gradle deploy -P[beamConfigs | beamExperiments]=config-or-experiment-file ``` The command will start an ec2 instance based on the provided configurations and run all simulations in serial. At the end of each simulation/experiment, outputs are uploaded to a public Amazon S3 [bucket](https://s3.us-east-2.amazonaws.com/beam-outputs/). To run each each simulation/experiment parallel on separate instances, set beamBatch to false. For customized runs, you can also use following parameters that can be specified from command line: * **beamBranch**: To specify the branch for simulation, current source branch will be used as default branch. * **beamCommit**: The commit SHA to run simulation. use HEAD if you want to run with latest commit, default is HEAD. * **beamConfigs**: A comma , separated list of beam.conf files. It should be relative path under the project home. You can create branch level defaults by specifying the branch name with .configs suffix like master.configs. Branch level default will be used if beamConfigs is not present. * **beamExperiments**: A comma , separated list of experiment.yml files. It should be relative path under the project home.You can create branch level defaults same as configs by specifying the branch name with .experiments suffix like master.experiments. Branch level default will be used if beamExperiments is not present. beamConfigs has priority over this, in other words, if both are provided then beamConfigs will be used. * **beamBatch**: Set to false in case you want to run as many instances as number of config/experiment files. Default is true. * **region**: Use this parameter to select the AWS region for the run, all instances would be created in specified region. Default region is us-east-2. * **shutdownWait**: As simulation ends, ec2 instance would automatically terminate. In case you want to use the instance, please specify the wait in minutes, default wait is 30 min. If any of the above parameter is not specified at the command line, then default values are assumed for optional parameters. These default values are specified in [gradle.properties](https://github.com/LBNL-UCB-STI/beam/blob/master/gradle.properties) file. To run a batch simulation, you can specify multiple configuration files separated by commas: ``` gradle deploy -PbeamConfigs=test/input/beamville/beam.conf,test/input/sf-light/sf-light.conf ``` Similarly for experiment batch, you can specify comma-separated experiment files: ``` gradle deploy -PbeamExperiments=test/input/beamville/calibration/transport-cost/experiments.yml,test/input/sf-light/calibration/transport-cost/experiments.yml ``` For demo and presentation material, please follow the [link](https://goo.gl/Db37yM) on google drive. #### AWS EC2 Start[¶](#aws-ec2-start "Permalink to this headline") To maintain ec2 instances, there are some utility tasks that reduce operation cost tremendously. You can start already available instances using a simple start gradle task under aws module. You can specify one or more instance ids by a comma saturated list as instanceIds argument. Below is syntax to use the command: ``` cd aws gradle start -PinstanceIds=<InstanceID1>[,<InstanceID2>] ``` As a result of task, instance DNS would be printed on the console. #### AWS EC2 Stop[¶](#aws-ec2-stop "Permalink to this headline") Just like starting instance, you can also stop already running instances using a simple stop gradle task under aws module. You can specify one or more instance ids by a comma saturated list as instanceIds argument. Below is syntax to use the command: ``` cd aws gradle stop -PinstanceIds=<InstanceID1>[,<InstanceID2>] ``` ### Performance Monitoring[¶](#performance-monitoring "Permalink to this headline") Beam uses [Kamon](http://kamon.io) as a performance monitoring framework. It comes with a nice API to instrument your application code for metric recoding. Kamon also provide many different pingable recorders like Log Reporter, StatsD, InfluxDB etc. You can configure your desired recorder with project configurations under Kamon/metrics section. When you start the application it will measure the instrumented components and recorder would publish either to console or specified backend where you can monitor/analyse the metrics. #### Beam Metrics Utility (MetricsSupport)[¶](#beam-metrics-utility-metricssupport "Permalink to this headline") Beam provides metric utility as part of performance monitoring framework using Kamon API. It makes developers life very easy, all you need is to extend your component from beam.sim.metrics.MetricsSupport trait and call your desired utility. As you extend the trait, it will add some handy entity recorder methods in your component, to measure the application behaviour. By using MetricSupport you measure following different metricises. > > * Count occurrences or number of invocation: > > > > ``` > countOccurrence("number-of-routing-requests", Metrics.VerboseLevel) > > ``` > > > In this example first argument of countOccurrence is the name of entity you want to record and second is the metric level. It is the simplest utility and just counts and resets to zero upon each flush. you can use it for counting errors or occurrences of specifics events. > > > * Execution time of some expression, function call or component: > > > > ``` > latency("time-to-calculate-route", Metrics.RegularLevel) { > calcRoute(request) > } > > ``` > > > In this snippet, first two arguments are same as of countOccurrence. Next, it takes the actual piece of code/expression for which you want to measure the execution time/latency. In the example above we are measuring the execution time to calculate a router in R5RoutingWorker, we named the entity as “request-router-time” and set metric level to Metrics.RegularLevel. When this method executes your entity recorder record the metrics and log with provided name. > > > #### Beam Metrics Configuration[¶](#beam-metrics-configuration "Permalink to this headline") After instrumenting your code you need configure your desired metric level, recorder backends and other Kamon configurations in your project configuration file (usually beam.conf). Update your metrics configurations as below: ``` beam.metrics.level = "verbose" kamon { trace { level = simple-trace } metric { #tick-interval = 5 seconds filters { trace.includes = [ "\*\*" ] akka-actor { includes = [ "beam-actor-system/user/router/\*\*", "beam-actor-system/user/worker-\*" ] excludes = [ "beam-actor-system/system/\*\*", "beam-actor-system/user/worker-helper" ] } akka-dispatcher { includes = [ "beam-actor-system/akka.actor.default-dispatcher" ] } } } statsd { hostname = 127.0.0.1 # replace with your container in case local loop didn't work port = 8125 } influxdb { hostname = 18.216.21.254 # specify InfluxDB server IP port = 8089 protocol = "udp" } modules { #kamon-log-reporter.auto-start = yes #kamon-statsd.auto-start = yes #kamon-influxdb.auto-start = yes } } ``` Make sure to update the **host** and **port** for StatsD or InfluxDB (either one(or both) of them you are using) with its relevant the server IP address in the abode config. Other then IP address you also need to confirm few thing in your environment like. * beam.metrics.level would not be pointing to the value off. * kamon-statsd.auto-start = yes, under kamon.modules. * build.gradle(Gradle build script) has kamon-statsd, kamon-influxdb or kamon-log-reporter available as dependencies, based on your kamon.modules settings and desired backend/logger. #### Setup Docker as Metric Backend[¶](#setup-docker-as-metric-backend "Permalink to this headline") Kamon’s [StatsD](http://kamon.io/documentation/0.6.x/kamon-statsd/overview/) reporter enables beam to publish matrices to a verity of backends. [Graphite](http://graphite.wikidot.com/) as the StatsD backend and [Grafana](http://grafana.org/) to create beautiful dashboards build a very good monitoring ecosystem. To make environment up and running in a few minutes, use Kamon’s provided docker image (beam dashboard need to import) from [docker hub](https://hub.docker.com/u/kamon/) or build using Dockerfile and supporting configuration files available in metrics directory under beam root. All you need is to install few prerequisite like docker, docker-compose, and make. To start a container you just need to run the following command in metrics directory (available at root of beam project): ``` $ make up ``` With the docker container following services start and exposes the listed ports: * 80: the Grafana web interface. * 81: the Graphite web port * 2003: the Graphite data port * 8125: the StatsD port. * 8126: the StatsD administrative port. Now your docker container is up and required components are configured, all you need to start beam simulation. As simulation starts, kamon would load its modules and start publishing metrics to the StatsD server (running inside the docker container). In your browser open <http://localhost:80> (or with IP you located in previous steps). Login with the default username (admin) and password (admin), open existing beam dashboard (or create a new one). If you get the docker image from docker hub, you need to import the beam dashboard from metrics/grafana/dashboards directory. * To import a dashboard open dashboard search and then hit the import button. * From here you can upload a dashboard json file, as upload complete the import process will let you change the name of the dashboard, pick graphite as data source. * A new dashboard will appear in dashboard list. Open beam dashboard (or what ever the name you specified while importing) and start monitoring different beam module level matrices in a nice graphical interface. To view the container log: ``` $ make tail ``` To stop the container: ``` $ make down ``` Cloud visualization services become more popular nowadays and save much effort and energy to prepare an environment. In future we are planing to use [Datadog](https://www.datadoghq.com/) (a cloud base monitoring and analytic platform) with beam. [Kamon Datadog integration](http://kamon.io/documentation/kamon-datadog/0.6.6/overview/) is the easiest way to have something (nearly) production ready. ##### How to get Docker IP?[¶](#how-to-get-docker-ip "Permalink to this headline") Docker with VirtualBox on macOS/Windows: use docker-machine IP instead of localhost. To find the docker container IP address, first you need to list the containers to get container id using: ``` $ docker ps ``` Then use the container id to find IP address of your container. Run the following command by providing container id in following command by replacing YOUR\_CONTAINER\_ID: ``` $ docker inspect YOUR_CONTAINER_ID ``` Now at the bottom, under NetworkSettings, locate IP Address of your docker container. ### Tagging Tests for Periodic CI[¶](#tagging-tests-for-periodic-ci "Permalink to this headline") ScalaTest allows you to define different test categories by tagging your tests. These tags categorise tests in different sets. And later you can filter these set of tests by specifying these tags with your build tasks. Beam also provide a custom tag Periodic to mark your tests for periodic CI runs. As you mark the test with this tag, your test would be included automatically into execution set and become the part of next scheduled run. It also be excluded immediately for regular gradle test task and CI. Follow the example below to tag your test with Periodic tag: ``` behavior of "Trajectory" it should "interpolate coordinates" taggedAs Periodic in { ... } ``` This code marks the test with com.beam.tags.Periodic tag. You can also specify multiple tags as a comma separated parameter list in taggedAs method. Following code demonstrate the use of multiple tags: ``` "The agentsim" must { ... "let everybody walk when their plan says so" taggedAs (Periodic, Slow) in { ... } ... } ``` You can find details about scheduling a continuous integration build under DevOps section [Configure Periodic Jobs](http://beam.readthedocs.io/en/latest/devops.html#configure-periodic-jobs). ### Instructions for forking BEAM[¶](#instructions-for-forking-beam "Permalink to this headline") These instructions are based on [this page](https://confluence.atlassian.com/bitbucket/current-limitations-for-git-lfs-with-bitbucket-828781638.html) 1. Clone BEAM repo ``` git clone https://github.com/LBNL-UCB-STI/beam cd beam ``` When asked for user name and password for LFS server (<http://52.15.173.229:8080>) enter anything but do not leave them blank. 2. Fetch Git LFS files ``` git lfs fetch origin ``` Many tutorials on cloning Git LFS repos say one should use ``` git lfs fetch --all origin ``` However, in BEAM this represents over 15 GB data and often fails. 3. Add new origin ``` git remote add new-origin <URL to new repo> ``` 4. Create internal master branch, master branch will used to track public repo ``` git branch master-internal git checkout master-internal ``` 5. Update .lfsconfig to have only the new LFS repo ``` [lfs] url = <URL to new LFS repo> ``` Note: for Bitbucket, the <URL to new LFS repo> is <URL to new repo>/info/lfs 6. Commit changes ``` git commit --all ``` 7. Push to new repo ``` git push new-origin --all ``` **There will be errors saying that many files are missing (LFS upload missing objects). That is OK.** Note As of this writing, the repo has around 250 MB LFS data. However, the push fails if the LFS server sets a low limit on LFS data. For example, it fails for Bitbucket free which sets a limit of 1 GB LFS data 8. Set master-internal as default branch in the repository’s website. 9. Clone the new repo ``` git clone <URL to new repo> cd <folder of new repo> ``` Note Cloning might take a few minutes since the repo is quite large. If everything turned out well, the cloning process should not ask for the credentials for BEAM’s LFS server (<http://52.15.173.229:8080>). 10. Add public repo as upstream remote ``` git remote add upstream https://github.com/LBNL-UCB-STI/beam ``` 11. Set master branch to track public remote and pull latest changes ``` git fetch upstream git checkout -b master upstream/master git pull ``` ### Scala tips[¶](#scala-tips "Permalink to this headline") #### Scala Collection[¶](#scala-collection "Permalink to this headline") ##### Use `mutable` buffer instead of `immutable var`:[¶](#use-mutable-buffer-instead-of-immutable-var "Permalink to this headline") ``` // Before var buffer = scala.collection.immutable.Vector.empty[Int] buffer = buffer :+ 1 buffer = buffer :+ 2 // After val buffer = scala.collection.mutable.ArrayBuffer.empty[Int] buffer += 1 buffer += 2 ``` **Additionally note that, for the best performance, use mutable inside of methods, but return an immutable** > > val mutableList = scala.collection.mutable.MutableList(1,2) > mutableList += 3 > mutableList.toList //returns scala.collection.immutable.List > > > > > > > //or return mutableList but explicitly set the method return type to > > //a common, assumed immutable one from scala.collection (more dangerous) > > > ##### Don’t create temporary collections, use [view](https://www.scala-lang.org/blog/2017/11/28/view-based-collections.html):[¶](#dont-create-temporary-collections-use-view "Permalink to this headline") ``` val seq: Seq[Int] = Seq(1, 2, 3, 4, 5) // Before seq.map(x => x + 2).filter(x => x % 2 == 0).sum // After seq.view.map(x => x + 2).filter(x => x % 2 == 0).sum ``` ##### Don’t emulate `collectFirst` and `collect`:[¶](#dont-emulate-collectfirst-and-collect "Permalink to this headline") ``` // collectFirst // Get first number >= 4 val seq: Seq[Int] = Seq(1, 2, 10, 20) val predicate: Int => Boolean = (x: Int) => { x >= 4 } // Before seq.filter(predicate).headOption // After seq.collectFirst { case num if predicate(num) => num } // collect // Get first char of string, if it's longer than 3 val s: Seq[String] = Seq("C#", "C++", "C", "Scala", "Haskel") val predicate: String => Boolean = (s: String) => { s.size > 3 } // Before s.filter(predicate).map { s => s.head } // After s.collect { case curr if predicate(curr) => curr.head } ``` ##### Prefer `nonEmpty` over `size > 0`:[¶](#prefer-nonempty-over-size-0 "Permalink to this headline") ``` //Before (1 to x).size > 0 //After (1 to x).nonEmpty //nonEmpty shortcircuits as soon as the first element is encountered ``` ##### Prefer not to use `\_1, \_2,...` for `Tuple` to improve readability:[¶](#prefer-not-to-use-1-2-for-tuple-to-improve-readability "Permalink to this headline") ``` // Get odd elements of sequence s val predicate: Int => Boolean = (idx: Int) => { idx % 2 == 1 } val s: Seq[String] = Seq("C#", "C++", "C", "Scala", "Haskel") // Before s.zipWithIndex.collect { case x if predicate(x._2) => x._1 // what is _1 or _2 ?? } // After s.zipWithIndex.collect { case (s, idx) if predicate(idx) => s } // Use destructuring bindings to extract values from tuple val tuple = ("Hello", 5) // Before val str = tuple._1 val len = tuple._2 // After val (str, len) = tuple ``` ##### Great article about [Scala Collection tips and tricks](https://pavelfatin.com/scala-collections-tips-and-tricks/#sequences-rewriting), must read[¶](#great-article-about-scala-collection-tips-and-tricks-must-read "Permalink to this headline") #### Use lazy logging[¶](#use-lazy-logging "Permalink to this headline") When you log, prefer to use API which are lazy. If you use `scala logging`, you have [it for free](https://github.com/lightbend/scala-logging#scala-logging-). When use `ActorLogging` in Akka, you should not use [string interpolation](https://docs.scala-lang.org/overviews/core/string-interpolation.html), but use method with replacement arguments: ``` // Before log.debug(s"Hello: $name") // After log.debug("Hello: {}", name) ``` BeamAgents[¶](#beamagents "Permalink to this headline") ------------------------------------------------------- BEAM is composed of Actors. Some of these Actors are BeamAgents. BeamAgents inherit the Akka FSM trait which provides a domain-specific language for programming agent actions as a finite state machine. *How are BeamAgents different from Actors?* In general, we reserve “BeamAgent” (also referred to as “Agent”) for entities in the simulation that exhibit agency. I.e. they don’t just change state but they have some degree of control or autonomy over themselves or other Agents. A Person or a Manager is a Agent, but a Vehicle is only a tool used by Agents, so it is not a BeamAgent in BEAM. Also, only BeamAgents can schedule callbacks with the BeamAgentScheduler. So any entity that needs to schedule itself (or be scheduled by other entities) to execute some process at a defined time within the simulation should be designed as a BeamAgent. Programming a BeamAgent involves constructing a finite state machine and the corresponding logic that responds to Akka messages from different states and then optionally transitions between states. ### Person Agents[¶](#person-agents "Permalink to this headline") ### Ride Hail Agents[¶](#ride-hail-agents "Permalink to this headline") ### Transit Driver Agents[¶](#transit-driver-agents "Permalink to this headline") Behaviors[¶](#behaviors "Permalink to this headline") ----------------------------------------------------- Person Agents in BEAM exhibit several within-day behaviors that govern their use of the transportation system. ### Mode Choice[¶](#mode-choice "Permalink to this headline") The most prominent behavior is mode choice. Mode choice can be specified either exogensously as a field in the persons plans, or it can be selected during replanning, or it can remain unset and be selected within the day. Within day mode choice is selected based on the attributes of the first trip of each tour. Once a mode is selected for the tour, the person attempts to stick with that mode for the duration of the tour. In all cases (whether mode is specified before the day or chosen within the day) person agents use WALK as a fallback option throughout if constraints otherwise prevent their previously determined mode from being possible for any given trip. E.g. if a person is in the middle of a RIDE\_HAIL tour, but the Ride Hail Manager is unable to match a driver to the person, then the person will walk. In BEAM the following modes are considers: * Walk * Bike * Drive (alone) * Walk to Transit * Drive to Transit (Park and Ride) * Ride Hail * Ride Hail to/from Transit There are two mode choice models that are possible within BEAM. #### Multinomial Logit Mode Choice[¶](#multinomial-logit-mode-choice "Permalink to this headline") The first is a simple multinomial logit choice model that has the following form for modal alternative j: V\_j = ASC\_j + Beta\_cost \* cost + Beta\_time \* time + Beta\_xfer \* num\_transfers The ASC (alternative specific constant) parameters as well as the Beta parameters can be configured in the BEAM configuration file and default to the following values: beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.cost = -1.0 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.time = -0.0047 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.transfer = -1.4 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.car\_intercept = 0.0 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.walk\_transit\_intercept = 0.0 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.drive\_transit\_intercept = 0.0 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.ride\_hail\_transit\_intercept = 0.0 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.ride\_hail\_intercept = 0.0 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.walk\_intercept = 0.0 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.bike\_intercept = 0.0 #### Latent Class Mode Choice[¶](#latent-class-mode-choice "Permalink to this headline") ### Parking[¶](#parking "Permalink to this headline") In BEAM, parking is issued at the granularity of a Traffic Analysis Zone (TAZ). Upon initialization, parking alternatives are read in from the CSV file listed in the BEAM config parameter *beam.agentsim.taz.parking*. Each row identifies the attributes of a parking alternative for a given TAZ, of which a given combination of attributes should be unique. Parking attributes include the following: | attribute | values | | --- | --- | | *parkingType* | Workplace, Public, Residential | | *pricingModel* | FlatFee, Block | | *chargingType* | NoCharger, Level1, Level2, DCFast, UltraFast | | *numStalls* | *integer* | | *feeInCents* | *integer* | | *reservedFor* | Any, RideHailManager | BEAM agents seek parking mid-tour, from within a leg of their trip. A search is run which starts at the trip destination and expands outward, seeking to find the closest TAZ centers with increasing search radii. Agents will pick the closest and cheapest parking alternative with attributes which match their use case. The location can be overridden for ride hail agents using the config parameter *beam.agentsim.agents.rideHail.refuelLocationType*, which may be set to “AtRequestLocation” or “AtTAZCenter”. The following should be considered when configuring a set of parking alternatives. The default behavior is to provide a nearly unbounded number of parking stalls for each combination of attributes, per TAZ, for the public, and provide no parking alternatives for ride hail agents. This behavior can be overridden manually by providing replacement values in the parking configuration file. Parking which is *reservedFor* a RideHailManager should only appear as *Workplace* parking. Free parking can be instantiated by setting *feeInCents* to zero. *numStalls* should be non-negative. Charging behavior is currently implemented for ride hail agents only. the *chargingType* attribute will result in the following charger power in kW: | *chargingType* | kW | | --- | --- | | NoCharger | 0.0 | | Level1 | 1.5 | | Level2 | 6.7 | | DCFast | 50.0 | | UltraFast | 250.0 | ### Refueling[¶](#refueling "Permalink to this headline") Event Specifications[¶](#event-specifications "Permalink to this headline") --------------------------------------------------------------------------- For an overview of events, including compatibility with MATSim, see [MATSim Events](index.html#matsim-events). The following lists each field in each event with some brief descriptions and contextual information where necessary. ### MATSim Events[¶](#matsim-events "Permalink to this headline") The following MATSim events are thrown within the AgentSim: #### ActivityStartEvent[¶](#activitystartevent "Permalink to this headline") * Time - Time of the start of the activity. * Activity Type - String denoting the type of activity (e.g. “Home” or “Work”) * Person - Person ID of the person agent engaged in the activity. * Link - Link ID of the nearest link to the activity location * Facility - Facility ID (unused in BEAM) #### ActivityEndEvent[¶](#activityendevent "Permalink to this headline") * Time - Time of the end of the activity. * Activity Type - String denoting the type of activity (e.g. “Home” or “Work”) * Person - Person ID of the person agent engaged in the activity. * Link - Link ID of the nearest link to the activity location * Facility - Facility ID (unused in BEAM) #### PersonDepartureEvent[¶](#persondepartureevent "Permalink to this headline") * Time - Time of the person departure. * Person - Person ID of the person departing. * Leg Mode - String denoting the trip mode of the trip to be attempted (trip mode is the overall mode of the trip, which is different than the mode of individual sub-legs of the trip, e.g. a trip with leg mode TRANSIT might have sub-legs of mode WALK, BUS, SUBWAY, WALK). * Link - Link ID of the nearest link to the departure location. #### PersonArrivalEvent[¶](#personarrivalevent "Permalink to this headline") * Time - Time of the person arrival. * Person - Person ID of the person arriving. * Leg Mode - String denoting the trip mode of the trip completed (trip mode is the overall mode of the trip, which is different than the mode of individual sub-legs of the trip, e.g. a trip with leg mode TRANSIT might have sub-legs of mode WALK, BUS, SUBWAY, WALK). * Link - Link ID of the nearest link to the arrival location. #### PersonEntersVehicleEvent[¶](#personentersvehicleevent "Permalink to this headline") * Time - Time of the vehicle entry. * Person - Person ID of the person entering the vehicle. * Vehicle - Vehicle ID of the vehicle being entered. #### PersonLeavesVehicleEvent[¶](#personleavesvehicleevent "Permalink to this headline") * Time - Time of the vehicle exit. * Person - Person ID of the person exiting the vehicle. * Vehicle - Vehicle ID of the vehicle being exited. ### BEAM Events[¶](#beam-events "Permalink to this headline") These events are specific to BEAM and are thrown within the AgentSim: #### ModeChoiceEvent[¶](#modechoiceevent "Permalink to this headline") Note that this event corresponds to the moment of choosing a mode, if mode choice is occurring dynamically within the day. If mode choice occurs outside of the simulation day, then this event is not thrown. Also, the time of choosing mode is not always the same as the departure time. * Time - Time of the mode choice. * Person - Person ID of the person making the mode choice. * Mode - The chosen trip mode (e.g. WALK\_TRANSIT or CAR) * Expected Maximum Utility - The logsum from the utility function used to evalute modal alternatives. If the mode choice model is not a random utility based model, then this will be left blank. * Location - Link ID of the nearest location. * Available Alternatives - Comma-separated list of the alternatives considered by the agent during the mode choice process. * Persona Vehicle Available - Boolean denoting whether this agent had a personal vehicle availalbe to them during the mode choice process. * Length - the length of the chosen trip in meters. * Tour index - the index of the chosen trip within the current tour of the agent (e.g. 0 means the first trip of the tour, 1 is the second trip, etc.) #### PathTraversalEvent[¶](#pathtraversalevent "Permalink to this headline") A Path Traversal is any time a vehicle moves within the BEAM AgentSim. * Length - Length of the movement in meters. * Fuel - fuel consumed during the movement in Joules. * Num Passengers - the number of passengers on board during the vehicle movement (the driver does not count as a passenger). * Links - Comma-separated list of link IDs indicating the path taken. * Mode - the sub-leg mode of the traversal (e.g. BUS or CAR or SUBWAY). * Departure Time - the time of departure. * Arrival Time - the time of arrival. * Vehicle - the ID of the vehilce making the movement. * Vehicle Type - String indicating the type of vehicle. * Start X - X coordinate of the starting location of the movement. Coordinates are output in WGS (lat/lon). * Start Y - Y coordinate of the starting location of the movement. Coordinates are output in WGS (lat/lon). * End X - X coordinate of the ending location of the movement. Coordinates are output in WGS (lat/lon). * End Y - Y coordinate of the ending location of the movement. Coordinates are output in WGS (lat/lon). * End Leg Fuel Level - Amount of fuel (in Joules) remaining in the vehicle at the end of the movement. Model Inputs[¶](#model-inputs "Permalink to this headline") ----------------------------------------------------------- ### Configuration file[¶](#configuration-file "Permalink to this headline") The BEAM configuration file controls where BEAM will source input data and the value of parameters. To see an example of the latest version of this file: <https://github.com/LBNL-UCB-STI/beam/blob/master/test/input/beamville/beam.conf> As of Fall 2018, BEAM is still under rapid development. So the configuration file will continue to evolve. Particularly, it should be expected that new parameters will be created to contol new model features and old configuration options may be modified, simplied, or eliminated. Furthermore, the BEAM configuration file contains a hybrid between parameters from MATSim (see namespace matsim in the config file). Not all of the matsim parameters are used by BEAM. Only the specific MATSim parameters described in this document are relevant. Modifying the other parameters will have no impact. In future releases of BEAM, the irrelevant parameters will be removed. In order to see example configuration options for a particular release of BEAM replace master in the above URL with the version number, e.g. for Version v0.6.2 go to this link: <https://github.com/LBNL-UCB-STI/beam/blob/v0.6.2/test/input/beamville/beam.conf> BEAM follows the [MATSim convention](http://archive.matsim.org/docs) for most of the inputs required to run a simulation, though specifying the road network and transit system is based on the [R5 requirements](https://github.com/conveyal/r5). Refer to these external documntation for details on the following inputs. * The person population and corresponding attributes files (e.g. population.xml and populationAttributes.xml) * The household population and corresponding attributes files (e.g. households.xml and householdAttributes.xml) * A directory containing network and transit data used by R5 (e.g. r5/) * The open street map network (e.g. r5/beamville.osm) * GTFS archives, one for each transit agency (e.g. r5/bus.zip) #### Config Options[¶](#config-options "Permalink to this headline") The following is a list of the most commonly used configuration options in approximate order of apearance in the beamville example config file (order need not be preserved so it is ok to rearrange options). A complete listing will be added to this documentation soon General parameters: ``` beam.agentsim.simulationName = "beamville" beam.agentsim.numAgents = 100 beam.agentsim.thresholdForWalkingInMeters = 1000 beam.agentsim.thresholdForMakingParkingChoiceInMeters = 100 beam.agentsim.schedulerParallelismWindow = 30 beam.agentsim.timeBinSize = 3600 ``` * simulationName: Used as a prefix when creating an output directory to store simulation results. * numAgents: This will limit the number of PersonAgents created in the simulation agents will be . Note that the number of agents is also limited by the total number of “person” elements in the population file specified by matsim.modules.plans.inputPlansFile. In other words, if there are 100 people in the plans and numAgents is set to 50, then 50 PersonAgents will be created. If numAgents is >=100, then 100 PersonAgents will be created. Sampling to a smaller number of agents is accomplished by sampling full households until the desired number of PersonAgents is reached. This keeps the household structure intact. * thresholdForWalkingInMeters: Used to determine whether a PersonAgent needs to route a walking path through the network to get to a parked vehicle. If the vehicle is closer than thresholdForWalkingInMeters in Euclidean distance, then the walking trip is assumed to be instantaneous. Note, for performance reasons, we do not recommend values for this threshold less than 100m. * thresholdForMakingParkingChoiceInMeters: Similar to thresholdForWalkingInMeters, this threshold determines the point in a driving leg when the PersonAgent initiates the parking choice processes. So for 1000m, the agent will drive until she is <=1km from the destination and then seek a parking space. * schedulerParallelismWindow: This controls the discrete event scheduling window used by BEAM to achieve within-day parallelism. The units of this parameter are in seconds and the larger the window, the better the performance of the simulation, but the less chronologically accurate the results will be. * timeBinSize: For most auto-generated output graphs and tables, this parameter will control the resolution of time-varying outputs. Mode choice parameters: ``` beam.agentsim.agents.modalBehaviors.modeChoiceClass = "ModeChoiceMultinomialLogit" beam.agentsim.agents.modalBehaviors.defaultValueOfTime = 8.0 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.transfer = -1.4 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.car_intercept = 0.0 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.walk_transit_intercept = 0.0 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.drive_transit_intercept = 2.0 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.ride_hail_transit_intercept = 0.0 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.ride_hail_intercept = -1.0 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.walk_intercept = -3.0 beam.agentsim.agents.modalBehaviors.mulitnomialLogit.params.bike_intercept = 0.0 beam.agentsim.agents.modalBehaviors.lccm.paramFile = ${beam.inputDirectory}"/lccm-long.csv" #Toll params beam.agentsim.toll.file=${beam.inputDirectory}"/toll-prices.csv" ``` * modeChoiceClass: Selects the choice algorithm to be used by agents to select mode when faced with a choice. Default of ModeChoiceMultinomialLogit is recommended but other algorithms include ModeChoiceMultinomialLogit ModeChoiceTransitIfAvailable ModeChoiceDriveIfAvailable ModeChoiceRideHailIfAvailable ModeChoiceUniformRandom ModeChoiceLCCM. * defaultValueOfTime: This value of time is used by the ModeChoiceMultinomialLogit choice algorithm unless the value of time is specified in the populationAttributes file. * params.transfer: Constant utility (where 1 util = 1 dollar) of making transfers during a transit trip. * params.car\_intercept: Constant utility (where 1 util = 1 dollar) of driving. * params.walk\_transit\_intercept: Constant utility (where 1 util = 1 dollar) of walking to transit. * params.drive\_transit\_intercept: Constant utility (where 1 util = 1 dollar) of driving to transit. * params.ride\_hail\_transit\_intercept: Constant utility (where 1 util = 1 dollar) of taking ride hail to/from transit. * params.ride\_hail\_intercept: Constant utility (where 1 util = 1 dollar) of taking ride hail. * params.walk\_intercept: Constant utility (where 1 util = 1 dollar) of walking. * params.bike\_intercept: Constant utility (where 1 util = 1 dollar) of biking. * lccm.paramFile: if modeChoiceClass is set to ModeChoiceLCCM this must point to a valid file with LCCM parameters. Otherwise, this parameter is ignored. * toll.file: File path to a file with static road tolls. Note, this input will change in future BEAM release where time-varying tolls will possible. Vehicles and Population: ``` #BeamVehicles Params beam.agentsim.agents.vehicles.beamFuelTypesFile = ${beam.inputDirectory}"/beamFuelTypes.csv" beam.agentsim.agents.vehicles.beamVehicleTypesFile = ${beam.inputDirectory}"/vehicleTypes.csv" beam.agentsim.agents.vehicles.beamVehiclesFile = ${beam.inputDirectory}"/vehicles.csv" ``` * useBikes: simple way to disable biking, set to true if vehicles file does not contain any data on biking. * beamFuelTypesFile: configure fuel fuel pricing. * beamVehicleTypesFile: configure vehicle properties including seating capacity, length, fuel type, fuel economy, and refueling parameters. * beamVehiclesFile: replacement to legacy MATSim vehicles.xml file. This must contain an Id and vehicle type for every vehicle id contained in households.xml. TAZs, Scaling, and Physsim Tuning: ``` #TAZ params beam.agentsim.taz.file=${beam.inputDirectory}"/taz-centers.csv" beam.agentsim.taz.parking = ${beam.inputDirectory}"/parking/taz-parking-default.csv" # Scaling and Tuning Params beam.agentsim.tuning.transitCapacity = 0.1 beam.agentsim.tuning.transitPrice = 1.0 beam.agentsim.tuning.tollPrice = 1.0 beam.agentsim.tuning.rideHailPrice = 1.0 # PhysSim Scaling Params beam.physsim.flowCapacityFactor = 0.0001 beam.physsim.storageCapacityFactor = 0.0001 beam.physsim.writeMATSimNetwork = false beam.physsim.ptSampleSize = 1.0 beam.physsim.jdeqsim.agentSimPhysSimInterfaceDebugger.enabled = false beam.physsim.skipPhysSim = false ``` * agentsim.taz.file: path to a file specifying the centroid of each TAZ. For performance BEAM approximates TAZ boundaries based on a nearest-centroid approach. The area of each centroid (in m^2) is also necessary to approximate average travel distances within each TAZ (used in parking choice process). * taz.parking: path to a file specifying the parking and charging infrastructure. If any TAZ contained in the taz file is not specified in the parking file, then ulimited free parking is assumed. * tuning.transitCapacity: Scale the number of seats per transit vehicle… actual seats are rounded to nearest whole number. Applies uniformly to all transit vehilces. * tuning.transitPrice: Scale the price of riding on transit. Applies uniformly to all transit trips. * tuning.tollPrice: Scale the price to cross tolls. * tuning.rideHailPrice: Scale the price of ride hailing. Applies uniformly to all trips and is independent of defaultCostPerMile and defaultCostPerMinute described above. I.e. price = (costPerMile + costPerMinute)\*rideHailPrice * physsim.flowCapacityFactor: Flow capacity parameter used by JDEQSim for traffic flow simulation. * physsim.storageCapacityFactor: Storage capacity parameter used by JDEQSim for traffic flow simulation. * physsim.writeMATSimNetwork: A copy of the network used by JDEQSim will be written to outputs folder (typically only needed for debugging). * physsim.ptSampleSize: A scaling factor used to reduce the seating capacity of all transit vehicles. This is typically used in the context of running a partial sample of the population, it is advisable to reduce the capacity of the transit vehicles, but not necessarily proportionately. This should be calibrated. * agentSimPhysSimInterfaceDebugger.enabled: Enables special debugging output. * skipPhysSim: Turns off the JDEQSim traffic flow simulation. If set to true, then network congestion will not change from one iteration to the next. Typically this is only used for debugging issues that are unrelated to the physsim. Warm Mode: ``` ################################################################## # Warm Mode ################################################################## beam.warmStart.enabled = false #PATH TYPE OPTIONS: PARENT\_RUN, ABSOLUTE\_PATH #PARENT\_RUN: can be a director or zip archive of the output directory (e.g. like what get's stored on S3). We should also be able to specify a URL to an S3 output. #ABSOLUTE\_PATH: a directory that contains required warm stats files (e.g. linkstats and eventually a plans). beam.warmStart.pathType = "PARENT\_RUN" beam.warmStart.path = "https://s3.us-east-2.amazonaws.com/beam-outputs/run149-base\_\_2018-06-27\_20-28-26\_2a2e2bd3.zip" ``` * warmStart.enabled: Allows you to point to the output of a previous BEAM run and the network travel times and final plan set from that run will be loaded and used to start a new BEAM run. * beam.warmStart.pathType: See above for descriptions. * beam.warmStart.path: path to the outputs to load. Can we a path on the local computer or a URL in which case outputs will be downloaded. Ride hail management: ``` ################################################################## # RideHail ################################################################## # Ride Hailing General Params beam.agentsim.agents.rideHail.numDriversAsFractionOfPopulation=0.1 beam.agentsim.agents.rideHail.defaultCostPerMile=1.25 beam.agentsim.agents.rideHail.defaultCostPerMinute=0.75 beam.agentsim.agents.rideHail.vehicleTypeId="BEV" beam.agentsim.agents.rideHail.refuelThresholdInMeters=5000.0 beam.agentsim.agents.rideHail.refuelLocationType="AtRequestLocation" # SurgePricing parameters beam.agentsim.agents.rideHail.surgePricing.surgeLevelAdaptionStep=0.1 beam.agentsim.agents.rideHail.surgePricing.minimumSurgeLevel=0.1 # priceAdjustmentStrategy(KEEP\_PRICE\_LEVEL\_FIXED\_AT\_ONE | CONTINUES\_DEMAND\_SUPPLY\_MATCHING) beam.agentsim.agents.rideHail.surgePricing.priceAdjustmentStrategy="KEEP\_PRICE\_LEVEL\_FIXED\_AT\_ONE" beam.agentsim.agents.rideHail.rideHailManager.radiusInMeters=5000 # initialLocation(HOME | UNIFORM\_RANDOM | ALL\_AT\_CENTER | ALL\_IN\_CORNER) beam.agentsim.agents.rideHail.initialLocation.name="HOME" beam.agentsim.agents.rideHail.initialLocation.home.radiusInMeters=10000 # allocationManager(DEFAULT\_MANAGER | REPOSITIONING\_LOW\_WAITING\_TIMES | EV\_MANAGER) beam.agentsim.agents.rideHail.allocationManager.name="EV\_MANAGER" beam.agentsim.agents.rideHail.allocationManager.timeoutInSeconds=300 beam.agentsim.agents.rideHail.allocationManager.randomRepositioning.repositioningShare=0.2 beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.repositionCircleRadisInMeters=3000.0 beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.minimumNumberOfIdlingVehiclesThreshholdForRepositioning=1 beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.percentageOfVehiclesToReposition=1.0 beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.timeWindowSizeInSecForDecidingAboutRepositioning=1200 beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.allowIncreasingRadiusIfDemandInRadiusLow=true beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.minDemandPercentageInRadius=0.1 # repositioningMethod(TOP\_SCORES | KMEANS) beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.repositioningMethod="TOP\_SCORES" beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.keepMaxTopNScores=5 beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.minScoreThresholdForRepositioning=0.00001 beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.distanceWeight=0.01 beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.waitingTimeWeight=4.0 beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.demandWeight=4.0 beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes.produceDebugImages=true beam.agentsim.agents.rideHail.iterationStats.timeBinSizeInSec=3600 ``` * numDriversAsFractionOfPopulation - Defines the # of ride hailing drivers to create, this ration is multiplied by the parameter beam.agentsim.numAgents to determine the actual number of drivers to create. Drivers begin the simulation located at or near the homes of existing agents, uniformly distributed. * defaultCostPerMile - One component of the 2 part price of ride hail calculation. * defaultCostPerMinute - One component of the 2 part price of ride hail calculation. * vehicleTypeId: What vehicle type is used for ride hail vehicles. This is primarily relevant for when allocationManager is EV\_MANAGER. * refuelThresholdInMeters: One the fuel level (state of charge for EVs) of the vehicle falls below the level corresponding to this parameter, the EV\_MANAGER will dispatch the vehicle to refuel. Note, do not make this value greate than 80% of the total vehicle range to avoid complications associated with EV fast charging. * refuelLocationType: One of AtRequestLocation or AtTAZCenter which controls whether the vehicle is assumed to charge at the it’s present location (AtRequestLocation) or whether it will drive to a nearby charging depot (AtTAZCenter). * allocationManager.name: Controls whether fleet management is simple (DEFAULT\_MANAGER for no repositioning, no refueling), includes repositioing (REPOSITIONING\_LOW\_WAITING\_TIMES) or includes both repositioning and refueling (EV\_MANAGER) * allocationManager.timeoutInSeconds: How frequently does the manager make fleet repositioning decisions. * beam.agentsim.agents.rideHail.allocationManager.repositionLowWaitingTimes: All of these parameters control the details of repositioning, more documentation will be posted for these soon. Model Outputs[¶](#model-outputs "Permalink to this headline") ------------------------------------------------------------- ### File: /modeChoice.csv[¶](#file-modechoice-csv "Permalink to this headline") Classname: ModeChosenAnalysisObject | field | description | | --- | --- | | iterations | iteration number | | car | Car chosen as travel mode | | drive\_transit | Drive to transit chosen as travel mode | | ride\_hail | Ride Hail chosen as travel mode | | walk | Walk chosen as travel mode | | walk\_transit | Walk to transit chosen as travel mode | ### File: /referenceModeChoice.csv[¶](#file-referencemodechoice-csv "Permalink to this headline") Classname: ModeChosenAnalysisObject | field | description | | --- | --- | | iterations | Bike chosen as travel mode | | bike | iteration number | | car | Car chosen as travel mode | | drive\_transit | Drive to transit chosen as travel mode | | ride\_hail | Ride Hail chosen as travel mode | | ride\_hail\_transit | Ride Hail to transit chosen as travel mode | | walk | Walk chosen as travel mode | | walk\_transit | Walk to transit chosen as travel mode | ### File: /realizedMode.csv[¶](#file-realizedmode-csv "Permalink to this headline") Classname: RealizedModeAnalysisObject | field | description | | --- | --- | | car | Car chosen as travel mode | | drive\_transit | Drive to transit chosen as travel mode | | other | Other modes of travel chosen | | ride\_hail | Ride Hail chosen as travel mode | | walk | Walk chosen as travel mode | | walk\_transit | Walk to transit chosen as travel mode | ### File: /rideHailRevenue.csv[¶](#file-ridehailrevenue-csv "Permalink to this headline") Classname: RideHailRevenueAnalysisObject | field | description | | --- | --- | | iteration # | iteration number | | revenue | Revenue generated from ride hail | ### File: /ITERS/it.0/0.averageTravelTimes.csv[¶](#file-iters-it-0-0-averagetraveltimes-csv "Permalink to this headline") Classname: PersonTravelTimeAnalysisObject | field | description | | --- | --- | | Mode | Travel mode chosen | | Hour,\* | Average time taken to travel by the chosen mode during the given hour of the day | ### File: /ITERS/it.0/0.energyUse.png.csv[¶](#file-iters-it-0-0-energyuse-png-csv "Permalink to this headline") Classname: FuelUsageAnalysisObject | field | description | | --- | --- | | Modes | Mode of travel chosen by the passenger | | Bin\_\* | Energy consumed by the vehicle while travelling by the chosen mode within the given time bin | ### File: /ITERS/it.0/0.physsimLinkAverageSpeedPercentage.csv[¶](#file-iters-it-0-0-physsimlinkaveragespeedpercentage-csv "Permalink to this headline") Classname: PhyssimCalcLinkSpeedStatsObject | field | description | | --- | --- | | Bin | A given time slot within a day | | AverageLinkSpeed | The average speed at which a vehicle can travel across the network during the given time bin | ### File: /ITERS/it.0/0.physsimFreeFlowSpeedDistribution.csv[¶](#file-iters-it-0-0-physsimfreeflowspeeddistribution-csv "Permalink to this headline") Classname: PhyssimCalcLinkSpeedDistributionStatsObject | field | description | | --- | --- | | freeSpeedInMetersPerSecond | The possible full speed at which a vehicle can drive through the given link (in m/s) | | numberOfLinks | Total number of links in the network that allow vehicles to travel with speeds up to the given free speed | | linkEfficiencyInPercentage | Average speed efficiency recorded by the the given network link in a day | | numberOfLinks | Total number of links having the corresponding link efficiency | ### File: /ITERS/it.0/0.rideHailWaitingStats.csv[¶](#file-iters-it-0-0-ridehailwaitingstats-csv "Permalink to this headline") Classname: RideHailWaitingAnalysisObject | field | description | | --- | --- | | Waiting Time | The time spent by a passenger waiting for a ride hail | | Hour | Hour of the day | | Count | Frequencies of times spent waiting for a ride hail during the entire day | ### File: /ITERS/it.0/0.rideHailIndividualWaitingTimes.csv[¶](#file-iters-it-0-0-ridehailindividualwaitingtimes-csv "Permalink to this headline") Classname: RideHailWaitingAnalysisObject | field | description | | --- | --- | | timeOfDayInSeconds | Time of a day in seconds | | personId | Unique id of the passenger travelling by the ride hail | | rideHailVehicleId | Unique id of the ride hail vehicle | | waitingTimeInSeconds | Time spent by the given passenger waiting for the arrival of the given ride hailing vehicle | ### File: /ITERS/it.0/0.rideHailSurgePriceLevel.csv[¶](#file-iters-it-0-0-ridehailsurgepricelevel-csv "Permalink to this headline") Classname: GraphSurgePricingObject | field | description | | --- | --- | | PriceLevel | Travel fare charged by the ride hail in the given hour | | Hour | Hour of the day | ### File: /ITERS/it.0/0.rideHailRevenue.csv[¶](#file-iters-it-0-0-ridehailrevenue-csv "Permalink to this headline") Classname: GraphSurgePricingObject | field | description | | --- | --- | | Revenue | Revenue earned by ride hail in the given hour | | Hour | Hour of the day | ### File: /ITERS/it.0/0.tazRideHailSurgePriceLevel.csv[¶](#file-iters-it-0-0-tazridehailsurgepricelevel-csv "Permalink to this headline") Classname: GraphSurgePricingObject | field | description | | --- | --- | | TazId | TAZ id | | DataType | Type of data , can be “priceLevel” or “revenue” | | Value | Value of the given data type , can indicate either price Level or revenue earned by the ride hail in the given hour | | Hour | Hour of the day | ### File: /ITERS/it.0/0.rideHailWaitingSingleStats.csv[¶](#file-iters-it-0-0-ridehailwaitingsinglestats-csv "Permalink to this headline") Classname: RideHailingWaitingSingleAnalysisObject | field | description | | --- | --- | | WaitingTime(sec) | Time spent by a passenger on waiting for a ride hail | | Hour\* | Hour of the day | ### File: /ITERS/it.0/0.rideHailInitialLocation.csv[¶](#file-iters-it-0-0-ridehailinitiallocation-csv "Permalink to this headline") Classname: BeamMobsim | field | description | | --- | --- | | rideHailAgentID | Unique id of the given ride hail agent | | xCoord | X co-ordinate of the starting location of the ride hail | | yCoord | Y co-ordinate of the starting location of the ride hail | ### File: /stopwatch.txt[¶](#file-stopwatch-txt "Permalink to this headline") Classname: StopWatchOutput | field | description | | --- | --- | | Iteration | Iteration number | | BEGIN iteration | Begin time of the iteration | | BEGIN iterationStartsListeners | Time at which the iteration start event listeners started | | END iterationStartsListeners | Time at which the iteration start event listeners ended | | BEGIN replanning | Time at which the replanning event started | | END replanning | Time at which the replanning event ended | | BEGIN beforeMobsimListeners | Time at which the beforeMobsim event listeners started | | BEGIN dump all plans | Begin dump all plans | | END dump all plans | End dump all plans | | END beforeMobsimListeners | Time at which the beforeMobsim event listeners ended | | BEGIN mobsim | Time at which the mobsim run started | | END mobsim | Time at which the mobsim run ended | | BEGIN afterMobsimListeners | Time at which the afterMobsim event listeners started | | END afterMobsimListeners | Time at which the afterMobsim event listeners ended | | BEGIN scoring | Time at which the scoring event started | | END scoring | Time at which the scoring event ended | | BEGIN iterationEndsListeners | Time at which the iteration ends event listeners ended | | BEGIN compare with counts | Time at which compare with counts started | | END compare with counts | Time at which compare with counts ended | | END iteration | Time at which the iteration ended | ### File: /scorestats.txt[¶](#file-scorestats-txt "Permalink to this headline") Classname: ScoreStatsOutput | field | description | | --- | --- | | ITERATION | Iteration number | | avg. EXECUTED | Average of the total execution time for the given iteration | | avg. WORST | Average of worst case time complexities for the given iteration | | avg. AVG | Average of average case time complexities for the given iteration | | avg. BEST | Average of best case time complexities for the given iteration | ### File: /summaryStats.txt[¶](#file-summarystats-txt "Permalink to this headline") Classname: SummaryStatsOutput ### File: /ITERS/it.0/0.countsCompare.txt[¶](#file-iters-it-0-0-countscompare-txt "Permalink to this headline") Classname: CountsCompareOutput | field | description | | --- | --- | | Link Id | Iteration number | | Count | Time taken by the agent to travel in a crowded transit | | Station Id | Amount of diesel consumed in megajoule | | Hour | Amount of food consumed in megajoule | | MATSIM volumes | Amount of electricity consumed in megajoule | | Relative Error | Amount of gasoline consumed in megajoule | | Normalized Relative Error | Time at which the beforeMobsim event listeners started | | GEH | GEH | ### File: /ITERS/it.0/0.events.csv[¶](#file-iters-it-0-0-events-csv "Permalink to this headline") Classname: EventOutput | field | description | | --- | --- | | person | Person(Agent) Id | | vehicle | vehicle id | | time | Start time of the vehicle | | type | Type of the event | | fuel | Type of fuel used in the vehicle | | duration | Duration of the travel | | cost | Cost of travel | | location.x | X co-ordinate of the location | | location.y | Y co-ordinate of the location | | parking\_type | Parking type chosen by the vehicle | | pricing\_model | Pricing model | | charging\_type | Charging type of the vehicle | | parking\_taz | Parking TAZ | | distance | Distance between source and destination | | location | Location of the vehicle | | mode | Mode of travel | | currentTourMode | Current tour mode | | expectedMaximumUtility | Expected maximum utility of the vehicle | | availableAlternatives | Available alternatives for travel for the passenger | | personalVehicleAvailable | Whether the passenger possesses a personal vehicle | | tourIndex | Tour index | | facility | Facility availed by the passenger | | departTime | Time of departure of the vehicle | | originX | X ordinate of the passenger origin point | | originY | Y ordinate of the passenger origin point | | destinationX | X ordinate of the passenger destination point | | destinationY | Y ordinate of the passenger destination point | | fuelType | Fuel type of the vehicle | | num\_passengers | Num of passengers travelling in the vehicle | | links | Number of links in the network | | departure\_time | Departure time of the vehicle | | arrival\_time | Arrival time of the vehicle | | vehicle\_type | Type of vehicle | | capacity | Total capacity of the vehicle | | start.x | X ordinate of the start point | | start.y | Y ordinate of the start point | | end.x | X ordinate of the vehicle end point | | end.y | Y ordinate of the vehicle end point | | end\_leg\_fuel\_level | Fuel level at the end of the travel | | seating\_capacity | Seating capacity of the vehicle | | costType | Type of cost of travel incurred on the passenger | ### File: /ITERS/it.0/0.legHistogram.txt[¶](#file-iters-it-0-0-leghistogram-txt "Permalink to this headline") Classname: LegHistogramOutput | field | description | | --- | --- | | time | Time | | time | Time | | departures\_all | Total number of departures on all modes | | arrivals\_all | Total number of arrivals on all modes | | duration | Duration of travel | | stuck\_all | Total number of travels that got stuck on all modes | | en-route\_all | Total number of travels by all modes | | departures\_car | Total number of departures by car | | arrivals\_car | Total number of departures by car | | stuck\_car | Total number of travels that got stuck while travelling by car | | en-route\_car | Total number of travels made by car | | departures\_drive\_transit | Total number of departures by drive to transit | | arrivals\_drive\_transit | Total number of arrivals by drive to transit | | stuck\_drive\_transit | Total number of travels that got stuck while travelling by drive to transit | | en-route\_drive\_transit | Total number of travels made by drive to transit | | departures\_ride\_hail | Total number of departures by ride hail | | arrivals\_ride\_hail | Total number of arrivals by ride hail | | stuck\_ride\_hail | Total number of travels that got stuck while travelling by ride hail | | en-route\_ride\_hail | Total number of travels made by ride hail | | departures\_walk | Total number of departures on foot | | arrivals\_walk | Total number of arrivals on foot | | stuck\_walk | Total number of travels that got stuck while travelling on foot | | en-route\_walk | Total number of travels made on foot | | departures\_walk\_transit | Total number of departures by walk to transit | | arrivals\_walk\_transit | Total number of arrivals by walk to transit | | stuck\_walk\_transit | Total number of travels that got stuck while travelling by walk to transit | | en-route\_walk\_transit | Total number of travels made by walk to transit | ### File: /ITERS/it.0/0.rideHailTripDistance.csv[¶](#file-iters-it-0-0-ridehailtripdistance-csv "Permalink to this headline") Classname: RideHailTripDistanceOutput | field | description | | --- | --- | | hour | Hour of the day | | numPassengers | Number of passengers travelling in the ride hail | | vkt | Total number of kilometers travelled by the ride hail vehicle | ### File: /ITERS/it.0/0.tripDuration.txt[¶](#file-iters-it-0-0-tripduration-txt "Permalink to this headline") Classname: TripDurationOutput | field | description | | --- | --- | | pattern | Pattern | | (5\*i)+ | Value | ### File: /ITERS/it.0/0.biasErrorGraphData.txt[¶](#file-iters-it-0-0-biaserrorgraphdata-txt "Permalink to this headline") Classname: BiasErrorGraphDataOutput | field | description | | --- | --- | | hour | Hour of the day | | mean relative error | Mean relative error | | mean bias | Mean bias value | ### File: /ITERS/it.0/0.biasNormalizedErrorGraphData.txt[¶](#file-iters-it-0-0-biasnormalizederrorgraphdata-txt "Permalink to this headline") Classname: BiasNormalizedErrorGraphDataOutput | field | description | | --- | --- | | hour | Hour of the day | | mean normalized relative error | Mean normalized relative error | | mean bias | Mean bias value | Protocols[¶](#protocols "Permalink to this headline") ----------------------------------------------------- Because BEAM is implemented using the Actor framework and simulations are executed asynchronously, there are many communications protocols between Actors and Agents that must be specified and followed. The following describes the key protocols with diagrams and narrative. ### Trip Planning[¶](#trip-planning "Permalink to this headline") #### RoutingRequests[¶](#routingrequests "Permalink to this headline") One of the more familiar protocols, any Actor can consult the router service for routing information by sending a RoutingRequest and receiving a RoutingResponse.  The RoutingRequest message contains: * Departure Window * Origin / Destination * Transit Modes to Consider * Vehicles to Consider (their Id, Location, Mode) * The Id of the Person for whom the request is ultimately made The RoutingResponse message contains: * A vector of EmbodiedBeamTrips EmbodiedBeamTrips contain: * A trip classifer (i.e. the overall mode for the trip which is restricted to WALK, BIKE, CAR, RIDE\_HAIL, TRANSIT) * A vector of EmbodiedBeamLegs EmbodiedBeamLegs contain: * A BeamLeg * A BeamVehicle Id * As Driver Boolean * Optional PassengerSchedule * Cost * UnbecomeDriveOnCompletion Boolean BeamLegs contain: * Start time * Mode (this is a more specific mode for Transit, e.g. SUBWAY or BUS) * Duration * BeamPath containing the path used through the network. BeamPaths contain: * Vector of link Ids * Optional transit stop info (for any Transit leg, the boarding and alighting stop) * A trajectory resolver which is resposible for translating the linkIds into coordinates BeamTransitSegments contain: * Origin stop * Destination stop #### ChoosesMode[¶](#choosesmode "Permalink to this headline") The mode choice protocol involves gathering information, making a choice, confirming reservations if necessary, and then adapting if the chosen trip cannot be executed.  *Gathering Information* 1. The Person receives the BeginModeChoiceTrigger from the scheduler. 2. Person sends a MobilityStatusInquiry to thier Household. 3. Household returns a MobilityStatusResponse. Based on this response, the person optionally includes vehicles in the ReservationRequest sent to the Router. 4. Person sends a ReservationRequest to the Router. 5. Person sends a RideHailInquiry to the RideHailManager. 6. Person schedules a FinalizeModeChoiceTrigger to occur in the future (respresenting non-zero time to make a choice). 7. Person stays in ChoosingMode state until all results are recieved: RoutingResponse, RideHailingInquiryResponse, FinalizeModeChoiceTrigger. With each response, the data is stored locally for use in the mode choice. *Choosing and Reserving* 1. The Person evaluates the ModeChoiceCalculator which returns a chosen itinerary (in the form of an EmboidedBeamTrip) from the list of possible alternatives. 2. If a reservation is required to accomplish the chosen itinerary, the person sends ReservationRequest to all drivers in the itinerary (in the case of a transit trip) or a ReserveRide message to the RideHailManager in the case of a ride hail trip). 3. If reservation requests were sent, the Person waits (still in ChoosingMode state) for all responses to be returned. If any response is negative, the Person removes the chosen itinerary from their choice set, sends RemovePassengerFromTrip messagse to all drivers in the trip (if transit) and begins the mode choice process anew. 4. If all reservation responses are received and positive or if the trip does not require reservations at all, the person releases any reserved personal vehicles by sending ReleaseVehicleReservation messags to the Household and a ResourceIsAvailableNotification to themself, the Person throws a PersonDepartureEvent and finally schedules a PersonDepartureTrigger to occur at the departure time of the trip. ### Traveling[¶](#traveling "Permalink to this headline") When a PersonAgent travels, she may transition from being a driver of a vehicle to being a passenger of a vehicle. The protocol for being a driver of a vehicle is listed separately below because that logic is implemented in its own trait (DrivesVehicle) to allow BeamAgents other than PersonAgents to drive vehicles. Some agents (e.g. TransitDrivers) may not be Persons and therefore do not “travel” but they do of course operate a vehicle and move around with it. #### Driver[¶](#driver "Permalink to this headline") [](_images/DrivesVehicleFSM.png)  *Starting Leg* 1. The Driver receives a StartLegTrigger from the Waiting state. 2. The Driver schedules NotifyLegStartTriggers for each rider in the PassengerSchedule associated with the current BeamLeg. 3. The Driver creates a list of borders from the PassengerSchedule associated with the BeamLeg to track which agents have yet to board the vehicle. 4. When all expected BoardVehicle messages are recieved by the Driver, the Driver schedules an EndLegTrigger and transitions to the Moving state. *Ending Leg* 1. The Driver receives an EndLegTrigger from the Moving state. 2. The Driver schedules NotifyLegEndTriggers for all riders in the PassengerSchedule associated with the current BeamLeg. 3. The Driver creates a list of alighters from the PassengerSchedule associated with the BeamLeg to track which agents have yet to alight the vehicle. 4. When all expected AlightingConfirmation messages are recieved from the vehicle, the Driver publishes a PathTraversalEvent and proceeds with the following steps. 5. If the Driver has more legs in the PassengerSchedule, she schedules a StartLegTrigger based on the start time of that BeamLeg. 6. Else the Driver schedules a PassengerScheduleEmptyTrigger to execute in the current tick. 7. The Driver transitions to the Waiting state. #### Traveler[¶](#traveler "Permalink to this headline")   *Starting Trip* 1. The PersonAgent receives a PersonDepartureTrigger from the scheduler while in Waiting state. She executes the ProcessNextLeg Method described below. *ProcessNextLeg Method* The following protocol is used more than once by the traveler so it is defined here as a function with no arguments. 1. The Person checks the value of \_currentEmbodiedLeg to see if unbecomeDriverOnCompletion is set to TRUE, if so, then the Person sends an UnbecomeDriver message to her vehicle and updates her \_currentVehicle accordingly. 2. If there are no more legs in the EbmodiedBeamTrip, the PersonAgent either schedules the ActivityEndTrigger and transitions to the PerformingActivity state or, if there are no remaining activities in the person’s plan, she transitions to the Finished state and schedules no further triggers. 3. If there are more legs in the EmbodiedBeamTrip, the PersonAgent processes the next leg in the trip. If asDriver for the next leg is FALSE, then the Person transitions to Waiting state and does nothing further. 4. If asDriver is true for the next leg, the Person creates a temporary passenger schedule for the next leg and sends it along with a BecomeDriver or a ModifyPassnegerSchedule message, depending on whether this person is already the driver of the vehicle or if becoming the driver for the first time. 5. The person stays in the current state (which could be Waiting or Moving depending on the circumstances). *Driving Mission Completed* 1. The PersonAgent receives a PassengerScheduleEmptyTrigger from the scheduler which indicates that as a driver, this Person has finished all legs in her PassengerSchedule. 2. The PersonAgent executes the ProcessNextLeg method. *Notify Start Leg* 1. The PersonAgent receives a NotifyLegStartTrigger. 2. If the private field \_currentEmbodiedLeg is non-empty or if the leg referred to in the trigger does not match the Person’s next leg or if the Person’s next leg has asDriver set to TRUE, this Person has received the NotifyLegStartTrigger too early, so she reschedules the NotifyLegStartTrigger to occur in the current tick, allowing other messages in her Actor mailbox to be processed first. 3. Otherwise, the PersonAgent sends an BoardVehicle message to the driver contained in the EmbodiedBeamLeg unless she is already a passenger in that vehicle. 4. The PersonAgent transitions to the Moving state. *Notify End Leg* 1. The PersonAgent receives a NotifyLegEndTrigger. 2. If the private field \_currentEmbodiedLeg is empty or the currentBeamLeg does not match the leg associated with the Trigger, this Person has received the NotifyLegEndTrigger too early, so she reschedules the NotifyLegEndTrigger to occur in the current tick, allowing other messages in her Actor mailbox to be processed first. 3. If another EmbodiedBeamLeg exists in her EmbodiedBeamTrip AND the BeamVehicle associated with the next EmbodiedBeamTrip is identical to the curren BeamVehicle, then she does nothing other than update her internal state to note the end of the leg and transition to Waiting. 4. Else she sends the current driver an AlightVehicle message and executes the ProcessNextLegModule method. ### Household[¶](#household "Permalink to this headline") During initialization, we execute the rank and escort heuristc. Escorts and household vehicles are assigned to members. 1. The PersonAgent retrieves mobility status from her Household using a MobilityStatusInquiry message. 2. Household returns a MobilityStatusReponse message which notifies the person about two topics: a) whether she is an escortee (e.g. a child), an estorter (e.g. a parent), or traveling alone; b) the Id and location of at most one Car and at most one Bike that the person may use for their tour. 3. If the PersonAgent is an escortee, then she will enter a waiting state until she receives a AssignTrip message from her escorter which contains the BeamTrip that she will follow, at which point she schedules a PersonDepartureTrigger. 4. Else the PersonAgent goes through the mode choice process. After choosing a BeamTrip, she sends an appropriate BeamTrip to her escortees using the AssignTrip message. 5. The PersonAgent sends a VehicleConfirmationNotice to the Household, confirming whether or not she is using the Car or Bike. The Household will use this information to offer unused vehicles as options to subsequent household members. #### Escort[¶](#escort "Permalink to this headline") ### RideHailing[¶](#ridehailing "Permalink to this headline") The process of hailing a ride from a TNC is modeled after the real-world experience:  1. The PersonAgent inquires about the availability and pricing of the service using a RideHailingInquiry message. 2. The RideHailingManager responds with a RideHailingInquiryResponse. 3. The PersonAgent may choose to use the ride hailing service in the mode choice process. 4. The PersonAgent sends a ReserveRide message attempting to book the service. 5. The RideHailingManager responds with a ReservationResponse which either confirms the reservation or notifies that the resource is unavailable. #### Inquiry[¶](#inquiry "Permalink to this headline") The RideHailingInquiry message contains: * inquiryId * customerId * pickUpLocation * departAt time * destinationLocation The RideHailingInquiryResponse message contains: * inquiryId * a Vector of TravelProposals * an optional ReservationError Each TravelProposal contains: * RideHailingAgentLocation * Time to Customer * estimatedPrice * estimatedTravelTime * Route to customer * Route from origin to destination #### Reserve[¶](#reserve "Permalink to this headline") The ReserveRide message contains: * inquiryId * customerId in the form of a VehiclePersonId * pickUpLocation * departAt time * destinationLocation) The ReservationResponse message contains the request Id and either a ReservationError or f the reservation is successfull, a ReserveConfirmInfo object with the following: * DepartFrom BeamLeg. * ArriveTo BeamLeg. * PassengerVehicleId object containin the passenger and vehicle Ids. * Vector of triggers to schedule. ### Transit[¶](#transit "Permalink to this headline") Transit itineraries are returned by the router in the Trip Planning Protocol. In order to follow one of these itineraries, the PersonAgent must reserve a spot on the transit vehicle according to the following protocol:  1. PersonAgent sends ReservationRequest to the Driver. 2. The BeamVehicle forwards the reservation request to the Driver of the vehicle. The driver is responsible for managing the schedule and accepting/rejecting reservations from customers. 3. The Driver sends a ReservationConfirmation directly to the PersonAgent. 4. When the BeamVehicle makes it to the confirmed stop for boarding, the Driver sends a BoardingNotice to the PersonAgent. 5. The PersonAgent sends an BoardVehicle message to the Driver. 7. Also, concurrently, when the BeamVehicle is at the stop, the Driver sends an AlightingNotice to all passengers registered to alight at that stop. 8. Notified passengers send an AlightVehicle message to the Driver. Because the reservation process ensures that vehicles will not exceed capacity, the Driver need not send an acknowledgement to the PersonAgent. ### Refueling[¶](#refueling "Permalink to this headline") ??? ### Modify Passenger Schedule Manager[¶](#modify-passenger-schedule-manager "Permalink to this headline") This protocol is deep into the weeds of the Ride Hail Manager but important for understanding how reservations and reposition requests are managed.  DevOps Guide[¶](#devops-guide "Permalink to this headline") ----------------------------------------------------------- ### Git LFS[¶](#git-lfs "Permalink to this headline") #### Setup git-lfs Server[¶](#setup-git-lfs-server "Permalink to this headline") 1. From the AWS Management Console, launch the Amazon EC2 instance from an Amazon Machine Image (AMI) that has Ubuntu 64-bit as base operating system. 2. Choose a security group that will allow SSH access as well as port 8080 to access your git lfs server. You should only enable ingress from the IP addresses you wish to allow access to your server. 3. On AWS Management Console go to Services menu from top bar and open the Amazon S3 console. 4. Click Create Bucket, it will opens a new dialog window. 6. On the Name and region tab, provide appropriate name (should be DNS compliant) and desired region, then Click Next button in the bottom. 7. Leave Set properties as is and click Next again. 8. On Set Permissions tab, set read and write access for your git-lfs user. and Click next. 9. Verify your settings on Review tab. If you want to change something, choose Edit. If your current settings are correct, choose Create bucket. 10. Connect to the ec2 instance via SSH. 11. Add NodeSource APT repository for Debian-based distributions repository AND the PGP key for verifying packages: > > $ curl -sL <https://deb.nodesource.com/setup_6.x> | sudo -E bash - 12. Install Node.js from the Debian-based distributions repository: > > $ sudo apt-get install -y nodejs 13. To confirm that Node.js was successfully installed on your system, you can run the following command: > > $ node -v If Node is installed, this command should print out something like this: > > v6.9.1 14. To get the most up-to-date npm, you can run the command: > > $ sudo npm install npm –global 15. Next, you can directly install git-lfs server using node package manager by executing following command: > > $ sudo npm install node-git-lfs 16. Git LFS server offers two method of configuration, via environment variable or configuration file. At this step you have to define some environment variables to configure the server: > > * LFS\_BASE\_URL - URL of the LFS server - **required** > * LFS\_PORT - HTTP portal of the LFS server, default to 3000 - **required** > * LFS\_STORE\_TYPE - Object store type, can be either s3 (for AWS S3) or grid (for MongoDB GridFS), default to s3 - **required** > * LFS\_AUTHENTICATOR\_TYPE - Authenticator type, can be basic (for basic username and password), none (for no authentication), default to none - **required** > * LFS\_AUTHENTICATOR\_USERNAME - Username - **required** > * LFS\_AUTHENTICATOR\_PASSWORD - Password - **required** > * AWS\_ACCESS\_KEY - AWS access key - **required** > * AWS\_SECRET\_KEY - AWS secret key - **required** > * LFS\_STORE\_S3\_BUCKET - AWS S3 bucket - **required** > * LFS\_STORE\_S3\_ENDPOINT - AWS S3 endpoint, normally this will be set by region > * LFS\_STORE\_S3\_REGION - AWS S3 region > > > Set Aws access key, secret ky and s3 details based on previous steps. 17. Now start git lfs server: > > $ node-git-lfs 18. At the end, create file named .lfsconfig in you repository with following contents, update host and port based on your environment. > > > [lfs] > url = “<http://host:port/LBNL-UCB-STI/beam.git>” > batch = true > access = basic > > This will setup everything you need to setup and install a custom gitl-lfs server on Amazon instance and github repository will start pointing to the your custom server. There is no special installation or requirement for the clint, only thing that you need is to provide lfs user name and password on you client when you pull your contents for the first time. ### Jenkins[¶](#jenkins "Permalink to this headline") #### Setup Jenkins Server[¶](#setup-jenkins-server "Permalink to this headline") 1. From the AWS Management Console, launch the Amazon EC2 instance from an Amazon Machine Image (AMI) that has Ubuntu 64-bit as base operating system. 2. Choose a security group that will allow SSH access as well as port 8080, 80 and 443 to access your Jenkins dashboard. You should only enable ingress from the IP addresses you wish to allow access to your server. 3. Connect to the instance via SSH. 4. Add oracle java apt repository: ``` $ sudo add-apt-repository ppa:webupd8team/java ``` 5. Run commands to update system package index and install Java installer script: ``` $ sudo apt update; sudo apt install oracle-java8-installer ``` 6. Add the repository key to the system: ``` $ wget -q -O - https://pkg.jenkins.io/debian/jenkins-ci.org.key | sudo apt-key add - . ``` 7. Append the Debian package repository address to the server’s sources: ``` $ echo deb https://pkg.jenkins.io/debian-stable binary/ | sudo tee /etc/apt/sources.list.d/jenkins.list ``` 8. Run update so that apt-get will use the new repository: ``` $ sudo apt-get update ``` 9. Install Jenkins and its dependencies, including Java: ``` $ sudo apt-get install jenkins ``` 10. Start Jenkins: ``` $ sudo service jenkins start ``` 11. Verify that it started successfully: ``` $ sudo service jenkins status ``` 12. If everything went well, the beginning of the output should show that the service is active and configured to start at boot: > > jenkins.service - LSB: Start Jenkins at boot time > Loaded: loaded (/etc/init.d/jenkins; bad; vendor preset: enabled) > Active:active (exited) since Thu 2017-04-20 16:51:13 UTC; 2min 7s ago > Docs: man:systemd-sysv-generator(8) 13. To set up installation, visit Jenkins on its default port, 8080, using the server domain name or IP address: > > <http://ip_address_of_ec2_instance:8080> An “Unlock Jenkins” screen would appear, which displays the location of the initial password  14. In the terminal window, use the cat command to display the password: ``` $ sudo cat /var/lib/jenkins/secrets/initialAdminPassword ``` 15. Copy the 32-character alphanumeric password from the terminal and paste it into the “Administrator password” field, then click “Continue”.  16. Click the “Install suggested plugins” option, which will immediately begin the installation process.  17. When the installation is complete, it prompt to set up the first administrative user. It’s possible to skip this step and continue as admin using the initial password used above, but its batter to take a moment to create the user.  18. Once the first admin user is in place, you should see a “Jenkins is ready!” confirmation screen.  19. Click “Start using Jenkins” to visit the main Jenkins dashboard.  At this point, Jenkins has been successfully installed. 20. Update your package lists and install Nginx: ``` $ sudo apt-get install nginx ``` 21. To check successful installation run: ``` $ nginx -v ``` 22. Move into the proper directory where you want to put your certificates: ``` $ cd /etc/nginx ``` 23. Generate a certificate: ``` $ sudo openssl req -x509 -nodes -days 365 -newkey rsa:2048 -keyout /etc/nginx/cert.key -out /etc/nginx/cert.crt ``` 24. Next you will need to edit the default Nginx configuration file: ``` $ sudo vi /etc/nginx/sites-enabled/default ``` 25. Update the file with following contents: > > > server { > listen 80; > return 301 <https://$host$request_uri>; > > } > > > > server { > listen 443; > server\_name beam-ci.tk; > > > ssl\_certificate /etc/nginx/cert.crt; > ssl\_certificate\_key /etc/nginx/cert.key; > > > ssl on; > ssl\_session\_cache builtin:1000 shared:SSL:10m; > ssl\_protocols TLSv1 TLSv1.1 TLSv1.2; > ssl\_ciphers HIGH:!aNULL:!eNULL:!EXPORT:!CAMELLIA:!DES:!MD5:!PSK:!RC4; > ssl\_prefer\_server\_ciphers on; > > > access\_log /var/log/nginx/jenkins.access.log; > > > > location / { > proxy\_set\_header Host $host; > proxy\_set\_header X-Real-IP $remote\_addr; > proxy\_set\_header X-Forwarded-For $proxy\_add\_x\_forwarded\_for; > proxy\_set\_header X-Forwarded-Proto $scheme; > > > # Fix the “It appears that your reverse proxy set up is broken” error. > proxy\_pass <http://localhost:8080>; > proxy\_read\_timeout 90; > > > proxy\_redirect <http://localhost:8080> <https://beam-ci.tk>; > > > > > } > > > > > } > > > 26. For Jenkins to work with Nginx, you need to update the Jenkins config to listen only on the localhost interface instead of all (0.0.0.0), to ensure traffic gets handled properly. This is an important step because if Jenkins is still listening on all interfaces, then it will still potentially be accessible via its original port (8080). 27. Modify the /etc/default/jenkins configuration file to make these adjustments: ``` $ sudo vi /etc/default/jenkins ``` 28. Locate the JENKINS\_ARGS line and update it to look like the following: ``` $ JENKINS_ARGS="--webroot=/var/cache/$NAME/war --httpListenAddress=127.0.0.1 --httpPort=$HTTP_PORT -ajp13Port=$AJP_PORT" ``` 29. Then go ahead and restart Jenkins: ``` $ sudo service jenkins restart ``` 30. After that restart Nginx: ``` $ sudo service nginx restart ``` You should now be able to visit your domain using either HTTP or HTTPS, and the Jenkins site will be served securely. You will see a certificate warning because you used a self-signed certificate. 31. Next you install certbot to setup nginx with as CA certificate. Certbot team maintains a PPA. Once you add it to your list of repositories all you’ll need to do is apt-get the following packages: ``` $ sudo add-apt-repository ppa:certbot/certbot ``` 32. Run apt update: ``` $ sudo apt-get update ``` 33. Install certbot for Nginx: ``` $ sudo apt-get install python-certbot-nginx ``` 34. Get a certificate and have Certbot edit Nginx configuration automatically, run the following command: ``` $ sudo certbot –nginx ``` 35. The Certbot packages on your system come with a cron job that will renew your certificates automatically before they expire. Since Let’s Encrypt certificates last for 90 days, it’s highly advisable to take advantage of this feature. You can test automatic renewal for your certificates by running this command: ``` $ sudo certbot renew –dry-run ``` 36. Restart Nginx: ``` $ sudo service nginx restart ``` 37. Go to AWS management console and update the Security Group associated with jenkins server by removing the port 8080, that you added in step 2. #### Setup Jenkins Slave[¶](#setup-jenkins-slave "Permalink to this headline") Now configure a Jenkins slave for pipeline configuration. You need the slave AMI to spawn automatic EC2 instance on new build jobs. 1. Create Amazon EC2 instance from an Amazon Machine Image (AMI) that has Ubuntu 64-bit as base operating system. 2. Choose a security group that will allow only SSH access to your master (and temporarily for your personal system). 3. Connect to the instance via SSH. 4. Add oracle java apt repository and git-lfs: ``` $ sudo add-apt-repository ppa:webupd8team/java* $ sudo curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash* ``` 5. Run commands to update system package index: ``` $ sudo apt update ``` 6. Install Java and other dependency components, there is no need to install any jenkins component or service. Jenkins automatically deploy an agent as it initiates the build: ``` $ sudo apt install git docker oracle-java8-installer git-lfs=2.3.4 ``` 7. SSH master that you created in last topic and from inside master again ssh your newly created slave, just to test the communication: ``` $ ssh ubuntu@<slave_ip_address> ``` 8. In EC2 Instances pane, click on your Jenkins slave instance you just configure, and create a new image.  9. On Create Image dialog, name the image and select “Delete on Termination”. It makes slave instance disposable, if there are any build artifacts, job should save them, that will send them to your master.  10. Once image creation process completes, just copy the AMI ID, you need it for master configuration.  11. Update the Slave security group and remove all other IP addresses except master. You should only enable ingress from the IP addresses you wish to allow access to your slave.  12. At the end drop slave instance, its not needed anymore. #### Configure Jenkins Master[¶](#configure-jenkins-master "Permalink to this headline") Now start configuring Jenkins master, so it can spawn new slave instance on demand. 1. Once Master and Slave are setup, login to Jenkins server administrative console as admin. 2. On the left-hand side, click Manage Jenkins, and then click Manage Plugins. 3. Click on the Available tab, and then enter Amazon EC2 plugin at the top right.  3. Select the checkbox next to Amazon EC2 plugin, and then click Install without restart. 4. Once the installation is done, click Go back to the top page. 4. On the sidebar, click on Credentials, hover (global) for finding the sub menu and add a credential.  6. Choose AWS Credentials, and limit the scope to System, complete the form, if you make an error, Jenkins will add an error below the secret key. Jenkins uses access key ID and secret access key to interface with Amazon EC2.  7. Click on Manage Jenkins, and then Configure System. 8. Scroll all the way down to the section that says Cloud. 9. Click Add a new cloud, and select Amazon EC2. A collection of new fields appears.  10. Select Amazon EC2 Credentials that you just created. EC2 Key Pair’s Private key is a key generated when creating a new EC2 image on AWS.  11. Complete the form, choose a Region, Instance Type, label and set Idle termination time. If the slave becomes idle during this time, the instance will be terminated.  12. In order for Jenkins to watch GitHub projects, you will need to create a Personal Access Token in your GitHub account. Now go to GitHub and signing into your account and click on user icon in the upper-right hand corner and select Settings from the drop down menu.  13. On Settings page, locate the Developer settings section on the left-hand menu and go to Personal access tokens and click on Generate new token button.  14. In the Token description box, add a description that will allow you to recognize it later.  15. In the Select scopes section, check the repo:status, repo:public\_repo and admin:org\_hook boxes. These will allow Jenkins to update commit statuses and to create webhooks for the project. If you are using a private repository, you will need to select the general repo permission instead of the repo sub items.  16. When you are finished, click Generate token at the bottom. 17. You will be redirected back to the Personal access tokens index page and your new token will displayed.  18. Copy the token now so that you can reference it later. Now that you have a token, you need to add it to your Jenkins server so it can automatically set up webhooks. Log into your Jenkins web interface using the administrative account you configured during installation. 19. On Jenkins main dashboard, click Credentials in the left hand menu.  20. Click the arrow next to (global) within the Jenkins scope. In the box that appears, click Add credentials.  21. From Kind drop down menu, select Secret text. In the Secret field, paste your GitHub personal access token. Fill out the Description field so that you will be able to identify this entry at a later date and press OK button in the bottom.  22. Jenkins dashboard, click Manage Jenkins in the left hand menu and then click Configure System.  23. Find the section with title GitHub. Click the Add GitHub Server button and then select GitHub Server.  24. In the Credentials drop down menu, select your GitHub personal access token that you added in the last section.  25. Click the Test connection button. Jenkins will make a test API call to your account and verify connectivity. On successful connectivity click Save. #### Configure Jenkins Jobs[¶](#configure-jenkins-jobs "Permalink to this headline") Once Jenkins is installed on master and its configured with slave, cloud and github. The only thing we need now, before configuring the jobs, is to install a set of plugins. 1. On the left-hand side of Jenkins dashboard, click Manage Jenkins, and then click Manage Plugins. 2. Click on the Available tab, and then enter plugin name at the top right to install following set of plugins. * Gradle Plugin: This plugin allows Jenkins to invoke Gradle build scripts directly. * Build Timeout: This plugin allows builds to be automatically terminated after the specified amount of time has elapsed. * HTML5 Notifier Plugin: The HTML5 Notifier Plugin provides W3C Web Notifications support for builds. * Notification Plugin: you can notify on deploying, on master failure/back to normal, etc. * HTTP Request Plugin: This plugin sends a http request to a url with some parameters. * embeddable-build-status: Fancy but I love to have a status badge on my README * Timestamper: It adds time information in our build output. * AnsiColor: Because some tools (lint, test) output string with bash color and Jenkins do not render the color without it. * Green Balls: Because green is better than blue! 3. Back in the main Jenkins dashboard, click New Item in the left hand menu: 4. Enter a name for your new pipeline in the Enter an item name field. Afterwards, select Freestyle Project as the item type and Click the OK button at the bottom to move on.  5. On the next screen, specify Project name and description.  6. Then check the GitHub project box. In the Project url field that appears, enter your project’s GitHub repository URL.  7. In the HTML5 Notification Configuration section left uncheck Skip HTML5 Notifications? Checkbox, to receive browser notifications against our builds  8. To configure Glip Notifications with Jenkins build you need to configure notification endpoint under Job Notification section. Select JSON in Format drop-down, HTML in Protocol and to obtain end point URL follow steps 8.1 through 8.3.  > > 8.1. Open Glip and go to your desired channel where you want to receive notifications and then click top right button for Conversation Settings. It will open a menu, click Add Integration menu item.  > > 8.2. On Add Integration dialog search Jenkins and click on the Jenkins Integration option.  > > 8.3. A new window would appear with integration steps, copy the URL from this window and use in the above step.  9. At the end of notification section check Execute concurrent build if necessary and Restrict where this project can run and specify the label that we mentioned in last section while configuring master.  10. In Source Code Management specify the beam github url against Repository URL and select appropriate credentials. Put \*\* for all branches, to activate build for all available bit hub branches.  11. Next, in the Build Triggers section, check the GitHub hook trigger for GITScm polling box.  12. Under Build Environment section, click Abort build if it’s stuck and specify the timeout. Enable timestamps to Console output and select xterm in ANSI color option and in the end specify the build name pattern for more readable build names.  13. Last but not least, in Build section add a gradle build step, check Use Gradle Wrapper and specify the gralde task for build.  #### Configure Periodic Jobs[¶](#configure-periodic-jobs "Permalink to this headline") You can schedule any Jenkins job to run periodically based on provided schedule. To configure periodic build follow the steps below: 1. First click on Configure menu item from menu on left hand side of Job/Project home page. 2. On the next (configuration) page, go to Build Triggers section.  3. Click on check box labeled Build periodically to enable the option. It will expand and ask for Schedule with a warning message some thing like, No schedules so will never run.  4. You have to specify a schedule by following the similar syntax of cron job as a line consists of 5 fields separated by TAB or whitespace: MINUTE HOUR DOM MONTH DOW * MINUTE Minutes within the hour (0–59) * HOUR The hour of the day (0–23) * DOM The day of the month (1–31) * MONTH The month (1–12) * DOW The day of the week (0–7) where 0 and 7 are Sunday. To schedule once daily every 24 hours for only 5 working days, we need to specify some thing like: ``` H 0 \* \* 1-5 ```  As you specify the schedule, warning would be replaced with a descriptive schedule. 5. Save the configurations and now you have setup job to run periodically. #### References[¶](#references "Permalink to this headline") <https://d0.awsstatic.com/whitepapers/DevOps/Jenkins_on_AWS.pdf> <https://www.digitalocean.com/community/tutorials/how-to-configure-nginx-with-ssl-as-a-reverse-proxy-for-jenkins> <https://www.digitalocean.com/community/tutorials/how-to-set-up-continuous-integration-pipelines-in-jenkins-on-ubuntu-16-04> <https://jmaitrehenry.ca/2016/08/04/how-to-install-a-jenkins-master-that-spawn-slaves-on-demand-with-aws-ec2> ### Automated Cloud Deployment[¶](#automated-cloud-deployment "Permalink to this headline") #### Automatic Image (AMI) Update[¶](#automatic-image-ami-update "Permalink to this headline") In Automated Cloud Deployment capability, there is a baseline image (AMI) that used to instantiate new EC2 instance. It contains copy of git repository and gradle dependency libraries. All of these are outdated in few days due to active development of BEAM. And when we start instance from an outdated image it take additional time to update them before starting the simulation/run. This process help Cloud Automatic Deployment to keep up to date image for fast execution. To trigger this update process a Cloud Watch Event is setup with one week frequency. This event triggers an AWS Lambda (named updateDependencies) and lambda then starts an instance from the outdated image with instructions to update the image with latest LFS files for pre configured branches (these branches are mentioned in its environment variables that we can configure easily without any change in lambda code). One LFS files and gradle dependencies are updated in the new instance, the instance invoke a new lambda (named updateBeamAMI) to take its new image. This new lambda creates an image of the instance, terminate the instance and update this new image id to Automated Cloud Deployment process for future use. This process is designed to get latest LFS files from different branches. To add a new branch or update existing one, an environment variable named BRANCHES need to update with space as branch name delimiter. Indices and tables[¶](#indices-and-tables "Permalink to this headline") ======================================================================= * [Index](genindex.html) * [Module Index](py-modindex.html) * [Search Page](search.html)

goldilocks

go

Goldilocks 0.1.1 documentation [Goldilocks](index.html#document-index) stable * [Goldilocks](index.html#document-readme) * [Installation](index.html#document-installation) * [Command Line Usage](index.html#document-cli) * [Basic Package Usage](index.html#document-basic) * [Advanced Package Usage](index.html#document-advanced) * [Exporting](index.html#document-exporting) * [Plotting](index.html#document-plotting) * [Custom Strategies](index.html#document-custom) * [Examples](index.html#document-example) * [Contributing](index.html#document-contributing) * [Credits](index.html#document-authors) * [History](index.html#document-changelog) [Goldilocks](index.html#document-index) * [Docs](index.html#document-index) » * Goldilocks 0.1.1 documentation * [Edit on GitHub](https://github.com/SamStudio8/goldilocks/blob/3bd08ae9bb42c6683ff27d44ae259731925809b4/docs/index.rst) --- Welcome to Goldilocks’s documentation ============================================================================================================== Contents: Goldilocks[¶](#goldilocks "Permalink to this headline") ------------------------------------------------------- [](http://badge.fury.io/py/goldilocks) [](https://travis-ci.org/SamStudio8/goldilocks) [](https://coveralls.io/r/SamStudio8/goldilocks) [](https://gitter.im/SamStudio8/goldilocks?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge) Locating genomic regions that are “just right”. * Documentation: <http://goldilocks.readthedocs.org>. ### What is it?[¶](#what-is-it "Permalink to this headline") **Goldilocks** is a Python package providing functionality for locating ‘interesting’ genomic regions for some definition of ‘interesting’. You can import it to your scripts, pass it sequence data and search for subsequences that match some criteria across one or more samples. Goldilocks was developed to support our work in the investigation of quality control for genetic sequencing. It was used to quickly locate regions on the human genome that expressed a desired level of variability, which were “just right” for later variant calling and comparison. The package has since been made more flexible and can be used to find regions of interest based on other criteria such as GC-content, density of target k-mers, defined confidence metrics and missing nucleotides. ### What can I use it for?[¶](#what-can-i-use-it-for "Permalink to this headline") Given some genetic sequences (from one or more samples, comprising of one or more chromosomes), Goldilocks will shard each chromosome in to subsequences of a desired size which may or may not overlap as required. For each chromosome from each sample, each subsequence or ‘region’ is passed to the user’s chosen strategy. The strategy simply defines what is of interest to the user in a language that Goldilocks can understand. Goldilocks is currently packaged with the following strategies: | Strategy | Census Description | | --- | --- | | GCRatioStrategy | Calculate GC-ratio for subregions across the genome. | | NucleotideCounterStrategy | Count given nucleotides for subregions across the genome. | | MotifCounterStrategy | Search for one or more particular motifs of interest of any and varying size in subregions across the genome. | | ReferenceConsensusStrategy | Calculate the (dis)similarity to a given reference across the genome. | | PositionCounterStrategy | Given a list of base locations, calculate density of those locations over subregions across the genome. | Once all regions have been ‘censused’, the results may be sorted by one of four mathematical operations: max, min, median and mean. So you may be interested in subregions of your sequence(s) that feature the most missing nucleotides, or subregions that contain the mean or median number of SNPs or the lowest GC-ratio. ### Why should I use it?[¶](#why-should-i-use-it "Permalink to this headline") Goldilocks is hardly the first tool capable of calculating GC-content across a genome, or to find k-mers of interest, or SNP density, so why should you use it as part of your bioinformatics pipeline? Whilst not the first program to be able to conduct these tasks, it is the first to be capable of doing them all together, sharing the same interfaces. Every strategy can quickly be swapped with another by changing one line of your code. Every strategy returns regions in the same format and so you need not waste time munging data to fit the rest of your pipeline. Strategies are also customisable and extendable, those even vaguely familiar with Python should be able to construct a strategy to meet their requirements. Goldilocks is maintained, documented and tested, rather than that hacky perl script that you inherited years ago from somebody who has now left your lab. ### Requirements[¶](#requirements "Permalink to this headline") To use; * numpy * matplotlib (for plotting) To test; * tox * pytest For coverage; * nose * python-coveralls ### Installation[¶](#installation "Permalink to this headline") ``` $ pip install goldilocks ``` ### Citation[¶](#citation "Permalink to this headline") Please cite us so we can continue to make useful software! ``` Nicholls, S. M., Clare, A., & Randall, J. C. (2016). Goldilocks: a tool for identifying genomic regions that are "just right." Bioinformatics (2016) 32 (13): 2047-2049. doi:10.1093/bioinformatics/btw116 ``` ``` @article{Nicholls01072016, author = {Nicholls, Samuel M. and Clare, Amanda and Randall, Joshua C.}, title = {Goldilocks: a tool for identifying genomic regions that are ‘just right’}, volume = {32}, number = {13}, pages = {2047-2049}, year = {2016}, doi = {10.1093/bioinformatics/btw116}, URL = {http://bioinformatics.oxfordjournals.org/content/32/13/2047.abstract}, eprint = {http://bioinformatics.oxfordjournals.org/content/32/13/2047.full.pdf+html}, journal = {Bioinformatics} } ``` ### License[¶](#license "Permalink to this headline") Goldilocks is distributed under the MIT license, see LICENSE. Installation[¶](#installation "Permalink to this headline") ----------------------------------------------------------- At the command line: ``` $ pip install goldilocks ``` Or, if you have virtualenvwrapper installed: ``` $ mkvirtualenv goldilocks $ pip install goldilocks ``` Command Line Usage[¶](#command-line-usage "Permalink to this headline") ----------------------------------------------------------------------- Goldilocks is also packaged with a basic command line tool to demonstrate some of its capabilities and to provide access to base functionality without requiring users to author a script of their own. For more complicated queries, you’ll need to import Goldilocks as a package to a script of your own. But for simple use-cases the tool might be enough for you. ### Usage[¶](#usage "Permalink to this headline") Goldilocks is invoked as follows: ``` goldilocks <strategy> <sort-op> [--tracks TRACK1 [TRACK2 ...]] -l LENGTH -s STRIDE [-@ THREADS] FAIDX1 [FAIDX2 ...] ``` Where a strategy is a census strategy listed as available... ``` $ goldilocks list Available Strategies * gc * ref * motif * nuc ``` ...and a sort operation is one of: * max * min * mean * median * none ### Example[¶](#example "Permalink to this headline") Tabulate all regions and their associated counts of nucleotides A, C, G, T and N. Window size 100Kbp, overlap 50Kbp. Census will spawn 4 processes. Regions in table will be sorted by co-ordinate: ``` goldilocks nuc none --tracks A C G T N -l 100000 -s 50000 -@ 4 /store/ref/hs37d5.fa.fai ``` Tabulate all regions and their associated GC-content. Same parameters as previous example but table will be sorted by maximum GC-content descending: ``` goldilocks gc max -l 100000 -s 50000 -@ 4 /store/ref/hs37d5.fa.fai ``` Basic Package Usage[¶](#basic-package-usage "Permalink to this headline") ------------------------------------------------------------------------- The following example assumes basic Python programming experience (and that you have installed Goldilocks), skip to the end if you think you know what you’re doing. ### Importing[¶](#importing "Permalink to this headline") To use Goldilocks you will need to import the [`goldilocks.goldilocks.Goldilocks`](index.html#goldilocks.goldilocks.Goldilocks "goldilocks.goldilocks.Goldilocks") class and your desired census strategy (e.g. NucleotideCounterStrategy) from [`goldilocks.strategies`](index.html#module-goldilocks.strategies "goldilocks.strategies") to your script: ``` from goldilocks import Goldilocks from goldilocks.strategies import NucleotideCounterStrategy ``` ### Providing Sequence Data as Dictionary[¶](#providing-sequence-data-as-dictionary "Permalink to this headline") If you do not have FASTA files, the [`goldilocks.goldilocks.Goldilocks`](index.html#goldilocks.goldilocks.Goldilocks "goldilocks.goldilocks.Goldilocks") class allows you to provide sequence data in the following structure: ``` sequence\_data = { "sample\_name\_or\_identifier": { "chr\_name\_or\_number": "my\_actual\_sequence", } } ``` For example: ``` sequence\_data = { "my\_sample": { 2: "NANANANANA", "one": "CATCANCAT", "X": "GATTACAGATTACAN" }, "my\_other\_sample": { 2: "GANGANGAN", "one": "TATANTATA", "X": "GATTACAGATTACAN" } } ``` The sequences are stored in a nested structure of Python dictionaries, each key of the sequence\_data dictionary represents the name or an otherwise unique identifier for a particular sample (e.g. “my\_sample”, “my\_other\_sample”), the value is a dictionary whose own keys represent chromosome names or numbers [[1]](#id3) and the corresponding values are the sequences themselves as a string [[2]](#id4). Regardless of how the chromosomes are identified, they must match across samples if one wishes to make comparisons across samples. | [[1]](#id1) | Goldilocks has no preference for use of numbers or strings for chromosome names but it would be sensible to use numbers where possible for cases where you might wish to sort by chromosome. | | [[2]](#id2) | In future it is planned that sequences may be further nested in a dictionary to fully support polyploid species. | ### Providing Sequence Data as FASTA[¶](#providing-sequence-data-as-fasta "Permalink to this headline") If your sequences are in FASTA format, you must first index them with samtools faidx, then for each sample, pass the path to the index to Goldilocks in the following structure: ``` sequence\_data = { "my\_sequence": {"file": "/path/to/fastaidx/1.fa.fai"}, "my\_other\_sequence": {"file": "/path/to/fastaidx/2.fa.fai"}, "my\_other\_other\_sequence": {"file": "/path/to/fastaidx/3.fa.fai"}, } ``` When supplying sequences in this format, note the following: > > * is\_faidx=True must be passed to the Goldilocks constructor (see below), > * It is assumed that the FASTA will be in the same directory with the same name as its index, just without the ”.fai” extension, > * The key in the sequence data dictionary for each sample, must be file, > * The i-th sequence in each FASTA will be censused together, thus the order in which your sequences appear matters. > > > It is anticipated in future these assumptions will be circumvented by additional options to the Goldilocks constructor. To specify the is\_faidx argument, call the constructor like so: ``` g = Goldilocks(NucleotideCounterStrategy(["N"]), sequence\_data, length=3, stride=1, is\_faidx=True) ``` Now Goldilocks will know to expect to open these file values as FASTA indexes, not sequence data! ### Conducting a Census[¶](#conducting-a-census "Permalink to this headline") Once you have organised your sequence data in to the appropriate structure, you may conduct the census with Goldilocks by passing your strategy (e.g. NucleotideCounterStrategy) and sequence data to the imported [`goldilocks.goldilocks.Goldilocks`](index.html#goldilocks.goldilocks.Goldilocks "goldilocks.goldilocks.Goldilocks") class: ``` g = Goldilocks(NucleotideCounterStrategy(["N"]), sequence\_data, length=3, stride=1) ``` Make sure you add the brackets after the name of the imported strategy, this ‘creates’ a usuable strategy for Goldilocks to work with. For each chromosome (i.e. ‘one’, ‘X’ and 2) Goldilocks will split each sequence from the corresponding chromosome in each of the two example samples in to triplets of bases (as our specified region length is 3) with an offset of 1 (as our stride is 1). For example, chromosome “one” of “my\_sample” will be split as follows: ``` CAT ATC TCA CAN ANC NCA CAT ``` In our example, the NucleotideCounterStrategy will then count the number of N bases that appear in each split, for each sample, for each chromosome. ### Getting the Regions[¶](#getting-the-regions "Permalink to this headline") Once the census is complete, you can extract all of the censused regions directly from your Goldilocks object. The example below demonstrates the format of the returned regions dictionary for the example data above: ``` > g.regions { 0: { 'chr': 2, 'ichr': 0, 'pos_end': 3, 'pos_start': 1, 'group_counts': { 'my_sample': {'default': 2}, 'my_other_sample': {'default': 1}, 'total': {'default': 3} }, } ... 27: { 'chr': 'one', 'ichr': 6, 'pos_end': 9, 'pos_start': 7, 'group_counts': { 'my_sample': {'default': 0}, 'my_other_sample': {'default': 0}, 'total': {'default': 0} }, } } ``` The returned structure is a dictionary whose keys represent the id of each region, with values corresponding to a dictionary of metadata for that particular id. The id is assigned incrementally (starting at 0) as each region is encountered by Goldilocks during the census and isn’t particularly important. Each region dictionary has the following metadata [[3]](#id6): | Key | Value | | --- | --- | | id | A unique id assigned to the region by Goldilocks | | chr | The chromosome the region appeared on (as found in the input data) | | ichr | This region is the ichr-th to appear on this chromosome (0-indexed) | | pos\_start | The 1-indexed base of the sequence where the region begins (inclusive) | | pos\_end | The 1-indexed base of the sequence where the region ends (inclusive) | | [[3]](#id5) | Goldilocks used to feature a group\_counts dictionary as part of the region metadata as shown in the example above, this was removed as it duplicated data stored in the group\_counts variable in the Goldilocks object needlessly. It has not been removed in the example output above as it helps explain what regions represent. | In the example output above, the first (0th) censused region appears on chromosome 2 [[4]](#id9) and includes bases 1-3. It is the first (0th) region to appear on this chromosome and over those three bases, the corresponding subsequence for “my\_sample” contained 2 N bases and the corresponding subsequence for “my\_other\_sample” contained 1. In total, over both samples, on chromosome 2, over bases 1-3, 3 N bases appeared. The last region, region 27 (28th) appears on chromosome “one” [[5]](#id10) and includes bases 7-9. It is the seventh (6th by 0-index) found on this chromosome and over those three bases neither of the two samples contained an N base. | [[4]](#id7) | As numbers are ordered before strings like “one” and “X” in Python. | | [[5]](#id8) | As “X” is ordered before “one” in Python. | ### Sorting Regions[¶](#sorting-regions "Permalink to this headline") Following a census, Goldilocks allows you to sort the regions found by four mathematical operations: max, min, mean and median. ``` g\_max = g.query("max") g\_min = g.query("min") g\_mean = g.query("mean") g\_median = g.query("median") ``` The result of a query is the original [`goldilocks.goldilocks.Goldilocks`](index.html#goldilocks.goldilocks.Goldilocks "goldilocks.goldilocks.Goldilocks") object with masked and sorted internal data. You can view a table-based representation of the regions with [`goldilocks.goldilocks.Goldilocks.export\_meta()`](index.html#goldilocks.goldilocks.Goldilocks.export_meta "goldilocks.goldilocks.Goldilocks.export_meta"). ``` > g_max.export_meta(sep='\t', group="total") [NOTE] Filtering values between 0.00 and 3.00 (inclusive) [NOTE] 28 processed, 28 match search criteria, 0 excluded, 0 limit chr pos_start pos_end total_default 2 1 3 3.0 2 3 5 3.0 2 5 7 3.0 2 7 9 3.0 2 2 4 2.0 2 4 6 2.0 2 6 8 2.0 2 8 10 2.0 X 13 15 2.0 one 4 6 2.0 one 5 7 2.0 one 3 5 1.0 one 6 8 1.0 X 1 3 0.0 X 2 4 0.0 X 3 5 0.0 X 4 6 0.0 X 5 7 0.0 X 6 8 0.0 X 7 9 0.0 X 8 10 0.0 X 9 11 0.0 X 10 12 0.0 X 11 13 0.0 X 12 14 0.0 one 1 3 0.0 one 2 4 0.0 one 7 9 0.0 ``` Note the regions in g\_max are now sorted by the number of N bases that appeared. Ties are currently resolved by the region that was seen first (has the lowest id). ### Setting Number of Processes[¶](#setting-number-of-processes "Permalink to this headline") Goldilocks supports multiprocessing and can spawn some number of additional processes to perform the census steps before aggregating all the region counters and answering queries. To specify the number of processes Goldilocks should use, specify a processes argument to the constructor: ``` g = Goldilocks(NucleotideCounterStrategy(["N"]), sequence\_data, length=3, stride=1, processes=4) ``` ### Full Example[¶](#full-example "Permalink to this headline") Census an example sequence for appearance of ‘N’ bases: ``` from goldilocks import Goldilocks from goldilocks.strategies import NucleotideCounterStrategy sequence\_data = { "my\_sample": { 2: "NANANANANA", "one": "CATCANCAT", "X": "GATTACAGATTACAN" }, "my\_other\_sample": { 2: "GANGANGAN", "one": "TATANTATA", "X": "GATTACAGATTACAN" } } g = Goldilocks(NucleotideCounterStrategy(["N"]), sequence\_data, length=3, stride=1, processes=4) g\_max\_n\_bases = g.query("max") g\_min\_n\_bases = g.query("min") g\_median\_n\_bases = g.query("median") g\_mean\_n\_bases = g.query("mean") ``` Advanced Package Usage[¶](#advanced-package-usage "Permalink to this headline") ------------------------------------------------------------------------------- The following assumes basic Python programming experience (and that you have installed Goldilocks and familiarised yourself with the basics), skip to the end if you think you know what you’re doing. ### Filtering Regions[¶](#filtering-regions "Permalink to this headline") #### Group[¶](#group "Permalink to this headline") By default when returning region data the “total” group is used, in our running example of counting missing nucleotides, this would represent the total number of ‘N’ bases seen in sequence data across each sample in the same genomic region on the same chromosome. But if you are more interested in a particular sample: ``` g.query("max", group="my\_sample") ``` #### Track[¶](#track "Permalink to this headline") When using tracks (for strategies that calculate multiple distinct values for each genomic region - such as different nucleotide bases or k-mers), you may wish to extract regions based on scores for a certain track: ``` g.query("max", track="AAA") ``` #### Absolute distance[¶](#absolute-distance "Permalink to this headline") You may be interested in regions within some distance of the mean: ``` g.query("mean", acutal\_distance=10) ``` #### Percentile distance[¶](#percentile-distance "Permalink to this headline") Or perhaps the “top 10%”, or the “middle 25%” around the mean: ``` g.query("max", percentile\_distance=10) g.query("mean", percentile\_distance=25) ``` When not using max or min, by default both actual and percentile differences calculate ‘around’ the mean or median value instead. If you’d like to control this behaviour you can specify a direction: Let’s fetch regions that have values falling within 25% above or below the mean respectively: ``` g.query("mean", percentile\_distance=25, direction=1) g.query("mean", percentile\_distance=25, direction=-1) ``` #### Multiple criteria[¶](#multiple-criteria "Permalink to this headline") You can of course use these at the same time (though actual and percentile distances are mutually exclusive), let’s fetch the top 10% of regions that contain the most “AAA” k-mers for all chromosomes in a hypothetical sample called “my\_sample”: ``` g.query("max", group="my\_sample", track="N", percentile\_distance=10) ``` ### Excluding Regions[¶](#excluding-regions "Permalink to this headline") The filter function also allows users to specify a dictionary of exclusion criteria. #### Starting position[¶](#starting-position "Permalink to this headline") To filter regions based on the 1-indexed starting position greater than or equal to 3: ``` g.query("min", exclusions={ "start\_gte": 3, }) ``` #### Ending position[¶](#ending-position "Permalink to this headline") To filter regions based on the 1-indexed ending position less than or equal to 9: ``` g.query("min", exclusions={ "end\_lte": 9, }) ``` #### Chromosome[¶](#chromosome "Permalink to this headline") You can filter regions that appear on particular chromosomes completely by providing a list: ``` g.query("min", exclusions={ "chr": ["X", 6], }) ``` #### Value of another count group[¶](#value-of-another-count-group "Permalink to this headline") When using groups, one may wish to exclude results where the value of another group is less than the one selected by the query. For example, for each region the following would result in regions where the count for my-other-sample is greater than my-sample: ``` g.query("min", group="my-sample", exclusions={ "region\_group\_lte": "my-other-sample", }) ``` #### Multiple Criteria[¶](#id1 "Permalink to this headline") You may want to use such exclusion criteria at the same time. Let’s say we have a bunch of sequence data from a species whose chromosomes all feature centromeres between bases 500-1000. Let’s ignore regions from that area. Let’s also exclude anything from chromosome ‘G’. If a single one of these criteria are true, a region will be excluded: ``` g.query("mean", exclusions={ "start\_gte": 500, "end\_lte": 1000, "chr": ['G'], }) ``` What if you want to exclude based on multiple criteria that should all be true? Let’s exclude regions that start before or on base 100 on chromosome X or Y [[1]](#id4). Note the use of use\_and=True! [[2]](#id5) ``` g.query("mean", exclusions={ "start\_lte": 100, "chr": ['X', 'Y'], }, use\_and=True) ``` #### Chromosome specific criteria[¶](#chromosome-specific-criteria "Permalink to this headline") Finally applying exclusions across all chromosomes might seem quite naive, what if we want to ignore centromeres on a real species? Introducing chromosome dependent exclusions; the syntax is the same as previously, just the exclusions dictionary is a dictionary of dictionaries with keys representing each chromosome. Note the use of use\_chrom=True: ``` g.query("median", exclusions={ "one": { "start\_lte": 3, "end\_gte": 4 }, 2: { "start\_gte": 9 }, "X": { "chr": True }}, use\_chrom=True) ``` It is important to note that currently Goldilocks does not sanity check the contents of the exclusions dictionary including the spelling of exclusion names or whether you have correctly set use\_chrom if you are providing chromosome specific filtering. However, on this latter point, if Goldilocks detects a key in the exclusions dictionary matches the name of a chromosome, it will print a warning (but continue regardless). | [[1]](#id2) | Support for chromosome matching is still ‘or’ based even when using use\_and=True, a region can’t appear on more than one chromosome and so this seemed a more natural and useful behaviour. | | [[2]](#id3) | Apart from the above caveat on chromosome matching always being or-based, currently there is no support for more complicated queries such as exclude if (statement1 and statement2) or statement3. It’s or, or and on all criteria! | ### Limiting Regions[¶](#limiting-regions "Permalink to this headline") One may also limit the number of results returned by Goldilocks: ``` g.query("mean", limit=10) ``` ### Full Example[¶](#full-example "Permalink to this headline") Almost all of these options can be used together! Let’s finish off our examples by finding the top 5 regions that are within an absolute distance of 1.0 from the maximum number of ‘N’ bases seen across all subsequences over the ‘my\_sample’ sample. We’ll exclude any region that appears on chromosome “one” and any regions on chromosome 2 that start on a base position greater than or equal to 5 *and* end on a base position less than or equal to 10. Although when filtering the default track is indeed ‘default’, we’ve explicity set that here too.: ``` g.query("max", group="my_sample", track="default", actual_distance=1, exclusions={ 2: { "start_gte": 5, "end_lte": 10 }, "one": { "chr":True } }, use_chrom=True, use_and=True, limit=5 ).export_meta(sep="\t") [NOTE] Filtering values between 1.00 and 2.00 (inclusive) [NOTE] 28 processed, 12 match search criteria, 7 excluded, 5 limit chr pos_start pos_end my_other_sample_default my_sample_default 2 1 3 1.0 2.0 2 3 5 1.0 2.0 2 2 4 1.0 1.0 2 4 6 1.0 1.0 X 13 15 1.0 1.0 ``` Exporting[¶](#exporting "Permalink to this headline") ----------------------------------------------------- Goldilocks provides functions for the exporting of all censused regions metadata or for filtered regions resulting from a query. The examples below follow on from the basic usage instructions earlier in the documentation. ### Census Data[¶](#census-data "Permalink to this headline") For a given sample one may export basic metadata for all regions that included sequence data from that particular sample. The header is as follows: | Key | Value | | --- | --- | | id | A unique id assigned to the region by Goldilocks | | track1 | The value for the region as calculated by the strategy used. By default if a list of tracks is not specified when the strategy is created, there will be just one track named ‘default’. For the majority of ‘basic’ strategies this will be the case. | | [track2 ... trackN] | Optional further fields will appear for additional tracks, the column header will feature the name of the track. For example, a k-mer counting strategy would feature a column for each k-mer specified to the strategy. | | chr | The chromosome the region appeared on (as found in the input data) | | pos\_start | The 1-indexed base of the sequence where the region begins (inclusive) | | pos\_end | The 1-indexed base of the sequence where the region ends (inclusive) | Using the my\_sample data: ``` ... g.export_meta("my_sample", sep="\t") id default chr pos_start pos_end 0 2 2 1 3 1 1 2 2 4 2 2 2 3 5 3 1 2 4 6 4 2 2 5 7 5 1 2 6 8 6 2 2 7 9 7 1 2 8 10 8 0 X 1 3 9 0 X 2 4 10 0 X 3 5 11 0 X 4 6 12 0 X 5 7 13 0 X 6 8 14 0 X 7 9 15 0 X 8 10 16 0 X 9 11 17 0 X 10 12 18 0 X 11 13 19 0 X 12 14 20 1 X 13 15 21 0 one 1 3 22 0 one 2 4 23 0 one 3 5 24 1 one 4 6 25 1 one 5 7 26 1 one 6 8 27 0 one 7 9 ``` ### FASTA[¶](#fasta "Permalink to this headline") From any sorting or filtering operation on censused regions, a new Goldilocks object is returned, providing function to output filtered sequence data to FASTA format. Following on from the example introduced earlier, the example below shows the subsequences of my\_sample in the FASTA format, ordered by their appearance in the filtered candidates list, from the highest number of ‘N’ bases, to the lowest. ``` ... candidates = g.query("max", group="my_sample") candidates.export_fasta("my_sample") >my_sample|Chr2|Pos1:3 NAN >my_sample|Chr2|Pos3:5 NAN >my_sample|Chr2|Pos5:7 NAN >my_sample|Chr2|Pos7:9 NAN >my_sample|Chr2|Pos2:4 ANA >my_sample|Chr2|Pos4:6 ANA >my_sample|Chr2|Pos6:8 ANA >my_sample|Chr2|Pos8:10 ANA >my_sample|ChrX|Pos13:15 CAN >my_sample|Chrone|Pos4:6 CAN >my_sample|Chrone|Pos5:7 ANC >my_sample|Chrone|Pos6:8 NCA >my_sample|ChrX|Pos1:3 GAT >my_sample|ChrX|Pos2:4 ATT >my_sample|ChrX|Pos3:5 TTA >my_sample|ChrX|Pos4:6 TAC >my_sample|ChrX|Pos5:7 ACA >my_sample|ChrX|Pos6:8 CAG >my_sample|ChrX|Pos7:9 AGA >my_sample|ChrX|Pos8:10 GAT >my_sample|ChrX|Pos9:11 ATT >my_sample|ChrX|Pos10:12 TTA >my_sample|ChrX|Pos11:13 TAC >my_sample|ChrX|Pos12:14 ACA >my_sample|Chrone|Pos1:3 CAT >my_sample|Chrone|Pos2:4 ATC >my_sample|Chrone|Pos3:5 TCA >my_sample|Chrone|Pos7:9 CAT ``` Plotting[¶](#plotting "Permalink to this headline") --------------------------------------------------- ### Scatter Graphs[¶](#scatter-graphs "Permalink to this headline") #### Simple Plot[¶](#simple-plot "Permalink to this headline") After executing a census one can use the `plot` function to create a scatter graph of results. The `x` axis is the location along the genome (with ordered chromosomes or contigs appearing sequentially) and the `y` axis is the value of the censused region according to the strategy used. The example below plots GC content ratio across the first three chromosomes of the `hs37d5` reference sequence, with a window size of 100,000 and a step or overlap of 50,000. Note that the plot title may be specified with the `title` keyword argument. ``` from goldilocks import Goldilocks from goldilocks.strategies import GCRatioStrategy sequence\_data = { "my\_sequence": {"file": "/store/ref/hs37d5.1-3.fa.fai"}, } g = Goldilocks(GCRatioStrategy(), sequence\_data, length="100K", stride="50K", is\_faidx=True) g.plot(title="GC Content over hs37d5 Chr1-3") ``` [](_images/5-1-1.png) ### Line Graphs[¶](#line-graphs "Permalink to this headline") #### Plot multiple contigs or chromosomes from one sample[¶](#plot-multiple-contigs-or-chromosomes-from-one-sample "Permalink to this headline") For long genomes or a census with a small window size, simple plots as shown in the previous section can appear too crowded and thus difficult to extract information from. One can instead plot, for a given input sample, a panel of census region data, by chromosome by specifying the name of the sample as the first parameter to the `plot` function as per the example below: ``` from goldilocks import Goldilocks from goldilocks.strategies import GCRatioStrategy sequence\_data = { "hs37d5": {"file": "/store/ref/hs37d5.1-3.fa.fai"}, "GRCh38": {"file": "/store/ref/Homo\_sapiens.GRCh38.dna.chromosome.1-3.fa.fai"}, } g = Goldilocks(GCRatioStrategy(), sequence\_data, length="1M", stride="250K", is\_faidx=True) g.plot("hs37d5", title="GC Content over hs37d5 Chr1-3") ``` [](_images/5-2-1.png) Note that both the `x` and `y` axes are shared between all panels to avoid the automatic creation of graphics with the potential to mislead readers on a first glance by not featuring the same axes ticks. #### Plot a contig or chromosome from multiple samples[¶](#plot-a-contig-or-chromosome-from-multiple-samples "Permalink to this headline") By default, data within the census is aggregated by region across all input samples (in the `sequence\_data` dictionary) for the entire genome. However, one may be interested in comparisons across samples, rather than between chromosomes in a single sample. One can plot the census results for a specific contig or chromosome for each of the input samples, by specifying the `chrom` keyword argument to the `plot` function. Take note that the argument refers to the sequence that appears as the i’th contig of each of the input FASTA and not the actual name or identifier of the chromosome itself. ``` from goldilocks import Goldilocks from goldilocks.strategies import GCRatioStrategy sequence\_data = { "hs37d5": {"file": "/store/ref/hs37d5.1.fa.fai"}, "GRCh38": {"file": "/store/ref/Homo\_sapiens.GRCh38.dna.chromosome.1.fa.fai"}, } g = Goldilocks(GCRatioStrategy(), sequence\_data, length="1M", stride="250K", is\_faidx=True) g.plot(chrom=1, title="GC Content over Chr1") ``` [](_images/5-2-2.png) ### Histograms[¶](#histograms "Permalink to this headline") #### Simple profile (binning) plot[¶](#simple-profile-binning-plot "Permalink to this headline") Rather than inspection of individual data points, one may want to know how census data behaves as a whole. The `plot` function provides functionality to *profile* the results of a census through a histogram. Users can do this by providing a list of bins to the `bins` keyword argument of the `plot` function, following a census. The example below shows the distribution of GC content ratio across the `hs37d5` reference sequence for all 100Kbp regions (and step of 50Kbp). The `x` axis is the bin and the `y` axis represents the number of censused regions that fell into a particular bin. ``` from goldilocks import Goldilocks from goldilocks.strategies import GCRatioStrategy sequence\_data = { "my\_sequence": {"file": "/store/ref/hs37d5.fa.fai"} } g = Goldilocks(GCRatioStrategy(), sequence\_data, length="100K", stride="50K", is\_faidx=True) g.plot(bins=[0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0], title="GC Content Profile of hs37d5" ) ``` [](_images/5-3-1.png) #### Simpler profile (binning) plot[¶](#simpler-profile-binning-plot "Permalink to this headline") It’s trivial to select some sensible bins for the plotting of GC content as we know that the value for each region must fall between 0 and 1. However, many strategies will have an unknown minimum and maximum value and it can thus be difficult to select a suitable binning strategy without resorting to trial and error. Thus the `plot` function permits a single integer to be provided to the `bins` keyword instead of a list. This will automatically create \(N+1\) equally sized bins (reserving a special bin for 0.0) between 0 and the maximum observed value for the census. It is also possible to manually set the size of the largest bin with the `bin\_max` keyword argument. The following example creates the same graph as the previous subsection, but without explicitly providing a list of bins. ``` from goldilocks import Goldilocks from goldilocks.strategies import GCRatioStrategy sequence\_data = { "my\_sequence": {"file": "/store/ref/hs37d5.fa.fai"}, } g = Goldilocks(GCRatioStrategy(), sequence\_data, length="100K", stride="50K", is\_faidx=True) g.plot(bins=10, bin\_max=1.0, title="GC Content Profile of hs37d5") ``` #### Proportional bin plot[¶](#proportional-bin-plot "Permalink to this headline") Often it can be useful to compare the size of bins in terms of their proportion rather than raw counts alone. This can be accomplished by specifying `prop=True` to `plot`. The `y` axis is now the percentage of all regions that were placed in a particular bin instead of the raw count. ``` from goldilocks import Goldilocks from goldilocks.strategies import GCRatioStrategy sequence\_data = { "my\_sequence": {"file": "/store/ref/hs37d5.fa.fai"} } g = Goldilocks(GCRatioStrategy(), sequence\_data, length="100K", stride="50K", is\_faidx=True) g.plot(bins=10, bin\_max=1.0, prop=True, title="GC Content Profile of hs37d5") ``` [](_images/5-3-3.png) #### Bin multiple contigs or chromosomes from one sample[¶](#bin-multiple-contigs-or-chromosomes-from-one-sample "Permalink to this headline") As demonstrated with the line plots earlier, one may also specify a sample name as the first parameter to `plot` to create a figure with each contig or chromosome’s histogram on an individual panel. ``` from goldilocks import Goldilocks from goldilocks.strategies import GCRatioStrategy sequence\_data = { "my\_sequence": {"file": "/store/ref/hs37d5.1-3.fa.fai"} } g = Goldilocks(GCRatioStrategy(), sequence\_data, length="100K", stride="50K", is\_faidx=True) g.plot("my\_sequence", bins=10, bin\_max=1.0, prop=True, title="GC Content Profiles of hs37d5 Chrs 1-3") ``` [](_images/5-3-4.png) #### Bin a contig or chromosome from multiple samples[¶](#bin-a-contig-or-chromosome-from-multiple-samples "Permalink to this headline") Similarly, one may want to profile a single contig or chromosome between each input group as previously demonstrated by the line graphs. ``` from goldilocks import Goldilocks from goldilocks.strategies import GCRatioStrategy sequence\_data = { "hs37d5": {"file": "/store/ref/hs37d5.1.fa.fai"}, "GRCh38": {"file": "/store/ref/Homo\_sapiens.GRCh38.dna.chromosome.1.fa.fai"} } g = Goldilocks(GCRatioStrategy(), sequence\_data, length="100K", stride="50K", is\_faidx=True) g.plot(chrom=1, bins=10, bin\_max=1.0, prop=True, title="GC Content Profiles over Chr 1") ``` [](_images/5-3-5.png) ### Advanced[¶](#advanced "Permalink to this headline") #### Plot data from multiple counting tracks from one sample’s chromosomes[¶](#plot-data-from-multiple-counting-tracks-from-one-samples-chromosomes "Permalink to this headline") The examples thus far have demonstrated plotting the results of a strategy responsible for counting one interesting property. But strategies are capable of counting multiple targets of interest simultaneously. Of course, one may wish to plot the results of all tracks rather than just the totals - especially for cases such as nucleotide counting where the sum of all counts will typically equal the size of the census region! The `plot` function accepts a list of track names to plot via the `tracks` keyword argument. Each counting track is then drawn on the same panel for the appropriate chromosome. A suitable legend is automatically placed at the top of the figure. ``` from goldilocks import Goldilocks from goldilocks.strategies import NucleotideCounterStrategy sequence\_data = { "hs37d5": {"file": "/store/ref/hs37d5.1-3.fa.fai"}, } g = Goldilocks(NucleotideCounterStrategy(["A", "C", "G", "T", "N"]), sequence\_data, length="1M", stride="500K", is\_faidx=True, processes=4) g.plot(group="hs37d5", prop=True, tracks=["A", "C", "G", "T", "N"]) ``` [](_images/5-4-1.png) Note that `prop` is not a required argument, but can still be used with the `tracks` list to plot counts proportionally. #### Plot data from multiple counting tracks for one chromosome across many samples[¶](#plot-data-from-multiple-counting-tracks-for-one-chromosome-across-many-samples "Permalink to this headline") As previously demonstrated, one can use the `chrom` keyword argument for `plot` to create a figure featuring a panel per input sample, displaying census results for a particular chromosome. Similarly, this feature is supported when plotting multiple tracks with the `tracks` keyword. ``` from goldilocks import Goldilocks from goldilocks.strategies import NucleotideCounterStrategy sequence\_data = { "hs37d5": {"file": "/store/ref/hs37d5.1.fa.fai"}, "GRCh38": {"file": "/store/ref/Homo\_sapiens.GRCh38.dna.chromosome.1.fa.fai"}, } g = Goldilocks(NucleotideCounterStrategy(["A", "C", "G", "T", "N"]), sequence\_data, length="1M", stride="500K", is\_faidx=True, processes=4) g.plot(chrom=1, prop=True, tracks=["A", "C", "G", "T", "N"]) ``` [](_images/5-4-2.png) ### Integration with external plotting tools[¶](#integration-with-external-plotting-tools "Permalink to this headline") #### ggplot2[¶](#ggplot2 "Permalink to this headline") Plotting packages such as `ggplot2` favour ``melted” input. The figure below was created using data from Goldilocks as part of our quality control study, the scatter plot compares the density of SNPs between the GWAS and SNP chip studies across the human genome. [](_images/megabase_plot.png) #### Circos[¶](#circos "Permalink to this headline") Goldilocks has an output format specifically designed to output information for use with the ``popular and pretty” `circos` visualisation tool. Below is an example of a figure that can be generated from data gathered by Goldilocks. The figure visualises the selection of regions from our original quality control study. The Python script used to generate the data and the Circos configuration follow. [](_images/circos_latest.png) [](_images/circos_chr3-paper-col.png) Python script ``` from goldilocks import Goldilocks from goldilocks.strategies import PositionCounterStrategy sequence\_data = { "gwas": {"file": "/encrypt/ngsqc/vcf/cd-seq.vcf.q"}, "ichip": {"file": "/encrypt/ngsqc/vcf/cd-ichip.vcf.q"}, } g = Goldilocks(PositionCounterStrategy(), sequence\_data, length="1M", stride="500K", is\_pos\_file=True) # Query for regions that meet all criteria across both sample groups # The output file goldilocks.circ is used to plot the yellow triangular indicators g.query("median", percentile\_distance=20, group="gwas", exclusions={"chr": [6]}) g.query("max", percentile\_distance=5, group="ichip") g.export\_meta(fmt="circos", group="total", value\_bool=True, chr\_prefix="hs", to="goldilocks.circ") # Reset the regions selected and saved by queries g.reset\_candidates() # Export all region counts for both groups individually # The -all.circ files are used to plot the scatter plots and heatmaps g.export\_meta(fmt="circos", group="gwas", chr\_prefix="hs", to="gwas-all.circ") g.export\_meta(fmt="circos", group="ichip", chr\_prefix="hs", to="ichip-all.circ") # Export region counts for the groups where the criteria are met # The -candidates.circ files are used to plot the yellow 'bricks' that # appear between the two middle heatmaps g.query("median", percentile\_distance=20, group="gwas") g.export\_meta(fmt="circos", group="gwas", to="gwas-candidates.circ") g.reset\_candidates() g.query("max", percentile\_distance=5, group="ichip") g.export\_meta(fmt="circos", group="ichip", to="ichip-candidates.circ") g.reset\_candidates() ``` Circos configuration ``` # circos.conf <colors> gold = 255, 204, 0 </colors> karyotype = data/karyotype/karyotype.human.hg19.txt chromosomes_units = 1000000 chromosomes_display_default = no chromosomes = hs3; <ideogram> <spacing> default = 0.01r break = 2u </spacing> # Ideogram position, fill and outline radius = 0.9r thickness = 80p fill = yes stroke_color = dgrey stroke_thickness = 3p # Bands show_bands = yes band_transparency = 4 fill_bands = yes band_stroke_thickness = 2 band_stroke_color = white # Labels show_label = no label_font = default label_radius = 1r + 75p label_size = 72 label_parallel = yes label_case = upper </ideogram> # Ticks show_ticks = yes show_tick_labels = yes <ticks> label_font = default radius = dims(ideogram,radius_outer) label_offset = 5p orientation = out label_multiplier = 1e-6 color = black chromosomes_display_default = yes <tick> spacing = 1u size = 10p thickness = 3p color = lgrey show_label = no </tick> <tick> spacing = 5u size = 20p thickness = 5p color = dgrey show_label = yes label_size = 24p label_offset = 0p format = %d </tick> <tick> spacing = 10u size = 30p thickness = 5p color = black show_label = yes label_size = 40p label_offset = 5p format = %d </tick> </ticks> track_width = 0.05 track_pad = 0.02 track_start = 0.95 <plots> <plot> type = scatter file = goldilocks.circ r1 = 0.98r r0 = 0.95r orientation = out glyph = triangle #glyph_rotation = 180 glyph_size = 50p color = gold stroke_thickness = 2p stroke_color = black min = 0 max = 1 </plot> <plot> type = scatter file = gwas-all.circ r1 = 0.95r r0 = 0.80r fill = no fill_color = black color = black_a1 stroke_color = black glyph = circle glyph_size = 12 <backgrounds> <background> color = vlgrey y0 = 207 </background> <background> color = vlgrey y1 = 207 y0 = 179 </background> <background> color = gold y1 = 179 y0 = 148 </background> <background> color = vlgrey y1 = 145 y0 = 122 </background> <background> color = vlgrey y1 = 122 y0 = 0 </background> </backgrounds> <axes> <axis> color = white thickness = 1 spacing = 0.05r </axis> </axes> <rules> <rule> condition = var(value) < 1 show = no </rule> </rules> </plot> <plot> type = heatmap file = gwas-all.circ # color list color = grey,vvlblue,vlblue,lblue,blue,dblue,vdblue,vvdblue,black r1 = 0.80r r0 = 0.75r scale_log_base = 0.75 color_mapping = 2 min = 1 max = 267 # 95% </plot> <plot> type = tile layers_overflow = collapse file = gwas-candidates.circ r1 = 0.7495r r0 = 0.73r orientation = in layers = 1 margin = 0.0u thickness = 30p padding = 8p color = gold stroke_thickness = 0 stroke_color = gold </plot> <plot> type = tile layers_overflow = collapse file = ichip-candidates.circ r1 = 0.73r r0 = 0.70r orientation = out layers = 1 margin = 0.0u thickness = 30p padding = 8p color = gold stroke_color = gold </plot> <plot> type = heatmap file = ichip-all.circ # color list color = grey,vvlgreen,vlgreen,lgreen,green,dgreen,vdgreen,vvdgreen,black r1 = 0.70r r0 = 0.65r min = 1 max = 1097.71 # 99% color_mapping = 2 scale_log_base = 0.2 </plot> <plot> type = scatter file = ichip-all.circ r1 = 0.65r r0 = 0.50r orientation = in fill_color = black stroke_color = black glyph = circle glyph_size = 12 color = black_a1 <backgrounds> <background> color = gold y0 = 379 </background> <background> color = vlgrey y1 = 379 y0 = 49 </background> <background> color = vlgrey y1 = 49 y0 = 0 </background> </backgrounds> <axes> <axis> color = white thickness = 1 spacing = 0.05r </axis> </axes> <rules> <rule> condition = var(value) < 1 show = no </rule> </rules> </plot> </plots> ################################################################ # The remaining content is standard and required. It is imported # from default files in the Circos distribution. # # These should be present in every Circos configuration file and # overridden as required. To see the content of these files, # look in etc/ in the Circos distribution. <image> # Included from Circos distribution. <<include etc/image.conf>> </image> # RGB/HSV color definitions, color lists, location of fonts, fill patterns. # Included from Circos distribution. <<include etc/colors_fonts_patterns.conf>> # Debugging, I/O an dother system parameters # Included from Circos distribution. <<include etc/housekeeping.conf>> anti_aliasing* = no ``` Custom Strategies[¶](#custom-strategies "Permalink to this headline") --------------------------------------------------------------------- One of the major features of Goldilocks is its extensibility. Strategies are both easily customisable and interchangeable, as they all share a common interface. This interface also provides a platform for users with some knowledge of Python to construct their own custom census rules. One such example follows below: ### A Simple ORF Finder[¶](#a-simple-orf-finder "Permalink to this headline") #### Code Sample[¶](#code-sample "Permalink to this headline") ``` # Import Goldilocks and the BaseStrategy class from goldilocks import Goldilocks from goldilocks.strategies import BaseStrategy # Define a new class for your custom strategy that inherits from BaseStrategy class MyCustomSimpleORFCounterStrategy(BaseStrategy): # Initialising function boilerplate, required to set-up some properties of the census def \_\_init\_\_(self, tracks=None, min\_codons=1): # Initialise the custom class with super super(MyCustomSimpleORFCounterStrategy, self).\_\_init\_\_( tracks=range(0,3), # Use range to specify a counter for # each of the three possible forward # reading frames in which to search # to search for open reading frames label="Forward Open Reading Frames" # Y-Axis Plot Label ) self.MIN\_CODONS = min\_codons # This function defines the actual behaviour of a census for a given region # of sequence and the current counting track (one of three reading frames) def census(self, sequence, track\_frame, \*\*kwargs): STARTS = ["ATG"] STOPS = ["TAA", "TGA", "TAG"] CODON\_SIZE = 3 # Split input sequence into codons. Open a frame if a START is found # and increment the ORF counter if a STOP is encountered afterward orfs = orf\_open = 0 for i in xrange(track\_frame, len(sequence), CODON\_SIZE): codon = sequence[i:i+CODON\_SIZE].upper() if codon in STARTS and orf\_open == 0: orf\_open = 1 elif codon in STOPS and orf\_open > 0: if orf\_open > self.MIN\_CODONS: orfs += 1 orf\_open = 0 elif orf\_open > 0: orf\_open += 1 return orfs # Organise and execute the census sequence\_data = { "hs37d5": {"file": "/store/ref/hs37d5.1-3.fa.fai"} } g = Goldilocks(MyCustomSimpleORFCounterStrategy(min\_codons=30), sequence\_data, length="1M", stride="1M", is\_faidx=True, processes=4) ``` #### Implementation Description[¶](#implementation-description "Permalink to this headline") Strategies are defined as Python classes, inheriting from the `BaseStrategy` class found in the `goldilocks.strategies` subpackage. The class requires just two function definitions to be compliant with the shared interface; `\_\_init\_\_`: the class initializer that takes care of the setup of the strategy’s internals via the `BaseStrategy` parent class, and `census`: the function actually responsible for the behaviour of the strategy itself. The example presented is a very simple open reading frame counter. It searches the three forward frames for start codons that are then followed by one of the three stop codons. The ``tracks” in this example are the three possible frames. Note on line 9 that our `\_\_init\_\_` provides a default argument for `tracks` of `None`. Thus this particular strategy does not need the `tracks` argument. Instead, the track list is provided by the strategy itself, and passed to the `BaseStrategy` `\_\_init\_\_` (line 12), forcing tracks to be the list [0, 1, 2]. The elements of this list are used as an integer offset from which to begin splitting input DNA sequences when conducting the census later, which is why on this occasion we don’t want to allow the user to specify their own tracks. Other strategies, such as the included `NucleotideCounterStrategy` just pass the `tracks` argument from the user through to the super `\_\_init\_\_`. For a given array of `sequence` data and a frame offset (`track\_frame`), the `census` function splits the sequence into nucleotide triplets from the offset and searches for open reading frames. A subsequence is considered an ORF by this strategy if the ATG START codon is encountered and later followed by any STOP codon. Our example finishes with the familiar specification of the location of input sequence data and the construction of the census itself. Here we specify a census of all 1Mbp regions with no overlap (that is, the stride is equal to the size of the regions) and instantiate our new `MyCustomSimpleORFCounterStrategy` with a keyword requiring valid ORFs to be at least 30 codons in length (excluding start and stop). Every strategy’s `census` function is expected to return a numerical result that can be used to rank and sort regions, in this scenario, `census` returns the number of ORFs found. Note also, strategies may specify any number of keyword arguments that are not found in the `BaseStrategy`. In our example, `min\_codons` can be set by a user to specify how many codons must lie between an opening and closing codon to be counted as an open reading frame. We store this value as a member of the strategy object on line 18 and use it on line 35 to ensure the `orfs` counter is only incremented when the length of the current open reading frame has exceeded the provided threshold. One could store any number of configurable parameters inside of the strategy class in this fashion. This framework allows one to increase the complexity of strategies while still providing a friendly and interchangeable interface for end users. Examples[¶](#examples "Permalink to this headline") --------------------------------------------------- The following includes some simple examples of what Goldilocks can be used for. ### Example One[¶](#example-one "Permalink to this headline") Read a pair of 1-indexed base position lists and output all regions falling within 2 of the maximum count of positions in regions across both, in a table. ``` from goldilocks import Goldilocks from goldilocks.strategies import PositionCounterStrategy data = { "my\_positions": { 1: [1,2,5,10,15,15,18,25,30,50,51,52,53,54,55,100] }, "my\_other\_positions": { 1: [1,3,5,7,9,12,15,21,25,51,53,59,91,92,93,95,99,100] } } g = Goldilocks(PositionCounterStrategy(), data, is\_pos=True, length=10, stride=1) g.query("max", actual\_distance=2).export\_meta(sep="\t", group="total") ``` ### Example Two[¶](#example-two "Permalink to this headline") Read a short sequence, census GC-ratio and output the top 5 regions as FASTA. ``` from goldilocks import Goldilocks from goldilocks.strategies import GCRatioStrategy data = { "my\_sequence": { 1: "ACCGAGAGATTT" } } g = Goldilocks(GCRatioStrategy(), data, 3, 1) g.query("max", limit=5).export\_fasta() ``` ### Example Three[¶](#example-three "Permalink to this headline") Read a short sequence and census the appearance of the “AAA” and “CCC” motif. Output a table of regions with the most occurrences of CCC (and at least one) and another table of regions featuring the most appearances of both motifs. Output only the maximum region (actual\_distance = 0) displaying both motifs to FASTA. ``` from goldilocks import Goldilocks from goldilocks.strategies import MotifCounterStrategy data = { "my\_sequence": { 1: "CCCAAACCCGGGCCCGGGAGAAACCC" } } g = Goldilocks(MotifCounterStrategy(["AAA", "CCC"]), data, 9, 1) g.query("max", track="CCC", gmin=1).export\_meta(sep="\t") g.query("max", group="total").export\_meta(sep="\t", group="total", track="default") g.query("max", group="total", actual\_distance=0).export\_fasta() ``` ### Example Four[¶](#example-four "Permalink to this headline") Read two samples of three short chromosomes and search for ‘N’ nucleotides. List and export a FASTA of regions that contain at least one N, sorted by number of N’s appearing across both samples. Below, an example of complex filtering. ``` from goldilocks import Goldilocks from goldilocks.strategies import NucleotideCounterStrategy data = { "sample\_one": { 1: "ANAGGGANACAN", 2: "ANAGGGANACAN", 3: "ANANNNANACAN", 4: "NNNNAANNAANN" }, "sample\_two": { 1: "ANAGGGANACAN", 2: "ANAGGGANACAN", 3: "ANANNNANACAN", 4: "NNNANNAANNAA" } } g = Goldilocks(NucleotideCounterStrategy(["N"]), data, 3, 1) g\_max = g.query("max", gmin=1) g\_max.export\_meta(sep="\t") g\_max.export\_fasta() g.query("min", gmin = 1, exclusions={ # Filter any region with a starting position <= 3 or >= 10 "start\_lte": 3, "start\_gte": 10, # Filter any regions on Chr1 1: { "chr": True }, # Filter NO regions on Chr2 # NOTE: This also prevents the superexclusions above being applied. 2: { "chr": False }, # Filter any region on Chr3 with an ending postion >= 9 3: { "start\_lte": 5 # NOTE: This overrides the start\_lte applied above } }, use\_chrom=True).export\_meta(sep="\t") ``` ### Example Five[¶](#example-five "Permalink to this headline") Read in four simple chromosomes from one sample and census the GC ratio. Plot both a scatter plot of all censused regions over both of the provided samples with position over the x-axis and value on the y-axis. Produce a second plot drawing a panel with a line graph for each chromosome with the same axes but data from one sample only. For the combined result of both samples and chromosomes, organise the result of the census for each region into desired bins and plot the result as a histogram. Repeat the process for the my\_sequence sample and produce a panelled histogram for each chromosome. ``` from goldilocks import Goldilocks from goldilocks.strategies import GCRatioStrategy data = { "my\_sequence": { 1: "ANAGGGANACANANAGGGANACANANAGGGANACANANAGGGANACANANAGGGACGCGCGCGGGGANACAN"\*500, 2: "ANAGGCGCGCNANAGGGANACGCGGGGCCCGACANANAGGGANACANANAGGGACGCGCGCGCGCCCGACAN"\*500, 3: "ANAGGCGCGCNANAGGGANACGCGGGGCCCGACANANAGGGANACANANAGGGACGCGCGCGCGCCCGACAN"\*500, 4: "GCGCGCGCGCGCGCGCGGGGGGGGGCGCCGCCNNNNNNNNNNNNNNNNGCGCGCGCGCGCGCGNNNNNNNNN"\*500 }, "my\_same\_sequence": { 1: "ANAGGGANACANANAGGGANACANANAGGGANACANANAGGGANACANANAGGGACGCGCGCGGGGANACAN"\*500, 2: "ANAGGCGCGCNANAGGGANACGCGGGGCCCGACANANAGGGANACANANAGGGACGCGCGCGCGCCCGACAN"\*500, 3: "ANAGGCGCGCNANAGGGANACGCGGGGCCCGACANANAGGGANACANANAGGGACGCGCGCGCGCCCGACAN"\*500, 4: "GCGCGCGCGCGCGCGCGGGGGGGGGCGCCGCCNNNNNNNNNNNNNNNNGCGCGCGCGCGCGCGNNNNNNNNN"\*500 } } g = Goldilocks(GCRatioStrategy(), data, 50, 10) g.plot() g.plot("my\_sequence") g.profile(bins=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]) g.profile("my\_sequence", bins=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.0]) ``` ### Example Six[¶](#example-six "Permalink to this headline") Read a set of simple chromosomes from two samples and tabulate the top 10% of regions demonstrating the worst consensus to the given reference over both samples. Plot the lack of consensus as line graphs for each chromosome, for each sample, then over all chromosomes for all samples on one graph. ``` from goldilocks import Goldilocks from goldilocks.strategies import ReferenceConsensusStrategy data = { "first\_sample": { 1: "NNNAANNNNNCCCCCNNNNNGGGGGNNNNNTTTTTNNNNNAAAAANNNNNCCCCCNNNNNGGGGGNNNNNTTTTTNNNNN", 2: "NNNNNCCCCCNNNNNTTTTTNNNNNAAAAANNNNNGGGGGNNNNNCCCCCNNNNNTTTTTNNNNNAAAAANNNNNGGGGN" }, "second\_sample": { 1: "NNNNNNNNNNCCCCCCCCCCNNNNNNNNNNTTTTTTTTTTNNNNNNNNNNCCCCCCCCCCNNNNNNNNNNTTTTTTTTTT", 2: "NNCCCCCCCCNNNNNNNNNNAAAAAAAAAANNNNNNNNNNCCCCCCCCCCNNNNNNNNNNAAAAAAAAAANNNNNNNNNN" } } ref = { 1: "AAAAAAAAAACCCCCCCCCCGGGGGGGGGGTTTTTTTTTTAAAAAAAAAACCCCCCCCCCGGGGGGGGGGTTTTTTTTTT", 2: "CCCCCCCCCCTTTTTTTTTTAAAAAAAAAAGGGGGGGGGGCCCCCCCCCCTTTTTTTTTTAAAAAAAAAAGGGGGGGGGG" } g = Goldilocks(ReferenceConsensusStrategy(reference=ref, polarity=-1), data, stride=10, length=10) g.query("max", percentile\_distance=10).export\_meta(group="total", track="default") g.plot("first\_sample") g.plot("second\_sample") g.plot() ``` ### Example Seven[¶](#example-seven "Permalink to this headline") Read a pair of 1-indexed base position lists from two samples. Sort regions with the least number of marked positions on Sample 1 and subsort by max marked positions in Sample 2. ``` from goldilocks import Goldilocks from goldilocks.strategies import PositionCounterStrategy data = { "my\_positions": { 1: [1,2,3,4,5,6,7,8,9,10, 11,13,15,17,19, 21, 31,39, 41] }, "other\_positions": { 1: [21,22,23,24,25,26,27,28, 31,33,39, 41,42,43,44,45,46,47,48,49,50] } } g = Goldilocks(PositionCounterStrategy(), data, is\_pos=True, length=10, stride=5) g.query("max", group="my\_positions").query("max", group="other\_positions").export\_meta(sep="\t") ``` Contributing[¶](#contributing "Permalink to this headline") ----------------------------------------------------------- Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given. You can contribute in many ways: ### Types of Contributions[¶](#types-of-contributions "Permalink to this headline") #### Report Bugs[¶](#report-bugs "Permalink to this headline") Report bugs at <https://github.com/samstudio8/goldilocks/issues>. If you are reporting a bug, please include: * Your operating system name and version. * Any details about your local setup that might be helpful in troubleshooting. * Detailed steps to reproduce the bug. #### Fix Bugs[¶](#fix-bugs "Permalink to this headline") Look through the GitHub issues for bugs. Anything tagged with “bug” is open to whoever wants to implement it. #### Implement Features[¶](#implement-features "Permalink to this headline") Look through the GitHub issues for features. Anything tagged with “feature” is open to whoever wants to implement it. #### Write Documentation[¶](#write-documentation "Permalink to this headline") Goldilocks could always use more documentation, whether as part of the official Goldilocks docs, in docstrings, or even on the web in blog posts, articles, and such. #### Submit Feedback[¶](#submit-feedback "Permalink to this headline") The best way to send feedback is to file an issue at <https://github.com/samstudio8/goldilocks/issues>. If you are proposing a feature: * Explain in detail how it would work. * Keep the scope as narrow as possible, to make it easier to implement. * Remember that this is a volunteer-driven project, and that contributions are welcome :) ### Get Started Ready to contribute? Here’s how to set up goldilocks for local development. 1. Fork the goldilocks repo on GitHub. 2. Clone your fork locally: ``` $ git clone git@github.com:your_name_here/goldilocks.git ``` 3. Install your local copy into a virtualenv. Assuming you have virtualenvwrapper installed, this is how you set up your fork for local development: ``` $ mkvirtualenv goldilocks $ cd goldilocks/ $ python setup.py develop ``` 4. Create a branch for local development: ``` $ git checkout -b name-of-your-bugfix-or-feature ``` Now you can make your changes locally. 5. When you’re done making changes, check that your changes pass flake8 and the tests, including testing other Python versions with tox: ``` $ flake8 goldilocks tests $ python setup.py test $ tox ``` To get flake8 and tox, just pip install them into your virtualenv. 6. Commit your changes and push your branch to GitHub: ``` $ git add . $ git commit -m "Your detailed description of your changes." $ git push origin name-of-your-bugfix-or-feature ``` 7. Submit a pull request through the GitHub website. ### Pull Request Guidelines[¶](#pull-request-guidelines "Permalink to this headline") Before you submit a pull request, check that it meets these guidelines: 1. The pull request should include tests. 2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst. 3. The pull request should work for Python 2.6, 2.7, and 3.3, 3.4, and for PyPy. Check <https://travis-ci.org/samstudio8/goldilocks/pull_requests> and make sure that the tests pass for all supported Python versions. ### Tips[¶](#tips "Permalink to this headline") To run a subset of tests: ``` $ python -m unittest tests.test_goldilocks ``` Credits[¶](#credits "Permalink to this headline") ------------------------------------------------- ### Development Lead[¶](#development-lead "Permalink to this headline") * Sam Nicholls <[sam@samnicholls.net](mailto:sam%40samnicholls.net)> ### Contributors[¶](#contributors "Permalink to this headline") None yet. Why not be the first? History[¶](#history "Permalink to this headline") ------------------------------------------------- ### 0.1.1 (2016-07-07)[¶](#id1 "Permalink to this headline") * Updated citation. Please cite us! <3 * [PR:ar0ch] Add lowercase matching in GCRatioStrategy Fixes ‘feature’ where lowercase letters are ignored by GCRatioStrategy. ### 0.1.0 (2016-03-08)[¶](#id2 "Permalink to this headline") * Goldilocks is published software! ### 0.0.83-beta[¶](#beta "Permalink to this headline") * -l and -s CLI arguments and corresponding length and stride parameters to Goldilocks constructor now support SI suffixes: K, M, G, T. util module contains parse\_si\_bp used to parse option strings and return the number of bases for length and stride. * Add length and stride to x-axis label of plots. * Add ignore\_query option to plot to override new default behaviour of plot that only plots points for regions remaining after a call to query. * Remove profile function, use plot with bins=N instead. * Add binning to plot to reduce code duplication. * Add chrom kwarg to plot to allow plotting of a single chromosome across multiple input genomes. * Fix support for plotting data from multiple contigs or chromosomes of a single input genome when provided as a FASTA. * Add ignore\_query kwarg to plot for ignoring the results of a query on the Goldilocks object when performing a plot afterwards. * Bins no longer have to be specified manually, use bins=N, this will create N+1 bins (a special 0 bin is reserved) between 0 and the largest observed value unless bin\_max is also provided. * Bins may have a hard upper limit set with bin\_max. This will override the default of the largest observed value regardless of whether bin\_max is smaller. * Plots can now be plotted proportionally with prop=True. * Improve labels for plotting. * Reduce duplication of plotting code inside plot. * Share Y axis across plot panels to prevent potentially misleading default plots. * Reduce duplication of code used for outputting metadata: * Add fmt kwarg to export\_meta that permits one of: + bed BED format (compulsory fields only) + circos A format compatible with the circos plotting tool + melt A format that will suit import to an R dataframe without the need for additional munging with reshape2 + table A plain tabular format that will suit for quick outputs with some munging * Remove print\_melt, use export\_meta with fmt=melt. * Add is\_pos\_file kwarg to Goldilocks, allows user to specify position based variants in the format CHRtPOS or CHR:POS in a newline delimited file. * Changed required idx key to file in sequence dictionaries. * Added custom strategy and plotting examples to the documentation. * The Goldilocks class is now imported as from goldilocks import Goldilocks. * The textwrap.wrap function is used to write out FASTA more cleanly. * A serious regression in the parsing of FASTA files introduced by v0.0.80 has been closed. * Improved plotting functionality for co-plotting groups, tracks of chromosome has been introduced. Tracks can now be plotted together on the same panel by providing their names as a list to the tracks keyword. * reset\_candidates allows users to “reset” the Goldilocks object after a query or sort has been performed on the regions. ### 0.0.82 (2016-01-29)[¶](#id3 "Permalink to this headline") * Changed example to use MotifCounterStrategy over removed KMerCounterStrategy. * Fix runtime NameError preventing PositionCounterStrategy from executing correctly. * Fix runtime NameError preventing ReferenceConsensusStrategy from executing correctly. * Add default count track to PositionCounterStrategy to prevent accidental multiple counting issue encountered when couting with the default track. * Add LICENSE * Paper accepted for press! ### 0.0.81 (2016-01-29)[¶](#id4 "Permalink to this headline") * Fix versioning error. ### 0.0.80 (2015-08-10)[¶](#id5 "Permalink to this headline") * Added multiprocessing capabilities during census step. * Added a simple command line interface. * Removed prepare-evaluate paradigm from strategies and now perform counts directly on input data in one step. * Skip slides (and set all counts to 0) if their end\_pos falls outside of the region on that particular genome’s chromosome/contig. * Rename KMerCounterStrategy to MotifCounterStrategy * Fixed bug causing use\_and to not work as expected for chromosomes not explicitly listed in the exceptions dict when also using use\_chrom. * Support use of FASTA files which must be supplied with a samtools faidx style index. * Stopped supporting Python 3 due to incompatability with buffer and memoryview. * Prevent query from deep copying itself on return. Note this means that a query will alter the original Goldilocks object. * Now using a 3D numpy matrix to store counters with memory shared to support multiprocessing during census. * Removed StrategyValue as these cannot be stored in shared memory. This makes ratio-based strategies a bit of a hack currently (but still work...) * tldr; Goldilocks is at least 2-4x faster than previously, even without multiprocessing ### 0.0.71 (2015-07-11)[¶](#id6 "Permalink to this headline") * Officially add MIT license to repository. * Deprecate \_filter. * Update and tidy examples.py. * is\_seq argument to initialisation removed and replaced with is\_pos. * Use is\_pos to indicate the expected input is positional, not sequence. * Force use of PositionCounterStrategy when is\_pos is True. * Sequence data now read in to 0-indexed arrays to avoid the overhead of string re-allocation by having to append a padding character to the beginning of very long strings. * Region metadata continues to use 1-indexed positions for user output. * VariantCounterStrategy now PositionCounterStrategy. * PositionCounterStrategy expects 1-indexed lists of positions; prepare populates the listed locations with 1 and then evaluate returns the sum as before. * test\_regression2 updated to account for converting 1-index to 0-index when manually handling the sequence for expected results. * query accepts gmax and gmin arguments to filter candidate regions by the group-track value. * CandidateList removed and replaced with simply returning a new Goldilocks. ### 0.0.6 (2015-06-23)[¶](#id7 "Permalink to this headline") * Goldilocks.sorted\_regions stores a list of region ids to represent the result of a sorting operation following a call to query. * Regions in Goldilocks.regions now always have a copy of their “id” as a key. * \_\_check\_exclusions now accepts a group and track for more complex exclusion-based operations. * region\_group\_lte and region\_group\_gte added to usable exclusion fields to remove regions where the value of the desired group/track combination is less/greater than or equal to the value of the group/track set by the current query. * query now returns a new Goldilocks instance, rather than a CandidateList. * Goldilocks.candidates property now allows access to regions, this property will maintain the order of sorted\_regions if it has one. * export\_meta now allows group=None * CandidateList class deleted. * Test data that is no longer used has been deleted. * Scripts for generating test data added to test\_gen/ directory. * Tests updated to reflect the fact CandidateList lists are no longer returned by query. * \_filter is to be deprecated in favour of query by 0.0.7 ### Beta (2014-10-08)[¶](#beta-2014-10-08 "Permalink to this headline") * Massively updated! Compatability with previous versions very broken. * Software retrofitted to be much more flexible to support a wider range of problems. ### 0.0.2 (2014-08-18)[¶](#id8 "Permalink to this headline") * Remove incompatible use of print ### 0.0.1 (2014-08-18)[¶](#id9 "Permalink to this headline") * Initial package Indices and tables[¶](#indices-and-tables "Permalink to this headline") ======================================================================= * [Index](genindex.html) * [Module Index](py-modindex.html) * [Search Page](search.html)

reaper

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Reaper v0.0.1-alpha documentation Reaper[¶](#reaper "Permalink to this headline") =============================================== Reaper is a PyQt5 GUI that scrapes Facebook, Twitter, Reddit and Youtube apis using the Python package `socialreaper` . To use Reaper, install `socialreaper` and `PyQt5`, then run `reaper.py` Adding API Keys[¶](#adding-api-keys "Permalink to this headline") ================================================================= Facebook[¶](#facebook "Permalink to this headline") --------------------------------------------------- Navigate to <https://developers.facebook.com/tools/explorer> From the My Apps menu select Add a New App. Fill in the details and choose the category Apps for Pages. Navigate to <https://developers.facebook.com/tools/explorer> and change the application to the name of your new app. Click Get User Access Token under the Get Token menu. Click Get Access Token. Test the new token by making an example query in the GET field like `wikipedia/posts`. Extend the access token expiry time by clicking the blue i icon next to the Access Token field. Click Open in Access Token Tool. Click Extend Access Token. Copy the new access token into the Api Key field in Reaper’s authentication. Twitter[¶](#twitter "Permalink to this headline") ------------------------------------------------- Navigate to <https://apps.twitter.com/> Click Create New App. Fill in the details and create the new app. Click the Keys and Access Tokens tab. Click Create my Access Token. Copy Consumer Key (API Key), Consumer Secret (API Secret), Access Token, Access Token Secret into their respective fields in Reaper’s authentication. Reddit[¶](#reddit "Permalink to this headline") ----------------------------------------------- Navigate to <https://www.reddit.com/prefs/apps/> Click create another app. Fill in the details and select script as the application type. Copy the app id (the string underneath the application’s name), and secret into their respective fields in Reaper’s authentication. Youtube[¶](#youtube "Permalink to this headline") ------------------------------------------------- Navigate to <http://console.developers.google.com/> Create a new project. Click Youtube Data Api. Next to the Youtube Data Api V3, click Enable. Click the Create credentials button. Select Other UI as the location you will be calling from. Select Public Data. Click What credentials do I need. Copy the api key into Reaper’s authentication. ### Related Topics * [Documentation overview](index.html#document-index) ### Quick search ©2017, Adam Smith. | Powered by [Sphinx 1.6.5](http://sphinx-doc.org/) & [Alabaster 0.7.10](https://github.com/bitprophet/alabaster)

secure

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secure.py 0.3.0 documentation secure.py[¶](#secure-py "Permalink to this headline") ===================================================== [](https://pypi.org/project/secure/) [](https://www.python.org/downloads/) [](https://pypi.org/project/secure/) [](https://github.com/ambv/black) secure.py 🔒 is a lightweight package that adds optional security headers for Python web frameworks. Supported Python web frameworks:[¶](#supported-python-web-frameworks "Permalink to this headline") -------------------------------------------------------------------------------------------------- [aiohttp](https://docs.aiohttp.org), [Bottle](https://bottlepy.org), [CherryPy](https://cherrypy.org), [Django](https://www.djangoproject.com), [Falcon](https://falconframework.org), [FastAPI](https://fastapi.tiangolo.com), [Flask](http://flask.pocoo.org), [hug](http://www.hug.rest), [Masonite](https://docs.masoniteproject.com), [Pyramid](https://trypyramid.com), [Quart](https://pgjones.gitlab.io/quart/), [Responder](https://python-responder.org), [Sanic](https://sanicframework.org), [Starlette](https://www.starlette.io/), [Tornado](https://www.tornadoweb.org/) Install[¶](#install "Permalink to this headline") ------------------------------------------------- **pip**: ``` $ pip install secure ``` **Pipenv**: ``` $ pipenv install secure ``` After installing secure.py: ``` import secure secure\_headers = secure.Secure() ``` Documentation[¶](#documentation "Permalink to this headline") ------------------------------------------------------------- ### Secure Headers[¶](#secure-headers "Permalink to this headline") Security Headers are HTTP response headers that, when set, can enhance the security of your web application by enabling browser security policies. You can assess the security of your HTTP response headers at [securityheaders.com](https://securityheaders.com) *Recommendations used by secure,py and more information regarding security headers can be found at the* [OWASP Secure Headers Project](https://www.owasp.org/index.php/OWASP_Secure_Headers_Project) *.* #### Server[¶](#server "Permalink to this headline") Contain information about server software **Default Value:** `NULL` *(obfuscate server information, not included by default)* #### Strict-Transport-Security (HSTS)[¶](#strict-transport-security-hsts "Permalink to this headline") Ensure application communication is sent over HTTPS **Default Value:** `max-age=63072000; includeSubdomains` #### X-Frame-Options (XFO)[¶](#x-frame-options-xfo "Permalink to this headline") Disable framing from different origins (clickjacking defense) **Default Value:** `SAMEORIGIN` #### X-XSS-Protection[¶](#x-xss-protection "Permalink to this headline") Enable browser cross-site scripting filters **Default Value:** `0` #### X-Content-Type-Options[¶](#x-content-type-options "Permalink to this headline") Prevent MIME-sniffing **Default Value:** `nosniff` #### Content-Security-Policy (CSP)[¶](#content-security-policy-csp "Permalink to this headline") Prevent cross-site injections **Default Value:** `script-src 'self'; object-src 'self'` *(not included by default)*\* #### Referrer-Policy[¶](#referrer-policy "Permalink to this headline") Enable full referrer if same origin, remove path for cross origin and disable referrer in unsupported browsers **Default Value:** `no-referrer` #### Cache-control[¶](#cache-control "Permalink to this headline") Prevent cacheable HTTPS response **Default Value:** `no-cache` #### Permissions-Policy[¶](#permissions-policy "Permalink to this headline") Disable browser features and APIs **Default Value:** accelerometer=(), ambient-light-sensor=(), autoplay=(),camera=(), encrypted-media=(), fullscreen=(),geolocation=(), gyroscope=(), magnetometer=(),microphone=(); midi=(), payment=(), picture-in-picture=(), speaker=(), sync-xhr=(), usb=(), vr=()” *(not included by default)* **Additional information:** * The `Strict-Transport-Security` (HSTS) header will tell the browser to **only** utilize secure HTTPS connections for the domain, and in the default configuration, including all subdomains. The HSTS header requires trusted certificates and users will unable to connect to the site if using self-signed or expired certificates. The browser will honor the HSTS header for the time directed in the `max-age` attribute *(default = 2 years)*, and setting the `max-age` to `0` will disable an already set HSTS header. Use the `hsts=False` option to not include the HSTS header in Secure Headers. * The `Content-Security-Policy` (CSP) header can break functionality and can (and should) be carefully constructed, use the `csp=True` option to enable default values. #### Usage[¶](#usage "Permalink to this headline") `secure\_headers.framework(response)` **Default HTTP response headers:** ``` Strict-Transport-Security: max-age=63072000; includeSubdomains X-Frame-Options: SAMEORIGIN X-XSS-Protection: 0 X-Content-Type-Options: nosniff Referrer-Policy: no-referrer, strict-origin-when-cross-origin Cache-control: no-cache, no-store, must-revalidate, max-age=0 Pragma: no-cache Expires: 0 ``` #### Options[¶](#options "Permalink to this headline") You can toggle the setting of headers with default values by passing `True` or `False` and override default values by passing a string to the following options: * `server` - set the Server header, e.g. `Server=“Secure”` *(string / bool / SecurePolicies, default=False)* * `hsts` - set the Strict-Transport-Security header *(string / bool / SecurePolicies, default=True)* * `xfo` - set the X-Frame-Options header *(string / bool / SecurePolicies, default=True)* * `xxp` - set the X-XSS-Protection header *(string / bool / SecurePolicies, default=True)* * `content` - set the X-Content-Type-Options header *(string / bool / SecurePolicies, default=True)* * `csp` - set the Content-Security-Policy *(string / bool / SecurePolicies, default=False)* \* * `referrer` - set the Referrer-Policy header *(string / bool / SecurePolicies, default=True)* * `cache` - set the Cache-control and Pragma headers *(string / bool / SecurePolicies, default=True)* * `feature` - set the Feature-Policy header *(SecurePolicies / string / bool / SecurePolicies, default=False)* **Example:** ``` from secure import SecureHeaders secure\_headers = SecureHeaders(csp=True, hsts=False, xfo="DENY") . . . secure\_headers.framework(response) ``` ### Policy Builder[¶](#policy-builder "Permalink to this headline") #### ContentSecurityPolicy()[¶](#contentsecuritypolicy "Permalink to this headline") **Directives:** `base\_uri(sources)`, `child\_src(sources)`, `connect\_src(sources)`, `default\_src(sources)`, `font\_src(sources)`, `form\_action(sources)`, `frame\_ancestors(sources)`, `frame\_src(sources)`, `img\_src(sources)`, `manifest\_src(sources)`, `media\_src(sources)`, `object\_src(sources)`, `plugin\_types(types)`, `report\_to(json\_object)`, `report\_uri(uri)`, `require\_sri\_for(values)`, `sandbox(values)`, `script\_src(sources)`, `style\_src(sources)`, `upgrade\_insecure\_requests()`, `worker\_src(sources)` **Example:** ``` csp\_policy = ( secure.ContentSecurityPolicy() .default\_src("'none'") .base\_uri("'self'") .connect\_src("'self'", "api.spam.com") .frame\_src("'none'") .img\_src("'self'", "static.spam.com") ) secure\_headers = secure.Secure(csp=csp\_policy) # default-src 'none'; base-uri 'self'; connect-src 'self' api.spam.com; frame-src 'none'; img-src 'self' static.spam.com ``` *You can check the effectiveness of your CSP Policy at the* [CSP Evaluator](https://csp-evaluator.withgoogle.com) #### StrictTransportSecurity()[¶](#stricttransportsecurity "Permalink to this headline") **Directives:** `include\_subDomains()`, `max\_age(seconds)`, `preload()` **Example:** ``` hsts\_value = ( secure.StrictTransportSecurity() .include\_subdomains() .preload() .max\_age(2592000) ) secure\_headers = secure.Secure(hsts=hsts\_value) # includeSubDomains; preload; max-age=2592000 ``` #### XFrameOptions()[¶](#xframeoptions "Permalink to this headline") **Directives:** `allow\_from(uri)`, `deny()`, `sameorigin()` **Example:** ``` xfo\_value = secure.XFrameOptions().deny() secure\_headers = secure.Secure(xfo=xfo\_value) # deny ``` #### ReferrerPolicy()[¶](#referrerpolicy "Permalink to this headline") **Directives:** `no\_referrer()`, `no\_referrer\_when\_downgrade()`, `origin()`, `origin\_when\_cross\_origin()`, `same\_origin()`, `strict\_origin()`, `strict\_origin\_when\_cross\_origin()`, `unsafe\_url()` **Example:** ``` referrer = secure.ReferrerPolicy().strict\_origin() secure\_headers = secure.Secure(referrer=referrer).headers() # strict-origin ``` #### PermissionsPolicy()[¶](#permissionspolicy "Permalink to this headline") **Directives:** `accelerometer(allowlist)`, `ambient\_light\_sensor(allowlist)`, `autoplay(allowlist)`, `camera(allowlist)`, `document\_domain(allowlist)`, `encrypted\_media(allowlist)`, `fullscreen(allowlist)`, `geolocation(allowlist)`, `gyroscope(allowlist)`, `magnetometer(allowlist)`, `microphone(allowlist)`, `midi(allowlist)`, `payment(allowlist)`, `picture\_in\_picture(allowlist)`, `speaker(allowlist)`, `sync\_xhr(allowlist)`, `usb(allowlist)`, `Values(allowlist)`, `vr(allowlist)` **Example:** ``` permissions = ( secure.PermissionsPolicy().geolocation("self", '"spam.com"').vibrate() ) secure\_headers = secure.Secure(permissions=permissions).headers() # geolocation=(self "spam.com"), vibrate=() ``` #### CacheControl()[¶](#cachecontrol "Permalink to this headline") **Directives:** `immutable()`, `max\_age(seconds)`, `max\_stale(seconds)`, `min\_fresh(seconds)`, `must\_revalidate()`, `no\_cache()`, `no\_store()`, `no\_transform()`, `only\_if\_cached()`, `private()`, `proxy\_revalidate()`, `public()`, `s\_maxage(seconds)`, `stale\_if\_error(seconds)`, `stale\_while\_revalidate(seconds)`, **Example:** ``` cache = secure.CacheControl().no\_cache() secure\_headers = secure.Secure(cache=cache).headers() # no-store ``` #### Usage[¶](#usage "Permalink to this headline") **Example:** ``` import uvicorn from fastapi import FastAPI import secure app = FastAPI() server = secure.Server().set("Secure") csp = ( secure.ContentSecurityPolicy() .default\_src("'none'") .base\_uri("'self'") .connect\_src("'self'" "api.spam.com") .frame\_src("'none'") .img\_src("'self'", "static.spam.com") ) hsts = secure.StrictTransportSecurity().include\_subdomains().preload().max\_age(2592000) referrer = secure.ReferrerPolicy().no\_referrer() permissions\_value = ( secure.PermissionsPolicy().geolocation("self", "'spam.com'").vibrate() ) cache\_value = secure.CacheControl().must\_revalidate() secure\_headers = secure.Secure( server=server, csp=csp, hsts=hsts, referrer=referrer, permissions=permissions\_value, cache=cache\_value, ) @app.middleware("http") async def set\_secure\_headers(request, call\_next): response = await call\_next(request) secure\_headers.framework.fastapi(response) return response @app.get("/") async def root(): return {"message": "Secure"} if \_\_name\_\_ == "\_\_main\_\_": uvicorn.run(app, port=8081, host="localhost") . . . ``` Response Headers: ``` server: Secure strict-transport-security: includeSubDomains; preload; max-age=2592000 x-frame-options: SAMEORIGIN x-xss-protection: 0 x-content-type-options: nosniff content-security-policy: default-src 'none'; base-uri 'self'; connect-src 'self'api.spam.com; frame-src 'none'; img-src 'self' static.spam.com referrer-policy: no-referrer cache-control: must-revalidate permissions-policy: geolocation=(self 'spam.com'), vibrate=() ``` ### Supported Frameworks[¶](#supported-frameworks "Permalink to this headline") #### Framework Agnostic[¶](#framework-agnostic "Permalink to this headline") Return Dictionary of Headers: `secure\_headers.headers()` **Example:** ``` secure\_headers.framework.headers(csp=True, feature=True) ``` **Return Value:** `{'Strict-Transport-Security': 'max-age=63072000; includeSubdomains', 'X-Frame-Options': 'SAMEORIGIN', 'X-XSS-Protection': '0', 'X-Content-Type-Options': 'nosniff', 'Content-Security-Policy': "script-src 'self'; object-src 'self'", 'Referrer-Policy': 'no-referrer, strict-origin-when-cross-origin', 'Cache-control': 'no-cache, no-store, must-revalidate', 'Pragma': 'no-cache', 'Feature-Policy': "accelerometer 'none'; ambient-light-sensor 'none'; autoplay 'none'; camera 'none'; encrypted-media 'none';fullscreen 'none'; geolocation 'none'; gyroscope 'none'; magnetometer 'none'; microphone 'none'; midi 'none';payment 'none'; picture-in-picture 'none'; speaker 'none'; sync-xhr 'none'; usb 'none'; vr 'none';"}` #### aiohttp[¶](#aiohttp "Permalink to this headline") `secure\_headers.framework.aiohttp(resp)` **Example:** ``` from aiohttp import web from aiohttp.web import middleware import secure secure\_headers = secure.Secure() . . . @middleware async def set\_secure\_headers(request, handler): resp = await handler(request) secure\_headers.framework.aiohttp(resp) return resp . . . app = web.Application(middlewares=[set\_secure\_headers]) . . . ``` #### Bottle[¶](#bottle "Permalink to this headline") `secure\_headers.framework.bottle(response)` **Example:** ``` from bottle import route, run, response, hook import secure secure\_headers = secure.Secure() . . . @hook("after\_request") def set\_secure\_headers(): secure\_headers.framework.bottle(response) . . . ``` #### CherryPy[¶](#cherrypy "Permalink to this headline") `"tools.response\_headers.headers": secure\_headers.framework.cherrypy()` **Example:** CherryPy [Application Configuration](http://docs.cherrypy.org/en/latest/config.html#application-config): ``` import cherrypy import secure secure\_headers = secure.Secure() . . . config = { "/": { "tools.response\_headers.on": True, "tools.response\_headers.headers": secure\_headers.framework.cherrypy(), } } . . . ``` #### Django[¶](#django "Permalink to this headline") `secure\_headers.framework.django(response)` **Example:** Django [Middleware Documentation](https://docs.djangoproject.com/en/2.1/topics/http/middleware/): ``` # securemiddleware.py import secure secure\_headers = secure.Secure() . . . def set\_secure\_headers(get\_response): def middleware(request): response = get\_response(request) secure\_headers.framework.django(response) return response return middleware . . . ``` ``` # settings.py ... MIDDLEWARE = [ 'app.securemiddleware.set\_secure\_headers' ] ... ``` #### FastAPI[¶](#fastapi "Permalink to this headline") `secure\_headers.framework.falcon(resp)` **Example:** ``` from fastapi import FastAPI import secure secure\_headers = secure.Secure() . . . @app.middleware("http") async def set\_secure\_headers(request, call\_next): response = await call\_next(request) secure\_headers.framework.fastapi(response) return response . . . ``` #### Falcon[¶](#falcon "Permalink to this headline") `secure\_headers.framework.falcon(resp)` **Example:** ``` import falcon import secure secure\_headers = secure.Secure() . . . class SetSecureHeaders(object): def process\_request(self, req, resp): secure\_headers.framework.falcon(resp) . . . app = api = falcon.API(middleware=[SetSecureHeaders()]) . . . ``` #### Flask[¶](#flask "Permalink to this headline") `secure\_headers.framework.flask(response)` **Example:** ``` from flask import Flask, Response import secure secure\_headers = secure.Secure() app = Flask(\_\_name\_\_) . . . @app.after\_request def set\_secure\_headers(response): secure\_headers.framework.flask(response) return response . . . ``` #### hug[¶](#hug "Permalink to this headline") `secure\_headers.framework.hug(response)` **Example:** ``` import hug import secure secure\_headers = secure.Secure() . . . @hug.response\_middleware() def set\_secure\_headers(request, response, resource): secure\_headers.framework.hug(response) . . . ``` #### Masonite[¶](#masonite "Permalink to this headline") `secure\_headers.framework.masonite(self.request)` **Example:** Masonite [Middleware](https://docs.masoniteproject.com/advanced/middleware#creating-middleware): ``` # SecureMiddleware.py from masonite.request import Request import secure secure\_headers = secure.Secure() class SecureMiddleware: def \_\_init\_\_(self, request: Request): self.request = request def before(self): secure\_headers.framework.masonite(self.request) . . . ``` ``` # middleware.py ... HTTP\_MIDDLEWARE = [ SecureMiddleware, ] ... ``` #### Pyramid[¶](#pyramid "Permalink to this headline") Pyramid [Tween](https://docs.pylonsproject.org/projects/pyramid/en/latest/narr/hooks.html#registering-tweens): ``` def set\_secure\_headers(handler, registry): def tween(request): response = handler(request) secure\_headers.framework.pyramid(response) return response return tween ``` **Example:** ``` from pyramid.config import Configurator from pyramid.response import Response import secure secure\_headers = secure.Secure() . . . def set\_secure\_headers(handler, registry): def tween(request): response = handler(request) secure\_headers.framework.pyramid(response) return response return tween . . . config.add\_tween(".set\_secure\_headers") . . . ``` #### Quart[¶](#quart "Permalink to this headline") `secure\_headers.framework.quart(response)` **Example:** ``` from quart import Quart, Response import secure secure\_headers = secure.Secure() app = Quart(\_\_name\_\_) . . . @app.after\_request async def set\_secure\_headers(response): secure\_headers.framework.quart(response) return response . . . ``` #### Responder[¶](#responder "Permalink to this headline") `secure\_headers.framework.responder(resp)` **Example:** ``` import responder import secure secure\_headers = secure.Secure() api = responder.API() . . . @api.route(before\_request=True) def set\_secure\_headers(req, resp): secure\_headers.framework.responder(resp) . . . ``` You should use Responder’s [built in HSTS](https://python-responder.org/en/latest/tour.html#hsts-redirect-to-https) and pass the `hsts=False` option. #### Sanic[¶](#sanic "Permalink to this headline") `secure\_headers.framework.sanic(response)` **Example:** ``` from sanic import Sanic import secure secure\_headers = secure.Secure() app = Sanic() . . . @app.middleware("response") async def set\_secure\_headers(request, response): secure\_headers.framework.sanic(response) . . . ``` *To set Cross Origin Resource Sharing (CORS) headers, please see* [sanic-cors](https://github.com/ashleysommer/sanic-cors) *.* #### Starlette[¶](#starlette "Permalink to this headline") `secure\_headers.framework.starlette(response)` **Example:** ``` from starlette.applications import Starlette import uvicorn import secure secure\_headers = secure.Secure() app = Starlette() . . . @app.middleware("http") async def set\_secure\_headers(request, call\_next): response = await call\_next(request) secure\_headers.framework.starlette(response) return response . . . ``` #### Tornado[¶](#tornado "Permalink to this headline") `secure\_headers.framework.tornado(self)` **Example:** ``` import tornado.ioloop import tornado.web import secure secure\_headers = secure.Secure() . . . class BaseHandler(tornado.web.RequestHandler): def set\_default\_headers(self): secure\_headers.framework.tornado(self) . . . ``` ### Resources[¶](#resources "Permalink to this headline") #### Frameworks[¶](#frameworks "Permalink to this headline") * [aiohttp](https://github.com/aio-libs/aiohttp) - Asynchronous HTTP client/server framework for asyncio and Python * [Bottle](https://github.com/bottlepy/bottle) - A fast and simple micro-framework for python web-applications. * [CherryPy](https://github.com/cherrypy/cherrypy) - A pythonic, object-oriented HTTP framework. * [Django](https://github.com/django/django/) - The Web framework for perfectionists with deadlines. * [Falcon](https://github.com/falconry/falcon) - A bare-metal Python web API framework for building high-performance microservices, app backends, and higher-level frameworks. * [Flask](https://github.com/pallets/flask) - The Python micro framework for building web applications. * [hug](https://github.com/timothycrosley/hug) - Embrace the APIs of the future. Hug aims to make developing APIs as simple as possible, but no simpler. * [Masonite](https://github.com/MasoniteFramework/masonite) - The Modern And Developer Centric Python Web Framework. * [Pyramid](https://github.com/Pylons/pyramid) - A Python web framework * [Quart](https://gitlab.com/pgjones/quart) - A Python ASGI web microframework. * [Responder](https://github.com/kennethreitz/responder) - A familiar HTTP Service Framework * [Sanic](https://github.com/huge-success/sanic) - An Async Python 3.5+ web server that’s written to go fast * [Starlette](https://github.com/encode/starlette) - The little ASGI framework that shines. ✨ * [Tornado](https://github.com/tornadoweb/tornado) - A Python web framework and asynchronous networking library, originally developed at FriendFeed. #### General[¶](#general "Permalink to this headline") * [OWASP - Secure Headers Project](https://www.owasp.org/index.php/OWASP_Secure_Headers_Project) * [OWASP - Session Management Cheat Sheet](https://www.owasp.org/index.php/Session_Management_Cheat_Sheet#Cookies) * [Mozilla Web Security](https://infosec.mozilla.org/guidelines/web_security) * [securityheaders.com](https://securityheaders.com) #### Policies[¶](#policies "Permalink to this headline") * **CSP:** [CSP Cheat Sheet | Scott Helme](https://scotthelme.co.uk/csp-cheat-sheet/), [Content-Security-Policy | MDN](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Security-Policy), [Content Security Policy Cheat Sheet | OWASP](https://www.owasp.org/index.php/Content_Security_Policy_Cheat_Sheet), [Content Security Policy CSP Reference & Examples](https://content-security-policy.com) * **XXP:** [X-XSS-Protection | MDN](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/X-XSS-Protection) * **XFO:** [X-Frame-Options | MDN](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/X-Frame-Options) * **HSTS:** [Strict-Transport-Security | MDN](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Strict-Transport-Security), [HTTP Strict Transport Security Cheat Sheet | OWASP](https://www.owasp.org/index.php/HTTP_Strict_Transport_Security_Cheat_Sheet) * **Referrer:** [A new security header: Referrer Policy | Scott Helme](https://scotthelme.co.uk/a-new-security-header-referrer-policy/), [Referrer-Policy | MDN](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Referrer-Policy) * **Feature:** [A new security header: Feature Policy | Scott Helme](https://scotthelme.co.uk/a-new-security-header-feature-policy/), [Feature-Policy | MDN](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Feature-Policy), [Introduction to Feature Policy | Google Developers](https://developers.google.com/web/updates/2018/06/feature-policy) * **Cache:** [Cache-Control | MDN](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Cache-Control) Indices and tables[¶](#indices-and-tables "Permalink to this headline") ======================================================================= * [Index](genindex.html) * [Module Index](py-modindex.html) * [Search Page](search.html) [secure.py](index.html#document-index) ====================================== ### Navigation Contents: * [Secure Headers](index.html#document-headers) * [Policy Builder](index.html#document-policies) * [Supported Frameworks](index.html#document-frameworks) * [Resources](index.html#document-resources) ### Related Topics * [Documentation overview](index.html#document-index) ### Quick search ©2021, Caleb Kinney. | Powered by [Sphinx 1.8.5](http://sphinx-doc.org/) & [Alabaster 0.7.12](https://github.com/bitprophet/alabaster)

cortex

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Cortex2.0 documentation [Cortex2.0](index.html#document-index) latest User Documentation * [Installation](index.html#document-install) * [Getting Started](index.html#document-getting_started) * [cortex](index.html#document-modules) * [Develop](index.html#document-develop) * [Custom demos](index.html#document-build) * [A walkthrough a custom classifier:](index.html#a-walkthrough-a-custom-classifier) * [Defining losses and results](index.html#defining-losses-and-results) * [Visualization](index.html#visualization) * [Putting it together](index.html#putting-it-together) [Cortex2.0](index.html#document-index) * [Docs](index.html#document-index) » * Cortex2.0 documentation * [Edit on GitHub](https://github.com/rdevon/cortex2.0/blob/master/docs/source/index.rst) --- Welcome to Cortex2.0[¶](#welcome-to-cortex2-0 "Permalink to this headline") =========================================================================== Installation[¶](#installation "Permalink to this headline") ----------------------------------------------------------- ### Prerequisites[¶](#prerequisites "Permalink to this headline") #### Visdom[¶](#visdom "Permalink to this headline") ``` $pip install visdom ``` ### From Source[¶](#from-source "Permalink to this headline") ``` $git clone https://github.com/rdevon/cortex2.0.git $cd cortex2.0 $pip install . ``` Getting Started[¶](#getting-started "Permalink to this headline") ----------------------------------------------------------------- ### Configuration[¶](#configuration "Permalink to this headline") The first thing to do is to set up the config.yaml. This file is user-specific (it got tracked at some point, so I need to fix this), and will tell cortex everything user-specific regarding data locations, visualation, and outputs. ``` $ rm -rf ~/.cortex.yml $ cortex setup ``` #### Configuration File Example[¶](#configuration-file-example "Permalink to this headline") Located at `~/.cortex.yml` ``` torchvision\_data\_path: /data/milatmp1/hjelmdev/data/ data\_paths: { Imagenet-12: /data/lisa/data/ImageNet2012\_jpeg, CelebA: /tmp/hjelmdev/CelebA}viz: { font: /usr/share/fonts/truetype/liberation/LiberationSerif-Regular.ttf, server: 'http://132.204.26.180'} out\_path: /data/milatmp1/hjelmdev/outs/ ``` These are as follows: * torchvision\_data\_path: the path to all torchvision-specific datasets (details can be found in torchvision.datasets) * data\_paths: user-specified custom datasets. Currently, only support is for image folders (a la imagenet), but other dataset types (e.g., text) are planned in the near-future. * vis: visdom specific arguments. * out\_path: Out path for experiment outputs #### Usage[¶](#usage "Permalink to this headline") > > cortex –help #### Built-ins[¶](#built-ins "Permalink to this headline") | setup: | Setup cortex configuration. | | GAN: | Generative adversarial network. | | VAE: | Variational autoencoder. | | AdversarialAutoencoder: | | | Adversarial Autoencoder. | | ALI: | Adversarially learned inference. | | ImageClassification: | | | Basic image classifier. | | GAN\_MINE: | GAN + MINE. | #### Options[¶](#options "Permalink to this headline") | `-h, --help` | show this help message and exit | | `-o OUT\_PATH, --out\_path OUT\_PATH` | | | Output path directory. All model results will go here. If a new directory, a new one will be created, as long as parent exists. | | `-n NAME, --name NAME` | | | Name of the experiment. If given, base name of output directory will be –name. If not given, name will be the base name of the –out\_path | | `-r RELOAD, --reload RELOAD` | | | Path to model to reload. | | `-M LOAD\_MODELS, --load\_models LOAD\_MODELS` | | | Path to model to reload. Does not load args, info, etc | | `-m META, --meta META` | | | TODO | | `-c CONFIG\_FILE, --config\_file CONFIG\_FILE` | | | Configuration yaml file. See exps/ for examples | | `-k, --clean` | Cleans the output directory. This cannot be undone! | | `-v VERBOSITY, --verbosity VERBOSITY` | | | Verbosity of the logging. (0, 1, 2) | | `-d DEVICE, --device DEVICE` | | | TODO | #### Usage Example[¶](#usage-example "Permalink to this headline") To run an experiment. ``` cortex GAN --d.source CIFAR10 --d.copy\_to\_local ``` #### Custom models[¶](#custom-models "Permalink to this headline") It is possible to run experiments with custom models made with Pytorch under the Cortex framework. For doing so, the model has to be added to the demos folder under the root of the project. You can have a look to the given demo autoencoder and classifier already implemented. The main difference is that, rather than registering the plugins, the run function of main.py has to be called. For example, ``` if \_\_name\_\_ == '\_\_main\_\_': classifier = MyClassifier() run(model=classifier) ``` To run an experiment with a custom model. ``` python my\_model.py --d.source <Dataset> --d.copy\_to\_local ``` cortex[¶](#cortex "Permalink to this headline") ----------------------------------------------- ### cortex package[¶](#cortex-package "Permalink to this headline") #### Subpackages[¶](#subpackages "Permalink to this headline") #### Submodules[¶](#submodules "Permalink to this headline") #### cortex.main module[¶](#cortex-main-module "Permalink to this headline") #### cortex.plugins module[¶](#cortex-plugins-module "Permalink to this headline") #### Module contents[¶](#module-contents "Permalink to this headline") Develop[¶](#develop "Permalink to this headline") ------------------------------------------------- ### Documentation[¶](#documentation "Permalink to this headline") Make sure that the cortex package is installed and configured. For development purpose, if you are making changes to documentation, for example modifications inside docstrings or changes in some .rst files #### Building Documentation[¶](#building-documentation "Permalink to this headline") To build the documentation, the docs.py script under the root of the project is facilitating the process. Before making a Pull Request to the remote repository, you should run the script. ``` $ python docs.py ``` #### Serving Documentation Locally[¶](#serving-documentation-locally "Permalink to this headline") If you want to have a look at your changes before making a Pull Request on GitHub, it is possible to serve locally the generated html files. ``` $ cd docs/build/html $ python -m http.server 8000 --bind 127.0.0.1 ``` Custom demos[¶](#custom-demos "Permalink to this headline") ----------------------------------------------------------- While cortex has built-in functionality, but it is meant to meant to be used with your own modules. An example of making a model that works with cortex can be found at: <https://github.com/rdevon/cortex/blob/master/demos/demo_classifier.py> and <https://github.com/rdevon/cortex/blob/master/demos/demo_custom_ae.py> Documentation on the API can be found here: <https://github.com/rdevon/cortex/blob/master/cortex/plugins.py> For instance, the demo autoencoder can be used as: ``` python cortex/demos/demo\_custom\_ae.py --help ``` A walkthrough a custom classifier:[¶](#a-walkthrough-a-custom-classifier "Permalink to this headline") ------------------------------------------------------------------------------------------------------ Let’s look a little more closely at the autoencoder demo above to see what’s going on. cortex relies on using and overriding methods of plugins classes. First, let’s look at the methods, `build`, `routine`, and `visualize`. These are special methods for the plugin that can be overridden to change the behavior of your model for your needs. The signature of these functions look like: ``` def build(self, dim\_z=64, dim\_encoder\_out=64): ... def routine(self, inputs, targets, ae\_criterion=F.mse\_loss): ... def visualize(self, inputs, targets): ... ``` Each of these functions have arguments and keyword arguments. Note that the keyword arguments showed up in the help in the above example. This is part of the functionality of cortex: it manages your hyperparameters to these functions, organizes them, and provides command line control automatically. Even the docstrings are used in the command line, so other users can get the usage docs directly from there. The arguments are *data*, which are to be manipulated as needed in those methods. These are for the most part handled automatically, but all of these methods can be used as normal functions as well. ### Building models[¶](#building-models "Permalink to this headline") The `build` function takes the hyperparameters and sets networks. ``` class Autoencoder(nn.Module): def \_\_init\_\_(self, encoder, decoder): super(Autoencoder, self).\_\_init\_\_() self.encoder = encoder self.decoder = decoder def forward(self, x, nonlinearity=None): encoded = self.encoder(x) decoded = self.decoder(encoded) return decoded ... def build(self, dim\_z=64, dim\_encoder\_out=64): encoder = nn.Sequential( nn.Linear(28, 256), nn.ReLU(True), nn.Linear(256, 28), nn.ReLU(True)) decoder = nn.Sequential( nn.Linear(28, 256), nn.ReLU(True), nn.Linear(256, 28), nn.Sigmoid()) self.nets.ae = Autoencoder(encoder, decoder) ``` All that’s being done here is the hyperparameters are being used to create an instance of an `nn.Module` subclass, which is being added to the set of “nets”. Note that they keyword `ae` is very important, as this is going to be how you retrieve your nets and define their losses farther down. Also note that cortex *only* currently supports `nn.Module` subclasses from Pytorch. Defining losses and results[¶](#defining-losses-and-results "Permalink to this headline") ----------------------------------------------------------------------------------------- Adding losses and results from your model is easy, just compute your graph given you models and data, then add the losses and results by setting those members: ``` def routine(self, inputs, targets, ae\_criterion=F.mse\_loss): encoded = self.nets.ae.encoder(inputs) outputs = self.nets.ae.decoder(encoded) r\_loss = ae\_criterion( outputs, inputs, size\_average=False) / inputs.size(0) self.losses.ae = r\_loss ``` Additional results can be added similarly. For instance, in the demo classifier: ``` def routine(self, inputs, targets, criterion=nn.CrossEntropyLoss()): ... classifier = self.nets.classifier outputs = classifier(inputs) predicted = torch.max(F.log\_softmax(outputs, dim=1).data, 1)[1] loss = criterion(outputs, targets) correct = 100. \* predicted.eq( targets.data).cpu().sum() / targets.size(0) self.losses.classifier = loss self.results.accuracy = correct ``` Visualization[¶](#visualization "Permalink to this headline") ------------------------------------------------------------- Cortex allows for visualization using visdom, and this can be defined in a similar way as above: ``` def visualize(self, images, inputs, targets): predicted = self.predict(inputs) self.add\_image(images.data, labels=(targets.data, predicted.data), name='gt\_pred') ``` See the ModelPlugin API for more more details. Putting it together[¶](#putting-it-together "Permalink to this headline") ------------------------------------------------------------------------- Finally, we can specify default arguments: ``` defaults = dict( data=dict( batch\_size=dict(train=64, test=64), inputs=dict(inputs='images')), optimizer=dict(optimizer='Adam', learning\_rate=1e-4), train=dict(save\_on\_lowest='losses.ae')) ``` and then add `cortex.main.run` to `\_\_main\_\_`: ``` if \_\_name\_\_ == '\_\_main\_\_': autoencoder = AE() run(model=autoencoder) ``` And that’s it. cortex also allows for lower-level functions to be overridden (e.g., train\_step, eval\_step, train\_loop, etc) with more customizability coming soon. For more examples of usage, see the built-in models: <https://github.com/rdevon/cortex/tree/master/cortex/built_ins/models>

resource

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Resource 1 documentation [Resource](index.html#document-index)   [Resource](index.html#document-index) * [Docs](index.html#document-index) » * Resource 1 documentation * [Edit on GitHub](https://github.com/RussellLuo/resource/blob/master/docs/index.rst) --- Welcome to Resource’s documentation ========================================================================================================== Contents: Indices and tables[¶](#indices-and-tables "Permalink to this headline") ======================================================================= * [*Index*](genindex.html) * [*Module Index*](py-modindex.html) * [*Search Page*](search.html)

gauge

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Gauge 0.0.2 documentation Gauge[¶](#gauge "Permalink to this headline") ============================================= [](https://gauge.readthedocs.io/en/stable/?badge=latest) Low-overhead streaming (or continuous) OpenTracing-compatible Python profiler. Quick links[¶](#quick-links "Permalink to this headline") --------------------------------------------------------- | Documentation | <https://gauge.readthedocs.io/en/stable/> | | Issue tracker | <https://github.com/AndreiPashkin/gauge/issues/> | Why another profiler?[¶](#why-another-profiler "Permalink to this headline") ---------------------------------------------------------------------------- The idea is to make a profiler which would be able to continuously *stream* the profile data to monitoring software (like [Jaeger](https://www.jaegertracing.io/) or [Elastic APM](https://www.elastic.co/apm/)) and also, have low-performance overhead so that it would be possible to deploy it on production. So it would be possible to monitor the performance of a microservice or some other long-running application in real-time over a long period and explore historical performance data. Currently, no open-source alternatives exist that are able to do that. Features[¶](#features "Permalink to this headline") --------------------------------------------------- * OpenTracing compatibility. Specifically with the following APMs: + [Elastic APM](https://www.elastic.co/apm/) (after [this fix](https://github.com/elastic/apm-agent-python/pull/824) is merged). + [Jaeger](https://www.jaegertracing.io/). * Very low overhead with 100 / samples per second sampling frequency. * Supports generators/coroutines. Compatibility[¶](#compatibility "Permalink to this headline") ------------------------------------------------------------- Currently, the project targets only CPython 3.5+. It is intended to be compatible with all major operating systems, but currently only tested on Ubuntu Linux. Project status[¶](#project-status "Permalink to this headline") --------------------------------------------------------------- Project in alpha stage of development and is actively worked on. It just passed the state where the main functionality is working more or less correctly and it is possible to actually install it and use. How does it work?[¶](#how-does-it-work "Permalink to this headline") -------------------------------------------------------------------- The profiler exploits [\_PyThread\_CurrentFrames()](https://github.com/python/cpython/blob/8ecc0c4d390d03de5cd2344aa44b69ed02ffe470/Python/pystate.c#L1155) function of CPython’s C-API which provides reasonable performance. This function is called with defined frequency in a background thread which collects raw data which is in turn aggregated and exported to somewhere in another background thread which is activated with a much lower frequency. Alternatives[¶](#alternatives "Permalink to this headline") ----------------------------------------------------------- | Profiler | Is low-overhead? | Is streaming? | Is commercial? | Is Open-Source? | Has permissive license? | | --- | --- | --- | --- | --- | --- | | [DataDog profiler](https://docs.datadoghq.com/tracing/profiler/getting_started/?tab=python) | Yes | Yes | Yes | No | | | [Py-Spy](https://github.com/benfred/py-spy) | Yes | No | No | Yes | Yes | | [Austin](https://github.com/P403n1x87/austin) | Yes | No | No | Yes | No | Documentation[¶](#documentation "Permalink to this headline") ------------------------------------------------------------- ### Usage[¶](#usage "Permalink to this headline") #### Installation[¶](#installation "Permalink to this headline") Gauge can be installed as a regular Python package. Currently, it is not published on PyPI so you have to clone [the repository](https://github.com/AndreiPashkin/gauge/) to install it. ##### Prerequisites[¶](#prerequisites "Permalink to this headline") * [CMake](https://cmake.org/) above 3.14. * Some sort of C++ 14 compatible compiler. * CPython 3.5+. * CPython’s headers. On recent Debian/Ubuntu version you would be able to install everything necessary with similar commands: ``` sudo apt-get update sudo apt-get install build-essential cmake python3.8 python3.8-dev ``` These exact commands work on Ubuntu 18.04 for example. ##### Installation steps[¶](#installation-steps "Permalink to this headline") 1. Clone the project from [the GitHub repo](https://github.com/AndreiPashkin/gauge/). 2. Execute `python setup.py install`. Warning The project doesn’t store C++ dependency libraries in the repository like some others do, instead - it downloads them on demand. Downloading can take some time so don’t be surprised to find yourself waiting a little bit during installation. That’s it. #### Quickstart[¶](#quickstart "Permalink to this headline") Gauge built on three types of entities - collectors, aggregators, exporters. Collectors - collect raw data about execution, aggregators - aggregate them, exporters - push them to somewhere (monitor software). To instantiate the profiler implementations of these entities has to be combined. ##### Minimal example[¶](#minimal-example "Permalink to this headline") ``` import gauge from elasticapm import Client from elasticapm.contrib.opentracing import Tracer client = Client({ 'SERVICE\_NAME': 'my-service', }) tracer = Tracer(client\_instance=client) collector = gauge.SamplingCollector() aggregator = gauge.SpanAggregator() collector.subscribe(aggregator) exporter = gauge.OpenTracingExporter(tracer=tracer) aggregator.subscribe(exporter) collector.start() # ... work work work collector.stop() aggregator.finish\_open\_spans() client.close() ``` ### Development[¶](#development "Permalink to this headline") This chapter is dedicated to those who dared to write some code for this project. #### Development environment[¶](#development-environment "Permalink to this headline") Here is how to create a local development environment: > > 1. First of all install everything described in [Prerequisites](index.html#prerequisites). > 2. In addition to that, a developer needs to install a debug-build of > CPython. It is better to use it when developing extensions for CPython - > you’ll get meaningful errors messages in case of reference-counting > errors for example. > > > On Ubuntu 18.04 it could be done with the following command: > > > > ``` > sudo apt-get install python3.8-dbg > > ``` > 3. Install [Tox](https://tox.readthedocs.io/en/latest/) with version above 3. > 4. Use Tox to deploy the development virtualenv: > > > > ``` > tox -edev > > ``` > 5. Activate the development virtualenv: `. .tox/dev/bin/activate`. > > > This is it. #### Architecture overview[¶](#architecture-overview "Permalink to this headline") Gauge is built upon three main entities - collectors, aggregators, exporters. * Collectors - collect raw data which is just snapshots of the current state of execution. * Aggregators - aggregate the raw data collected by the collector. The task of an aggregator is to turn a collection of raw snapshots into something meaningful: > > > + Spans - potentially incomplete calls with start and end represented as > separate objects). > + Calls - complete calls with known start and end. > + Statistics - summary aggregated data (like it’s done in PStats module in > Python’s standard library). > * Exporters - the task of the exporters is to simply save data or push it to some kind of remote monitoring service. #### Commit message format[¶](#commit-message-format "Permalink to this headline") * First line - 50 symbols max. in the imperative mood, conforming to punctuation rules of the English language. * Subsequent lines - 72 symbols max. * Optionally - comma-separated list of referenced issues/PRs on the last line of the message body, preceded by an empty line. ##### Example good commit message[¶](#example-good-commit-message "Permalink to this headline") ``` Add foo() method into Bar class. foo() does a really important thing and this makes Bar even more awesome. #42, #36, #11 ``` #### Contribution guide[¶](#contribution-guide "Permalink to this headline") Nothing complex for now - just create an issue on the issue tracker or comment on the existing one and then make a pull request ;) Indices and tables[¶](#indices-and-tables "Permalink to this headline") ----------------------------------------------------------------------- * [Index](genindex.html) * [Module Index](py-modindex.html) * [Search Page](search.html) [Gauge](index.html#document-index) ================================== ### Navigation Contents: * [Introduction](index.html#document-index) * [Usage](index.html#document-Usage) * [Development](index.html#document-Development) ### Related Topics * [Documentation overview](index.html#document-index) ### Quick search ©2020, Andrei Pashkin. | Powered by [Sphinx 1.8.5](http://sphinx-doc.org/) & [Alabaster 0.7.12](https://github.com/bitprophet/alabaster)

merlin

go

Merlin Documentation Release 1.11.0 The Merlin Development Team Oct 09, 2023 CONTENTS 1 Merlin Overview 3 1.1 Tutorial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 1.2 Getting Started . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 1.3 FAQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 1.4 Command line . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 1.5 Workflows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 1.6 Workflow Specification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 1.7 Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84 1.8 Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 1.9 Merlin Server . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98 1.10 Celery . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102 1.11 Virtual environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 1.12 Spack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106 1.13 Contributing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 1.14 Docker . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 i ii Merlin Documentation, Release 1.11.0 Merlin is a tool for running machine learning based workflows. The goal of Merlin is to make it easy to build, run, and process the kinds of large scale HPC workflows needed for cognitive simulation. CONTENTS 1 Merlin Documentation, Release 1.11.0 2 CONTENTS CHAPTER ONE MERLIN OVERVIEW Merlin is a distributed task queuing system, designed to allow complex HPC workflows to scale to large numbers of simulations (we’ve done 100 Million on the Sierra Supercomputer). Why would you want to run that many simulations? To become your own Big Data generator. Data sets of this size can be large enough to train deep neural networks that can mimic your HPC application, to be used for such things as design optimization, uncertainty quantification and statistical experimental inference. Merlin’s been used to study inertial confinement fusion, extreme ultraviolet light generation, structural mechanics and atomic physics, to name a few. How does it work? In essence, Merlin coordinates complex workflows through a persistent external queue server that lives outside of your HPC systems, but that can talk to nodes on your cluster(s). As jobs spin up across your ecosystem, workers on those allocations pull work from a central server, which coordinates the task dependencies for your workflow. Since this coordination is done via direct connections to the workers (i.e. not through a file system), your workflow can scale to very large numbers of workers, which means a very large number of simulations with very little overhead. Furthermore, since the workers pull their instructions from the central server, you can do a lot of other neat things, like having multiple batch allocations contribute to the same work (think surge computing), or specialize workers to different machines (think CPU workers for your application and GPU workers that train your neural network). Another neat feature is that these workers can add more work back to central server, which enables a variety of dynamic workflows, such as may be necessary for the intelligent sampling of design spaces or reinforcement learning tasks. Merlin does all of this by leveraging some key HPC and cloud computing technologies, building off open source components. It uses maestro to provide an interface for describing workflows, as well as for defining workflow task dependencies. It translates those dependencies into concrete tasks via celery, which can be configured for a variety of backend technologies (rabbitmq and redis are currently supported). Although not a hard dependency, we encourage the use of flux for interfacing with HPC batch systems, since it can scale to a very large number of jobs. The integrated system looks a little something like this: 3 Merlin Documentation, Release 1.11.0 For more details, check out the rest of the documentation. Need help? merlin@llnl.gov 1.1 Tutorial Estimated time • 3 hours Grab your laptop and coffee, and dive into this 7-module tutorial to become a Merlin expert. This hands-on tutorial introduces Merlin through some example workflows. In it, you will install Merlin on your local machine, stand up a virtual server and run both a simple workflow and a quasi-real-life physicsy simulation that couples a physics application with visualization and machine learning. You’ll also learn how to use some advanced features and help make Merlin better. Finally we offer some tips and tricks for porting and scaling up your application. 1.1.1 0. Before you start It will be helpful to have these steps already completed before you start the tutorial modules: • Make sure you have python 3.6 or newer. • Make sure you have pip version 22.3 or newer. • You can upgrade pip to the latest version with: pip install --upgrade pip 4 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 • OR you can upgrade to a specific version with: pip install --upgrade pip==x.y.z • Make sure you have GNU make tools and compilers. • (OPTIONAL) Install docker. • Download OpenFOAM image with: docker pull cfdengine/openfoam • Download redis image with: docker pull redis 1.1.2 Introduction This module introduces you to Merlin, some of the technology behind it, and how it works. Prerequisites • Curiosity Estimated time • 20 minutes You will learn • What Merlin is and why you might consider it • Why it was built and what are some target use cases • How it is designed and what the underlying tech is Table of Contents: • What is Merlin? • Why Merlin? What’s the need? • How can Merlin run so many simulations? • So what exactly does Merlin do? • How is it designed? • What is in this Tutorial? 1.1. Tutorial 5 Merlin Documentation, Release 1.11.0 What is Merlin? Summary Merlin is a toolkit designed to enable HPC-focused simulation workflows with distributed cloud compute technologies. This helps simulation workflows push to immense scale. (Like 100 million.) At its core, Merlin translates a text-based, command-line focused workflow description into a set of discrete tasks. These tasks live on a centralized broker (e.g. a separate server) that persists outside of your HPC batch allocation. Autonomous workers in different allocations (even on different machines) can then connect to this server, pull off and execute these tasks asynchronously. Why Merlin? What’s the need? That sounds complicated. Why would you care to do this? The short answer: machine learning The longer answer: machine learning and data science are becoming an integral part of scientific inquiry. The problem is that machine learning models are data hungry: it takes lots and lots of simulations to train machine learning models on their outputs. Unfortunately HPC systems were designed to execute a few large hero simulations, not many smaller simulations. Naively pushing standard HPC workflow tools to hundreds of thousands and millions of simulations can lead to some serious problems. Workflows, applications and machines are becoming more complex, but subject matter experts need to devote time and attention to their applications and often require fine command-line level control. Furthermore, they rarely have the time to devote to learning workflow systems. With the expansion of data-driven computing, the HPC scientist needs to be able to run more simulations through complex multi-component workflows. Merlin targets HPC workflows that require many simulations. These include: Table 1: Merlin Targeted Use Cases Emulator building Running enough simulations to build an emulator (or “surrogate model”) of an expen- sive computer code, such as needed for uncertainty quantification Iterative sampling Executing some simulations and then choosing new ones to run based on the results obtained thus far Active learning Iteratively sampling coupled with emulator building to efficiently train a machine learning model Design optimization Using a computer code to optimize a model design, perhaps robustly or under uncer- tainty Reinforcement learning Building a machine learning model by subsequently exposing it to lots of trials, giving it a reward/penalty for the outcomes of those trials Hierarchical simulation Running low-fidelity simulations to inform which higher fidelity simulations to execute Heterogeneous workflows Workflows that require different steps to execute on different hardware and/or systems Many scientific and engineering problems require running lots of simulations. But accomplishing these tasks effectively in an unstable bleeding edge HPC environment can be dicey. The tricks that work for 100 simulations won’t work for 10 thousand, let alone 100 million. We made Merlin to make high-frequency extreme scale computing easy. 6 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 How can Merlin run so many simulations? The good news is that distributed cloud compute technology has really pushed the frontier of scalability. Merlin helps bring this tech to traditional scientific HPC. Traditionally, HPC workflow systems tie workflow steps to HPC resources and coordinate the execution of tasks and management of resources one of two ways: Table 2: Traditional HPC Workflow Philosophies External Coordination • Separate batch jobs for each task • External daemon tracks dependencies and jobs • Progress monitored with periodic polling (of files or batch system) Internal Coordination • Multiple tasks bundled into larger batch jobs • Internal daemon tracks dependencies and re- sources • Progress monitored via polling (of filesystem or message passing) External coordination ties together independent batch jobs each executing workflow sub-tasks with an external mon- itor. This monitor could be a daemon or human that monitors either the batch or file system via periodic polling and orchestrates task launch dependencies. External coordination can tailor the resources to the task, but cannot easily run lots of concurrent simulations (since batch systems usually limit the number of jobs a user can queue at once). Internal coordination puts the monitor within a larger batch job that allocates resources inside that job for the specific tasks at hand. Internal coordination can run many more concurrent tasks by bundling smaller jobs into larger jobs, but cannot tailor the resources to the task at hand. This precludes workflows that, for instance, require one step on CPU hardware and another on a GPU machine. Instead of tying resources to tasks, Merlin does this: 1.1. Tutorial 7 Merlin Documentation, Release 1.11.0 Table 3: Merlin’s Workflow Philosophy Centralized Coordination of Producers & Consumers • Batch jobs and workers decoupled from tasks • Centralized queues visible to multiple jobs • Progress and dependencies handled via direct worker connections to central message server and results database Merlin decouples workflow tasks from workflow resources. Merlin avoids a command-and-control approach to HPC resource management for a workflow. Instead of having the workflow coordinator ask for and manage HPC resources and tasks, the Merlin coordinator just manages tasks. Task- agnostic resources can then independently connect (and disconnect) to the coordinator. In Merlin, this producer-consumer workflow happens through two commands: merlin run <workflow file> (producer) and merlin run-worker <workflow file> (consumer). The merlin run command populates the central queue(s) with work to do and the merlin run-worker command drains the queue(s) by executing the task instructions. Each new instance of merlin run-worker creates a new consumer. These consumers can exist on different machines in different batch allocations, anywhere that can see the central server. Likewise merlin run can populate the queue from any system that can see the queue server, including other workers. In principle, this means a researcher can push new work onto an already running batch allocation of workers, or re-direct running jobs to work on higher-priority work. The benefits of producer-consumer workflows The increased flexibility that comes from decoupling what HPC applications you run from where you run them can be extremely enabling. Merlin allows you to • Scale to very large number of simulations by avoiding common HPC bottlenecks • Automatically take advantage of free nodes to process your workflow faster • Create iterative workflows, like as needed for active machine learning • Dynamically add more tasks to already-running jobs • Have cross-machine and cross-batch-job workflows, with different steps executing on different resources, but still coordinated The producer-consumer approach to workflows allows for increased flexibility and scalability. For this reason it has become a mainstay of cloud-compute microservices, which allow for extremely distributed asynchronous computing. 8 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 Many asynchronous task and workflow systems exist, but the majority are focused around this microservices model, where a system is set up (and managed) by experts that build a single workflow. This static workflow gets tested and hardened and exists as a service for their users (e.g. an event on a website triggers a discrete set of tasks). HPC, and in particular scientific HPC brings its own set of challenges that make a direct application of microservices to HPC workflows challenging. Table 4: Challenges for bringing microservices to scientific HPC Work- flows Challenge Requirement Workflows can change from day-to-day as researchers Workflows need to be dynamic, not static. explore new simulations, configurations, and questions. Workflow components are usually different executables, Workflows need to intuitively support multiple lan- pre- and post-processing scripts and data aggregation guages. steps written in different languages. These components often need command-line-level con- Workflows need to support shell syntax and environment trol of task instructions. variables. Components frequently require calls to a batch system Workflows need a natural way to launch parallel jobs scheduler for parallel job execution. that use more resources then a single worker. Tasks can independently create large quantities of data. Dataflow models could be bottlenecks. Workflows should take advantage of parallel file systems. HPC systems (in particular leadership class machines) Workflows need to be able to restart, retry and rerun can experience unforeseen outages. failed steps without needing to run the entire workflow. Merlin was built specifically to address the challenges of porting microservices to HPC simulations. So what exactly does Merlin do? Merlin wraps a heavily tested and well used asynchronous task queuing library in a skin and syntax that is natural for HPC simulations. In essence, we extend maestro by hooking it up to celery. We leverage maestro’s HPC-friendly workflow description language and translate it to discrete celery tasks. Why not just plain celery? Celery is extremely powerful, but this power can be a barrier for many science and engineering subject matter experts, who might not be python coders. While this may not be an issue for web developers, it presents a serious challenge to many scientists who are used to running their code from a shell command line. By wrapping celery commands in maestro steps, we not only create a familiar environment for users (since maestro steps look like shell commands), but we also create structure around celery dependencies. Maestro also has interfaces to common batch schedulers (e.g. slurm and flux)[*]_ for parallel job control. So why Merlin and not just plain maestro? The main reason: to run lots of simulations for machine learning applications. Basically Merlin scales maestro. Maestro follows an external coordinator model. Maestro workflow DAGs (directed acyclic graphs) need to be unrolled (concretized) ahead of time, so that batch dependencies can be calculated and managed. This graph problem becomes very expensive as the number of tasks approaches a few hundred. (Not to mention most batch systems will prevent a user from queuing more than a few hundred concurrent batch jobs.) In other words, using maestro alone to run thousands of simulations is not practical. But with celery, we can dynamically create additional tasks. This means that the DAG can get unrolled by the very same workers that will execute the tasks, offering a natural parallelism (i.e. much less waiting before starting the work). What does this mean in practice? Merlin can quickly queue a lot of simulations. 1.1. Tutorial 9 Merlin Documentation, Release 1.11.0 How quickly? The figure below shows task queuing rates when pushing a simple workflow on the Quartz Supercomputer to 40 million samples. This measures how quickly simulation ensembles of various sample sizes can get enqueued. As you can see, by exploiting celery’s dynamic task queuing (tasks that create tasks), Merlin can enqueue hundreds of thousands of simulations per second. These jobs can then be consumed in parallel, at a rate that depends on the number of workers you have. Furthermore, this ability to dynamically add tasks to the queue means that workflows can become more flexible and responsive. A worker executing a step can launch additional workflows without having to stand up resources to execute and monitor the execution of those additional steps. The only downside to being able to enqueue work this quickly is the inability of batch schedulers to keep up. This is why we recommend pairing Merlin with flux, which results in a scalable but easy-to-use workflow system: • Maestro describes the workflow tasks • Merlin orchestrates the task executions • Flux schedules the HPC resources Here’s an example of how Merlin, maestro and flux can all work together to launch a workflow on multiple machines. 10 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 The scientist describes her workflow with a maestro-like <workflow file>. Her workflow consists of two steps: 1. Run many parallel CPU-only jobs, varying her simulation parameters of interest 2. Use a GPU to train a deep learning model on the results of those simulations She then types merlin run <workflow file>, which translates that maestro file into celery commands and sends those tasks to two separate queues on a centralized server (one for CPU work and one for GPU work). She then launches a batch allocation on the CPU machine, which contains the command merlin run-workers <workflow file> --steps 1. Workers start up under flux, pull work from the server’s CPU queue and call flux to launch the parallel simulations asynchronously. She also launches a separate batch request on the GPU machine with merlin run-workers <workflow file> --steps 2. These workers connect to the central queue associated with the GPU step. When the simulations in step 1 finish, step 2 will automatically start. In this fashion, Merlin allows the scientist to coordinate a highly scalable asynchronous multi-machine heterogeneous workflow. This is of course a simple example, but it does show how the producer-consumer philosophy in HPC workflows can be quite enabling. Merlin’s goal is to make it easy for HPC-focused subject matter experts to take advantage of the advances in cloud computing. How is it designed? Merlin leverages a number of open source technologies, developed and battle-hardened in the world of distributed computing. We decided to do this instead of having to build, test and maintain stand-alone customized (probably buggy) versions of software that will probably not be as fully featured. There are differing philosophies on how much third-party software to rely upon. On the one hand, building our system off ubiquitous open source message passing libraries increases the confidence in our software stack’s performance, especially at scale (for instance, celery is robust enough to keep Instagram running). However, doing so means that when something breaks deep down, it can be difficult to fix (if at all). Indeed if there’s an underlying “feature” that we’d like to work around, we could be stuck. Furthermore, the complexity of the software stack can be quite large, 1.1. Tutorial 11 Merlin Documentation, Release 1.11.0 such that our team couldn’t possibly keep track of it all. These are valid concerns; however, we’ve found it much easier to quickly develop a portable system with a small team by treating (appropriately chosen) third party libraries as underlying infrastructure. (Sure you could build and use your own compiler, but should you?) Merlin manages the increased risk that comes with relying on software that is out of our control by: 1. Building modular software that can easily be reconfigured / swapped for other tech 2. Participating as developers for those third-party packages upon which rely (for instance we often kick enhance- ments and bug fixes to maestro) 3. Using continuous integration and testing to catch more errors as they occur This section talks about some of those underlying technologies, what they are, and why they were chosen. A brief technical dive into some underlying tech Merlin extends maestro with celery, which in turn can be configured to interface with a variety of message queue brokers and results backends. In practice, we like to use RabbitMQ and Redis for our broker and backend respectively, because of their features and reliability, especially at scale. Table 5: Key Merlin Tech Components Component Reasoning maestro shell-like workflow descriptions, batch system interfaces celery highly scalable, supports multiple brokers and backends RabbitMQ resilience, support for multiple users and queues Redis database speed, scalability cryptography secure Redis results flux (optional) portability and scalability of HPC resource allocation The different components interact to populate and drain the message queue broker of workflow tasks. When a call is made to merlin run, maestro turns the workflow description (composed of “steps” with “parameters” and “samples”) into a task dependency graph. Merlin translates this graph into discrete celery task commands*0 0 The flux and slurm interfaces used by Merlin differ from the versions bundled with maestro to decouple job launching from batch submission. 12 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 Calls to merlin run-worker cause celery workers to connect to both the message broker and results database. The workers pull tasks from the broker and begin to execute the instructions therein. When finished, a worker posts the results (task status metadata, such as “SUCCESS” or “FAIL”) to the results database and automatically grabs another task from the queue. When additional workers come along (through other explicit calls to merlin run-worker), they connect to the broker and help out with the workflow. Multiple vs. Single Queues RabbitMQ brokers can have multiple distinct queues. To take advantage of this feature, Merlin lets you assign workflow steps and workers to different queues. (Steps must be assigned to a single queue, but workers can connect to multiple queues at once.) The advantage of a single queue is simplicity, both in workflow design and scalability. However, having multiple queues allows for prioritization of work (the express checkout lane at the grocery store) and customization of workers (specialized assembly line workers tailored for a specific task). What is in this Tutorial? This tutorial will show you how to: • Install Merlin and test that it works correctly • Build a basic workflow and scale it up, introducing you to Merlin’s syntax and how it differs from maestro. • Run a “real” physics simulation based workflow, with post-processing of results, visualization and machine learning. • Use Merlin’s advanced features to do things like interface with batch systems, distribute a workflow across machines and dynamically add new samples to a running workflow. • Contribute to Merlin, through code enhancements and bug reports. • Port your own application, with tips and tricks for building and scaling up workflows. 1.1.3 Installation Prerequisites • shell (bash, csh, etc, if running on Windows, use a linux container) • python3 >= python3.6 • pip3 • wget • build tools (make, C/C++ compiler) • (OPTIONAL) docker (required for Module 4: Run a Real Simulation) • (OPTIONAL) file editor for docker config file editing Estimated time • 20 minutes You will learn • How to install merlin in a virtual environment using pip. 1.1. Tutorial 13 Merlin Documentation, Release 1.11.0 • How to install a container platform eg. singularity, docker, or podman. • How to configure merlin. • How to test/verify the installation. Table of Contents: • Installing Merlin – Redis Server • Configuring Merlin • Checking/Verifying Installation • (OPTIONAL) Docker Advanced Installation – RabbitMQ Server – Redis TLS Server This section details the steps necessary to install merlin and its dependencies. Merlin will then be configured for the local machine and the configuration will be checked to ensure a proper installation. Installing Merlin A merlin installation is required for the subsequent modules of this tutorial. Once merlin is installed, it requires servers to operate. While you are able to host your own servers, we will use merlin’s containerized servers in this tutorial. However, if you prefer to host your own servers you can host a redis server that is accessible to your current machine. Your computer/organization may already have a redis server available you can use, please check with your local system administrator. Create a virtualenv using python3 to install merlin. python3 -m venv --prompt merlin merlin_venv Activate the virtualenv. source merlin_venv/bin/activate or source merlin_venv/bin/activate.csh The (merlin) <shell prompt> will appear after activating. You should upgrade pip and setuptools before proceeding. pip3 install setuptools pip -U Install merlin through pip. pip3 install merlin Check to make sure merlin installed correctly. which merlin 14 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 You should see that it was installed in your virtualenv, like so: ~/<path_to_virtualenv>/merlin_venv/bin/merlin If this is not the output you see, you may need to restart your virtualenv and try again. When you are done with the virtualenv you can deactivate it using deactivate, but leave the virtualenv activated for the subsequent steps. deactivate Redis Server A redis server is required for the celery results backend server, this same server can also be used for the celery broker. We will be using merlin’s containerized server however we will need to download one of the supported container platforms avaliable. For the purpose of this tutorial we will be using singularity. # Update and install singularity dependencies apt-get update && apt-get install -y \ build-essential \ libssl-dev \ uuid-dev \ libgpgme11-dev \ squashfs-tools \ libseccomp-dev \ pkg-config # Download dependency go wget https://go.dev/dl/go1.18.1.linux-amd64.tar.gz # Extract go into local tar -C /usr/local -xzf go1.18.1.linux-amd64.tar.gz # Remove go tar file rm go1.18.1.linux-amd64.tar.gz # Update PATH to include go export PATH=$PATH:/usr/local/go/bin # Download singularity wget https://github.com/sylabs/singularity/releases/download/v3.9.9/singularity-ce-3.9.9. ˓→tar.gz # Extract singularity tar -xzf singularity-ce-3.9.9.tar.gz # Configure and install singularity cd singularity-ce-3.9.9 ./mconfig && \ make -C ./builddir && \ sudo make -C ./builddir install 1.1. Tutorial 15 Merlin Documentation, Release 1.11.0 Configuring Merlin Merlin requires a configuration script for the celery interface. Run this configuration method to create the app.yaml configuration file. merlin config --broker redis The merlin config command above will create a file called app.yaml in the ~/.merlin directory. If you are running a redis server locally then you are all set, look in the ~/.merlin/app.yaml file to see the configuration, it should look like the configuration below. broker: name: redis server: localhost port: 6379 db_num: 0 results_backend: name: redis server: localhost port: 6379 db_num: 0 More detailed information on configuring Merlin can be found in the configuration section. Checking/Verifying Installation First launch the merlin server containers by using the merlin server commands merlin server init merlin server start A subdirectory called merlin_server/ will have been created in the current run directory. This contains all of the proper configuration for the server containers merlin creates. Configuration can be done through the merlin server config command, however users have the flexibility to edit the files directly in the directory. Additionally an precon- figured app.yaml file has been created in the merlin_server/ subdirectory to utilize the merlin server containers . To use it locally simply copy it to the run directory with a cp command. cp ./merlin_server/app.yaml . You can also make this server container your main server configuration by replacing the one located in your home directory. Make sure you make back-ups of your current app.yaml file in case you want to use your previous configura- tions. Note: since merlin servers are created locally on your run directory you are allowed to create multiple instances of merlin server with their unique configurations for different studies. Simply create different directories for each study and run merlin server init in each directory to create an instance for each. mv ~/.merlin/app.yaml ~/.merlin/app.yaml.bak cp ./merlin_server/app.yaml ~/.merlin/ The merlin info command will check that the configuration file is installed correctly, display the server configuration strings, and check server access. merlin info If everything is set up correctly, you should see: 16 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 . . . Merlin Configuration ------------------------- config_file | <user home>/.merlin/app.yaml is_debug | False merlin_home | <user home>/.merlin merlin_home_exists | True broker server | redis://localhost:6379/0 results server | redis://localhost:6379/0 Checking server connections: ---------------------------- broker server connection: OK results server connection: OK Python Configuration ------------------------- . . . (OPTIONAL) Docker Advanced Installation RabbitMQ Server This optional section details the setup of a rabbitmq server for merlin. A rabbitmq server can be started to provide the broker, the redis server will still be required for the backend. Merlin is configured to use ssl encryption for all communication with the rabbitmq server. An ssl server requires ssl certificates to encrypt the communication through the python ssl module python ssl . This tutorial can use self-signed certificates created by the user for use in the rabbitmq server. The rabbitmq server uses Transport Layer Security (TLS) (often known as “Secure Sockets Layer”). Information on rabbitmq with TLS can be found here: rabbit TLS A set of self-signed keys is created through the tls-gen package. These keys are then copied to a common directory for use in the rabbitmq server and python. git clone https://github.com/michaelklishin/tls-gen.git cd tls-gen/basic make CN=my-rabbit CLIENT_ALT_NAME=my-rabbit SERVER_ALT_NAME=my-rabbit make verify mkdir -p ${HOME}/merlinu/cert_rabbitmq cp result/* ${HOME}/merlinu/cert_rabbitmq The rabbitmq docker service can be added to the previous docker-compose.yml file. version: '3' networks: (continues on next page) 1.1. Tutorial 17 Merlin Documentation, Release 1.11.0 (continued from previous page) mernet: driver: bridge services: redis: image: 'redis:latest' container_name: my-redis ports: - "6379:6379" networks: - mernet rabbitmq: image: rabbitmq:3-management container_name: my-rabbit tty: true ports: - "15672:15672" - "15671:15671" - "5672:5672" - "5671:5671" environment: - RABBITMQ_SSL_CACERTFILE=/cert_rabbitmq/ca_certificate.pem - RABBITMQ_SSL_KEYFILE=/cert_rabbitmq/server_key.pem - RABBITMQ_SSL_CERTFILE=/cert_rabbitmq/server_certificate.pem - RABBITMQ_SSL_VERIFY=verify_none - RABBITMQ_SSL_FAIL_IF_NO_PEER_CERT=false - RABBITMQ_DEFAULT_USER=merlinu - RABBITMQ_DEFAULT_VHOST=/merlinu - RABBITMQ_DEFAULT_PASS=guest volumes: - ~/merlinu/cert_rabbitmq:/cert_rabbitmq networks: - mernet merlin: image: 'llnl/merlin' container_name: my-merlin tty: true volumes: - ~/merlinu/:/home/merlinu networks: - mernet When running the rabbitmq broker server, the config can be created with the default merlin config command. If you have already run the previous command then remove the ~/.merlin/app.yaml or ~/merlinu/.merlin/app.yaml file , and run the merlin config command again. merlin config The app.yaml file will need to be edited to add the rabbitmq settings in the broker section of the app.yaml file. The server: should be changed to my-rabbit. The rabbitmq server will be accessed on the default TLS port, 5671. 18 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 broker: name: rabbitmq server: my-rabbit password: ~/.merlin/rabbit.pass results_backend: name: redis server: my-redis port: 6379 db_num: 0 To complete the config create a file ~/merlinu/.merlin/rabbit.pass and add the password guest. The aliases defined previously can be used with this set of docker containers. Redis TLS Server This optional section details the setup of a redis server with TLS for merlin. The reddis TLS configuration can be found in the Security with redis section. A newer redis (version 6 or greater) must be used to enable TLS. A set of self-signed keys is created through the tls-gen package. These keys are then copied to a common directory for use in the redis server and python. git clone https://github.com/michaelklishin/tls-gen.git cd tls-gen/basic make CN=my-redis CLIENT_ALT_NAME=my-redis SERVER_ALT_NAME=my-redis make verify mkdir -p ${HOME}/merlinu/cert_redis cp result/* ${HOME}/merlinu/cert_redis The configuration below does not use client verification --tls-auth-clients no so the ssl files do not need to be defined as shown in the Security with redis section. version: '3' networks: mernet: driver: bridge services: redis: image: 'redis' container_name: my-redis command: - --port 0 - --tls-port 6379 - --tls-ca-cert-file /cert_redis/ca_certificate.pem - --tls-key-file /cert_redis/server_key.pem - --tls-cert-file /cert_redis/server_certificate.pem - --tls-auth-clients no ports: - "6379:6379" volumes: (continues on next page) 1.1. Tutorial 19 Merlin Documentation, Release 1.11.0 (continued from previous page) - "~/merlinu/cert_redis:/cert_redis" networks: - mernet rabbitmq: image: rabbitmq:3-management container_name: my-rabbit tty: true ports: - "15672:15672" - "15671:15671" - "5672:5672" - "5671:5671" volumes: - "~/merlinu/rabbbitmq.conf:/etc/rabbitmq/rabbitmq.conf" - "~/merlinu/cert_rabbitmq:/cert_rambbitmq" networks: - mernet The rabbitmq.conf file contains the configuration, including ssl, for the rabbitmq server. default_vhost = /merlinu default_user = merlinu default_pass = guest listeners.ssl.default = 5671 ssl.options.ccertfile = /cert_rabbitmq/ca_certificate.pem ssl.options.certfile = /cert_rabbitmq/server_certificate.pem ssl.options.keyfile = /cert_rabbitmq/server_key.pem ssl.options.verify = verify_none ssl.options.fail_if_no_peer_cert = false Once this docker-compose file is run, the merlin app.yaml file is changed to use the redis TLS server rediss instead of redis. 1.1.4 Hello, World! This hands-on module walks through the steps of building and running a simple merlin workflow. Prerequisites • Module 2: Installation Estimated time • 30 minutes You will learn • The components of a merlin workflow specification. • How to run a simple merlin workflow. 20 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 • How to interpret the results of your workflow. Table of Contents: • Get Example Files • Specification File – Section: description – Section: global.parameters – Section: study • Try It! • Run Distributed! • Using Samples Get Example Files merlin example is a command line tool that makes it easy to get a basic workflow up and running. To see a list of all the examples provided with merlin you can run: $ merlin example list For this tutorial we will be using the hello example. Run the following commands: $ merlin example hello $ cd hello/ This will create and move into directory called hello, which contains these files: • my_hello.yaml – this spec file is partially blank. You will fill in the gaps as you follow this module’s steps. • hello.yaml – this is a complete spec without samples. You can always reference it as an example. • hello_samples.yaml – same as before, but with samples added. • make_samples.py – this is a small python script that generates samples. • requirements.txt – this is a text file listing this workflow’s python dependencies. Specification File Central to Merlin is something called a specification file, or a “spec” for short. The spec defines all aspects of your workflow. The spec is formatted in yaml. If you’re unfamiliar with yaml, it’s worth reading up on for a few minutes. Warning: Stray whitespace can break yaml; make sure your indentation is consistent. Let’s build our spec piece by piece. For each spec section listed below, fill in the blank yaml entries of my_hello.yaml with the given material. 1.1. Tutorial 21 Merlin Documentation, Release 1.11.0 Section: description Just what it sounds like. Name and briefly summarize your workflow. description: name: hello world workflow description: say hello in 2 languages Section: global.parameters Global parameters are constants that you want to vary across simulations. Steps that contain a global parameter or depend on other steps that contain a global parameter are run for each index over parameter values. The label is the pattern for a filename that will be created for each value. global.parameters: GREET: values : ["hello","hola"] label : GREET.%% WORLD: values : ["world","mundo"] label : WORLD.%% Note: %% is a special token that defines where the value in the label is placed. In this case the parameter labels will be GREET.hello, GREET.hola, etc. The label can take a custom text format, so long as the %% token is included to be able to substitute the parameter’s value in the appropriate place. So this will give us 1) an English result, and 2) a Spanish one (you could add as many more languages as you want, as long as both parameters hold the same number of values). Section: study This is where you define workflow steps. While the convention is to list steps as sequentially as possible, the only factor in determining step order is the dependency directed acyclic graph (DAG) created by the depends field. study: - name: step_1 description: say hello run: cmd: echo "$(GREET), $(WORLD)!" - name: step_2 description: print a success message run: cmd: print("Hurrah, we did it!") depends: [step_1] shell: /usr/bin/env python3 Note: The - denotes a list item in YAML. To add elements, simply add new elements prefixed with a hyphen 22 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 $(GREET) and $(WORLD) expand the global parameters separately into their two values. .. $(step_1.workspace) gets the path to step_1. The default value for shell is /bin/bash. In step_2 we override this to use python instead. Steps must be defined as nodes in a DAG, so no cyclical dependencies are allowed. Our step DAG currently looks like this: Since our global parameters have 2 values, this is actually what the DAG looks like: It looks like running step_2 twice is redundant. Instead of doing that, we can collapse it back into a single step, by having it wait for both parameterized versions of step_1 to finish. Add _* to the end of the step name in step_1’s depend entry. Go from this: depends: [step_1] . . . to this: depends: [step_1_*] Now the DAG looks like this: 1.1. Tutorial 23 Merlin Documentation, Release 1.11.0 Your full hello world spec my_hello.yaml should now look like this (an exact match of hello.yaml): description: name: hello description: a very simple merlin workflow global.parameters: GREET: values : ["hello","hola"] label : GREET.%% WORLD: values : ["world","mundo"] label : WORLD.%% study: - name: step_1 description: say hello run: cmd: echo "$(GREET), $(WORLD)!" - name: step_2 description: print a success message run: cmd: print("Hurrah, we did it!") depends: [step_1_*] shell: /usr/bin/env python3 The order of the spec sections doesn’t matter. Note: At this point, my_hello.yaml is still maestro-compatible. The primary difference is that maestro won’t un- derstand anything in the merlin block, which we will still add later. If you want to try it, run: $ maestro run my_hello.yaml 24 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 Try It! First, we’ll run merlin locally. On the command line, run: $ merlin run --local my_hello.yaml If your spec is bugless, you should see a few messages proclaiming successful step completion, like this (for now we’ll ignore the warning): * *~~~~~ *~~*~~~* __ __ _ _ / ~~~~~ | \/ | | (_) ~~~~~ | \ / | ___ _ __| |_ _ __ ~~~~~* | |\/| |/ _ \ '__| | | '_ \ *~~~~~~~ | | | | __/ | | | | | | | ~~~~~~~~~~ |_| |_|\___|_| |_|_|_| |_| *~~~~~~~~~~~ ~~~*~~~* Machine Learning for HPC Workflows [2020-02-07 09:35:49: WARNING] Workflow specification missing encouraged 'merlin' section! Run 'merlin example' for examples. Using default configuration with no sampling. [2020-02-07 09:35:49: INFO] Study workspace is 'hello_20200207-093549'. [2020-02-07 09:35:49: INFO] Reading app config from file ~/.merlin/app.yaml [2020-02-07 09:35:49: INFO] Calculating task groupings from DAG. [2020-02-07 09:35:49: INFO] Converting graph to celery tasks. [2020-02-07 09:35:49: INFO] Launching tasks. [2020-02-07 09:35:49: INFO] Executing step 'step1_HELLO.hello' in 'hello_20200207-093549/ ˓→step1/HELLO.hello'... [2020-02-07 09:35:54: INFO] Step 'step1_HELLO.hello' in 'hello_20200207-093549/step1/ ˓→HELLO.hello' finished successfully. [2020-02-07 09:35:54: INFO] Executing step 'step2_HELLO.hello' in 'hello_20200207-093549/ ˓→step2/HELLO.hello'... [2020-02-07 09:35:59: INFO] Step 'step2_HELLO.hello' in 'hello_20200207-093549/step2/ ˓→HELLO.hello' finished successfully. [2020-02-07 09:35:59: INFO] Executing step 'step1_HELLO.hola' in 'hello_20200207-093549/ ˓→step1/HELLO.hola'... [2020-02-07 09:36:04: INFO] Step 'step1_HELLO.hola' in 'hello_20200207-093549/step1/ ˓→HELLO.hola' finished successfully. [2020-02-07 09:36:04: INFO] Executing step 'step2_HELLO.hola' in 'hello_20200207-093549/ ˓→step2/HELLO.hola'... [2020-02-07 09:36:09: INFO] Step 'step2_HELLO.hola' in 'hello_20200207-093549/step2/ ˓→HELLO.hola' finished successfully. Great! But what happened? We can inspect the output directory to find out. Look for a directory named hello_<TIMESTAMP>. That’s your output directory. Within, there should be a directory for each step of the workflow, plus one called merlin_info. The whole file tree looks like this: 1.1. Tutorial 25 Merlin Documentation, Release 1.11.0 A lot of stuff, right? Here’s what it means: • The 3 yaml files inside merlin_info/ are called the provenance specs. They are copies of the original spec that was run, some showing under-the-hood variable expansions. • MERLIN_FINISHED files indicate that the step ran successfully. • .sh files contain the command for the step. • .out files contain the step’s stdout. Look at one of these, and it should contain your “hello” message. • .err files contain the step’s stderr. Hopefully empty, and useful for debugging. Run Distributed! Important: Before trying this, make sure you’ve properly set up your merlin config file app.yaml. Run $ merlin info for information on your merlin configuration. Now we will run the same workflow, but in parallel on our task server: $ merlin run my_hello.yaml If your merlin configuration is set up correctly, you should see something like this: * *~~~~~ *~~*~~~* __ __ _ _ (continues on next page) 26 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 (continued from previous page) / ~~~~~ | \/ | | (_) ~~~~~ | \ / | ___ _ __| |_ _ __ ~~~~~* | |\/| |/ _ \ '__| | | '_ \ *~~~~~~~ | | | | __/ | | | | | | | ~~~~~~~~~~ |_| |_|\___|_| |_|_|_| |_| *~~~~~~~~~~~ ~~~*~~~* Machine Learning for HPC Workflows [2020-02-07 13:06:23: WARNING] Workflow specification missing encouraged 'merlin' section! Run 'merlin example' for examples. Using default configuration with no sampling. [2020-02-07 13:06:23: INFO] Study workspace is 'studies/simple_chain_20200207-130623'. [2020-02-07 13:06:24: INFO] Reading app config from file ~/.merlin/app.yaml [2020-02-07 13:06:25: INFO] broker: amqps://user:******@broker:5671//user [2020-02-07 13:06:25: INFO] backend: redis://user:******@backend:6379/0 [2020-02-07 13:06:25: INFO] Calculating task groupings from DAG. [2020-02-07 13:06:25: INFO] Converting graph to celery tasks. [2020-02-07 13:06:25: INFO] Launching tasks. That means we have launched our tasks! Now we need to launch the workers that will complete those tasks. Run this: $ merlin run-workers my_hello.yaml Here’s the expected merlin output message for running workers: * *~~~~~ *~~*~~~* __ __ _ _ / ~~~~~ | \/ | | (_) ~~~~~ | \ / | ___ _ __| |_ _ __ ~~~~~* | |\/| |/ _ \ '__| | | '_ \ *~~~~~~~ | | | | __/ | | | | | | | ~~~~~~~~~~ |_| |_|\___|_| |_|_|_| |_| *~~~~~~~~~~~ ~~~*~~~* Machine Learning for HPC Workflows [2020-02-07 13:14:38: INFO] Launching workers from 'hello.yaml' [2020-02-07 13:14:38: WARNING] Workflow specification missing encouraged 'merlin' section! Run 'merlin example' for examples. Using default configuration with no sampling. [2020-02-07 13:14:38: INFO] Starting celery workers [2020-02-07 13:14:38: INFO] ['celery worker -A merlin -n default_worker.%%h -l INFO -Q␣ ˓→merlin'] Immediately after that, this will pop up: -------------- celery@worker_name.%machine770 v4.4.0 (cliffs) --- ***** ----- -- ******* ---- Linux-3.10.0-1062.9.1.1chaos.ch6.x86_64-x86_64-with-redhat-7.7-Maipo␣ (continues on next page) 1.1. Tutorial 27 Merlin Documentation, Release 1.11.0 (continued from previous page) ˓→ 2020-02-12 09:53:10 - *** --- * --- - ** ---------- [config] - ** ---------- .> app: merlin:0x2aaab20619e8 - ** ---------- .> transport: amqps://user:**@server:5671//user - ** ---------- .> results: redis://user:**@server:6379/0 - *** --- * --- .> concurrency: 36 (prefork) -- ******* ---- .> task events: OFF (enable -E to monitor tasks in this worker) --- ***** ----- -------------- [queues] .> merlin exchange=merlin(direct) key=merlin [tasks] . merlin.common.tasks.add_merlin_expanded_chain_to_chord . merlin.common.tasks.expand_tasks_with_samples . merlin.common.tasks.merlin_step . merlin:chordfinisher . merlin:queue_merlin_study [2020-02-12 09:53:11,549: INFO] Connected to amqps://user:**@server:5671//user [2020-02-12 09:53:11,599: INFO] mingle: searching for neighbors [2020-02-12 09:53:12,807: INFO] mingle: sync with 2 nodes [2020-02-12 09:53:12,807: INFO] mingle: sync complete [2020-02-12 09:53:12,835: INFO] celery@worker_name.%machine770 ready. You may not see all of the info logs listed after the Celery C is displayed. If you’d like to see them you can change the merlin workers’ log levels with the --worker-args tag: $ merlin run-workers --worker-args "-l INFO" my_hello.yaml The terminal you ran workers in is now being taken over by Celery, the powerful task queue library that merlin uses internally. The workers will continue to report their task status here until their tasks are complete. Workers are persistent, even after work is done. Send a stop signal to all your workers with this command: $ merlin stop-workers . . . and a successful worker stop will look like this, with the name of specific worker(s) reported: $ merlin stop-workers * *~~~~~ *~~*~~~* __ __ _ _ / ~~~~~ | \/ | | (_) ~~~~~ | \ / | ___ _ __| |_ _ __ ~~~~~* | |\/| |/ _ \ '__| | | '_ \ *~~~~~~~ | | | | __/ | | | | | | | ~~~~~~~~~~ |_| |_|\___|_| |_|_|_| |_| *~~~~~~~~~~~ ~~~*~~~* Machine Learning for HPC Workflows (continues on next page) 28 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 (continued from previous page) [2020-03-06 09:20:08: INFO] Stopping workers... [2020-03-06 09:20:08: INFO] Reading app config from file .merlin/app.yaml [2020-03-06 09:20:08: INFO] broker: amqps://user:******@server:5671//user [2020-03-06 09:20:08: INFO] backend: redis://mlsi:******@server:6379/0 all_workers: ['celery@default_worker.%machine'] spec_worker_names: [] workers_to_stop: ['celery@default_worker.%machine'] [2020-03-06 09:20:10: INFO] Sending stop to these workers: ['celery@default_worker. ˓→%machine'] Using Samples It’s a little boring to say “hello world” in just two different ways. Let’s instead say hello to many people! To do this, we’ll need samples. Specifically, we’ll change WORLD from a global parameter to a sample. While parameters are static, samples are generated dynamically, and can be more complex data types. In this case, WORLD will go from being “world” or “mundo” to being a randomly-generated name. First, we remove the global parameter WORLD so it does not conflict with our new sample. Parameters now look like this: global.parameters: GREET: values : ["hello", "hola"] label : GREET.%% Now add these yaml sections to your spec: env: variables: N_SAMPLES: 3 This makes N_SAMPLES into a user-defined variable that you can use elsewhere in your spec. merlin: samples: generate: cmd: python3 $(SPECROOT)/make_samples.py --filepath=$(MERLIN_INFO)/samples. ˓→csv --number=$(N_SAMPLES) file: $(MERLIN_INFO)/samples.csv column_labels: [WORLD] This is the merlin block, an exclusively merlin feature. It provides a way to generate samples for your workflow. In this case, a sample is the name of a person. For simplicity we give column_labels the name WORLD, just like before. It’s also important to note that $(SPECROOT) and $(MERLIN_INFO) are reserved variables. The $(SPECROOT) variable is a shorthand for the directory path of the spec file and the $(MERLIN_INFO) variable is a shorthand for the directory holding the provenance specs and sample generation results. More information on Merlin variables can be found on the variables page. 1.1. Tutorial 29 Merlin Documentation, Release 1.11.0 It’s good practice to shift larger chunks of code to external scripts. At the same location of your spec, make a new file called make_samples.py: import argparse import names import numpy as np # argument parsing parser = argparse.ArgumentParser(description="Make some samples (names of people).") parser.add_argument("--number", type=int, action="store", help="the number of samples␣ ˓→you want to make") parser.add_argument("--filepath", type=str, help="output file") args = parser.parse_args() # sample making all_names = np.loadtxt(names.FILES["first:female"], dtype=str, usecols=0) selected_names = np.random.choice(all_names, size=args.number) result = "" name_list = list(selected_names) result = "\n".join(name_list) with open(args.filepath, "w") as f: f.write(result) Since our environment variable N_SAMPLES is set to 3, this sample-generating command should churn out 3 different names. Before we can run this, we must install the script’s external python library dependencies (names: a simple package that generates random names, and numpy: a scientific computing package): $ pip3 install -r requirements.txt Here’s our DAG with samples: 30 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 Here’s your new and improved my_hello.yaml, which now should match hello_samples.yaml: description: name: hello_samples description: a very simple merlin workflow, with samples env: variables: N_SAMPLES: 3 global.parameters: GREET: values : ["hello","hola"] label : GREET.%% study: - name: step_1 description: say hello run: cmd: echo "$(GREET), $(WORLD)!" - name: step_2 description: print a success message run: cmd: print("Hurrah, we did it!") depends: [step_1_*] shell: /usr/bin/env python3 merlin: samples: generate: cmd: python3 $(SPECROOT)/make_samples.py --filepath=$(MERLIN_INFO)/samples. ˓→csv --number=$(N_SAMPLES) file: $(MERLIN_INFO)/samples.csv column_labels: [WORLD] Run the workflow again! Once finished, this is what the insides of step_1 look like: 1.1. Tutorial 31 Merlin Documentation, Release 1.11.0 • Numerically-named directories like 00, 01, and 02 are sample directories. Instead of storing sample output in a single flattened location, merlin stores them in a tree-like sample index, which helps get around file system constraints when working with massive amounts of data. Lastly, let’s flex merlin’s muscle a bit and scale up our workflow to 1000 samples. To do this, you could internally change the value in the spec from 3 to 1000. OR you could just run this: $ merlin run my_hello.yaml --vars N_SAMPLES=1000 $ merlin run-workers my_hello.yaml Once again, to send a warm stop signal to your workers, run: $ merlin stop-workers Congratulations! You concurrently greeted 1000 friends in English and Spanish! 1.1.5 Run a Real Simulation Summary This module aims to do a parameter study on a well-known benchmark problem for viscous incompressible fluid flow. Prerequisites • Module 0: Before you start • Module 2: Installation • Module 3: Hello World Estimated time • 60 minutes You will learn • How to run the simulation OpenFOAM, using merlin. • How to use machine learning on OpenFOAM results, using merlin. Table of Contents: • Introduction – Before Moving On • Specification file – Variables 32 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 – Samples and scripts – Setting up – Running the simulation – Combining outputs – Machine Learning and visualization – Putting it all together • Run the workflow Introduction We aim to do a parameter study on the lid-driven cavity problem. Fig. 1: Fig 1. Lid-driven cavity problem setup Fig. 2: Fig 2. Example of a flow in steady state In this problem, we have a viscous fluid within a square cavity that has three non-slip walls and one moving wall (moving lid). We are interested in learning how varying the viscosity and lid speed affects the average enstrophy and kinetic energy of the fluid after it reaches steady state. We will be using the velocity squared as a proxy for kinetic energy. This module will be going over: • Setting up our inputs using the merlin block • Running multiple simulations in parallel • Combining the outputs of these simulations into a an array • Predictive modeling and visualization 1.1. Tutorial 33 Merlin Documentation, Release 1.11.0 Before Moving On check that the virtual environment with merlin installed is activated and that redis server is set up using this command: $ merlin info This is covered more in depth here: Checking/Verifying Installation There are two ways to do this example: with docker and without docker. To go through the version with docker, get the necessary files for this module by running: $ merlin example openfoam_wf $ cd openfoam_wf/ For the version without docker you should run: $ merlin example openfoam_wf_no_docker $ cd openfoam_wf_no_docker/ Note: From here on, this tutorial will focus solely on the docker version of running openfoam. However, the docker version of this tutorial is almost identical to the no docker version. If you’re using the no docker version of this tutorial you can still follow along but check the openfoam_no_docker_template.yaml file in each step to see what differs. In the openfoam_wf directory you should see the following: • openfoam_wf.yaml – this spec file is partially blank. You will fill in the gaps as you follow this module’s steps. • openfoam_wf_template.yaml – this is a complete spec file. You can always reference it as an example. • scripts – This directory contains all the necessary scripts for this module. – We’ll be exploring these scripts as we go with the tutorial. • requirements.txt – this is a text file listing this workflow’s python dependencies. To start, open openfoam_wf.yaml using your favorite text editor. It should look something like this: Listing 1: openfoam_wf.yaml description: name: openfoam_wf description: | A parameter study that includes initializing, running, post-processing, collecting, learning and visualizing OpenFOAM runs using docker. env: variables: OUTPUT_PATH: SCRIPTS: (continues on next page) 34 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 Fig. 3: Fig 3. openfoam_wf directory structure 1.1. Tutorial 35 Merlin Documentation, Release 1.11.0 (continued from previous page) N_SAMPLES: merlin: samples: generate: cmd: | file: column_labels: resources: workers: nonsimworkers: args: -l INFO --concurrency <INPUT CONCURRENCY HERE> steps: simworkers: args: -l INFO --concurrency <INPUT CONCURRENCY HERE> --prefetch- ˓→multiplier 1 -Ofair steps: study: - name: setup description: | Installs necessary python packages and imports the cavity directory from the docker container run: cmd: | - name: sim_runs description: | Edits the Lidspeed and viscosity then runs OpenFOAM simulation using the icoFoam solver run: cmd: | depends: task_queue: simqueue - name: combine_outputs description: | Combines the outputs of the previous step run: cmd: | depends: - name: learn description: | Learns the output of the openfoam simulations using input parameters and outputs error visualization from the experiment run: cmd: | (continues on next page) 36 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 (continued from previous page) depends: Specification file We are going to build a spec file that produces this DAG: Fig. 4: Fig 4. Module 4 DAG Variables First we specify some variables to make our life easier. Locate the env block in our yaml spec env: variables: OUTPUT_PATH: ./openfoam_wf_output SCRIPTS: N_SAMPLES: The OUTPUT_PATH variable is set to tell merlin where you want your output directory to be. The default is <spec_name>_<TIMESTAMP> which in our case would simply be openfoam_wf_<TIMESTAMP> We’ll fill out the next two variables as we go. 1.1. Tutorial 37 Merlin Documentation, Release 1.11.0 Samples and scripts One merlin best practice is to copy any scripts your workflow may use from your SPECROOT directory into the MERLIN_INFO directory. This is done to preserve the original scripts in case they are modified during the time merlin is running. We will do that first. We will put this in the merlin sample generation section, since it runs before anything else. Edit the merlin block to look like the following: merlin: samples: generate: cmd: | cp -r $(SPECROOT)/scripts $(MERLIN_INFO)/ # Generates the samples python $(MERLIN_INFO)/scripts/make_samples.py -n 10 -outfile=$(MERLIN_ ˓→INFO)/samples file: $(MERLIN_INFO)/samples.npy column_labels: [LID_SPEED, VISCOSITY] We will be using the scripts directory a lot so we’ll set the variable SCRIPTS to $(MERLIN_INFO)/scripts for con- venience. We would also like to have a more central control over the number of samples generated so we’ll create an N_SAMPLES variable: env: variables: OUTPUT_PATH: ./openfoam_wf_output SCRIPTS: $(MERLIN_INFO)/scripts N_SAMPLES: 10 and update the merlin block to be: merlin: samples: generate: cmd: | cp -r $(SPECROOT)/scripts $(MERLIN_INFO)/ # Generates the samples python $(SCRIPTS)/make_samples.py -n N_SAMPLES -outfile=$(MERLIN_INFO)/ ˓→samples file: $(MERLIN_INFO)/samples.npy column_labels: [LID_SPEED, VISCOSITY] Just like in the Using Samples step of the hello world module, we generate samples using the merlin block. We are only concerned with how the variation of two initial conditions, lidspeed and viscosity, affects outputs of the system. These are the column_labels. The make_samples.py script is designed to make log uniform random samples. Now, we can move on to the steps of our study block. 38 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 Setting up Our first step in our study block is concerned with making sure we have all the required python packages for this workflow. The specific packages are found in the requirements.txt file. We will also need to copy the lid driven cavity deck from the OpenFOAM docker container and adjust the write controls. This last part is scripted already for convenience. Locate the setup step in the study block and edit it to look like the following: study: - name: setup description: | Installs necessary python packages and imports the cavity directory from the docker container run: cmd: | pip install -r $(SPECROOT)/requirements.txt # Set up the cavity directory in the MERLIN_INFO directory source $(SCRIPTS)/cavity_setup.sh $(MERLIN_INFO) This step does not need to be parallelized so we will assign it to lower concurrency (a setting that controls how many workers can be running at the same time) Locate the resources section in the merlin block and edit the concurrency and add the setup step: resources: workers: nonsimworkers: args: -l INFO --concurrency 1 steps: [setup] Running the simulation Moving on to the sim_runs step, we want to: 1. Copy the cavity deck from the MERLIN_INFO directory into each of the current step’s subdirectories 2. Edit the default input values (lidspeed and viscosity) in these cavity decks using the sed command 3. Run the simulation using the run_openfoam executable through the OpenFOAM docker container 4. Post-process the results (also using the run_openfoam executable) This part should look like: - name: sim_runs description: | Edits the Lidspeed and viscosity then runs OpenFOAM simulation using the icoFoam solver run: cmd: | cp -r $(MERLIN_INFO)/cavity cavity/ cd cavity (continues on next page) 1.1. Tutorial 39 Merlin Documentation, Release 1.11.0 (continued from previous page) ## Edits default values for viscosity and lidspeed with # values specified by samples section of the merlin block sed -i '' "18s/.*/nu [0 2 -1 0 0 0 0] $(VISCOSITY);/" constant/ ˓→transportProperties sed -i '' "26s/.*/ value uniform ($(LID_SPEED) 0 0);/" 0/U cd .. cp $(SCRIPTS)/run_openfoam . # Creating a unique OpenFOAM docker container for each sample and using it to␣ ˓→run the simulation CONTAINER_NAME='OPENFOAM_ICO_$(MERLIN_SAMPLE_ID)' docker container run -ti --rm -v $(pwd):/cavity -w /cavity --name=${CONTAINER_ ˓→NAME} cfdengine/openfoam ./run_openfoam $(LID_SPEED) docker wait ${CONTAINER_NAME} depends: [setup] task_queue: simqueue This step runs many simulations in parallel so it would run faster if we assign it a worker with a higher concurrency. Navigate back to the resources section in the merlin block resources: workers: nonsimworkers: args: -l INFO --concurrency 1 steps: [setup] simworkers: args: -l INFO --concurrency 10 --prefetch-multiplier 1 -Ofair steps: [sim_runs] The quantities of interest are the average enstrophy and kinetic energy at each cell. The enstrophy is calculated through an OpenFOAM post processing function of the the flow fields while the kinetic energy is approximated by calculated using the square of the velocity vector at each grid point. The velocity field is normally outputted normally as a result of running the default solver for this particular problem. The run_openfoam executable calculates the appropriate timestep deltaT so that we have a Courant number of less than 1. It also uses the icoFoam solver on the cavity decks and gives us VTK files that are helpful for visualizing the flow fields using visualization tools such as VisIt or ParaView. Combining outputs Navigate to the next step in our study block combine_outputs. The purpose of this step is to extracts the data from each of the simulation runs from the previous step (sim_runs) and combines it for future use. The combine_outputs.py script in the $(SCRIPTS) directory is provided for convenience. It takes two inputs. The first informs it of the base directory of the sim_runs directory and the second specifies the subdirectories for each run. The script then goes into each of the directories and combines the velocity and enstrophy for each timestep of each run in a .npz file. - name: combine_outputs description: Combines the outputs of the previous step run: (continues on next page) 40 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 (continued from previous page) cmd: | python $(SCRIPTS)/combine_outputs.py -data $(sim_runs.workspace) -merlin_paths ˓→$(MERLIN_PATHS_ALL) depends: [sim_runs_*] This step depends on all the previous step’s simulation runs which is why we have the star. However, it does not need to be parallelized so we assign it to the nonsimworkers in the workers section of the merlin block. workers: nonsimworkers: args: -l INFO --concurrency 1 steps: [setup, combine_outputs] Machine Learning and visualization In the learn step, we want to: 1. Post-process the .npz file from the previous step. 2. Learn the mapping between our inputs and chosen outputs 3. Graph important features The provided learn.py script does all the above and outputs the trained sklearn model and a png of the graphs plotted in the current directory. - name: learn description: Learns the output of the openfoam simulations using input parameters run: cmd: | python $(SCRIPTS)/learn.py -workspace $(MERLIN_WORKSPACE) depends: [combine_outputs] This step is also dependent on the previous step for the .npz file and will only need one worker therefore we will: nonsimworkers: args: -l INFO --concurrency 1 steps: [setup, combine_outputs, learn] Putting it all together By the end, your openfoam_wf.yaml should look like the template version in the same directory: Listing 2: openfoam_wf_template.yaml description: name: openfoam_wf_template description: | A parameter study that includes initializing, running, post-processing, collecting, learning and visualizing OpenFOAM runs using docker. (continues on next page) 1.1. Tutorial 41 Merlin Documentation, Release 1.11.0 (continued from previous page) env: variables: OUTPUT_PATH: ./openfoam_wf_output SCRIPTS: $(MERLIN_INFO)/scripts N_SAMPLES: 100 merlin: samples: generate: cmd: | cp -r $(SPECROOT)/scripts $(MERLIN_INFO)/ # Generates the samples python $(SCRIPTS)/make_samples.py -n $(N_SAMPLES) -outfile=$(MERLIN_ ˓→INFO)/samples file: $(MERLIN_INFO)/samples.npy column_labels: [LID_SPEED, VISCOSITY] resources: workers: nonsimworkers: args: -l INFO --concurrency 1 steps: [setup, combine_outputs, learn] simworkers: args: -l INFO --concurrency 10 --prefetch-multiplier 1 -Ofair steps: [sim_runs] study: - name: setup description: | Installs necessary python packages and imports the cavity directory from the docker container run: cmd: | pip install -r $(SPECROOT)/requirements.txt # Set up the cavity directory in the MERLIN_INFO directory source $(SCRIPTS)/cavity_setup.sh $(MERLIN_INFO) - name: sim_runs description: | Edits the Lidspeed and viscosity then runs OpenFOAM simulation using the icoFoam solver run: cmd: | cp -r $(MERLIN_INFO)/cavity cavity/ cd cavity ## Edits default values for viscosity and lidspeed with (continues on next page) 42 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 (continued from previous page) # values specified by samples section of the merlin block sed -i '' "18s/.*/nu [0 2 -1 0 0 0 0] $(VISCOSITY);/"␣ ˓→constant/transportProperties sed -i '' "26s/.*/ value uniform ($(LID_SPEED) 0 0);/" 0/U cd .. cp $(SCRIPTS)/run_openfoam . # Creating a unique OpenFOAM docker container for each sample and using it␣ ˓→to run the simulation CONTAINER_NAME='OPENFOAM_ICO_$(MERLIN_SAMPLE_ID)' docker container run -ti --rm -v $(pwd):/cavity -w /cavity --name=$ ˓→{CONTAINER_NAME} cfdengine/openfoam ./run_openfoam $(LID_SPEED) docker wait ${CONTAINER_NAME} depends: [setup] task_queue: simqueue - name: combine_outputs description: Combines the outputs of the previous step run: cmd: | python $(SCRIPTS)/combine_outputs.py -data $(sim_runs.workspace) -merlin_ ˓→paths $(MERLIN_PATHS_ALL) depends: [sim_runs_*] - name: learn description: Learns the output of the openfoam simulations using input parameters run: cmd: | python $(SCRIPTS)/learn.py -workspace $(MERLIN_WORKSPACE) depends: [combine_outputs] Run the workflow Now that you are done with the Specification file, use the following commands from inside the openfoam_wf directory to run the workflow on our task server. Note: Running with fewer samples is the one of the best ways to debug $ merlin run openfoam_wf.yaml $ merlin run-workers openfoam_wf.yaml But wait! We realize that 10 samples is not enough to train a good model. We would like to restart with 100 samples instead of 10 (should take about 6 minutes): After sending the workers to start on their queues, we need to first stop the workers: $ merlin stop-workers --spec openfoam_wf.yaml 1.1. Tutorial 43 Merlin Documentation, Release 1.11.0 Note: • The –spec flag only stops workers from a specific YAML spec We stopped these tasks from running but if we were to run the workflow again (with 100 samples instead of 10), we would continue running the 10 samples first! This is because the queues are still filled with the previous attempt’s tasks. We need to purge these queues first in order to repopulate them with the appropriate tasks. This is where we use the merlin purge command: $ merlin purge openfoam_wf.yaml Now we are free to repopulate the queues with the 100 samples: $ merlin run openfoam_wf.yaml --vars N_SAMPLES=100 $ merlin run-workers openfoam_wf.yaml To see your results, look inside the learn output directory. You should see a png that looks like this: 44 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 Related articles • https://cfd.direct/openfoam/user-guide/v6-cavity/ • https://www.cfdengine.com/blog/how-to-install-openfoam-anywhere-with-docker/ 1.1. Tutorial 45 Merlin Documentation, Release 1.11.0 1.1.6 Advanced Topics Prerequisites • Module 2: Installation • Module 3: Hello World • Module 4: Running a Real Simulation • Python virtual environment containing the following packages – merlin – pandas – faker – maestrowf Estimated time • 15 minutes You will learn • Run workflows using HPC batch schedulers • Distribute workflows across multiple batch allocations and machines • Setup iterative workflow specs suited for optimization and dynamic sampling applications Table of Contents: • Setup • Interfacing with HPC systems • Multi-machine workflows • Dynamic task queuing and sampling Setup The code for the following examples can be obtained from command line, invoking: merlin example hpc_demo This will copy the three merlin workflow specifications from this section and the supporting python scripts. Each specification may need some modification to adapt it to the batch scheduler you will be using. In addition, the dynamic sampling workflow will need an additional modification to set the path of the virtual environment, which is set as a variable in the env block. 46 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 Interfacing with HPC systems Another block is added to the merlin workflow specification when running on HPC systems, the batch block. This block contains information about the batch scheduler system such as batch type, batch queue to use, and banks to charge to. There are additional optional arguments for addressing any special configurations or launch command arguments, varying based on batch type. In addition, the shell type used by each steps cmd scripts can be specified here. The number of nodes in a batch allocation can be defined here, but it will be overridden in the worker config. batch: # Required keys: type: flux bank: testbank queue: pbatch # Optional keys: flux_path: <optional path to flux bin> flux_start_opts: <optional flux start options> flux_exec_workers: <optional, flux argument to launch workers on all nodes. (True)> launch_pre: <Any configuration needed before the srun or jsrun launch> launch_args: <Optional extra arguments for the parallel launch command> worker_launch: <Override the parallel launch defined in merlin> shell: <the interpreter to use for the script after the shebang> # e.g. /bin/bash, /bin/tcsh, python, /usr/bin/env perl, etc. nodes: <num nodes> # The number of nodes to use for all workers This can be overridden in the workers config. If this is unset the number of nodes will be queried from the environment, failing that, the number of nodes will be set to 1. Inside the study step specifications are a few additional keys that become more useful on HPC systems: nodes, procs, and task_queue. Adding on the actual study steps to the above batch block specifies the actual resources each steps processes will take. study: - name: sim-runs description: Run simulations run: cmd: $(LAUNCHER) echo "$(VAR1) $(VAR2)" > simrun.out nodes: 4 procs: 144 task_queue: sim_queue - name: post-process description: Post-Process simulations on second allocation run: cmd: | cd $(runs1.workspace)/$(MERLIN_SAMPLE_PATH) $(LAUNCHER) <parallel-post-proc-script> nodes: 1 procs: 36 (continues on next page) 1.1. Tutorial 47 Merlin Documentation, Release 1.11.0 (continued from previous page) depends: [sim-runs] task_queue: post_proc_queue In addition to the batch block is the resources section inside the merlin block. This can be used to put together custom celery workers. Here you can override batch types and node counts on a per worker basis to accommodate steps with different resource requirements. In addition, this is where the task_queue becomes useful, as it groups the different allocation types, which can be assigned to each worker here by specifying step names. Arguments to celery itself can also be defined here with the args key. Of particular interest will be: --concurrency <num_threads> --prefetch-multiplier <num_tasks> -0 fair Concurrency can be used to run multiple workers in an allocation, thus is recommended to be set to the number of simulations or step work items that fit into the number of nodes in the batch allocation in which these workers are spawned. Note that some schedulers, such as flux, can support more jobs than the node has resources for. This may not impact the throughput, but it can prevent over-subscription errors that might otherwise stop the workflow. The prefetch multiplier is more related to packing in tasks into the time of the allocation. For long running tasks it is recommended to set this to 1. For short running tasks, this can reduce overhead from talking to the rabbit servers by requesting <num_threads> x <num_tasks> tasks at a time from the server. The -0 fair option enables workers running tasks from different queues to run on the same allocation. The example block below extends the previous with workers configured for long running simulation jobs as well as shorter running post processing tasks that can cohabit an allocation merlin: resources: task_server: celery overlap: False # Customize workers workers: simworkers: args: --concurrency 1 steps: [sim-runs] nodes: 4 machines: [host1] postworkers: args: --concurrency 4 --prefetch-multiplier 2 steps: [post-proc-runs] nodes: 1 machines: [host1] Putting it all together with the parameter blocks we have an HPC batch enabled study specification. In this demo workflow, sample_names generates one many single core jobs, with concurrency set to 36 for this particular machine that has 36 cores per node. The collect step on the other hand consists of a single job that uses all cores on the node, and is assigned to a queue that has a concurrency of 1. 48 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 description: name: hpc_demo description: Demo running a workflow on HPC machines env: variables: OUTPUT_PATH: ./name_studies # Collect individual sample files into one for further processing COLLECT: $(SPECROOT)/sample_collector.py # Process single iterations' results POST_PROC: $(SPECROOT)/sample_processor.py # Process all iterations CUM_POST_PROC: $(SPECROOT)/cumulative_sample_processor.py # Number of threads for post proc scripts POST_NPROCS: 36 PYTHON: <INSERT PATH TO VIRTUALENV HERE> batch: type: flux bank: testbank queue: pdebug shell: /bin/bash nodes: 1 ######################################## # Study definition ######################################## study: - name: sample_names description: Record samples from the random name generator run: cmd: | $(LAUNCHER) echo "$(NAME)" $(LAUNCHER) echo "$(NAME)" > name_sample.out nodes: 1 procs: 1 task_queue: name_queue - name: collect description: Collect all samples generated run: cmd: | echo $(MERLIN_GLOB_PATH) echo $(sample_names.workspace) ls $(sample_names.workspace)/$(MERLIN_GLOB_PATH)/name_sample.out | xargs ˓→$(PYTHON) $(COLLECT) -out collected_samples.txt --np $(POST_NPROCS) nodes: 1 (continues on next page) 1.1. Tutorial 49 Merlin Documentation, Release 1.11.0 (continued from previous page) procs: 1 depends: [sample_names_*] task_queue: post_proc_queue - name: post-process description: Post-Process collection of samples, counting occurrences of unique␣ ˓→names run: cmd: | $(PYTHON) $(POST_PROC) $(collect.workspace)/collected_samples.txt --results␣ ˓→iter_$(ITER)_results.json nodes: 1 procs: 1 depends: [collect] task_queue: post_proc_queue ######################################## # Worker and sample configuration ######################################## merlin: resources: task_server: celery overlap: False workers: nameworkers: args: --concurrency 36 --prefetch-multiplier 3 steps: [sample_names] nodes: 1 machines: [borax, quartz] postworkers: args: --concurrency 1 --prefetch-multiplier 1 steps: [post-process] nodes: 1 machines: [borax, quartz] ################################################### samples: column_labels: [NAME] file: $(MERLIN_INFO)/samples.csv generate: cmd: | $(PYTHON) $(SPECROOT)/faker_sample.py -n 200 -outfile=$(MERLIN_INFO)/samples.csv 50 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 Multi-machine workflows Spreading this workflow across multiple machines is a simple modification of the above workflow: simply add addi- tional host names to the machines list in the worker config. A caveat for this feature is that all host systems will need to have access to the same workspace/filesystem. The following resource block demonstrates usage of one host for larger simulation steps, and a second host for the smaller post processing steps. In this case you simply need an alloc on each host, and can simply execute run-workers on each, with run only needed once up front to send the tasks to the queue server. Dynamic task queuing and sampling Iterative workflows, such as optimization or machine learning, can be implemented in merlin via recursive workflow specifications that use dynamic task queuing. The example spec below is a simple implementation of this using an iteration counter $(ITER) and a predetermined limit, $(MAX_ITER) to limit the number of times to generate new samples and spawn a new instantiation of the workflow. The iteration counter takes advantage of the ability to override workflow variables on the command line. description: name: dynamic_sampling_demo description: Demo dynamic sampling workflow env: variables: OUTPUT_PATH: ./name_studies ITER_OUTPUT: $(SPECROOT)/$(OUTPUT_PATH)/iter_outputs # Iteration and cumulative␣ ˓→results COLLECT: $(SPECROOT)/sample_collector.py POST_PROC: $(SPECROOT)/sample_processor.py # Process single iterations' results CUM_POST_PROC: $(SPECROOT)/cumulative_sample_processor.py # Process all iterations POST_NPROCS: 36 # Number of threads for post proc scripts PYTHON: /usr/WS2/white242/merlin_dev_2/venv_merlin_py3_7/bin/python ITER: 1 MAX_ITER: 10 batch: type: flux bank: testbank queue: pdebug shell: /bin/bash nodes: 1 ######################################## # Study definition ######################################## study: - name: sample_names description: Record samples from the random name generator run: cmd: | $(LAUNCHER) echo "$(NAME)" $(LAUNCHER) echo "$(NAME)" > name_sample.out nodes: 1 procs: 1 (continues on next page) 1.1. Tutorial 51 Merlin Documentation, Release 1.11.0 (continued from previous page) task_queue: name_queue - name: collect description: Collect all samples generated run: cmd: | echo $(MERLIN_GLOB_PATH) echo $(sample_names.workspace) ls $(sample_names.workspace)/$(MERLIN_GLOB_PATH)/name_sample.out | xargs ˓→$(PYTHON) $(COLLECT) -out collected_samples.txt --np $(POST_NPROCS) nodes: 1 procs: 1 depends: [sample_names_*] task_queue: post_proc_queue - name: post-process description: Post-Process collection of samples, counting occurrences of unique␣ ˓→names run: cmd: | $(PYTHON) $(POST_PROC) $(collect.workspace)/collected_samples.txt --results ˓→$(ITER_OUTPUT)/iter_$(ITER)_results.json nodes: 1 procs: 1 depends: [collect] task_queue: post_proc_queue - name: run-more-samples description: Generate new set of samples and rerun, or generate some descriptive␣ ˓→plots/statistics run: cmd: | if [ $(ITER) -ge $(MAX_ITER) ] ; then echo "done" $(PYTHON) $(CUM_POST_PROC) $(ITER_OUTPUT)/iter_*_results.json --np $(POST_ ˓→NPROCS) --hardcopy $(ITER_OUTPUT)/cumulative_results.png else next_iter=$(ITER) ((next_iter=next_iter+1)) echo "Starting iteration " $next_iter cd $(SPECROOT) merlin run $(SPECROOT)/faker_demo.yaml --vars ITER=$next_iter fi nodes: 1 procs: 1 depends: [post-process] task_queue: post_proc_queue (continues on next page) 52 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 (continued from previous page) ######################################## # Worker and sample configuration ######################################## merlin: resources: task_server: celery overlap: False # Customize workers NOTE: abuse this for scaling study: prefetch mult increase # - celery->rabbit query overhead for fast jobs workers: nameworkers: args: --concurrency 36 --prefetch-multiplier 3 steps: [sample_names] nodes: 1 machines: [borax, quartz] # NOTE: specifying wrong step leaves orphaned queue -> purge first! # also, invalid host name appears to fail silently postworkers: args: --concurrency 1 --prefetch-multiplier 1 steps: [post-process] nodes: 1 machines: [borax, quartz] ################################################### samples: column_labels: [NAME] file: $(MERLIN_INFO)/samples.csv generate: cmd: | $(PYTHON) $(SPECROOT)/faker_sample.py -n 200 -outfile=$(MERLIN_INFO)/samples. ˓→csv This workflow specification is intended to be invoke within an allocation of nodes on your HPC cluster, e.g. within and sxterm. The last step to queue up new samples for the next iteration, merlin run faker_demo.yaml ..., only doesn’t need to also call run-workers since the workers from the first instantiation are still alive. Thus the new samples will immediately start processing on the existing allocation. Another change in this workflow relative to the single stage version is managing the workspaces and outputs. The strategy used here is to create a new directory for collecting each iterations final outputs, $(ITER_OUTPUT), facilitating collective post processing at the end without having to worry about traversing into each iterations’ local workspaces. The workflow itself isn’t doing anything practical; it’s simply repeatedly sampling from a fake name generator in an attempt to count the number of unique names that are possible. The figure below shows results from running 20 iterations, with the number of unique names faker can generate appearing to be slightly more than 300. 1.1. Tutorial 53 Merlin Documentation, Release 1.11.0 1.1.7 Contribute to Merlin Estimated time • 10 minutes You will learn • How to post issues to the merlin repo. • Guidelines for contributing to merlin. Table of Contents: • Issues – Bug Reports – Feature Requests – Questions 54 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 • Contributing Issues Found a bug? Have an idea? Or just want to ask a question? Create a new issue on GitHub. Bug Reports To report a bug, simply navigate to Issues, click “New Issue”, then click “Bug report”. Then simply fill out a few fields such as “Describe the bug” and “Expected behavior”. Try to fill out every field as it will help us figure out your bug sooner. Feature Requests We are still adding new features to merlin. To suggest one, simply navigate to Issues, click “New Issue”, then click “Feature request”. Then fill out a few fields such as “What problem is this feature looking to solve?” Questions Note: Who knows? Your question may already be answered in the FAQ. We encourage questions to be asked in a collaborative setting: on GitHub, direct any questions to General Questions in Issues. Any questions can also be sent to merlin@llnl.gov. Contributing Merlin is an open source project, so contributions are welcome. Contributions can be anything from bugfixes, docu- mentation, or even new core features. Contributing to Merlin is easy! Just send us a pull request from your fork. Before you send it, summarize your change in the [Unreleased] section of CHANGELOG.md and make sure develop is the destination branch. We also appreciate squash commits before pull requests are merged. Merlin uses a rough approximation of the Git Flow branching model. The develop branch contains the latest contribu- tions, and main is always tagged and points to the latest stable release. If you’re a contributor, try to test and run on develop. That’s where all the magic is happening (and where we hope bugs stop). More detailed information on contributing can be found on the Contributing page. 1.1. Tutorial 55 Merlin Documentation, Release 1.11.0 1.1.8 Port Your Own Application Prerequisites • Module 2: Installation • Module 3: Hello World • Module 4: Running a Real Simulation Estimated time • 15 minutes You will learn • Tips for building workflows • Tips for scaling • Debugging Table of Contents: • Tips for porting your app, building workflows • Tips for debugging your workflows • Tips for scaling workflows • Misc tips Tips for porting your app, building workflows The first step of building a new workflow, or porting an existing app to a workflow, is to describe it as a set of discrete, and ideally focused steps. Decoupling the steps and making them generic when possible will facilitate more rapid composition of future workflows. This will also require mapping out the dependencies and parameters that get passed between/shared across these steps. Setting up a template using tools such as cookiecutter can be useful for more production style workflows that will be frequently reused. Additionally, make use of the built-in examples accessible from the merlin command line with merlin example. Use dry runs merlin run --dry --local to prototype without actually populating task broker’s queues. Similarly, once the dry run prototype looks good, try it on a small number of parameters before throwing millions at it. Merlin inherits much of the input language and workflow specification philosophy from Maestro. Thus a good first step is to learn to use that tool. As seen in the Module 5: Advanced Topics there are also use cases that combine Merlin and Maestro. Make use of exit keys such as MERLIN_RESTART or MERLIN_RETRY in your step logic. 56 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 Tips for debugging your workflows The scripts defined in the workflow steps are also written to the output directories; this is a useful debugging tool as it can both catch parameter and variable replacement errors, as well as provide a quick way to reproduce, edit, and retry the step offline before fixing the step in the workflow specification. The <stepname>.out and <stepname>.err files log all of the output to catch any runtime errors. Additionally, you may need to grep for 'WARNING' and 'ERROR' in the worker logs. When a bug crops up in a running study with many parameters, there are a few other commands to make use of. Rather than trying to spam Ctrl-c to kill all the workers, you will want to instead use merlin stop-workers --spec <workflow_name>.yaml to stop the workers for that workflow. This should then be followed up with merlin purge <workflow_name>.yaml to clear out the task queue to prevent the same buggy tasks from continuing to run the next time run-workers is invoked. Tips for scaling workflows Most of the worst bottlenecks you’ll encounter when scaling up your workflow are caused by the file system. This can be caused by using too much space or too many files, even in a single workflow if you’re not careful. There is a certain number of inodes created just based upon the sample counts even without accounting for the steps being executed. This can be mitigated by avoiding reading/writing to the file system when possible. If file creation is unavoidable, you may need to consider adding cleanup steps to your workflow: dynamically pack up the previous step in a tarball, transfer to another file system or archival system, or even just delete files. Misc tips Avoid reliance upon storage at the $(SPECROOT) level. This is particularly dangerous if using symlinks as it can violate the provenance of what was run, possibly ruining the utility of the dataset that was generated. It is preferred to make local copies of any input decks and supporting scripts and data sets inside the workflows’ workspace. This of course has limits, regarding shared/system libraries that any programs running in the steps may need; alternate means of recording this information in a log file or something similar may be needed in this case. 1.2 Getting Started 1.2.1 Quick Start pip3 install merlin All set up? See the Merlin Commands section for using merlin. Check out the Tutorial! 1.2.2 Developer Setup The developer setup can be done via pip or via make. This section will cover how to do both. Additionally, there is an alternative method to setup merlin on supercomputers. See the Spack section for more details. 1.2. Getting Started 57 Merlin Documentation, Release 1.11.0 Pip Setup To install with the additional developer dependencies, use: pip3 install "merlin[dev]" or: pip3 install -e "git+https://github.com/LLNL/merlin.git@develop#egg=merlin[dev]" Make Setup Visit the Merlin repository on github. Create a fork of the repo and clone it onto your system. Change directories into the merlin repo: $ cd merlin/ Install Merlin with the developer dependencies: $ make install-dev This will create a virtualenv, start it, and install Merlin and it’s dependencies for you. More documentation about using Virtualenvs with Merlin can be found at Using Virtualenvs with Merlin. We can make sure it’s installed by running: $ merlin --version If you don’t see a version number, you may need to restart your virtualenv and try again. Configuring Merlin Once Merlin has been installed, the installation needs to be configured. Documentation for merlin configuration is in the Configuring Merlin section. That’s it. To start running Merlin see the Merlin Workflows. (Optional) Testing Merlin Warning: With python 3.6 you may see some tests fail and a unicode error presented. To fix this, you need to reset the LC_ALL environment variable to en_US.utf8. If you have make installed and the Merlin repository cloned, you can run the test suite provided in the Makefile by running: $ make tests This will run both the unit tests suite and the end-to-end tests suite. If you’d just like to run the unit tests you can run: 58 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 $ make unit-tests Similarly, if you’d just like to run the end-to-end tests you can run: $ make e2e-tests 1.2.3 Custom Setup This section documents how to install Merlin without using the Makefile. This setup is more complicated; however, allows for more customization of the setup configurations. Clone the Merlin repository: git clone https://github.com/LLNL/merlin.git Create a virtualenv Merlin uses virtualenvs to manage package dependencies which can be installed via Pip, Python’s default package manager. More documentation about using Virtualenvs with Merlin can be found at Using Virtualenvs with Merlin. To create a new virtualenv and activate it: $ python3 -m venv venv_merlin_$SYS_TYPE_py3_6 $ source venv_merlin_$SYS_TYPE_py3/bin/activate # Or activate.csh for .cshrc Install Python Package Dependencies Merlin uses Pip to manage Python dependencies. Merlin dependencies can be found in the requirements directory in the Merlin repository. To install the standard set of dependencies run: (merlin3_7) $ pip install -r requirements.txt This will install all the required dependencies for Merlin and development development dependencies. Installing Merlin Merlin can be installed in editable mode. From within the Merlin repository: (merlin3_7) $ pip install -e . Any changes made to the Merlin source code should automatically reflect in the virtualenv. Tip: If changes to Merlin’s source code do not reflect when running Merlin try running pip install -e . from within the Merlin repository. 1.2. Getting Started 59 Merlin Documentation, Release 1.11.0 1.3 FAQ Frequently Asked Questions • General – What is Merlin? – Where can I get help with Merlin? • Setup & Installation – How can I build Merlin? – Do I have to build Merlin? – What are the setup instructions at LLNL? – How do I reconfigure for different servers? • Component Technology – What underlying libraries does Merlin use? – What security features are in Merlin? – What is celery? – What is maestro? • Designing and Building Workflows – Where are some example workflows? – How do I launch a workflow? – How do I describe workflows in Merlin? – What is a DAG? – What if my workflow can’t be described by a DAG? – How do I implement workflow looping / iteration? – Can steps be restarted? – How do I put a time delay in before a restart or retry? – I have a long running batch task that needs to restart, what should I do? – How do I mark a step failure? – What fields can be added to steps? – How do I specify the language used in a step? • Running Workflows – How do I set up a workspace without executing step scripts? – How do I start workers? – How do I see what workers are connected? – How do I stop workers? – How do I re-run failed steps in a workflow? 60 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 – What tasks are in my queue? – How do I purge tasks? – Why is stuff still running after I purge? – Why am I running old tasks? – Where do tasks get run? – Can I run different steps from my workflow on different machines? – What is Slurm? – What is LSF? – What is flux? – What is PBS? – How do I use flux on LC? – What is LAUNCHER? – How do I use LAUNCHER? – What is level_max_dirs? – What is pgen? – Where can I learn more about merlin? 1.3.1 General What is Merlin? Merlin is a distributed task queue system designed to facilitate the large scale execution of HPC ensembles, like those needed to build machine learning models of complex simulations. Where can I get help with Merlin? In addition to this documentation, the Merlin developers can be reached at merlin@llnl.gov. You can also reach out to the merlin user group mailing list: merlin-users@listserv.llnl.gov. 1.3.2 Setup & Installation How can I build Merlin? Merlin can be installed via pip in a python virtual environment or via spack. See Getting started. 1.3. FAQ 61 Merlin Documentation, Release 1.11.0 Do I have to build Merlin? If you’re at LLNL and want to run on LC, you can use one of the public deployments. For more information, check out the LLNL access page in confluence. What are the setup instructions at LLNL? See “Do I have to build Merlin” How do I reconfigure for different servers? The server configuration is set in ~/.merlin/app.yaml. Details can be found here. 1.3.3 Component Technology What underlying libraries does Merlin use? • Celery – What is celery? • Maestro – What is maestro? What security features are in Merlin? Merlin encrypts network traffic of step results, implying that all results are encrypted with a unique user-based key, which is auto-generated and placed in ~/.merlin/. This allows for multiple users to share a results database. This is important since some backends, like redis do not allow for multiple distinct users. What is celery? Celery is an asynchronous task/job queue based on distributed message passing. It is focused on real-time operation, but supports scheduling as well. See Celery’s GitHub page and Celery’s website for more details. What is maestro? Maestro is a tool and library for specifying and conducting general workflows. See Maestro’s GitHub page for more details. 62 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 1.3.4 Designing and Building Workflows yaml specification file Where are some example workflows? $ merlin example list How do I launch a workflow? To launch a workflow locally, use merlin run --local <spec>. To launch a distributed workflow, use merlin run-workers <spec>, and merlin run <spec>. These may be done in any order. How do I describe workflows in Merlin? A Merlin workflow is described with a yaml specification file. What is a DAG? DAG is an acronym for ‘directed acyclic graph’. This is the way your workflow steps are represented as tasks. What if my workflow can’t be described by a DAG? There are certain workflows that cannot be explicitly defined by a single DAG; however, in our experience, many can. Furthermore, those workflows that cannot usually do employ DAG sub-components. You probably can gain much of the functionality you want by combining a DAG with control logic return features (like step restart and additional calls to merlin run). How do I implement workflow looping / iteration? Combining exit $(MERLIN_RETRY) with max_retries can allow you to loop a single step. Entire workflow looping / iteration can be accomplished by finishing off your DAG with a final step that makes another call to merlin run. Can steps be restarted? Yes. To build this into a workflow, use exit $(MERLIN_RETRY) within a step to retry a failed cmd section. The max number of retries in given step can be specified with the max_retries field. Alternatively, use exit $(MERLIN_RESTART) to run the optional <step>.run.restart section. To delay a retry or restart directive, add the retry_delay field to the step. Note: retry_delay only works in server mode (ie not --local mode). To restart failed steps after a workflow is done running, see How do I re-run failed steps in a workflow?. 1.3. FAQ 63 Merlin Documentation, Release 1.11.0 How do I put a time delay in before a restart or retry? Add the retry_delay field to the step. This specifies how many seconds before the task gets run after the restart. Set this value to large enough for your problem to finish. See the merlin example restart_delay example for syntax. Note: retry_delay only works in server mode (ie not --local mode). I have a long running batch task that needs to restart, what should I do? Before your allocation ends, use $(MERLIN_RESTART) or $(MERLIN_RETRY) but with a retry_delay on your step for longer that your allocation has left. The server will hold onto the step for that long (in seconds) before releasing it, allowing your batch allocation to end without the worker grabbing the step right away. For instance, your step could look something like this name: batch_task description: A long running task that needs to restart run: cmd: | # Run my code, but end 60 seconds before my allocation my_code --end_early 60s if [ -e restart_needed_flag ]; then exit $(MERLIN_RESTART) fi retry_delay: 120 # wait at least 2 minutes before restarting How do I mark a step failure? Each step is ultimately designated as: * a success $(MERLIN_SUCCESS) – writes a MERLIN_FINISHED file to the step’s workspace directory * a soft failure $(MERLIN_SOFT_FAIL) – allows the workflow to continue * a hard failure $(MERLIN_HARD_FAIL) – stops the whole workflow by shutting down all workers on that step Normally this happens behinds the scenes, so you don’t need to worry about it. To hard-code this into your step logic, use a shell command such as exit $(MERLIN_HARD_FAIL). Note: The $(MERLIN_HARD_FAIL) exit code will shutdown all workers connected to the queue associated with the failed step. To shutdown all workers use the $(MERLIN_STOP_WORKERS) exit code To rerun all failed steps in a workflow, see How do I re-run failed steps in a workflow?. If you really want a previously successful step to be re-run, you can first manually remove the MERLIN_FINISHED file. 64 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 What fields can be added to steps? Steps have a name, description, and run field, as shown below. name: <string> description: <string> run: cmd: <shell command for this step> Also under run, the following fields are optional: run: depends: <list of step names> task_queue: <task queue name for this step> shell: <e.g., /bin/bash, /usr/bin/env python3> max_retries: <integer> retry_delay: <integer: seconds> nodes: <integer> procs: <integer> How do I specify the language used in a step? You can add the field shell under the run portion of your step to change the language you write your step in. The default is /bin/bash, but you can do things like /usr/bin/env python as well. Use merlin example feature_demo to see an example of this. 1.3.5 Running Workflows $ merlin run <yaml file> For more details, see Merlin commands. How do I set up a workspace without executing step scripts? $ merlin run --dry <yaml file> How do I start workers? $ merlin run-workers <yaml file> 1.3. FAQ 65 Merlin Documentation, Release 1.11.0 How do I see what workers are connected? $ merlin query-workers This command gives you fine control over which workers you’re looking for via a regex on their name, the queue names associated with workers, or even by providing the name of a spec file where workers are defined. For more info, see Searching for any workers (merlin query-workers). How do I stop workers? Interactively outside of a workflow (e.g. at the command line), you can do this with $ merlin stop-workers This gives you fine control over which kinds of workers to stop, for instance via a regex on their name, or the queue names you’d like to stop. From within a step, you can exit with the $(MERLIN_STOP_WORKERS) code, which will issue a time-delayed call to stop all of the workers, or with the $(MERLIN_HARD_FAIL) directive, which will stop all workers connected to the current step. This helps prevent the suicide race condition where a worker could kill itself before removing the step from the workflow, causing the command to be left there for the next worker and creating a really bad loop. You can of course call merlin stop-workers from within a step, but be careful to make sure the worker executing it won’t be stopped too. For more tricks, see Stopping workers (merlin stop-workers). How do I re-run failed steps in a workflow? $ merlin restart <spec> What tasks are in my queue? How do I purge tasks? $ merlin purge <yaml file> Why is stuff still running after I purge? You probably have workers executing tasks. Purging removes them from the server queue, but any currently running or reserved tasks are being held by the workers. You need to shut down these workers first: $ merlin stop-workers $ merlin purge <yaml file> 66 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 Why am I running old tasks? You might have old tasks in your queues. Try merlin purge <yaml>. You might also have rogue workers. To find out, try merlin query-workers. Where do tasks get run? Can I run different steps from my workflow on different machines? Yes. Under the merlin block you can specify which machines your workers are allowed on. In order for this to work, you must then use merlin run-workers separately on each of the specified machines. merlin: resources: workers: worker_name: machines: [hostA, hostB, hostC] What is Slurm? A job scheduler. See Slurm documentation . What is LSF? Another job scheduler. See IBM’s LSF documentation . What is flux? Flux is a hierarchical scheduler and launcher for parallel simulations. It allows the user to specify the same launch command that will work on different HPC clusters with different default schedulers such as SLURM or LSF. Merlin versions earlier than 1.9.2 used the non-flux native scheduler to launch a flux instance. Subsequent merlin versions can launch the merlin workers using a native flux scheduler. More information can be found at the Flux web page. Older versions of flux may need the --mpi=none argument if flux is launched on a system using the SLURM scheduler. This argument can be added in the launch_args variable in the batch section. batch: type: flux launch_args: --mpi=none What is PBS? Another job scheduler. See Portable Batch System . This functionality is only available to launch a flux scheduler. 1.3. FAQ 67 Merlin Documentation, Release 1.11.0 How do I use flux on LC? The --mpibind=off option is currently required when using flux with a slurm launcher on LC toss3 systems. Set this in the batch section as shown in the example below. batch: type: flux launch_args: --mpibind=off What is LAUNCHER? $LAUNCHER is a reserved word that may be used in a step command. It serves as an abstraction to launch a job with parallel schedulers like What is Slurm?, What is LSF?, and What is flux?. How do I use LAUNCHER? Instead of this: run: cmd: srun -N 1 -n 3 python script.py Do something like this: batch: type: slurm run: cmd: $(LAUNCHER) python script.py nodes: 1 procs: 3 The arguments the LAUNCHER syntax will use: procs: The total number of MPI tasks nodes: The total number of MPI nodes walltime: The total walltime of the run (hh:mm:ss or mm:ss or ss) (not available in lsf) cores per task: The number of hardware threads per MPI task gpus per task: The number of GPUs per MPI task SLURM specific run flags: slurm: Verbatim flags only for the srun parallel launch (srun -n <nodes> -n <procs> <slurm>) FLUX specific run flags: flux: Verbatim flags for the flux parallel launch (flux mini run <flux>) LSF specific run flags: bind: Flag for MPI binding of tasks on a node (default: -b rs) num resource set: Number of resource sets launch_distribution: The distribution of resources (default: plane:{procs/nodes}) lsf: Verbatim flags only for the lsf parallel launch (jsrun . . . <lsf>) 68 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 What is level_max_dirs? level_max_dirs is an optional field that goes under the merlin.samples section of a yaml spec. It caps the number of sample directories that can be generated at a single level of a study’s sample hierarchy. This is useful for getting around filesystem constraints when working with massive amounts of data. Defaults to 25. What is pgen? pgen stands for “parameter generator”. It’s a way to override the parameters in the global.parameters spec section, instead generating them programatically with a python script. Merlin offers the same pgen functionality as Maestro. See this guide for details on using pgen. It’s a Maestro doc, but the exact same flags can be used in conjunction with merlin run. Where can I learn more about merlin? Check out our paper on arXiv. 1.4 Command line The merlin executable defines a number of commands to create tasks, launch workers to run the tasks and remove tasks from the task server. The tasks are communicated to a task server, or broker, that are then requested by workers on an allocation to run. The celery python module is used to implement the tasks and worker functionality. 1.4.1 Help (merlin --help) Descriptions of the Merlin commands are outputted when the -h or --help commands are used. $ merlin [<command name>] --help 1.4.2 Version (merlin --version) See the version by using the --version or -v flag. $ merlin --version 1.4.3 Log Level (merlin -lvl debug) More information, generally pertaining to bugs, can be output by increasing the logging level using the -lvl or --level argument. Options for the level argument are: debug, info, warning, error. $ merlin -lvl debug run <input.yaml> 1.4. Command line 69 Merlin Documentation, Release 1.11.0 1.4.4 Create the Config File (merlin config) Create a default config file in the ${HOME}/.merlin directory using the config command. This file can then be edited for your system configuration. $ merlin config [--task_server] [--output_dir <dir>] [--broker <rabbitmq|redis>] The --task_server option will select the appropriate configuration for the given task server. Currently only celery is implemented. The --output_dir or -o will output the configuration in the given directory. This file can then be edited and copied into ${HOME}/.merlin. The --broker command will write the initial app.yaml config file for a rabbitmq or redis broker. The default is rabbitmq. The backend will be redis in both cases. The redis backend in the rabbitmq config shows the use on encryption for the backend. 1.4.5 Generate working examples (merlin example) If you want to run an example workflow, use Merlin’s merlin example: $ merlin example list This will list the available example workflows and a description for each one. To select one: $ merlin example <example_name> This will copy the example workflow to the current working directory. It is possible to specify another path to copy to. $ merlin example <example_name> -p path/to/dir If the specified directory does not exist Merlin will automatically create it. This will generate the example workflow at the specified location, ready to be run. 1.4.6 Information (merlin info) Information about your merlin and python configuration can be printed out by using the info command. This is helpful for debugging. Included in this command is a server check which will check for server connections. The connection check will timeout after 60 seconds. $ merlin info 1.4.7 Monitor (merlin monitor) Batch submission scripts may not keep the batch allocation alive if there is not a blocking process in the submission script. The merlin monitor command addresses this by providing a blocking process that checks for tasks in the queues every (sleep) seconds. When the queues are empty, the blocking process will exit and allow the allocation to end. $ merlin monitor <input.yaml> [--steps <steps>] [--vars <VARIABLES=<VARIABLES>>] [-- ˓→sleep <duration>][--task_server celery] 70 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 Use the --steps option to identify specific steps in the specification that you want to query. The --vars option will specify desired Merlin variable values to override those found in the specification. The list is space-delimited and should be given after the input yaml file. Example: --vars LEARN=path/to/new_learn.py EPOCHS=3 The --sleep argument is the duration in seconds between checks for workers. The default is 60 seconds. The only currently available option for --task_server is celery, which is the default when this flag is excluded. The monitor function will check for celery workers for up to 10*(sleep) seconds before monitoring begins. The loop happens when the queue(s) in the spec contain tasks, but no running workers are detected. This is to protect against a failed worker launch. 1.4.8 Purging Tasks (merlin purge) Once the merlin run command succeeds, the tasks are now on the task server waiting to be run by the workers. If you would like to remove the tasks from the server, then use the purge command. Attention: Any tasks reserved by workers will not be purged from the queues. All workers must be first stopped so the tasks can be returned to the task server and then they can be purged. You probably want to use merlin stop-workers first. To purge all tasks in all queues defined by the workflow yaml file from the task server, run: $ merlin purge <input.yaml> [-f] [--steps <steps>] [--vars <VARIABLES=<VARIABLES>>] This will ask you if you would like to remove the tasks, you can use the -f option if you want to skip this. If you have different queues in your workflow yaml file, you can choose which queues are purged by using the --steps argument and giving a space-delimited list of steps. $ merlin purge <input.yaml> --steps step1 step2 The --vars option will specify desired Merlin variable values to override those found in the specification. The list is space-delimited and should be given after the input yaml file. Example: --vars QUEUE_NAME=new_queue EPOCHS=3 1.4.9 Searching for any workers (merlin query-workers) If you want to see all workers that are currently connected to the task server you can use: $ merlin query-workers This will broadcast a command to all connected workers and print the names of any that respond and the queues they’re attached to. This is useful for interacting with workers, such as via merlin stop-workers --workers. The --queues option will look for workers associated with the names of the queues you provide here. For example, if you want to see the names of all workers attached to the queues named demo and merlin you would use: merlin query-workers --queues demo merlin The --spec option will query for workers defined in the spec file you provide. For example, if simworker and nonsimworker are defined in a spec file called example_spec.yaml then to query for these workers you would use: 1.4. Command line 71 Merlin Documentation, Release 1.11.0 merlin query-workers --spec example_spec.yaml The --workers option will query for workers based on the worker names you provide here. For example, if you wanted to query a worker named step_1_worker you would use: merlin query-workers --workers step_1_worker This flag can also take regular expressions as input. For instance, if you had several workers running but only wanted to find the workers whose names started with step you would use: merlin query-workers --workers ^step 1.4.10 Restart the workflow (merlin restart) To restart a previously started merlin workflow, use the restart command and the path to root of the merlin workspace that was generated during the previously run workflow. This will define the tasks and queue them on the task server also called the broker. $ merlin restart [--local] <path/to/workspace_timestamp> Merlin currently writes file called MERLIN_FINISHED to the directory of each step that was finished successfully. It uses this to determine which steps to skip during execution of a workflow. The --local option will run tasks sequentially in your current shell. 1.4.11 Run the workflow (merlin run) To run the merlin workflow use the run command and the path to the input yaml file <input.yaml>. This will define the tasks and queue them on the task server also called the broker. $ merlin run [--local] <input.yaml> [--vars <VARIABLES=<VARIABLES>>] [--samplesfile ˓→<SAMPLES_FILE>] [--dry] The --local option will run tasks sequentially in your current shell. The --vars option will specify desired Merlin variable values to override those found in the specification. The list is space-delimited and should be given after the input yaml file. Example: --vars LEARN=path/to/new_learn.py EPOCHS=3 The --samplesfile will allow the user to specify a file containing samples. Valid choices: .npy, .csv, .tab. Should be given after the input yaml file. The --no-errors option is used for testing, it will silence the errors thrown when flux is not present. 72 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 Dry Run ‘Dry run’ means telling workers to create a study’s workspace and all of its necessary subdirectories and scripts (with variables expanded) without actually executing the scripts. To dry-run a workflow, use --dry: $ merlin run --local --dry <input.yaml> In a distributed fashion: $ merlin run --dry <input.yaml> ; merlin run-workers <input.yaml> You can also specify dry runs from the workflow specification file: batch: dry_run: True If you wish to execute a workflow after dry-running it, simply use restart. 1.4.12 Run the Workers (merlin run-workers) The tasks queued on the broker are run by a collection of workers. These workers can be run local in the current shell or in parallel on a batch allocation. The workers are launched using the run-workers command which reads the configuration for the worker launch from the <input.yaml> file. The batch and merlin resources section are both used to configure the worker launch. The top level batch section can be overridden in the merlin workers resource section. Parallel workers should be scheduled using the system’s batch scheduler. Once the workers are running, tasks from the broker will be processed. To launch workers for your workflow: $ merlin run-workers [--echo] <input.yaml> [--worker-args <worker args>] [--steps ˓→<WORKER_STEPS>] [--vars <VARIABLES=<VARIABLES>>] The --echo option will echo the celery workers run command to stdout and not run any workers. The --worker-args option will pass the values, in quotes, to the celery workers. Should be given after the input yaml file. The --steps option is the specific steps in the input yaml file you want to run the corresponding workers. The default is ‘all’ steps. Should be given after the input yaml file. The --vars option will specify desired Merlin variable values to override those found in the specification. The list is space-delimited and should be given after the input yaml file. Example: --vars LEARN=path/to/new_learn.py EPOCHS=3 An example of launching a simple celery worker using srun: $ srun -n 1 celery -A merlin worker -l INFO A parallel batch allocation launch is configured to run a single worker process per node. This worker process will then launch a number of worker threads to process the tasks. The number of threads can be configured by the users and will be the number of parallel jobs that can be run at once on the allocation plus threads for any non-parallel tasks. If there are 36 cores on a node and all the tasks are single core, the user may want to start 36 threads per node. If the parallel jobs uses 8 tasks, then the user should run 4 or 5 threads. For the celery workers the number of threads is set using the --concurrency argument, see the Configuring celery workers section. A full SLURM batch submission script to run the workflow on 4 nodes is shown below. 1.4. Command line 73 Merlin Documentation, Release 1.11.0 #!/bin/bash #SBATCH -N 4 #SBATCH -J Merlin #SBATCH -t 30:00 #SBATCH -p pdebug #SBATCH --mail-type=ALL #SBATCH -o merlin_workers_%j.out # Assumes you are run this in the same dir as the yaml file. YAML_FILE=input.yaml # Source the merlin virtualenv source <path to merlin venv>/bin/activate # Remove all tasks from the queues for this run. #merlin purge -f ${YAML_FILE} # Submit the tasks to the task server merlin run ${YAML_FILE} # Print out the workers command merlin run-workers ${YAML_FILE} --echo # Run the workers on the allocation merlin run-workers ${YAML_FILE} # Delay until the workers cease running merlin monitor 1.4.13 Status (merlin status) $ merlin status <input.yaml> [--steps <steps>] [--vars <VARIABLES=<VARIABLES>>] [--csv ˓→<csv file>] [--task_server celery] Use the --steps option to identify specific steps in the specification that you want to query. The --vars option will specify desired Merlin variable values to override those found in the specification. The list is space-delimited and should be given after the input yaml file. Example: --vars LEARN=path/to/new_learn.py EPOCHS=3 The --csv option takes in a filename, to dump status reports to. The only currently available option for --task_server is celery, which is the default when this flag is excluded. 74 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 1.4.14 Stopping workers (merlin stop-workers) To send out a stop signal to some or all connected workers, use: $ merlin stop-workers [--spec <input.yaml>] [--queues <queues>] [--workers <regex>] [-- ˓→task_server celery] The default behavior will send a stop to all connected workers across all workflows, having them shutdown softly. The --spec option targets only workers named in the merlin block of the spec file. The --queues option allows you to pass in the names of specific queues to stop. For example: # Stop all workers on these queues, no matter their name $ merlin stop-workers --queues queue1 queue2 The --workers option allows you to pass in regular expressions of names of workers to stop: # Stop all workers whose name matches this pattern, no matter the queue # Note the ".*" convention at the start, per regex $ merlin stop-workers --workers ".*@my_other_host*" The only currently available option for --task_server is celery, which is the default when this flag is excluded. Attention: If you’ve named workers identically (you shouldn’t) only one might get the signal. In this case, you can send it again. 1.4.15 Hosting Local Server (merlin server) To create a local server for merlin to connect to. Merlin server creates and configures a server on the current directory. This allows multiple instances of merlin server to exist for different studies or uses. The init subcommand initalizes a new instance of merlin server. The status subcommand checks to the status of the merlin server. The start subcommand starts the merlin server. The stop subcommand stops the merlin server. The restart subcommand performs stop command followed by a start command on the merlin server. The config subcommand edits configurations for the merlin server. There are multiple flags to allow for different configurations. • The -ip IPADDRESS, --ipaddress IPADDRESS option set the binded IP address for merlin server. • The -p PORT, --port PORT option set the binded port for merlin server. • The -pwd PASSWORD, --password PASSWORD option set the password file for merlin server. • The --add-user USER PASSWORD option add a new user for merlin server. • The --remove-user REMOVE_USER option remove an exisiting user from merlin server. • The -d DIRECTORY, --directory DIRECTORY option set the working directory for merlin server. • The -ss SNAPSHOT_SECONDS, --snapshot-seconds SNAPSHOT_SECONDS option set the number of sec- onds before each snapshot. 1.4. Command line 75 Merlin Documentation, Release 1.11.0 • The -sc SNAPSHOT_CHANGES, --snapshot-changes SNAPSHOT_CHANGES option set the number of database changes before each snapshot. • The -sf SNAPSHOT_FILE, --snapshot-file SNAPSHOT_FILE option set the name of snapshots. • The -am APPEND_MODE, --append-mode APPEND_MODE option set the appendonly mode. Options are al- ways, everysec, no. • The -af APPEND_FILE, --append-file APPEND_FILE option set the filename for server append/change file. More information can be found on Merlin Server 1.5 Workflows The Merlin package provides a few example workflows. These may be useful in seeing how the software works, and in designing your own workflow. This section provides documentation on running these Merlin workflow examples. 1.5.1 Overview List the built-in Merlin workflow examples with merlin example list. The Merlin team is working on adding a more diverse array of example workflows like these. In particular, look at the .yaml files within these directories. These are known as Merlin specifications, and are foundational to determining a workflow. 1.5.2 Get started with the demo ensemble Merlin provides a demo workflow that highlights some features of the software. Tip: Have at least two terminals open; one to monitor workers, and the other to provide them tasks. Create your workflow example: $ merlin example feature_demo To run the distributed version of feature_demo, run the following: $ merlin run feature_demo/feature_demo.yaml This will queue the tasks to the configured broker. To process the queued tasks, use the run-workers Merlin CLI command. Adding this command to a parallel batch submission script will launch the workers in parallel. $ merlin run-workers feature_demo/feature_demo.yaml 76 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 1.6 Workflow Specification The merlin input file or spec file is separated into several sections. An annotated version is given below. Note: The Merlin input file is a yaml file and must adhere to yaml syntax. The yaml spec relies on the indentation in the file. The input file can take a number of variables, beyond the examples shown here. For a complete list and descriptions of the variables, see Variables. #################################### # Description Block (Required) #################################### # The description block is where the description of the study is placed. This # section is meant primarily for documentation purposes so that when a # specification is passed to other users they can glean a general understanding # of what this study is meant to achieve. #------------------------------- # Required keys: # name - Name of the study # description - Description of what this study does. #------------------------------- # NOTE: You can add other keys to this block for custom documentation. Merlin # currently only looks for the required set. #################################### description: description: Run a scan through Merlin name: MERLIN #################################### # Batch Block (Required) #################################### # The batch system to use for each allocation #------------------------------- # Required keys: # type - The scheduler type to use (local|slurm|flux|lsf) # bank - The allocation bank # queue - The batch queue #################################### batch: type: flux bank: testbank queue: pbatch flux_path: <optional path to flux bin> flux_start_opts: <optional flux start options> flux_exec: <optional, flux exec command to launch workers on all nodes if using flux and flux_exec_workers is True (flux exec) > flux_exec_workers: <optional, flux argument to launch workers on all nodes. (True)> launch_pre: <Any configuration needed before the srun or jsrun launch> launch_args: <Optional extra arguments for the parallel launch command> (continues on next page) 1.6. Workflow Specification 77 Merlin Documentation, Release 1.11.0 (continued from previous page) worker_launch: <Override the parallel launch defined in merlin> shell: <the interpreter to use for the script after the shebang> # e.g. /bin/bash, /bin/tcsh, python, /usr/bin/env perl, etc. nodes: <num nodes> # The number of nodes to use for all workers This can be overridden in the workers config. If this is unset the number of nodes will be queried from the environment, failing that, the number of nodes will be set to 1. walltime: The total walltime of the batch allocation (hh:mm:ss or mm:ss or ss) ##################################### # Environment Block #################################### # The environment block is where items describing the study's environment are # defined. This includes static information that the study needs to know about # and dependencies that the workflow requires for execution. #------------------------------- # NOTE: This block isn't strictly required as a study may not depend on anything. ######################################################################## env: #------------------------------- # Variables #------------------------------- # Values that the workflow substitutes into steps and are similar in # concept to Unix environment variables. These variables are not dependent # on values in the environment and so are more portable. # # Note that variables defined here can alter the runtime shell # variable definitions. # Do not define a variable named "shell" here. #------------------------------- variables: # Set a custom output path for the study workspace. This path is where # Merlin will place all temporary files, state files, and any output. # The resulting path is usually a timestamped folder within OUTPUT_PATH # and in this case would be # './sample_output/merlin/merlin_sample1_<timestamp>'. # NOTE: If not specified, # OUTPUT_PATH is assumed to be the path where Merlin was launched from. OUTPUT_PATH: ./sample_output/merlin # OUTPUT_PATH is a keyword # variable that Merlin looks for # to replace with the study # directory created for the # ensemble #################################### # Study Block (Required) #################################### # The study block is where the steps in the workflow are defined. This section # of the specification represents the unexpanded set of tasks that the study # is composed of. (continues on next page) 78 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 (continued from previous page) # # # A description of what gets turned into tasks and what type of task # would be a good addition # # study lists the various steps, each of which has these fields # name: step name # description: what the step does # run: # cmd: the command to run for multilines use cmd: | # The $(LAUNCHER) macro can be used to substitute a parallel launcher # based on the batch:type:. # It will use the nodes and procs values for the task. # task_queue: the queue to assign the step to (optional. default: merlin) # shell: the shell to use for the command (eg /bin/bash /usr/bin/env python) # (optional. default: /bin/bash) # depends: a list of steps this step depends upon (ie parents) # procs: The total number of MPI tasks # nodes: The total number of MPI nodes # walltime: The total walltime of the run (hh:mm:ss, mm:ss or ss) (not available in␣ ˓→lsf) # cores per task: The number of hardware threads per MPI task # gpus per task: The number of GPUs per MPI task # SLURM specific run flags: # slurm: Verbatim flags only for the srun parallel launch (srun -n <nodes> -n <procs> ˓→<slurm>) # FLUX specific run flags: # flux: Verbatim flags for the flux parallel launch (flux mini run <flux>) # LSF specific run flags: # bind: Flag for MPI binding of tasks on a node # num resource set: Number of resource sets # launch_distribution : The distribution of resources (default: plane:{procs/nodes}) # exit_on_error: Flag to exit on error (default: 1) # lsf: Verbatim flags only for the lsf parallel launch (jsrun ... <lsf> ####################################################################### study: - name: runs1 description: Run on alloc1 run: cmd: $(LAUNCHER) echo "$(VAR1) $(VAR2)" > simrun.out nodes: 1 procs: 1 task_queue: queue1 shell: /bin/bash - name: post-process description: Post-Process runs on alloc1 run: cmd: | cd $(runs1.workspace)/$(MERLIN_SAMPLE_PATH) <post-process> nodes: 1 (continues on next page) 1.6. Workflow Specification 79 Merlin Documentation, Release 1.11.0 (continued from previous page) procs: 1 depends: [runs1] task_queue: queue1 - name: runs2 description: Run on alloc2 run: cmd: | touch learnrun.out $(LAUNCHER) echo "$(VAR1) $(VAR2)" >> learnrun.out exit $(MERLIN_RETRY) # some syntax to send a retry error code nodes: 1 procs: 1 task_queue: lqueue max_retries: 3 # maximum number of retries retry_delay: 10 # delay retry for N seconds (default 1) batch: type: <override the default batch type> - name: monitor description: Monitor on alloc1 run: cmd: date > monitor.out nodes: 1 procs: 1 task_queue: mqueue #################################### # Parameter Block (Required) #################################### # The parameter block contains all the things we'd like to vary in the study. # Currently, there are two modes of operating in the specification: # 1. If a parameter block is specified, the study is expanded and considered a # parameterized study. # 2. If a parameter block is not specified, the study is treated as linear and # the resulting study is not expanded. # # There are three keys per parameter: # 1. A list of values that the parameter takes. # 2. A label that represents a "pretty printed" version of the parameter. The # parameter values is specified by the '%%' moniker (for example, for SIZE -- # when SIZE is equal to 10, the label will be 'SIZE.10'). To access the label # for SIZE, for example, the token '$(SIZE.label)' is used. # Labels can take one of two forms: A single string with the '%%' marker or # a list of per value labels (must be the same length as the list of values). # # NOTE: A specified parameter does not necessarily have to be used in every step # or at all. If a parameter is specified and not used, it simply will not be # factored into expansion or the naming of expanded steps or their workspaces. # NOTE: You can also specify custom generation of parameters using a Python # file containing the definition of a function as follows: # (continues on next page) 80 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 (continued from previous page) # 'def get_custom_generator():' # # The 'get_custom_generator' function is required to return a ParameterGenerator # instance populated with custom filled values. In order to use the file, simply # call Merlin using 'merlin run <specification path>'. ######################################################################## global.parameters: STUDY: label: STUDY.%% values: [MERLIN1, MERLIN2] SIZE: values : [10, 20] label : SIZE.%% ITERATIONS: values : [10, 20] label : ITER.%% #################################### # Merlin Block (Required) #################################### # The merlin specific block will add any configuration to # the DAG created by the study description. # including task server config, data management and sample definitions. # # merlin will replace all SPECROOT instances with the directory where # the input yaml was run. ####################################################################### merlin: #################################### # Resource definitions # # Define the task server configuration and workers to run the tasks. # #################################### resources: task_server: celery # Flag to determine if multiple workers can pull tasks # from overlapping queues. (default = False) overlap: False # Customize workers. Workers can have any user-defined name (e.g., simworkers,␣ ˓→learnworkers). workers: simworkers: args: <celery worker args> <optional> steps: [runs1, post-process, monitor] # [all] when steps is omitted nodes: <Number of nodes for this worker or batch num nodes> # A list of machines to run the given steps can be specified # in the machines keyword. <optional> # A full OUTPUT_PATH and the steps argument are required (continues on next page) 1.6. Workflow Specification 81 Merlin Documentation, Release 1.11.0 (continued from previous page) # when using this option. Currently all machines in the # list must have access to the OUTPUT_PATH. machines: [host1, host2] learnworkers: args: <celery worker args> <optional> steps: [runs2] nodes: <Number of nodes for this worker or batch num nodes> # An optional batch section in the worker can override the # main batch config. This is useful if other workers are running # flux, but some component of the workflow requires the native # scheduler or cannot run under flux. Another possibility is to # have the default type as local and workers needed for flux or # slurm steps. batch: type: local machines: [host3] ################################################### # Sample definitions # # samples file can be one of # .npy (numpy binary) # .csv (comma delimited: '#' = comment line) # .tab (tab/space delimited: '#' = comment line) ################################################### samples: column_labels: [VAR1, VAR2] file: $(SPECROOT)/samples.npy generate: cmd: | python $(SPECROOT)/make_samples.py -dims 2 -n 10 -outfile=$(INPUT_PATH)/samples. ˓→npy "[(1.3, 1.3, 'linear'), (3.3, 3.3, 'linear')]" level_max_dirs: 25 #################################### # User Block (Optional) #################################### # The user block allows other variables in the workflow file to be propagated # through to the workflow (including in variables .partial.yaml and .expanded.yaml). # User block uses yaml anchors, which defines a chunk of configuration and use # their alias to refer to that specific chunk of configuration elsewhere. ####################################################################### user: study: run: hello: &hello_run cmd: | python3 $(HELLO) -outfile hello_world_output_$(MERLIN_SAMPLE_ID).json ˓→$(X0) $(X1) $(X2) max_retries: 1 collect: &collect_run (continues on next page) 82 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 (continued from previous page) cmd: | echo $(MERLIN_GLOB_PATH) echo $(hello.workspace) ls $(hello.workspace)/X2.$(X2)/$(MERLIN_GLOB_PATH)/hello_world_output_*. ˓→json > files_to_collect.txt spellbook collect -outfile results.json -instring "$(cat files_to_ ˓→collect.txt)" translate: &translate_run cmd: spellbook translate -input $(collect.workspace)/results.json -output␣ ˓→results.npz -schema $(FEATURES) learn: &learn_run cmd: spellbook learn -infile $(translate.workspace)/results.npz make_samples: &make_samples_run cmd: spellbook make-samples -n $(N_NEW) -sample_type grid -outfile grid_ ˓→$(N_NEW).npy predict: &predict_run cmd: spellbook predict -infile $(make_new_samples.workspace)/grid_$(N_NEW). ˓→npy -outfile prediction_$(N_NEW).npy -reg $(learn.workspace)/random_forest_reg.pkl verify: &verify_run cmd: | if [[ -f $(learn.workspace)/random_forest_reg.pkl && -f $(predict. ˓→workspace)/prediction_$(N_NEW).npy ]] then touch FINISHED exit $(MERLIN_SUCCESS) else exit $(MERLIN_SOFT_FAIL) fi python3: run: &python3_run cmd: | print("OMG is this in python?") print("Variable X2 is $(X2)") shell: /usr/bin/env python3 python2: run: &python2_run cmd: | print "OMG is this in python2? Change is bad." print "Variable X2 is $(X2)" shell: /usr/bin/env python2 1.6. Workflow Specification 83 Merlin Documentation, Release 1.11.0 1.7 Configuration This section provides documentation for configuring Merlin’s connections with task servers and results backends. 1.7.1 Merlin server configuration Merlin works best configuring celery to run with a RabbitMQ broker and a redis backend. Merlin uses celery chords which require a results backend be configured. The Amqp (rpc Rabbitmq) server does not support chords but the Redis, Database, Memcached and more, support chords. Merlin’s configuration is controlled by an app.yaml file, such as the one below: celery: # directory where Merlin looks for the following: # mysql-ca-cert.pem rabbit-client-cert.pem rabbit-client-key.pem redis.pass certs: /path/to/celery/config broker: # can be rabbitmq, redis, rediss, or redis+sock name: rabbitmq #username: # defaults to your username unless changed here password: ~/.merlin/rabbit-password # server URL server: server.domain.com ### for rabbitmq connections ### #vhost: # defaults to your username unless changed here ### for redis+sock connections ### #socketname: the socket name your redis connection can be found on. #path: The path to the socket. ### for redis/rediss connections ### #port: The port number redis is listening on (default 6379) #db_num: The data base number to connect to. # ssl security #keyfile: /var/ssl/private/client-key.pem #certfile: /var/ssl/amqp-server-cert.pem #ca_certs: /var/ssl/myca.pem # This is optional and can be required, optional or none # (required is the default) #cert_reqs: required results_backend: # Can be redis,rediss, mysql, db+ or memcached server # Only a few of these are directly configured by merlin name: redis dbname: dbname username: username (continues on next page) 84 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 (continued from previous page) # name of file where redis password is stored. password: redis.pass server: server.domain.com # merlin will generate this key if it does not exist yet, # and will use it to encrypt all data over the wire to # your redis server. encryption_key: ~/.merlin/encrypt_data_key port: 6379 db_num: 0 # ssl security #keyfile: /var/ssl/private/client-key.pem #certfile: /var/ssl/amqp-server-cert.pem #ca_certs: /var/ssl/myca.pem # This is optional and can be required, optional or none # (required is the default) #cert_reqs: required The default location for the app.yaml is in the merlin repo under the merlin/config directory. This default can be overridden by files in one of two other locations. The current working directory is first checked for the app.yaml file, then the user’s ~/.merlin directory is checked. broker/name: can be rabbitmq, redis, or redis+sock. As their names imply, rabbitmq will use RabbitMQ as a task broker (preferred for multi user configurations), redis will use redis as a task broker, and redis+sock will connect to a redis task broker using a socket. 1.7.2 Broker: rabbitmq, amqps, amqp Merlin constructs the following connection string from the relevant options in the broker section of the app.yaml file. If the port argument is not defined, the default rabbitmq TLS port, 5671, will be used. See the Security with RabbitMQ section for more info about security with this broker. When the broker is amqp, the default port will be 5672. The prototype url for this configuration is: {conn}://{username}:{password}@{server}:{port}/{vhost} Here conn is amqps (with ssl) when name is rabbitmq or amqps and amqp (without ssl) when name is amqp. broker: name: rabbitmq #username: # defaults to your username unless changed here password: ~/.merlin/rabbit-password # server URL server: server.domain.com #vhost: # defaults to your username unless changed here 1.7. Configuration 85 Merlin Documentation, Release 1.11.0 1.7.3 Broker: redis Merlin constructs the following connection string from the relevant options in the broker section of the app.yaml file. The prototype url for this configuration is: redis://:{password}@{server}:{port}/{db_num} broker: name: redis server: localhost port: 6379 1.7.4 Broker: rediss Newer versions of Redis (version 6 or greater) can be configured with ssl. The rediss name is used to enable this support. See the Security with redis section for more info. The prototype url for this configuration is: rediss://:{password}@{server}:{port}/{db_num} broker: name: rediss server: localhost port: 6379 1.7.5 Broker: redis+socket Merlin constructs the following connection string from the relevant options in the broker section of the app.yaml file. The prototype url for this configuration is: redis+socket://{path}?virtual_host={db_num} broker: name: redis+socket path: /tmp/username/redis.sock db_num: 0 86 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 1.7.6 Broker: url A url option is available to specify the broker connection url, in this case the server name is ignored. The url must include all the entire connection url except the ssl if the broker name is recognized by the ssl processing system. Currently the ssl system will only configure the Rabbitmq and Redis servers. The prototype url for this configuration is: {url} broker: url: redis://localhost:6379/0 1.7.7 Broker: Security Security with RabbitMQ Merlin can only be configured to communicate with RabbitMQ over an SSL connection and does not permit use of a RabbitMQ server configured without SSL. The default value of the broker_use_ssl keyword is True. The keys can be given in the broker config as show below. broker: # can be redis, redis+sock, or rabbitmq name: rabbitmq #username: # defaults to your username unless changed here password: ~/.merlin/rabbit-password # server URL server: server.domain.com ### for rabbitmq, redis+sock connections ### #vhost: # defaults to your username unless changed here # ssl security keyfile: /var/ssl/private/client-key.pem certfile: /var/ssl/amqp-server-cert.pem ca_certs: /var/ssl/myca.pem # This is optional and can be required, optional or none # (required is the default) cert_reqs: required This results in a value for broker_use_ssl given below: broker_use_ssl = { 'keyfile': '/var/ssl/private/client-key.pem', 'certfile': '/var/ssl/amqp-server-cert.pem', 'ca_certs': '/var/ssl/myca.pem', 'cert_reqs': ssl.CERT_REQUIRED } 1.7. Configuration 87 Merlin Documentation, Release 1.11.0 Security with redis The same ssl config and resulting ssl_use_broker can be used with the rediss:// url when using a redis server version 6 or greater with ssl. broker: name: rediss #username: # defaults to your username unless changed here # server URL server: server.domain.com port: 6379 db_num: 0 # ssl security keyfile: /var/ssl/private/client-key.pem certfile: /var/ssl/amqp-server-cert.pem ca_certs: /var/ssl/myca.pem # This is optional and can be required, optional or none # (required is the default) cert_reqs: required The resulting broker_use_ssl configuration for a rediss server is given below. broker_use_ssl = { 'ssl_keyfile': '/var/ssl/private/client-key.pem', 'ssl_certfile': '/var/ssl/amqp-server-cert.pem', 'ssl_ca_certs': '/var/ssl/myca.pem', 'ssl_cert_reqs': ssl.CERT_REQUIRED } 1.7.8 Results backend: redis Merlin constructs the following connection string from relevant options in the results_backend section of the app.yaml file. The prototype url for this configuration is: redis://:{password}{server}:{port}/{db_num} results_backend: name: redis server: localhost port: 6379 88 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 1.7.9 Results backend: rediss Newer versions of Redis (version 6 or greater) can be configured with ssl. The rediss name is used to enable this support. See the Security with redis section for more info. The prototype url for this configuration is: rediss://:{password}{server}:{port}/{db_num} results_backend: name: rediss server: localhost port: 6379 1.7.10 Results backend: url A url option is available to specify the results connection url, in this case the server name is ignored. The url must include the entire connection url including the ssl configuration. The prototype url for this configuration is: {url} results_backend: url: redis://localhost:6379/0 The url option can also be used to define a server that is not explicitly handled by the merlin configuration system. results_backend: url: db+postgresql://scott:tiger@localhost/mydatabase Resolving password fields The results_backend/password is interpreted in the following way. First, it is treated as an absolute path to a file containing your backend password. If that path doesn’t exist, it then looks for a file of that name under the directory defined under celery/certs. If that file doesn’t exist, it then looks treats results_backend/password as the password itself. The broker/password is simply the full path to a file containing your password for the user defined by broker/ username. 1.7. Configuration 89 Merlin Documentation, Release 1.11.0 1.7.11 Results backend: Security Security with redis Redis versions less than 6 do not natively support multiple users or SSL. We address security concerns here by redefining the core celery routine that communicates with redis to encrypt all data it sends to redis and then decrypt anything it receives. Each user should have their own encryption key as defined by results_backend/encryption_key in the app.yaml file. Merlin will generate a key if that key does not yet exist. Redis versions 6 or greater can use the ssl keys as in the broker section. The ssl config with redis (rediss) in the results backend is the placed in the redis_backend_use_ssl celery argument. The values in this argument are the same as the broker. redis_backend_use_ssl = { 'ssl_keyfile': '/var/ssl/private/client-key.pem', 'ssl_certfile': '/var/ssl/amqp-server-cert.pem', 'ssl_ca_certs': '/var/ssl/myca.pem', 'ssl_cert_reqs': ssl.CERT_REQUIRED } 1.8 Variables There are a number of variables which can be placed in a merlin input .yaml file that can control workflow execution, such as via string expansion and control flow. Note: Only user variables and OUTPUT_PATH may be reassigned or overridden from the command line. 1.8.1 Directory structure context The directory structure of merlin output looks like this: SPECROOT <spec.yaml> ... OUTPUT_PATH MERLIN_WORKSPACE MERLIN_INFO <name>.orig.yaml <name>.partial.yaml <name>.expanded.yaml <step_name>.workspace WORKSPACE 90 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 1.8. Variables 91 Merlin Documentation, Release 1.11.0 1.8.2 Reserved variables Table 6: Study variables that Merlin uses. May be referenced within a specification file, but not reassigned or overridden. Variable Description Example Expansion $(SPECROOT) Directory path of the specification file. /globalfs/user/ ˓→merlin_workflows $(OUTPUT_PATH) Directory path the study output will be written to. If not defined will default to the current working directory. ./studies May be reassigned or overridden. $(MERLIN_TIMESTAMP) The time a study began. May be used as a unique iden- tifier. "YYYYMMDD-HHMMSS" $(MERLIN_WORKSPACE) Output directory generated by a study at OUTPUT_PATH. Ends with MERLIN_TIMESTAMP. $(OUTPUT_PATH)/ ˓→ensemble_name_ ˓→$(MERLIN_ ˓→TIMESTAMP) $(WORKSPACE) The workspace directory for a single step. $(OUTPUT_PATH)/ ˓→ensemble_name_ ˓→$(MERLIN_ ˓→TIMESTAMP)/step_ ˓→name/`` $(MERLIN_INFO) Directory within MERLIN_WORKSPACE that holds the provenance specs and sample generation results. Com- $(MERLIN_WORKSPACE)/ monly used to hold samples.npy. ˓→merlin_info/ $(MERLIN_SAMPLE_ID) Sample index in an ensemble 0 1 2 3 $(MERLIN_SAMPLE_PATH) Path in the sample directory tree to a sample’s directory, i.e. where the task is actually run. /0/0/0/ /0/0/1/ /0/ ˓→0/2/ /0/0/3/ $(MERLIN_GLOB_PATH) All of the directories in a simulation tree as a glob (*) string /*/*/*/* $(MERLIN_PATHS_ALL) A space delimited string of all of the paths; can be used as is in bash for loop for instance with: 0/0/0 for path in $(MERLIN_PATHS_ALL) 0/0/1 do 0/0/2 ls $path 0/0/3 done $(MERLIN_SAMPLE_VECTOR)Vector of merlin sample values $(SAMPLE_COLUMN_1) ˓→$(SAMPLE_COLUMN_ ˓→2) ... $(MERLIN_SAMPLE_NAMES) Names of merlin sample values 92 Chapter 1. Merlin Overview SAMPLE_COLUMN_1␣ ˓→SAMPLE_COLUMN_2 .. ˓→. Merlin Documentation, Release 1.11.0 The LAUNCHER and VLAUNCHER Variables $(LAUNCHER) is a special case of a reserved variable since it’s value can be changed. It serves as an abstraction to launch a job with parallel schedulers like slurm, lsf , and flux and it can be used within a step command. For example, say we start with this run cmd inside our step: run: cmd: srun -N 1 -n 3 python script.py We can modify this to use the $(LAUNCHER) variable like so: batch: type: slurm run: cmd: $(LAUNCHER) python script.py nodes: 1 procs: 3 In other words, the $(LAUNCHER) variable would become srun -N 1 -n 3. Similarly, the $(VLAUNCHER) variable behaves similarly to the $(LAUNCHER) variable. The key distinction lies in its source of information. Instead of drawing certain configuration options from the run section of a step, it retrieves spe- cific shell variables. These shell variables are automatically generated by Merlin when you include the $(VLAUNCHER) variable in a step command, but they can also be customized by the user. Currently, the following shell variables are: Table 7: VLAUNCHER Variables Variable Description Default ${MERLIN_NODES} The number of nodes 1 ${MERLIN_PROCS} The number of tasks/procs 1 ${MERLIN_CORES} The number of cores per task/proc 1 ${MERLIN_GPUS} The number of gpus per task/proc 0 Let’s say we have the following defined in our yaml file: batch: type: flux run: cmd: | MERLIN_NODES=4 MERLIN_PROCS=2 MERLIN_CORES=8 MERLIN_GPUS=2 $(VLAUNCHER) python script.py The $(VLAUNCHER) variable would be substituted to flux run -N 4 -n 2 -c 8 -g 2. 1.8. Variables 93 Merlin Documentation, Release 1.11.0 1.8.3 User variables Variables defined by a specification file in the env section, as in this example: env: variables: ID: 42 EXAMPLE_VAR: hello As long as they’re defined in order, you can nest user variables like this: env: variables: EXAMPLE_VAR: hello WORKER_NAME: $(EXAMPLE_VAR)_worker Like all other Merlin variables, user variables may be used anywhere (as a yaml key or value) within a specification as below: cmd: echo "$(EXAMPLE_VAR), world!" ... $(WORKER_NAME): args: ... If you want to programmatically define the study name, you can include variables in the description.name field as long as it makes a valid filename: description: name: my_$(EXAMPLE_VAR)_study_$(ID) description: example of programmatic study name The above would produce a study called my_hello_study_42. 1.8.4 Environment variables Merlin expands Unix environment variables for you. The values of the user variables below would be expanded: env: variables: MY_HOME: ~/ MY_PATH: $PATH USERNAME: ${USER} However, Merlin leaves environment variables found in shell scripts (think cmd and restart) alone. So this step: - name: step1 description: an example run: cmd: echo $PATH ; echo $(MY_PATH) . . . would be expanded as: 94 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 - name: step1 description: an example run: cmd: echo $PATH ; echo /an/example/:/path/string/ 1.8. Variables 95 Merlin Documentation, Release 1.11.0 96 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 1.8.5 Step return variables Table 8: Special return code variables for task steps. Variable Description Example Usage $(MERLIN_SUCCESS) This step was successful. Keep going to the next task. Default step behavior if no exit code given. echo "hello, world!" exit $(MERLIN_ ˓→SUCCESS) $(MERLIN_RESTART) Run this step’s restart command, or re-run cmd if restart is absent. The default maximum number of run: retries+restarts for any given step is 30. You can over- cmd: | ride this by adding a max_retries field under the run touch my_file. field in the specification. Issues a warning. Default will ˓→txt retry in 1 second. To override the delay time, specify echo "hi mom!"␣ retry_delay. ˓→>> my_file.txt exit $(MERLIN_ ˓→RESTART) restart: | echo "bye, mom! ˓→" >> my_file.txt max_retries: 23 retry_delay: 10 $(MERLIN_RETRY) Retry this step’s cmd command. The default maximum number of retries for any given step is 30. You can over- run: ride this by adding a max_retries field under the run cmd: | field in the specification. Issues a warning. Default will touch my_file. retry in 1 second. To override the delay time, specify ˓→txt retry_delay. echo "hi mom!"␣ ˓→>> my_file.txt exit $(MERLIN_ ˓→RETRY) max_retries: 23 retry_delay: 10 $(MERLIN_SOFT_FAIL) Mark this step as a failure, note in the warning log but keep going. Unknown return codes get translated to soft echo "Uh-oh, this␣ fails, so that they can be logged. ˓→sample didn't work ˓→" exit $(MERLIN_SOFT_ ˓→FAIL) $(MERLIN_HARD_FAIL) Something went terribly wrong and I need to stop the whole workflow. Raises a HardFailException and echo "Oh no, we've␣ stops all workers connected to that step. Workers will ˓→created skynet!␣ stop after a 60 second delay to allow the step to be ac- ˓→Abort!" knowledged by the server. exit $(MERLIN_HARD_ ˓→FAIL) Note: Workers in isolated parts of the workflow not consuming from the bad step will continue. You can stop all workers with $(MERLIN_STOP_WORKERS). $(MERLIN_STOP_WORKERS) Launch a task to stop all active workers. To allow the 1.8. Variables current task to finish and acknowledge the results to the # send a signal to␣ 97 server, will happen in 60 seconds. ˓→all workers to␣ ˓→stop exit $(MERLIN_STOP_ Merlin Documentation, Release 1.11.0 1.9 Merlin Server The merlin server command allows users easy access to containerized broker and results servers for merlin workflows. This allows users to run merlin without a dedicated external server. The main configuration will be stored in the subdirectory called “server/” by default in the main merlin configuration “~/.merlin”. However different server images can be created for different use cases or studies just by simplying creating a new directory to store local configuration files for merlin server instances. Below is an example of how merlin server can be utilized. First create and navigate into a directory to store your local merlin configuration for a specific use case or study. mkdir study1/ cd study1/ Afterwards you can instantiate merlin server in this directory by running merlin server init A main server configuration will be created in the ~/.merlin/server and a local configuration will be created in a subdi- rectory called “merlin_server/” We should expect the following files in each directory ~/study1$ ls ~/.merlin/server/ docker.yaml merlin_server.yaml podman.yaml singularity.yaml ~/study1$ ls merlin_server ~/study1$ ls merlin_server/ redis.conf redis_latest.sif The main configuration in “~/.merlin/server” deals with defaults and technical commands that might be used for setting up the merlin server local configuration and its containers. Each container has their own configuration file to allow users to be able to switch between different containerized services freely. The local configuration “merlin_server” folder contains configuration files specific to a certain use case or run. In the case above you can see that we have a redis singularity container called “redis_latest.sif” with the redis configuration file called “redis.conf”. This redis configuration will allow the user to configurate redis to their specified needs without have to manage or edit the redis container. When the server is run this configuration will be dynamically read, so settings can be changed between runs if needed. Once the merlin server has been initialized in the local directory the user will be allowed to run other merlin server commands such as “run, status, stop” to interact with the merlin server. A detailed list of commands can be found in the Merlin Server Commands page. Note: Running “merlin server init” again will NOT override any exisiting configuration that the users might have set or edited. By running this command again any missing files will be created for the users with exisiting defaults. HOWEVER it is highly advised that users back up their configuration in case an error occurs where configuration files are overriden. 98 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 1.9.1 Merlin Server Configuration Below are a sample list of configurations for the merlin server command Main Configuration ~/.merlin/server/ merlin_server.yaml container: # Select the format for the recipe e.g. singularity, docker, podman (currently␣ ˓→singularity is the only working option.) format: singularity # The image name image: redis_latest.sif # The url to pull the image from url: docker://redis # The config file config: redis.conf # Subdirectory name to store configurations Default: merlin_server/ config_dir: merlin_server/ # Process file containing information regarding the redis process pfile: merlin_server.pf process: # Command for determining the process of the command status: pgrep -P {pid} #ps -e | grep {pid} # Command for killing process kill: kill {pid} singularity.yaml singularity: command: singularity # init_command: \{command} .. (optional or default) run_command: \{command} run {image} {config} stop_command: kill # \{command} (optional or kill default) pull_command: \{command} pull {image} {url} Local Configuration merlin_server/ redis.conf bind 127.0.0.1 -::1 protected-mode yes port 6379 logfile "" dir ./ ... see documentation on redis configuration here for more detail merlin_server.pf 1.9. Merlin Server 99 Merlin Documentation, Release 1.11.0 bits: '64' commit: '00000000' hostname: ubuntu image_pid: '1111' mode: standalone modified: '0' parent_pid: 1112 port: '6379' version: 6.2.6 1.9.2 Merlin Server Commands Merlin server has a list of commands for interacting with the broker and results server. These commands allow the user to manage and monitor the exisiting server and create instances of servers if needed. Initializing Merlin Server (merlin server init) The merlin server init command creates configurations for merlin server commands. A main merlin sever configuration subdirectory is created in “~/.merlin/server” which contains configuration for local merlin configuration, and configurations for different containerized services that merlin server supports, which includes singularity (docker and podman implemented in the future). A local merlin server configuration subdirectory called “merlin_server/” will also be created when this command is run. This will contain a container for merlin server and associated configuration files that might be used to start the server. For example, for a redis server a “redis.conf” will contain settings which will be dynamically loaded when the redis server is run. This local configuration will also contain information about currently running containers as well. Note: If there is an exisiting subdirectory containing a merlin server configuration then only missing files will be replaced. However it is recommended that users backup their local configurations. Checking Merlin Server Status (merlin server status) Displays the current status of the merlin server. Starting up a Merlin Server (merlin server start) Starts the container located in the local merlin server configuration. Note: If this command seems to hang and never release control back to you, follow these steps: 1. Kill the command with Ctrl+C 2. Run either export LC_ALL="C.UTF-8" or export LC_ALL="C" 3. Re-run the merlin server start command 100 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 Stopping an exisiting Merlin Server (merlin server stop) Stop any exisiting container being managed and monitored by merlin server. Restarting a Merlin Server instance (merlin server restart) Restarting an existing container that is being managed and monitored by merlin server. Configurating Merlin Server instance (merlin server config) Place holder for information regarding merlin server config command Possible Flags -ip IPADDRESS, --ipaddress IPADDRESS Set the binded IP address for the merlin server container. (default: None) -p PORT, --port PORT Set the binded port for the merlin server container. (default: None) -pwd PASSWORD, --password PASSWORD Set the password file to be used for merlin server container. (default: None) --add-user ADD_USER ADD_USER Create a new user for merlin server instance. (Provide both username and password) (default: None) --remove-user REMOVE_USER Remove an exisiting user. (default: None) -d DIRECTORY, --directory DIRECTORY Set the working directory of the merlin server container. (default: None) -ss SNAPSHOT_SECONDS, --snapshot-seconds SNAPSHOT_SECONDS Set the number of seconds merlin server waits before checking if a snapshot is needed. (default: None) -sc SNAPSHOT_CHANGES, --snapshot-changes SNAPSHOT_CHANGES Set the number of changes that are required to be made to the merlin server before a snapshot is made. (default: None) -sf SNAPSHOT_FILE, --snapshot-file SNAPSHOT_FILE Set the snapshot filename for database dumps. (default: None) -am APPEND_MODE, --append-mode APPEND_MODE The appendonly mode to be set. The avaiable options are always, everysec, no. (default: None) -af APPEND_FILE, --append-file APPEND_FILE Set append only filename for merlin server container. (default: None) 1.9. Merlin Server 101 Merlin Documentation, Release 1.11.0 1.10 Celery Merlin uses Celery, a Python based distributed task management system. Merlin uses Celery to queue work which is processed by Celery workers. Merlin queues tasks to the broker which receives and routes tasks. Merlin by default is configured to use RabbitMQ. Celery has many functions, it defines the interface to the task broker, the backend results database and the workers that will run the tasks. The broker and backend are configured through the app.yaml file. A configuration for the rabbit ampq server is shown below. celery: # directory where Merlin looks for the following: # mysql-ca-cert.pem rabbit-client-cert.pem rabbit-client-key.pem redis.pass certs: /path/to/celery/config broker: # can be rabbitmq, redis, rediss, or redis+sock name: rabbitmq #username: # defaults to your username unless changed here password: ~/.merlin/rabbit-password # server URL server: server.domain.com ### for rabbitmq connections ### #vhost: # defaults to your username unless changed here ### for redis+sock connections ### #socketname: the socket name your redis connection can be found on. #path: The path to the socket. ### for redis/rediss connections ### #port: The port number redis is listening on (default 6379) #db_num: The data base number to connect to. # ssl security #keyfile: /var/ssl/private/client-key.pem #certfile: /var/ssl/amqp-server-cert.pem #ca_certs: /var/ssl/myca.pem # This is optional and can be required, optional or none # (required is the default) #cert_reqs: required results_backend: # Can be redis,rediss, mysql, db+ or memcached server # Only a few of these are directly configured by merlin name: redis dbname: dbname username: username # name of file where redis password is stored. (continues on next page) 102 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 (continued from previous page) password: redis.pass server: server.domain.com # merlin will generate this key if it does not exist yet, # and will use it to encrypt all data over the wire to # your redis server. encryption_key: ~/.merlin/encrypt_data_key port: 6379 db_num: 0 # ssl security #keyfile: /var/ssl/private/client-key.pem #certfile: /var/ssl/amqp-server-cert.pem #ca_certs: /var/ssl/myca.pem # This is optional and can be required, optional or none # (required is the default) #cert_reqs: required The default location for the app.yaml is in the merlin repo under the config directory. This default can be overridden by files in one of two other locations. The current working directory is first checked for the app.yaml file, then the user’s ~/.merlin directory is checked. The celery command needs application configuration for the specific module that includes celery, this is specified using the -A <module> syntax. All celery commands should include the -A argument. celery -A merlin The merlin run command will define the tasks from the steps in the yaml file and then send them to the broker through the celery broker interface. If these tasks are no longer needed or are incorrect, they can be purged by using one of these commands: celery -A merlin -Q <queue list> purge # This is the equivalent of merlin purge <yaml file> e.g. celery -A merlin -Q merlin,queue2,queue3 purge or with rabbitmq: celery -A merlin amqp queue.purge <queue name> e.g. celery -A merlin amqp queue.purge merlin a third option with rabbitmq is deleting the queue celery -A merlin amqp queue.delete <queue> e.g. celery -A merlin amqp queue.delete merlin 1.10. Celery 103 Merlin Documentation, Release 1.11.0 1.10.1 Configuring celery workers The common configurations used for the celery workers in the celery workers guide are not the best for HPC applica- tions. Here are some parameters you may want to use for HPC specific workflows. These options can be altered by setting the args for an entry of type worker in the merlin resources section. The number of threads to use on each node of the HPC allocation is set through the --concurrency keyword. A good choice for this is the number of simulations that can be run per node. celery -A merlin worker --concurrency <num threads> e.g. # If the HPC simulation is a simple 1D short running sim # then on Lassen you might want to use all Hardware threads. celery -A merlin worker --concurrency 160 # If the HPC simulation will take the whole node you may want # to limit this to only a few threads. celery -A merlin worker --concurrency 2 The --prefetch-multiplier argument sets how many tasks are requested from the task server per worker thread. If --concurrency is 2 and --prefetch-multiplier is 3, then 6 tasks will be requested from the task server by the worker threads. Since HPC tasks are generally not short running tasks, the recommendation is to set this to 1. celery -A merlin worker --prefetch-multiplier <num_tasks> e.g. celery -A merlin worker --prefetch-multiplier 1 The -O fair option is another parameter used for long running celery tasks. With this set, celery will only send tasks to threads that are available to run them. celery -A merlin worker -O fair The -n option allows the workers to be given a unique name so multiple workers running tasks from different queues may share the allocation resources. The names are automatically set to <queue name>.%h, where <queue name> is from the task_queue config or merlin (default) and %h will resolve to the hostname of the compute node. celery -A merlin worker -n <name> e.g. celery -A merlin worker -n merlin.%h or celery -A merlin worker -n queue_1.%h On the toss3 nodes, the CPU affinity can be set for the worker processes. This is enabled by setting the environment variable CELERY_AFFINIITY to the number of CPUs to skip. e.g. export CELERY_AFFINIITY=4 This will skip 4 CPUs between each celery worker thread. 104 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 1.11 Virtual environments This section provides a quick reference for using virtual environments for the Merlin project. 1.11.1 Creating a virtual environment To create a new virtual environment: $ python3 -m venv venv Caution: A virtual environment will need to be created for each system type. It’s recommended to name the virtual environment venv_<system> to make it easier to switch between them. This documentation will use venv for simplicity to reference the virtual environment. Tip: Virtual environments provide an isolated environment for working on Python projects to avoid dependency conflicts. Activating a Virtualenv Once the virtual environment is created it can be activated like so: $ source venv/bin/activate (venv) $ This will set the Python and Pip path to the virtual environment at venv/bin/python and venv/bin/pip respectively. The virtual environment name should now display in the terminal, which means it is active. Any calls to pip will install to the virtual environment. Tip: To verify that Python and Pip are pointing to the virtual environment, run $ which python and $ which pip. Deactivating a Virtualenv Virtualenvs can be exited via the following: (venv) $ deactivate $ 1.11. Virtual environments 105 Merlin Documentation, Release 1.11.0 1.12 Spack The virtualenv method is not the only method to install merlin in a separate python install. The spack method will build python and all required modules for a specific set of configuration options. These options include the compiler version, system type and python version. Merlin will then be installed in this specific version allowing for multiple python versions on a single system without the need for a virtualenv. The py-merlin package builds with python3.6+. 1.12.1 Checkout spack Get the latest version of spack from Github. This is independent from merlin so make sure merlin and spack are in separate directories. git clone https://github.com/spack/spack.git # The merlin spack package is in the develop branch git checkout develop 1.12.2 Setup spack cd to spack directory Source the setup-env.sh or setup-env.csh. This will put spack in your path and setup module access for later use. This should be done every time the modules are used. source ./share/spack/setup-env.sh Add compilers if you haven’t already: spack compiler add To see the compilers. spack compiler list 1.12.3 Build merlin Build merlin, this will take a long time, be prepared to wait. It will build python and all python modules merlin needs including numpy. spack install py-merlin The build will be done with the default compiler, in general this is the newest gcc compiler. You can choose a different compiler by using the % syntax, this will create an entirely separate build and module. spack install py-merlin%gcc@7.1.0 A different python version can be specified as part of the package config. To build merlin with python-3.6.8 you would type: spack install py-merlin^python@3.6.8 A tree of all of the packages and their dependencies needed to build the merlin package can be shown by using the spec keyword. 106 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 spack spec py-merlin 1.12.4 Activate merlin To use merlin you can activate the module. spack activate py-merlin or spack activate py-merlin%gcc@7.1.0 or spack activate py-merlin^python@3.6.8 1.12.5 Load python The associated python module can then be loaded into your environment, this will only work if you have sourced the setup-env.sh or setup-env.csh. module avail python example: ------ <path to>/spack/share/spack/modules/linux-rhel7-x86_64 ------- python-3.6.8-gcc-8.1.0-4ilk3kn (L) This will give you a list, the spack version will have a long hash associated with the name. module load python-3.6.8-<compiler>-<hash> e.g. module load python-3.6.8-gcc-8.1.0-4ilk3kn At this point the module specific python, merlin, maestro and celery will all be in your path. 1.13 Contributing Welcome to the Merlin developer documentation! This section provides instructions for contributing to Merlin. 1.13.1 Getting Started Follow the Getting Started documentation to setup your Merlin development environment. Once your development is setup create a branch: $ git checkout -b feature/<username>/description Note: Other common types of branches besides feature are: bugfix, hotfix, or refactor. 1.13. Contributing 107 Merlin Documentation, Release 1.11.0 Select the branch type that best represents the development. Merlin follows a gitflow workflow. Updates to the develop branch are made via pull requests. 1.13.2 Developer Guide This section provides Merlin’s guide for contributing features/bugfixes to Merlin. 1.13.3 Pull Request Checklist Warning: All pull requests must pass make tests prior to consideration! To expedite review, please ensure that pull requests • Are from a meaningful branch name (e.g. feature/my_name/cool_thing) • Are being merged into the appropriate branch • Include testing for any new features – unit tests in tests/unit – integration tests in tests/integration • Include descriptions of the changes – a summary in the pull request – details in CHANGELOG.md • Ran make fix-style to adhere to style guidelines • Pass make tests; output included in pull request • Increment version number appropriately – in CHANGELOG.md – in merlin.__init__.py • Have squashed commits 1.13.4 Testing All pull requests must pass unit and integration tests. To ensure that they do run $ make tests 108 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 1.13.5 Python Code Style Guide This section documents Merlin’s style guide. Unless otherwise specified, PEP-8 is the preferred coding style and PEP- 0257 for docstrings. Note: make fix-style will automatically fix any style issues. Merlin has style checkers configured. They can be run from the Makefile: $ make check-style 1.13.6 Adding New Features to YAML Spec File In order to conform to Maestro’s verification format introduced in Maestro v1.1.7, we now use json schema validation to verify our spec file. If you are adding a new feature to Merlin that requires a new block within the yaml spec file or a new property within a block, then you are going to need to update the merlinspec.json file located in the merlin/spec/ directory. You also may want to add additional verifications within the specification.py file located in the same directory. Note: If you add custom verifications beyond the pattern checking that the json schema checks for, then you should also add tests for this verification in the test_specification.py file located in the merlin/tests/unit/spec/ directory. Follow the steps for adding new tests in the docstring of the TestCustomVerification class. Adding a New Property To add a new property to a block in the yaml file, you need to create a template for that property and place it in the correct block in merlinspec.json. For example, say I wanted to add a new property called example that’s an integer within the description block. I would modify the description block in the merlinspec.json file to look like this: "DESCRIPTION": { "type": "object", "properties": { "name": {"type": "string", "minLength": 1}, "description": {"type": "string", "minLength": 1}, "example": {"type": "integer", "minimum": 1} }, "required": ["name", "description"] } If you need help with json schema formatting, check out the step-by-step getting started guide. That’s all that’s required of adding a new property. If you want to add your own custom verifications make sure to create unit tests for them (see the note above for more info). 1.13. Contributing 109 Merlin Documentation, Release 1.11.0 Adding a New Block Adding a new block is slightly more complicated than adding a new property. You will not only have to update the merlinspec.json schema file but also add calls to verify that block within specification.py. To add a block to the json schema, you will need to define the template for that entire block. For example, if I wanted to create a block called country with two properties labeled name and population that are both required, it would look like so: "COUNTRY": { "type": "object", "properties": { "name": {"type": "string", "minLength": 1}, "population": { "anyOf": [ {"type": "string", "minLength": 1}, {"type": "integer", "minimum": 1} ] } }, "required": ["name", "capital"] } Here, name can only be a string but population can be both a string and an integer. For help with json schema formatting, check out the step-by-step getting started guide. The next step is to enable this block in the schema validation process. To do this we need to: 1. Create a new method called verify_<your_block_name>() within the MerlinSpec class 2. Call the YAMLSpecification.validate_schema() method provided to us via Maestro in your new method 3. Add a call to verify_<your_block_name>() inside the verify() method If you add your own custom verifications on top of this, please add unit tests for them. 1.14 Docker Merlin has a simple Dockerfile description for running a container with all requirements installed. 1.14.1 Build the container The docker container can be built by building in the top level merlin directory. docker build -t merlin . This will create a merlin:latest image in your docker image collection with a user “merlinu” and a WORKDIR set to /home/merlinu. docker images 110 Chapter 1. Merlin Overview Merlin Documentation, Release 1.11.0 1.14.2 Run the container The container can be run in detached mode to provide both the merlin and celery commands docker run --rm -td --name my-merlin merlin alias merlin="docker exec my-merlin merlin" alias celery="docker exec my-merlin celery" Examples can be run through docker containers by first starting a server for the broker and backend. The server can be a redis or rabbitmq , for this demonstration a redis server will be used. The backend will always be a redis server. docker pull redis docker run -d -p 6379:6379 --name my-redis redis A local output directory can be defined by using the --volume docker arguments. It is recommended that a fixed directory be used for the --volume argument. The merlin docker container is linked to the redis server above by using the --link option. # Create local working directory mkdir $HOME/merlinu cd $HOME/merlinu docker pull llnl/merlin docker run --rm -td --name my-merlin --link my-redis --volume "$HOME/merlinu":/home/ ˓→merlinu llnl/merlin alias merlin="docker exec my-merlin merlin" alias celery="docker exec my-merlin celery" # Create the $HOME/merlinu/.merlin/app.yaml using redis merlin config --broker redis <edit $HOME/merlinu/.merlin/app.yaml and change the broker and backend server: variables␣ ˓→to my-redis> # Copy an example to the local dir merlin example feature_demo # Run a test run without workers merlin run feature_demo/feature_demo.yaml --dry --local # Define the tasks and load them on the broker merlin run feature_demo/feature_demo.yaml # Start workers to pull tasks from the server and run them in the container merlin run-workers feature_demo/feature_demo.yaml A shell can started in the container by using the --entrypoint command. If the user would like to examine the container contents, they can use a shell as the entry point. docker run --rm -ti --volume "$HOME/merlinu":/home/merlinu --entrypoint="/bin/bash"␣ ˓→merlin 1.14. Docker 111

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redis-py 2.10.5 documentation [redis-py](#) 3.5.3 [redis-py](#) * » * redis-py 2.10.5 documentation * [Edit on GitHub](https://github.com/redis/redis-py/blob/3.5.3/docs/index) --- Welcome to redis-py’s documentation ========================================================================================================== Indices and tables[¶](#indices-and-tables "Permalink to this headline") ----------------------------------------------------------------------- * [Index](genindex.html) * [Module Index](py-modindex.html) * [Search Page](search.html) Contents:[¶](#contents "Permalink to this headline") ---------------------------------------------------- *exception* redis.AuthenticationError[[source]](_modules/redis/exceptions.html#AuthenticationError)[¶](#redis.AuthenticationError "Permalink to this definition") *exception* redis.AuthenticationWrongNumberOfArgsError[[source]](_modules/redis/exceptions.html#AuthenticationWrongNumberOfArgsError)[¶](#redis.AuthenticationWrongNumberOfArgsError "Permalink to this definition") An error to indicate that the wrong number of args were sent to the AUTH command *class* redis.BlockingConnectionPool(*max\_connections=50*, *timeout=20*, *connection\_class=<class 'redis.connection.Connection'>*, *queue\_class=<class 'queue.LifoQueue'>*, *\*\*connection\_kwargs*)[[source]](_modules/redis/connection.html#BlockingConnectionPool)[¶](#redis.BlockingConnectionPool "Permalink to this definition") Thread-safe blocking connection pool: ``` >>> from redis.client import Redis >>> client = Redis(connection\_pool=BlockingConnectionPool()) ``` It performs the same function as the default `:py:class: ~redis.connection.ConnectionPool` implementation, in that, it maintains a pool of reusable connections that can be shared by multiple redis clients (safely across threads if required). The difference is that, in the event that a client tries to get a connection from the pool when all of connections are in use, rather than raising a `:py:class: ~redis.exceptions.ConnectionError` (as the default `:py:class: ~redis.connection.ConnectionPool` implementation does), it makes the client wait (“blocks”) for a specified number of seconds until a connection becomes available. Use `max\_connections` to increase / decrease the pool size: ``` >>> pool = BlockingConnectionPool(max\_connections=10) ``` Use `timeout` to tell it either how many seconds to wait for a connection to become available, or to block forever: > > # Block forever. > >>> pool = BlockingConnectionPool(timeout=None) > > > # Raise a `ConnectionError` after five seconds if a connection is > # not available. > >>> pool = BlockingConnectionPool(timeout=5) > > > disconnect()[[source]](_modules/redis/connection.html#BlockingConnectionPool.disconnect)[¶](#redis.BlockingConnectionPool.disconnect "Permalink to this definition") Disconnects all connections in the pool. get\_connection(*command\_name*, *\*keys*, *\*\*options*)[[source]](_modules/redis/connection.html#BlockingConnectionPool.get_connection)[¶](#redis.BlockingConnectionPool.get_connection "Permalink to this definition") Get a connection, blocking for `self.timeout` until a connection is available from the pool. If the connection returned is `None` then creates a new connection. Because we use a last-in first-out queue, the existing connections (having been returned to the pool after the initial `None` values were added) will be returned before `None` values. This means we only create new connections when we need to, i.e.: the actual number of connections will only increase in response to demand. make\_connection()[[source]](_modules/redis/connection.html#BlockingConnectionPool.make_connection)[¶](#redis.BlockingConnectionPool.make_connection "Permalink to this definition") Make a fresh connection. release(*connection*)[[source]](_modules/redis/connection.html#BlockingConnectionPool.release)[¶](#redis.BlockingConnectionPool.release "Permalink to this definition") Releases the connection back to the pool. *exception* redis.BusyLoadingError[[source]](_modules/redis/exceptions.html#BusyLoadingError)[¶](#redis.BusyLoadingError "Permalink to this definition") *exception* redis.ChildDeadlockedError[[source]](_modules/redis/exceptions.html#ChildDeadlockedError)[¶](#redis.ChildDeadlockedError "Permalink to this definition") Error indicating that a child process is deadlocked after a fork() *class* redis.Connection(*host='localhost'*, *port=6379*, *db=0*, *password=None*, *socket\_timeout=None*, *socket\_connect\_timeout=None*, *socket\_keepalive=False*, *socket\_keepalive\_options=None*, *socket\_type=0*, *retry\_on\_timeout=False*, *encoding='utf-8'*, *encoding\_errors='strict'*, *decode\_responses=False*, *parser\_class=<class 'redis.connection.PythonParser'>*, *socket\_read\_size=65536*, *health\_check\_interval=0*, *client\_name=None*, *username=None*)[[source]](_modules/redis/connection.html#Connection)[¶](#redis.Connection "Permalink to this definition") Manages TCP communication to and from a Redis server can\_read(*timeout=0*)[[source]](_modules/redis/connection.html#Connection.can_read)[¶](#redis.Connection.can_read "Permalink to this definition") Poll the socket to see if there’s data that can be read. check\_health()[[source]](_modules/redis/connection.html#Connection.check_health)[¶](#redis.Connection.check_health "Permalink to this definition") Check the health of the connection with a PING/PONG connect()[[source]](_modules/redis/connection.html#Connection.connect)[¶](#redis.Connection.connect "Permalink to this definition") Connects to the Redis server if not already connected disconnect()[[source]](_modules/redis/connection.html#Connection.disconnect)[¶](#redis.Connection.disconnect "Permalink to this definition") Disconnects from the Redis server on\_connect()[[source]](_modules/redis/connection.html#Connection.on_connect)[¶](#redis.Connection.on_connect "Permalink to this definition") Initialize the connection, authenticate and select a database pack\_command(*\*args*)[[source]](_modules/redis/connection.html#Connection.pack_command)[¶](#redis.Connection.pack_command "Permalink to this definition") Pack a series of arguments into the Redis protocol pack\_commands(*commands*)[[source]](_modules/redis/connection.html#Connection.pack_commands)[¶](#redis.Connection.pack_commands "Permalink to this definition") Pack multiple commands into the Redis protocol read\_response()[[source]](_modules/redis/connection.html#Connection.read_response)[¶](#redis.Connection.read_response "Permalink to this definition") Read the response from a previously sent command send\_command(*\*args*, *\*\*kwargs*)[[source]](_modules/redis/connection.html#Connection.send_command)[¶](#redis.Connection.send_command "Permalink to this definition") Pack and send a command to the Redis server send\_packed\_command(*command*, *check\_health=True*)[[source]](_modules/redis/connection.html#Connection.send_packed_command)[¶](#redis.Connection.send_packed_command "Permalink to this definition") Send an already packed command to the Redis server *exception* redis.ConnectionError[[source]](_modules/redis/exceptions.html#ConnectionError)[¶](#redis.ConnectionError "Permalink to this definition") *class* redis.ConnectionPool(*connection\_class=<class 'redis.connection.Connection'>*, *max\_connections=None*, *\*\*connection\_kwargs*)[[source]](_modules/redis/connection.html#ConnectionPool)[¶](#redis.ConnectionPool "Permalink to this definition") Generic connection pool disconnect(*inuse\_connections=True*)[[source]](_modules/redis/connection.html#ConnectionPool.disconnect)[¶](#redis.ConnectionPool.disconnect "Permalink to this definition") Disconnects connections in the pool If `inuse\_connections` is True, disconnect connections that are current in use, potentially by other threads. Otherwise only disconnect connections that are idle in the pool. *classmethod* from\_url(*url*, *db=None*, *decode\_components=False*, *\*\*kwargs*)[[source]](_modules/redis/connection.html#ConnectionPool.from_url)[¶](#redis.ConnectionPool.from_url "Permalink to this definition") Return a connection pool configured from the given URL. For example: ``` redis://[[username]:[password]]@localhost:6379/0 rediss://[[username]:[password]]@localhost:6379/0 unix://[[username]:[password]]@/path/to/socket.sock?db=0 ``` Three URL schemes are supported: * ``redis://` <<https://www.iana.org/assignments/uri-schemes/prov/redis>>`\_ creates a normal TCP socket connection * ``rediss://` <<https://www.iana.org/assignments/uri-schemes/prov/rediss>>`\_ creates a SSL wrapped TCP socket connection * `unix://` creates a Unix Domain Socket connection There are several ways to specify a database number. The parse function will return the first specified option: > > 1. A `db` querystring option, e.g. redis://localhost?db=0 > 2. If using the redis:// scheme, the path argument of the url, e.g. > redis://localhost/0 > 3. The `db` argument to this function. > > > If none of these options are specified, db=0 is used. The `decode\_components` argument allows this function to work with percent-encoded URLs. If this argument is set to `True` all `%xx` escapes will be replaced by their single-character equivalents after the URL has been parsed. This only applies to the `hostname`, `path`, `username` and `password` components. Any additional querystring arguments and keyword arguments will be passed along to the ConnectionPool class’s initializer. The querystring arguments `socket\_connect\_timeout` and `socket\_timeout` if supplied are parsed as float values. The arguments `socket\_keepalive` and `retry\_on\_timeout` are parsed to boolean values that accept True/False, Yes/No values to indicate state. Invalid types cause a `UserWarning` to be raised. In the case of conflicting arguments, querystring arguments always win. get\_connection(*command\_name*, *\*keys*, *\*\*options*)[[source]](_modules/redis/connection.html#ConnectionPool.get_connection)[¶](#redis.ConnectionPool.get_connection "Permalink to this definition") Get a connection from the pool get\_encoder()[[source]](_modules/redis/connection.html#ConnectionPool.get_encoder)[¶](#redis.ConnectionPool.get_encoder "Permalink to this definition") Return an encoder based on encoding settings make\_connection()[[source]](_modules/redis/connection.html#ConnectionPool.make_connection)[¶](#redis.ConnectionPool.make_connection "Permalink to this definition") Create a new connection release(*connection*)[[source]](_modules/redis/connection.html#ConnectionPool.release)[¶](#redis.ConnectionPool.release "Permalink to this definition") Releases the connection back to the pool *exception* redis.DataError[[source]](_modules/redis/exceptions.html#DataError)[¶](#redis.DataError "Permalink to this definition") *exception* redis.InvalidResponse[[source]](_modules/redis/exceptions.html#InvalidResponse)[¶](#redis.InvalidResponse "Permalink to this definition") *exception* redis.PubSubError[[source]](_modules/redis/exceptions.html#PubSubError)[¶](#redis.PubSubError "Permalink to this definition") *exception* redis.ReadOnlyError[[source]](_modules/redis/exceptions.html#ReadOnlyError)[¶](#redis.ReadOnlyError "Permalink to this definition") *class* redis.Redis(*host='localhost'*, *port=6379*, *db=0*, *password=None*, *socket\_timeout=None*, *socket\_connect\_timeout=None*, *socket\_keepalive=None*, *socket\_keepalive\_options=None*, *connection\_pool=None*, *unix\_socket\_path=None*, *encoding='utf-8'*, *encoding\_errors='strict'*, *charset=None*, *errors=None*, *decode\_responses=False*, *retry\_on\_timeout=False*, *ssl=False*, *ssl\_keyfile=None*, *ssl\_certfile=None*, *ssl\_cert\_reqs='required'*, *ssl\_ca\_certs=None*, *ssl\_check\_hostname=False*, *max\_connections=None*, *single\_connection\_client=False*, *health\_check\_interval=0*, *client\_name=None*, *username=None*)[[source]](_modules/redis/client.html#Redis)[¶](#redis.Redis "Permalink to this definition") Implementation of the Redis protocol. This abstract class provides a Python interface to all Redis commands and an implementation of the Redis protocol. Connection and Pipeline derive from this, implementing how the commands are sent and received to the Redis server acl\_cat(*category=None*)[[source]](_modules/redis/client.html#Redis.acl_cat)[¶](#redis.Redis.acl_cat "Permalink to this definition") Returns a list of categories or commands within a category. If `category` is not supplied, returns a list of all categories. If `category` is supplied, returns a list of all commands within that category. acl\_deluser(*username*)[[source]](_modules/redis/client.html#Redis.acl_deluser)[¶](#redis.Redis.acl_deluser "Permalink to this definition") Delete the ACL for the specified `username` acl\_genpass()[[source]](_modules/redis/client.html#Redis.acl_genpass)[¶](#redis.Redis.acl_genpass "Permalink to this definition") Generate a random password value acl\_getuser(*username*)[[source]](_modules/redis/client.html#Redis.acl_getuser)[¶](#redis.Redis.acl_getuser "Permalink to this definition") Get the ACL details for the specified `username`. If `username` does not exist, return None acl\_list()[[source]](_modules/redis/client.html#Redis.acl_list)[¶](#redis.Redis.acl_list "Permalink to this definition") Return a list of all ACLs on the server acl\_load()[[source]](_modules/redis/client.html#Redis.acl_load)[¶](#redis.Redis.acl_load "Permalink to this definition") Load ACL rules from the configured `aclfile`. Note that the server must be configured with the `aclfile` directive to be able to load ACL rules from an aclfile. acl\_save()[[source]](_modules/redis/client.html#Redis.acl_save)[¶](#redis.Redis.acl_save "Permalink to this definition") Save ACL rules to the configured `aclfile`. Note that the server must be configured with the `aclfile` directive to be able to save ACL rules to an aclfile. acl\_setuser(*username*, *enabled=False*, *nopass=False*, *passwords=None*, *hashed\_passwords=None*, *categories=None*, *commands=None*, *keys=None*, *reset=False*, *reset\_keys=False*, *reset\_passwords=False*)[[source]](_modules/redis/client.html#Redis.acl_setuser)[¶](#redis.Redis.acl_setuser "Permalink to this definition") Create or update an ACL user. Create or update the ACL for `username`. If the user already exists, the existing ACL is completely overwritten and replaced with the specified values. `enabled` is a boolean indicating whether the user should be allowed to authenticate or not. Defaults to `False`. `nopass` is a boolean indicating whether the can authenticate without a password. This cannot be True if `passwords` are also specified. `passwords` if specified is a list of plain text passwords to add to or remove from the user. Each password must be prefixed with a ‘+’ to add or a ‘-’ to remove. For convenience, the value of `add\_passwords` can be a simple prefixed string when adding or removing a single password. `hashed\_passwords` if specified is a list of SHA-256 hashed passwords to add to or remove from the user. Each hashed password must be prefixed with a ‘+’ to add or a ‘-’ to remove. For convenience, the value of `hashed\_passwords` can be a simple prefixed string when adding or removing a single password. `categories` if specified is a list of strings representing category permissions. Each string must be prefixed with either a ‘+’ to add the category permission or a ‘-’ to remove the category permission. `commands` if specified is a list of strings representing command permissions. Each string must be prefixed with either a ‘+’ to add the command permission or a ‘-’ to remove the command permission. `keys` if specified is a list of key patterns to grant the user access to. Keys patterns allow ‘\*’ to support wildcard matching. For example, ‘\*’ grants access to all keys while ‘cache:[\*](#id1)’ grants access to all keys that are prefixed with ‘cache:’. `keys` should not be prefixed with a ‘~’. `reset` is a boolean indicating whether the user should be fully reset prior to applying the new ACL. Setting this to True will remove all existing passwords, flags and privileges from the user and then apply the specified rules. If this is False, the user’s existing passwords, flags and privileges will be kept and any new specified rules will be applied on top. `reset\_keys` is a boolean indicating whether the user’s key permissions should be reset prior to applying any new key permissions specified in `keys`. If this is False, the user’s existing key permissions will be kept and any new specified key permissions will be applied on top. `reset\_passwords` is a boolean indicating whether to remove all existing passwords and the ‘nopass’ flag from the user prior to applying any new passwords specified in ‘passwords’ or ‘hashed\_passwords’. If this is False, the user’s existing passwords and ‘nopass’ status will be kept and any new specified passwords or hashed\_passwords will be applied on top. acl\_users()[[source]](_modules/redis/client.html#Redis.acl_users)[¶](#redis.Redis.acl_users "Permalink to this definition") Returns a list of all registered users on the server. acl\_whoami()[[source]](_modules/redis/client.html#Redis.acl_whoami)[¶](#redis.Redis.acl_whoami "Permalink to this definition") Get the username for the current connection append(*key*, *value*)[[source]](_modules/redis/client.html#Redis.append)[¶](#redis.Redis.append "Permalink to this definition") Appends the string `value` to the value at `key`. If `key` doesn’t already exist, create it with a value of `value`. Returns the new length of the value at `key`. bgrewriteaof()[[source]](_modules/redis/client.html#Redis.bgrewriteaof)[¶](#redis.Redis.bgrewriteaof "Permalink to this definition") Tell the Redis server to rewrite the AOF file from data in memory. bgsave()[[source]](_modules/redis/client.html#Redis.bgsave)[¶](#redis.Redis.bgsave "Permalink to this definition") Tell the Redis server to save its data to disk. Unlike save(), this method is asynchronous and returns immediately. bitcount(*key*, *start=None*, *end=None*)[[source]](_modules/redis/client.html#Redis.bitcount)[¶](#redis.Redis.bitcount "Permalink to this definition") Returns the count of set bits in the value of `key`. Optional `start` and `end` paramaters indicate which bytes to consider bitfield(*key*, *default\_overflow=None*)[[source]](_modules/redis/client.html#Redis.bitfield)[¶](#redis.Redis.bitfield "Permalink to this definition") Return a BitFieldOperation instance to conveniently construct one or more bitfield operations on `key`. bitop(*operation*, *dest*, *\*keys*)[[source]](_modules/redis/client.html#Redis.bitop)[¶](#redis.Redis.bitop "Permalink to this definition") Perform a bitwise operation using `operation` between `keys` and store the result in `dest`. bitpos(*key*, *bit*, *start=None*, *end=None*)[[source]](_modules/redis/client.html#Redis.bitpos)[¶](#redis.Redis.bitpos "Permalink to this definition") Return the position of the first bit set to 1 or 0 in a string. `start` and `end` difines search range. The range is interpreted as a range of bytes and not a range of bits, so start=0 and end=2 means to look at the first three bytes. blpop(*keys*, *timeout=0*)[[source]](_modules/redis/client.html#Redis.blpop)[¶](#redis.Redis.blpop "Permalink to this definition") LPOP a value off of the first non-empty list named in the `keys` list. If none of the lists in `keys` has a value to LPOP, then block for `timeout` seconds, or until a value gets pushed on to one of the lists. If timeout is 0, then block indefinitely. brpop(*keys*, *timeout=0*)[[source]](_modules/redis/client.html#Redis.brpop)[¶](#redis.Redis.brpop "Permalink to this definition") RPOP a value off of the first non-empty list named in the `keys` list. If none of the lists in `keys` has a value to RPOP, then block for `timeout` seconds, or until a value gets pushed on to one of the lists. If timeout is 0, then block indefinitely. brpoplpush(*src*, *dst*, *timeout=0*)[[source]](_modules/redis/client.html#Redis.brpoplpush)[¶](#redis.Redis.brpoplpush "Permalink to this definition") Pop a value off the tail of `src`, push it on the head of `dst` and then return it. This command blocks until a value is in `src` or until `timeout` seconds elapse, whichever is first. A `timeout` value of 0 blocks forever. bzpopmax(*keys*, *timeout=0*)[[source]](_modules/redis/client.html#Redis.bzpopmax)[¶](#redis.Redis.bzpopmax "Permalink to this definition") ZPOPMAX a value off of the first non-empty sorted set named in the `keys` list. If none of the sorted sets in `keys` has a value to ZPOPMAX, then block for `timeout` seconds, or until a member gets added to one of the sorted sets. If timeout is 0, then block indefinitely. bzpopmin(*keys*, *timeout=0*)[[source]](_modules/redis/client.html#Redis.bzpopmin)[¶](#redis.Redis.bzpopmin "Permalink to this definition") ZPOPMIN a value off of the first non-empty sorted set named in the `keys` list. If none of the sorted sets in `keys` has a value to ZPOPMIN, then block for `timeout` seconds, or until a member gets added to one of the sorted sets. If timeout is 0, then block indefinitely. client\_getname()[[source]](_modules/redis/client.html#Redis.client_getname)[¶](#redis.Redis.client_getname "Permalink to this definition") Returns the current connection name client\_id()[[source]](_modules/redis/client.html#Redis.client_id)[¶](#redis.Redis.client_id "Permalink to this definition") Returns the current connection id client\_kill(*address*)[[source]](_modules/redis/client.html#Redis.client_kill)[¶](#redis.Redis.client_kill "Permalink to this definition") Disconnects the client at `address` (ip:port) client\_kill\_filter(*\_id=None*, *\_type=None*, *addr=None*, *skipme=None*)[[source]](_modules/redis/client.html#Redis.client_kill_filter)[¶](#redis.Redis.client_kill_filter "Permalink to this definition") Disconnects client(s) using a variety of filter options :param id: Kills a client by its unique ID field :param type: Kills a client by type where type is one of ‘normal’, ‘master’, ‘slave’ or ‘pubsub’ :param addr: Kills a client by its ‘address:port’ :param skipme: If True, then the client calling the command will not get killed even if it is identified by one of the filter options. If skipme is not provided, the server defaults to skipme=True client\_list(*\_type=None*)[[source]](_modules/redis/client.html#Redis.client_list)[¶](#redis.Redis.client_list "Permalink to this definition") Returns a list of currently connected clients. If type of client specified, only that type will be returned. :param \_type: optional. one of the client types (normal, master, > > replica, pubsub) > > > client\_pause(*timeout*)[[source]](_modules/redis/client.html#Redis.client_pause)[¶](#redis.Redis.client_pause "Permalink to this definition") Suspend all the Redis clients for the specified amount of time :param timeout: milliseconds to pause clients client\_setname(*name*)[[source]](_modules/redis/client.html#Redis.client_setname)[¶](#redis.Redis.client_setname "Permalink to this definition") Sets the current connection name client\_unblock(*client\_id*, *error=False*)[[source]](_modules/redis/client.html#Redis.client_unblock)[¶](#redis.Redis.client_unblock "Permalink to this definition") Unblocks a connection by its client id. If `error` is True, unblocks the client with a special error message. If `error` is False (default), the client is unblocked using the regular timeout mechanism. config\_get(*pattern='\*'*)[[source]](_modules/redis/client.html#Redis.config_get)[¶](#redis.Redis.config_get "Permalink to this definition") Return a dictionary of configuration based on the `pattern` config\_resetstat()[[source]](_modules/redis/client.html#Redis.config_resetstat)[¶](#redis.Redis.config_resetstat "Permalink to this definition") Reset runtime statistics config\_rewrite()[[source]](_modules/redis/client.html#Redis.config_rewrite)[¶](#redis.Redis.config_rewrite "Permalink to this definition") Rewrite config file with the minimal change to reflect running config config\_set(*name*, *value*)[[source]](_modules/redis/client.html#Redis.config_set)[¶](#redis.Redis.config_set "Permalink to this definition") Set config item `name` with `value` dbsize()[[source]](_modules/redis/client.html#Redis.dbsize)[¶](#redis.Redis.dbsize "Permalink to this definition") Returns the number of keys in the current database debug\_object(*key*)[[source]](_modules/redis/client.html#Redis.debug_object)[¶](#redis.Redis.debug_object "Permalink to this definition") Returns version specific meta information about a given key decr(*name*, *amount=1*)[[source]](_modules/redis/client.html#Redis.decr)[¶](#redis.Redis.decr "Permalink to this definition") Decrements the value of `key` by `amount`. If no key exists, the value will be initialized as 0 - `amount` decrby(*name*, *amount=1*)[[source]](_modules/redis/client.html#Redis.decrby)[¶](#redis.Redis.decrby "Permalink to this definition") Decrements the value of `key` by `amount`. If no key exists, the value will be initialized as 0 - `amount` delete(*\*names*)[[source]](_modules/redis/client.html#Redis.delete)[¶](#redis.Redis.delete "Permalink to this definition") Delete one or more keys specified by `names` dump(*name*)[[source]](_modules/redis/client.html#Redis.dump)[¶](#redis.Redis.dump "Permalink to this definition") Return a serialized version of the value stored at the specified key. If key does not exist a nil bulk reply is returned. echo(*value*)[[source]](_modules/redis/client.html#Redis.echo)[¶](#redis.Redis.echo "Permalink to this definition") Echo the string back from the server eval(*script*, *numkeys*, *\*keys\_and\_args*)[[source]](_modules/redis/client.html#Redis.eval)[¶](#redis.Redis.eval "Permalink to this definition") Execute the Lua `script`, specifying the `numkeys` the script will touch and the key names and argument values in `keys\_and\_args`. Returns the result of the script. In practice, use the object returned by `register\_script`. This function exists purely for Redis API completion. evalsha(*sha*, *numkeys*, *\*keys\_and\_args*)[[source]](_modules/redis/client.html#Redis.evalsha)[¶](#redis.Redis.evalsha "Permalink to this definition") Use the `sha` to execute a Lua script already registered via EVAL or SCRIPT LOAD. Specify the `numkeys` the script will touch and the key names and argument values in `keys\_and\_args`. Returns the result of the script. In practice, use the object returned by `register\_script`. This function exists purely for Redis API completion. execute\_command(*\*args*, *\*\*options*)[[source]](_modules/redis/client.html#Redis.execute_command)[¶](#redis.Redis.execute_command "Permalink to this definition") Execute a command and return a parsed response exists(*\*names*)[[source]](_modules/redis/client.html#Redis.exists)[¶](#redis.Redis.exists "Permalink to this definition") Returns the number of `names` that exist expire(*name*, *time*)[[source]](_modules/redis/client.html#Redis.expire)[¶](#redis.Redis.expire "Permalink to this definition") Set an expire flag on key `name` for `time` seconds. `time` can be represented by an integer or a Python timedelta object. expireat(*name*, *when*)[[source]](_modules/redis/client.html#Redis.expireat)[¶](#redis.Redis.expireat "Permalink to this definition") Set an expire flag on key `name`. `when` can be represented as an integer indicating unix time or a Python datetime object. flushall(*asynchronous=False*)[[source]](_modules/redis/client.html#Redis.flushall)[¶](#redis.Redis.flushall "Permalink to this definition") Delete all keys in all databases on the current host. `asynchronous` indicates whether the operation is executed asynchronously by the server. flushdb(*asynchronous=False*)[[source]](_modules/redis/client.html#Redis.flushdb)[¶](#redis.Redis.flushdb "Permalink to this definition") Delete all keys in the current database. `asynchronous` indicates whether the operation is executed asynchronously by the server. *classmethod* from\_url(*url*, *db=None*, *\*\*kwargs*)[[source]](_modules/redis/client.html#Redis.from_url)[¶](#redis.Redis.from_url "Permalink to this definition") Return a Redis client object configured from the given URL For example: ``` redis://[[username]:[password]]@localhost:6379/0 rediss://[[username]:[password]]@localhost:6379/0 unix://[[username]:[password]]@/path/to/socket.sock?db=0 ``` Three URL schemes are supported: * ``redis://` <<http://www.iana.org/assignments/uri-schemes/prov/redis>>`\_ creates a normal TCP socket connection * ``rediss://` <<http://www.iana.org/assignments/uri-schemes/prov/rediss>>`\_ creates a SSL wrapped TCP socket connection * `unix://` creates a Unix Domain Socket connection There are several ways to specify a database number. The parse function will return the first specified option: > > 1. A `db` querystring option, e.g. redis://localhost?db=0 > 2. If using the redis:// scheme, the path argument of the url, e.g. > redis://localhost/0 > 3. The `db` argument to this function. > > > If none of these options are specified, db=0 is used. Any additional querystring arguments and keyword arguments will be passed along to the ConnectionPool class’s initializer. In the case of conflicting arguments, querystring arguments always win. geoadd(*name*, *\*values*)[[source]](_modules/redis/client.html#Redis.geoadd)[¶](#redis.Redis.geoadd "Permalink to this definition") Add the specified geospatial items to the specified key identified by the `name` argument. The Geospatial items are given as ordered members of the `values` argument, each item or place is formed by the triad longitude, latitude and name. geodist(*name*, *place1*, *place2*, *unit=None*)[[source]](_modules/redis/client.html#Redis.geodist)[¶](#redis.Redis.geodist "Permalink to this definition") Return the distance between `place1` and `place2` members of the `name` key. The units must be one of the following : m, km mi, ft. By default meters are used. geohash(*name*, *\*values*)[[source]](_modules/redis/client.html#Redis.geohash)[¶](#redis.Redis.geohash "Permalink to this definition") Return the geo hash string for each item of `values` members of the specified key identified by the `name` argument. geopos(*name*, *\*values*)[[source]](_modules/redis/client.html#Redis.geopos)[¶](#redis.Redis.geopos "Permalink to this definition") Return the positions of each item of `values` as members of the specified key identified by the `name` argument. Each position is represented by the pairs lon and lat. georadius(*name*, *longitude*, *latitude*, *radius*, *unit=None*, *withdist=False*, *withcoord=False*, *withhash=False*, *count=None*, *sort=None*, *store=None*, *store\_dist=None*)[[source]](_modules/redis/client.html#Redis.georadius)[¶](#redis.Redis.georadius "Permalink to this definition") Return the members of the specified key identified by the `name` argument which are within the borders of the area specified with the `latitude` and `longitude` location and the maximum distance from the center specified by the `radius` value. The units must be one of the following : m, km mi, ft. By default `withdist` indicates to return the distances of each place. `withcoord` indicates to return the latitude and longitude of each place. `withhash` indicates to return the geohash string of each place. `count` indicates to return the number of elements up to N. `sort` indicates to return the places in a sorted way, ASC for nearest to fairest and DESC for fairest to nearest. `store` indicates to save the places names in a sorted set named with a specific key, each element of the destination sorted set is populated with the score got from the original geo sorted set. `store\_dist` indicates to save the places names in a sorted set named with a specific key, instead of `store` the sorted set destination score is set with the distance. georadiusbymember(*name*, *member*, *radius*, *unit=None*, *withdist=False*, *withcoord=False*, *withhash=False*, *count=None*, *sort=None*, *store=None*, *store\_dist=None*)[[source]](_modules/redis/client.html#Redis.georadiusbymember)[¶](#redis.Redis.georadiusbymember "Permalink to this definition") This command is exactly like `georadius` with the sole difference that instead of taking, as the center of the area to query, a longitude and latitude value, it takes the name of a member already existing inside the geospatial index represented by the sorted set. get(*name*)[[source]](_modules/redis/client.html#Redis.get)[¶](#redis.Redis.get "Permalink to this definition") Return the value at key `name`, or None if the key doesn’t exist getbit(*name*, *offset*)[[source]](_modules/redis/client.html#Redis.getbit)[¶](#redis.Redis.getbit "Permalink to this definition") Returns a boolean indicating the value of `offset` in `name` getrange(*key*, *start*, *end*)[[source]](_modules/redis/client.html#Redis.getrange)[¶](#redis.Redis.getrange "Permalink to this definition") Returns the substring of the string value stored at `key`, determined by the offsets `start` and `end` (both are inclusive) getset(*name*, *value*)[[source]](_modules/redis/client.html#Redis.getset)[¶](#redis.Redis.getset "Permalink to this definition") Sets the value at key `name` to `value` and returns the old value at key `name` atomically. hdel(*name*, *\*keys*)[[source]](_modules/redis/client.html#Redis.hdel)[¶](#redis.Redis.hdel "Permalink to this definition") Delete `keys` from hash `name` hexists(*name*, *key*)[[source]](_modules/redis/client.html#Redis.hexists)[¶](#redis.Redis.hexists "Permalink to this definition") Returns a boolean indicating if `key` exists within hash `name` hget(*name*, *key*)[[source]](_modules/redis/client.html#Redis.hget)[¶](#redis.Redis.hget "Permalink to this definition") Return the value of `key` within the hash `name` hgetall(*name*)[[source]](_modules/redis/client.html#Redis.hgetall)[¶](#redis.Redis.hgetall "Permalink to this definition") Return a Python dict of the hash’s name/value pairs hincrby(*name*, *key*, *amount=1*)[[source]](_modules/redis/client.html#Redis.hincrby)[¶](#redis.Redis.hincrby "Permalink to this definition") Increment the value of `key` in hash `name` by `amount` hincrbyfloat(*name*, *key*, *amount=1.0*)[[source]](_modules/redis/client.html#Redis.hincrbyfloat)[¶](#redis.Redis.hincrbyfloat "Permalink to this definition") Increment the value of `key` in hash `name` by floating `amount` hkeys(*name*)[[source]](_modules/redis/client.html#Redis.hkeys)[¶](#redis.Redis.hkeys "Permalink to this definition") Return the list of keys within hash `name` hlen(*name*)[[source]](_modules/redis/client.html#Redis.hlen)[¶](#redis.Redis.hlen "Permalink to this definition") Return the number of elements in hash `name` hmget(*name*, *keys*, *\*args*)[[source]](_modules/redis/client.html#Redis.hmget)[¶](#redis.Redis.hmget "Permalink to this definition") Returns a list of values ordered identically to `keys` hmset(*name*, *mapping*)[[source]](_modules/redis/client.html#Redis.hmset)[¶](#redis.Redis.hmset "Permalink to this definition") Set key to value within hash `name` for each corresponding key and value from the `mapping` dict. hscan(*name*, *cursor=0*, *match=None*, *count=None*)[[source]](_modules/redis/client.html#Redis.hscan)[¶](#redis.Redis.hscan "Permalink to this definition") Incrementally return key/value slices in a hash. Also return a cursor indicating the scan position. `match` allows for filtering the keys by pattern `count` allows for hint the minimum number of returns hscan\_iter(*name*, *match=None*, *count=None*)[[source]](_modules/redis/client.html#Redis.hscan_iter)[¶](#redis.Redis.hscan_iter "Permalink to this definition") Make an iterator using the HSCAN command so that the client doesn’t need to remember the cursor position. `match` allows for filtering the keys by pattern `count` allows for hint the minimum number of returns hset(*name*, *key=None*, *value=None*, *mapping=None*)[[source]](_modules/redis/client.html#Redis.hset)[¶](#redis.Redis.hset "Permalink to this definition") Set `key` to `value` within hash `name`, `mapping` accepts a dict of key/value pairs that that will be added to hash `name`. Returns the number of fields that were added. hsetnx(*name*, *key*, *value*)[[source]](_modules/redis/client.html#Redis.hsetnx)[¶](#redis.Redis.hsetnx "Permalink to this definition") Set `key` to `value` within hash `name` if `key` does not exist. Returns 1 if HSETNX created a field, otherwise 0. hstrlen(*name*, *key*)[[source]](_modules/redis/client.html#Redis.hstrlen)[¶](#redis.Redis.hstrlen "Permalink to this definition") Return the number of bytes stored in the value of `key` within hash `name` hvals(*name*)[[source]](_modules/redis/client.html#Redis.hvals)[¶](#redis.Redis.hvals "Permalink to this definition") Return the list of values within hash `name` incr(*name*, *amount=1*)[[source]](_modules/redis/client.html#Redis.incr)[¶](#redis.Redis.incr "Permalink to this definition") Increments the value of `key` by `amount`. If no key exists, the value will be initialized as `amount` incrby(*name*, *amount=1*)[[source]](_modules/redis/client.html#Redis.incrby)[¶](#redis.Redis.incrby "Permalink to this definition") Increments the value of `key` by `amount`. If no key exists, the value will be initialized as `amount` incrbyfloat(*name*, *amount=1.0*)[[source]](_modules/redis/client.html#Redis.incrbyfloat)[¶](#redis.Redis.incrbyfloat "Permalink to this definition") Increments the value at key `name` by floating `amount`. If no key exists, the value will be initialized as `amount` info(*section=None*)[[source]](_modules/redis/client.html#Redis.info)[¶](#redis.Redis.info "Permalink to this definition") Returns a dictionary containing information about the Redis server The `section` option can be used to select a specific section of information The section option is not supported by older versions of Redis Server, and will generate ResponseError keys(*pattern='\*'*)[[source]](_modules/redis/client.html#Redis.keys)[¶](#redis.Redis.keys "Permalink to this definition") Returns a list of keys matching `pattern` lastsave()[[source]](_modules/redis/client.html#Redis.lastsave)[¶](#redis.Redis.lastsave "Permalink to this definition") Return a Python datetime object representing the last time the Redis database was saved to disk lindex(*name*, *index*)[[source]](_modules/redis/client.html#Redis.lindex)[¶](#redis.Redis.lindex "Permalink to this definition") Return the item from list `name` at position `index` Negative indexes are supported and will return an item at the end of the list linsert(*name*, *where*, *refvalue*, *value*)[[source]](_modules/redis/client.html#Redis.linsert)[¶](#redis.Redis.linsert "Permalink to this definition") Insert `value` in list `name` either immediately before or after [`where`] `refvalue` Returns the new length of the list on success or -1 if `refvalue` is not in the list. llen(*name*)[[source]](_modules/redis/client.html#Redis.llen)[¶](#redis.Redis.llen "Permalink to this definition") Return the length of the list `name` lock(*name*, *timeout=None*, *sleep=0.1*, *blocking\_timeout=None*, *lock\_class=None*, *thread\_local=True*)[[source]](_modules/redis/client.html#Redis.lock)[¶](#redis.Redis.lock "Permalink to this definition") Return a new Lock object using key `name` that mimics the behavior of threading.Lock. If specified, `timeout` indicates a maximum life for the lock. By default, it will remain locked until release() is called. `sleep` indicates the amount of time to sleep per loop iteration when the lock is in blocking mode and another client is currently holding the lock. `blocking\_timeout` indicates the maximum amount of time in seconds to spend trying to acquire the lock. A value of `None` indicates continue trying forever. `blocking\_timeout` can be specified as a float or integer, both representing the number of seconds to wait. `lock\_class` forces the specified lock implementation. `thread\_local` indicates whether the lock token is placed in thread-local storage. By default, the token is placed in thread local storage so that a thread only sees its token, not a token set by another thread. Consider the following timeline: > > > time: 0, thread-1 acquires my-lock, with a timeout of 5 seconds.thread-1 sets the token to “abc” > > > > time: 1, thread-2 blocks trying to acquire my-lock using theLock instance. > > > > time: 5, thread-1 has not yet completed. redis expires the lockkey. > > > > time: 5, thread-2 acquired my-lock now that it’s available.thread-2 sets the token to “xyz” > > > > time: 6, thread-1 finishes its work and calls release(). if thetoken is *not* stored in thread local storage, then > thread-1 would see the token value as “xyz” and would be > able to successfully release the thread-2’s lock. > > > > > In some use cases it’s necessary to disable thread local storage. For example, if you have code where one thread acquires a lock and passes that lock instance to a worker thread to release later. If thread local storage isn’t disabled in this case, the worker thread won’t see the token set by the thread that acquired the lock. Our assumption is that these cases aren’t common and as such default to using thread local storage. lpop(*name*)[[source]](_modules/redis/client.html#Redis.lpop)[¶](#redis.Redis.lpop "Permalink to this definition") Remove and return the first item of the list `name` lpush(*name*, *\*values*)[[source]](_modules/redis/client.html#Redis.lpush)[¶](#redis.Redis.lpush "Permalink to this definition") Push `values` onto the head of the list `name` lpushx(*name*, *value*)[[source]](_modules/redis/client.html#Redis.lpushx)[¶](#redis.Redis.lpushx "Permalink to this definition") Push `value` onto the head of the list `name` if `name` exists lrange(*name*, *start*, *end*)[[source]](_modules/redis/client.html#Redis.lrange)[¶](#redis.Redis.lrange "Permalink to this definition") Return a slice of the list `name` between position `start` and `end` `start` and `end` can be negative numbers just like Python slicing notation lrem(*name*, *count*, *value*)[[source]](_modules/redis/client.html#Redis.lrem)[¶](#redis.Redis.lrem "Permalink to this definition") Remove the first `count` occurrences of elements equal to `value` from the list stored at `name`. The count argument influences the operation in the following ways:count > 0: Remove elements equal to value moving from head to tail. count < 0: Remove elements equal to value moving from tail to head. count = 0: Remove all elements equal to value. lset(*name*, *index*, *value*)[[source]](_modules/redis/client.html#Redis.lset)[¶](#redis.Redis.lset "Permalink to this definition") Set `position` of list `name` to `value` ltrim(*name*, *start*, *end*)[[source]](_modules/redis/client.html#Redis.ltrim)[¶](#redis.Redis.ltrim "Permalink to this definition") Trim the list `name`, removing all values not within the slice between `start` and `end` `start` and `end` can be negative numbers just like Python slicing notation memory\_purge()[[source]](_modules/redis/client.html#Redis.memory_purge)[¶](#redis.Redis.memory_purge "Permalink to this definition") Attempts to purge dirty pages for reclamation by allocator memory\_stats()[[source]](_modules/redis/client.html#Redis.memory_stats)[¶](#redis.Redis.memory_stats "Permalink to this definition") Return a dictionary of memory stats memory\_usage(*key*, *samples=None*)[[source]](_modules/redis/client.html#Redis.memory_usage)[¶](#redis.Redis.memory_usage "Permalink to this definition") Return the total memory usage for key, its value and associated administrative overheads. For nested data structures, `samples` is the number of elements to sample. If left unspecified, the server’s default is 5. Use 0 to sample all elements. mget(*keys*, *\*args*)[[source]](_modules/redis/client.html#Redis.mget)[¶](#redis.Redis.mget "Permalink to this definition") Returns a list of values ordered identically to `keys` migrate(*host*, *port*, *keys*, *destination\_db*, *timeout*, *copy=False*, *replace=False*, *auth=None*)[[source]](_modules/redis/client.html#Redis.migrate)[¶](#redis.Redis.migrate "Permalink to this definition") Migrate 1 or more keys from the current Redis server to a different server specified by the `host`, `port` and `destination\_db`. The `timeout`, specified in milliseconds, indicates the maximum time the connection between the two servers can be idle before the command is interrupted. If `copy` is True, the specified `keys` are NOT deleted from the source server. If `replace` is True, this operation will overwrite the keys on the destination server if they exist. If `auth` is specified, authenticate to the destination server with the password provided. move(*name*, *db*)[[source]](_modules/redis/client.html#Redis.move)[¶](#redis.Redis.move "Permalink to this definition") Moves the key `name` to a different Redis database `db` mset(*mapping*)[[source]](_modules/redis/client.html#Redis.mset)[¶](#redis.Redis.mset "Permalink to this definition") Sets key/values based on a mapping. Mapping is a dictionary of key/value pairs. Both keys and values should be strings or types that can be cast to a string via str(). msetnx(*mapping*)[[source]](_modules/redis/client.html#Redis.msetnx)[¶](#redis.Redis.msetnx "Permalink to this definition") Sets key/values based on a mapping if none of the keys are already set. Mapping is a dictionary of key/value pairs. Both keys and values should be strings or types that can be cast to a string via str(). Returns a boolean indicating if the operation was successful. object(*infotype*, *key*)[[source]](_modules/redis/client.html#Redis.object)[¶](#redis.Redis.object "Permalink to this definition") Return the encoding, idletime, or refcount about the key parse\_response(*connection*, *command\_name*, *\*\*options*)[[source]](_modules/redis/client.html#Redis.parse_response)[¶](#redis.Redis.parse_response "Permalink to this definition") Parses a response from the Redis server persist(*name*)[[source]](_modules/redis/client.html#Redis.persist)[¶](#redis.Redis.persist "Permalink to this definition") Removes an expiration on `name` pexpire(*name*, *time*)[[source]](_modules/redis/client.html#Redis.pexpire)[¶](#redis.Redis.pexpire "Permalink to this definition") Set an expire flag on key `name` for `time` milliseconds. `time` can be represented by an integer or a Python timedelta object. pexpireat(*name*, *when*)[[source]](_modules/redis/client.html#Redis.pexpireat)[¶](#redis.Redis.pexpireat "Permalink to this definition") Set an expire flag on key `name`. `when` can be represented as an integer representing unix time in milliseconds (unix time \* 1000) or a Python datetime object. pfadd(*name*, *\*values*)[[source]](_modules/redis/client.html#Redis.pfadd)[¶](#redis.Redis.pfadd "Permalink to this definition") Adds the specified elements to the specified HyperLogLog. pfcount(*\*sources*)[[source]](_modules/redis/client.html#Redis.pfcount)[¶](#redis.Redis.pfcount "Permalink to this definition") Return the approximated cardinality of the set observed by the HyperLogLog at key(s). pfmerge(*dest*, *\*sources*)[[source]](_modules/redis/client.html#Redis.pfmerge)[¶](#redis.Redis.pfmerge "Permalink to this definition") Merge N different HyperLogLogs into a single one. ping()[[source]](_modules/redis/client.html#Redis.ping)[¶](#redis.Redis.ping "Permalink to this definition") Ping the Redis server pipeline(*transaction=True*, *shard\_hint=None*)[[source]](_modules/redis/client.html#Redis.pipeline)[¶](#redis.Redis.pipeline "Permalink to this definition") Return a new pipeline object that can queue multiple commands for later execution. `transaction` indicates whether all commands should be executed atomically. Apart from making a group of operations atomic, pipelines are useful for reducing the back-and-forth overhead between the client and server. psetex(*name*, *time\_ms*, *value*)[[source]](_modules/redis/client.html#Redis.psetex)[¶](#redis.Redis.psetex "Permalink to this definition") Set the value of key `name` to `value` that expires in `time\_ms` milliseconds. `time\_ms` can be represented by an integer or a Python timedelta object pttl(*name*)[[source]](_modules/redis/client.html#Redis.pttl)[¶](#redis.Redis.pttl "Permalink to this definition") Returns the number of milliseconds until the key `name` will expire publish(*channel*, *message*)[[source]](_modules/redis/client.html#Redis.publish)[¶](#redis.Redis.publish "Permalink to this definition") Publish `message` on `channel`. Returns the number of subscribers the message was delivered to. pubsub(*\*\*kwargs*)[[source]](_modules/redis/client.html#Redis.pubsub)[¶](#redis.Redis.pubsub "Permalink to this definition") Return a Publish/Subscribe object. With this object, you can subscribe to channels and listen for messages that get published to them. pubsub\_channels(*pattern='\*'*)[[source]](_modules/redis/client.html#Redis.pubsub_channels)[¶](#redis.Redis.pubsub_channels "Permalink to this definition") Return a list of channels that have at least one subscriber pubsub\_numpat()[[source]](_modules/redis/client.html#Redis.pubsub_numpat)[¶](#redis.Redis.pubsub_numpat "Permalink to this definition") Returns the number of subscriptions to patterns pubsub\_numsub(*\*args*)[[source]](_modules/redis/client.html#Redis.pubsub_numsub)[¶](#redis.Redis.pubsub_numsub "Permalink to this definition") Return a list of (channel, number of subscribers) tuples for each channel given in `\*args` randomkey()[[source]](_modules/redis/client.html#Redis.randomkey)[¶](#redis.Redis.randomkey "Permalink to this definition") Returns the name of a random key readonly()[[source]](_modules/redis/client.html#Redis.readonly)[¶](#redis.Redis.readonly "Permalink to this definition") Enables read queries for a connection to a Redis Cluster replica node readwrite()[[source]](_modules/redis/client.html#Redis.readwrite)[¶](#redis.Redis.readwrite "Permalink to this definition") Disables read queries for a connection to a Redis Cluster slave node register\_script(*script*)[[source]](_modules/redis/client.html#Redis.register_script)[¶](#redis.Redis.register_script "Permalink to this definition") Register a Lua `script` specifying the `keys` it will touch. Returns a Script object that is callable and hides the complexity of deal with scripts, keys, and shas. This is the preferred way to work with Lua scripts. rename(*src*, *dst*)[[source]](_modules/redis/client.html#Redis.rename)[¶](#redis.Redis.rename "Permalink to this definition") Rename key `src` to `dst` renamenx(*src*, *dst*)[[source]](_modules/redis/client.html#Redis.renamenx)[¶](#redis.Redis.renamenx "Permalink to this definition") Rename key `src` to `dst` if `dst` doesn’t already exist restore(*name*, *ttl*, *value*, *replace=False*)[[source]](_modules/redis/client.html#Redis.restore)[¶](#redis.Redis.restore "Permalink to this definition") Create a key using the provided serialized value, previously obtained using DUMP. rpop(*name*)[[source]](_modules/redis/client.html#Redis.rpop)[¶](#redis.Redis.rpop "Permalink to this definition") Remove and return the last item of the list `name` rpoplpush(*src*, *dst*)[[source]](_modules/redis/client.html#Redis.rpoplpush)[¶](#redis.Redis.rpoplpush "Permalink to this definition") RPOP a value off of the `src` list and atomically LPUSH it on to the `dst` list. Returns the value. rpush(*name*, *\*values*)[[source]](_modules/redis/client.html#Redis.rpush)[¶](#redis.Redis.rpush "Permalink to this definition") Push `values` onto the tail of the list `name` rpushx(*name*, *value*)[[source]](_modules/redis/client.html#Redis.rpushx)[¶](#redis.Redis.rpushx "Permalink to this definition") Push `value` onto the tail of the list `name` if `name` exists sadd(*name*, *\*values*)[[source]](_modules/redis/client.html#Redis.sadd)[¶](#redis.Redis.sadd "Permalink to this definition") Add `value(s)` to set `name` save()[[source]](_modules/redis/client.html#Redis.save)[¶](#redis.Redis.save "Permalink to this definition") Tell the Redis server to save its data to disk, blocking until the save is complete scan(*cursor=0*, *match=None*, *count=None*, *\_type=None*)[[source]](_modules/redis/client.html#Redis.scan)[¶](#redis.Redis.scan "Permalink to this definition") Incrementally return lists of key names. Also return a cursor indicating the scan position. `match` allows for filtering the keys by pattern `count` provides a hint to Redis about the number of keys toreturn per batch. `\_type` filters the returned values by a particular Redis type.Stock Redis instances allow for the following types: HASH, LIST, SET, STREAM, STRING, ZSET Additionally, Redis modules can expose other types as well. scan\_iter(*match=None*, *count=None*, *\_type=None*)[[source]](_modules/redis/client.html#Redis.scan_iter)[¶](#redis.Redis.scan_iter "Permalink to this definition") Make an iterator using the SCAN command so that the client doesn’t need to remember the cursor position. `match` allows for filtering the keys by pattern `count` provides a hint to Redis about the number of keys toreturn per batch. `\_type` filters the returned values by a particular Redis type.Stock Redis instances allow for the following types: HASH, LIST, SET, STREAM, STRING, ZSET Additionally, Redis modules can expose other types as well. scard(*name*)[[source]](_modules/redis/client.html#Redis.scard)[¶](#redis.Redis.scard "Permalink to this definition") Return the number of elements in set `name` script\_exists(*\*args*)[[source]](_modules/redis/client.html#Redis.script_exists)[¶](#redis.Redis.script_exists "Permalink to this definition") Check if a script exists in the script cache by specifying the SHAs of each script as `args`. Returns a list of boolean values indicating if if each already script exists in the cache. script\_flush()[[source]](_modules/redis/client.html#Redis.script_flush)[¶](#redis.Redis.script_flush "Permalink to this definition") Flush all scripts from the script cache script\_kill()[[source]](_modules/redis/client.html#Redis.script_kill)[¶](#redis.Redis.script_kill "Permalink to this definition") Kill the currently executing Lua script script\_load(*script*)[[source]](_modules/redis/client.html#Redis.script_load)[¶](#redis.Redis.script_load "Permalink to this definition") Load a Lua `script` into the script cache. Returns the SHA. sdiff(*keys*, *\*args*)[[source]](_modules/redis/client.html#Redis.sdiff)[¶](#redis.Redis.sdiff "Permalink to this definition") Return the difference of sets specified by `keys` sdiffstore(*dest*, *keys*, *\*args*)[[source]](_modules/redis/client.html#Redis.sdiffstore)[¶](#redis.Redis.sdiffstore "Permalink to this definition") Store the difference of sets specified by `keys` into a new set named `dest`. Returns the number of keys in the new set. sentinel(*\*args*)[[source]](_modules/redis/client.html#Redis.sentinel)[¶](#redis.Redis.sentinel "Permalink to this definition") Redis Sentinel’s SENTINEL command. sentinel\_get\_master\_addr\_by\_name(*service\_name*)[[source]](_modules/redis/client.html#Redis.sentinel_get_master_addr_by_name)[¶](#redis.Redis.sentinel_get_master_addr_by_name "Permalink to this definition") Returns a (host, port) pair for the given `service\_name` sentinel\_master(*service\_name*)[[source]](_modules/redis/client.html#Redis.sentinel_master)[¶](#redis.Redis.sentinel_master "Permalink to this definition") Returns a dictionary containing the specified masters state. sentinel\_masters()[[source]](_modules/redis/client.html#Redis.sentinel_masters)[¶](#redis.Redis.sentinel_masters "Permalink to this definition") Returns a list of dictionaries containing each master’s state. sentinel\_monitor(*name*, *ip*, *port*, *quorum*)[[source]](_modules/redis/client.html#Redis.sentinel_monitor)[¶](#redis.Redis.sentinel_monitor "Permalink to this definition") Add a new master to Sentinel to be monitored sentinel\_remove(*name*)[[source]](_modules/redis/client.html#Redis.sentinel_remove)[¶](#redis.Redis.sentinel_remove "Permalink to this definition") Remove a master from Sentinel’s monitoring sentinel\_sentinels(*service\_name*)[[source]](_modules/redis/client.html#Redis.sentinel_sentinels)[¶](#redis.Redis.sentinel_sentinels "Permalink to this definition") Returns a list of sentinels for `service\_name` sentinel\_set(*name*, *option*, *value*)[[source]](_modules/redis/client.html#Redis.sentinel_set)[¶](#redis.Redis.sentinel_set "Permalink to this definition") Set Sentinel monitoring parameters for a given master sentinel\_slaves(*service\_name*)[[source]](_modules/redis/client.html#Redis.sentinel_slaves)[¶](#redis.Redis.sentinel_slaves "Permalink to this definition") Returns a list of slaves for `service\_name` set(*name*, *value*, *ex=None*, *px=None*, *nx=False*, *xx=False*, *keepttl=False*)[[source]](_modules/redis/client.html#Redis.set)[¶](#redis.Redis.set "Permalink to this definition") Set the value at key `name` to `value` `ex` sets an expire flag on key `name` for `ex` seconds. `px` sets an expire flag on key `name` for `px` milliseconds. `nx` if set to True, set the value at key `name` to `value` onlyif it does not exist. `xx` if set to True, set the value at key `name` to `value` onlyif it already exists. `keepttl` if True, retain the time to live associated with the key.(Available since Redis 6.0) set\_response\_callback(*command*, *callback*)[[source]](_modules/redis/client.html#Redis.set_response_callback)[¶](#redis.Redis.set_response_callback "Permalink to this definition") Set a custom Response Callback setbit(*name*, *offset*, *value*)[[source]](_modules/redis/client.html#Redis.setbit)[¶](#redis.Redis.setbit "Permalink to this definition") Flag the `offset` in `name` as `value`. Returns a boolean indicating the previous value of `offset`. setex(*name*, *time*, *value*)[[source]](_modules/redis/client.html#Redis.setex)[¶](#redis.Redis.setex "Permalink to this definition") Set the value of key `name` to `value` that expires in `time` seconds. `time` can be represented by an integer or a Python timedelta object. setnx(*name*, *value*)[[source]](_modules/redis/client.html#Redis.setnx)[¶](#redis.Redis.setnx "Permalink to this definition") Set the value of key `name` to `value` if key doesn’t exist setrange(*name*, *offset*, *value*)[[source]](_modules/redis/client.html#Redis.setrange)[¶](#redis.Redis.setrange "Permalink to this definition") Overwrite bytes in the value of `name` starting at `offset` with `value`. If `offset` plus the length of `value` exceeds the length of the original value, the new value will be larger than before. If `offset` exceeds the length of the original value, null bytes will be used to pad between the end of the previous value and the start of what’s being injected. Returns the length of the new string. shutdown(*save=False*, *nosave=False*)[[source]](_modules/redis/client.html#Redis.shutdown)[¶](#redis.Redis.shutdown "Permalink to this definition") Shutdown the Redis server. If Redis has persistence configured, data will be flushed before shutdown. If the “save” option is set, a data flush will be attempted even if there is no persistence configured. If the “nosave” option is set, no data flush will be attempted. The “save” and “nosave” options cannot both be set. sinter(*keys*, *\*args*)[[source]](_modules/redis/client.html#Redis.sinter)[¶](#redis.Redis.sinter "Permalink to this definition") Return the intersection of sets specified by `keys` sinterstore(*dest*, *keys*, *\*args*)[[source]](_modules/redis/client.html#Redis.sinterstore)[¶](#redis.Redis.sinterstore "Permalink to this definition") Store the intersection of sets specified by `keys` into a new set named `dest`. Returns the number of keys in the new set. sismember(*name*, *value*)[[source]](_modules/redis/client.html#Redis.sismember)[¶](#redis.Redis.sismember "Permalink to this definition") Return a boolean indicating if `value` is a member of set `name` slaveof(*host=None*, *port=None*)[[source]](_modules/redis/client.html#Redis.slaveof)[¶](#redis.Redis.slaveof "Permalink to this definition") Set the server to be a replicated slave of the instance identified by the `host` and `port`. If called without arguments, the instance is promoted to a master instead. slowlog\_get(*num=None*)[[source]](_modules/redis/client.html#Redis.slowlog_get)[¶](#redis.Redis.slowlog_get "Permalink to this definition") Get the entries from the slowlog. If `num` is specified, get the most recent `num` items. slowlog\_len()[[source]](_modules/redis/client.html#Redis.slowlog_len)[¶](#redis.Redis.slowlog_len "Permalink to this definition") Get the number of items in the slowlog slowlog\_reset()[[source]](_modules/redis/client.html#Redis.slowlog_reset)[¶](#redis.Redis.slowlog_reset "Permalink to this definition") Remove all items in the slowlog smembers(*name*)[[source]](_modules/redis/client.html#Redis.smembers)[¶](#redis.Redis.smembers "Permalink to this definition") Return all members of the set `name` smove(*src*, *dst*, *value*)[[source]](_modules/redis/client.html#Redis.smove)[¶](#redis.Redis.smove "Permalink to this definition") Move `value` from set `src` to set `dst` atomically sort(*name*, *start=None*, *num=None*, *by=None*, *get=None*, *desc=False*, *alpha=False*, *store=None*, *groups=False*)[[source]](_modules/redis/client.html#Redis.sort)[¶](#redis.Redis.sort "Permalink to this definition") Sort and return the list, set or sorted set at `name`. `start` and `num` allow for paging through the sorted data `by` allows using an external key to weight and sort the items.Use an “\*” to indicate where in the key the item value is located `get` allows for returning items from external keys rather than thesorted data itself. Use an “\*” to indicate where in the key the item value is located `desc` allows for reversing the sort `alpha` allows for sorting lexicographically rather than numerically `store` allows for storing the result of the sort intothe key `store` `groups` if set to True and if `get` contains at least twoelements, sort will return a list of tuples, each containing the values fetched from the arguments to `get`. spop(*name*, *count=None*)[[source]](_modules/redis/client.html#Redis.spop)[¶](#redis.Redis.spop "Permalink to this definition") Remove and return a random member of set `name` srandmember(*name*, *number=None*)[[source]](_modules/redis/client.html#Redis.srandmember)[¶](#redis.Redis.srandmember "Permalink to this definition") If `number` is None, returns a random member of set `name`. If `number` is supplied, returns a list of `number` random members of set `name`. Note this is only available when running Redis 2.6+. srem(*name*, *\*values*)[[source]](_modules/redis/client.html#Redis.srem)[¶](#redis.Redis.srem "Permalink to this definition") Remove `values` from set `name` sscan(*name*, *cursor=0*, *match=None*, *count=None*)[[source]](_modules/redis/client.html#Redis.sscan)[¶](#redis.Redis.sscan "Permalink to this definition") Incrementally return lists of elements in a set. Also return a cursor indicating the scan position. `match` allows for filtering the keys by pattern `count` allows for hint the minimum number of returns sscan\_iter(*name*, *match=None*, *count=None*)[[source]](_modules/redis/client.html#Redis.sscan_iter)[¶](#redis.Redis.sscan_iter "Permalink to this definition") Make an iterator using the SSCAN command so that the client doesn’t need to remember the cursor position. `match` allows for filtering the keys by pattern `count` allows for hint the minimum number of returns strlen(*name*)[[source]](_modules/redis/client.html#Redis.strlen)[¶](#redis.Redis.strlen "Permalink to this definition") Return the number of bytes stored in the value of `name` substr(*name*, *start*, *end=- 1*)[[source]](_modules/redis/client.html#Redis.substr)[¶](#redis.Redis.substr "Permalink to this definition") Return a substring of the string at key `name`. `start` and `end` are 0-based integers specifying the portion of the string to return. sunion(*keys*, *\*args*)[[source]](_modules/redis/client.html#Redis.sunion)[¶](#redis.Redis.sunion "Permalink to this definition") Return the union of sets specified by `keys` sunionstore(*dest*, *keys*, *\*args*)[[source]](_modules/redis/client.html#Redis.sunionstore)[¶](#redis.Redis.sunionstore "Permalink to this definition") Store the union of sets specified by `keys` into a new set named `dest`. Returns the number of keys in the new set. swapdb(*first*, *second*)[[source]](_modules/redis/client.html#Redis.swapdb)[¶](#redis.Redis.swapdb "Permalink to this definition") Swap two databases time()[[source]](_modules/redis/client.html#Redis.time)[¶](#redis.Redis.time "Permalink to this definition") Returns the server time as a 2-item tuple of ints: (seconds since epoch, microseconds into this second). touch(*\*args*)[[source]](_modules/redis/client.html#Redis.touch)[¶](#redis.Redis.touch "Permalink to this definition") Alters the last access time of a key(s) `\*args`. A key is ignored if it does not exist. transaction(*func*, *\*watches*, *\*\*kwargs*)[[source]](_modules/redis/client.html#Redis.transaction)[¶](#redis.Redis.transaction "Permalink to this definition") Convenience method for executing the callable func as a transaction while watching all keys specified in watches. The ‘func’ callable should expect a single argument which is a Pipeline object. ttl(*name*)[[source]](_modules/redis/client.html#Redis.ttl)[¶](#redis.Redis.ttl "Permalink to this definition") Returns the number of seconds until the key `name` will expire type(*name*)[[source]](_modules/redis/client.html#Redis.type)[¶](#redis.Redis.type "Permalink to this definition") Returns the type of key `name` unlink(*\*names*)[[source]](_modules/redis/client.html#Redis.unlink)[¶](#redis.Redis.unlink "Permalink to this definition") Unlink one or more keys specified by `names` unwatch()[[source]](_modules/redis/client.html#Redis.unwatch)[¶](#redis.Redis.unwatch "Permalink to this definition") Unwatches the value at key `name`, or None of the key doesn’t exist wait(*num\_replicas*, *timeout*)[[source]](_modules/redis/client.html#Redis.wait)[¶](#redis.Redis.wait "Permalink to this definition") Redis synchronous replication That returns the number of replicas that processed the query when we finally have at least `num\_replicas`, or when the `timeout` was reached. watch(*\*names*)[[source]](_modules/redis/client.html#Redis.watch)[¶](#redis.Redis.watch "Permalink to this definition") Watches the values at keys `names`, or None if the key doesn’t exist xack(*name*, *groupname*, *\*ids*)[[source]](_modules/redis/client.html#Redis.xack)[¶](#redis.Redis.xack "Permalink to this definition") Acknowledges the successful processing of one or more messages. name: name of the stream. groupname: name of the consumer group. [\*](#id3)ids: message ids to acknowlege. xadd(*name*, *fields*, *id='\*'*, *maxlen=None*, *approximate=True*)[[source]](_modules/redis/client.html#Redis.xadd)[¶](#redis.Redis.xadd "Permalink to this definition") Add to a stream. name: name of the stream fields: dict of field/value pairs to insert into the stream id: Location to insert this record. By default it is appended. maxlen: truncate old stream members beyond this size approximate: actual stream length may be slightly more than maxlen xclaim(*name*, *groupname*, *consumername*, *min\_idle\_time*, *message\_ids*, *idle=None*, *time=None*, *retrycount=None*, *force=False*, *justid=False*)[[source]](_modules/redis/client.html#Redis.xclaim)[¶](#redis.Redis.xclaim "Permalink to this definition") Changes the ownership of a pending message. name: name of the stream. groupname: name of the consumer group. consumername: name of a consumer that claims the message. min\_idle\_time: filter messages that were idle less than this amount of milliseconds message\_ids: non-empty list or tuple of message IDs to claim idle: optional. Set the idle time (last time it was delivered) of the > > message in ms > > > time: optional integer. This is the same as idle but instead of arelative amount of milliseconds, it sets the idle time to a specific Unix time (in milliseconds). retrycount: optional integer. set the retry counter to the specifiedvalue. This counter is incremented every time a message is delivered again. force: optional boolean, false by default. Creates the pending messageentry in the PEL even if certain specified IDs are not already in the PEL assigned to a different client. justid: optional boolean, false by default. Return just an array of IDsof messages successfully claimed, without returning the actual message xdel(*name*, *\*ids*)[[source]](_modules/redis/client.html#Redis.xdel)[¶](#redis.Redis.xdel "Permalink to this definition") Deletes one or more messages from a stream. name: name of the stream. [\*](#id5)ids: message ids to delete. xgroup\_create(*name*, *groupname*, *id='$'*, *mkstream=False*)[[source]](_modules/redis/client.html#Redis.xgroup_create)[¶](#redis.Redis.xgroup_create "Permalink to this definition") Create a new consumer group associated with a stream. name: name of the stream. groupname: name of the consumer group. id: ID of the last item in the stream to consider already delivered. xgroup\_delconsumer(*name*, *groupname*, *consumername*)[[source]](_modules/redis/client.html#Redis.xgroup_delconsumer)[¶](#redis.Redis.xgroup_delconsumer "Permalink to this definition") Remove a specific consumer from a consumer group. Returns the number of pending messages that the consumer had before it was deleted. name: name of the stream. groupname: name of the consumer group. consumername: name of consumer to delete xgroup\_destroy(*name*, *groupname*)[[source]](_modules/redis/client.html#Redis.xgroup_destroy)[¶](#redis.Redis.xgroup_destroy "Permalink to this definition") Destroy a consumer group. name: name of the stream. groupname: name of the consumer group. xgroup\_setid(*name*, *groupname*, *id*)[[source]](_modules/redis/client.html#Redis.xgroup_setid)[¶](#redis.Redis.xgroup_setid "Permalink to this definition") Set the consumer group last delivered ID to something else. name: name of the stream. groupname: name of the consumer group. id: ID of the last item in the stream to consider already delivered. xinfo\_consumers(*name*, *groupname*)[[source]](_modules/redis/client.html#Redis.xinfo_consumers)[¶](#redis.Redis.xinfo_consumers "Permalink to this definition") Returns general information about the consumers in the group. name: name of the stream. groupname: name of the consumer group. xinfo\_groups(*name*)[[source]](_modules/redis/client.html#Redis.xinfo_groups)[¶](#redis.Redis.xinfo_groups "Permalink to this definition") Returns general information about the consumer groups of the stream. name: name of the stream. xinfo\_stream(*name*)[[source]](_modules/redis/client.html#Redis.xinfo_stream)[¶](#redis.Redis.xinfo_stream "Permalink to this definition") Returns general information about the stream. name: name of the stream. xlen(*name*)[[source]](_modules/redis/client.html#Redis.xlen)[¶](#redis.Redis.xlen "Permalink to this definition") Returns the number of elements in a given stream. xpending(*name*, *groupname*)[[source]](_modules/redis/client.html#Redis.xpending)[¶](#redis.Redis.xpending "Permalink to this definition") Returns information about pending messages of a group. name: name of the stream. groupname: name of the consumer group. xpending\_range(*name*, *groupname*, *min*, *max*, *count*, *consumername=None*)[[source]](_modules/redis/client.html#Redis.xpending_range)[¶](#redis.Redis.xpending_range "Permalink to this definition") Returns information about pending messages, in a range. name: name of the stream. groupname: name of the consumer group. min: minimum stream ID. max: maximum stream ID. count: number of messages to return consumername: name of a consumer to filter by (optional). xrange(*name*, *min='-'*, *max='+'*, *count=None*)[[source]](_modules/redis/client.html#Redis.xrange)[¶](#redis.Redis.xrange "Permalink to this definition") Read stream values within an interval. name: name of the stream. start: first stream ID. defaults to ‘-‘, > > meaning the earliest available. > > > finish: last stream ID. defaults to ‘+’,meaning the latest available. count: if set, only return this many items, beginning with theearliest available. xread(*streams*, *count=None*, *block=None*)[[source]](_modules/redis/client.html#Redis.xread)[¶](#redis.Redis.xread "Permalink to this definition") Block and monitor multiple streams for new data. streams: a dict of stream names to stream IDs, where > > IDs indicate the last ID already seen. > > > count: if set, only return this many items, beginning with theearliest available. block: number of milliseconds to wait, if nothing already present. xreadgroup(*groupname*, *consumername*, *streams*, *count=None*, *block=None*, *noack=False*)[[source]](_modules/redis/client.html#Redis.xreadgroup)[¶](#redis.Redis.xreadgroup "Permalink to this definition") Read from a stream via a consumer group. groupname: name of the consumer group. consumername: name of the requesting consumer. streams: a dict of stream names to stream IDs, where > > IDs indicate the last ID already seen. > > > count: if set, only return this many items, beginning with theearliest available. block: number of milliseconds to wait, if nothing already present. noack: do not add messages to the PEL xrevrange(*name*, *max='+'*, *min='-'*, *count=None*)[[source]](_modules/redis/client.html#Redis.xrevrange)[¶](#redis.Redis.xrevrange "Permalink to this definition") Read stream values within an interval, in reverse order. name: name of the stream start: first stream ID. defaults to ‘+’, > > meaning the latest available. > > > finish: last stream ID. defaults to ‘-‘,meaning the earliest available. count: if set, only return this many items, beginning with thelatest available. xtrim(*name*, *maxlen*, *approximate=True*)[[source]](_modules/redis/client.html#Redis.xtrim)[¶](#redis.Redis.xtrim "Permalink to this definition") Trims old messages from a stream. name: name of the stream. maxlen: truncate old stream messages beyond this size approximate: actual stream length may be slightly more than maxlen zadd(*name*, *mapping*, *nx=False*, *xx=False*, *ch=False*, *incr=False*)[[source]](_modules/redis/client.html#Redis.zadd)[¶](#redis.Redis.zadd "Permalink to this definition") Set any number of element-name, score pairs to the key `name`. Pairs are specified as a dict of element-names keys to score values. `nx` forces ZADD to only create new elements and not to update scores for elements that already exist. `xx` forces ZADD to only update scores of elements that already exist. New elements will not be added. `ch` modifies the return value to be the numbers of elements changed. Changed elements include new elements that were added and elements whose scores changed. `incr` modifies ZADD to behave like ZINCRBY. In this mode only a single element/score pair can be specified and the score is the amount the existing score will be incremented by. When using this mode the return value of ZADD will be the new score of the element. The return value of ZADD varies based on the mode specified. With no options, ZADD returns the number of new elements added to the sorted set. zcard(*name*)[[source]](_modules/redis/client.html#Redis.zcard)[¶](#redis.Redis.zcard "Permalink to this definition") Return the number of elements in the sorted set `name` zcount(*name*, *min*, *max*)[[source]](_modules/redis/client.html#Redis.zcount)[¶](#redis.Redis.zcount "Permalink to this definition") Returns the number of elements in the sorted set at key `name` with a score between `min` and `max`. zincrby(*name*, *amount*, *value*)[[source]](_modules/redis/client.html#Redis.zincrby)[¶](#redis.Redis.zincrby "Permalink to this definition") Increment the score of `value` in sorted set `name` by `amount` zinterstore(*dest*, *keys*, *aggregate=None*)[[source]](_modules/redis/client.html#Redis.zinterstore)[¶](#redis.Redis.zinterstore "Permalink to this definition") Intersect multiple sorted sets specified by `keys` into a new sorted set, `dest`. Scores in the destination will be aggregated based on the `aggregate`, or SUM if none is provided. zlexcount(*name*, *min*, *max*)[[source]](_modules/redis/client.html#Redis.zlexcount)[¶](#redis.Redis.zlexcount "Permalink to this definition") Return the number of items in the sorted set `name` between the lexicographical range `min` and `max`. zpopmax(*name*, *count=None*)[[source]](_modules/redis/client.html#Redis.zpopmax)[¶](#redis.Redis.zpopmax "Permalink to this definition") Remove and return up to `count` members with the highest scores from the sorted set `name`. zpopmin(*name*, *count=None*)[[source]](_modules/redis/client.html#Redis.zpopmin)[¶](#redis.Redis.zpopmin "Permalink to this definition") Remove and return up to `count` members with the lowest scores from the sorted set `name`. zrange(*name*, *start*, *end*, *desc=False*, *withscores=False*, *score\_cast\_func=<class 'float'>*)[[source]](_modules/redis/client.html#Redis.zrange)[¶](#redis.Redis.zrange "Permalink to this definition") Return a range of values from sorted set `name` between `start` and `end` sorted in ascending order. `start` and `end` can be negative, indicating the end of the range. `desc` a boolean indicating whether to sort the results descendingly `withscores` indicates to return the scores along with the values. The return type is a list of (value, score) pairs `score\_cast\_func` a callable used to cast the score return value zrangebylex(*name*, *min*, *max*, *start=None*, *num=None*)[[source]](_modules/redis/client.html#Redis.zrangebylex)[¶](#redis.Redis.zrangebylex "Permalink to this definition") Return the lexicographical range of values from sorted set `name` between `min` and `max`. If `start` and `num` are specified, then return a slice of the range. zrangebyscore(*name*, *min*, *max*, *start=None*, *num=None*, *withscores=False*, *score\_cast\_func=<class 'float'>*)[[source]](_modules/redis/client.html#Redis.zrangebyscore)[¶](#redis.Redis.zrangebyscore "Permalink to this definition") Return a range of values from the sorted set `name` with scores between `min` and `max`. If `start` and `num` are specified, then return a slice of the range. `withscores` indicates to return the scores along with the values. The return type is a list of (value, score) pairs score\_cast\_func` a callable used to cast the score return value zrank(*name*, *value*)[[source]](_modules/redis/client.html#Redis.zrank)[¶](#redis.Redis.zrank "Permalink to this definition") Returns a 0-based value indicating the rank of `value` in sorted set `name` zrem(*name*, *\*values*)[[source]](_modules/redis/client.html#Redis.zrem)[¶](#redis.Redis.zrem "Permalink to this definition") Remove member `values` from sorted set `name` zremrangebylex(*name*, *min*, *max*)[[source]](_modules/redis/client.html#Redis.zremrangebylex)[¶](#redis.Redis.zremrangebylex "Permalink to this definition") Remove all elements in the sorted set `name` between the lexicographical range specified by `min` and `max`. Returns the number of elements removed. zremrangebyrank(*name*, *min*, *max*)[[source]](_modules/redis/client.html#Redis.zremrangebyrank)[¶](#redis.Redis.zremrangebyrank "Permalink to this definition") Remove all elements in the sorted set `name` with ranks between `min` and `max`. Values are 0-based, ordered from smallest score to largest. Values can be negative indicating the highest scores. Returns the number of elements removed zremrangebyscore(*name*, *min*, *max*)[[source]](_modules/redis/client.html#Redis.zremrangebyscore)[¶](#redis.Redis.zremrangebyscore "Permalink to this definition") Remove all elements in the sorted set `name` with scores between `min` and `max`. Returns the number of elements removed. zrevrange(*name*, *start*, *end*, *withscores=False*, *score\_cast\_func=<class 'float'>*)[[source]](_modules/redis/client.html#Redis.zrevrange)[¶](#redis.Redis.zrevrange "Permalink to this definition") Return a range of values from sorted set `name` between `start` and `end` sorted in descending order. `start` and `end` can be negative, indicating the end of the range. `withscores` indicates to return the scores along with the values The return type is a list of (value, score) pairs `score\_cast\_func` a callable used to cast the score return value zrevrangebylex(*name*, *max*, *min*, *start=None*, *num=None*)[[source]](_modules/redis/client.html#Redis.zrevrangebylex)[¶](#redis.Redis.zrevrangebylex "Permalink to this definition") Return the reversed lexicographical range of values from sorted set `name` between `max` and `min`. If `start` and `num` are specified, then return a slice of the range. zrevrangebyscore(*name*, *max*, *min*, *start=None*, *num=None*, *withscores=False*, *score\_cast\_func=<class 'float'>*)[[source]](_modules/redis/client.html#Redis.zrevrangebyscore)[¶](#redis.Redis.zrevrangebyscore "Permalink to this definition") Return a range of values from the sorted set `name` with scores between `min` and `max` in descending order. If `start` and `num` are specified, then return a slice of the range. `withscores` indicates to return the scores along with the values. The return type is a list of (value, score) pairs `score\_cast\_func` a callable used to cast the score return value zrevrank(*name*, *value*)[[source]](_modules/redis/client.html#Redis.zrevrank)[¶](#redis.Redis.zrevrank "Permalink to this definition") Returns a 0-based value indicating the descending rank of `value` in sorted set `name` zscan(*name*, *cursor=0*, *match=None*, *count=None*, *score\_cast\_func=<class 'float'>*)[[source]](_modules/redis/client.html#Redis.zscan)[¶](#redis.Redis.zscan "Permalink to this definition") Incrementally return lists of elements in a sorted set. Also return a cursor indicating the scan position. `match` allows for filtering the keys by pattern `count` allows for hint the minimum number of returns `score\_cast\_func` a callable used to cast the score return value zscan\_iter(*name*, *match=None*, *count=None*, *score\_cast\_func=<class 'float'>*)[[source]](_modules/redis/client.html#Redis.zscan_iter)[¶](#redis.Redis.zscan_iter "Permalink to this definition") Make an iterator using the ZSCAN command so that the client doesn’t need to remember the cursor position. `match` allows for filtering the keys by pattern `count` allows for hint the minimum number of returns `score\_cast\_func` a callable used to cast the score return value zscore(*name*, *value*)[[source]](_modules/redis/client.html#Redis.zscore)[¶](#redis.Redis.zscore "Permalink to this definition") Return the score of element `value` in sorted set `name` zunionstore(*dest*, *keys*, *aggregate=None*)[[source]](_modules/redis/client.html#Redis.zunionstore)[¶](#redis.Redis.zunionstore "Permalink to this definition") Union multiple sorted sets specified by `keys` into a new sorted set, `dest`. Scores in the destination will be aggregated based on the `aggregate`, or SUM if none is provided. *exception* redis.RedisError[[source]](_modules/redis/exceptions.html#RedisError)[¶](#redis.RedisError "Permalink to this definition") *exception* redis.ResponseError[[source]](_modules/redis/exceptions.html#ResponseError)[¶](#redis.ResponseError "Permalink to this definition") *class* redis.SSLConnection(*ssl\_keyfile=None*, *ssl\_certfile=None*, *ssl\_cert\_reqs='required'*, *ssl\_ca\_certs=None*, *ssl\_check\_hostname=False*, *\*\*kwargs*)[[source]](_modules/redis/connection.html#SSLConnection)[¶](#redis.SSLConnection "Permalink to this definition") redis.StrictRedis[¶](#redis.StrictRedis "Permalink to this definition") alias of [`redis.client.Redis`](#redis.Redis "redis.client.Redis") *exception* redis.TimeoutError[[source]](_modules/redis/exceptions.html#TimeoutError)[¶](#redis.TimeoutError "Permalink to this definition") *class* redis.UnixDomainSocketConnection(*path=''*, *db=0*, *username=None*, *password=None*, *socket\_timeout=None*, *encoding='utf-8'*, *encoding\_errors='strict'*, *decode\_responses=False*, *retry\_on\_timeout=False*, *parser\_class=<class 'redis.connection.PythonParser'>*, *socket\_read\_size=65536*, *health\_check\_interval=0*, *client\_name=None*)[[source]](_modules/redis/connection.html#UnixDomainSocketConnection)[¶](#redis.UnixDomainSocketConnection "Permalink to this definition") *exception* redis.WatchError[[source]](_modules/redis/exceptions.html#WatchError)[¶](#redis.WatchError "Permalink to this definition") redis.from\_url(*url*, *db=None*, *\*\*kwargs*)[[source]](_modules/redis/utils.html#from_url)[¶](#redis.from_url "Permalink to this definition") Returns an active Redis client generated from the given database URL. Will attempt to extract the database id from the path url fragment, if none is provided.

optimization

go

Surrogate optimizer Release 0.0.0 Oct 06, 2020 Contents: 1 app 1 1.1 core package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Submodules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.2 Module contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.2 datamod package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.2.1 Submodules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.2.2 Module contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 1.3 metamod package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.3.1 Submodules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 1.3.2 Module contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 1.4 optimod package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 1.4.1 Submodules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 1.4.2 Module contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 1.5 run module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 1.6 visumod package . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 1.6.1 Submodules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 1.6.2 Module contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 2 Indices and tables 35 Python Module Index 37 Index 39 i ii CHAPTER 1 app 1.1 core package 1.1.1 Submodules core.model module This modules sets up the overall properties of the model. core.model.evaluators A dictionary of available evaluators. Type dict class core.model.Model Bases: object This is the core class of the framework. evaluator The selected evaluator. Type object range_in Input parameter allowable ranges. Type np.array dim_in Number of input dimensions. Type int dim_out Number of output dimensions. Type int 1 Surrogate optimizer, Release 0.0.0 n_const Number of constraints. Type int n_obj Number of objectives. Type int Notes The ranges need to be specified if direct evaluation. core.optimization module Module containing the optimization surrogate. core.optimization.ref_points Reference points for benchmark problems. Type dict class core.optimization.Optimization(model) Bases: object The class to define the optimization problem. model The model object. Type core.Model iterations Number of optimization iterations. Type int converged Convergence status. Type bool algorithm Optization algorithm object. termination Termination object. n_const NUmber of constraints. Type int ref_point Reference point for hypervolume calculation. Type np.array direct Whether a direct optimization is performed. Type bool 2 Chapter 1. app Surrogate optimizer, Release 0.0.0 range_in Input parameter allowable ranges. Type np.array function Function used to evaluate the candidates. problem Problem object. Type datamod.problems.Custom surrogate Surrogate object. Type core.Surrogate res Results object. Type pymoo.model.result.Result optimization_stats Optimization benchmark statistics. Type dict optimum_model Candidates evaluated by the original model. Type np.array optimum_surrogate Candidates evaluated by the surrogate. Type np.array error_measure Maximum of the error metrics. Type float error Benchmark percent error. Type np.array benchmark() Determine the benchmark optimization accuracy. optimize() Wrapper function to perform optimization. plot_results() Plot the optimized candidates. report() Report the optimal solutions and their verification accuracy. set_problem(surrogate) Wrapper function to set the problem. Parameters surrogate (core.Surrogate) – Surrogate object. 1.1. core package 3 Surrogate optimizer, Release 0.0.0 Notes Direct optimization does not normalize. verify() Wrapper function to verify the optimized solutions. core.settings module This module provides auxiliary functions to handle settings and files writing and loading. core.settings.settings A shared dictionary of settings accross the framework. Type dict core.settings.ask_to_overwrite(path, id_current, text) Parameters • path (str) – Path to the already defined results folder. • id_current (int) – ID of the problem to be solved. • text (str) – Reason for everwriting. core.settings.check_valid_settings() Check if valid settings are used. core.settings.dump_json(file, data) Writes a JSON file. Parameters • file (str) – Path and name of the file to be written. • data (dict) – The data to be saved. core.settings.dump_object(name, *args) Pickle dumps the given objects. Parameters • name (str) – Path and name of the target file. • args (any) – Any object to be pickle dumped. core.settings.get_input_from_id(problem_id, problem_folder) Get filename from problem ID. Parameters • problem_id (int) – ID of the problem to be solved. • problem_folder (str) – Directory which contains the input files. Returns Path to the input file of the requested ID. Return type file (str) core.settings.get_results_folders() Retrieves all current results folders. Returns Path to the directory with result folders. all_result_folder (list): List of all results folders. Return type data_folder (str) 4 Chapter 1. app Surrogate optimizer, Release 0.0.0 core.settings.load_json(file) Read a JSON file. Parameters file (str) – Path and name of the file to be loaded. Returns The loaded data. Return type data (dict) core.settings.load_object(name) Loads the pickled object. Parameters name (str) – Path and name of the file to load. Returns The loaded object. Return type obj (any) core.settings.make_workfolder(file, fresh) Initialize the workdirectory. Parameters • file (str) – Path and name of the current input file. • fresh (bool) – Whether this is a new problem to be solved. Returns Path to the current results data folder. Return type folder_path (str) core.settings.restart_check(id_current, file) Parameters • id_current (int) – ID of the problem to be solved. • file (str) – Path and name of the current input file. Notes For load_surrogate, only checking that the problem name is the same. core.settings.update_settings(problem_id) Updates the shared settings dictionary with the settings specified in the input file. Parameters problem_id (int) – ID of the problem to be solved. core.surrogate module This module handles the surrogate’s training. core.surrogate.max_samples maximum number of training samples above which the training is stopped as unsuccessful Type int class core.surrogate.Surrogate(model) Bases: object The class to define the surrogate. model The model object. 1.1. core package 5 Surrogate optimizer, Release 0.0.0 Type core.Model name Name of the surrogate. Type str trained Whether the surrogate is trained. Type bool diverging Whether the amount of training samples has exceeded the maximal allowable. Type bool hp_optimized Whether the hyperparameters of the surrogate have been optimized. Type bool optimized_to_samples Amount of training samples during last hyperparameter optimization. Type int reoptimization_ratio Sample increase ratio for reoptimization. Type float no_samples Current number of training samples. Type int sampling_iterations Number of training iterations. Type int convergence_metric Dictionary of convergence metrics. Type dict file Path to the training database. Type str verification_file Path to the verification database. Type str verification Verfication samples. Type datamod.get_data data Training samples. Type datamod.get_data 6 Chapter 1. app Surrogate optimizer, Release 0.0.0 range_norm Range of validity in normalized coordinates. Type np.array surrogates List of cross validation surrogates. Type list surrogate Surrogate trained on all available data. Type object samples New input samples in the current iteration. Type np.array best_hp Optimal hyperparameters. Type kerastuner.engine.hyperparameters.HyperParameters accuracy Benchmark accuracy statistics. Type dict append_verification() Add verification results to database. check_convergence() Check the convergence of the surrogate’s training. evaluate_samples(verify=False) Wrapper function to call the evaluted problem solver. Parameters verify (bool) – whether this is a verification run load_results(verify=False) Wrapper function to load the results from the results file Parameters verify (bool) – whether this is a verification run optimize_hyperparameters() Wrapper function to optimize the surrogate’s hyperparameters. plot_response(inputs, output, density=30, constants=None, iteration=None) Plot the model’s response based on the surrogate. Parameters • inputs (list) – Input dimensions to plot. • output (int) – Output dimension to plot. • density (int) – Sampling density of the reponse plot. • constants (list) – Values of the fixed input dimensions. • iteration (int) – Iteration number. reload() Reloads the surrogate. 1.1. core package 7 Surrogate optimizer, Release 0.0.0 report() Plot the convergence metric and report on the trained surrogate. sample() Wrapper function to obtain the new sample points. Notes Be careful with geometric, grows fast. If non-adaptive sampling is used, adaptive must be set to None. save() Saves the surrogate. train() Wrapper function to (re)train the surrogate. 1.1.2 Module contents 1.2 datamod package 1.2.1 Submodules datamod.evaluator module Contains the classes to evaluate the new samples, typically using an external software. class datamod.evaluator.Evaluator Bases: object General evaluator class. save_results Function to write the results into the results database. iteration Iteration number. Type int generate_results(samples, file, iteration, verify) Generate the response and save the result to the database. Parameters • samples (np.array) – Samples to evaluate. • file (str) – Path and name of the database file. • iteration (int) – Iteration number. • verify (bool) – Whether this is a verification evaluation. class datamod.evaluator.EvaluatorANSYS Bases: datamod.evaluator.Evaluator Evaluate the samples using ANSYS. ansys_project_folder Path to the folder of the ANSYS project. 8 Chapter 1. app Surrogate optimizer, Release 0.0.0 Type str input_param_name Names of the input parameters. Type list setup ANSYS settings. Type dict valid_licences Licences required for running ANSYS: Type list can_run_ansys(minimal_amount=2) Determine whether there is a sufficient amount of available licenses to run the simulation. Parameters minimal_amount (int) – Minimal required amount of available licenses. Returns Whether it is possible to run ANSYS or not. Return type status (bool) Notes Returns True whenever at least one license is available. check_licenses() Request the license server for infomation about license usage. Returns Licence names with the number of unused licenses. Return type license_status (dict) evaluate(samples, verify) Evaluate the samples. Parameters • samples (np.array) – Samples to evaluate. • verify (bool) – Whether this is a verification evaluation. Returns Output values at the samples. Return type results (np.array) get_info() Get information about the problem. Returns Input parameter allowable ranges. dim_in (int): Number of input dimensions. dim_out (int): Number of output dimensions. n_constr (int): Number of constraints. Return type range_in (np.array) scrape_license_info(line) Extract the number of available ANSYS licenses. Parameters line (str) – Line containing the license usage information. Returns Name of the license. available (int): Number of available unused licenses. Return type license_name (str) 1.2. datamod package 9 Surrogate optimizer, Release 0.0.0 class datamod.evaluator.EvaluatorANSYSAPDL Bases: datamod.evaluator.EvaluatorANSYS Evaluate the samples through ANSYS APDL. Notes Not documented thorougly as it is a dev version for the particular problem. call_ansys() Evaluate the requested input files. evaluate(samples, verify) Evaluate the samples. Parameters • samples (np.array) – Samples to evaluate. • verify (bool) – Whether this is a verification evaluation. Returns Output values at the samples. Return type response (np.array) get_results(verify) Retrieve the results from text files. Parameters verify (bool) – Whether this is a verification evaluation. Returns Output values at the samples. Return type response (np.array) update_input(samples) Update the unput files. Parameters samples (np.array) – Input samples to evaluate. class datamod.evaluator.EvaluatorANSYSWB Bases: datamod.evaluator.EvaluatorANSYS Evaluate the samples through ANSYS Workbench. workbench_project Path and name to the ANSYS workbench project. Type str template Path and name to the journal file template. Type str output Path and name to the newly created journal folder. Type str iteration Iteration number. Type int call_ansys() Evaluate the requested input files. 10 Chapter 1. app Surrogate optimizer, Release 0.0.0 evaluate(samples, verify) Evaluate the samples. Parameters • samples (np.array) – Samples to evaluate. • verify (bool) – Whether this is a verification evaluation. Returns Output values at the samples. Return type results (np.array) Notes A trick with iterations get_results(verify) Retrieve the results from CSV files. Parameters verify (bool) – Whether this is a verification evaluation. Returns Output values at the samples. Return type response (np.array) update_input(samples) Create an input journal from the template. Parameters samples (np.array) – Samples to evaluate. class datamod.evaluator.EvaluatorBenchmark Bases: datamod.evaluator.Evaluator Evaluate a benchmark problem on the given sample. problem Benchmark problem. results List of values requested from the problem. Type list evaluate(samples, verify) Evaluate the samples. Parameters • samples (np.array) – Samples to evaluate. • verify (bool) – Whether this is a verification evaluation. Returns Output values at the samples. Return type response (np.array) Warning: Return_values_of in problem.evaluate doesn’t work - Pymoo implementation problem. get_info() Get information about the benchmark problem. 1.2. datamod package 11 Surrogate optimizer, Release 0.0.0 Returns Input parameter allowable ranges. dim_in (int): Number of input dimensions. dim_out (int): Number of output dimensions. n_constr (int): Number of constraints. Return type range_in (np.array) Notes return_values_of is wrongly implemented in pymoo class datamod.evaluator.EvaluatorData Bases: datamod.evaluator.Evaluator Obtain the response from a data file. source_file Path and name of the data file. Type str evaluate(samples, verify) Evaluate the samples. Parameters • samples (np.array) – Samples to evaluate. • verify (bool) – Whether this is a verification evaluation. Returns Output values at the samples. Return type response (np.array) get_info() Load information about a data-defined problem. Returns Input parameter allowable ranges. dim_in (int): Number of input dimensions. dim_out (int): Number of output dimensions. n_constr (int): Number of constraints. Return type range_in (np.array) get_samples(no_samples, no_new_samples) Get the coordinates of the new samples. Returns Coordinates of the new samples. Return type samples(np.array) class datamod.evaluator.RealoadNotAnEvaluator Bases: datamod.evaluator.Evaluator Just a programming convenience when reloading a surrogate, doesnt evaluate anything in fact. get_info() Get information about the problem. Returns Input parameter allowable ranges. dim_in (int): Number of input dimensions. dim_out (int): Number of output dimensions. n_constr (int): Number of constraints. Return type range_in (np.array) 12 Chapter 1. app Surrogate optimizer, Release 0.0.0 datamod.problems module Function definitions. This module contains function definitions. datamod.problems.problems Classes of the custom defined problems. Type dict class datamod.problems.CubicSquared Bases: pymoo.model.problem.Problem Squared function. class datamod.problems.Custom(function, xl, xu, n_obj, n_constr) Bases: pymoo.model.problem.Problem Class for custom built problems using a surrogate. function The response function. class datamod.problems.GettingStarted Bases: pymoo.model.problem.Problem Pymoo example problem. References http://pymoo.org/getting_started.html class datamod.problems.MatlabPeaks Bases: pymoo.model.problem.Problem MATLAB peaks function definition. References https://www.mathworks.com/help/matlab/ref/peaks.html class datamod.problems.Squared Bases: pymoo.model.problem.Problem Squared function. datamod.results module This module contains functions to handle the results database. datamod.results.append_verification_to_database(file, verification_file) Appends the results from the verification to the training database. Parameters • file (str) – Path and name of the training database file. • verification_file (str) – Path and name of the verification database file. 1.2. datamod package 13 Surrogate optimizer, Release 0.0.0 datamod.results.header_names(dim_in, n_obj, n_const) Determines the header names. Parameters • dim_in (int) – Number of input dimensions. • n_const (int) – Number of constraints. • n_obj (int) – Number of objectives. Returns Header names. Return type headers (list) datamod.results.load_results(file) Loads the data from the result database. Parameters file (str) – Path and name of the database file. Returns Number of input dimensions. col_names (np.array): Name if stored variables. data (np.array): Samples. Return type dim_in (int) datamod.results.make_data_file(file, dim_in, n_obj, n_constr) Initialize the results file header. Parameters • file (str) – Path and name of the database file. • dim_in (int) – Number of input dimensions. • n_const (int) – Number of constraints. • n_obj (int) – Number of objectives. datamod.results.make_response_files(folder, dim_in, n_obj, n_constr) Sets up the training and verification database files. Parameters • folder (str) – Path to the current results data folder. • dim_in (int) – Number of input dimensions. • n_const (int) – Number of constraints. • n_obj (int) – Number of objectives. Returns List of the database files paths. Return type files (list) datamod.results.write_results(file, inputs, outputs) Write the samples to the database. Parameters • inputs (np.array) – Input coordinates. • outputs (np.array) – Output coordinates. 14 Chapter 1. app Surrogate optimizer, Release 0.0.0 datamod.sampling module This is the sampling module. This module provides sampling methods. datamod.sampling.adaptive_methods Exploration and exploitation criteria for adaptive sampling methods. Type dict datamod.sampling.sample_bounds Sampling range. Type tuple datamod.sampling.samplings Defined sampling classes. Type dict class datamod.sampling.Halton(**kwargs) Bases: smt.sampling_methods.sampling_method.SamplingMethod Halton sampling. References https://gist.github.com/tupui/cea0a91cc127ea3890ac0f002f887bae datamod.sampling.determine_samples(no_samples, dim_in) Determine the number of new samples. Parameters • dim_in (int) – Number of input dimensions. • no_samples (int) – number of current samples. Returns Number of new samples. Return type new_samples (int) datamod.sampling.resample_adaptive(points_new, surrogates, data, range_in, iteration) Determine the coordinates of the new samples using an adaptive DoE. Parameters • points_new (int) – Number of new samples. • range_in (np.array) – Range of input variables. • surrogates (list) – Surrogates from cross-validation. • data (datamod.get_data) – Training samples. • range_in – Range of input variables. • iteration (int) – Iteration number. Returns Coordinates of the new samples. Return type coordinates (np.array) datamod.sampling.resample_static(points_new, points_now, range_in) Determine the coordinates of the new samples using a static DoE. 1.2. datamod package 15 Surrogate optimizer, Release 0.0.0 Parameters • points_new (int) – Number of new samples. • points_now (int) – number of current samples. • range_in (np.array) – Range of input variables. Returns Coordinates of the new samples. Return type coordinates (np.array) datamod.sampling.response_grid(density, inputs, ranges) Returns a grid of samples with the desired ranges. Parameters • density (int) – Density of the samples plot. • inputs (list) – Input dimensions to plot. • ranges (np.array) – Range of input variables. Returns Grid samples. Return type sample_desired (np.array) datamod.sampling.sample(name, points, n_dim) Sampling on a unit hypercube - typically [-1,1], using a selected DoE. Parameters • name (str) – Sampling strategy. • points (int) – Number of requested samples. • n_dim (int) – Number of input dimensions. Returns New samples. Return type samples (np.array) Raises NameError – if the sampling is not defined. Notes Grid actually doesn’t make full grid. datamod.sampling.sample_adaptive(data, samples, predictions, no_points_new, iteration) Sampling using an adaptive DoE. Parameters • data (datamod.get_data) – Training samples. • samples (np.array) – Proposed samples. • predictions (np.array) – Predictions of the surrogates from cross-validation. • no_points_new (int) – Number of new samples. • iteration (int) – Iteration number. Returns Normalized coordinates of the new samples. Return type candidates (np.array) 16 Chapter 1. app Surrogate optimizer, Release 0.0.0 datamod.sampling.scale_samples(range_in, samples) Scales the samples to the desired range. Parameters • range_in (np.array) – Range of input variables. • samples (np.array) – Samples to be scaled. Returns Coordinates of the new samples. Return type coordinates (np.array) 1.2.2 Module contents Data handling module. The aim of the datamod package is to handle the data. class datamod.get_data(file) Bases: object Import data from an external file. dim_in Number of input dimensions. Type int col_names Names of columns. Type list dim_out Number of output dimensions. Type int coordinates Samples coordinates. Type np.array response Sample response. Type np.array input Normalized input samples. Type np.array output Normalized output samples. Type np.array norm_in Input normalization factors. Type np.array norm_out Output normalization factors. 1.2. datamod package 17 Surrogate optimizer, Release 0.0.0 Type np.array range_in Range of the input data. Type np.array range_out Range of the output data. Type np.array datamod.get_range(data) Determine the range of the data. Parameters data (np.array) – Data to analyze. Returns Ranges of the given data. Return type ranges (np.array) datamod.load_problem(name) Load a pre-defined benchmark problem. Parameters name (str) – Name of the desired problem. Returns Benchmark problem. range_in (np.array): Input parameter allowable ranges. dim_in (int): Number of input dimensions. dim_out (int): Number of output dimensions. n_constr (int): Number of constraints. Return type problem () datamod.normalize(data) Normalize the data to the [-1,1] range. Parameters data (np.array) – Data to normalize. datamod.scale(data, ranges) Scale data from [-1,1] range to original range. Parameters • data (np.array) – Data to scale. • ranges (np.array) – Normalization ranges. Returns (np.array): data_scale: Scaled data. 1.3 metamod package 1.3.1 Submodules metamod.ANN module Custom ANN definition. This module contains the definition of an ANN comptable with the SMT Toolbox. class metamod.ANN.ANN_base(**kwargs) Bases: smt.surrogate_models.surrogate_model.SurrogateModel ANN general class. 18 Chapter 1. app Surrogate optimizer, Release 0.0.0 tbd Settings to be declared. Type dict_keys log_dir Directory to store the ANN model. Type str validation_points Validation points. Type dict optimized Whether the hyperparameters have been optimized. Type bool set_validation_values(xt, yt, name=None) Set validation data (values). Parameters • xt (np.ndarray[nt, nx] or np.ndarray[nt]) – The input values for the nt training points. • yt (np.ndarray[nt, ny] or np.ndarray[nt]) – The output values for the nt training points. • name (str or None) – An optional label for the group of training points being set. This is only used in special situations (e.g., multi-fidelity applications). write_stats(dictionary_as_list, name) Writes the statistics about the surrogate’s training. Parameters • dictionary_as_list (list) – Training statistics. • name (str) – Name of the file to be written. metamod.ANN_pt module Custom ANN definition using TensorFlow. This module contains the definition of an ANN comptable with the SMT Toolbox. metamod.ANN_pt.activations Available activation functions. Type dict metamod.ANN_pt.initializers Available kernel initializers. Type dict 1.3. metamod package 19 Surrogate optimizer, Release 0.0.0 Notes Keras tuner logs are not stored in data due to max path length issues in kerastuner. See also: PyTorch documentation - https://pytorch.org/docs/stable/index.html class metamod.ANN_pt.ANN_pt(**kwargs) Bases: metamod.ANN.ANN_base ANN class. name Name of the surrogate model. Type str model The model of the ANN. early_stop Number of training epchs before early stopping. Type int build_hypermodel(hp) Build the hypermodel of the ANN with Keras Tuner hyperparameters. Parameters hp (kerastuner.engine.hyperparameters.HyperParameters) – Hyperparameters. Returns The model of the ANN. Return type model () Notes No kernel regularizers implemented so far. See also: Keras Tuner documentation: https://keras-team.github.io/keras-tuner/ pretrain(inputs, outputs, iteration) Optimize the hyperparameters of the ANN. Parameters • inputs (np.array) – All input data. • outputs (np.array) – All output data. • iteration (int) – Iteration number. Returns Optimal hyperparameters. Return type best_hp (kerastuner.engine.hyperparameters.HyperParameters) Notes Optimization objective fixed on val_loss. 20 Chapter 1. app Surrogate optimizer, Release 0.0.0 save() Save the ANN into an external file. class metamod.ANN_pt.SparseModel(neurons_hyp, activation_hyp, kernel_regularizer, in_dim, lay- ers_hyp, out_dim, init, bias_init) Bases: torch.nn.modules.module.Module Defines an ANN with a subnetwork for each output. activation Activation function. Type function subnetworks Neural network. early_stopping(history, metric, tolerance, patience) Check whether the early stopping condition has been met. Parameters • history (metamod.ANN_pt.TrainHistory) – Metrics history during the train- ing. • metric (str) – Early stopping decision metric. • tolerance (float) – Early stopping tolerance. • patience (int) – Early stopping patience. Returns Whether to early stop the training. Return type stop (bool) fit(epochs, train_in, train_out, test_in=None, test_out=None, optimizing=False) Train the ANN. Parameters • epochs (int) – • train_in (torch.Tensor) – Train input data. • train_out (torch.Tensor) – Train output data. • test_in (torch.Tensor) – Test input data. • test_out (torch.Tensor) – Test output data. • optimizing (bool) – Whether this is an optimization run. Returns Metrics history during the training. Return type history (metamod.ANN_pt.TrainHistory) forward(x) Forward-pass throught the network. Parameters x (torch.Tensor) – Input tensor. Returns Output tensor. Return type out (torch.Tensor) class metamod.ANN_pt.TrainHistory Bases: object This class stored the metrics history. 1.3. metamod package 21 Surrogate optimizer, Release 0.0.0 history Stores training and validation losses. Type dict store(loss, loss_eval=None) metamod.ANN_pt.swish(x) Swish activation function. Parameters x (torch.Tensor) – Weighted inputs. Returns Neuron’s activations. Return type activation (torch.Tensor) metamod.ANN_tf module Custom ANN definition using TensorFlow. This module contains the definition of an ANN comptable with the SMT Toolbox Notes Keras tuner logs are not stored in data due to max path length issues in kerastuner. See also: Tensorflow Keras documentation - https://www.tensorflow.org/api_docs/python/tf/keras class metamod.ANN_tf.ANN(**kwargs) Bases: metamod.ANN.ANN_base ANN class. name Name of the surrogate model. Type str model The model of the ANN. early_stop Number of training epchs before early stopping. Type int build_dense_model(neurons_hyp, activation_hyp, kernel_regularizer, in_dim, layers_hyp, out_dim) Defines an fully connected ANN. Parameters • neurons_hyp (int) – Number of neurons per layer. • activation_hyp (str) – Activation function name. • () (kernel_regularizer) – • in_dim (int) – Number of input dimensions. • layers_hyp (int) – Number of hidden layers. • out_dim (int) – Number of output dimensions. 22 Chapter 1. app Surrogate optimizer, Release 0.0.0 Returns The model of the ANN. Return type model () build_hypermodel(hp) General claass to build the ANN using TensorFlow with Keras Tuner hyperparameters defined. Parameters hp (kerastuner.engine.hyperparameters.HyperParameters) – Hyperparameters. Returns The model of the ANN. Return type model () Notes Hyperparameters initialized using default values in config file. build_sparse_model(neurons_hyp, activation_hyp, kernel_regularizer, in_dim, layers_hyp, out_dim) Defines an ANN with a subnetwork for each output. Parameters • neurons_hyp (int) – Number of neurons per layer. • activation_hyp (str) – Activation function name. • () (kernel_regularizer) – • in_dim (int) – Number of input dimensions. • layers_hyp (int) – Number of hidden layers. • out_dim (int) – Number of output dimensions. Returns The model of the ANN. Return type model () get_callbacks() Set-up the requiested callbacks to be entered into the model. Returns A list of callbacks. Return type callbacks (list) Notes MyStopping is never used. pretrain(inputs, outputs, iteration) Optimize the hyperparameters of the ANN. Parameters • inputs (np.array) – All input data. • outputs (np.array) – All output data. • iteration (int) – Iteration number. Returns Optimal hyperparameters. Return type best_hp (kerastuner.engine.hyperparameters.HyperParameters) 1.3. metamod package 23 Surrogate optimizer, Release 0.0.0 Notes Optimization objective fixed on val_loss. prune_model(model, target_sparsity) Extends the ANN’s model with priuning layers. Parameters • () (model) – The model of the ANN. • target_sparsity (float) – Target constant sparsity of the network. Returns The model of the ANN. Return type model () Notes Only constant sparsity active. save() Save the ANN into an external file. metamod.deploy module Module to assist the deployment of the surrogate. metamod.deploy.get_input_coordinates(density, requested_dims, range_norm) Obtain the grid samples. Parameters • density (int) – Sampling density of the reponse plot. • requested_dims (list) – Input dimensions to plot. • range_norm (np.array) – Range of validity in normalized coordinates. Returns Grid samples. Return type samples (np.array) metamod.deploy.get_plotting_coordinates(density, requested_dims, dim_in, normaliza- tion_factors, range_norm, constants) Obtain the grid samples for plotting. Parameters • density (int) – Sampling density of the reponse plot. • requested_dims (list) – Input dimensions to plot. • dim_in (int) – Number of input dimensions. • normalization_factors (np.array) – Input normalization factors. • range_norm (np.array) – Range of validity in normalized coordinates. • constants (list) – Values of the fixed input dimensions. Returns Grid samples. Return type samples (np.array) 24 Chapter 1. app Surrogate optimizer, Release 0.0.0 metamod.performance module Module to access the performance of the surrogate. metamod.performance.defined_metrics Available metrics. Type dict metamod.performance.RMSE(*args, **kwargs) Returns the root mean squared error. metamod.performance.benchmark_accuracy(surrogate) Parameters surrogate (core.Surrogate) – Trained surrogate. Returns Benchmark accuracy statistics. Return type diffs (dict) metamod.performance.check_convergence(metrics) Checks whether the metric meets the convergence criterion. Parameters metrics (list) – List of the convergence metrics for each iteration. Returns Convergence status. Return type trained (bool) Notes Need to add convergence if data is loaded and there is no more data to load metamod.performance.convergence_operator() Obtain either greater than or lower than operator based on the convergence metric type. Returns Direction logical operator. Return type op (function) metamod.performance.evaluate_metrics(inputs, outputs, predict) Evaluates surrogate accuracy metrics based on test samples. Parameters • inputs (np.array) – Test samples. • outputs (np.array) – Test response. • predict (SurrogateModel.predict_values) – Surrogate’s prediction method. Returns Surrogate accuracy metrics. Return type metrics (dict) metamod.performance.report_divergence() Report the problem ID if the surrogate training fails to converge with the maximal amount of training samples. metamod.performance.retrieve_metric(surrogates) Calculates the mean and variance of the assessed metric. Parameters surrogates (list) – List of cross validation surrogates. Returns Mean and variance of the assessed metric. Return type output_metrics (dict) 1.3. metamod package 25 Surrogate optimizer, Release 0.0.0 metamod.validation module This module provides cross-validation splitting techniques. metamod.validation.split_methods CV validation techniques. Type dict metamod.validation.invalid_param() Raise error if validation parameter is invalid. Raises NameError – if validation parameter is invalid. metamod.validation.set_validation(validation, param) Select the desired validation technique model. Parameters • validation (str) – Validation technique name. • param (float/int) – Validation technique parameter. Returns Indices for the split. Return type split (np.array) metamod.validation.split_holdout(param) Select the desired validation technique model. Parameters param (float) – Holdout ratio. Returns Indices for the split. Return type split (np.array) metamod.validation.split_kfold(param) Select the desired validation technique model. Parameters param (int) – Number of splits. Returns Indices for the split. Return type split (np.array) metamod.validation.split_rlt(param) Select the desired validation technique model. Parameters param (float) – Holdout ratio. Returns Indices for the split. Return type split (np.array) 1.3.2 Module contents Surrogate package. The aim of the metamod package is to produce and run a surrogate model. metamod.cross_validate(data, iteration, best_hp) Train the defined surrogate on the provided data. Parameters • data (datamod.get_data) – Training samples. 26 Chapter 1. app Surrogate optimizer, Release 0.0.0 • iteration (int) – Iteration number. • best_hp (kerastuner.engine.hyperparameters.HyperParameters) – Optimal hyperparameters. Returns List of cross validation surrogates. Return type surrogates (list) metamod.optimize_hyperparameters(data, iteration) Train the defined surrogate on the provided data. Parameters • data (datamod.get_data) – Training samples. • iteration (int) – Iteration number. Returns Optimal hyperparameters. Return type best_hp (kerastuner.engine.hyperparameters.HyperParameters) metamod.reload_info() Get information about the problem. Returns Input parameter allowable ranges. dim_in (int): Number of input dimensions. dim_out (int): Number of output dimensions. n_constr (int): Number of constraints. Return type range_in (np.array) metamod.set_surrogate(name, dim_in, dim_out) Select the desired surrogate model. Parameters • name (str) – Name of the surrogate. • dim_in (int) – Number of input dimensions. • dim_out (int) – Number of output dimensions. Returns Initialized surrogate model. Return type surrogate (object) Raises NameError – If the surrogate is not defined- metamod.train_surrogate(data, best_hp) Parameters • data (datamod.get_data) – Training samples. • best_hp (kerastuner.engine.hyperparameters.HyperParameters) – Optimal hyperparameters. Returns Surroggate trained on all training samples. Return type model (object) 1.4 optimod package 1.4.1 Submodules 1.4. optimod package 27 Surrogate optimizer, Release 0.0.0 optimod.performance module Module to access the performance of the optimization. optimod.performance.calculate_hypervolume(data, ref_point) Text. Parameters • data (np.array) – Pareto front. • ref_point (np.array) – Reference point. Returns Hypervolume size. Return type hv (float) optimod.performance.verify_results(results, surrogate) Text. Parameters • results (np.array) – Pareto set results. • surrogate (core.Surrogate) – Surrogate object. Returns Set of verification indices. Return type idx (np.array) optimod.termination module Module to provide the termination object. optimod.termination.default_termination(n_obj, params) Text. Parameters • n_obj (int) – Number of objectives. • params (list) – Termination parameters. Returns Termination object. Return type termination () 1.4.2 Module contents Optimization package. The aim of the optimod package is to perform optimization. optimod.get_operator(name, setup) Text. Parameters • name (str) – Operator name to retrieve. • setup (dict) – Optimization setup parameters. Returns Retrieved operator. Return type operator () 28 Chapter 1. app Surrogate optimizer, Release 0.0.0 optimod.set_algorithm(name, no_obj, setup) Text. Parameters • name (str) – Name of the optimization algorithm. • n_obj (int) – Number of objectives. • setup (dict) – Optimization setup parameters. Returns Optization algorithm object. Return type algorithm () optimod.set_optimization(no_obj) Set the selected optimization technique. Parameters n_obj (int) – Number of objectives. Returns Optization algorithm object. termination (): Termination object. Return type algorithm () optimod.solve_problem(problem, algorithm, termination, direct) Solve the defined problem. Parameters • problem (datamod.problems.Custom) – Problem to solve. • () (termination) – Optimization algorithm. • () – Termination method. • direct (bool) – Whether this is a direct optimization run. Returns Results object. Return type res (pymoo.model.result.Result) Notes Save_history will work with ANN surrogate from Tensorflow only if line 290 in site- packagespymoomodellgorithm is changed from deepcopy to copy. Optional seed not implemented. optimod.unnormalize_res(res, norm_in, norm_out) Unnormliazes results. Parameters • res (pymoo.model.result.Result) – Results object (unnormalized). • norm_in (np.array) – Input normalization factors. • norm_out (np.array) – Output normalization factors. Returns Results object (normalized). Return type res (pymoo.model.result.Result) Notes Implemented for: F,X. 1.4. optimod package 29 Surrogate optimizer, Release 0.0.0 1.5 run module This is the main module of the framework. run.problem_ids List of problem IDs to solve. Type list run.model Model object. Type core.Model run.build_surrogate Whether to build a surrogate. Type bool run.load_surrogate Whether to load a surrogate. Type bool run.train_from_data Whether to train from data. Type bool run.perform_optimization Whether to perform optimization. Type bool run.surrogate Surrogate object. Type core.Surrogate run.optimization Optimization object. Type core.Optimization run.optimize(surrogate) Optimization. Parameters surrogate (core.Surrogate/None) – The surrogate object run.reload_surrogate() Reloads a saved surrogate model. run.train_surrogate() Training of the surrogate model. 1.6 visumod package 1.6.1 Submodules 30 Chapter 1. app Surrogate optimizer, Release 0.0.0 visumod.plots module This module provides the actual plots. visumod.plots.adaptive_candidates(candidates, data, iteration) Plot the adapative sampling candidate points. Parameters • candidates (np.array) – Candidate samples. • data (np.array) – Combined adaptive sampling metric. • iteration (int) – Iteration number. visumod.plots.curve(data, name, labels, units, lower_bound=False) A curve plot. Parameters • data (np.array) – Data to plot. • name (str) – Filename. • labels (list) – Axis labels. • units (list) – Units of plotted quantities. • lower_bound (bool) – Whether to put a lower plot bound at 0. visumod.plots.get_blackblue_cmap() Get a custom black-blue colormap. Returns Defined colormap. Return type newmap (matplotlib.colors.LinearSegmentedColormap) visumod.plots.get_plot_args(data, label) Get plot arguments. Parameters • data (np.array) – Data to plot. • label (str) – Variable name for label. Returns Plot arguments. Return type plot_args (dict) visumod.plots.heatmap(correlation) A heatmap plot. Parameters correlation (np.array) – Correlation matrix. visumod.plots.learning_curves(training_loss, validation_loss, data_train, prediction_train, data_test, prediction_test, progress) A 2-figure plot of learning curves and plot correlations. Parameters • training_loss (list) – Loss history on the training data. • validation_loss (list) – Loss history on the testing data. • () (prediction_test) – Training output data. • () – Training output prediction. 1.6. visumod package 31 Surrogate optimizer, Release 0.0.0 • () – Testing output data. • () – Testing output prediction. • progress (list) – Training progress status. visumod.plots.pareto_fronts(pf_true, pf_calc) A scatter plot of Pareto fronts comparison. Parameters • pf_true (np.array) – True Pareto front. • pf_calc (np.array) – Calculated Pareto front. visumod.plots.pcp(data, name) A parallel component plot. Parameters • data (np.array) – Data to plot. • name (str) – Filename. visumod.plots.save_figure(name, plot=None, iteration=None) Save the given figure. Parameters • name (str) – Filename. • () (plot) – Pymoo plot obejct. • iteration (int) – Iteration number. visumod.plots.scatter(data, name, lower_bound=False, compare=False) A scatter plot. Parameters • data (np.array) – Data to plot. • name (str) – Filename. • lower_bound (bool) – Whether to put a lower plot bound at 0. • compare (bool) – Whetther this is a surrogate comparison plot. visumod.plots.scatter_pymoo(data, name, label=None, **kwargs) Plot either a scatter, curve or surface plot. Parameters • data (np.array) – Data to plot. • name (str) – Visualization type. • label (str) – Variable name for label. Notes Surface plot not used. visumod.plots.surface_pymoo(data, iteration) A surface plot using Pymoo. Parameters 32 Chapter 1. app Surrogate optimizer, Release 0.0.0 • data (np.array) – Data to plot. • iteration (int) – Iteration number. 1.6.2 Module contents This is the visualization module. visumod.compare_pareto_fronts(pf_true, pf_calc) Compare 2D Pareto fronts. Parameters • pf_true (np.array) – True Pareto front. • pf_calc (np.array) – Calculated Pareto front. visumod.compare_surrogate(inputs, outputs, predict, iteration) Plot the comparison of raw data and surrogate response. Parameters • inputs (np.array) – Input data. • outputs (np.array) – Output data. • predict (method) – Predict method of the surrogate. • iteration (int) – Iteration number. visumod.correlation_heatmap(predict, dim_in) Plot the correleation heatmap between variables. Parameters • predict (method) – Predict method of the surrogate. • dim_in (int) – Number of input dimensions. visumod.plot_adaptive_candidates(candidates, data, iteration) Plot candidates for adaptive sampling. Parameters • candidates (np.array) – Candidate samples. • data (np.array) – Combined adaptive sampling metric. • iteration (int) – Iteration number. visumod.plot_raw(data, iteration, normalized=False) Plot either a scatter, curve or surface plot. Parameters • data (np.array) – Raw data samples. • iteration (int) – Iteration number. • normalized (bool) – Whether the data is normalized. 1.6. visumod package 33 Surrogate optimizer, Release 0.0.0 Notes Surface plot not used yet. visumod.plot_training_history(history, train_in, train_out, test_in, test_out, predict, progress) Plot the evolution of the training and testing error. Parameters • history (tensorflow.python.keras.callbacks.History/metamod. ANN_pt.TrainHistory) – Metrics history during the training. • train_in (np.array/torch.Tensor) – Training input data. • train_out (np.array/torch.Tensor) – Training output data. • test_in (np.array/torch.Tensor) – Testing input data. • test_out (np.array/torch.Tensor) – Testing output data. • predict (method) – Predict method of the surrogate. • progress (list) – Training progress status. visumod.sample_size_convergence(metrics) Plot the sample size convergence. Parameters metrics (dict) – Dictionary of convergence metrics. visumod.surrogate_response(inputs, outputs, iteration) Plot the surrogate response. Parameters • inputs (np.array) – Input data. • outputs (np.array) – Output to plot. • iteration (int) – Iteration number. visumod.vis_design_space(data, iteration) Visualize the design space in design coordinates. Parameters • res (pymoo.model.result.Result) – Results object. • iteration (int) – Iteration number. visumod.vis_objective_space(data, iteration) Visualize the design space in objective coordinates. Parameters • res (pymoo.model.result.Result) – Results object. • iteration (int) – Iteration number. visumod.vis_objective_space_pcp(data, iteration) Visualize the design space in objective coordinates with the parallel coordinates plot. Parameters • data (np.array) – Multidimensional Pareto front. • iteration (int) – Iteration number. 34 Chapter 1. app CHAPTER 2 Indices and tables • genindex • modindex • search 35 Surrogate optimizer, Release 0.0.0 36 Chapter 2. Indices and tables Python Module Index c core, 8 core.model, 1 core.optimization, 2 core.settings, 4 core.surrogate, 5 d datamod, 17 datamod.evaluator, 8 datamod.problems, 13 datamod.results, 13 datamod.sampling, 15 m metamod, 26 metamod.ANN, 18 metamod.ANN_pt, 19 metamod.ANN_tf, 22 metamod.deploy, 24 metamod.performance, 25 metamod.validation, 26 o optimod, 28 optimod.performance, 28 optimod.termination, 28 r run, 30 v visumod, 33 visumod.plots, 31 37 Surrogate optimizer, Release 0.0.0 38 Python Module Index Index A C accuracy (core.surrogate.Surrogate attribute), 7 calculate_hypervolume() (in module opti- activation (metamod.ANN_pt.SparseModel at- mod.performance), 28 tribute), 21 call_ansys() (data- activations (in module metamod.ANN_pt), 19 mod.evaluator.EvaluatorANSYSAPDL adaptive_candidates() (in module vi- method), 10 sumod.plots), 31 call_ansys() (data- adaptive_methods (in module datamod.sampling), mod.evaluator.EvaluatorANSYSWB method), 15 10 algorithm (core.optimization.Optimization attribute), can_run_ansys() (data- 2 mod.evaluator.EvaluatorANSYS method), ANN (class in metamod.ANN_tf ), 22 9 ANN_base (class in metamod.ANN), 18 check_convergence() (core.surrogate.Surrogate ANN_pt (class in metamod.ANN_pt), 20 method), 7 ansys_project_folder (data- check_convergence() (in module meta- mod.evaluator.EvaluatorANSYS attribute), mod.performance), 25 8 check_licenses() (data- append_verification() mod.evaluator.EvaluatorANSYS method), (core.surrogate.Surrogate method), 7 9 append_verification_to_database() (in check_valid_settings() (in module module datamod.results), 13 core.settings), 4 ask_to_overwrite() (in module core.settings), 4 col_names (datamod.get_data attribute), 17 compare_pareto_fronts() (in module visumod), B 33 benchmark() (core.optimization.Optimization compare_surrogate() (in module visumod), 33 method), 3 converged (core.optimization.Optimization attribute), benchmark_accuracy() (in module meta- 2 mod.performance), 25 convergence_metric (core.surrogate.Surrogate at- best_hp (core.surrogate.Surrogate attribute), 7 tribute), 6 build_dense_model() (metamod.ANN_tf.ANN convergence_operator() (in module meta- method), 22 mod.performance), 25 build_hypermodel() (metamod.ANN_pt.ANN_pt coordinates (datamod.get_data attribute), 17 method), 20 core (module), 8 build_hypermodel() (metamod.ANN_tf.ANN core.model (module), 1 method), 23 core.optimization (module), 2 build_sparse_model() (metamod.ANN_tf.ANN core.settings (module), 4 method), 23 core.surrogate (module), 5 build_surrogate (in module run), 30 correlation_heatmap() (in module visumod), 33 cross_validate() (in module metamod), 26 39 Surrogate optimizer, Release 0.0.0 CubicSquared (class in datamod.problems), 13 EvaluatorBenchmark (class in datamod.evaluator), curve() (in module visumod.plots), 31 11 Custom (class in datamod.problems), 13 EvaluatorData (class in datamod.evaluator), 12 evaluators (in module core.model), 1 D data (core.surrogate.Surrogate attribute), 6 F datamod (module), 17 file (core.surrogate.Surrogate attribute), 6 datamod.evaluator (module), 8 fit() (metamod.ANN_pt.SparseModel method), 21 datamod.problems (module), 13 forward() (metamod.ANN_pt.SparseModel method), datamod.results (module), 13 21 datamod.sampling (module), 15 function (core.optimization.Optimization attribute), 3 default_termination() (in module opti- function (datamod.problems.Custom attribute), 13 mod.termination), 28 defined_metrics (in module meta- G mod.performance), 25 generate_results() (data- determine_samples() (in module data- mod.evaluator.Evaluator method), 8 mod.sampling), 15 get_blackblue_cmap() (in module visumod.plots), dim_in (core.model.Model attribute), 1 31 dim_in (datamod.get_data attribute), 17 get_callbacks() (metamod.ANN_tf.ANN method), dim_out (core.model.Model attribute), 1 23 dim_out (datamod.get_data attribute), 17 get_data (class in datamod), 17 direct (core.optimization.Optimization attribute), 2 get_info() (datamod.evaluator.EvaluatorANSYS diverging (core.surrogate.Surrogate attribute), 6 method), 9 dump_json() (in module core.settings), 4 get_info() (datamod.evaluator.EvaluatorBenchmark dump_object() (in module core.settings), 4 method), 11 get_info() (datamod.evaluator.EvaluatorData E method), 12 early_stop (metamod.ANN_pt.ANN_pt attribute), 20 get_info() (datamod.evaluator.RealoadNotAnEvaluator early_stop (metamod.ANN_tf.ANN attribute), 22 method), 12 early_stopping() (metamod.ANN_pt.SparseModel get_input_coordinates() (in module meta- method), 21 mod.deploy), 24 error (core.optimization.Optimization attribute), 3 get_input_from_id() (in module core.settings), 4 error_measure (core.optimization.Optimization at- get_operator() (in module optimod), 28 tribute), 3 get_plot_args() (in module visumod.plots), 31 evaluate() (datamod.evaluator.EvaluatorANSYS get_plotting_coordinates() (in module meta- method), 9 mod.deploy), 24 evaluate() (datamod.evaluator.EvaluatorANSYSAPDL get_range() (in module datamod), 18 method), 10 get_results() (data- evaluate() (datamod.evaluator.EvaluatorANSYSWB mod.evaluator.EvaluatorANSYSAPDL method), 11 method), 10 evaluate() (datamod.evaluator.EvaluatorBenchmark get_results() (data- method), 11 mod.evaluator.EvaluatorANSYSWB method), evaluate() (datamod.evaluator.EvaluatorData 11 method), 12 get_results_folders() (in module core.settings), evaluate_metrics() (in module meta- 4 mod.performance), 25 get_samples() (datamod.evaluator.EvaluatorData evaluate_samples() (core.surrogate.Surrogate method), 12 method), 7 GettingStarted (class in datamod.problems), 13 Evaluator (class in datamod.evaluator), 8 evaluator (core.model.Model attribute), 1 H EvaluatorANSYS (class in datamod.evaluator), 8 Halton (class in datamod.sampling), 15 EvaluatorANSYSAPDL (class in datamod.evaluator), header_names() (in module datamod.results), 13 9 heatmap() (in module visumod.plots), 31 EvaluatorANSYSWB (class in datamod.evaluator), 10 history (metamod.ANN_pt.TrainHistory attribute), 21 40 Index Surrogate optimizer, Release 0.0.0 hp_optimized (core.surrogate.Surrogate attribute), 6 name (metamod.ANN_pt.ANN_pt attribute), 20 name (metamod.ANN_tf.ANN attribute), 22 I no_samples (core.surrogate.Surrogate attribute), 6 initializers (in module metamod.ANN_pt), 19 norm_in (datamod.get_data attribute), 17 input (datamod.get_data attribute), 17 norm_out (datamod.get_data attribute), 17 input_param_name (data- normalize() (in module datamod), 18 mod.evaluator.EvaluatorANSYS attribute), 9 O invalid_param() (in module metamod.validation), Optimization (class in core.optimization), 2 26 optimization (in module run), 30 iteration (datamod.evaluator.Evaluator attribute), 8 optimization_stats iteration (datamod.evaluator.EvaluatorANSYSWB (core.optimization.Optimization attribute), attribute), 10 3 iterations (core.optimization.Optimization at- optimize() (core.optimization.Optimization method), tribute), 2 3 optimize() (in module run), 30 L optimize_hyperparameters() learning_curves() (in module visumod.plots), 31 (core.surrogate.Surrogate method), 7 load_json() (in module core.settings), 4 optimize_hyperparameters() (in module meta- load_object() (in module core.settings), 5 mod), 27 load_problem() (in module datamod), 18 optimized (metamod.ANN.ANN_base attribute), 19 load_results() (core.surrogate.Surrogate method), optimized_to_samples (core.surrogate.Surrogate 7 attribute), 6 load_results() (in module datamod.results), 14 optimod (module), 28 load_surrogate (in module run), 30 optimod.performance (module), 28 log_dir (metamod.ANN.ANN_base attribute), 19 optimod.termination (module), 28 optimum_model (core.optimization.Optimization at- M tribute), 3 make_data_file() (in module datamod.results), 14 optimum_surrogate make_response_files() (in module data- (core.optimization.Optimization attribute), mod.results), 14 3 make_workfolder() (in module core.settings), 5 output (datamod.evaluator.EvaluatorANSYSWB MatlabPeaks (class in datamod.problems), 13 attribute), 10 max_samples (in module core.surrogate), 5 output (datamod.get_data attribute), 17 metamod (module), 26 metamod.ANN (module), 18 P metamod.ANN_pt (module), 19 pareto_fronts() (in module visumod.plots), 32 metamod.ANN_tf (module), 22 pcp() (in module visumod.plots), 32 metamod.deploy (module), 24 perform_optimization (in module run), 30 metamod.performance (module), 25 plot_adaptive_candidates() (in module vi- metamod.validation (module), 26 sumod), 33 Model (class in core.model), 1 plot_raw() (in module visumod), 33 model (core.optimization.Optimization attribute), 2 plot_response() (core.surrogate.Surrogate model (core.surrogate.Surrogate attribute), 5 method), 7 model (in module run), 30 plot_results() (core.optimization.Optimization model (metamod.ANN_pt.ANN_pt attribute), 20 method), 3 model (metamod.ANN_tf.ANN attribute), 22 plot_training_history() (in module visumod), 34 N pretrain() (metamod.ANN_pt.ANN_pt method), 20 n_const (core.model.Model attribute), 1 pretrain() (metamod.ANN_tf.ANN method), 23 n_const (core.optimization.Optimization attribute), 2 problem (core.optimization.Optimization attribute), 3 n_obj (core.model.Model attribute), 2 problem (datamod.evaluator.EvaluatorBenchmark at- name (core.surrogate.Surrogate attribute), 6 tribute), 11 problem_ids (in module run), 30 Index 41 Surrogate optimizer, Release 0.0.0 problems (in module datamod.problems), 13 save_results (datamod.evaluator.Evaluator at- prune_model() (metamod.ANN_tf.ANN method), 24 tribute), 8 scale() (in module datamod), 18 R scale_samples() (in module datamod.sampling), 16 range_in (core.model.Model attribute), 1 scatter() (in module visumod.plots), 32 range_in (core.optimization.Optimization attribute), 2 scatter_pymoo() (in module visumod.plots), 32 range_in (datamod.get_data attribute), 18 scrape_license_info() (data- range_norm (core.surrogate.Surrogate attribute), 6 mod.evaluator.EvaluatorANSYS method), range_out (datamod.get_data attribute), 18 9 RealoadNotAnEvaluator (class in data- set_algorithm() (in module optimod), 28 mod.evaluator), 12 set_optimization() (in module optimod), 29 ref_point (core.optimization.Optimization attribute), set_problem() (core.optimization.Optimization 2 method), 3 ref_points (in module core.optimization), 2 set_surrogate() (in module metamod), 27 reload() (core.surrogate.Surrogate method), 7 set_validation() (in module metamod.validation), reload_info() (in module metamod), 27 26 reload_surrogate() (in module run), 30 set_validation_values() (meta- reoptimization_ratio (core.surrogate.Surrogate mod.ANN.ANN_base method), 19 attribute), 6 settings (in module core.settings), 4 report() (core.optimization.Optimization method), 3 setup (datamod.evaluator.EvaluatorANSYS attribute), 9 report() (core.surrogate.Surrogate method), 7 solve_problem() (in module optimod), 29 report_divergence() (in module meta- source_file (datamod.evaluator.EvaluatorData at- mod.performance), 25 tribute), 12 res (core.optimization.Optimization attribute), 3 SparseModel (class in metamod.ANN_pt), 21 resample_adaptive() (in module data- split_holdout() (in module metamod.validation), mod.sampling), 15 26 resample_static() (in module datamod.sampling), split_kfold() (in module metamod.validation), 26 15 split_methods (in module metamod.validation), 26 response (datamod.get_data attribute), 17 split_rlt() (in module metamod.validation), 26 response_grid() (in module datamod.sampling), 16 Squared (class in datamod.problems), 13 restart_check() (in module core.settings), 5 store() (metamod.ANN_pt.TrainHistory method), 22 results (datamod.evaluator.EvaluatorBenchmark at- subnetworks (metamod.ANN_pt.SparseModel at- tribute), 11 tribute), 21 retrieve_metric() (in module meta- surface_pymoo() (in module visumod.plots), 32 mod.performance), 25 Surrogate (class in core.surrogate), 5 RMSE() (in module metamod.performance), 25 surrogate (core.optimization.Optimization attribute), run (module), 30 3 surrogate (core.surrogate.Surrogate attribute), 7 S surrogate (in module run), 30 sample() (core.surrogate.Surrogate method), 8 surrogate_response() (in module visumod), 34 sample() (in module datamod.sampling), 16 surrogates (core.surrogate.Surrogate attribute), 7 sample_adaptive() (in module datamod.sampling), swish() (in module metamod.ANN_pt), 22 16 sample_bounds (in module datamod.sampling), 15 T sample_size_convergence() (in module vi- tbd (metamod.ANN.ANN_base attribute), 18 sumod), 34 template (datamod.evaluator.EvaluatorANSYSWB at- samples (core.surrogate.Surrogate attribute), 7 tribute), 10 sampling_iterations (core.surrogate.Surrogate termination (core.optimization.Optimization at- attribute), 6 tribute), 2 samplings (in module datamod.sampling), 15 train() (core.surrogate.Surrogate method), 8 save() (core.surrogate.Surrogate method), 8 train_from_data (in module run), 30 save() (metamod.ANN_pt.ANN_pt method), 20 train_surrogate() (in module metamod), 27 save() (metamod.ANN_tf.ANN method), 24 train_surrogate() (in module run), 30 save_figure() (in module visumod.plots), 32 trained (core.surrogate.Surrogate attribute), 6 42 Index Surrogate optimizer, Release 0.0.0 TrainHistory (class in metamod.ANN_pt), 21 U unnormalize_res() (in module optimod), 29 update_input() (data- mod.evaluator.EvaluatorANSYSAPDL method), 10 update_input() (data- mod.evaluator.EvaluatorANSYSWB method), 11 update_settings() (in module core.settings), 5 V valid_licences (data- mod.evaluator.EvaluatorANSYS attribute), 9 validation_points (metamod.ANN.ANN_base at- tribute), 19 verification (core.surrogate.Surrogate attribute), 6 verification_file (core.surrogate.Surrogate at- tribute), 6 verify() (core.optimization.Optimization method), 4 verify_results() (in module opti- mod.performance), 28 vis_design_space() (in module visumod), 34 vis_objective_space() (in module visumod), 34 vis_objective_space_pcp() (in module vi- sumod), 34 visumod (module), 33 visumod.plots (module), 31 W workbench_project (data- mod.evaluator.EvaluatorANSYSWB attribute), 10 write_results() (in module datamod.results), 14 write_stats() (metamod.ANN.ANN_base method), 19 Index 43

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Kent Yao’s Blog Sugar 发布 0.1.0 Kent Yao 2021 年 01 月 27 日 Contents: 1 Indices and tables 1 i ii CHAPTER 1 Indices and tables • genindex • modindex • search 1

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Scrape 0.1a3 documentation ### Navigation * [Scrape 0.1a3 documentation](index.html#document-index) » [[S]crape](_images/scrape_logo2.png) - the Documentation[¶](#shdr-the-documentation "Permalink to this headline") ================================================================================================================= [S]crape is a tool developed to help researchers extract selective data from web publications. It is particularly useful for serial web publications which have similar structure over many issues. You interactively develop a selection and extraction set of commands, and run them across a series of issues, generating output (JSON or CSV). [[S]crape](_images/scrape_logo2.png) Overview[¶](#shdr-overview "Permalink to this headline") --------------------------------------------------------------------------------------------- ### Context[¶](#context "Permalink to this headline") [![[S]crape](_images/scrape_logo2.png)](_images/scrape_logo2.png) operates within the following environment: ![[S]crape Context](_images/graphviz-5b80bd82137457c5d465f221bd1045db439506e5.png) ### Modes of Operation[¶](#modes-of-operation "Permalink to this headline") You operate [S]crape in one of two ways: > > * interactively; > * batch; > > > [S]crape starts and opens targets through a browser [[1]](#id3), and gets it’s data from that browser. You can interactively highlight sections in the browser and scrape will give you unambiguous code to select that data. As with a *Google* search, skill will help make the search term (xpath or css selector) more general, yet specific enough to return your desired result. Inspect results as you work interactively until you are satisfied and then save a range of commands from your history to develop your script. At anytime you can load and run scripts against an opened target. Thus you build up a complete script incrementally. In batch mode, you can automatically run it over a pattern or list of targets. You can test your scripts interactively in headless mode (that is, without a browser). You can also run batch either with a browser, or headless. ### Knowledge You Should Have[¶](#knowledge-you-should-have "Permalink to this headline") You should have a general understanding of *HTML* and *CSS* structure and form. You don’t need to know much, but you should be able to understand and recognize what you are looking at when looking at small portions of web page source, and have an understanding of what type of thing you are trying to extract, i.e. path, attribute, or text. You will need some basic understanding of [XPATH](http://www.w3schools.com/xpath/xpath_syntax.asp) syntax and [CSS Selectors](http://www.w3schools.com/cssref/css_selectors.asp) as you will be using these to describe what you are looking for. When manually highlighting something in your browser, [S]crape will return an *XPATH*. Often a *CSS selector* is both shorter and more accurately selective. [S]crape allows you to view context near your selection. This makes it easy to pick a different form of selector and test it before saving it to your script. ### [[S]crape](_images/scrape_logo2.png) Shell[¶](#isp-shell "Permalink to this headline") In interactive use, [S]crape is similar to a typical command shell, such as `sh` or `bash`, or `cmd` on Windows. In command interpreters, there are typically built-in commands and a way to execute external commands. Shells also provide variables, and some sort of program control. [S]crape has a rich set of built-in commands, and allows calling external commands through your system’s shell. You can also add built-in commands by writing extensions to [S]crape in Python (*plugins*). Since [S]crape outputs tables [[2]](#tables), variable names are like table column names. This means every variable in [S]crape is a list (you can think of them as arrays), and every table an associative array of variables. In fact, you can save the result of your [S]crape as either `csv`, `json` or `yaml`. There are other important kinds of variables in [S]crape. | vars: | Output variables are the normal variables, and are used to specify output table column names. | | local: | Local variables are similar to output variables, only they are omitted from tables. These are used for intermediate results. Local variables have scope per output table. | | global: | These variables persist across output table changes. | [S]crape is least like shells in that there is no familiar loop control. This simplifies traversing an *HTML* tree and extracting data. Instead of looping, you traverse to locations in the XPATH tree of the input file. We refer to selected (current) XPATH locations as *nodes*. Typical [S]crape operation involves traversing a document’s tree, extracting selected content from those nodes, and repeating. In place of program control, you control which nodes you search from. Multiple nodes can be active (for example all the list items of some part of the document), so scripts tend to be rather short. Some general control mechanisms [S]crape provides are: | root: | Normally, navigation through the document is incremental. This sets the root of the tree to the starting `<html>` tag. When the root of the document tree is set, it’s children are the active children, so in this case, normally `<head>` and `<body>` tags will be *current* starting nodes. | | body: | This resets the root node to the `<body>` tag. | | grab: | [S]crape opens a browser when it starts, and communicates with it. `grab` gets a highlited region from your browser, giving you an `xpath` to it. | A majority of [S]crape commands involve selecting a node using an xpath selector, a css-selector, or a combination of path and text search. The remaining commands deal with interactive use (history, view variables, run scripts, save or load scripts), and outputing results (tables). --- Footnotes | [[1]](#id1) | [S]crape uses a [Selenium](http://seleniumhg.org) Client Driver to run your browser. At this time [S]crape only supports Firefox. | | [[2]](#id2) | [S]crape was initially designed to output CSV, but this is a bit too restricting. For one thing, to change the view of the data (the order of way the data is populated into columns, the number and contents of tables) one would need to re-scrape the source. This is why you have a choice of saving variables as JSON or YAML also. Then, you could rebuild, re-shape your tables from your saved data source. | [[S]crape](_images/scrape_logo2.png) Installation[¶](#shdr-installation "Permalink to this headline") ----------------------------------------------------------------------------------------------------- [![[S]crape](_images/scrape_logo2.png)](_images/scrape_logo2.png) requires the following: > > * a recent version of [python](http://python.org/download/releases/) 2.7 > * a recent version of [Firefox](http://www.mozilla.org/firefox) [[1]](#browser) > > > Installing [S]crape will install or upgrade the following python libraries: > > * argparse > * lxml > * cssselect > * PyYAML > * selenium > * [S]crape‘s version of envoy > * a [S]crape plugin library > > > Installation requires you have a compiler on your machine. For Linux systems, this should already be the case. For Macintosh OS/X systems, downloaded Xcode free from the [Mac App Store](https://itunes.apple.com/us/app/xcode/id497799835) (also install the command line tools). Alternatively, you may be able to install just the command-line tools (see <https://github.com/kennethreitz/osx-gcc-installer> - we have not tried this). For Windows platforms, this is not a straightforward process. See the `MS Windows` section at <http://lxml.de/installation.html>. ### Installing [[S]crape](_images/scrape_logo2.png)[¶](#installing-isp "Permalink to this headline") Prerequisites: * [python](http://python.org/download/releases/) 2.7 * a recent version of [Firefox](http://www.mozilla.org/firefox) **Recommended:** * [virtualenv](https://pypi.python.org/pypi/virtualenv) * [pip](http://www.pip-installer.org/) I suggest you use python’s `virtualenv`, particularly your first time with [S]crape (see [virtualenv](https://pypi.python.org/pypi/virtualenv)). This will ensure you have an isolated, clean python install of [S]crape to start. Once you have this working, you may consider installing this your system’s python site-libraries. To properly use `virtualenv`, you’ll need `pip`. Ensure you have `pip` installed: ``` $ which pip ``` If you don’t have pip installed, then install it: ``` $ easy_install pip ``` If you do have pip, be sure it’s up-to-date: ``` $ pip install --upgrade pip ``` Todo Have yet to debug the scrape.gz install file (installation does not mirror setup.py). Now, install the current version of [S]crape. Currently, you must do this from sources. Clone a copy of [S]crape and run setup.py: ``` $ hg clone ssh://hg@bitbucket.org/yarko/scrape $ cd scrape $ python setup.py install ``` --- Footnotes | [[1]](#id1) | Firefox is the only browser officially supported for [S]crape. As an alternative, you may try a current version of [Chrome](http://www.google.com/chrome), but note that you will need to download a [chrome-webdriver](https://code.google.com/p/chromedriver/). For some combinations of versions of Chrome, chrome-webdriver and selenium, timeouts didn’t properly work. For some medical journal sites with continuous stream advertising, Chrome would not respond (would never return when called from scrape). | Tutorials[¶](#id1 "Permalink to this headline") ----------------------------------------------- Select the tutorials which are appropriate for what you want to do. To start, I recommend you follow the installation checkout and tutorial on this page, followed by the example PyCon project. These brief tutorials will introduce the concepts and strategies for using scrape, as well as give an overview of some of the most useful commands. ![[S]crape tutorials](_images/graphviz-1a584fa556b22fe87b08ee9e8ad17a12546f2160.png) ### Introduction to [S]crape[¶](#introduction-to-sp "Permalink to this headline") #### Getting Started[¶](#getting-started "Permalink to this headline") ##### Overview[¶](#overview "Permalink to this headline") ##### Introduction[¶](#introduction "Permalink to this headline") We’ll start by making sure you have [S]crape installed. To start, I’ll assume you have installed [S]crape in a `virtualenv`. [Activate](https://pypi.python.org/pypi/virtualenv) the virtual environment where you have installed [S]crape, and run `scrape`: ``` $ scrape http://scrape.readthedocs.org ``` This should start [S]crape and open its documentation in Firefox [[2]](#fox). > > > > > > [](_images/webdriver.png) > > > > > * your Firefox should have “WebDriver” displayed in the lower-right; > > > + this indicates that this Firefox is being controlled from [S]crape. > * you should see a log of the plugins registered (scrape comes distributed with one - `affiliations`); > > > + if you don’t see `scrape: INFO - ...registering plugins:` in your [S]crape shell, then likely something is incomplete in your installation. You can continue with the exercises, but you will need to install plugins when you need them.[](_images/startup-prompt.png) > > > At the `[S]crape >>>` prompt type the following: ``` [S]crape >>> help ``` > > [](_images/help.png) > [S]crape gives you access to an HTML file or web page. It does this by parsing your web page into a *tree* of HTML *nodes*. You then traverse the tree of nodes, scraping the information you want from a selected nodes. [S]crape starts by setting the root node of your HTML page [[1]](#root) to the `<html>` node. Let’s show the contents of the current node: ``` [S]crape >>> show node ``` You should see the source for the two subnodes (children) of the `<html>` tag, the `<head>` and `<body>` tags of the [S]crape document page. This is the content of the document, rooted at `html` [[3]](#blank). Just to confirm, lets count the current number of selected nodes: ``` [S]crape >>> nodes ``` When you will be looking at larger, more verbose selections, it can also be helpful to review just the tags of the selected nodes: ``` [S]crape >>> tags ``` The general starategy for using [S]crape is: > > * select a scrape target (a web page); > * declare a table name (which will hold a set of variables); > * navigate the web page’s tree; > * declare a variable to collect information; > * capture the desired information; > * repeat as desired; > * save a table to a file; > * lastly, save your interactive commands to a script to run later. > > > When looking a [S]crape target, it’s useful to also open the source view. When you are starting with a new page, you can easily search the various tags and attributes of the `html` elements. Do that now - right-click on your webpage, and select `View Page Source`. For this tutorial, we’ll save the headers to develop an outline for this page. The outline we’d like to make consists of the headers, `Contents:`, `Alternatives`, and so forth. In your source window, search for `Contents:`. > > [](_images/h2.png) > `Contents:` is in an `<h2>` tag, as is `Alternatives`; this looks like a reasonable target for our scrape. [S]crape provides a simplified interface to the libxml2 library, so that most of the information you will find about `xpath` selectors and `cssselectors` will work as you expect. [S]crape also combines, extends and adds other commands for interactive use. For example, `find\_by\_text` will search nodes selected by an xpath expression for a string. You might like view <http://www.w3schools.com/xpath/xpath_syntax.asp> for reference during this tutorial. Let’s find the subheadings on our target page to see if this will give us the page outline we’d like: ``` [S]crape >>> findall .//h2 [S]crape >>> show node ``` This should find all the `<H2>` nodes under the current node. More than one node is found - `show` displays all of the currently selected nodes. > > [](_images/showh2.png) > There are four active nodes, as verified by: ``` [S]crape >>> nodes ``` The text of these nodes seems like it would serve nicely as an outline, so lets capture those. First, declare a table name and a variable to collect output (if you don’t declare a table name, the default is `scrape\_table`). Setting `var my\_name` will select a variable to collect data. The variable does not need to exist (it will be created). If you change tables before you’ve saved their output, they are stored so you can later add to their variables (and output). ``` [S]crape >>> table outline [S]crape >>> var topics ``` When you save the output from this table, it will be saved in a file `outline.csv`. You can also save the output as `json` or `yaml`. Once you save a table, its values are emptied. So far, this table has one column - one variable. To see what the various variables of a table currently have, we issue the `show out` command to show pending output (the current table’s contents). [S]crape variables are lists of values. Varible names are shown with a colon (`my\_var:`), and their values are shown preceded by a ‘-‘. The `text` command will collect text contents of the currently selected `HTML` nodes into the current variable. ``` [S]crape >>> show out [S]crape >>> text [S]crape >>> show out ``` > > [](_images/topics.png) > There was no output pending prior to the `text` command. If you wanted to save this now, the `table` command (with no argument) will output the current table to a `csv` file with the same name (if one already exsits, it will not be overwritten; the name will be numerically extended). If you want to save your script for later, look at your history. Only scrape commands which act on pages are saved in history. You can choose which parts of history you save to a script file. ``` [S]crape >>> history [S]crape >>> help save ``` If you’d like, save your script now. You can edit saved [S]crape scripts with a text editor. You can add comments, which begin with ‘#’ and extend to the end of the line. There is an alternate form for selecting tables and variables, which may help the commands in your script (and what they apply to) stand out. If you’d like, in place of: ``` table outline var topics ``` you can equivalently write: ``` [ outline ] < topics > ``` To exit [S]crape, see `help EOF`. After our brief interactive session with [S]crape, here’s what our script looks like: ``` ## # [S]crape script to get outline of a page # # - gets the text of <h2> headings; # [ outline ] < topics > findall .//h2 text table # save outline.csv ``` ###### Summary[¶](#summary "Permalink to this headline") In this introductory tutorial, we’ve > > * shown one way to select nodes; > * defined tables and variables; > * saved selected content; > * saved a [S]crape script; > > > Please continue with the next tutorial. Happy [![[S]crape](_images/scrape_logo2.png)](_images/scrape_logo2.png) ing! --- Footnotes | [[1]](#id2) | You can easily set to the root of the document at any time to either the entire document, or the body - see `help doc` and `help body`. | | [[2]](#id1) | Be sure you’ve installed [S]crape and a current Firefox browser. | | [[3]](#id3) | Note that when you look at an empty page (`about:blank`), scrape will create a minimal parse tree for you (`<html><head/><body/></head>`). | ### Developing a Project[¶](#developing-a-project "Permalink to this headline") This is the second tutorial in a series. From the introductory tutorial, we saw how to select a destination (file or URL). Initially, it’s also beneficial to view the destination’s source along with the browser window. You can either search for what interests you in the source window, or use `inspect element` to get to the item that interests you. Once you’ve quickly found the item of interest, you can start trying various tree traversal commands to get to related items in [S]crape, view the nodes found, and save some part of their content in [S]crape variables for output. You can also save your script activity into a script, which you can edit and run later in [S]crape. In this tutorial we’ll see how to backtrack and make corrections. We’ll also see how the various [S]crape commands behave when applied to multiple nodes. Note *A word about* [S]crape ing *public sites*: Be a *Good Citizen*! * avoid repeatedly hitting a site, and loading its servers; * always check for copyright, and observe fair use doctrines. #### PyCon Volunteer Reporting[¶](#pycon-volunteer-reporting "Permalink to this headline") Here’s our project: the US PyCon 2013 Conference is coming up. PyCon is a community conference and depends heavily on voluneers. We want to track how many volunteers we still need for session staff [[1]](#sessstaff). The conference site lists the sessions and staff on <http://us.pycon.org/2013/schedule/sessions>. Since this will likely change dynamically, we’ll use a snapshot version we saved, just as you would when first developing a script (in order to spare repeatedly hitting a site’s servers). Having a static copy will also make it easier to follow along with the tutorial (also, after the conference, there will be no unfulfilled needs, so the web data won’t be as interesting): > > * download [`tutorial2.zip`](_downloads/tutorial2.zip). > > > #### Getting Oriented with [S]crape Commands[¶](#getting-oriented-with-s-commands "Permalink to this headline") Let’s review what we’ve learned so far. When you open [S]crape with a `URL`, [S]crape opens the url in a browser and parses it into a tree of nodes held in scrape. These nodes are what you navigate. Using xpath and cssselect you select nodes and extract data. The ability to inspect aspects during the process is useful, as well as being able to run scripts in batch. In this tutorial we’ll introduce some of the rhyme and reason behind [S]crape. Since [S]crape has over 60 commands, let’s start by describing some structure around the commands (we will only introduce some of them in this tutorial). ![[S]crape Context](_images/graphviz-5b80bd82137457c5d465f221bd1045db439506e5.png) [S]crape commands affect each of these areas. Most of the action happens in the hub - in [S]crape itself. The type of commands in [S]crape are: > > * navigation > * content extraction (capturing) > * interaction > * settings > * variables > > > #### A Starting Strategy[¶](#a-starting-strategy "Permalink to this headline") The first time you open a target `URL` it can be useful to open the page’s source from the browser (I have them side-by-side at first). To open the page source right-click in the browser page: [](_images/page_source.png) For smaller pages, it can be useful to search in the source for what interested you in the browser. For larger pages, it can sometimes be easier to simply highlight what interests you in the web page, and use the [S]crape `grab` command to give you a small context. From there, it can be easier to search for the larger context in the source window, so you can get a good view of the context around your interest. Let’s do that now. Unzip the tutorial file (I’ve replaced the >1M in images with a single pixel gif to keep things manageable). You should have a file `sessions.html` and a directory `sessions\_files`. Assuming you’ve unzipped in the current directory run `scrape`: ``` $ scrape sessions.html ``` To orient ourselves, use a few of the interaction commands from the *Introductory Tutorial*: ``` [S]crape >>> nodes 2 [S]crape >>> tags ['head', 'body'] [S]crape >>> ``` In this case, we are not concerned with any of the meta-data which might be in the `<head/>`: ``` [S]crape >>> body [S]crape >>> nodes 7 [S]crape >>> tags ['header', 'div', 'script', 'script', 'script', 'script', 'div'] [S]crape >>> ``` Looking at our browser window, the sessions are named and listed as visual blocks. Here are the parts interesting for our task: [](_images/session_view1.png) Scrolling to the bottom of the browser page, we see there are 42 sessions. We can see that each session has a `Session Chair` and a `Session Runner`. If no one has signed up, the page shows: `No volunteers signed up`. We need a total of 84 volunteers. We’ll need to gather information after the session name (e.g. `Session #1`). Unfortunately, there’s a lot of `HTML` code for headers, sponsors, and so forth - but let’s go to our browser’s source window and search for `Sessions`. It looks like our info is all contained in an `HTML` list. [](_images/list_view1.png) Let’s just start by seeing what happens when we try to get the list of sessions. If we try findclass: ``` [S]crape >>> findclass unstyled [S]crape >>> nodes 43 [S]crape >>> ``` It looks like we might have gotten the 42 session (their content looks to be held in `<ul class="unstyled">` lists), and the outermost list holding them. You can look at what was selected with `show node`, but it’s a little easier to digest at this point in the browser-source window. This is close to what we wanted, but not quite. #### Adjusting Course[¶](#adjusting-course "Permalink to this headline") If you use further [S]crape navigation commands (such as *findclass*), they will act from each of the currently selected nodes. We’re not where we want to be, so let’s back up: ``` [S]crape >>> body ``` Some smaller ways you can back up in the tree: > > * doc, or root (aliases) > * getprevious > * getparent > > > See the *help* for these, and experiment with them. Now, let’s try a couple of other commands to see if you can get to the 42 nodes of interest (look for hints in the browser-source view). Here are a few examples (I’ll omit the output, so be sure to follow along at your computer): ``` [S]crape >>> flindclass unstyled [S]crape >>> nodes [S]crape >>> tags [S]crape >>> body [S]crape >>> help cssselect [S]crape >>> cssselect div.box-content ul.unstyled [S]crape >>> nodes [S]crape >>> tags ``` There are a couple of ways to get to what we want (you may find others). Cssselectors are easy to write and powerful. Xpath expressions are explicit and functional (if you learn xpath expressions, you can take advantage of that knowledge for navigating `XML` documents also). I find that either `cssselect h1+ul.unstyled` or `find .//ul[@class='unstyled']` work. The css expression says: > > *get all the elements* `ul` *which immediately follow an* `h1`, *and which have class* `unstyled`. The xpath expression says: > > *get the next (single)* `ul` *node with class* `unstyled`. The `.//tag\_name` form says look anywhere (any depth) under the current node. I prefer the xpath expression - for this case, it seems more suitable, closer to what we intend. ``` [S]crape >>> body [S]crape >>> find .//ul[@class="unstyled"] [S]crape >>> nodes [S]crape >>> show node ``` This looks like the spot we were interested in, in the browser-source. #### Saving Output[¶](#saving-output "Permalink to this headline") So that we can have context, let’s collect the session name. Let’s also scrape the text of the first `ul` under that - the session volunteers. I want to have 42 names, and 42 pieces of volunteer information. Thus we can determine which sessions have needs. The first `ul` under each session name will do this for us. First, lets try to select the sessions. From the last `show` command, we can see *Session #42*. ``` [S]crape >>> findall ./li [S]crape >>> nodes [S]crape >>> show ``` Note that findall has a single `'/'` - this will find only direct children of our current `ul` node. Now lets get our session names: ``` [S]crape >>> find ./a [S]crape >>> nodes [S]crape >>> show ``` We use find (not findall) because we only want the first `a` tag under each of our 42 nodes. This time, the *show* command is a joy to look at - it’s clear that we have the session names, that our 42 nodes are indeed exactly what we want. The text of these nodes contain the session names we want. We’re ready to setup some variables: ``` [S]crape >>> [sessions_table] [S]crape >>> <session> [S]crape >>> text [S]crape >>> show out ``` We have our 42 session names waiting to be output. But still we need to add information about the volunteer status of each. Thankfully, we have the browser-source window to refer to. We can see that after the `<a>` containing our session names we want the `ul` nodes which are the first children of the `div` tag following `li`. The **getnext** command will get the next sibling node (the `div` we want). From there we will get the `ul` directly under: ``` [S]crape >>> getnext [S]crape >>> nodes # confirm [S]crape >>> show [S]crape >>> find ./ul [S]crape >>> nodes # still looks good ``` Where *text* will get the text inside the tag (up to the next child tag), text\_content will get *all* the text inside a tag, even that inside other enclosed nodes. We’re ready to save the status of the volunteers - we’ll put this in a *volunteer* variable. ``` [S]crape >>> <volunteer> [S]crape >>> text_content [S]crape >>> show out ``` There is a good deal of *white space*, but we’ll easily deal with that outside of scrape. I think the form of *show out* (yaml) would be easy to read into a python script which will do the counting. ``` [S]crape >>> yaml sessions.yaml ``` You could have also saved this as either *json* or *csv* (the latter using the *table* command). Either json or yaml is convenient for loading into python data structures. I chose yaml because it is easy on the eyes when viewing the scraped data file. #### Running Another Day[¶](#running-another-day "Permalink to this headline") We’ll need to run this script quite often to keep the current volunteer needs up to date, so we’ll need to save our script. Have a look at your history: ``` [S]crape >>> history ``` Notice that history shows your navigation commands, but not your interactive insepction commands. Scripts are saved from this command history, so inspection commands are not stored there. You could edit your script file (comments start with `'#'`), and eliminate any false starts, and test the edited result, or you could select which part of your history to save, and go from there. You decide: ``` [S]crape >>> help save [S]crape >>> save sessions.scrape ``` Before you exit [S]crape, edit your file, and test it by running it against the current page (I use the *gvim* editor; you should use your favorite): ``` [S]crape >>> clear volunteers session # clear variables [S]crape >>> show out # should now have no output pending [S]crape >>> load sessions.scrape [S]crape >>> show out ``` Loading a script runs it against the current document tree. You can run your script in *headless* mode: ``` $ scrape -H -s sessions.scrape http://us.pycon.org/2013/schedule/sessions ``` I leave it to you to develop a script to count and report on volunteer needs, based on *sessions.yaml*. Mine was under 12 lines of python. Whatever you use for postprocessing, you can also run it from your *sessions.scrape* by adding something like this to the bottom of your script: ``` # after saving your yaml / json / csv file: !python my_script.py sessions.yaml ``` #### Summary[¶](#summary "Permalink to this headline") After this exercise, your script should look similar: ``` ## Count volunteer signups for PyCon Sessions # # open http://us.pycon.org/2013/schedule/sessions/ # body # I save to a different name than this table, which would be default; [s_table] find .//ul[@class='unstyled'] findall ./li find ./a <session> # column1: the session name text getnext find ./ul <txt> # column2: who's signed up to staff the session; text_content yaml sessions !python session_volunteer_counter.py sessions.yaml ``` Let’s look at which commands in [S]crape we used: > > * navigation: > + body > + cssselect > + find > + findall > + findclass > + getnext > * capturing: > + text > + text\_content > * interaction: > + help > + history > + nodes > + show > + show out > + tags > * settings: > + headless (-H) > * variables: > + clear > + “[...]”, or table > + “<...>”, or var > * output: > + yaml > * scripts: > + load > + save > * shell: > + ”!”, or shell > > > Happy [![[S]crape](_images/scrape_logo2.png)](_images/scrape_logo2.png) ing! --- Footnotes | [[1]](#id1) | Session staff consist of chairs and runners. Chairs introduce speakers, manage questions and keep track of time. Runners get speakers to their talk on time and ensure they have everything they need. A typical session consists of 3 talks. Sessions run simultaneously in multiple rooms. | ### [*Introduction to [S]crape*](index.html#document-tutorials/intro)[¶](#introduction-to-s-crape "Permalink to this headline") > > The [*“Getting Started” Tutorial*](index.html#document-tutorials/intro) > will help you confirm your installation, and > introduce the basic [![[S]crape](_images/scrape_logo2.png)](_images/scrape_logo2.png) concepts. ### [*Developing a Project*](index.html#document-tutorials/pycon)[¶](#developing-a-project "Permalink to this headline") > > In this [*Intermediate Tutorial*](index.html#document-tutorials/pycon), > we’ll look at using [![[S]crape](_images/scrape_logo2.png)](_images/scrape_logo2.png) to report on volunteers > signed up for running talks at a national conference. > In the process, will look at the different groups > of commands in [![[S]crape](_images/scrape_logo2.png)](_images/scrape_logo2.png) and introduce some > useful patterns for developing scripts. Copyright Notice[¶](#copyright-notice "Permalink to this headline") ------------------------------------------------------------------- Copyright (c) 2013, The University of Chicago. All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: > > * Redistributions of source code must retain the above copyright > notice, this list of conditions and the following disclaimer. > * Redistributions in binary form must reproduce the above > copyright notice, this list of conditions and the following > disclaimer in the documentation and/or other materials provided > with the distribution. > * Neither the name of The University of Chicago nor the names > of its contributors may be used to endorse or promote products > derived from this software without specific prior written > permission. > > > THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS “AS IS” AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. [*Overview*](index.html#document-overview)[¶](#overview "Permalink to this headline") ------------------------------------------------------------------------------------- > > What’s involved, required knowledge, and the basic modes of operation. [*Installation*](index.html#document-installation)[¶](#installation "Permalink to this headline") ------------------------------------------------------------------------------------------------- > > Software and browser requirements. [*Tutorials*](index.html#document-tutorials/contents)[¶](#tutorials "Permalink to this headline") ------------------------------------------------------------------------------------------------- > > Get started with a concrete examples. Alternatives[¶](#alternatives "Permalink to this headline") ----------------------------------------------------------- Examples of just some of the alternatives to [S]crape include: > > * [scrapy](http://doc.scrapy.org) > * [twill](http://twill.idyll.org) > * [dryscrape](http://dryscrape.readthedocs.org) > * [django-dynamic-scraper](http://django-dynamic-scraper.readthedocs.org) > > > You can also look through the *notable tools* section on [Wikipedia](http://www.wikipedia.org/wiki/Web_scraping). Many of these are either not interactive, or are programmers libraries or toolkits. [S]crape is an interactive script development tool which, with a modicum of knowledge, is both powerful and simple. [S]crape scripts use [S]crape commands, shell commands, and commands provided by extensions. [*Copyright Notice*](index.html#document-LICENSE)[¶](#license "Permalink to this headline") ------------------------------------------------------------------------------------------- > > This license applies to the program, [![[S]crape](_images/scrape_logo2.png)](_images/scrape_logo2.png), and its documentation. Work in Progress[¶](#work-in-progress "Permalink to this headline") ------------------------------------------------------------------- This document is currently a work-in-progress. Here are a list of known items left to do: Todo Have yet to debug the scrape.gz install file (installation does not mirror setup.py). (The [*original entry*](index.html#index-0) is located in /home/docs/checkouts/readthedocs.org/user\_builds/scrape/checkouts/latest/source/installation.rst, line 92.) Indices and tables[¶](#indices-and-tables "Permalink to this headline") ----------------------------------------------------------------------- * [Index](genindex.html) * [Module Index](py-modindex.html) * [Search Page](search.html) Please enable JavaScript to view the [comments powered by Disqus.](http://disqus.com/?ref_noscript) [comments powered by Disqus](http://disqus.com) ### [Table Of Contents](index.html#document-index) * [Overview](index.html#document-overview) + [Context](index.html#context) + [Modes of Operation](index.html#modes-of-operation) + [Knowledge You Should Have](index.html#knowledge-you-should-have) + [Shell](index.html#isp-shell) * [Installation](index.html#document-installation) + [Installing](index.html#installing-isp) * [Tutorials](index.html#document-tutorials/contents) + [Introduction to [S]crape](index.html#document-tutorials/intro) - [Getting Started](index.html#getting-started) * [Overview](index.html#overview) * [Introduction](index.html#introduction) + [Summary](index.html#summary) + [Developing a Project](index.html#document-tutorials/pycon) - [PyCon Volunteer Reporting](index.html#pycon-volunteer-reporting) - [Getting Oriented with [S]crape Commands](index.html#getting-oriented-with-s-commands) - [A Starting Strategy](index.html#a-starting-strategy) - [Adjusting Course](index.html#adjusting-course) - [Saving Output](index.html#saving-output) - [Running Another Day](index.html#running-another-day) - [Summary](index.html#summary) + [`Introduction to [S]crape`](index.html#introduction-to-s-crape) + [`Developing a Project`](index.html#developing-a-project) * [Copyright Notice](index.html#document-LICENSE) ### Quick search Enter search terms or a module, class or function name. ### Edit this document! Anyone with a Github account can edit and submit changes directly through the Web. 1. Click to edit: [Scrape 0.1a3 documentation](https://github.com/yarko/scrapedoc/blob/master/source/index.rst) 2. **Edit** using GitHub's editor in your web browser (click 'Edit' tab on the top right) 3. Fill in the **Commit message** the bottom of the page describing *why* you made the changes. If you've completed your changes, press the **Propose file change** button. 4. If you've completed your changes, click **Send a pull request**. 5. Your changes are now queued for review under the project's [Pull requests](https://github.com/yarko/scrapedoc/pulls) tab on GitHub! For an introduction to the documentation format see [the reST primer](http://docutils.sourceforge.net/docs/user/rst/quickstart.html). ### Navigation * [Scrape 0.1a3 documentation](index.html#document-index) » © Copyright 2012-2013, Yarko Tymciurak, and The University of Chicago. Created using [Sphinx](http://sphinx.pocoo.org/) 1.3.1. 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Search — Entity-Component-System 0.1 documentation ### Navigation * [index](genindex.html "General Index") * [modules](py-modindex.html "Python Module Index") | * [Entity-Component-System 0.1 documentation](index.html) » Search ====== Please activate JavaScript to enable the search functionality. From here you can search these documents. Enter your search words into the box below and click "search". Note that the search function will automatically search for all of the words. Pages containing fewer words won't appear in the result list. ### Navigation * [index](genindex.html "General Index") * [modules](py-modindex.html "Python Module Index") | * [Entity-Component-System 0.1 documentation](index.html) » © Copyright 2013 Kevin Ward, Sean Fisk. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3. Index — Entity-Component-System 0.1 documentation ### Navigation * [index](# "General Index") * [modules](py-modindex.html "Python Module Index") | * [Entity-Component-System 0.1 documentation](index.html) » Index ===== [**A**](#A) | [**C**](#C) | [**D**](#D) | [**E**](#E) | [**N**](#N) | [**P**](#P) | [**R**](#R) | [**S**](#S) | [**U**](#U) A - | | | | --- | --- | | [add\_component() (ecs.managers.EntityManager method)](api.html#ecs.managers.EntityManager.add_component) | [add\_system() (ecs.managers.SystemManager method)](api.html#ecs.managers.SystemManager.add_system) | C - | | | | --- | --- | | [Component (class in ecs.models)](api.html#ecs.models.Component) [component\_for\_entity() (ecs.managers.EntityManager method)](api.html#ecs.managers.EntityManager.component_for_entity) | [create\_entity() (ecs.managers.EntityManager method)](api.html#ecs.managers.EntityManager.create_entity) | D - | | | | --- | --- | | [database (ecs.managers.EntityManager attribute)](api.html#ecs.managers.EntityManager.database) | [DuplicateSystemTypeError](api.html#ecs.exceptions.DuplicateSystemTypeError) | E - | | | | --- | --- | | [ecs.\_\_init\_\_ (module)](api.html#module-ecs.__init__) [ecs.exceptions (module)](api.html#module-ecs.exceptions) [ecs.managers (module)](api.html#module-ecs.managers) | [ecs.models (module)](api.html#module-ecs.models) [Entity (class in ecs.models)](api.html#ecs.models.Entity) [EntityManager (class in ecs.managers)](api.html#ecs.managers.EntityManager) | N - | | | --- | | [NonexistentComponentTypeForEntity](api.html#ecs.exceptions.NonexistentComponentTypeForEntity) | P - | | | --- | | [pairs\_for\_type() (ecs.managers.EntityManager method)](api.html#ecs.managers.EntityManager.pairs_for_type) | R - | | | | --- | --- | | [remove\_component() (ecs.managers.EntityManager method)](api.html#ecs.managers.EntityManager.remove_component) [remove\_entity() (ecs.managers.EntityManager method)](api.html#ecs.managers.EntityManager.remove_entity) | [remove\_system() (ecs.managers.SystemManager method)](api.html#ecs.managers.SystemManager.remove_system) | S - | | | | --- | --- | | [System (class in ecs.models)](api.html#ecs.models.System) [SystemManager (class in ecs.managers)](api.html#ecs.managers.SystemManager) | [systems (ecs.managers.SystemManager attribute)](api.html#ecs.managers.SystemManager.systems) | U - | | | --- | | [update() (ecs.managers.SystemManager method)](api.html#ecs.managers.SystemManager.update) [(ecs.models.System method)](api.html#ecs.models.System.update) | ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](# "General Index") * [modules](py-modindex.html "Python Module Index") | * [Entity-Component-System 0.1 documentation](index.html) » © Copyright 2013 Kevin Ward, Sean Fisk. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3. API Documentation — Entity-Component-System 0.1 documentation ### Navigation * [index](genindex.html "General Index") * [modules](py-modindex.html "Python Module Index") | * [previous](index.html "Entity-Component-System") | * [Entity-Component-System 0.1 documentation](index.html) » API Documentation[¶](#api-documentation "Permalink to this headline") ===================================================================== ecs Package[¶](#module-ecs.__init__ "Permalink to this headline") ----------------------------------------------------------------- An entity system in Python. [exceptions](http://docs.python.org/library/exceptions.html#exceptions "(in Python v2.7)") Module[¶](#module-ecs.exceptions "Permalink to this headline") --------------------------------------------------------------------------------------------------------------------------------------------------------- Exceptions that may be raised. *exception* ecs.exceptions.DuplicateSystemTypeError(*system\_type*)[[source]](_modules/ecs/exceptions.html#DuplicateSystemTypeError)[¶](#ecs.exceptions.DuplicateSystemTypeError "Permalink to this definition") Bases: exceptions.Exception Error indicating that the system type already exists in the system manager. | Parameters: | **system\_type** (type) – type of the system | *exception* ecs.exceptions.NonexistentComponentTypeForEntity(*entity\_instance*, *component\_type*)[[source]](_modules/ecs/exceptions.html#NonexistentComponentTypeForEntity)[¶](#ecs.exceptions.NonexistentComponentTypeForEntity "Permalink to this definition") Bases: exceptions.Exception Error indicating that a component type does not exist for a certain entity. | Parameters: | * **entity** (Entity) – entity without component type * **component\_type** (type) – component type not in entity | managers Module[¶](#module-ecs.managers "Permalink to this headline") --------------------------------------------------------------------- Entity and System Managers. *class* ecs.managers.EntityManager[[source]](_modules/ecs/managers.html#EntityManager)[¶](#ecs.managers.EntityManager "Permalink to this definition") Provide database-like access to components based on an entity key. add\_component(*entity\_id*, *component\_instance*)[[source]](_modules/ecs/managers.html#EntityManager.add_component)[¶](#ecs.managers.EntityManager.add_component "Permalink to this definition") Add a component to the database and associates it with the given entity\_id. entity\_id can be an [ecs.models.Entity](#ecs.models.Entity "ecs.models.Entity") object or a plain int. | Parameters: | * **entity\_id** (int or [ecs.models.Entity](#ecs.models.Entity "ecs.models.Entity")) – GUID of the entity * **component\_instance** ([ecs.models.Component](#ecs.models.Component "ecs.models.Component")) – component to add to the entity | component\_for\_entity(*entity\_id*, *component\_type*)[[source]](_modules/ecs/managers.html#EntityManager.component_for_entity)[¶](#ecs.managers.EntityManager.component_for_entity "Permalink to this definition") Return the instance of component\_type for the entity\_id from the database. | Parameters: | * **entity\_id** (int) – entity GUID * **component\_type** (type) – a type of created component | | Returns: | list of (entity\_id, component\_instance) tuples | | Return type: | tuple of (int, [ecs.models.Component](#ecs.models.Component "ecs.models.Component")) | | Raises : | NonexistentComponentTypeForEntity when component\_type does not exist on entity\_instance | create\_entity()[[source]](_modules/ecs/managers.html#EntityManager.create_entity)[¶](#ecs.managers.EntityManager.create_entity "Permalink to this definition") Return a new entity instance with the current lowest GUID value. Does not store a reference to it, and does not make any entries in the database referencing it. | Returns: | the new entity | | Return type: | [ecs.models.Entity](#ecs.models.Entity "ecs.models.Entity") | database[[source]](_modules/ecs/managers.html#EntityManager.database)[¶](#ecs.managers.EntityManager.database "Permalink to this definition") Get this manager’s database. Direct modification is not permitted. | Returns: | the database | | Return type: | [dict](http://docs.python.org/library/stdtypes.html#dict "(in Python v2.7)") | pairs\_for\_type(*component\_type*)[[source]](_modules/ecs/managers.html#EntityManager.pairs_for_type)[¶](#ecs.managers.EntityManager.pairs_for_type "Permalink to this definition") Return a list of (entity\_id, component\_instance) tuples for all entities in the database possessing a component of component\_type. Return an empty list if there are no components of this type in the database. Can use in a loop like this, where Renderable is a component type: ``` for entity, renderable\_component in entity\_manager.pairs\_for\_type(Renderable): pass # do something ``` | Parameters: | **component\_type** (type) – a type of created component | | Returns: | list of (entity\_id, component\_instance) tuples | | Return type: | tuple of (int, [ecs.models.Component](#ecs.models.Component "ecs.models.Component")) | remove\_component(*entity\_id*, *component\_type*)[[source]](_modules/ecs/managers.html#EntityManager.remove_component)[¶](#ecs.managers.EntityManager.remove_component "Permalink to this definition") Remove the component of component\_type associated with entity\_id from the database. Doesn’t do any kind of data-teardown. It is up to the system calling this code to do that. In the future, a callback system may be used to implement type-specific destructors. | Parameters: | * **entity\_id** (int) – GUID of the entity * **component\_type** ([ecs.models.Component](#ecs.models.Component "ecs.models.Component")) – component to remove from the entity | remove\_entity(*entity\_id*)[[source]](_modules/ecs/managers.html#EntityManager.remove_entity)[¶](#ecs.managers.EntityManager.remove_entity "Permalink to this definition") Remove all components from the database that are associated with entity\_id, with the side-effect that the entity is also no longer in the database. | Parameters: | **entity\_id** (int) – entity GUID | *class* ecs.managers.SystemManager[[source]](_modules/ecs/managers.html#SystemManager)[¶](#ecs.managers.SystemManager "Permalink to this definition") A container and manager for [ecs.models.System](#ecs.models.System "ecs.models.System") objects. add\_system(*system\_instance*)[[source]](_modules/ecs/managers.html#SystemManager.add_system)[¶](#ecs.managers.SystemManager.add_system "Permalink to this definition") Add a [ecs.models.System](#ecs.models.System "ecs.models.System") instance to the manager. | Parameters: | **system\_instance** ([ecs.models.System](#ecs.models.System "ecs.models.System")) – instance of a system | remove\_system(*system\_type*)[[source]](_modules/ecs/managers.html#SystemManager.remove_system)[¶](#ecs.managers.SystemManager.remove_system "Permalink to this definition") Tell the manager to no longer run the system of this type. | Parameters: | **system\_type** (type) – type of system to remove | systems[[source]](_modules/ecs/managers.html#SystemManager.systems)[¶](#ecs.managers.SystemManager.systems "Permalink to this definition") Get this manager’s list of systems. | Returns: | system list | | Return type: | list of [ecs.models.System](#ecs.models.System "ecs.models.System") | update(*dt*)[[source]](_modules/ecs/managers.html#SystemManager.update)[¶](#ecs.managers.SystemManager.update "Permalink to this definition") Run all systems in order, for this frame. | Parameters: | **dt** (float) – delta time, or elapsed time for this frame | models Module[¶](#module-ecs.models "Permalink to this headline") ----------------------------------------------------------------- Entity, Component, and System classes. *class* ecs.models.Component[[source]](_modules/ecs/models.html#Component)[¶](#ecs.models.Component "Permalink to this definition") Class from which all components should derive. *class* ecs.models.Entity(*guid*)[[source]](_modules/ecs/models.html#Entity)[¶](#ecs.models.Entity "Permalink to this definition") Encapsulation of a GUID to use in the entity database. | Parameters: | **guid** (int) – globally unique identifier | *class* ecs.models.System[[source]](_modules/ecs/models.html#System)[¶](#ecs.models.System "Permalink to this definition") An object that represents an operation on a set of objects from the game database. The [update()](#ecs.models.System.update "ecs.models.System.update") method must be implemented. update(*dt*)[[source]](_modules/ecs/models.html#System.update)[¶](#ecs.models.System.update "Permalink to this definition") Run the system for this frame. This method is called by the system manager, and is where the functionality of the system is implemented. | Parameters: | **dt** (float) – delta time, or elapsed time for this frame | ### [Table Of Contents](index.html) * [API Documentation](#) + [ecs Package](#module-ecs.__init__) + [exceptions Module](#module-ecs.exceptions) + [managers Module](#module-ecs.managers) + [models Module](#module-ecs.models) #### Previous topic [Entity-Component-System](index.html "previous chapter") ### This Page * [Show Source](_sources/api.txt) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](genindex.html "General Index") * [modules](py-modindex.html "Python Module Index") | * [previous](index.html "Entity-Component-System") | * [Entity-Component-System 0.1 documentation](index.html) » © Copyright 2013 Kevin Ward, Sean Fisk. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3. Entity-Component-System — Entity-Component-System 0.1 documentation ### Navigation * [index](genindex.html "General Index") * [modules](py-modindex.html "Python Module Index") | * [next](api.html "API Documentation") | * [Entity-Component-System 0.1 documentation](#) » Entity-Component-System[¶](#entity-component-system "Permalink to this headline") ================================================================================= * [API Documentation](api.html) + [ecs Package](api.html#module-ecs.__init__) + [exceptions Module](api.html#module-ecs.exceptions) + [managers Module](api.html#module-ecs.managers) + [models Module](api.html#module-ecs.models) #### Next topic [API Documentation](api.html "next chapter") ### This Page * [Show Source](_sources/index.txt) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](genindex.html "General Index") * [modules](py-modindex.html "Python Module Index") | * [next](api.html "API Documentation") | * [Entity-Component-System 0.1 documentation](#) » © Copyright 2013 Kevin Ward, Sean Fisk. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3. Python Module Index — Entity-Component-System 0.1 documentation ### Navigation * [index](genindex.html "General Index") * [modules](# "Python Module Index") | * [Entity-Component-System 0.1 documentation](index.html) » Python Module Index =================== [**e**](#cap-e) | | | | | --- | --- | --- | | | | | | | **e** | | | - | ecs | | | | [ecs.\_\_init\_\_](api.html#module-ecs.__init__) | | | | [ecs.exceptions](api.html#module-ecs.exceptions) | | | | [ecs.managers](api.html#module-ecs.managers) | | | | [ecs.models](api.html#module-ecs.models) | | ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](genindex.html "General Index") * [modules](# "Python Module Index") | * [Entity-Component-System 0.1 documentation](index.html) » © Copyright 2013 Kevin Ward, Sean Fisk. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3. Overview: module code — Entity-Component-System 0.1 documentation ### Navigation * [index](../genindex.html "General Index") * [modules](../py-modindex.html "Python Module Index") | * [Entity-Component-System 0.1 documentation](../index.html) » All modules for which code is available ======================================= * [ecs.exceptions](ecs/exceptions.html) * [ecs.managers](ecs/managers.html) * [ecs.models](ecs/models.html) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../genindex.html "General Index") * [modules](../py-modindex.html "Python Module Index") | * [Entity-Component-System 0.1 documentation](../index.html) » © Copyright 2013 Kevin Ward, Sean Fisk. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3. ecs.managers — Entity-Component-System 0.1 documentation ### Navigation * [index](../../genindex.html "General Index") * [modules](../../py-modindex.html "Python Module Index") | * [Entity-Component-System 0.1 documentation](../../index.html) » * [Module code](../index.html) » Source code for ecs.managers ============================ ``` """Entity and System Managers.""" from ecs.exceptions import ( NonexistentComponentTypeForEntity, DuplicateSystemTypeError) from ecs.models import Entity [[docs]](../../api.html#ecs.managers.EntityManager)class EntityManager(object): """Provide database-like access to components based on an entity key.""" def \_\_init\_\_(self): self.\_database = {} self.\_next\_guid = 0 @property [[docs]](../../api.html#ecs.managers.EntityManager.database) def database(self): """Get this manager's database. Direct modification is not permitted. :return: the database :rtype: :class:`dict` """ return self.\_database [[docs]](../../api.html#ecs.managers.EntityManager.create_entity) def create\_entity(self): """Return a new entity instance with the current lowest GUID value. Does not store a reference to it, and does not make any entries in the database referencing it. :return: the new entity :rtype: :class:`ecs.models.Entity` """ entity = Entity(self.\_next\_guid) self.\_next\_guid += 1 return entity [[docs]](../../api.html#ecs.managers.EntityManager.add_component) def add\_component(self, entity\_id, component\_instance): """Add a component to the database and associates it with the given ``entity\_id``. ``entity\_id`` can be an :class:`ecs.models.Entity` object or a plain :class:`int`. :param entity\_id: GUID of the entity :type entity\_id: :class:`int` or :class:`ecs.models.Entity` :param component\_instance: component to add to the entity :type component\_instance: :class:`ecs.models.Component` """ component\_type = type(component\_instance) if component\_type not in self.\_database: self.\_database[component\_type] = {} self.\_database[component\_type][entity\_id] = component\_instance [[docs]](../../api.html#ecs.managers.EntityManager.remove_component) def remove\_component(self, entity\_id, component\_type): """Remove the component of ``component\_type`` associated with ``entity\_id`` from the database. Doesn't do any kind of data-teardown. It is up to the system calling this code to do that. In the future, a callback system may be used to implement type-specific destructors. :param entity\_id: GUID of the entity :type entity\_id: :class:`int` :param component\_type: component to remove from the entity :type component\_type: :class:`ecs.models.Component` """ try: del self.\_database[component\_type][entity\_id] if self.\_database[component\_type] == {}: del self.\_database[component\_type] except KeyError: pass [[docs]](../../api.html#ecs.managers.EntityManager.pairs_for_type) def pairs\_for\_type(self, component\_type): """Return a list of ``(entity\_id, component\_instance)`` tuples for all entities in the database possessing a component of ``component\_type``. Return an empty list if there are no components of this type in the database. Can use in a loop like this, where ``Renderable`` is a component type: .. code-block:: python for entity, renderable\_component in \ entity\_manager.pairs\_for\_type(Renderable): pass # do something :param component\_type: a type of created component :type component\_type: :class:`type` :return: list of ``(entity\_id, component\_instance)`` tuples :rtype: :class:`tuple` of (:class:`int`, :class:`ecs.models.Component`) """ try: return self.\_database[component\_type].items() except KeyError: return [] [[docs]](../../api.html#ecs.managers.EntityManager.component_for_entity) def component\_for\_entity(self, entity\_id, component\_type): """Return the instance of ``component\_type`` for the ``entity\_id`` from the database. :param entity\_id: entity GUID :type entity\_id: :class:`int` :param component\_type: a type of created component :type component\_type: :class:`type` :return: list of ``(entity\_id, component\_instance)`` tuples :rtype: :class:`tuple` of (:class:`int`, :class:`ecs.models.Component`) :raises: :exc:`NonexistentComponentTypeForEntity` when \ ``component\_type`` does not exist on ``entity\_instance`` """ try: return self.\_database[component\_type][entity\_id] except KeyError: raise NonexistentComponentTypeForEntity( entity\_id, component\_type) [[docs]](../../api.html#ecs.managers.EntityManager.remove_entity) def remove\_entity(self, entity\_id): """Remove all components from the database that are associated with ``entity\_id``, with the side-effect that the entity is also no longer in the database. :param entity\_id: entity GUID :type entity\_id: :class:`int` """ # Don't use iterkeys(), otherwise we will get a RuntimeError about # mutating the length of the dictionary at runtime. for comp\_type in self.\_database.keys(): try: del self.\_database[comp\_type][entity\_id] if self.\_database[comp\_type] == {}: del self.\_database[comp\_type] except KeyError: pass [[docs]](../../api.html#ecs.managers.SystemManager)class SystemManager(object): """A container and manager for :class:`ecs.models.System` objects.""" def \_\_init\_\_(self): self.\_systems = [] self.\_system\_types = {} # Allow getting the list of systems but not directly setting it. @property [[docs]](../../api.html#ecs.managers.SystemManager.systems) def systems(self): """Get this manager's list of systems. :return: system list :rtype: :class:`list` of :class:`ecs.models.System` """ return self.\_systems [[docs]](../../api.html#ecs.managers.SystemManager.add_system) def add\_system(self, system\_instance): """Add a :class:`ecs.models.System` instance to the manager. :param system\_instance: instance of a system :type system\_instance: :class:`ecs.models.System` """ system\_type = type(system\_instance) if system\_type in self.\_system\_types: raise DuplicateSystemTypeError(system\_type) self.\_system\_types[system\_type] = system\_instance self.\_systems.append(system\_instance) [[docs]](../../api.html#ecs.managers.SystemManager.remove_system) def remove\_system(self, system\_type): """Tell the manager to no longer run the system of this type. :param system\_type: type of system to remove :type system\_type: :class:`type` """ self.\_systems.remove(self.\_system\_types[system\_type]) del self.\_system\_types[system\_type] [[docs]](../../api.html#ecs.managers.SystemManager.update) def update(self, dt): """Run all systems in order, for this frame. :param dt: delta time, or elapsed time for this frame :type dt: :class:`float` """ # Iterating over a list of systems instead of values in a dictionary is # noticeably faster. We maintain a list in addition to a dictionary # specifically for this purpose. for system in self.\_systems: system.update(dt) ``` ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../../genindex.html "General Index") * [modules](../../py-modindex.html "Python Module Index") | * [Entity-Component-System 0.1 documentation](../../index.html) » * [Module code](../index.html) » © Copyright 2013 Kevin Ward, Sean Fisk. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3. ecs.models — Entity-Component-System 0.1 documentation ### Navigation * [index](../../genindex.html "General Index") * [modules](../../py-modindex.html "Python Module Index") | * [Entity-Component-System 0.1 documentation](../../index.html) » * [Module code](../index.html) » Source code for ecs.models ========================== ``` """Entity, Component, and System classes.""" from \_\_future\_\_ import print\_function from abc import ABCMeta, abstractmethod [[docs]](../../api.html#ecs.models.Entity)class Entity(object): """Encapsulation of a GUID to use in the entity database.""" def \_\_init\_\_(self, guid): """:param guid: globally unique identifier :type guid: :class:`int` """ self.\_guid = guid def \_\_str\_\_(self): """Stringify. :return: GUID as a string :rtype: :class:`str` """ return str(self.\_guid) def \_\_hash\_\_(self): """Hash function for this object. :return: the hash value :rtype: :class:`int` """ return self.\_guid def \_\_eq\_\_(self, other): """Equality method. :param other: other entity :type other: :class:`Entity` :return: ``True`` if equal :rtype: :class:`bool` """ return self.\_guid == hash(other) [[docs]](../../api.html#ecs.models.Component)class Component(object): """Class from which all components should derive.""" pass [[docs]](../../api.html#ecs.models.System)class System(object): """An object that represents an operation on a set of objects from the game database. The :meth:`update` method must be implemented. """ \_\_metaclass\_\_ = ABCMeta @abstractmethod [[docs]](../../api.html#ecs.models.System.update) def update(self, dt): """Run the system for this frame. This method is called by the system manager, and is where the functionality of the system is implemented. :param dt: delta time, or elapsed time for this frame :type dt: :class:`float` """ print("System's update() method was called " "with time delta of {}".format(dt)) ``` ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../../genindex.html "General Index") * [modules](../../py-modindex.html "Python Module Index") | * [Entity-Component-System 0.1 documentation](../../index.html) » * [Module code](../index.html) » © Copyright 2013 Kevin Ward, Sean Fisk. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3. ecs.exceptions — Entity-Component-System 0.1 documentation ### Navigation * [index](../../genindex.html "General Index") * [modules](../../py-modindex.html "Python Module Index") | * [Entity-Component-System 0.1 documentation](../../index.html) » * [Module code](../index.html) » Source code for ecs.exceptions ============================== ``` """Exceptions that may be raised.""" [[docs]](../../api.html#ecs.exceptions.NonexistentComponentTypeForEntity)class NonexistentComponentTypeForEntity(Exception): """Error indicating that a component type does not exist for a certain entity.""" def \_\_init\_\_(self, entity\_instance, component\_type): """:param entity: entity without component type :type entity: :class:`Entity` :param component\_type: component type not in entity :type component\_type: :class:`type` """ self.entity\_instance = entity\_instance self.component\_type = component\_type def \_\_str\_\_(self): return "Nonexistent component type: `{0}' for entity: `{1}'".format( self.component\_type.\_\_name\_\_, self.entity\_instance) [[docs]](../../api.html#ecs.exceptions.DuplicateSystemTypeError)class DuplicateSystemTypeError(Exception): """Error indicating that the system type already exists in the system manager.""" def \_\_init\_\_(self, system\_type): """:param system\_type: type of the system :type system\_type: :class:`type` """ self.system\_type = system\_type def \_\_str\_\_(self): return "Duplicate system type: `{0}'".format(self.system\_type.\_\_name\_\_) ``` ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../../genindex.html "General Index") * [modules](../../py-modindex.html "Python Module Index") | * [Entity-Component-System 0.1 documentation](../../index.html) » * [Module code](../index.html) » © Copyright 2013 Kevin Ward, Sean Fisk. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3.

httpstream

go

HTTPStream — HTTPStream 1.1.0 documentation ### Navigation * [index](genindex/ "General Index") * [HTTPStream 1.1.0 documentation](#) » HTTPStream[¶](#httpstream "Permalink to this headline") ======================================================= HTTPStream is a simple and pragmatic HTTP client library for Python that provides support for incremental JSON document retrieval and RFC 6570 URI Templates. Resources[¶](#resources "Permalink to this headline") ----------------------------------------------------- A resource is an entity that exists on a distributed system, such as the World Wide Web, and is identified by a URI. Commonly associated with the REST architectural style, web resources are objects upon which HTTP methods like GET and POST can be actioned. HTTPStream is built around a core [Resource](#httpstream.Resource "httpstream.Resource") class that embodies the concept of the web resource and instances can be constructed by simply using the URI by which they are uniquely identified: ``` >>> from httpstream import Resource >>> resource = Resource("http://example.com/foo/bar") ``` Although the Resource class can be used directly, applications may alternatively either inherit from or wrap this class to provide more meaningful naming: ``` from httpstream import Resource class InheritedMailbox(Resource): def \_\_init\_\_(self, uri): Resource.\_\_init\_\_(self, uri) def deliver(self, message): self.post(message) class WrappedMailbox(object): def \_\_init\_\_(self, uri): self.\_resource = Resource(uri) def deliver(self, message): self.\_resource.post(message) ``` For simple HTTP access, resources can of course be created and used in an immediate inline context: ``` >>> from httpstream import Resource >>> results = Resource("https://api.duckduckgo.com/?q=neo4j&format=json").get().content ``` Methods such as [get](#httpstream.Resource.get "httpstream.Resource.get") return a file-like [Response](#httpstream.Response "httpstream.Response") object. The response content can be either iterated through or retrieved at once: ``` resource = Resource("http://example.com/") # print each line of the response in turn with resource.get() as response: for line in response: print line # print the entire response content at once with resource.get() as response: print response.content ``` *class* httpstream.Resource(*uri*)[¶](#httpstream.Resource "Permalink to this definition") A web resource identified by a URI. get(*headers=None*, *redirect\_limit=5*, *\*\*kwargs*)[¶](#httpstream.Resource.get "Permalink to this definition") Issue a GET request to this resource. | Parameters: | * **headers** (*dict*) – headers to be included in the request (optional) * **redirect\_limit** – maximum number of redirects to be handled automatically (optional, default=5) * **product** – name or (name, version) tuple for the client product to be listed in the User-Agent header (optional) * **chunk\_size** – number of bytes to retrieve per chunk (optional, default=4096) | | Returns: | file-like Response object from which content can be read | put(*body=None*, *headers=None*, *\*\*kwargs*)[¶](#httpstream.Resource.put "Permalink to this definition") Issue a PUT request to this resource. post(*body=None*, *headers=None*, *\*\*kwargs*)[¶](#httpstream.Resource.post "Permalink to this definition") Issue a POST request to this resource. delete(*headers=None*, *\*\*kwargs*)[¶](#httpstream.Resource.delete "Permalink to this definition") Issue a DELETE request to this resource. head(*headers=None*, *redirect\_limit=5*, *\*\*kwargs*)[¶](#httpstream.Resource.head "Permalink to this definition") Issue a HEAD request to this resource. resolve(*reference*, *strict=True*)[¶](#httpstream.Resource.resolve "Permalink to this definition") Resolve a URI reference against the URI for this resource, returning a new resource represented by the new target URI. Implicit Resources[¶](#implicit-resources "Permalink to this headline") ----------------------------------------------------------------------- A shorthand is also available for implicit resource creation: ``` >>> from httpstream import get >>> results = get("https://api.duckduckgo.com/?q=neo4j&format=json").content ``` httpstream.get(*uri*, *headers=None*, *redirect\_limit=5*, *\*\*kwargs*)[¶](#httpstream.get "Permalink to this definition") httpstream.put(*uri*, *body=None*, *headers=None*, *\*\*kwargs*)[¶](#httpstream.put "Permalink to this definition") httpstream.post(*uri*, *body=None*, *headers=None*, *\*\*kwargs*)[¶](#httpstream.post "Permalink to this definition") httpstream.delete(*uri*, *headers=None*, *\*\*kwargs*)[¶](#httpstream.delete "Permalink to this definition") httpstream.head(*uri*, *headers=None*, *redirect\_limit=5*, *\*\*kwargs*)[¶](#httpstream.head "Permalink to this definition") Resource Templates[¶](#resource-templates "Permalink to this headline") ----------------------------------------------------------------------- ``` >>> from httpstream import ResourceTemplate >>> searcher = ResourceTemplate("https://api.duckduckgo.com/?q={query}&format=json") >>> results = searcher.expand(query="neo4j").get().content ``` *class* httpstream.ResourceTemplate(*uri\_template*)[¶](#httpstream.ResourceTemplate "Permalink to this definition") expand(*\*\*values*)[¶](#httpstream.ResourceTemplate.expand "Permalink to this definition") Expand this template into a full URI using the values provided. uri\_template[¶](#httpstream.ResourceTemplate.uri_template "Permalink to this definition") The URI template string of this resource template. Requests & Responses[¶](#requests-responses "Permalink to this headline") ------------------------------------------------------------------------- HTTPStream defines four types of response objects. A standard Response is generated on receipt of a 2xx status code and a ClientError and ServerError may be raised on receipt of 4xx and 5xx statuses respectively. The fourth response type is Redirection which is generally consumed internally but may also be returned under certain circumstances. Response objects are file-like and as such may be read or iterated through The iter\_lines and iter\_json methods may be used to step through known types of content. The response object itself may also be iterated directly and an appropriate type of iterator is selected depending on the type of content available. The example below shows how to print each line of textual content as it is received: ``` >>> for line in res.get(): ... print line ``` *class* httpstream.Request(*method*, *uri*, *body=None*, *headers=None*)[¶](#httpstream.Request "Permalink to this definition") body[¶](#httpstream.Request.body "Permalink to this definition") Content of the request. headers[¶](#httpstream.Request.headers "Permalink to this definition") Dictionary of headers attached to the request. method *= None*[¶](#httpstream.Request.method "Permalink to this definition") HTTP method of this request submit(*redirect\_limit=0*, *product=None*, *\*\*response\_kwargs*)[¶](#httpstream.Request.submit "Permalink to this definition") Submit this request and return a [Response](#httpstream.Response "httpstream.Response") object. uri[¶](#httpstream.Request.uri "Permalink to this definition") URI of the request. *class* httpstream.Response(*http*, *uri*, *request*, *response*, *\*\*kwargs*)[¶](#httpstream.Response "Permalink to this definition") File-like object allowing consumption of an HTTP response. chunk\_size *= None*[¶](#httpstream.Response.chunk_size "Permalink to this definition") Default chunk size for this response close()[¶](#httpstream.Response.close "Permalink to this definition") Close the response, discarding all remaining content and releasing the underlying connection object. closed[¶](#httpstream.Response.closed "Permalink to this definition") Indicates whether or not the response is closed. content[¶](#httpstream.Response.content "Permalink to this definition") Fetch all content, returning a value appropriate for the content type. content\_length[¶](#httpstream.Response.content_length "Permalink to this definition") The length of content as provided by the Content-Length header field. If the content is chunked, this returns None. content\_type[¶](#httpstream.Response.content_type "Permalink to this definition") The type of content as provided by the Content-Type header field. encoding[¶](#httpstream.Response.encoding "Permalink to this definition") The content character set encoding. headers[¶](#httpstream.Response.headers "Permalink to this definition") The response headers. is\_chunked[¶](#httpstream.Response.is_chunked "Permalink to this definition") Indicates whether or not the content is chunked. is\_json[¶](#httpstream.Response.is_json "Permalink to this definition") Indicates whether or not the content is JSON. is\_text[¶](#httpstream.Response.is_text "Permalink to this definition") Indicates whether or not the content is text. is\_tsj[¶](#httpstream.Response.is_tsj "Permalink to this definition") Indicates whether or not the content is tab-separated JSON. iter\_chunks(*chunk\_size=None*)[¶](#httpstream.Response.iter_chunks "Permalink to this definition") Iterate through the content as chunks of text. Chunk sizes may vary slightly from that specified due to multi-byte characters. If no chunk size is specified, a default of 4096 is used. iter\_json()[¶](#httpstream.Response.iter_json "Permalink to this definition") Iterate through the content as individual JSON values. iter\_lines(*keep\_ends=False*)[¶](#httpstream.Response.iter_lines "Permalink to this definition") Iterate through the content as lines of text. iter\_tsj()[¶](#httpstream.Response.iter_tsj "Permalink to this definition") Iterate through the content as lines of tab-separated JSON. json[¶](#httpstream.Response.json "Permalink to this definition") Fetch all content, decoding from JSON and returning the decoded value. read(*size=None*)[¶](#httpstream.Response.read "Permalink to this definition") Fetch some or all of the response content, returning as a bytearray. reason[¶](#httpstream.Response.reason "Permalink to this definition") The reason phrase attached to this response. request[¶](#httpstream.Response.request "Permalink to this definition") The Request object which preceded this response. status\_code[¶](#httpstream.Response.status_code "Permalink to this definition") The status code of the response text[¶](#httpstream.Response.text "Permalink to this definition") Fetches all content as a string. tsj[¶](#httpstream.Response.tsj "Permalink to this definition") Fetches all content, decoding from tab-separated JSON and returning the decoded values. uri[¶](#httpstream.Response.uri "Permalink to this definition") The URI from which the response came. *class* httpstream.Redirection(*http*, *uri*, *request*, *response*, *\*\*kwargs*)[¶](#httpstream.Redirection "Permalink to this definition") Bases: httpstream.http.Response *class* httpstream.ClientError(*http*, *uri*, *request*, *response*, *\*\*kwargs*)[¶](#httpstream.ClientError "Permalink to this definition") Bases: exceptions.Exception, httpstream.http.Response *class* httpstream.ServerError(*http*, *uri*, *request*, *response*, *\*\*kwargs*)[¶](#httpstream.ServerError "Permalink to this definition") Bases: exceptions.Exception, httpstream.http.Response Incremental JSON Parsing[¶](#incremental-json-parsing "Permalink to this headline") ----------------------------------------------------------------------------------- *class* httpstream.JSONStream(*source*)[¶](#httpstream.JSONStream "Permalink to this definition") Streaming JSON decoder. This class both expects Unicode input and will produce Unicode output. httpstream.assembled(*iterable*)[¶](#httpstream.assembled "Permalink to this definition") Returns a JSON-derived value from a set of key-value pairs as produced by the JSONStream process. This operates in a similar way to the built-in dict function. Internally, this uses the merged function on each pair to build the return value. ``` >>> data = [ ... (("drink",), "lemonade"), ... (("cutlery", 0), "knife"), ... (("cutlery", 1), "fork"), ... (("cutlery", 2), "spoon"), ... ] >>> assembled(data) {'cutlery': ['knife', 'fork', 'spoon'], 'drink': 'lemonade'} ``` | Parameters: | **iterable** – key-value pairs to be merged into assembled value | httpstream.grouped(*iterable*, *level=1*)[¶](#httpstream.grouped "Permalink to this definition") URIs[¶](#uris "Permalink to this headline") ------------------------------------------- *class* httpstream.URI(*value*)[¶](#httpstream.URI "Permalink to this definition") Uniform Resource Identifier. See also [RFC 3986](http://tools.ietf.org/html/rfc3986) absolute\_path\_reference[¶](#httpstream.URI.absolute_path_reference "Permalink to this definition") The path, query and fragment parts of this URI or None if undefined. **Component Definition:** ``` https://bob@example.com:8080/data/report.html?date=2000-12-25#summary \_______________________________________/ | absolute_path_reference ``` | Returns: | combined string values of path, query and fragment parts or None | | Return type: | percent-encoded string or None | authority[¶](#httpstream.URI.authority "Permalink to this definition") The authority part of this URI or None if undefined. **Component Definition:** ``` https://bob@example.com:8080/data/report.html?date=2000-12-25#summary \__________________/ | authority ``` | Return type: | Authority instance or None | fragment[¶](#httpstream.URI.fragment "Permalink to this definition") The *fragment* part of this URI or None if undefined. **Component Definition:** ``` https://bob@example.com:8080/data/report.html?date=2000-12-25#summary \_____/ | fragment ``` | Returns: | | | Return type: | unencoded string or None | hierarchical\_part[¶](#httpstream.URI.hierarchical_part "Permalink to this definition") The authority and path parts of this URI or None if undefined. **Component Definition:** ``` https://bob@example.com:8080/data/report.html?date=2000-12-25#summary \___________________________________/ | hierarchical_part ``` | Returns: | combined string values of authority and path parts or None | | Return type: | percent-encoded string or None | host[¶](#httpstream.URI.host "Permalink to this definition") The *host* part of this URI or None if undefined. ``` >>> URI(None).host None >>> URI("").host None >>> URI("http://example.com").host 'example.com' >>> URI("http://example.com:8080/data").host 'example.com' ``` **Component Definition:** ``` https://bob@example.com:8080/data/report.html?date=2000-12-25#summary \_________/ | host ``` | Returns: | | | Return type: | unencoded string or None | host\_port[¶](#httpstream.URI.host_port "Permalink to this definition") The *host* and *port* parts of this URI separated by a colon or None if both are undefined. ``` >>> URI(None).host\_port None >>> URI("").host\_port None >>> URI("http://example.com").host\_port 'example.com' >>> URI("http://example.com:8080/data").host\_port 'example.com:8080' >>> URI("http://bob@example.com:8080/data").host\_port 'example.com:8080' ``` **Component Definition:** ``` https://bob@example.com:8080/data/report.html?date=2000-12-25#summary \______________/ | host_port ``` | Returns: | | | Return type: | percent-encoded string or None | path[¶](#httpstream.URI.path "Permalink to this definition") The *path* part of this URI or None if undefined. **Component Definition:** ``` https://bob@example.com:8080/data/report.html?date=2000-12-25#summary \_______________/ | path ``` | Returns: | | | Return type: | Path instance or None | port[¶](#httpstream.URI.port "Permalink to this definition") The *port* part of this URI or None if undefined. ``` >>> URI(None).port None >>> URI("").port None >>> URI("http://example.com").port None >>> URI("http://example.com:8080/data").port 8080 ``` **Component Definition:** ``` https://bob@example.com:8080/data/report.html?date=2000-12-25#summary \__/ | port ``` | Returns: | | | Return type: | integer or None | query[¶](#httpstream.URI.query "Permalink to this definition") The *query* part of this URI or None if undefined. **Component Definition:** ``` https://bob@example.com:8080/data/report.html?date=2000-12-25#summary \_____________/ | query ``` | Return type: | Query instance or None | resolve(*reference*, *strict=True*)[¶](#httpstream.URI.resolve "Permalink to this definition") Transform a reference relative to this URI to produce a full target URI. See also [RFC 3986 § 5.2.2](http://tools.ietf.org/html/rfc3986#section-5.2.2) scheme[¶](#httpstream.URI.scheme "Permalink to this definition") The scheme part of this URI or None if undefined. **Component Definition:** ``` https://bob@example.com:8080/data/report.html?date=2000-12-25#summary \___/ | scheme ``` | Return type: | unencoded string or None | string[¶](#httpstream.URI.string "Permalink to this definition") The full percent-encoded string value of this URI or None if undefined. ``` >>> URI(None).string None >>> URI("").string '' >>> URI("http://example.com").string 'example.com' >>> URI("foo/bar").string 'foo/bar' >>> URI("http://bob@example.com:8080/data/report.html?date=2000-12-25#summary").string 'http://bob@example.com:8080/data/report.html?date=2000-12-25#summary' ``` **Component Definition:** ``` https://bob@example.com:8080/data/report.html?date=2000-12-25#summary \___________________________________________________________________/ | string ``` | Return type: | percent-encoded string or None | Note Unlike string, the \_\_str\_\_ method will always return a string, even when the URI is undefined; in this case, an empty string is returned instead of None. user\_info[¶](#httpstream.URI.user_info "Permalink to this definition") The user information part of this URI or None if undefined. **Component Definition:** ``` https://bob@example.com:8080/data/report.html?date=2000-12-25#summary \_/ | user_info ``` | Returns: | string value of user information part or None | | Return type: | unencoded string or None | *class* httpstream.Authority(*string*)[¶](#httpstream.Authority "Permalink to this definition") A host name plus optional port and user information detail. **Syntax** authority := [ user\_info "@" ] host [ ":" port ] See also [RFC 3986 § 3.2](http://tools.ietf.org/html/rfc3986#section-3.2) host[¶](#httpstream.Authority.host "Permalink to this definition") The host part of this authority component, an empty string if host is empty or None if undefined. ``` >>> Authority(None).host None >>> Authority("").host '' >>> Authority("example.com").host 'example.com' >>> Authority("example.com:8080").host 'example.com' >>> Authority("bob@example.com").host 'example.com' >>> Authority("bob@example.com:8080").host 'example.com' ``` | Returns: | | host\_port[¶](#httpstream.Authority.host_port "Permalink to this definition") The host and port parts of this authority component or None if undefined. ``` >>> Authority(None).host\_port None >>> Authority("").host\_port '' >>> Authority("example.com").host\_port 'example.com' >>> Authority("example.com:8080").host\_port 'example.com:8080' >>> Authority("bob@example.com").host\_port 'example.com' >>> Authority("bob@example.com:8080").host\_port 'example.com:8080' ``` | Returns: | | port[¶](#httpstream.Authority.port "Permalink to this definition") The port part of this authority component or None if undefined. ``` >>> Authority(None).port None >>> Authority("").port None >>> Authority("example.com").port None >>> Authority("example.com:8080").port 8080 >>> Authority("bob@example.com").port None >>> Authority("bob@example.com:8080").port 8080 ``` | Returns: | | string[¶](#httpstream.Authority.string "Permalink to this definition") The full string value of this authority component or :py:const:`None if undefined. ``` >>> Authority(None).string None >>> Authority("").string '' >>> Authority("example.com").string 'example.com' >>> Authority("example.com:8080").string 'example.com:8080' >>> Authority("bob@example.com").string 'bob@example.com' >>> Authority("bob@example.com:8080").string 'bob@example.com:8080' ``` | Returns: | | user\_info[¶](#httpstream.Authority.user_info "Permalink to this definition") The user information part of this authority component or None if undefined. ``` >>> Authority(None).user\_info None >>> Authority("").user\_info None >>> Authority("example.com").user\_info None >>> Authority("example.com:8080").user\_info None >>> Authority("bob@example.com").user\_info 'bob' >>> Authority("bob@example.com:8080").user\_info 'bob' ``` | Returns: | | *class* httpstream.Path(*string*)[¶](#httpstream.Path "Permalink to this definition") remove\_dot\_segments()[¶](#httpstream.Path.remove_dot_segments "Permalink to this definition") Implementation of RFC3986, section 5.2.4 segments[¶](#httpstream.Path.segments "Permalink to this definition") string[¶](#httpstream.Path.string "Permalink to this definition") with\_trailing\_slash()[¶](#httpstream.Path.with_trailing_slash "Permalink to this definition") without\_trailing\_slash()[¶](#httpstream.Path.without_trailing_slash "Permalink to this definition") *class* httpstream.Query(*string*)[¶](#httpstream.Query "Permalink to this definition") *classmethod* decode(*string*)[¶](#httpstream.Query.decode "Permalink to this definition") *classmethod* encode(*iterable*)[¶](#httpstream.Query.encode "Permalink to this definition") string[¶](#httpstream.Query.string "Permalink to this definition") URI Templates[¶](#uri-templates "Permalink to this headline") ------------------------------------------------------------- *class* httpstream.URITemplate(*template*)[¶](#httpstream.URITemplate "Permalink to this definition") A URI Template is a compact sequence of characters for describing a range of Uniform Resource Identifiers through variable expansion. This class exposes a full implementation of RFC6570. expand(*\*\*values*)[¶](#httpstream.URITemplate.expand "Permalink to this definition") Expand into a URI using the values supplied string[¶](#httpstream.URITemplate.string "Permalink to this definition") See also [RFC 6570](http://tools.ietf.org/html/rfc6570) Percent Encoding[¶](#percent-encoding "Permalink to this headline") ------------------------------------------------------------------- Percent encoding is used within URI components to allow inclusion of certain characters which are not within a permitted set. httpstream.percent\_encode(*data*, *safe=None*)[¶](#httpstream.percent_encode "Permalink to this definition") Percent encode a string of data, optionally keeping certain characters unencoded. httpstream.percent\_decode(*data*)[¶](#httpstream.percent_decode "Permalink to this definition") Percent decode a string of data. See also [RFC 3986 § 2.1](http://tools.ietf.org/html/rfc3986#section-2.1) Errors[¶](#errors "Permalink to this headline") ----------------------------------------------- *exception* httpstream.NetworkAddressError(*message*, *host\_port=None*)[¶](#httpstream.NetworkAddressError "Permalink to this definition") host\_port[¶](#httpstream.NetworkAddressError.host_port "Permalink to this definition") *exception* httpstream.SocketError(*code*, *host\_port=None*)[¶](#httpstream.SocketError "Permalink to this definition") code[¶](#httpstream.SocketError.code "Permalink to this definition") host\_port[¶](#httpstream.SocketError.host_port "Permalink to this definition") *exception* httpstream.RedirectionError(*\*args*, *\*\*kwargs*)[¶](#httpstream.RedirectionError "Permalink to this definition") [](#) ### [Table Of Contents](#) * [HTTPStream](#) + [Resources](#resources) + [Implicit Resources](#implicit-resources) + [Resource Templates](#resource-templates) + [Requests & Responses](#requests-responses) + [Incremental JSON Parsing](#incremental-json-parsing) + [URIs](#uris) + [URI Templates](#uri-templates) + [Percent Encoding](#percent-encoding) + [Errors](#errors) ### This Page * [Show Source](_sources/index.txt) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](genindex/ "General Index") * [HTTPStream 1.1.0 documentation](#) » © Copyright 2013, Nigel Small. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3. Index — HTTPStream 1.1.0 documentation ### Navigation * [index](# "General Index") * [HTTPStream 1.1.0 documentation](../) » Index ===== [**A**](#A) | [**B**](#B) | [**C**](#C) | [**D**](#D) | [**E**](#E) | [**F**](#F) | [**G**](#G) | [**H**](#H) | [**I**](#I) | [**J**](#J) | [**M**](#M) | [**N**](#N) | [**P**](#P) | [**Q**](#Q) | [**R**](#R) | [**S**](#S) | [**T**](#T) | [**U**](#U) | [**W**](#W) A - | | | | --- | --- | | [absolute\_path\_reference (httpstream.URI attribute)](../#httpstream.URI.absolute_path_reference) [assembled() (in module httpstream)](../#httpstream.assembled) | [Authority (class in httpstream)](../#httpstream.Authority) [authority (httpstream.URI attribute)](../#httpstream.URI.authority) | B - | | | --- | | [body (httpstream.Request attribute)](../#httpstream.Request.body) | C - | | | | --- | --- | | [chunk\_size (httpstream.Response attribute)](../#httpstream.Response.chunk_size) [ClientError (class in httpstream)](../#httpstream.ClientError) [close() (httpstream.Response method)](../#httpstream.Response.close) [closed (httpstream.Response attribute)](../#httpstream.Response.closed) | [code (httpstream.SocketError attribute)](../#httpstream.SocketError.code) [content (httpstream.Response attribute)](../#httpstream.Response.content) [content\_length (httpstream.Response attribute)](../#httpstream.Response.content_length) [content\_type (httpstream.Response attribute)](../#httpstream.Response.content_type) | D - | | | | --- | --- | | [decode() (httpstream.Query class method)](../#httpstream.Query.decode) | [delete() (httpstream.Resource method)](../#httpstream.Resource.delete) [(in module httpstream)](../#httpstream.delete) | E - | | | | --- | --- | | [encode() (httpstream.Query class method)](../#httpstream.Query.encode) [encoding (httpstream.Response attribute)](../#httpstream.Response.encoding) | [expand() (httpstream.ResourceTemplate method)](../#httpstream.ResourceTemplate.expand) [(httpstream.URITemplate method)](../#httpstream.URITemplate.expand) | F - | | | --- | | [fragment (httpstream.URI attribute)](../#httpstream.URI.fragment) | G - | | | | --- | --- | | [get() (httpstream.Resource method)](../#httpstream.Resource.get) [(in module httpstream)](../#httpstream.get) | [grouped() (in module httpstream)](../#httpstream.grouped) | H - | | | | --- | --- | | [head() (httpstream.Resource method)](../#httpstream.Resource.head) [(in module httpstream)](../#httpstream.head) [headers (httpstream.Request attribute)](../#httpstream.Request.headers) [(httpstream.Response attribute)](../#httpstream.Response.headers) [hierarchical\_part (httpstream.URI attribute)](../#httpstream.URI.hierarchical_part) | [host (httpstream.Authority attribute)](../#httpstream.Authority.host) [(httpstream.URI attribute)](../#httpstream.URI.host) [host\_port (httpstream.Authority attribute)](../#httpstream.Authority.host_port) [(httpstream.NetworkAddressError attribute)](../#httpstream.NetworkAddressError.host_port) [(httpstream.SocketError attribute)](../#httpstream.SocketError.host_port) [(httpstream.URI attribute)](../#httpstream.URI.host_port) | I - | | | | --- | --- | | [is\_chunked (httpstream.Response attribute)](../#httpstream.Response.is_chunked) [is\_json (httpstream.Response attribute)](../#httpstream.Response.is_json) [is\_text (httpstream.Response attribute)](../#httpstream.Response.is_text) [is\_tsj (httpstream.Response attribute)](../#httpstream.Response.is_tsj) | [iter\_chunks() (httpstream.Response method)](../#httpstream.Response.iter_chunks) [iter\_json() (httpstream.Response method)](../#httpstream.Response.iter_json) [iter\_lines() (httpstream.Response method)](../#httpstream.Response.iter_lines) [iter\_tsj() (httpstream.Response method)](../#httpstream.Response.iter_tsj) | J - | | | | --- | --- | | [json (httpstream.Response attribute)](../#httpstream.Response.json) | [JSONStream (class in httpstream)](../#httpstream.JSONStream) | M - | | | --- | | [method (httpstream.Request attribute)](../#httpstream.Request.method) | N - | | | --- | | [NetworkAddressError](../#httpstream.NetworkAddressError) | P - | | | | --- | --- | | [Path (class in httpstream)](../#httpstream.Path) [path (httpstream.URI attribute)](../#httpstream.URI.path) [percent\_decode() (in module httpstream)](../#httpstream.percent_decode) [percent\_encode() (in module httpstream)](../#httpstream.percent_encode) | [port (httpstream.Authority attribute)](../#httpstream.Authority.port) [(httpstream.URI attribute)](../#httpstream.URI.port) [post() (httpstream.Resource method)](../#httpstream.Resource.post) [(in module httpstream)](../#httpstream.post) [put() (httpstream.Resource method)](../#httpstream.Resource.put) [(in module httpstream)](../#httpstream.put) | Q - | | | | --- | --- | | [Query (class in httpstream)](../#httpstream.Query) | [query (httpstream.URI attribute)](../#httpstream.URI.query) | R - | | | | --- | --- | | [read() (httpstream.Response method)](../#httpstream.Response.read) [reason (httpstream.Response attribute)](../#httpstream.Response.reason) [Redirection (class in httpstream)](../#httpstream.Redirection) [RedirectionError](../#httpstream.RedirectionError) [remove\_dot\_segments() (httpstream.Path method)](../#httpstream.Path.remove_dot_segments) [Request (class in httpstream)](../#httpstream.Request) | [request (httpstream.Response attribute)](../#httpstream.Response.request) [resolve() (httpstream.Resource method)](../#httpstream.Resource.resolve) [(httpstream.URI method)](../#httpstream.URI.resolve) [Resource (class in httpstream)](../#httpstream.Resource) [ResourceTemplate (class in httpstream)](../#httpstream.ResourceTemplate) [Response (class in httpstream)](../#httpstream.Response) | S - | | | | --- | --- | | [scheme (httpstream.URI attribute)](../#httpstream.URI.scheme) [segments (httpstream.Path attribute)](../#httpstream.Path.segments) [ServerError (class in httpstream)](../#httpstream.ServerError) [SocketError](../#httpstream.SocketError) | [status\_code (httpstream.Response attribute)](../#httpstream.Response.status_code) [string (httpstream.Authority attribute)](../#httpstream.Authority.string) [(httpstream.Path attribute)](../#httpstream.Path.string) [(httpstream.Query attribute)](../#httpstream.Query.string) [(httpstream.URI attribute)](../#httpstream.URI.string) [(httpstream.URITemplate attribute)](../#httpstream.URITemplate.string) [submit() (httpstream.Request method)](../#httpstream.Request.submit) | T - | | | | --- | --- | | [text (httpstream.Response attribute)](../#httpstream.Response.text) | [tsj (httpstream.Response attribute)](../#httpstream.Response.tsj) | U - | | | | --- | --- | | [URI (class in httpstream)](../#httpstream.URI) [uri (httpstream.Request attribute)](../#httpstream.Request.uri) [(httpstream.Response attribute)](../#httpstream.Response.uri) [uri\_template (httpstream.ResourceTemplate attribute)](../#httpstream.ResourceTemplate.uri_template) | [URITemplate (class in httpstream)](../#httpstream.URITemplate) [user\_info (httpstream.Authority attribute)](../#httpstream.Authority.user_info) [(httpstream.URI attribute)](../#httpstream.URI.user_info) | W - | | | | --- | --- | | [with\_trailing\_slash() (httpstream.Path method)](../#httpstream.Path.with_trailing_slash) | [without\_trailing\_slash() (httpstream.Path method)](../#httpstream.Path.without_trailing_slash) | [](../) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](# "General Index") * [HTTPStream 1.1.0 documentation](../) » © Copyright 2013, Nigel Small. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3. Overview: module code — HTTPStream 1.1.0 documentation ### Navigation * [index](../genindex/ "General Index") * [HTTPStream 1.1.0 documentation](../) » All modules for which code is available ======================================= * [httpstream](httpstream/) [](../) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../genindex/ "General Index") * [HTTPStream 1.1.0 documentation](../) » © Copyright 2013, Nigel Small. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3. httpstream — HTTPStream 1.1.0 documentation ### Navigation * [index](../../genindex/ "General Index") * [HTTPStream 1.1.0 documentation](../../) » * [Module code](../) » Source code for httpstream ========================== ``` #!/usr/bin/env python # -\*- coding: utf-8 -\*- # Copyright 2013, Nigel Small # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """ HTTPStream """ \_\_author\_\_ = "Nigel Small" \_\_copyright\_\_ = "2013, Nigel Small" \_\_email\_\_ = "nigel@nigelsmall.com" \_\_license\_\_ = "Apache License, Version 2.0" \_\_version\_\_ = "1.1.0" from .http import \* from .jsonstream import \* from .uri import \* ``` [](../../) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../../genindex/ "General Index") * [HTTPStream 1.1.0 documentation](../../) » * [Module code](../) » © Copyright 2013, Nigel Small. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3. Search — HTTPStream 1.1.0 documentation ### Navigation * [index](../genindex/ "General Index") * [HTTPStream 1.1.0 documentation](../) » Search ====== Please activate JavaScript to enable the search functionality. From here you can search these documents. Enter your search words into the box below and click "search". Note that the search function will automatically search for all of the words. Pages containing fewer words won't appear in the result list. [](../) ### Navigation * [index](../genindex/ "General Index") * [HTTPStream 1.1.0 documentation](../) » © Copyright 2013, Nigel Small. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3.

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color latest documentation [color](index.html#document-index) latest [color](index.html#document-index) * [Docs](index.html#document-index) » * color latest documentation * [Edit on GitHub](https://github.com/zoranf/Color-Toggle-Plugin/blob/develop/docs/index.rst) --- [INDEX](#id1)[¶](#index "Permalink to this headline") ===================================================== Contents * [INDEX](#index)

bunkerized-nginx

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bunkerized-nginx v1.3.2 documentation [bunkerized-nginx](#) latest Contents * [Introduction](index.html#document-introduction) * [Integrations](index.html#document-integrations) * [Quickstart guide](index.html#document-quickstart_guide) * [Special folders](index.html#document-special_folders) * [Security tuning](index.html#document-security_tuning) * [Web UI](index.html#document-web_ui) * [List of environment variables](index.html#document-environment_variables) * [Troubleshooting](index.html#document-troubleshooting) * [Plugins](index.html#document-plugins) [bunkerized-nginx](#) * » * bunkerized-nginx v1.3.2 documentation * [Edit on GitHub](https://github.com/bunkerity/bunkerized-nginx/blob/v1.3.2/docs/index) --- bunkerized-nginx official documentation[¶](#bunkerized-nginx-official-documentation "Permalink to this heading") ================================================================================================================ Introduction[¶](#introduction "Permalink to this heading") ----------------------------------------------------------  > > Make security by default great again ! > > > bunkerized-nginx is a web server based on the notorious nginx and focused on security. It integrates into existing environments (Linux, Docker, Swarm, Kubernetes, …) to make your web services “secured by default” without any hassle. The security best practices are automatically applied for you while keeping control of every settings to meet your own use case.  Non-exhaustive list of features : * HTTPS support with transparent Let’s Encrypt automation * State-of-the-art web security : HTTP security headers, prevent leaks, TLS hardening, … * Integrated ModSecurity WAF with the OWASP Core Rule Set * Automatic ban of strange behaviors * Antibot challenge through cookie, javascript, captcha or recaptcha v3 * Block TOR, proxies, bad user-agents, countries, … * Block known bad IP with DNSBL * Prevent bruteforce attacks with rate limiting * Plugins system for external security checks (ClamAV, CrowdSec, …) * Easy to configure with environment variables or web UI * Seamless integration into existing environments : Linux, Docker, Swarm, Kubernetes, … Fooling automated tools/scanners :  You can find a live demo at <https://demo-nginx.bunkerity.com>, feel free to do some security tests. Integrations[¶](#integrations "Permalink to this heading") ---------------------------------------------------------- ### Docker[¶](#docker "Permalink to this heading") You can get official prebuilt Docker images of bunkerized-nginx for x86, x64, armv7 and aarch64/arm64 architectures on Docker Hub : ``` $ docker pull bunkerity/bunkerized-nginx ``` Or you can build it from source if you wish : ``` $ git clone https://github.com/bunkerity/bunkerized-nginx.git $ cd bunkerized-nginx $ docker build -t bunkerized-nginx . ``` To use bunkerized-nginx as a Docker container you have to pass specific environment variables, mount volumes and redirect ports to make it accessible from the outside.  To demonstrate the use of the Docker image, we will create a simple “Hello World” static file that will be served by bunkerized-nginx. **One important thing to know is that the container runs as an unprivileged user with UID and GID 101. The reason behind this behavior is the security : in case a vulnerability is exploited the attacker won’t have full privileges inside the container. But there is also a downside because bunkerized-nginx (heavily) make use of volumes, you will need to adjust the rights on the host.** First create the environment on the host : ``` $ mkdir bunkerized-hello bunkerized-hello/www bunkerized-hello/certs $ cd bunkerized-hello $ chown root:101 www certs $ chmod 750 www $ chmod 770 certs ``` The www folder will contain our static files that will be served by bunkerized-nginx. Whereas the certs folder will store the automatically generated Let’s Encrypt certificates. Let’s create a dummy static page into the www folder : ``` $ echo "Hello bunkerized World !" > www/index.html $ chown root:101 www/index.html $ chmod 740 www/index.html ``` It’s time to run the container : ``` $ docker run \ -p 80:8080 \ -p 443:8443 \ -v "${PWD}/www:/www:ro" \ -v "${PWD}/certs:/etc/letsencrypt" \ -e SERVER\_NAME=www.example.com \ -e AUTO\_LETS\_ENCRYPT=yes \ bunkerity/bunkerized-nginx ``` Or if you prefer docker-compose : ``` version: '3' services: mybunkerized: image: bunkerity/bunkerized-nginx ports: - 80:8080 - 443:8443 volumes: - ./www:/www:ro - ./certs:/etc/letsencrypt environment: - SERVER\_NAME=www.example.com - AUTO\_LETS\_ENCRYPT=yes ``` Important things to note : * Replace www.example.com with your own domain (it must points to your server IP address if you want Let’s Encrypt to work) * Automatic Let’s Encrypt is enabled thanks to `AUTO\_LETS\_ENCRYPT=yes` (since the default is `AUTO\_LETS\_ENCRYPT=no` you can remove the environment variable to disable Let’s Encrypt) * The container is exposing TCP/8080 for HTTP and TCP/8443 for HTTPS * The /www volume is used to deliver static files and can be mounted as read-only for security reason * The /etc/letsencrypt volume is used to store certificates and must be mounted as read/write Inspect the container logs until bunkerized-nginx is started then visit http(s)://www.example.com to confirm that everything is working as expected. This example is really simple but, as you can see in the [list of environment variables](https://bunkerized-nginx.readthedocs.io/en/latest/environment_variables.html), you may get a lot of environment variables depending on your use case. To make things cleanier, you can write the environment variables to a file : ``` $ cat variables.env SERVER\_NAME=www.example.com AUTO\_LETS\_ENCRYPT=yes ``` And load the file when creating the container : ``` $ docker run ... --env-file "${PWD}/variables.env" ... bunkerity/bunkerized-nginx ``` Or if you prefer docker-compose : ``` ... services: mybunkerized: ... env\_file: - ./variables.env ... ... ``` ### Docker autoconf[¶](#docker-autoconf "Permalink to this heading") The downside of using environment variables is that the container needs to be recreated each time there is an update which is not very convenient. To counter that issue, you can use another image called bunkerized-nginx-autoconf which will listen for Docker events and automatically configure bunkerized-nginx instance in real time without recreating the container. Instead of defining environment variables for the bunkerized-nginx container, you simply add labels to your web services and bunkerized-nginx-autoconf will “automagically” take care of the rest.  First of all, you will need a network to allow communication between bunkerized-nginx and your web services : ``` $ docker network create services-net ``` We will also make use of a named volume to share the configuration between autoconf and bunkerized-nginx : ``` $ docker volume create bunkerized-vol ``` You can now create the bunkerized-nginx container : ``` $ docker run \ --name mybunkerized \ -l bunkerized-nginx.AUTOCONF \ --network services-net \ -p 80:8080 \ -p 443:8443 \ -v "${PWD}/www:/www:ro" \ -v "${PWD}/certs:/etc/letsencrypt" \ -v bunkerized-vol:/etc/nginx \ -e MULTISITE=yes \ -e SERVER\_NAME= \ -e AUTO\_LETS\_ENCRYPT=yes \ bunkerity/bunkerized-nginx ``` The autoconf one can now be started : ``` $ docker run \ --name myautoconf \ --volumes-from mybunkerized:rw \ -v /var/run/docker.sock:/var/run/docker.sock:ro \ bunkerity/bunkerized-nginx-autoconf ``` Here is the docker-compose equivalent : ``` version: '3' services: mybunkerized: image: bunkerity/bunkerized-nginx restart: always ports: - 80:8080 - 443:8443 volumes: - ./certs:/etc/letsencrypt - ./www:/www:ro - bunkerized-vol:/etc/nginx environment: - SERVER\_NAME= - MULTISITE=yes - AUTO\_LETS\_ENCRYPT=yes labels: - "bunkerized-nginx.AUTOCONF" networks: - services-net myautoconf: image: bunkerity/bunkerized-nginx-autoconf restart: always volumes\_from: - mybunkerized volumes: - /var/run/docker.sock:/var/run/docker.sock:ro depends\_on: - mybunkerized volumes: bunkerized-vol: networks: services-net: name: services-net ``` Important things to note : * autoconf is generating config files and other artefacts for the bunkerized-nginx, they need to share the same volumes * autoconf must have access to the Docker socket in order to get events, access to labels and send SIGHUP signal (reload order) to bunkerized-nginx * bunkerized-nginx must have the bunkerized-nginx.AUTOCONF label * bunkerized-nginx must be started in [multisite mode](https://bunkerized-nginx.readthedocs.io/en/latest/quickstart_guide.html#multisite) with the `MULTISITE=yes` environment variable * When setting the `SERVER\_NAME` environment variable to an empty value, bunkerized-nginx won’t generate any web service configuration at startup * The `AUTO\_LETS\_ENCRYPT=yes` will be applied to all subsequent web service configuration, unless overriden by the web service labels Check the logs of both autoconf and bunkerized-nginx to see if everything is working as expected. You can now create a new web service and add environment variables as labels with the `bunkerized-nginx.` prefix to let the autoconf service “automagically” do the configuration for you : ``` $ docker run \ --name myservice \ --network services-net \ -l bunkerized-nginx.SERVER_NAME=www.example.com \ -l bunkerized-nginx.USE_REVERSE_PROXY=yes \ -l bunkerized-nginx.REVERSE_PROXY_URL=/ \ -l bunkerized-nginx.REVERSE_PROXY_HOST=http://myservice \ tutum/hello-world ``` docker-compose equivalent : ``` version: "3" services: myservice: image: tutum/hello-world networks: services-net: aliases: - myservice labels: - "bunkerized-nginx.SERVER\_NAME=www.example.com" - "bunkerized-nginx.USE\_REVERSE\_PROXY=yes" - "bunkerized-nginx.REVERSE\_PROXY\_URL=/" - "bunkerized-nginx.REVERSE\_PROXY\_HOST=http://myservice" networks: services-net: external: name: services-net ``` Please note that if you want to override the `AUTO\_LETS\_ENCRYPT=yes` previously defined in the bunkerized-nginx container, you simply need to add the `bunkerized-nginx.AUTO\_LETS\_ENCRYPT=no` label. Look at the logs of both autoconf and bunkerized-nginx to check if the configuration has been generated and loaded by bunkerized-nginx. You should now be able to visit http(s)://www.example.com. When your container is not needed anymore, you can delete it as usual. The autoconf should get the event and generate the configuration again. ### Docker Swarm[¶](#docker-swarm "Permalink to this heading") The deployment and configuration is very similar to the “Docker autoconf” one but with services instead of containers. A service based on the bunkerized-nginx-autoconf image needs to be scheduled on a manager node (don’t worry it doesn’t expose any network port for obvious security reasons). This service will listen for Docker Swarm events like service creation or deletion and generate the configuration according to the labels of each service. Once configuration generation is done, the bunkerized-nginx-autoconf service will send the configuration files and a reload order to all the bunkerized-nginx tasks so they can apply the new configuration. If you need to deliver static files (e.g., html, images, css, js, …) a shared folder accessible from all bunkerized-nginx instances is needed (you can use a storage system like NFS, GlusterFS, CephFS on the host or a [Docker volume plugin](https://docs.docker.com/engine/extend/)).  In this setup we will deploy bunkerized-nginx in global mode on all workers and autoconf as a single replica on a manager. First of all, you will need to create 2 networks, one for the communication between bunkerized-nginx and autoconf and the other one for the communication between bunkerized-nginx and the web services : ``` $ docker network create -d overlay --attachable bunkerized-net $ docker network create -d overlay --attachable services-net ``` We can now start the bunkerized-nginx as a service : ``` $ docker service create \ --name mybunkerized \ --mode global \ --constraint node.role==worker \ -l bunkerized-nginx.AUTOCONF \ --network bunkerized-net \ -p published=80,target=8080,mode=host \ -p published=443,target=8443,mode=host \ -e SWARM\_MODE=yes \ -e USE\_API=yes \ -e API\_URI=/ChangeMeToSomethingHardToGuess \ -e SERVER\_NAME= \ -e MULTISITE=yes \ -e AUTO\_LETS\_ENCRYPT=yes \ bunkerity/bunkerized-nginx $ docker service update \ --network-add services-net \ mybunkerized ``` Once bunkerized-nginx has been started you can start the autoconf as a service : ``` $ docker service create \ --name myautoconf \ --replicas 1 \ --constraint node.role==manager \ --network bunkerized-net \ --mount type=bind,source=/var/run/docker.sock,destination=/var/run/docker.sock,ro \ --mount type=volume,source=cache-vol,destination=/cache \ --mount type=volume,source=certs-vol,destination=/etc/letsencrypt \ -e SWARM\_MODE=yes \ -e API\_URI=/ChangeMeToSomethingHardToGuess \ bunkerity/bunkerized-nginx-autoconf ``` Or do the same with docker-compose if you wish : ``` version: '3.8' services: nginx: image: bunkerity/bunkerized-nginx ports: - published: 80 target: 8080 mode: host protocol: tcp - published: 443 target: 8443 mode: host protocol: tcp environment: - SWARM\_MODE=yes - USE\_API=yes - API\_URI=/ChangeMeToSomethingHardToGuess # must match API\_URI from autoconf - MULTISITE=yes - SERVER\_NAME= - AUTO\_LETS\_ENCRYPT=yes networks: - bunkerized-net - services-net deploy: mode: global placement: constraints: - "node.role==worker" # mandatory label labels: - "bunkerized-nginx.AUTOCONF" autoconf: image: bunkerity/bunkerized-nginx-autoconf volumes: - /var/run/docker.sock:/var/run/docker.sock:ro - cache-vol:/cache - certs-vol:/etc/letsencrypt environment: - SWARM\_MODE=yes - API\_URI=/ChangeMeToSomethingHardToGuess # must match API\_URI from nginx networks: - bunkerized-net deploy: replicas: 1 placement: constraints: - "node.role==manager" # This will create the networks for you networks: bunkerized-net: driver: overlay attachable: true name: bunkerized-net services-net: driver: overlay attachable: true name: services-net # And the volumes too volumes: cache-vol: certs-vol: ``` Check the logs of both autoconf and bunkerized-nginx services to see if everything is working as expected. You can now create a new service and add environment variables as labels with the `bunkerized-nginx.` prefix to let the autoconf service “automagically” do the configuration for you : ``` $ docker service create \ --name myservice \ --constraint node.role==worker \ --network services-net \ -l bunkerized-nginx.SERVER_NAME=www.example.com \ -l bunkerized-nginx.USE_REVERSE_PROXY=yes \ -l bunkerized-nginx.REVERSE_PROXY_URL=/ \ -l bunkerized-nginx.REVERSE_PROXY_HOST=http://myservice \ tutum/hello-world ``` docker-compose equivalent : ``` version: "3" services: myservice: image: tutum/hello-world networks: - services-net deploy: placement: constraints: - "node.role==worker" labels: - "bunkerized-nginx.SERVER\_NAME=www.example.com" - "bunkerized-nginx.USE\_REVERSE\_PROXY=yes" - "bunkerized-nginx.REVERSE\_PROXY\_URL=/" - "bunkerized-nginx.REVERSE\_PROXY\_HOST=http://myservice" networks: services-net: external: name: services-net ``` Please note that if you want to override the `AUTO\_LETS\_ENCRYPT=yes` previously defined in the bunkerized-nginx service, you simply need to add the `bunkerized-nginx.AUTO\_LETS\_ENCRYPT=no` label. Look at the logs of both autoconf and bunkerized-nginx to check if the configuration has been generated and loaded by bunkerized-nginx. You should now be able to visit http(s)://www.example.com. When your service is not needed anymore, you can delete it as usual. The autoconf should get the event and generate the configuration again. ### Kubernetes[¶](#kubernetes "Permalink to this heading") **This integration is still in beta, please fill an issue if you find a bug or have an idea on how to improve it.** The bunkerized-nginx-autoconf acts as an Ingress Controller and connects to the k8s API to get cluster events and generate a new configuration when it’s needed. Once the configuration is generated, the Ingress Controller sends the configuration files and a reload order to the bunkerized-nginx instances running in the cluster. If you need to deliver static files (e.g., html, images, css, js, …) a shared folder accessible from all bunkerized-nginx instances is needed (you can use a storage system like NFS, GlusterFS, CephFS on the host or a [Kubernetes Volume that supports ReadOnlyMany access](https://kubernetes.io/docs/concepts/storage/persistent-volumes/#access-modes)).  The first step to do is to declare the RBAC authorization that will be used by the Ingress Controller to access the Kubernetes API. A ready-to-use declaration is available here : ``` apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRole metadata: name: bunkerized-nginx-ingress-controller rules: - apiGroups: [""] resources: ["services", "pods"] verbs: ["get", "watch", "list"] - apiGroups: ["extensions"] resources: ["ingresses"] verbs: ["get", "watch", "list"] --- apiVersion: v1 kind: ServiceAccount metadata: name: bunkerized-nginx-ingress-controller --- apiVersion: rbac.authorization.k8s.io/v1 kind: ClusterRoleBinding metadata: name: bunkerized-nginx-ingress-controller subjects: - kind: ServiceAccount name: bunkerized-nginx-ingress-controller namespace: default apiGroup: "" roleRef: kind: ClusterRole name: bunkerized-nginx-ingress-controller apiGroup: rbac.authorization.k8s.io ``` Next, you can deploy bunkerized-nginx as a DaemonSet : ``` apiVersion: apps/v1 kind: DaemonSet metadata: name: bunkerized-nginx labels: app: bunkerized-nginx spec: selector: matchLabels: name: bunkerized-nginx template: metadata: labels: name: bunkerized-nginx # this label is mandatory bunkerized-nginx: "yes" spec: containers: - name: bunkerized-nginx image: bunkerity/bunkerized-nginx ports: - containerPort: 8080 hostPort: 80 - containerPort: 8443 hostPort: 443 env: - name: KUBERNETES\_MODE value: "yes" - name: DNS\_RESOLVERS value: "coredns.kube-system.svc.cluster.local" - name: USE\_API value: "yes" - name: API\_URI value: "/ChangeMeToSomethingHardToGuess" - name: SERVER\_NAME value: "" - name: MULTISITE value: "yes" --- apiVersion: v1 kind: Service metadata: name: bunkerized-nginx-service # this label is mandatory labels: bunkerized-nginx: "yes" # this annotation is mandatory annotations: bunkerized-nginx.AUTOCONF: "yes" spec: clusterIP: None selector: name: bunkerized-nginx ``` You can now deploy the autoconf which will act as the ingress controller : ``` apiVersion: v1 kind: PersistentVolumeClaim metadata: name: pvc-nginx spec: accessModes: - ReadWriteOnce resources: requests: storage: 5Gi --- apiVersion: apps/v1 kind: Deployment metadata: name: bunkerized-nginx-ingress-controller labels: app: bunkerized-nginx-autoconf spec: replicas: 1 selector: matchLabels: app: bunkerized-nginx-autoconf template: metadata: labels: app: bunkerized-nginx-autoconf spec: serviceAccountName: bunkerized-nginx-ingress-controller volumes: - name: vol-nginx persistentVolumeClaim: claimName: pvc-nginx initContainers: - name: change-data-dir-permissions command: - chown - -R - 101:101 - /etc/letsencrypt - /cache image: busybox volumeMounts: - name: vol-nginx mountPath: /etc/letsencrypt subPath: letsencrypt - name: vol-nginx mountPath: /cache subPath: cache securityContext: runAsNonRoot: false runAsUser: 0 runAsGroup: 0 containers: - name: bunkerized-nginx-autoconf image: bunkerity/bunkerized-nginx-autoconf env: - name: KUBERNETES\_MODE value: "yes" - name: API\_URI value: "/ChangeMeToSomethingHardToGuess" volumeMounts: - name: vol-nginx mountPath: /etc/letsencrypt subPath: letsencrypt - name: vol-nginx mountPath: /cache subPath: cache ``` Check the logs of both bunkerized-nginx and autoconf deployments to see if everything is working as expected. You can now deploy your web service and make it accessible from within the cluster : ``` apiVersion: apps/v1 kind: Deployment metadata: name: myapp labels: app: myapp spec: replicas: 1 selector: matchLabels: app: myapp template: metadata: labels: app: myapp spec: containers: - name: myapp image: containous/whoami --- apiVersion: v1 kind: Service metadata: name: myapp spec: type: ClusterIP selector: app: myapp ports: - protocol: TCP port: 80 targetPort: 80 ``` Last but not least, it’s time to define your Ingress resource to make your web service publicly available : ``` apiVersion: networking.k8s.io/v1 kind: Ingress metadata: name: bunkerized-nginx-ingress # this label is mandatory labels: bunkerized-nginx: "yes" annotations: # add any global and default environment variables here as annotations with the "bunkerized-nginx." prefix # examples : #bunkerized-nginx.AUTO\_LETS\_ENCRYPT: "yes" #bunkerized-nginx.USE\_ANTIBOT: "javascript" #bunkerized-nginx.REDIRECT\_HTTP\_TO\_HTTPS: "yes" #bunkerized-nginx.www.example.com\_REVERSE\_PROXY\_WS: "yes" #bunkerized-nginx.www.example.com\_USE\_MODSECURITY: "no" spec: tls: - hosts: - www.example.com rules: - host: "www.example.com" http: paths: - pathType: Prefix path: "/" backend: service: name: myapp port: number: 80 ``` Check the logs to see if the configuration has been generated and bunkerized-nginx reloaded. You should be able to visit http(s)://www.example.com. Note that an alternative would be to add annotations directly to your services (a common use-case is for [PHP applications](https://bunkerized-nginx.readthedocs.io/en/latest/quickstart_guide.html#php-applications) because the Ingress resource is only for reverse proxy) without editing the Ingress resource : ``` apiVersion: v1 kind: Service metadata: name: myapp # this label is mandatory labels: bunkerized-nginx: "yes" annotations: bunkerized-nginx.SERVER\_NAME: "www.example.com" bunkerized-nginx.AUTO\_LETS\_ENCRYPT: "yes" bunkerized-nginx.USE\_REVERSE\_PROXY: "yes" bunkerized-nginx.REVERSE\_PROXY\_URL: "/" bunkerized-nginx.REVERSE\_PROXY\_HOST: "http://myapp.default.svc.cluster.local" spec: type: ClusterIP selector: app: myapp ports: - protocol: TCP port: 80 targetPort: 80 ``` ### Linux[¶](#linux "Permalink to this heading") **This integration is still in beta, please fill an issue if you find a bug or have an idea on how to improve it.** List of supported Linux distributions : * Debian buster (10) * Ubuntu focal (20.04) * CentOS 7 * Fedora 34 * Arch Linux Unlike containers, Linux integration can be tedious because bunkerized-nginx has a bunch of dependencies that need to be installed before we can use it. Fortunately, we provide a helper script to make the process easier and automatic. Once installed, the configuration is really simple, all you have to do is to edit the `/opt/bunkerized-nginx/variables.env` configuration file and run the `bunkerized-nginx` command to apply it. First of all you will need to install bunkerized-nginx. The recommended way is to use the official installer script : ``` $ curl -fsSL https://github.com/bunkerity/bunkerized-nginx/releases/download/v1.3.2/linux-install.sh -o /tmp/bunkerized-nginx.sh ``` Before executing it, you should also check the signature : ``` $ curl -fsSL https://github.com/bunkerity/bunkerized-nginx/releases/download/v1.3.2/linux-install.sh.asc -o /tmp/bunkerized-nginx.sh.asc $ gpg --auto-key-locate hkps://keys.openpgp.org --locate-keys contact@bunkerity.com $ gpg --verify /tmp/bunkerized-nginx.sh.asc /tmp/bunkerized-nginx.sh ``` You can now install bunkerized-nginx (and take a coffee because it may take a while) : ``` $ chmod +x /tmp/bunkerized-nginx.sh $ /tmp/bunkerized-nginx.sh ``` To demonstrate the configuration on Linux, we will create a simple “Hello World” static file that will be served by bunkerized-nginx. Static files are stored inside the `/opt/bunkerized-nginx/www` folder and the unprivileged nginx user must have read access on it : ``` $ echo "Hello bunkerized World !" > /opt/bunkerized-nginx/www/index.html $ chown root:nginx /opt/bunkerized-nginx/www/index.html $ chmod 740 /opt/bunkerized-nginx/www/index.html ``` Here is the example configuration file that needs to be written at `/opt/bunkerized-nginx/variables.env` : ``` HTTP_PORT=80 HTTPS_PORT=443 DNS_RESOLVERS=8.8.8.8 8.8.4.4 SERVER_NAME=www.example.com AUTO_LETS_ENCRYPT=yes ``` Important things to note : * Replace www.example.com with your own domain (it must points to your server IP address if you want Let’s Encrypt to work) * Automatic Let’s Encrypt is enabled thanks to `AUTO\_LETS\_ENCRYPT=yes` (since the default is `AUTO\_LETS\_ENCRYPT=no` you can remove the environment variable to disable Let’s Encrypt) * The default values for `HTTP\_PORT` and `HTTPS\_PORT` are `8080` and `8443` hence the explicit declaration with standard ports values * Replace the `DNS\_RESOLVERS` value with your own DNS resolver(s) if you need nginx to resolve internal DNS requests (e.g., reverse proxy to an internal service) You can now apply the configuration by running the **bunkerized-nginx** command : ``` $ bunkerized-nginx ``` Visit http(s)://www.example.com to confirm that everything is working as expected. Quickstart guide[¶](#quickstart-guide "Permalink to this heading") ------------------------------------------------------------------ ### Reverse proxy[¶](#reverse-proxy "Permalink to this heading") The following environment variables can be used to deploy bunkerized-nginx as a reverse proxy in front of your web services : * `USE\_REVERSE\_PROXY` : activate/deactivate the reverse proxy mode * `REVERSE\_PROXY\_URL` : public path prefix * `REVERSE\_PROXY\_HOST` : full address of the proxied service Here is a basic example : ``` SERVER_NAME=www.example.com USE_REVERSE_PROXY=yes REVERSE_PROXY_URL=/ REVERSE_PROXY_HOST=http://my-service.example.local:8080 ``` If you have multiple web services you can configure multiple reverse proxy rules by appending a number to the environment variables names : ``` SERVER_NAME=www.example.com USE_REVERSE_PROXY=yes REVERSE_PROXY_URL_1=/app1 REVERSE_PROXY_HOST_1=http://app1.example.local:8080 REVERSE_PROXY_URL_2=/app2 REVERSE_PROXY_HOST_2=http://app2.example.local:8080 ``` #### Docker[¶](#docker "Permalink to this heading") When using Docker, the recommended way is to create a network so bunkerized-nginx can communicate with the web service using the container name : ``` $ docker network create services-net $ docker run -d \ --name myservice \ --network services-net \ tutum/hello-world $ docker run -d \ --network services-net \ -p 80:8080 \ -p 443:8443 \ -v "${PWD}/certs:/etc/letsencrypt" \ -e SERVER\_NAME=www.example.com \ -e AUTO\_LETS\_ENCRYPT=yes \ -e USE\_REVERSE\_PROXY=yes \ -e REVERSE\_PROXY\_URL=/ \ -e REVERSE\_PROXY\_HOST=http://myservice \ bunkerity/bunkerized-nginx ``` docker-compose equivalent : ``` version: '3' services: mybunkerized: image: bunkerity/bunkerized-nginx ports: - 80:8080 - 443:8443 volumes: - ./certs:/etc/letsencrypt environment: - SERVER\_NAME=www.example.com - AUTO\_LETS\_ENCRYPT=yes - USE\_REVERSE\_PROXY=yes - REVERSE\_PROXY\_URL=/ - REVERSE\_PROXY\_HOST=http://myservice networks: - services-net depends\_on: - myservice myservice: image: tutum/hello-world networks: - services-net networks: services-net: ``` #### Docker autoconf[¶](#docker-autoconf "Permalink to this heading") When the Docker autoconf stack is running, you simply need to start the container hosting your web service and add the environment variables as labels : ``` $ docker run -d \ --name myservice \ --network services-net \ -l bunkerized-nginx.SERVER_NAME=www.example.com \ -l bunkerized-nginx.USE_REVERSE_PROXY=yes \ -l bunkerized-nginx.REVERSE_PROXY_URL=/ \ -l bunkerized-nginx.REVERSE_PROXY_HOST=http://myservice \ tutum/hello-world ``` docker-compose equivalent : ``` version: '3' services: myservice: image: tutum/hello-world networks: services-net: aliases: - myservice labels: - bunkerized-nginx.SERVER\_NAME=www.example.com - bunkerized-nginx.USE\_REVERSE\_PROXY=yes - bunkerized-nginx.REVERSE\_PROXY\_URL=/ - bunkerized-nginx.REVERSE\_PROXY\_HOST=http://myservice networks: services-net: external: name: services-net ``` #### Docker Swarm[¶](#docker-swarm "Permalink to this heading") When the Docker Swarm stack is running, you simply need to start the Swarm service hosting your web service and add the environment variables as labels : ``` $ docker service create \ --name myservice \ --network services-net \ --constraint node.role==worker \ -l bunkerized-nginx.SERVER_NAME=www.example.com \ -l bunkerized-nginx.USE_REVERSE_PROXY=yes \ -l bunkerized-nginx.REVERSE_PROXY_URL=/ \ -l bunkerized-nginx.REVERSE_PROXY_HOST=http://myservice \ tutum/hello-world ``` docker-compose equivalent : ``` version: '3' services: myservice: image: tutum/hello-world networks: services-net: aliases: - myservice deploy: placement: constraints: - "node.role==worker" labels: - bunkerized-nginx.SERVER\_NAME=www.example.com - bunkerized-nginx.USE\_REVERSE\_PROXY=yes - bunkerized-nginx.REVERSE\_PROXY\_URL=/ - bunkerized-nginx.REVERSE\_PROXY\_HOST=http://myservice networks: services-net: external: name: services-net ``` #### Kubernetes[¶](#kubernetes "Permalink to this heading") Example deployment and service declaration : ``` apiVersion: apps/v1 kind: Deployment metadata: name: myservice labels: app: myservice spec: replicas: 1 selector: matchLabels: app: myservice template: metadata: labels: app: myservice spec: containers: - name: myservice image: tutum/hello-world --- apiVersion: v1 kind: Service metadata: name: myservice spec: type: ClusterIP selector: app: myservice ports: - protocol: TCP port: 80 targetPort: 80 ``` The most straightforward way to add a reverse proxy in the Kubernetes cluster is to declare it in the Ingress resource : ``` apiVersion: networking.k8s.io/v1 kind: Ingress metadata: name: bunkerized-nginx-ingress # this label is mandatory labels: bunkerized-nginx: "yes" spec: tls: - hosts: - www.example.com rules: - host: "www.example.com" http: paths: - pathType: Prefix path: "/" backend: service: name: myservice port: number: 80 ``` An alternative “hackish” way is to use environment variables as annotations prefixed with “bunkerized-nginx.” inside the Service resource of your web service : ``` apiVersion: v1 kind: Service metadata: name: myservice # this label is mandatory labels: bunkerized-nginx: "yes" annotations: bunkerized-nginx.SERVER\_NAME: "www.example.com" bunkerized-nginx.AUTO\_LETS\_ENCRYPT: "yes" bunkerized-nginx.USE\_REVERSE\_PROXY: "yes" bunkerized-nginx.REVERSE\_PROXY\_URL: "/" bunkerized-nginx.REVERSE\_PROXY\_HOST: "http://myservice.default.svc.cluster.local" spec: type: ClusterIP selector: app: myservice ports: - protocol: TCP port: 80 targetPort: 80 ``` #### Linux[¶](#linux "Permalink to this heading") Example of a basic configuration file : ``` HTTP_PORT=80 HTTPS_PORT=443 DNS_RESOLVERS=8.8.8.8 8.8.4.4 SERVER_NAME=www.example.com AUTO_LETS_ENCRYPT=yes USE_REVERSE_PROXY=yes REVERSE_PROXY_URL=/ # Local proxied application REVERSE_PROXY_HOST=http://127.0.0.1:8080 # Remote proxied application #REVERSE_PROXY_HOST=http://service.example.local:8080 ``` ### PHP applications[¶](#php-applications "Permalink to this heading") The following environment variables can be used to configure bunkerized-nginx in front of PHP-FPM web applications : * `REMOTE\_PHP` : host/ip of a remote PHP-FPM instance * `REMOTE\_PHP\_PATH` : absolute path containing the PHP files (from the remote instance perspective) * `LOCAL\_PHP` : absolute path of the local unix socket used by a local PHP-FPM instance * `LOCAL\_PHP\_PATH` : absolute path containing the PHP files (when using local instance) Here is a basic example with a remote instance : ``` SERVER_NAME=www.example.com REMOTE_PHP=my-php.example.local REMOTE_PHP_PATH=/var/www/html ``` And another example with a local instance : ``` SERVER_NAME=www.example.com LOCAL_PHP=/var/run/php7-fpm.sock LOCAL_PHP_PATH=/opt/bunkerized-nginx/www ``` #### Docker[¶](#id1 "Permalink to this heading") When using Docker, the recommended way is to create a network so bunkerized-nginx can communicate with the PHP-FPM instance using the container name : ``` $ docker network create services-net $ docker run -d \ --name myservice \ --network services-net \ -v "${PWD}/www:/app" \ php:fpm $ docker run -d \ --network services-net \ -p 80:8080 \ -p 443:8443 \ -v "${PWD}/www:/www:ro" \ -v "${PWD}/certs:/etc/letsencrypt" \ -e SERVER\_NAME=www.example.com \ -e AUTO\_LETS\_ENCRYPT=yes \ -e REMOTE\_PHP=myservice \ -e REMOTE\_PHP\_PATH=/app \ bunkerity/bunkerized-nginx ``` docker-compose equivalent : ``` version: '3' services: mybunkerized: image: bunkerity/bunkerized-nginx ports: - 80:8080 - 443:8443 volumes: - ./www:/www:ro - ./certs:/etc/letsencrypt environment: - SERVER\_NAME=www.example.com - AUTO\_LETS\_ENCRYPT=yes - REMOTE\_PHP=myservice - REMOTE\_PHP\_PATH=/app networks: - services-net depends\_on: - myservice myservice: image: php:fpm networks: - services-net volumes: - ./www:/app networks: services-net: ``` #### Docker autoconf[¶](#id2 "Permalink to this heading") When the Docker autoconf stack is running, you simply need to start the container hosting your PHP-FPM instance and add the environment variables as labels : ``` $ docker run -d \ --name myservice \ --network services-net \ -v "${PWD}/www/www.example.com:/app" \ -l bunkerized-nginx.SERVER_NAME=www.example.com \ -l bunkerized-nginx.REMOTE_PHP=myservice \ -l bunkerized-nginx.REMOTE_PHP_PATH=/app \ php:fpm ``` ``` version: '3' services: myservice: image: php:fpm volumes: - ./www/www.example.com:/app networks: services-net: aliases: - myservice labels: - bunkerized-nginx.SERVER\_NAME=www.example.com - bunkerized-nginx.REMOTE\_PHP=myservice - bunkerized-nginx.REMOTE\_PHP\_PATH=/app networks: services-net: external: name: services-net ``` #### Docker Swarm[¶](#id3 "Permalink to this heading") When the Docker Swarm stack is running, you simply need to start the Swarm service hosting your PHP-FPM instance and add the environment variables as labels : ``` $ docker service create \ --name myservice \ --constraint node.role==worker \ --network services-net \ --mount type=bind,source=/shared/www/www.example.com,destination=/app \ -l bunkerized-nginx.SERVER_NAME=www.example.com \ -l bunkerized-nginx.REMOTE_PHP=myservice \ -l bunkerized-nginx.REMOTE_PHP_PATH=/app \ php:fpm ``` docker-compose equivalent : ``` version: "3" services: myservice: image: php:fpm networks: services-net: aliases: - myservice volumes: - /shared/www/www.example.com:/app deploy: placement: constraints: - "node.role==worker" labels: - "bunkerized-nginx.SERVER\_NAME=www.example.com" - "bunkerized-nginx.REMOTE\_PHP=myservice" - "bunkerized-nginx.REMOTE\_PHP\_PATH=/app" networks: services-net: external: name: services-net ``` #### Kubernetes[¶](#id4 "Permalink to this heading") You need to use environment variables as annotations prefixed with `bunkerized-nginx.` inside the Service resource of your PHP-FPM instance : ``` apiVersion: apps/v1 kind: Deployment metadata: name: myservice labels: app: myservice spec: replicas: 1 selector: matchLabels: app: myservice template: metadata: labels: app: myservice spec: containers: - name: myservice image: php:fpm volumeMounts: - name: php-files mountPath: /app volumes: - name: php-files hostPath: path: /shared/www/www.example.com type: Directory --- apiVersion: v1 kind: Service metadata: name: myservice # this label is mandatory labels: bunkerized-nginx: "yes" annotations: bunkerized-nginx.SERVER\_NAME: "www.example.com" bunkerized-nginx.AUTO\_LETS\_ENCRYPT: "yes" bunkerized-nginx.REMOTE\_PHP: "myservice.default.svc.cluster.local" bunkerized-nginx.REMOTE\_PHP\_PATH: "/app" spec: type: ClusterIP selector: app: myservice ports: - protocol: TCP port: 9000 targetPort: 9000 ``` #### Linux[¶](#id5 "Permalink to this heading") Example of a basic configuration file : ``` HTTP_PORT=80 HTTPS_PORT=443 DNS_RESOLVERS=8.8.8.8 8.8.4.4 SERVER_NAME=www.example.com AUTO_LETS_ENCRYPT=yes # Case 1 : the PHP-FPM instance is on the same machine # you just need to adjust the socket path LOCAL_PHP=/run/php/php7.3-fpm.sock LOCAL_PHP_PATH=/opt/bunkerized-nginx/www # Case 2 : the PHP-FPM instance is on another machine #REMOTE_PHP=myapp.example.local #REMOTE_PHP_PATH=/app ``` Don’t forget that bunkerized-nginx runs as an unprivileged user/group both named `nginx`. When using a local PHP-FPM instance, you will need to take care of the rights and permissions of the socket and web files. For example, if your PHP-FPM is running as the `www-data` user, you can create a new group called `web-users` and add `nginx` and `www-data` into it : ``` $ groupadd web-users $ usermod -a -G web-users nginx $ usermod -a -G web-users www-data ``` Once it’s done, you will need to tweak your PHP-FPM configuration file (e.g., `/etc/php/7.3/fpm/pool.d/www.conf`) to edit the default group of the processes and the permissions of the socket file : ``` [www] ... user = www-data group = web-users ... listen = /run/php/php7.3-fpm.sock listen.owner = www-data listen.group = web-users listen.mode = 0660 ... ``` Last but not least, you will need to edit the permissions of `/opt/bunkerized-nginx/www` to make sure that nginx can read and PHP-FPM can write (in case your PHP app needs it) : ``` $ chown root:web-users /opt/bunkerized-nginx/www $ chmod 750 /opt/bunkerized-nginx/www $ find /opt/bunkerized-nginx/www/* -exec chown www-data:nginx {} \; $ find /opt/bunkerized-nginx/www/* -type f -exec chmod 740 {} \; $ find /opt/bunkerized-nginx/www/* -type d -exec chmod 750 {} \; ``` ### Multisite[¶](#multisite "Permalink to this heading") If you have multiple services to protect, the easiest way to do it is by enabling the “multisite” mode. When using multisite, bunkerized-nginx will create one server block per server defined in the `SERVER\_NAME` environment variable. You can configure each servers independently by adding the server name as a prefix. Here is an example : ``` SERVER_NAME=app1.example.com app2.example.com MULTISITE=yes app1.example.com_USE_REVERSE_PROXY=yes app1.example.com_REVERSE_PROXY_URL=/ app1.example.com_REVERSE_PROXY_HOST=http://app1.example.local:8080 app2.example.com_REMOTE_PHP=app2.example.local app2.example.com_REMOTE_PHP_PATH=/var/www/html ``` When using the multisite mode, some [special folders](https://bunkerized-nginx.readthedocs.io/en/latest/special_folders.html) must have a specific structure with subfolders named the same as the servers defined in the `SERVER\_NAME` environment variable. Let’s take the **app2.example.com** as an example : if some static files need to be served by nginx, you need to place them under **www/app2.example.com**. #### Docker[¶](#id6 "Permalink to this heading") When using Docker, the recommended way is to create a network so bunkerized-nginx can communicate with the web services using the container name : ``` $ docker network create services-net $ docker run -d \ --name myapp1 \ --network services-net \ tutum/hello-world $ docker run -d \ --name myapp2 \ --network services-net \ -v "${PWD}/www/app2.example.com:/app" \ php:fpm $ docker run -d \ --network services-net \ -p 80:8080 \ -p 443:8443 \ -v "${PWD}/www:/www:ro" \ -v "${PWD}/certs:/etc/letsencrypt" \ -e "SERVER\_NAME=app1.example.com app2.example.com" \ -e MULTISITE=yes \ -e AUTO\_LETS\_ENCRYPT=yes \ -e app1.example.com_USE_REVERSE_PROXY=yes \ -e app1.example.com_REVERSE_PROXY_URL=/ \ -e app1.example.com_REVERSE_PROXY_HOST=http://myapp1 \ -e app2.example.com_REMOTE_PHP=myapp2 \ -e app2.example.com_REMOTE_PHP_PATH=/app \ bunkerity/bunkerized-nginx ``` docker-compose equivalent : ``` version: '3' services: mybunkerized: image: bunkerity/bunkerized-nginx ports: - 80:8080 - 443:8443 volumes: - ./www:/www:ro - ./certs:/etc/letsencrypt environment: - SERVER\_NAME=app1.example.com app2.example.com - MULTISITE=yes - AUTO\_LETS\_ENCRYPT=yes - app1.example.com\_USE\_REVERSE\_PROXY=yes - app1.example.com\_REVERSE\_PROXY\_URL=/ - app1.example.com\_REVERSE\_PROXY\_HOST=http://myapp1 - app2.example.com\_REMOTE\_PHP=myapp2 - app2.example.com\_REMOTE\_PHP\_PATH=/app networks: - services-net depends\_on: - myapp1 - myapp2 myapp1: image: tutum/hello-world networks: - services-net myapp2: image: php:fpm volumes: - ./www/app2.example.com:/app networks: - services-net networks: services-net: ``` #### Docker autoconf[¶](#id7 "Permalink to this heading") **The multisite feature must be activated when using the Docker autoconf integration.** When the Docker autoconf stack is running, you simply need to start the containers hosting your web services and add the environment variables as labels : ``` $ docker run -d \ --name myapp1 \ --network services-net \ -l bunkerized-nginx.SERVER_NAME=app1.example.com \ -l bunkerized-nginx.USE_REVERSE_PROXY=yes \ -l bunkerized-nginx.REVERSE_PROXY_URL=/ \ -l bunkerized-nginx.REVERSE_PROXY_HOST=http://myapp1 \ tutum/hello-world $ docker run -d \ --name myapp2 \ --network services-net \ -v "${PWD}/www/app2.example.com:/app" \ -l bunkerized-nginx.SERVER_NAME=app2.example.com \ -l bunkerized-nginx.REMOTE_PHP=myapp2 \ -l bunkerized-nginx.REMOTE_PHP_PATH=/app \ php:fpm ``` docker-compose equivalent : ``` version: '3' services: myapp1: image: tutum/hello-world networks: services-net: aliases: - myapp1 labels: - bunkerized-nginx.SERVER\_NAME=app1.example.com - bunkerized-nginx.USE\_REVERSE\_PROXY=yes - bunkerized-nginx.REVERSE\_PROXY\_URL=/ - bunkerized-nginx.REVERSE\_PROXY\_HOST=http://myapp1 myapp2: image: php:fpm networks: services-net: aliases: - myapp2 volumes: - ./www/app2.example.com:/app labels: - bunkerized-nginx.SERVER\_NAME=app2.example.com - bunkerized-nginx.REMOTE\_PHP=myapp2 - bunkerized-nginx.REMOTE\_PHP\_PATH=/app networks: services-net: external: name: services-net ``` #### Docker Swarm[¶](#id8 "Permalink to this heading") **The multisite feature must be activated when using the Docker Swarm integration.** When the Docker Swarm stack is running, you simply need to start the Swarm service hosting your web services and add the environment variables as labels : ``` $ docker service create \ --name myapp1 \ --network services-net \ --constraint node.role==worker \ -l bunkerized-nginx.SERVER_NAME=app1.example.com \ -l bunkerized-nginx.USE_REVERSE_PROXY=yes \ -l bunkerized-nginx.REVERSE_PROXY_URL=/ \ -l bunkerized-nginx.REVERSE_PROXY_HOST=http://myapp1 \ tutum/hello-world $ docker service create \ --name myapp2 \ --constraint node.role==worker \ --network services-net \ --mount type=bind,source=/shared/www/app2.example.com,destination=/app \ -l bunkerized-nginx.SERVER_NAME=app2.example.com \ -l bunkerized-nginx.REMOTE_PHP=myapp2 \ -l bunkerized-nginx.REMOTE_PHP_PATH=/app \ php:fpm ``` docker-compose equivalent : ``` version: "3" services: myapp1: image: tutum/hello-world networks: services-net: aliases: - myapp1 deploy: placement: constraints: - "node.role==worker" labels: - bunkerized-nginx.SERVER\_NAME=app1.example.com - bunkerized-nginx.USE\_REVERSE\_PROXY=yes - bunkerized-nginx.REVERSE\_PROXY\_URL=/ - bunkerized-nginx.REVERSE\_PROXY\_HOST=http://myapp1 myapp2: image: php:fpm networks: services-net: aliases: - myapp2 volumes: - /shared/www/app2.example.com:/app deploy: placement: constraints: - "node.role==worker" labels: - "bunkerized-nginx.SERVER\_NAME=app2.example.com" - "bunkerized-nginx.REMOTE\_PHP=myapp2" - "bunkerized-nginx.REMOTE\_PHP\_PATH=/app" networks: services-net: external: name: services-net ``` #### Kubernetes[¶](#id9 "Permalink to this heading") **The multisite feature must be activated when using the Kubernetes integration.** ``` apiVersion: apps/v1 kind: Deployment metadata: name: myapp1 labels: app: myapp1 spec: replicas: 1 selector: matchLabels: app: myapp1 template: metadata: labels: app: myapp1 spec: containers: - name: myapp1 image: tutum/hello-world --- apiVersion: v1 kind: Service metadata: name: myapp1 # this label is mandatory labels: bunkerized-nginx: "yes" annotations: bunkerized-nginx.SERVER\_NAME: "app1.example.com" bunkerized-nginx.AUTO\_LETS\_ENCRYPT: "yes" bunkerized-nginx.USE\_REVERSE\_PROXY: "yes" bunkerized-nginx.REVERSE\_PROXY\_URL: "/" bunkerized-nginx.REVERSE\_PROXY\_HOST: "http://myapp1.default.svc.cluster.local" spec: type: ClusterIP selector: app: myapp1 ports: - protocol: TCP port: 80 targetPort: 80 --- apiVersion: apps/v1 kind: Deployment metadata: name: myapp2 labels: app: myapp2 spec: replicas: 1 selector: matchLabels: app: myapp2 template: metadata: labels: app: myapp2 spec: containers: - name: myapp2 image: php:fpm volumeMounts: - name: php-files mountPath: /app volumes: - name: php-files hostPath: path: /shared/www/app2.example.com type: Directory --- apiVersion: v1 kind: Service metadata: name: myapp2 # this label is mandatory labels: bunkerized-nginx: "yes" annotations: bunkerized-nginx.SERVER\_NAME: "app2.example.com" bunkerized-nginx.AUTO\_LETS\_ENCRYPT: "yes" bunkerized-nginx.REMOTE\_PHP: "myapp2.default.svc.cluster.local" bunkerized-nginx.REMOTE\_PHP\_PATH: "/app" spec: type: ClusterIP selector: app: myapp2 ports: - protocol: TCP port: 9000 targetPort: 9000 ``` #### Linux[¶](#id10 "Permalink to this heading") Example of a basic configuration file : ``` HTTP_PORT=80 HTTPS_PORT=443 DNS_RESOLVERS=8.8.8.8 8.8.4.4 SERVER_NAME=app1.example.com app2.example.com MULTISITE=yes AUTO_LETS_ENCRYPT=yes app1.example.com_USE_REVERSE_PROXY=yes app1.example.com_REVERSE_PROXY_URL=/ # Local proxied application app1.example.com_REVERSE_PROXY_HOST=http://127.0.0.1:8080 # Remote proxied application #app1.example.com_REVERSE_PROXY_HOST=http://service.example.local:8080 # If the PHP-FPM instance is on the same machine # you just need to adjust the socket path app2.example.com_LOCAL_PHP=/run/php/php7.3-fpm.sock app2.example.com_LOCAL_PHP_PATH=/opt/bunkerized-nginx/www/app2.example.com # Else if the PHP-FPM instance is on another machine #app2.example.com_REMOTE_PHP=myapp.example.local #app2.example.com_REMOTE_PHP_PATH=/app ``` Don’t forget that bunkerized-nginx runs as an unprivileged user/group both named `nginx`. When using a local PHP-FPM instance, you will need to take care of the rights and permissions of the socket and web files. See the Linux section of PHP application for more information. Special folders[¶](#special-folders "Permalink to this heading") ---------------------------------------------------------------- Please note that bunkerized-nginx runs as an unprivileged user (UID/GID 101 when using the Docker image) and you should set the rights on the host accordingly to the files and folders on your host. ### Multisite[¶](#multisite "Permalink to this heading") When the special folder “supports” the multisite mode, you can create subfolders named as the server names used in the configuration. When doing it only the subfolder files will be “used” by the corresponding web service. ### Web files[¶](#web-files "Permalink to this heading") This special folder is used by bunkerized-nginx to deliver static files. The typical use case is when you have a PHP application that also contains static assets like CSS, JS and images. Location (container) : `/www` Location (Linux) : `/opt/bunkerized-nginx/www` Multisite : `yes` Read-only : `yes` Examples : * [Basic website with PHP](https://github.com/bunkerity/bunkerized-nginx/tree/master/examples/basic-website-with-php) * [Multisite basic](https://github.com/bunkerity/bunkerized-nginx/blob/master/examples/multisite-basic) ### http configurations[¶](#http-configurations "Permalink to this heading") This special folder contains .conf files that will be loaded by nginx at http context. The typical use case is when you need to add custom directives into the `http { }` block of nginx. Location (container) : `/http-confs` Location (Linux) : `/opt/bunkerized-nginx/http-confs` Multisite : `no` Read-only : `yes` Examples : * [Load balancer](https://github.com/bunkerity/bunkerized-nginx/tree/master/examples/load-balancer) ### server configurations[¶](#server-configurations "Permalink to this heading") This special folder contains .conf files that will be loaded by nginx at server context. The typical use case is when you need to add custom directives into the `server { }` block of nginx. Location (container) : `/server-confs` Location (Linux) : `/opt/bunkerized-nginx/server-confs` Multisite : `yes` Read-only : `yes` Examples : * [Wordpress](https://github.com/bunkerity/bunkerized-nginx/blob/master/examples/wordpress) * [Multisite custom confs](https://github.com/bunkerity/bunkerized-nginx/tree/master/examples/multisite-custom-confs) ### ModSecurity configurations[¶](#modsecurity-configurations "Permalink to this heading") This special folder contains .conf files that will be loaded by ModSecurity after the OWASP Core Rule Set is loaded. The typical use case is to edit loaded CRS rules to avoid false positives. Location (container) : `/modsec-confs` Location (Linux) : `/opt/bunkerized-nginx/modsec-confs` Multisite : `yes` Read-only : `yes` Examples : * [Wordpress](https://github.com/bunkerity/bunkerized-nginx/blob/master/examples/wordpress) * [Multisite custom confs](https://github.com/bunkerity/bunkerized-nginx/tree/master/examples/multisite-custom-confs) ### CRS configurations[¶](#crs-configurations "Permalink to this heading") This special folder contains .conf file that will be loaded by ModSecurity before the OWASP Core Rule Set is loaded. The typical use case is when you want to specify exclusions for the CRS. Location (container) : `/modsec-crs-confs` Location (Linux) : `/opt/bunkerized-nginx/modsec-crs-confs` Multisite : `yes` Read-only : `yes` Examples : * [Wordpress](https://github.com/bunkerity/bunkerized-nginx/blob/master/examples/wordpress) * [Multisite custom confs](https://github.com/bunkerity/bunkerized-nginx/tree/master/examples/multisite-custom-confs) ### Cache[¶](#cache "Permalink to this heading") This special folder is used to cache some data like blacklists and avoid downloading them again if it is not necessary. The typical use case is to avoid the overhead when you are testing bunkerized-nginx in a container and you have to recreate it multiple times. Location (container) : `/cache` Location (Linux) : `/opt/bunkerized-nginx/cache` Multisite : `no` Read-only : `no` ### Plugins[¶](#plugins "Permalink to this heading") This special folder is the placeholder for the plugins loaded by bunkerized-nginx. See the [plugins section](https://bunkerized-nginx.readthedocs.io/en/latest/plugins.html) for more information. Location (container) : `/plugins` Location (Linux) : `/opt/bunkerized-nginx/plugins` Multisite : `no` Read-only : `no` ### ACME challenge[¶](#acme-challenge "Permalink to this heading") This special folder is used as the web root for Let’s Encrypt challenges. The typical use case is to share the same folder when you are using bunkerized-nginx in a clustered environment like Docker Swarm or Kubernetes. Location (container) : `/acme-challenge` Location (Linux) : `/opt/bunkerized-nginx/acme-challenge` Multisite : `no` Read-only : `no` Security tuning[¶](#security-tuning "Permalink to this heading") ---------------------------------------------------------------- bunkerized-nginx comes with a set of predefined security settings that you can (and you should) tune to meet your own use case. ### Miscellaneous[¶](#miscellaneous "Permalink to this heading") Here is a list of miscellaneous environment variables related more or less to security : * `MAX\_CLIENT\_SIZE=10m` : maximum size of client body * `ALLOWED\_METHODS=GET|POST|HEAD` : list of HTTP methods that clients are allowed to use * `DISABLE\_DEFAULT\_SERVER=no` : enable/disable the default server (i.e. : should your server respond to unknown Host header ?) * `SERVER\_TOKENS=off` : enable/disable sending the version number of nginx ### HTTPS[¶](#https "Permalink to this heading") #### Settings[¶](#settings "Permalink to this heading") Here is a list of environment variables and the corresponding default value related to HTTPS : * `LISTEN\_HTTP=yes` : you can set it to `no` if you want to disable HTTP access * `REDIRECT\_HTTP\_TO\_HTTPS=no` : enable/disable HTTP to HTTPS redirection * `HTTPS\_PROTOCOLS=TLSv1.2 TLSv1.3` : list of TLS versions to use * `HTTP2=yes` : enable/disable HTTP2 when HTTPS is enabled * `COOKIE\_AUTO\_SECURE\_FLAG=yes` : enable/disable adding Secure flag when HTTPS is enabled * `STRICT\_TRANSPORT\_SECURITY=max-age=31536000` : force users to visit the website in HTTPS (more info [here](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Security-Policy)) #### Let’s Encrypt[¶](#let-s-encrypt "Permalink to this heading") Using Let’s Encrypt with the `AUTO\_LETS\_ENCRYPT=yes` environment variable is the easiest way to add HTTPS supports to your web services if they are connected to internet and you have public DNS A record(s). You can also set the `EMAIL\_LETS\_ENCRYPT` environment variable if you want to receive notifications from Let’s Encrypt like expiration alerts. #### Custom certificate(s)[¶](#custom-certificate-s "Permalink to this heading") If you have security constraints (e.g., local network, custom PKI, …) you can use custom certificates of your choice and tell bunkerized-nginx to use them with the following environment variables : * `USE\_CUSTOM\_HTTPS=yes` * `CUSTOM\_HTTPS\_CERT=/path/inside/container/to/cert.pem` * `CUSTOM\_HTTPS\_KEY=/path/inside/container/to/key.pem` Here is a an example on how to use custom certificates : ``` $ ls /etc/ssl/my-web-app cert.pem key.pem $ docker run -p 80:8080 \ -p 443:8443 \ -v /etc/ssl/my-web-app:/certs:ro \ -e USE\_CUSTOM\_HTTPS=yes \ -e CUSTOM\_HTTPS\_CERT=/certs/cert.pem \ -e CUSTOM\_HTTPS\_KEY=/certs/key.pem \ ... bunkerity/bunkerized-nginx ``` Please note that if you have one or more intermediate certificate(s) in your chain of trust, you will need to provide the bundle to `CUSTOM\_HTTPS\_CERT` (more info [here](https://nginx.org/en/docs/http/configuring_https_servers.html#chains)). You can reload the certificate(s) (i.e., in case of a renewal) by sending a reload order to bunkerized-nginx. Docker reload : ``` docker kill --signal=SIGHUP my-container ``` Swarm and Kubernetes reload (repeat for each node) : ``` $ curl http://node-local-ip:80/api-uri/reload ``` Linux reload : ``` $ /usr/sbin/nginx -s reload ``` #### Self-signed certificate[¶](#self-signed-certificate "Permalink to this heading") This method is not recommended in production but can be used to quickly deploy HTTPS for testing purposes. Just use the `GENERATE\_SELF\_SIGNED\_SSL=yes` environment variable and bunkerized-nginx will generate a self-signed certificate for you : ``` $ docker run -p 80:8080 \ -p 443:8443 \ -e GENERATE\_SELF\_SIGNED\_SSL=yes \ ... bunkerity/bunkerized-nginx ``` ### Headers[¶](#headers "Permalink to this heading") Some important HTTP headers related to client security are sent with a default value. Sometimes it can break a web application or can be tuned to provide even more security. The complete list is available [here](https://bunkerized-nginx.readthedocs.io/en/latest/environment_variables.html#security-headers). You can also remove headers (e.g., too verbose ones) by using the `REMOVE\_HEADERS` environment variable which takes a list of header name separated with space (default value = `Server X-Powered-By X-AspNet-Version X-AspNetMvc-Version`). If you want to keep your application headers and tell bunkerized-nginx to not override it, just set the corresponding environment variable to an empty value (e.g., `CONTENT\_SECURITY\_POLICY=`, `PERMISSIONS\_POLICY=`, …). ### ModSecurity[¶](#modsecurity "Permalink to this heading") ModSecurity is integrated and enabled by default alongside the OWASP Core Rule Set within bunkerized-nginx. To change this behaviour you can use the `USE\_MODSECURITY=no` or `USE\_MODSECURITY\_CRS=no` environment variables. We strongly recommend to keep both ModSecurity and the OWASP Core Rule Set enabled. The only downsides are the false positives that may occur. But they can be fixed easily and the CRS team maintains a list of exclusions for common application (e.g., wordpress, nextcloud, drupal, cpanel, …). Tuning the CRS with bunkerized-nginx is pretty simple : you can add configuration before and after the rules are loaded. You just need to mount your .conf files into the `/modsec-crs-confs` (before CRS is loaded) and `/modsec-confs` (after CRS is loaded) volumes. If you are using Linux integration the [special folders](https://bunkerized-nginx.readthedocs.io/en/latest/special_folders.html) are `/opt/bunkerized-nginx/modsec-confs` and `/opt/bunkerized-nginx/modsec-crs-confs`. Here is a Docker example to illustrate it : ``` $ cat /data/exclusions-crs/wordpress.conf SecAction \ "id:900130,\ phase:1,\ nolog,\ pass,\ t:none,\ setvar:tx.crs\_exclusions\_wordpress=1" $ cat /data/tuning-crs/remove-false-positives.conf SecRule REQUEST_FILENAME "/wp-admin/admin-ajax.php" "id:1,ctl:ruleRemoveByTag=attack-xss,ctl:ruleRemoveByTag=attack-rce" SecRule REQUEST_FILENAME "/wp-admin/options.php" "id:2,ctl:ruleRemoveByTag=attack-xss" SecRule REQUEST_FILENAME "^/wp-json/yoast" "id:3,ctl:ruleRemoveById=930120" $ docker run -p 80:8080 \ -p 443:8443 \ -v /data/exclusions-crs:/modsec-crs-confs:ro \ -v /data/tuning-crs:/modsec-confs:ro \ ... bunkerity/bunkerized-nginx ``` ### Bad behaviors detection[¶](#bad-behaviors-detection "Permalink to this heading") When attackers search for and/or exploit vulnerabilities they might generate some suspicious HTTP status codes that a “regular” user won’t generate within a period of time. If we detect that kind of behavior we can ban the offending IP address and force the attacker to come with a new one. That kind of security measure is implemented and enabled by default in bunkerized-nginx. Here is the list of the related environment variables and their default value : * `USE\_BAD\_BEHAVIOR=yes` : enable/disable “bad behavior” detection and automatic ban of IP * `BAD\_BEHAVIOR\_STATUS\_CODES=400 401 403 404 405 429 444` : the list of HTTP status codes considered as “suspicious” * `BAD\_BEHAVIOR\_THRESHOLD=10` : the number of “suspicious” HTTP status codes required before we ban the corresponding IP address * `BAD\_BEHAVIOR\_BAN\_TIME=86400` : the duration time (in seconds) of the ban * `BAD\_BEHAVIOR\_COUNT\_TIME=60` : the duration time (in seconds) to wait before resetting the counter of “suspicious” HTTP status codes for a given IP ### Antibot challenge[¶](#antibot-challenge "Permalink to this heading") Attackers will certainly use automated tools to exploit/find some vulnerabilities on your web services. One countermeasure is to challenge the users to detect if they look like a bot. It might be effective against script kiddies or “lazy” attackers. You can use the `USE\_ANTIBOT` environment variable to add that kind of checks whenever a new client is connecting. The available challenges are : `cookie`, `javascript`, `captcha` and `recaptcha`. More info [here](https://bunkerized-nginx.readthedocs.io/en/latest/environment_variables.html#antibot). ### External blacklists[¶](#external-blacklists "Permalink to this heading") #### Distributed[¶](#distributed "Permalink to this heading") **This feature is in beta and will be improved regularly.** You can benefit from a distributed blacklist shared among all of the bunkerized-nginx users. Each time a bunkerized-nginx instance detect a bad request, the offending IP is sent to a remote API and will enrich a database. An extract of the top malicious IP is downloaded on a periodic basis and integrated into bunkerized-nginx as a blacklist. This feature is controlled with the `USE\_REMOTE\_API=yes` environment variable. **To avoid poisoning, in addition to the various security checks made by the API we only mark IP as bad in the database if it has been seen by one of our honeypots under our control.** #### DNSBL[¶](#dnsbl "Permalink to this heading") Automatic checks on external DNS BlackLists are enabled by default with the `USE\_DNSBL=yes` environment variable. The list of DNSBL zones is also configurable, you just need to edit the `DNSBL\_LIST` environment variable which contains the following value by default `bl.blocklist.de problems.dnsbl.sorbs.net sbl.spamhaus.org xbl.spamhaus.org`. #### User-Agents[¶](#user-agents "Permalink to this heading") Sometimes script kiddies or lazy attackers don’t put a “legitimate” value inside the **User-Agent** HTTP header so we can block them. This is controlled with the `BLOCK\_USER\_AGENT=yes` environment variable. The blacklist is composed of two files from [here](https://raw.githubusercontent.com/mitchellkrogza/nginx-ultimate-bad-bot-blocker/master/_generator_lists/bad-user-agents.list) and [here](https://raw.githubusercontent.com/JayBizzle/Crawler-Detect/master/raw/Crawlers.txt). If a legitimate User-Agent is blacklisted, you can use the `WHITELIST\_USER\_AGENT` while still keeping the `BLOCK\_USER\_AGENT=yes` (more info [here](https://bunkerized-nginx.readthedocs.io/en/latest/environment_variables.html#custom-whitelisting)). #### TOR exit nodes[¶](#tor-exit-nodes "Permalink to this heading") Blocking TOR exit nodes might not be a good decision depending on your use case. We decided to enable it by default with the `BLOCK\_TOR\_EXIT\_NODE=yes` environment variable. If privacy is a concern for you and/or your clients, you can override the default value (i.e : `BLOCK\_TOR\_EXIT\_NODE=no`). Please note that you have a concrete example on how to use bunkerized-nginx with a .onion hidden service [here](https://github.com/bunkerity/bunkerized-nginx/tree/master/examples/tor-hidden-service). #### Proxies[¶](#proxies "Permalink to this heading") This list contains IP addresses and networks known to be open proxies (downloaded from [here](https://iplists.firehol.org/files/firehol_proxies.netset)). Unless privacy is important for you and/or your clients, you should keep the default environment variable `BLOCK\_PROXIES=yes`. #### Abusers[¶](#abusers "Permalink to this heading") This list contains IP addresses and networks known to be abusing (downloaded from [here](https://iplists.firehol.org/files/firehol_abusers_30d.netset)). You can control this feature with the `BLOCK\_ABUSERS` environment variable (default : `yes`). #### Referrers[¶](#referrers "Permalink to this heading") This list contains bad referrers domains known for spamming (downloaded from [here](https://raw.githubusercontent.com/mitchellkrogza/nginx-ultimate-bad-bot-blocker/master/_generator_lists/bad-referrers.list)). If one value is found inside the **Referer** HTTP header, request will be blocked. You can control this feature with the `BLOCK\_REFERRER` environment variable (default = `yes`). ### Limiting[¶](#limiting "Permalink to this heading") #### Requests[¶](#requests "Permalink to this heading") To limit bruteforce attacks or rate limit access to your API you can use the “request limit” feature so attackers will be limited to X request(s) within a period of time for the same resource. That kind of protection might be useful against other attacks too (e.g., blind SQL injection). Here is the list of related environment variables and their default value : * `USE\_LIMIT\_REQ=yes` : enable/disable request limiting * `LIMIT\_REQ\_URL=` : the URL you want to protect, use `/` to apply the limit for all URL * `LIMIT\_REQ\_RATE=1r/s` : the rate to apply for the resource, valid period are : `s` (second), `m` (minute), `h` (hour) and `d` (day) * `LIMIT\_REQ\_BURST=5 : the number of request tu put in a queue before effectively rejecting requests * `LIMIT\_REQ\_DELAY=1` : the number of seconds to wait before we proceed requests in queue Please note that you can apply different rate to different URL by appending a number as suffix (more info [here](https://bunkerized-nginx.readthedocs.io/en/latest/environment_variables.html#requests-limiting)). #### Connections[¶](#connections "Permalink to this heading") Opening too many connections from the same IP address might be considered as suspicious (unless it’s a shared IP and everyone is sending requests to your web service). It can be a dos/ddos attempt too. Bunkerized-nginx levarages the [ngx\_http\_conn\_module](http://nginx.org/en/docs/http/ngx_http_limit_conn_module.html) from nginx to prevent users opening too many connections. Here is the list of related environment variables and their default value : * `USE\_LIMIT\_CONN=yes` : enable disable connection limiting * `LIMIT\_CONN\_MAX=50` : maximum number of connections per IP ### Country[¶](#country "Permalink to this heading") If the location of your clients is known, you may want to add another security layer by whitelisting or blacklisting some countries. You can use the `BLACKLIST\_COUNTRY` or `WHITELIST\_COUNTRY` environment variables depending on your approach. They both take a list of 2 letters country code separated with space. ### Authentication[¶](#authentication "Permalink to this heading") You can quickly protect sensitive resources (e.g. : admin panels) by requiring HTTP authentication. Here is the list of related environment variables and their default value : * `USE\_AUTH\_BASIC=no` : enable/disable auth basic * `AUTH\_BASIC\_LOCATION=sitewide` : location of the sensitive resource (e.g. `/admin`) or `sitewide` to force authentication on the whole service * `AUTH\_BASIC\_USER=changeme` : the username required * `AUTH\_BASIC\_PASSWORD=changeme` : the password required * `AUTH\_BASIC\_TEXT=Restricted area` : the text that will be displayed to the user Please note that bunkerized-nginx also supports [Authelia](https://github.com/authelia/authelia) for authentication (see the corresponding [environment variables](https://bunkerized-nginx.readthedocs.io/en/latest/environment_variables.html#authelia) and a [full example](https://github.com/bunkerity/bunkerized-nginx/tree/master/examples/authelia)). ### Whitelisting[¶](#whitelisting "Permalink to this heading") Adding extra security can sometimes trigger false positives. Also, it might be not useful to do the security checks for specific clients because we decided to trust them. Bunkerized-nginx supports two types of whitelist : by IP address and by reverse DNS. Here is the list of related environment variables and their default value : * `USE\_WHITELIST\_IP=yes` : enable/disable whitelisting by IP address * `WHITELIST\_IP\_LIST=23.21.227.69 40.88.21.235 50.16.241.113 50.16.241.114 50.16.241.117 50.16.247.234 52.204.97.54 52.5.190.19 54.197.234.188 54.208.100.253 54.208.102.37 107.21.1.8` : list of IP addresses and/or network CIDR blocks to whitelist (default contains the IP addresses of the [DuckDuckGo crawler](https://help.duckduckgo.com/duckduckgo-help-pages/results/duckduckbot/)) * `USE\_WHITELIST\_REVERSE=yes` : enable/disable whitelisting by reverse DNS * `WHITELIST\_REVERSE\_LIST=.googlebot.com .google.com .search.msn.com .crawl.yahoot.net .crawl.baidu.jp .crawl.baidu.com .yandex.com .yandex.ru .yandex.net` : the list of reverse DNS suffixes to trust (default contains the list of major search engines crawlers) ### Blacklisting[¶](#blacklisting "Permalink to this heading") Sometimes it isn’t necessary to spend some resources for a particular client because we know for sure that he is malicious. Bunkerized-nginx nginx supports two types of blacklisting : by IP address and by reverse DNS. Here is the list of related environment variables and their default value : * `USE\_BLACKLIST\_IP=yes` : enable/disable blacklisting by IP address * `BLACKLIST\_IP\_LIST=` : list of IP addresses and/or network CIDR blocks to blacklist * `USE\_BLACKLIST\_REVERSE=yes` : enable/disable blacklisting by reverse DNS * `BLACKLIST\_REVERSE\_LIST=.shodan.io` : the list of reverse DNS suffixes to never trust ### Plugins[¶](#plugins "Permalink to this heading") Some security features can be added through the plugins system (e.g., ClamAV, CrowdSec, …). You will find more info in the [plugins section](https://bunkerized-nginx.readthedocs.io/en/latest/plugins.html). ### Container hardening[¶](#container-hardening "Permalink to this heading") You will find a ready to use docker-compose.yml file focused on container hardening [here](https://github.com/bunkerity/bunkerized-nginx/tree/master/examples/hardened). #### Drop capabilities[¶](#drop-capabilities "Permalink to this heading") By default, bunkerized-nginx runs as non-root user inside the container and should not use any of the default [capabilities](https://docs.docker.com/engine/security/#linux-kernel-capabilities) allowed by Docker. You can safely remove all capabilities to harden the container : ``` docker run ... --drop-cap=all ... bunkerity/bunkerized-nginx ``` #### No new privileges[¶](#no-new-privileges "Permalink to this heading") Bunkerized-nginx should never tries to gain additional privileges through setuid/setgid executables. You can safely add the **no-new-privileges** [security configuration](https://docs.docker.com/engine/reference/run/#security-configuration) when creating the container : ``` docker run ... --security-opt no-new-privileges ... bunkerity/bunkerized-nginx ``` #### Read-only[¶](#read-only "Permalink to this heading") Since the locations where bunkerized-nginx needs to write are known, we can run the container with a read-only root file system and only allow writes to specific locations by adding volumes and a tmpfs mount : ``` docker run ... --read-only --tmpfs /tmp -v cache-vol:/cache -v conf-vol:/etc/nginx -v /path/to/web/files:/www:ro -v /where/to/store/certificates:/etc/letsencrypt bunkerity/bunkerized-nginx ``` #### User namespace remap[¶](#user-namespace-remap "Permalink to this heading") Another hardening trick is [user namespace remapping](https://docs.docker.com/engine/security/userns-remap/) : it allows you to map the UID/GID of users inside a container to another UID/GID on the host. For example, you can map the user nginx with UID/GID 101 inside the container to a non-existent user with UID/GID 100101 on the host. Let’s assume you have the /etc/subuid and /etc/subgid files like this : ``` user:100000:65536 ``` It means that everything done inside the container will be remapped to UID/GID 100101 (100000 + 101) on the host. Please note that you must set the rights on the volumes (e.g. : /etc/letsencrypt, /www, …) according to the remapped UID/GID : ``` $ chown root:100101 /path/to/letsencrypt $ chmod 770 /path/to/letsencrypt $ docker run ... -v /path/to/letsencrypt:/etc/letsencrypt ... bunkerity/bunkerized-nginx ``` Web UI[¶](#web-ui "Permalink to this heading") ---------------------------------------------- ### Overview[¶](#overview "Permalink to this heading")  ### Usage[¶](#usage "Permalink to this heading") The web UI has its own set of environment variables to configure it : * `ADMIN\_USERNAME` and `ADMIN\_PASSWORD` : credentials for accessing the web UI * `ABSOLUTE\_URI` : the full public URI that points to the web UI * `API\_URI` : path of the bunkerized-nginx API (must match the corresponding `API\_URI` of the bunkerized-nginx instance) * `DOCKER\_HOST` : Docker API endpoint address (default = `unix:///var/run/docker.sock`) Since the web UI is a web service itself, we can use bunkerized-nginx as a reverse proxy in front of it. **Using the web UI in a Docker environment exposes a security risk because you need to mount the Docker API socket into the web UI container. It’s highly recommended to use a middleware like [tecnativa/docker-socket-proxy](https://github.com/Tecnativa/docker-socket-proxy) to reduce the risk as much as possible.** **You need to apply the security best practices because the web UI contains code and that code might be vulnerable : complex admin password, hard to guess public URI, network isolation from others services, HTTPS only, …** #### Docker[¶](#docker "Permalink to this heading") First of all, we will need to setup two networks one for ui communication and the other one for the services : ``` $ docker network create ui-net $ docker network create services-net ``` We also need a volume to shared the generated configuration from the web UI to the bunkerized-nginx instances : ``` $ docker volume create bunkerized-vol ``` Next we will create the “Docker API proxy” container that will be in the front of the Docker socket and deny access to sensitive things : ``` $ docker run -d \ --name my-docker-proxy \ --network ui-net \ -v /var/run/docker.sock:/var/run/docker.sock:ro \ -e CONTAINERS=1 \ -e SWARM=1 \ -e SERVICES=1 \ tecnativa/docker-socket-proxy ``` We can now create the web UI container based on bunkerized-nginx-ui image : ``` $ docker run -d \ --name my-bunkerized-ui \ --network ui-net \ -v bunkerized-vol:/etc/nginx \ -e ABSOLUTE\_URI=https://admin.example.com/admin-changeme/ \ -e DOCKER\_HOST=tcp://my-docker-proxy:2375 \ -e API\_URI=/ChangeMeToSomethingHardToGuess \ -e ADMIN\_USERNAME=admin \ -e ADMIN\_PASSWORD=changeme \ bunkerity/bunkerized-nginx-ui ``` Last but not least, you need to start the bunkerized-nginx and configure it as a reverse proxy for the web UI web service : ``` $ docker create \ --name my-bunkerized \ --network ui-net \ -p 80:8080 \ -p 443:8443 \ -v bunkerized-vol:/etc/nginx \ -v "${PWD}/certs:/etc/letsencrypt" \ -e SERVER\_NAME=admin.example.com \ -e MULTISITE=yes \ -e USE\_API=yes \ -e API\_URI=/ChangeMeToSomethingHardToGuess \ -e AUTO\_LETS\_ENCRYPT=yes \ -e REDIRECT\_HTTP\_TO\_HTTPS=yes \ -e admin.example.com_USE_REVERSE_PROXY=yes \ -e admin.example.com_REVERSE_PROXY_URL=/admin-changeme/ \ -e admin.example.com_REVERSE_PROXY_HOST=http://my-bunkerized-ui:5000 \ -e "admin.example.com\_REVERSE\_PROXY\_HEADERS=X-Script-Name /admin-changeme" \ -e admin.example.com_USE_MODSECURITY=no \ -l bunkerized-nginx.UI \ bunkerity/bunkerized-nginx $ docker network connect services-net my-bunkerized $ docker start my-bunkerized ``` The web UI should now be accessible at https://admin.example.com/admin-changeme/. docker-compose equivalent : ``` version: '3' services: my-bunkerized: image: bunkerity/bunkerized-nginx restart: always depends\_on: - my-bunkerized-ui networks: - services-net - ui-net ports: - 80:8080 - 443:8443 volumes: - ./letsencrypt:/etc/letsencrypt - bunkerized-vol:/etc/nginx environment: - SERVER\_NAME=admin.example.com # replace with your domain - MULTISITE=yes - USE\_API=yes - API\_URI=/ChangeMeToSomethingHardToGuess # change it to something hard to guess + must match API\_URI from myui service - AUTO\_LETS\_ENCRYPT=yes - REDIRECT\_HTTP\_TO\_HTTPS=yes - admin.example.com\_USE\_REVERSE\_PROXY=yes - admin.example.com\_REVERSE\_PROXY\_URL=/admin-changeme/ # change it to something hard to guess - admin.example.com\_REVERSE\_PROXY\_HOST=http://my-bunkerized-ui:5000 - admin.example.com\_REVERSE\_PROXY\_HEADERS=X-Script-Name /admin-changeme # must match REVERSE\_PROXY\_URL - admin.example.com\_USE\_MODSECURITY=no labels: - "bunkerized-nginx.UI" my-bunkerized-ui: image: bunkerity/bunkerized-nginx-ui restart: always depends\_on: - my-docker-proxy networks: - ui-net volumes: - bunkerized-vol:/etc/nginx environment: - ABSOLUTE\_URI=https://admin.example.com/admin-changeme/ # change it to your full URI - DOCKER\_HOST=tcp://my-docker-proxy:2375 - API\_URI=/ChangeMeToSomethingHardToGuess # must match API\_URI from bunkerized-nginx - ADMIN\_USERNAME=admin # change it to something hard to guess - ADMIN\_PASSWORD=changeme # change it to a good password my-docker-proxy: image: tecnativa/docker-socket-proxy restart: always networks: - ui-net volumes: - /var/run/docker.sock:/var/run/docker.sock:ro environment: - CONTAINERS=1 - SWARM=1 - SERVICES=1 networks: ui-net: services-net: name: services-net volumes: bunkerized-vol: ``` #### Linux[¶](#linux "Permalink to this heading") First of all, you need to edit the web UI configuration file located at `/opt/bunkerized-nginx/ui/variables.env` : ``` ABSOLUTE_URI=https://admin.example.com/admin-changeme/ DOCKER_HOST= ADMIN_USERNAME=admin ADMIN_PASSWORD=changeme ``` Make sure that the web UI service is automatically started on boot : ``` $ systemctl enable bunkerized-nginx-ui ``` Now you can start the web UI service : ``` $ systemctl start bunkerized-nginx-ui ``` Edit the bunkerized-nginx configurations located at `/opt/bunkerized-nginx/variables.env` : ``` HTTP_PORT=80 HTTPS_PORT=443 DNS_RESOLVERS=8.8.8.8 8.8.4.4 SERVER_NAME=admin.example.com MULTISITE=yes AUTO_LETS_ENCRYPT=yes REDIRECT_HTTP_TO_HTTPS=yes admin.example.com_USE_REVERSE_PROXY=yes admin.example.com_REVERSE_PROXY_URL=/admin-changeme/ # Local bunkerized-nginx-ui admin.example.com_REVERSE_PROXY_HOST=http://127.0.0.1:5000 # Remote bunkerized-nginx-ui #REVERSE_PROXY_HOST=http://service.example.local:5000 admin.example.com_REVERSE_PROXY_HEADERS=X-Script-Name /admin-changeme admin.example.com_USE_MODSECURITY=no ``` And run the `bunkerized-nginx` command to apply changes : ``` $ bunkerized-nginx ``` The web UI should now be accessible at https://admin.example.com/admin-changeme/. List of environment variables[¶](#list-of-environment-variables "Permalink to this heading") -------------------------------------------------------------------------------------------- ### nginx[¶](#nginx "Permalink to this heading") #### Misc[¶](#misc "Permalink to this heading") `MULTISITE` Values : *yes* | *no* Default value : *no* Context : *global* When set to *no*, only one server block will be generated. Otherwise one server per host defined in the `SERVER\_NAME` environment variable will be generated. Any environment variable tagged as *multisite* context can be used for a specific server block with the following format : *host\_VARIABLE=value*. If the variable is used without the host prefix it will be applied to all the server blocks (but still can be overriden). `SERVER\_NAME` Values : *<first name> <second name> …* Default value : *www.bunkerity.com* Context : *global*, *multisite* Sets the host names of the webserver separated with spaces. This must match the Host header sent by clients. Useful when used with `MULTISITE=yes` and/or `AUTO\_LETSENCRYPT=yes` and/or `DISABLE\_DEFAULT\_SERVER=yes`. `MAX\_CLIENT\_SIZE` Values : *0* | *Xm* Default value : *10m* Context : *global*, *multisite* Sets the maximum body size before nginx returns a 413 error code. Setting to 0 means “infinite” body size. `ALLOWED\_METHODS` Values : *allowed HTTP methods separated with | char* Default value : *GET|POST|HEAD* Context : *global*, *multisite* Only the HTTP methods listed here will be accepted by nginx. If not listed, nginx will close the connection. `DISABLE\_DEFAULT\_SERVER` Values : *yes* | *no* Default value : *no* Context : *global* If set to yes, nginx will only respond to HTTP request when the Host header match a FQDN specified in the `SERVER\_NAME` environment variable. For example, it will close the connection if a bot access the site with direct ip. `SERVE\_FILES` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to yes, nginx will serve files from /www directory within the container. A use case to not serving files is when you setup bunkerized-nginx as a reverse proxy. `DNS\_RESOLVERS` Values : *<two IP addresses separated with a space>* Default value : *127.0.0.11* Context : *global* The IP addresses of the DNS resolvers to use when performing DNS lookups. `ROOT\_FOLDER` Values : *<any valid path to web files>* Default value : */www* Context : *global* The default folder where nginx will search for web files. Don’t change it unless you know what you are doing. `ROOT\_SITE\_SUBFOLDER` Values : *<any valid directory name>* Default value : Context : *global*, *multisite* The subfolder where nginx will search for site web files. `LOG\_FORMAT` Values : *<any values accepted by the log\_format directive>* Default value : *$host $remote\_addr - $remote\_user [$time\_local] “$request” $status $body\_bytes\_sent “$http\_referer” “$http\_user\_agent”* Context : *global* The log format used by nginx to generate logs. More info [here](http://nginx.org/en/docs/http/ngx_http_log_module.html#log_format). `LOG\_LEVEL` Values : *debug, info, notice, warn, error, crit, alert, or emerg* Default value : *info* Context : *global* The level of logging : *debug* means more logs and *emerg* means less logs. More info [here](https://nginx.org/en/docs/ngx_core_module.html#error_log). `HTTP\_PORT` Values : *<any valid port greater than 1024>* Default value : *8080* Context : *global* The HTTP port number used by nginx inside the container. `HTTPS\_PORT` Values : *<any valid port greater than 1024>* Default value : *8443* Context : *global* The HTTPS port number used by nginx inside the container. `WORKER\_CONNECTIONS` Values : *<any positive integer>* Default value : 1024 Context : *global* Sets the value of the [worker\_connections](https://nginx.org/en/docs/ngx_core_module.html#worker_connections) directive. `WORKER\_RLIMIT\_NOFILE` Values : *<any positive integer>* Default value : 2048 Context : *global* Sets the value of the [worker\_rlimit\_nofile](https://nginx.org/en/docs/ngx_core_module.html#worker_rlimit_nofile) directive. `WORKER\_PROCESSES` Values : *<any positive integer or auto>* Default value : auto Context : *global* Sets the value of the [worker\_processes](https://nginx.org/en/docs/ngx_core_module.html#worker_processes) directive. `INJECT\_BODY` Values : *<any HTML code>* Default value : Context : *global*, *multisite* Use this variable to inject any HTML code you want before the </body> tag (e.g. : `\<script src="https://..."\>`) `REDIRECT\_TO` Values : *<any valid absolute URI>* Default value : Context : *global*, *multisite* Use this variable if you want to redirect one server to another (e.g., redirect apex to www : `REDIRECT\_TO=https://www.example.com`). `REDIRECT\_TO\_REQUEST\_URI` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* When set to yes and `REDIRECT\_TO` is set it will append the requested path to the redirection (e.g., https://example.com/something redirects to https://www.example.com/something). `CUSTOM\_HEADER` Values : *<HeaderName: HeaderValue>* Default value : Context : *global*, *multisite* Add custom HTTP header of your choice to clients. You can add multiple headers by appending a number as a suffix of the environment variable : `CUSTOM\_HEADER\_1`, `CUSTOM\_HEADER\_2`, `CUSTOM\_HEADER\_3`, … #### Information leak[¶](#information-leak "Permalink to this heading") `SERVER\_TOKENS` Values : *on* | *off* Default value : *off* Context : *global* If set to on, nginx will display server version in Server header and default error pages. `REMOVE\_HEADERS` Values : <*list of headers separated with space*> Default value : *Server X-Powered-By X-AspNet-Version X-AspNetMvc-Version* Context : *global*, *multisite* List of header to remove when sending responses to clients. #### Custom error pages[¶](#custom-error-pages "Permalink to this heading") `ERRORS` Values : *<error1=/page1 error2=/page2>* Default value : Context : *global*, *multisite* Use this kind of environment variable to define custom error page depending on the HTTP error code. Replace errorX with HTTP code. Example : `ERRORS=404=/404.html 403=/403.html` the /404.html page will be displayed when 404 code is generated (same for 403 and /403.html page). The path is relative to the root web folder. #### HTTP basic authentication[¶](#http-basic-authentication "Permalink to this heading") `USE\_AUTH\_BASIC` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* If set to yes, enables HTTP basic authentication at the location `AUTH\_BASIC\_LOCATION` with user `AUTH\_BASIC\_USER` and password `AUTH\_BASIC\_PASSWORD`. `AUTH\_BASIC\_LOCATION` Values : *sitewide* | */somedir* | *<any valid location>* Default value : *sitewide* Context : *global*, *multisite* The location to restrict when `USE\_AUTH\_BASIC` is set to *yes*. If the special value *sitewide* is used then auth basic will be set at server level outside any location context. `AUTH\_BASIC\_USER` Values : *<any valid username>* Default value : *changeme* Context : *global*, *multisite* The username allowed to access `AUTH\_BASIC\_LOCATION` when `USE\_AUTH\_BASIC` is set to yes. `AUTH\_BASIC\_PASSWORD` Values : *<any valid password>* Default value : *changeme* Context : *global*, *multisite* The password of `AUTH\_BASIC\_USER` when `USE\_AUTH\_BASIC` is set to yes. `AUTH\_BASIC\_TEXT` Values : *<any valid text>* Default value : *Restricted area* Context : *global*, *multisite* The text displayed inside the login prompt when `USE\_AUTH\_BASIC` is set to yes. #### Reverse proxy[¶](#reverse-proxy "Permalink to this heading") `USE\_REVERSE\_PROXY` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* Set this environment variable to *yes* if you want to use bunkerized-nginx as a reverse proxy. `REVERSE\_PROXY\_URL` Values : <*any valid location path*> Default value : Context : *global*, *multisite* Only valid when `USE\_REVERSE\_PROXY` is set to *yes*. Let’s you define the location path to match when acting as a reverse proxy. You can set multiple url/host by adding a suffix number to the variable name like this : `REVERSE\_PROXY\_URL\_1`, `REVERSE\_PROXY\_URL\_2`, `REVERSE\_PROXY\_URL\_3`, … `REVERSE\_PROXY\_HOST` Values : <*any valid proxy\_pass value*> Default value : Context : *global*, *multisite* Only valid when `USE\_REVERSE\_PROXY` is set to *yes*. Let’s you define the proxy\_pass destination to use when acting as a reverse proxy. You can set multiple url/host by adding a suffix number to the variable name like this : `REVERSE\_PROXY\_HOST\_1`, `REVERSE\_PROXY\_HOST\_2`, `REVERSE\_PROXY\_HOST\_3`, … `REVERSE\_PROXY\_WS` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* Only valid when `USE\_REVERSE\_PROXY` is set to *yes*. Set it to *yes* when the corresponding `REVERSE\_PROXY\_HOST` is a WebSocket server. You can set multiple url/host by adding a suffix number to the variable name like this : `REVERSE\_PROXY\_WS\_1`, `REVERSE\_PROXY\_WS\_2`, `REVERSE\_PROXY\_WS\_3`, … `REVERSE\_PROXY\_BUFFERING` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* Only valid when `USE\_REVERSE\_PROXY` is set to *yes*. Set it to *yes* then the [proxy\_buffering](https://nginx.org/en/docs/http/ngx_http_proxy_module.html#proxy_buffering) directive will be set to `on` or `off` otherwise. You can set multiple url/host by adding a suffix number to the variable name like this : `REVERSE\_PROXY\_BUFFERING\_1`, `REVERSE\_PROXY\_BUFFERING\_2`, `REVERSE\_PROXY\_BUFFERING\_3`, … `REVERSE\_PROXY\_KEEPALIVE` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* Only valid when `USE\_REVERSE\_PROXY` is set to *yes*. Set it to *yes* to enable keepalive connections with the backend (needs a HTTP 1.1 backend) or *no* otherwise. You can set multiple url/host by adding a suffix number to the variable name like this : `REVERSE\_PROXY\_KEEPALIVE\_1`, `REVERSE\_PROXY\_KEEPALIVE\_2`, `REVERSE\_PROXY\_KEEPALIVE\_3`, … `REVERSE\_PROXY\_HEADERS` Values : *<list of custom headers separated with a semicolon like this : header1 value1;header2 value2…>* Default value : Context : *global*, *multisite* Only valid when `USE\_REVERSE\_PROXY` is set to *yes*. You can set multiple url/host by adding a suffix number to the variable name like this : `REVERSE\_PROXY\_HEADERS\_1`, `REVERSE\_PROXY\_HEADERS\_2`, `REVERSE\_PROXY\_HEADERS\_3`, … `PROXY\_REAL\_IP` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* Set this environment variable to *yes* if you’re using bunkerized-nginx behind a reverse proxy. This means you will see the real client address instead of the proxy one inside your logs. Security tools will also then work correctly. `PROXY\_REAL\_IP\_FROM` Values : *<list of trusted IP addresses and/or networks separated with spaces>* Default value : *192.168.0.0/16 172.16.0.0/12 10.0.0.0/8* Context : *global*, *multisite* When `PROXY\_REAL\_IP` is set to *yes*, lets you define the trusted IPs/networks allowed to send the correct client address. `PROXY\_REAL\_IP\_HEADER` Values : *X-Forwarded-For* | *X-Real-IP* | *custom header* Default value : *X-Forwarded-For* Context : *global*, *multisite* When `PROXY\_REAL\_IP` is set to *yes*, lets you define the header that contains the real client IP address. `PROXY\_REAL\_IP\_RECURSIVE` Values : *on* | *off* Default value : *on* Context : *global*, *multisite* When `PROXY\_REAL\_IP` is set to *yes*, setting this to *on* avoid spoofing attacks using the header defined in `PROXY\_REAL\_IP\_HEADER`. #### Compression[¶](#compression "Permalink to this heading") `USE\_GZIP` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* When set to *yes*, nginx will use the gzip algorithm to compress responses sent to clients. `GZIP\_COMP\_LEVEL` Values : <*any integer between 1 and 9*> Default value : *5* Context : *global*, *multisite* The gzip compression level to use when `USE\_GZIP` is set to *yes*. `GZIP\_MIN\_LENGTH` Values : <*any positive integer*> Default value : *1000* Context : *global*, *multisite* The minimum size (in bytes) of a response required to compress when `USE\_GZIP` is set to *yes*. `GZIP\_TYPES` Values : <*list of mime types separated with space*> Default value : *application/atom+xml application/javascript application/json application/rss+xml application/vnd.ms-fontobject application/x-font-opentype application/x-font-truetype application/x-font-ttf application/x-javascript application/xhtml+xml application/xml font/eot font/opentype font/otf font/truetype image/svg+xml image/vnd.microsoft.icon image/x-icon image/x-win-bitmap text/css text/javascript text/plain text/xml* Context : *global*, *multisite* List of response MIME type required to compress when `USE\_GZIP` is set to *yes*. `USE\_BROTLI` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* When set to *yes*, nginx will use the brotli algorithm to compress responses sent to clients. `BROTLI\_COMP\_LEVEL` Values : <*any integer between 1 and 9*> Default value : *5* Context : *global*, *multisite* The brotli compression level to use when `USE\_BROTLI` is set to *yes*. `BROTLI\_MIN\_LENGTH` Values : <*any positive integer*> Default value : *1000* Context : *global*, *multisite* The minimum size (in bytes) of a response required to compress when `USE\_BROTLI` is set to *yes*. `BROTLI\_TYPES` Values : <*list of mime types separated with space*> Default value : *application/atom+xml application/javascript application/json application/rss+xml application/vnd.ms-fontobject application/x-font-opentype application/x-font-truetype application/x-font-ttf application/x-javascript application/xhtml+xml application/xml font/eot font/opentype font/otf font/truetype image/svg+xml image/vnd.microsoft.icon image/x-icon image/x-win-bitmap text/css text/javascript text/plain text/xml* Context : *global*, *multisite* List of response MIME type required to compress when `USE\_BROTLI` is set to *yes*. #### Cache[¶](#cache "Permalink to this heading") `USE\_CLIENT\_CACHE` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* When set to *yes*, clients will be told to cache some files locally. `CLIENT\_CACHE\_EXTENSIONS` Values : <*list of extensions separated with |*> Default value : *jpg|jpeg|png|bmp|ico|svg|tif|css|js|otf|ttf|eot|woff|woff2* Context : *global*, *multisite* List of file extensions that clients should cache when `USE\_CLIENT\_CACHE` is set to *yes*. `CLIENT\_CACHE\_CONTROL` Values : <*Cache-Control header value*> Default value : *public, max-age=15552000* Context : *global*, *multisite* Content of the [Cache-Control](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Cache-Control) header to send when `USE\_CLIENT\_CACHE` is set to *yes*. `CLIENT\_CACHE\_ETAG` Values : *on* | *off* Default value : *on* Context : *global*, *multisite* Whether or not nginx will send the [ETag](https://en.wikipedia.org/wiki/HTTP_ETag) header when `USE\_CLIENT\_CACHE` is set to *yes*. `USE\_OPEN\_FILE\_CACHE` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* When set to *yes*, nginx will cache open fd, existence of directories, … See [open\_file\_cache](http://nginx.org/en/docs/http/ngx_http_core_module.html#open_file_cache). `OPEN\_FILE\_CACHE` Values : <*any valid open\_file\_cache parameters*> Default value : *max=1000 inactive=20s* Context : *global*, *multisite* Parameters to use with open\_file\_cache when `USE\_OPEN\_FILE\_CACHE` is set to *yes*. `OPEN\_FILE\_CACHE\_ERRORS` Values : *on* | *off* Default value : *on* Context : *global*, *multisite* Whether or not nginx should cache file lookup errors when `USE\_OPEN\_FILE\_CACHE` is set to *yes*. `OPEN\_FILE\_CACHE\_MIN\_USES` Values : <\*any valid integer \*> Default value : *2* Context : *global*, *multisite* The minimum number of file accesses required to cache the fd when `USE\_OPEN\_FILE\_CACHE` is set to *yes*. `OPEN\_FILE\_CACHE\_VALID` Values : <*any time value like Xs, Xm, Xh, …*> Default value : *30s* Context : *global*, *multisite* The time after which cached elements should be validated when `USE\_OPEN\_FILE\_CACHE` is set to *yes*. `USE\_PROXY\_CACHE` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* When set to *yes*, nginx will cache responses from proxied applications. See [proxy\_cache](http://nginx.org/en/docs/http/ngx_http_proxy_module.html#proxy_cache). `PROXY\_CACHE\_PATH\_ZONE\_SIZE` Values : <*any valid size like Xk, Xm, Xg, …*> Default value : *10m* Context : *global*, *multisite* Maximum size of cached metadata when `USE\_PROXY\_CACHE` is set to *yes*. `PROXY\_CACHE\_PATH\_PARAMS` Values : <*any valid parameters to proxy\_cache\_path directive*> Default value : *max\_size=100m* Context : *global*, *multisite* Parameters to use for [proxy\_cache\_path](http://nginx.org/en/docs/http/ngx_http_proxy_module.html#proxy_cache_path) directive when `USE\_PROXY\_CACHE` is set to *yes*. `PROXY\_CACHE\_METHODS` Values : <*list of HTTP methods separated with space*> Default value : *GET HEAD* Context : *global*, *multisite* The HTTP methods that should trigger a cache operation when `USE\_PROXY\_CACHE` is set to *yes*. `PROXY\_CACHE\_MIN\_USES` Values : <*any positive integer*> Default value : *2* Context : *global*, *multisite* The minimum number of requests before the response is cached when `USE\_PROXY\_CACHE` is set to *yes*. `PROXY\_CACHE\_KEY` Values : <*list of variables*> Default value : *$scheme$host$request\_uri* Context : *global*, *multisite* The key used to uniquely identify a cached response when `USE\_PROXY\_CACHE` is set to *yes*. `PROXY\_CACHE\_VALID` Values : <*status=time list separated with space*> Default value : *200=10m 301=10m 302=1h* Context : *global*, *multisite* Define the caching time depending on the HTTP status code (list of status=time separated with space) when `USE\_PROXY\_CACHE` is set to *yes*. `PROXY\_NO\_CACHE` Values : <*list of variables*> Default value : *$http\_authorization* Context : *global*, *multisite* Conditions that must be met to disable caching of the response when `USE\_PROXY\_CACHE` is set to *yes*. `PROXY\_CACHE\_BYPASS` Values : <*list of variables*> Default value : *$http\_authorization* Context : *global*, *multisite* Conditions that must be met to bypass the cache when `USE\_PROXY\_CACHE` is set to *yes*. ### HTTPS[¶](#https "Permalink to this heading") #### Let’s Encrypt[¶](#let-s-encrypt "Permalink to this heading") `AUTO\_LETS\_ENCRYPT` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* If set to yes, automatic certificate generation and renewal will be setup through Let’s Encrypt. This will enable HTTPS on your website for free. You will need to redirect the 80 port to 8080 port inside container and also set the `SERVER\_NAME` environment variable. `EMAIL\_LETS\_ENCRYPT` Values : *contact@yourdomain.com* Default value : *contact@first-domain-in-server-name* Context : *global*, *multisite* Define the contact email address declare in the certificate. `USE\_LETS\_ENCRYPT\_STAGING` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* When set to yes, it tells certbot to use the [staging environment](https://letsencrypt.org/docs/staging-environment/) for Let’s Encrypt certificate generation. Useful when you are testing your deployments to avoid being rate limited in the production environment. #### HTTP[¶](#http "Permalink to this heading") `LISTEN\_HTTP` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to no, nginx will not in listen on HTTP (port 80). Useful if you only want HTTPS access to your website. `REDIRECT\_HTTP\_TO\_HTTPS` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* If set to yes, nginx will redirect all HTTP requests to HTTPS. #### Custom certificate[¶](#custom-certificate "Permalink to this heading") `USE\_CUSTOM\_HTTPS` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* If set to yes, HTTPS will be enabled with certificate/key of your choice. `CUSTOM\_HTTPS\_CERT` Values : *<any valid path inside the container>* Default value : Context : *global*, *multisite* Full path of the certificate or bundle file to use when `USE\_CUSTOM\_HTTPS` is set to yes. If your chain of trust contains one or more intermediate certificate(s), you will need to bundle them into a single file (more info [here](https://nginx.org/en/docs/http/configuring_https_servers.html#chains)). `CUSTOM\_HTTPS\_KEY` Values : *<any valid path inside the container>* Default value : Context : *global*, *multisite* Full path of the key file to use when `USE\_CUSTOM\_HTTPS` is set to yes. #### Self-signed certificate[¶](#self-signed-certificate "Permalink to this heading") `GENERATE\_SELF\_SIGNED\_SSL` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* If set to yes, HTTPS will be enabled with a container generated self-signed certificate. `SELF\_SIGNED\_SSL\_EXPIRY` Values : *integer* Default value : *365* (1 year) Context : *global*, *multisite* Needs `GENERATE\_SELF\_SIGNED\_SSL` to work. Sets the expiry date for the self generated certificate. `SELF\_SIGNED\_SSL\_COUNTRY` Values : *text* Default value : *Switzerland* Context : *global*, *multisite* Needs `GENERATE\_SELF\_SIGNED\_SSL` to work. Sets the country for the self generated certificate. `SELF\_SIGNED\_SSL\_STATE` Values : *text*, *multisite* Default value : *Switzerland* Context : *global*, *multisite* Needs `GENERATE\_SELF\_SIGNED\_SSL` to work. Sets the state for the self generated certificate. `SELF\_SIGNED\_SSL\_CITY` Values : *text* Default value : *Bern* Context : *global*, *multisite* Needs `GENERATE\_SELF\_SIGNED\_SSL` to work. Sets the city for the self generated certificate. `SELF\_SIGNED\_SSL\_ORG` Values : *text* Default value : *AcmeInc* Context : *global*, *multisite* Needs `GENERATE\_SELF\_SIGNED\_SSL` to work. Sets the organisation name for the self generated certificate. `SELF\_SIGNED\_SSL\_OU` Values : *text* Default value : *IT* Context : *global*, *multisite* Needs `GENERATE\_SELF\_SIGNED\_SSL` to work. Sets the organisitional unit for the self generated certificate. `SELF\_SIGNED\_SSL\_CN` Values : *text* Default value : *bunkerity-nginx* Context : *global*, *multisite* Needs `GENERATE\_SELF\_SIGNED\_SSL` to work. Sets the CN server name for the self generated certificate. #### Misc[¶](#id1 "Permalink to this heading") `HTTP2` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to yes, nginx will use HTTP2 protocol when HTTPS is enabled. `HTTPS\_PROTOCOLS` Values : *TLSv1.2* | *TLSv1.3* | *TLSv1.2 TLSv1.3* Default value : *TLSv1.2 TLSv1.3* Context : *global*, *multisite* The supported version of TLS. We recommend the default value *TLSv1.2 TLSv1.3* for compatibility reasons. ### ModSecurity[¶](#modsecurity "Permalink to this heading") `USE\_MODSECURITY` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to yes, the ModSecurity WAF will be enabled. You can include custom rules by adding .conf files into the /modsec-confs/ directory inside the container (i.e : through a volume). `USE\_MODSECURITY\_CRS` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to yes, the [OWASP ModSecurity Core Rule Set](https://coreruleset.org/) will be used. It provides generic rules to detect common web attacks. You can customize the CRS (i.e. : add WordPress exclusions) by adding custom .conf files into the /modsec-crs-confs/ directory inside the container (i.e : through a volume). Files inside this directory are included before the CRS rules. If you need to tweak (i.e. : SecRuleUpdateTargetById) put .conf files inside the /modsec-confs/ which is included after the CRS rules. `MODSECURITY\_SEC\_AUDIT\_ENGINE` Values : *On* | *Off* | *RelevantOnly* Default value : *RelevantOnly* Context : *global*, *multisite* Sets the value of the [SecAuditEngine directive](https://github.com/SpiderLabs/ModSecurity/wiki/Reference-Manual-%28v2.x%29#SecAuditEngine) of ModSecurity. ### Security headers[¶](#security-headers "Permalink to this heading") If you want to keep your application headers and tell bunkerized-nginx to not override it, just set the corresponding environment variable to an empty value (e.g., `CONTENT\_SECURITY\_POLICY=`, `PERMISSIONS\_POLICY=`, …). `X\_FRAME\_OPTIONS` Values : *DENY* | *SAMEORIGIN* | *ALLOW-FROM https://www.website.net* Default value : *DENY* Context : *global*, *multisite* Policy to be used when the site is displayed through iframe. Can be used to mitigate clickjacking attacks. More info [here](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/X-Frame-Options). `X\_XSS\_PROTECTION` Values : *0* | *1* | *1; mode=block* Default value : *1; mode=block* Context : *global*, *multisite* Policy to be used when XSS is detected by the browser. Only works with Internet Explorer. More info [here](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/X-XSS-Protection). `X\_CONTENT\_TYPE\_OPTIONS` Values : *nosniff* Default value : *nosniff* Context : *global*, *multisite* Tells the browser to be strict about MIME type. More info [here](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/X-Content-Type-Options). `REFERRER\_POLICY` Values : *no-referrer* | *no-referrer-when-downgrade* | *origin* | *origin-when-cross-origin* | *same-origin* | *strict-origin* | *strict-origin-when-cross-origin* | *unsafe-url* Default value : *no-referrer* Context : *global*, *multisite* Policy to be used for the Referer header. More info [here](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Referrer-Policy). `FEATURE\_POLICY` Values : *<directive> <allow list>* Default value : *accelerometer ‘none’; ambient-light-sensor ‘none’; autoplay ‘none’; battery ‘none’; camera ‘none’; display-capture ‘none’; document-domain ‘none’; encrypted-media ‘none’; fullscreen ‘none’; geolocation ‘none’; gyroscope ‘none’; magnetometer ‘none’; microphone ‘none’; midi ‘none’; payment ‘none’; picture-in-picture ‘none’; publickey-credentials-get ‘none’; sync-xhr ‘none’; usb ‘none’; wake-lock ‘none’; web-share ‘none’; xr-spatial-tracking ‘none”* Context : *global*, *multisite* Tells the browser which features can be used on the website. More info [here](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Feature-Policy). `PERMISSIONS\_POLICY` Values : *feature=(allow list)* Default value : *accelerometer=(), ambient-light-sensor=(), autoplay=(), battery=(), camera=(), display-capture=(), document-domain=(), encrypted-media=(), fullscreen=(), geolocation=(), gyroscope=(), interest-cohort=(), magnetometer=(), microphone=(), midi=(), payment=(), picture-in-picture=(), publickey-credentials-get=(), screen-wake-lock=(), sync-xhr=(), usb=(), web-share=(), xr-spatial-tracking=()* Context : *global*, *multisite* Tells the browser which features can be used on the website. More info [here](https://www.w3.org/TR/permissions-policy-1/). `COOKIE\_FLAGS` Values : *\* HttpOnly* | *MyCookie secure SameSite=Lax* | *…* Default value : *\* HttpOnly SameSite=Lax* Context : *global*, *multisite* Adds some security to the cookies set by the server. Accepted value can be found [here](https://github.com/AirisX/nginx_cookie_flag_module). `COOKIE\_AUTO\_SECURE\_FLAG` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* When set to *yes*, the *secure* will be automatically added to cookies when using HTTPS. `STRICT\_TRANSPORT\_SECURITY` Values : *max-age=expireTime [; includeSubDomains] [; preload]* Default value : *max-age=31536000* Context : *global*, *multisite* Tells the browser to use exclusively HTTPS instead of HTTP when communicating with the server. More info [here](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Strict-Transport-Security). `CONTENT\_SECURITY\_POLICY` Values : *<directive 1>; <directive 2>; …* Default value : *object-src ‘none’; frame-ancestors ‘self’; form-action ‘self’; block-all-mixed-content; sandbox allow-forms allow-same-origin allow-scripts allow-popups allow-downloads; base-uri ‘self’;* Context : *global*, *multisite* Policy to be used when loading resources (scripts, forms, frames, …). More info [here](https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/Content-Security-Policy). ### Blocking[¶](#blocking "Permalink to this heading") #### Antibot[¶](#antibot "Permalink to this heading") `USE\_ANTIBOT` Values : *no* | *cookie* | *javascript* | *captcha* | *recaptcha* Default value : *no* Context : *global*, *multisite* If set to another allowed value than *no*, users must complete a “challenge” before accessing the pages on your website : * *cookie* : asks the users to set a cookie * *javascript* : users must execute a javascript code * *captcha* : a text captcha must be resolved by the users * *recaptcha* : use [Google reCAPTCHA v3](https://developers.google.com/recaptcha/intro) score to allow/deny users `ANTIBOT\_URI` Values : *<any valid uri>* Default value : */challenge* Context : *global*, *multisite* A valid and unused URI to redirect users when `USE\_ANTIBOT` is used. Be sure that it doesn’t exist on your website. `ANTIBOT\_SESSION\_SECRET` Values : *random* | *<32 chars of your choice>* Default value : *random* Context : *global*, *multisite* A secret used to generate sessions when `USE\_ANTIBOT` is set. Using the special *random* value will generate a random one. Be sure to use the same value when you are in a multi-server environment (so sessions are valid in all the servers). `ANTIBOT\_RECAPTCHA\_SCORE` Values : *<0.0 to 1.0>* Default value : *0.7* Context : *global*, *multisite* The minimum score required when `USE\_ANTIBOT` is set to *recaptcha*. `ANTIBOT\_RECAPTCHA\_SITEKEY` Values : *<public key given by Google>* Default value : Context : *global*, *multisite* The sitekey given by Google when `USE\_ANTIBOT` is set to *recaptcha*. `ANTIBOT\_RECAPTCHA\_SECRET` Values : *<private key given by Google>* Default value : Context : *global*, *multisite* The secret given by Google when `USE\_ANTIBOT` is set to *recaptcha*. #### Distributed blacklist[¶](#distributed-blacklist "Permalink to this heading") `USE\_REMOTE\_API` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to yes, the instance will participate into the distributed blacklist shared among all other instances. The blacklist will be automaticaly downloaded on a periodic basis. `REMOTE\_API\_SERVER` Values : *<any valid full URL>* Default value : Context : *global* Full URL of the remote API used for the distributed blacklist. #### External blacklists[¶](#external-blacklists "Permalink to this heading") `BLOCK\_USER\_AGENT` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to yes, block clients with “bad” user agent. Blacklist can be found [here](https://raw.githubusercontent.com/mitchellkrogza/nginx-ultimate-bad-bot-blocker/master/_generator_lists/bad-user-agents.list) and [here](https://raw.githubusercontent.com/JayBizzle/Crawler-Detect/master/raw/Crawlers.txt). `BLOCK\_TOR\_EXIT\_NODE` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* Is set to yes, will block known TOR exit nodes. Blacklist can be found [here](https://iplists.firehol.org/?ipset=tor_exits). `BLOCK\_PROXIES` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* Is set to yes, will block known proxies. Blacklist can be found [here](https://iplists.firehol.org/?ipset=firehol_proxies). `BLOCK\_ABUSERS` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* Is set to yes, will block known abusers. Blacklist can be found [here](https://iplists.firehol.org/?ipset=firehol_abusers_30d). `BLOCK\_REFERRER` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* Is set to yes, will block known bad referrer header. Blacklist can be found [here](https://raw.githubusercontent.com/mitchellkrogza/nginx-ultimate-bad-bot-blocker/master/_generator_lists/bad-referrers.list). #### DNSBL[¶](#dnsbl "Permalink to this heading") `USE\_DNSBL` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to *yes*, DNSBL checks will be performed to the servers specified in the `DNSBL\_LIST` environment variable. `DNSBL\_LIST` Values : *<list of DNS zones separated with spaces>* Default value : *bl.blocklist.de problems.dnsbl.sorbs.net sbl.spamhaus.org xbl.spamhaus.org* Context : *global*, *multisite* The list of DNSBL zones to query when `USE\_DNSBL` is set to *yes*. #### CrowdSec[¶](#crowdsec "Permalink to this heading") `USE\_CROWDSEC` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* If set to *yes*, [CrowdSec](https://github.com/crowdsecurity/crowdsec) will be enabled. Please note that you need a CrowdSec instance running see example [here](https://github.com/bunkerity/bunkerized-nginx/tree/master/examples/crowdsec). `CROWDSEC\_HOST` Values : *<full URL to the CrowdSec instance API>* Default value : Context : *global* The full URL to the CrowdSec API. `CROWDSEC\_KEY` Values : *<CrowdSec bouncer key>* Default value : Context : *global* The CrowdSec key given by *cscli bouncer add BouncerName*. #### Custom whitelisting[¶](#custom-whitelisting "Permalink to this heading") `USE\_WHITELIST\_IP` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to *yes*, lets you define custom IP addresses to be whitelisted through the `WHITELIST\_IP\_LIST` environment variable. `WHITELIST\_IP\_LIST` Values : *<list of IP addresses and/or network CIDR blocks separated with spaces>* Default value : *23.21.227.69 40.88.21.235 50.16.241.113 50.16.241.114 50.16.241.117 50.16.247.234 52.204.97.54 52.5.190.19 54.197.234.188 54.208.100.253 54.208.102.37 107.21.1.8* Context : *global*, *multisite* The list of IP addresses and/or network CIDR blocks to whitelist when `USE\_WHITELIST\_IP` is set to *yes*. The default list contains IP addresses of the [DuckDuckGo crawler](https://help.duckduckgo.com/duckduckgo-help-pages/results/duckduckbot/). `USE\_WHITELIST\_REVERSE` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to *yes*, lets you define custom reverse DNS suffixes to be whitelisted through the `WHITELIST\_REVERSE\_LIST` environment variable. `WHITELIST\_REVERSE\_LIST` Values : *<list of reverse DNS suffixes separated with spaces>* Default value : *.googlebot.com .google.com .search.msn.com .crawl.yahoot.net .crawl.baidu.jp .crawl.baidu.com .yandex.com .yandex.ru .yandex.net* Context : *global*, *multisite* The list of reverse DNS suffixes to whitelist when `USE\_WHITELIST\_REVERSE` is set to *yes*. The default list contains suffixes of major search engines. `WHITELIST\_USER\_AGENT` Values : *<list of regexes separated with spaces>* Default value : Context : *global*, *multisite* Whitelist user agent from being blocked by `BLOCK\_USER\_AGENT`. `WHITELIST\_URI` Values : *<list of URI separated with spaces>* Default value : Context : *global*, *multisite* URI listed here have security checks like bad user-agents, bad IP, … disabled. Useful when using callbacks for example. #### Custom blacklisting[¶](#custom-blacklisting "Permalink to this heading") `USE\_BLACKLIST\_IP` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to *yes*, lets you define custom IP addresses to be blacklisted through the `BLACKLIST\_IP\_LIST` environment variable. `BLACKLIST\_IP\_LIST` Values : *<list of IP addresses and/or network CIDR blocks separated with spaces>* Default value : Context : *global*, *multisite* The list of IP addresses and/or network CIDR blocks to blacklist when `USE\_BLACKLIST\_IP` is set to *yes*. `USE\_BLACKLIST\_REVERSE` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to *yes*, lets you define custom reverse DNS suffixes to be blacklisted through the `BLACKLIST\_REVERSE\_LIST` environment variable. `BLACKLIST\_REVERSE\_LIST` Values : *<list of reverse DNS suffixes separated with spaces>* Default value : *.shodan.io* Context : *global*, *multisite* The list of reverse DNS suffixes to blacklist when `USE\_BLACKLIST\_REVERSE` is set to *yes*. #### Requests limiting[¶](#requests-limiting "Permalink to this heading") `USE\_LIMIT\_REQ` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to yes, the amount of HTTP requests made by a user for a given resource will be limited during a period of time. `LIMIT\_REQ\_URL` Values : *<any valid url>* Default value : Context : *global*, *multisite* The URL where you want to apply the request limiting. Use special value of `/` to apply it globally for all URL. You can set multiple rules by adding a suffix number to the variable name like this : `LIMIT\_REQ\_URL\_1`, `LIMIT\_REQ\_URL\_2`, `LIMIT\_REQ\_URL\_3`, … `LIMIT\_REQ\_RATE` Values : *Xr/s* | *Xr/m* | *Xr/h* | *Xr/d* Default value : *1r/s* Context : *global*, *multisite* The rate limit to apply when `USE\_LIMIT\_REQ` is set to *yes*. Default is 1 request to the same URI and from the same IP per second. Possible value are : `s` (second), `m` (minute), `h` (hour) and `d` (day)). You can set multiple rules by adding a suffix number to the variable name like this : `LIMIT\_REQ\_RATE\_1`, `LIMIT\_REQ\_RATE\_2`, `LIMIT\_REQ\_RATE\_3`, … `LIMIT\_REQ\_BURST` Values : *<any valid integer>* Default value : *5* Context : *global*, *multisite* The number of requests to put in queue before rejecting requests. You can set multiple rules by adding a suffix number to the variable name like this : `LIMIT\_REQ\_BURST\_1`, `LIMIT\_REQ\_BURST\_2`, `LIMIT\_REQ\_BURST\_3`, … `LIMIT\_REQ\_DELAY` Values : *<any valid float>* Default value : *1* Context : *global*, *multisite* The number of seconds to wait before requests in queue are processed. Values like `0.1`, `0.01` or `0.001` are also accepted. You can set multiple rules by adding a suffix number to the variable name like this : `LIMIT\_REQ\_DELAY\_1`, `LIMIT\_REQ\_DELAY\_2`, `LIMIT\_REQ\_DELAY\_3`, … `LIMIT\_REQ\_CACHE` Values : *Xm* | *Xk* Default value : *10m* Context : *global* The size of the cache to store information about request limiting. #### Connections limiting[¶](#connections-limiting "Permalink to this heading") `USE\_LIMIT\_CONN` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to yes, the number of connections made by an ip will be limited during a period of time. (ie. very small/weak ddos protection) More info connections limiting [here](http://nginx.org/en/docs/http/ngx_http_limit_conn_module.html). `LIMIT\_CONN\_MAX` Values : *<any valid integer>* Default value : *50* Context : *global*, *multisite* The maximum number of connections per ip to put in queue before rejecting requests. `LIMIT\_CONN\_CACHE` Values : *Xm* | *Xk* Default value : *10m* Context : *global* The size of the cache to store information about connection limiting. #### Countries[¶](#countries "Permalink to this heading") `BLACKLIST\_COUNTRY` Values : *<country code 1> <country code 2> …* Default value : Context : *global*, *multisite* Block some countries from accessing your website. Use 2 letters country code separated with space. `WHITELIST\_COUNTRY` Values : *<country code 1> <country code 2> …* Default value : Context : *global*, *multisite* Only allow specific countries accessing your website. Use 2 letters country code separated with space. ### PHP[¶](#php "Permalink to this heading") `REMOTE\_PHP` Values : *<any valid IP/hostname>* Default value : Context : *global*, *multisite* Set the IP/hostname address of a remote PHP-FPM to execute .php files. `REMOTE\_PHP\_PATH` Values : *<any valid absolute path>* Default value : */app* Context : *global*, *multisite* The path where the PHP files are located inside the server specified in `REMOTE\_PHP`. `LOCAL\_PHP` Values : *<any valid absolute path>* Default value : Context : *global*, *multisite* Set the absolute path of the unix socket file of a local PHP-FPM instance to execute .php files. `LOCAL\_PHP\_PATH` Values : *<any valid absolute path>* Default value : */app* Context : *global*, *multisite* The path where the PHP files are located inside the server specified in `LOCAL\_PHP`. ### Bad behavior[¶](#bad-behavior "Permalink to this heading") `USE\_BAD\_BEHAVIOR` Values : *yes* | *no* Default value : *yes* Context : *global*, *multisite* If set to yes, bunkerized-nginx will block users getting too much “suspicious” HTTP codes in a period of time. `BAD\_BEHAVIOR\_STATUS\_CODES` Values : *<HTTP status codes separated with space>* Default value : *400 401 403 404 405 429 444* Context : *global*, *multisite* List of HTTP status codes considered as “suspicious”. `BAD\_BEHAVIOR\_THRESHOLD` Values : *<any positive integer>* Default value : *10* Context : *global*, *multisite* The number of “suspicious” HTTP status code before the corresponding IP is banned. `BAD\_BEHAVIOR\_BAN\_TIME` Values : *<any positive integer>* Default value : *86400* Context : *global*, *multisite* The duration time (in seconds) of a ban when the corresponding IP has reached the `BAD\_BEHAVIOR\_THRESHOLD`. `BAD\_BEHAVIOR\_COUNT\_TIME` Values : *<any positive integer>* Default value : *60* Context : *global*, *multisite* The duration time (in seconds) before the counter of “suspicious” HTTP is reset. ### Authelia[¶](#authelia "Permalink to this heading") `USE\_AUTHELIA` Values : *yes* | *no* Default value : *no* Context : *global*, *multisite* Enable or disable [Authelia](https://www.authelia.com/) support. See the [authelia example](https://github.com/bunkerity/bunkerized-nginx/tree/master/examples/authelia) for more information on how to setup Authelia with bunkerized-nginx. `AUTHELIA\_BACKEND` Values : *<any valid http(s) address>* Default value : Context : *global*, *multisite* The public Authelia address that users will be redirect to when they will be asked to login (e.g. : `https://auth.example.com`). `AUTHELIA\_UPSTREAM` Values : *<any valid http(s) address>* Default value : Context : *global*, *multisite* The private Authelia address when doing requests from nginx (e.g. : http://my-authelia.local:9091). `AUTHELIA\_MODE` Values : *portal* | *auth-basic* Default value : *portal* Context : *global*, *multisite* Choose authentication mode : show a web page (`portal`) or a simple auth basic prompt (`auth-basic`). ### misc[¶](#id2 "Permalink to this heading") `SWARM\_MODE` Values : *yes* | *no* Default value : *no* Context : *global* Only set to *yes* when you use *bunkerized-nginx* with Docker Swarm integration. `KUBERNETES\_MODE` Values : *yes* | *no* Default value : *no* Context : *global* Only set to *yes* when you use bunkerized-nginx with Kubernetes integration. `USE\_API` Values : *yes* | *no* Default value : *no* Context : *global* Only set to *yes* when you use bunkerized-nginx with Swarm/Kubernetes integration or with the web UI. `API\_URI` Values : *random* | *<any valid URI path>* Default value : *random* Context : *global* Only set to *yes* when you use bunkerized-nginx with Swarm/Kubernetes integration or with the web UI. `API\_WHITELIST\_IP` Values : *<list of IP/CIDR separated with space>* Default value : *192.168.0.0/16 172.16.0.0/12 10.0.0.0/8* Context : *global* List of IP/CIDR block allowed to send API order using the `API\_URI` uri. `USE\_REDIS` Undocumented. Reserved for future use. `REDIS\_HOST` Undocumented. Reserved for future use. Troubleshooting[¶](#troubleshooting "Permalink to this heading") ---------------------------------------------------------------- ### Logs[¶](#logs "Permalink to this heading") When troubleshooting, the logs are your best friends. We try our best to provide user-friendly logs to help you understand what happened. If you are using container based integrations, you can get the logs using your manager/orchestrator (e.g., docker logs, docker service logs, kubectl logs, …). For Linux integration, everything is stored inside the `/var/log` folder. You can edit the `LOG\_LEVEL` environment variable to increase or decrease the verbosity of logs with the following values : `debug`, `info`, `notice`, `warn`, `error`, `crit`, `alert` or `emerg` (with `debug` being the most verbose level). ### Permissions[¶](#permissions "Permalink to this heading") Don’t forget that bunkerized-nginx runs as an unprivileged user with UID/GID 101 when using container based integrations or simply the `nginx` user on Linux. Double check the permissions of files and folders for each special folders (see the [volumes list](https://bunkerized-nginx.readthedocs.io/en/latest/special_folders.html)). ### ModSecurity[¶](#modsecurity "Permalink to this heading") The OWASP Core Rule Set can sometimes leads to false positives. Here is what you can do : * Check if your application has exclusions rules (e.g : wordpress, nextcloud, drupal, …) * Edit the matched rules to exclude some parameters, URIs, … * Remove the matched rules if editing it is too much a hassle Some additional resources : * [Wordpress example](https://github.com/bunkerity/bunkerized-nginx/tree/master/examples/wordpress) * [Handling false positive](https://www.netnea.com/cms/apache-tutorial-8_handling-false-positives-modsecurity-core-rule-set/) * [Adding exceptions and tuning](https://coreruleset.org/docs/exceptions.html) ### Bad behavior[¶](#bad-behavior "Permalink to this heading") The [bad behavior](https://bunkerized-nginx.readthedocs.io/en/latest/security_tuning.html#bad-behaviors-detection) feature comes with a set of status codes considered as “suspicious”. You may need to tweak the corresponding list to avoid false positives within your application. ### Whitelisting[¶](#whitelisting "Permalink to this heading") It’s a common case that a bot gets flagged as suspicious and can’t access your website. Instead of disabling the corresponding security feature(s) we recommend a whitelisting approach. Here is a list of environment variables you can use : * `WHITELIST\_IP\_LIST` * `WHITELIST\_REVERSE\_LIST` * `WHITELIST\_URI` * `WHITELIST\_USER\_AGENT` More information [here](https://bunkerized-nginx.readthedocs.io/en/latest/environment_variables.html#custom-whitelisting). Plugins[¶](#plugins "Permalink to this heading") ------------------------------------------------ Bunkerized-nginx comes with a plugin system that lets you extend the core with extra security features. ### Official plugins[¶](#official-plugins "Permalink to this heading") * [ClamAV](https://github.com/bunkerity/bunkerized-nginx-clamav) : automatically scan uploaded files and deny access if a virus is detected * [CrowdSec](https://github.com/bunkerity/bunkerized-nginx-crowdsec) : CrowdSec bouncer integration within bunkerized-nginx ### Community plugins[¶](#community-plugins "Permalink to this heading") If you have made a plugin and want it to be listed here, feel free to [create a pull request](https://github.com/bunkerity/bunkerized-nginx/pulls) and edit that section. ### Use a plugin[¶](#use-a-plugin "Permalink to this heading") The generic way of using a plugin consists of : * Download the plugin into your local drive (e.g., git clone) * Edit the settings inside the plugin.json files (e.g., myplugin/plugin.json) * If you are using a container based integration, you need to mount it to the [plugins special folder](https://bunkerized-nginx.readthedocs.io/en/latest/special_folders.html#plugins) inside the container (e.g., /where/is/myplugin:/plugins/myplugin) * If you are using Linux integration, copy the downloaded plugin folder to the [plugins special folder](https://bunkerized-nginx.readthedocs.io/en/latest/special_folders.html#plugins) (e.g., cp -r myplugin /plugins) To check if the plugin is loaded you should see log entries like that : ``` 2021/06/05 09:19:47 [error] 104#104: [PLUGINS] *NOT AN ERROR* plugin MyPlugin/1.0 has been loaded ``` ### Write a plugin[¶](#write-a-plugin "Permalink to this heading") A plugin is composed of a plugin.json which contains metadata (e.g. : name, settings, …) and a set of LUA files for the plugin code. #### plugin.json[¶](#plugin-json "Permalink to this heading") ``` { "id": "myplugin", "name": "My Plugin", "description": "Short description of my plugin.", "version": "1.0", "settings": { "MY\_SETTING": "value1", "ANOTHER\_SETTING": "value2", } } ``` The `id` value is really important because it must match the subfolder name inside the `plugins` volume. Choose one which isn’t already used to avoid conflicts. Settings names and default values can be choosen freely. There will be no conflict when you retrieve them because they will be prefixed with your plugin id (e.g. : `myplugin\_MY\_SETTING`). #### Main code[¶](#main-code "Permalink to this heading") ``` local M = {} local logger = require "logger" -- this function will be called at startup -- the name MUST be init without any argument function M.init () -- the logger.log function lets you write into the logs -- only ERROR level is available in init() logger.log(ngx.ERR, "MyPlugin", "\*NOT AN ERROR\* init called") -- here is how to retrieve a setting local my\_setting = ngx.shared.plugins\_data:get("pluginid\_MY\_SETTING") logger.log(ngx.ERR, "MyPlugin", "\*NOT AN ERROR\* my\_setting = " .. my\_setting) return true end -- this function will be called for each request -- the name MUST be check without any argument function M.check () -- the logger.log function lets you write into the logs logger.log(ngx.NOTICE, "MyPlugin", "check called") -- here is how to retrieve a setting local my\_setting = ngx.shared.plugins\_data:get("pluginid\_MY\_SETTING") -- a dummy example to show how to block a request if my\_setting == "block" then ngx.exit(ngx.HTTP\_FORBIDDEN) end end return M ``` That file must have the same name as the `id` defined in the plugin.json with a .lua suffix (e.g. : `myplugin.lua`). Under the hood, bunkerized-nginx uses the [lua nginx module](https://github.com/openresty/lua-nginx-module) therefore you should be able to access to the whole **ngx.\*** functions. #### Dependencies[¶](#dependencies "Permalink to this heading") Since the core already uses some external libraries you can use it in your own plugins too (see the [compile.sh](https://github.com/bunkerity/bunkerized-nginx/blob/master/compile.sh) file and the [core lua files](https://github.com/bunkerity/bunkerized-nginx/tree/master/lua)). In case you need to add dependencies, you can do it by placing the corresponding files into the same folder of your main plugin code. Here is an example with a file named **dependency.lua** : ``` local M = {} function M.my\_function () return "42" end return M ``` To include it from you main code you will need to prefix it with your plugin id like that : ``` ... local my\_dependency = require "pluginid.dependency" function M.check () ... local my\_value = my\_dependency.my\_function() ... end ... ```

elastic

go

Elastic v5.1.0 documentation [Elastic](index.html#document-index) stable * [Physical Principles](index.html#document-intro) + [Elasticity of crystals](index.html#elasticity-of-crystals) + [Numerical derivation of elastic matrix](index.html#numerical-derivation-of-elastic-matrix) + [Crystal symmetry and elastic matrix derivation](index.html#crystal-symmetry-and-elastic-matrix-derivation) * [Installation](index.html#document-cli-usage) + [Conda](index.html#conda) + [PyPi](index.html#pypi) + [Manual](index.html#manual) + [Testing](index.html#testing) * [Usage](index.html#usage) * [Library usage](index.html#document-lib-usage) + [Simple Parallel Calculation](index.html#simple-parallel-calculation) + [Birch-Murnaghan Equation of State](index.html#birch-murnaghan-equation-of-state) + [Calculation of the elastic tensor](index.html#calculation-of-the-elastic-tensor) * [Implementation](index.html#document-modules) + [Modules](index.html#modules) - [Parallel Calculator Module](index.html#parallel-calculator-module) - [Elastic Module](index.html#elastic-module) * [Indices and tables](index.html#document-endmatter) * [References](index.html#references) [Elastic](index.html#document-index) * [Docs](index.html#document-index) » * Elastic v5.1.0 documentation * [Edit on GitHub](https://github.com/jochym/Elastic/blob/09fb7f2e9e2d0780cac7b6c900582a32e4bd070d/docs/source/index.rst) --- Calculation of elastic properties of crystals[¶](#calculation-of-elastic-properties-of-crystals "Permalink to this headline") ============================================================================================================================= [](http://dx.doi.org/10.5281/zenodo.18759) --- **New version 5.0 released** The new version is API *incompatible* with the previous versions. It provides a new command line utility as the main user interface to the package - which hoppefully will broaden the user base byond python users. --- Elastic is a set of python routines for calculation of elastic properties of crystals (elastic constants, equation of state, sound velocities, etc.). It is a fifth version of the in-house code I have written over several years and is implemented as a extension to the [ASE](https://wiki.fysik.dtu.dk/ase/) system and a script providing interface to the library not requiring knowledge of python or ASE system. The code was a basis for some of my publications and was described briefly in these papers. The code was available to anyone, presented at our [Workshop on ab initio Calculations in Geosciences](http://wolf.ifj.edu.pl/workshop/work2008/) and used by some of my co-workers but was never properly published with full documentation, project page etc. In 2010, I have decided to re-implement elastic as a module for the [ASE](https://wiki.fysik.dtu.dk/ase/) system and publish it properly under the GPL as versions 3 and 4. Later, in 2017, needs of users nudged me into implementing the command-line front-end to the library which is included with version 5.0 of the package. The version 5.0 also changes API of the library from mix-in class to the set of simple functions providing functionality of the module. The workflow of the package was also changed to prepare data - calculate - post-process style, which is better suited to serious research work. The source code started live on the [launchpad project page](https://launchpad.net/elastic) and later in 2014 moved to the [github repository](https://github.com/jochym/Elastic) with corresponding [elastic web page](https://jochym.github.io/Elastic/) and on-line documentation placed at [Elastic website](http://wolf.ifj.edu.pl/elastic/) (you are probably reading from it already). The project is free software and I welcome patches, ideas and other feedback. Physical Principles[¶](#physical-principles "Permalink to this headline") ------------------------------------------------------------------------- Elastic is based on the standard elasticity theory (see [[LL]](index.html#ll) for the detailed introduction) and *finite deformation* approach to the calculation of elastic tensor of the crystal. I have described basic physical principles on which the code rests in my habilitation thesis. Here I will include slightly edited second chapter of the thesis introducing the method and some implementation details. ### Elasticity of crystals[¶](#elasticity-of-crystals "Permalink to this headline") The classical, linear theory of elasticity of crystalline materials has been formulated already in the 18th and 19th century by Cauchy, Euler, Poisson, Young and many other great mathematicians and physicists of that time. The standard textbook formulation (e.g. classical book by Landau et al. [[LL]](index.html#ll)) can be, in principle, directly used as a basis for numerical determination of the elastic tensor and other mechanical properties of the crystal. Nevertheless, practical implementation of these formulas have some non-obvious aspects, worthy of explicit presentation. The *finite deformation* method developed and used in the mentioned papers [[TiC]](index.html#tic), [[ZrC]](index.html#zrc) is based on the fundamental relationship between stress and strain of the solid crystalline body with a particular symmetry. This is a simple tensor equation, sometimes called generalised *Hook’s law* (in standard tensor notation): \[\sigma\_{\lambda\xi} = C\_{\lambda\xi\mu\nu} s\_{\mu\nu}\] This formula simply states that the stress in the crystal \(\sigma\_{\lambda\xi}\) is a linear function of the strain \(s\_{\mu\nu}\) incurred by its deformation, and the elasticity tensor \(C\_{\lambda\xi\mu\nu}\) is just a tensor proportionality coefficient. The Greek indexes run through coordinates x, y, z. The elasticity tensor inherits symmetries of the crystal and has some intrinsic symmetries of its own. Therefore, only a small number of its components are independent. This fact leads to customary representation of this entity in the form of the matrix with components assigned according to Voight’s notation. Thus, instead of the rank-4 three dimensional tensor we have \(6 \times 6\) matrix \(C\_{ij}\) where the indexes \(i, j = 1 \ldots 6\). The stress and strain tensors are represented as six-dimensional vectors. The symmetries of the elastic tensor are directly translated into symmetries of the \(C\_{ij}\) matrix. The Voight’s notation is commonly used in tensor calculus. For this particular case we can write it as an index assignment where each pair of Greek indexes is replaced with a corresponding Latin index (i, j, k, l, m, n): xx=1, yy=2, zz=3, yz=4, xz=5, xy=6. While this convention makes presentation of elastic constants much easier - since it is just a square table of numbers - it slightly complicates algebraic procedures as we lose the simplicity of the tensor formalism. Every class of crystal implies, through its symmetry, a different number of independent elements in the \(C\_{ij}\) matrix. For example, the cubic lattice has just three independent elements in the elastic matrix: \(C\_{11}, C\_{12}, C\_{44}\), and the matrix itself has the following shape: \[\begin{split}\left[\begin{array}{cccccc} C\_{11} & C\_{12} & C\_{12} & 0 & 0 & 0\\ C\_{12} & C\_{11} & C\_{12} & 0 & 0 & 0\\ C\_{12} & C\_{12} & C\_{11} & 0 & 0 & 0\\ 0 & 0 & 0 & C\_{44} & 0 & 0\\ 0 & 0 & 0 & 0 & C\_{44} & 0\\ 0 & 0 & 0 & 0 & 0 & C\_{44}\end{array}\right]\end{split}\] Less symmetric crystals have, naturally, a higher number of independent elastic constants and lower symmetry of the \(C\_{ij}\) matrix (see [[LL]](index.html#ll) for full introduction to theory of elasticity). ### Numerical derivation of elastic matrix[¶](#numerical-derivation-of-elastic-matrix "Permalink to this headline") Numerical derivation of the \(C\_{ij}\) matrix may be approached in many different ways. Basically, we can employ the same methods as used effectively in experimental work. From all experimental procedures we can select three classes which are relevant to our discussion: 1. Based on the measured sound velocity, including various methods based on determination of lattice dynamics of the crystal. 2. Based on the strain-energy relation. 3. Based on the measured stress-strain relations for some particular, simple strains. While the first method is frequently used in laboratory measurements, it is not direct and is not well suited to numerical derivation. For example, you can measure the tangent of all acoustic branches of phonon dispersion curves in several directions to get enough data points to solve the set of equations for most of the independent components of the \(C\_{ij}\) matrix. The tangent of the acoustic branch is connected with the sound velocity and with components of elastic matrix by a set of equations of the general form: \[\varrho v\_{k}^{2}=L(C\_{ij})\] where \(L(C\_{ij})\) is a linear combination of independent components of elastic tensor, \(v\_{k}\) is a long-wave sound velocity in particular direction, which is equivalent to the slope of the acoustic branch of phonon dispersion curve in this direction, and \(\varrho\) is crystal density. Full set of these equations for the cubic crystal is included in [[TiC]](index.html#tic). Unfortunately, it is difficult and non-practical to use this method to obtain more then few of the simplest of components, since the numerical properties of the non-linear formulas involved lead to the error pile-up in the results. It is particularly susceptible to errors in long-wave sound velocities – due to the quadratic function in above equation. Unfortunately, these asymptotic velocities are particularly weakly constrained by most of available computational methods. The same formulas can also be used to obtain elastic matrix from straight-forward sound velocity measurements. The same unfavourable numerical properties lead to high demands on accuracy of the measurements – but in this case these requirements could be quite easily met in experiment since sound velocity can be measured with very high precision. The second method is not practical for laboratory measurements - it is not easy to accurately measure energy of the deformed crystal. Furthermore, the strain-energy relation is non-linear and we need to extract a derivative of the function – the procedure is quite complex, needs more data points and is prone to errors. The third method is well suited for experimental work as well as computational derivation of the elastic matrix. The numerical properties of the formulas – being just a set of linear equations – are well known and provide stable and well-controlled error propagation. Furthermore, while the sound velocity is not directly accessible to computational quantum mechanical methods, the stresses induced by strains on the crystal are almost universally provided by DFT based programs and often do not require any additional computational effort. The comparison of these methods used for computational derivation of the elastic matrix is included in [[TiC]](index.html#tic), [[ZrC]](index.html#zrc). The comparison shows that the finite deformation (stress-strain) method compares favourably to the pure energy-derivative method. The results clearly show that the strain–stress relationship approach described here is much better suited for computational derivation of elastic matrix and provides lower error level than other two methods. ### Crystal symmetry and elastic matrix derivation[¶](#crystal-symmetry-and-elastic-matrix-derivation "Permalink to this headline") As mentioned above, the symmetry of the crystal determines the number and position of independent components of the \(C\_{ij}\) matrix. Therefore, the stress-strain relation is effectively modified by the symmetry of the case by a simple fact that most, of the coefficients are not independent from one another. We aim to derive the complete set of \(C\_{ij}\) elements from the set of computational or experimental measurements of strain and stress tensors \(s^{a}\), \(\sigma^{a}\) where the upper Latin index a numbers a calculation/experiment setup. In the case described here the “measurement” is a particular computational setup with the crystal deformed in various ways in order to provide enough data points to derive all independent components of the \(C\_{ij}\) matrix. The set of necessary deformations can be determined by the symmetry of the crystal and contains tetragonal and sheer deformations along some or all axis – as the symmetry of the case dictates. To improve the accuracy of the results the deformations may be of different sizes (typically 0.1-1% in length or 0.1-1 degree in angle). Having a set of calculation data \(\{s^{a}, \sigma^{a}\}\), we can rewrite generalised Hook’s law to form a set of linear equations (in Voight notation for \(i,j\) indexes): \(C\_{ij}s\_{j}^{a}=\sigma\_{i}^{a}\). This set can be further transformed for each symmetry case to the form in which the independent components of the \(C\_{ij}\) matrix create a vector of unknowns and the symmetry relations and strains \(s\_{j}^{a}\) create a new equation matrix \(S\). \(S\_{ju}(s^{a})C\_{u}=\sigma\_{j}^{a}\). The \(S(s)\) matrix is a linear function of the strain vector s with all symmetry relations taken into account. The index a runs over all data sets we have in the calculation while index u runs over all independent components of the \(C\_{ij}\) matrix. For the cubic crystal the above equation takes explicit form: \[\begin{split}\left[\begin{array}{ccc} s\_{1} & s\_{2}+s\_{3} & 0\\ s\_{2} & s\_{1}+s\_{3} & 0\\ s\_{3} & s\_{1}+s\_{2} & 0\\ 0 & 0 & 2s\_{4}\\ 0 & 0 & 2s\_{5}\\ 0 & 0 & 2s\_{6}\end{array}\right]^{a}\left[\begin{array}{c} C\_{11}\\ C\_{12}\\ C\_{44}\end{array}\right]=\left[\begin{array}{c} \sigma\_{1}\\ \sigma\_{2}\\ \sigma\_{3}\\ \sigma\_{4}\\ \sigma\_{5}\\ \sigma\_{6}\end{array}\right]^{a}.\end{split}\] Note the a index of S and \(\sigma\), which creates a set of \(n\times6\) linear equations for 3 unknowns \(\left[C\_{11},C\_{12},C\_{44}\right]\), where n is a number of independent calculations of stresses incurred in crystal by strains. In principle, the above relations could be expressed in the non-symmetry specific form with either a full set of indexes and the symmetry information encoded in the single matrix of constant elements or even in the pure tensor formulation with the four-index elastic tensor \(C\) and two-index stress and strain tensors. While this type of formulation is definitely more regular and sometimes easier to manipulate in formal transformations, it is not very useful for numerical calculations or writing computer code – multi-dimensional arrays are difficult to manipulate and are prone to many trivial notation errors. Thus, it is better to split the general formula to crystal classes with different number of \(C\_{ij}\) components (i.e. length of the \(C\_{u}\) vector) and separate shape of the \(S\) matrix. This is an approach used by Elastic. For example, in the orthorhombic crystal the vector of independent \(C\_{ij}\) components has nine elements and the S matrix is a \(9\times6\) one: \[\begin{split}\left[\begin{array}{ccccccccc} s\_{1} & 0 & 0 & s\_{2} & s\_{3} & 0 & 0 & 0 & 0\\ 0 & s\_{2} & 0 & s\_{1} & 0 & s\_{3} & 0 & 0 & 0\\ 0 & 0 & s\_{3} & 0 & s\_{1} & s\_{2} & 0 & 0 & 0\\ 0 & 0 & 0 & 0 & 0 & 0 & 2s\_{4} & 0 & 0\\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2s\_{5} & 0\\ 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2s\_{6}\end{array}\right]^{a}\left[\begin{array}{c} C\_{11}\\ C\_{22}\\ C\_{33}\\ C\_{12}\\ C\_{13}\\ C\_{23}\\ C\_{44}\\ C\_{55}\\ C\_{66}\end{array}\right]=\left[\begin{array}{c} \sigma\_{1}\\ \sigma\_{2}\\ \sigma\_{3}\\ \sigma\_{4}\\ \sigma\_{5}\\ \sigma\_{6}\end{array}\right]^{a}.\end{split}\] The elements of the matrix S have direct relation to the terms of expansion of the elastic free energy as a function of deformation (strain tensor) F(s). For example, the orthorhombic equation can be derived from the free energy formula (see [[LL]](index.html#ll) for derivation): \[\begin{split}F(s) = \frac{1}{2}C\_{11}s\_{1}^{2}+ \frac{1}{2}C\_{22}s\_{2}^{2}+ \frac{1}{2}C\_{33}s\_{3}^{2}+ C\_{12}s\_{1}s\_{2}+C\_{13}s\_{1}s\_{3}+C\_{23}s\_{2}s\_{3}+ \\ 2C\_{44}s\_{4}^{2}+2C\_{55}s\_{5}^{2}+2C\_{66}s\_{6}^{2}\end{split}\] The elements of the S matrix are simply coefficients of first derivatives of the F(s) over respective strain components. Alternatively, we can rewrite the S(s) matrix in the compact form as a mixed derivative: \[S\_{iu}=A\frac{\partial^{2}F}{\partial s\_{i}\partial C\_{u}},\] where A is a multiplier taking into account the double counting of the off-diagonal components in the free energy formula (see note at the end of the exercises in [[LL]](index.html#ll)). The multiplier \(A=1\) for \(i \leq 4\), and \(1/2\) otherwise. The above general formula turns out to be quite helpful in less trivial cases of trigonal or hexagonal classes. For instance, the hexagonal elastic free energy (see [[LL]](index.html#ll) for rather lengthy formula) leads to the following set of equations: \[\begin{split}\left[\begin{array}{ccccc} s\_{1} & 0 & s\_{2} & s\_{3} & 0\\ s\_{2} & 0 & s\_{1} & s\_{3} & 0\\ 0 & s\_{3} & 0 & s\_{1}+s\_{2} & 0\\ 0 & 0 & 0 & 0 & 2s\_{4}\\ 0 & 0 & 0 & 0 & 2s\_{5}\\ s\_{6} & 0 & -s\_{6} & 0 & 0\end{array}\right]^{a}\left[\begin{array}{c} C\_{11}\\ C\_{33}\\ C\_{12}\\ C\_{13}\\ C\_{44}\end{array}\right]=\left[\begin{array}{c} \sigma\_{1}\\ \sigma\_{2}\\ \sigma\_{3}\\ \sigma\_{4}\\ \sigma\_{5}\\ \sigma\_{6}\end{array}\right]^{a}.\end{split}\] The set of linear equations, with calculated strains and stresses inserted into the \(S^{a}\) matrix and \(\sigma^{a}\) vector, could be constructed for any crystal – only the form of the S matrix and the length of the \(C\_{u}\) vector will be different for each symmetry. The set of equations is usually over-determined. Therefore, it cannot be solved in the strict linear-algebra sense since no exact solution could exist. Nevertheless, this set of equations can be solved in approximate sense – i.e. minimising the length of the residual vector of the solution. Fortunately, a very clever algorithm capable of dealing with just this type of linear equations has been known for a long time. It is called Singular Value Decomposition [[SVD]](index.html#svd). Not only does it provide the approximate solution minimising the residual vector of the equation but also is stable against numerically ill-conditioned equations or equations which provide too little data to determine all components of the solution. The SVD provides also some indication of the quality of the obtained solution in the form of the vector of singular values, which could be used to judge whether the solution is well-determined. It is a well known algorithm and its implementations are available in every self-respecting numerical linear algebra library. The implementation used in the Elastic code is the one included in the Scientific Python library [SciPy](http://www.scipy.org/). Installation[¶](#installation "Permalink to this headline") ----------------------------------------------------------- [](https://anaconda.org/conda-forge/elastic) [](https://conda.anaconda.org/conda-forge) [](https://anaconda.org/conda-forge/elastic) ### Conda[¶](#conda "Permalink to this headline") The installation procedure is quite simple if you use, *highly recommended* [conda package manager](http://conda.pydata.org/miniconda.html): ``` conda install -c conda-forge elastic ``` The above command installs elastic with all dependencies into your current conda environment. If you want to add my anaconda.org channel into your conda installation you need to run following command: ``` conda config --add channels conda-forge ``` The above method has additional benefit of providing current installation of ASE and spglib libraries. ### PyPi[¶](#pypi "Permalink to this headline") The package is published simultaneously on conda and pypi. The second recommended way to install elastic is with pip: ``` pip install elastic ``` which should install the package and all its dependencies. Note that the number of dependencies is rather large and some of them are fairly large. Most of them, however, are just standard scientific python packages - almost all are present in standard anaconda install. ### Manual[¶](#manual "Permalink to this headline") To install the code *pedestrian way* you need to install following python packages (most, if not all, are available in major linux distributions): * [SciPy and NumPy](http://www.scipy.org/) libraries * [matplotlib](http://matplotlib.org/) (not strictly required, but needed for testing and plotting) * [ASE](https://wiki.fysik.dtu.dk/ase/) system * [spglib](https://atztogo.github.io/spglib/) space group library * Some ASE calculator (VASP, GPAW, abinit, …), but be warned that for now the code was developed using VASP only. I will be happy to help you extending it to other calculators. The code can be used without supported ASE calculator using command line interface and external, independent calculation tool. This is highly system-dependent and I am unable to provide detailed support for this type of install - I use conda install of ASE/elastic myself! Some legacy [installation guides](https://github.com/jochym/qe-doc/blob/master/Installation.ipynb) which may help you with manual process could be find at the [QE-doc project pages](https://jochym.github.io/qe-doc/). Essentially, you need to clone the repository and run: ``` python setup.py install ``` in the main directory. But this is really not recommended way to install. Use it at your own risk and if you know what you are doing. ### Testing[¶](#testing "Permalink to this headline") The simplest verification if everything works is just using the `elastic` utility to see the help screen: ``` elastic --help ``` or version of the package: ``` elastic --version ``` Additionally the whole package has a set of unittests based on hypothesis package. The tests are self-contained and do not require any external packages like DFT programs (e.g. VASP). You can run these tests by executing following command in the source directory: ``` python -m unittest discover -s test -b ``` Usage[¶](#usage "Permalink to this headline") --------------------------------------------- Starting from ver. 5.0, the command line utility `elastic` is a primary interface to the package and the direct python programming with the library is relegated to non-standard calculations. The basic calculation scheme can be summarized with the following list: * Prepare the basic structure data in the form of VASP POSCAR file or abinit input file. Support for other programs can be added relatively easily. Contact the author if you need it. The structure should be fully optimized represent what you consider to be ground state of the system. * run `elastic` on the structure to generate deformed structures probing the properties of the system: ``` elastic -v --cij gen -n 5 -s 2 POSCAR ``` which generates a set of deformed systems named `cij\_XXX.POSCAR`, where `XXX` is replaced by numbers with 000 corresponding to undisturbed structure. * run your DFT program (VASP, abinit, etc.) on all systems. This step depends on your particular system, and you need to handle it yourself. You need to make sure that for each system the internal degrees of freedom are optimized and full stress tensor gets calculated. Example bash script handling this task on my cluster looks like this: ``` #!/bin/bash # Command to run vasp in current directory RUN\_VASP="/opt/bin/run-vasp541" for s in $\* ; do d=${s%%.POSCAR} echo -n $d ": " mkdir calc-$d ( cd calc-$d ln -s ../INCAR ../KPOINTS ../POTCAR ../vasprun.conf . ln -s ../$s POSCAR $RUN\_VASP ) done ``` This produces a set of directories: `calc-cij\_XXX` with completed single-point calculations. * run `elastic` again to post-process the calculations. We do that by feeding it with output from the DFT calculations. Remember to put undisturbed structure at the first position: ``` elastic -v --cij proc calc-cij_000/vasprun.xml calc-cij_*/vasprun.xml ``` You can test this procedure using data provided as a reference in the `tests/data` directory. If you run the script on the provided data you should get following output: ``` elastic -v --cij proc calc-cij\_000/vasprun.xml calc-cij\_\*/vasprun.xml Cij solution ------------------------------ Solution rank: 3 Square of residuals: 0.00053 Relative singular values: 1.0000 0.7071 0.6354 Elastic tensor (GPa): C\_11 C\_12 C\_44 ------------------------------ 321.15 95.88 143.44 ``` The data provided correspond to cubic MgO crystal. The DFT calculation setup is tuned to provide quick results for testing and *should not* be used as a guide for production calculations. You *need* to determine proper calculation setup for your system. Library usage[¶](#library-usage "Permalink to this headline") ------------------------------------------------------------- ### Simple Parallel Calculation[¶](#simple-parallel-calculation "Permalink to this headline") Once you have everything installed and running you can run your first real calculation. The first step is to import the modules to your program (the examples here use VASP calculator) ``` from ase.spacegroup import crystal import ase.units as units import numpy import matplotlib.pyplot as plt from parcalc import ClusterVasp, ParCalculate from elastic import get\_pressure, BMEOS, get\_strain from elastic import get\_elementary\_deformations, scan\_volumes from elastic import get\_BM\_EOS, get\_elastic\_tensor ``` next we need to create our example MgO crystal: ``` a = 4.194 cryst = crystal(['Mg', 'O'], [(0, 0, 0), (0.5, 0.5, 0.5)], spacegroup=225, cellpar=[a, a, a, 90, 90, 90]) ``` We need a calculator for our job, here we use VASP and ClusterVasp defined in the parcalc module. You can probably replace this calculator by any other ASE calculator but this was not tested yet. Thus let us define the calculator: ``` # Create the calculator running on one, eight-core node. # This is specific to the setup on my cluster. # You have to adapt this part to your environment calc = ClusterVasp(nodes=1, ppn=8) # Assign the calculator to the crystal cryst.set\_calculator(calc) # Set the calculation parameters calc.set(prec = 'Accurate', xc = 'PBE', lreal = False, nsw=30, ediff=1e-8, ibrion=2, kpts=[3,3,3]) # Set the calculation mode first. # Full structure optimization in this case. # Not all calculators have this type of internal minimizer! calc.set(isif=3) ``` Finally, run our first calculation. Obtain relaxed structure and residual pressure after optimization: ``` print("Residual pressure: %.3f bar" % ( get\_pressure(cryst.get\_stress()))) ``` ``` Residual pressure: 0.000 bar ``` If this returns proper pressure (close to zero) we can use the obtained structure for further calculations. For example we can scan the volume axis to obtain points for equation of state fitting. This will demonstrate the ability to run several calculations in parallel - if you have a cluster of machines at your disposal this will speed up the calculation considerably. ``` # Lets extract optimized lattice constant. # MgO is cubic so a is a first diagonal element of lattice matrix a=cryst.get\_cell()[0,0] # Clean up the directory calc.clean() systems=[] # Iterate over lattice constant in the +/-5% range for av in numpy.linspace(a\*0.95,a\*1.05,5): systems.append(crystal(['Mg', 'O'], [(0, 0, 0), (0.5, 0.5, 0.5)], spacegroup=225, cellpar=[av, av, av, 90, 90, 90])) # Define the template calculator for this run # We can use the calc from above. It is only used as a template. # Just change the params to fix the cell volume calc.set(isif=2) # Run the calculation for all systems in sys in parallel # The result will be returned as list of systems res res=ParCalculate(systems,calc) # Collect the results v=[] p=[] for s in res : v.append(s.get\_volume()) p.append(get\_pressure(s.get\_stress())) # Plot the result (you need matplotlib for this plt.plot(v,p,'o') plt.show() ``` ``` Workers started: 5 ```  ### Birch-Murnaghan Equation of State[¶](#birch-murnaghan-equation-of-state "Permalink to this headline") Let us now use the tools provided by the modules to calculate equation of state for the crystal and verify it by plotting the data points against fitted EOS curve. The EOS used by the module is a well established Birch-Murnaghan formula (P - pressure, V - volume, B - parameters): \[P(V)= \frac{B\_0}{B'\_0}\left[ \left({\frac{V}{V\_0}}\right)^{-B'\_0} - 1 \right]\] Now we repeat the setup and optimization procedure from the example 1 above but using a new Crystal class (see above we skip this part for brevity). Then comes a new part (IDOF - Internal Degrees of Freedom): ``` # Switch to cell shape+IDOF optimizer calc.set(isif=4) # Calculate few volumes and fit B-M EOS to the result # Use +/-3% volume deformation and 5 data points deform=scan\_volumes(cryst, n=5,lo=0.97,hi=1.03) # Run the calculations - here with Cluster VASP res=ParCalculate(deform,calc) # Post-process the results fit=get\_BM\_EOS(cryst, systems=res) # Get the P(V) data points just calculated pv=numpy.array(cryst.pv) # Sort data on the first column (V) pv=pv[pv[:, 0].argsort()] # Print just fitted parameters print("V0=%.3f A^3 ; B0=%.2f GPa ; B0'=%.3f ; a0=%.5f A" % ( fit[0], fit[1]/units.GPa, fit[2], pow(fit[0],1./3))) v0=fit[0] # B-M EOS for plotting fitfunc = lambda p, x: numpy.array([BMEOS(xv,p[0],p[1],p[2]) for xv in x]) # Ranges - the ordering in pv is not guarateed at all! # In fact it may be purely random. x=numpy.array([min(pv[:,0]),max(pv[:,0])]) y=numpy.array([min(pv[:,1]),max(pv[:,1])]) # Plot the P(V) curves and points for the crystal # Plot the points plt.plot(pv[:,0]/v0,pv[:,1]/units.GPa,'o') # Mark the center P=0 V=V0 plt.axvline(1,ls='--') plt.axhline(0,ls='--') # Plot the fitted B-M EOS through the points xa=numpy.linspace(x[0],x[-1],20) plt.plot(xa/v0,fitfunc(fit,xa)/units.GPa,'-') plt.title('MgO pressure vs. volume') plt.xlabel('$V/V\_0$') plt.ylabel('P (GPa)') plt.show() ``` ``` Workers started: 5 V0=74.233 A^3 ; B0=168.19 GPa ; B0'=4.270 ; a0=4.20275 A ```  If you set up everything correctly you should obtain fitted parameters printed out in the output close to: \[V\_0 = 73.75 \text{ A}^3 \quad B\_0 = 170 \text{ GPa} \quad B'\_0 = 4.3 \quad a\_0 = 4.1936 \text{ A}\] ### Calculation of the elastic tensor[¶](#calculation-of-the-elastic-tensor "Permalink to this headline") Finally let us calculate an elastic tensor for the same simple cubic crystal - magnesium oxide (MgO). For this we need to create the crystal and optimize its structure (see :ref:`parcalc` above). Once we have an optimized structure we can switch the calculator to internal degrees of freedom optimization (IDOF) and calculate the elastic tensor: ``` # Switch to IDOF optimizer calc.set(isif=2) # Create elementary deformations systems = get\_elementary\_deformations(cryst, n=5, d=0.33) # Run the stress calculations on deformed cells res = ParCalculate(systems, calc) # Elastic tensor by internal routine Cij, Bij = get\_elastic\_tensor(cryst, systems=res) print("Cij (GPa):", Cij/units.GPa) ``` ``` Workers started: 10 Cij (GPa): [ 338.46921273 103.64272667 152.2150523 ] ``` To make sure we are getting the correct answer let us make the calculation for \(C\_{11}, C\_{12}\) by hand. We will deform the cell along a (x) axis by +/-0.2% and fit the \(3^{rd}\) order polynomial to the stress-strain data. The linear component of the fit is the element of the elastic tensor: ``` from elastic.elastic import get\_cart\_deformed\_cell # Create 10 deformation points on the a axis systems = [] for d in numpy.linspace(-0.2,0.2,10): systems.append(get\_cart\_deformed\_cell(cryst, axis=0, size=d)) # Calculate the systems and collect the stress tensor for each system r = ParCalculate(systems, cryst.calc) ss=[] for s in r: ss.append([get\_strain(s, cryst), s.get\_stress()]) ss=numpy.array(ss) lo=min(ss[:,0,0]) hi=max(ss[:,0,0]) mi=(lo+hi)/2 wi=(hi-lo)/2 xa=numpy.linspace(mi-1.1\*wi,mi+1.1\*wi, 50) ``` ``` Workers started: 10 ``` ``` # Make a plot plt.plot(ss[:,0,0],ss[:,1,0]/units.GPa,'.') plt.plot(ss[:,0,0],ss[:,1,1]/units.GPa,'.') plt.axvline(0,ls='--') plt.axhline(0,ls='--') # Now fit the polynomials to the data to get elastic constants # C11 component f=numpy.polyfit(ss[:,0,0],ss[:,1,0],3) c11=f[-2]/units.GPa # Plot the fitted function plt.plot(xa,numpy.polyval(f,xa)/units.GPa,'-', label='$C\_{11}$') # C12 component f=numpy.polyfit(ss[:,0,0],ss[:,1,1],3) c12=f[-2]/units.GPa # Plot the fitted function plt.plot(xa,numpy.polyval(f,xa)/units.GPa,'-', label='$C\_{12}$') plt.xlabel('Relative strain') plt.ylabel('Stress componnent (GPa)') plt.title('MgO, strain-stress relation ($C\_{11}, C\_{12}$)') plt.legend(loc='best') # Here are the results. They should agree with the results # of the internal routine. print('C11 = %.3f GPa, C12 = %.3f GPa => K= %.3f GPa' % ( c11, c12, (c11+2\*c12)/3)) plt.show() ``` ``` C11 = 325.005 GPa, C12 = 102.441 GPa => K= 176.629 GPa ```  If you set up everything correctly you should obtain fitted parameters printed out in the output close to: ``` Cij (GPa): [ 340 100 180] ``` With the following result of fitting: ``` C11 = 325 GPa, C12 = 100 GPa => K= 180 GPa ``` The actual numbers depend on the details of the calculations setup but should be fairly close to the above results. Implementation[¶](#implementation "Permalink to this headline") --------------------------------------------------------------- Elastic is implemented as an extension module to [ASE](https://wiki.fysik.dtu.dk/ase/) system The Elastic package provides, basically, one main python module and one auxiliary module ([Parallel Calculator Module](#par-calc-mod)) which can be useful outside of the scope of the main code. The [Parallel Calculator Module](#par-calc-mod) is not distributed separately but can be just placed by itself somewhere in your python path and used with any part of the ASE. I hope it will be incorporated in the main project sometime in the future. ### Modules[¶](#modules "Permalink to this headline") #### Parallel Calculator Module[¶](#parallel-calculator-module "Permalink to this headline") Parallel calculator module is an extension of the standard [ASE](https://wiki.fysik.dtu.dk/ase/) calculator working in the parallel cluster environment. It is very useful in all situations where you need to run several, independent calculations and you have a large cluster of machines at your disposal (probably with some queuing system). This implementation uses VASP but the code can be easily adapted for use with other ASE calculators with minor changes. The final goal is to provide a universal module for parallel calculator execution in the cluster environment. The SIESTA code by Georgios Tritsaris <[gtritsaris@seas.harvard.edu](mailto:gtritsaris%40seas.harvard.edu)> Not fully tested after merge. *class* `parcalc.parcalc.``ClusterAims`(*nodes=1*, *ppn=8*, *\*\*kwargs*)[[source]](_modules/parcalc/parcalc.html#ClusterAims)[¶](#parcalc.parcalc.ClusterAims "Permalink to this definition") Encapsulating Aims calculator for the cluster environment. *class* `parcalc.parcalc.``ClusterSiesta`(*nodes=1*, *ppn=8*, *\*\*kwargs*)[[source]](_modules/parcalc/parcalc.html#ClusterSiesta)[¶](#parcalc.parcalc.ClusterSiesta "Permalink to this definition") Siesta calculator. Not fully tested by me - so this should be considered beta quality. Nevertheless it is based on working implementation *class* `parcalc.parcalc.``ClusterVasp`(*nodes=1*, *ppn=8*, *block=True*, *ncl=False*, *\*\*kwargs*)[[source]](_modules/parcalc/parcalc.html#ClusterVasp)[¶](#parcalc.parcalc.ClusterVasp "Permalink to this definition") Adaptation of VASP calculator to the cluster environment where you often have to make some preparations before job submission. You can easily adapt this class to your particular environment. It is also easy to use this as a template for other type of calculator. `calc_finished`()[[source]](_modules/parcalc/parcalc.html#ClusterVasp.calc_finished)[¶](#parcalc.parcalc.ClusterVasp.calc_finished "Permalink to this definition") Check if the lockfile is in the calculation directory. It is removed by the script at the end regardless of the success of the calculation. This is totally tied to implementation and you need to implement your own scheme! `calculate`(*atoms*)[[source]](_modules/parcalc/parcalc.html#ClusterVasp.calculate)[¶](#parcalc.parcalc.ClusterVasp.calculate "Permalink to this definition") Blocking/Non-blocking calculate method If we are in blocking mode we just run, wait for the job to end and read in the results. Easy … The non-blocking mode is a little tricky. We need to start the job and guard against it reading back possible old data from the directory - the queuing system may not even started the job when we get control back from the starting script. Thus anything we read after invocation is potentially garbage - even if it is a converged calculation data. We handle it by custom run function above which raises an exception after submitting the job. This skips post-run processing in the calculator, preserves the state of the data and signals here that we need to wait for results. `clean`()[[source]](_modules/parcalc/parcalc.html#ClusterVasp.clean)[¶](#parcalc.parcalc.ClusterVasp.clean "Permalink to this definition") Method which cleans up after a calculation. The default files generated by Vasp will be deleted IF this method is called. `prepare_calc_dir`()[[source]](_modules/parcalc/parcalc.html#ClusterVasp.prepare_calc_dir)[¶](#parcalc.parcalc.ClusterVasp.prepare_calc_dir "Permalink to this definition") Prepare the calculation directory for VASP execution. This needs to be re-implemented for each local setup. The following code reflects just my particular setup. `run`()[[source]](_modules/parcalc/parcalc.html#ClusterVasp.run)[¶](#parcalc.parcalc.ClusterVasp.run "Permalink to this definition") Blocking/Non-blocing run method. In blocking mode it just runs parent run method. In non-blocking mode it raises the \_\_NonBlockingRunException to bail out of the processing of standard calculate method (or any other method in fact) and signal that the data is not ready to be collected. `parcalc.parcalc.``ParCalculate`(*systems*, *calc*, *cleanup=True*, *block=True*, *prefix='Calc\_'*)[[source]](_modules/parcalc/parcalc.html#ParCalculate)[¶](#parcalc.parcalc.ParCalculate "Permalink to this definition") Run calculators in parallel for all systems. Calculators are executed in isolated processes and directories. The resulting objects are returned in the list (one per input system). *class* `parcalc.parcalc.``RemoteCalculator`(*restart=None*, *ignore\_bad\_restart\_file=False*, *label=None*, *atoms=None*, *calc=None*, *block=False*, *\*\*kwargs*)[[source]](_modules/parcalc/parcalc.html#RemoteCalculator)[¶](#parcalc.parcalc.RemoteCalculator "Permalink to this definition") Remote calculator based on ASE calculator class. This class is only involved with the machanics of remotly executing the software and transporting the data. The calculation is delegated to the actual calculator class. *classmethod* `ParallelCalculate`(*syslst, properties=['energy'], system\_changes=['positions', 'numbers', 'cell', 'pbc', 'initial\_charges', 'initial\_magmoms']*)[[source]](_modules/parcalc/parcalc.html#RemoteCalculator.ParallelCalculate)[¶](#parcalc.parcalc.RemoteCalculator.ParallelCalculate "Permalink to this definition") Run a series of calculations in parallel using (implicitely) some remote machine/cluster. The function returns the list of systems ready for the extraction of calculated properties. `read_results`()[[source]](_modules/parcalc/parcalc.html#RemoteCalculator.read_results)[¶](#parcalc.parcalc.RemoteCalculator.read_results "Permalink to this definition") Read energy, forces, … from output file(s). `run_calculation`(*atoms=None, properties=['energy'], system\_changes=['positions', 'numbers', 'cell', 'pbc', 'initial\_charges', 'initial\_magmoms']*)[[source]](_modules/parcalc/parcalc.html#RemoteCalculator.run_calculation)[¶](#parcalc.parcalc.RemoteCalculator.run_calculation "Permalink to this definition") Internal calculation executor. We cannot use FileIOCalculator directly since we need to support remote execution. This calculator is different from others. It prepares the directory, launches the remote process and raises the exception to signal that we need to come back for results when the job is finished. `write_input`(*atoms=None, properties=['energy'], system\_changes=['positions', 'numbers', 'cell', 'pbc', 'initial\_charges', 'initial\_magmoms']*)[[source]](_modules/parcalc/parcalc.html#RemoteCalculator.write_input)[¶](#parcalc.parcalc.RemoteCalculator.write_input "Permalink to this definition") Write input file(s). `parcalc.parcalc.``work_dir`(*\*args*, *\*\*kwds*)[[source]](_modules/parcalc/parcalc.html#work_dir)[¶](#parcalc.parcalc.work_dir "Permalink to this definition") Context menager for executing commands in some working directory. Returns to the previous wd when finished. Usage: >>> with work\_dir(path): … subprocess.call(‘git status’) #### Elastic Module[¶](#elastic-module "Permalink to this headline") Elastic is a module for calculation of \(C\_{ij}\) components of elastic tensor from the strain-stress relation. The strain components here are ordered in standard way which is different to ordering in previous versions of the code (up to 4.0). The ordering is: \(u\_{xx}, u\_{yy}, u\_{zz}, u\_{yz}, u\_{xz}, u\_{xy}\). The general ordering of \(C\_{ij}\) components is (except for triclinic symmetry and taking into account customary names of constants - e.g. \(C\_{16} \rightarrow C\_{14}\)): \[C\_{11}, C\_{22}, C\_{33}, C\_{12}, C\_{13}, C\_{23}, C\_{44}, C\_{55}, C\_{66}, C\_{16}, C\_{26}, C\_{36}, C\_{45}\] The functions with the name of bravais lattices define the symmetry of the \(C\_{ij}\) matrix. The matrix is N columns by 6 rows where the columns corespond to independent elastic constants of the given crystal, while the rows corespond to the canonical deformations of a crystal. The elements are the second partial derivatives of the free energy formula for the crystal written down as a quadratic form of the deformations with respect to elastic constant and deformation. *Note:* The elements for deformations \(u\_{xy}, u\_{xz}, u\_{yz}\) have to be divided by 2 to properly match the usual definition of elastic constants. See: [[LL]](index.html#ll) L.D. Landau, E.M. Lifszyc, “Theory of elasticity” There is some usefull summary also at: [ScienceWorld](http://scienceworld.wolfram.com/physics/Elasticity.html) --- `elastic.elastic.``get_BM_EOS`(*cryst*, *systems*)[[source]](_modules/elastic/elastic.html#get_BM_EOS)[¶](#elastic.elastic.get_BM_EOS "Permalink to this definition") Calculate Birch-Murnaghan Equation of State for the crystal. The B-M equation of state is defined by: \[P(V)= \frac{B\_0}{B'\_0}\left[ \left({\frac{V}{V\_0}}\right)^{-B'\_0} - 1 \right]\] It’s coefficients are estimated using n single-point structures ganerated from the crystal (cryst) by the scan\_volumes function between two relative volumes. The BM EOS is fitted to the computed points by least squares method. The returned value is a list of fitted parameters: \(V\_0, B\_0, B\_0'\) if the fit succeded. If the fitting fails the `RuntimeError('Calculation failed')` is raised. The data from the calculation and fit is stored in the bm\_eos and pv members of cryst for future reference. You have to provide properly optimized structures in cryst and systems list. | Parameters: | * **cryst** – Atoms object, basic structure * **systems** – A list of calculated structures | | Returns: | tuple of EOS parameters \(V\_0, B\_0, B\_0'\). | `elastic.elastic.``get_bulk_modulus`(*cryst*)[[source]](_modules/elastic/elastic.html#get_bulk_modulus)[¶](#elastic.elastic.get_bulk_modulus "Permalink to this definition") Calculate bulk modulus using the Birch-Murnaghan equation of state. The EOS must be previously calculated by get\_BM\_EOS routine. The returned bulk modulus is a \(B\_0\) coefficient of the B-M EOS. The units of the result are defined by ASE. To get the result in any particular units (e.g. GPa) you need to divide it by ase.units.<unit name>: ``` get\_bulk\_modulus(cryst)/ase.units.GPa ``` | Parameters: | **cryst** – ASE Atoms object | | Returns: | float, bulk modulus \(B\_0\) in ASE units. | `elastic.elastic.``get_cart_deformed_cell`(*base\_cryst*, *axis=0*, *size=1*)[[source]](_modules/elastic/elastic.html#get_cart_deformed_cell)[¶](#elastic.elastic.get_cart_deformed_cell "Permalink to this definition") Return the cell deformed along one of the cartesian directions Creates new deformed structure. The deformation is based on the base structure and is performed along single axis. The axis is specified as follows: 0,1,2 = x,y,z ; sheers: 3,4,5 = yz, xz, xy. The size of the deformation is in percent and degrees, respectively. | Parameters: | * **base\_cryst** – structure to be deformed * **axis** – direction of deformation * **size** – size of the deformation | | Returns: | new, deformed structure | `elastic.elastic.``get_cij_order`(*cryst*)[[source]](_modules/elastic/elastic.html#get_cij_order)[¶](#elastic.elastic.get_cij_order "Permalink to this definition") Give order of of elastic constants for the structure | Parameters: | **cryst** – ASE Atoms object | | Returns: | Order of elastic constants as a tuple of strings: C\_ij | `elastic.elastic.``get_deformed_cell`(*base\_cryst*, *axis=0*, *size=1*)[[source]](_modules/elastic/elastic.html#get_deformed_cell)[¶](#elastic.elastic.get_deformed_cell "Permalink to this definition") Return the cell (with atoms) deformed along one cell parameter (0,1,2 = a,b,c ; 3,4,5 = alpha,beta,gamma) by size percent or size degrees (axis/angles). `elastic.elastic.``get_elastic_tensor`(*cryst*, *systems*)[[source]](_modules/elastic/elastic.html#get_elastic_tensor)[¶](#elastic.elastic.get_elastic_tensor "Permalink to this definition") Calculate elastic tensor of the crystal. The elastic tensor is calculated from the stress-strain relation and derived by fitting this relation to the set of linear equations build from the symmetry of the crystal and strains and stresses of the set of elementary deformations of the unit cell. It is assumed that the crystal is converged and optimized under intended pressure/stress. The geometry and stress on the cryst is taken as the reference point. No additional optimization will be run. Structures in cryst and systems list must have calculated stresses. The function returns tuple of \(C\_{ij}\) elastic tensor, raw Birch coefficients \(B\_{ij}\) and fitting results: residuals, solution rank, singular values returned by numpy.linalg.lstsq. | Parameters: | * **cryst** – Atoms object, basic structure * **systems** – list of Atoms object with calculated deformed structures | | Returns: | tuple(\(C\_{ij}\) float vector, tuple(\(B\_{ij}\) float vector, residuals, solution rank, singular values) | `elastic.elastic.``get_elementary_deformations`(*cryst*, *n=5*, *d=2*)[[source]](_modules/elastic/elastic.html#get_elementary_deformations)[¶](#elastic.elastic.get_elementary_deformations "Permalink to this definition") Generate elementary deformations for elastic tensor calculation. The deformations are created based on the symmetry of the crystal and are limited to the non-equivalet axes of the crystal. | Parameters: | * **cryst** – Atoms object, basic structure * **n** – integer, number of deformations per non-equivalent axis * **d** – float, size of the maximum deformation in percent and degrees | | Returns: | list of deformed structures | `elastic.elastic.``get_lattice_type`(*cryst*)[[source]](_modules/elastic/elastic.html#get_lattice_type)[¶](#elastic.elastic.get_lattice_type "Permalink to this definition") Find the symmetry of the crystal using spglib symmetry finder. Derive name of the space group and its number extracted from the result. Based on the group number identify also the lattice type and the Bravais lattice of the crystal. The lattice type numbers are (the numbering starts from 1): Triclinic (1), Monoclinic (2), Orthorombic (3), Tetragonal (4), Trigonal (5), Hexagonal (6), Cubic (7) | Parameters: | **cryst** – ASE Atoms object | | Returns: | tuple (lattice type number (1-7), lattice name, space group name, space group number) | `elastic.elastic.``get_pressure`(*s*)[[source]](_modules/elastic/elastic.html#get_pressure)[¶](#elastic.elastic.get_pressure "Permalink to this definition") Return *external* isotropic (hydrostatic) pressure in ASE units. If the pressure is positive the system is under external pressure. This is a convenience function to convert output of get\_stress function into external pressure. | Parameters: | **cryst** – stress tensor in Voight (vector) notation as returned by the get\_stress() method. | | Returns: | float, external hydrostatic pressure in ASE units. | `elastic.elastic.``get_strain`(*cryst*, *refcell=None*)[[source]](_modules/elastic/elastic.html#get_strain)[¶](#elastic.elastic.get_strain "Permalink to this definition") Calculate strain tensor in the Voight notation Computes the strain tensor in the Voight notation as a conventional 6-vector. The calculation is done with respect to the crystal geometry passed in refcell parameter. | Parameters: | * **cryst** – deformed structure * **refcell** – reference, undeformed structure | | Returns: | 6-vector of strain tensor in the Voight notation | `elastic.elastic.``get_vecang_cell`(*cryst*, *uc=None*)[[source]](_modules/elastic/elastic.html#get_vecang_cell)[¶](#elastic.elastic.get_vecang_cell "Permalink to this definition") Compute A,B,C, alpha,beta,gamma cell params from the unit cell matrix (uc) or cryst. Angles in radians. `elastic.elastic.``hexagonal`(*u*)[[source]](_modules/elastic/elastic.html#hexagonal)[¶](#elastic.elastic.hexagonal "Permalink to this definition") The matrix is constructed based on the approach from L&L using auxiliary coordinates: \(\xi=x+iy\), \(\eta=x-iy\). The components are calculated from free energy using formula introduced in [Crystal symmetry and elastic matrix derivation](index.html#symmetry) with appropriate coordinate changes. The order of constants is as follows: \[C\_{11}, C\_{33}, C\_{12}, C\_{13}, C\_{44}\] | Parameters: | **u** – vector of deformations: [ \(u\_{xx}, u\_{yy}, u\_{zz}, u\_{yz}, u\_{xz}, u\_{xy}\) ] | | Returns: | Symmetry defined stress-strain equation matrix | `elastic.elastic.``monoclinic`(*u*)[[source]](_modules/elastic/elastic.html#monoclinic)[¶](#elastic.elastic.monoclinic "Permalink to this definition") Monoclinic group, The ordering of constants is: \[C\_{11}, C\_{22}, C\_{33}, C\_{12}, C\_{13}, C\_{23}, C\_{44}, C\_{55}, C\_{66}, C\_{16}, C\_{26}, C\_{36}, C\_{45}\] | Parameters: | **u** – vector of deformations: [ \(u\_{xx}, u\_{yy}, u\_{zz}, u\_{yz}, u\_{xz}, u\_{xy}\) ] | | Returns: | Symmetry defined stress-strain equation matrix | `elastic.elastic.``orthorombic`(*u*)[[source]](_modules/elastic/elastic.html#orthorombic)[¶](#elastic.elastic.orthorombic "Permalink to this definition") Equation matrix generation for the orthorombic lattice. The order of constants is as follows: \[C\_{11}, C\_{22}, C\_{33}, C\_{12}, C\_{13}, C\_{23}, C\_{44}, C\_{55}, C\_{66}\] | Parameters: | **u** – vector of deformations: [ \(u\_{xx}, u\_{yy}, u\_{zz}, u\_{yz}, u\_{xz}, u\_{xy}\) ] | | Returns: | Symmetry defined stress-strain equation matrix | `elastic.elastic.``regular`(*u*)[[source]](_modules/elastic/elastic.html#regular)[¶](#elastic.elastic.regular "Permalink to this definition") Equation matrix generation for the regular (cubic) lattice. The order of constants is as follows: \[C\_{11}, C\_{12}, C\_{44}\] | Parameters: | **u** – vector of deformations: [ \(u\_{xx}, u\_{yy}, u\_{zz}, u\_{yz}, u\_{xz}, u\_{xy}\) ] | | Returns: | Symmetry defined stress-strain equation matrix | `elastic.elastic.``scan_pressures`(*cryst*, *lo*, *hi*, *n=5*, *eos=None*)[[source]](_modules/elastic/elastic.html#scan_pressures)[¶](#elastic.elastic.scan_pressures "Permalink to this definition") Scan the pressure axis from lo to hi (inclusive) using B-M EOS as the volume predictor. Pressure (lo, hi) in GPa `elastic.elastic.``scan_volumes`(*cryst*, *lo=0.98*, *hi=1.02*, *n=5*, *scale\_volumes=True*)[[source]](_modules/elastic/elastic.html#scan_volumes)[¶](#elastic.elastic.scan_volumes "Permalink to this definition") Provide set of crystals along volume axis from lo to hi (inclusive). No volume cell optimization is performed. Bounds are specified as fractions (1.10 = 10% increase). If scale\_volumes==False the scalling is applied to lattice vectors instead of volumes. | Parameters: | * **lo** – lower bound of the V/V\_0 in the scan * **hi** – upper bound of the V/V\_0 in the scan * **n** – number of volume sample points * **scale\_volumes** – If True scale the unit cell volume or, if False, scale the length of lattice axes. | | Returns: | a list of deformed systems | `elastic.elastic.``tetragonal`(*u*)[[source]](_modules/elastic/elastic.html#tetragonal)[¶](#elastic.elastic.tetragonal "Permalink to this definition") Equation matrix generation for the tetragonal lattice. The order of constants is as follows: \[C\_{11}, C\_{33}, C\_{12}, C\_{13}, C\_{44}, C\_{14}\] | Parameters: | **u** – vector of deformations: [ \(u\_{xx}, u\_{yy}, u\_{zz}, u\_{yz}, u\_{xz}, u\_{xy}\) ] | | Returns: | Symmetry defined stress-strain equation matrix | `elastic.elastic.``triclinic`(*u*)[[source]](_modules/elastic/elastic.html#triclinic)[¶](#elastic.elastic.triclinic "Permalink to this definition") Triclinic crystals. *Note*: This was never tested on the real case. Beware! The ordering of constants is: \[C\_{11}, C\_{22}, C\_{33}, C\_{12}, C\_{13}, C\_{23}, C\_{44}, C\_{55}, C\_{66}, C\_{16}, C\_{26}, C\_{36}, C\_{46}, C\_{56}, C\_{14}, C\_{15}, C\_{25}, C\_{45}\] | Parameters: | **u** – vector of deformations: [ \(u\_{xx}, u\_{yy}, u\_{zz}, u\_{yz}, u\_{xz}, u\_{xy}\) ] | | Returns: | Symmetry defined stress-strain equation matrix | `elastic.elastic.``trigonal`(*u*)[[source]](_modules/elastic/elastic.html#trigonal)[¶](#elastic.elastic.trigonal "Permalink to this definition") The matrix is constructed based on the approach from L&L using auxiliary coordinates: \(\xi=x+iy\), \(\eta=x-iy\). The components are calculated from free energy using formula introduced in [Crystal symmetry and elastic matrix derivation](index.html#symmetry) with appropriate coordinate changes. The order of constants is as follows: \[C\_{11}, C\_{33}, C\_{12}, C\_{13}, C\_{44}, C\_{14}\] | Parameters: | **u** – vector of deformations: [ \(u\_{xx}, u\_{yy}, u\_{zz}, u\_{yz}, u\_{xz}, u\_{xy}\) ] | | Returns: | Symmetry defined stress-strain equation matrix | Indices and tables[¶](#indices-and-tables "Permalink to this headline") ----------------------------------------------------------------------- * [Index](genindex.html) * [Module Index](py-modindex.html) * [Search Page](search.html) References[¶](#references "Permalink to this headline") ------------------------------------------------------- [](https://doi.org/10.5281/zenodo.593721) The Elastic package should be cited using one or both of the following papers (TiC, ZrC) and its own reference.: | [Elastic] | P.T. Jochym, *Module for calculating elastic tensor of crystals*, software, <https://github.com/jochym/Elastic/>, [doi:10.5281/zenodo.593721](http://dx.doi.org/10.5281/zenodo.593721). | | [TiC] | P.T. Jochym, K. Parlinski and M. Sternik, *TiC lattice dynamics from ab initio calculations*, European Physical Journal B; **10**, 1 (1999) 9-13 ; [doi:10.1007/s100510050823](http://dx.doi.org/10.1007/s100510050823) | | [ZrC] | P.T. Jochym and K. Parlinski, *Ab initio lattice dynamics and elastic constants of ZrC*, European Physical Journal B; **15**, 2 (2000) 265-268 ; [doi:10.1007/s100510051124](http://dx.doi.org/10.1007/s100510051124) | | [LL] | L.D. Landau, E.M. Lifszyc, [Theory of elasticity](http://books.google.com/books?id=tpY-VkwCkAIC), Elsevier (1986) ; ISBN: 075062633X, 9780750626330 | | [SVD] | G. Golub and W. Kahan, *Calculating the Singular Values and Pseudo-Inverse of a Matrix*, J. Soc. Indus,.Appl. Math.: Ser. B **2**, (1964) pp. 205-224 ; [doi:10.1137/0702016](http://dx.doi.org/10.1137/0702016) ; [Wikipedia article on SVD](http://en.wikipedia.org/wiki/Singular_value_decomposition) | * [Search Page](search.html) This page uses [Google Analytics](http://analytics.google.com/) to collect statistics. You can disable it by blocking the JavaScript coming from www.google-analytics.com.

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Search — render 0.1 documentation ### Navigation * [index](genindex.html "General Index") * [modules](py-modindex.html "Python Module Index") | * [render 0.1 documentation](index.html) » Search ====== Please activate JavaScript to enable the search functionality. From here you can search these documents. Enter your search words into the box below and click "search". Note that the search function will automatically search for all of the words. Pages containing fewer words won't appear in the result list. ### Related Topics * [Documentation overview](index.html) © Copyright 2013, Santhosh Thottingal. Index — render 0.1 documentation ### Navigation * [index](# "General Index") * [modules](py-modindex.html "Python Module Index") | * [render 0.1 documentation](index.html) » Index ===== [**G**](#G) | [**R**](#R) | [**W**](#W) G - | | | | --- | --- | | [get\_info() (render.core.Render method)](index.html#render.core.Render.get_info) | [get\_module\_name() (render.core.Render method)](index.html#render.core.Render.get_module_name) | R - | | | | --- | --- | | [Render (class in render.core)](index.html#render.core.Render) [render.core (module)](index.html#module-render.core) | [render\_text() (render.core.Render method)](index.html#render.core.Render.render_text) | W - | | | --- | | [wiki2pdf() (render.core.Render method)](index.html#render.core.Render.wiki2pdf) | ### Related Topics * [Documentation overview](index.html) ### Quick search Enter search terms or a module, class or function name. © Copyright 2013, Santhosh Thottingal. render - a complex script rendering utility based on pangocairo — render 0.1 documentation ### Navigation * [index](genindex.html "General Index") * [modules](py-modindex.html "Python Module Index") | * [render 0.1 documentation](#) » render - a complex script rendering utility based on pangocairo[¶](#render-a-complex-script-rendering-utility-based-on-pangocairo "Permalink to this headline") =============================================================================================================================================================== This is an experimental python package for generating renderings of complex scripts. This is based on pypdflib and pangocairo. The module currently has two functions * a wiki to pdf generater * a complex script renderer Installation[¶](#installation "Permalink to this headline") ----------------------------------------------------------- You will need to have pango and cairo installed on your system along with pycairo. You will probably have them if you have pygtk on your system. pycairo does not install in virtualenvs,so use you distributions package manger to install pycairo or use pip to install pycairo system wide. API reference[¶](#module-render.core "Permalink to this headline") ------------------------------------------------------------------ *class* render.core.Render[[source]](_modules/render/core.html#Render)[¶](#render.core.Render "Permalink to this definition") The render class. Instantiate to get access to the methods. get\_info()[[source]](_modules/render/core.html#Render.get_info)[¶](#render.core.Render.get_info "Permalink to this definition") returns info on the module get\_module\_name()[[source]](_modules/render/core.html#Render.get_module_name)[¶](#render.core.Render.get_module_name "Permalink to this definition") returns the module name render\_text(*text*, *file\_type='png'*, *path=None*, *filename=None*, *width=0*, *height=0*, *color='Black'*, *font='Serif'*, *font\_size=12*)[[source]](_modules/render/core.html#Render.render_text)[¶](#render.core.Render.render_text "Permalink to this definition") | Parameters: | * **text** (*str.*) – the text to be rendered. * **file\_type** (*str.*) – required output format. accepts png, svg and pdf * **filename** (*str.*) – filename for the output * **path** (*str.*) – the file path for the output. defaults to the current directory * **width** (*int.*) – width of the output * **height** (*int.*) – height of the output * **color** (*str.*) – the background color for the rendering. * **font** (*str.*) – the font to be used * **font\_size** (*int*) – font size to be used. defaults to 12 | | Returns: | the path to the generated rendering. | generates a rendering of the supplied text. wiki2pdf(*url*, *path=None*, *font='Serif'*)[[source]](_modules/render/core.html#Render.wiki2pdf)[¶](#render.core.Render.wiki2pdf "Permalink to this definition") | Parameters: | * **url** (*str.*) – the url for the wiki page * **font** (*str.*) – the font to be used for the pdf. * **path** – output path.Defaults to current dir | | Returns: | the path to the generated pdf. | Indices and tables[¶](#indices-and-tables "Permalink to this headline") ======================================================================= * [*Index*](genindex.html) * [*Module Index*](py-modindex.html) * [*Search Page*](search.html) ### [Table Of Contents](#) * [render - a complex script rendering utility based on pangocairo](#) + [Installation](#installation) + [API reference](#module-render.core) * [Indices and tables](#indices-and-tables) ### Related Topics * [Documentation overview](#) ### This Page * [Show Source](_sources/index.txt) ### Quick search Enter search terms or a module, class or function name. © Copyright 2013, Santhosh Thottingal. Python Module Index — render 0.1 documentation ### Navigation * [index](genindex.html "General Index") * [modules](# "Python Module Index") | * [render 0.1 documentation](index.html) » Python Module Index =================== [**r**](#cap-r) | | | | | --- | --- | --- | | | | | | | **r** | | | - | render | | | | [render.core](index.html#module-render.core) | | ### Related Topics * [Documentation overview](index.html) ### Quick search Enter search terms or a module, class or function name. © Copyright 2013, Santhosh Thottingal. krTheme Sphinx Style — render 0.1 documentation ### Navigation * [index](../genindex.html "General Index") * [modules](../py-modindex.html "Python Module Index") | * [render 0.1 documentation](../index.html) » krTheme Sphinx Style[¶](#krtheme-sphinx-style "Permalink to this headline") =========================================================================== This repository contains sphinx styles Kenneth Reitz uses in most of his projects. It is a drivative of Mitsuhiko’s themes for Flask and Flask related projects. To use this style in your Sphinx documentation, follow this guide: 1. put this folder as \_themes into your docs folder. Alternatively you can also use git submodules to check out the contents there. 2. add this to your conf.py: ``` sys.path.append(os.path.abspath('\_themes')) html\_theme\_path = ['\_themes'] html\_theme = 'kr' ``` The following themes exist: **kr** the standard flask documentation theme for large projects **kr\_small** small one-page theme. Intended to be used by very small addon libraries. ### Related Topics * [Documentation overview](../index.html) ### This Page * [Show Source](../_sources/_themes/README.txt) ### Quick search Enter search terms or a module, class or function name. © Copyright 2013, Santhosh Thottingal. Overview: module code — render 0.1 documentation ### Navigation * [index](../genindex.html "General Index") * [modules](../py-modindex.html "Python Module Index") | * [render 0.1 documentation](../index.html) » All modules for which code is available ======================================= * [render.core](render/core.html) ### Related Topics * [Documentation overview](../index.html) ### Quick search Enter search terms or a module, class or function name. © Copyright 2013, Santhosh Thottingal. render.core — render 0.1 documentation ### Navigation * [index](../../genindex.html "General Index") * [modules](../../py-modindex.html "Python Module Index") | * [render 0.1 documentation](../../index.html) » * [Module code](../index.html) » Source code for render.core =========================== ``` #!/usr/bin/python # -\*- coding: utf-8 -\*- # Copyright 2010 Santhosh Thottingal <santhosh.thottingal@gmail.com> # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU Affero General Public License as published by # the Free Software Foundation; either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU Affero General Public License for more details. # # You should have received a copy of the GNU General Public License # along with this program; if not, write to the Free Software # Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA. import os import cairo import hashlib import pango import pangocairo import hyphenation from wiki2pdf import Wikiparser from styles import \* [[docs]](../../index.html#render.core.Render)class Render: """ The render class. Instantiate to get access to the methods. """ def \_\_init\_\_(self): self.tmp\_folder = "/static/tmp" [[docs]](../../index.html#render.core.Render.wiki2pdf) def wiki2pdf(self, url, path=None, font='Serif'): """ :param url: the url for the wiki page :type url: str. :param font: the font to be used for the pdf. :type font: str. :param path: output path.Defaults to current dir :returns: the path to the generated pdf. """ if path is None: path = os.path.abspath(os.path.curdir) m = hashlib.md5() m.update(url.encode("utf-8")) filename = m.hexdigest()[0:5]+".pdf" filename = os.path.join(path, filename) print(filename) parser = Wikiparser(url, filename, font) parser.parse() #else: # print ("File already exists.") return (os.path.join(self.tmp\_folder, filename)) [[docs]](../../index.html#render.core.Render.render_text) def render\_text(self, text, file\_type='png',path=None, filename=None, width=0, height=0, color="Black", font='Serif', font\_size=12): """ :param text: the text to be rendered. :type text: str. :param file\_type: required output format. accepts png, svg and pdf :type file\_type: str. :param filename: filename for the output :type filename: str. :param path: the file path for the output. defaults to the current directory :type path: str. :param width: width of the output :type width: int. :param height: height of the output :type height: int. :param color: the background color for the rendering. :type color: str. :param font: the font to be used :type font: str. :param font\_size: font size to be used. defaults to 12 :type font\_size: int :returns: the path to the generated rendering. generates a rendering of the supplied text. """ surface = None print width width = int(width) height = int(height) font\_size = int(font\_size) text = text.decode("utf-8") m = hashlib.md5() m.update(text.encode("utf-8")) if filename is None: filename = m.hexdigest()[0:5]+"."+file\_type if path is None: path = os.getcwd() outputfile = os.path.join(path, filename) if file\_type == 'png': surface = cairo.ImageSurface(cairo.FORMAT\_ARGB32, int(width), int(height)) if file\_type == 'svg': surface = cairo.SVGSurface(outputfile, int(width), int(height)) if file\_type == 'pdf': surface = cairo.PDFSurface(outputfile, int(width), int(height)) context = cairo.Context(surface) try: text = hyphenation.getInstance().hyphenate(text, u'\u00AD') except: print("error while hyphenating. Proceeding without Hyphenation") width = int(width) left\_margin = 10 top\_margin = 20 bottom\_margin = 50 position\_x = left\_margin position\_y = top\_margin rgba = get\_color(color) context.set\_source\_rgba(float(rgba.red), float(rgba.green), float(rgba.blue), float(rgba.alpha)) pc = pangocairo.CairoContext(context) paragraph\_layout = pc.create\_layout() paragraph\_font\_description = pango.FontDescription() paragraph\_font\_description.set\_family(font) paragraph\_font\_description.set\_size((int)(int(font\_size) \* pango.SCALE)) paragraph\_layout.set\_font\_description(paragraph\_font\_description) if width > 0: paragraph\_layout.set\_width((int)((width-2\*left\_margin) \* pango.SCALE)) paragraph\_layout.set\_justify(True) paragraph\_layout.set\_text(text+"\n") context.move\_to(position\_x, position\_y) pango\_layout\_iter = paragraph\_layout.get\_iter() line\_width = 0 while not pango\_layout\_iter.at\_last\_line(): first\_line = True context.move\_to(position\_x, position\_y) while not pango\_layout\_iter.at\_last\_line(): ink\_rect, logical\_rect = pango\_layout\_iter.get\_line\_extents() line = pango\_layout\_iter.get\_line\_readonly() has\_next\_line = pango\_layout\_iter.next\_line() # Decrease paragraph spacing if ink\_rect[2] == 0: # It is para break dy = font\_size / 2 position\_y += dy if not first\_line: self.context.rel\_move\_to(0, dy) else: xstart = 1.0 \* logical\_rect[0] / pango.SCALE context.rel\_move\_to(xstart, 0) if width > 0 and height > 0: pc.show\_layout\_line(line) line\_height = (int)(logical\_rect[3] / pango.SCALE) line\_width = (int)(logical\_rect[2] / pango.SCALE) context.rel\_move\_to(-xstart, line\_height) position\_y += line\_height first\_line = False if width == 0 or height == 0: if width == 0: width = line\_width if height == 0: height = position\_y return self.render\_text(text, file\_type, path,filename, width + 2.5\*left\_margin, height, color, font, font\_size) if file\_type == 'png': surface.write\_to\_png(str(outputfile)) else: context.show\_page() return outputfile [[docs]](../../index.html#render.core.Render.get_module_name) def get\_module\_name(self): """ returns the module name """ return "Script Renderer" [[docs]](../../index.html#render.core.Render.get_info) def get\_info(self): """ returns info on the module """ return "Provides rendered images for Complex scripts" def getInstance(): return Render() ``` ### Related Topics * [Documentation overview](../../index.html) + [Module code](../index.html) ### Quick search Enter search terms or a module, class or function name. © Copyright 2013, Santhosh Thottingal.

configuration

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Configuration v1.2.0 documentation [](index.html#document-index) * [Repository](https://github.com/slickframework/configuration) * [Getting started](index.html#document-manual/getting-started) + [Basic usage](index.html#basic-usage) + [Creating a Configuration](index.html#creating-a-configuration) + [Retrieving values](index.html#retrieving-values) + [Default values](index.html#default-values) * [Configuration Chain](index.html#document-manual/multiple-configurations) + [Priority Configuration Chain](index.html#priority-configuration-chain) + [Combined configuration](index.html#combined-configuration) * [Configuration interface](index.html#document-api/configuration) * [Contributing](index.html#document-manual/contrib) + [Pull requests](index.html#pull-requests) + [Running tests](index.html#running-tests) + [Security](index.html#security) * [License](index.html#document-manual/license) Slick Configuration[¶](#slick-configuration "Permalink to this headline") ========================================================================= `slick/configuration` is a simple package that deals with configuration files. It has a very simple interface that you can use to set your own configuration drivers. By default it uses the PHP arrays for configuration as it does not need any parser and therefore is more performance friendly. Installation[¶](#installation "Permalink to this headline") ----------------------------------------------------------- `slick/configuration` is a php 5.6+ library that you’ll have in your project development environment. Before you begin, ensure that you have PHP 5.6 or higher installed. You can install `slick/configuration` with all its dependencies through Composer. Follow instructions on the [composer website](https://getcomposer.org/download/) if you don’t have it installed yet. You can use this Composer command to install `slick/configuration`: ``` $ composer require slick/configuration ``` ### Getting started[¶](#getting-started "Permalink to this headline") To create a `ConfigurationInterface` you should use the `Slick\Configuration` factory class as it really helps you with the creation process. #### Basic usage[¶](#basic-usage "Permalink to this headline") Lets start by creating a configuration file: ``` <?php /\*\* \* App configuration file \*/ namespace settings; $settings = []; $settings['application'] = [ 'version' => 'v1.0.0', 'environment' => 'develop' ]; return $settings; ``` we save this file as `./settings.php`. We are using plain PHP arrays for configuration files. Don’t forget to add the `return` statement at the end of the file so that the defined array could be assigned when initializing the driver. #### Creating a Configuration[¶](#creating-a-configuration "Permalink to this headline") Now we will use the `Slick\Configuration\Configuration` factory o create our `Slick\Configuration\ConfigurationInterface`: ``` use Slick\Configuration\Configuration; $settings = Configuration::get('settings'); ``` Its really simple. #### Retrieving values[¶](#retrieving-values "Permalink to this headline") Now lets use it. ``` print\_r($settings->get('application')); # the output form above is: # Array ( # [version] => v1.0.0, # [environment] => develop # ) ``` You can set any level of nesting in your configuration array but as you add another level to the array it becomes harder to use. Please check the example bellow: ``` $value = $settings->get('application')['version']; // OR $appSettings = $settings->get('application'); $value = $appSettings['version']; ``` To simplify you ca use a “dot notation” to reach a deeper level. ``` $value = $settings->get('application.version'); ``` #### Default values[¶](#default-values "Permalink to this headline") It is possible to have a default value when no key is found on a configuration driver. By default if a key is not found a `NULL` is returned but if you specify a value it will be returned by the `ConfigurationInterface::get()` method: ``` $value = $settings->get('application.rowsPerPage', 10); print $value; # the output form above is: # 10 ``` ### Configuration Chain[¶](#configuration-chain "Permalink to this headline") Starting form v1.2.0, `Slick\Configuration` is capable of combine multiple configuration drivers with a single configuration interface. This allows you to add a more important configuration source (like environment variables) to be also check when you try to retrieve a configuration value. #### Priority Configuration Chain[¶](#priority-configuration-chain "Permalink to this headline") v1.2.0 added a `ConfigurationChainInterface` that allows clients to retrieve a value from a combined chain of `ConfigurationInterface` objects instead of a single configuration source. It also adds a `PriorityConfigurationChain` that implements `ConfigurationChainInterface` and its by default the returned value from `Configuration::initialize()` or `Configuration::get()` factory methods. The priority is given by a integer value that determines the order that a key is searched in the chain. Lower value will be checked first. #### Combined configuration[¶](#combined-configuration "Permalink to this headline") Lets try an example. This is our PHP file with an associative array with configuration settings: ``` <?php /\*\* \* App configuration file \*/ namespace settings; $settings = []; $settings['application'] = [ 'version' => 'v1.0.0', 'environment' => 'develop' ]; return $settings; ``` Now we will create a combined configuration that will have a `Environment` driver and a `Php` with the values from the file we have just create. ``` use Slick\Configuration\Configuration; $settings = Configuration::get([ [null, Configuration::DRIVER\_ENV, 10], ['settings', Configuration::DRIVER\_PHP, 20] ]); ``` This configuration setup will create the `Environment` driver as the first configuration driver that will be check and then, if not found, the `Php` one. Lets assume that we has define and environment variable as `APPLICATION\_VERSION=v1.2.3` and lets get that value from the configuration chain: ``` print\_r($settings->get('application.version')); # the output form above is: # v1.2.3 ``` You can combine any number of configuration drivers in one chain and set the priority on with the search will occur. Tip The example shown here is a very simple way of handling environment variables that can be set on Docker containers where you some times can’t create files. ### Configuration interface[¶](#namespace-Slick\Configuration "Permalink to this headline") *class* `Slick\Configuration\``ConfigurationInterface`[¶](#Slick\Configuration\ConfigurationInterface "Permalink to this definition") ConfigurationInterface, defines a configuration driver `Slick\Configuration\ConfigurationInterface::``get`(*$key*[, *$default = NULL*])[¶](#Slick\Configuration\ConfigurationInterface::get "Permalink to this definition") Returns the value store with provided key or the default value. | Parameters: | * **$key** (*string*) – The key used to store the value in configuration. * **$default** (*mixed*) – The default value if no value was stored. | | Returns: | The configuration stored value or the default if not found. | `Slick\Configuration\ConfigurationInterface::``get`(*$key*, *$value*) Set/Store the provided value with a given key. | Parameters: | * **$key** (*string*) – The key used to store the value in configuration. * **$value** (*mixed*) – The value to store under the provided key. | | Returns: | Self instance for method call chains. | ### Contributing[¶](#contributing "Permalink to this headline") Contributions are **welcome** and will be fully **credited**. We accept contributions via Pull Requests on [Github](https://github.com/slickframework/configuration). #### Pull requests[¶](#pull-requests "Permalink to this headline") * [PSR-2 Coding Standard](https://github.com/php-fig/fig-standards/blob/master/accepted/PSR-2-coding-style-guide.md) - Check the code style with `$ composer check-style` and fix it with `$ composer fix-style`. * **Add tests!** - Your patch won’t be accepted if it doesn’t have tests. * **Document any change in behaviour** - Make sure the README.md and any other relevant documentation are kept up-to-date. * **Consider our release cycle** - We try to follow [SemVer v2.0.0](http://semver.org). Randomly breaking public APIs is not an option. * **Create feature branches** - Don’t ask us to pull from your master branch. * **One pull request per feature** - If you want to do more than one thing, send multiple pull requests. * **Send coherent history** - Make sure each individual commit in your pull request is meaningful. If you had to make multiple intermediate commits while developing, please [squash them](http://www.git-scm.com/book/en/v2/Git-Tools-Rewriting-History#Changing-Multiple-Commit-Messages) before submitting. #### Running tests[¶](#running-tests "Permalink to this headline") We use [phpspec](http://www.phpspec.net) for unit tests. ``` # unit tests $ vendor/bin/phpspec run -fdot ``` #### Security[¶](#security "Permalink to this headline") If you discover any security related issues, please email [slick.framework@gmail.com](mailto:slick.framework%40gmail.com) instead of using the issue tracker. ### License[¶](#license "Permalink to this headline") Licensed using the [MIT license](http://opensource.org/licenses/MIT). > > Copyright (c) 2014-2017 [The Slick Team](https://github.com/orgs/slickframework/people) <[slick.framework@gmail.com](mailto:slick.framework%40gmail.com)> > > > Permission is hereby granted, free of charge, to any person obtaining a copy > of this software and associated documentation files (the “Software”), to deal > in the Software without restriction, including without limitation the rights > to use, copy, modify, merge, publish, distribute, sublicense, and/or sell > copies of the Software, and to permit persons to whom the Software is > furnished to do so, subject to the following conditions: > > > The above copyright notice and this permission notice shall be included in > all copies or substantial portions of the Software. > > > THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR > IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, > FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE > AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER > LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, > OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN > THE SOFTWARE. > > > --- Please enable JavaScript to view the [comments powered by Disqus.](https://disqus.com/?ref_noscript)

mu

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Mu 1.2.0 documentation Mu: A Python Code Editor[¶](#mu-a-python-code-editor "Permalink to this headline") ==================================================================================  Note **This documentation is NOT for users of Mu**. Rather, it is for software developers who want to improve Mu. Read our [Developer Setup](index.html#document-setup) documentation for the technical details needed to get started. For tutorials, how-to guides and user related discussion, please see the project’s website for users of Mu at: <https://codewith.mu/> If you’re interested in the fun, educational, inspiring and sometimes hilarious ways in which people use Mu, check out: <https://madewith.mu/> Quickstart[¶](#quickstart "Permalink to this headline") ------------------------------------------------------- Mu works with Python 3.5 to 3.8 (both inclusive). You need to have one of these Python versions installed in order to work on developing Mu ([here is a comprehensive guide for how to do this](https://realpython.com/installing-python/)). We also assume you know how to [use Python virtual environments](https://docs.python.org/3/library/venv.html). Clone the repository: ``` git clone https://github.com/mu-editor/mu.git ``` Create a virtualenv in Python 3.8: ``` python3.8 -m venv .venv ``` Activate the environment, on macOS and Linux the command is: ``` source .venv/bin/activate ``` For Windows, the command is: ``` . .\.venv\Scripts\activate.ps1 ``` Once your virtual environment is activated, upgrade `pip`: ``` python -m pip install --upgrade pip ``` Install Mu and its development dependencies: ``` pip install -e ".[dev]" ``` Start Mu: ``` mu-editor ``` Run the test suite: ``` make check ``` Read on to learn more about Mu, its aims and how you can contribute. What?[¶](#what "Permalink to this headline") -------------------------------------------- Mu is a very simple Python editor for kids, teachers and beginner programmers. It’s written in Python and works on Windows, OSX, Linux and Raspberry Pi. > > “[Papert] realized, ‘Oh, we could take the real content out here as a > version in the child’s world that is still the real thing.’ It’s not a fake > version of math. It’s kind of like little league, or even T-ball. In sports > they do this all the time. In music, they do it all the time. The idea is, > you never let the child do something that isn’t the real thing – but you > have to work your ass off to figure out what the real thing is in the > context of the way their minds are working at that developmental level.” > – [Alan Kay](https://www.fastcompany.com/40435064/what-alan-kay-thinks-about-the-iphone-and-technology-now) > > > Mu aspires to be “the real thing” as a development environment for beginner programmers taking their first steps with Python. As a rule of thumb, if you’re able to ask “why doesn’t Mu have [feature X]?” then you’re probably too advanced for using Mu as a development environment. In which case, you should graduate to a more advanced editor. Why?[¶](#why "Permalink to this headline") ------------------------------------------ There isn’t a cross platform Python code editor that is: * Easy to use; * Available on all major platforms; * Well documented (even for beginners); * Simply coded; * Currently maintained; and, * Thoroughly tested. Mu addresses these needs. Mu was originally created as a contribution from the [Python Software Foundation](http://python.org/psf) for the BBC’s [micro:bit project](http://microbit.org/). Many people asked if Mu could be turned into a generic beginner’s code editor and, thanks to the wonderful support of the [Raspberry Pi Foundation](http://raspberrypi.org/) the work needed to make such changes was done over the summer of 2017. The following video of a talk given at [PyCon 2018](https://us.pycon.org/2018/) outlines the story of Mu: How?[¶](#how "Permalink to this headline") ------------------------------------------ Mu’s outlook is: * Less is more (remove all unnecessary distractions); * Keep it simple (so Mu is easy to understand); * Walk the path of least resistance (Mu should be easy to use); * Have fun (learning should be a positive experience). Mu’s own code is simple, clearly organised and well tested. It’s copiously commented and mostly found in a few obviously named Python files. This has been done on purpose: we want teachers and kids to take ownership of this project and organising the code in this way aids the first steps required to get involved. If you’re looking for ways to get involved check out some of the [Suggested First Steps](index.html#document-first-steps) for new contributors. Furthermore, we put our users at the centre of our development work. Extensive interviews with teachers, observations of lessons and exceptionally clear and helpful feedback from the education team at the Raspberry Pi Foundation (perhaps the most successful computing in education project in history) have informed the design choices for Mu. Who?[¶](#who "Permalink to this headline") ------------------------------------------ **You!** Contributions are welcome without prejudice from *anyone* irrespective of age, gender, religion, race or sexuality. If you’re thinking, “but they don’t mean me”, *then we especially mean YOU*. Good quality code and engagement with respect, humour and intelligence wins every time. Read about [Contributing to Mu](index.html#document-contributing) and perhaps try out some [Suggested First Steps](index.html#document-first-steps). We want the Mu community to be a friendly place. Therefore, we expect contributors to follow our [Code of Conduct](index.html#document-code_of_conduct). Contents:[¶](#contents "Permalink to this headline") ---------------------------------------------------- ### Contributing to Mu[¶](#contributing-to-mu "Permalink to this headline") Hey! Many thanks for wanting to improve Mu. Contributions are welcome without prejudice from *anyone* irrespective of age, gender, religion, race or sexuality. If you’re thinking, “but they don’t mean me”, *then we especially mean YOU*. Good quality code and engagement with respect, humour and intelligence wins every time. * If you’re from a background which isn’t well-represented in most geeky groups, get involved - *we want to help you make a difference*. * If you’re from a background which *is* well-represented in most geeky groups, get involved - *we want your help making a difference*. * If you’re worried about not being technical enough, get involved - *your fresh perspective will be invaluable*. * If you think you’re an imposter, get involved. * If your day job isn’t code, get involved. * This isn’t a group of experts, just people. Get involved! * We are interested in educational, social and technical problems. If you are too, get involved. * This is a new community. *No-one knows what they are doing*, so, get involved. We expect contributors to follow our [code\_of\_conduct](https://mu.readthedocs.io/en/latest/code_of_conduct.html). Check out our [developer setup](https://mu.readthedocs.io/en/latest/setup.html) documentation for instructions to configure a working development environment for Mu. Feedback may be given for contributions and, where necessary, changes will be politely requested and discussed with the originating author. Respectful yet robust argument is most welcome. Warning **Contributions are subject to the following caveats**: the contribution was created by the contributor who, by submitting the contribution, is confirming that they have the authority to submit the contribution and place it under the license as defined in the LICENSE file found within this repository (see [GNU General Public License](index.html#document-license)). If this is a significant contribution the contributor should add themselves to the AUTHORS file found in the root of Mu’s repository, otherwise they agree, for the sake of convenience, that copyright passes exclusively to Nicholas H.Tollervey on behalf of the Mu project. #### Checklist[¶](#checklist "Permalink to this headline") * If your contribution includes non-obvious technical decision making please make sure you document this in the [design decisions](https://mu.readthedocs.io/en/latest/design.html) section. * Your code should be commented in *plain English* (British spelling). * If your contribution is for a major block of work and you’ve not done so already, add yourself to the AUTHORS file following the convention found therein. * We have 100% test coverage - include tests to maintain this! * **Before submitting code ensure coding standards and test coverage by running**: ``` make check ``` * If in doubt, ask a question. The only stupid question is the one that’s never asked. * Most importantly, **Have fun!** :-) ### Code of Conduct[¶](#code-of-conduct "Permalink to this headline") We expect contributors to follow the [Python Software Foundation’s Code of Conduct](https://www.python.org/psf/codeofconduct/), reproduced below. The Python community is made up of members from around the globe with a diverse set of skills, personalities, and experiences. It is through these differences that our community experiences great successes and continued growth. When you’re working with members of the community, we encourage you to follow these guidelines which help steer our interactions and strive to keep Python a positive, successful, and growing community. A member of the Python community is: #### Open[¶](#open "Permalink to this headline") Members of the community are open to collaboration, whether it’s on PEPs, patches, problems, or otherwise. We’re receptive to constructive comment and criticism, as the experiences and skill sets of other members contribute to the whole of our efforts. We’re accepting of all who wish to take part in our activities, fostering an environment where anyone can participate and everyone can make a difference. #### Considerate[¶](#considerate "Permalink to this headline") Members of the community are considerate of their peers – other Python users. We’re thoughtful when addressing the efforts of others, keeping in mind that often times the labor was completed simply for the good of the community. We’re attentive in our communications, whether in person or online, and we’re tactful when approaching differing views. #### Respectful[¶](#respectful "Permalink to this headline") Members of the community are respectful. We’re respectful of others, their positions, their skills, their commitments, and their efforts. We’re respectful of the volunteer efforts that permeate the Python community. We’re respectful of the processes set forth in the community, and we work within them. When we disagree, we are courteous in raising our issues. Overall, we’re good to each other. We contribute to this community not because we have to, but because we want to. If we remember that, these guidelines will come naturally. ### Developer Setup[¶](#developer-setup "Permalink to this headline") The source code is hosted on GitHub. Fork the repository with the following command: ``` git clone https://github.com/mu-editor/mu.git ``` **Mu does not and never will use or support Python 2**. You should use Python 3.5 or above. #### Windows, OSX, Linux[¶](#windows-osx-linux "Permalink to this headline") Create a working development environment by installing all the dependencies into your virtualenv with: ``` pip install -e ".[dev]" ``` Note The Mu package distribution, as specified in `setup.py`, declares both runtime and extra dependencies. The above mentioned `pip install -e ".[dev]"` installs all runtime dependencies and most development ones: it should serve nearly everyone. For the sake of completeness, however, here are a few additional details. The `[dev]` extra is actually the aggregation of the following extras: * `[tests]` specifies the testing dependencies, needed by `make test`. * `[docs]` specifies the doc building dependencies, needed by `make docs`. * `[i18n]` specifies the translation dependencies, needed by `make translate\_\*`. * `[package]` specifies the packaging dependencies needed by `make win32`, `make win64`, `make macos`, or `make dist`. Additionally, the following extras are defined: * `[utils]` specifies the dependencies needed to run the utilities under the `utils` directory. It has been specifically excluded from the `[dev]` extra for two reasons: i) on the Windows platform, it requires a C compiler to be installed (as of this writing), and ii) running such utilities is seldom needed in Mu’s development process. * `[all]` includes all the dependencies in all extras. Warning Sometimes, having several different versions of PyQt installed on your machine can cause problems (see [this issue](https://github.com/mu-editor/mu/issues/297) for example). Using a virtualenv will ensure your development environment is safely isolated from such problematic version conflicts. If in doubt, throw away your virtualenv and start again with a fresh install as per the instructions above. On Windows, use the venv module from the standard library to avoid an issue with the Qt modules missing a DLL: ``` py -3 -mvenv .venv ``` Virtual environment setup can vary depending on your operating system. To learn more about virtual environments, see this [in-depth guide from Real Python](https://realpython.com/python-virtual-environments-a-primer/). #### Running Development Mu[¶](#running-development-mu "Permalink to this headline") Note From this point onwards, instructions assume that you’re using a virtual environment. To run the local development version of Mu, in the root of the repository type: ``` python run.py ``` An alternative form is to type: ``` python -m mu ``` Yet another one is typing: ``` mu-editor ``` #### Raspberry Pi[¶](#raspberry-pi "Permalink to this headline") If you are working on a Raspberry Pi there are additional steps to create a working development environment: 1. Install required dependencies from Raspbian repository: ``` sudo apt-get install python3-pyqt5 python3-pyqt5.qsci python3-pyqt5.qtserialport python3-pyqt5.qtsvg python3-dev python3-gpiozero python3-pgzero libxmlsec1-dev libxml2 libxml2-dev ``` 2. If you are running Raspbian Buster or newer you can also install this optional package: ``` sudo apt-get install python3-pyqt5.qtchart ``` 3. Create a virtualenv that uses Python 3 and allows the virtualenv access to the packages installed on your system via the `--system-site-packages` flag: ``` sudo pip3 install virtualenv virtualenv -p /usr/bin/python3 --system-site-packages ~/mu-venv ``` 4. Activate the virtual environment ``` source ~/mu-venv/bin/activate ``` 5. Clone mu: ``` (mu-venv) $ git clone https://github.com/mu-editor/mu.git ~/mu-source ``` 6. With the virtualenv enabled, pip install the Python packages for the Raspberry Pi with: ``` (mu-venv) $ cd ~/mu-source (mu-venv) $ pip install -e ".[dev]" ``` 7. Run mu: ``` python run.py ``` An alternative form is to type: ``` python -m mu ``` Or even: ``` mu-editor ``` Warning These instructions for Raspberry Pi only work with Raspbian version “Stretch”. If you use `pip` to install Mu on a Raspberry Pi, then the PyQt related packages will not be automatically installed from PyPI. This is why you need to use `apt-get` to install them instead, as described in step 1, above. #### Using `make`[¶](#using-make "Permalink to this headline") There is a Makefile that helps with most of the common workflows associated with development. Typing `make` on its own will list the options thus: ``` $ make There is no default Makefile target right now. Try: make run - run the local development version of Mu. make clean - reset the project and remove auto-generated assets. make pyflakes - run the PyFlakes code checker. make pycodestyle - run the PEP8 style checker. make test - run the test suite. make coverage - view a report on test coverage. make check - run all the checkers and tests. make dist - make a dist/wheel for the project. make publish-test - publish the project to PyPI test instance. make publish-live - publish the project to PyPI production. make docs - run sphinx to create project documentation. make translate - create a messages.pot file for translations. make translateall - as with translate but for all API strings. make win32 - create a 32bit Windows installer for Mu. make win64 - create a 64bit Windows installer for Mu. make macos - create a macOS native application for Mu. make video - create an mp4 video representing code commits. ``` Everything should be working if you can successfully run: ``` make check ``` (You’ll see the results from various code quality tools, the test suite and code coverage.) Note On Windows there is a `make.cmd` file that works in a similar way to the `make` command on Unix-like operating systems. Warning In order to use the MicroPython REPL via USB serial you may need to add yourself to the `dialout` group on Linux. #### Before Submitting[¶](#before-submitting "Permalink to this headline") Before contributing code please make sure you’ve read [Contributing to Mu](index.html#document-contributing) and follow the checklist for contributing changes. We expect everyone participating in the development of Mu to act in accordance with the PSF’s [Code of Conduct](index.html#document-code_of_conduct). ### Suggested First Steps[¶](#suggested-first-steps "Permalink to this headline") We love helpful, collaborative and friendly contributions! If you would like to ease into contributing to Mu we’d like to suggest the following things to try out, depending upon your skills and interests. If your contribution includes changes to code or documentation, you should read [Contributing to Mu](index.html#document-contributing) to learn about our expectations for submitting changes and improvements. #### Bug Reports[¶](#bug-reports "Permalink to this headline") If you think you have found a problem, then we want to hear about it! We keep track of issues via our [GitHub repository](https://github.com/mu-editor/mu/issues/). You’ll need to have an account on GitHub (joining GitHub is [very easy](https://github.com/join)) in order to submit such feedback. When you [create an issue](https://github.com/mu-editor/mu/issues/new) we expect certain pieces of information from you: * What you were doing (including all the necessary steps needed to recreate the situation you encountered). * What you expected to happen, what actually happened and why you think this difference is problematic. * Attach a copy of the logs generated by Mu (click on the cog icon in the bottom-right corner of Mu to display these logs, click on the logs and use CTRL-A to select all, then CTRL-C to copy and CTRL-V to paste the contents into the issue). Please try to be precise and provide as much information as possible. For what are obvious reaons, I hope you can see why we’re unable to respond to issues that say some variation of, “when I click this button, it breaks”. ;-) #### Coding[¶](#coding "Permalink to this headline") The first thing to do is follow the instructions for [Developer Setup](index.html#document-setup). You should read the explanation of [Mu’s Architecture](index.html#document-architecture) to learn how Mu fits together. As of time of writing, Mu is a very small project with only around 4000 lines of Python code. However, it’s important to know where to find different aspects of Mu’s functionality and understand why Mu was put together in the way that it has been. Assuming you’ve read and understood the architecture documentation an obvious and simple way to get started is to change the code in `logic.py` to suggest an alternative (better) message of the day. When Mu starts up, so the user sees that the status bar may contain textual messages from the application, a “message of the day” is displayed. These messages are defined near the top of `logic.py`. If you’d rather try something more substantial, why not explore the [list of currently open issues](https://github.com/mu-editor/mu/issues/), fix one of them and create a pull request for your solution? #### Translations[¶](#translations "Permalink to this headline") Mu uses Python’s standard libraries and tools to translate the application into other languages. If you are fluent in a language that is currently not covered by Mu, then we would love you to help by providing us with a translation. Full details of this process can be found in our guide on the [Internationalisation of Mu](index.html#document-translations). #### Documentation[¶](#documentation "Permalink to this headline") The documentation associated with Mu is not limited to addressing technical aspects of the editor (like the documentation you’re reading right now). Our documentation encompasses tutorials, how-to guides, learning resources and other non-technical information for our users. Such non-technical documentation is a part of [Mu’s website](https://codewith.mu/). If you are a teacher, learner or other interested party who wishes to contribute a tutorial, how-to or learning resource you should learn how to make such changes by reading the guide to [Developing Mu’s Website](index.html#document-website). #### User Experience Research[¶](#user-experience-research "Permalink to this headline") Our users are at the centre of everything we do. We have two sorts of user in mind: * Beginner programmers with little or no experience using a development environment. * Those who support beginner programmers: teachers, club leaders, parents and other mentors. When it comes to teaching and learning sometimes you just have to do what the experienced person says: for example, the professional musician explaining to the beginner how to hold an instrument “correctly”. There is some notion of correctness that the experienced person understands is the best way to do something. This also applies to learning to write code: we need to find ways to introduce the practice and conventions of programming in an effective manner. As Alan Kay said of Papert: > > “He realized, ‘Oh, we could take the real content out here as a > version in the child’s world that is still the real thing.’ It’s not a fake > version of math. It’s kind of like little league, or even T-ball. In sports > they do this all the time. In music, they do it all the time. The idea is, > you never let the child do something that isn’t the real thing – but you > have to work your ass off to figure out what the real thing is in the > context of the way their minds are working at that developmental level.” > – [Alan Kay](https://www.fastcompany.com/40435064/what-alan-kay-thinks-about-the-iphone-and-technology-now) > > > How do we know what “the real thing” is in the context of a code editor for a beginner programmer? That’s where [User Experience](index.html#document-user-experience) comes in and we would love contributions from professional developers, beginner programmers and teachers to make sure Mu is an effective educational tool. ### User Experience[¶](#user-experience "Permalink to this headline") We care deeply about all our users - we want using Mu to be a positive experience. In particular we focus on our primary users: * Beginner programmers, * Those who support beginner programmers. They are at the centre of all Mu related development. In order to understand how Mu can best support our users we need to learn about their needs, world view and how this reflects upon and influences their experience of using Mu (hence the name of this section). This includes taking into account cultural differences, special educational needs, level of education and other aspects of a person’s life that may impact on their accessibility to technology. These are not problems to be “solved”. Rather, this illustrates that Mu is an application to be evolved in a way to inclusively address the needs of users. As a result, it is important to keep Mu simple so that it is easy to make the inevitable changes needed. It’s also important to point out that we will make mistakes and may need to revise how Mu works. Therefore Mu should be simple enough that it is easy to fix. It’s important to differentiate between design and usability. Plenty of software looks beautiful but is difficult to use. With Mu, we aim to put usability and a great experience before looks. This beautiful yet inconvenient wine glass from [the uncomfortable](https://www.theuncomfortable.com/) illustrates what I mean (used with permission, see [Copyright Information](index.html#document-copyright)).  #### What is UX?[¶](#what-is-ux "Permalink to this headline") I have some wonderful friends in the tech world and one in particular, [Ann Carrier](https://twitter.com/pixeldiva), was kind enough to explain her work in “user experience” which I’ll reproduce below. It beautifully captures how I’d like user experience to relate to Mu. I sent her a series of questions to help me understand what I needed to do to bring about great UX in Mu and Ann gave some great answers. What is UX and why is it important / useful? > > UX is short for User Experience. User Experience means the overall > experience of a person using a product such as a website or computer > application, especially in terms of how easy or pleasing it is to use. > > > It’s a really important part of the process of creating a product for > people to use. It focuses on finding out about the needs of the people who > will be using the product or service. This can also include how they > behave, or what they do right now to achieve whatever it is they need to > do. Once you understand this, you can then go about the process of > designing something to meet those needs. You can even include users in the > process through interviews, usability assessments or other workshops. There > is absolutely nothing better than seeing people use a thing you’ve designed > to help you figure out all the things you haven’t quite got right! > > > Can you describe the processes and techniques you use as part of your job? > > I start with research. That takes many forms. Whether it’s desk research > into the latest apps or design patterns, or into the other products in the > area we want to build a product in, or speaking to the people who are going > to be using the product, this stage is vital. > > > Once we have a better idea of the problem we’re trying to solve, and the > context which surrounds that problem, we can begin to try and solve it. > There are many ways to do this, but I always find it helpful to draw things > out. This is especially helpful when you’re working in a team. Having a > diagram (even if it’s just boxes, lines and dodgy handwriting) that > everyone can see and suggest changes to helps you know that everyone has a > shared understanding. > > > Once we have a shared understanding of the problem, and if there is one, > the current workflows or processes we can then begin to look ahead to how > this thing could work, in a magical perfect world that in reality, rarely > exists. When we have this, we can then look at what steps we are going to > take to get there. This allows us to have a firm “North Star” in mind, and > with every step, re-assess not only whether that’s the same direction we > want to keep going in, but whether or not any solutions will take us closer > towards, or further away from it. At this stage, we can and should also > write down the success criteria - in other words, how will we know this is > working? > > > Next up is sketching out ideas for how the interface will look. Coming up > with lots of ideas at this point is really useful. The first idea you have > is rarely the best, so it’s good to try and get a lot of ideas on to paper > so that you can figure out the best ones. One technique you can use is to > try to come up with 6 different solutions to a problem. And if you’re > struggling to come up with all 6, then try things like solving the problem > the opposite way to the last idea, or something completely wacky. It’s > amazing how useful this can be. Sometimes, the right idea is just a tiny > bit to the side of the absolutely wrong way of doing a thing. > > > Sketching doesn’t need to be just done by designers, and you certainly > don’t need to be an artist. As long as you can draw boxes and arrows, and > your handwriting is neat enough that you can read it afterwards, you’re > good. The point of a sketch is to communicate an idea. If everyone you’re > working with can understand what you’re trying to describe with the sketch, > then that’s all it needs to do. > > > Once you’ve got more of an idea of what the solution will be, and talked > that over with anyone who needs to be involved (Product Managers, > Engineers, Testers, Users) then you can move on to more high fidelity > designs. These are called mockups and they show what the user interface > could look like, once it’s been built. Even at this stage, changes - known > as iterations - can and will still happen, because at every point you’re > finding out more and more and refining your idea, until you get to the > point where it’s built and in the hands of your users. > > > How does your UX work fit in with the wider software development project? > > In a good team, UX people are involved the whole way through, from the > first ideas, through research, exploration of solutions right to the end > when the product is out in the world. That said, user experience isn’t just > the responsibility of people with UX in their job titles. Everyone has a > part to play in delivering a good user experience to the people using the > product. > > > What advice would you give to people doing UX for the first time? > > If a good carpenter’s rule is “measure twice, cut once” a good rule for UX > people is “listen more than you talk”. Do your research. Find out about the > people who will be using the product. What they need. What they want. What > problems they’ve currently got. How they work. Then keep talking to them as > you design and build. > > > Is there anything else we should know about UX that’s not been covered by your answers to the above? > > There’s a great Shaker proverb which says: > > > > > > > “Don’t make something unless it is both made necessary and useful; > > but if it is both necessary and useful, don’t hesitate to make it > > beautiful.” > > > > > > > > > This gives you a great set of questions to ask of yourself whenever you’re > approaching a project. > > > Necessary: > > > 1. what problem are we trying to solve? > 2. is the proposed solution needed (can it be solved a different way?) > 3. will it solve the problem? > > > Useful (and usable): > > > 4. does the solution solve the problem for the people who need it? > 5. does it work well? > > > Beautiful: > > > 6. does it look good? (beautiful things make people happy!) > > > #### UX and Mu[¶](#ux-and-mu "Permalink to this headline") The “story so far” of Mu and UX starts with Carrie Anne Philbin’s (director of education at the Raspberry Pi Foundation) [keynote address to EuroPython 2015](https://www.youtube.com/watch?v=_gU7sfTrz4c). This formed the basis for usability decisions when Mu was first created. While running workshops to test a browser based editor for the BBC’s micro:bit, we’d heard from teachers that while the browser was very convenient in terms of setting things up, it was a pain to have to continually download scripts and then copy them onto the device and they also wanted easy access to MicroPython’s REPL. I wondered “how hard can it be?” and set out to create an editor based on Carrie Anne’s comments about the needs of teachers and learners when it came to code editing. Halfway into the keynote Carrie Anne talks about a development environments for beginner programmers in Python: She starts by explaining the problems with online editors. Often they require users to sign up, thus excluding a large number of children who, for legal (child protection) reasons, are not allowed to sign up because they have not reached the minimum age (usually around 14 years old) for them to be allowed to create their own accounts. Online editors introduce bureaucratic problems too: often schools use a “whitelist” system with their firewalls - they block everything except those sites on the whitelist. Getting a site onto the school’s whitelist is often an onerously bureaucratic and slow task. Furthermore, assuming the website is available, many online editors expect their users to have access to modern hardware and browsers. This is often not the case and intractable technical problems result. Finally, a significant minority of children still don’t have access to the internet, even in relatively advanced countries like the UK. For these reasons, a native developmnet environment is preferred. Carrie Anne then explores two offerings for students to use as native code editors. PyCharm has an educational edition that is both free and open. However, Carrie Anne claims it’s not very obvious for either beginner developers or teachers how best to use the application. She mentions there are too many opportunities for users to fail because of the plethora of buttons and menus. As a teacher, she wants something simple and obvious. Next, she turns her attention to Idle - the editor that comes bundled with Python. It’s good that Idle is free, has some syntax highlighting, auto-indents Python code, is cross platform, leightweight and simple. However, there are no line numbers, error reporting is incomrehensible to beginners and, most importantly, there are two separate windows that often get lost or confused with each other (one for code, the other for a sort of REPL). She suggests we turn out attention to a project called [SonicPi](http://sonic-pi.net/), a sort of programmable music tool for the Raspberry Pi, as an example of the sorts of features teachers and learners desire in a coding environment. She enumerates features that may not immediately seem important for beginner programmers and teachers. * All the panels are in the same window and it’s obvious what each one does. * There’s built-in help. * There are a limited number of obviously named buttons that encompass the core tasks required of the user. * Zooming in and out is a killer feature for teachers. * Simple things like line numbers and help for aligning code make a huge difference. Finally, she challenges the audience by asking, “Why can’t we have something like that for Python?” Being of a teacher-ish disposition she sets the assembled conference homework to be due in 2016. When I started work on Mu I watched the video mentioned above and sketched a rough outline of how Mu might work in terms of usability, reproduced below.  Notice that while the details are obviously different, the core interface looks like Mu (if you’re wondering what “micro:ed” refers to - it was Mu’s original name until the BBC got shirty about it and I changed it to Mu). I simply took Carrie Anne’s suggestions and made the simplest thing possible. Since then I’ve interviewed many teachers, observed lots of lessons and workshops and gathered feedback from users online. Changes to the usability of Mu generally follow a pattern: * We find evidence of several people wanting a change to make their lives easier (we tend to ignore single case exacmples of desired changes). * We use our issue tracking system built into GitHub as a way to come up with a tangible plan. * We create the simplest possible solution and ask for feedback. * Iterate! #### Resources for UX[¶](#resources-for-ux "Permalink to this headline") In addition to providing answers about UX, Ann very kindly pointed me to various resources on the web that helped me to understand the challenges and work needed to do actionable UX research. Andrew Travers has [blogged about](https://web.archive.org/web/20210516205833/https://trvrs.co/book/) a free pocket guide he has written on [interviewing for research](https://s3-eu-west-1.amazonaws.com/interviewing-for-research/InterviewingForResearch.pdf). I found this invaluable reading and helped me to prepare for the observations and interviews I conducted as part of the process of developing Mu. This is where I would start if I were new to UX research and wanted to get a quick overview of things to do. The Government Digital Service of the UK Government has an international reputation for software development greatness. The foundation of this reputation are the documents it releases, for free, that outline the “best practices” and expectations about process that GDS have about various aspects of the software development process. Their [service manual on user research](https://www.gov.uk/service-manual/user-research) is a comprehensive outline of the various tasks, processes and outcomes needed to do effective UX research. I particularly found the section on [analysis of UX research](https://www.gov.uk/service-manual/user-research/analyse-a-research-session) helpful. Finally, it’s good to read the suggestions, heuristics and best practice for working with users who have additional requirements when using software. Again, the UK government’s GDS has a number of resources, although I found this blog post on the [Dos and don’ts on designing for accessibility](https://accessibility.blog.gov.uk/2016/09/02/dos-and-donts-on-designing-for-accessibility/) (and the associated posters) to be a rich seam of useful advice. All their resources in this context can be found on their page about [accessibility and assisted digital](https://www.gov.uk/service-manual/helping-people-to-use-your-service). Mu has a long way to go on its path to being an inclusive and accessible code editor, but what is certain is that UX is a core driver of this journey. ### Mu’s Architecture[¶](#mu-s-architecture "Permalink to this headline") This section provides a high level overview of how the various parts of Mu fit together. #### Key Concepts[¶](#key-concepts "Permalink to this headline") The key concepts you should know are: * Mu uses the [PyQT5 framework](https://riverbankcomputing.com/software/pyqt/intro) (that makes the [Qt](https://www.qt.io/) GUI toolkit available to Python) for making its user interface. * Mu is a modal editor: the behaviour of Mu changes, depending on mode. * There are a number of core features and behaviours that are always available and never vary, no matter the mode. * The text area into which users type code is based on a [Scintilla](http://www.scintilla.org/) based widget. * Mu is easy to internationalise using Python’s standard `gettext` based modules and tools. * Mu’s code base is small, well documented and has 100% unit test coverage. #### Code Structure[¶](#code-structure "Permalink to this headline") The code is found in the `mu` directory and organised in the following way: * The application is created and configured in `app.py`. * Most of the fundamental logic for Mu is in `logic.py`. * Un-packaged third party code used by Mu is found in `contrib`. * The Python3 debugger consists of a debug client and debug runner found in the `debugger` namespace. A description of how the debugger works can be found in [Python Runner/Debugger](index.html#document-debugger). * Interacting with the UI layer is done via the `Window` class in the `interface.main` module. Mu specific UI code used by the `Window` class found in the other modules in the namespace. * Internationalization (I18n) related assets are found under `locale`. Learn how this works via [Internationalisation of Mu](index.html#document-translations). * Modes are found under the `modes` namespace. They all inherit from a `BaseMode` class and there’s a tutorial for [Modes in Mu](index.html#document-modes). * Graphical assets, fonts and CSS descriptions for the themes are all found under `resources`. All classes, methods and functions have documentation *written for humans*. These are extracted into the [Mu API Reference](index.html#document-api). [Mu’s Test Suite](index.html#document-tests) is in the `test` directory and filenames for tests relate directly to the file they test in the Mu code base. The module / directory structure mirrors the organisation of the Mu code base. We use PyTest’s assert based unit testing. All tests have a comment describing their intent. The documentation you’re reading right now (i.e. that written for developers) is found in the `docs` directory. We use [Sphinx](http://www.sphinx-doc.org/en/stable/) to write our docs and host them on [ReadTheDocs](https://mu.readthedocs.io/en/latest/). Other documentation (tutorials, user help and so on) is on [Developing Mu’s Website](index.html#document-website). The `utils` directory contains various scripts used to scrape and / or build the API documentation used by Mu’s autocomplete and call tip functionality. The other assets in the root directory of the project are mainly for documentation (such as our Code of Conduct), configuration (for testing) or packaging for various platforms (see [Packaging Mu](index.html#document-packaging)). If you want to make changes please read [Contributing to Mu](index.html#document-contributing). ### Modes in Mu[¶](#modes-in-mu "Permalink to this headline") Mu is a modal editor: it behaves differently depending on the currently selected mode. The name of the current mode is always displayed in the bottom right hand corner of Mu’s window. Clicking on the mode button opens up a dialog box to allow users to select a new mode. #### What Are Modes?[¶](#what-are-modes "Permalink to this headline") Modes are a way to customise how Mu should behave. This simplifies Mu: rather than trying to provide every possible feature at once (and thus become a nightmare of complexity for the user), modes bring related features together in a simple and easy to use manner. Modes are able to add buttons to the user interface, handle certain events (such as when one of the mode’s buttons is clicked) and provide contextual information for Mu (such as where files should be saved or what API metadata is available). It’s also possible for one mode to transition to another and some modes are only available as transitional modes (i.e. they may not be selected by the user). A good example of such a “transitional” mode is the Python 3 debugger, which can only be accessed from the standard Python 3 mode. Mu contains the following modes, although it is very easy to add more (the images below are used with permission, see [Copyright Information](index.html#document-copyright)). ##### Adafruit Mode[¶](#adafruit-mode "Permalink to this headline")  [Adafruit](http://adafruit.com/) make extraordinarily awesome boards for embedded development. Many of these boards run Adafruit’s own flavour of [MicroPython](http://micropython.org/) called [CircuitPython](https://www.adafruit.com/circuitpython). The Adafruit mode inherits from a base MicroPython mode that provides USB/serial connectivity to the board. Because source code is stored directly on the Adafruit boards, this mode ensures that filesystem based operations actually happen on the connected device. If no such device is found, the mode will warn you. ##### BBC micro:bit Mode[¶](#bbc-micro-bit-mode "Permalink to this headline")  The [BBC micro:bit](http://microbit.org/) is a small computing device for young coders that is capable of running MicroPython. Mu was originally created as volunteer led effort as part of the Python Software Foundation’s contribution to the project. Just like the Adafruit mode, micro:bit mode inherits from a base MicroPython mode so there’s a REPL based interface to the device. It also provides functionality to “flash” (i.e. copy) your code onto the device and a simple user interface to the simple file system on the device. ##### Pygame Zero / PyGame Mode[¶](#pygame-zero-pygame-mode "Permalink to this headline")  [PyGame](http://pygame.org/) (or, more correctly: “pygame”) is a cross platform set of Python libraries for writing games. [Pygame Zero](https://pygame-zero.readthedocs.io/en/stable/) is a wrapper for pygame that makes it easy for beginners to make games. If both pygame and Pygame Zero are installed (as they are if you used the official Windows installer), Mu’s Pygame Zero mode makes it easy for beginner programmers to create games. This mode provides a “Play” button that uses Pygame Zero’s game-runner to launch the user’s games. Two further buttons open the operating system’s file system explorer for the directories containing images and sounds used in the user’s games. This makes it easy for the user to copy and paste new game assets into the right place. The standard Python3 mode (see below) is probably a better environment for more advanced pygame-only development. Mu ensures that all the game assets required by the [Pygame Zero introductory tutorial](https://pygame-zero.readthedocs.io/en/stable/introduction.html) are available by default. ##### Standard Python3 Mode[¶](#standard-python3-mode "Permalink to this headline")  This mode is for creating simple Python 3 programs. As with the other modes, there is a REPL for live programming, but in this case it is an iPython based REPL that uses [project Jupyter](http://jupyter.org/). As with other Jupyter notebooks, it’s possible to embed graphics and charts into the REPL so it becomes a interesting to read and work with. There are two ways to run your script in this mode: 1. Click the “Run” button: will launch the script using Python’s interactive mode (so you’ll be dropped into a basic interactive Python shell upon the script’s completion). 2. Click the “Debug” button: Python mode transitions to the debug mode - a graphical way to inspect and watch your code execute. Because of the overhead needed to start the graphical debugger it takes longer to start running your script. This is especially noticable on the Raspberry Pi. Python 2 isn’t supported by Mu and never will be. ##### Debug Mode[¶](#debug-mode "Permalink to this headline") It’s only possible to enter debug mode from standard Python mode. It’s purpose is to manage the execution and inspection of your code. Clicking the margin of the editor toggles “break points” that tell the debugger where to pause. Once paused it’s possible to inspect the state of various objects at that moment in the code’s execution and step over, into and out of lines of code. You’re able to watch Python execute your code, allowing you to discover where there may be bugs. Once the code has finished the debug mode transitions back to standard Python mode. #### Create a New Mode[¶](#create-a-new-mode "Permalink to this headline") It’s very easy to create new mode for Mu. The following tutorial explains how we created the Pygame Zero mode. ##### Create a Class[¶](#create-a-class "Permalink to this headline") The most important aspects of a mode are encapsulated in a class that represents the mode. These classes live in the `mu.modes` namespace and **must** inherit from the `mu.modes.base.BaseMode` class. If your new mode is for a MicroPython based device, you should inherit from the `mu.modes.base.MicroPythonMode` class, since this includes various helpful utilities for such things as finding a connected device and running a REPL over a USB-serial connection. The naming convention is to create a new module in which is found the class representing the mode. For example, for Pygame Zero, the new module is `mu.modes.pygamezero` in which is found the `PyGameZeroMode` class that inherits form `BaseMode`. ##### Integrate the Mode[¶](#integrate-the-mode "Permalink to this headline") Mu needs to know that the new mode is available to use. This is fulfilled by a couple of relatively simple steps: * Add the mode’s class to the `\_\_all\_\_` list in the `\_\_init\_\_.py` file for the `mu.modes` namespace. * In `mu.app.py` import the new mode from `mu.modes` and add an instance of the mode’s class to the dictionary returned by the `setup\_modes` function. (All modes are instantiated with the available `editor` and `view` objects that represnt the editor’s logic and UI layer respectively.) ##### Update the Class’s Behaviour[¶](#update-the-class-s-behaviour "Permalink to this headline") The core elements of your new mode’s class that need updating include some attributes and three methods. The attributes that must be changed are: * `name` – the full name of the mode, for example, “PyGame Zero”. * `description` – a short description of the mode to be displayed in the mode picker. For example, “Make games with Pygame Zero”. * `icon` – an icon used to represent the mode in the mode picker. This must be a `.png` image file found in the `mu/resources/images` directory. Additional attritbutes with safe default values set in the `BaseMode` class which may be of value for you to change are: * `save\_timeout` – the number of seconds to wait before auto-saving work. If this value is 0 (zero) Mu will not auto-save changed files when in this mode. * `builtins` – a list of strings defining symbols that Mu’s code checker must assume are builtins (above and beyond Python’s standard builtins). Note When creating strings that will be seen by users please remember to use the conventions for internationalization (i18n). Put simply, enclose your strings in a call to `\_` like this: ``` \_('This string will be translated automatically') ``` Please see [Internationalisation of Mu](index.html#document-translations) for more details. You should pay attention to three methods of your class: `actions`, `api` and `workspace\_dir`. You must override `actions` and `api` (see below) and *may* want to override `workspace\_dir`. The purpose of the `workspace\_dir` method is to return a string representation of the path to the directory containing the code created with this mode. The default implementation in `BaseMode` is generally safe to use although some CircuitPython based boards may want to use this method to point to a connected device (if attached) or a safe default on the user’s filesystem (if no device is attached). See how it’s done in the `AdafruitMode` class. If in doubt, just use the method inherited from `BaseMode`. However, you **must** override the `actions` method. It must return a list of dictionaries that describe the buttons to be added to Mu’s user interface. Each dictionary must contain the following key/value pairs: * `name` – the name of the button which doubles as the name of the icon found in `mu/resources/images` used as the visual representation of the button. To create a new button start with the blank `button.png` image and use either an icon from the [FontAwesome](https://fontawesome.bootstrapcheatsheets.com/) set of icons, or some other graphical device that looks visually similar. Make sure that the colour of the image is correct blue of (hex value) #336699. Please remember to centre it within the button and make sure it has the same sort of scale as the existing buttons. * `display\_name` – the string displayed immediately underneath the button in Mu’s user interface. * `description` – the string displayed as a tool-top when the mouse pointer hovers over the button, but the button remains unclicked. * `handler` – a reference to a method you have created in your mode’s class that is called, with an event object, when the button is clicked. * `shortcut` – a string representation of the keyboard shortcut for the button. Valid examples include, `'F5'` (for function key 5) or, `'Ctrl+Shift+I'` (for control-shift-I). By way of illustration, here’s the list of dictionaries returned in the Pygame Zero mode: ``` [ { 'name': 'play', 'display\_name': \_('Play'), 'description': \_('Play your PyGame Zero game.'), 'handler': self.play\_toggle, 'shortcut': 'F5', }, { 'name': 'images', 'display\_name': \_('Images'), 'description': \_('Show the images used by PyGame Zero.'), 'handler': self.show\_images, 'shortcut': 'Ctrl+Shift+I', }, { 'name': 'sounds', 'display\_name': \_('Sounds'), 'description': \_('Show the sounds used by PyGame Zero.'), 'handler': self.show\_sounds, 'shortcut': 'Ctrl+Shift+S', }, ] ``` Notice how the handlers are references to methods of the `PyGameZeroMode` class, the details of which are left to the creator of the mode. Mu simply calls the handler and expects the author of the mode to know what they’re doing. Interactions with the Mu editor are via two objects referenced within the class: * `self.editor` – represents an object containing the core logic of the editor (an instance of `mu.logic.Editor`). * `self.view` – references the main GUI object through which all display and user interface related operations should pass (an instance of `mu.interface.main.Window`). Please see the [Mu API Reference](index.html#document-api) for specific details of what these two objects offer. Finally, you **must** also override the `api` method, whose role is to provide a list of strings that conform to Scintilla’s protocol for defining and documenting API’s to be used with autocomplete and call-tips. The protocol is: ``` 'foo.bar(arg1, args2="baz") \nMulti line\n\nEnglish description.` ``` Happily, various scripts in the `utils` directory can be used, cloned and modified to autogenerate this documentation from source code. The reason the extraction of such API related information is automated is so it makes it very quick and easy to revise such data as APIs change in the future. Take a look at the `pgzero\_api.py` file and you’ll find a simple recipe for extracting such information from Python modules. Three modules for Python’s standard library (`json`, `inspect` and `importlib`) are used to import the modules we’re interested in, inspect the signatures of the callable objects found therein and emit a JSON based output (called `pgzero\_api.json`). The resulting JSON is a list of JSON objects containing three attributes: * `name` – the module name + object name. * `args` – a list of the arguments taken by the callable Python object being described. * `description` – the docstring associated with the Python object. Here’s an example of such an object from the emitted `pgzero\_api.json` file: ``` { "description": "Interface to the screen.", "name": "screen.Screen", "args": [ "surface" ] } ``` Given such JSON serialised data, the `mkapi.py` command will take such a file as input and emit to stdout a list of strings for the API that conform to Scintilla’s protocol to be used by autocomplete and call-tips. In the case of the Pygame Zero mode, the output from the `mkapi.py` command ended up in `mu.modes.api.PYGAMEZERO\_APIS`. The list itself is in the `pygamezero.py` file in the `mu/modes/api` directory, and the `\_\_init\_\_.py` found therein exposes it via the `\_\_all\_\_` list. Back in the `PyGameZeroMode` class the `api` method simply returns a concatenated list of the APIs that a user of the mode may use: ``` from mu.modes.api import (PYTHON3_APIS, SHARED_APIS, PI_APIS, PYGAMEZERO_APIS) ... later in the PyGameZeroMode class ... def api(self): return SHARED_APIS + PYTHON3_APIS + PI_APIS + PYGAMEZERO_APIS ``` With these relatively simple steps, it’s possible to create quite powerful modes. Most importantly, taking a look at the existing modes in the `mu.modes` namespace will reveal how to do most of the things you’ll need. However, there is one final aspect of creating a mode that we need to address. ##### Unit Test the Mode[¶](#unit-test-the-mode "Permalink to this headline") **We will not accept any new modes without 100% unit test coverage.** Please read the guide about [Mu’s Test Suite](index.html#document-tests) for how Mu is tested and the various expectations we have when it comes to writing tests. If you are unsure about the best way to go about testing your mode please feel free to ask for help. We would much rather get a pull request for a “spike” (draft) version of a new mode and work with the original author on testing the code, than have no pull request at all. If in doubt, ask. We’re a friendly bunch and [Contributing to Mu](index.html#document-contributing) is easy. ### Internationalisation of Mu[¶](#internationalisation-of-mu "Permalink to this headline") A really useful and relatively simple way to contribute to Mu is to translate the user interface into a different language. The steps to do this are very simple and there exist plenty of tools to help you. You can contribute in three ways: * Improve or extend an existing translation. * Create a completely new translation for a new language. * Make a translation of [Mu’s website](https://codewith.mu/) (see the [Developing Mu’s Website](index.html#document-website) guide for how to do this). In both cases you’ll be using assets found in the `mu/locale` directory. Mu uses Python’s standard [gettext](https://docs.python.org/3.6/library/i18n.html) based internationalization API so we can make use of standard tools to help translators, such as [babel](https://babel.pocoo.org/en/latest/) or [Poedit](https://poedit.net/). Non-technical users If you are not a technical user and you are not familiar with the tools and jargon we use in this guide, please reach out to us by [creating a new issue in GitHub](https://github.com/mu-editor/mu/issues/new). We will help you set up a user-friendly tool that you can use to contribute new or improved translations, and integrate them into the next Mu release. We welcome translations from all users! #### How To[¶](#how-to "Permalink to this headline") Updating or creating a new translation for Mu’s user interface requires [setting up a development environment](index.html#document-setup) beforehand and, from there, is a four-step process: ##### 1. Produce an up to date `mu.po` file[¶](#produce-an-up-to-date-mu-po-file "Permalink to this headline") Open a CLI shell, change the working directory to Mu’s repository root, and run: ``` $ make translate_begin LANG=xx_XX ``` Where `xx\_XX` is the identifier for the target language. This creates (or updates, if it already exists) the `mu.po` file under the `mu/locale/xx\_XX/LC\_MESSAGES/` directory – this is where the original British English messages are associated with their localized translations. ##### 2. Translate Mu user interface strings[¶](#translate-mu-user-interface-strings "Permalink to this headline") Use a tool of your choice to edit the `mu.po` file: * Those looking for a GUI based tool can try out [Poedit](https://poedit.net). * Others might prefer a plain text editor, which will be sufficient. ##### 3. Check the translation result[¶](#check-the-translation-result "Permalink to this headline") As you progress, check the translation results by launching Mu with: ``` $ make translate_test LANG=xx_XX ``` As before, `xx\_XX` is the identifier for the target language. When done checking, quit Mu, and go back to step 2. as many times as needed. ##### 4. Submit your translation work[¶](#submit-your-translation-work "Permalink to this headline") This process produced two new or updated files, both under the `mu/locale/xx\_XX/LC\_MESSAGES/` directory: * `mu.po` containing the text based source of the translation strings. * `mu.mo` containing a compiled version of the above, used by Mu at runtime. Commit your changes and create a pull request via GitHub. Thanks! ### Python Runner/Debugger[¶](#python-runner-debugger "Permalink to this headline") An obvious requirement for a Python editor is to run your Python code. For standard Python, Mu does this in two ways: * With the Python runner (press the “Run” button). * With the graphical debugger (click the “Debug” button). Note For MicroPython based modes, the code is run on the attached embedded device and not directly by Mu. For example, saving your code on an Adafruit board restarts the device and Circuit Python evaluates your code. Both the Python runner and grapical debugger were created with the financial support of the Raspberry Pi Foundation. If you are creating a new standard Python mode for Mu, you should *at least* make available the Python runner (please see [Modes in Mu](index.html#document-modes) for more information about how to do this). Both methods of running Python code essentially work in the same way: they fire up a new child process and connect its stdin, stdout, stderr to the `PythonProcessPane` found in the `mu.interface.panes` namespace so you’re able to interact with it in a terminal like environment. However, the Python runner starts immediately whereas the debugger has to set up a bunch of debug-related scaffolding, which makes it start slower. This is especially noticeable on the less powerful Raspberry Pi machine. Basically, if you just want to run your script, use the Python runner. #### Python Runner[¶](#python-runner "Permalink to this headline") The essentials of the Python runner are in the afore mentioned `PythonProcessPane` class. The `start\_process` method is used to create the new child process. The resulting process becomes a `process` attribute on the instance of the `PythonProcessPane`. You have some control over how the child process behaves. * You should supply the `script\_name` to run. * You must also provide a `working\_directory` within which the script will run (this is usually the user’s `mu\_code` directory). * The `interactive` flag (which defaults to `True`) will mean the user will drop into a simple Python REPL when the script completes. The default is at the request of the Raspberry Pi Foundation who explain that it is often handy for beginner developers to run their script and then explore the resulting context interactively. * If the `debugger` flag is set to `True` (the default is `False`) then the debug runner (see below) is started in a child process for the referenced script. This overrides the `interactive` flag to being `False`. * Any `command\_args` for the referenced script should be a list of strings. The default is no `command\_args` (i.e. None). Handlers are configured to handle various events, such as when the process finishes or when a user type a character. The `PythonProcessPane` includes basic command history and input editing features. It’ll also respond to CTRL-C and CTRL-D. Copy and paste can be accessed via a context menu. #### Graphical Debugger[¶](#graphical-debugger "Permalink to this headline") The graphical debugger exists to give beginner programmers an easy way to observe their code while it is running and allows you to use breakpoints, step over and into code as well as use a simple object inspector to view the status of objects in scope.  When a user clicks the “Debug” button Mu transitions to “debug” mode which exposes the functionality of the debugger client which, in turn, communicates with the debug runner process which is actually driving the user’s script. The debugger is designed to be as simple as possible in order to introduce beginner programmers to the basic concepts of a debugger in the easiest way. It does **NOT** strive to be extensive or particularly powerful. Rather, its aim is to encourage beginner programmers to explore their code while it is running. In this sense if conforms to the Mu outlook of providing the first steps for a beginner programmer with a view to them quickly graduating to a “proper” development environment once they’ve found their feet. Most of the debugger’s functionality can be found in the `mu.debugger` namespace. Coordination is done in the `mu.modes.debugger.DebugMode` class. ##### Debug Client[¶](#debug-client "Permalink to this headline") The debug client exists within the Mu process. It spins up an instance of the `mu.debugger.client.CommandBufferHandler` class in a separate thread to handle inter-process communication in a non-blocking manner, so the UI thread is never blocked. The `mu.debugger.client.Debugger` class is used to react to incoming events from, and as an API for Mu to issue commands to the debug runner. It uses a reference to a `view` object to update the user inteface as events are detected. ##### Debug Runner[¶](#debug-runner "Permalink to this headline") The debug runner exists on a new child process and makes use of Python’s [bdb debugger framework](https://docs.python.org/3/library/bdb.html). It spins up a new thread to run the `command\_buffer` function that listens for incoming commands. The most interesting aspects of the runner are found in the `mu.debugger.runner.Debugger` class which inherits from the `bdb.Bdb` class found in Python’s standard library. It responds to commands from the client and sends messages when various events occur during the debugging process. These messages are picked up by the debug client and reflected in Mu’s UI. The `mu.debugger.runner.run` function is the entry point for the debug runner and, as specified in Mu’s `setup.py`, is accessed via the `mu-debug` command. This command expects at least one argument: the name of the script to be debugged. Any further arguments are passed on to the script to be debugged. ### Mu’s Test Suite[¶](#mu-s-test-suite "Permalink to this headline") We have tests so we can make changes with confidence. We use several different sorts of test: * [PyFlakes](https://github.com/PyCQA/pyflakes) for checking for errors in our code. * [pycodestyle](http://pycodestyle.pycqa.org/en/latest/intro.html) for making sure our coding style conforms with most of the conventions of [PEP8](https://www.python.org/dev/peps/pep-0008/). * [PyTest](https://pytest.readthedocs.io/en/latest/) as a framework for writing our unit tests. * [Coverage](https://coverage.readthedocs.io/) for checking the coverage of our unit tests. Warning We currently have 100% test coverage. It means **every line of code in Mu has been exercised by at least one unit test**. We would like to keep it this way! We can’t claim that Mu is bug-free, but we can claim that we’ve expressed an opinion about how every line of code should behave. Furthermore, our opinion of how such code behaves may **NOT** be accurate or even desirable. ;-) In addition, we regularly make use of the excellent [LGTM](https://lgtm.com/projects/g/mu-editor/mu/) online code quality service written, in part, by friend-of-Mu, [Dr.Mark Shannon](https://sites.google.com/site/makingcpythonfast/). #### Running the Tests[¶](#running-the-tests "Permalink to this headline") Running the tests couldn’t be simpler: just use the `make` command: ``` $ make check ``` This will run **ALL** the tests of each type. To run specific types of test please try: `make pyflakes`, `make pycodestyle`, `make test` or `make coverage`. Warning The test suite will only work if you have installed all the requirements for developing Mu. Please see [Developer Setup](index.html#document-setup) for more information on how to achieve this. #### Writing a New Test[¶](#writing-a-new-test "Permalink to this headline") All the unit tests are in the `tests` subdirectory in the root of Mu’s repository. The tests are organised to mirror the code structure of the application itself. For example, the tests for the `mu.modes.base` namespace are in the `tests.modes.test\_base.py` file. As mentioned above, we use PyTest as a framework for writing our unit tests. Please refer to their [extensive documentation](https://pytest.readthedocs.io/en/latest/) for more details. In terms of our expectation for writing a test, we expect it to look something like the following: ``` def test\_MyClass\_function\_name\_extra\_info(): """ This is a description of the INTENTION of the test. For example, we may want to know why this test is important, any special context information and even a reference to a bug report if required. """ assert True # As per PyTest conventions, use simple asserts. ``` We also expect your test code to pass PyFlakes and PEP checks. If in doubt, don’t hesitate to get in touch and ask. ### Packaging Mu[¶](#packaging-mu "Permalink to this headline") Because our target users (beginner programmers and those who support them) may not be confident with the technical requirements for installing packages, we need to make obtaining and setting up Mu as simple and easy as possible. Furthermore, we aim to make the creation of packages automatic and as simple as possible. By automating this process we ensure that the knowledge and steps needed to package Mu is stored in software (so everyone can see how we do it) and we don’t rely on a volunteer to take time and effort to make things happen. If you submit code and it is accepted into our master branch, within minutes you should have a set of packages for different platforms that includes your changes. Such builds can be [found here](http://mu-builds.s3-website.eu-west-2.amazonaws.com/). Of course, such builds are not “official” releases. We’ll only do that every so often when major updates land. These will take the form of [releases found in our GitHub repository](https://github.com/mu-editor/mu/releases). Such releases will include the “official” installers for supported platforms. The installers referenced on [Mu’s website](http://codewith.mu/) will always be the latest stable release of Mu on GitHub. Note Huge thanks to [Carlos Pereira Atencio](https://twitter.com/carlosperate) who made considerable efforts to automate and configure the packaging of Mu. Without the contributions of volunteers like Carlos, projects like Mu simply wouldn’t exist. If you find Mu useful why not say thank you to Carlos via Twitter..? Thank you Carlos! :-) We package Mu in various different ways so it is as widely available as possible. What follows is a brief description of how each package is generated (some of them require the manual intervention of others outside the Mu project). #### Python Package[¶](#python-package "Permalink to this headline") If you have Python 3.5 or later installed on Windows, OSX or 64-bit Linux and you are familiar with Python’s built-in packaging system, you can install Mu into a virtual environment with `pip`: ``` $ pip install mu-editor ``` Note By design, `pip` will not create any shortcuts for applications that it installs. If you want to add a shortcut for Mu to your desktop/start menu you can use Martin O’Hanlon’s amazingly useful [Shortcut tool](https://shortcut.readthedocs.io/en/latest/) like this: ``` $ pip install shortcut $ shortcut mu ``` As per conventions, the `setup.py` file contains all the details used by `pip` to install it. We use [twine](https://github.com/pypa/twine) to push releases to PyPI and I (Nicholas - maintainer) simply use a Makefile to automate this: ``` $ make publish-test $ make publish-live ``` The `make publish-live` command is what updates PyPI. The `make publish-test` command uses the test instance of PyPI so we can confirm the release looks, behaves and works as expected before pushing to live. #### Raspberry Pi[¶](#raspberry-pi "Permalink to this headline") Raspberry Pi OS (previously called Raspbian) is the official operating system for the Raspberry Pi and features Mu as Recommended Software. Raspberry Pi OS uses the Mu packages contributed to Debian by [Nick Morrott](https://twitter.com/nickmorrott). To install Mu on Raspberry Pi OS from the command line, type: ``` $ sudo apt install mu-editor ``` Alternatively, Mu can be installed from the Recommended Software menu in the Programming section. Warning Since Mu for Raspberry Pi OS is packaged by a third party, our latest releases may not be immediately available. #### Windows Installer[¶](#windows-installer "Permalink to this headline") Packaging for Windows is essential for the widespread use of Mu since most computers in schools run this operating system. Furthermore, feedback from school network administrators tells us that they prefer installers since these are easier to install “in bulk” to computing labs. There are two versions of the installer: one for 32bit Windows and the other for 64bit Windows. The 32bit version has been tested on Windows 7 and the 64bit version has been tested on Windows 10. Support for anything other than Windows 10 is important, but a “best effort” affair. If you find you’re having problems please [submit a bug report](https://github.com/mu-editor/mu/issues/new). The latest *unsigned* builds for Mu on Windows [can be found here](http://mu-builds.s3-website.eu-west-2.amazonaws.com/?prefix=windows/). Mu for Windows contains its own version of Python packaged in such a way that makes it only usable within the context of Mu (Python’s so-called [isolated mode](https://docs.python.org/3.4/whatsnew/3.4.html#whatsnew-isolated-mode)). Of course, the version of Python in Mu will have as much or little access to computing resources as the host operating system will allow. Packaging is automated using the [Appveyor](https://www.appveyor.com/) cloud based continuous integration solution for Windows. The [.appveyor.yml](https://github.com/mu-editor/mu/blob/master/.appveyor.yml) file found in the root of Mu’s repository, configures and describes this process. You can see the history of such builds [here](https://ci.appveyor.com/project/carlosperate/mu/history). We use the [NSIS](http://nsis.sourceforge.net/Main%5FPage) tool to build the installers. This process if coordinated by the amazing [pynsist](https://pynsist.readthedocs.io/en/latest/) utility. Note Pynsist is the creation of [Thomas Kluyver](https://twitter.com/takluyver), who has done an amazing job creating many useful tools and utilities for the wider Python community (for example, Thomas is also responsible for the Jupyter widget Mu uses for the REPL in Python 3 mode). On several occasions Thomas has volunteered his time to help Mu. Like Carlos, Thomas is another example of the invaluable efforts that go into making Mu. Once again, if you find Mu useful, please don’t hesitate to thank Thomas via Twitter. Thank you Thomas! The required configuration file for `pynsist` is automatically generated at packaging time, under a temporary working directory. The motive for that arises from the need to ensure that Mu’s dependencies are sourced from a single place, which is `setup.py`. The `win\_installer.py` script handles that, runs `pynsist`, moves the resulting installer executable to the `dist` directory, and cleans up. If you’re interested in learning more, the script includes comments with detailed notes (also, check out the `pynsist` [specification for configuration files](https://pynsist.readthedocs.io/en/latest/cfgfile.html)). The automated builds are unsigned, so Windows will complain about the software coming from an untrusted source. The official releases will be signed by me (Nicholas Tollervey - the current maintainer) on my local machine using a private key and uploaded to GitHub and associated with the relevant release. [The instructions for cryptographically signing installers](https://pynsist.readthedocs.io/en/latest/faq.html#code-signing) explain this process more fully (the details of which are described [by Mozilla](https://developer.mozilla.org.cach3.com/en-US/docs/Mozilla/Developer_guide/Build_Instructions/Signing_an_executable_with_Authenticode)). Use the `make` command to build your own installers: ``` $ make win32 $ make win64 ``` This will clean the repository before running the `win\_installer.py` command for the requested bitness. Because Mu depends on the availability of tkinter, part of the build process is to download the appropriate tkinter-related resources from [Mu’s tkinter assets repository](https://github.com/mu-editor/mu_tkinter). If asked, the command for automatically installing Mu, system wide, should use the following flags: ``` mu-editor\_win64.exe /S /AllUsers ``` The `/S` flag tells the installer to work in “silent” mode (i.e. you won’t see the windows shown in the screenshots above) and the `/AllUsers` flag makes Mu available to all users of the system (i.e. it’s installed “system wide”). #### OSX App Installer[¶](#osx-app-installer "Permalink to this headline") We use Travis to automate the building of the .app and .dmg installer (see the `.travis` file in the root of Mu’s GIT repository for the steps involved). This process is controlled by [Briefcase (part of the BeeWare suite of tools](https://briefcase.readthedocs.io/en/latest/)) which piggy-backs onto the `setup.py` script to build the necessary assets. To ensure Mu has Python 3 available for it to both run and use for evaluating users’ scripts, we have created a portable/embeddable Python runtime whose automated build scripts can be found [in this repository](https://github.com/mu-editor/mu_portable_python_macos). This is the Python version used by Mu (not the one on the user’s machine). The end result of submitting a commit to Mu’s master branch is an automatically generated installable for OSX. These assets are un-signed, so OSX will complain about Mu coming from an unknown developer. However, for full releases we sign the .app with our Apple developer key (a manual process). #### Linux Packages[¶](#linux-packages "Permalink to this headline") We don’t automatically create packages for Linux distros. However, we liaise with upstream developers to ensure that Mu finds its way into both Debian and Fedora based distributions. ##### Debian[¶](#debian "Permalink to this headline") Mu (and the MicroPython runtime) were packaged for Debian and Ubuntu by [Nick Morrott](https://twitter.com/nickmorrott) and have been available to install since the releases of Debian 10 “buster” and Ubuntu 19.04 “Disco Dingo”. To install Mu on Debian/Ubuntu from the command line, type: ``` $ sudo apt install mu-editor ``` Warning Since Mu for Debian/Ubuntu is packaged by a third party, our latest releases may not be immediately available. ##### Fedora[¶](#fedora "Permalink to this headline") Mu was packaged by [Kushal Das](https://twitter.com/kushaldas) for Fedora. However this is an old version of Mu and, as with the Raspberry Pi version, relies on a third party to package it so may lag behind the latest version. Note Last, but not least, Kushal does a huge amount of work for both the Fedora and Python communities and is passionate about sustaining our Python community through education outreach. With people like Kushal putting in the time and effort to package tools like Mu and mentor beginner programmers who use Mu our community would flourish less. If you find Mu useful, please don’t hesitate to thank Kushal via Twitter. Thank you Kushal. ### Developing Mu’s Website[¶](#developing-mu-s-website "Permalink to this headline") The purpose of Mu’s main website [https://codewith.mu/](https://codewith.mu) is to provide four things: * Instructions for getting Mu. * Learning oriented tutorials to show users how to get started with Mu. * Goal oriented “how-to” guides that show how to solve a specific problems or achieve particular tasks. * Links to other community-related resources such as the developer documentation you’re reading right now, and online community discussions. The site itself is hosted for free on [GitHub Pages](https://pages.github.com/) as a [Jekyll created static site](https://jekyllrb.com/). The source code is found in the [mu-editor.github.io](https://github.com/mu-editor/mu-editor.github.io) repository. As soon as a new change lands in the master branch of the site’s repository, GitHub automatically rebuilds the site and deploys it. This means everything is simple and automated. We expect everyone participating in the development of the website to act in accordance with the PSF’s [Code of Conduct](index.html#document-code_of_conduct). #### Developer Setup[¶](#developer-setup "Permalink to this headline") 1. Follow the instructions for your operating system to install the [Jekyll static site generator](https://jekyllrb.com/docs/installation/). 2. Get the source code from GitHub: ``` git clone https://github.com/mu-editor/mu-editor.github.io.git ``` 3. From within the root directory of the website’s source code, use Jekyll to build and serve the site locally: ``` jekyll serve ``` 4. Point your browser to <http://127.0.0.1:4000> to see the locally running version. As you make changes to the website’s source, Jekyll will automatically update the locally running version so you’ll immediately see your updates. Warning If the instructions above don’t work, and since Jekyll isn’t supported for all environments, a [Vagrant](https://www.vagrantup.com/) image can be used for instead. Assuming you have Vagrant installed: ``` git clone https://github.com/lcreid/rails-5-jade.git cd rails-5-jade vagrant up vagrant ssh git clone https://github.com/mu-editor/mu-editor.github.io.git cd mu-editor.github.io bundle install jekyll serve --host 0.0.0.0 --force\_polling ``` You may need to restart your VM to ensure the port forwarding works properly. The source code is arranged as a typical Jekyll website except it’s not a blog, so there are no articles in the `\_posts` directory. Since we need our website to be easily translatable all the content will be in a directory named after the ISO language code of the translation. For example, all the original English content is in the `en` directory in the root of the repository. All images should be in the `img` directory. If an image is for a specific translation of the website, it should be in a subdirectory of `img` which is named after the ISO language code (for example, as there is for `img/en`).  We use GIF based screen captures throughout the site (such as on the front page). The dimensions for such captures of Mu are 1140x660 pixels and must not include the window title bar (provided by the operating system). So far, we have found the [peek](https://github.com/phw/peek) utility on Linux an excellent choice for making such GIF based screen captures. When adding such animated screen grabs please ensure the `img` element has the following classes (for the sake of visual consistency): `img-responsive center-block img-rounded movie`. #### Internationalisation of the Website[¶](#internationalisation-of-the-website "Permalink to this headline") There are two ways to contribute to the translation of Mu’s website: * Add / update existing content for your target language. * Start a completely new translation for your target language. When adding content to an existing translation of the website please remember that files can be either HTML or Markdown. At the top of each file is a YAML based header that must contain three entries: `layout` which must always be `default`, `title` which should be the title of the page you’re creating and `i18n` which much be the ISO language code for your translation (this is used so the correctly translated version of the site’s menu is displayed). For example, the YAML header for the `index.html` site in the `en` sub-directory looks like this: ``` --- layout: default title: Code With Mu i18n: en --- ``` The workflow for creating a new translation of the website is: 1. Create a new directory named after the [ISO language code](https://en.wikipedia.org/wiki/ISO_639-1) for the new translation. For example, if we were creating a new French translation of the site, we’d create a `fr` directory in the root of the repository. 2. Ensure there’s a version of the `index.html` file found in the root of the repository, translated into the target language in the new directory you created in step 1. Also ensure you copy the structure of the main sections of the website found in the `en` version of the site. 3. In the `\_includes` directory found in the root of the repository, you must add the new language as a list item in the `lang\_list.html` template. Ensure that the href for the link points to the new directory, and the name of the translation is in the target language. For example, this is how an entry for French would look (note the use of the French word for “French”): ``` <li><a href="/fr/">Français</a></li> ``` 4. In the same `\_includes` directory, create a copy of the `nav\_en.html` but with the `en` section of the name replaced with the ISO code for the new target language. For example, if we were to do this for a French translation, our new file would be called `nav\_fr.html`. This file defines how the site’s navigation bar should look. Make sure you translate the English version into your target language and remember to update the href values to use the new directory created in step 1. 5. Remember that the YAML headers for your new translation should have an `i18n` value with the expected ISO language code for the new target language. For example, if we were writing a new page for the French translation, the `i18n` entry would have the value `fr`. Assuming you followed all the steps above, you should see your new language in the “language” dropdown in the site navigation. Clicking on it should take you to the `index.html` page in the new directory you created for the target language, and the site navigation should reflect the newly translated navigation template. From this point on, it’s just a case of adding content to the newly translated version of the site in much the same way as it is done in the “default” `en` directory. ### Mu API Reference[¶](#mu-api-reference "Permalink to this headline") This API reference is automatically generated from the docstrings found within the source code. It’s meant as an easy to use and easy to share window into the code base. Take a look around! The code is simple and short. #### `mu.app`[¶](#mu-app "Permalink to this headline") The Mu application is created and configured in this module. Mu - a “micro” Python editor for beginner programmers. Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). Based upon work done for Puppy IDE by Dan Pope, Nicholas Tollervey and Damien George. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *class* mu.app.AnimatedSplash(*animation*, *parent=None*)[[source]](_modules/mu/app.html#AnimatedSplash)[¶](#mu.app.AnimatedSplash "Permalink to this definition") An animated splash screen for gifs. Includes a text area for logging output. draw\_log(*text*)[[source]](_modules/mu/app.html#AnimatedSplash.draw_log)[¶](#mu.app.AnimatedSplash.draw_log "Permalink to this definition") Draw the log entries onto the splash screen. Will only display the last self.log\_lines number of log entries. The logs will be displayed at the bottom of the splash screen, justified left. draw\_text(*text*)[[source]](_modules/mu/app.html#AnimatedSplash.draw_text)[¶](#mu.app.AnimatedSplash.draw_text "Permalink to this definition") Draw text into splash screen. failed(*text*)[[source]](_modules/mu/app.html#AnimatedSplash.failed)[¶](#mu.app.AnimatedSplash.failed "Permalink to this definition") Something has gone wrong during start-up, so signal this, display a helpful message along with instructions for what to do. set\_frame()[[source]](_modules/mu/app.html#AnimatedSplash.set_frame)[¶](#mu.app.AnimatedSplash.set_frame "Permalink to this definition") Update the splash screen with the next frame of the animation. *class* mu.app.StartupWorker[[source]](_modules/mu/app.html#StartupWorker)[¶](#mu.app.StartupWorker "Permalink to this definition") A worker class for running blocking tasks on a separate thread during application start-up. The animated splash screen will be shown until this thread is finished. run()[[source]](_modules/mu/app.html#StartupWorker.run)[¶](#mu.app.StartupWorker.run "Permalink to this definition") Blocking and long running tasks for application startup should be called from here. mu.app.excepthook(*\*exc\_args*)[[source]](_modules/mu/app.html#excepthook)[¶](#mu.app.excepthook "Permalink to this definition") Log exception and exit cleanly. mu.app.is\_linux\_wayland()[[source]](_modules/mu/app.html#is_linux_wayland)[¶](#mu.app.is_linux_wayland "Permalink to this definition") Checks environmental variables to try to determine if Mu is running on wayland. mu.app.run()[[source]](_modules/mu/app.html#run)[¶](#mu.app.run "Permalink to this definition") Creates all the top-level assets for the application, sets things up and then runs the application. Specific tasks include: * set up logging * create an application object * create an editor window and status bar * display a splash screen while starting * close the splash screen after startup timer ends mu.app.setup\_logging()[[source]](_modules/mu/app.html#setup_logging)[¶](#mu.app.setup_logging "Permalink to this definition") Configure logging. mu.app.setup\_modes(*editor*, *view*)[[source]](_modules/mu/app.html#setup_modes)[¶](#mu.app.setup_modes "Permalink to this definition") Create a simple dictionary to hold instances of the available modes. *PREMATURE OPTIMIZATION ALERT* This may become more complex in future so splitting things out here to contain the mess. ;-) #### `mu.logic`[¶](#mu-logic "Permalink to this headline") Most of the fundamental logic for Mu is in this module. Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). Based upon work done for Puppy IDE by Dan Pope, Nicholas Tollervey and Damien George. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *class* mu.logic.Device(*vid*, *pid*, *port*, *serial\_number*, *manufacturer*, *long\_mode\_name*, *short\_mode\_name*, *board\_name=None*)[[source]](_modules/mu/logic.html#Device)[¶](#mu.logic.Device "Permalink to this definition") Device object, containing both information about the connected device, the port it’s connected through and the mode it works with. *property* name[¶](#mu.logic.Device.name "Permalink to this definition") Returns the device name. *class* mu.logic.DeviceList(*modes*, *parent=None*)[[source]](_modules/mu/logic.html#DeviceList)[¶](#mu.logic.DeviceList "Permalink to this definition") add\_device(*new\_device*)[[source]](_modules/mu/logic.html#DeviceList.add_device)[¶](#mu.logic.DeviceList.add_device "Permalink to this definition") Add a new device to the device list, maintains alphabetical ordering check\_usb()[[source]](_modules/mu/logic.html#DeviceList.check_usb)[¶](#mu.logic.DeviceList.check_usb "Permalink to this definition") Ensure connected USB devices are polled. If there’s a change and a new recognised device is attached, inform the user via a status message. If a single device is found and Mu is in a different mode ask the user if they’d like to change mode. data(*index*, *role*)[[source]](_modules/mu/logic.html#DeviceList.data)[¶](#mu.logic.DeviceList.data "Permalink to this definition") Reimplements QAbstractListModel.data(): returns data for the specified index and role. In this case only implmented for ToolTipRole and DisplayRole remove\_device(*device*)[[source]](_modules/mu/logic.html#DeviceList.remove_device)[¶](#mu.logic.DeviceList.remove_device "Permalink to this definition") Remove the given device from the device list rowCount(*parent*)[[source]](_modules/mu/logic.html#DeviceList.rowCount)[¶](#mu.logic.DeviceList.rowCount "Permalink to this definition") Number of devices *class* mu.logic.Editor(*view*)[[source]](_modules/mu/logic.html#Editor)[¶](#mu.logic.Editor "Permalink to this definition") Application logic for the editor itself. ask\_to\_change\_mode(*new\_mode*, *mode\_name*, *heading*)[[source]](_modules/mu/logic.html#Editor.ask_to_change_mode)[¶](#mu.logic.Editor.ask_to_change_mode "Permalink to this definition") Open a dialog asking the user, whether to change mode from mode\_name to new\_mode. The dialog can be customized by the heading-parameter. autosave()[[source]](_modules/mu/logic.html#Editor.autosave)[¶](#mu.logic.Editor.autosave "Permalink to this definition") Cycles through each tab and, if changed, saves it to the filesystem. change\_mode(*mode*)[[source]](_modules/mu/logic.html#Editor.change_mode)[¶](#mu.logic.Editor.change_mode "Permalink to this definition") Given the name of a mode, will make the necessary changes to put the editor into the new mode. check\_code()[[source]](_modules/mu/logic.html#Editor.check_code)[¶](#mu.logic.Editor.check_code "Permalink to this definition") Uses PyFlakes and PyCodeStyle to gather information about potential problems with the code in the current tab. check\_for\_shadow\_module(*path*)[[source]](_modules/mu/logic.html#Editor.check_for_shadow_module)[¶](#mu.logic.Editor.check_for_shadow_module "Permalink to this definition") Check if the filename in the path is a shadow of a module already in the Python path. For example, many learners will save their first turtle based script as turtle.py, thus causing Python to never find the built in turtle module because of the name conflict. If the filename shadows an existing module, return True, otherwise, return False. connect\_to\_status\_bar(*status\_bar*)[[source]](_modules/mu/logic.html#Editor.connect_to_status_bar)[¶](#mu.logic.Editor.connect_to_status_bar "Permalink to this definition") Connect the editor with the Window-statusbar. Should be called after Editor.setup(), to ensure modes are initialized debug\_toggle\_breakpoint(*margin*, *line*, *modifiers*)[[source]](_modules/mu/logic.html#Editor.debug_toggle_breakpoint)[¶](#mu.logic.Editor.debug_toggle_breakpoint "Permalink to this definition") How to handle the toggling of a breakpoint. device\_changed(*device*)[[source]](_modules/mu/logic.html#Editor.device_changed)[¶](#mu.logic.Editor.device_changed "Permalink to this definition") Slot for receiving signals that the current device has changed. If the device change requires mode change, the user will be asked through a dialog. direct\_load(*path*)[[source]](_modules/mu/logic.html#Editor.direct_load)[¶](#mu.logic.Editor.direct_load "Permalink to this definition") For loading files passed from command line or the OS launch. find\_again(*forward=True*)[[source]](_modules/mu/logic.html#Editor.find_again)[¶](#mu.logic.Editor.find_again "Permalink to this definition") Handle find again (F3 and Shift+F3) functionality. find\_again\_backward(*forward=False*)[[source]](_modules/mu/logic.html#Editor.find_again_backward)[¶](#mu.logic.Editor.find_again_backward "Permalink to this definition") Handle find again backward (Shift+F3) functionality. find\_replace()[[source]](_modules/mu/logic.html#Editor.find_replace)[¶](#mu.logic.Editor.find_replace "Permalink to this definition") Handle find / replace functionality. If find/replace dialog is dismissed, do nothing. Otherwise, check there’s something to find, warn if there isn’t. If there is, find (and, optionally, replace) then confirm outcome with a status message. get\_dialog\_directory(*default=None*)[[source]](_modules/mu/logic.html#Editor.get_dialog_directory)[¶](#mu.logic.Editor.get_dialog_directory "Permalink to this definition") Return the directory folder which a load/save dialog box should open into. In order of precedence this function will return: 0. If not None, the value of default. 1. The last location used by a load/save dialog. 2. The directory containing the current file. 3. The mode’s reported workspace directory. get\_tab(*path*)[[source]](_modules/mu/logic.html#Editor.get_tab)[¶](#mu.logic.Editor.get_tab "Permalink to this definition") Given a path, returns either an existing tab for the path or creates / loads a new tab for the path. has\_python\_extension(*filename*)[[source]](_modules/mu/logic.html#Editor.has_python_extension)[¶](#mu.logic.Editor.has_python_extension "Permalink to this definition") Check whether the given filename matches recognized Python extensions. load(*\*args*, *default\_path=None*)[[source]](_modules/mu/logic.html#Editor.load)[¶](#mu.logic.Editor.load "Permalink to this definition") Loads a Python (or other supported) file from the file system or extracts a Python script from a hex file. load\_cli(*paths*)[[source]](_modules/mu/logic.html#Editor.load_cli)[¶](#mu.logic.Editor.load_cli "Permalink to this definition") Given a set of paths, passed in by the user when Mu starts, this method will attempt to load them and log / report a problem if Mu is unable to open a passed in path. new()[[source]](_modules/mu/logic.html#Editor.new)[¶](#mu.logic.Editor.new "Permalink to this definition") Adds a new tab to the editor. quit(*\*args*, *\*\*kwargs*)[[source]](_modules/mu/logic.html#Editor.quit)[¶](#mu.logic.Editor.quit "Permalink to this definition") Exit the application. rename\_tab(*tab\_id=None*)[[source]](_modules/mu/logic.html#Editor.rename_tab)[¶](#mu.logic.Editor.rename_tab "Permalink to this definition") How to handle double-clicking a tab in order to rename the file. If activated by the shortcut, activate against the current tab. restore\_session(*paths=None*)[[source]](_modules/mu/logic.html#Editor.restore_session)[¶](#mu.logic.Editor.restore_session "Permalink to this definition") Attempts to recreate the tab state from the last time the editor was run. If paths contains a collection of additional paths specified by the user, they are also “restored” at the same time (duplicates will be ignored). save(*\*args*, *default=None*)[[source]](_modules/mu/logic.html#Editor.save)[¶](#mu.logic.Editor.save "Permalink to this definition") Save the content of the currently active editor tab. save\_tab\_to\_file(*tab*, *show\_error\_messages=True*)[[source]](_modules/mu/logic.html#Editor.save_tab_to_file)[¶](#mu.logic.Editor.save_tab_to_file "Permalink to this definition") Given a tab, will attempt to save the script in the tab to the path associated with the tab. If there’s a problem this will be logged and reported and the tab status will continue to show as Modified. select\_mode(*event=None*)[[source]](_modules/mu/logic.html#Editor.select_mode)[¶](#mu.logic.Editor.select_mode "Permalink to this definition") Select the mode that editor is supposed to be in. setup(*modes*)[[source]](_modules/mu/logic.html#Editor.setup)[¶](#mu.logic.Editor.setup "Permalink to this definition") Define the available modes and ensure there’s a default working directory. show\_admin(*event=None*)[[source]](_modules/mu/logic.html#Editor.show_admin)[¶](#mu.logic.Editor.show_admin "Permalink to this definition") Cause the editor’s admin dialog to be displayed to the user. Ensure any changes to the envars is updated. show\_help()[[source]](_modules/mu/logic.html#Editor.show_help)[¶](#mu.logic.Editor.show_help "Permalink to this definition") Display browser based help about Mu. show\_status\_message(*message*, *duration=5*)[[source]](_modules/mu/logic.html#Editor.show_status_message)[¶](#mu.logic.Editor.show_status_message "Permalink to this definition") Displays the referenced message for duration seconds. sync\_package\_state(*old\_packages*, *new\_packages*)[[source]](_modules/mu/logic.html#Editor.sync_package_state)[¶](#mu.logic.Editor.sync_package_state "Permalink to this definition") Given the state of the old third party packages, compared to the new third party packages, ensure that pip uninstalls and installs the packages so the currently available third party packages reflects the new state. tidy\_code()[[source]](_modules/mu/logic.html#Editor.tidy_code)[¶](#mu.logic.Editor.tidy_code "Permalink to this definition") Prettify code with Black. toggle\_comments()[[source]](_modules/mu/logic.html#Editor.toggle_comments)[¶](#mu.logic.Editor.toggle_comments "Permalink to this definition") Ensure all highlighted lines are toggled between comments/uncommented. toggle\_theme()[[source]](_modules/mu/logic.html#Editor.toggle_theme)[¶](#mu.logic.Editor.toggle_theme "Permalink to this definition") Switches between themes (night, day or high-contrast). zoom\_in()[[source]](_modules/mu/logic.html#Editor.zoom_in)[¶](#mu.logic.Editor.zoom_in "Permalink to this definition") Make the editor’s text bigger zoom\_out()[[source]](_modules/mu/logic.html#Editor.zoom_out)[¶](#mu.logic.Editor.zoom_out "Permalink to this definition") Make the editor’s text smaller. *class* mu.logic.MuFlakeCodeReporter[[source]](_modules/mu/logic.html#MuFlakeCodeReporter)[¶](#mu.logic.MuFlakeCodeReporter "Permalink to this definition") The class instantiates a reporter that creates structured data about code quality for Mu. Used by the PyFlakes module. flake(*message*)[[source]](_modules/mu/logic.html#MuFlakeCodeReporter.flake)[¶](#mu.logic.MuFlakeCodeReporter.flake "Permalink to this definition") PyFlakes found something wrong with the code. syntaxError(*filename*, *message*, *line\_no*, *column*, *source*)[[source]](_modules/mu/logic.html#MuFlakeCodeReporter.syntaxError)[¶](#mu.logic.MuFlakeCodeReporter.syntaxError "Permalink to this definition") Records a syntax error in the file called filename. The message argument contains an explanation of the syntax error, line\_no indicates the line where the syntax error occurred, column indicates the column on which the error occurred and source is the source code containing the syntax error. unexpectedError(*filename*, *message*)[[source]](_modules/mu/logic.html#MuFlakeCodeReporter.unexpectedError)[¶](#mu.logic.MuFlakeCodeReporter.unexpectedError "Permalink to this definition") Called if an unexpected error occured while trying to process the file called filename. The message parameter contains a description of the problem. mu.logic.check\_flake(*filename*, *code*, *builtins=None*)[[source]](_modules/mu/logic.html#check_flake)[¶](#mu.logic.check_flake "Permalink to this definition") Given a filename and some code to be checked, uses the PyFlakesmodule to return a dictionary describing issues of code quality per line. See: <https://github.com/PyCQA/pyflakes> If a list symbols is passed in as “builtins” these are assumed to be additional builtins available when run by Mu. mu.logic.check\_pycodestyle(*code*, *config\_file=False*)[[source]](_modules/mu/logic.html#check_pycodestyle)[¶](#mu.logic.check_pycodestyle "Permalink to this definition") Given some code, uses the PyCodeStyle module (was PEP8) to return a list of items describing issues of coding style. See: <https://pycodestyle.readthedocs.io/en/latest/intro.html> mu.logic.extract\_envars(*raw*)[[source]](_modules/mu/logic.html#extract_envars)[¶](#mu.logic.extract_envars "Permalink to this definition") Returns a list of environment variables given a string containing NAME=VALUE definitions on separate lines. mu.logic.read\_and\_decode(*filepath*)[[source]](_modules/mu/logic.html#read_and_decode)[¶](#mu.logic.read_and_decode "Permalink to this definition") Read the contents of a file, mu.logic.save\_and\_encode(*text*, *filepath*, *newline='\n'*)[[source]](_modules/mu/logic.html#save_and_encode)[¶](#mu.logic.save_and_encode "Permalink to this definition") Detect the presence of an encoding cookie and use that encoding; if none is present, do not add one and use the Mu default encoding. If the codec is invalid, log a warning and fall back to the default. mu.logic.sniff\_encoding(*filepath*)[[source]](_modules/mu/logic.html#sniff_encoding)[¶](#mu.logic.sniff_encoding "Permalink to this definition") Determine the encoding of a file: * If there is a BOM, return the appropriate encoding * If there is a PEP 263 encoding cookie, return the appropriate encoding * Otherwise return None for read\_and\_decode to attempt several defaults mu.logic.sniff\_newline\_convention(*text*)[[source]](_modules/mu/logic.html#sniff_newline_convention)[¶](#mu.logic.sniff_newline_convention "Permalink to this definition") Determine which line-ending convention predominates in the text. Windows usually has U+000D U+000A Posix usually has U+000A But editors can produce either convention from either platform. And a file which has been copied and edited around might even have both! mu.logic.write\_and\_flush(*fileobj*, *content*)[[source]](_modules/mu/logic.html#write_and_flush)[¶](#mu.logic.write_and_flush "Permalink to this definition") Write content to the fileobj then flush and fsync to ensure the data is, in fact, written. This is especially necessary for USB-attached devices #### `mu.debugger`[¶](#mu-debugger "Permalink to this headline") The debugger consists of two parts: * Client - used by Mu to process messages from the process being debugged. * Runner - created in a new process to run the code to be debugged. Messages are passed via inter-process communication. ##### `mu.debugger.client`[¶](#mu-debugger-client "Permalink to this headline") Code used by the Mu application to communicate with the process being debugged. A debug client for the Mu editor. Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *class* mu.debugger.client.Breakpoint(*bpnum*, *filename*, *line*, *enabled=True*, *temporary=False*, *funcname=None*)[[source]](_modules/mu/debugger/client.html#Breakpoint)[¶](#mu.debugger.client.Breakpoint "Permalink to this definition") Represents a breakpoint, identified by a breakpoint number (bpnum). Users set breakpoints to stop the debugger at a certain line (potentially in a named function) in a file. *class* mu.debugger.client.CommandBufferHandler(*debugger*)[[source]](_modules/mu/debugger/client.html#CommandBufferHandler)[¶](#mu.debugger.client.CommandBufferHandler "Permalink to this definition") Represents the work to be done on a separate thread for connecting and processing incoming messages. Emits signals to indicate when messages are receievd or the connection fails at appropriate moments during the lifetime of a debug session. on\_command[¶](#mu.debugger.client.CommandBufferHandler.on_command "Permalink to this definition") Signal emitted when a command is received. on\_fail[¶](#mu.debugger.client.CommandBufferHandler.on_fail "Permalink to this definition") Emitted when there was a connection failure. worker()[[source]](_modules/mu/debugger/client.html#CommandBufferHandler.worker)[¶](#mu.debugger.client.CommandBufferHandler.worker "Permalink to this definition") Buffer input from a socket, emit complete debugger commands as signals. *exception* mu.debugger.client.ConnectionNotBootstrapped[[source]](_modules/mu/debugger/client.html#ConnectionNotBootstrapped)[¶](#mu.debugger.client.ConnectionNotBootstrapped "Permalink to this definition") The connection to the runner hasn’t been completed. *class* mu.debugger.client.Debugger(*host*, *port*, *proc=None*)[[source]](_modules/mu/debugger/client.html#Debugger)[¶](#mu.debugger.client.Debugger "Permalink to this definition") Represents the networked debugger client. breakpoint(*breakpoint*)[[source]](_modules/mu/debugger/client.html#Debugger.breakpoint)[¶](#mu.debugger.client.Debugger.breakpoint "Permalink to this definition") Given a breakpoint number or (filename, line), return an object representing the referenced breakpoint. breakpoints(*filename*)[[source]](_modules/mu/debugger/client.html#Debugger.breakpoints)[¶](#mu.debugger.client.Debugger.breakpoints "Permalink to this definition") Return all the breakpoints associated with the referenced file. clear\_breakpoint(*breakpoint*)[[source]](_modules/mu/debugger/client.html#Debugger.clear_breakpoint)[¶](#mu.debugger.client.Debugger.clear_breakpoint "Permalink to this definition") Clear an existing breakpoint. create\_breakpoint(*filename*, *line*, *temporary=False*)[[source]](_modules/mu/debugger/client.html#Debugger.create_breakpoint)[¶](#mu.debugger.client.Debugger.create_breakpoint "Permalink to this definition") Create a new, enabled breakpoint at the specified line of the given file. disable\_breakpoint(*breakpoint*)[[source]](_modules/mu/debugger/client.html#Debugger.disable_breakpoint)[¶](#mu.debugger.client.Debugger.disable_breakpoint "Permalink to this definition") Disable an existing breakpoint. do\_next()[[source]](_modules/mu/debugger/client.html#Debugger.do_next)[¶](#mu.debugger.client.Debugger.do_next "Permalink to this definition") Go to the next line in the current stack frame. do\_return()[[source]](_modules/mu/debugger/client.html#Debugger.do_return)[¶](#mu.debugger.client.Debugger.do_return "Permalink to this definition") Return to the previous stack frame. do\_run()[[source]](_modules/mu/debugger/client.html#Debugger.do_run)[¶](#mu.debugger.client.Debugger.do_run "Permalink to this definition") Run the debugger until the next breakpoint. do\_step()[[source]](_modules/mu/debugger/client.html#Debugger.do_step)[¶](#mu.debugger.client.Debugger.do_step "Permalink to this definition") Step through one stack frame. enable\_breakpoint(*breakpoint*)[[source]](_modules/mu/debugger/client.html#Debugger.enable_breakpoint)[¶](#mu.debugger.client.Debugger.enable_breakpoint "Permalink to this definition") Enable an existing breakpoint. ignore\_breakpoint(*breakpoint*, *count*)[[source]](_modules/mu/debugger/client.html#Debugger.ignore_breakpoint)[¶](#mu.debugger.client.Debugger.ignore_breakpoint "Permalink to this definition") Ignore an existing breakpoint for “count” iterations. (N.B. Use a count of 0 to restore the breakpoint. on\_bootstrap(*breakpoints*)[[source]](_modules/mu/debugger/client.html#Debugger.on_bootstrap)[¶](#mu.debugger.client.Debugger.on_bootstrap "Permalink to this definition") The runner has finished setting up. on\_breakpoint\_clear(*bpnum*)[[source]](_modules/mu/debugger/client.html#Debugger.on_breakpoint_clear)[¶](#mu.debugger.client.Debugger.on_breakpoint_clear "Permalink to this definition") The runner has cleared the referenced breakpoint. on\_breakpoint\_create(*\*\*bp\_data*)[[source]](_modules/mu/debugger/client.html#Debugger.on_breakpoint_create)[¶](#mu.debugger.client.Debugger.on_breakpoint_create "Permalink to this definition") The runner has created a breakpoint. on\_breakpoint\_disable(*bpnum*)[[source]](_modules/mu/debugger/client.html#Debugger.on_breakpoint_disable)[¶](#mu.debugger.client.Debugger.on_breakpoint_disable "Permalink to this definition") The runner has disabled a breakpoint referenced by breakpoint number. on\_breakpoint\_enable(*bpnum*)[[source]](_modules/mu/debugger/client.html#Debugger.on_breakpoint_enable)[¶](#mu.debugger.client.Debugger.on_breakpoint_enable "Permalink to this definition") The runner has enabled the breakpoint referenced by breakpoint number. on\_breakpoint\_ignore(*bpnum*, *count*)[[source]](_modules/mu/debugger/client.html#Debugger.on_breakpoint_ignore)[¶](#mu.debugger.client.Debugger.on_breakpoint_ignore "Permalink to this definition") The runner will ignore the referenced breakpoint “count” iterations. on\_call(*args*)[[source]](_modules/mu/debugger/client.html#Debugger.on_call)[¶](#mu.debugger.client.Debugger.on_call "Permalink to this definition") The runner has called a function with the specified arguments. on\_command(*command*)[[source]](_modules/mu/debugger/client.html#Debugger.on_command)[¶](#mu.debugger.client.Debugger.on_command "Permalink to this definition") Handle a command emitted by the client thread. on\_error(*message*)[[source]](_modules/mu/debugger/client.html#Debugger.on_error)[¶](#mu.debugger.client.Debugger.on_error "Permalink to this definition") The runner has sent an error message. on\_exception(*name*, *value*)[[source]](_modules/mu/debugger/client.html#Debugger.on_exception)[¶](#mu.debugger.client.Debugger.on_exception "Permalink to this definition") The runner has encountered a named exception with an associated value. on\_fail(*message*)[[source]](_modules/mu/debugger/client.html#Debugger.on_fail)[¶](#mu.debugger.client.Debugger.on_fail "Permalink to this definition") Handle if there’s a connection failure with the debug runner. on\_finished()[[source]](_modules/mu/debugger/client.html#Debugger.on_finished)[¶](#mu.debugger.client.Debugger.on_finished "Permalink to this definition") The debug runner has finished running the script to be debugged. on\_info(*message*)[[source]](_modules/mu/debugger/client.html#Debugger.on_info)[¶](#mu.debugger.client.Debugger.on_info "Permalink to this definition") The runner has sent an informative message. on\_line(*filename*, *line*)[[source]](_modules/mu/debugger/client.html#Debugger.on_line)[¶](#mu.debugger.client.Debugger.on_line "Permalink to this definition") The runner has moved to the specified line in the referenced file. on\_postmortem(*\*args*, *\*\*kwargs*)[[source]](_modules/mu/debugger/client.html#Debugger.on_postmortem)[¶](#mu.debugger.client.Debugger.on_postmortem "Permalink to this definition") The runner encountered a fatal error and has died. on\_restart()[[source]](_modules/mu/debugger/client.html#Debugger.on_restart)[¶](#mu.debugger.client.Debugger.on_restart "Permalink to this definition") The runner has restarted. on\_return(*retval*)[[source]](_modules/mu/debugger/client.html#Debugger.on_return)[¶](#mu.debugger.client.Debugger.on_return "Permalink to this definition") The runner has returned from a function with the specified return value. on\_stack(*stack*)[[source]](_modules/mu/debugger/client.html#Debugger.on_stack)[¶](#mu.debugger.client.Debugger.on_stack "Permalink to this definition") The runner has sent an update to the stack. on\_warning(*message*)[[source]](_modules/mu/debugger/client.html#Debugger.on_warning)[¶](#mu.debugger.client.Debugger.on_warning "Permalink to this definition") The runner has sent a warning message. output(*event*, *\*\*data*)[[source]](_modules/mu/debugger/client.html#Debugger.output)[¶](#mu.debugger.client.Debugger.output "Permalink to this definition") Send a command to the debug runner. start()[[source]](_modules/mu/debugger/client.html#Debugger.start)[¶](#mu.debugger.client.Debugger.start "Permalink to this definition") Start the debugger session. stop()[[source]](_modules/mu/debugger/client.html#Debugger.stop)[¶](#mu.debugger.client.Debugger.stop "Permalink to this definition") Shut down the debugger session. *exception* mu.debugger.client.UnknownBreakpoint[[source]](_modules/mu/debugger/client.html#UnknownBreakpoint)[¶](#mu.debugger.client.UnknownBreakpoint "Permalink to this definition") The client encountered an unknown breakpoint. ##### `mu.debugger.runner`[¶](#mu-debugger-runner "Permalink to this headline") The runner code controls the debug process. A debug runner for the Mu editor. Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *exception* mu.debugger.runner.ClientClose[[source]](_modules/mu/debugger/runner.html#ClientClose)[¶](#mu.debugger.runner.ClientClose "Permalink to this definition") Cause the debugger to wait for a new client to connect. *class* mu.debugger.runner.DebugState(*value*)[[source]](_modules/mu/debugger/runner.html#DebugState)[¶](#mu.debugger.runner.DebugState "Permalink to this definition") Enumerates the three possible states of a debugging session. *class* mu.debugger.runner.Debugger(*socket*, *host*, *port*, *skip=None*)[[source]](_modules/mu/debugger/runner.html#Debugger)[¶](#mu.debugger.runner.Debugger "Permalink to this definition") Instances of this class represent and drive the debugging process. do\_break(*filename*, *line*, *temporary=False*)[[source]](_modules/mu/debugger/runner.html#Debugger.do_break)[¶](#mu.debugger.runner.Debugger.do_break "Permalink to this definition") Set a breakpoint. do\_clear(*bpnum*)[[source]](_modules/mu/debugger/runner.html#Debugger.do_clear)[¶](#mu.debugger.runner.Debugger.do_clear "Permalink to this definition") Handle how a breakpoint must be removed when it is a temporary one. do\_close()[[source]](_modules/mu/debugger/runner.html#Debugger.do_close)[¶](#mu.debugger.runner.Debugger.do_close "Permalink to this definition") Respond to a closed socket (not a user commend, but needs handling). do\_continue()[[source]](_modules/mu/debugger/runner.html#Debugger.do_continue)[¶](#mu.debugger.runner.Debugger.do_continue "Permalink to this definition") Stop only at breakpoints or when finished. If there are no breakpoints on script start, do a set\_trace to stop at the first available line. However, use the continue\_flag to ensure set\_continue is always called thereafter. do\_disable(*bpnum*)[[source]](_modules/mu/debugger/runner.html#Debugger.do_disable)[¶](#mu.debugger.runner.Debugger.do_disable "Permalink to this definition") Disable the breakpoint referenced by its breakpoint number (bpnum). do\_enable(*bpnum*)[[source]](_modules/mu/debugger/runner.html#Debugger.do_enable)[¶](#mu.debugger.runner.Debugger.do_enable "Permalink to this definition") Enables the breakpoint referenced by its breakpoint number (bpnum). do\_ignore(*bpnum*, *count*)[[source]](_modules/mu/debugger/runner.html#Debugger.do_ignore)[¶](#mu.debugger.runner.Debugger.do_ignore "Permalink to this definition") Ignore the breakpoint referenced by its breakpoint number (bpnum), count number of times. do\_next()[[source]](_modules/mu/debugger/runner.html#Debugger.do_next)[¶](#mu.debugger.runner.Debugger.do_next "Permalink to this definition") Stop on the next line in or below the given frame. do\_quit()[[source]](_modules/mu/debugger/runner.html#Debugger.do_quit)[¶](#mu.debugger.runner.Debugger.do_quit "Permalink to this definition") Set the quitting attribute to True. This raises BdbQuit in the next call to one of the dispatch\_\*() methods. do\_restart()[[source]](_modules/mu/debugger/runner.html#Debugger.do_restart)[¶](#mu.debugger.runner.Debugger.do_restart "Permalink to this definition") Restart the program by raising an exception to be caught by the debugger. do\_return()[[source]](_modules/mu/debugger/runner.html#Debugger.do_return)[¶](#mu.debugger.runner.Debugger.do_return "Permalink to this definition") Stop when returning from the current frame. do\_step()[[source]](_modules/mu/debugger/runner.html#Debugger.do_step)[¶](#mu.debugger.runner.Debugger.do_step "Permalink to this definition") Stop after one line of code. interact(*frame*, *traceback*)[[source]](_modules/mu/debugger/runner.html#Debugger.interact)[¶](#mu.debugger.runner.Debugger.interact "Permalink to this definition") Contains the loop processing interactions with the debugger. output(*event*, *\*\*data*)[[source]](_modules/mu/debugger/runner.html#Debugger.output)[¶](#mu.debugger.runner.Debugger.output "Permalink to this definition") Dumps data related to a referenced event to the socket. output\_stack()[[source]](_modules/mu/debugger/runner.html#Debugger.output_stack)[¶](#mu.debugger.runner.Debugger.output_stack "Permalink to this definition") Dump the current stack. If this is a normal situation, the top two frames are BDB and the runner executing the program. If there is an exception, there are two further extra frames. All these frames can be ignored. reset()[[source]](_modules/mu/debugger/runner.html#Debugger.reset)[¶](#mu.debugger.runner.Debugger.reset "Permalink to this definition") Reset state. setup(*frame*, *traceback*)[[source]](_modules/mu/debugger/runner.html#Debugger.setup)[¶](#mu.debugger.runner.Debugger.setup "Permalink to this definition") Start state should be set correctly. user\_call(*frame*, *argument\_list*)[[source]](_modules/mu/debugger/runner.html#Debugger.user_call)[¶](#mu.debugger.runner.Debugger.user_call "Permalink to this definition") This method is called from dispatch\_call() when there is the possibility that a break might be necessary anywhere inside the called function. user\_exception(*frame*, *exc\_info*)[[source]](_modules/mu/debugger/runner.html#Debugger.user_exception)[¶](#mu.debugger.runner.Debugger.user_exception "Permalink to this definition") This method is called from dispatch\_exception() when stop\_here() yields True. For when an exception occurs, but only if we are to stop at or just below this level. user\_line(*frame*)[[source]](_modules/mu/debugger/runner.html#Debugger.user_line)[¶](#mu.debugger.runner.Debugger.user_line "Permalink to this definition") This method is called from dispatch\_line() when either stop\_here() or break\_here() yields True. For when we stop or break at this line. user\_return(*frame*, *return\_value*)[[source]](_modules/mu/debugger/runner.html#Debugger.user_return)[¶](#mu.debugger.runner.Debugger.user_return "Permalink to this definition") This method is called from dispatch\_return() when stop\_here() yields True. For when a return trap is set here. *exception* mu.debugger.runner.Restart[[source]](_modules/mu/debugger/runner.html#Restart)[¶](#mu.debugger.runner.Restart "Permalink to this definition") Cause the debugger to restart for the target Python program. mu.debugger.runner.command\_buffer(*debugger*)[[source]](_modules/mu/debugger/runner.html#command_buffer)[¶](#mu.debugger.runner.command_buffer "Permalink to this definition") Buffer input from a socket, yield complete debugger commands. mu.debugger.runner.run(*hostname*, *port*, *filename*, *args*)[[source]](_modules/mu/debugger/runner.html#run)[¶](#mu.debugger.runner.run "Permalink to this definition") Run a Python script identified by “filename” with the specified arguments in a debugger session that’s listening at hostname/port. #### `mu.interface`[¶](#mu-interface "Permalink to this headline") This module contains all the PyQt related code needed to create the user interface for Mu. All interaction with the user interface is done via the `Window` class in `mu.interface.main`. All the other sub-modules contain different bespoke aspects of the user interface. ##### `mu.interface.main`[¶](#mu-interface-main "Permalink to this headline") Contains the core user interface assets used by other parts of the application. Contains the main Window definition for Mu’s UI. Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *class* mu.interface.main.ButtonBar(*parent*)[[source]](_modules/mu/interface/main.html#ButtonBar)[¶](#mu.interface.main.ButtonBar "Permalink to this definition") Represents the bar of buttons across the top of the editor and defines their behaviour. addAction(*name*, *display\_name*, *tool\_text*)[[source]](_modules/mu/interface/main.html#ButtonBar.addAction)[¶](#mu.interface.main.ButtonBar.addAction "Permalink to this definition") Creates an action associated with an icon and name and adds it to the widget’s slots. connect(*name*, *handler*, *shortcut=None*)[[source]](_modules/mu/interface/main.html#ButtonBar.connect)[¶](#mu.interface.main.ButtonBar.connect "Permalink to this definition") Connects a named slot to a handler function and optional hot-key shortcuts. reset()[[source]](_modules/mu/interface/main.html#ButtonBar.reset)[¶](#mu.interface.main.ButtonBar.reset "Permalink to this definition") Resets the button states. set\_responsive\_mode(*width*, *height*)[[source]](_modules/mu/interface/main.html#ButtonBar.set_responsive_mode)[¶](#mu.interface.main.ButtonBar.set_responsive_mode "Permalink to this definition") Compact button bar for when window is very small. *class* mu.interface.main.FileTabs[[source]](_modules/mu/interface/main.html#FileTabs)[¶](#mu.interface.main.FileTabs "Permalink to this definition") Extend the base class so we can override the removeTab behaviour. addTab(*widget*, *title*)[[source]](_modules/mu/interface/main.html#FileTabs.addTab)[¶](#mu.interface.main.FileTabs.addTab "Permalink to this definition") Add a new tab to the switcher change\_tab(*tab\_id*)[[source]](_modules/mu/interface/main.html#FileTabs.change_tab)[¶](#mu.interface.main.FileTabs.change_tab "Permalink to this definition") Update the application title to reflect the name of the file in the currently selected tab. removeTab(*tab\_id*)[[source]](_modules/mu/interface/main.html#FileTabs.removeTab)[¶](#mu.interface.main.FileTabs.removeTab "Permalink to this definition") Ask the user before closing the file. *class* mu.interface.main.StatusBar(*parent=None*, *mode='python'*)[[source]](_modules/mu/interface/main.html#StatusBar)[¶](#mu.interface.main.StatusBar "Permalink to this definition") Defines the look and behaviour of the status bar along the bottom of the UI. connect\_logs(*handler*, *shortcut*)[[source]](_modules/mu/interface/main.html#StatusBar.connect_logs)[¶](#mu.interface.main.StatusBar.connect_logs "Permalink to this definition") Connect the mouse press event and keyboard shortcut for the log widget to the referenced handler function. connect\_mode(*handler*, *shortcut*)[[source]](_modules/mu/interface/main.html#StatusBar.connect_mode)[¶](#mu.interface.main.StatusBar.connect_mode "Permalink to this definition") Connect the mouse press event and keyboard shortcut for the mode widget to the referenced handler function. device\_connected(*device*)[[source]](_modules/mu/interface/main.html#StatusBar.device_connected)[¶](#mu.interface.main.StatusBar.device_connected "Permalink to this definition") Show a tooltip whenever a new device connects set\_message(*message*, *pause=5000*)[[source]](_modules/mu/interface/main.html#StatusBar.set_message)[¶](#mu.interface.main.StatusBar.set_message "Permalink to this definition") Displays a message in the status bar for a certain period of time. set\_mode(*mode*)[[source]](_modules/mu/interface/main.html#StatusBar.set_mode)[¶](#mu.interface.main.StatusBar.set_mode "Permalink to this definition") Updates the mode label to the new mode. *class* mu.interface.main.Window(*parent=None*)[[source]](_modules/mu/interface/main.html#Window)[¶](#mu.interface.main.Window "Permalink to this definition") Defines the look and characteristics of the application’s main window. add\_debug\_inspector()[[source]](_modules/mu/interface/main.html#Window.add_debug_inspector)[¶](#mu.interface.main.Window.add_debug_inspector "Permalink to this definition") Display a debug inspector to view the call stack. add\_filesystem(*home*, *file\_manager*, *board\_name='board'*)[[source]](_modules/mu/interface/main.html#Window.add_filesystem)[¶](#mu.interface.main.Window.add_filesystem "Permalink to this definition") Adds the file system pane to the application. add\_jupyter\_repl(*kernel\_manager*, *kernel\_client*)[[source]](_modules/mu/interface/main.html#Window.add_jupyter_repl)[¶](#mu.interface.main.Window.add_jupyter_repl "Permalink to this definition") Adds a Jupyter based REPL pane to the application. add\_micropython\_plotter(*name*, *connection*, *data\_flood\_handler*)[[source]](_modules/mu/interface/main.html#Window.add_micropython_plotter)[¶](#mu.interface.main.Window.add_micropython_plotter "Permalink to this definition") Adds a plotter that reads data from a serial connection. add\_micropython\_repl(*name*, *connection*)[[source]](_modules/mu/interface/main.html#Window.add_micropython_repl)[¶](#mu.interface.main.Window.add_micropython_repl "Permalink to this definition") Adds a MicroPython based REPL pane to the application. add\_plotter(*plotter\_pane*, *name*)[[source]](_modules/mu/interface/main.html#Window.add_plotter)[¶](#mu.interface.main.Window.add_plotter "Permalink to this definition") Adds the referenced plotter pane to the application. add\_python3\_plotter(*mode*)[[source]](_modules/mu/interface/main.html#Window.add_python3_plotter)[¶](#mu.interface.main.Window.add_python3_plotter "Permalink to this definition") Add a plotter that reads from either the REPL or a running script. Since this function will only be called when either the REPL or a running script are running (but not at the same time), it’ll just grab data emitted by the REPL or script via data\_received. add\_python3\_runner(*interpreter*, *script\_name*, *working\_directory*, *interactive=False*, *debugger=False*, *command\_args=None*, *envars=None*, *python\_args=None*)[[source]](_modules/mu/interface/main.html#Window.add_python3_runner)[¶](#mu.interface.main.Window.add_python3_runner "Permalink to this definition") Display console output for the interpreter with the referenced pythonpath running the referenced script. The script will be run within the workspace\_path directory. If interactive is True (default is False) the Python process will run in interactive mode (dropping the user into the REPL when the script completes). If debugger is True (default is False) the script will be run within a debug runner session. The debugger overrides the interactive flag (you cannot run the debugger in interactive mode). If there is a list of command\_args (the default is None) then these will be passed as further arguments into the command run in the new process. If envars is given, these will become part of the environment context of the new chlid process. If python\_args is given, these will be passed as arguments to the Python runtime used to launch the child process. add\_repl(*repl\_pane*, *name*)[[source]](_modules/mu/interface/main.html#Window.add_repl)[¶](#mu.interface.main.Window.add_repl "Permalink to this definition") Adds the referenced REPL pane to the application. add\_snek\_repl(*name*, *connection*, *force\_interrupt=True*, *wait\_input=False*)[[source]](_modules/mu/interface/main.html#Window.add_snek_repl)[¶](#mu.interface.main.Window.add_snek_repl "Permalink to this definition") Adds a Snek based REPL pane to the application. add\_tab(*path*, *text*, *api*, *newline*)[[source]](_modules/mu/interface/main.html#Window.add_tab)[¶](#mu.interface.main.Window.add_tab "Permalink to this definition") Adds a tab with the referenced path and text to the editor. annotate\_code(*feedback*, *annotation\_type*)[[source]](_modules/mu/interface/main.html#Window.annotate_code)[¶](#mu.interface.main.Window.annotate_code "Permalink to this definition") Given a list of annotations about the code in the current tab, add the annotations to the editor window so the user can make appropriate changes. change\_mode(*mode*)[[source]](_modules/mu/interface/main.html#Window.change_mode)[¶](#mu.interface.main.Window.change_mode "Permalink to this definition") Given a an object representing a mode, recreates the button bar with the expected functionality. connect\_find\_again(*handlers*, *shortcut*)[[source]](_modules/mu/interface/main.html#Window.connect_find_again)[¶](#mu.interface.main.Window.connect_find_again "Permalink to this definition") Create keyboard shortcuts and associate them with handlers for doing a find again in forward or backward direction. Any given shortcut will be used for forward find again, while Shift+shortcut will find again backwards. connect\_find\_replace(*handler*, *shortcut*)[[source]](_modules/mu/interface/main.html#Window.connect_find_replace)[¶](#mu.interface.main.Window.connect_find_replace "Permalink to this definition") Create a keyboard shortcut and associate it with a handler for doing a find and replace. connect\_tab\_rename(*handler*, *shortcut*)[[source]](_modules/mu/interface/main.html#Window.connect_tab_rename)[¶](#mu.interface.main.Window.connect_tab_rename "Permalink to this definition") Connect the double-click event on a tab and the keyboard shortcut to the referenced handler (causing the Save As dialog). connect\_toggle\_comments(*handler*, *shortcut*)[[source]](_modules/mu/interface/main.html#Window.connect_toggle_comments)[¶](#mu.interface.main.Window.connect_toggle_comments "Permalink to this definition") Create a keyboard shortcut and associate it with a handler for toggling comments on highlighted lines. connect\_zoom(*widget*)[[source]](_modules/mu/interface/main.html#Window.connect_zoom)[¶](#mu.interface.main.Window.connect_zoom "Permalink to this definition") Connects a referenced widget to the zoom related signals and sets the zoom of the widget to the current zoom level. copy\_to\_repl()[[source]](_modules/mu/interface/main.html#Window.copy_to_repl)[¶](#mu.interface.main.Window.copy_to_repl "Permalink to this definition") Copies currently selected text in the editor, into the active REPL widget and sets focus to the REPL widget. The final line pasted into the REPL waits for RETURN to be pressed by the user (this appears to be the default behaviour for pasting into the REPL widget). *property* current\_tab[¶](#mu.interface.main.Window.current_tab "Permalink to this definition") Returns the currently focussed tab. focus\_tab(*tab*)[[source]](_modules/mu/interface/main.html#Window.focus_tab)[¶](#mu.interface.main.Window.focus_tab "Permalink to this definition") Force focus on the referenced tab. get\_load\_path(*folder*, *extensions='\*'*, *allow\_previous=True*)[[source]](_modules/mu/interface/main.html#Window.get_load_path)[¶](#mu.interface.main.Window.get_load_path "Permalink to this definition") Displays a dialog for selecting a file to load. Returns the selected path. Defaults to start in the referenced folder unless a previous folder has been used and the allow\_previous flag is True (the default behaviour) get\_microbit\_path(*folder*)[[source]](_modules/mu/interface/main.html#Window.get_microbit_path)[¶](#mu.interface.main.Window.get_microbit_path "Permalink to this definition") Displays a dialog for locating the location of the BBC micro:bit in the host computer’s filesystem. Returns the selected path. Defaults to start in the referenced folder. get\_save\_path(*folder*)[[source]](_modules/mu/interface/main.html#Window.get_save_path)[¶](#mu.interface.main.Window.get_save_path "Permalink to this definition") Displays a dialog for selecting a file to save. Returns the selected path. Defaults to start in the referenced folder. handle\_python\_anywhere\_complete(*domain*)[[source]](_modules/mu/interface/main.html#Window.handle_python_anywhere_complete)[¶](#mu.interface.main.Window.handle_python_anywhere_complete "Permalink to this definition") Displays a confirmation that all the API calls completed OK and provides a link to the user’s website. handle\_python\_anywhere\_error(*error\_message*)[[source]](_modules/mu/interface/main.html#Window.handle_python_anywhere_error)[¶](#mu.interface.main.Window.handle_python_anywhere_error "Permalink to this definition") Display a friendly message to indicate a problem was encountered when uploading to PythonAnywhere. hide\_device\_selector()[[source]](_modules/mu/interface/main.html#Window.hide_device_selector)[¶](#mu.interface.main.Window.hide_device_selector "Permalink to this definition") Hides the device selector in the status bar highlight\_text(*target\_text*, *forward=True*)[[source]](_modules/mu/interface/main.html#Window.highlight_text)[¶](#mu.interface.main.Window.highlight_text "Permalink to this definition") Highlight the first match from the current position of the cursor in the current tab for the target\_text. Returns True if there’s a match. *property* modified[¶](#mu.interface.main.Window.modified "Permalink to this definition") Returns a boolean indication if there are any modified tabs in the editor. on\_context\_menu()[[source]](_modules/mu/interface/main.html#Window.on_context_menu)[¶](#mu.interface.main.Window.on_context_menu "Permalink to this definition") Called when a user right-clicks on an editor pane. If the REPL is active AND there is selected text in the current editor pane, modify the default context menu to include a paste to REPL option. Otherwise, just display the default context menu. on\_stdout\_write(*data*)[[source]](_modules/mu/interface/main.html#Window.on_stdout_write)[¶](#mu.interface.main.Window.on_stdout_write "Permalink to this definition") Called when either a running script or the REPL write to STDOUT. open\_directory\_from\_os(*path*)[[source]](_modules/mu/interface/main.html#Window.open_directory_from_os)[¶](#mu.interface.main.Window.open_directory_from_os "Permalink to this definition") Given the path to a directory, open the OS’s built in filesystem explorer for that path. Works with Windows, OSX and Linux. remove\_debug\_inspector()[[source]](_modules/mu/interface/main.html#Window.remove_debug_inspector)[¶](#mu.interface.main.Window.remove_debug_inspector "Permalink to this definition") Removes the debug inspector pane from the application. remove\_filesystem()[[source]](_modules/mu/interface/main.html#Window.remove_filesystem)[¶](#mu.interface.main.Window.remove_filesystem "Permalink to this definition") Removes the file system pane from the application. remove\_plotter()[[source]](_modules/mu/interface/main.html#Window.remove_plotter)[¶](#mu.interface.main.Window.remove_plotter "Permalink to this definition") Removes the plotter pane from the application. remove\_python\_runner()[[source]](_modules/mu/interface/main.html#Window.remove_python_runner)[¶](#mu.interface.main.Window.remove_python_runner "Permalink to this definition") Removes the runner pane from the application. remove\_repl()[[source]](_modules/mu/interface/main.html#Window.remove_repl)[¶](#mu.interface.main.Window.remove_repl "Permalink to this definition") Removes the REPL pane from the application. replace\_text(*target\_text*, *replace*, *global\_replace*)[[source]](_modules/mu/interface/main.html#Window.replace_text)[¶](#mu.interface.main.Window.replace_text "Permalink to this definition") Given target\_text, replace the first instance after the cursor with “replace”. If global\_replace is true, replace all instances of “target”. Returns the number of times replacement has occurred. reset\_annotations()[[source]](_modules/mu/interface/main.html#Window.reset_annotations)[¶](#mu.interface.main.Window.reset_annotations "Permalink to this definition") Resets the state of annotations on the current tab. resizeEvent(*resizeEvent*)[[source]](_modules/mu/interface/main.html#Window.resizeEvent)[¶](#mu.interface.main.Window.resizeEvent "Permalink to this definition") Respond to window getting too small for the button bar to fit well. screen\_size()[[source]](_modules/mu/interface/main.html#Window.screen_size)[¶](#mu.interface.main.Window.screen_size "Permalink to this definition") Returns an (width, height) tuple with the screen geometry. select\_mode(*modes*, *current\_mode*)[[source]](_modules/mu/interface/main.html#Window.select_mode)[¶](#mu.interface.main.Window.select_mode "Permalink to this definition") Display the mode selector dialog and return the result. set\_checker\_icon(*icon*)[[source]](_modules/mu/interface/main.html#Window.set_checker_icon)[¶](#mu.interface.main.Window.set_checker_icon "Permalink to this definition") Set the status icon to use on the check button set\_read\_only(*is\_readonly*)[[source]](_modules/mu/interface/main.html#Window.set_read_only)[¶](#mu.interface.main.Window.set_read_only "Permalink to this definition") Set all tabs read-only. set\_theme(*theme*)[[source]](_modules/mu/interface/main.html#Window.set_theme)[¶](#mu.interface.main.Window.set_theme "Permalink to this definition") Sets the theme for the REPL and editor tabs. set\_timer(*duration*, *callback*)[[source]](_modules/mu/interface/main.html#Window.set_timer)[¶](#mu.interface.main.Window.set_timer "Permalink to this definition") Set a repeating timer to call “callback” every “duration” seconds. set\_usb\_checker(*duration*, *callback*)[[source]](_modules/mu/interface/main.html#Window.set_usb_checker)[¶](#mu.interface.main.Window.set_usb_checker "Permalink to this definition") Sets up a timer that polls for USB changes via the “callback” every “duration” seconds. set\_zoom()[[source]](_modules/mu/interface/main.html#Window.set_zoom)[¶](#mu.interface.main.Window.set_zoom "Permalink to this definition") Sets the zoom to current zoom\_position level. setup(*breakpoint\_toggle*, *theme*)[[source]](_modules/mu/interface/main.html#Window.setup)[¶](#mu.interface.main.Window.setup "Permalink to this definition") Sets up the window. Defines the various attributes of the window and defines how the user interface is laid out. show\_admin(*log*, *settings*, *packages*, *mode*, *device\_list*)[[source]](_modules/mu/interface/main.html#Window.show_admin)[¶](#mu.interface.main.Window.show_admin "Permalink to this definition") Display the administrative dialog with referenced content of the log and settings. Return a dictionary of the settings that may have been changed by the admin dialog. show\_annotations()[[source]](_modules/mu/interface/main.html#Window.show_annotations)[¶](#mu.interface.main.Window.show_annotations "Permalink to this definition") Show the annotations added to the current tab. show\_confirmation(*message*, *information=None*, *icon=None*)[[source]](_modules/mu/interface/main.html#Window.show_confirmation)[¶](#mu.interface.main.Window.show_confirmation "Permalink to this definition") Displays a modal message to the user to which they need to confirm or cancel. If information is passed in this will be set as the additional informative text in the modal dialog. Since this mechanism will be used mainly for warning users that something is awry the default icon is set to “Warning”. It’s possible to override the icon to one of the following settings: NoIcon, Question, Information, Warning or Critical. show\_device\_selector()[[source]](_modules/mu/interface/main.html#Window.show_device_selector)[¶](#mu.interface.main.Window.show_device_selector "Permalink to this definition") Reveals the device selector in the status bar show\_find\_replace(*find*, *replace*, *global\_replace*)[[source]](_modules/mu/interface/main.html#Window.show_find_replace)[¶](#mu.interface.main.Window.show_find_replace "Permalink to this definition") Display the find/replace dialog. If the dialog’s OK button was clicked return a tuple containing the find term, replace term and global replace flag. show\_message(*message*, *information=None*, *icon=None*)[[source]](_modules/mu/interface/main.html#Window.show_message)[¶](#mu.interface.main.Window.show_message "Permalink to this definition") Displays a modal message to the user. If information is passed in this will be set as the additional informative text in the modal dialog. Since this mechanism will be used mainly for warning users that something is awry the default icon is set to “Warning”. It’s possible to override the icon to one of the following settings: NoIcon, Question, Information, Warning or Critical. size\_window(*x=None*, *y=None*, *w=None*, *h=None*)[[source]](_modules/mu/interface/main.html#Window.size_window)[¶](#mu.interface.main.Window.size_window "Permalink to this definition") Makes the editor 80% of the width\*height of the screen and centres it when none of x, y, w and h is passed in; otherwise uses the passed in values to position and size the editor window. If the X or Y value will be off the screen, these are reset to None (thus stopping the window being drawn in a hard-to-reach place). See issue #1613 for context. stop\_timer()[[source]](_modules/mu/interface/main.html#Window.stop_timer)[¶](#mu.interface.main.Window.stop_timer "Permalink to this definition") Stop the repeating timer. sync\_packages(*to\_remove*, *to\_add*)[[source]](_modules/mu/interface/main.html#Window.sync_packages)[¶](#mu.interface.main.Window.sync_packages "Permalink to this definition") Display a modal dialog that indicates the status of the add/remove package management operation. *property* tab\_count[¶](#mu.interface.main.Window.tab_count "Permalink to this definition") Returns the number of active tabs. toggle\_comments()[[source]](_modules/mu/interface/main.html#Window.toggle_comments)[¶](#mu.interface.main.Window.toggle_comments "Permalink to this definition") Toggle comments on/off for all selected line in the currently active tab. update\_debug\_inspector(*locals\_dict*)[[source]](_modules/mu/interface/main.html#Window.update_debug_inspector)[¶](#mu.interface.main.Window.update_debug_inspector "Permalink to this definition") Given the contents of a dict representation of the locals in the current stack frame, update the debug inspector with the new values. update\_title(*filename=None*)[[source]](_modules/mu/interface/main.html#Window.update_title)[¶](#mu.interface.main.Window.update_title "Permalink to this definition") Updates the title bar of the application. If a filename (representing the name of the file currently the focus of the editor) is supplied, append it to the end of the title. upload\_to\_python\_anywhere(*instance*, *username*, *token*, *app\_name*, *files*)[[source]](_modules/mu/interface/main.html#Window.upload_to_python_anywhere)[¶](#mu.interface.main.Window.upload_to_python_anywhere "Permalink to this definition") Show a progress dialog as the files are uploaded to PythonAnywhere. wheelEvent(*event*)[[source]](_modules/mu/interface/main.html#Window.wheelEvent)[¶](#mu.interface.main.Window.wheelEvent "Permalink to this definition") Trap a CTRL-scroll event so the user is able to zoom in and out. *property* widgets[¶](#mu.interface.main.Window.widgets "Permalink to this definition") Returns a list of references to the widgets representing tabs in the editor. zoom\_in()[[source]](_modules/mu/interface/main.html#Window.zoom_in)[¶](#mu.interface.main.Window.zoom_in "Permalink to this definition") Handles zooming in. zoom\_out()[[source]](_modules/mu/interface/main.html#Window.zoom_out)[¶](#mu.interface.main.Window.zoom_out "Permalink to this definition") Handles zooming out. ##### `mu.interface.dialogs`[¶](#mu-interface-dialogs "Permalink to this headline") Bespoke modal dialogs required by Mu. UI related code for dialogs used by Mu. Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *class* mu.interface.dialogs.AdminDialog(*parent=None*)[[source]](_modules/mu/interface/dialogs.html#AdminDialog)[¶](#mu.interface.dialogs.AdminDialog "Permalink to this definition") Displays administrative related information and settings (logs, environment variables, third party packages etc…). settings()[[source]](_modules/mu/interface/dialogs.html#AdminDialog.settings)[¶](#mu.interface.dialogs.AdminDialog.settings "Permalink to this definition") Return a dictionary representation of the raw settings information generated by this dialog. Such settings will need to be processed / checked in the “logic” layer of Mu. *class* mu.interface.dialogs.ESPFirmwareFlasherWidget[[source]](_modules/mu/interface/dialogs.html#ESPFirmwareFlasherWidget)[¶](#mu.interface.dialogs.ESPFirmwareFlasherWidget "Permalink to this definition") Used for configuring how to interact with the ESP: * Override MicroPython. append\_data(*msg*)[[source]](_modules/mu/interface/dialogs.html#ESPFirmwareFlasherWidget.append_data)[¶](#mu.interface.dialogs.ESPFirmwareFlasherWidget.append_data "Permalink to this definition") Add data to the end of the text area. esptool\_finished(*exitCode*, *exitStatus*)[[source]](_modules/mu/interface/dialogs.html#ESPFirmwareFlasherWidget.esptool_finished)[¶](#mu.interface.dialogs.ESPFirmwareFlasherWidget.esptool_finished "Permalink to this definition") Called when the subprocess that executes ‘esptool.py is finished. read\_process()[[source]](_modules/mu/interface/dialogs.html#ESPFirmwareFlasherWidget.read_process)[¶](#mu.interface.dialogs.ESPFirmwareFlasherWidget.read_process "Permalink to this definition") Read data from the child process and append it to the text area. Try to keep reading until there’s no more data from the process. *class* mu.interface.dialogs.EnvironmentVariablesWidget[[source]](_modules/mu/interface/dialogs.html#EnvironmentVariablesWidget)[¶](#mu.interface.dialogs.EnvironmentVariablesWidget "Permalink to this definition") Used for editing and displaying environment variables used with Python 3 mode. *class* mu.interface.dialogs.FindReplaceDialog(*parent=None*)[[source]](_modules/mu/interface/dialogs.html#FindReplaceDialog)[¶](#mu.interface.dialogs.FindReplaceDialog "Permalink to this definition") Display a dialog for getting: * A term to find, * An optional value to replace the search term, * A flag to indicate if the user wishes to replace all. find()[[source]](_modules/mu/interface/dialogs.html#FindReplaceDialog.find)[¶](#mu.interface.dialogs.FindReplaceDialog.find "Permalink to this definition") Return the value the user entered to find. replace()[[source]](_modules/mu/interface/dialogs.html#FindReplaceDialog.replace)[¶](#mu.interface.dialogs.FindReplaceDialog.replace "Permalink to this definition") Return the value the user entered for replace. replace\_flag()[[source]](_modules/mu/interface/dialogs.html#FindReplaceDialog.replace_flag)[¶](#mu.interface.dialogs.FindReplaceDialog.replace_flag "Permalink to this definition") Return the value of the global replace flag. *class* mu.interface.dialogs.LocaleWidget[[source]](_modules/mu/interface/dialogs.html#LocaleWidget)[¶](#mu.interface.dialogs.LocaleWidget "Permalink to this definition") Used for manually setting the locale (and thus the language) used by Mu. get\_locale()[[source]](_modules/mu/interface/dialogs.html#LocaleWidget.get_locale)[¶](#mu.interface.dialogs.LocaleWidget.get_locale "Permalink to this definition") Return the user-selected language code. *class* mu.interface.dialogs.LogWidget[[source]](_modules/mu/interface/dialogs.html#LogWidget)[¶](#mu.interface.dialogs.LogWidget "Permalink to this definition") Used to display Mu’s logs. *class* mu.interface.dialogs.MicrobitSettingsWidget[[source]](_modules/mu/interface/dialogs.html#MicrobitSettingsWidget)[¶](#mu.interface.dialogs.MicrobitSettingsWidget "Permalink to this definition") Used for configuring how to interact with the micro:bit: * Minification flag. * Override runtime version to use. *class* mu.interface.dialogs.ModeItem(*name*, *description*, *icon*, *parent=None*)[[source]](_modules/mu/interface/dialogs.html#ModeItem)[¶](#mu.interface.dialogs.ModeItem "Permalink to this definition") Represents an available mode listed for selection. *class* mu.interface.dialogs.ModeSelector(*parent=None*)[[source]](_modules/mu/interface/dialogs.html#ModeSelector)[¶](#mu.interface.dialogs.ModeSelector "Permalink to this definition") Defines a UI for selecting the mode for Mu. get\_mode()[[source]](_modules/mu/interface/dialogs.html#ModeSelector.get_mode)[¶](#mu.interface.dialogs.ModeSelector.get_mode "Permalink to this definition") Return details of the newly selected mode. select\_and\_accept()[[source]](_modules/mu/interface/dialogs.html#ModeSelector.select_and_accept)[¶](#mu.interface.dialogs.ModeSelector.select_and_accept "Permalink to this definition") Handler for when an item is double-clicked. *class* mu.interface.dialogs.PackageDialog(*parent=None*)[[source]](_modules/mu/interface/dialogs.html#PackageDialog)[¶](#mu.interface.dialogs.PackageDialog "Permalink to this definition") Display the output of the pip commands needed to remove or install packages. Because the QProcess mechanism we’re using is asynchronous, we have to manage the pip requests via pip\_queue. When one request is signalled as finished we start the next. finish()[[source]](_modules/mu/interface/dialogs.html#PackageDialog.finish)[¶](#mu.interface.dialogs.PackageDialog.finish "Permalink to this definition") Set the UI to a valid end state. next\_pip\_command()[[source]](_modules/mu/interface/dialogs.html#PackageDialog.next_pip_command)[¶](#mu.interface.dialogs.PackageDialog.next_pip_command "Permalink to this definition") Run the next pip command, finishing if there is none. run\_pip(*command*, *packages*)[[source]](_modules/mu/interface/dialogs.html#PackageDialog.run_pip)[¶](#mu.interface.dialogs.PackageDialog.run_pip "Permalink to this definition") Run a pip command in a subprocess and pipe the output to the dialog’s text area. setup(*to\_remove*, *to\_add*)[[source]](_modules/mu/interface/dialogs.html#PackageDialog.setup)[¶](#mu.interface.dialogs.PackageDialog.setup "Permalink to this definition") Create the UI for the dialog. *class* mu.interface.dialogs.PackagesWidget[[source]](_modules/mu/interface/dialogs.html#PackagesWidget)[¶](#mu.interface.dialogs.PackagesWidget "Permalink to this definition") Used for editing and displaying 3rd party packages installed via pip to be used with Python 3 mode. *class* mu.interface.dialogs.PythonAnywhereWidget[[source]](_modules/mu/interface/dialogs.html#PythonAnywhereWidget)[¶](#mu.interface.dialogs.PythonAnywhereWidget "Permalink to this definition") For configuring the user’s username and API token for interacting with the PythonAnywhere API to deploy a website from web mode. valid\_instances *= ['www', 'eu']*[¶](#mu.interface.dialogs.PythonAnywhereWidget.valid_instances "Permalink to this definition") Valid server hosting instances for PythonAnywhere. ##### `mu.interface.editor`[¶](#mu-interface-editor "Permalink to this headline") Contains the customised Scintilla based editor used for textual display and entry. UI related capabilities for the text editor widget embedded in each tab in Mu. Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *class* mu.interface.editor.CssLexer[[source]](_modules/mu/interface/editor.html#CssLexer)[¶](#mu.interface.editor.CssLexer "Permalink to this definition") Fixes problems with comments in CSS. description(*style*)[[source]](_modules/mu/interface/editor.html#CssLexer.description)[¶](#mu.interface.editor.CssLexer.description "Permalink to this definition") Ensures “Comment” is returned when the lexer encounters a comment (this is due to a bug in the base class, for which this is a work around). *class* mu.interface.editor.EditorPane(*path*, *text*, *newline='\n'*)[[source]](_modules/mu/interface/editor.html#EditorPane)[¶](#mu.interface.editor.EditorPane "Permalink to this definition") Represents the text editor. annotate\_code(*feedback*, *annotation\_type='error'*)[[source]](_modules/mu/interface/editor.html#EditorPane.annotate_code)[¶](#mu.interface.editor.EditorPane.annotate_code "Permalink to this definition") Given a list of annotations add them to the editor pane so the user can act upon them. configure()[[source]](_modules/mu/interface/editor.html#EditorPane.configure)[¶](#mu.interface.editor.EditorPane.configure "Permalink to this definition") Set up the editor component. connect\_margin(*func*)[[source]](_modules/mu/interface/editor.html#EditorPane.connect_margin)[¶](#mu.interface.editor.EditorPane.connect_margin "Permalink to this definition") Connect clicking the margin to the passed in handler function, via a filtering handler that ignores clicks on margin 4. contextMenuEvent(*event*)[[source]](_modules/mu/interface/editor.html#EditorPane.contextMenuEvent)[¶](#mu.interface.editor.EditorPane.contextMenuEvent "Permalink to this definition") A context menu (right click) has been actioned. debugger\_at\_line(*line*)[[source]](_modules/mu/interface/editor.html#EditorPane.debugger_at_line)[¶](#mu.interface.editor.EditorPane.debugger_at_line "Permalink to this definition") Set the line to be highlighted with the DEBUG\_INDICATOR. dropEvent(*event*)[[source]](_modules/mu/interface/editor.html#EditorPane.dropEvent)[¶](#mu.interface.editor.EditorPane.dropEvent "Permalink to this definition") Run by Qt when *something* is dropped on this editor find\_next\_match(*text*, *from\_line=- 1*, *from\_col=- 1*, *case\_sensitive=True*, *wrap\_around=True*)[[source]](_modules/mu/interface/editor.html#EditorPane.find_next_match)[¶](#mu.interface.editor.EditorPane.find_next_match "Permalink to this definition") Finds the next text match from the current cursor, or the given position, and selects it (the automatic selection is the only available QsciScintilla behaviour). Returns True if match found, False otherwise. highlight\_selected\_matches()[[source]](_modules/mu/interface/editor.html#EditorPane.highlight_selected_matches)[¶](#mu.interface.editor.EditorPane.highlight_selected_matches "Permalink to this definition") Checks the current selection, if it is a single word it then searches and highlights all matches. Since we’re interested in exactly one word: \* Ignore an empty selection \* Ignore anything which spans more than one line \* Ignore more than one word \* Ignore anything less than one word *property* label[¶](#mu.interface.editor.EditorPane.label "Permalink to this definition") The label associated with this editor widget (usually the filename of the script we’re editing). range\_from\_positions(*start\_position*, *end\_position*)[[source]](_modules/mu/interface/editor.html#EditorPane.range_from_positions)[¶](#mu.interface.editor.EditorPane.range_from_positions "Permalink to this definition") Given a start-end pair, such as are provided by a regex match, return the corresponding Scintilla line-offset pairs which are used for searches, indicators etc. NOTE: Arguments must be byte offsets into the underlying text bytes. reset\_annotations()[[source]](_modules/mu/interface/editor.html#EditorPane.reset_annotations)[¶](#mu.interface.editor.EditorPane.reset_annotations "Permalink to this definition") Clears all the assets (indicators, annotations and markers). reset\_check\_indicators()[[source]](_modules/mu/interface/editor.html#EditorPane.reset_check_indicators)[¶](#mu.interface.editor.EditorPane.reset_check_indicators "Permalink to this definition") Clears all the text indicators related to the check code functionality. reset\_debugger\_highlight()[[source]](_modules/mu/interface/editor.html#EditorPane.reset_debugger_highlight)[¶](#mu.interface.editor.EditorPane.reset_debugger_highlight "Permalink to this definition") Reset all the lines so the DEBUG\_INDICATOR is no longer displayed. We need to check each line since there’s no way to tell what the currently highlighted line is. This approach also has the advantage of resetting the *whole* editor pane. reset\_search\_indicators()[[source]](_modules/mu/interface/editor.html#EditorPane.reset_search_indicators)[¶](#mu.interface.editor.EditorPane.reset_search_indicators "Permalink to this definition") Clears all the text indicators from the search functionality. selection\_change\_listener()[[source]](_modules/mu/interface/editor.html#EditorPane.selection_change_listener)[¶](#mu.interface.editor.EditorPane.selection_change_listener "Permalink to this definition") Runs every time the text selection changes. This could get triggered multiple times while the mouse click is down, even if selection has not changed in itself. If there is a new selection is passes control to highlight\_selected\_matches. set\_api(*api\_definitions*)[[source]](_modules/mu/interface/editor.html#EditorPane.set_api)[¶](#mu.interface.editor.EditorPane.set_api "Permalink to this definition") Sets the API entries for tooltips, calltips and the like. set\_theme(*theme=<class 'mu.interface.themes.DayTheme'>*)[[source]](_modules/mu/interface/editor.html#EditorPane.set_theme)[¶](#mu.interface.editor.EditorPane.set_theme "Permalink to this definition") Connect the theme to a lexer and return the lexer for the editor to apply to the script text. set\_zoom(*size='m'*)[[source]](_modules/mu/interface/editor.html#EditorPane.set_zoom)[¶](#mu.interface.editor.EditorPane.set_zoom "Permalink to this definition") Sets the font zoom to the specified base point size for all fonts given a t-shirt size. show\_annotations()[[source]](_modules/mu/interface/editor.html#EditorPane.show_annotations)[¶](#mu.interface.editor.EditorPane.show_annotations "Permalink to this definition") Display all the messages to be annotated to the code. *property* title[¶](#mu.interface.editor.EditorPane.title "Permalink to this definition") The title associated with this editor widget (usually the filename of the script we’re editing). If the script has been modified since it was last saved, the label will end with an asterisk. toggle\_comments()[[source]](_modules/mu/interface/editor.html#EditorPane.toggle_comments)[¶](#mu.interface.editor.EditorPane.toggle_comments "Permalink to this definition") Iterate through the selected lines and toggle their comment/uncomment state. So, lines that are not comments become comments and vice versa. toggle\_line(*raw\_line*)[[source]](_modules/mu/interface/editor.html#EditorPane.toggle_line)[¶](#mu.interface.editor.EditorPane.toggle_line "Permalink to this definition") Given a raw\_line, will return the toggled version of it. wheelEvent(*event*)[[source]](_modules/mu/interface/editor.html#EditorPane.wheelEvent)[¶](#mu.interface.editor.EditorPane.wheelEvent "Permalink to this definition") Stops QScintilla from doing the wrong sort of zoom handling. *class* mu.interface.editor.PythonLexer(*\*args*, *\*\*kwargs*)[[source]](_modules/mu/interface/editor.html#PythonLexer)[¶](#mu.interface.editor.PythonLexer "Permalink to this definition") A Python specific “lexer” that’s used to identify keywords of the Python language so the editor can do syntax highlighting. keywords(*flag*)[[source]](_modules/mu/interface/editor.html#PythonLexer.keywords)[¶](#mu.interface.editor.PythonLexer.keywords "Permalink to this definition") Returns a list of Python keywords. ##### `mu.interface.panes`[¶](#mu-interface-panes "Permalink to this headline") Contains code used to populate the various panes found in the user interface (REPL, file list, debug inspector etc…). Contains the UI classes used to populate the various panes used by Mu. Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *class* mu.interface.panes.DebugInspector[[source]](_modules/mu/interface/panes.html#DebugInspector)[¶](#mu.interface.panes.DebugInspector "Permalink to this definition") Presents a tree like representation of the current state of the call stack to the user. record\_collapsed(*index*)[[source]](_modules/mu/interface/panes.html#DebugInspector.record_collapsed)[¶](#mu.interface.panes.DebugInspector.record_collapsed "Permalink to this definition") Remove collapsed dicts from set, so they render collapsed. record\_expanded(*index*)[[source]](_modules/mu/interface/panes.html#DebugInspector.record_expanded)[¶](#mu.interface.panes.DebugInspector.record_expanded "Permalink to this definition") Keep track of expanded dicts for displaying in debugger. set\_font\_size(*new\_size=14*)[[source]](_modules/mu/interface/panes.html#DebugInspector.set_font_size)[¶](#mu.interface.panes.DebugInspector.set_font_size "Permalink to this definition") Sets the font size for all the textual elements in this pane. set\_zoom(*size*)[[source]](_modules/mu/interface/panes.html#DebugInspector.set_zoom)[¶](#mu.interface.panes.DebugInspector.set_zoom "Permalink to this definition") Set the current zoom level given the “t-shirt” size. *class* mu.interface.panes.DebugInspectorItem(*\*args*)[[source]](_modules/mu/interface/panes.html#DebugInspectorItem)[¶](#mu.interface.panes.DebugInspectorItem "Permalink to this definition") *class* mu.interface.panes.FileSystemPane(*home*)[[source]](_modules/mu/interface/panes.html#FileSystemPane)[¶](#mu.interface.panes.FileSystemPane "Permalink to this definition") Contains two QListWidgets representing the micro:bit and the user’s code directory. Users transfer files by dragging and dropping. Highlighted files can be selected for deletion. disable()[[source]](_modules/mu/interface/panes.html#FileSystemPane.disable)[¶](#mu.interface.panes.FileSystemPane.disable "Permalink to this definition") Stops interaction with the list widgets. enable()[[source]](_modules/mu/interface/panes.html#FileSystemPane.enable)[¶](#mu.interface.panes.FileSystemPane.enable "Permalink to this definition") Allows interaction with the list widgets. on\_delete\_fail(*filename*)[[source]](_modules/mu/interface/panes.html#FileSystemPane.on_delete_fail)[¶](#mu.interface.panes.FileSystemPane.on_delete_fail "Permalink to this definition") Fired when a deletion on the device for the given file failed. on\_get\_fail(*filename*)[[source]](_modules/mu/interface/panes.html#FileSystemPane.on_get_fail)[¶](#mu.interface.panes.FileSystemPane.on_get_fail "Permalink to this definition") Fired when getting the referenced file on the device failed. on\_ls(*microbit\_files*)[[source]](_modules/mu/interface/panes.html#FileSystemPane.on_ls)[¶](#mu.interface.panes.FileSystemPane.on_ls "Permalink to this definition") Displays a list of the files on the micro:bit. Since listing files is always the final event in any interaction between Mu and the micro:bit, this enables the controls again for further interactions to take place. on\_ls\_fail()[[source]](_modules/mu/interface/panes.html#FileSystemPane.on_ls_fail)[¶](#mu.interface.panes.FileSystemPane.on_ls_fail "Permalink to this definition") Fired when listing files fails. on\_put\_fail(*filename*)[[source]](_modules/mu/interface/panes.html#FileSystemPane.on_put_fail)[¶](#mu.interface.panes.FileSystemPane.on_put_fail "Permalink to this definition") Fired when the referenced file cannot be copied onto the device. set\_font\_size(*new\_size=14*)[[source]](_modules/mu/interface/panes.html#FileSystemPane.set_font_size)[¶](#mu.interface.panes.FileSystemPane.set_font_size "Permalink to this definition") Sets the font size for all the textual elements in this pane. set\_zoom(*size*)[[source]](_modules/mu/interface/panes.html#FileSystemPane.set_zoom)[¶](#mu.interface.panes.FileSystemPane.set_zoom "Permalink to this definition") Set the current zoom level given the “t-shirt” size. show\_message(*message*)[[source]](_modules/mu/interface/panes.html#FileSystemPane.show_message)[¶](#mu.interface.panes.FileSystemPane.show_message "Permalink to this definition") Emits the set\_message signal. show\_warning(*message*)[[source]](_modules/mu/interface/panes.html#FileSystemPane.show_warning)[¶](#mu.interface.panes.FileSystemPane.show_warning "Permalink to this definition") Emits the set\_warning signal. *class* mu.interface.panes.JupyterREPLPane(*\*args*, *\*\*kwargs*)[[source]](_modules/mu/interface/panes.html#JupyterREPLPane)[¶](#mu.interface.panes.JupyterREPLPane "Permalink to this definition") REPL = Read, Evaluate, Print, Loop. Displays a Jupyter iPython session. setFocus()[[source]](_modules/mu/interface/panes.html#JupyterREPLPane.setFocus)[¶](#mu.interface.panes.JupyterREPLPane.setFocus "Permalink to this definition") Override base setFocus so the focus happens to the embedded \_control within this widget. set\_font\_size(*new\_size=14*)[[source]](_modules/mu/interface/panes.html#JupyterREPLPane.set_font_size)[¶](#mu.interface.panes.JupyterREPLPane.set_font_size "Permalink to this definition") Sets the font size for all the textual elements in this pane. set\_theme(*theme*)[[source]](_modules/mu/interface/panes.html#JupyterREPLPane.set_theme)[¶](#mu.interface.panes.JupyterREPLPane.set_theme "Permalink to this definition") Sets the theme / look for the REPL pane. set\_zoom(*size*)[[source]](_modules/mu/interface/panes.html#JupyterREPLPane.set_zoom)[¶](#mu.interface.panes.JupyterREPLPane.set_zoom "Permalink to this definition") Set the current zoom level given the “t-shirt” size. *class* mu.interface.panes.LocalFileList(*home*)[[source]](_modules/mu/interface/panes.html#LocalFileList)[¶](#mu.interface.panes.LocalFileList "Permalink to this definition") Represents a list of files in the Mu directory on the local machine. contextMenuEvent(*self*, *QContextMenuEvent*)[[source]](_modules/mu/interface/panes.html#LocalFileList.contextMenuEvent)[¶](#mu.interface.panes.LocalFileList.contextMenuEvent "Permalink to this definition") dropEvent(*self*, *QDropEvent*)[[source]](_modules/mu/interface/panes.html#LocalFileList.dropEvent)[¶](#mu.interface.panes.LocalFileList.dropEvent "Permalink to this definition") on\_get(*microbit\_file*)[[source]](_modules/mu/interface/panes.html#LocalFileList.on_get)[¶](#mu.interface.panes.LocalFileList.on_get "Permalink to this definition") Fired when the get event is completed for the given filename. *class* mu.interface.panes.MicroPythonDeviceFileList(*home*)[[source]](_modules/mu/interface/panes.html#MicroPythonDeviceFileList)[¶](#mu.interface.panes.MicroPythonDeviceFileList "Permalink to this definition") Represents a list of files on a MicroPython device. contextMenuEvent(*self*, *QContextMenuEvent*)[[source]](_modules/mu/interface/panes.html#MicroPythonDeviceFileList.contextMenuEvent)[¶](#mu.interface.panes.MicroPythonDeviceFileList.contextMenuEvent "Permalink to this definition") dropEvent(*self*, *QDropEvent*)[[source]](_modules/mu/interface/panes.html#MicroPythonDeviceFileList.dropEvent)[¶](#mu.interface.panes.MicroPythonDeviceFileList.dropEvent "Permalink to this definition") on\_delete(*microbit\_file*)[[source]](_modules/mu/interface/panes.html#MicroPythonDeviceFileList.on_delete)[¶](#mu.interface.panes.MicroPythonDeviceFileList.on_delete "Permalink to this definition") Fired when the delete event is completed for the given filename. on\_put(*microbit\_file*)[[source]](_modules/mu/interface/panes.html#MicroPythonDeviceFileList.on_put)[¶](#mu.interface.panes.MicroPythonDeviceFileList.on_put "Permalink to this definition") Fired when the put event is completed for the given filename. *class* mu.interface.panes.MicroPythonREPLPane(*connection*, *theme='day'*, *parent=None*)[[source]](_modules/mu/interface/panes.html#MicroPythonREPLPane)[¶](#mu.interface.panes.MicroPythonREPLPane "Permalink to this definition") REPL = Read, Evaluate, Print, Loop. This widget represents a REPL client connected to a device running MicroPython. The device MUST be flashed with MicroPython for this to work. clear()[[source]](_modules/mu/interface/panes.html#MicroPythonREPLPane.clear)[¶](#mu.interface.panes.MicroPythonREPLPane.clear "Permalink to this definition") Clears the text of the REPL. context\_menu()[[source]](_modules/mu/interface/panes.html#MicroPythonREPLPane.context_menu)[¶](#mu.interface.panes.MicroPythonREPLPane.context_menu "Permalink to this definition") Creates custom context menu with just copy and paste. delete\_selection()[[source]](_modules/mu/interface/panes.html#MicroPythonREPLPane.delete_selection)[¶](#mu.interface.panes.MicroPythonREPLPane.delete_selection "Permalink to this definition") Returns true if deletion happened, returns false if there was no selection to delete. insertFromMimeData(*source*)[[source]](_modules/mu/interface/panes.html#MicroPythonREPLPane.insertFromMimeData)[¶](#mu.interface.panes.MicroPythonREPLPane.insertFromMimeData "Permalink to this definition") Insert mime data by sending it to the REPL keyPressEvent(*data*)[[source]](_modules/mu/interface/panes.html#MicroPythonREPLPane.keyPressEvent)[¶](#mu.interface.panes.MicroPythonREPLPane.keyPressEvent "Permalink to this definition") Called when the user types something in the REPL. Correctly encodes it and sends it to the connected device. mouseReleaseEvent(*mouseEvent*)[[source]](_modules/mu/interface/panes.html#MicroPythonREPLPane.mouseReleaseEvent)[¶](#mu.interface.panes.MicroPythonREPLPane.mouseReleaseEvent "Permalink to this definition") Called whenever a user have had a mouse button pressed, and releases it. We pass it through to the normal way Qt handles button pressed, but also sends as cursor movement signal to the device (except if a selection is made, for selections we first move the cursor on deselection) move\_cursor\_to(*new\_position*)[[source]](_modules/mu/interface/panes.html#MicroPythonREPLPane.move_cursor_to)[¶](#mu.interface.panes.MicroPythonREPLPane.move_cursor_to "Permalink to this definition") Move the cursor, by sending vt100 left/right signals through serial. The Qt cursor is first returned to the known location of the device cursor. Then the appropriate number of move left or right signals are send. The Qt cursor is not moved to the new\_position here, but will be moved once receiving a response (in process\_tty\_data). process\_tty\_data(*data*)[[source]](_modules/mu/interface/panes.html#MicroPythonREPLPane.process_tty_data)[¶](#mu.interface.panes.MicroPythonREPLPane.process_tty_data "Permalink to this definition") Given some incoming bytes of data, work out how to handle / display them in the REPL widget. If received input is incomplete, stores remainder in self.unprocessed\_input. Updates the self.device\_cursor\_position to match that of the device for every input received. set\_devicecursor\_to\_qtcursor()[[source]](_modules/mu/interface/panes.html#MicroPythonREPLPane.set_devicecursor_to_qtcursor)[¶](#mu.interface.panes.MicroPythonREPLPane.set_devicecursor_to_qtcursor "Permalink to this definition") Call this whenever the cursor has been moved by the user, to send the cursor movement to the device. set\_font\_size(*new\_size=14*)[[source]](_modules/mu/interface/panes.html#MicroPythonREPLPane.set_font_size)[¶](#mu.interface.panes.MicroPythonREPLPane.set_font_size "Permalink to this definition") Sets the font size for all the textual elements in this pane. set\_qtcursor\_to\_devicecursor()[[source]](_modules/mu/interface/panes.html#MicroPythonREPLPane.set_qtcursor_to_devicecursor)[¶](#mu.interface.panes.MicroPythonREPLPane.set_qtcursor_to_devicecursor "Permalink to this definition") Resets the Qt TextCursor to where we know the device has the cursor placed. set\_zoom(*size*)[[source]](_modules/mu/interface/panes.html#MicroPythonREPLPane.set_zoom)[¶](#mu.interface.panes.MicroPythonREPLPane.set_zoom "Permalink to this definition") Set the current zoom level given the “t-shirt” size. *class* mu.interface.panes.MuFileList[[source]](_modules/mu/interface/panes.html#MuFileList)[¶](#mu.interface.panes.MuFileList "Permalink to this definition") Contains shared methods for the two types of file listing used in Mu. show\_confirm\_overwrite\_dialog()[[source]](_modules/mu/interface/panes.html#MuFileList.show_confirm_overwrite_dialog)[¶](#mu.interface.panes.MuFileList.show_confirm_overwrite_dialog "Permalink to this definition") Display a dialog to check if an existing file should be overwritten. Returns a boolean indication of the user’s decision. *class* mu.interface.panes.PlotterPane(*parent=None*)[[source]](_modules/mu/interface/panes.html#PlotterPane)[¶](#mu.interface.panes.PlotterPane "Permalink to this definition") This plotter widget makes viewing sensor data easy! This widget represents a chart that will look for tuple data from the MicroPython REPL, Python 3 REPL or Python 3 code runner and will auto-generate a graph. add\_data(*values*)[[source]](_modules/mu/interface/panes.html#PlotterPane.add_data)[¶](#mu.interface.panes.PlotterPane.add_data "Permalink to this definition") Given a tuple of values, ensures there are the required number of line series, add the data to the line series, update the range of the chart so the chart displays nicely. process\_tty\_data(*data*)[[source]](_modules/mu/interface/panes.html#PlotterPane.process_tty_data)[¶](#mu.interface.panes.PlotterPane.process_tty_data "Permalink to this definition") Takes raw bytes and, if a valid tuple is detected, adds the data to the plotter. The the length of the bytes data > 1024 then a data\_flood signal is emitted to ensure Mu can take action to remain responsive. set\_theme(*theme*)[[source]](_modules/mu/interface/panes.html#PlotterPane.set_theme)[¶](#mu.interface.panes.PlotterPane.set_theme "Permalink to this definition") Sets the theme / look for the plotter pane. *class* mu.interface.panes.PythonProcessPane(*parent=None*)[[source]](_modules/mu/interface/panes.html#PythonProcessPane)[¶](#mu.interface.panes.PythonProcessPane "Permalink to this definition") Handles / displays a Python process’s stdin/out with working command history and simple buffer editing. append(*msg*)[[source]](_modules/mu/interface/panes.html#PythonProcessPane.append)[¶](#mu.interface.panes.PythonProcessPane.append "Permalink to this definition") Append text to the text area. backspace()[[source]](_modules/mu/interface/panes.html#PythonProcessPane.backspace)[¶](#mu.interface.panes.PythonProcessPane.backspace "Permalink to this definition") Removes a character from the current buffer – to the left of cursor. clear\_input\_line()[[source]](_modules/mu/interface/panes.html#PythonProcessPane.clear_input_line)[¶](#mu.interface.panes.PythonProcessPane.clear_input_line "Permalink to this definition") Remove all the characters currently in the input buffer line. context\_menu()[[source]](_modules/mu/interface/panes.html#PythonProcessPane.context_menu)[¶](#mu.interface.panes.PythonProcessPane.context_menu "Permalink to this definition") Creates custom context menu with just copy and paste. delete()[[source]](_modules/mu/interface/panes.html#PythonProcessPane.delete)[¶](#mu.interface.panes.PythonProcessPane.delete "Permalink to this definition") Removes a character from the current buffer – to the right of cursor. finished(*code*, *status*)[[source]](_modules/mu/interface/panes.html#PythonProcessPane.finished)[¶](#mu.interface.panes.PythonProcessPane.finished "Permalink to this definition") Handle when the child process finishes. history\_back()[[source]](_modules/mu/interface/panes.html#PythonProcessPane.history_back)[¶](#mu.interface.panes.PythonProcessPane.history_back "Permalink to this definition") Replace the current input line with the next item BACK from the current history position. history\_forward()[[source]](_modules/mu/interface/panes.html#PythonProcessPane.history_forward)[¶](#mu.interface.panes.PythonProcessPane.history_forward "Permalink to this definition") Replace the current input line with the next item FORWARD from the current history position. insert(*msg*)[[source]](_modules/mu/interface/panes.html#PythonProcessPane.insert)[¶](#mu.interface.panes.PythonProcessPane.insert "Permalink to this definition") Insert text to the text area at the current cursor position. insertFromMimeData(*source*)[[source]](_modules/mu/interface/panes.html#PythonProcessPane.insertFromMimeData)[¶](#mu.interface.panes.PythonProcessPane.insertFromMimeData "Permalink to this definition") Insert mime data by sending it to the REPL keyPressEvent(*data*)[[source]](_modules/mu/interface/panes.html#PythonProcessPane.keyPressEvent)[¶](#mu.interface.panes.PythonProcessPane.keyPressEvent "Permalink to this definition") Called when the user types something in the REPL. on\_process\_halt()[[source]](_modules/mu/interface/panes.html#PythonProcessPane.on_process_halt)[¶](#mu.interface.panes.PythonProcessPane.on_process_halt "Permalink to this definition") Called when the the user has manually halted a running process. Ensures that the remaining data from the halted process’s stdout is handled properly. When the process is halted the user is dropped into the Python prompt and this method ensures the UI is updated in a clean, non-blocking way. parse\_input(*key*, *text*, *modifiers*)[[source]](_modules/mu/interface/panes.html#PythonProcessPane.parse_input)[¶](#mu.interface.panes.PythonProcessPane.parse_input "Permalink to this definition") Correctly encodes user input and sends it to the connected process. The key is a Qt.Key\_Something value, text is the textual representation of the input, and modifiers are the control keys (shift, CTRL, META, etc) also used. parse\_paste(*text*)[[source]](_modules/mu/interface/panes.html#PythonProcessPane.parse_paste)[¶](#mu.interface.panes.PythonProcessPane.parse_paste "Permalink to this definition") Recursively takes characters from text to be parsed as input. We do this so the event loop has time to respond to output from the process to which the characters are sent (for example, when a newline is sent). Yes, this is a quick and dirty hack, but ensures the pasted input is also evaluated in an interactive manner rather than as a single-shot splurge of data. Essentially, it’s simulating someone typing in the characters of the pasted text *really fast* but in such a way that the event loop cycles. read\_from\_stdout()[[source]](_modules/mu/interface/panes.html#PythonProcessPane.read_from_stdout)[¶](#mu.interface.panes.PythonProcessPane.read_from_stdout "Permalink to this definition") Process incoming data from the process’s stdout. replace\_input\_line(*text*)[[source]](_modules/mu/interface/panes.html#PythonProcessPane.replace_input_line)[¶](#mu.interface.panes.PythonProcessPane.replace_input_line "Permalink to this definition") Replace the current input line with the passed in text. set\_font\_size(*new\_size=14*)[[source]](_modules/mu/interface/panes.html#PythonProcessPane.set_font_size)[¶](#mu.interface.panes.PythonProcessPane.set_font_size "Permalink to this definition") Sets the font size for all the textual elements in this pane. set\_start\_of\_current\_line()[[source]](_modules/mu/interface/panes.html#PythonProcessPane.set_start_of_current_line)[¶](#mu.interface.panes.PythonProcessPane.set_start_of_current_line "Permalink to this definition") Set the flag to indicate the start of the current line (used before waiting for input). This flag is used to discard the preceeding text in the text entry field when Mu parses new input from the user (i.e. any text beyond the self.start\_of\_current\_line). set\_zoom(*size*)[[source]](_modules/mu/interface/panes.html#PythonProcessPane.set_zoom)[¶](#mu.interface.panes.PythonProcessPane.set_zoom "Permalink to this definition") Set the current zoom level given the “t-shirt” size. start\_process(*interpreter*, *script\_name*, *working\_directory*, *interactive=True*, *debugger=False*, *command\_args=None*, *envars=None*, *python\_args=None*)[[source]](_modules/mu/interface/panes.html#PythonProcessPane.start_process)[¶](#mu.interface.panes.PythonProcessPane.start_process "Permalink to this definition") Start the child Python process. Will use the referenced interpreter to run the Python script\_name within the context of the working directory. If interactive is True (the default) the Python process will run in interactive mode (dropping the user into the REPL when the script completes). If debugger is True (the default is False) then the script will run within a debug runner session. If there is a list of command\_args (the default is None), then these will be passed as further arguments into the script to be run. If there is a list of environment variables, these will be part of the context of the new child process. If python\_args is given, these are passed as arguments to the Python interpreter used to launch the child process. try\_read\_from\_stdout()[[source]](_modules/mu/interface/panes.html#PythonProcessPane.try_read_from_stdout)[¶](#mu.interface.panes.PythonProcessPane.try_read_from_stdout "Permalink to this definition") Ensure reading from stdout only happens if there is NOT already current attempts to read from stdout. write\_to\_stdin(*data*)[[source]](_modules/mu/interface/panes.html#PythonProcessPane.write_to_stdin)[¶](#mu.interface.panes.PythonProcessPane.write_to_stdin "Permalink to this definition") Writes data from the Qt application to the child process’s stdin. *class* mu.interface.panes.SnekREPLPane(*connection*, *theme='day'*, *parent=None*)[[source]](_modules/mu/interface/panes.html#SnekREPLPane)[¶](#mu.interface.panes.SnekREPLPane "Permalink to this definition") REPL = Read, Evaluate, Print, Loop. This widget represents a REPL client connected to a device running Snek. The device MUST be flashed with Snek for this to work. execute(*commands*)[[source]](_modules/mu/interface/panes.html#SnekREPLPane.execute)[¶](#mu.interface.panes.SnekREPLPane.execute "Permalink to this definition") Execute a series of commands over a period of time (scheduling remaining commands to be run in the next iteration of the event loop). insertFromMimeData(*source*)[[source]](_modules/mu/interface/panes.html#SnekREPLPane.insertFromMimeData)[¶](#mu.interface.panes.SnekREPLPane.insertFromMimeData "Permalink to this definition") Insert mime data by sending it to the REPL keyPressEvent(*data*)[[source]](_modules/mu/interface/panes.html#SnekREPLPane.keyPressEvent)[¶](#mu.interface.panes.SnekREPLPane.keyPressEvent "Permalink to this definition") Called when the user types something in the REPL. Correctly encodes it and sends it to the connected device. process\_bytes(*data*)[[source]](_modules/mu/interface/panes.html#SnekREPLPane.process_bytes)[¶](#mu.interface.panes.SnekREPLPane.process_bytes "Permalink to this definition") Given some incoming bytes of data, work out how to handle / display them in the REPL widget. send\_commands(*commands*)[[source]](_modules/mu/interface/panes.html#SnekREPLPane.send_commands)[¶](#mu.interface.panes.SnekREPLPane.send_commands "Permalink to this definition") Send commands to the REPL via raw mode. set\_devicecursor\_to\_qtcursor()[[source]](_modules/mu/interface/panes.html#SnekREPLPane.set_devicecursor_to_qtcursor)[¶](#mu.interface.panes.SnekREPLPane.set_devicecursor_to_qtcursor "Permalink to this definition") Call this whenever the cursor has been moved by the user, to send the cursor movement to the device. ##### `mu.interface.themes`[¶](#mu-interface-themes "Permalink to this headline") Theme related code so Qt changes for each pre-defined theme. Theme and presentation related code for the Mu editor. Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *class* mu.interface.themes.ContrastTheme[[source]](_modules/mu/interface/themes.html#ContrastTheme)[¶](#mu.interface.themes.ContrastTheme "Permalink to this definition") Defines a Python related theme including the various font colours for syntax highlighting. This is the high contrast theme. *class* mu.interface.themes.DayTheme[[source]](_modules/mu/interface/themes.html#DayTheme)[¶](#mu.interface.themes.DayTheme "Permalink to this definition") Defines a Python related theme including the various font colours for syntax highlighting. This is a light theme. *class* mu.interface.themes.Font(*color='#181818'*, *paper='#FEFEF7'*, *bold=False*, *italic=False*)[[source]](_modules/mu/interface/themes.html#Font)[¶](#mu.interface.themes.Font "Permalink to this definition") Utility class that makes it easy to set font related values within the editor. *classmethod* get\_database()[[source]](_modules/mu/interface/themes.html#Font.get_database)[¶](#mu.interface.themes.Font.get_database "Permalink to this definition") Create a font database and load the MU builtin fonts into it. This is a cached classmethod so the font files aren’t re-loaded every time a font is refereced load(*size=14*)[[source]](_modules/mu/interface/themes.html#Font.load)[¶](#mu.interface.themes.Font.load "Permalink to this definition") Load the font from the font database, using the correct size and style *property* stylename[¶](#mu.interface.themes.Font.stylename "Permalink to this definition") Map the bold and italic boolean flags here to a relevant font style name. *class* mu.interface.themes.NightTheme[[source]](_modules/mu/interface/themes.html#NightTheme)[¶](#mu.interface.themes.NightTheme "Permalink to this definition") Defines a Python related theme including the various font colours for syntax highlighting. This is the dark theme. *class* mu.interface.themes.Theme[[source]](_modules/mu/interface/themes.html#Theme)[¶](#mu.interface.themes.Theme "Permalink to this definition") Defines a font and other theme specific related information. #### `mu.modes`[¶](#mu-modes "Permalink to this headline") Contains the definitions of the various modes Mu into which Mu can be put. All the core functionality is in the `mu.modes.base` module. ##### `mu.modes.base`[¶](#mu-modes-base "Permalink to this headline") Core functionality and base classes for all Mu’s modes. The definitions of API autocomplete and call tips can be found in the `mu.modes.api` namespace. Contains the base classes for Mu editor modes. Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *class* mu.modes.base.BaseMode(*editor*, *view*)[[source]](_modules/mu/modes/base.html#BaseMode)[¶](#mu.modes.base.BaseMode "Permalink to this definition") Represents the common aspects of a mode. actions()[[source]](_modules/mu/modes/base.html#BaseMode.actions)[¶](#mu.modes.base.BaseMode.actions "Permalink to this definition") Return an ordered list of actions provided by this module. An action is a name (also used to identify the icon) , description, and handler. activate()[[source]](_modules/mu/modes/base.html#BaseMode.activate)[¶](#mu.modes.base.BaseMode.activate "Permalink to this definition") Executed when the mode is activated add\_plotter()[[source]](_modules/mu/modes/base.html#BaseMode.add_plotter)[¶](#mu.modes.base.BaseMode.add_plotter "Permalink to this definition") Mode specific implementation of adding and connecting a plotter to incoming streams of data tuples. api()[[source]](_modules/mu/modes/base.html#BaseMode.api)[¶](#mu.modes.base.BaseMode.api "Permalink to this definition") Return a list of API specifications to be used by auto-suggest and call tips. assets\_dir(*asset\_type*)[[source]](_modules/mu/modes/base.html#BaseMode.assets_dir)[¶](#mu.modes.base.BaseMode.assets_dir "Permalink to this definition") Determine (and create) the directory for a set of assets This supports the [Images] and [Sounds] &c. buttons in pygamezero mode and possibly other modes, too. If a tab is current and has an active file, the assets directory is looked for under that path; otherwise the workspace directory is used. If the assets directory does not exist it is created builtins *= None*[¶](#mu.modes.base.BaseMode.builtins "Permalink to this definition") Symbols to assume as builtins when checking code style. deactivate()[[source]](_modules/mu/modes/base.html#BaseMode.deactivate)[¶](#mu.modes.base.BaseMode.deactivate "Permalink to this definition") Executed when the mode is activated device\_changed(*new\_device*)[[source]](_modules/mu/modes/base.html#BaseMode.device_changed)[¶](#mu.modes.base.BaseMode.device_changed "Permalink to this definition") Invoked when the user changes device. ensure\_state()[[source]](_modules/mu/modes/base.html#BaseMode.ensure_state)[¶](#mu.modes.base.BaseMode.ensure_state "Permalink to this definition") Executed when the mode is finished setting up. Used to ensure button / UI state according to current state of settings. on\_data\_flood()[[source]](_modules/mu/modes/base.html#BaseMode.on_data_flood)[¶](#mu.modes.base.BaseMode.on_data_flood "Permalink to this definition") Handle when the plotter is being flooded by data (which usually causes Mu to become unresponsive). In this case, remove the plotter and display a warning dialog to explain what’s happened and how to fix things (usually, put a time.sleep(x) into the code generating the data). open\_file(*path*)[[source]](_modules/mu/modes/base.html#BaseMode.open_file)[¶](#mu.modes.base.BaseMode.open_file "Permalink to this definition") Some files are not plain text and each mode can attempt to decode them. When overridden, should return the text and newline convention for the file. remove\_plotter()[[source]](_modules/mu/modes/base.html#BaseMode.remove_plotter)[¶](#mu.modes.base.BaseMode.remove_plotter "Permalink to this definition") If there’s an active plotter, hide it. Save any data captured while the plotter was active into a directory called ‘data\_capture’ in the workspace directory. The file contains CSV data and is named with a timestamp for easy identification. return\_focus\_to\_current\_tab()[[source]](_modules/mu/modes/base.html#BaseMode.return_focus_to_current_tab)[¶](#mu.modes.base.BaseMode.return_focus_to_current_tab "Permalink to this definition") After, eg, stopping the plotter or closing the REPL return the focus to the currently-active tab is there is one. save\_timeout *= 5*[¶](#mu.modes.base.BaseMode.save_timeout "Permalink to this definition") Number of seconds to wait before saving work. set\_buttons(*\*\*kwargs*)[[source]](_modules/mu/modes/base.html#BaseMode.set_buttons)[¶](#mu.modes.base.BaseMode.set_buttons "Permalink to this definition") Given the names and boolean settings of buttons associated with actions for the current mode, toggles them into the boolean enabled state. stop()[[source]](_modules/mu/modes/base.html#BaseMode.stop)[¶](#mu.modes.base.BaseMode.stop "Permalink to this definition") Called if/when the editor quits when in this mode. Override in child classes to clean up state, stop child processes etc. workspace\_dir()[[source]](_modules/mu/modes/base.html#BaseMode.workspace_dir)[¶](#mu.modes.base.BaseMode.workspace_dir "Permalink to this definition") Return the location on the filesystem for opening and closing files. The default is to use a directory in the users home folder, however in some network systems this in inaccessible. This allows a key in the settings file to be used to set a custom path. write\_plotter\_data\_to\_csv(*csv\_filepath*)[[source]](_modules/mu/modes/base.html#BaseMode.write_plotter_data_to_csv)[¶](#mu.modes.base.BaseMode.write_plotter_data_to_csv "Permalink to this definition") Write any plotter data out to a CSV file when the plotter is closed *class* mu.modes.base.FileManager(*port*)[[source]](_modules/mu/modes/base.html#FileManager)[¶](#mu.modes.base.FileManager "Permalink to this definition") Used to manage filesystem operations on connected MicroPython devices in a manner such that the UI remains responsive. Provides an FTP-ish API. Emits signals on success or failure of different operations. delete(*device\_filename*)[[source]](_modules/mu/modes/base.html#FileManager.delete)[¶](#mu.modes.base.FileManager.delete "Permalink to this definition") Delete the referenced file on the device’s filesystem. Emit the name of the file when complete, or emit a failure signal. get(*device\_filename*, *local\_filename*)[[source]](_modules/mu/modes/base.html#FileManager.get)[¶](#mu.modes.base.FileManager.get "Permalink to this definition") Get the referenced device filename and save it to the local filename. Emit the name of the filename when complete or emit a failure signal. ls()[[source]](_modules/mu/modes/base.html#FileManager.ls)[¶](#mu.modes.base.FileManager.ls "Permalink to this definition") List the files on the micro:bit. Emit the resulting tuple of filenames or emit a failure signal. on\_start()[[source]](_modules/mu/modes/base.html#FileManager.on_start)[¶](#mu.modes.base.FileManager.on_start "Permalink to this definition") Run when the thread containing this object’s instance is started so it can emit the list of files found on the connected device. put(*local\_filename*, *target=None*)[[source]](_modules/mu/modes/base.html#FileManager.put)[¶](#mu.modes.base.FileManager.put "Permalink to this definition") Put the referenced local file onto the filesystem on the micro:bit. Emit the name of the file on the micro:bit when complete, or emit a failure signal. *class* mu.modes.base.MicroPythonMode(*editor*, *view*)[[source]](_modules/mu/modes/base.html#MicroPythonMode)[¶](#mu.modes.base.MicroPythonMode "Permalink to this definition") Includes functionality that works with a USB serial based REPL. activate()[[source]](_modules/mu/modes/base.html#MicroPythonMode.activate)[¶](#mu.modes.base.MicroPythonMode.activate "Permalink to this definition") Invoked whenever the mode is activated. add\_plotter()[[source]](_modules/mu/modes/base.html#MicroPythonMode.add_plotter)[¶](#mu.modes.base.MicroPythonMode.add_plotter "Permalink to this definition") Check if REPL exists, and if so, enable the plotter pane! add\_repl()[[source]](_modules/mu/modes/base.html#MicroPythonMode.add_repl)[¶](#mu.modes.base.MicroPythonMode.add_repl "Permalink to this definition") Detect a connected MicroPython based device and, if found, connect to the REPL and display it to the user. compatible\_board(*port*)[[source]](_modules/mu/modes/base.html#MicroPythonMode.compatible_board)[¶](#mu.modes.base.MicroPythonMode.compatible_board "Permalink to this definition") A compatible board must match on vendor ID, but only needs to match on product ID or manufacturer ID, if they are supplied in the list of valid boards (aren’t None). deactivate()[[source]](_modules/mu/modes/base.html#MicroPythonMode.deactivate)[¶](#mu.modes.base.MicroPythonMode.deactivate "Permalink to this definition") Invoked whenever the mode is deactivated. device\_changed(*new\_device*)[[source]](_modules/mu/modes/base.html#MicroPythonMode.device_changed)[¶](#mu.modes.base.MicroPythonMode.device_changed "Permalink to this definition") Invoked when the user changes device. find\_devices(*with\_logging=True*)[[source]](_modules/mu/modes/base.html#MicroPythonMode.find_devices)[¶](#mu.modes.base.MicroPythonMode.find_devices "Permalink to this definition") Returns the port and serial number, and name for the first MicroPython-ish device found connected to the host computer. If no device is found, returns the tuple (None, None, None). on\_data\_flood()[[source]](_modules/mu/modes/base.html#MicroPythonMode.on_data_flood)[¶](#mu.modes.base.MicroPythonMode.on_data_flood "Permalink to this definition") Ensure the REPL is stopped if there is data flooding of the plotter. remove\_plotter()[[source]](_modules/mu/modes/base.html#MicroPythonMode.remove_plotter)[¶](#mu.modes.base.MicroPythonMode.remove_plotter "Permalink to this definition") Remove plotter pane. Disconnects serial connection to device. remove\_repl()[[source]](_modules/mu/modes/base.html#MicroPythonMode.remove_repl)[¶](#mu.modes.base.MicroPythonMode.remove_repl "Permalink to this definition") If there’s an active REPL, disconnect and hide it. toggle\_plotter(*event*)[[source]](_modules/mu/modes/base.html#MicroPythonMode.toggle_plotter)[¶](#mu.modes.base.MicroPythonMode.toggle_plotter "Permalink to this definition") Toggles the plotter on and off. toggle\_repl(*event*)[[source]](_modules/mu/modes/base.html#MicroPythonMode.toggle_repl)[¶](#mu.modes.base.MicroPythonMode.toggle_repl "Permalink to this definition") Toggles the REPL on and off. *class* mu.modes.base.REPLConnection(*port*, *baudrate=115200*)[[source]](_modules/mu/modes/base.html#REPLConnection)[¶](#mu.modes.base.REPLConnection "Permalink to this definition") close()[[source]](_modules/mu/modes/base.html#REPLConnection.close)[¶](#mu.modes.base.REPLConnection.close "Permalink to this definition") Close and clean up the currently open serial link. execute(*commands*)[[source]](_modules/mu/modes/base.html#REPLConnection.execute)[¶](#mu.modes.base.REPLConnection.execute "Permalink to this definition") Execute a series of commands over a period of time (scheduling remaining commands to be run in the next iteration of the event loop). open()[[source]](_modules/mu/modes/base.html#REPLConnection.open)[¶](#mu.modes.base.REPLConnection.open "Permalink to this definition") Open the serial link send\_commands(*commands*)[[source]](_modules/mu/modes/base.html#REPLConnection.send_commands)[¶](#mu.modes.base.REPLConnection.send_commands "Permalink to this definition") Send commands to the REPL via raw mode. mu.modes.base.get\_default\_workspace()[[source]](_modules/mu/modes/base.html#get_default_workspace)[¶](#mu.modes.base.get_default_workspace "Permalink to this definition") Return the location on the filesystem for opening and closing files. The default is to use a directory in the users home folder, however in some network systems this in inaccessible. This allows a key in the settings file to be used to set a custom path. ##### `mu.modes.circuitpython`[¶](#mu-modes-circuitpython "Permalink to this headline") CircuitPython mode for Adafruit boards (and others). A mode for working with Circuit Python boards. Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *class* mu.modes.circuitpython.CircuitPythonMode(*editor*, *view*)[[source]](_modules/mu/modes/circuitpython.html#CircuitPythonMode)[¶](#mu.modes.circuitpython.CircuitPythonMode "Permalink to this definition") Represents the functionality required by the CircuitPython mode. actions()[[source]](_modules/mu/modes/circuitpython.html#CircuitPythonMode.actions)[¶](#mu.modes.circuitpython.CircuitPythonMode.actions "Permalink to this definition") Return an ordered list of actions provided by this module. An action is a name (also used to identify the icon) , description, and handler. api()[[source]](_modules/mu/modes/circuitpython.html#CircuitPythonMode.api)[¶](#mu.modes.circuitpython.CircuitPythonMode.api "Permalink to this definition") Return a list of API specifications to be used by auto-suggest and call tips. compatible\_board(*port*)[[source]](_modules/mu/modes/circuitpython.html#CircuitPythonMode.compatible_board)[¶](#mu.modes.circuitpython.CircuitPythonMode.compatible_board "Permalink to this definition") Use adafruit\_board\_toolkit to find out whether a board is running CircuitPython. The toolkit sees if the CDC Interface name is appropriate. connected *= True*[¶](#mu.modes.circuitpython.CircuitPythonMode.connected "Permalink to this definition") is the board connected. force\_interrupt *= False*[¶](#mu.modes.circuitpython.CircuitPythonMode.force_interrupt "Permalink to this definition") NO keyboard interrupt on serial connection. save\_timeout *= 0*[¶](#mu.modes.circuitpython.CircuitPythonMode.save_timeout "Permalink to this definition") No auto-save on CP boards. Will restart. workspace\_dir()[[source]](_modules/mu/modes/circuitpython.html#CircuitPythonMode.workspace_dir)[¶](#mu.modes.circuitpython.CircuitPythonMode.workspace_dir "Permalink to this definition") Return the default location on the filesystem for opening and closing files. ##### `mu.modes.debugger`[¶](#mu-modes-debugger "Permalink to this headline") The Python 3 debugger mode. The mode Mu is is when it’s debugging a Python 3 script. Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *class* mu.modes.debugger.DebugMode(*editor*, *view*)[[source]](_modules/mu/modes/debugger.html#DebugMode)[¶](#mu.modes.debugger.DebugMode "Permalink to this definition") Represents the functionality required by the Python 3 visual debugger. actions()[[source]](_modules/mu/modes/debugger.html#DebugMode.actions)[¶](#mu.modes.debugger.DebugMode.actions "Permalink to this definition") Return an ordered list of actions provided by this module. An action is a name (also used to identify the icon) , description, and handler. api()[[source]](_modules/mu/modes/debugger.html#DebugMode.api)[¶](#mu.modes.debugger.DebugMode.api "Permalink to this definition") Return a list of API specifications to be used by auto-suggest and call tips. button\_continue(*event*)[[source]](_modules/mu/modes/debugger.html#DebugMode.button_continue)[¶](#mu.modes.debugger.DebugMode.button_continue "Permalink to this definition") Button clicked to continue running the script. button\_step\_in(*event*)[[source]](_modules/mu/modes/debugger.html#DebugMode.button_step_in)[¶](#mu.modes.debugger.DebugMode.button_step_in "Permalink to this definition") Button clicked to step into the current block of code. button\_step\_out(*event*)[[source]](_modules/mu/modes/debugger.html#DebugMode.button_step_out)[¶](#mu.modes.debugger.DebugMode.button_step_out "Permalink to this definition") Button clicked to step out of the current block of code. button\_step\_over(*event*)[[source]](_modules/mu/modes/debugger.html#DebugMode.button_step_over)[¶](#mu.modes.debugger.DebugMode.button_step_over "Permalink to this definition") Button clicked to step over the current line of code. button\_stop(*event*)[[source]](_modules/mu/modes/debugger.html#DebugMode.button_stop)[¶](#mu.modes.debugger.DebugMode.button_stop "Permalink to this definition") Button clicked to stop the current script and return to Python3 mode. debug\_on\_bootstrap()[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_bootstrap)[¶](#mu.modes.debugger.DebugMode.debug_on_bootstrap "Permalink to this definition") Once the debugger is bootstrapped ensure all the current breakpoints are set. Do not set breakpoints (and remove the marker) if: * The marker is not visible (the line is -1) * The marker is not a duplicate of an existing line. * The line with the marker is not a valid breakpoint line. debug\_on\_breakpoint\_clear(*breakpoint*)[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_breakpoint_clear)[¶](#mu.modes.debugger.DebugMode.debug_on_breakpoint_clear "Permalink to this definition") Handle the clearing of the referenced breakpoint. Currently an unimplemented extra feature. debug\_on\_breakpoint\_disable(*breakpoint*)[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_breakpoint_disable)[¶](#mu.modes.debugger.DebugMode.debug_on_breakpoint_disable "Permalink to this definition") Handle when a breakpoint is disabled. debug\_on\_breakpoint\_enable(*breakpoint*)[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_breakpoint_enable)[¶](#mu.modes.debugger.DebugMode.debug_on_breakpoint_enable "Permalink to this definition") Handle when a breakpoint is enabled. debug\_on\_breakpoint\_ignore(*breakpoint*, *count*)[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_breakpoint_ignore)[¶](#mu.modes.debugger.DebugMode.debug_on_breakpoint_ignore "Permalink to this definition") Handle when a breakpoint is to be ignored by the debugger. Currently an unimplemented extra feature. debug\_on\_call(*args*)[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_call)[¶](#mu.modes.debugger.DebugMode.debug_on_call "Permalink to this definition") Handle when the debugger has called a function with the referenced args. Make sure the debugger steps into the function. debug\_on\_error(*message*)[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_error)[¶](#mu.modes.debugger.DebugMode.debug_on_error "Permalink to this definition") Handle when the debugger sends an error message. debug\_on\_exception(*name*, *value*)[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_exception)[¶](#mu.modes.debugger.DebugMode.debug_on_exception "Permalink to this definition") Handle when the debugger encounters a named exception with an associated value. Clear the highlighted line and allow the script to run until the end so the error message is printed to stdout. debug\_on\_fail(*message*)[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_fail)[¶](#mu.modes.debugger.DebugMode.debug_on_fail "Permalink to this definition") Called when, for any reason, the debug client was unable to connect to the debug runner. On a Raspberry Pi this is usually because it’s an underpowereed machine and it takes time to start the debug runner process. (However, the debug client waits for 10 seconds for the runner to start.) debug\_on\_finished()[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_finished)[¶](#mu.modes.debugger.DebugMode.debug_on_finished "Permalink to this definition") Called when the runner has completed running the script to be debugged. debug\_on\_info(*message*)[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_info)[¶](#mu.modes.debugger.DebugMode.debug_on_info "Permalink to this definition") Handle when the debugger sends an informative textual message. debug\_on\_line(*filename*, *line*)[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_line)[¶](#mu.modes.debugger.DebugMode.debug_on_line "Permalink to this definition") Handle when the debugger has moved to the referenced line in the file. debug\_on\_postmortem(*args*, *kwargs*)[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_postmortem)[¶](#mu.modes.debugger.DebugMode.debug_on_postmortem "Permalink to this definition") Handle when something catastrophic happens to the debugger. debug\_on\_restart()[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_restart)[¶](#mu.modes.debugger.DebugMode.debug_on_restart "Permalink to this definition") Handle when the debugger restarts. Currenty an unimplemented extra feature. debug\_on\_return(*return\_value*)[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_return)[¶](#mu.modes.debugger.DebugMode.debug_on_return "Permalink to this definition") Handle when the debugger returns from a function call with the referenced return value. Make sure the debugger steps out of the function to the caller. debug\_on\_stack(*stack*)[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_stack)[¶](#mu.modes.debugger.DebugMode.debug_on_stack "Permalink to this definition") Handle when the debugger sends an updated stack. debug\_on\_warning(*message*)[[source]](_modules/mu/modes/debugger.html#DebugMode.debug_on_warning)[¶](#mu.modes.debugger.DebugMode.debug_on_warning "Permalink to this definition") Handle when the debugger sends a warning message. disable\_buttons()[[source]](_modules/mu/modes/debugger.html#DebugMode.disable_buttons)[¶](#mu.modes.debugger.DebugMode.disable_buttons "Permalink to this definition") Disable all debug control buttons except ‘stop’. disable\_buttons\_later(*\**, *milliseconds=100*)[[source]](_modules/mu/modes/debugger.html#DebugMode.disable_buttons_later)[¶](#mu.modes.debugger.DebugMode.disable_buttons_later "Permalink to this definition") Set a timer to disable all debug control buttons except ‘stop’. enable\_buttons()[[source]](_modules/mu/modes/debugger.html#DebugMode.enable_buttons)[¶](#mu.modes.debugger.DebugMode.enable_buttons "Permalink to this definition") Enable all debug control buttons except ‘stop’: if the timer started in disable\_buttons\_later is active, stops it and does nothing else. finished()[[source]](_modules/mu/modes/debugger.html#DebugMode.finished)[¶](#mu.modes.debugger.DebugMode.finished "Permalink to this definition") Called when the debugged Python process is finished. start()[[source]](_modules/mu/modes/debugger.html#DebugMode.start)[¶](#mu.modes.debugger.DebugMode.start "Permalink to this definition") Start debugging the current script. stop()[[source]](_modules/mu/modes/debugger.html#DebugMode.stop)[¶](#mu.modes.debugger.DebugMode.stop "Permalink to this definition") Stop the debug runner and reset the UI. toggle\_breakpoint(*line*, *tab*)[[source]](_modules/mu/modes/debugger.html#DebugMode.toggle_breakpoint)[¶](#mu.modes.debugger.DebugMode.toggle_breakpoint "Permalink to this definition") Toggle a breakpoint in the debugger. ##### `mu.modes.microbit`[¶](#mu-modes-microbit "Permalink to this headline") The original BBC micro:bit mode. The mode for working with the BBC micro:bit. Contains most of the origial functionality from Mu when it was only a micro:bit related editor. Copyright (c) 2015-2021 Nicholas H.Tollervey and others (see the AUTHORS file). This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *class* mu.modes.microbit.DeviceFlasher(*path\_to\_microbit*, *python\_script=None*, *path\_to\_runtime=None*)[[source]](_modules/mu/modes/microbit.html#DeviceFlasher)[¶](#mu.modes.microbit.DeviceFlasher "Permalink to this definition") Used to flash the micro:bit in a non-blocking manner. run()[[source]](_modules/mu/modes/microbit.html#DeviceFlasher.run)[¶](#mu.modes.microbit.DeviceFlasher.run "Permalink to this definition") Flash the device. If we are sending a custom hex we need to manually read it and copy it into the micro:bit drive otherwise use uFlash. *class* mu.modes.microbit.MicrobitMode(*editor*, *view*)[[source]](_modules/mu/modes/microbit.html#MicrobitMode)[¶](#mu.modes.microbit.MicrobitMode "Permalink to this definition") Represents the functionality required by the micro:bit mode. actions()[[source]](_modules/mu/modes/microbit.html#MicrobitMode.actions)[¶](#mu.modes.microbit.MicrobitMode.actions "Permalink to this definition") Return an ordered list of actions provided by this module. An action is a name (also used to identify the icon) , description, and handler. add\_fs()[[source]](_modules/mu/modes/microbit.html#MicrobitMode.add_fs)[¶](#mu.modes.microbit.MicrobitMode.add_fs "Permalink to this definition") Add the file system navigator to the UI. api()[[source]](_modules/mu/modes/microbit.html#MicrobitMode.api)[¶](#mu.modes.microbit.MicrobitMode.api "Permalink to this definition") Return a list of API specifications to be used by auto-suggest and call tips. copy\_main(*script*)[[source]](_modules/mu/modes/microbit.html#MicrobitMode.copy_main)[¶](#mu.modes.microbit.MicrobitMode.copy_main "Permalink to this definition") If script argument contains any code, copy it onto the connected micro:bit as main.py, then restart the board (CTRL-D). deactivate()[[source]](_modules/mu/modes/microbit.html#MicrobitMode.deactivate)[¶](#mu.modes.microbit.MicrobitMode.deactivate "Permalink to this definition") Invoked whenever the mode is deactivated. device\_changed(*new\_device*)[[source]](_modules/mu/modes/microbit.html#MicrobitMode.device_changed)[¶](#mu.modes.microbit.MicrobitMode.device_changed "Permalink to this definition") Invoked when the user changes device. find\_microbit()[[source]](_modules/mu/modes/microbit.html#MicrobitMode.find_microbit)[¶](#mu.modes.microbit.MicrobitMode.find_microbit "Permalink to this definition") Finds a micro:bit path, serial port and board ID. flash()[[source]](_modules/mu/modes/microbit.html#MicrobitMode.flash)[¶](#mu.modes.microbit.MicrobitMode.flash "Permalink to this definition") Performs multiple checks to see if it needs to flash MicroPython into the micro:bit and then sends via serial the Python script from the currently active tab. In some error cases it attaches the code directly into the MicroPython hex and flashes that (this method is much slower and deprecated). WARNING: This method is getting more complex due to several edge cases. Ergo, it’s a target for refactoring. flash\_and\_send(*script*, *microbit\_path*, *rt\_path=None*)[[source]](_modules/mu/modes/microbit.html#MicrobitMode.flash_and_send)[¶](#mu.modes.microbit.MicrobitMode.flash_and_send "Permalink to this definition") Start the MicroPython hex flashing process in a new thread with a custom hex file, or the one provided by uFlash. Then send the user script via serial. flash\_attached(*script*, *microbit\_path*)[[source]](_modules/mu/modes/microbit.html#MicrobitMode.flash_attached)[¶](#mu.modes.microbit.MicrobitMode.flash_attached "Permalink to this definition") Start the MicroPython hex flashing process in a new thread with the hex file provided by uFlash and the script added to the filesystem in the hex. flash\_failed(*error*)[[source]](_modules/mu/modes/microbit.html#MicrobitMode.flash_failed)[¶](#mu.modes.microbit.MicrobitMode.flash_failed "Permalink to this definition") Called when the thread used to flash the micro:bit encounters a problem. flash\_finished()[[source]](_modules/mu/modes/microbit.html#MicrobitMode.flash_finished)[¶](#mu.modes.microbit.MicrobitMode.flash_finished "Permalink to this definition") Called when the thread used to flash the micro:bit has finished. fs *= None*[¶](#mu.modes.microbit.MicrobitMode.fs "Permalink to this definition") Reference to filesystem navigator. get\_device\_micropython\_version()[[source]](_modules/mu/modes/microbit.html#MicrobitMode.get_device_micropython_version)[¶](#mu.modes.microbit.MicrobitMode.get_device_micropython_version "Permalink to this definition") Retrieves the MicroPython version from a micro:bit board. Errors bubble up, so caller must catch them. minify\_if\_needed(*python\_script\_bytes*)[[source]](_modules/mu/modes/microbit.html#MicrobitMode.minify_if_needed)[¶](#mu.modes.microbit.MicrobitMode.minify_if_needed "Permalink to this definition") Minify the script if is too large to fit in flash via uFlash appended method. Raises exceptions if minification fails or cannot be performed. on\_data\_flood()[[source]](_modules/mu/modes/microbit.html#MicrobitMode.on_data_flood)[¶](#mu.modes.microbit.MicrobitMode.on_data_flood "Permalink to this definition") Ensure the Files button is active before the REPL is killed off when a data flood of the plotter is detected. open\_file(*path*)[[source]](_modules/mu/modes/microbit.html#MicrobitMode.open_file)[¶](#mu.modes.microbit.MicrobitMode.open_file "Permalink to this definition") Tries to open a MicroPython hex file with an embedded Python script. Returns the embedded Python script and newline convention. remove\_fs()[[source]](_modules/mu/modes/microbit.html#MicrobitMode.remove_fs)[¶](#mu.modes.microbit.MicrobitMode.remove_fs "Permalink to this definition") Remove the file system navigator from the UI. toggle\_files(*event*)[[source]](_modules/mu/modes/microbit.html#MicrobitMode.toggle_files)[¶](#mu.modes.microbit.MicrobitMode.toggle_files "Permalink to this definition") Check for the existence of the REPL or plotter before toggling the file system navigator for the micro:bit on or off. toggle\_plotter(*event*)[[source]](_modules/mu/modes/microbit.html#MicrobitMode.toggle_plotter)[¶](#mu.modes.microbit.MicrobitMode.toggle_plotter "Permalink to this definition") Check for the existence of the file pane before toggling plotter. toggle\_repl(*event*)[[source]](_modules/mu/modes/microbit.html#MicrobitMode.toggle_repl)[¶](#mu.modes.microbit.MicrobitMode.toggle_repl "Permalink to this definition") Check for the existence of the file pane before toggling REPL. ##### `mu.modes.pygamezero`[¶](#mu-modes-pygamezero "Permalink to this headline") The Pygame Zero / pygame mode. The Pygame Zero mode for the Mu editor. Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *class* mu.modes.pygamezero.PyGameZeroMode(*editor*, *view*)[[source]](_modules/mu/modes/pygamezero.html#PyGameZeroMode)[¶](#mu.modes.pygamezero.PyGameZeroMode "Permalink to this definition") Represents the functionality required by the PyGameZero mode. actions()[[source]](_modules/mu/modes/pygamezero.html#PyGameZeroMode.actions)[¶](#mu.modes.pygamezero.PyGameZeroMode.actions "Permalink to this definition") Return an ordered list of actions provided by this module. An action is a name (also used to identify the icon) , description, and handler. api()[[source]](_modules/mu/modes/pygamezero.html#PyGameZeroMode.api)[¶](#mu.modes.pygamezero.PyGameZeroMode.api "Permalink to this definition") Return a list of API specifications to be used by auto-suggest and call tips. play\_toggle(*event*)[[source]](_modules/mu/modes/pygamezero.html#PyGameZeroMode.play_toggle)[¶](#mu.modes.pygamezero.PyGameZeroMode.play_toggle "Permalink to this definition") Handles the toggling of the play button to start/stop a script. run\_game()[[source]](_modules/mu/modes/pygamezero.html#PyGameZeroMode.run_game)[¶](#mu.modes.pygamezero.PyGameZeroMode.run_game "Permalink to this definition") Run the current game. show\_fonts(*event*)[[source]](_modules/mu/modes/pygamezero.html#PyGameZeroMode.show_fonts)[¶](#mu.modes.pygamezero.PyGameZeroMode.show_fonts "Permalink to this definition") Open the directory containing the font assets used by Pygame Zero. This should open the host OS’s file system explorer so users can drag new files into the opened folder. show\_images(*event*)[[source]](_modules/mu/modes/pygamezero.html#PyGameZeroMode.show_images)[¶](#mu.modes.pygamezero.PyGameZeroMode.show_images "Permalink to this definition") Open the directory containing the image assets used by Pygame Zero. This should open the host OS’s file system explorer so users can drag new files into the opened folder. show\_music(*event*)[[source]](_modules/mu/modes/pygamezero.html#PyGameZeroMode.show_music)[¶](#mu.modes.pygamezero.PyGameZeroMode.show_music "Permalink to this definition") Open the directory containing the music assets used by Pygame Zero. This should open the host OS’s file system explorer so users can drag new files into the opened folder. show\_sounds(*event*)[[source]](_modules/mu/modes/pygamezero.html#PyGameZeroMode.show_sounds)[¶](#mu.modes.pygamezero.PyGameZeroMode.show_sounds "Permalink to this definition") Open the directory containing the sound assets used by Pygame Zero. This should open the host OS’s file system explorer so users can drag new files into the opened folder. stop\_game()[[source]](_modules/mu/modes/pygamezero.html#PyGameZeroMode.stop_game)[¶](#mu.modes.pygamezero.PyGameZeroMode.stop_game "Permalink to this definition") Stop the currently running game. ##### `mu.modes.python3`[¶](#mu-modes-python3 "Permalink to this headline") The Python 3 editing mode. The Python3 mode for the Mu editor. Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. *class* mu.modes.python3.KernelRunner(*kernel\_name*, *cwd*, *envars*)[[source]](_modules/mu/modes/python3.html#KernelRunner)[¶](#mu.modes.python3.KernelRunner "Permalink to this definition") Used to control the iPython kernel in a non-blocking manner so the UI remains responsive. start\_kernel()[[source]](_modules/mu/modes/python3.html#KernelRunner.start_kernel)[¶](#mu.modes.python3.KernelRunner.start_kernel "Permalink to this definition") Create the expected context, start the kernel, obtain a client and emit a signal when both are started. stop\_kernel()[[source]](_modules/mu/modes/python3.html#KernelRunner.stop_kernel)[¶](#mu.modes.python3.KernelRunner.stop_kernel "Permalink to this definition") Clean up the context, stop the client connections to the kernel, affect an immediate shutdown of the kernel and emit a “finished” signal. *class* mu.modes.python3.MuKernelManager(*\*args*, *\*\*kwargs*)[[source]](_modules/mu/modes/python3.html#MuKernelManager)[¶](#mu.modes.python3.MuKernelManager "Permalink to this definition") start\_kernel(*\*\*kw*)[[source]](_modules/mu/modes/python3.html#MuKernelManager.start_kernel)[¶](#mu.modes.python3.MuKernelManager.start_kernel "Permalink to this definition") Starts a kernel on this host in a separate process. Subclassed to allow checking that the kernel uses the same Python as Mu itself. *class* mu.modes.python3.PythonMode(*editor*, *view*)[[source]](_modules/mu/modes/python3.html#PythonMode)[¶](#mu.modes.python3.PythonMode "Permalink to this definition") Represents the functionality required by the Python 3 mode. actions()[[source]](_modules/mu/modes/python3.html#PythonMode.actions)[¶](#mu.modes.python3.PythonMode.actions "Permalink to this definition") Return an ordered list of actions provided by this module. An action is a name (also used to identify the icon) , description, and handler. add\_plotter()[[source]](_modules/mu/modes/python3.html#PythonMode.add_plotter)[¶](#mu.modes.python3.PythonMode.add_plotter "Permalink to this definition") Add a plotter pane. add\_repl()[[source]](_modules/mu/modes/python3.html#PythonMode.add_repl)[¶](#mu.modes.python3.PythonMode.add_repl "Permalink to this definition") Create a new Jupyter REPL session in a non-blocking way. api()[[source]](_modules/mu/modes/python3.html#PythonMode.api)[¶](#mu.modes.python3.PythonMode.api "Permalink to this definition") Return a list of API specifications to be used by auto-suggest and call tips. debug(*event*)[[source]](_modules/mu/modes/python3.html#PythonMode.debug)[¶](#mu.modes.python3.PythonMode.debug "Permalink to this definition") Debug the script using the debug mode. on\_data\_flood()[[source]](_modules/mu/modes/python3.html#PythonMode.on_data_flood)[¶](#mu.modes.python3.PythonMode.on_data_flood "Permalink to this definition") Ensure the process (REPL or runner) causing the data flood is stopped *before* the base on\_data\_flood is called to turn off the plotter and tell the user what to fix. on\_kernel\_start(*kernel\_manager*, *kernel\_client*)[[source]](_modules/mu/modes/python3.html#PythonMode.on_kernel_start)[¶](#mu.modes.python3.PythonMode.on_kernel_start "Permalink to this definition") Handles UI update when the kernel runner has started the iPython kernel. on\_kernel\_stop()[[source]](_modules/mu/modes/python3.html#PythonMode.on_kernel_stop)[¶](#mu.modes.python3.PythonMode.on_kernel_stop "Permalink to this definition") Handles UI updates for when the kernel runner has shut down the running iPython kernel. remove\_plotter()[[source]](_modules/mu/modes/python3.html#PythonMode.remove_plotter)[¶](#mu.modes.python3.PythonMode.remove_plotter "Permalink to this definition") Remove the plotter pane, dump data and clean things up. remove\_repl()[[source]](_modules/mu/modes/python3.html#PythonMode.remove_repl)[¶](#mu.modes.python3.PythonMode.remove_repl "Permalink to this definition") Remove the Jupyter REPL session. run\_script()[[source]](_modules/mu/modes/python3.html#PythonMode.run_script)[¶](#mu.modes.python3.PythonMode.run_script "Permalink to this definition") Run the current script. run\_toggle(*event*)[[source]](_modules/mu/modes/python3.html#PythonMode.run_toggle)[¶](#mu.modes.python3.PythonMode.run_toggle "Permalink to this definition") Handles the toggling of the run button to start/stop a script. stop\_script()[[source]](_modules/mu/modes/python3.html#PythonMode.stop_script)[¶](#mu.modes.python3.PythonMode.stop_script "Permalink to this definition") Stop the currently running script. toggle\_plotter()[[source]](_modules/mu/modes/python3.html#PythonMode.toggle_plotter)[¶](#mu.modes.python3.PythonMode.toggle_plotter "Permalink to this definition") Toggles the plotter on and off. toggle\_repl(*event*)[[source]](_modules/mu/modes/python3.html#PythonMode.toggle_repl)[¶](#mu.modes.python3.PythonMode.toggle_repl "Permalink to this definition") Toggles the REPL on and off #### `mu.resources`[¶](#mu-resources "Permalink to this headline") Contains utility functions for working with binary assets used by Mu (mainly images). Copyright (c) 2015-2017 Nicholas H.Tollervey and others (see the AUTHORS file). Based upon work done for Puppy IDE by Dan Pope, Nicholas Tollervey and Damien George. This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with this program. If not, see <<http://www.gnu.org/licenses/>>. mu.resources.load\_font\_data(*name*)[[source]](_modules/mu/resources.html#load_font_data)[¶](#mu.resources.load_font_data "Permalink to this definition") Load the (binary) content of a font as bytes mu.resources.load\_icon(*name*)[[source]](_modules/mu/resources.html#load_icon)[¶](#mu.resources.load_icon "Permalink to this definition") Load an icon from the resources directory. mu.resources.load\_movie(*name*)[[source]](_modules/mu/resources.html#load_movie)[¶](#mu.resources.load_movie "Permalink to this definition") Load an animated GIF from the resources directory. mu.resources.load\_pixmap(*name*, *size=None*)[[source]](_modules/mu/resources.html#load_pixmap)[¶](#mu.resources.load_pixmap "Permalink to this definition") Load a pixmap from the resources directory. mu.resources.load\_stylesheet(*name*)[[source]](_modules/mu/resources.html#load_stylesheet)[¶](#mu.resources.load_stylesheet "Permalink to this definition") Load a CSS stylesheet from the resources directory. mu.resources.path(*name*, *resource\_dir='images/'*, *ext=''*)[[source]](_modules/mu/resources.html#path)[¶](#mu.resources.path "Permalink to this definition") Return the filename for the referenced image. ### Design Decisions[¶](#design-decisions "Permalink to this headline") The following documents concern the decision making aspects behind various aspects of Mu. This is a recent practice (started by Tim Golden) so these documents do not cover many aspects of Mu. However, moving forward newer technical decisions will be documented in this way. #### Reading and writing code files[¶](#reading-and-writing-code-files "Permalink to this headline") ##### Decision[¶](#decision "Permalink to this headline") By default Mu will save files encoded as UTF-8 without a PEP 263 encoding cookie. However, if the file as loaded started with an encoding cookie, then the file will be saved again with that encoding. When reading files, Mu will detect UTF8/16 BOMs and encoding cookies. In their absence, UTF-8 will be attempted. If that fails, the OS default will be used (ie locale.getpreferredencoding()). If the file cannot be decoded according to these rules, refuse to guess. Instead, produce an informative error popup. ##### Background[¶](#background "Permalink to this headline") Originally Mu used the built-in open() function for reading and writing its files without specifying an encoding. In that situation Python would request the preferred encoding for the locale and use that. If the user then used a character in their code which had no mapping in that encoding, the save/autosave functionality would raise an uncaught exception and the user would lose their code. ##### Discussion and Implementation[¶](#discussion-and-implementation "Permalink to this headline") It was initially suggested that we simply read/write everything as UTF-8 which can encode the entire universe of Unicode codepoints. However, files which had previously been saved by Mu under a different encoding might produce mojibake or simply raise UnicodeDecodeError. To overcome the difficulty of using UTF-8 going forwards without losing backwards compatibility, the compromise was adopted of *writing* UTF-8 with an encoding cookie, while *reading* according to the rules above. It will still possible for a file to fail decoding on the way in (eg because the locale-default encoding is used, but the file is encoded otherwise). In that situation we might have attempted to reload using, eg, latin-1 which can decode every byte to *something*. But the result would have been mojibake and – crucially – the autosave mechanism would have kicked in 30 seconds later, overwriting the user’s original file for good. Instead it was decided to offer an informative message box which could explain the situation in enough terms to offer the user a way forward without risking the integrity of their code. UPDATE: After initial implementation of the encoding cookie, it was thought that it was too arcane for beginner coders. It was decided then to save as UTF-8 by default, although without a cookie. But if a file already has an encoding cookie, that should be preserved and the encoding honoured. ###### Implemented via:[¶](#implemented-via "Permalink to this headline") * <https://github.com/mu-editor/mu/pull/390> * <https://github.com/mu-editor/mu/pull/399> * <https://github.com/mu-editor/mu/pull/418> ###### Discussion in:[¶](#discussion-in "Permalink to this headline") (Initial) * <https://github.com/mu-editor/mu/issues/370> * <https://github.com/mu-editor/mu/pull/364> * <https://github.com/mu-editor/mu/pull/371> (Later) * <https://github.com/mu-editor/mu/issues/402> * <https://github.com/mu-editor/mu/issues/696> #### Line-endings[¶](#line-endings "Permalink to this headline") ##### Decision[¶](#decision "Permalink to this headline") Use n internally in Mu. Detect the majority line-ending when loading a file and store it on the tab object within the editor. Then use that newline convention when saving. By default, eg for a new / empty file, use the platform line-ending. ##### Background[¶](#background "Permalink to this headline") Mu is designed to run on any platform which supports Python / Qt. This includes Windows and any \*nix variant, including OS/X. Windows traditionally uses rn (ASCII 13 + ASCII 10) for line-endings while \*nix usually recognises a single n (ASCII 10). Although many editors now detect and adapt to either convention, it’s common enough for beginners to use, eg, Windows notepad which only honours and only generates the rn convention. When reading / writing files, Python offers several forms of line-ending manipulation via the newline= parameter in the built-in open() function. Mu originally used Universal newlines (newline=None; the default), but then switched to retaining newlines (newline=””) in PR #133 The effect of this last change is to retain whatever convention or mix of conventions is present in the source file. In effect it is overriding any newline manipulation to present to the editor control the characters originally present in the file. When the file is saved, the same characters are written out. However this creates a quandary when programatically manipulating the editor text: do we use the most widespread n as a line-ending; or do we use the platform convention os.linesep; or do we use the convention used in the file itself, which may or may not follow the platform convention? ##### Discussion and Implementation[¶](#discussion-and-implementation "Permalink to this headline") My proposal here is that Mu operate its own line-ending manipulation. When reading the file, note the majority line-ending convention but convert wholly to n. When writing the file, use the convention noted on the way in. This is essentially the same as we would do when reading encoded Unicode from a file. This way the line-endings are honoured so that, eg, a file can be read/written in Notepad without problems. And the Mu code can be sure of using n as a line-ending convention when manipulating the text. In terms of the current implementation, the convention from the incoming file could presumably be stored on the tab object. ###### Implemented via:[¶](#implemented-via "Permalink to this headline") * <https://github.com/mu-editor/mu/pull/390> * <https://github.com/mu-editor/mu/pull/399> ###### Discussion in:[¶](#discussion-in "Permalink to this headline") * (original change) <https://github.com/mu-editor/mu/pull/133> * <https://github.com/mu-editor/mu/pull/371> * <https://github.com/mu-editor/mu/issues/380> #### Run the user’s code inside its own virtual environment[¶](#run-the-user-s-code-inside-its-own-virtual-environment "Permalink to this headline") ##### Decision[¶](#decision "Permalink to this headline") Mu, whether pip-installed or via installer will maintain a separate virtual environment for running the user’s code. Initially this will contain all the dependencies used by any of our modes, plus any packages the user installs in addition. The dependencies needed for the running of the editor itself (mostly PyQt stuff but currently including some serial packages which have “leaked” from the modes) will be kept in a separate environment, virtual or otherwise. The installers will have to support this approach by bundling wheels (as our assumption is that some schools at least will have restricted or no internet access). For now we’re not breaking out modes into plugins or separate environments although that’s a natural extension of this work. If that were done later, each mode could specify its own dependencies which could be installed on demand. ##### Background[¶](#background "Permalink to this headline") * Getting Mu to unpack and run out of the box on the three main platforms: Windows, OS X & Linux has always proven challenging. * In addition, within the Mu codebase, the code to run the user’s code is scattered and contains a fragile re-implementation of a virtual environment. * Installing 3rd-party modules was also a little fragile as we had to run pip with a –target parameter * There are other issues, especially around the Jupyter console and, on Windows, its use of the pywin32 packages which have slightly odd path handling. ##### Discussion and Implementation[¶](#discussion-and-implementation "Permalink to this headline") This started off with work by @ntoll to have the 3rd-party apps installed into a virtual environment rather than with the –target parameter which would try to force them into a directory where Mu could find them. This stalled somewhat, especially on Windows, and I (@tjguk) took over that branch. Having focused on the getting a virtual environment running for the 3rd-party installs, I realised that having a venv for the whole of the code runtime might help solve some of the other issues. After a few merges to get some changes in, especially those by @tmontes to the installer, we started PR#1072 <https://github.com/mu-editor/mu/pull/1072>. There is now a virtual\_environment.py module which initially brings together various pieces of code which were scattered around the codebase and adds support for creating and installing into a virtual environment. The various places where the user code is run (mostly within the modes package, but including inside panes.py) have been updated to use this virtual environment logic. Possible future work might involve adding a “run process” method to the class itself. As far as possible this should remove the need to hack up special PYTHONPATH, \*.pth and site.py logic. ###### Implemented via:[¶](#implemented-via "Permalink to this headline") * <https://github.com/mu-editor/mu/pull/1068> * <https://github.com/mu-editor/mu/pull/1072> * <https://github.com/mu-editor/mu/pull/1056> * <https://github.com/mu-editor/mu/pull/1058> ###### Discussion in:[¶](#discussion-in "Permalink to this headline") * <https://github.com/mu-editor/mu/issues/1061> * <https://github.com/mu-editor/mu/issues/1070> #### Session & Settings Data[¶](#session-settings-data "Permalink to this headline") ##### Decision[¶](#decision "Permalink to this headline") Centralise access to settings inside a standalone module offering a dictalike-interface. The settings can be loaded from and saved to files. This currently uses JSON (as we have historically) but <https://github.com/mu-editor/mu/issues/1203> is tracking the possibility of using TOML or some other format. Settings objects have defaults which are overridden by values loaded from file or set programatically. When the settings are saved, only values overriding the defaults are saved. The `load` method can be called several times for the same settings; values in each one override any corresponding existing values. The last loaded filename is the file which the settings will be saved to. Both load and save attempt to be robust, carrying on with warnings in the log if files can’t be found, open, read etc. The existing files (session.json, settings.json) are implemented as singletons in the settings module, and settings.json is autosaved. New settings to support venv functionality – in particular, baseline packages – is also added. At its simplest <https://github.com/mu-editor/mu/pull/1200> does no more than implement this set of functionality. The few places in existing code where settings were used or altered have been updated to use the new objects and functionality. ##### Not Implemented / Hooks[¶](#not-implemented-hooks "Permalink to this headline") During the design and/or based on previous discussions, several ideas were floated which are at least supported by the new implementation. * Safe mode / Readonly mode / Reset mode As described below, there are situations where teachers or admins would like to reset settings for use in a club or classroom setting. The new implementation supports this idea via the `reset` method and `readonly` flags without actually implementing it as such. Such functionality might, in the future, be managed by means of command-line switches or some other flag. * File format: JSON, YAML, TOML… The implementation tries to be agnostic as to file format. At present it uses the historically-implemented JSON format. But the choice of serialiser is centralised towards the top of the module and shouldn’t be hard to change, especially for any serialiser which uses the conventional `.dumps`, `.loads` API. cf <https://github.com/mu-editor/mu/issues/1203> * One file / Two files? The new settings implementation facilitates any number of files each of which can have an arbitrary hierarchy. Whether we end up with one settings file containing, eg, session settings and board settings, or several files each specific to an area can be decided later. Nothing in this implementation precludes either approach. * Interpolation Because it is easy to implement and doesn’t seem risky, this implementation applies `os.path.expandvars` to any values retrieved. This will do platform-sensitive env var expansion so admins can specify, eg, a workspace directory of %USERPROFILE%mu\_code or $HOME/.mu/mu\_code. Value interpolation (where one settings value can rely on another) has *not* been implemented. It’s potentially quite an involved piece of work, and the benefit is not so clear. * Indicating failure to users This is obviously a wider issue, but while this implementation tries to be robust when loading / saving settings, it only writes to the standard logs and then fails quietly. The problem here is that we’re possibly not operating within the UI. At the least, we don’t have a good overall story for a UI which isn’t part of the central editor itself. ##### Background[¶](#background "Permalink to this headline") Mu maintains two files, automatically saved on exit, to hold user settings and session data. The former contains critical parameters without which the editor probably won’t function. The latter contains more or less cosmetic items which can be cleared (eg by a “Reset” button) without losing functionality. Historically access to these files has been somewhat scattered around the codebase, making it difficult for modules to access them coherently. The first aim of the re-implementation is to create globally-accessible singletons, much as is conventional for logging. Those “settings” objects would offer a dictionary-like interface so that code could easily do: ``` import settings def set\_new\_theme(theme): ... settings.session['theme'] = theme.name ``` The second aim is, possibly, to reconsider the use of the settings, or their structures, or which / how many files they are and where they’re situated. Any such refactoring or restructuring should be a lot easier with a newer implementation. ##### Discussion and Implementation[¶](#discussion-and-implementation "Permalink to this headline") Open questions: * How many / which files do we need? * Should we combine both settings / sessions into one file? Is there a meaningful difference which we want to maintain? [+1] * Should we register exit handlers so the files are always saved on closedown? [+1] * Should we write files to disc as soon as they are updated? [-0] * Should we re-read files to allow users to update them mid-session? [-1] * Should we implement read-only mode (ie the existing file is loaded but not written back)? [+0] * Should we implement safe mode (ie the file is neither loaded nor written back)? [+1] * Should we implement reset mode (ie the file is not loaded but is written back)? [+0] * Should we break out the virtual environment settings (venv location, baseline packages) into its own file? [+1] * Could we add a boards.json file to allow users to add new/variant configurations? [+0] * What levels of config do we need? Defaults? One/multiple settings files? Override at instance level? * Do we still need to look in the application directory as well as the data directory? [-0] * What format should the files use? [cf <https://github.com/mu-editor/mu/issues/1203>] * Should we save everything every time? [-0.5] * Do we need interpolation of other settings? (eg ROOT\_DIR = abc; WORK\_DIR = %(ROOT\_DIR)/xyz) * Do we need interpolation of env vars? (eg ROOT\_DIR = %USERPROFILE%mu\_code) [+0.5] * Should we merge `settings.py` into `config.py` [+0] * Should settings (as opposed to sessions) be read-only? [+1] ###### Exit Handlers[¶](#exit-handlers "Permalink to this headline") Registered exit handlers to ensure that files are saved when Mu exits. (This could probably be alternatively achieved within the Qt app). The advantage of this is that the save is automatic; the disadvantage is that it’s a little hidden. Not currently writing to disc as soon as updated: having an exit handler ensures the settings will be written, even in the event of an unhandled exception. And it’s not clear what advantage an “autosave” would offer. ###### Levels of Config[¶](#levels-of-config "Permalink to this headline") Allowing three levels of data: the defaults for each setting type, held in a class dictionary; possible overrides at class instantiation [I’m not clear where this would be used; it can probably go]; and the .json files. The `load` function merges into the existing settings. Most commonly this means it’ll be preceded by a call to `reset`. But it could be used to implement a cascade of settings, eg where an admin sets site-wide settings which are then overridden by user settings. ###### Amnesia / Read-only / Reset modes[¶](#amnesia-read-only-reset-modes "Permalink to this headline") To support the possible “modes” above – amnesia, read-only etc. there is a `readonly` flag on each settings object, preventing it from being written to disc; and a `reset` method which will return to default settings. This last can be used either to “forget” any loaded or set settings; or before reloading from a different file. So *Safe mode* is implemented by calling `reset` without `load` and setting `readonly`. *Read-only mode* is implemented by calling `reset` followed by `load` and setting `readonly` And *Reset mode* is implemented by calling `reset` without `load` and *not* setting `readonly` The use cases here would be mostly for admins or leaders who needed, eg, to ensure that new sessions were started for every user, or who needed to debug or recover from a corrupt settings file. ###### Failure modes[¶](#failure-modes "Permalink to this headline") It’s critical that we should recover well from not being able to read or to write settings files, whether that’s a file system failure or invalid JSON. Regardless of the approach we should definitely log any exception, or log a warning where there’s no exception as such but, say, a missing file. ####### Reading[¶](#reading "Permalink to this headline") * A failure to find/open a settings file is considered usual: it’s expected that, the first time around, a user settings file won’t exist to be read. The loader will log a warning and carry on as though it had found it empty * A failure to read the JSON from a settings file is more complicated. For pragmatic purposes, the intention is here is: log a warning; quarantine the file; and carry on as though it had been found empty. That way the editor continues to work, albeit in “reset” mode, and the failing file is available for debugging. Not quite clear: should we automatically enter read-only mode in this situation? ####### Writing[¶](#writing "Permalink to this headline") * A failure to open a settings file to write to is more problematic, and there’s not very much we can do. Log the exception (eg AccessDenied or whatever). Perhaps – given that the text won’t be great – pushign the JSON output to the logs as debug might give some manual fallback. * A failure to *write* JSON is less probable – although it does happen during testing where the JSON lib attempts to serialise a Mock object. Here, we can’t really do more than log the exception and fail gracefully. ###### Levels of Config & Defaults[¶](#levels-of-config-defaults "Permalink to this headline") The thrust of this proposal expects the Settings subclass to hold a dictionary of defaults at class level. These are applied first before any file is loaded. Any information from a loaded file is overlaid, so the file data “wins”. Any values not present in the file remain per the default. Although not implemented in any way at present, the mechanism allows for several files to be loaded in succession, typically for a site-wide file, set up by an administrator, followed by a user-specific file. In this scenario, the data would be read: Defaults < Site settings < User settings with later data replacing earlier data. The presence of the defaults in the Settings subclass should also make for a more consistent use of defaults across the codebase. Eg if in general device timeouts should be 2 seconds but can be changed, one piece of code might do: ``` timeout\_s = settings.user.get('timeout\_s', 2) ``` while another piece elsewhere might do: ``` timeout\_s = settings.user.get('timeout\_s', 3) ``` If the defaults are present in the class, the .get method could be implemented so the default, instead of None as conventional, returns the class default: ``` timeout\_s = settings.user.get('timeout\_s') # with no explicit timeout\_s setting, timeout\_s is now the default value ``` Taking this further, it’s not clear that we even need to load the defaults as such; we could always just fall back to them in the event of a .get KeyError or even a \_\_getitem\_\_ KeyError. Taking that approach would also means we wouldn’t need the “dirty data” mechanism because anything in the `\_Settings` object’s own `\_dict` should be saved out at the end. ###### Saving Everything?[¶](#saving-everything "Permalink to this headline") Implicit in the new design is the idea that settings are saved out to file(s) at the end of every session. Originally, the effect of the defaults was that, say, a workspace directory would inherit the default which will then be written out to the settings file at the end of the session. Even if that file had not originally had a settings for the workspace directory. On reflection, I’ve re-implemented for now a “dirty” setting for each attribute. Only “dirty” attributes are written out to file. Anything loaded from a file is considered “dirty” even if it remains unchanged for the duration of the session. Anything updated during the session – and this will typically be user-configurable items like Zoom level, Theme &c. – is also tagged as “dirty” and will be written out to file. ###### Implemented via:[¶](#implemented-via "Permalink to this headline") * <https://github.com/mu-editor/mu/pull/1200> ###### Discussion in:[¶](#discussion-in "Permalink to this headline") * <https://github.com/mu-editor/mu/issues/1184> * <https://github.com/mu-editor/mu/issues/1203> ### Release Process[¶](#release-process "Permalink to this headline") Our continuous integration setup provides the following automation: * Running of the unit test suite on Windows, OSX and Linux for each commit. * Code quality checks via [LGTM.com](https://lgtm.com/projects/g/mu-editor/mu/). Mu has an A+ rating for code quality. * Generation of installables for Windows 32bit and Windows 64bit for each commit on our master branch. * Creation of a stand-alone .app for Mac OSX for each commit on our master branch. However, such automation does not make a release. What follows is a check-list of steps needed to cut a release. #### User Activity Checks[¶](#user-activity-checks "Permalink to this headline") To ensure nothing is broken from the user’s point of view the following key user activities should be completed on Windows, OSX and Linux (to ensure the cross platform nature of Mu is consistent): * Start Mu from a clean state (delete your Mu configuration file, and mu\_code directory). *Outcome: Mu should ask for an initial mode and a fresh mu\_code directory is created. Upon restart, Mu doesn’t repeat this process.* * Click the “Mode” button, select a new mode. *Outcome: the mode selection dialog should appear and you’ll find yourself in a new mode.* * Click the “New” button. *Outcome: a new blank tab will appear.* * Click the “Load” button. *Outcome: the operating system’s file selector dialog should appear. The selected file should open in a new tab.* * With a new tab, click the “Save” button. *Outcome: the operating system’s file naming dialog should appear, and the tab will be updated with the newly named filename.* * While in Python mode, plug in an Adafruit board. *Outcome: Mu should suggest switching to Adafruit mode.* * In Adafruit mode, load, edit and save a file on the device. *Outcome: upon saving the file, the device will reboot and run your code.* * In Adafruit mode, click the “Serial” button. *Outcome: a serial connection to the attached device is shown in a pane at the bottom of Mu’s window. Pressing Ctrl-C should drop you to the CircuitPython REPL.* * In Adafruit mode, use some code [like this](https://github.com/adafruit/Adafruit_Learning_System_Guides/blob/master/Sensor_Plotting_With_Mu_CircuitPython/light.py) on the Adafruit device (in the case of this example, a Circuit Playground Express) to emit tuple based data. Click the “Plotter” button while the code is running. *Outcome: the plotter should display the output as a graph.* * While in Python mode, plug in a micro:bit board. *Outcome: Mu should suggest switching to micro:bit mode.* * In micro:bit mode (from now on, assuming you have a micro:bit device connected), while the current tab is completely empty, click the “Flash” button. *Outcome: Mu should do a complete fresh flash of “vanilla” MicroPython.* * In micro:bit mode, write some simple working code and click the “Flash” button. *Outcome: since MicroPython is already flashed on the device, only the file will be copied over and the device will soft-reboot.* * In micro:bit mode, click on the “Files” button. *Outcome: the files pane will appear and contain a true reflection of the current state of the file system on the device and in your mu\_code directory.* * In micro:bit mode, while the “Files” pane is active, copy to/from the device, delete a file on the device and open files listed on your computer by right-clicking them. *Outcome: the file pane state should update and no error message appear.* * In micro:bit mode, click on the “REPL” button. *Outcome: you should see and be able to interact with the REPL of MicroPython running on the connected device.* * In micro:bit mode, use some code to emit tuple based data. Click on the “Plotter” button while the code is running. *Outcome: the plotter should display the output as a graph.* * In PyGameZero mode, with an empty file, click the “Play” button. *Outcome: a blank Pygame window will appear.* * In PyGameZero mode, with correctly working code, click the “Play” button. *Outcome: the game runs.* * In PyGameZero mode, click each of the “Images”, “Fonts”, “Sounds” and “Music” buttons. *Outcome: the operating system’s file manager should open in the correct directory for each of these types of game asset.* * In Python mode, enter a simple script and click “Run”. *Outcome: the script should run with input/output being handled by a pane at the bottom of the Mu window.* * In Python mode, add a new breakpoint to your code, click “Debug”. *Outcome: the visual debugger should start and stop at your breakpoint.* * In Python mode, with no breakpoints present, click “Debug”. *Outcome: the visual debugger will start and stop at the first valid line of code.* * While the visual debugger is active, add and remove breakpoints. *Outcome: the UI will update (red dots will appear etc) and the debugger will respect such changes (stopping at new breakpoint, ignoring removed breakpoints).* * While in the visual debugger click the “Stop”, “Continue”, “Step Over”, “Step In”, “Step Out” buttons. *Outcome: the conventional behaviour for each button should happen. “Stop” will stop the script. “Continue” will run to the next break or end of script. “Step Over” will move to the next valid line of code. “Step In” will move into the called funtion. “Step Out” will move out of the current function. As all this happens, the input/output pane and object inspector should update as the code progresses.* * In Python mode, click on the “REPL” button. *Outcome: an iPython based REPL should appear in a new pane at the bottom of Mu’s window. Clicking the button again toggles the REPL off.* * In Python mode, use some code to emit tuple based data. Click on the “Plotter” button while the code is running. *Outcome: the plotter should display the output as a graph.* * Click “Zoom-In”. *Outcome: the font size should increase.* * Click “Zoom-out”. *Outcome: the font size should decrease.* * Click “Theme” several times. *Outcome: the theme/look should toggle.* * With incorrect code in the current tab, click “Check”. *Outcome: problems like syntax errors or undefined names should be highlighted with annotations on the correct line. If appropriate, they will be underlined.* * Click the “Help” button. *Outcome: the operating system’s default browser should open at the help page for the current version of Mu.* * With unsaved code in the current tab, click “Quit”. *Outcome: Mu should warn you may lose unsaved work and prompt you to confirm.* * With all work saved, click “Quit”. *Outcome: Mu should quit.* * Click on the “cog” icon in the bottom right of Mu’s Window. *Outcome: the “admin” dialog should open with the “logs” tab in focus.* * In the editor panel, type CTRL-K while code is selected. *Outcome: the selected code should toggle between commented and uncommented.* * Type CTRL-F. *Outcome: the find/replace dialog should appear.* #### Pre-Packaging Checklist[¶](#pre-packaging-checklist "Permalink to this headline") * All autogenerated API information used by Mu for auto-completion and call tips should be regenerated. * The developer documentation should be checked, re-read and regenerated locally to ensure everything is presented correctly. * The CHANGELOG.rst file should be updated to reflect the differences since the last officially packaged release. * If this is a major release make sure the resources for the old version of Mu on the [project website](https://codewith.mu/) are archived under the correctly versioned URL scheme. * Make sure the current resources in the source for the project website reference the new version of Mu. #### Packaging Processes[¶](#packaging-processes "Permalink to this headline") Official final releases will be signed by Nicholas H.Tollervey (the creator and current maintainer of Mu). This is a manual step only Nicholas can do (since only he has the cryptographic keys to make this work). Once the release packages for Windows (32bit and 64bit) and OSX have been created and signed they should be checked so no warning messages appear about untrusted sources during the installation process. The instructions for signing the Windows installers are explain in [this wonderful article on Adafruit’s website](https://learn.adafruit.com/how-to-sign-windows-drivers-installer/making-an-installer). But the essence is that the command issued should look something like: ``` "C:\Program Files (x86)\Windows Kits\10\bin\10.0.17134.0\x86\signtool" sign /v /n "Nicholas H.Tollervey" /tr http://timestamp.globalsign.com/?signature=sha2 /td sha256 mu-editor_1.0.1_win32.exe ``` Signing the Mac app involves issuing the following two commands: ``` codesign --deep --force --verbose --sign "CERT\_ID" mu-editor.app dmgbuild -s package/dmg\_settings.py "Mu Editor" dist/mu-editor.dmg ``` The appropriate installer should be checked on the following operating systems: * Windows 7 (32bit) * Windows 10 (64bit) * Latest OSX. For native Python packaging, ensure Mu is installable via `pip install .` run in the root of the source repository in a virtualenv. #### Pre-Release Checklist[¶](#pre-release-checklist "Permalink to this headline") * Create an announcement blog post for [MadeWithMu](https://madewith.mu/). * Tweet an announcement for the timing of the upcoming release. * Compose (but do not publish) a tweet to announce Mu’s release. * Ensure the source code for the developer docs, the project website and MadeWithMu is all ready to be published. * Prepare a press release and circulation list. * Check other possible channels for announcements, community websites etc. #### Release Process[¶](#id1 "Permalink to this headline") * Build the developer documentation on ReadTheDocs. Make a note of the link to the latest release in the resulting page on the CHANGELOG. * Create a new release on GitHub and attach the signed 32bit and 64bit Windows installers and OSX dmg. Reference the changelog from step 1 in the release notes. * Update the download page on the project website to the URLs for the installers added to the release in step 2. Update the live version of the website. * Push the latest version to PyPI (`make publish-test` then `make publish-live`). * Publish the blog post announcement to MadeWithMu. * Tweet with link to the announcement blog post and changelog. * Mention release in Gitter, Adafruit’s CircuitPython Discord. * Send out press release / news item to circulation list / friends. * Hit other possible announcement channels. #### Post-Release Tasks[¶](#post-release-tasks "Permalink to this headline") * Monitor Gitter chat channel for problems. * Clean up fixed issues in GitHub. * Update Roadmap.rst with reference to the next release. * Send out thanks / gifts where appropriate. ### Roadmap (Mappa MUndi)[¶](#roadmap-mappa-mundi "Permalink to this headline") (Apologies for the pun: <https://en.wikipedia.org/wiki/Mappa_mundi>) Mu started as a shonky hack. Now many people are interested in our small editor for educational use and we owe it to them to be clear what our plans are, how we work together and how *anyone* can get involved (see `CONTRIBUTING.rst`). I believe it worth repeating the Mu philosophy we have followed so far: * Less is More: Mu has only the most essential features, so users are not intimidated by a baffling interface. * Path of Least Resistance: Whatever the task, there is always only one obvious way to do it with Mu. * Keep it Simple: It’s quick and easy to learn Mu ~ complexity impedes a novice programmer’s first steps. * Have fun: Learning should inspire fun ~ Mu helps learners quickly create and test working code. Python aims to make code readable, Mu aims to make it writeable. With this in mind: #### Next Point Release[¶](#next-point-release "Permalink to this headline") 1.0.1 is a bug fix / translations release and will only include: * Update to Adafruit boards and future proofing for as-yet-unknown boards. * Swedish translation. * Updated and complete Chinese translation. * Blocking IO from Python 3 sub-process flooding data is fixed. * New MicroPython runtime for micro:bit (bug fixes). * Improvements to the stability of micro:bit flashing. Expected delivery: mid-September 2018. #### Next Minor Release[¶](#next-minor-release "Permalink to this headline") 1.1.0 will introduce some new “beta” modes: * ESP mode for embedded devices from ESP. * Web mode for creating simple dynamic websites. It will also add some new features: * Use of “black” for code style / quality checking. * Configuration of UI for purposes of better presentation: + Change size of buttons. + Tool-tips and auto-complete toggle. + Colour configuration for “Custom” theme (help dyslexic users via colour). + Transparent background (makes screen-casting easier). * Update minifier. * More translations. * Cleanups to the documentation. * Bug fix release of MicroPython for micro:bit. Expected delivery: late-November 2018. ### Mu’s Developers[¶](#mu-s-developers "Permalink to this headline") Mu was created and [mostly written by](https://github.com/mu-editor/mu/graphs/contributors) Nicholas H.Tollervey ([ntoll@ntoll.org](mailto:ntoll%40ntoll.org)). Some of Nicholas’s work has been [magnificently supported](http://ntoll.org/article/mu-pi) by the [Raspberry Pi Foundation](http://raspberrypi.org/). Happily, many people have volunteered wonderful and varied contributions to Mu. These include (but are not limited to): * Tim Golden ([mail@timgolden.me.uk](mailto:mail%40timgolden.me.uk)) * Peter Inglesby * Carlos Pereira Atencio ([carlosperate@embeddedlog.com](mailto:carlosperate%40embeddedlog.com)) * Nick Sarbicki ([nick.a.sarbicki@gmail.com](mailto:nick.a.sarbicki%40gmail.com)) * Kushal Das ([mail@kushaldas.in](mailto:mail%40kushaldas.in)) * Tibs / Tony Ibbs ([tibs@tonyibbs.co.uk](mailto:tibs%40tonyibbs.co.uk)) * Zander Brown * Alistair Broomhead ([alistair.broomhead@gmail.com](mailto:alistair.broomhead%40gmail.com)) * Frank Morton ([fmorton@mac.com](mailto:fmorton%40mac.com)) * Keith Packard ([keithp@keithp.com](mailto:keithp%40keithp.com)) We welcome contributions from anyone! Please see [Contributing to Mu](index.html#document-contributing) for more information. If you have made a contribution to Mu and would like to be recognised, please feel to add yourself to the list above. ### Release History[¶](#release-history "Permalink to this headline") #### 1.2.0[¶](#id1 "Permalink to this headline") This release fixes some minor bugs, addresses some usability gremlins and adjusts some capabilities to make things tidier. Much of this work was done over the summer at the code-sprints at EuroPython 2022 in Dublin. Kudos and thanks to all the new contributors to Mu. Please provide bug reports or feedback via: <https://github.com/mu-editor/mu/issues/new> * Thanks to @keith-packard for Snek mode. [Snek](<https://sneklang.org/>) is a Python inspired language for processors too small even to run MicroPython. * @tmontes contributed changes so Mu builds to Linux AppImages (an easy way to package application for Linux). * Minor fixes by @stratakis in the Russian translation. * @carlosperate fixed many minor glitches and gremlins. * @carlosperate was on fire with fixes needed to ensure Mu continues to work with very old versions of OSX (as used in many educational institutions). * Again, thanks to @carlosperate, AppImage with Wayland no longer the setting of an environment variable to make it work properly. * The web mode includes simple and easy to use integration with beginner and education friendly web hosts, PythonAnywhere. * @agdales, @Jeffrey04, @johannaengland and @AnjaVerboven contributed new messages of the day as part of their onboarding at EuroPython. * @tonybaloney contributed several Windows based fixes and clean-ups. * @johannaengland and @prcutler were on fire tidying up and fixing docs at EuroPython. * A bug was fixed in the web mode relating to the resolution and/or recreation of the assets directory (in which images, css and templates were to be found). * Or friend at Adafruit, @tennewt made the necessary changes so Mu handles OSC commands gracefully (see the [PR](<https://github.com/mu-editor/mu/pull/2326>) for more details). * New contributor, @zigit ensured “Unexpected Maker” based ESP boards are correctly detected. * Thanks to @Jayman2000, error messages are correctly capitalized (or not) to avoid potential confusion. #### 1.1.1[¶](#id2 "Permalink to this headline") Inevitably, while testing 1.1.0 we found we’d missed something and caught a late breaking bug. Please provide bug reports or feedback via: <https://github.com/mu-editor/mu/issues/new> * Thanks to @MinoruInachi (with feedback from @odaki) for a revised Japanese translation for Mu. * Due to complicated dependency problems, we’ve updated the bundled version of Flask to 1.1.4. Thanks to @carlosperate for quickly resolving this problem. #### 1.1.0 (final)[¶](#final "Permalink to this headline") What a journey to get to the 1.1.0 release of Mu. Many thanks to all the contributors who have made this version possible. All your efforts, no matter large or small, are really appreciated. Please provide bug reports or feedback via: <https://github.com/mu-editor/mu/issues/new> * Minor clean ups in the Makefile. * Thank you to @microbit-mark for updating the board IDs to support version 2.2 of the device. * Updates to the Chinese translation by @CSharperMantle. 謝謝。 * Updates to the Slovak translation by @bletvaska. Ďakujem. * The foundations of a brand new Russian translation of Mu by @grovz with contributions from @iamdbychkov. спасибо! #### 1.1.0-beta.7[¶](#beta-7 "Permalink to this headline") This is a beta release and may contain bugs or unfinished features. Please provide bug reports or feedback via: <https://github.com/mu-editor/mu/issues/new> * We expect this release to be the last beta before the final 1.1 release in the new year of 2022. Season’s greetings to everyone using or contributing to Mu, and here’s wishing you all a flourishing and fulfilling 2022. * As always there have been the usual minor bug fixes and clean ups from the core team of maintainers. Thank you so much for all that you do to support the continued development of Mu. * Thanks to the ever-green @keith-packard for his contribution to ensure icons on the button bar continuously scale based on the window width. This looks really smooth and slick. * Tinsel laden @tmontes has made a number of contributions around tooling for internationalization (i18n) of Mu. These include using the [Babel](http://babel.pocoo.org/en/latest/) package for generating the required translation files from our source code, and updating the `Makefile` (and `make.py`) so the process can be automated. * Xmassy @xbecas is a new joiner to the core team and we’re very please to have him with us since he has done a **huge** amount of work on updating and curating the translation files needed for i18n. Thanks to his work, translators for all the other existing locales need not have to go through the string generation/update steps (he’s done that for you already!). * Both @xbecas and @tmontes have made extensive updates to our pt-PT (Portuguese) translation. Feliz Natal e Próspero Ano Novo. * This was swiftly followed by a welcome contribution by @rffontenelle the red-nosed translator, who made extensive updates to the pt-BR (Brazilian Portuguese) translation. Many thanks Rafael, you continue to demonstrate why the Brazilian FLOSS community is such a vibrant place, and we hope your work will help beginner coders in Brazil take their first steps to join your community. Boas Festas! * Now that the upstream PyGame / PyGameZero packages have been updated and repackaged, @tmontes has ensured we use these (rather than our own custom builds) in our installers for Windows and OSX. Many thanks to our friends and collaborators in those projects (cc/ @illume and @lordmauve). * Once in royal @devdanzin’s repos, stood some lowly bugs to fix. These include ensuring empty path handling is properly handled by `get\_save\_path`, correct highlighting of both f-strings and triple quoted strings in the editor widget, fixing a comment-toggling bug that deleted the first character of the next line under certain circumstances and more robust handling of environment variables. Wow, @devdanzin was on fire..! (…and has further work in development, thank you so much for your continued contributions.) * Carolling @carlosperate has put a huge effort in. He has triaged various crash reports, administered our continuous integration pipeline, and reviewed and merged much of the work described above. He also ensured our version numbering for Mu is no longer odd, and meets the guidelines set out in [PEP440](https://www.python.org/dev/peps/pep-0440/). * Good Tim Golden (@tjguk) fast typed out, a venv that’s crisp and even. His outstanding work on making Python virtual environments work in some of the most inhospitable computing environments ever found is miraculous. Tim’s genius is to know exactly the right intervention to make, and in this case his epic addition of `-I` to the Mu codebase will help ensure the user’s virtual environments are properly isolated. * @tiago has updated the [pup](https://github.com/mu-editor/pup) packager we use to create our installer. This should fix a problem found on the new ARM based Macs. He has also made significant progress on a cross-distro Linux package which we hope will make an appearance in the not-too-distant future. * Finally, Nicholas (@ntoll) promises never to do another Christmas themed changelog. #### 1.1.0-beta.6[¶](#beta-6 "Permalink to this headline") This is a beta release and may contain bugs or unfinished features. Please provide bug reports or feedback via: <https://github.com/mu-editor/mu/issues/new> * Another delayed release due to busy-ness of the volunteer team involved in Mu. Thank you for your patience, bug reports and code patches. * There have been the usual minor bug fixes and clean ups from various regular contributors and some new ones too. Thank you for your careful and well targetted changes. * Carlos (@carlosperate) fixed some packaging problems relating to the iPython kernel bundled with Mu. * Martin (@dybber) fixed a couple of problems relating to the stopping of child processes (Flask and scripts stopped via KeyboardInterrupt in Linux). * The web mode checks for the availability of templates in the local directory tree before starting up. If a template directory isn’t found in the expected location, then the user sees a helpful message describing the problem and what they need to do to fix it. * Mu’s splash screen no longer always appears on top of everything else on the user’s desktop. The splash screen now also logs the progress of installing the various packages needed on first install. Thanks to Carlos for these changes. * A new admin/settings option has been added to allow users to manually change the translation Mu uses for its interface. Updating this setting requires a restart of Mu. Zander (@ZanderBrown) contributed the icon/glyph to indicate the relevant tab is for translation related settings (not entirely obvious if Mu’s UI is using a language you don’t understand and you’re looking for the setting that relates to translations). The icon makes this clear. * On some desktop windowing systems there is a bug that means windows re-open at a position higher up the screen, and so may appear off the top of the screen. We’ve ensured this never happens with Mu. If Mu starts with any part of the window off the screen, the window is moved to be within the dimensions of the screen. This was a weird one to track down and fix. * Many thanks to Ethan Spoelstra (@espoelstra) who contributed a change so Crostini on ChromeOS is used to return the correct CIRCUITPY path if it exists. * Huge thanks to Keith Packard (@keith-packard) for several contributions to this release of Mu. Keith refactored the way in which Mu handles pasting in the REPL window so it works correctly and more broadly across operating systems. * Keith also fixed some font related issues in the REPL. * Keith was on fire with a couple more contributions relating to SVG icons in the buttons in Mu. We’re very grateful to Ben Williams (@Rybec) for putting in the work to make our button icons SVG files. Keith made the code changes to implement this. * Thanks to Miro Hrončok (@hroncok) for pointing out a change in Python 10 which would break some of our UI calls into PyQt, and who provided a patch to fix things. * Some minor clarifications in our developer documentation (<https://mu.rtfd.io>). #### 1.1.0-beta.5[¶](#beta-5 "Permalink to this headline") This is a beta release and may contain bugs or unfinished features. Please provide bug reports or feedback via: <https://github.com/mu-editor/mu/issues/new> * We had hoped for a regular (fortnightly) release tempo. Due to the voluntary nature of Mu’s development and because some of the updates in this release were quite challenging (see below), this release is a LOT later than we had planned. * Several of us made minor updates and fixes (such as ensuring various packages had explicit dependency versions listed, updating versions for Mu’s own dependencies and so on). * Right clicking on highlighted text in the editor, with the REPL active, now has an additional option added to the context menu: to correctly paste the text from the editor into the REPL. Thanks to Professor Chris Rogers of Tufts University for suggesting this feature. * The multi-talented Dan Halbert of Adafruit very kindly fixed a bug in the Adafruit board handling when on run on new Apple M1 hardware. Thank you Dan for your valuable contribution. * A huge amount of work by Tim and Carlos has gone into analysing the crash reports from recent beta releases of Mu. This has resulted in significant effort to address many of the bugs encountered, many of which related to edge cases encountered by the new virtual environment feature. Tim and Carlos have created many fixes and checks to ensure these bugs are either completely fixed or are, at least, mitigated in more helpful ways. This has been a challenging and “fiddly” bit of work, so kudos and thanks, as always, to both Tim and Carlos for their continued efforts. * Carlos has also updated the version of MicroPython used in the BBC micro:bit mode to the latest 2.0.0-beta.5 version. * In addition, Carlos has ensured that the micro:bit mode flashes files onto the micro:bit using the correctly memory aligned hex string. * Github user ajs256 has ensured the crash reporter doesn’t kick in when a `KeyboardInterrupt` is triggered in Mu (CTRL-C). * Sometimes in Mu for Linux, the expected `.py` file extension wasn’t added to new files. This depended on the user’s graphical shell. Mu now checks the output from the shell and, if requires, will add `.py` itself. * Various fixes to Mu’s logging make it more robust, clear and useful. * Tiago fixed a late breaking bug in packaging Mu for OSX. All fixed in a matter of hours. Amazing work! There are perhaps a couple more features we want to land in the coming weeks, and then we will start the work of ensuring internationalization is fully up to date, the website reflects the new features and various changes, and PUP will be able to produce redistributable appimages for Linux. Then we will have reached 1.1.0-final. :-) #### 1.1.0-beta.4[¶](#beta-4 "Permalink to this headline") This is a beta release and may contain bugs or unfinished features. Please provide bug reports or feedback via: <https://github.com/mu-editor/mu/issues/new> * During the beta phase, we’re moving to a fortnightly release cadence. Since this release is a week late, expect the next one in a week’s time - 26th April. * Carlos made many changes to clean up the specification for required modules used by the installer. This will make supporting and tracking Mu’s dependencies MUCH easier. Thank you Carlos! * Huge thanks to Dan Halbert of Adafruit who contributed a significant amount of refactoring to the CircuitPython mode. As a result Mu now uses the adafruit-board-toolkit module for device identification, among many other helpful changes [described in Dan’s pull request](<https://github.com/mu-editor/mu/pull/1371>). Thank you Dan..! * Carlos was on fire… he also fixed a bug in the file-copy dialog when the context menu was opened with an empty list of files. * Carlos (again), fixed some outstanding documentation issues for supporting Raspbian Buster (and newer). These are now at <https://mu.rtfd.io/>. * Carlos (again, again) tidied up various aspects of the Makefile so there is only a single source of truth for running various utilities and commands. * Logging was another focus for this release. Now that we have a few weeks worth of crash reports we’ve been able to look at the parts of the application that cause most grief and add extra-logging in various locations. Tim put in a great effort to make sure the “first run” and other virtual environment based aspects of Mu now have clearer and more useful logging and throw more useful exceptions, closer to the source of the problem, for the resulting crash report. Carlos ensured the IPython kernel installation was properly logged. * We ensured various key packages were pinned to particular versions to maximise compatibility with older versions of Python still found in schools. There are many pull requests and work items currently in flight and they’ll be landing very soon as the overall quality and robustness of Mu significantly improves. Many thanks to everyone who continues to help, support and contribute to the ongoing development of Mu. #### 1.1.0-beta.3[¶](#beta-3 "Permalink to this headline") This is a beta release and may contain bugs or unfinished features. Please provide bug reports or feedback via: <https://github.com/mu-editor/mu/issues/new> * During beta phase, we’re moving to a fortnightly release cadence. Expect beta 4 on the 12th April. * The final version of the Mu splash screen was delivered. Huge thanks to the extraordinarily talented Steve Hawkes ([@hawkz](<https://github.com/hawkz>)) of [The Developer Society](<https://www.dev.ngo/>) for his generous artistic support, patience and humorous approach. * Thanks to a recent update in [PyGame Zero](<https://pypi.org/project/pgzero/>), we’re back to using the official package from PyPI, rather than our patched fork, in the installer. * Both Tim and Carlos have contributed updates, fixes and tests to address a bug affecting Windows users who may have a space in the file path upon which Mu is found. This was a difficult bug to reproduce but Tim did a lot of digging to isolate the cause with as much confidence as is possible when it comes to such things. Carlos did a bunch of thankless and fiddly test related work so testing with spaces in the path is part of our test suite. Work on this is ongoing so expect further improvements in upcoming releases. As always, many thanks for these efforts. * Tim addressed a wheel/sdist related problem that was causing odd side effects with regard to dependancies. * A strange bug, where it was not possible to install third-party packages on first run of Mu, opened up a deep rabbit hole of investigation. In the end Tim was able to fix this AND address the source of a warning message from Qt when Mu was starting for the first time. * The splash screen code was rewritten in such a way that objects relating to the splash screen will always be garbage-collected by Python and destroyed by Qt5. Previously, they existed for the full duration of the application, not really causing any problems, but “in limbo” nonetheless. * The crash reporting tool has had a minor update so the user is reminded to attach their log file to the bug report, along with an indication of where to find the log file. #### 1.1.0-beta.2[¶](#beta-2 "Permalink to this headline") This is a beta release and may contain bugs or unfinished features. Please provide bug reports or feedback via: <https://github.com/mu-editor/mu/issues/new> * This is the first public beta release (beta 1 was created for testing by the core development team). * Many minor bug fixes to the existing new features found in beta 1 (see below). * Many thanks to Martin Dybdal for his work on improving the admin panel. * Carlos made significant changes so Mu can be packaged with very recent versions of Python. Carlos also made various changes relating to the status of Python packages contained within the official installer. * Many thanks to Dan Pope for assistance with an upgraded version of PyGameZero (which uses the latest version of PyGame - kudos to René and the other developers of PyGame for the recent improvements). * Various fixes to the UI so that panes are easier to resize and the themes are correctly applied to the REPL (thanks again to Martin for these fixes). * Carlos also contributed fixes relating to the micro:bit mode (compatibility with versions 1 and 2). * Tim has made herculean efforts to ensure the creation and checking of Mu’s virtual environment is robust and easy to maintain. * A new crash reporting feature has been added. If Mu breaks the user will be redirected to the endpoint codewith.mu/crash with details of the crash and an option to create a bug report. This ensures Mu crashes are handled more gracefully, and the user is able to see the error that caused the crash. * A new animated splash screen has been added so the initial creation of Mu’s virtual environment happens in such a way that the user can see progress is being made, and updates are logged on the splash screen for the user. If Mu encounters a problem at this early stage, the splash screen recovers and the new crash reporting feature kicks in. The current animation was created by Steve Hawkes (thank you) with a much more polished version promised very soon..! * Behind the scenes, Tiago has continued to make outstanding work on the pup tool we use to create the installers for Windows 64/32 bit and MacOS X. This beta release will be the first to use installers created with pup. * **Known bug** - on first ever start of Mu, if in Python3 mode the package manager will not work. Re-starting Mu fixes this (i.e. from second and subsequent starts). We’re tracking this problem via [this issue](<https://github.com/mu-editor/mu/issues/1358>). #### 1.1.0-beta.1[¶](#beta-1 "Permalink to this headline") This is a beta release and may contain bugs or unfinished features. Please provide bug reports or feedback via: <https://github.com/mu-editor/mu/issues/new> * A new mode for ESP8266/ESP32 devices running MicroPython. This work and a significant amount of related refactoring was contributed with Viking like energy and efficiency by Martin Dybdal. This work has meant it was relatively easy to create two further new modes… * New mode for Lego Spike devices (thanks to Chris and Ethan at Tufts University for the help and support). * New mode for Raspberry Pi Pico (thanks to Zander, Martin and Carlos for the extensive testing). * Updates to the Microbit mode made by Spanish source-code wrangler extraordinaire (and resident Microbit expert) Carlos Pereira Atencio. The Microbit mode now supports versions 1 and 2 of the board. * Various bits of artwork used in the application have been updated (including a new [temporary] animated splash screen). Thanks to devdanzin for choreographing the initial work on the splash screen at short notice. * A complete re-write of the virtualenv and third party package handlers by the hugely talented Tim Golden. This was a long term and difficult refactoring project which Tim has delivered with great aplomb. This should make package handling much smoother and simpler. * Various smallish UI fixes, enhancements and smoothing by devdanzin. Thank you for these contributions - they really make a difference to the ease of use and friendly feel of Mu. * This version of Mu is packaged with stand-alone installers for Windows and OSX by the wonder that is PUP - a new packaging tool by our very own Tiago Montes ~ Portugal’s Premier Python Packager Par-excellence. We have big plans for PUP… watch this space. :-) * Many many many minor bug fixes contributed by many many many people to whom we are eternally grateful. We hope to release beta.2 very soon. #### 1.0.3[¶](#id3 "Permalink to this headline") Bugfix. * Updated to the latest version of Qt to fix syntax highlighting issues in OSX. * Ensure CWD is set to the directory containing the script to be run in Python3 mode. * Updated website with instructions in light of OSX changes. #### 1.1.0-alpha.2[¶](#alpha-2 "Permalink to this headline") The second alpha release of 1.1. This version may contain bugs and is unfinished (more new features will be arriving in alpha 3). Please provide bug reports or feedback via: <https://github.com/mu-editor/mu/issues/new> * **NEW FEATURE** A brand new web mode for creating simple dynamic web applications with the Flask web framework. Currently users are able to edit Python, HTML and CSS files, run a local server and view their website in thier browser. We expect to add a deployment option thanks to PythonAnywhere by the time alpha 3 is released. * **NEW FEATURE** A new Slovak translation of Mu thanks to Miroslav Biňas (GitHub user [bletvaska](https://github.com/bletvaska)). * **ACHIEVEMENT UNLOCKED** Fixed a problematic bug where students got into a seemingly impossible loop because the auto-save feature encountered errors and got in the way of renaming a file. We are THRILLED TO BITS that the fix for this problem was contributed by [Sean Tibor](http://teachingpython.fm), a teacher from Fort Lauderdale, Florida. **Teachers coding the tools they use to teach has been a core aim for Mu, and Sean gets the gold medal (or perhaps a beer when I next see him) for unlocking this achievement.** * **RENAME** At the suggestion of Adafruit’s Dan Halbert, the “Adafruit” mode has been renamed to “CircuitPython” mode to reflect the growing number of manufacturers who support CircuitPython. Many thanks to [Benjamin Shockley](http://benjaminshockley.com/) for putting the work in to make this happen. * **NEW DEVICES** Several new non-Adafruit boards have been added to the renamed CircuitPython mode. Many thanks to [Shawn Hymel](http://shawnhymel.com) (SparkFun) and [Gustavo Reynaga](http://www.gustavoreynaga.com/) (Electronic Cats) for contributing these valuable changes. * Add some new free-to-reuse image and sound assets for use in PyGameZero example games. * Middle mouse wheel scrolling with the CTRL or CMD (on Mac) keys will zoom the UI in a consistent manner across all platforms. * Minor documentation updates / corrections thanks to [Luke Slevinsky](https://lukeslev.github.io/). * Refinement of the built-in educational libraries as we start to unbundle a slew of software from Mu’s installer so users can install such packages from within Mu. Many thanks to the formidably talented [Martin O’Hanlon](https://www.stuffaboutcode.com/) for his help. * PyGameZero mode will look for game assets relative to the location of the game file, rather than just within the user’s workspace. Thanks to the evergreen [Tim Golden](http://timgolden.me.uk/) for this helpful update. * Minor corrections to the French localisation by GitHub user [ogoletti](https://github.com/ogoletti). * UI related convenience in the new ESP mode so that the current / most recent filesystem path is used when using the file copy pane. Many thanks (as always) to [Martin Dybdal](http://dybber.dk/) for his continued work on all things ESP related in Mu. * A tidy up of the file save dialog so it uses Qt’s built in dialog features. Thanks to [Tiago Montes](https://tmont.es/) for being his usual awesome self. * Tabs are restored on startup in the correct order. Once again, this is the work of Tiago Montes. * The mechanism for generating the various installers and packages for Mu has been significantly refactored so that there is, if possible, always a single source for configuration information. The significant amount of effort to make this happen was, once again (again), contributed by Tiago Montes. * Window size and location is also restored on startup. Tiago Montes, who implemented this change, has been **ON FIRE** during this development phase. * A small (but important) change to the tool-tip for the sleep function found in MicroPython on the micro:bit has been submitted to the pedagogical legend and friend of Mu that is [Dave Ames](https://dave-ames.net/). * A helpful message is now sent to the output pane when the graphical debugger starts in Python 3 mode. The Shakespeare like talents of long term Mu-tineer [Steve Stagg](https://sta.gg/) are behind this Nobel-prize-worthy literary contribution. * Re-add support for user defined syntax check overrides. Many thanks to [Leroy Levin](https://github.com/leroyle) for making this happen..! * Ensure that `pip` is updated while creating the Windows installers. Thanks to [Yu Wang](https://github.com/bigeyex) for making this change. * Various minor updates and fixes to aid code readibility. #### 1.1.0-alpha.1[¶](#alpha-1 "Permalink to this headline") The first alpha release of 1.1. This version may contain bugs and is unfinished (more new features will be added in later alpha releases or, depending on feedback, we may change the behaviour of existing features). Please provide bug reports or feedback via: <https://github.com/mu-editor/mu/issues/new> * **NEW FEATURE** Installation of third party packages from PyPI. Click on the cog icon to open the admin dialog and select the “Third Party Packages” tab. * **NEW FEATURE** Code tidy via the wonderful code formatter [Black](https://black.readthedocs.io/en/stable/). Click the new “Tidy” button to reformat and tidy your code so it looks more readable. If your code has errors, these will be pointed out. Many thanks to Black’s creator and maintainer, Łukasz Langa, for this contribution. * **NEW FEATURE** A new ESP8266 / ESP32 mode for working with these WiFi enabled cheap IoT boards. Many thanks to Martin Dybdal for driving this work forward and doing the heavy lifting. Thanks also to Murilo Polese for testing and very constructive input in the review stage of this feature. * **OS CHANGE** Due to Qt’s and Travis’s lack of support, Mu will only run on Mac OS 10.12 and above. * Ensure line-number margin is not too sensitive to inaccurate clicking from young coders trying to position the cursor at the beginning of the line. Thanks to Tiago Montes for this enhancement. * Fix some typos in the French translation. Thank you to GitHub user @camillem. * Fix a bug relating to Adafruit boards when a file on a board which is then unplugged is saved, Mu used to crash. Thanks to Melissa LeBlanc-Williams for the report of this problem. * Fix problem with a missing newline at the end of a file. Thanks to Melissa LeBlanc-Williams for the eagle-eyes and fix. * Fix for PYTHONPATH related problems on Windows (the current directory is now on the path when a script is run). Thanks to Tim Golden for this fix. * Update to locale detection (use Qt’s QLocale class). Thanks to Tiago Montes for making this happen. * Fix bug relating to match selection of non-ASCII characters. Thank you to Tiago Montes for this work. * Fixed various encoding related issues on OSX. * Various minor / trivial bug fixes and tidy ups. #### 1.0.2[¶](#id4 "Permalink to this headline") Another bugfix and translation release. No new features were added. Unless there are show-stoppers, the next release will be 1.1 with new features. * Updated OSX to macOS, as per Apple’s usage of the terms. Thanks Craig Steele. * Updates and improvements to the Chinese translation. Thank John Guan. * Improved locale detection on macOS. Many thanks to Tiago Montes. * Cosmetic stripping of trailing spaces on save. Thanks to Tim Golden. * Update PyQt version so pip installed Mu works with Python 3.5. Thanks to Carlos Pereira Atencio. * Fix incorrect setting of dataTerminalReady flag. Thanks to GitHub user @wu6692776. * Spanish language improvements and fixes by Juan Biondi, @yeyeto2788 and Carlos Pereira Atencio. * Improvements and fixes to the German translation by Eberhard Fahle. * Fix encoding bug on Windows which caused crashes and lost files. Many thanks to Tim Golden for this work. * Keyboard focus loss when closing REPL is now fixed. Thanks again Tim Golden. * More devices for Adafruit mode along with a capability to work with future devices which have the Adafruit vendor ID. Thanks to Limor Friend for this contribution. * Fix a bug introduced in 1.0.1 where output from a child Python process was being truncated. * Fix an off-by-one error when reading bytes from UART on MicroPython devices. * Ensure zoom is consistent and remembered between panes and sessions. * Ensure mu\_code and/or current directory of current script are on Python path in Mu installed from the installer on Windows. Thanks to Tim Golden and Tim McCurrach for helping to test the fix. * Added Argon, Boron and Xenon boards to Adafruit mode since they’re also supported by Adafruit’s CircuitPython. * The directory used to start a load/save dialog is either what the user last selected, the current directory of the current file or the mode’s working directory (in order of precedence). This is reset when the mode is changed. * Various minor typo and bug fixes. #### 1.0.1[¶](#id5 "Permalink to this headline") This is a bugfix and new translation release. No new features were added. The next release will be 1.1.0 with some new features. * Added a German translation by René Raab. * Added various new Adafruit boards, thanks Limor! * Added a Vietnamese translation by GitHub user @doanminhdang. * Fix bug in MicroPython REPL when dealing with colour escape sequences, thanks Martin Dybdal of Coding Pirates! Arrr. * Ensured anyone trying to setup on an incompatible version of Python is given a friendly message explaining the problem. Thanks to the hugely talented René Dudfield for migrating this helpful function from PyGame! * Added a Brasilian translation by Marco A L Barbosa. * Added missing API docs for PyGameZero. Thanks to Justin Riley. * Added a Swedish translation by Filip Korling. * Fixes to various metadata configuration entries by Nick Morrott. * Updated to a revised Chinese translation. Thanks to John Guan. * Added the Mappa MUndi (roadmap) to the developer documentation. * Added a Polish translation by Filip Kłębczyk. * Fixes and enhancements to the UI to aid dyslexic users by Tim McCurrach. * Updated to version 1.0.0.final for MicroPython on the BBC micro:bit. Many thanks to Damien George of the MicroPython project for his amazing work. * Many other minor bugs caught and fixed by the likes of Zander and Carlos! #### 1.0.0[¶](#id6 "Permalink to this headline") * Fix for font related issues in OSX Mojave. Thanks to Steve Stagg for spotting and fixing. * Fix for encoding issue encountered during code checking. Thanks to Tim Golden for a swift fix. * Fix for orphaned modal dialog. Thanks for spotting this Zander Brown. * Minor revisions to hot-key sequences to avoid duplications. All documented at <https://codewith.mu/en/tutorials/1.0/shortcuts>. * Update to latest version of uflash and MicroPython 1.0.0-rc.2 for micro:bit. * Updated to latest GuiZero in Windows installers. * Update third party API documentation used by QScintilla for code completion and call tips. Includes CircuitPython 3 and PyGame Zero 1.2. * Added swag related graphics to the repository (non-functional change). #### 1.0.0.rc.1[¶](#rc-1 "Permalink to this headline") * Various UI style clean ups to make sure the look of Mu is more consistent between platforms. Thanks to Zander Brown for this valuable work. * Added French translation of the user interface. Thanks to Gerald Quintana. * Added Japanese translation of the user interface. Thanks to @MinoruInachi. * Added Spanish translation of the user interface. Thanks to Carlos Pereira Atencio with help from Oier Echaniz. * Added Portuguese translation of the user interface. Thanks to Tiago Montes. * Fixed various edge cases relating to the new-style flashing of micro:bits. * Fixed off-by-one error in the visual debugger highlighting of code (caused by Windows newlines not correctly handled). * Fixed shadow module related problem relating to Adafruit mode. It’s now possible to save “code.py” files onto boards. * Updated to latest version of uflash and MicroPython 1.0.0-rc.1 for micro:bit. * Various minor bugs and niggles have been fixed. #### 1.0.0.beta.17[¶](#beta-17 "Permalink to this headline") * Update to the latest version of uflash with the latest version of MicroPython for the BBC micro:bit. * Change flashing the BBC micro:bit to become more efficient (based on the copying of files to the boards small “fake” filesystem, rather than re-flashing the whole device in one go). * Ensure user agrees to GPL3 license when installing on OSX. * Fix Windows “make” file to correctly report errors thanks to Tim Golden. * The debugger in Python mode now correctly handles user-generated exceptions. * The debugger in Python mode updates the stack when no breakpoints are set. * Major update of the OSX based automated build system. * Modal dialog boxes should behave better on GTK based desktops thanks to Zander Brown. * Right click to access context menu in file panes in micro:bit mode so local files can be opened in Mu. * Fix bug where REPL, Files and Plotter buttons got into a bad state on mode change. * Update to use PyQt 5.11. * On save, check for shadow modules (i.e. user’s are not allowed to save code whose filename would override an existing module name). * Automatic comment toggling via Ctrl-K shortcut. * A simple find and replace diaolog is now available via the Ctrl-F shortcut. * Various minor bugs and niggles have been squashed. #### 1.0.0.beta.16[¶](#beta-16 "Permalink to this headline") * Updated flashing in micro:bit mode so it is more robust and doesn’t block on Windows. Thank you to Carlos Pereira Atencio for issue #350 and the polite reminder. * Updated the mu-debug runner so if the required filename for the target isn’t passed into the command, a helpful message is displayed to the user. * Developer documentation updates. * Updated to the latest version of uflash, which contains the latest stable release of MicroPython for the micro:bit. Many thanks to Damien George for all his continuing hard work on MicroPython for the micro:bit. * Inclusion of tkinter, turtle, gpiozero, guizero, pigpio, pillow and requests libraries as built-in modules. * Update to latest version of Pygame Zero. * Fix plotter axis label bug which wouldn’t display numbers if value was a float. * Separate session and settings into two different files. Session includes user defined changes to configuration whereas settings contains sys-admin-y configuration. * Update the CSS for the three themes so they display consistently on all supported platforms. Thanks to Zander Brown for his efforts on this. * Move the mode selection to the “Mode” button in the top left of the window. * Support for different encodings and default to UTF-8 where possible. Many thanks to Tim Golden for all the hard work on this rather involved fix. * Consistent end of line support on all platforms. Once again, many thanks to Tim Golden for his work on this difficult problem. * Use `mu-editor` instead of `mu` to launch the editor from the command line. * More sanity when dealing with cross platform paths and ensure filetypes are treated in a case insensitive manner. * Add support for minification of Python scripts to be flashed onto a micro:bit thanks to Zander Brown’s nudatus module. * Clean up logging about device discovery (it’s much less verbose). * Drag and drop files onto Mu to open them. Thanks to Zander Brown for this *really useful* feature. * The old logs dialog is now an admin dialog which allows users to inspect the logs, but also make various user defined configuration changes to Mu. * Plotter now works in Python 3 mode. * Fix problem in OSX with the `mount` command when detecting Circuit Python boards. Thanks to Frank Morton for finding and fixing this. * Add data flood avoidance to the plotter. * OSX automated packaging. Thanks to Russell Keith-Magee and the team at BeeWare for their invaluable help with this problematic task. * Refactoring and bug fixing of the visual debugger’s user interface. Thank you to Martin O’Hanlon and Carlos Pereira Atencio for their invaluable bug reports and testing. * Various fixes to the way the UI and themes are displayed (crisper icons on HiDPI displays and various other fixes). Thanks to Steve Stagg for putting lipstick on the pig. ;-) * A huge number of minor bug fixes, UI clean-ups and simplifications. #### 1.0.0.beta.15[¶](#beta-15 "Permalink to this headline") * A new plotter works with CircuitPython and micro:bit modes. If you emit tuples of numbers via the serial connection (e.g. `print((1, 2, 3))` as three arbitrary values) over time these will be plotted as line graphs. Many thanks to Limor “ladyada” Fried for contributing code for this feature. * Major refactoring of how Mu interacts with connected MicroPython based boards in order to enable the plotter and REPL to work independently. * Mu has a new mode for Pygame Zero (version 1.1). Thanks to Dan Pope for Pygame Zero and Rene Dudfield for being Pygame maintainer. * It’s now possible to run mu “python3 -m mu”. Thanks to Cefn Hoile for the contribution. * Add support for pirkey Adafruit board. Thanks again Adafruit. * Updated all the dependencies to the latest upstream versions. * Various minor bug fixes and guards to make Mu more robust (although this will always be bugs!). #### 1.0.0.beta.14[¶](#beta-14 "Permalink to this headline") * Add new PythonProcessPanel to better handle interactions with child Python3 processes. Includes basic command history and command editing. * Move the old “run” functionality in Python3 mode into a new “Debug” button. * Create a new “Run” button in Python3 mode that uses the new PythonProcessPanel. * Automation of 32bit and 64bit Windows installers (thanks to Thomas Kluyver for his fantastic pynsist tool). * Add / revise developer documentation in light of changes above. * (All the changes mentioned above were supported by the Raspberry Pi Foundation – Thank you!) * Update / add USB PIDs for Adafruit boards (thanks Adafruit for the heads up). * Minor cosmetic changes. * Additional test cases. #### 1.0.0.beta.13[¶](#beta-13 "Permalink to this headline") * Fix to solve problem when restoring CircuitPython session when device is not connected. * Fix to solve “data terminal ready” (DTR) problem when CircuitPython expects DTR to be set (and it isn’t by default in Qt). * Added initial work on developer documentation found here: <http://mu.rtfd.io/> * Updates to USB PIDs for Adafruit boards. * Added functionally equivalent “make.py” for Windows based developers. * Major refactor of the micro:bit related “files” UI pane: it no longer blocks the main UI thread. #### 1.0.0.beta.12[¶](#beta-12 "Permalink to this headline") * Update “save” related behaviour so “save as” pops up when the filename in the tab is double clicked. * Update the debugger so the process stops at the end of the run. * Ensure the current working directory for the REPL is set to mu\_mode. * Add additional documentation about Raspberry Pi related API. * Update micro:bit runtime to lates MicroPython beta. * Make a start on developer documentation. #### 1.0.0.beta.11[¶](#beta-11 "Permalink to this headline") * Updated Python 3 REPL to make use of an out of process iPython kernel (to avoid problems with blocking Mu’s UI). * Reverted Save related functionality to prior behaviour. * The “Save As” dialog for re-naming a file is launched when you click the filename in the tab associated with the code. #### 1.0.0.beta.10[¶](#beta-10 "Permalink to this headline") * Ensured “Save” button prompts user to confirm (or replace) the filename of an existing file. Allows Mu to have something like “Save As”. * Updated to latest microfs library for working with the micro:bit’s filesystem. * Fixed three code quality warnings found by <https://lgtm.com/projects/g/mu-editor/mu/alerts/?mode=list> * Updated API generation so the output is ordered (helps when diffing the generated files). * Updated Makefile to create Python packages/wheels and deploy to PyPI. * Explicit versions for packages found within install\_requires in setup.py. * Minor documentation changes. #### 1.0.0.beta.9[¶](#beta-9 "Permalink to this headline") * Debian related packaging updates. * Fixed a problem relating to how Windows stops the debug runner. * Fixed a problem relating to how Windows paths are expressed that was stopping the debug runner from starting. #### 1.0.0.beta.8[¶](#beta-8 "Permalink to this headline") * Updated splash image to reflect trademark usage of logos. * Refactored the way the Python runner executes so that it drops into the Python shell when it completes. * The debug runner now reports when it has finished running a script. #### 1.0.0.beta.7[¶](#id7 "Permalink to this headline") * Update PyInstaller icons. * Fix some tests that fail on older version of Python 3. * Add scripts to extract API information from Adafruit and Python 3. * Add generated API documentation to Mu so autosuggest and call tips have data. * Ensure translation files are distributed. #### 1.0.0.beta.6[¶](#id8 "Permalink to this headline") * Pip installable. * Updated theme handling: day, night and high-contrast (as per user feedback). * Keyboard shortcuts. #### 1.0.0.beta.\*[¶](#beta "Permalink to this headline") * Added modes to allow Mu to be a general Python editor. (Python3, Adafruit and micro:bit.) * Added simple visual debugger. * Added iPython based REPL for Python3 mode. * Many minor UI changes based on UX feedback. * Many bug fixes. #### 0.9.13[¶](#id9 "Permalink to this headline") * Add ability to change default Python directory in the settings file. Thanks to Zander Brown for the contribution. See #179. #### 0.9.12[¶](#id10 "Permalink to this headline") * Change the default Python directory from `~/python` to `~/mu\_code`. This fixes issue #126. * Add instructions for installing PyQt5 and QScintilla on Mac OS. * Update to latest version of uFlash. * Add highlighting of search mathes. * Check if the script produced is > 8k. * Use a settings file local to the Mu executable if available. * Fix bug with highlighting code errors in Windows. * Check to overwrite an existing file on the micro:bit FS. * Start changelog ### GNU General Public License[¶](#gnu-general-public-license "Permalink to this headline") *Version 3, 29 June 2007* *Copyright © 2007 Free Software Foundation, Inc* <<http://fsf.org>> Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. #### Preamble[¶](#preamble "Permalink to this headline") The GNU General Public License is a free, copyleft license for software and other kinds of works. The licenses for most software and other practical works are designed to take away your freedom to share and change the works. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change all versions of a program–to make sure it remains free software for all its users. We, the Free Software Foundation, use the GNU General Public License for most of our software; it applies also to any other work released this way by its authors. You can apply it to your programs, too. When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for them if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs, and that you know you can do these things. To protect your rights, we need to prevent others from denying you these rights or asking you to surrender the rights. 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An interactive user interface displays “Appropriate Legal Notices” to the extent that it includes a convenient and prominently visible feature that **(1)** displays an appropriate copyright notice, and **(2)** tells the user that there is no warranty for the work (except to the extent that warranties are provided), that licensees may convey the work under this License, and how to view a copy of this License. If the interface presents a list of user commands or options, such as a menu, a prominent item in the list meets this criterion. ##### 1. Source Code[¶](#source-code "Permalink to this headline") The “source code” for a work means the preferred form of the work for making modifications to it. “Object code” means any non-source form of a work. 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The Corresponding Source for a work in source code form is that same work. ##### 2. Basic Permissions[¶](#basic-permissions "Permalink to this headline") All rights granted under this License are granted for the term of copyright on the Program, and are irrevocable provided the stated conditions are met. This License explicitly affirms your unlimited permission to run the unmodified Program. The output from running a covered work is covered by this License only if the output, given its content, constitutes a covered work. This License acknowledges your rights of fair use or other equivalent, as provided by copyright law. You may make, run and propagate covered works that you do not convey, without conditions so long as your license otherwise remains in force. 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Ancillary propagation of a covered work occurring solely as a consequence of using peer-to-peer transmission to receive a copy likewise does not require acceptance. However, nothing other than this License grants you permission to propagate or modify any covered work. These actions infringe copyright if you do not accept this License. Therefore, by modifying or propagating a covered work, you indicate your acceptance of this License to do so. ##### 10. Automatic Licensing of Downstream Recipients[¶](#automatic-licensing-of-downstream-recipients "Permalink to this headline") Each time you convey a covered work, the recipient automatically receives a license from the original licensors, to run, modify and propagate that work, subject to this License. You are not responsible for enforcing compliance by third parties with this License. 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A contributor’s “essential patent claims” are all patent claims owned or controlled by the contributor, whether already acquired or hereafter acquired, that would be infringed by some manner, permitted by this License, of making, using, or selling its contributor version, but do not include claims that would be infringed only as a consequence of further modification of the contributor version. For purposes of this definition, “control” includes the right to grant patent sublicenses in a manner consistent with the requirements of this License. Each contributor grants you a non-exclusive, worldwide, royalty-free patent license under the contributor’s essential patent claims, to make, use, sell, offer for sale, import and otherwise run, modify and propagate the contents of its contributor version. In the following three paragraphs, a “patent license” is any express agreement or commitment, however denominated, not to enforce a patent (such as an express permission to practice a patent or covenant not to sue for patent infringement). To “grant” such a patent license to a party means to make such an agreement or commitment not to enforce a patent against the party. If you convey a covered work, knowingly relying on a patent license, and the Corresponding Source of the work is not available for anyone to copy, free of charge and under the terms of this License, through a publicly available network server or other readily accessible means, then you must either **(1)** cause the Corresponding Source to be so available, or **(2)** arrange to deprive yourself of the benefit of the patent license for this particular work, or **(3)** arrange, in a manner consistent with the requirements of this License, to extend the patent license to downstream recipients. “Knowingly relying” means you have actual knowledge that, but for the patent license, your conveying the covered work in a country, or your recipient’s use of the covered work in a country, would infringe one or more identifiable patents in that country that you have reason to believe are valid. If, pursuant to or in connection with a single transaction or arrangement, you convey, or propagate by procuring conveyance of, a covered work, and grant a patent license to some of the parties receiving the covered work authorizing them to use, propagate, modify or convey a specific copy of the covered work, then the patent license you grant is automatically extended to all recipients of the covered work and works based on it. A patent license is “discriminatory” if it does not include within the scope of its coverage, prohibits the exercise of, or is conditioned on the non-exercise of one or more of the rights that are specifically granted under this License. You may not convey a covered work if you are a party to an arrangement with a third party that is in the business of distributing software, under which you make payment to the third party based on the extent of your activity of conveying the work, and under which the third party grants, to any of the parties who would receive the covered work from you, a discriminatory patent license **(a)** in connection with copies of the covered work conveyed by you (or copies made from those copies), or **(b)** primarily for and in connection with specific products or compilations that contain the covered work, unless you entered into that arrangement, or that patent license was granted, prior to 28 March 2007. Nothing in this License shall be construed as excluding or limiting any implied license or other defenses to infringement that may otherwise be available to you under applicable patent law. ##### 12. No Surrender of Others’ Freedom[¶](#no-surrender-of-others-freedom "Permalink to this headline") If conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot convey a covered work so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not convey it at all. For example, if you agree to terms that obligate you to collect a royalty for further conveying from those to whom you convey the Program, the only way you could satisfy both those terms and this License would be to refrain entirely from conveying the Program. ##### 13. Use with the GNU Affero General Public License[¶](#use-with-the-gnu-affero-general-public-license "Permalink to this headline") Notwithstanding any other provision of this License, you have permission to link or combine any covered work with a work licensed under version 3 of the GNU Affero General Public License into a single combined work, and to convey the resulting work. The terms of this License will continue to apply to the part which is the covered work, but the special requirements of the GNU Affero General Public License, section 13, concerning interaction through a network will apply to the combination as such. ##### 14. Revised Versions of this License[¶](#revised-versions-of-this-license "Permalink to this headline") The Free Software Foundation may publish revised and/or new versions of the GNU General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Program specifies that a certain numbered version of the GNU General Public License “or any later version” applies to it, you have the option of following the terms and conditions either of that numbered version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of the GNU General Public License, you may choose any version ever published by the Free Software Foundation. If the Program specifies that a proxy can decide which future versions of the GNU General Public License can be used, that proxy’s public statement of acceptance of a version permanently authorizes you to choose that version for the Program. Later license versions may give you additional or different permissions. However, no additional obligations are imposed on any author or copyright holder as a result of your choosing to follow a later version. ##### 15. Disclaimer of Warranty[¶](#disclaimer-of-warranty "Permalink to this headline") THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM “AS IS” WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. ##### 16. Limitation of Liability[¶](#limitation-of-liability "Permalink to this headline") IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. ##### 17. Interpretation of Sections 15 and 16[¶](#interpretation-of-sections-15-and-16 "Permalink to this headline") If the disclaimer of warranty and limitation of liability provided above cannot be given local legal effect according to their terms, reviewing courts shall apply local law that most closely approximates an absolute waiver of all civil liability in connection with the Program, unless a warranty or assumption of liability accompanies a copy of the Program in return for a fee. *END OF TERMS AND CONDITIONS* #### How to Apply These Terms to Your New Programs[¶](#how-to-apply-these-terms-to-your-new-programs "Permalink to this headline") If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively state the exclusion of warranty; and each file should have at least the “copyright” line and a pointer to where the full notice is found. > > <one line to give the program’s name and a brief idea of what it does.> > Copyright (C) <year> <name of author> > > > This program is free software: you can redistribute it and/or modify > it under the terms of the GNU General Public License as published by > the Free Software Foundation, either version 3 of the License, or > (at your option) any later version. > > > This program is distributed in the hope that it will be useful, > but WITHOUT ANY WARRANTY; without even the implied warranty of > MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the > GNU General Public License for more details. > > > You should have received a copy of the GNU General Public License > along with this program. If not, see <<http://www.gnu.org/licenses/>>. > > > Also add information on how to contact you by electronic and paper mail. If the program does terminal interaction, make it output a short notice like this when it starts in an interactive mode: > > <program> Copyright (C) <year> <name of author> > This program comes with ABSOLUTELY NO WARRANTY; for details type ‘show w’. > This is free software, and you are welcome to redistribute it > under certain conditions; type ‘show c’ for details. > > > The hypothetical commands show w and show c should show the appropriate parts of the General Public License. Of course, your program’s commands might be different; for a GUI interface, you would use an “about box”. You should also get your employer (if you work as a programmer) or school, if any, to sign a “copyright disclaimer” for the program, if necessary. For more information on this, and how to apply and follow the GNU GPL, see <<http://www.gnu.org/licenses/>>. The GNU General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Lesser General Public License instead of this License. But first, please read <<http://www.gnu.org/philosophy/why-not-lgpl.html>>. ### Copyright Information[¶](#copyright-information "Permalink to this headline") #### Mu Copyright[¶](#mu-copyright "Permalink to this headline") Mu, its source code and associated assets are copyright Nicholas H.Tollervey and others (those who have made significant contributions to Mu can be found in this list of [Mu’s Developers](index.html#document-authors)). #### Notes on Image Copyright Status[¶](#notes-on-image-copyright-status "Permalink to this headline") All images used in Mu’s developer documentation fall under Mu’s copyright status, except for the following images sourced from third parties: ##### beautifully\_useless.jpg[¶](#beautifully-useless-jpg "Permalink to this headline") Permission was sought and obtained from Katerina Kamprani (the creator of [the uncomfortable](http://theuncomfortable.com)): ``` Hello Nicholas, Thank you very much for asking and for your kind words. As long as there is no commercial use of the image, and since you mention my project, it is absolutely fine! I am very happy the images help to prove a point! Many greetings from Athens, Greece, Katerina ``` ##### circuit\_playground.jpg[¶](#circuit-playground-jpg "Permalink to this headline") Permission was sought and obtained from [Adafruit Industries](https://adafruit.com/), the source of the image: ``` yup! 100%! please use! > Hi Folks, > > Is it OK to use a picture of a Circuit Playground Express taken > from your website in the developer docs for Mu. Like this..? > > https://mu.readthedocs.io/en/latest/modes.html#adafruit-mode ``` ##### pygame.png[¶](#pygame-png "Permalink to this headline") Permission was sought and obtained from René Dudfield, the current core maintainer of [Pygame](https://pygame.org): ``` public domain Go for it! Feel free to do whatever weird(and not weird) things you like with it. It's a modification (by me) of a logo by Gareth Noyce, who also put the logo files in public domain. Gareth Noyce said of the logo files: They're public domain but I'd like attribution if they're used anywhere. Just a "logo by Gareth Noyce" would do, but I won't be complaining if people forget. :)' ``` ##### python.png[¶](#python-png "Permalink to this headline") This is a copy of the Python logo owned by the [Python Software Foundation](https://python.org/psf) (PSF). Mu was originally written by a PSF Fellow on behalf of the PSF as part of the PSF’s contribution to the BBC’s micro:bit project. Furthermore, the PSF say of the [use of the Python logo](https://www.python.org/community/logos/)): > > “Projects and companies that use Python are encouraged to incorporate > the Python logo on their websites, brochures, packaging, and elsewhere > to indicate suitability for use with Python or implementation in > Python. Use of the “two snakes” logo element alone, without the > accompanying wordmark is permitted on the same terms as the combined > logo. > > > In general, we want the logo to be used as widely as possible to > indicate use of Python or suitability for Python.” > > > ##### lego.png[¶](#lego-png "Permalink to this headline") This is a copped version of the [Creative Commons licensed](https://creativecommons.org/licenses/by-sa/2.0/) photograph taken by [Jeff Eaton](https://www.flickr.com/people/jeffeaton/) that can be [found here](https://www.flickr.com/photos/jeffeaton/7298224068). ##### pyboard.png[¶](#pyboard-png "Permalink to this headline") The [MicroPython Logo](https://commons.wikimedia.org/wiki/File:MicroPython_new_logo.svg) is covered by the MIT license. ##### web.png[¶](#web-png "Permalink to this headline") The [Flask Logo](https://commons.wikimedia.org/wiki/File:Flask_logo.svg) has been released to the public domain. [](#) [Mu](#) ======= A Python code editor for beginner programmers. ### Navigation * [Contributing to Mu](index.html#document-contributing) * [Code of Conduct](index.html#document-code_of_conduct) * [Developer Setup](index.html#document-setup) * [Suggested First Steps](index.html#document-first-steps) * [User Experience](index.html#document-user-experience) * [Mu’s Architecture](index.html#document-architecture) * [Modes in Mu](index.html#document-modes) * [Internationalisation of Mu](index.html#document-translations) * [Python Runner/Debugger](index.html#document-debugger) * [Mu’s Test Suite](index.html#document-tests) * [Packaging Mu](index.html#document-packaging) * [Developing Mu’s Website](index.html#document-website) * [Mu API Reference](index.html#document-api) * [Design Decisions](index.html#document-design) * [Release Process](index.html#document-release) * [Roadmap (Mappa MUndi)](index.html#document-roadmap) * [Mu’s Developers](index.html#document-authors) * [Release History](index.html#document-changes) * [GNU General Public License](index.html#document-license) * [Copyright Information](index.html#document-copyright) ### Quick search ©2017-2021, Nicholas H.Tollervey and Mu Contributors. | Powered by [Sphinx 4.3.2](http://sphinx-doc.org/) & [Alabaster 0.7.12](https://github.com/bitprophet/alabaster)

pilosa

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Python Client for Pilosa 0.3.30 documentation [Python Client for Pilosa](index.html#document-index) stable Contents: * [pilosa package](index.html#document-pilosa) + [Submodules](index.html#submodules) + [pilosa.client module](index.html#pilosa-client-module) + [pilosa.exceptions module](index.html#pilosa-exceptions-module) + [pilosa.orm module](index.html#pilosa-orm-module) + [pilosa.response module](index.html#pilosa-response-module) + [pilosa.validator module](index.html#pilosa-validator-module) + [pilosa.version module](index.html#pilosa-version-module) + [Module contents](index.html#module-contents) [Python Client for Pilosa](index.html#document-index) * [Docs](index.html#document-index) » * Python Client for Pilosa 0.3.30 documentation * [Edit on GitHub](https://github.com/pilosa/python-pilosa/blob/28597d10623db6f5e5da05e731310931a899d0f4/doc/index.rst) --- Welcome to Python Client for Pilosa’s documentation ========================================================================================================================================== Python client for [Pilosa](https://www.pilosa.com) high performance distributed row index. pilosa package[¶](#pilosa-package "Permalink to this headline") --------------------------------------------------------------- ### Submodules[¶](#submodules "Permalink to this headline") ### pilosa.client module[¶](#pilosa-client-module "Permalink to this headline") ### pilosa.exceptions module[¶](#pilosa-exceptions-module "Permalink to this headline") ### pilosa.orm module[¶](#pilosa-orm-module "Permalink to this headline") ### pilosa.response module[¶](#pilosa-response-module "Permalink to this headline") ### pilosa.validator module[¶](#pilosa-validator-module "Permalink to this headline") ### pilosa.version module[¶](#pilosa-version-module "Permalink to this headline") ### Module contents[¶](#module-contents "Permalink to this headline") Requirements[¶](#requirements "Permalink to this headline") ----------------------------------------------------------- * Python 2.7 and higher or Python 3.4 and higher. Install[¶](#install "Permalink to this headline") ------------------------------------------------- Pilosa client is on [PyPI](https://pypi.python.org/pypi/pilosa). You can install the library using `pip`: ``` pip install pilosa ``` Quick overview[¶](#quick-overview "Permalink to this headline") --------------------------------------------------------------- Assuming [Pilosa](https://github.com/pilosa/pilosa) server is running at `localhost:10101` (the default): ``` import pilosa # Create the default client client = pilosa.Client() # Retrieve the schema schema = client.schema() # Create an Index object myindex = schema.index("myindex") # Create a Field object myfield = myindex.field("myfield") # make sure the index and field exists on the server client.sync\_schema(schema) # Send a SetBit query. PilosaError is thrown if execution of the query fails. client.query(myfield.set(5, 42)) # Send a Bitmap query. PilosaError is thrown if execution of the query fails. response = client.query(myfield.row(5)) # Get the result result = response.result # Act on the result if result: columns = result.row.columns print("Got columns: ", columns) # You can batch queries to improve throughput response = client.query( myindex.batch\_query( myfield.row(5), myfield.row(10), ) ) for result in response.results: # Act on the result print(result) ``` Indices and tables[¶](#indices-and-tables "Permalink to this headline") ======================================================================= * [Index](genindex.html) * [Module Index](py-modindex.html) * [Search Page](search.html)

resty

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resty 0.1.1 documentation ### Navigation * [resty 0.1.1 documentation](index.html#document-index) » Welcome to resty’s documentation ==================================================================================================== `resty` library provides user-friendly abstractions that allow you to easily interact with RESTful API. This documentation contains guidance on how to install and use the library. `resty` is: * simple high-level API interaction * easy to use * smart Contents: Installation[¶](#installation "Permalink to this headline") ----------------------------------------------------------- This tutorial will walk you through the process of installing resty ### Install `resty`[¶](#install-resty "Permalink to this headline") The installation can be easy done by using the *easy\_install*: ``` easy_install resty ``` You can upgrade your older version too: ``` easy_install --upgrade resty ``` Or using *pip*: ``` pip install resty ``` Getting started tutorial[¶](#getting-started-tutorial "Permalink to this headline") ----------------------------------------------------------------------------------- This tutorial aims to get you started with `resty` as quickly as possible. It is made up of a number of examples of increasing complexity, each of which shows one or more useful `resty` features. If you have any comments about these tutorials, or suggestions for things we should cover in them, then please contact us via [Github](https://github.com/pbs/resty/) ### Step 1: Configure the client[¶](#step-1-configure-the-client "Permalink to this headline") `resty` provides two types of clients: * the default `client` - uses summaries if available therefore the interactions with the api are optimized * `dumb\_client` - does not use summaries and relies strictly on links Note The summary is composed from all additional attributes (not required) that a minimum document representation has. The minimum documentation representation is used whenever a document has a relationship to another document. ### Step 2: Load the entrypoint and select a service[¶](#step-2-load-the-entrypoint-and-select-a-service "Permalink to this headline") The URL where the Service document is located is named the API Entrypoint. In the provided example we used `http://services.pbs.org/` url as an entrypoint and selected the zipcodes service from the available services: ``` >>> from resty import client >>> c = client.load("http://services.pbs.org/") >>> c <resty.types.Service object at 0x26abd10> >>> zipcode\_collection = c.service("zipcodes") <resty.types.Collection object at 0x2597e90> ``` Note The call to `c.service("zicpodes")` will select the top level collection named `zipcodes`. ### Step 3: Use filters[¶](#step-3-use-filters "Permalink to this headline") The `filters` are used to describe complex interactions that usually require some sort of human input. One particularly common situation is searching trough the elements of a collection. Templates are available only in collections. Since `zipcode\_collection` returns a collection we can filter it based on zip. ``` >>> filtered\_zipcodes = zipcode\_collection.filter('zip', zipcode='22202') ``` Note The `filter` method takes one positional argument that represents the filter name and a number of keyword arguments where the keys represent the placeholder names and the desired values. ### Step 4: Iterating through items[¶](#step-4-iterating-through-items "Permalink to this headline") Items represents the list of objects available in that collection. In the above example the `filtered\_zipcodes` returns a collection with a single object. Let’s select the first object from the list: ``` >>> zipcode\_resource = filtered\_zipcodes.items()[0] >>> zipcode\_resource <resty.types.Resource object at 0x259fc50> ``` ### Step 5: Accessing metadata and usefull content[¶](#step-5-accessing-metadata-and-usefull-content "Permalink to this headline") At this point we have a `zipcode\_resource` and we can extract informations like metadata and content specific informations ``` >>> print zipcode\_resource.content.zipcode u'22202' >>> print zipcode\_resource.class\_ u'Zipcode' ``` Note For example when representing a document in json, properties which are prefixed with $ are considered metadata. ### Step 6: Using links to interact with available relationships[¶](#step-6-using-links-to-interact-with-available-relationships "Permalink to this headline") Using the `related` method one can get from a document to a related document by specifying the relationship name. Let’s see all the callsigns that are available for zipcode 22202 with their corresponding confidence: ``` >>> callsign\_collection = zipcode\_resource.related('search') >>> for c in callsign\_collection.items(): >>> print c.related('related').content.callsign, c.content.confidence WETA 100 WMPB 100 WWPB 100 WHUT 100 WFPT 100 WVPY 100 WMPT 100 WGTV 80 KRMA 80 WTTW 80 WTVS 0 KCTS 0 KSPS 0 WGBH 0 WNED 0 ``` Note The `related` method takes one argument that represents the relationship name. In case there are multiple relationship with the same name one can pass an additional argument representing the related resource class or in case of a colletion the type of the elements. In the above example the call to `c.related('related')` is equivalent to `c.related('related', 'Callsign')` What’s new in resty 0.1?[¶](#what-s-new-in-resty-0-1 "Permalink to this headline") ---------------------------------------------------------------------------------- We are pleased to announce the initial release of the `resty` library. As this is the first version of `resty` library, these are the main features. ### Features[¶](#features "Permalink to this headline") * simple high-level API interaction * easy to use * smart Indices and tables[¶](#indices-and-tables "Permalink to this headline") ======================================================================= * [Index](genindex.html) * [Module Index](py-modindex.html) * [Search Page](search.html) ### [Table Of Contents](index.html#document-index) * [Installation](index.html#document-installing) + [Install `resty`](index.html#install-resty) * [Getting started tutorial](index.html#document-tutorial) + [Step 1: Configure the client](index.html#step-1-configure-the-client) + [Step 2: Load the entrypoint and select a service](index.html#step-2-load-the-entrypoint-and-select-a-service) + [Step 3: Use filters](index.html#step-3-use-filters) + [Step 4: Iterating through items](index.html#step-4-iterating-through-items) + [Step 5: Accessing metadata and usefull content](index.html#step-5-accessing-metadata-and-usefull-content) + [Step 6: Using links to interact with available relationships](index.html#step-6-using-links-to-interact-with-available-relationships) * [What’s new in resty 0.1?](index.html#document-releases) + [Features](index.html#features) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [resty 0.1.1 documentation](index.html#document-index) » © Copyright 2012, Core Services Team. Created using [Sphinx](http://sphinx-doc.org/) 1.3.5.

gravity

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Gravity 1.0.3 documentation [Gravity](index.html#document-index) stable Contents: * [Overview](index.html#document-readme) * [Quick Start](index.html#quick-start) + [Installation](index.html#installation) + [Usage](index.html#usage) * [Documentation Conventions](index.html#document-conventions) * [Installation and Configuration](index.html#document-installation) + [Installation](index.html#installation) + [Configuration](index.html#configuration) + [Configuration Options](index.html#configuration-options) + [Galaxy Job Handlers](index.html#galaxy-job-handlers) + [Gravity State](index.html#gravity-state) * [Basic Usage](index.html#document-basic_usage) + [Managing a Single Galaxy](index.html#managing-a-single-galaxy) + [Managing a Production Galaxy](index.html#managing-a-production-galaxy) * [Advanced Usage](index.html#document-advanced_usage) + [Zero-Downtime Restarts](index.html#zero-downtime-restarts) + [Service Instances](index.html#service-instances) + [Managing Multiple Galaxies](index.html#managing-multiple-galaxies) * [Subcommands](index.html#document-subcommands) + [start](index.html#start) + [stop](index.html#stop) + [restart](index.html#restart) + [graceful](index.html#graceful) + [update](index.html#update) + [shutdown](index.html#shutdown) + [follow](index.html#follow) + [list](index.html#list) + [show](index.html#show) + [pm](index.html#pm) + [exec](index.html#exec) * [History](index.html#document-history) + [1.0.3](index.html#section-1) + [1.0.2](index.html#section-2) + [1.0.1](index.html#section-3) + [1.0.0](index.html#section-4) + [0.13.6](index.html#section-5) + [0.13.5](index.html#section-6) + [0.13.4](index.html#section-7) + [0.13.3](index.html#section-8) + [0.13.2](index.html#section-9) + [0.13.1](index.html#section-10) + [0.13.0](index.html#section-11) + [0.12.0](index.html#section-12) + [0.11.0](index.html#section-13) + [0.10.0](index.html#section-14) + [0.9](index.html#section-15) + [0.8.3](index.html#section-16) + [0.8.2](index.html#section-17) + [0.8.1](index.html#section-18) + [0.8](index.html#section-19) + [0.7](index.html#section-20) + [Older](index.html#older) [Gravity](index.html#document-index) * [Docs](index.html#document-index) » * Gravity 1.0.3 documentation * [Edit on GitHub](https://github.com/galaxyproject/gravity/blob/087af2867b7490e9f949440d7235f11ed98ec408/docs/index.rst) --- Gravity[¶](#gravity "Permalink to this headline") ================================================= [](https://github.com/galaxyproject/gravity) Process management for [Galaxy](http://galaxyproject.org/) servers. [](http://gravity.readthedocs.io/en/latest/) [](https://pypi.python.org/pypi/gravity/) [](https://github.com/galaxyproject/gravity/actions/workflows/test.yaml) * License: MIT * Documentation: <https://gravity.readthedocs.io> * Code: <https://github.com/galaxyproject/gravity> Overview[¶](#overview "Permalink to this headline") --------------------------------------------------- Modern Galaxy servers run multiple disparate processes: [gunicorn](https://gunicorn.org/) for serving the web application, [celery](https://docs.celeryq.dev/) for asynchronous tasks, [tusd](https://tus.io/) for fault-tolerant uploads, standalone Galaxy processes for job handling, and more. Gravity is Galaxy’s process manager, to make configuring and running these services simple. Installing Gravity will give you two executables, `galaxyctl` which is used to manage the starting, stopping, and logging of Galaxy’s various processes, and `galaxy`, which can be used to run a Galaxy server in the foreground. Quick Start[¶](#quick-start "Permalink to this headline") --------------------------------------------------------- ### Installation[¶](#installation "Permalink to this headline") Python 3.7 or later is required. Gravity can be installed independently of Galaxy, but it is also a dependency of Galaxy since Galaxy 22.01. If you’ve installed Galaxy, then Gravity is already installed in Galaxy’s virtualenv. To install independently: ``` $ pip install gravity ``` ### Usage[¶](#usage "Permalink to this headline") From the root directory of a source checkout of Galaxy, after first run (or running Galaxy’s `./scripts/common\_startup.sh`), activate Galaxy’s virtualenv, which will put Gravity’s `galaxyctl` and `galaxy` commands on your `$PATH`: ``` $ . ./.venv/bin/activate $ galaxyctl --help Usage: galaxyctl [OPTIONS] COMMAND [ARGS]... Manage Galaxy server configurations and processes. ... additional help output ``` You can start and run Galaxy in the foreground using the `galaxy` command: ``` $ galaxy Registered galaxy config: /srv/galaxy/config/galaxy.yml Creating or updating service gunicorn Creating or updating service celery Creating or updating service celery-beat celery: added process group 2022-01-20 14:44:24,619 INFO spawned: 'celery' with pid 291651 celery-beat: added process group 2022-01-20 14:44:24,620 INFO spawned: 'celery-beat' with pid 291652 gunicorn: added process group 2022-01-20 14:44:24,622 INFO spawned: 'gunicorn' with pid 291653 celery STARTING celery-beat STARTING gunicorn STARTING ==> /srv/galaxy/var/gravity/log/gunicorn.log <== ...log output follows... ``` Galaxy will continue to run and output logs to stdout until terminated with `CTRL+C`. More detailed configuration and usage examples, especially those concerning production Galaxy servers, can be found in [the full documentation](https://gravity.readthedocs.io). Documentation Conventions[¶](#documentation-conventions "Permalink to this headline") ------------------------------------------------------------------------------------- Examples in this documentation assume a Galaxy layout like the one used in the [Galaxy Installation with Ansible](https://training.galaxyproject.org/training-material/topics/admin/tutorials/ansible-galaxy/tutorial.html) tutorial: ``` /srv/galaxy/server # Galaxy code /srv/galaxy/config # config files /srv/galaxy/venv # virtualenv ``` Installation and Configuration[¶](#installation-and-configuration "Permalink to this headline") ----------------------------------------------------------------------------------------------- ### Installation[¶](#installation "Permalink to this headline") Python 3.7 or later is required. Gravity can be installed independently of Galaxy, but it is also a dependency of Galaxy since Galaxy 22.01. If you’ve installed Galaxy, then Gravity is already installed in Galaxy’s virtualenv. To install independently: ``` $ pip install gravity ``` To make your life easier, you are encourged to install into a [virtualenv](https://virtualenv.pypa.io/). The easiest way to do this is with Python’s built-in [venv](https://docs.python.org/3/library/venv.html) module: ``` $ python3 -m venv ~/gravity $ . ~/gravity/bin/activate ``` ### Configuration[¶](#configuration "Permalink to this headline") Gravity needs to know where your Galaxy configuration file is, and depending on your Galaxy layout, some additional details like the paths to its virtualenv and root directory. By default, Gravity’s configuration is defined in Galaxy’s configuration file (`galaxy.yml`) to be easy and familiar for Galaxy administrators. Gravity’s configuration is defined underneath the `gravity` key, and Galaxy’s configuration is defined underneath the `galaxy` key. For example: ``` --- gravity: gunicorn: bind: localhost:8192 galaxy: database\_connection: postgresql:///galaxy ``` #### Configuration Search Paths[¶](#configuration-search-paths "Permalink to this headline") If you run `galaxy` or `galaxyctl` from the root of a Galaxy source checkout and do not specify the config file option, `config/galaxy.yml` or `config/galaxy.yml.sample` will be automatically used. To avoid having to run from the Galaxy root directory or to work with a config file in a different location, you can explicitly point Gravity at your Galaxy configuration file with the `--config-file` (`-c`) option or the `$GRAVITY\_CONFIG\_FILE` (or `$GALAXY\_CONFIG\_FILE`, as set by Galaxy’s `run.sh` script) environment variable. Then it’s possible to run the `galaxyctl` command from anywhere. Often times it’s convenient to put the environment variable in the Galaxy user’s shell environment file, e.g.: ``` $ echo "export GRAVITY_CONFIG_FILE='/srv/galaxy/config/galaxy.yml'" >> ~/.bash_profile ``` When running Gravity as root, the following configuration files will automatically be searched for and read, unless `--config-file` is specified or `$GRAVITY\_CONFIG\_FILE` is set: * `/etc/galaxy/gravity.yml` * `/etc/galaxy/galaxy.yml` * `/etc/galaxy/gravity.d/\*.y(a?)ml` #### Splitting Gravity and Galaxy Configurations[¶](#splitting-gravity-and-galaxy-configurations "Permalink to this headline") For more advanced deployments, it is *not* necessary to write your entire Galaxy configuration to the Gravity config file. You can write only the Gravity configuration, and then point to your Galaxy config file with the `galaxy\_config\_file` option in the Gravity config. This can be useful for cases such as your Galaxy server being split across multiple hosts. For example, on a deployment where the web (gunicorn) and job handler processes run on different hosts, one might have: In `gravity.yml` on the web host: ``` --- gravity: galaxy\_config\_file: galaxy.yml log\_dir: /var/log/galaxy gunicorn: bind: localhost:8888 celery: enable: false enable\_beat: false ``` In `gravity.yml` on the job handler host: ``` --- gravity: galaxy\_config\_file: galaxy.yml log\_dir: /var/log/galaxy gunicorn: enable: false celery: enable: true enable\_beat: true handlers: handler: processes: 2 ``` See the [Managing Multiple Galaxies](index.html#managing-multiple-galaxies) section for additional examples. ### Configuration Options[¶](#configuration-options "Permalink to this headline") The following options in the `gravity` section of `galaxy.yml` can be used to configure Gravity: ``` # Configuration for Gravity process manager. # ``uwsgi:`` section will be ignored if Galaxy is started via Gravity commands (e.g ``./run.sh``, ``galaxy`` or ``galaxyctl``). gravity: # Process manager to use. # ``supervisor`` is the default process manager when Gravity is invoked as a non-root user. # ``systemd`` is the default when Gravity is invoked as root. # Valid options are: supervisor, systemd # process\_manager: # What command to write to the process manager configs # `gravity` (`galaxyctl exec <service-name>`) is the default # `direct` (each service's actual command) is also supported. # Valid options are: gravity, direct # service\_command\_style: gravity # Use the process manager's \*service instance\* functionality for services that can run multiple instances. # Presently this includes services like gunicorn and Galaxy dynamic job handlers. Service instances are only supported if # ``service\_command\_style`` is ``gravity``, and so this option is automatically set to ``false`` if # ``service\_command\_style`` is set to ``direct``. # use\_service\_instances: true # umask under which services should be executed. Setting ``umask`` on an individual service overrides this value. # umask: '022' # Memory limit (in GB), processes exceeding the limit will be killed. Default is no limit. If set, this is default value # for all services. Setting ``memory\_limit`` on an individual service overrides this value. Ignored if ``process\_manager`` # is ``supervisor``. # memory\_limit: # Specify Galaxy config file (galaxy.yml), if the Gravity config is separate from the Galaxy config. Assumed to be the # same file as the Gravity config if a ``galaxy`` key exists at the root level, otherwise, this option is required. # galaxy\_config\_file: # Specify Galaxy's root directory. # Gravity will attempt to find the root directory, but you can set the directory explicitly with this option. # galaxy\_root: # User to run Galaxy as, required when using the systemd process manager as root. # Ignored if ``process\_manager`` is ``supervisor`` or user-mode (non-root) ``systemd``. # galaxy\_user: # Group to run Galaxy as, optional when using the systemd process manager as root. # Ignored if ``process\_manager`` is ``supervisor`` or user-mode (non-root) ``systemd``. # galaxy\_group: # Set to a directory that should contain log files for the processes controlled by Gravity. # If not specified defaults to ``<galaxy\_data\_dir>/gravity/log``. # log\_dir: # Set to Galaxy's virtualenv directory. # If not specified, Gravity assumes all processes are on PATH. This option is required in most circumstances when using # the ``systemd`` process manager. # virtualenv: # Select the application server. # ``gunicorn`` is the default application server. # ``unicornherder`` is a production-oriented manager for (G)unicorn servers that automates zero-downtime Galaxy server restarts, # similar to uWSGI Zerg Mode used in the past. # Valid options are: gunicorn, unicornherder # app\_server: gunicorn # Override the default instance name. # this is hidden from you when running a single instance. # instance\_name: \_default\_ # Configuration for Gunicorn. Can be a list to run multiple gunicorns for rolling restarts. gunicorn: # Enable Galaxy gunicorn server. # enable: true # The socket to bind. A string of the form: ``HOST``, ``HOST:PORT``, ``unix:PATH``, ``fd://FD``. An IP is a valid HOST. # bind: localhost:8080 # Controls the number of Galaxy application processes Gunicorn will spawn. # Increased web performance can be attained by increasing this value. # If Gunicorn is the only application on the server, a good starting value is the number of CPUs \* 2 + 1. # 4-12 workers should be able to handle hundreds if not thousands of requests per second. # workers: 1 # Gunicorn workers silent for more than this many seconds are killed and restarted. # Value is a positive number or 0. Setting it to 0 has the effect of infinite timeouts by disabling timeouts for all workers entirely. # If you disable the ``preload`` option workers need to have finished booting within the timeout. # timeout: 300 # Extra arguments to pass to Gunicorn command line. # extra\_args: # Use Gunicorn's --preload option to fork workers after loading the Galaxy Application. # Consumes less memory when multiple processes are configured. Default is ``false`` if using unicornherder, else ``true``. # preload: # umask under which service should be executed # umask: # Value of supervisor startsecs, systemd TimeoutStartSec # start\_timeout: 15 # Value of supervisor stopwaitsecs, systemd TimeoutStopSec # stop\_timeout: 65 # Amount of time to wait for a server to become alive when performing rolling restarts. # restart\_timeout: 300 # Memory limit (in GB). If the service exceeds the limit, it will be killed. Default is no limit or the value of the # ``memory\_limit`` setting at the top level of the Gravity configuration, if set. Ignored if ``process\_manager`` is # ``supervisor``. # memory\_limit: # Extra environment variables and their values to set when running the service. A dictionary where keys are the variable # names. # environment: {} # Configuration for Celery Processes. celery: # Enable Celery distributed task queue. # enable: true # Enable Celery Beat periodic task runner. # enable\_beat: true # Number of Celery Workers to start. # concurrency: 2 # Log Level to use for Celery Worker. # Valid options are: DEBUG, INFO, WARNING, ERROR # loglevel: DEBUG # Queues to join # queues: celery,galaxy.internal,galaxy.external # Pool implementation # Valid options are: prefork, eventlet, gevent, solo, processes, threads # pool: threads # Extra arguments to pass to Celery command line. # extra\_args: # umask under which service should be executed # umask: # Value of supervisor startsecs, systemd TimeoutStartSec # start\_timeout: 10 # Value of supervisor stopwaitsecs, systemd TimeoutStopSec # stop\_timeout: 10 # Memory limit (in GB). If the service exceeds the limit, it will be killed. Default is no limit or the value of the # ``memory\_limit`` setting at the top level of the Gravity configuration, if set. Ignored if ``process\_manager`` is # ``supervisor``. # memory\_limit: # Extra environment variables and their values to set when running the service. A dictionary where keys are the variable # names. # environment: {} # Configuration for gx-it-proxy. gx\_it\_proxy: # Set to true to start gx-it-proxy # enable: false # gx-it-proxy version # version: '>=0.0.5' # Public-facing IP of the proxy # ip: localhost # Public-facing port of the proxy # port: 4002 # Routes file to monitor. # Should be set to the same path as ``interactivetools\_map`` in the ``galaxy:`` section. This is ignored if # ``interactivetools\_map is set``. # sessions: database/interactivetools\_map.sqlite # Include verbose messages in gx-it-proxy # verbose: true # Forward all requests to IP. # This is an advanced option that is only needed when proxying to remote interactive tool container that cannot be reached through the local network. # forward\_ip: # Forward all requests to port. # This is an advanced option that is only needed when proxying to remote interactive tool container that cannot be reached through the local network. # forward\_port: # Rewrite location blocks with proxy port. # This is an advanced option that is only needed when proxying to remote interactive tool container that cannot be reached through the local network. # reverse\_proxy: false # umask under which service should be executed # umask: # Value of supervisor startsecs, systemd TimeoutStartSec # start\_timeout: 10 # Value of supervisor stopwaitsecs, systemd TimeoutStopSec # stop\_timeout: 10 # Memory limit (in GB). If the service exceeds the limit, it will be killed. Default is no limit or the value of the # ``memory\_limit`` setting at the top level of the Gravity configuration, if set. Ignored if ``process\_manager`` is # ``supervisor``. # memory\_limit: # Extra environment variables and their values to set when running the service. A dictionary where keys are the variable # names. # environment: {} # Configuration for tusd server (https://github.com/tus/tusd). # The ``tusd`` binary must be installed manually and made available on PATH (e.g in galaxy's .venv/bin directory). tusd: # Enable tusd server. # If enabled, you also need to set up your proxy as outlined in https://docs.galaxyproject.org/en/latest/admin/nginx.html#receiving-files-via-the-tus-protocol. # enable: false # Path to tusd binary # tusd\_path: tusd # Host to bind the tusd server to # host: localhost # Port to bind the tusd server to # port: 1080 # Directory to store uploads in. # Must match ``tus\_upload\_store`` setting in ``galaxy:`` section. # upload\_dir: # Comma-separated string of enabled tusd hooks. # # Leave at the default value to require authorization at upload creation time. # This means Galaxy's web process does not need to be running after creating the initial # upload request. # # Set to empty string to disable all authorization. This means data can be uploaded (but not processed) # without the Galaxy web process being available. # # You can find a list of available hooks at https://github.com/tus/tusd/blob/master/docs/hooks.md#list-of-available-hooks. # hooks\_enabled\_events: pre-create # Extra arguments to pass to tusd command line. # extra\_args: # umask under which service should be executed # umask: # Value of supervisor startsecs, systemd TimeoutStartSec # start\_timeout: 10 # Value of supervisor stopwaitsecs, systemd TimeoutStopSec # stop\_timeout: 10 # Memory limit (in GB). If the service exceeds the limit, it will be killed. Default is no limit or the value of the # ``memory\_limit`` setting at the top level of the Gravity configuration, if set. Ignored if ``process\_manager`` is # ``supervisor``. # memory\_limit: # Extra environment variables and their values to set when running the service. A dictionary where keys are the variable # names. # environment: {} # Configuration for Galaxy Reports. reports: # Enable Galaxy Reports server. # enable: false # Path to reports.yml, relative to galaxy.yml if not absolute # config\_file: reports.yml # The socket to bind. A string of the form: ``HOST``, ``HOST:PORT``, ``unix:PATH``, ``fd://FD``. An IP is a valid HOST. # bind: localhost:9001 # Controls the number of Galaxy Reports application processes Gunicorn will spawn. # It is not generally necessary to increase this for the low-traffic Reports server. # workers: 1 # Gunicorn workers silent for more than this many seconds are killed and restarted. # Value is a positive number or 0. Setting it to 0 has the effect of infinite timeouts by disabling timeouts for all workers entirely. # timeout: 300 # URL prefix to serve from. # The corresponding nginx configuration is (replace <url\_prefix> and <bind> with the values from these options): # # location /<url\_prefix>/ { # proxy\_pass http://<bind>/; # } # # If <bind> is a unix socket, you will need a ``:`` after the socket path but before the trailing slash like so: # proxy\_pass http://unix:/run/reports.sock:/; # url\_prefix: # Extra arguments to pass to Gunicorn command line. # extra\_args: # umask under which service should be executed # umask: # Value of supervisor startsecs, systemd TimeoutStartSec # start\_timeout: 10 # Value of supervisor stopwaitsecs, systemd TimeoutStopSec # stop\_timeout: 10 # Memory limit (in GB). If the service exceeds the limit, it will be killed. Default is no limit or the value of the # ``memory\_limit`` setting at the top level of the Gravity configuration, if set. Ignored if ``process\_manager`` is # ``supervisor``. # memory\_limit: # Extra environment variables and their values to set when running the service. A dictionary where keys are the variable # names. # environment: {} # Configure dynamic handlers in this section. # See https://docs.galaxyproject.org/en/latest/admin/scaling.html#dynamically-defined-handlers for details. # handlers: {} ``` ### Galaxy Job Handlers[¶](#galaxy-job-handlers "Permalink to this headline") Gravity has support for reading Galaxy’s job configuration: it can read statically configured job handlers in the `job\_conf.yml` or `job\_conf.xml` files, or the job configuration inline from the `job\_config` option in `galaxy.yml`. However, unless you need to statically define handlers, it is simpler to configure Gravity to run [dynamically defined handlers](https://docs.galaxyproject.org/en/latest/admin/scaling.html#dynamically-defined-handlers) as detailed in the Galaxy scaling documentation. When using dynamically defined handlers, be sure to explicitly set the [job handler assignment method](https://docs.galaxyproject.org/en/master/admin/scaling.html#job-handler-assignment-methods) to `db-skip-locked` or `db-transaction-isolation` to prevent the web process from also handling jobs. ### Gravity State[¶](#gravity-state "Permalink to this headline") Older versions of Gravity stored a considerable amount of *config state* in `$GRAVITY\_STATE\_DIR/configstate.yaml`. As of version 1.0.0, Gravity does not store state information, and this file can be removed if left over from an older installation. Although Gravity no longer uses the config state file, it does still use a state directory for storing supervisor configs, the default log directory (if `log\_dir` is unchanged), and the celery-beat database. This directory defaults to `<galaxy\_root>/database/gravity/` by way of the `data\_dir` option in the `galaxy` section of `galaxy.yml` (which defaults to `<galaxy\_root>/database/`). If running multiple Galaxy servers with the same Gravity configuration as described in [Managing Multiple Galaxies](index.html#managing-multiple-galaxies) and if doing so using supervisor rather than systemd, the supervisor configurations will be stored in `$XDG\_CONFIG\_HOME/galaxy-gravity` (`$XDG\_CONFIG\_HOME` defaults to `~/.config/galaxy-gravity`) In any case, you can override the path to the state directory using the `--state-dir` option, or the `$GRAVITY\_STATE\_DIR` environment variable. Note Galaxy 22.01 and 22.05 automatically set `$GRAVITY\_STATE\_DIR` to `<galaxy\_root>/database/gravity` in the virtualenv’s activation script, `activate`. This can be removed from the activate script when using Gravity 1.0.0 or later. Basic Usage[¶](#basic-usage "Permalink to this headline") --------------------------------------------------------- A basic example of starting and running a simple Galaxy server from a source clone in the foreground is provided in the ref:Quick Start guide. This section covers more typical usage for production Galaxy servers. ### Managing a Single Galaxy[¶](#managing-a-single-galaxy "Permalink to this headline") If you have not installed Gravity separate from the Galaxy virtualenv, simply activate Galaxy’s virtualenv, which will put Gravity’s `galaxyctl` and `galaxy` commands on your `$PATH`: ``` $ . /srv/galaxy/venv/bin/activate $ galaxyctl --help Usage: galaxyctl [OPTIONS] COMMAND [ARGS]... Manage Galaxy server configurations and processes. Options: -d, --debug Enables debug mode. -c, --config-file FILE Gravity (or Galaxy) config file to operate on. Can also be set with $GRAVITY_CONFIG_FILE or $GALAXY_CONFIG_FILE --state-dir DIRECTORY Where process management configs and state will be stored. -h, --help Show this message and exit. Commands: configs List registered config files. deregister Deregister config file(s). exec Run a single Galaxy service in the foreground, with logging... follow Follow log files of configured instances. graceful Gracefully reload configured services. instances List all known instances. pm Invoke process manager (supervisorctl, systemctl) directly. register Register config file(s). rename Update path of registered config file. restart Restart configured services. show Show details of registered config. shutdown Shut down process manager. start Start configured services. status Display server status. stop Stop configured services. update Update process manager from config changes. ``` If you run `galaxy` or `galaxyctl` from the root of a Galaxy source checkout and do not specify the config file option, `config/galaxy.yml` or `config/galaxy.yml.sample` will be automatically used. This is handy for working with local clones of Galaxy for testing or development. You can skip Galaxy’s lengthy and repetitive `run.sh` configuration steps when starting and stopping Galaxy in between code updates (you should still run `run.sh` after performing a `git pull` to make sure your dependencies are up to date). Gravity can either run Galaxy via the [supervisor](http://supervisord.org/) process manager (the default) or [systemd](https://www.freedesktop.org/wiki/Software/systemd/). For production servers, **it is recommended that you run Gravity as root in systemd mode**. See the [Managing a Production Galaxy](#managing-a-production-galaxy) section for details. As shown in the Quick Start, the `galaxy` command will run a Galaxy server in the foreground. The `galaxy` command is actually a shortcut for two separate steps: 1. read the provided `galaxy.yml` and write out the corresponding process manager configurations, and 2. start and run Galaxy in the foreground using the process manager ([supervisor](http://supervisord.org/)). You can perform these steps separately (and in this example, start Galaxy as a backgrounded daemon instead of in the foreground): ``` $ galaxyctl update Adding service gunicorn Adding service celery Adding service celery-beat $ galaxyctl start celery STARTING celery-beat STARTING gunicorn STARTING Log files are in /srv/galaxy/var/gravity/log ``` When running as a daemon, the `stop` subcommand stops your Galaxy server: ``` $ galaxyctl stop celery-beat: stopped gunicorn: stopped celery: stopped All processes stopped, supervisord will exit Shut down ``` Most Gravity subcommands (such as `stop`, `start`, `restart`, …) are straightforward, but a few subcommands are worth pointing out: [update](index.html#update), [graceful](index.html#graceful), and [exec](index.html#exec). All subcommands are documented in the [Subcommands](index.html#subcommands) section and their respective `--help` output. ### Managing a Production Galaxy[¶](#managing-a-production-galaxy "Permalink to this headline") By default, Gravity runs Galaxy processes under [supervisor](http://supervisord.org/), but setting the `process\_manager` option to `systemd` in Gravity’s configuration will cause it to run under [systemd](https://www.freedesktop.org/wiki/Software/systemd/) instead. systemd is the default init system under most modern Linux distributions, and using systemd is strongly encouraged for production Galaxy deployments. Gravity manages [systemd service unit files](https://www.freedesktop.org/software/systemd/man/systemd.unit.html) corresponding to all of the Galaxy services that it is aware of, much like how it manages supervisor program config files in supervisor mode. If you run `galaxyctl update` as a non-root user, the unit files will be installed in `~/.config/systemd/user` and run via [systemd user mode](https://www.freedesktop.org/software/systemd/man/user@.service.html). This can be useful for testing and development, but in production it is recommended to run Gravity as root, so that it installs the service units in `/etc/systemd/system` and are managed by the privileged systemd instance. Even when Gravity is run as root, Galaxy itself still runs as a non-root user, specified by the `galaxy\_user` option in the Gravity configuration. It is also recommended, when running as root, that you install Gravity independent of Galaxy, rather than use the copy installed in Galaxy’s virtualenv: ``` # python3 -m venv /opt/gravity # /opt/gravity/bin/pip install gravity ``` Caution Because systemd unit file names have semantic meaning (the filename is the service’s name) and systemd does not have a facility for isolating unit files controlled by an application, Gravity considers all unit files in the unit dir (`/etc/systemd/system`) that are named like `galaxy-\*` to be controlled by Gravity. **If you have existing unit files that are named as such, Gravity will overwrite or remove them.** In systemd mode, and especially when run as root, some Gravity options are required: ``` gravity: process\_manager: systemd # required if running as root galaxy\_user: GALAXY-USERNAME # optional, defaults to primary group of the user set above galaxy\_group: GALAXY-GROUPNAME # required virtualenv: /srv/galaxy/venv # probably necessary if your galaxy.yml is not in galaxy\_root/config galaxy\_root: /srv/galaxy/server ``` See the [Configuration](index.html#configuration) section for more details on these options and others. The `log\_dir` option is ignored when using systemd. Logs are instead captured by systemd’s logging facility, `journald`. You can use `galaxyctl` to manage Galaxy process starts/stops/restarts/etc. and follow the logs, just as you do under supervisor, but you can also use `systemctl` and `journalctl` directly to manage process states and inspect logs (respectively). Only `galaxyctl update` is necessary, in order to write and/or remove the appropriate systemd service units based on your configuration. For example: ``` # export GRAVITY\_CONFIG\_FILE=/srv/galaxy/config/galaxy.yml # . /srv/galaxy/venv/bin/activate (venv) # galaxyctl update Adding service galaxy-gunicorn.service Adding service galaxy-celery.service Adding service galaxy-celery-beat.service ``` After this point, operations can be performed with either `galaxyctl` or `systemctl`. Some examples of equivalent commands: | Gravity | systemd | | --- | --- | | `galaxy` | `systemctl start galaxy.target && journalctl -f -u 'galaxy-\*'` | | `galaxyctl start` | `systemctl start galaxy.target` | | `galaxyctl start SERVICE ...` | `systemctl start galaxy-SERVICE.service galaxy-...` | | `galaxyctl restart` | `systemctl restart galaxy.target` | | `galaxyctl restart SERVICE ...` | `systemctl restart galaxy-SERVICE.service galaxy-...` | | `galaxyctl graceful` | `systemctl reload-or-restart galaxy.target` | | `galaxyctl graceful SERVICE ...` | `systemctl reload-or-restart galaxy-SERVICE.service galaxy-...` | | `galaxyctl stop` | `systemctl start galaxy.target` | | `galayxctl follow` | `journalctl -f -u 'galaxy-\*'` | Advanced Usage[¶](#advanced-usage "Permalink to this headline") --------------------------------------------------------------- ### Zero-Downtime Restarts[¶](#zero-downtime-restarts "Permalink to this headline") Prior to Gravity 1.0, the preferred solution for performing zero-downtime restarts was [unicornherder](https://github.com/alphagov/unicornherder). However, due to limitations in the unicornherder software, it does not always successfully perform zero-downtime restarts. Because of this, Gravity is now able to perform rolling restarts of gunicorn services if more than one gunicorn is configured. To run multiple gunicorn processes, configure the `gunicorn` section of the Gravity configuration as a *list*. Each item in the list is a gunicorn configuration, and can have all of the same parameters as a single gunicorn configuration: ``` gravity: gunicorn: - bind: unix:/srv/galaxy/var/gunicorn0.sock workers: 4 - bind: unix:/srv/galaxy/var/gunicorn1.sock workers: 4 ``` Caution This will start multiple Galaxy servers with the same `server\_name`. If you have not configured separate Galaxy processes to act as job handlers, your gunicorn processes will handle them, resulting in job errors due to handling the same job multiple times. See the Gravity and Galaxy documentation on configuring handlers. Your proxy server can balance load between the two gunicorns. For example, with nginx: ``` upstream galaxy { server unix:/srv/galaxy/var/gunicorn0.sock; server unix:/srv/galaxy/var/gunicorn1.sock; } http { location / { proxy\_pass http://galaxy; } } ``` By default, Gravity will wait 300 seconds for the gunicorn server to respond to web requests after initiating the restart. To change this timeout this, set the `restart\_timeout` option on each configured `gunicorn` instance. ### Service Instances[¶](#service-instances "Permalink to this headline") In the case of multiple gunicorn instances as described in [Zero-Downtime Restarts](#zero-downtime-restarts) and multiple dynamic handlers as described in [Galaxy Job Handlers](index.html#galaxy-job-handlers), Gravity will create multiple *service instances* of each service. This allows multiple processes to be run from a single service definition. In supervisor, this means that the service names as presented by supervisor are appended with `:INSTANCE\_NUMBER`, e.g.: ``` $ galaxyctl status celery RUNNING pid 121363, uptime 0:02:33 celery-beat RUNNING pid 121364, uptime 0:02:33 gunicorn:0 RUNNING pid 121365, uptime 0:02:33 gunicorn:1 RUNNING pid 121366, uptime 0:02:33 ``` However, `galaxyctl` commands that take a service name still use the base service name, e.g.: ``` $ galaxyctl stop gunicorn gunicorn:0: stopped gunicorn:1: stopped Not all processes stopped, supervisord not shut down (hint: see `galaxyctl status`) ``` In systemd, the service names as presented by systemd are appended with `@INSTANCE\_NUMBER`, e.g.: ``` $ galaxyctl status UNIT LOAD ACTIVE SUB DESCRIPTION galaxy-celery-beat.service loaded active running Galaxy celery-beat galaxy-celery.service loaded active running Galaxy celery galaxy-gunicorn@0.service loaded active running Galaxy gunicorn (process 0) galaxy-gunicorn@1.service loaded active running Galaxy gunicorn (process 1) galaxy.target loaded active active Galaxy ``` As with supervisor, `galaxyctl` commands that take a service name still use the base service name. If you prefer not to work with service instances and want Galaxy to write a service configuration file for each instance of each service, you can do so by setting `use\_service\_instances` in the Gravity configuration to `false`. ### Managing Multiple Galaxies[¶](#managing-multiple-galaxies "Permalink to this headline") Gravity can manage multiple instances of Galaxy simultaneously. This is useful especially in the case where you have multiple production Galaxy instances on a single server and are managing them with Gravity installed outside of a Galaxy virtualenv, as root. There are multiple ways to achieve this: 1. Pass multiple `--config-file` options to `galaxyctl`, or set a list of colon-separated config paths in `$GRAVITY\_CONFIG\_FILE`: > > > ``` > $ galaxyctl --config-file /srv/galaxy/test/config/galaxy.yml \ > --config-file /srv/galaxy/main/config/galaxy.yml list --version > TYPE INSTANCE NAME VERSION CONFIG PATH > galaxy test 22.05 /srv/galaxy/test/config/galaxy.yml > galaxy main 22.09.dev0 /srv/galaxy/main/config/galaxy.yml > $ export GRAVITY_CONFIG_FILE='/srv/galaxy/test/config/galaxy.yml:/srv/galaxy/main/config/galaxy.yml' > $ galaxyctl list --version > TYPE INSTANCE NAME VERSION CONFIG PATH > galaxy test 22.05 /srv/galaxy/test/config/galaxy.yml > galaxy main 22.09.dev0 /srv/galaxy/main/config/galaxy.yml > > ``` > > > 2. If running as root, any config files located in `/etc/galaxy/gravity.d` will automatically be loaded. 3. Specify multiple Gravity configurations in a single config file, as a list. In this case, the Galaxy and Gravity configurations must be in separate files as described in [Splitting Gravity and Galaxy Configurations](index.html#splitting-gravity-and-galaxy-configurations): > > > ``` > gravity: > - instance\_name: test > process\_manager: systemd > galaxy\_config\_file: /srv/galaxy/test/config/galaxy.yml > galaxy\_root: /srv/galaxy/test/server > virtualenv: /srv/galaxy/test/venv > galaxy\_user: gxtest > gunicorn: > bind: unix:/srv/galaxy/test/var/gunicorn.sock > handlers: > handler: > pools: > - job-handlers > - workflow-schedulers > > - instance\_name: main > process\_manager: systemd > galaxy\_config\_file: /srv/galaxy/main/config/galaxy.yml > galaxy\_root: /srv/galaxy/main/server > virtualenv: /srv/galaxy/main/venv > galaxy\_user: gxmain > gunicorn: > bind: unix:/srv/galaxy/main/var/gunicorn.sock > workers: 8 > handlers: > handler: > processes: 4 > pools: > - job-handlers > - workflow-schedulers > > ``` > > > In all cases, when using multiple Gravity instances, each Galaxy instance managed by Gravity must have a unique **instance name**. When working with a single instance, the default name `\_default\_` is used automatically and mostly hidden from you. When working with multiple instances, set the `instance\_name` option in each instance’s Gravity config to a unique name. Although it is strongly encouraged to use systemd for running multiple instances, it is possible to use supervisor. Please see the [Gravity State](index.html#gravity-state) section for important details on how and where Gravity stores the supervisor configuration and log files. Subcommands[¶](#subcommands "Permalink to this headline") --------------------------------------------------------- Use `galaxyctl --help` for help. Subcommands also support `--help`, e.g. `galaxy register --help` ### start[¶](#start "Permalink to this headline") Start and run Galaxy and associated processes in daemonized (background) mode, or `-f` to run in the foreground and follow log files. The `galaxy` command is a shortcut for `galaxyctl start -f`. ### stop[¶](#stop "Permalink to this headline") Stop daemonized Galaxy server processes. If using supervisor mode and no processes remain running after this step (which should be the case when working with a single Galaxy instance), `supervisord` will terminate. ### restart[¶](#restart "Permalink to this headline") Restart Galaxy server processes. This is done in a relatively “brutal” fashion: processes are signaled (by the process manager) to exit, and then are restarted. See the `graceful` subcommand to restart gracefully. ### graceful[¶](#graceful "Permalink to this headline") Restart Galaxy with minimal interruption. If running with a single [gunicorn](https://gunicorn.org/) without `preload`, this means holding the web socket open while restarting (connections to Galaxy will block). With `preload`, gunicorn is restarted and some clients may experience connection failures. If running with multiple gunicorns, a rolling restart is performed, where Gravity restarts each gunicorn, waits for it to respond to requests after restarting, and then moves to the next one. This process should be transparent to clients. See [Zero-Downtime Restarts](index.html#zero-downtime-restarts) for configuration details. If running with [unicornherder](https://github.com/alphagov/unicornherder), a new Galaxy application will be started and the old one shut down only once the new one is accepting connections. This should also be transparent to clients, but limitations in the unicornherder software may allow interruptions to occur. ### update[¶](#update "Permalink to this headline") Figure out what has changed in the Galaxy/Gravity config(s), which could be: * changes to the Gravity configuration options in `galaxy.yml` * adding or removing handlers in `job\_conf.yml` or `job\_conf.xml` This may cause service restarts if there are any changes. Any needed changes to supervisor or systemd configs will be performed and then `supervisorctl update` or `systemctl daemon-reload` will be called. If you wish to *remove* any existing process manager configurations for Galaxy servers managed by Gravity, the `--clean` flag to `update` can be used for this purpose. ### shutdown[¶](#shutdown "Permalink to this headline") Stop all processes and cause `supervisord` to terminate. Similar to `stop` but there is no ambiguity as to whether `supervisord` remains running. The equivalent of `stop` when using systemd. ### follow[¶](#follow "Permalink to this headline") Follow (e.g. using `tail -f` (supervisor) or `journalctl -f` (systemd)) log files of all Galaxy services, or a subset (if named as arguments). ### list[¶](#list "Permalink to this headline") List config files known to Gravity. ### show[¶](#show "Permalink to this headline") Show Gravity configuration details for a Galaxy instance. ### pm[¶](#pm "Permalink to this headline") Pass through directly to the process manager (e.g. supervisor). Run `galaxyctl pm` to invoke the supervisorctl shell, or `galaxyctl pm [command]` to call a supervisorctl or systemctl command directly. See the [supervisor](http://supervisord.org/) documentation or `galaxyctl pm help` for help. ### exec[¶](#exec "Permalink to this headline") Directly execute a single Galaxy service in the foreground, e.g. `galaxyctl exec gunicorn`, `galaxyctl exec tusd`, etc. When Gravity writes out configs for the underlying process manager, it must provide a *command* (program and arguments) to execute and some number of *environment variables* that must be set for each individual Galaxy service (gunicorn, celery, etc.) to execute. By default, rather than write this information directly to the process manager configuration, Gravity sets the command to `galaxyctl exec --config-file=<gravity-config-path> <service-name>`. The `exec` subcommand instructs Gravity to use the [exec(3)](https://pubs.opengroup.org/onlinepubs/9699919799/functions/exec.html) system call to execute the actual service command with its proper arguments and environment. This is done so that it is is not necesary to rewrite the process manager configs and update the process manager every time a parameter is changed, only when services are added or removed entirely. Gravity can instead be instructed to write the actual service command and environment variables directly to the process manager configurations by setting `service\_command\_style` to `direct`. Thus, although `exec` is mostly an internal subcommand, developers and admins may find it useful when debugging in order to quickly and easily start just a single service and view only that service’s logs in the foreground. History[¶](#history "Permalink to this headline") ------------------------------------------------- ### 1.0.3[¶](#section-1 "Permalink to this headline") * Don’t create supervisor conf dir unless necessary, create the gravity data dir as the correct user by @natefoo in <https://github.com/galaxyproject/gravity/pull/105> ### 1.0.2[¶](#section-2 "Permalink to this headline") * Pin a minimum package version of gx-it-proxy by @natefoo in <https://github.com/galaxyproject/gravity/pull/102> ### 1.0.1[¶](#section-3 "Permalink to this headline") * Added configuration of gx-it-proxy to support path-based proxying by @sveinugu in <https://github.com/galaxyproject/gravity/pull/100> ### 1.0.0[¶](#section-4 "Permalink to this headline") Version 1.0.0 represents a significant update to Gravity, its features and functionality. Although Gravity 1.x is intended to be backwards compatible with 0.x, you are strongly encouraged to [read the documentation](<https://gravity.readthedocs.io/en/latest/>) if upgrading to Gravity 1.x or to Galaxy 23.0 (which depends on Gravity 1.x) in order to get the most out of the new features. * Support systemd as a process manager by @natefoo in <https://github.com/galaxyproject/gravity/pull/77> * Full stateless mode when working with single instances and other improvements for 1.0 by @natefoo in <https://github.com/galaxyproject/gravity/pull/80> * Multi-unicorn rolling restart and general multi-instance service support by @natefoo in <https://github.com/galaxyproject/gravity/pull/81> * Don’t clobber other Galaxies’ systemd units when managed by different Gravity config files by @natefoo in <https://github.com/galaxyproject/gravity/pull/83> * Don’t restart tusd on graceful by @natefoo in <https://github.com/galaxyproject/gravity/pull/85> * Read job\_conf.yml by default if job\_config\_file is unset by @natefoo in <https://github.com/galaxyproject/gravity/pull/86> * Fixes for spaces in the galaxy root path, fix the galaxy entrypoint by @natefoo in <https://github.com/galaxyproject/gravity/pull/87> * Update existing env with program env when running exec, rather than the other way around by @natefoo in <https://github.com/galaxyproject/gravity/pull/93> * Hide the “exec” ServiceCommandStyle from documentation since it is only used internally by @natefoo in <https://github.com/galaxyproject/gravity/pull/94> * Updates for settings documentation generation by @natefoo in <https://github.com/galaxyproject/gravity/pull/95> * Set $VIRTUAL\_ENV if virtualenv is set in config by @natefoo in <https://github.com/galaxyproject/gravity/pull/97> * Always add venv bin dir to $PATH if virtualenv is set by @natefoo in <https://github.com/galaxyproject/gravity/pull/98> ### 0.13.6[¶](#section-5 "Permalink to this headline") * Fix graceful method for gunicorn `--preload` by @Slugger70 in <https://github.com/galaxyproject/gravity/pull/76> * Add `--version` option to get Gravity version by @natefoo in <https://github.com/galaxyproject/gravity/pull/79> * Fix stopping of gx-it-proxy by @abretaud in <https://github.com/galaxyproject/gravity/pull/91> ### 0.13.5[¶](#section-6 "Permalink to this headline") * If virtualenv is set in the Gravity config, automatically add its bin dir to $PATH if the gx-it-proxy is enabled by @natefoo in <https://github.com/galaxyproject/gravity/pull/71> * Support converting settings to command line arguments in a generalized way by @natefoo in <https://github.com/galaxyproject/gravity/pull/73> ### 0.13.4[¶](#section-7 "Permalink to this headline") * Fixes for startup test by @natefoo in <https://github.com/galaxyproject/gravity/pull/68> * Fix setting environment vars on handlers by @natefoo in <https://github.com/galaxyproject/gravity/pull/70> ### 0.13.3[¶](#section-8 "Permalink to this headline") * Don’t use gunicorn logging options with unicornherder by @natefoo in <https://github.com/galaxyproject/gravity/pull/65> ### 0.13.2[¶](#section-9 "Permalink to this headline") * Don’t override PATH in subprocess call by @jdavcs in <https://github.com/galaxyproject/gravity/pull/62> * Only send pre create hook by @mvdbeek in <https://github.com/galaxyproject/gravity/pull/64> ### 0.13.1[¶](#section-10 "Permalink to this headline") * Set correct default for environment settings by @natefoo in <https://github.com/galaxyproject/gravity/pull/58> * Don’t catch exceptions in the deregister, register, and rename subcommands by @natefoo in <https://github.com/galaxyproject/gravity/pull/59> * `processes` in the `handling` dict in the job conf dict is a dict, not a list by @natefoo in <https://github.com/galaxyproject/gravity/pull/60> ### 0.13.0[¶](#section-11 "Permalink to this headline") * Add options to enable/disable gunicorn, celery, and celery-beat services by @natefoo in <https://github.com/galaxyproject/gravity/pull/47> * Add ability to include gravity config from a separate file and document by @natefoo in <https://github.com/galaxyproject/gravity/pull/48> * Only default to preload = true for gunicorn if not using unicornherder by @natefoo in <https://github.com/galaxyproject/gravity/pull/49> * Add option to specify tusd path by @natefoo in <https://github.com/galaxyproject/gravity/pull/50> * Support setting per-service environment variables by @natefoo in <https://github.com/galaxyproject/gravity/pull/56> ### 0.12.0[¶](#section-12 "Permalink to this headline") * Fix typo in `log\_dir` description by @nsoranzo in <https://github.com/galaxyproject/gravity/pull/44> * Shortcut individual services fix by @natefoo in <https://github.com/galaxyproject/gravity/pull/45> * Add additional options to celery beat / celery workers by @mvdbeek in <https://github.com/galaxyproject/gravity/pull/46> ### 0.11.0[¶](#section-13 "Permalink to this headline") * Allow setting supervisor socket path via environment variable by @mvdbeek in <https://github.com/galaxyproject/gravity/pull/36> * Automatically switch to non-sample galaxy.yml if it exists by @mvdbeek in <https://github.com/galaxyproject/gravity/pull/39> * Add pydantic config schema by @mvdbeek in <https://github.com/galaxyproject/gravity/pull/42> * Add –quiet option to galaxy and galaxyctl start by @mvdbeek in <https://github.com/galaxyproject/gravity/pull/40> * Add support for yaml job config by @mvdbeek in <https://github.com/galaxyproject/gravity/pull/37> * Add –preload support for gunicorn by @mvdbeek in <https://github.com/galaxyproject/gravity/pull/41> * Support running tusd by @natefoo in <https://github.com/galaxyproject/gravity/pull/23> ### 0.10.0[¶](#section-14 "Permalink to this headline") * Fix for the case where a job\_conf.xml exists but no handlers are defined by @natefoo in <https://github.com/galaxyproject/gravity/pull/24> * Do not raise error if config file section is empty by @nsoranzo in <https://github.com/galaxyproject/gravity/pull/25> * Add tests for static handlers and a defined job\_conf.xml with no handlers by @natefoo in <https://github.com/galaxyproject/gravity/pull/26> * Fix minor typos in readme by @ic4f in <https://github.com/galaxyproject/gravity/pull/27> * Move configuration to gravity key of galaxy.yml file by @mvdbeek in <https://github.com/galaxyproject/gravity/pull/28> * Fix for resolved galaxy.yml.sample symlink by @mvdbeek in <https://github.com/galaxyproject/gravity/pull/31> * Support managing gx-it-proxy via gravity by @mvdbeek in <https://github.com/galaxyproject/gravity/pull/32> ### 0.9[¶](#section-15 "Permalink to this headline") * Gunicorn/fastAPI support, click support, tests by @mvdbeek in <https://github.com/galaxyproject/gravity/pull/14> * Don’t test on Python 3.6, which is unsupported by @natefoo in <https://github.com/galaxyproject/gravity/pull/17> * Update README. Also some various small bugfixes and fixes for other stuff mentioned in the README by @natefoo in <https://github.com/galaxyproject/gravity/pull/18> * Add unicornherder support by @natefoo in <https://github.com/galaxyproject/gravity/pull/15> * Expose the log following used by start -f as its own subcommand. by @natefoo in <https://github.com/galaxyproject/gravity/pull/16> * Better integration with Galaxy’s run.sh by @natefoo in <https://github.com/galaxyproject/gravity/pull/19> * Use relative paths in supervisord by @natefoo in <https://github.com/galaxyproject/gravity/pull/21> * Converted CLI from [argparse](https://docs.python.org/3/library/argparse.html) to [click](http://click.pocoo.org/). * Stole ideas and code from [planemo](https://github.com/galaxyproject/planemo) in general. * Improve the AttributeDict so that it can have “hidden” items (anything that starts with a `\_`) that won’t be serialized. Also, it serializes itself and can be created via deserialization from a classmethod. This simplifies using it to persist state data in the new GravityState subclass. ### 0.8.3[¶](#section-16 "Permalink to this headline") * Merge `galaxycfg` and `galaxyadm` commands to `galaxy`. ### 0.8.2[¶](#section-17 "Permalink to this headline") * Allow for passing names of individual services directly to `supervisorctl` via the `start`, `stop`, and `restart` methods. * Fix a bug where uWSGI would not start when using the automatic virtualenv install method. ### 0.8.1[¶](#section-18 "Permalink to this headline") * Version bump because I deleted the 0.8 files from PyPI, and despite the fact that it lets you delete them, it doesn’t let you upload once they have been uploaded once… ### 0.8[¶](#section-19 "Permalink to this headline") * Add auto-register to `galaxy start` if it’s called from the root (or subdirectory) of a Galaxy root directory. * Make `galaxycfg remove` accept instance names as params in addition to config file paths. * Use the same hash generated for an instance name as the hash for a generated virtualenv name, so virtualenvs are more easily identified as belonging to a config. * Renamed from `galaxyadmin` to `gravity` (thanks John Chilton). ### 0.7[¶](#section-20 "Permalink to this headline") * Added the `galaxyadm` subcommand `graceful` on a suggestion from Nicola Soranzo. * Install uWSGI into the config’s virtualenv if requested. * Removed any dependence on Galaxy and eggs. * Moved project to its own repository from the Galaxy clone I’d been working from. ### Older[¶](#older "Permalink to this headline") * Works in progress as part of the Galaxy code. Indices and tables[¶](#indices-and-tables "Permalink to this headline") ----------------------------------------------------------------------- * [Index](genindex.html) * [Search Page](search.html)

osm

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osm 1.1 documentation [osm](index.html#document-index) latest * [Installation](index.html#document-installation) + [Via pip](index.html#via-pip) + [Clone repository](index.html#clone-repository) * [Command Line Interface](index.html#document-cli) + [Getting help](index.html#getting-help) + [Build and run the simulation campaign](index.html#build-and-run-the-simulation-campaign) + [Parsing simulation results](index.html#parsing-simulation-results) + [Analyzing simulation results](index.html#analyzing-simulation-results) * [osm](index.html#document-modules) + [campaign module](index.html#document-campaign) + [custom\_analyzer module](index.html#document-custom_analyzer) + [merge module](index.html#document-merge) [osm](index.html#document-index) * [Docs](index.html#document-index) » * osm 1.1 documentation * [Edit on GitHub](https://github.com/Pbarbecho/osm/blob/master/doc/index.rst) --- Automating large-scale simulations for OMNeT++[¶](#automating-large-scale-simulations-for-omnet "Permalink to this headline") ============================================================================================================================= OSM allows to OMNeT++ users to quickly and easily execute large-scale network simulations. Three shell commands (including help context) are available: ``` # Build and lauch the simulation campaign $osm launcher [OPTIONS] INIFILE MAKEFILE # Summarize result files located in output folder $osm parser [OPTIONS] # Analyze summarized file $osm analyzer [OPTIONS] ``` How to cite us[¶](#how-to-cite-us "Permalink to this headline") --------------------------------------------------------------- If you use SMO for your OMNeT++ experiment analysis, we would appreciate a citation of our work: 16. 1. 2. Bautista, L. F. Urquiza-Aguiar, L. L. Cárdenas and M. A. Igartua, “Large-Scale Simulations Manager Tool for OMNeT++: Expediting Simulations and Post-Processing Analysis,” in IEEE Access, vol. 8, pp. 159291-159306, 2020, doi: 10.1109/ACCESS.2020.3020745. Feature highlights[¶](#feature-highlights "Permalink to this headline") ----------------------------------------------------------------------- * Supports Python >= 3.5; * Fine grane control of the simulation campaign; * Customizable/interactive plotting * Runs parallelized simulations and post-processing for large number of files (common in large-scale simulations); User’s guide[¶](#user-s-guide "Permalink to this headline") ----------------------------------------------------------- ### Installation[¶](#installation "Permalink to this headline") First, we recommend to install a virtualenv: ``` $pip3 install pipenv ``` #### Via pip[¶](#via-pip "Permalink to this headline") Then, OSM is developed using pipenv. It can be installed from the master branch: ``` pip3 install --user -U https://github.com/Pbarbecho/osm/archive/master.zip ``` This will install the osm package and its linked dependencies in your current python library path. In case of a virtual environment is installed, the osm installation can be executed by calling pipenv osm –help. #### Clone repository[¶](#clone-repository "Permalink to this headline") As an stating point, cloning the osm project into a osm folder can be issued by executing the following command: ``` git clone https://github.com/Pbarbecho/osm.git osm ``` ### Command Line Interface[¶](#command-line-interface "Permalink to this headline") In order to easy use of OMNeT++ simulation manager (OSM), it includes a command line interface. OSM cope with the typical tasks that includes the large-scale simulations workflow. #### Getting help[¶](#getting-help "Permalink to this headline") OSM cli comes with –help option. Each sub-commands are detailed below:: ``` osm --help Usage: osm [OPTIONS] COMMAND [ARGS]... CLI OSM Simulation manager. Execute large-scale OMNeT++ simulations. Options: -v verbose --help Show this message and exit. Commands: analyzer Customized filtering and plotting. launcher Build the simulation campaign. parser Merge result files into one single output file (.npy, .mat,... ``` #### Build and run the simulation campaign[¶](#build-and-run-the-simulation-campaign "Permalink to this headline") Simulation campaign can be run through the launcher sub-command. The same command is used to pass users configurations as detailed below. Once the launcher sub-command is executed, information of the build simulation campaign is prompted and the uset choices to execute simulations. To extend information of osm launcher command usage, the –help flag can be used: ``` $osm launcher --help Usage: osm launcher [OPTIONS] INIFILE MAKEFILE Build and run the simulation campaign. Options: --omnet-path PATH OMNET++ installation directory. --output-dir PATH Path to directory where results are saved. -n, --ned-files-dir TEXT Path of NED files used in the model separated by ':' (e.g. .:../../src). Consider current directory is omnetpp.ini file. --max-processes INTEGER The maximum number of parallel simulations. [ default available cpus are used ] -t, --sim-time INTEGER Simulation time. Common for all scenarios in simulation campaign. [default: 300] -r, --repetitions INTEGER Number of repetitions. [default: 1] -a, --analyze Analyze a group of files from a previous simulation campaign, looking for missing files. -add, --additional-files-path PATH Path to parameters studied file (variables.txt.csv) and structure file (structure.csv) files. [default: parents directory] --help Show this message and exit. ``` Two arguments are required: > > 1. INIFILE -> The location of the project configuration file (omnetpp.ini) > 2. MAKEFILE -> Location of the executable file of the project. > > > As an option: > > * The location of the NED files required by the project should be passed. In case non path was pass, by default the current location (/) will be used. > * In case of a custom recording of output files, the additional files directory (variables, and structure files) is required. > * The OMNeT++ installation path, the output directory where simulation results will be save and the directory to additional files (structure and variables) in case of customized recording. > > > Example of launcher entry: ``` osm -v launcher --max-processes 20 -t 500 -r 20 -n '.:../../src/veins/' -add ~/additionals/ --output-dir ~/results/ ~/veins/omnetpp.ini ~/veins/src/veins\_executable ``` Simulation campaign summary: ``` ============================ Simulation campaign summary ============================ Scenarios to simulate [scenario]: ['Barcelona', 'Berlin', 'Tokio'] Iteration variables: 2 = [4, 3] Repetitions per scenario: 20 Simulation time: 500s Total Runs: 720 Build simulation campaign (\*Y/N): ``` #### Parsing simulation results[¶](#parsing-simulation-results "Permalink to this headline") The sub-command parser, can be used to merge into a unique output file simulation campaign results. The –additional-files-path or –add includes the path to ‘variables’ and ‘structure’ files. > > * Variables file: Include the iteration variable with the set of values. The OMNeT++ syntax is used to declare iteration variables.: > > > > ``` > \*.node[\*0].veinsmobility.accidentDuration = {0, 50, 300}s > \*.node[\*].appl.beaconInterval = {1, 30, 60, 90}s > > ``` > * Structure file: Include the structure (n-dimension) of results files. This file is pass in combination with the variable file to map columns with the corresponding parameter (Column name). Columns are listed separated wih a comma as follows: > > > > ``` > Type,NodeID,tx/rx,recAddress,Speed,MsgId,Length,CH,Time > > ``` > > > Results are exported to any of the supported output formats .npy, .mat, .csv for later process within MATLAB or within the OSM analyzer. Further details of parser command usage can be listed with the –help flag: ``` $osm launcher --help Usage: osm parser [OPTIONS] Merge result files into one single output file (.npy, .mat, .csv). Options: --max-processes INTEGER The maximum number of parallel simulations. By default available cpus are used. -i, --input-dir PATH Directory containing simulations results. -o, --output-dir PATH Path to directory where output file is saved. -O, --output-filename TEXT Filename with supported extension .npy (Numpy), .mat (Matlab) or csv (Comma- separated values). -add, --additional-files-path PATH Path to iteration varibles and structure files. [default: parents directory] --help Show this message and exit. ``` In case of no input, output and max processes options are included with the parser command, by default the installation path create the input folder we the simulation is launched. In the same manner, the output folder and file’s name (results.csv) are created we parser command is executed without options. By default de maximum number of processes is used. The following command, will try to automatically parse result files into an output file with sim.csv format: ``` $osm parser --add ~/additionals/ --input-dir ~/results/ --output-dir ~/summary/sim.csv ``` #### Analyzing simulation results[¶](#analyzing-simulation-results "Permalink to this headline") The analyzer command includes a customizable python script. Here, parsed results files can be filtered and sorted for plotting. The plotting phase is simplified by using the common structure of results (pandas dataframes). An interactive plotting is available with the option -itp. It try to automatically open a web browser (default firefox) where columns can be easily drag and drop to generate custom plots. Extended information is available with the –help flag: ``` $osm analyzer --help Usage: osm analyzer [OPTIONS] Customized filtering and plotting. Options: -i, --input-cvs-file PATH Input .csv file with merge results -o, --output-dir PATH Path to directory where custom analyzed factors are saved. -plt, --interactive-pivot-table GUI in firefox to drag columns and plot resutls dataframe. --help Show this message and exit. ``` The output of the analyzer, includes figures and data used to generate plots: ``` Files generated: 0) summary\_%PL\_df.png 1) summary\_%PL\_df.csv 2) summary\_speed.png 3) summary\_speed.csv ```  Code Documentation[¶](#code-documentation "Permalink to this headline") ----------------------------------------------------------------------- ### osm[¶](#osm "Permalink to this headline") #### campaign module[¶](#module-campaign "Permalink to this headline") `campaign.``add_iteration_variables_to_scenarions_ini_file`(*temp\_ini\_file*, *iteration\_variables\_dictionary*)[[source]](_modules/campaign.html#add_iteration_variables_to_scenarions_ini_file)[¶](#campaign.add_iteration_variables_to_scenarions_ini_file "Permalink to this definition") Scenarios with same iteration variables.txt `campaign.``allocate_processors`(*df\_scenarios*, *Processors*)[[source]](_modules/campaign.html#allocate_processors)[¶](#campaign.allocate_processors "Permalink to this definition") `campaign.``ask_for_scenarios_to_simulate`(*sim\_scenarios\_list*)[[source]](_modules/campaign.html#ask_for_scenarios_to_simulate)[¶](#campaign.ask_for_scenarios_to_simulate "Permalink to this definition") > > Select scenarios to include in the simulation campaign > > > > Args: > sim\_scenarios\_list(list): List of scenarios found in .ini file specified as argument > > @param sim\_scenarios\_list: @return: `campaign.``build_simulation_campaign`(*max\_processors*, *output\_dir*, *omnet\_path*, *sim\_time*, *repetitions*, *sim\_scenarios\_list*, *variables\_path*, *inifile*, *analyze*, *makefile*, *verbose*, *NED\_files\_dir*)[[source]](_modules/campaign.html#build_simulation_campaign)[¶](#campaign.build_simulation_campaign "Permalink to this definition") > > Execute parallel simulations of simulation campaign elements. If there is not enough processors, > a bath is used for queue simulations and distribute among processors. > > > Args: > > > @param max\_processors: (int) The max number of cpus to use. By default, all cpus are used. @param output\_dir: (path) The space of structure.csv to export. @param omnet\_path: (path) The OMNET++ installation path @param sim\_time: (int) The simulation time. Common for all scenarios. @param repetitions: (int) The number of runs for each iteration parameter. @param sim\_scenarios: (list) List of selected scenarios to include in the campaign @param inifile: (path) Path to .ini file of veins project. @param analyze: (bool) If true execute simulation campaign. Otherwise analyze result files @param makefile: (path) Path to executable veins project. @return: `campaign.``clear_memory`()[[source]](_modules/campaign.html#clear_memory)[¶](#campaign.clear_memory "Permalink to this definition") Clean memory cache at the end of simulation execution `campaign.``create_temp_ini_file`(*output\_results*, *repetitions*, *veins\_ini\_file\_name*, *iteration\_variables\_dictionary*)[[source]](_modules/campaign.html#create_temp_ini_file)[¶](#campaign.create_temp_ini_file "Permalink to this definition") Instantiates a temp.ini file with simulation campaign configurations. | Parameters: | * **output\_results** (*path*) – The space of structure.csv to export. * **repetitions** (*int*) – The number of runs for each iteration parameter. * **veins\_ini\_file\_name** (*path*) – Path to .ini file of veins project. | `campaign.``execute_sim`(*veins\_exec\_project\_path*, *max\_processors*, *omnet\_path*, *scenario*, *batch*, *sim\_time*, *runs*, *temp\_ini\_name*, *verbose*, *NED\_files\_dir*)[[source]](_modules/campaign.html#execute_sim)[¶](#campaign.execute_sim "Permalink to this definition") Execute scenario simulation using OMNET++ funcionality (opp\_run all OMNET++ simulation manual 11.20 Running Simulation Campaigns) Args: @param veins\_exec\_project\_path: (path) Path to executable veins project. @param max\_processors: (int) The max number of cpus to use. By default, all cpus are used. @param omnet\_path: (path) Path to the OMNET++ installation. @param scenario: Scenario to simulate @param batch: Batch of simulations @param sim\_time: (int) The simulation time. Common for all scenarios. @param runs: Bundle of runs (e.g. 0,1,2,3….) @param temp\_ini\_name: (string) VEINs ini configuration file name @return: :param verbose: `campaign.``folder_permissions`(*veins\_exec\_project\_path*)[[source]](_modules/campaign.html#folder_permissions)[¶](#campaign.folder_permissions "Permalink to this definition") `campaign.``get_scenarios`(*veins\_ini\_file*)[[source]](_modules/campaign.html#get_scenarios)[¶](#campaign.get_scenarios "Permalink to this definition") Try to read simulation scenarios in VEINs project .ini file OMNET++ defines [Config ] as the structure.csv in ini file to declare an scenario. This function return a list with [Config ] declarations found in ini file. `campaign.``isNotBlank`(*myString*)[[source]](_modules/campaign.html#isNotBlank)[¶](#campaign.isNotBlank "Permalink to this definition") Check if string is empty or null. Args: @param myString: (string) Any string @return: (bool) `campaign.``missing_files`(*total\_sims*, *output\_dir*)[[source]](_modules/campaign.html#missing_files)[¶](#campaign.missing_files "Permalink to this definition") Check in results folder if there are missing files of simulation campaign @param total\_sims: (int) Total number of runs = scenarios \* iter variable \* repetitions\_per\_scenario @param output\_dir: (path) The space of structure.csv to export. @return: `campaign.``new_folder`(*new\_directory*)[[source]](_modules/campaign.html#new_folder)[¶](#campaign.new_folder "Permalink to this definition") Create new folder and replace if it exists. Used to creates the results folder where results files are saved. | Parameters: | **new\_directory** (*path*) – Path of the new folder | `campaign.``parallel`(*max\_processors*, *omnet\_path*, *batch*, *sim\_time*, *runs\_bundle*, *temp\_ini\_name*, *sim\_scenarios\_list*, *makefile*, *verbose*, *NED\_files\_dir*)[[source]](_modules/campaign.html#parallel)[¶](#campaign.parallel "Permalink to this definition") Execute parallel summary. If the number of cpus < # of summary a batch of runs is set Args: @param max\_processors: (int) The max number of cpus to use. By default, all cpus are used. @param omnet\_path: (path) Path to the OMNET++ installation. @param batch: (int) Number of simulations per cpu @param sim\_time: (int) The simulation time. Common for all scenarios. @param runs\_bundle: (int) Bundle of runs (e.g. 0,1,2,3..) @param temp\_ini\_name: (string) VEINs ini configuration file name @param iter\_var\_per\_scenario: (dict) Dictionary with scenarios as keys and iterations as values @param makefile: (path) Path to executable veins project. @return: `campaign.``read_iteration_variables_from_file`(*scenarios\_to\_sim*, *iter\_parameters\_file\_path*)[[source]](_modules/campaign.html#read_iteration_variables_from_file)[¶](#campaign.read_iteration_variables_from_file "Permalink to this definition") Get the number of iteration structure.csv (defined as specified in OMNET++ Simulation manual 10.4 Parameter Studies) of each scenario (sim\_scenarios) defined in .ini file. e.g. OMNET++ study parameter definition -> [\*](#id1).numHosts = ${1, 2, 5, 10..50 step 10} Args: sim\_scenarios (list): List of selected scenarios to include in the campaign veins\_ini\_file\_path (path): Path to .ini file of veins project. Return a dictionary with scenarios as dic keys and read\_iteration\_variables\_from\_file as values. Values in read\_iteration\_variables\_from\_file dictionary are defined as follows: scenario1: [parameters1, parameter2, … , # of read\_iteration\_variables\_from\_file] scenario2: [parameters1, parameter2, … , # of read\_iteration\_variables\_from\_file] `campaign.``run`(*output\_dir*, *max\_processors*, *omnet\_path*, *sim\_time*, *repetitions*, *analyze*, *iter\_path*, *inifile*, *makefile*, *verbose*, *NED\_files\_dir*)[[source]](_modules/campaign.html#run)[¶](#campaign.run "Permalink to this definition") Return the results relative to the desired parameter space in the form of an xarray data structure.csv. Args: output\_dir: (path) The space of structure.csv to export. max\_processors: (int) The max number of cpus to use. By default, all cpus are used. omnet\_path: (path) Path to the OMNET++ installation. By default the script try to find the installation path. sim\_time: (int) The simulation time. Common for all scenarios. repetitions: (int) The number of runs for each iteration parameter. analyze: (bool) If true execute simulation campaign. Otherwise analyze result files from a simulation campaign to find missing simulations. inifile: (path) Path to .ini file of veins project. makefile: (path) Path to executable veins project. `campaign.``run_simulations`()[[source]](_modules/campaign.html#run_simulations)[¶](#campaign.run_simulations "Permalink to this definition") `campaign.``scenario_runs_set`(*scenario\_iteration\_variables\_dictionary*, *repetitions*)[[source]](_modules/campaign.html#scenario_runs_set)[¶](#campaign.scenario_runs_set "Permalink to this definition") Generate runs list per scenario in OMNET++ format (opp\_run all OMNET++ simulation manual 11.20 Running Simulation Campaigns) for create batches e.g. -r 0,1,2,3. | Parameters: | * **scenario\_iteration\_variables\_dictionary** (*dict*) – Dictionary with scenarios as keys and the number of iteration variables.txt as values * **repetitions** (*int*) – The number of runs for each iteration parameter. | `campaign.``sim_campaign_info`(*scenarios\_to\_sim*, *iteration\_variables\_dictionary*, *repetitions*, *simtime*, *total\_sims*)[[source]](_modules/campaign.html#sim_campaign_info)[¶](#campaign.sim_campaign_info "Permalink to this definition") Print simulation campaign summary: Args: #### custom\_analyzer module[¶](#custom-analyzer-module "Permalink to this headline") #### merge module[¶](#merge-module "Permalink to this headline") Links[¶](#links "Permalink to this headline") --------------------------------------------- > > Github: <https://github.com/Pbarbecho/osm>

chubaofs

go

ChubaoFS documentation [ChubaoFS](index.html#document-index) stable Getting Started * [Introduction](index.html#document-overview) + [High Level Architecture](index.html#high-level-architecture) + [Features](index.html#features) * [Run Cluster by Yum Tools](index.html#document-user-guide/yum) * [Run Cluster Manually](index.html#document-manual-deploy) + [Building](index.html#building) + [Deployment](index.html#deployment) + [Mount Client](index.html#mount-client) + [Upgrading](index.html#upgrading) Design Documentation * [Resource Manager (Master)](index.html#document-design/master) + [Utilization-Based Distribution/Placement](index.html#utilization-based-distribution-placement) + [Replica Placement](index.html#replica-placement) + [Meta Partition Splitting](index.html#meta-partition-splitting) + [Exception Handling](index.html#exception-handling) * [Metadata Subsystem](index.html#document-design/metanode) + [Internal Structure](index.html#internal-structure) + [Replication](index.html#replication) + [Failure Recovery](index.html#failure-recovery) * [Data Subsystem](index.html#document-design/datanode) + [Features](index.html#features) + [HTTP APIs](index.html#http-apis) * [Object Subsystem (ObjectNode)](index.html#document-design/objectnode) + [Structure](index.html#structure) + [Features](index.html#features) + [Semantic Transform](index.html#semantic-transform) + [User](index.html#user) + [Authentication](index.html#authentication) + [Invisible Temporary Data](index.html#invisible-temporary-data) + [Object Mode Conflict (Important)](index.html#object-mode-conflict-important) + [Supported S3 Features](index.html#supported-s3-features) + [Unsupported S3 Features](index.html#unsupported-s3-features) + [Supported APIs](index.html#supported-apis) + [Supported SDKs](index.html#supported-sdks) * [Client](index.html#document-design/client) + [Client Caching](index.html#client-caching) + [Integration with FUSE](index.html#integration-with-fuse) User Documentation * [Resource Manager (Master)](index.html#document-user-guide/master) + [Features](index.html#features) + [Configurations](index.html#configurations) + [Start Service](index.html#start-service) * [Meta Subsystem](index.html#document-user-guide/metanode) + [Notice](index.html#notice) * [Data Subsystem](index.html#document-user-guide/datanode) + [How To Start DataNode](index.html#how-to-start-datanode) + [Configurations](index.html#configurations) + [Notice](index.html#notice) * [Object Subsystem (ObjectNode)](index.html#document-user-guide/objectnode) + [How To start ObjectNode](index.html#how-to-start-objectnode) + [Configurations](index.html#configurations) + [Fetch Authentication Keys](index.html#fetch-authentication-keys) + [Using Object Storage Interface](index.html#using-object-storage-interface) * [Console](index.html#document-user-guide/console) + [How To Start Console](index.html#how-to-start-console) + [Configurations](index.html#configurations) + [Notice](index.html#notice) * [Client](index.html#document-user-guide/client) + [Prerequisite](index.html#prerequisite) + [Prepare Config File](index.html#prepare-config-file) + [Mount](index.html#mount) + [Unmount](index.html#unmount) * [Monitor](index.html#document-user-guide/monitor) + [Metrics](index.html#metrics) + [Grafana DashBoard Config](index.html#grafana-dashboard-config) * [Tune FUSE Performance](index.html#document-user-guide/fuse) + [Fetch Linux kernel source code](index.html#fetch-linux-kernel-source-code) + [Optimize FUSE linux kernel module](index.html#optimize-fuse-linux-kernel-module) + [Build against current running Linux kernel](index.html#build-against-current-running-linux-kernel) + [Install kernel module](index.html#install-kernel-module) * [Run Cluster by Yum Tools](index.html#document-user-guide/yum) * [Run Cluster on Docker](index.html#document-user-guide/docker) Administration * [Resource Manager (Master) API](index.html#document-administration) + [Cluster](index.html#document-admin-api/master/cluster) + [Metanode Related](index.html#document-admin-api/master/metanode) + [Datanode Related](index.html#document-admin-api/master/datanode) + [Volume](index.html#document-admin-api/master/volume) + [Meta Partition](index.html#document-admin-api/master/meta-partition) + [Data Partition](index.html#document-admin-api/master/data-partition) + [Master Management](index.html#document-admin-api/master/management) + [User](index.html#document-admin-api/master/user) * [Meta Node API](index.html#meta-node-api) + [Meta Partition](index.html#document-admin-api/metanode/partition) + [Inode](index.html#document-admin-api/metanode/inode) + [Dentry](index.html#document-admin-api/metanode/dentry) * [Command Line Interface](index.html#command-line-interface) + [CLI](index.html#document-admin-api/cli/cli) use case * [Use Cases](index.html#document-use-case) + [Machine Learning](index.html#machine-learning) + [ElasticSearch](index.html#elasticsearch) + [Nginx Log Storage](index.html#nginx-log-storage) + [Spark](index.html#spark) + [MySQL Database Backup](index.html#mysql-database-backup) Evaluation * [Performance](index.html#document-evaluation) + [Environment](index.html#environment) + [Small File Performance and Scalability](index.html#small-file-performance-and-scalability) + [IO Performance and Scalability](index.html#io-performance-and-scalability) - [1. Sequential Read](index.html#sequential-read) - [2. Sequential Write](index.html#sequential-write) - [3. Random Read](index.html#random-read) - [4. Random Write](index.html#random-write) + [Metadata Performance and Scalability](index.html#metadata-performance-and-scalability) * [Integrity](index.html#integrity) * [Workload](index.html#workload) * [Scalability](index.html#scalability) Contributing * [Contributing to ChubaoFS](index.html#document-contributing) + [Bug Reports](index.html#bug-reports) + [Patch Guidelines](index.html#patch-guidelines) + [Credits](index.html#credits) FAQ * [Environment Requirements and Capacity Planning](index.html#document-env) + [Environment Requirements](index.html#environment-requirements) + [Capacity Planning](index.html#capacity-planning) + [Multi-Zone Deploy](index.html#multi-zone-deploy) * [Q&A](index.html#document-faq) + [Compile](index.html#compile) + [Node & Disk Failure](index.html#node-disk-failure) + [Performance of Data and Metadata](index.html#performance-of-data-and-metadata) + [Capacity Management](index.html#capacity-management) + [Zone](index.html#zone) + [Node status is abnormal](index.html#node-status-is-abnormal) + [Upgrade](index.html#upgrade) + [Update Configuration Online](index.html#update-configuration-online) + [Update Configuration Offline](index.html#update-configuration-offline) + [Handle Logs](index.html#handle-logs) + [Data Loss and Consistence](index.html#data-loss-and-consistence) + [Fuse Client](index.html#fuse-client) [ChubaoFS](index.html#document-index) * [Docs](index.html#document-index) » * ChubaoFS documentation * [Edit on GitHub](https://github.com/chubaofs/chubaofs/blob/501719b9b4782c078aee421093d29c2f83b07fe8/docs/source/index.rst) --- Welcome to ChubaoFS(Chubao File System)[¶](#welcome-to-chubaofs-chubao-file-system "Permalink to this headline") ================================================================================================================ Introduction[¶](#introduction "Permalink to this headline") ----------------------------------------------------------- ChubaoFS(Chubao File System) is a distributed file system that is designed to natively support large scale container platforms. ### High Level Architecture[¶](#high-level-architecture "Permalink to this headline") [](_images/cfs-arch.png) ChubaoFS consists of a *metadata subsystem*, a *data subsystem*, and a *resource manager*, and can be accessed by different *clients* (as a set of application processes) hosted on the containers through different file system instances called *volumes*. The metadata subsystem stores the file metadata, and consists of a set of *meta nodes*. Each meta node consists of a set of *meta partitions*. The data subsystem stores the file contents, and consists of a set of *data nodes*. Each data node consists of a set of *data partitions*. The volume is a logical concept in ChubaoFS and consists of one or multiple meta partitions and one or multiple data partitions. Each partition can only be assigned to a single volume. From a client’s perspective, the volume can be viewed as a file system instance that contains data accessible by the containers. A volume can be mounted to multiple containers so that files can be shared among different clients simultaneously, and needs to be created at the very beginning before the any file operation. A ChubaoFS cluster deployed at each data center can have hundreds of thousands of volumes, whose data sizes vary from a few gigabytes to several terabytes. Generally speaking, the resource manager periodically communicates with the metadata subsystem and data subsystem to manage the meta nodes and data nodes, respectively. Each client periodically communicates with the resource manager to obtain the up-to-date view of the mounted volume. A file operation usually initiates the communications from the client to the corresponding meta node and data node directly, without the involvement of the resource manager. The updated view of the mounted volume, as well as the file metadata are usually cached at the client side to reduce the communication overhead. ### Features[¶](#features "Permalink to this headline") #### Scalable Meta Management[¶](#scalable-meta-management "Permalink to this headline") The metadata operations could make up as much as half of typical file system workloads. On our platform, this becomes even more important as there could be heavy accesses to the metadata of files by tens of thousands of clients simultaneously. A single node that stores the file metadata could easily become the performance bottleneck. As a result, we employ a distributed metadata subsystem to manage the file metadata. In this way, the metadata requests from different clients can be forwarded to different nodes, which improves the scalability of the entire system. The metadata subsystem can be considered as an in-memory datastore of the file metadata. It can have thousands of meta nodes, each of which can have hundreds of billions of meta partitions. Each meta partition on a meta node stores the file metadata in memory by maintaining a set of inodes and a set of dentries. We employ two b-trees called inodeTree and dentryTree for fast lookup of inodes and dentries in the memory. The inodeTree is indexed by the inode id, and the dentryTree is indexed by the dentry name and the parent inode id. We also maintain a range of the inode ids (denoted as start and end) stored on a meta partition for splitting. #### General-Purpose Storage Engine[¶](#general-purpose-storage-engine "Permalink to this headline") To reduce the storage cost, many applications and services are served from the same shared storage infrastructure (aka “multi-tenancy”). The workloads of different applications and services are mixed together, where the file size can vary from a few kilobytes to hundreds of gigabytes, and the files can be written in a sequential or random fashion. For example, the log files usually need to be written sequentially in the execution order of the code; some data analytics in the machine learning domain are based on the data stored on the underlying file system; and a database engine running on top of the file system can modify the stored data frequently. A dedicated file system needs to be able to serve for all these different workloads with excellent performance. #### Strong Replication Consistency[¶](#strong-replication-consistency "Permalink to this headline") E-commence venders who move their line of business applications to the cloud usually prefer strong consistency. For example, an image processing service may not want to provide the customer with an outdated image that does not match the product description. This can be easily achieved if there is only one copy of the file. But to ensure a distributed file system to continue operating properly in the event of machines failures, which can be caused by various reasons such as faulty hard drives, bad motherboards, etc, there are usually multiple replicas of the same file. As a result, in a desired file system, the data read from any of the replicas must be consistent with each other. #### Relaxed POSIX Semantics and Metadata Atomicity[¶](#relaxed-posix-semantics-and-metadata-atomicity "Permalink to this headline") In a POSIX-compliant distributed file system, the behavior of serving multiple processes on multiple client nodes should be the same as the behavior of a local file system serving multiple processes on a single node with direct attached storage. ChubaoFS provides POSIX-compliant APIs. However, the POSIX consistency semantics, as well as the atomicity requirement between the inode and dentry of the same file, have been carefully relaxed in order to better align with the needs of applications and to improve the system performance. Run Cluster by Yum Tools[¶](#run-cluster-by-yum-tools "Permalink to this headline") ----------------------------------------------------------------------------------- Yum tools to run a ChubaoFS cluster for CentOS 7+ is provided. The list of RPM packages dependencies can be installed with: ``` $ yum install http://storage.jd.com/chubaofsrpm/latest/cfs-install-latest-el7.x86_64.rpm $ cd /cfs/install $ tree -L 2 . ├── install_cfs.yml ├── install.sh ├── iplist ├── src └── template ├── client.json.j2 ├── console.json.j2 ├── create_vol.sh.j2 ├── datanode.json.j2 ├── grafana │   ├── grafana.ini │   ├── init.sh │   └── provisioning ├── master.json.j2 ├── metanode.json.j2 └── objectnode.json.j2 ``` Set parameters of the ChubaoFS cluster in **iplist**. * **[master]**, **[datanode]** , **[metanode]** , **[console]**, **[monitor]** , **[client]** modules includes member IP addresses of each role. * **[cfs:vars]** module includes parameters for SSH connection. So make sure the port, username and password for SSH connection is unified before start. * **#master config** module includes parameters of Master. Properties[¶](#id1 "Permalink to this table") | Key | Type | Description | Mandatory | | --- | --- | --- | --- | | master\_clusterName | string | The cluster identifier | Yes | | master\_listen | string | Http port which api service listen on | Yes | | master\_prof | string | golang pprof port | Yes | | master\_logDir | string | Path for log file storage | Yes | | master\_logLevel | string | Level operation for logging. Default is *info*. | No | | master\_retainLogs | string | the number of raft logs will be retain. | Yes | | master\_walDir | string | Path for raft log file storage. | Yes | | master\_storeDir | string | Path for RocksDB file storage,path must be exist | Yes | | master\_exporterPort | int | The prometheus exporter port | No | | master\_metaNodeReservedMem | string | If the metanode memory is below this value, it will be marked as read-only. Unit: byte. 1073741824 by default. | No | * **#datanode config** module includes parameters of DataNodes. Properties[¶](#id2 "Permalink to this table") | Key | Type | Description | Mandatory | | --- | --- | --- | --- | | datanode\_listen | string | Port of TCP network to be listen | Yes | | datanode\_prof | string | Port of HTTP based prof and api service | Yes | | datanode\_logDir | string | Path for log file storage | Yes | | datanode\_logLevel | string | Level operation for logging. Default is *info* | No | | datanode\_raftHeartbeat | string | Port of raft heartbeat TCP network to be listen | Yes | | datanode\_raftReplica | string | Port of raft replicate TCP network to be listen | Yes | | datanode\_raftDir | string | Path for raft log file storage | No | | datanode\_exporterPort | string | Port for monitor system | No | | datanode\_disks | string slice | Format: *PATH:RETAIN*. PATH: Disk mount point, must exists. RETAIN: Retain space. (Ranges: 20G-50G.) RETAIN: The minimum free space(Bytes) reserved for the path. | Yes | * **#metanode config** module includes parameters of MetaNodes. Properties[¶](#id3 "Permalink to this table") | Key | Type | Description | Mandatory | | | --- | --- | --- | --- | --- | | metanode\_listen | string | Listen and accept port of the server | Yes | | | metanode\_prof | string | Pprof port | Yes | | | metanode\_logLevel | string | Level operation for logging. Default is *info* | No | | | metanode\_metadataDir | string | MetaNode store snapshot directory | Yes | | | metanode\_logDir | string | Log directory | Yes | | | metanode\_raftDir | string | Raft wal directory | Yes | | | metanode\_raftHeartbeatPort | string | Raft heartbeat port | Yes | | | metanode\_raftReplicaPort | string | Raft replicate port | Yes | | | metanode\_exporterPort | string | Port for monitor system | No | | | metanode\_totalMem | string | Max memory metadata used. The value needs to be higher than the value of *metaNodeReservedMem* in the master configuration. Unit: byte | Yes | | * **#objectnode config** module includes parameters of ObjectNodes. | Key | Type | Description | Mandatory | | --- | --- | --- | --- | | objectnode\_listen | string | Listen and accept port of the server | Yes | | objectnode\_domains | string slice | Domain of S3-like interface which makes wildcard domain support Format: `DOMAIN` | No | | objectnode\_logDir | string | Log directory | Yes | | objectnode\_logLevel | string | Level operation for logging. Default: `error` | No | | objectnode\_exporterPort | string | Port for monitor system | No | | objectnode\_enableHTTPS | string | Enable HTTPS | Yes | * **#console config** module includes parameters of Console. Properties[¶](#id4 "Permalink to this table") | Key | Type | Description | Mandatory | | --- | --- | --- | --- | | console\_logDir | string | Path for log file storage | Yes | | console\_logLevel | string | Level operation for logging. Default is *info* | No | | console\_listen | string | Port of TCP network to be listen, default is 80 | Yes | * **#client config** module includes parameters of Client. Properties[¶](#id5 "Permalink to this table") | Key | Type | Description | Mandatory | | --- | --- | --- | --- | | client\_mountPoint | string | Mount point of the client | Yes | | client\_volName | string | Volume name | No | | client\_owner | string | Owner id | Yes | | client\_SizeGB | string | Initial size to create the volume if it not exists | No | | client\_logDir | string | Path for log file storage | Yes | | client\_logLevel | string | Level operation for logging. Default is *info* | No | | client\_exporterPort | string | Port exposed to monitor system | Yes | | client\_profPort | string | Pprof port | No | ``` [master] 10.196.59.198 10.196.59.199 10.196.59.200 [datanode] ... [cfs:vars] ansible\_ssh\_port=22 ansible\_ssh\_user=root ansible\_ssh\_pass="password" ... #master config ... #datanode config ... datanode\_disks = '"/data0:10737418240","/data1:10737418240"' ... #metanode config ... metanode\_totalMem = "28589934592" ... #objectnode config ... #console config ... ``` For more configurations, please refer to [Resource Manager (Master)](index.html#document-user-guide/master); [Meta Subsystem](index.html#document-user-guide/metanode); [Data Subsystem](index.html#document-user-guide/datanode); [Client](index.html#document-user-guide/client); [Monitor](index.html#document-user-guide/monitor); [Console](index.html#document-user-guide/console). Start the resources of ChubaoFS cluster with script **install.sh** . (make sure the Master is started first) ``` $ bash install.sh -h Usage: install.sh -r | --role [datanode | metanode | master | objectnode | console | monitor | client | all | createvol ] $ bash install.sh -r master $ bash install.sh -r metanode $ bash install.sh -r datanode $ bash install.sh -r objectnode $ bash install.sh -r console $ bash install.sh -r monitor $ bash install.sh -r client ``` Check mount point at **/cfs/mountpoint** on **client** node defined in **iplist** . Open <http://consul.prometheus-cfs.local> in browser for monitoring system(the IP of monitoring system is defined in **iplist** ). Run Cluster Manually[¶](#run-cluster-manually "Permalink to this headline") --------------------------------------------------------------------------- ### Building[¶](#building "Permalink to this headline") Use following command to build server and client: ``` make build ``` If the build is successful, cfs-server and cfs-client will be found in directory build/bin ### Deployment[¶](#deployment "Permalink to this headline") #### Start Resource Manager (Master)[¶](#start-resource-manager-master "Permalink to this headline") ``` nohup ./cfs-server -c master.json & ``` Sample *master.json* is shown as follows, ``` { "role": "master", "ip": "10.196.59.198", "listen": "17010", "prof":"17020", "id":"1", "peers": "1:10.196.59.198:17010,2:10.196.59.199:17010,3:10.196.59.200:17010", "retainLogs":"2000", "logDir": "/cfs/master/log", "logLevel":"info", "walDir":"/cfs/master/data/wal", "storeDir":"/cfs/master/data/store", "consulAddr": "http://consul.prometheus-cfs.local", "exporterPort": 9500, "clusterName":"chubaofs01", "metaNodeReservedMem": "1073741824" } ``` For detailed explanations of *master.json*, please refer to [Resource Manager (Master)](index.html#document-user-guide/master). #### Start Metanode[¶](#start-metanode "Permalink to this headline") ``` nohup ./cfs-server -c meta.json & ``` Sample *meta.json is* shown as follows, ``` { "role": "metanode", "listen": "17210", "prof": "17220", "logLevel": "info", "metadataDir": "/cfs/metanode/data/meta", "logDir": "/cfs/metanode/log", "raftDir": "/cfs/metanode/data/raft", "raftHeartbeatPort": "17230", "raftReplicaPort": "17240", "totalMem": "8589934592", "consulAddr": "http://consul.prometheus-cfs.local", "exporterPort": 9501, "masterAddr": [ "10.196.59.198:17010", "10.196.59.199:17010", "10.196.59.200:17010" ] } ``` For detailed explanations of *meta.json*, please refer to [Meta Subsystem](index.html#document-user-guide/metanode). #### Start Datanode[¶](#start-datanode "Permalink to this headline") 1. Prepare data directories **Recommendation** Using independent disks can reach better performance. **Disk preparation** > > 1.1 Check available disks > > > > > > > > > ``` > > fdisk -l > > > > ``` > > > > > > > > > 1.2 Build local Linux file system on the selected devices > > > > > > > > > ``` > > mkfs.xfs -f /dev/sdx > > > > ``` > > > > > > > > > 1.3 Make mount point > > > > > > > > > ``` > > mkdir /data0 > > > > ``` > > > > > > > > > 1.4 Mount the device on mount point > > > > > > > > > ``` > > mount /dev/sdx /data0 > > > > ``` > > > > > > > > > 2. Start datanode ``` nohup ./cfs-server -c datanode.json & ``` Sample *datanode.json* is shown as follows, ``` { "role": "datanode", "listen": "17310", "prof": "17320", "logDir": "/cfs/datanode/log", "raftDir": "/cfs/datanode/log", "logLevel": "info", "raftHeartbeat": "17330", "raftReplica": "17340", "consulAddr": "http://consul.prometheus-cfs.local", "exporterPort": 9502, "masterAddr": [ "10.196.59.198:17010", "10.196.59.199:17010", "10.196.59.200:17010" ], "disks": [ "/data0:10737418240", "/data1:10737418240" ] } ``` For detailed explanations of *datanode.json*, please refer to [Data Subsystem](index.html#document-user-guide/datanode). #### Start ObjectNode[¶](#start-objectnode "Permalink to this headline") ``` nohup ./cfs-server -c objectnode.json & ``` Sample *objectnode.json is* shown as follows, ``` { "role": "objectnode", "domains": [ "object.cfs.local" ], "listen": 17410, "masterAddr": [ "10.196.59.198:17010", "10.196.59.199:17010", "10.196.59.200:17010" ], "logLevel": "info", "logDir": "/cfs/Logs/objectnode" } ``` For detailed explanations of *objectnode.json*, please refer to [Object Subsystem (ObjectNode)](index.html#document-user-guide/objectnode). #### Start Console[¶](#start-console "Permalink to this headline") ``` nohup ./cfs-server -c console.json & ``` Sample *console.json is* shown as follows, ``` { "role": "console", "logDir": "/cfs/log/", "logLevel": "debug", "listen": "80", "masterAddr": [ "192.168.0.11:17010", "192.168.0.12:17010", "192.168.0.13:17010" ], "objectNodeDomain": "object.chubao.io", "monitor\_addr": "http://192.168.0.102:9090", "dashboard\_addr": "http://192.168.0.103", "monitor\_app": "cfs", "monitor\_cluster": "cfs" } ``` For detailed explanations of *console.json*, please refer to [Console](index.html#document-user-guide/console). #### Create Volume[¶](#create-volume "Permalink to this headline") By default, there are only a few data partitions allocated upon volume creation, and will be dynamically expanded according to actual usage. ``` curl -v "http://10.196.59.198:17010/admin/createVol?name=test&capacity=10000&owner=cfs" ``` For performance evaluation, extra data partitions shall be pre-created according to the amount of data nodes and disks to reach maximum performance. ``` curl -v "http://10.196.59.198:17010/dataPartition/create?name=test&count=120" ``` ### Mount Client[¶](#mount-client "Permalink to this headline") 1. Run `modprobe fuse` to insert FUSE kernel module. 2. Run `yum install -y fuse` to install libfuse. 3. Run `cfs-client -c fuse.json` to start a client daemon. Sample *fuse.json* is shown as follows, ``` { "mountPoint": "/cfs/mountpoint", "volName": "ltptest", "owner": "ltptest", "masterAddr": "10.196.59.198:17010,10.196.59.199:17010,10.196.59.200:17010", "logDir": "/cfs/client/log", "profPort": "17510", "exporterPort": "9504", "logLevel": "info" } ``` For detailed explanations of *fuse.json*, please refer to [Client](index.html#document-user-guide/client). Note that end user can start more than one client on a single machine, as long as mountpoints are different. ### Upgrading[¶](#upgrading "Permalink to this headline") 1. freeze the cluster ``` curl -v "http://10.196.59.198:17010/cluster/freeze?enable=true" ``` 2. upgrade each module 3. closed freeze flag ``` curl -v "http://10.196.59.198:17010/cluster/freeze?enable=false" ``` Resource Manager (Master)[¶](#resource-manager-master "Permalink to this headline") ----------------------------------------------------------------------------------- The resource manager manages the file system by processing different types of tasks, such as creating/deleting/updating/loading partitions and keeping track of the resource status (such as the memory/disk utilization). The resource manager is also responsible for creating new volumes and adding new meta/data nodes to the ChubaoFS cluster. It has multiple replicas, among which the consistency is maintained by a consensus algorithm such as Raft, and persisted to a key value store such as RocksDB for backup and recovery. ### Utilization-Based Distribution/Placement[¶](#utilization-based-distribution-placement "Permalink to this headline") The resource manager is a utilization-based distribution strategy to places the file metadata and contents across different meta and data nodes. Because each node can have multiple partitions, and the partitions in a volume do not need to reside on the same node, this distribution can be controlled at a finer granularity, resulting a more efficient resource management. Specifically, the distribution of file metadata and contents works follows: First, when mounting a volume, the client asks the resource manager for a set of available meta and data partitions. These partitions are usually the ones on the nodes with the lowest memory/disk utilizations. Later on, when writing a file, the client can only choose the meta and data partitions in a random fashion from the ones allocated by the resource manager. Second, when the resource manager finds that all the partitions in a volume is about to be full, it automatically adds a set of new partitions to this volume. These partitions are usually the ones on the nodes with the lowest memory/disk utilizations. Note that, when a partition is full, or a threshold (i.e., the number of files on a meta partition or the number of extents on a data partition) is reached, no new data can be stored on this partition, although it can still be modified or deleted. ### Replica Placement[¶](#replica-placement "Permalink to this headline") When choosing partitions for the replicas, the resource manager ensures that two replicas of the same partition never reside on the same node. ### Meta Partition Splitting[¶](#meta-partition-splitting "Permalink to this headline") There is a special requirement when splitting a meta partition. In particular, if a meta partition is about to reach its upper limit of the number of stored inodes and dentries, a splitting task needs to be performed with the requirement to ensure that the inode ids stored at the newly created partition are unique from the ones stored at the original partition. To meet this requirement, when splitting a meta partition, the resource manager cuts off the inode range of the meta partition in advance at a upper bound *end*, a value greater than highest inode id used so far (denoted as *maxInodeID*), and sends a split request to the meta node to (1) update the inode range from *1* to *end* for the original meta partition, and (2) create a new meta partition with the inode range from *end + 1* to *infinity* for this volume. As a result, the inode range for these two meta partitions becomes *[1, end]* and *[end + 1, infinity]*, respectively. If there is another file needs to be created, then its inode id will be chosen as *maxInodeID + 1* in the original meta partition, or *end + 1* in the newly created meta partition. The *maxInodeID* of each meta partition can be obtained by the periodical communication between the resource manager and the the meta nodes. ### Exception Handling[¶](#exception-handling "Permalink to this headline") When a request to a meta/data partition times out (e.g., due to network outage), the remaining replicas of this partition are marked as read-only. When a meta/data partition is no longer available (e.g., due to hardware failures), all the data on this partition will eventually be migrated to a new available partition manually. This unavailability is identified by the multiple failures reported by the node when operating the files. Metadata Subsystem[¶](#metadata-subsystem "Permalink to this headline") ----------------------------------------------------------------------- The metadata operations could make up as much as half of typical file system workloads. this can be important as there could be heavy accesses to the metadata of files by tens of thousands of clients simultaneously. A single node that stores the file metadata could easily become the performance/storage bottleneck. As a result, we employ a distributed metadata subsystem to manage the file metadata. In this way, the metadata requests from different clients can be forwarded to different nodes, which improves the scalability of the entire system. ### Internal Structure[¶](#internal-structure "Permalink to this headline") The metadata subsystem can be considered as an in-memory datastore of the file metadata. It can have thousands of meta nodes, each of which can have a set of meta partitions. Each meta partition on a meta node stores the file metadata in memory by maintaining a set of *inodes* and a set of *dentries*. Generally speaking, An inode is an object that represents the underlying file (or directory), and can be identified by an unsigned 64-bit integer called the *inode id*. A dentry is an object that represents the directory hierarchy and can be identified by a string name and the id of the parent inode. For example, if we have two directories *foo* and *bar*, where *foo* is the parent directory of *bar*, then there are two inodes: one for *foo* called *i1*, and the other for *bar* called *i2*, and one dentry to represent the hierarchy of these two directories where *i2* is the current inode and *i1* is the parent inode. A meta partition can only store the inodes and dentries of the files from the same volume. We employ two b-trees called *inodeTree* and *dentryTree* for fast lookup of inodes and dentries in the memory. The *inodeTree* is indexed by the inode id, and the *dentryTree* is indexed by the dentry name and the parent inode id. We also maintain a range of the inode ids (denoted as *start* and *end*) stored on a meta partition for splitting (see [Resource Manager (Master)](index.html#document-design/master)). ### Replication[¶](#replication "Permalink to this headline") The replication during file write is performed in terms of meta partitions. The replication consistency is ensured by a revision of the Raft consensus protocol called the MultiRaft, which has the advantage of reduced heartbeat network traffic comparing to the original version. ### Failure Recovery[¶](#failure-recovery "Permalink to this headline") The in-memory meta partitions are persisted to the local disk by snapshots and logs for backup and recovery. Some techniques such as log compaction are used to reduce the log files sizes and shorten the recovery time. It is worth noting that, a failure that happens during a metadata operation could result an *orphan* inode with which has no dentry to be associated. The memory and disk space occupied by this inode can be hard to free. To minimize the chance of this case to happen, the client always issues a retry after a failure until the request succeeds or the maximum retry limit is reached. Data Subsystem[¶](#data-subsystem "Permalink to this headline") --------------------------------------------------------------- The data subsystem is optimized for the storage of large and small files, which can be accessed in a sequential or random fashion.  ### Features[¶](#features "Permalink to this headline") * Large File Storage For large files, the contents are stored as a sequence of one or multiple extents, which can be distributed across different data partitions on different data nodes. Writing a new file to the extent store always causes the data to be written at the zero-offset of a new extent, which eliminates the need for the offset within the extent. The last extent of a file does not need to fill up its size limit by padding (i.e., the extent does not have holes), and never stores the data from other files. * Small File Storage The contents of multiple small files are aggregated and stored in a single extent, and the physical offset of each file content in the extent is recorded in the corresponding meta node. ChubaoFS relies on the punch hole interface, textit{fallocate()}footnote{url{<http://man7.org/linux/man-pages/man2/fallocate.2.html>}}, to textit{asynchronous} free the disk space occupied by the to-be-deleted file. The advantage of this design is to eliminate the need of implementing a garbage collection mechanism and therefore avoid to employ a mapping from logical offset to physical offset in an extent~cite{haystack}. Note that this is different from deleting large files, where the extents of the file can be removed directly from the disk. * Replication The replication is performed in terms of partitions during file writes. Depending on the file write pattern, ChubaoFS adopts different replication strategies. When a file is sequentially written into ChubaoFS, a primary-backup replication protocol is used to ensure the strong consistency with optimized IO throughput.  When overwriting an existing file portion during random writes, we employ a MultiRaft-based replication protocol, which is similar to the one used in the metadata subsystem, to ensure the strong consistency.  * Failure Recovery Because of the existence of two different replication protocols, when a failure on a replica is discovered, we first start the recovery process in the primary-backup-based replication by checking the length of each extent and making all extents aligned. Once this processed is finished, we then start the recovery process in our MultiRaft-based replication. ### HTTP APIs[¶](#http-apis "Permalink to this headline") | API | Method | Parameters | Description | | --- | --- | --- | --- | | /disks | GET | N/A | Get disk list and informations. | | /partitions | GET | N/A | Get parttion list and infomartions. | | /partition | GET | partitionId[int] | Get detail of specified partition. | | /extent | GET | partitionId[int]&extentId[int] | Get extent informations. | | /stats | GET | N/A | Get status of the datanode. | Object Subsystem (ObjectNode)[¶](#object-subsystem-objectnode "Permalink to this headline") ------------------------------------------------------------------------------------------- The object subsystem provides S3-compatible object storage interface. It makes possible for ChubaoFS becomes a fusion storage that expose two type interface (POSIX and S3-compatible). So that user can operate files stored in ChubaoFS by using native Amazon S3 SDKs. ### Structure[¶](#structure "Permalink to this headline") > >  > The ObjectNode is a functional subsystem node. It fetch volume view (volume topology) on demand from Resource Manager (Master). Each ObjectNode communicate with metadata subsystem (MetaNode) and data subsystem (DataNode) directly. ObjectNode is stateless design with high scalability and it have ability to operate all files stored in the ChubaoFS cluster which it belong to directly without any volume-mount operation. ### Features[¶](#features "Permalink to this headline") * Provides object storage interface compatible with native Amazon S3 SDKs. * Fusion storage expose two type interface (POSIX and S3-compatible). * Stateless and high scalability ### Semantic Transform[¶](#semantic-transform "Permalink to this headline") Based on our POSIX-compatible design. Every file operate request comes from object storage interface need to be made semantic transform to POSIX. | POSIX | Object Storage | | --- | --- | | `Cluster` | `Region` | | `Volume` | `Bucket` | | `Path` | `Key` | **Example:** > > > > > >  > > > > > Put object ‘*example/a/b.txt*’ will be create and write data to file ‘*/a/b.txt*’ in volume ‘*example*’. > > > ### User[¶](#user "Permalink to this headline") Before using the object storage service, you need to create a user through the Master. While creating users, *AccessKey* and *SecretKey* will be generated for each user. ChubaoFS uses the field **Owner** of the volume as the user ID. There are two ways to create users: * When creating a volume through the API of the Master, if there is no user with the same name as the owner of the volume in the cluster, a user with the user ID of Owner will be automatically created. * Create a user by calling the user management API of the Master. ### Authentication[¶](#authentication "Permalink to this headline") The signature validation algorithm in object storage interface is fully compatible with native Amazon S3 service. The authentication consisting of *AccessKey* and *SecretKey* generated by Resource Manager (Master) with user creation, which can be obtained through the Master API. The *AccessKey* is a 16-character string unique in the entire ChubaoFS cluster. The user has all access permissions to the volume owned by him. Users can grant other users specified permissions to access volumes under their own names. The permissions are divided into the following three categories: * Readonly or readwrite permission. * Permission for a single operation, such as *GetObject*, *PutObject*, etc. * Custom permission. When a user uses the object storage service to execute a certain operation, ChubaoFS will identify whether the user has the corresponding permission. ### Invisible Temporary Data[¶](#invisible-temporary-data "Permalink to this headline") In order to make write operation in object storage interface atomically. Every write operation will create and write data to an invisible temporary. The volume operator in ObjectNode puts file data into temporary which only have ‘**inode**’ without ‘**dentry**’ in metadata. When all the file data stored successfully, the volume operator create or update ‘**dentry**’ in metadata makes it visible to users. ### Object Mode Conflict (Important)[¶](#object-mode-conflict-important "Permalink to this headline") The POSIX and object storage are two different types of storage product, and the object storage is a Key-Value pair storage service. So that the object with key ‘*a/b/c*’ and the object with key ‘*a/b*’ are different object without any conflict. But ChubaoFS is based on POSIX design. According to semantic transformation rule, the ‘*b*’ part in key ‘*a/b/c*’ transform to folder ‘*b*’ under the folder ‘*a*’ , and in key ‘*a/b*’ transform to file ‘*b*’ under the folder ‘*a*’. The object key like this is conflict in ChubaoFS. When the object being operated has conflicts with the existing object due to the above mode inconsistency, the ObjectNode will return a `409` status code to the client. ### Supported S3 Features[¶](#supported-s3-features "Permalink to this headline") * File object operations. * Directory object operations. * Multipart upload. * Parallel download for high-level SDK APIs. * Tagging for bucket and object. * User-defined metadata for object. * IP address and network segment black and white list for bucket ACL. * Signature Algorithm V2 and V4. * Cross-Origin Resource Sharing (CORS). ### Unsupported S3 Features[¶](#unsupported-s3-features "Permalink to this headline") * Version * Restore deleted objects * Locking objects * Lifecycle configuration for bucket and object. * Hosting Websites * Encryption * BitTorrent ### Supported APIs[¶](#supported-apis "Permalink to this headline") | API | Reference | | --- | --- | | `AbortMultipartUpload` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_AbortMultipartUpload.html> | | `CompleteMultipartUpload` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_CompleteMultipartUpload.html> | | `CopyObject` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_CopyObject.html> | | `CreateBucket` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_CreateBucket.html> | | `CreateMultipartUpload` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_CreateMultipartUpload.html> | | `DeleteBucket` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_DeleteBucket.html> | | `DeleteBucketCors` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_DeleteBucketCors.html> | | `DeleteBucketPolicy` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_DeleteBucketPolicy.html> | | `DeleteBucketTagging` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_DeleteBucketTagging.html> | | `DeleteObject` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_DeleteObject.html> | | `DeleteObjects` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_DeleteObjects.html> | | `DeleteObjectTagging` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_DeleteObjectTagging.html> | | `GetBucketAcl` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_GetBucketAcl.html> | | `GetBucketCors` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_GetBucketCors.html> | | `GetBucketLocation` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_GetBucketLocation.html> | | `GetBucketPolicy` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_GetBucketPolicy.html> | | `GetBucketTagging` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_GetBucketTagging.html> | | `GetObject` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_GetObject.html> | | `GetObjectAcl` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_GetObjectAcl.html> | | `GetObjectTagging` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_GetObjectTagging.html> | | `HeadBucket` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_HeadBucket.html> | | `HeadObject` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_HeadObject.html> | | `ListBuckets` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_ListBuckets.html> | | `ListMultipartUploads` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_ListMultipartUploads.html> | | `ListObjects` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_ListObjects.html> | | `ListObjectsV2` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_ListObjectsV2.html> | | `ListParts` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_ListParts.html> | | `PutBucketAcl` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_PutBucketAcl.html> | | `PutBucketCors` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_PutBucketCors.html> | | `PutBucketPolicy` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_PutBucketPolicy.html> | | `PutBucketTagging` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_PutBucketTagging.html> | | `PutObject` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_PutObject.html> | | `PutObjectAcl` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_PutObjectAcl.html> | | `PutObjectTagging` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_PutObjectTagging.html> | | `UploadPart` | <https://docs.aws.amazon.com/AmazonS3/latest/API/API_UploadPart.html> | ### Supported SDKs[¶](#supported-sdks "Permalink to this headline") Object Node provides S3-compatible object storage interface, so that you can operate files by using native Amazon S3 SDKs. | Name | Language | Link | | --- | --- | --- | | AWS SDK for Java | `Java` | <https://aws.amazon.com/sdk-for-java/> | | AWS SDK for JavaScript | `JavaScript` | <https://aws.amazon.com/sdk-for-browser/> | | AWS SDK for JavaScript in Node.js | `JavaScript` | <https://aws.amazon.com/sdk-for-node-js/> | | AWS SDK for Go | `Go` | <https://docs.aws.amazon.com/sdk-for-go/> | | AWS SDK for PHP | `PHP` | <https://aws.amazon.com/sdk-for-php/> | | AWS SDK for Ruby | `Ruby` | <https://aws.amazon.com/sdk-for-ruby/> | | AWS SDK for .NET | `.NET` | <https://aws.amazon.com/sdk-for-net/> | | AWS SDK for C++ | `C++` | <https://aws.amazon.com/sdk-for-cpp/> | | Boto3 | `Python` | <http://boto.cloudhackers.com> | Client[¶](#client "Permalink to this headline") ----------------------------------------------- The client runs on each container executing application code and exposes a file system interface to applications and can access a mounted volume via a user-space file system interface such as FUSE. ### Client Caching[¶](#client-caching "Permalink to this headline") The client process runs entirely in the user space with its own cache, which has been used in the following cases. To reduce the communication with the resource manager, the client caches the addresses of the available meta and data partitions assigned to the mounted volume, which can be obtained at the startup, and periodically synchronizes this available partitions with the resource manager. To reduce the communication with the meta nodes, the client also caches the returned inodes and dentries when creating new files, as well as the data partition id, the extent id and the offset, after the file has been written to the data node successfully. When a file is opened for read/write, the client will force the cache metadata to be synchronous with the meta node. To reduce the communication with the data nodes, the client caches the most recently identified leader. Our observation is that, when reading a file, the client may not know which data node is the current leader because the leader could change after a failure recovery. As a result, the client may try to send the read request to each replica one by one until a leader is identified. However, since the leader does not change frequently, by caching the last identified leader, the client can have minimized number of retries in most cases. ### Integration with FUSE[¶](#integration-with-fuse "Permalink to this headline") The ChubaoFS client has been integrated with FUSE to provide a file system interface in the user space. In the past, low performance is considered the main disadvantage of such user-space file systems. But over the years, FUSE has made several improvement on its performance such as multithreading and write-back cache. In the future, we plan to develop our own POSIX-compliant file system interface in the kernel space to completely eliminate the overhead from FUSE. Currently the write-back cache feature does not work well in ChubaoFS due to the following reason. The default write behavior of FUSE is called directIO, which bypasses the kernel’s page cache. This results in performance problems on writing small files as each write pushes the file data to the user daemon. The solution FUSE implemented was to make the page cache support a write-back policy that aggregates small data first, and then make writes asynchronous. With that change, file data can be pushed to the user daemon in larger blobs at a time. However, in real production, we found that the write-back cache is not very useful, because a write operation usually invoke another process that tries to balance the dirty pages (pages in the main memory that have been modified during writing data to disk are marked as “dirty” and have to be flushed to disk before they can be freed), which incurs extra overhead. This overhead becomes more obvious when small files are continuously written through FUSE. Resource Manager (Master)[¶](#resource-manager-master "Permalink to this headline") ----------------------------------------------------------------------------------- The cluster contains dataNodes,metaNodes,vols,dataPartitions and metaPartitions,they are managed by master server. The master server caches the metadata in mem,persist to GoLevelDB,and ensure consistence by raft protocol. The master server manages dataPartition id to dataNode server mapping,metaPartition id to metaNode server mapping. At lease 3 master nodes are required in respect to high availability. ### Features[¶](#features "Permalink to this headline") * Multi-tenant, Resource Isolation * dataNodes,metaNodes shared,vol owns dataPartition and metaPartition exclusive * Asynchronous communication with dataNode and metaNode ### Configurations[¶](#configurations "Permalink to this headline") ChubaoFS use **JSON** as configuration file format. Properties :header: “Key”, “Type”, “Description”, “Mandatory”[¶](#id1 "Permalink to this table") | role | string | Role of process and must be set to master | Yes | | ip | string | host ip | Yes | | listen | string | Http port which api service listen on | Yes | | prof | string | golang pprof port | Yes | | id | string | identy different master node | Yes | | peers | string | the member information of raft group | Yes | | logDir | string | Path for log file storage | Yes | | logLevel | string | Level operation for logging. Default is *error*. | No | | retainLogs | string | the number of raft logs will be retain. | Yes | | walDir | string | Path for raft log file storage. | Yes | | storeDir | string | Path for RocksDB file storage,path must be exist | Yes | | clusterName | string | The cluster identifier | Yes | | exporterPort | int | The prometheus exporter port | No | | consulAddr | string | The consul register addr for prometheus exporter | No | | metaNodeReservedMem | string | If the metanode memory is below this value, it will be marked as read-only. Unit: byte. 1073741824 by default. | No | | heartbeatPort | string | Raft heartbeat port,5901 by default | No | | replicaPort | string | Raft replica Port,5902 by default | No | | nodeSetCap | string | the capacity of node set,18 by default | No | | missingDataPartitionInterval | string | how much time it has not received the heartbeat of replica,the replica is considered missing ,24 hours by default | No | | dataPartitionTimeOutSec | string | how much time it has not received the heartbeat of replica, the replica is considered not alive ,10 minutes by default | No | | numberOfDataPartitionsToLoad | string | the maximum number of partitions to check at a time,40 by default | No | | secondsToFreeDataPartitionAfterLoad | string | the task that release the memory occupied by loading data partition task can be start, only after secondsToFreeDataPartitionAfterLoad seconds ,300 by default | No | | tickInterval | string | the interval of timer which check heartbeat and election timeout,500 ms by default | No | | electionTick | string | how many times the tick timer has reset,the election is timeout,5 by default | No | **Example:** ``` { "role": "master", "id":"1", "ip": "10.196.59.198", "listen": "17010", "prof":"17020", "peers": "1:10.196.59.198:17010,2:10.196.59.199:17010,3:10.196.59.200:17010", "retainLogs":"20000", "logDir": "/cfs/master/log", "logLevel":"info", "walDir":"/cfs/master/data/wal", "storeDir":"/cfs/master/data/store", "exporterPort": 9500, "consulAddr": "http://consul.prometheus-cfs.local", "clusterName":"chubaofs01", "metaNodeReservedMem": "1073741824" } ``` ### Start Service[¶](#start-service "Permalink to this headline") ``` nohup ./master -c config.json > nohup.out & ``` Meta Subsystem[¶](#meta-subsystem "Permalink to this headline") --------------------------------------------------------------- Metanode is the manager of meta partitions and replicated by MultiRaft. Each metanode manages various of partitions. Each partition covers an inode range, and maintains two in-memory btrees: inode btree and dentry btree. At lease 3 meta nodes are required in respect to high availability. Properties[¶](#id1 "Permalink to this table") | Key | Type | Description | Mandatory | | | --- | --- | --- | --- | --- | | role | string | Role of process and must be set to *metanode* | Yes | | | listen | string | Listen and accept port of the server | Yes | | | prof | string | Pprof port | Yes | | | localIP | string | IP of network to be choose | No. If not specified, the ip address used to communicate with the master is used. | | | logLevel | string | Level operation for logging. Default is *error* | No | | | metadataDir | string | MetaNode store snapshot directory | Yes | | | logDir | string | Log directory | Yes | | | raftDir | string | Raft wal directory | Yes | | | raftHeartbeatPort | string | Raft heartbeat port | Yes | | | raftReplicaPort | string | Raft replicate port | Yes | | | consulAddr | string | Addresses of monitor system | No | | | exporterPort | string | Port for monitor system | No | | | masterAddr | string | Addresses of master server | Yes | | | zoneName | string | Specified zone. `default` by default. | No | | | totalMem | string | Max memory metadata used. The value needs to be higher than the value of *metaNodeReservedMem* in the master configuration. Unit: byte | Yes | | | deleteBatchCount | int64 | when deleting inodes, how many are deleted at a time ,500 by default | No | | Example: ``` { "role": "metanode", "listen": "17210", "prof": "17220", "logLevel": "debug", "metadataDir": "/cfs/metanode/data/meta", "logDir": "/cfs/metanode/log", "raftDir": "/cfs/metanode/data/raft", "raftHeartbeatPort": "17230", "raftReplicaPort": "17240", "consulAddr": "http://consul.prometheus-cfs.local", "exporterPort": 9501, "totalMem": "8589934592", "masterAddr": [ "10.196.59.198:17010", "10.196.59.199:17010", "10.196.59.200:17010" ] } ``` ### Notice[¶](#notice "Permalink to this headline") > > * listen, raftHeartbeatPort, raftReplicaPort can’t be modified after boot startup first time; > * Above config would be stored under directory raftDir in constcfg file. If need modified forcely，you must delete this file manually; > * These configuration items associated with master’s metanode infomation . If they have been modified, master would’t be found old metanode; > > > Data Subsystem[¶](#data-subsystem "Permalink to this headline") --------------------------------------------------------------- ### How To Start DataNode[¶](#how-to-start-datanode "Permalink to this headline") Start a DataNode process by execute the server binary of ChubaoFS you built with `-c` argument and specify configuration file. At least 4 data nodes are required in respect to high availability. ``` nohup cfs-server -c datanode.json & ``` ### Configurations[¶](#configurations "Permalink to this headline") Properties[¶](#id1 "Permalink to this table") | Key | Type | Description | Mandatory | | --- | --- | --- | --- | | role | string | Role of process and must be set to *datanode* | Yes | | listen | string | Port of TCP network to be listen | Yes | | localIP | string | IP of network to be choose | No,If not specified, the ip address used to communicate with the master is used. | | prof | string | Port of HTTP based prof and api service | Yes | | logDir | string | Path for log file storage | Yes | | logLevel | string | Level operation for logging. Default is *error* | No | | raftHeartbeat | string | Port of raft heartbeat TCP network to be listen | Yes | | raftReplica | string | Port of raft replicate TCP network to be listen | Yes | | raftDir | string | Path for raft log file storage | No | | consulAddr | string | Addresses of monitor system | No | | exporterPort | string | Port for monitor system | No | | masterAddr | string slice | Addresses of master server | Yes | | zoneName | string | Specified zone. `default` by default. | No | | disks | string slice | Format: *PATH:RETAIN*. PATH: Disk mount point. RETAIN: Retain space. (Ranges: 20G-50G.) | Yes | **Example:** ``` { "role": "datanode", "listen": "17310", "prof": "17320", "logDir": "/cfs/datanode/log", "logLevel": "info", "raftHeartbeat": "17330", "raftReplica": "17340", "raftDir": "/cfs/datanode/log", "consulAddr": "http://consul.prometheus-cfs.local", "exporterPort": 9502, "masterAddr": [ "10.196.59.198:17010", "10.196.59.199:17010", "10.196.59.200:17010" ], "disks": [ "/data0:10737418240", "/data1:10737418240" ] } ``` ### Notice[¶](#notice "Permalink to this headline") > > * Since datanode uses **SEEK\_HOLE** and **SEEK\_DATA** operations which is supported by XFS (since Linux 3.5) and ext4 (since Linux 3.8), users should pay attention to the Linux kernel version on which datanodes are deployed. > * listen, raftHeartbeat, raftReplica can’t be modified after boot startup first time. > * Above config would be stored under directory raftDir in constcfg file. If need modified forcely, you must delete this file manually. > * These configuration items associated with master’s datanode infomation. If they have been modified, master would’t be found old datanode. > > > Object Subsystem (ObjectNode)[¶](#object-subsystem-objectnode "Permalink to this headline") ------------------------------------------------------------------------------------------- ### How To start ObjectNode[¶](#how-to-start-objectnode "Permalink to this headline") Start a ObjectNode process by execute the server binary of ChubaoFS you built with `-c` argument and specify configuration file. ``` nohup cfs-server -c objectnode.json & ``` *Note: If you do not intend to use the object storage service, you do not need to start the ObjectNode.* ### Configurations[¶](#configurations "Permalink to this headline") Object Node using JSON format configuration file. **Properties** | Key | Type | Description | Mandatory | | --- | --- | --- | --- | | role | string | Role of process and must be set to `objectnode` | Yes | | listen | string | Listen and accept port of the server | Yes | | domains | string slice | Domain of S3-like interface which makes wildcard domain support Format: `DOMAIN` | No | | logDir | string | Log directory | Yes | | logLevel | string | Level operation for logging. Default: `error` | No | | masterAddr | string slice | Format: `HOST:PORT`. HOST: Hostname, domain or IP address of master (resource manager). PORT: port number which listened by this master | Yes | | authNodes | string slice | Format: *HOST:PORT*. HOST: Hostname, domain or IP address of AuthNode. PORT: port number which listened by this AuthNode | Yes | | exporterPort | string | Port for monitor system | No | | prof | string | Pprof port | Yes | **Example:** ``` { "role": "objectnode", "listen": "17410", "domains": [ "object.cfs.local" ], "logDir": "/cfs/Logs/objectnode", "logLevel": "info", "masterAddr": [ "10.196.59.198:17010", "10.196.59.199:17010", "10.196.59.200:17010" ], "exporterPort": 9503, "prof": "7013" } ``` ### Fetch Authentication Keys[¶](#fetch-authentication-keys "Permalink to this headline") First, you need create a user through **User API**, and then get keys information. Refer to [User](index.html#document-admin-api/master/user). You can also use Command Line Interface (CLI) tool to create and get user’s AccessKey and SecretKey: ``` $ cli user create [USER_ID] $ cli user info [USER_ID] ``` ### Using Object Storage Interface[¶](#using-object-storage-interface "Permalink to this headline") Object Subsystem (ObjectNode) provides S3-compatible object storage interface, so that you can operate files by using native Amazon S3 SDKs. For detail about list of supported APIs, see **Supported S3-compatible APIs** at [Object Subsystem (ObjectNode)](index.html#document-design/objectnode) For detail about list of supported SDKs, see **Supported SDKs** at [Object Subsystem (ObjectNode)](index.html#document-design/objectnode) #### Using S3cmd[¶](#using-s3cmd "Permalink to this headline") Use s3cmd to access the ObjectNode deployed locally. **Installation** Install the `s3cmd` from <https://s3tools.org/s3cmd> . **Configuration** Edit s3cmd configuration file `$HOME/.s3cfg` ``` host\_base = 127.0.0.1 host\_bucket = 127.0.0.1 use\_https = False access\_key = YOUR_ACCESS_KEY secret\_key = YOUR_SECRET_KEY ``` **Example: making a bucket (volume)** ``` s3cmd mb s3://my_volume Bucket 's3://my\_volume/' created ``` **Example: uploading an local file to ChubaoFS** ``` s3cmd put data.dat s3://my_volume upload: 'data.dat' -> 's3://my\_volume/data.dat' ``` **Example: listing buckets (volumes)** ``` s3cmd ls 2020-05-10 15:29 s3://my_volume ``` **Example: listing files stored in a ChubaoFS volume** ``` s3cmd ls s3://my_volume DIR s3://my_volume/backup/ 2020-05-10 15:31 10485760 s3://my_volume/data.dat 2020005-10 15:33 10 s3://my_volume/hello.txt ``` **Example: deleting file stored in a ChubaoFS volume** ``` s3cmd rm s3://my_volume/data.dat delete: 's3://my\_volume/data.dat' ``` Detail usage for `s3cmd` see <https://s3tools.org/usage> . #### Using AWS Java SDK[¶](#using-aws-java-sdk "Permalink to this headline") Use AWS Java SDK to access the ObjectNode deployed locally. **Install by Maven:** ``` <dependency> <groupId>software.amazon.awssdk</groupId> <artifactId>s3</artifactId> <version>2.10.71</version> </dependency> ``` **Example: uploading file to ChubaoFS volume (PutObject)** ``` Regions clientRegion = Region.of("\*\*\* Region name \*\*\*"); // Setup region (the cluster name) String endpoint = "http://127.0.0.1"; // Setup endpoint String accessKey = "\*\*\* Access Key \*\*\*"; // Setup AccessKey String secretKey = "\*\*\* Secret Key \*\*\*"; // Setup SecretKey String bucketName = "\*\*\* Bucket name \*\*\*"; // Setup target bucket (ChubaoFS Volume) String objectKey = "\*\*\* File object key name \*\*\*"; // Setup object key []byte data = "\*\*\* Example File Data as String \*\*".getBytes(); try { // Setup credential AwsCredentialsProvider credentialsProvider = StaticCredentialsProvider.create(AwsBasicCredentials.create(accessKey, secretKey)); // Init S3 client S3Configuration configuration = S3Configuration.builder() .chunkedEncodingEnabled(true) .build(); S3Client client = S3Client.builder() .region(region) .credentialsProvider(credentialsProvider) .endpointOverride(URI.create(endpoint)) .serviceConfiguration(configuration) .build(); // Upload file PutObjectRequest request = PutObjectRequest.builder() .bucket(bucketName) .key(objectKey) .build(); RequestBody body = RequestBody.fromBytes(data); s3Client.putObject(request, body) } catch (Exception e) { e.printStackTrace(); } ``` **Example: downloading file stored in ChubaoFS volume (GetObject)** ``` Regions clientRegion = Region.of("\*\*\* Region name \*\*\*"); // Setup region String endpoint = "http://127.0.0.1"; // Setup endpoint String accessKey = "\*\*\* Access Key \*\*\*"; // Setup AccessKey String secretKey = "\*\*\* Secret Key \*\*\*"; // Setup SecretKey String bucketName = "\*\*\* Bucket name \*\*\*"; // Setup target bucket (ChubaoFS Volume) String objectKey = "\*\*\* File object key name \*\*\*"; // Setup object key try { // Setup credential AwsCredentialsProvider credentialsProvider = StaticCredentialsProvider.create(AwsBasicCredentials.create(accessKey, secretKey)); // Init S3 client S3Configuration configuration = S3Configuration.builder() .chunkedEncodingEnabled(true) .build(); S3Client client = S3Client.builder() .region(region) .credentialsProvider(credentialsProvider) .endpointOverride(URI.create(endpoint)) .serviceConfiguration(configuration) .build(); // Get file data GetObjectRequest request = GetObjectRequest.builder() .bucket(bucketName) .key(objectKey) .build(); InputStream is = s3Client.getObject(request) while (true) { if (is.read() == -1) { break } } } catch (Exception e) { e.printStackTrace(); } ``` #### Using AWS Python SDK (Boto3)[¶](#using-aws-python-sdk-boto3 "Permalink to this headline") Use AWS Python SDK (Boto3) to access the ObjectNode deployed locally. **Install Boto3 by PIP:** ``` $ pip install boto3 ``` **Example: uploading file to ChubaoFS volume (PutObject)** ``` import boto3 endpoint = " \*\* endpoint url \*\* " # example: http://127.0.0.1 region\_name = " \*\* region name \*\* " access\_key = " \*\* your access key \*\* " # your access key secret\_key = " \*\* your secret key \*\* " # your secret key bucket = " \*\* your bucket (volume) \*\* " # your volume name key = " \*\* your object key (file path in CFS) \*\* " # your object name def put\_file(): session = boto3.Session( aws\_access\_key\_id=access\_key, aws\_secret\_access\_key=secret\_key) client = session.client(service\_name="s3", region\_name=region\_name, endpoint\_url=endpoint) client.put\_object(Bucket=bucket, Key=key, Body=bytes("hello world")) ``` **Example: downloading file stored in ChubaoFS volume (GetObject)** ``` import boto3 endpoint = " \*\* endpoint url \*\* " # example: http://127.0.0.1 region\_name = " \*\* region name \*\* " access\_key = " \*\* your access key \*\* " # your access key secret\_key = " \*\* your secret key \*\* " # your secret key bucket = " \*\* your bucket (volume) \*\* " # your volume name key = " \*\* your object key (file path in CFS) \*\* " # your object name def get\_file(): session = boto3.Session( aws\_access\_key\_id=access\_key, aws\_secret\_access\_key=secret\_key) client = session.client(service\_name="s3", region\_name=region\_name, endpoint\_url=endpoint) result = client.get\_object(Bucket=bucket, Key=key) print(result["Body"].read()) ``` Console[¶](#console "Permalink to this headline") ------------------------------------------------- ### How To Start Console[¶](#how-to-start-console "Permalink to this headline") Start a Console process by execute the server binary of ChubaoFS you built with `-c` argument and specify configuration file. ``` nohup cfs-server -c console.json & ``` ### Configurations[¶](#configurations "Permalink to this headline") Properties[¶](#id1 "Permalink to this table") | Key | Type | Description | Mandatory | | --- | --- | --- | --- | | role | string | Role of process and must be set to *console* | Yes | | logDir | string | Path for log file storage | Yes | | logLevel | string | Level operation for logging. Default is *error* | No | | listen | string | Port of TCP network to be listen, default is 80 | Yes | | masterAddr | string slice | Addresses of master server | Yes | | objectNodeDomain | string | object domain for sign url for down | Yes | | monitor\_addr | string | Prometheus the address | Yes | | dashboard\_addr | string | console menu forward to Grafana | Yes | | monitor\_app | string | the tag for monitor, it same as master config | Yes | | monitor\_cluster | string | the tag for monitor, it same as master config | Yes | **Example:** ``` { "role": "console", "logDir": "/cfs/log/", "logLevel": "debug", "listen": "80", "masterAddr": [ "192.168.0.11:17010", "192.168.0.12:17010", "192.168.0.13:17010" ], "monitor\_addr": "http://192.168.0.102:9090", "dashboard\_addr": "http://grafana.chubao.io", "monitor\_app": "cfs", "monitor\_cluster": "spark" } ``` ### Notice[¶](#notice "Permalink to this headline") > > * add 127.0.0.1 console.chubao.io grafana.chubao.io in /etc/hosts > * you can visit it by http://127.0.0.1:80 > * in console default user is root default password is ChubaoFSRoot > * If you are upgrading your ChubaoFS from v2.0.0 or earlier version, the password may not be compatible, you can use curl -H “Content-Type:application/json” -X POST –data ‘{“id”:”testuser”,”pwd”:”12345”,”type”:2}’ “http://10.196.59.198:17010/user/create” to create new user to use it > > > Client[¶](#client "Permalink to this headline") ----------------------------------------------- ### Prerequisite[¶](#prerequisite "Permalink to this headline") Insert FUSE kernel module and install libfuse. ``` modprobe fuse yum install -y fuse ``` ### Prepare Config File[¶](#prepare-config-file "Permalink to this headline") fuse.json ``` { "mountPoint": "/cfs/mountpoint", "volName": "ltptest", "owner": "ltptest", "masterAddr": "10.196.59.198:17010,10.196.59.199:17010,10.196.59.200:17010", "logDir": "/cfs/client/log", "logLevel": "info", "profPort": "27510" } ``` Supported Configurations[¶](#id1 "Permalink to this table") | Name | Type | Description | Mandatory | | --- | --- | --- | --- | | mountPoint | string | Mount point | Yes | | volName | string | Volume name | Yes | | owner | string | Owner name as authentication | Yes | | masterAddr | string | Resource manager IP address | Yes | | logDir | string | Path to store log files | No | | logLevel | string | Log level：debug, info, warn, error | No | | profPort | string | Golang pprof port | No | | exporterPort | string | Performance monitor port | No | | consulAddr | string | Performance monitor server address | No | | lookupValid | string | Lookup valid duration in FUSE kernel module, unit: sec | No | | attrValid | string | Attr valid duration in FUSE kernel module, unit: sec | No | | icacheTimeout | string | Inode cache valid duration in client | No | | enSyncWrite | string | Enable DirectIO sync write, i.e. make sure data is fsynced in data node | No | | autoInvalData | string | Use AutoInvalData FUSE mount option | No | | rdonly | bool | Mount as read-only file system | No | | writecache | bool | Leverage the write cache feature of kernel FUSE. Requires the kernel FUSE module to support write cache. | No | | keepcache | bool | Keep kernel page cache. Requires the writecache option is enabled. | No | | token | string | Specify the capability of a client instance. | No | | readRate | int | Read Rate Limit. Unlimited by default. | No | | writeRate | int | Write Rate Limit. Unlimited by default. | No | | followerRead | bool | Enable read from follower. False by default. | No | | accessKey | string | Access key of user who owns the volume. | No | | secretKey | string | Secret key of user who owns the volume. | No | | disableDcache | bool | Disable Dentry Cache. False by default. | No | | subdir | string | Mount sub directory. | No | | fsyncOnClose | bool | Perform fsync upon file close. True by default. | No | | maxcpus | int | The maximum number of available CPU cores. Limit the CPU usage of the client process. | No | | enableXattr | bool | Enable xattr support. False by default. | No | ### Mount[¶](#mount "Permalink to this headline") Use the example *fuse.json*, and client is mounted on the directory */mnt/fuse*. All operations to */mnt/fuse* would be performed on the backing distributed file system. ``` ./cfs-client -c fuse.json ``` ### Unmount[¶](#unmount "Permalink to this headline") It is recommended to use standard Linux `umount` command to terminate the mount. Monitor[¶](#monitor "Permalink to this headline") ------------------------------------------------- ChubaoFS use prometheus as metrics collector. It simply config as follow in master, metanode, datanode, client’s config file： ``` { "exporterPort": 9505, "consulAddr": "http://consul.prometheus-cfs.local" } ``` * exporterPort：prometheus exporter Port. when set, can export prometheus metrics from URL(<http://$hostip:$exporterPort/metrics>). If not set, prometheus exporter will unavailable； * consulAddr: consul register address, it can work with prometheus to auto discover deployed ChubaoFS nodes, if not set, consul register will not work. Using grafana as prometheus metrics web front：  Also, user can use prometheus alertmanager to capture and route alerts to the correct receiver. please visit prometheus alertmanger [web-doc](https://prometheus.io/docs/alerting/alertmanager/) ### Metrics[¶](#metrics "Permalink to this headline") * Cluster > > > + The number of nodes: `MasterCount` , `MetaNodeCount` , `DataNodeCount` , `ObjectNodeCount` > + The number of clients: `ClientCount` > + The number of volumes: `VolumeCount` > + The size of nodes: `DataNodeSize` , `MetaNodeSize` > + The used ratio of nodes: `DataNodeUsedRatio` , `MetaNodeUsedRatio` > + The number of inactive nodes: `DataNodeInactive` , `MetaNodesInactive` > + The capacity of volumes: `VolumeTotalSize` > + The used ratio of volumes: `VolumeUsedRatio` > + The number of error disks: `DiskError` > * Volume > > > + The used size of volumes: `VolumeUsedSize` > + The used ratio of volumes: `VolumeUsedRatio` > + The capacity change rate of volumes: `VolumeSizeRate` > * Master > > > + The number of invalid masters: `master\_nodes\_invalid` > + The number of invalid metanodes: `metanode\_inactive` > + The number of invalid datanodes:： `datanode\_inactive` > + The number of invalid clients:： `fuseclient\_inactive` > * MetaNode > > > + The duration of each operation (Time) and the number of operations per second (Ops) on the metanode, which can be selected from the `MetaNodeOp` drop-down list. > * DataNode > > > + The duration of each operation (Time) and the number of operations per second (Ops) on the datanode, which can be selected from the `DataNodeOp` drop-down list. > * ObjectNode > > > + The duration of each operation (Time) and the number of operations per second (Ops) on the objectnode, which can be selected from the `objectNodeOp` drop-down list. > * FuseClient > > > + The duration of each operation (Time) and the number of operations per second (Ops) on the client, which can be selected from the `fuseOp` drop-down list. > *Recommended focus metrics: cluster status, node or disk failure, total size, growth rate, etc.* ### Grafana DashBoard Config[¶](#grafana-dashboard-config "Permalink to this headline") ``` { "annotations": { "list": [ { "builtIn": 1, "datasource": "-- Grafana --", "enable": true, "hide": true, "iconColor": "rgba(0, 211, 255, 1)", "name": "Annotations & Alerts", "type": "dashboard" } ] }, "editable": true, "gnetId": null, "graphTooltip": 0, "id": 21, "iteration": 1578879879321, "links": [], "panels": [ { "collapsed": false, "gridPos": { "h": 1, "w": 24, "x": 0, "y": 0 }, "id": 40, "panels": [], "title": "Cluster", "type": "row" }, { "cacheTimeout": null, "colorBackground": false, "colorValue": false, "colors": [ "#299c46", "rgba(237, 129, 40, 0.89)", "#d44a3a" ], "datasource": null, "description": "master node total count of cluster", "format": "none", "gauge": { "maxValue": 100, "minValue": 0, "show": false, "thresholdLabels": false, "thresholdMarkers": true }, "gridPos": { "h": 4, "w": 4, "x": 0, "y": 1 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true }, { "format": "ops", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fill": 1, "gridPos": { "h": 7, "w": 8, "x": 8, "y": 19 }, "id": 148, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/.\*\_ops\_.\*/", "yaxis": 2 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "[[app]]\_fuseclient\_OpMetaOpen{app=\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaOpen\_{{instance}}", "refId": "B" }, { "expr": "[[app]]\_fuseclient\_OpMetaReadDir{app=\"$app\", cluster=\"$cluster\"}", 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"[[app]]\_fuseclient\_OpMetaBatchInodeGet{app=~\"$app\", cluster=\"$cluster\", instance=\"$instance\"}", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "OpMetaBatchInodeGet\_{{instance}}", "refId": "A" }, { "expr": "[[app]]\_fuseclient\_OpMetaCreateInode{app=~\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "CreateInode\_{{instance}}", "refId": "D" }, { "expr": "[[app]]\_fuseclient\_OpMetaDeleteInode{app=~\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "DeleteInode\_{{instance}}", "refId": "F" }, { "expr": "[[app]]\_fuseclient\_OpMetaEvictInode{app=~\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "EvictInode\_{{instance}}", "refId": "G" }, { "expr": "[[app]]\_fuseclient\_OpMetaInodeGet{app=~\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "InodeGet\_{{instance}}", "refId": "J" }, { "expr": "[[app]]\_fuseclient\_OpMetaTruncate{app=~\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "Truncate\_{{instance}}", "refId": "B" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaBatchInodeGet\_count{app=~\"$app\", cluster=\"$cluster\", instance=\"$instance\"}[1m])", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "OpMetaBatchInodeGet\_count\_{{instance}}", "refId": "C" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaCreateInode{app=~\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "CreateInode\_count\_{{instance}}", "refId": "E" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaDeleteInode{app=~\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "DeleteInode\_count\_{{instance}}", "refId": "H" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaEvictInode{app=~\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "EvictInode\_count\_{{instance}}", "refId": "I" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaInodeGet{app=~\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "InodeGet\_count\_{{instance}}", "refId": "K" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaTruncate{app=~\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "Truncate\_count\_{{instance}}", "refId": "L" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "MetaBatchInodeGet", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "ns", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "ops", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "default", "fill": 1, "gridPos": { "h": 7, "w": 8, "x": 0, "y": 26 }, "id": 152, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/.\*\_count\_.\*/", "yaxis": 2 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "[[app]]\_fuseclient\_filecreate{app=~\"$app\",cluster=\"$cluster\", instance=\"$instance\"}", "format": "time\_series", "intervalFactor": 1, "legendFormat": "filecreate\_{{instance}}", "refId": "A" }, { "expr": "rate([[app]]\_fuseclient\_filecreate\_count{app=~\"$app\",cluster=\"$cluster\", instance=\"$instance\"}[1m])", "format": "time\_series", "intervalFactor": 1, "legendFormat": "filecreate\_count\_{{instance}}", "refId": "B" }, { "expr": "[[app]]\_fuseclient\_filesync{app=~\"$app\",cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "filesync\_{{instance}}", "refId": "C" }, { "expr": "rate([[app]]\_fuseclient\_filesync\_count{app=~\"$app\",cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "filesync\_count\_{{instance}}", "refId": "D" }, { "expr": "[[app]]\_fuseclient\_filewrite{app=~\"$app\",cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "filewrite\_{{instance}}", "refId": "E" }, { "expr": "rate([[app]]\_fuseclient\_filewrite\_count{app=~\"$app\",cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "filewrite\_count\_{{instance}}", "refId": "F" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "FileCreate", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "ns", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "ops", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fill": 1, "gridPos": { "h": 7, "w": 8, "x": 8, "y": 26 }, "id": 158, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/.\*\_count\_.\*/", "yaxis": 2 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "[[app]]\_fuseclient\_OpMetaInodeGet{app=~\"$app\", cluster=\"$cluster\", instance=\"$instance\"}", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "InodeGet\_{{instance}}", "refId": "J" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaInodeGet\_count{app=~\"$app\", cluster=\"$cluster\", instance=\"$instance\"}[1m])", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "InodeGet\_count\_{{instance}}", "refId": "K" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "InodeGet", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "ns", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "ops", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fill": 1, "gridPos": { "h": 7, "w": 8, "x": 16, "y": 26 }, "id": 155, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/.\*\_count\_.\*/", "yaxis": 2 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "[[app]]\_fuseclient\_OpMetaBatchInodeGet{app=~\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "OpMetaBatchInodeGet\_{{instance}}", "refId": "A" }, { "expr": "[[app]]\_fuseclient\_OpMetaCreateInode{app=~\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "CreateInode\_{{instance}}", "refId": "D" }, { "expr": "[[app]]\_fuseclient\_OpMetaDeleteInode{app=~\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "DeleteInode\_{{instance}}", "refId": "F" }, { "expr": "[[app]]\_fuseclient\_OpMetaEvictInode{app=~\"$app\", cluster=\"$cluster\", instance=\"$instance\"}", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "EvictInode\_{{instance}}", "refId": "G" }, { "expr": "[[app]]\_fuseclient\_OpMetaInodeGet{app=~\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "InodeGet\_{{instance}}", "refId": "J" }, { "expr": "[[app]]\_fuseclient\_OpMetaTruncate{app=~\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "Truncate\_{{instance}}", "refId": "B" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaBatchInodeGet\_count{app=~\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "OpMetaBatchInodeGet\_count\_{{instance}}", "refId": "C" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaCreateInode{app=~\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "CreateInode\_count\_{{instance}}", "refId": "E" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaDeleteInode{app=~\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "DeleteInode\_count\_{{instance}}", "refId": "H" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaEvictInode\_count{app=~\"$app\", cluster=\"$cluster\", instance=\"$instance\"}[1m])", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "EvictInode\_count\_{{instance}}", "refId": "I" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaInodeGet{app=~\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "InodeGet\_count\_{{instance}}", "refId": "K" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaTruncate{app=~\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "Truncate\_count\_{{instance}}", "refId": "L" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "EvictInode", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "ns", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "ops", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "default", "fill": 1, "gridPos": { "h": 7, "w": 8, "x": 0, "y": 33 }, "id": 153, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/.\*\_count\_.\*/", "yaxis": 2 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "[[app]]\_fuseclient\_filecreate{app=~\"$app\",cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "filecreate\_{{instance}}", "refId": "A" }, { "expr": "rate([[app]]\_fuseclient\_filecreate\_count{app=~\"$app\",cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "filecreate\_count\_{{instance}}", "refId": "B" }, { "expr": "[[app]]\_fuseclient\_filesync{app=~\"$app\",cluster=\"$cluster\", instance=\"$instance\"}", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "filesync\_{{instance}}", "refId": "C" }, { "expr": "rate([[app]]\_fuseclient\_filesync\_count{app=~\"$app\",cluster=\"$cluster\", instance=\"$instance\"}[1m])", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "filesync\_count\_{{instance}}", "refId": "D" }, { "expr": "[[app]]\_fuseclient\_filewrite{app=~\"$app\",cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "filewrite\_{{instance}}", "refId": "E" }, { "expr": "rate([[app]]\_fuseclient\_filewrite\_count{app=~\"$app\",cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "filewrite\_count\_{{instance}}", "refId": "F" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "FileSync", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "ns", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "ops", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "default", "fill": 1, "gridPos": { "h": 7, "w": 8, "x": 8, "y": 33 }, "id": 100, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/.\*\_count\_.\*/", "yaxis": 2 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "[[app]]\_fuseclient\_fileread{app=~\"$app\",cluster=\"$cluster\", instance=\"$instance\"}", "format": "time\_series", "intervalFactor": 1, "legendFormat": "fileread\_{{instance}}", "refId": "A" }, { "expr": "rate([[app]]\_fuseclient\_fileread\_count{app=~\"$app\",cluster=\"$cluster\", instance=\"$instance\"}[1m])", "format": "time\_series", "intervalFactor": 1, "legendFormat": "fileread\_count\_{{instance}}", "refId": "B" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "FileRead", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "ns", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "ops", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": "default", "fill": 1, "gridPos": { "h": 7, "w": 8, "x": 16, "y": 33 }, "id": 154, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/.\*\_count\_.\*/", "yaxis": 2 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "[[app]]\_fuseclient\_filecreate{app=~\"$app\",cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "filecreate\_{{instance}}", "refId": "A" }, { "expr": "rate([[app]]\_fuseclient\_filecreate\_count{app=~\"$app\",cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "filecreate\_count\_{{instance}}", "refId": "B" }, { "expr": "[[app]]\_fuseclient\_filesync{app=~\"$app\",cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "filesync\_{{instance}}", "refId": "C" }, { "expr": "rate([[app]]\_fuseclient\_filesync\_count{app=~\"$app\",cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "filesync\_count\_{{instance}}", "refId": "D" }, { "expr": "[[app]]\_fuseclient\_filewrite{app=~\"$app\",cluster=\"$cluster\", instance=\"$instance\"}", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "filewrite\_{{instance}}", "refId": "E" }, { "expr": "rate([[app]]\_fuseclient\_filewrite\_count{app=~\"$app\",cluster=\"$cluster\", instance=\"$instance\"}[1m])", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "filewrite\_count\_{{instance}}", "refId": "F" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "FileWrite", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "ns", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "ops", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fill": 1, "gridPos": { "h": 7, "w": 8, "x": 0, "y": 40 }, "id": 147, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/.\*\_ops\_.\*/", "yaxis": 2 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "[[app]]\_fuseclient\_OpMetaOpen{app=\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaOpen\_{{instance}}", "refId": "B" }, { "expr": "[[app]]\_fuseclient\_OpMetaReadDir{app=\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaReaddir\_{{instance}}", "refId": "K" }, { "expr": "[[app]]\_fuseclient\_OpMetaSetattr{app=\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaSetattr\_{{instance}}", "refId": "L" }, { "expr": "[[app]]\_fuseclient\_OpMetaLookup{app=\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaLookup\_{{instance}}", "refId": "A" }, { "expr": "[[app]]\_fuseclient\_OpMetaTruncate{app=\"$app\", cluster=\"$cluster\", instance=\"$instance\"}", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "MetaTruncate\_{{instance}}", "refId": "C" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaOpen\_count{app=\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaOpen\_ops\_{{instance}}", "refId": "D" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaReadDir{app=\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaReaddir\_ops\_{{instance}}", "refId": "E" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaSetattr{app=\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaSetattr\_ops\_{{instance}}", "refId": "F" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaLookup{app=\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaLookup\_ops\_{{instance}}", "refId": "G" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaTruncate\_count{app=\"$app\", cluster=\"$cluster\", instance=\"$instance\"}[1m])", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "MetaTruncate\_ops\_{{instance}}", "refId": "H" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "MetaTruncate", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "ns", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "ops", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fill": 1, "gridPos": { "h": 7, "w": 8, "x": 8, "y": 40 }, "id": 149, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/.\*\_ops\_.\*/", "yaxis": 2 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "[[app]]\_fuseclient\_OpMetaOpen{app=\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaOpen\_{{instance}}", "refId": "B" }, { "expr": "[[app]]\_fuseclient\_OpMetaReadDir{app=\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaReaddir\_{{instance}}", "refId": "K" }, { "expr": "[[app]]\_fuseclient\_OpMetaSetattr{app=\"$app\", cluster=\"$cluster\", instance=\"$instance\"}", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "MetaSetattr\_{{instance}}", "refId": "L" }, { "expr": "[[app]]\_fuseclient\_OpMetaLookup{app=\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaLookup\_{{instance}}", "refId": "A" }, { "expr": "[[app]]\_fuseclient\_OpMetaTruncate{app=\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaTruncate\_{{instance}}", "refId": "C" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaOpen\_count{app=\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaOpen\_ops\_{{instance}}", "refId": "D" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaReadDir{app=\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaReaddir\_ops\_{{instance}}", "refId": "E" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaSetattr\_cout{app=\"$app\", cluster=\"$cluster\", instance=\"$instance\"}[1m])", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "MetaSetattr\_ops\_{{instance}}", "refId": "F" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaLookup\_count{app=\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaLookup\_ops\_{{instance}}", "refId": "G" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaTruncate\_count{app=\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaTruncate\_ops\_{{instance}}", "refId": "H" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "MetaSetattr", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "ns", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "ops", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fill": 1, "gridPos": { "h": 7, "w": 8, "x": 16, "y": 40 }, "id": 150, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/.\*\_ops\_.\*/", "yaxis": 2 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "[[app]]\_fuseclient\_OpMetaOpen{app=\"$app\", cluster=\"$cluster\", instance=\"$instance\"}", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "MetaOpen\_{{instance}}", "refId": "B" }, { "expr": "[[app]]\_fuseclient\_OpMetaReadDir{app=\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaReaddir\_{{instance}}", "refId": "K" }, { "expr": "[[app]]\_fuseclient\_OpMetaSetattr{app=\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaSetattr\_{{instance}}", "refId": "L" }, { "expr": "[[app]]\_fuseclient\_OpMetaLookup{app=\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaLookup\_{{instance}}", "refId": "A" }, { "expr": "[[app]]\_fuseclient\_OpMetaTruncate{app=\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaTruncate\_{{instance}}", "refId": "C" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaOpen\_count{app=\"$app\", cluster=\"$cluster\", instance=\"$instance\"}[1m])", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "MetaOpen\_ops\_{{instance}}", "refId": "D" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaReadDir{app=\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaReaddir\_ops\_{{instance}}", "refId": "E" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaSetattr{app=\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaSetattr\_ops\_{{instance}}", "refId": "F" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaLookup{app=\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaLookup\_ops\_{{instance}}", "refId": "G" }, { "expr": "rate([[app]]\_fuseclient\_OpMetaTruncate{app=\"$app\", cluster=\"$cluster\"}[1m])", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "MetaTruncate\_ops\_{{instance}}", "refId": "H" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "MetaOpen", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { 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"legendFormat": "FlushInRead\_{{instance}}", "refId": "A" }, { "expr": "[[app]]\_fuseclient\_StreamPrepareReadRequests{app=~\"$app\", cluster=\"$cluster\", instance=\"$instance\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "PrepareReadRequest\_{{instance}}", "refId": "B" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "FlushInRead", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "ns", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fill": 1, "gridPos": { "h": 7, "w": 8, "x": 8, "y": 47 }, "id": 161, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [ { "alias": "/.\*\_count\_.\*/", "yaxis": 2 } ], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "[[app]]\_fuseclient\_OpMetaCreateDentry{app=~\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "OpMetaCreateDentry\_{{instance}}", "refId": "C" }, { "expr": "[[app]]\_fuseclient\_OpMetaDeleteDentry{app=~\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "OpMetaDeleteDentry\_{{instance}}", "refId": "E" }, { "expr": "[[app]]\_fuseclient\_OpMetaUpdateDentry{app=~\"$app\", cluster=\"$cluster\", instance=\"$instance\"}", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "OpMetaUpdateDentry\_{{instance}}", "refId": "A" }, { "expr": "[[app]]\_fuseclient\_OpMetaCreateDentry\_count{app=~\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "OpMetaCreateDentry\_count\_{{instance}}", "refId": "B" }, { "expr": "[[app]]\_fuseclient\_OpMetaDeleteDentry\_count{app=~\"$app\", cluster=\"$cluster\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "OpMetaDeleteDentry\_count\_{{instance}}", "refId": "D" }, { "expr": "[[app]]\_fuseclient\_OpMetaUpdateDentry\_count{app=~\"$app\", cluster=\"$cluster\", instance=\"$instance\"}", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "OpMetaUpdateDentry\_count\_{{instance}}", "refId": "F" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "MetaUpdateDentry", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "ns", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "ops", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fill": 1, "gridPos": { "h": 7, "w": 8, "x": 16, "y": 47 }, "id": 98, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "[[app]]\_fuseclient\_StreamWrite{instance=~\"$instance\",cluster=\"$cluster\", instance=\"$instance\"}", "format": "time\_series", "intervalFactor": 1, 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"intervalFactor": 1, "legendFormat": "SyncOverwrite", "refId": "F" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "StreamSDKLatency", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "ns", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fill": 1, "gridPos": { "h": 7, "w": 8, "x": 0, "y": 54 }, "id": 159, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": 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false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "go\_goroutines{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "intervalFactor": 1, "legendFormat": "go\_goroutines\_{{instance}}", "refId": "A" }, { "expr": "go\_threads{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "go\_threads\_{{instance}}", "refId": "B" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "go\_goroutines", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "locale", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fill": 1, "gridPos": { "h": 6, "w": 7, "x": 7, "y": 2 }, "id": 169, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "go\_goroutines{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "go\_goroutines\_{{instance}}", "refId": "A" }, { "expr": "go\_threads{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "go\_threads\_{{instance}}", "refId": "B" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "go\_threads", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "locale", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fill": 1, "gridPos": { "h": 6, "w": 7, "x": 14, "y": 2 }, "id": 63, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", 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"avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "go\_memstats\_alloc\_bytes{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "alloc\_bytes\_{{instance}}", "refId": "A" }, { "expr": "go\_memstats\_alloc\_bytes\_total{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "alloc\_bytes\_total\_{{instance}}", "refId": "B" }, { "expr": "go\_memstats\_heap\_alloc\_bytes{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "heap\_alloc\_bytes\_{{instance}}", "refId": "C" }, { "expr": "go\_memstats\_heap\_inuse\_bytes{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "heap\_inuse\_bytes\_{{instance}}", "refId": "D" }, { "expr": "go\_memstats\_sys\_bytes{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "sys\_bytes\_{{instance}}", "refId": "E" }, { "expr": "go\_memstats\_mallocs\_total{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "mallocs\_total\_{{instance}}", "refId": "F" }, { "expr": "go\_memstats\_frees\_total{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "frees\_total\_{{instance}}", "refId": "G" }, { "expr": "go\_memstats\_other\_sys\_bytes{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "other\_sys\_bytes\_{{instance}}", "refId": "H" }, { "expr": "go\_memstats\_mcache\_sys\_bytes{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "mcache\_sys\_bytes\_{{instance}}", "refId": "I" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "other\_sys", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "decbytes", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } }, { "aliasColors": {}, "bars": false, "dashLength": 10, "dashes": false, "datasource": null, "fill": 1, "gridPos": { "h": 6, "w": 7, "x": 14, "y": 26 }, "id": 174, "legend": { "avg": false, "current": false, "max": false, "min": false, "show": true, "total": false, "values": false }, "lines": true, "linewidth": 1, "links": [], "nullPointMode": "null", "percentage": false, "pointradius": 5, "points": false, "renderer": "flot", "seriesOverrides": [], "spaceLength": 10, "stack": false, "steppedLine": false, "targets": [ { "expr": "go\_memstats\_alloc\_bytes{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "alloc\_bytes\_{{instance}}", "refId": "A" }, { "expr": "go\_memstats\_alloc\_bytes\_total{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "alloc\_bytes\_total\_{{instance}}", "refId": "B" }, { "expr": "go\_memstats\_heap\_alloc\_bytes{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "heap\_alloc\_bytes\_{{instance}}", "refId": "C" }, { "expr": "go\_memstats\_heap\_inuse\_bytes{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "heap\_inuse\_bytes\_{{instance}}", "refId": "D" }, { "expr": "go\_memstats\_sys\_bytes{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "sys\_bytes\_{{instance}}", "refId": "E" }, { "expr": "go\_memstats\_mallocs\_total{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "mallocs\_total\_{{instance}}", "refId": "F" }, { "expr": "go\_memstats\_frees\_total{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "frees\_total\_{{instance}}", "refId": "G" }, { "expr": "go\_memstats\_other\_sys\_bytes{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": true, "intervalFactor": 1, "legendFormat": "other\_sys\_bytes\_{{instance}}", "refId": "H" }, { "expr": "go\_memstats\_mcache\_sys\_bytes{app=\"$app\", cluster=\"$cluster\", role=\"$role\"}", "format": "time\_series", "hide": false, "intervalFactor": 1, "legendFormat": "mcache\_sys\_bytes\_{{instance}}", "refId": "I" } ], "thresholds": [], "timeFrom": null, "timeShift": null, "title": "mcache\_sys", "tooltip": { "shared": true, "sort": 0, "value\_type": "individual" }, "type": "graph", "xaxis": { "buckets": null, "mode": "time", "name": null, "show": true, "values": [] }, "yaxes": [ { "format": "decbytes", "label": null, "logBase": 1, "max": null, "min": null, "show": true }, { "format": "short", "label": null, "logBase": 1, "max": null, "min": null, "show": true } ], "yaxis": { "align": false, "alignLevel": null } } ], "title": "GoRuntime", "type": "row" } ], "refresh": false, "schemaVersion": 16, "style": "dark", "tags": [], "templating": { "list": [ { "allValue": null, "current": { "selected": true, "text": "cfs", "value": "cfs" }, "hide": 2, "includeAll": false, "label": "App", "multi": false, "name": "app", "options": [ { "selected": true, "text": "cfs", "value": "cfs" } ], "query": "cfs", "type": "custom" }, { "allValue": null, "current": { "tags": [], "text": "spark", "value": "spark" }, "datasource": "default", "hide": 0, "includeAll": false, "label": "Cluster", "multi": false, "name": "cluster", "options": [], "query": "label\_values(go\_info{app=~\"$app\"}, cluster)", "refresh": 1, "regex": "", "sort": 0, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false }, { "allValue": null, "current": { "text": "fuseclient", "value": "fuseclient" }, "datasource": "default", "hide": 0, "includeAll": false, "label": "Role", "multi": false, "name": "role", "options": [], "query": "label\_values(go\_info{app=~\"$app\", cluster=~\"$cluster\"}, role)", "refresh": 1, "regex": "", "sort": 0, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false }, { "allValue": null, "current": { "text": "10.170.6.247:9500", "value": "10.170.6.247:9500" }, "datasource": "default", "hide": 0, "includeAll": false, "label": "Instance", "multi": false, "name": "instance", "options": [], "query": "label\_values(go\_info{app=~\"$app\", role=~\"$role\", cluster=~\"$cluster\"}, instance)", "refresh": 1, "regex": "", "sort": 0, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false }, { "allValue": null, "current": { "text": "10.170.6.247", "value": "10.170.6.247" }, "datasource": "default", "hide": 2, "includeAll": false, "label": "Host", "multi": false, "name": "hostip", "options": [], "query": "label\_values(go\_info{instance=~\"$instance\", cluster=~\"$cluster\"}, instance)", "refresh": 1, "regex": "/([^:]+):.\*/", "sort": 0, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false }, { "allValue": null, "current": { "tags": [], "text": "clickhourse", "value": "clickhourse" }, "datasource": "default", "hide": 0, "includeAll": false, "label": "Vol", "multi": false, "name": "vol", "options": [], "query": "label\_values(cfs\_master\_vol\_total\_GB{app=\"$app\",cluster=\"$cluster\"}, volName)", "refresh": 1, "regex": "", "sort": 0, "tagValuesQuery": "", "tags": [], "tagsQuery": "", "type": "query", "useTags": false } ] }, "time": { "from": "now-1h", "to": "now" }, "timepicker": { "refresh\_intervals": [ "5s", "10s", "30s", "1m", "5m", "15m", "30m", "1h", "2h", "1d" ], "time\_options": [ "5m", "15m", "1h", "6h", "12h", "24h", "2d", "7d", "30d" ] }, "timezone": "", "title": "chubaofs", "uid": "J8XJyOmZk", "version": 484 } ``` Tune FUSE Performance[¶](#tune-fuse-performance "Permalink to this headline") ----------------------------------------------------------------------------- ### Fetch Linux kernel source code[¶](#fetch-linux-kernel-source-code "Permalink to this headline") Download the corresponding src rpm, and use the following commands to install source code. ``` rpm -i kernel-3.10.0-327.28.3.el7.src.rpm 2>&1 | grep -v exist cd ~/rpmbuild/SPECS rpmbuild -bp --target=$(uname -m) kernel.spec ``` The source code will be installed in `~/rpmbuild/BUILD/` ### Optimize FUSE linux kernel module[¶](#optimize-fuse-linux-kernel-module "Permalink to this headline") In order to achieve maximum throughput performance, several FUSE kernel parameters have to be modified, such as `FUSE\_MAX\_PAGES\_PER\_REQ` and `FUSE\_DEFAULT\_MAX\_BACKGROUND`. Update source code according to the following lines. ``` /\* fs/fuse/fuse\_i.h \*/ #define FUSE\_MAX\_PAGES\_PER\_REQ 256 /\* fs/fuse/inode.c \*/ #define FUSE\_DEFAULT\_MAX\_BACKGROUND 32 ``` ### Build against current running Linux kernel[¶](#build-against-current-running-linux-kernel "Permalink to this headline") ``` yum install kernel-devel-3.10.0-327.28.3.el7.x86_64 cd ~/rpmbuild/BUILD/kernel-3.10.0-327.28.3.el7/linux-3.10.0-327.28.3.el7.x86_64/fs/fuse make -C /lib/modules/`uname -r`/build M=$PWD ``` ### Install kernel module[¶](#install-kernel-module "Permalink to this headline") ``` cp fuse.ko /lib/modules/`uname -r`/kernel/fs/fuse rmmod fuse depmod -a modprobe fuse ``` Run Cluster on Docker[¶](#run-cluster-on-docker "Permalink to this headline") ----------------------------------------------------------------------------- Under the docker directory, a helper tool called run\_docker.sh is provided to run ChubaoFS with docker-compose. To start a minimal ChubaoFS cluster from scratch, note that **/data/disk** is arbitrary, and make sure there are at least 30G available space. ``` $ docker/run_docker.sh -r -d /data/disk ``` If client starts successfully, use mount command in client docker shell to check mount status: ``` $ mount | grep chubaofs ``` Open <http://127.0.0.1:3000> in browser, login with admin/123456 to view grafana monitor metrics. Or run server and client seperately by following commands: ``` $ docker/run_docker.sh -b $ docker/run_docker.sh -s -d /data/disk $ docker/run_docker.sh -c $ docker/run_docker.sh -m ``` For more usage: ``` $ docker/run_docker.sh -h ``` Prometheus and Grafana confg can be found in docker/monitor directory. Resource Manager (Master) API[¶](#resource-manager-master-api "Permalink to this headline") ------------------------------------------------------------------------------------------- ### Cluster[¶](#cluster "Permalink to this headline") #### Overview[¶](#overview "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/admin/getCluster" | python -m json.tool ``` Display the base information of the cluster, such as the detail of metaNode, dataNode, vol and so on. response ``` { "Name": "test", "LeaderAddr": "10.196.59.198:17010", "DisableAutoAlloc": false, "Applied": 225, "MaxDataPartitionID": 100, "MaxMetaNodeID": 3, "MaxMetaPartitionID": 1, "DataNodeStatInfo": {}, "MetaNodeStatInfo": {}, "VolStatInfo": {}, "BadPartitionIDs": {}, "BadMetaPartitionIDs": {}, "MetaNodes": {}, "DataNodes": {} } ``` #### Freeze[¶](#freeze "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/cluster/freeze?enable=true" ``` If cluster is freezed, the vol never allocates dataPartitions automatically. Parameters[¶](#id1 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | enable | bool | if enable is true, the cluster is freezed | #### Statistics[¶](#statistics "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/cluster/stat" ``` Show cluster space information by zone. response ``` { "DataNodeStatInfo": { "TotalGB": 1, "UsedGB": 0, "IncreasedGB": -2, "UsedRatio": "0.0" }, "MetaNodeStatInfo": { "TotalGB": 1, "UsedGB": 0, "IncreasedGB": -8, "UsedRatio": "0.0" }, "ZoneStatInfo": { "zone1": { "DataNodeStat": { "TotalGB": 1, "UsedGB": 0, "AvailGB": 0, "UsedRatio": 0, "TotalNodes": 0, "WritableNodes": 0 }, "MetaNodeStat": { "TotalGB": 1, "UsedGB": 0, "AvailGB": 0, "UsedRatio": 0, "TotalNodes": 0, "WritableNodes": 0 } } } } ``` #### Topology[¶](#topology "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/topo/get" ``` Show cluster topology information by zone. response ``` [ { "Name": "zone1", "Status": "available", "NodeSet": { "700": { "DataNodeLen": 0, "MetaNodeLen": 0, "MetaNodes": [], "DataNodes": [] } } }, { "Name": "zone2", "Status": "available", "NodeSet": { "800": { "DataNodeLen": 0, "MetaNodeLen": 0, "MetaNodes": [], "DataNodes": [] } } } ] ``` #### Update Zone[¶](#update-zone "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/zone/update?name=zone1&enable=false" ``` Set the status of the zone to available or unavailable. Parameters[¶](#id2 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | name | string | zone name | | enable | bool | if enable is true, the cluster is available | #### Get Zone[¶](#get-zone "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/zone/list" ``` Get name and status of all zones. response ``` [ { "Name": "zone1", "Status": "available", "NodeSet": {} }, { "Name": "zone2", "Status": "available", "NodeSet": {} } ] ``` #### Get Node Info[¶](#get-node-info "Permalink to this headline") ``` curl -v "http://192.168.0.11:17010/admin/getNodeInfo" ``` Get node info of cluster. response ``` { "code": 0, "msg": "success", "data": { "batchCount": 0, "deleteWorkerSleepMs": 0, "markDeleteRate": 0 } } ``` #### Set Node Info[¶](#set-node-info "Permalink to this headline") ``` curl -v "http://192.168.0.11:17010/admin/setNodeInfo?batchCount=100&markDeleteRate=100&deleteWorkerSleepMs=1000" ``` Set node info of cluster. Parameters[¶](#id3 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | batchCount | uint64 | metanode delete batch count | | deleteWorkerSleepMs | uint64 | metanode delete worker sleep time with millisecond. if 0 for no sleep | | markDeleteRate | uint64 | datanode batch markdelete limit rate. if 0 for no infinity limit | ### Metanode Related[¶](#metanode-related "Permalink to this headline") #### GET[¶](#get "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/metaNode/get?addr=10.196.59.202:17210" | python -m json.tool ``` Show the base information of the metaNode, such as addr, total memory, used memory and so on. Parameters[¶](#id1 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | addr | string | the addr which communicate with master | response ``` { "ID": 3, "Addr": "10.196.59.202:17210", "IsActive": true, "Zone": "zone1", "MaxMemAvailWeight": 66556215048, "TotalWeight": 67132641280, "UsedWeight": 576426232, "Ratio": 0.008586377967698518, "SelectCount": 0, "Carry": 0.6645600532184904, "Threshold": 0.75, "ReportTime": "2018-12-05T17:26:28.29309577+08:00", "MetaPartitionCount": 1, "NodeSetID": 2, "PersistenceMetaPartitions": {} } ``` #### Decommission[¶](#decommission "Permalink to this headline") ``` curl -v "http://127.0.0.1/metaNode/decommission?addr=127.0.0.1:9021" ``` Remove the metaNode from cluster, meta partitions which locate the metaNode will be migrate other available metaNode asynchronous. Parameters[¶](#id2 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | addr | string | the addr which communicate with master | #### Threshold[¶](#threshold "Permalink to this headline") ``` curl -v "http://127.0.0.1/threshold/set?threshold=0.75" ``` If the used memory percent arrives the threshold, the status of the meta partitions which locate the metaNode will be read only. Parameters[¶](#id3 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | threshold | float64 | the max percent of memory which metaNode can use | ### Datanode Related[¶](#datanode-related "Permalink to this headline") #### GET[¶](#get "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/dataNode/get?addr=10.196.59.201:17310" | python -m json.tool ``` Show the base information of the dataNode, such as addr, disk total size, disk used size and so on. Parameters[¶](#id1 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | addr | string | the addr which communicate with master | response ``` { "TotalWeight": 39666212700160, "UsedWeight": 2438143586304, "AvailableSpace": 37228069113856, "ID": 2, "Zone": "zone1", "Addr": "10.196.59.201:17310", "ReportTime": "2018-12-06T10:56:38.881784447+08:00", "IsActive": true "UsageRatio": 0.06146650815226848, "SelectTimes": 5, "Carry": 1.0655859145960367, "DataPartitionReports": {}, "DataPartitionCount": 21, "NodeSetID": 3, "PersistenceDataPartitions": {}, "BadDisks": {} } ``` #### Decommission[¶](#decommission "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/dataNode/decommission?addr=10.196.59.201:17310" ``` Remove the dataNode from cluster, data partitions which locate the dataNode will be migrate other available dataNode asynchronous. Parameters[¶](#id2 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | addr | string | the addr which communicate with master | ### Volume[¶](#volume "Permalink to this headline") #### Create[¶](#create "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/admin/createVol?name=test&capacity=100&owner=cfs&mpCount=3" ``` Allocate a set of data partition and a meta partition to the user. Default create 10 data partition and 3 meta partition when create volume. ChubaoFS uses the **Owner** parameter as the user ID. When creating a volume, if there is no user named the owner of the volume, a user with the user ID same as **Owner** will be automatically created; if a user named Owner already exists in the cluster, the volume will be owned by the user. For details, please see:: doc: /admin-api/master/user Parameters[¶](#id1 "Permalink to this table") | Parameter | Type | Description | Mandatory | Default | | --- | --- | --- | --- | --- | | name | string | volume name | Yes | None | | capacity | int | the quota of vol, unit is GB | Yes | None | | owner | string | the owner of vol, and user ID of a user | Yes | None | | mpCount | int | the amount of initial meta partitions | No | 3 | | enableToken | bool | whether to enable the token mechanism to control client permissions | No | false | | size | int | the size of data partitions, unit is GB | No | 120 | | followerRead | bool | enable read from follower | No | false | | crossZone | bool | cross zone or not. If it is true, parameter *zoneName* must be empty | No | false | | zoneName | string | specified zone | No | default (if *crossZone* is false) | #### Delete[¶](#delete "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/vol/delete?name=test&authKey=md5(owner)" ``` Mark the vol status to MarkDelete first, then delete data partition and meta partition asynchronous, finally delete meta data from persist store. While deleting the volume, the policy information related to the volume will be deleted from all user information. Parameters[¶](#id2 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | name | string | volume name | | authKey | string | calculates the 32-bit MD5 value of the owner field as authentication information | #### Get[¶](#get "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/client/vol?name=test&authKey=md5(owner)" | python -m json.tool ``` Show the base information of the vol, such as name, the detail of data partitions and meta partitions and so on. Parameters[¶](#id3 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | name | string | volume name | | authKey | string | calculates the 32-bit MD5 value of the owner field as authentication information | response ``` { "Name": "test", "Owner": "user", "Status": "0", "FollowerRead": "true", "MetaPartitions": {}, "DataPartitions": {}, "DataPartitions": {}, "CreateTime": 0 } ``` #### Stat[¶](#stat "Permalink to this headline") ``` curl -v http://10.196.59.198:17010/client/volStat?name=test ``` Show the status information of volume. Parameters[¶](#id4 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | name | string | volume name | response ``` { "Name": "test", "TotalSize": 322122547200000000, "UsedSize": 155515112832780000, "UsedRatio": "0.48", "EnableToken": false } ``` #### Update[¶](#update "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/vol/update?name=test&capacity=100&authKey=md5(owner)" ``` Increase the quota of volume, or adjust other parameters. Parameters[¶](#id5 "Permalink to this table") | Parameter | Type | Description | Mandatory | | --- | --- | --- | --- | | name | string | volume name | Yes | | authKey | string | calculates the 32-bit MD5 value of the owner field as authentication information | Yes | | capacity | int | the quota of vol, unit is GB | Yes | | zoneName | string | update zone name | Yes | | enableToken | bool | whether to enable the token mechanism to control client permissions. `False` by default. | No | | followerRead | bool | enable read from follower | No | #### List[¶](#list "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/vol/list?keywords=test" ``` List all volumes information, and can be filtered by keywords. Parameters[¶](#id6 "Permalink to this table") | Parameter | Type | Description | Mandatory | | --- | --- | --- | --- | | keywords | string | get volumes information which contains this keyword | No | response ``` [ { "Name": "test1", "Owner": "cfs", "CreateTime": 0, "Status": 0, "TotalSize": 155515112832780000, "UsedSize": 155515112832780000 }, { "Name": "test2", "Owner": "cfs", "CreateTime": 0, "Status": 0, "TotalSize": 155515112832780000, "UsedSize": 155515112832780000 } ] ``` #### Add Token[¶](#add-token "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/token/add?name=test&tokenType=1&authKey=md5(owner)" ``` Add the token that controls read and write permissions. Parameters[¶](#id7 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | name | string | the name of vol | | tokenType | int | 1 is readonly token, 2 is readWrite token | | authKey | string | calculates the 32-bit MD5 value of the owner field as authentication information | #### Update Token[¶](#update-token "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/token/update?name=test&token=xx&tokenType=1&authKey=md5(owner)" ``` Update token type. Parameters[¶](#id8 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | name | string | the name of vol | | token | string | the token value | | tokenType | int | 1 is readonly token, 2 is readWrite token | | authKey | string | calculates the 32-bit MD5 value of the owner field as authentication information | #### Delete Token[¶](#delete-token "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/token/delete?name=test&token=xx&authKey=md5(owner)" ``` Delete specified token. Parameters[¶](#id9 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | name | string | the name of vol | | token | string | the token value | | authKey | string | calculates the 32-bit MD5 value of the owner field as authentication information | #### Get Token[¶](#get-token "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/token/get?name=test&token=xx" ``` Show token information. Parameters[¶](#id10 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | name | string | the name of vol | | token | string | the token value | response ``` { "TokenType":2, "Value":"siBtuF9hbnNqXzJfMTU48si3nzU4MzE1Njk5MDM1NQ==", "VolName":"test" } ``` ### Meta Partition[¶](#meta-partition "Permalink to this headline") #### Create[¶](#create "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/metaPartition/create?name=test&start=10000" ``` Split meta partition manually. If max meta partition of the vol which range is `[0,end)` and `end` larger than `start` parameter, old meta partition range will be `[0,start]`, new meta partition will be `[start+1,end)`. Parameters[¶](#id1 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | name | string | the name of vol | | start | uint64 | the start value of meta partition which will be create | #### Get[¶](#get "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/metaPartition/get?id=1" | python -m json.tool ``` Show base information of meta partition, such as id, start, end and so on. Parameters[¶](#id2 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | id | uint64 | the id of meta partition | response ``` { "PartitionID": 1, "Start": 0, "End": 9223372036854776000, "MaxNodeID": 1, "VolName": "test", "Replicas": {}, "ReplicaNum": 3, "Status": 2, "IsRecover": true, "Hosts": {}, "Peers": {}, "Zones": {}, "MissNodes": {}, "LoadResponse": {} } ``` #### Decommission[¶](#decommission "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/metaPartition/decommission?id=13&addr=10.196.59.202:17210" ``` Remove the replica of meta partition, and create new replica asynchronous. Parameters[¶](#id3 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | id | uint64 | the id of meta partition | | addr | string | the addr of replica which will be decommission | #### Load[¶](#load "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/metaPartition/load?id=1" ``` Send load task to the metaNode which meta partition locate on, then check the crc of each replica in the meta partition. Parameters[¶](#id4 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | id | uint64 | the id of data partition | ### Data Partition[¶](#data-partition "Permalink to this headline") #### Create[¶](#create "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/dataPartition/create?count=400&name=test" ``` Create a set of data partition. Parameters[¶](#id1 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | count | int | the num of dataPartitions will be create | | name | string | the name of vol | #### Get[¶](#get "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/dataPartition/get?id=100" | python -m json.tool ``` Get information of the specified data partition. Parameters[¶](#id2 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | id | uint64 | the id of data partition | response ``` { "PartitionID": 100, "LastLoadedTime": 1544082851, "ReplicaNum": 3, "Status": 2, "Replicas": {}, "Hosts": {}, "Peers": {}, "Zones": {}, "MissingNodes": {}, "VolName": "test", "VolID": 2, "FileInCoreMap": {}, "FilesWithMissingReplica": {} } ``` #### Decommission[¶](#decommission "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/dataPartition/decommission?id=13&addr=10.196.59.201:17310" ``` Remove the replica of data partition, and create new replica asynchronous. Parameters[¶](#id3 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | id | uint64 | the id of data partition | | addr | string | the addr of replica which will be decommission | #### Load[¶](#load "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/dataPartition/load?id=1" ``` Send load task to the dataNode which data partition locate on, then check the crc of each file in the data partition asynchronous. Parameters[¶](#id4 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | id | uint64 | the id of data partition | #### Offline Disk[¶](#offline-disk "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/disk/decommission?addr=10.196.59.201:17310&disk=/cfs1" ``` Synchronously offline all the data partitions on the disk, and create a new replica for each data partition in the cluster. Parameters[¶](#id5 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | addr | string | replica address | | disk | string | disk path | ### Master Management[¶](#master-management "Permalink to this headline") #### Add[¶](#add "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/raftNode/add?addr=10.196.59.197:17010&id=3" ``` Add a new master node to master raft group. Parameters[¶](#id1 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | addr | string | the addr of master server, format is ip:port | | id | uint64 | the node id of master server | #### Remove[¶](#remove "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/raftNode/remove?addr=10.196.59.197:17010&id=3" ``` Remove the master node from master raft group. Parameters[¶](#id2 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | addr | string | the addr of master server, format is ip:port | | id | uint64 | the node id of master server | ### User[¶](#user "Permalink to this headline") #### Create[¶](#create "Permalink to this headline") ``` curl -H "Content-Type:application/json" -X POST --data '{"id":"testuser","pwd":"12345","type":3}' "http://10.196.59.198:17010/user/create" ``` Create a user in the cluster to access object storage service. When the cluster starts, the `root` user is automatically created (the value of `type` is `0x1`). ChubaoFS treats every `Owner` of volume as a `user`. For example, if the value of **Owner** is `testuser` when creating a volume, the volume is owned by user `testuser`. If there is no user ID with the same value as the **Owner** when creating the volume, the user named the value of **Owner** will be automatically created when creating the volume. body key[¶](#id1 "Permalink to this table") | Key | Type | Description | Range | Mandatory | Default | | --- | --- | --- | --- | --- | --- | | id | string | user ID | Consists of letters, numbers and underscores, no more than 20 characters | Yes | None | | pwd | string | user’s password | Unlimited | No | `ChubaoFSUser` | | ak | string | Access Key | Consists of 16-bits letters and numbers | No | Random value | | sk | string | Secret Key | Consists of 32-bits letters and numbers | No | Random value | | type | int | user type | 2: [admin] / 3: [normal user] | Yes | None | #### Delete[¶](#delete "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/user/delete?user=testuser" ``` Delete the specified user in the cluster. Parameters[¶](#id2 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | user | string | user ID | #### Get[¶](#get "Permalink to this headline") Show basic user information, including user ID, Access Key, Secret Key, list of owned volumes, list of permissions granted by other users, user type, creation time. The field `policy` shows the volumes which the user has permission, of which `own\_vols` indicates that volumes owned by the user, and `authorized\_vols` indicates the volume authorized by other users to the user with restrictions. There are two ways to obtain: ##### Query by User ID[¶](#query-by-user-id "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/user/info?user=testuser" | python -m json.tool ``` Parameters[¶](#id3 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | user | string | user ID | ##### Query by Access Key[¶](#query-by-access-key "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/user/akInfo?ak=0123456789123456" | python -m json.tool ``` Parameters[¶](#id4 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | ak | string | Access Key | response ``` { "user\_id": "testuser", "access\_key": "0123456789123456", "secret\_key": "ZVY5RHlrnOrCjImW9S3MajtYZyxSegcf", "policy": { "own\_vols": ["vol1"], "authorized\_vols": { "ltptest": [ "perm:builtin:ReadOnly", "perm:custom:PutObjectAction" ] } }, "user\_type": 3, "create\_time": "2020-05-11 09:25:04" } ``` #### List Users[¶](#list-users "Permalink to this headline") ``` curl -v "http://10.196.59.198:17010/user/list?keywords=test" | python -m json.tool ``` Query information about all users in a cluster whose user ID contains the keyword. Parameters[¶](#id5 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | keywords | string | check user ID contains this or not | #### Update[¶](#update "Permalink to this headline") ``` curl -H "Content-Type:application/json" -X POST --data '{"user\_id":"testuser","access\_key":"KzuIVYCFqvu0b3Rd","secret\_key":"iaawlCchJeeuGSnmFW72J2oDqLlSqvA5","type":3}' "http://10.196.59.198:17010/user/update" ``` Update the specified user’s information, including access key, secret key and user type. body key[¶](#id6 "Permalink to this table") | Key | Type | Description | Mandatory | | --- | --- | --- | --- | | user\_id | string | user ID value after updating | Yes | | access\_key | string | Access Key value after updating | No | | secret\_key | string | Secret Key value after updating | No | | type | int | user type value after updating | No | #### Update Permission[¶](#update-permission "Permalink to this headline") ``` curl -H "Content-Type:application/json" -X POST --data '{"user\_id":"testuser","volume":"vol","policy":["perm:builtin:ReadOnly","perm:custom:PutObjectAction"]}' "http://10.196.59.198:17010/user/updatePolicy" ``` Update the specified user’s permission to a volume. There are three types of values for `policy`: * Grant read-only or read-write permission, the value is `perm:builtin:ReadOnly` or `perm:builtin:Writable`. * Grant a permission of the specified action, the format is `action:oss:XXX`, take *GetObject* action as an example, the value of policy is `action:oss:GetObject`. * Grant a custom permission, the format is `perm:custom:XXX`, where *XXX* is customized by the user. After the permissions are specified, the user can only access the volume within the specified permissions when using the object storage. If the user already has permissions for this volume, this operation will overwrite the original permissions. body key[¶](#id7 "Permalink to this table") | Key | Type | Description | Mandatory | | --- | --- | --- | --- | | user\_id | string | user ID to be set | Yes | | volume | string | volume name to be set | Yes | | policy | string slice | policy to be set | Yes | #### Remove Permission[¶](#remove-permission "Permalink to this headline") ``` curl -H "Content-Type:application/json" -X POST --data '{"user\_id":"testuser","volume":"vol"}' "http://10.196.59.198:17010/user/removePolicy" ``` Remove all permissions of a specified user for a volume. body key[¶](#id8 "Permalink to this table") | Key | Type | Description | Mandatory | | --- | --- | --- | --- | | user\_id | string | user ID to be deleted | Yes | | volume | string | volume name to be deleted | Yes | #### Transfer Volume[¶](#transfer-volume "Permalink to this headline") ``` curl -H "Content-Type:application/json" -X POST --data '{"volume":"vol","user\_src":"user1","user\_dst":"user2","force":"true"}' "http://10.196.59.198:17010/user/transferVol" ``` Transfer the ownership of the specified volume. This operation removes the specified volume from the `owner\_vols` of source user name and adds it to the `owner\_vols` of target user name; At the same time, the value of the field `Owner` in the volume structure will also be updated to the target user ID. body key[¶](#id9 "Permalink to this table") | Key | Type | Description | Mandatory | | --- | --- | --- | --- | | volume | string | Volume name to be transfered | Yes | | user\_src | string | Original owner of the volume, and must be the same as the `Owner` of the volume | Yes | | user\_dst | string | Target user ID after transferring | Yes | | force | bool | Force to transfer the volume. If the value is set to true, even if the value of `user\_src` is different from the value of the owner of the volume, the volume will also be transferred to the target user | No | Meta Node API[¶](#meta-node-api "Permalink to this headline") ------------------------------------------------------------- ### Meta Partition[¶](#meta-partition "Permalink to this headline") #### Get Partitions[¶](#get-partitions "Permalink to this headline") ``` curl -v http://10.196.59.202:17210/getPartitions ``` Get all meta-partition base information of the metanode. #### Get Partition by ID[¶](#get-partition-by-id "Permalink to this headline") ``` curl -v http://10.196.59.202:17210/getPartitionById?pid=100 ``` Get the specified partition information, this result contains: leader address, raft group peer and cursor. Parameters[¶](#id1 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | pid | integer | meta-partition id | ### Inode[¶](#inode "Permalink to this headline") #### Get Inode[¶](#get-inode "Permalink to this headline") ``` curl -v http://10.196.59.202:17210/getInode?pid=100&ino=1024 ``` Get inode information Parameters[¶](#id1 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | pid | integer | meta-partition id | | ino | integer | inode id | #### Get Extents by Inode[¶](#get-extents-by-inode "Permalink to this headline") ``` curl -v http://10.196.59.202:17210/getExtentsByInode?pid=100&ino=1024 ``` Get inode all extents information Parameters[¶](#id2 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | pid | integer | meta-partition id | | ino | integer | inode id | #### Get All Inodes[¶](#get-all-inodes "Permalink to this headline") ``` curl -v http://10.196.59.202:17210/getAllInodes?pid=100 ``` Get all inodes of the specified partition Parameters[¶](#id3 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | pid | integer | meta-partition id | ### Dentry[¶](#dentry "Permalink to this headline") #### Get Dentry[¶](#get-dentry "Permalink to this headline") ``` curl -v 'http://10.196.59.202:17210/getDentry?pid=100&name=""&parentIno=1024' ``` Get dentry information Parameters[¶](#id1 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | pid | integer | meta partition id | | name | string | file or directory name | | parentIno | integer | file or directory parent directory inode | #### Get Directory[¶](#get-directory "Permalink to this headline") ``` curl -v "http://10.196.59.202:17210/getDirectory?pid=100&parentIno=1024" ``` Get all files of the parent inode is 1024 Parameters[¶](#id2 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | pid | integer | partition id | | ino | integer | inode id | #### Get All Dentry[¶](#get-all-dentry "Permalink to this headline") ``` curl -v "http://10.196.59.202:17210/getAllDentry?pid=100" ``` Parameters[¶](#id3 "Permalink to this table") | Parameter | Type | Description | | --- | --- | --- | | pid | integer | partition id | Command Line Interface[¶](#command-line-interface "Permalink to this headline") ------------------------------------------------------------------------------- ### CLI[¶](#cli "Permalink to this headline") Using the Command Line Interface (CLI) can manage the cluster conveniently. With this tool, you can view the status of the cluster and each node, and manage each node, volumes, or users. As the CLI continues to improve, it will eventually achieve 100% coverage of the APIs of each node in the cluster. #### Compile and Configure[¶](#compile-and-configure "Permalink to this headline") After downloading the ChubaoFS source code, execute the command `go build` in the directory `chubaofs/cli` to generate `cli`. At the same time, a configuration file named `.cfs-cli.json` will be generated in the directory `root`, and the master address can be changed to the current cluster master address. You can also get or set the master address by executing the command `./cli config info` or `./cli config set`. #### Bug Shooting[¶](#bug-shooting "Permalink to this headline") The logs of `cfs-cli` tool are in the directory `/tmp/cfs/cli`, which offer detail running information for bug shooting. #### Usage[¶](#usage "Permalink to this headline") In the directory `chubaofs/cli`, execute the command `./cli --help` or `./cli -h` to get the CLI help document. CLI is mainly divided into seven types of management commands. Commands List[¶](#id1 "Permalink to this table") | Command | description | | --- | --- | | cli cluster | Manage cluster components | | cli metanode | Manage meta nodes | | cli datanode | Manage data nodes | | cli datapartition | Manage data partitions | | cli metapartition | Manage meta partitions | | cli config | Manage configuration for cli tool | | cli completion | Generating bash completions | | cli volume, vol | Manage cluster volumes | | cli user | Manage cluster users | | cli compatibility | Compatibility test | ##### Cluster Management[¶](#cluster-management "Permalink to this headline") ``` ./cli cluster info #Show cluster summary information ``` ``` ./cli cluster stat #Show cluster status information ``` ``` ./cli cluster freeze [true/false] #Turn on or turn off the automatic allocation of the data partitions. ``` ``` ./cli cluster threshold [float] #Set the threshold of memory on each meta node. ``` ##### MetaNode Management[¶](#metanode-management "Permalink to this headline") ``` ./cli metanode list #List information of meta nodes ``` ``` ./cli metanode info [Address] #Show detail information of a meta node ``` ``` ./cli metanode decommission [Address] #Decommission partitions in a meta node to other nodes ``` ##### DataNode Management[¶](#datanode-management "Permalink to this headline") ``` ./cli datanode list #List information of data nodes ``` ``` ./cli datanode info [Address] #Show detail information of a data node ``` ``` ./cli datanode decommission [Address] #Decommission partitions in a data node to other nodes ``` ##### DataPartition Management[¶](#datapartition-management "Permalink to this headline") ``` ./cli datapartition info [Partition ID] #Display detail information of a data partition ``` ``` ./cli datapartition decommission [Address] [Partition ID] #Decommission a replication of the data partition to a new address ``` ``` ./cli datapartition add-replica [Address] [Partition ID] #Add a replication of the data partition on a new address ``` ``` ./cli datapartition del-replica [Address] [Partition ID] #Delete a replication of the data partition from a fixed address ``` ``` ./cli datapartition check #Diagnose partitions, display the partitions those are corrupt or lack of replicas ``` ##### MetaPartition Management[¶](#metapartition-management "Permalink to this headline") ``` ./cli metapartition info [Partition ID] #Display detail information of a meta partition ``` ``` ./cli metapartition decommission [Address] [Partition ID] #Decommission a replication of the meta partition to a new address ``` ``` ./cli metapartition add-replica [Address] [Partition ID] #Add a replication of the meta partition on a new address ``` ``` ./cli metapartition del-replica [Address] [Partition ID] #Delete a replication of the meta partition from a fixed address ``` ``` ./cli metapartition check #Diagnose partitions, display the partitions those are corrupt or lack of replicas ``` ##### Config Management[¶](#config-management "Permalink to this headline") ``` ./cli config info #Show configurations of cli ``` ``` ./cli config set [flags] #Set configurations of cli Flags: --addr string #Specify master address [{HOST}:{PORT}] --timeout uint16 #Specify timeout for requests [Unit: s] (default 60) ``` ##### Completion Management[¶](#completion-management "Permalink to this headline") ``` ./cli completion #Generate bash completions ``` ##### Volume Management[¶](#volume-management "Permalink to this headline") ``` ./cli volume create [VOLUME NAME] [USER ID] [flags] #Create a new volume Flags: --capacity uint #Specify volume capacity [Unit: GB] (default 10) --dp-size uint #Specify size of data partition size [Unit: GB] (default 120) --follower-read #Enable read form replica follower (default true) --mp-count int #Specify init meta partition count (default 3) -y, --yes #Answer yes for all questions ``` ``` ./cli volume delete [VOLUME NAME] [flags] #Delete a volume from cluster Flags: -y, --yes #Answer yes for all questions ``` ``` ./cli volume info [VOLUME NAME] [flags] #Show volume information Flags: -d, --data-partition #Display data partition detail information -m, --meta-partition #Display meta partition detail information ``` ``` ./cli volume add-dp [VOLUME] [NUMBER] #Create and add more data partition to a volume ``` ``` ./cli volume list #List cluster volumes ``` ``` ./cli volume transfer [VOLUME NAME] [USER ID] [flags] #Transfer volume to another user. (Change owner of volume) Flags： -f, --force #Force transfer without current owner check -y, --yes #Answer yes for all questions ``` ##### User Management[¶](#user-management "Permalink to this headline") ``` ./cli user create [USER ID] [flags] #Create a new user Flags： --access-key string #Specify user access key for object storage interface authentication --secret-key string #Specify user secret key for object storage interface authentication --password string #Specify user password --user-type string #Specify user type [normal | admin] (default "normal") -y, --yes #Answer yes for all questions ``` ``` ./cli user delete [USER ID] [flags] #Delete specified user Flags： -y, --yes #Answer yes for all questions ``` ``` ./cli user info [USER ID] #Show detail information about specified user ``` ``` ./cli user list #List cluster users ``` ``` ./cli user perm [USER ID] [VOLUME] [PERM] #Setup volume permission for a user #The value of [PERM] is READONLY, RO, READWRITE, RW or NONE ``` ``` ./cli user update [USER ID] [flags] #Update information about specified user Flags： --access-key string #Update user access key --secret-key string #Update user secret key --user-type string #Update user type [normal | admin] -y, --yes #Answer yes for all questions ``` ##### Compatibility Test[¶](#compatibility-test "Permalink to this headline") ``` ./cli cptest meta [Snapshot Path] [Host] [Partition ID] #Metadata compatibility test Parameters： [Snapshot Path] string #The path which snapshot file located [Host] string #The metanode host which generated the snapshot file [Partition ID] string #The meta partition ID which to be compared ``` Example: > > 1. Use the old version to prepare metadata, stop writing metadata,after waiting for the latest snapshot to be generated(about 5 minutes), copy the snapshot file to the local machine > 2. Execute the metadata comparison command on local machine > > > > ``` > [Verify result] > All dentry are consistent > All inodes are consistent > All meta has checked > > ``` > > > Use Cases[¶](#use-cases "Permalink to this headline") ----------------------------------------------------- ChubaoFS is a distributed file system that is compatible with most POSIX file system semantics. When ChubaoFS is mounted, it can be as simple as using a local file system. Basically, it can be used in any case where a file system is needed, replacing the local file system, and realizing infinitely expandable storage without physical boundaries. It has been applied in various scenarios, and the following are some of the extracted scenes. ### Machine Learning[¶](#machine-learning "Permalink to this headline") Disadvantages of using a local disk to store training data sets: * The local disk space is small, and there are multiple models. The training data set of each model reaches the TB level. If you use a local disk to store the training data set, you need to reduce the size of the training data set. * Training data sets need to be updated frequently, requiring more disk space. * Risk of loss of training data set if machine crashes. The advantages of using ChubaoFS to store training data sets: * Unlimited disk space, easy to expand capacity. It can automatically expand disk capacity according to the percentage of disk usage, enabling storage system capacity expansion on demand, greatly saving storage costs. * Multiple replicas of data to ensure high data reliability without worrying about losing data. * Compatible with POSIX file system interface, no changes required by the application. ### ElasticSearch[¶](#elasticsearch "Permalink to this headline") Using local disks to store data often encounters the following problems: * Disk usage is uneven and disk IO cannot be fully utilized. * Local disk space is limited. The advantages of using ChubaoFS as a backend storage: * Unlimited disk space, easy to expand capacity. It can automatically expand disk capacity according to the percentage of disk usage, enabling storage system capacity expansion on demand, greatly saving storage costs. * Disk IO usage is uniform and disk IO is fully utilized. * Ensure data is highly reliable without worrying about losing data. ### Nginx Log Storage[¶](#nginx-log-storage "Permalink to this headline") Do you often worry about running out of local disk space? With ChubaoFS, you can store datas in a distributed file system without worrying about running out of disk space. If you use a local disk to store logs, you may often worry about the following issues: * Docker local disk space is small. * If the docker container crashes, the log is lost and unrecoverable. * The mixed deployment of physical machine and docker machine is difficult to manage and has high operation and maintenance cost. The advantages of using ChubaoFS to store nginx logs are: * The disk space is unlimited and easy to expand. The disk capacity is automatically expanded according to the percentage of disk usage. The storage system can be expanded on demand, which greatly saves storage costs. * Ensure data is highly reliable and do not worry about losing data. * Multiple replicas to solve the problem of unable to write to the log caused by disk error and datanode crashes. * Compatible with the POSIX file system interface, no changes required by the application. * The operation and maintenance of ChubaoFS are simple, and one person can easily manage the cluster of tens of thousands of machines. ### Spark[¶](#spark "Permalink to this headline") In the big data set scenario, are you worried about the amount of data stored in Spark intermediate calculation results in order to carefully calculate each task? You can store the shuffle results to ChubaoFS, and no longer worry about the disk has no free space which causes the task to fail. This enables the separation of storage and computation. The pain points of using local disk to store shuffle intermediate results: * Insufficient disk space. * Too many temporary directory files to create new files. The advantages of using ChubaoFS: * Unlimited disk space, easy to expand capacity. It can automatically expand disk capacity according to the percentage of disk usage, enabling storage system capacity expansion on demand, greatly saving storage costs. * Meta node manages file metadata, which can be expanded horizontally and the number of files is unlimited. ### MySQL Database Backup[¶](#mysql-database-backup "Permalink to this headline") Disadvantages of using OSS(Object Storage Service) to back up MySQL database: * Need to use the OSS SDK or RESTful API to develop backup programs which increases the difficulty of operation and maintenance. * If backup file fails, troubleshooting is more difficult. * After backing up files to OSS, it is not convenient to check whether the files are successfully uploaded. * Backup files are processed by multiple layers of services, which affects performance. Advantages of using ChubaoFS to back up MySQL databases: * Easy to use, compatible with POSIX file interface, and can be used as a local file system. * Complete and detailed operation logs are stored in the local file system, making it easy to troubleshoot problems. * Simply execute the ls command to verify that the file was successfully uploaded. * Supports PageCache and WriteCache, and file read and write performance is significantly improved compared to OSS. Performance[¶](#performance "Permalink to this headline") --------------------------------------------------------- ### Environment[¶](#environment "Permalink to this headline") **Cluster Information** | Instance | Nodes | CPU | Memory | Storage | Network | Description | | Master | 3 | 32 | 32 GB | 120 GB SSD | 10 Gb/s | | | MetaNode | 10 | 32 | 32 GB | 16 x 1TB SSD | 10 Gb/s | hybrid deployment | | DataNode | 10 | 32 | 32 GB | 16 x 1TB SSD | 10 Gb/s | hybrid deployment | **Volume Setup** | Parameter | Default | Recommend | Description | | FollowerRead | True | True | | | Capacity | 10 GB | 300 000 000 GB | | | Data Replica Number | 3 | 3 | | | Meta Replica Number | 3 | 3 | | | Data Partition Size | 120 GB | 120 GB | Logical upper limit with no pre-occupied space. | | Data Partition Count | 10 | 1500 | | | Meta Partition Count | 3 | 10 | | | Cross Zone | False | False | | Set volume parameters by following: ``` $ cfs-cli volume create test-vol {owner} --capacity=300000000 --mp-count=10 Create a new volume: Name : test-vol Owner : ltptest Dara partition size : 120 GB Meta partition count: 10 Capacity : 300000000 GB Replicas : 3 Allow follower read : Enabled Confirm (yes/no)[yes]: yes Create volume success. $ cfs-cli volume add-dp test-vol 1490 ``` **client configuration** | Parameter | Default | Recommend | | rate limit | -1 | -1 | ``` #get current iops, default:-1(no limits on iops)： $ http://[ClientIP]:[ProfPort]/rate/get #set iops $ http://[ClientIP]:[ProfPort]/rate/set?write=800&read=800 ``` ### Small File Performance and Scalability[¶](#small-file-performance-and-scalability "Permalink to this headline") Small file operation performance and scalability benchmark test by [mdtest](https://github.com/LLNL/mdtest). **Setup** ``` #!/bin/bash set -e TARGET\_PATH="/mnt/test/mdtest" # mount point of ChubaoFS volume for FILE_SIZE in 1024 2048 4096 8192 16384 32768 65536 131072 # file size do mpirun --allow-run-as-root -np 512 --hostfile hfile64 mdtest -n 1000 -w $i -e $FILE\_SIZE -y -u -i 3 -N 1 -F -R -d $TARGET\_PATH; done ``` **Benchmark** [](_images/cfs-small-file-benchmark.png) | File Size (KB) | 1 | 2 | 4 | 8 | 16 | 32 | 64 | 128 | | Creation (TPS) | 70383 | 70383 | 73738 | 74617 | 69479 | 67435 | 47540 | 27147 | | Read (TPS) | 108600 | 118193 | 118346 | 122975 | 116374 | 110795 | 90462 | 62082 | | Removal (TPS) | 87648 | 84651 | 83532 | 79279 | 85498 | 86523 | 80946 | 84441 | | Stat (TPS) | 231961 | 263270 | 264207 | 252309 | 240244 | 244906 | 273576 | 242930 | ### IO Performance and Scalability[¶](#io-performance-and-scalability "Permalink to this headline") IO Performance and benchmark scalability test by [fio](https://github.com/axboe/fio). *Note: Multiple clients mount the same volume. And the process refers to the fio process.* #### 1. Sequential Read[¶](#sequential-read "Permalink to this headline") **Setup** ``` #!/bin/bash fio -directory={} \ -ioengine=psync \ -rw=read \ # sequential read -bs=128k \ # block size -direct=1 \ # enable direct IO -group_reporting=1 \ -fallocate=none \ -time_based=1 \ -runtime=120 \ -name=test_file_c{} \ -numjobs={} \ -nrfiles=1 \ -size=10G ``` **Bandwidth(MB/s)** [](_images/cfs-fio-sequential-read-bandwidth.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 148.000 | 626.000 | 1129.000 | 1130.000 | | 2 Clients | 284.000 | 1241.000 | 2258.000 | 2260.000 | | 4 Clients | 619.000 | 2640.000 | 4517.000 | 4515.000 | | 8 Clients | 1193.000 | 4994.000 | 9006.000 | 9034.000 | **IOPS** [](_images/cfs-fio-sequential-read-iops.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 1180.000 | 5007.000 | 9031.000 | 9040.000 | | 2 Clients | 2275.000 | 9924.000 | 18062.000 | 18081.000 | | 4 Clients | 4954.000 | 21117.000 | 36129.000 | 36112.000 | | 8 Clients | 9531.000 | 39954.000 | 72048.000 | 72264.000 | **Latency(Microsecond)** [](_images/cfs-fio-sequential-read-latency.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 842.200 | 794.340 | 1767.310 | 7074.550 | | 2 Clients | 874.255 | 801.690 | 1767.370 | 7071.715 | | 4 Clients | 812.363 | 760.702 | 1767.710 | 7077.065 | | 8 Clients | 837.707 | 799.851 | 1772.620 | 7076.967 | #### 2. Sequential Write[¶](#sequential-write "Permalink to this headline") **Setup** ``` #!/bin/bash fio -directory={} \ -ioengine=psync \ -rw=write \ # sequential write -bs=128k \ # block size -direct=1 \ # enable direct IO -group_reporting=1 \ -fallocate=none \ -name=test_file_c{} \ -numjobs={} \ -nrfiles=1 \ -size=10G ``` **Bandwidth(MB/s)** [](_images/cfs-fio-sequential-write-bandwidth.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 52.200 | 226.000 | 956.000 | 1126.000 | | 2 Clients | 104.500 | 473.000 | 1763.000 | 2252.000 | | 4 Clients | 225.300 | 1015.000 | 2652.000 | 3472.000 | | 8 Clients | 480.600 | 1753.000 | 3235.000 | 3608.000 | **IOPS** [](_images/cfs-fio-sequential-write-iops.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 417 | 1805 | 7651 | 9004 | | 2 Clients | 835 | 3779 | 14103 | 18014 | | 4 Clients | 1801 | 8127 | 21216 | 27777 | | 8 Clients | 3841 | 14016 | 25890 | 28860 | **Latency(Microsecond)** [](_images/cfs-fio-sequential-write-latency.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 2385.400 | 2190.210 | 2052.360 | 7081.320 | | 2 Clients | 2383.610 | 2081.850 | 2233.790 | 7079.450 | | 4 Clients | 2216.305 | 1947.688 | 2946.017 | 8842.903 | | 8 Clients | 2073.921 | 2256.120 | 4787.496 | 17002.425 | #### 3. Random Read[¶](#random-read "Permalink to this headline") **Setup** ``` #!/bin/bash fio -directory={} \ -ioengine=psync \ -rw=randread \ # random read -bs=4k \ # block size -direct=1 \ # enable direct IO -group_reporting=1 \ -fallocate=none \ -time_based=1 \ -runtime=120 \ -name=test_file_c{} \ -numjobs={} \ -nrfiles=1 \ -size=10G ``` **Bandwidth(MB/s)** [](_images/cfs-fio-random-read-bandwidth.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 6.412 | 39.100 | 216.000 | 534.000 | | 2 Clients | 14.525 | 88.100 | 409.000 | 1002.000 | | 4 Clients | 33.242 | 200.200 | 705.000 | 1693.000 | | 8 Clients | 59.480 | 328.300 | 940.000 | 2369.000 | **IOPS** [](_images/cfs-fio-random-read-iops.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 1641 | 10240 | 56524.800 | 140288 | | 2 Clients | 3718 | 23142.4 | 107212.8 | 263168 | | 4 Clients | 8508 | 52428.8 | 184627.2 | 443392 | | 8 Clients | 15222 | 85072.8 | 246681.6 | 621056 | **Latency(Microsecond)** [](_images/cfs-fio-random-read-latency.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 603.580 | 395.420 | 287.510 | 466.320 | | 2 Clients | 532.840 | 351.815 | 303.460 | 497.100 | | 4 Clients | 469.025 | 317.140 | 355.105 | 588.847 | | 8 Clients | 524.709 | 382.862 | 530.811 | 841.985 | #### 4. Random Write[¶](#random-write "Permalink to this headline") **Setup** ``` #!/bin/bash fio -directory={} \ -ioengine=psync \ -rw=randwrite \ # random write -bs=4k \ # block size -direct=1 \ # enable direct IO -group_reporting=1 \ -fallocate=none \ -time_based=1 \ -runtime=120 \ -name=test_file_c{} \ -numjobs={} \ -nrfiles=1 \ -size=10G ``` **Bandwidth(MB/s)** [](_images/cfs-fio-random-write-bandwidth.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 3.620 | 17.500 | 118.000 | 318.000 | | 2 Clients | 7.540 | 44.800 | 230.000 | 476.000 | | 4 Clients | 16.245 | 107.700 | 397.900 | 636.000 | | 8 Clients | 39.274 | 208.100 | 487.100 | 787.100 | **IOPS** [](_images/cfs-fio-random-write-iops.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 926.000 | 4476.000 | 31027.200 | 83251.200 | | 2 Clients | 1929.000 | 11473.000 | 60313.600 | 124620.800 | | 4 Clients | 4156.000 | 27800.000 | 104243.200 | 167014.400 | | 8 Clients | 10050.000 | 53250.000 | 127692.800 | 206745.600 | **Latency** [](_images/cfs-fio-random-write-latency.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 1073.150 | 887.570 | 523.820 | 784.030 | | 2 Clients | 1030.010 | 691.530 | 539.525 | 1042.685 | | 4 Clients | 955.972 | 575.183 | 618.445 | 1552.205 | | 8 Clients | 789.883 | 598.393 | 1016.185 | 2506.424 | ### Metadata Performance and Scalability[¶](#metadata-performance-and-scalability "Permalink to this headline") Metadata performance and scalability benchmark test by [mdtest](https://github.com/LLNL/mdtest). **Setup** ``` #!/bin/bash TEST\_PATH=/mnt/cfs/mdtest # mount point of ChubaoFS volume for CLIENTS in 1 2 4 8 # number of clients do mpirun --allow-run-as-root -np $CLIENTS --hostfile hfile01 mdtest -n 5000 -u -z 2 -i 3 -d $TEST\_PATH; done ``` **Dir Creation** [](_images/cfs-mdtest-dir-creation.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 448.618 | 2421.001 | 14597.97 | 43055.15 | | 2 Clients | 956.947 | 5917.576 | 28930.431 | 72388.765 | | 4 Clients | 2027.02 | 13213.403 | 54449.056 | 104771.356 | | 8 Clients | 4643.755 | 27416.904 | 89641.301 | 119542.62 | **Dir Removal** [](_images/cfs-mdtest-dir-removal.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 399.779 | 2118.005 | 12351.635 | 34903.672 | | 2 Clients | 833.353 | 5176.812 | 24471.674 | 50242.973 | | 4 Clients | 1853.617 | 11462.927 | 46413.313 | 91128.059 | | 8 Clients | 4441.435 | 24133.617 | 74401.336 | 115013.557 | **Dir Stat** [](_images/cfs-mdtest-dir-stat.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 283232.761 | 1215309.524 | 4231088.104 | 12579177.02 | | 2 Clients | 572834.143 | 2169669.058 | 8362749.217 | 18120970.71 | | 4 Clients | 1263474.549 | 3333746.786 | 10160929.29 | 31874265.88 | | 8 Clients | 2258670.069 | 8715752.83 | 22524794.98 | 77533648.04 | **File Creation** [](_images/cfs-mdtest-file-creation.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 448.888 | 2400.803 | 13638.072 | 27785.947 | | 2 Clients | 925.68 | 5664.166 | 25889.163 | 50434.484 | | 4 Clients | 2001.137 | 12986.968 | 50330.952 | 91387.825 | | 8 Clients | 4479.831 | 25933.437 | 86667.966 | 112746.199 | **File Removal** [](_images/cfs-mdtest-file-removal.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 605.143 | 3678.138 | 18631.342 | 47035.912 | | 2 Clients | 1301.151 | 8365.667 | 34005.25 | 64860.041 | | 4 Clients | 3032.683 | 14017.426 | 50938.926 | 80692.761 | | 8 Clients | 7170.386 | 32056.959 | 68761.908 | 88357.563 | **Tree Creation** [](_images/cfs-mdtest-tree-creation.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 305.778 | 229.562 | 86.299 | 23.917 | | 2 Clients | 161.31 | 211.119 | 76.301 | 24.439 | | 4 Clients | 260.562 | 223.153 | 81.209 | 23.867 | | 8 Clients | 350.038 | 220.744 | 81.621 | 17.144 | **Tree Removal** [](_images/cfs-mdtest-tree-removal.png) | | 1 Process | 4 Processes | 16 Processes | 64 Processes | | 1 Client | 137.462 | 70.881 | 31.235 | 7.057 | | 2 Clients | 217.026 | 113.36 | 23.971 | 7.128 | | 4 Clients | 231.622 | 113.539 | 30.626 | 7.299 | | 8 Clients | 185.156 | 83.923 | 20.607 | 5.515 | Integrity[¶](#integrity "Permalink to this headline") ----------------------------------------------------- * Linux Test Project / fs Workload[¶](#workload "Permalink to this headline") --------------------------------------------------- * Database backup * Java application logs * Code git repo * Database systems MyRocks, MySQL Innodb, HBase, Scalability[¶](#scalability "Permalink to this headline") --------------------------------------------------------- * Volume Scalability: tens to millions of cfs volumes * Metadata Scalability: a big volume with billions of files/directories Contributing to ChubaoFS[¶](#contributing-to-chubaofs "Permalink to this headline") ----------------------------------------------------------------------------------- ### Bug Reports[¶](#bug-reports "Permalink to this headline") Please make sure the bug is not already reported by [searching the repository](https://github.com/chubaofs/chubaofs/search?q=&type=Issues&utf8=%E2%9C%93) with reasonable keywords. Then, [open an issue](https://github.com/chubaofs/chubaofs/issues) with steps to reproduce. ### Patch Guidelines[¶](#patch-guidelines "Permalink to this headline") In order for the patch to be accepted with higher possibility, there are a few things you might want to pay attention to: * [Filesystem stress tests](https://github.com/linux-test-project/ltp/blob/master/runtest/fs) is required before opening a pull request by ``` runltp -f fs -d [MOUNTPOINT] ``` * A good commit message describing the bug fix or the new feature is preferred. * [DCO](https://github.com/apps/dco) is required, so please add Signed-off-by to the commit. ### Credits[¶](#credits "Permalink to this headline") Sections of this documents have been borrowed from [CoreDNS](https://github.com/coredns/coredns/blob/master/CONTRIBUTING.md) and [Fluentd](https://github.com/fluent/fluentd/blob/master/CONTRIBUTING.md) Environment Requirements and Capacity Planning[¶](#environment-requirements-and-capacity-planning "Permalink to this headline") ------------------------------------------------------------------------------------------------------------------------------- ### Environment Requirements[¶](#environment-requirements "Permalink to this headline") The following table lists the system and hardware requirements of the performance test environment and production environment. You can also refer to the capacity planning chapter to accurately customize the deployment plan based on your cluster’s actual capacity planning. Note that since the DataNode used some features of linux kernal, so that the kernel version of servers which used for deploy DataNode must be later than 3.10. In order to speed up read and write of meta data, the meta data is stored in memory, while the DataNode mainly occupies disk resources. To maximize the use of node resources, you can mix-deploy DataNode and MetaNode on the same node. | Role | Spec | Test | Product | | Master | CPU | >=4C | >=8C | | | Memory | >=4G | >=16G | | | Kernel | >=3.10 | >=3.10 | | | Nodes | 3 | 3 | | DataNode | CPU | >=4C | >=4C | | | Memory | >=4G | >=8G | | | Kernel | >=3.10 | >=3.10 | | | Disk Capacity | >=1TB | >=2TB | | | Disk Type | sata | ssd | sata | ssd | | | File System | xfs | etx4 | xfs | etx4 | | | Nodes | >=3 | 100~1000 | | MetaNode | CPU | >=4C | >=8C | | | Memory | >=8G | >=16G | | | Kernel | >=3.10 | >=3.10 | | | Nodes | >=4 | 100~1000 | | Client | CPU | >=2C | >=2C | | | Memory | >=4G | >=1G | | | Kernel | >=3.10 | >=3.10 | ### Capacity Planning[¶](#capacity-planning "Permalink to this headline") First of all, you have to assess the highest expected number of files and storage capacity of the cluster in the future. Secondly, you need to know the machine resources you currently have, and the total memory, CPU cores, and disks on each machine. If you have been clear about those statistics, you can use the empirical reference values ​​given in the second section to see which scale your current environment belongs to, what file size it can carry,or you need to prepare for the current file experience requirements How many resources to prevent frequent expansion of machine resources. | Total File Count | Total File Size | Total memory | Total Disk Space | | --- | --- | --- | --- | | 1,000,000,000 | 10PB | 2048 GB | 10PB | The higher the proportion of large files, the greater the MetaNode pressure. Of course, if you feel that the current resources are adequately used, you don’t need to meet the capacity growth requirements all at once. Then you can pay attention to the capacity warning information of MetaNode/DataNode in time. When the memory or disk is about to run out, dynamically increase MetaNode/DataNode to adjust the capacity. In other words, if you find that the disk space is not enough, you can increase the disk or increase DataNode. If you find that all MetaNode memory is too full, you can increase MetaNode to relieve memory pressure. ### Multi-Zone Deploy[¶](#multi-zone-deploy "Permalink to this headline") If you want the cluster to support fault tolerance in the computer room, you can deploy a ChubaoFS cluster across computer rooms. At the same time, it should be noted that since the communication delay between computer rooms is higher than that of a single computer room, if the requirements for high availability are greater than low latency, you can choose a cross-computer room deployment solution. If you have higher performance requirements, it is recommended to deploy clusters in a single computer room. Configuration scheme: Modify the zoneName parameter in the DataNode/MetaNode configuration file, specify the name of the computer room where you are, and then start the DataNode/MetaNode process, the computer room will be stored and recorded by the Master along with the registration of DataNode/MetaNode. Create a single zone volume: ``` $ cfs-cli volume create {name} --zone-name={zone} ``` In order to prevent volume initialization failure in a single computer room, please ensure that the DataNode of a single computer room is not less than 3 and MetaNode is not less than 4. Create a cross-zone volume: ``` $ cfs-cli volume create {name} --cross-zone=true ``` Q&A[¶](#q-a "Permalink to this headline") ----------------------------------------- * If you are new to ChubaoFS and want to start quickly, please refer to [Run Cluster on Docker](index.html#document-user-guide/docker) * If you are interested in ChubaoFS and want to conduct a performance test before applying it in the product environment, please refer to [Performance](index.html#document-evaluation) * If you have completed the assessment of ChubaoFS and want to put it into product environment, and want to learn how to carry out capacity planning and environment preparing, please refer to [Environment Requirements and Capacity Planning](index.html#document-env) * If you want to know about best practices of ChubaoFS, please refer to [Use Cases](index.html#document-use-case) * If you encounter some problems in the product environment, the following content may help to solve your problems. For the convenience of description, we define the following keyword abbreviations | Full Name | Abbreviation | | --- | --- | | Data Partition | dp | | Meta Partition | mp | | Data Partition Replica | dpr | | Meta Partition Replica | mpr | | NodeSet | ns | | DataNode | dn | | MetaNode | mn | ### Compile[¶](#compile "Permalink to this headline") 1. Compile ChubaoFS on one machine, but it cannot be started when deployed to other machines. First please make sure to use the `PORTABLE=1 make static\_lib` command to compile RocksDB, then use the `ldd` command to check whether the dependent libraries are installed on the machine. After installing the missing libraries, execute the `ldconfig` command. 2. A problem similar to undefined reference to ‘ZSTD\_versionNumber’. There are two solutions. * It can be compiled by adding the specified library to CGO\_LDFLAGS. For example: `CGO\_LDFLAGS="-L/usr/local/lib -lrocksdb -lzstd"`. This requires that the `zstd` library is also installed on other deployment machines. * Remove the script that automatically detects whether the ‘zstd’ library is installed. Example of file location: rockdb-5.9.2/build\_tools/build\_detect\_platform The deleted content is as follows. ``` # Test whether zstd library is installed $CXX $CFLAGS $COMMON\_FLAGS -x c++ - -o /dev/null 2>/dev/null <<EOF #include <zstd.h> int main() {} EOF if [ "$?" = 0 ]; then COMMON\_FLAGS="$COMMON\_FLAGS -DZSTD" PLATFORM\_LDFLAGS="$PLATFORM\_LDFLAGS -lzstd" JAVA\_LDFLAGS="$JAVA\_LDFLAGS -lzstd" fi ``` ### Node & Disk Failure[¶](#node-disk-failure "Permalink to this headline") If a node or disk fails, you can take the failed node or disk offline by decommission command. 1. Decommission Datanode/Metanode > > > ``` > $ cfs-cli metanode/datanode decommission 192.168.0.21:17210 > > ``` > > > > * At what time to decommission mn/dn？ > If a node fails and cannot reboot, it has to be removed from the cluster and the partitions on this machine is automatically migrated to other healthy nodes. > > > > * The decommission caused an acute increasing on disk io and network io. > The decommission will trigger automatic partition migration witch consumes a lot of network resources. Therefore, try to execute the decommission during off peak hours and avoid taking multiple nodes decommission at the same time. > > > > * What is the sign of decommission completion？ > > > > > > > > > ``` > > $ cfs-cli datapartition check > > > > ``` > > > > > > If bad partition ids is empty, the decommission is done. > > > > > > > > + Common error1: There is no mn available, all mn memory or disks are full at this time, new mn needs to be added to the cluster > > + Common error2: The port number is wrong, the identifier of each mn should be a combination of ip+port , and port is the listen port in the mn configuration file, which cannot be wrong > > > 2. Decommission Disk If a disk fails but the node is healthy, you can partially decommission the disk. Similarly, as with dn/mn decommission, please try to avoid multiple decommission operations during off peak hours. ``` $ cfs-cli disk decommission {disk} 192.168.0.11:17310 ``` If correct, all the dp on the disk is migrated to other disks or nodes. The common error is similar to decommission dn. 3. Decommission Data Partition/Meta Partition > > > ``` > $ cfs-cli datapartition decommission 192.168.0.11:17310 {Partition ID} > > ``` > > > If correct, the dp is migrated to other nodes. > > * At what time to decommission a partition？ > > > > > > > > > + There is too many partitions on one node, decommission some partitions to reduce the pressure on this node. > > + Take a small step instead of dn/mn decommission to prevent overloading of the cluster. > > > * The common error is similar to decommission dn/mn. > > > 4. If the disk is full, will it explode? It is recommended set reserved space in the dn startup json file, it is behind the disk path parameter, disk: “{disk path}:{reserved space}”. If the remaining space is less than reserved space, the dn turns readonly. ### Performance of Data and Metadata[¶](#performance-of-data-and-metadata "Permalink to this headline") 1. How does ChubaoFS compare with its alternatives ? * Ceph Ceph (<https://ceph.com/>) is a widely-used free-software storage platform. It can be configured for object storage, block storage, as well as file system. But many nice features provided by Ceph (e.g., various storage backends) also make it very complicated and difficult to learn and deploy. In addition, in certain scenarios such as metadata operations and small file operations, its performance in a multi-client environment can be hard to optimize. * GFS and HDFS GFS and its open source implementation HDFS (<https://github.com/apache/hadoop>) are designed for storing large files with sequential access. Both of them adopt the master-slave architecture, where the single master stores all the file metadata. Unlike GFS and HDFS, ChubaoFS employs a separate metadata subsystem to provide a scalable solution for the metadata storage so that the resource manager has less chance to become the bottleneck. * Hadoop Ozone Hadoop Ozone is a scalable distributed object storage system designed for Hadoop. It was originally proposed to solve the problem of HDFS namenode expansion. It reconstructs the namenode metadata management part of hdfs and reuses the datanode of hdfs. ChubaoFS has many of the same design concepts like ozone such as: supporting for volume isolation, compatible with both raft/master-slave synchronous replication mechanisms, implenting for s3-compatible interfaces. In addition, ChubaoFS’s POSIX fuse-client interface supports random file reading and writing, and optimizes reading and writing of small files. * Haystack Haystack from Facebook takes after log-structured filesystems to serve long tail of requests seen by sharing photos in a large social network. The key insight is to avoid disk operations when accessing metadata. ChubaoFS adopts similar ideas by putting the file metadata into the main memory. However, different from Haystack, the actually physical offsets instead of logical indices of the file contents are stored in the memory, and deleting a file is achieved by the punch hole interface provided by the underlying file system instead of relying on the garbage collector to perform merging and compacting regularly for more efficient disk utilization. In addition, Haystack does not guarantee the strong consistency among the replicas when deleting the files, and it needs to perform merging and compacting regularly for more efficient disk utilization, which could be a performance killer. ChubaoFS takes a different design principle to separate the storage of file metadata and contents. In this way, we can have more flexible and cost-effective deployments of meta and data nodes. * Public Cloud Windows Azure Storage (<https://azure.microsoft.com/en-us/>) is a cloud storage system that provides strong consistency and multi-tenancy to the clients. Different from ChubaoFS, it builds an extra partition layer to handle random writes before streaming data into the lower level. AWS EFS (<https://aws.amazon.com/efs/>) is a cloud storage service that provides scalable and elastic file storage. Depending on the use cases, there could be a considerable amount of cost associated with using these cloud storage services. * Others GlusterFS (<https://www.gluster.org/>) is a scalable distributed file system that aggregates disk storage resources from multiple servers into a single global namespace. MooseFS (<https://moosefs.com/>) is a fault- tolerant, highly available, POSIX-compliant, and scalable distributed file system. However, similar to HDFS, it employs a single master to manage the file metadata. 2. If the scale of metadata is huge, how to improve cluster performance? The metadata of ChubaoFS is stored in the memory. Expanding memory of the mn or expanding the amount of mn horizontally will significantly improve the metadata performance and support a large number of small files. 3. If a dn/mn is added to the cluster, will it be automatically rebalanced, for example the dp/mp on the old node are migrated to the new node? No. Considering that the rebalance may cause overloading and risks data loss, it will not automatically rebalance. If you want the new node to carry more dp/mp to disperse the pressure of the old node, you can create new dp for this volume and the new dp may locate on new nodes, or you can decommission the dp on the old node. 4. There are scenes of batch delete files witch cause cluster overloads You can set and view the background file deletion rate by following command, the default value is 0, which means unlimited. It is recommended to set markdeleterate=1000, and then dynamically adjust it according to the cpu status of the nodes in the cluster. ``` $ cfs-cli cluster info $ cfs-cli cluster delelerate -h Set delete parameters Usage: cfs-cli cluster delelerate [flags] Flags: --auto-repair-rate string DataNode auto repair rate --delete-batch-count string MetaNode delete batch count --delete-worker-sleep-ms string MetaNode delete worker sleep time with millisecond. if 0 for no sleep -h, --help help for delelerate --mark-delete-rate string DataNode batch mark delete limit rate. if 0 for no infinity limit ``` ### Capacity Management[¶](#capacity-management "Permalink to this headline") 1. What if the capacity of Volume is used-out? ``` $ cfs-cli volume expand {volume name} {capacity / GB} ``` 2. How to optimize the read/write performance from the Volume side？ The more dp that can be read and written, the better read and write performance of the Volume. ChubaoFS adopts a dynamic space allocation mechanism. After creating a Volume, it will pre-allocate a certain data partition dp for the Volume. When the number of dp that can be read and written is less than 10, the dp number will be automatically expanded by a step of 10. If you want to manually increase the number of readable and writable dp, you can use the following command: ``` $ cfs-cli volume create-dp {volume name} {number} ``` The default size of a dp is 120GB. Please add dp based on the actual cost to avoid overdrawing all dp. 3. How to reclaim the excess space of Volume ``` $ cfs-cli volume shrink {volume name} {capacity / GB} ``` If the set value is less than %120 of the used amount, the operation fails. 5. What if the cluster space is not enough? Prepare the new dn and mn, start it by json configuration file with the current master hosts, and the dn/mn will be automatically added to the cluster. ### Zone[¶](#zone "Permalink to this headline") Setting the zone can prevent the failure of a single zone witch causes the entire cluster unavailable. If parameter zoneName is set correctly, the node will automatically join the zone(cell). 1. See zones list ``` $ cfs-cli zone list ``` 2. What if accidentally set the volume zone by mistake? ``` $ cfs-cli volume update {volume name} --zone-name={zone name} ``` 2. What happens if MetaNde and DataNode do not set zone? Most of the parameters in the cluster have default values, and the default zone name is default. Please note that there must be enough dn and mn in a zone at the same time, otherwise, creating a volume in the zone will either fail to initialize data partitions or initialize meta partitions. 3. The meaning of NodeSet？ Each zone will have several NodeSets, and each NodeSet can carry 18 nodes by default. Because ChubaoFS implements multi-raft, each node starts a raft server process, and each raft server manages m raft instances on the node. If the other replication group members of these raft members are distributed on N nodes, then the raft heartbeat will be transmitted between N nodes. As the cluster scale expands, N will become larger. Through the NodeSet restriction, the heartbeat is relatively independent within the NodeSet, which avoids the heartbeat storm of the cluster dimension. We use the multi raft and nodeset mechanisms together to avoid the problem of the raft heartbeat storm.  4. How to distribute dp/mp of a volume in NodeSet? dp/mp is evenly distributed in NodeSet. On dp/mp creating, it will locate on the NodeSet behind the NodeSet of last dp/mp overall. 5. How to plan the amount of NodeSet？ For 3-replicas dp/mp, a dp/mp will select the ns only when there are at least 3 available nodes in ns. count (ns) >= 18 \* n + 3 ### Node status is abnormal[¶](#node-status-is-abnormal "Permalink to this headline") View node status through cli tool ``` $ cfs-cli datanode list [Data nodes] ID ADDRESS WRITABLE STATUS 7 192.168.0.31:17310 No Inactive 8 192.168.0.32:17310 No Inactive 9 192.168.0.33:17310 Yes Active 10 192.168.0.35:17310 Yes Active 11 192.168.0.34:17310 Yes Active $ cfs-cli metanode list [Meta nodes] ID ADDRESS WRITABLE STATUS 2 192.168.0.21:17210 No Inactive 3 192.168.0.22:17210 No Inactive 4 192.168.0.23:17210 Yes Active 5 192.168.0.25:17210 Yes Active 6 192.168.0.24:17210 Yes Active ``` 1. Reasons for Datanode WRITABLE=No > > * The node is waiting for the decommission to complete > * Node disk is out of use > * The node has just started and is restoring data locally > > > 2. Reasons for Metanode WRITABLE=No > > * The node is waiting for the decommission to complete > * The node memory has reached totalMemory > * The node has just started and is restoring data locally > > > 3. If one of the three masters in the cluster is broken, can the remaining two restarts provide services normally? Yes. Since the Master uses the RAFT, it can provide services normally when the number of remaining nodes exceeds 50% of the total number of nodes. 4. Reasons for STATUS=Inactive > > * The network connection between the node and the master is interrupted. You need to check the network status and restore the network connection > * The node process hangs, you need to check whether the server process of the node is abnormally terminated, at this time restart the process to recover > > > ### Upgrade[¶](#upgrade "Permalink to this headline") 1. Steps > > 1. Download and unzip the latest binary file compression package from ChubaoFS official website <https://github.com/chubaofs/chubaofs/releases> > 2. Freeze the cluster > > > > ``` > $ cfs-cli cluster freeze true > > ``` > > > 3. Confirm the startup configuration file, do not change important information such as the data directory and port in the configuration file > 4. Stop the old server process > 5. Start the new server process > 6. Check that the node status recovered healthy after the upgrade, IsActive: Active > > > > ``` > $ cfs-cli datanode info 192.168.0.33:17310 > [Data node info] > ID : 9 > Address : 192.168.0.33:17310 > Carry : 0.06612836801123345 > Used ratio : 0.0034684352702178426 > Used : 96 GB > Available : 27 TB > Total : 27 TB > Zone : default > IsActive : Active > Report time : 2020-07-27 10:23:20 > Partition count : 16 > Bad disks : [] > Persist partitions : [2 3 5 7 8 10 11 12 13 14 15 16 17 18 19 20] > > ``` > > > 7. Upgrade the next node (in order to reduce the impact on the client, especially in a relatively large user volume, you need to upgrade the MetaNode nodes one by one), the upgrade sequence is shown bellow > > >  > 2. After upgrading a master, it is found that the monitoring system is not displayed in time? Check whether the configuration of this master node is correct, especially the id; check the master error log to find out whether there is a large number of no leader errors, and query the keyword leaderChange in the master warn log to check the reason for the leader change, and then check the raft warn log for further analysis. 3. Can I modify the port number of the configuration during the upgrade? No, ip+port constitutes the unique identifier of mn and dn instances, and will be treated as a new node after modification. ### Update Configuration Online[¶](#update-configuration-online "Permalink to this headline") 1. Update mn threshold ``` $ cfs-cli cluster set threshold { value } ``` 2. Update cluster configuration 3. Update Volume configuration ``` $ cfs-cli volume set -h Set configuration of the volume Usage: cfs-cli volume set [VOLUME NAME] [flags] Flags: --authenticate string Enable authenticate --capacity uint Specify volume capacity [Unit: GB] --enable-token string ReadOnly/ReadWrite token validation for fuse client --follower-read string Enable read form replica follower -h, --help help for set --replicas int Specify volume replicas number -y, --yes Answer yes for all questions --zonename string Specify volume zone name ``` 4. Update log level An interface to update the log level of master, MetaNode, and DataNode online is provided: ``` $ http://127.0.0.1:{profPort}/loglevel/set?level={log-level} ``` Supported log-level: debug,info,warn,error,critical,read,write,fatal ### Update Configuration Offline[¶](#update-configuration-offline "Permalink to this headline") 1. Update master IP After the ip address of the three-node master is replaced, all mn, dn and other applications that reference the master ip address need to be restarted after modifying the configuration. 2. Update DataNode/MetaNode port number It is not recommended to modify the dn/mn port. Because dn/mn is registered through ip:port in the master. If the port is modified, the master will consider it as a brand new node, and the old node is in the state of Inactive. 3. Update MetaNode totalMemory totalMemory refers to the total memory size of MetaNode. When the memory usage of MetaNode is higher than this value, MetaNode becomes read-only. Usually this value is smaller than the node memory. If MetaNode and DataNode are deployed in a mixed deployment, extra memory needs to be reserved for the DataNode. 4. Update DataNode reservedSpace In the dn startup configuration json file, the number in the second half of the disk parameter is the value of Reserved Space, unit(byte). ``` { ... "disks": [ "/cfs/disk:10737418240" ], ... } ``` 5. For more configurations, see [Resource Manager (Master)](index.html#document-user-guide/master) [Data Subsystem](index.html#document-user-guide/datanode) [Meta Subsystem](index.html#document-user-guide/metanode)[Client](index.html#document-user-guide/client). ### Handle Logs[¶](#handle-logs "Permalink to this headline") 1. What to do if tens of GB of logs are generated every day, which takes up too much disk space? In a production environment, you can set the log level to warn or error, which will significantly reduce the amount of logs. ``` $ http://127.0.0.1:{profPort}/loglevel/set?level={log-level} ``` Supported log-level: debug,info,warn,error,critical,read,write,fatal 2. Datanode warn log > > > ``` > checkFileCrcTaskErr clusterID[xxx] partitionID:xxx File:xxx badCrc On xxx: > > ``` > > > Analysis: The Master schedules the dn to check the crc data every few hours. This error indicates that the crc check failed and the file data is wrong. At this time, it is necessary to further analyze the error according to the partitionID and File in the warn message and with the assistance of the info log. > > > 3. Datanode error log 4. Master error log > > > ``` > clusterID[xxx] addr[xxx]_op[xx] has no response util time out > > ``` > > > Analysis:The response timed out when the Master sends the [Op] command to mn or dn, check the network between Master and mn/dn; check whether the dn/mn service process is alive. > > > 5. Master warn log 6. Metanode error log > > > ``` > Error metaPartition(xx) changeLeader to (xx): > > ``` > > > Analysis:Leader change, a normal action. > > > > ``` > inode count is not equal, vol[xxx], mpID[xx] > > ``` > > > Analysis:The number of inode is inconsistent. Because as long as two of the three copies are successful, the write is successful, so there will be inconsistencies in the three copies. Check the log for the specific reason. > > > 7. Metanode warn log 8. Client warn log > > > ``` > operation.go:189: dcreate: packet(ReqID(151)Op(OpMetaCreateDentry)PartitionID(0)ResultCode(ExistErr)) mp(PartitionID(1) Start(0) End(16777216) Members([192.168.0.23:17210 192.168.0.24:17210 192.168.0.21:17210]) LeaderAddr(192.168.0.23:17210) Status(2)) req({ltptest 1 1 16777218 test.log 420}) result(ExistErr) > > ``` > > > Analysis: ExistErr indicates that the file name already exists during the rename operation. It is an upper-level business operation problem. Maintainers can ignore this. > > > > ``` > extent_handler.go:498: allocateExtent: failed to create extent, eh(ExtentHandler{ID(xxx)Inode(xxx)FileOffset(xxxx)StoreMode(1)}) err(createExtent: ResultCode NOK, packet(ReqID(xxxxx)Op(OpCreateExtent)Inode(0)FileOffset(0)Size(86)PartitionID(xxxxx)ExtentID(xx)ExtentOffset(0)CRC(0)ResultCode(IntraGroupNetErr)) datapartionHosts(1.1.0.0:17310) ResultCode(IntraGroupNetErr)) > > ``` > > > Analysis: The client sends a request to create an extent to an mp and fails, it will soon try to request another mp. > > > 9. Client error log > > > ``` > appendExtentKey: packet(%v) mp(%v) req(%v) result(NotExistErr) > > ``` > > > Analysis: This error indicates that the file was deleted before written, which is an upper-level business operation problem. Maintainers can ignore this. > > > > ``` > conn.go:103:sendToMetaPartition: retry failed req(ReqID(xxxx)Op(OpMetaInodeGet)PartitionID(0)ResultCode(Unknown ResultCode(0)))mp(PartitionID(xxxx) Start(xxx) End(xxx) Members([xxx xxxx xxxx]) LeaderAddr(xxxx) Status(2)) mc(partitionID(xxxx) addr(xxx)) err([conn.go 129] Failed to read from conn, req(ReqID(xxxx)Op(OpMetaInodeGet)PartitionID(0)ResultCode(Unknown ResultCode(0))) :: read tcp 10.196.0.10:42852->11.196.1.11:9021: i/o timeout) resp(<nil>) > > ``` > > > Analysis 1:The network connection between the client and the MetaNode is abnormal. According to the error message “10.196.0.10:42852->11.196.1.11:9021”, check whether the network between the two addresses is normal > > > Analysis 2:Check if the MetaNode process hangs on “11.196.1.11:9021” > > > 10. Raft warn log 11. Raft error log > > > ``` > raft.go:446: [ERROR] raft partitionID[1105] replicaID[6] not active peer["nodeID":"6","peerID":"0","priority":"0","type":"PeerNormal"] > > ``` > > > Analysis: This is caused by excessive network pressure and increased delay. After the raft election interval is exceeded, the raft replication group loses the leader. After the network is restored, re-elect the leader, the error will disappear by itself. > > > ### Data Loss and Consistence[¶](#data-loss-and-consistence "Permalink to this headline") 1. All data of a single dn/mn is lost This situation can be equivalent to a dn/mn failure. You can log off the node through decommission, and then restart the node to re-register the node to the Master, and the Master will treat it as a new member. 2. Accidentally deleted file data in a dp directory in dn dn has the function of automatically repairing data. If the data has not been repaired for a long time, you can manually restart the current dn process, which will trigger the data repair process. ### Fuse Client[¶](#fuse-client "Permalink to this headline") 1. Memory and performance optimization issues > > * The Fuse client occupies too much memory, which has a large impact on other services > > > > > > > Offline: Set the readRate and writeRate parameters in the configuration file and restart the client. > > > > > > Online: <http:/>/{clientIP}:{profPort} /rate/set?write=800&read=800 > > > > > > > * For more, see(<https://chubaofs.readthedocs.io/zh_CN/latest/user-guide/fuse.html>) > > > 2. Mount issues > > * Does it support subdirectory mounting? > > > Yes. Set subdir in the configuration file > > > * What are the reasons for the mount failure > > > > > > > > > + If you see the following output, > > > > ``` > > $ ... err(readFromProcess: sub-process: fusermount: exec: "fusermount": executable file not found in $PATH) > > > > ``` > > > > > > Check if fuse is installed, if not, install it > > > > > > > > ``` > > $ rpm –qa|grep fuse > > yum install fuse > > > > ``` > > > > > > > > + Check if the mount directory exists > > + Check whether the mount point directory is empty > > + Check whether the mount point has been umount > > + Check whether the mount point status is normal. If the following message appears on the mount point mnt, you need to umount first, and then start the client > > > > ``` > > $ ls -lih > > ls: cannot access 'mnt': Transport endpoint is not connected > > total 0 > > 6443448706 drwxr-xr-x 2 root root 73 Jul 29 06:19 bin > > 811671493 drwxr-xr-x 2 root root 43 Jul 29 06:19 conf > > 6444590114 drwxr-xr-x 3 root root 28 Jul 29 06:20 log > > ? d????????? ? ? ? ? ? mnt > > 540443904 drwxr-xr-x 2 root root 45 Jul 29 06:19 script > > > > ``` > > > > > > > > + Check whether the configuration file is correct, master address, volume name and other information > > + If none of the above problems exist, locate the error through the client error log to see if it is the mount failure caused by the MetaNode or master service > > > > > 3. IO issues > > * IOPS is too high and the client’s memory usage exceeds 3GB or even higher. Is there any way to limit IOPS? > > > Limit the frequency of client response to io requests by modifying the client rate limit. > > > > ``` > #see current iops: > $ http://[ClientIP]:[profPort]/rate/get > #set iops, default:-1(no limits) > $ http://[ClientIP]:[profPort]/rate/set?write=800&read=800 > > ``` > > > * io delay too high for ls or other operations > > > > > > > > > + Because the client reads and writes files through the http protocol, please check whether the network status is healthy > > + Check whether there is an overloaded mn, whether the mn process is hanging, you can restart mn, or expand a new mn to the cluster and take the mp on the overloaded mn decommission to relieve the pressure of mn > > > > > 4. Does ChubaoFS provide strong consistence guarantees? No.ChubaoFS has relaxed POSIX consistency semantics, i.e., instead of providing strong consistency guarantees, it only ensures sequential consistency for file/directory operations, and does not have any leasing mechanism to prevent multiple clients writing to the same file/directory. It depends on the upperlevel application to maintain a more restrict consistency level if necessary. 5. Is it feasible to kill the client to directly stop the client service No. It is recommended to umount first. After umount, the client process will automatically stop.

photon

go

Photon 0.4 documentation [Photon](index.html#document-index) stable * [The core](index.html#document-core) * [Tools](index.html#document-tools) * [Utility](index.html#document-util) [Photon](index.html#document-index) * [Docs](index.html#document-index) » * Photon 0.4 documentation * [Edit on GitHub](https://github.com/spookey/photon/blob/936de5e8f1c62c46e8100cfb5bae2594907c13a6/docs/index.rst) --- Photon[¶](#photon "Permalink to this headline") =============================================== [](_images/photon_logo.png) Welcome to the Photon Documentation. Photon Intro[¶](#photon-intro "Permalink to this headline") ----------------------------------------------------------- It could be best described as a **shell backend as python module** Contributions are highly welcome [[1]](#contributions), also feel free to use the [issue tracker](http://github.com/spookey/photon/issues) if you encounter any problems. | Repository: | [github.com/spookey/photon](http://github.com/spookey/photon/) | | Documentation: | [photon.readthedocs.org](http://photon.readthedocs.org/en/latest/) | | Package: | [pypi.python.org/pypi/photon\_core](https://pypi.python.org/pypi/photon_core/) | ### Examples[¶](#examples "Permalink to this headline") The **/examples** directory contains some basic receipts on how to use Photon in your scripts. Photon helps at [Freifunk MWU](http://freifunk-mwu.de/) to solve some tasks: > > * See our [collection of backend-scripts](https://github.com/freifunk-mwu/backend-scripts) for some scripts using photon, running in production. > * To automatically compile gluon firmware for routers, we wrote the [gluon builder](https://github.com/freifunk-mwu/gluon-builder-ffmwu). > > > Installation[¶](#installation "Permalink to this headline") ----------------------------------------------------------- Photon is available as package on pypi, it is called `photon\_core` [[2]](#photon-core). You can install/update the package via pip3 [[3]](#pip3): ``` pip3 install photon_core ``` ``` pip3 install -U photon_core ``` Bleeding-Edge Development is still at an very early stage, expect anything to change completely in near future. As long we still have a leading zero in the version (see *info* file) use *pip3* with the `--pre` switch: ``` pip3 install -U photon_core --pre ``` Versions Tags in the git repository will be released as a new pypi package version. Versions of a pypi package has always it’s git tag. And vice versa. Not every version increase will be tagged/released. I will only do so if I feel the urge to do so. | [[1]](#id1) | Teach me how to write good code, help me to improve. | | [[2]](#id2) | because photon itself was already taken :/ | | [[3]](#id3) | Photon is written in python3 ~ be careful with easy\_install | Structure[¶](#structure "Permalink to this headline") ----------------------------------------------------- Photon aimes to be modular and can be divided into [The core](index.html#core), it’s [Utility](index.html#util) and some [Tools](index.html#tools), provided through [Photon](index.html#photon) itself. If you just want to use Photon in your Scripts as a normal User you may especially be interested in the parts [Photon](index.html#photon) and [Tools](index.html#tools). ### The core[¶](#the-core "Permalink to this headline") All three modules depend on the [Utility](index.html#util): See also [Files](index.html#util-files), [Locations](index.html#util-locations), [Structures](index.html#util-structures), [System](index.html#util-system) [Settings](#settings) and [Meta](#meta) could be used independently or both together. Bundling [Settings](#settings) and [Meta](#meta) together plus adding the [Tools](index.html#tools), [Photon](#photon) provides a interface to use in your scripts. See also [Git Tool](index.html#tools-git), [Mail Tool](index.html#tools-mail), [Ping Tool](index.html#tools-ping), [Signal Tool](index.html#tools-signal) #### Settings[¶](#settings "Permalink to this headline") *class* `settings.``Settings`(*defaults*, *config='config.yaml'*, *verbose=True*)[[source]](_modules/settings.html#Settings)[¶](#settings.Settings "Permalink to this definition") Settings is a class which provides access to compiled settings loaded from YAML-files. The YAML-files will be read with specific loaders which enables certain logic within the configuration. It is possible to: > > * Insert references to existing fields via anchors and `!str\_join` or `!loc\_join` > * Insert keywords like **hostname** or **timestamp** using `!str\_join` > * Combine path-segments using `!loc\_join` > * Insert keywords like **home\_dir** or **conf\_dir** using `!loc\_join` > > > It is also possible to import or merge further content. | Parameters: | * **defaults** – The initial configuration to load. Will be located using [`util.locations.search\_location()`](index.html#util.locations.search_location "util.locations.search_location") + The common way is to use a short-filename to locate it next to the script using Photon. + Can also be a full path. + Can also passed directly as a dict + Bring your own defaults! Tears down (using [`util.system.shell\_notify()`](index.html#util.system.shell_notify "util.system.shell_notify") with state set to `True`) whole application if not found or none passed. * **config** – Where to store the loaded output from the defaults. Will be located using [`util.locations.search\_location()`](index.html#util.locations.search_location "util.locations.search_location") + File must already exist, will be created in ‘conf\_dir’ from [`util.locations.get\_locations()`](index.html#util.locations.get_locations "util.locations.get_locations") otherwise - Therefore use a short name (or full path) if one should be created Note The last loaded file wins + The config is intended to provide a editable file for the end-user + If a value differs from the original values in defaults, the value in config wins - Other values which not exist in config will be set from defaults - If a value in config contains a loader call which expresses the same as the value in defaults it will be skipped. + Be careful using **timestamp** s in a config. The timestamp of the first launch will always be used. + Simply delete all lines within the config to completely reset it to the defaults + Can be skipped by explicitly setting it to `None` * **verbose** – Sets the verbose flag for the underlying [Utility](index.html#util) functions | See also [`util.structures.yaml\_str\_join()`](index.html#util.structures.yaml_str_join "util.structures.yaml_str_join") and [`util.structures.yaml\_loc\_join()`](index.html#util.structures.yaml_loc_join "util.structures.yaml_loc_join") as well as the [Example Settings File](#settings-file-example) `get`[¶](#settings.Settings.get "Permalink to this definition") | Returns: | Current settings | `load`(*skey*, *sdesc*, *sdict=None*, *loaders=None*, *merge=False*, *writeback=False*)[[source]](_modules/settings.html#Settings.load)[¶](#settings.Settings.load "Permalink to this definition") Loads a dictionary into current settings | Parameters: | * **skey** – Type of data to load. Is be used to reference the data in the files sections within settings * **sdesc** – Either filename of yaml-file to load or further description of imported data when sdict is used * **sdict** ([*dict*](http://docs.python.org/3/library/stdtypes.html#dict "(in Python v3.5)")) – Directly pass data as dictionary instead of loading it from a yaml-file. Make sure to set skey and sdesc accordingly * **loaders** ([*list*](http://docs.python.org/3/library/stdtypes.html#list "(in Python v3.5)")) – Append custom loaders to the YAML-loader. * **merge** – Merge received data into current settings or place it under skey within meta * **writeback** – Write back loaded (and merged/imported) result back to the original file. This is used to generate the summary files | | Returns: | The loaded (or directly passed) content | See also [`util.structures.yaml\_str\_join()`](index.html#util.structures.yaml_str_join "util.structures.yaml_str_join") and [`util.structures.yaml\_loc\_join()`](index.html#util.structures.yaml_loc_join "util.structures.yaml_loc_join") ##### Example Settings File[¶](#example-settings-file "Permalink to this headline") defaults.sample.yaml | | | | --- | --- | | ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 ``` | ``` # The syntax of the settings files is YAML: 01\_syntax: dictionary: 'value is a string' dictionary\_with\_list: ['value', 'is', 'a', 'list'] dictionary\_with\_list2: - this - is - another - list # ---- # YAML supports backreferences by anchors. # First yo have define a dictionary value as anchor: 02\_anchor: prefix: &MY\_PRFX 'Photon is a software that ' # Then use them together with !str\_join: poll: yay: !str\_join [\*MY\_PRFX, 'realy helps me'] nay: !str\_join [\*MY\_PRFX, 'sucks'] # This turns into: # yay: Photon is a software that really helps me # nay: Photon is a software that sucks # (The anchor ('&'-sign) must appear before the Reference ('\*'-sign) in the YAML-file. # (Note the whitespace.) # ---- # !str\_join can listen to the keywords - 'hostname' & 'timestamp': 03\_keywords: message: - !str\_join ['my machine "', 'hostname', '" is the best'] - !str\_join ['yours, herbert. date: ', 'timestamp'] # This turns into: # message: # - my machine "blechschachtel" is the best # - 'yours, herbert. date: YYYY.MM.DD-HH.MM.SS' # (with current date expanded) # ---- # Use !loc\_join to combine files and paths: 04\_locations: simple\_file: !loc\_join ['/', 'usr', 'local', 'bin', 'myscript.sh'] same\_simple\_file: !loc\_join ['/usr/local/bin', 'myscript.sh'] # This turns into: # simple\_file: /usr/local/bin/myscript.sh # same\_simple\_file: /usr/local/bin/myscript.sh # But be careful with leading '/'-signs: not\_the\_simple\_file: !loc\_join ['/usr/local', '/bin', 'myscript.sh'] # This turns into not what we wanted: # not\_the\_simple\_file: /bin/myscript.sh # It can also listen to keywords: in\_the\_home\_dir: !loc\_join ['home\_dir', 'my\_directory'] # in\_the\_home\_dir: /home/herbert/my\_directory # ---- # Combine them alltogether: 05\_combined: name: &MY\_ASS my\_awesome\_server\_software main: &OH\_MY !loc\_join ['home\_dir', \*MY\_ASS, 'main'] main\_run: !loc\_join [\*OH\_MY, 'run.py'] backup\_dir: !loc\_join ['data\_dir', \*MY\_ASS, !str\_join ['backup-', 'timestamp']] git-remote: !str\_join - 'https://github.com/user404/' - \*MY\_ASS - .git # This turns into: # name: my\_awesome\_server\_software # main: /home/herbert/my\_awesome\_server\_software/main # main\_run: /home/herbert/my\_awesome\_server\_software/main/run.py # backup\_dir: /home/herbert/.local/share/photon/my\_awesome\_server\_software/backup-YYYY.MM.DD-HH.MM.SS # git-remote: https://github.com/user404/my\_awesome\_server\_software.git ``` | See also The [wikipedia page on YAML](http://en.wikipedia.org/wiki/YAML) for some syntax reference. See also * !loc\_join: [`util.structures.yaml\_loc\_join()`](index.html#util.structures.yaml_loc_join "util.structures.yaml_loc_join") (get locations by keyword and join paths) * !str\_join: [`util.structures.yaml\_str\_join()`](index.html#util.structures.yaml_str_join "util.structures.yaml_str_join") (get variables by keyword and join strings) See also [Example Settings File](#settings-file-example), [Mail Tool Example](index.html#tools-mail-example), [Ping Tool Example](index.html#tools-ping-example) #### Meta[¶](#meta "Permalink to this headline") *class* `meta.``Meta`(*meta='meta.json'*, *verbose=True*)[[source]](_modules/meta.html#Meta)[¶](#meta.Meta "Permalink to this definition") Meta is a class which bounds to an actual json-file on disk. It provides a logger storing the entries in that json-file. It is also possible to import contents. By staging out to a different directory meta-files are left behind for further debugging or to see what was going on. | Parameters: | * **meta** – Initial, clean meta file to use. See [`stage()`](#meta.Meta.stage "meta.Meta.stage") for more * **verbose** – Sets the verbose flag for the underlying [Utility](index.html#util) functions | `load`(*mkey*, *mdesc*, *mdict=None*, *merge=False*)[[source]](_modules/meta.html#Meta.load)[¶](#meta.Meta.load "Permalink to this definition") Loads a dictionary into current meta | Parameters: | * **mkey** – Type of data to load. Is be used to reference the data from the ‘header’ within meta * **mdesc** – Either filename of json-file to load or further description of imported data when mdict is used * **mdict** ([*dict*](http://docs.python.org/3/library/stdtypes.html#dict "(in Python v3.5)")) – Directly pass data as dictionary instead of loading it from a json-file. Make sure to set mkey and mdesc accordingly * **merge** – Merge received data into current meta or place it under ‘import’ within meta | | Returns: | The loaded (or directly passed) content | `log`[¶](#meta.Meta.log "Permalink to this definition") | Parameters: | **elem** – Add a new log entry to the meta.* Can be anything. * The log is a dictionary with keys generated from the output of [`util.system.get\_timestamp()`](index.html#util.system.get_timestamp "util.system.get_timestamp") and elem as value | | Returns: | Current meta | `stage`(*name*, *clean=False*)[[source]](_modules/meta.html#Meta.stage)[¶](#meta.Meta.stage "Permalink to this definition") Switch stage | Parameters: | * **name** – Filename of new meta file. Will be located using [`util.locations.search\_location()`](index.html#util.locations.search_location "util.locations.search_location") + File must not already exist, will be created in ‘data\_dir’ from [`util.locations.get\_locations()`](index.html#util.locations.get_locations "util.locations.get_locations") + Can also be a full path to place it anywhere desired * **clean** – What to do with preexisting meta files? + `False`: Merge current meta with preexisting one + `True`: Replace preexisting meta with current one | #### Photon[¶](#photon "Permalink to this headline") *class* `photon.``Photon`(*defaults*, *config='config.yaml'*, *meta='meta.json'*, *verbose=True*)[[source]](_modules/photon.html#Photon)[¶](#photon.Photon "Permalink to this definition") Photon uses [The core](#core) and some functions from [Utility](index.html#util) in its [`m()`](#photon.Photon.m "photon.Photon.m")-method. The [`m()`](#photon.Photon.m "photon.Photon.m")-method itself is used in each tool to interact with photon to: > > * Launch shell commands, and receive the results > * Add messages to the meta-file > * Show the messages if necessary > * Tear down application completely in case of any serious problems > > > Further, Photon provides direct handlers for [`settings.Settings`](#settings.Settings "settings.Settings") and [`meta.Meta`](#meta.Meta "meta.Meta") and a handler for each tool from [Tools](index.html#tools) by it’s methods. | Parameters: | * **defaults** – Pass defaults down to [`settings.Settings`](#settings.Settings "settings.Settings") * **config** – Pass config down to [`settings.Settings`](#settings.Settings "settings.Settings") * **meta** – Pass meta down to [`meta.Meta`](#meta.Meta "meta.Meta") * **verbose** – Sets the global verbose flag. Passes it down to the underlying [Utility](index.html#util) functions and [The core](#core) | | Variables: | * [**settings**](index.html#module-settings "settings") – The settings handler initialized with defaults and config * [**meta**](index.html#module-meta "meta") – The meta handler initialized with meta | At startup the loaded settings are imported into meta `git_handler`(*\*args*, *\*\*kwargs*)[[source]](_modules/photon.html#Photon.git_handler)[¶](#photon.Photon.git_handler "Permalink to this definition") | Returns: | A new git handler | See also [Git Tool](index.html#tools-git) `m`(*msg*, *state=False*, *more=None*, *cmdd=None*, *critical=True*, *verbose=None*)[[source]](_modules/photon.html#Photon.m)[¶](#photon.Photon.m "Permalink to this definition") Mysterious mega method managing multiple meshed modules magically Note If this function is used, the code contains facepalms: `m(` * It is possible to just show a message, or to run a command with message. * But it is not possible to run a command without a message, use the verbose-flag to hide your debug message. | Parameters: | * **msg** – Add a message. Shown depending on verbose (see below) * **state** – Pass state down to [`util.system.shell\_notify()`](index.html#util.system.shell_notify "util.system.shell_notify") * **more** – Pass more down to [`util.system.shell\_notify()`](index.html#util.system.shell_notify "util.system.shell_notify") * **cmdd** ([*dict*](http://docs.python.org/3/library/stdtypes.html#dict "(in Python v3.5)")) – If given, [`util.system.shell\_run()`](index.html#util.system.shell_run "util.system.shell_run") is launched with it’s values * **critical** – If set to `True`: Tears down (using [`util.system.shell\_notify()`](index.html#util.system.shell_notify "util.system.shell_notify") with state set to `True`) whole application on failure of cmdd contents. + Similar to [`util.system.shell\_run()`](index.html#util.system.shell_run "util.system.shell_run") critical-flag * **verbose** – Overrules parent’s class verbose-flag. + If left to `None`, the verbose value Photon was started with is used + Messages are shown/hidden if explicitly set to `True`/`False` | | Returns: | A dictionary specified the following: * ‘more’: more if it is not a dictionary otherwise it gets merged in if more is specified * The output of [`util.system.shell\_run()`](index.html#util.system.shell_run "util.system.shell_run") gets merged in if cmdd is specified * ‘failed’: `True` if command failed [`util.system.shell\_notify()`](index.html#util.system.shell_notify "util.system.shell_notify") is used with this dictionary to pipe it’s output into [`meta.Meta.log()`](#meta.Meta.log "meta.Meta.log") before returning. | `mail_handler`(*punchline=None*, *add\_meta=False*, *add\_settings=True*, *\*args*, *\*\*kwargs*)[[source]](_modules/photon.html#Photon.mail_handler)[¶](#photon.Photon.mail_handler "Permalink to this definition") | Parameters: | * **punchline** – Adds a punchline before further text * **add\_meta** – Appends current meta to the mail * **add\_settings** – Appends current settings to the mail | | Returns: | A new mail handler | See also [Mail Tool](index.html#tools-mail) `ping_handler`(*\*args*, *\*\*kwargs*)[[source]](_modules/photon.html#Photon.ping_handler)[¶](#photon.Photon.ping_handler "Permalink to this definition") | Returns: | A new ping handler | See also [Ping Tool](index.html#tools-ping) `s2m`[¶](#photon.Photon.s2m "Permalink to this definition") Imports settings to meta `signal_handler`(*\*args*, *\*\*kwargs*)[[source]](_modules/photon.html#Photon.signal_handler)[¶](#photon.Photon.signal_handler "Permalink to this definition") | Returns: | A new signal handler | See also [Signal Tool](index.html#tools-signal) `template_handler`(*\*args*, *\*\*kwargs*)[[source]](_modules/photon.html#Photon.template_handler)[¶](#photon.Photon.template_handler "Permalink to this definition") | Returns: | A new template handler | See also [Template Tool](index.html#tools-template) `photon.``check_m`(*pm*)[[source]](_modules/photon.html#check_m)[¶](#photon.check_m "Permalink to this definition") Shared helper function for all [Tools](index.html#tools) to check if the passed m-function is indeed [`photon.Photon.m()`](#photon.Photon.m "photon.Photon.m") | Params pm: | Suspected m-function | | Returns: | Now to be proven correct m-function, tears down whole application otherwise. | ### Tools[¶](#tools "Permalink to this headline") This are the tools for the user using Photon. You should not directly use them, instead they will get provided to you by [Photon](index.html#photon). See also [Settings](index.html#settings), [Meta](index.html#meta), [Photon](index.html#photon) Some functionality here is bought from the [Utility](index.html#util): See also [Files](index.html#util-files), [Locations](index.html#util-locations), [Structures](index.html#util-structures), [System](index.html#util-system) #### Git Tool[¶](#module-tools.git "Permalink to this headline") *class* `tools.git.``Git`(*m*, *local*, *remote\_url=None*, *mbranch=None*)[[source]](_modules/tools/git.html#Git)[¶](#tools.git.Git "Permalink to this definition") The git tool helps to deal with git repositories. | Parameters: | * **local** – The local folder of the repository + If `None` given (default), it will be ignored if there is already a git repo at local + If no git repo is found at local, a new one gets cloned from remote\_url * **remote\_url** – The remote URL of the repository + Tears down (using [`util.system.shell\_notify()`](index.html#util.system.shell_notify "util.system.shell_notify") with state set to `True`) whole application if remote\_url is set to `None` but a new clone is necessary * **mbranch** – The repository’s main branch. Is set to master when left to `None` | `_checkout`(*treeish*)[[source]](_modules/tools/git.html#Git._checkout)[¶](#tools.git.Git._checkout "Permalink to this definition") Helper function to checkout something | Parameters: | **treeish** – String for ‘tag‘, ‘branch‘, or remote tracking ‘-B banch‘ | `_get_branch`(*remotes=False*)[[source]](_modules/tools/git.html#Git._get_branch)[¶](#tools.git.Git._get_branch "Permalink to this definition") Helper function to determine current branch | Parameters: | **remotes** – List the remote-tracking branches | `_get_remote`(*cached=True*)[[source]](_modules/tools/git.html#Git._get_remote)[¶](#tools.git.Git._get_remote "Permalink to this definition") Helper function to determine remote | Parameters: | **cached** – Use cached values or query remotes | `_log`(*num=None*, *format=None*)[[source]](_modules/tools/git.html#Git._log)[¶](#tools.git.Git._log "Permalink to this definition") Helper function to receive git log | Parameters: | * **num** – Number of entries * **format** – Use formatted output with specified format string | `_pull`()[[source]](_modules/tools/git.html#Git._pull)[¶](#tools.git.Git._pull "Permalink to this definition") Helper function to pull from remote `branch`[¶](#tools.git.Git.branch "Permalink to this definition") | Parameters: | **branch** – Checks out specified branch (tracking if it exists on remote). If set to `None`, ‘master’ will be checked out | | Returns: | The current branch (This could also be ‘master (Detatched-Head)’ - Be warned) | `cleanup`[¶](#tools.git.Git.cleanup "Permalink to this definition") Commits all local changes (if any) into a working branch, merges it with ‘master’. Checks out your old branch afterwards. Tears down (using [`util.system.shell\_notify()`](index.html#util.system.shell_notify "util.system.shell_notify") with state set to `True`) whole application if conflicts are discovered `commit`[¶](#tools.git.Git.commit "Permalink to this definition") | Parameters: | **tag** – Checks out specified commit. If set to `None` the latest commit will be checked out | | Returns: | A list of all commits, descending | `local`[¶](#tools.git.Git.local "Permalink to this definition") | Returns: | The local folder of the repository | `log`[¶](#tools.git.Git.log "Permalink to this definition") | Returns: | The last 10 commit entries as dictionary | * ‘commit’: The commit-ID * ‘message’: First line of the commit message `publish`[¶](#tools.git.Git.publish "Permalink to this definition") Runs [`cleanup()`](#tools.git.Git.cleanup "tools.git.Git.cleanup") first, then pushes the changes to the [`remote`](#tools.git.Git.remote "tools.git.Git.remote"). `remote`[¶](#tools.git.Git.remote "Permalink to this definition") | Returns: | Current remote | `remote_url`[¶](#tools.git.Git.remote_url "Permalink to this definition") | Returns: | The remote URL of the repository | `short_commit`[¶](#tools.git.Git.short_commit "Permalink to this definition") | Returns: | A list of all commits, descending | See also [`commit`](#tools.git.Git.commit "tools.git.Git.commit") `status`[¶](#tools.git.Git.status "Permalink to this definition") | Returns: | Current repository status as dictionary: | * ‘clean’: `True` if there are no changes `False` otherwise * ‘untracked’: A list of untracked files (if any and not ‘clean’) * ‘modified’: A list of modified files (if any and not ‘clean’) * ‘deleted’: A list of deleted files (if any and not ‘clean’) * ‘conflicting’: A list of conflicting files (if any and not ‘clean’) `tag`[¶](#tools.git.Git.tag "Permalink to this definition") | Parameters: | **tag** – Checks out specified tag. If set to `None` the latest tag will be checked out | | Returns: | A list of all tags, sorted as version numbers, ascending | #### Mail Tool[¶](#module-tools.mail "Permalink to this headline") *class* `tools.mail.``Mail`(*m*, *to*, *sender*, *subject=None*, *cc=None*, *bcc=None*)[[source]](_modules/tools/mail.html#Mail)[¶](#tools.mail.Mail "Permalink to this definition") The Mail tool helps to send out mails. | Parameters: | * **to** – Where to send the mail (['user@example.com](mailto:'user%40example.com)‘) * **sender** – Yourself (['me@example.com](mailto:'me%40example.com)‘) + set a reverse DNS entry for `example.com` so your mail does not get caught up in spamfilters. * **subject** – The subject line * **cc** – One or a list of CCs * **bcc** – One or a list of BCCs | `send`[¶](#tools.mail.Mail.send "Permalink to this definition") | Returns: | A dictionary with the following:* ‘sender’: The sender * ‘recipients’: All recipients, compiled from to, cc and bcc * ‘result’: The [`smtplib.SMTP.sendmail()`](http://docs.python.org/3/library/smtplib.html#smtplib.SMTP.sendmail "(in Python v3.5)")-result * ‘exception’: The exception message (if any) | Note You need to have a postfix/sendmail running and listening on localhost. `text`[¶](#tools.mail.Mail.text "Permalink to this definition") | Parameters: | **text** – Add some more text | | Returns: | All text & headers as raw mail source | ##### Mail Tool Example[¶](#mail-tool-example "Permalink to this headline") mail.sample.yaml | | | | --- | --- | | ``` 1 2 3 4 5 ``` | ``` mail: recipient: you@example.com sender: me@example.com subject: 'Fire!' punchline: 'Dear Sir or Madam, I am writing to inform you about a fire in the building ...' ``` | mail.sample.py | | | | --- | --- | | ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 ``` | ``` from photon import Photon photon = Photon('mail.sample.yaml') settings = photon.settings.get['mail'] mail = photon.mail\_handler( to=settings['recipient'], sender=settings['sender'], subject=settings['subject'], punchline=settings['punchline'], add\_meta=True ) ### # Shows the message source so far print(mail.text) ### # Add some more text (do this as often as you like): mail.text = ''' Dear Sir or Madam, bla bla No, that's too formal.. ''' ### # Guess what happens here: mail.send ``` | See also [Example Settings File](index.html#settings-file-example), [Mail Tool Example](#tools-mail-example), [Ping Tool Example](#tools-ping-example) #### Ping Tool[¶](#module-tools.ping "Permalink to this headline") *class* `tools.ping.``Ping`(*m*, *six=False*, *net\_if=None*, *num=5*, *max\_pool\_size=None*)[[source]](_modules/tools/ping.html#Ping)[¶](#tools.ping.Ping "Permalink to this definition") The Ping tool helps to send pings, returning detailed results each probe, and calculates a summary of all probes. | Parameters: | * **six** – Either use `ping` or `ping6` * **net\_if** – Specify network interface to send pings from * **num** – How many pings to send each probe * **max\_pool\_size** – Hosts passed to [`probe()`](#tools.ping.Ping.probe "tools.ping.Ping.probe") in form of a list, will be processed in parallel. Specify the maximum size of the thread pool workers here. If skipped, the number of current CPUs is used | `probe`[¶](#tools.ping.Ping.probe "Permalink to this definition") | Parameters: | **hosts** – One or a list of hosts (URLs, IP-addresses) to send pings to* If you need to check multiple hosts, it is best to pass them together as a list. * This will probe all hosts in parallel, with `max\_pool\_size` workers. | | Returns: | A dictionary with all hosts probed as keys specified as following: | * ‘up’: `True` or `False` depending if ping was successful * ‘loss’: The packet loss as list (if ‘up’) * ‘ms’: A list of times each packet sent (if ‘up’) * ‘rtt’: A dictionary with the fields *avg*, *min*, *max* & *stddev* (if ‘up’) `status`[¶](#tools.ping.Ping.status "Permalink to this definition") | Returns: | A dictionary with the following: | * ‘num’: Total number of hosts already probed * ‘up’: Number of hosts up * ‘down’: Number of hosts down * ‘ratio’: Ratio between ‘up’/’down’ as float Ratio: * `100%` up == 1.0 * `10%` up == 0.1 * `0%` up == 0.0 ##### Ping Tool Example[¶](#ping-tool-example "Permalink to this headline") ping.sample.yaml | | | | --- | --- | | ``` 1 2 3 4 5 6 7 8 9 10 11 ``` | ``` hosts: addresses: - '127.0.0.1' - '127.0.0.2' - '127.0.0.3' urls: - exampla.com - example.com - exampli.com - examplo.com - examplu.com ``` | ping.sample.py | | | | --- | --- | | ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 ``` | ``` from pprint import pprint from photon import Photon photon = Photon('ping.sample.yaml') hosts = photon.settings.get['hosts'] ping = photon.ping\_handler() ### # Let's start off with localhost to demonstrate the handling of the probe-function: pprint(hosts) a = hosts['addresses'][0] ping.probe = a if ping.probe[a]['up']: print('%s is reachable - %s ms rtt in average' %(a, ping.probe[a]['rtt']['avg'])) else: print('%s could not be reached!' %(a)) pprint(ping.probe) print('-' \* 8) ### # You can also pass a complete list to probe. This will be faster, because the list is processed in parallel. # The status per host will be overwritten with new information if it encounters the same host again: ping.probe = hosts['addresses'] pprint(ping.probe) print('These are the statistics so far:') pprint(ping.status) print('-' \* 8) ### # Another round of pings to demonstrate the handling of the status-function: ping.probe = hosts['urls'] if ping.status['ratio'] <= 0.75: print('more than three quarters of all addresses are not reachable!!1!') print('The statistics have changed now:') pprint(ping.status) ``` | See also [Example Settings File](index.html#settings-file-example), [Mail Tool Example](#tools-mail-example), [Ping Tool Example](#tools-ping-example) #### Signal Tool[¶](#module-tools.signal "Permalink to this headline") *class* `tools.signal.``Signal`(*m*, *pid*, *sudo=True*, *cmdd\_if\_no\_pid=None*)[[source]](_modules/tools/signal.html#Signal)[¶](#tools.signal.Signal "Permalink to this definition") The Signal tool can send signals to processes via `kill`, returning the results. | Parameters: | * **pid** – Either the full path to the pidfile (e.g. `/var/run/proc.pid`) or the pid as number * **sudo** – Prepend sudo before command. (Make sure to be root yourself if set to `False` or expect errors. Further for unattended operation add the user to `sudoers` file.) | `_Signal__signal`(*sig*, *verbose=None*)[¶](#tools.signal.Signal._Signal__signal "Permalink to this definition") Helper class preventing code duplication.. | Parameters: | * **sig** – Signal to use (e.g. “HUP”, “ALRM”) * **verbose** – Overwrite [`photon.Photon.m()`](index.html#photon.Photon.m "photon.Photon.m")‘s verbose | | Returns: | [`photon.Photon.m()`](index.html#photon.Photon.m "photon.Photon.m")‘s result of killing pid with specified pid | `alrm`[¶](#tools.signal.Signal.alrm "Permalink to this definition") | Returns: | [`photon.Photon.m()`](index.html#photon.Photon.m "photon.Photon.m")‘s result of killing pid using SIGALRM | `hup`[¶](#tools.signal.Signal.hup "Permalink to this definition") | Returns: | [`photon.Photon.m()`](index.html#photon.Photon.m "photon.Photon.m")‘s result of killing pid using SIGHUP | `int`[¶](#tools.signal.Signal.int "Permalink to this definition") | Returns: | [`photon.Photon.m()`](index.html#photon.Photon.m "photon.Photon.m")‘s result of killing pid using SIGINT with visible shell warning | `kill`[¶](#tools.signal.Signal.kill "Permalink to this definition") | Returns: | [`photon.Photon.m()`](index.html#photon.Photon.m "photon.Photon.m")‘s result of killing pid using SIGKILL with visible shell warning | `quit`[¶](#tools.signal.Signal.quit "Permalink to this definition") | Returns: | [`photon.Photon.m()`](index.html#photon.Photon.m "photon.Photon.m")‘s result of killing pid using SIGQUIT with visible shell warning | `stop`[¶](#tools.signal.Signal.stop "Permalink to this definition") | Returns: | [`photon.Photon.m()`](index.html#photon.Photon.m "photon.Photon.m")‘s result of killing pid using SIGSTOP with visible shell warning | `usr1`[¶](#tools.signal.Signal.usr1 "Permalink to this definition") | Returns: | [`photon.Photon.m()`](index.html#photon.Photon.m "photon.Photon.m")‘s result of killing pid using SIGUSR1 | `usr2`[¶](#tools.signal.Signal.usr2 "Permalink to this definition") | Returns: | [`photon.Photon.m()`](index.html#photon.Photon.m "photon.Photon.m")‘s result of killing pid using SIGUSR2 | #### Template Tool[¶](#module-tools.template "Permalink to this headline") *class* `tools.template.``Template`(*m*, *template*, *fields=None*)[[source]](_modules/tools/template.html#Template)[¶](#tools.template.Template "Permalink to this definition") The Template tool helps to process on strings. | Parameters: | * **template** – The initial template to start with. + If it’s value is recognized by [`util.locations.search\_location()`](index.html#util.locations.search_location "util.locations.search_location") (a.k.a is a filename) the file contents will be loaded as template. Note If the file is not found, you will be doing string processing on the filename instead of the contents! * **fields** – Initially set up fields. Can be done later, using [`sub()`](#tools.template.Template.sub "tools.template.Template.sub") | The templating-language itself are normal [Template strings](http://docs.python.org/3/library/string.html#template-strings "(in Python v3.5)"), see there for syntax. `raw`[¶](#tools.template.Template.raw "Permalink to this definition") | Returns: | The raw template | `sub`[¶](#tools.template.Template.sub "Permalink to this definition") | Parameters: | **fields** – Set fields to substitute | | Returns: | Substituted Template with given fields. If no fields were set up beforehand, [`raw()`](#tools.template.Template.raw "tools.template.Template.raw") is used. | `write`(*filename*, *append=True*, *backup=True*)[[source]](_modules/tools/template.html#Template.write)[¶](#tools.template.Template.write "Permalink to this definition") | Parameters: | * **filename** – File to write into * **append** – Either append to existing content (if not already included) or completely replace filename * **backup** – Create a backup of filename before writing. Only applies when append is set | ### Utility[¶](#utility "Permalink to this headline") This is the toolbox used by [The core](index.html#core): See also [Settings](index.html#settings), [Meta](index.html#meta), [Photon](index.html#photon) As well as used by the [Tools](index.html#tools): See also [Git Tool](index.html#tools-git), [Mail Tool](index.html#tools-mail), [Ping Tool](index.html#tools-ping), [Signal Tool](index.html#tools-signal) Note If you have no explicit reason, do **not** use the functions here directly. * Always try to work trough [`photon.Photon`](index.html#photon.Photon "photon.Photon") and it’s handlers. * If you discover you are repeatedly calling backend functions consider adding a tool for that job! #### Files[¶](#module-util.files "Permalink to this headline") `util.files.``read_file`(*filename*)[[source]](_modules/util/files.html#read_file)[¶](#util.files.read_file "Permalink to this definition") Reads files | Parameters: | **filename** – The full path of the file to read | | Returns: | The content of the file as string (if filename exists) | Note If filename‘s content is empty, `None` will also returned. To check if a file really exists use [`util.locations.search\_location()`](#util.locations.search_location "util.locations.search_location") `util.files.``read_json`(*filename*)[[source]](_modules/util/files.html#read_json)[¶](#util.files.read_json "Permalink to this definition") Reads json files | Parameters: | **filename** – The full path to the json file | | Returns: | Loaded json content as represented data structure | `util.files.``read_yaml`(*filename*, *add\_constructor=None*)[[source]](_modules/util/files.html#read_yaml)[¶](#util.files.read_yaml "Permalink to this definition") Reads YAML files | Parameters: | * **filename** – The full path to the YAML file * **add\_constructor** – A list of yaml constructors (loaders) | | Returns: | Loaded YAML content as represented data structure | See also [`util.structures.yaml\_str\_join()`](#util.structures.yaml_str_join "util.structures.yaml_str_join"), [`util.structures.yaml\_loc\_join()`](#util.structures.yaml_loc_join "util.structures.yaml_loc_join") `util.files.``write_file`(*filename*, *content*)[[source]](_modules/util/files.html#write_file)[¶](#util.files.write_file "Permalink to this definition") Writes files | Parameters: | * **filename** – The full path of the file to write (enclosing folder must already exist) * **content** – The content to write | | Returns: | The size of the data written | `util.files.``write_json`(*filename*, *content*)[[source]](_modules/util/files.html#write_json)[¶](#util.files.write_json "Permalink to this definition") Writes json files | Parameters: | * **filename** – The full path to the json file * **content** – The content to dump | | Returns: | The size written | `util.files.``write_yaml`(*filename*, *content*)[[source]](_modules/util/files.html#write_yaml)[¶](#util.files.write_yaml "Permalink to this definition") Writes YAML files | Parameters: | * **filename** – The full path to the YAML file * **content** – The content to dump | | Returns: | The size written | #### Locations[¶](#module-util.locations "Permalink to this headline") `util.locations.``backup_location`(*src*, *loc=None*)[[source]](_modules/util/locations.html#backup_location)[¶](#util.locations.backup_location "Permalink to this definition") Writes Backups of locations | Parameters: | * **src** – The source file/folder to backup * **loc** – The target folder to backup into The backup will be called src + [`util.system.get\_timestamp()`](#util.system.get_timestamp "util.system.get_timestamp"). \* If loc left to none, the backup gets written in the same folder like src resides in + Otherwise the specified path will be used. | `util.locations.``change_location`(*src*, *tgt*, *move=False*, *verbose=True*)[[source]](_modules/util/locations.html#change_location)[¶](#util.locations.change_location "Permalink to this definition") Copies/moves/deletes locations | Parameters: | * **src** – Source location where to copy from * **tgt** – Target location where to copy to + To backup src, set tgt explicitly to `True`. tgt will be set to src + ‘\_backup\_’ + [`util.system.get\_timestamp()`](#util.system.get_timestamp "util.system.get_timestamp") then * **move** – Deletes original location after copy (a.k.a. move) + To delete src , set tgt explicitly to `False` and move to `True` (be careful!!1!) * **verbose** – Show warnings | `util.locations.``get_locations`()[[source]](_modules/util/locations.html#get_locations)[¶](#util.locations.get_locations "Permalink to this definition") Compiles default locations | Returns: | A dictionary with folders as values: | * ‘home\_dir’: Your home-directory (`~`) * ‘call\_dir’: Where you called the first Python script from. (`argv[0]`) * ‘conf\_dir’: The `XDG\_CONFIG\_HOME`-directory + `photon` (`~/.config/photon`) * ‘data\_dir’: The `XDG\_DATA\_HOME`-directory + `photon` (`~/.local/share/photon`) Note * Both [`search\_location()`](#util.locations.search_location "util.locations.search_location") and [`make\_locations()`](#util.locations.make_locations "util.locations.make_locations") have the argument locations. * If locations is set to `None` (by default), it will be filled with the output of [`get\_locations()`](#util.locations.get_locations "util.locations.get_locations"). `util.locations.``make_locations`(*locations=None*, *verbose=True*)[[source]](_modules/util/locations.html#make_locations)[¶](#util.locations.make_locations "Permalink to this definition") Creates folders | Parameters: | * **locations** – A list of folders to create (can be a dictionary, see note below) * **verbose** – Warn if any folders were created | Note * If locations is not a list, but a dictionary, all values in the dictionary will be used (as specified in [`util.structures.to\_list()`](#util.structures.to_list "util.structures.to_list")) * If locations is set to `None` (by default), it will be filled with the output of [`get\_locations()`](#util.locations.get_locations "util.locations.get_locations"). `util.locations.``search_location`(*loc*, *locations=None*, *critical=False*, *create\_in=None*, *verbose=True*)[[source]](_modules/util/locations.html#search_location)[¶](#util.locations.search_location "Permalink to this definition") Locates files with a twist: > > * Check the existence of a file using the full path in loc > * Search for the filename loc in locations > * Create it’s enclosing folders if the file does not exist. use create\_in > > > | Parameters: | * **loc** – Filename to search * **locations** – A list of possible locations to search within (can be a dictionary, see note below) * **critical** – Tears down (using [`util.system.shell\_notify()`](#util.system.shell_notify "util.system.shell_notify") with state set to `True`) whole application if file was not found * **create\_in** – If loc was not found, the folder create\_in is created. If locations is a dictionary, create\_in can also specify a key of locations. The value will be used then. * **verbose** – Pass verbose flag to [`make\_locations()`](#util.locations.make_locations "util.locations.make_locations") | | Returns: | The full path of loc in matched location | Note * If locations is not a list, but a dictionary, all values in the dictionary will be used (as specified in [`util.structures.to\_list()`](#util.structures.to_list "util.structures.to_list")) * If locations is set to `None` (by default), it will be filled with the output of [`get\_locations()`](#util.locations.get_locations "util.locations.get_locations"). #### Structures[¶](#module-util.structures "Permalink to this headline") `util.structures.``dict_merge`(*o*, *v*)[[source]](_modules/util/structures.html#dict_merge)[¶](#util.structures.dict_merge "Permalink to this definition") Recursively climbs through dictionaries and merges them together. | Parameters: | * **o** – The first dictionary * **v** – The second dictionary | | Returns: | A dictionary (who would have guessed?) | Note Make sure o & v are indeed dictionaries, bad things will happen otherwise! `util.structures.``to_list`(*i*, *use\_keys=False*)[[source]](_modules/util/structures.html#to_list)[¶](#util.structures.to_list "Permalink to this definition") Converts items to a list. | Parameters: | * **i** – Item to convert + If i is `None`, the result is an empty list + If i is ‘string’, the result won’t be `['s', 't', 'r',...]` rather more like `['string']` + If i is a nested dictionary, the result will be a flattened list. * **use\_keys** – If i is a dictionary, use the keys instead of values | | Returns: | All items in i as list | `util.structures.``yaml_loc_join`(*l*, *n*)[[source]](_modules/util/structures.html#yaml_loc_join)[¶](#util.structures.yaml_loc_join "Permalink to this definition") YAML loader to join paths The keywords come directly from [`util.locations.get\_locations()`](#util.locations.get_locations "util.locations.get_locations"). See there! | Returns: | A path seperator (`/`) joined string with keywords extended. Used in [`settings.Settings.load()`](index.html#settings.Settings.load "settings.Settings.load") | See also The YAML files mentioned in [Example Settings File](index.html#settings-file-example), [Mail Tool Example](index.html#tools-mail-example), [Ping Tool Example](index.html#tools-ping-example) `util.structures.``yaml_str_join`(*l*, *n*)[[source]](_modules/util/structures.html#yaml_str_join)[¶](#util.structures.yaml_str_join "Permalink to this definition") YAML loader to join strings The keywords are as following: * hostname: Your hostname (from [`util.system.get\_hostname()`](#util.system.get_hostname "util.system.get_hostname")) * timestamp: Current timestamp (from [`util.system.get\_timestamp()`](#util.system.get_timestamp "util.system.get_timestamp")) | Returns: | A non character joined string with keywords extended. Used in [`settings.Settings.load()`](index.html#settings.Settings.load "settings.Settings.load") | Note Be careful with timestamps when using a config in [Settings](index.html#settings). See also The YAML files mentioned in [Example Settings File](index.html#settings-file-example), [Mail Tool Example](index.html#tools-mail-example), [Ping Tool Example](index.html#tools-ping-example) #### System[¶](#module-util.system "Permalink to this headline") `util.system.``get_hostname`()[[source]](_modules/util/system.html#get_hostname)[¶](#util.system.get_hostname "Permalink to this definition") Determines the current hostname by probing `uname -n`. Falls back to `hostname` in case of problems. Tears down (using [`util.system.shell\_notify()`](#util.system.shell_notify "util.system.shell_notify") with state set to `True`) whole application if both failed (usually they don’t but consider this if you are debugging weird problems..) | Returns: | The hostname as string. Domain parts will be split off | `util.system.``get_timestamp`(*time=True*, *precice=False*)[[source]](_modules/util/system.html#get_timestamp)[¶](#util.system.get_timestamp "Permalink to this definition") What time is it? | Parameters: | * **time** – Append `-%H.%M.%S` to the final string. * **precice** – Append `-%f` to the final string. Is only recognized when time is set to `True` | | Returns: | A timestamp string of now in the format `%Y.%m.%d-%H.%M.%S-%f` | See also [strftime.org](http://strftime.org/) is awesome! `util.system.``shell_notify`(*msg*, *state=False*, *more=None*, *exitcode=None*, *verbose=True*)[[source]](_modules/util/system.html#shell_notify)[¶](#util.system.shell_notify "Permalink to this definition") A pretty long wrapper for a [`print()`](http://docs.python.org/3/library/functions.html#print "(in Python v3.5)") function. But this [`print()`](http://docs.python.org/3/library/functions.html#print "(in Python v3.5)") is the only one in Photon. Note This method is just a helper method within photon. If you need this functionality use [`photon.Photon.m()`](index.html#photon.Photon.m "photon.Photon.m") instead | Parameters: | * **msg** – The message to show * **state** – The message will be prefixed with [state] + If `False` (default): Prefixed with ~ + If `None`: Prefixed with [WARNING] + If `True`: Prefixed with [FATAL] and the exitcode will be set (see below) * **more** – Something to add to the message (see below) + Anything you have. Just for further information. + Will be displayed after the message, pretty printed using [`pprint.pformat()`](http://docs.python.org/3/library/pprint.html#pprint.pformat "(in Python v3.5)") * **exitcode** – Tears down (using [`util.system.shell\_notify()`](#util.system.shell_notify "util.system.shell_notify") with state set to `True`) whole application with given code * **verbose** – Show message or not (see below) + If set to `False`, you can use [`shell\_notify()`](#util.system.shell_notify "util.system.shell_notify") for the dictionary it returns. + Will be overruled if exitcode is set. | | Returns: | A dictionary containing untouched msg, more and verbose | `util.system.``shell_run`(*cmd*, *cin=None*, *cwd=None*, *timeout=10*, *critical=True*, *verbose=True*)[[source]](_modules/util/system.html#shell_run)[¶](#util.system.shell_run "Permalink to this definition") Runs a shell command within a controlled environment. Note This method is just a helper method within photon. If you need this functionality use [`photon.Photon.m()`](index.html#photon.Photon.m "photon.Photon.m") instead | Parameters: | * **cmd** – The command to run + A string one would type into a console like **git push -u origin master**. + Will be split using [`shlex.split()`](http://docs.python.org/3/library/shlex.html#shlex.split "(in Python v3.5)"). + It is possible to use a list here, but then no splitting is done. * **cin** – Add something to stdin of cmd * **cwd** – Run cmd insde specified current working directory * **timeout** – Catch infinite loops (e.g. `ping`). Exit after timeout seconds * **critical** – If set to `True`: Tears down (using [`util.system.shell\_notify()`](#util.system.shell_notify "util.system.shell_notify") with state set to `True`) whole application on failure of cmd * **verbose** – Show messages and warnings | | Returns: | A dictionary containing the results from running cmd with the following: * ‘command’: cmd * ‘stdin’: cin (If data was set in cin) * ‘cwd’: cwd (If cwd was set) * ‘exception’: exception message (If an exception was thrown) * ‘timeout’: timeout (If a timeout exception was thrown) * ‘stdout’: List from stdout (If any) * ‘stderr’: List from stderr (If any) * ‘returncode’: The returncode (If not any exception) * ‘out’: The most urgent message as joined string. (‘exception’ > ‘stderr’ > ‘stdout’) | I am lost: * [Index](genindex.html) * [Module Index](py-modindex.html) * [Search Page](search.html) Info[¶](#module-info "Permalink to this headline") -------------------------------------------------- The *info* file The *info* file is not vital to Photon, it just helps to share common values between documentation and the package builder (*setup* file). `info.``author`()[[source]](_modules/info.html#author)[¶](#info.author "Permalink to this definition") | Returns: | The main author (last entry of [`contributors()`](#info.contributors "info.contributors")) | `info.``contributors`()[[source]](_modules/info.html#contributors)[¶](#info.contributors "Permalink to this definition") | Returns: | A list of all contributors | `info.``contributors_str`()[[source]](_modules/info.html#contributors_str)[¶](#info.contributors_str "Permalink to this definition") | Returns: | The [`contributors()`](#info.contributors "info.contributors") as comma joined string | `info.``email`()[[source]](_modules/info.html#email)[¶](#info.email "Permalink to this definition") | Returns: | Main [`author()`](#info.author "info.author")‘s mail | `info.``pkg_name`()[[source]](_modules/info.html#pkg_name)[¶](#info.pkg_name "Permalink to this definition") | Returns: | The package name (on pypi) | `info.``release`()[[source]](_modules/info.html#release)[¶](#info.release "Permalink to this definition") | Returns: | Current release string | | Current: | 0.4 | `info.``url`()[[source]](_modules/info.html#url)[¶](#info.url "Permalink to this definition") | Returns: | The repo url (on github) | `info.``version`()[[source]](_modules/info.html#version)[¶](#info.version "Permalink to this definition") | Returns: | Current version string | | Current: | 0.4 (Release: 0.4) |

glab

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glab latest documentation [glab](index.html#document-index) latest [glab](index.html#document-index) * [Docs](index.html#document-index) » * glab latest documentation * [Edit on GitLab](https://gitlab.com/gitlab-org/cli/blob/main/docs/index.rst) ---

face

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Face 1.0 documentation (Show Table Of Contents) (Hide Table Of Contents) Welcome to Face’s documentation ================================================================================================== Face is an ORM built under php5.4. It is aimed to be flexible and to provide powerful features to abstract and normalize database interactions. In the latest weeks face has known a lot of refactoring and a lot are on the way. The doc was not relevant anymore and has been temporally closed until the new doc is written. We plan the first stable release of face for end of 2015. Stay tuned ! © Copyright 2013, Soufiane GHZAL. Created using [Sphinx](http://sphinx-doc.org/) 1.3.5.

json

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json 0.1.0 documentation [json](index.html#document-index) stable Getting Started: * [Features](index.html#document-features) * [Build json](index.html#document-build) + [Unix (Autotools):](index.html#unix-autotools) + [Windows (MSBuild):](index.html#windows-msbuild) + [Documentation (Sphinx):](index.html#documentation-sphinx) * [Getting Started](index.html#document-getting_started) + [Allocations:](index.html#allocations) + [Design and Data Structure:](index.html#design-and-data-structure) API: * [API documentation](index.html#document-api) + [parse json](index.html#document-json) + [utils / helpers](index.html#document-util) + [print](index.html#document-print) [json](index.html#document-index) * [Docs](index.html#document-index) » * json 0.1.0 documentation * [Edit on GitHub](https://github.com/recp/json/blob/2d4b48ae623f1954176422555ac5053e7f4497d5/docs/source/index.rst) --- json Documentation[¶](#json-documentation "Permalink to this headline") ======================================================================= **json** is lighweight JSON parser written in C99 (compatible with C89). Features[¶](#features "Permalink to this headline") --------------------------------------------------- * header-only or optional compiled library * option to store members and arrays as reverse order or normal * doesn’t alloc memory for keys and values only for tokens * creates DOM-like data structure to make it easy to iterate though * simple api * provides some util functions to print json, get int32, int64, float, double… * very small library * and other… Build json[¶](#build-json "Permalink to this headline") ------------------------------------------------------- **json** library does not have external dependencies. **NOTE:** If you only need to inline versions, you don’t need to build **json**, you don’t need to link it to your program. Just import cglm to your project as dependency / external lib by copy-paste then use it as usual ### Unix (Autotools):[¶](#unix-autotools "Permalink to this headline") | | | | --- | --- | | ``` 1 2 3 4 5 ``` | ``` $ sh autogen.sh $ ./configure $ make $ make check # run tests (optional) $ [sudo] make install # install to system (optional) ``` | **make** will build json to **.libs** sub folder in project folder. If you don’t want to install **json** to your system’s folder you can get static and dynamic libs in this folder. ### Windows (MSBuild):[¶](#windows-msbuild "Permalink to this headline") Windows related build files, project files are located in win folder, make sure you are inside in json/win folder. Code Analysis are enabled, it may take awhile to build. | | | | --- | --- | | ``` 1 2 ``` | ``` $ cd win $ .\build.bat ``` | if *msbuild* is not worked (because of multi versions of Visual Studio) then try to build with *devenv*: | | | | --- | --- | | ``` 1 ``` | ``` $ devenv json.sln /Build Release ``` | Currently tests are not available on Windows. ### Documentation (Sphinx):[¶](#documentation-sphinx "Permalink to this headline") **json** uses sphinx framework for documentation, it allows lot of formats for documentation. To see all options see sphinx build page: <https://www.sphinx-doc.org/en/master/man/sphinx-build.html> Example build: | | | | --- | --- | | ``` 1 2 3 4 5 6 7 ``` | ``` $ cd json/docs $ sphinx-build source build or $ cd json/docs $ sh ./build-docs.sh ``` | Getting Started[¶](#getting-started "Permalink to this headline") ----------------------------------------------------------------- **json** uses **json\_** prefix for all functions e.g. json\_parse(). There are only a few types which represents json document, json object, json array and json value (as string). * **json\_doc\_t** represents JSON document. It stores root JSON node and allocated memory. * **json\_t** represents JSON object. Arrays also are json object. * **json\_array\_t** represents JSON array. It inherits **json\_t**, so you can cast array to json object. * **json\_type\_t** represents JSON type. ### Allocations:[¶](#allocations "Permalink to this headline") *json* doesn’t alloc any memory for JSON contents, keys and values… It ONLY allocs memory for DOM-tree (json tokens), that’s it. It creates pointers to actual data, so you must retain JSON data until you have finished to process json. After you finished to parse JSON, this is the order that you must use to free-ing things: 1. free original JSON data 2. free json\_doc actually the order doesn’t matter but you must free the json doc which is returned from json\_parse(). ### Design and Data Structure:[¶](#design-and-data-structure "Permalink to this headline") **json** creates a TREE to traverse JSON. Every json object’s child node has **key** pointer. A value of **json\_t** may be one of these: * Child node * String contents you must use **type** member of json object to identify the value type. If you need to integer, float or boolean values, then you can use util functions to get these values. These functions will be failed if the value is not string. **VERY IMPORTANT:** **key** and **value** ARE JUST POINTERS to original data. Because of this, you will see that json object has **keySize** and **valueSize** members. When comparing two keys, you must use *keySize*. Instead of *strcmp()* you could use *strncmp()* and its friends, because it has *size* parameter which is our *keySize* You can also use built-in helper to compare two keys: **json\_key\_eq()** Also when copying values you must also use *valueSize*. You could use *json\_string\_dup()* to duplicate strings. It is better to not copy contents as possible as much. **UTILITIES / HELPERS:** json library also provides some inline utiliy functions to make things easier while handling json data. API documentation[¶](#api-documentation "Permalink to this headline") --------------------------------------------------------------------- ### parse json[¶](#parse-json "Permalink to this headline") Header: json/json.h #### JSON Document[¶](#json-document "Permalink to this headline") JSON document is returned when parsing json contents is done. This object stores root JSON object and allocated memories. It creates pointers to actual data, so you must retain JSON data until you have finished to process json. You After you processed the parsed JSON, then you must free this document. #### Table of contents (click to go):[¶](#table-of-contents-click-to-go "Permalink to this headline") Functions: 1. [`json\_parse()`](#c.json_parse "json_parse") 2. [`json\_free()`](#c.json_free "json_free") 3. [`json\_get()`](#c.json_get "json_get") 4. [`json\_array()`](#c.json_array "json_array") #### Functions documentation[¶](#functions-documentation "Permalink to this headline") json\_doc\_t\* `json_parse`(const char \* \_\_restrict*contents*)[¶](#c.json_parse "Permalink to this definition") parse json string this function parses JSON string and retuns a document which contains: * JSON object * allocated memory after JSON is processed, the object must be freed with json\_free() this library doesn’t alloc any memory for JSON itsef and doesn’t copy JSON contents into a data structure. It only allocs memory for tokens. So don’t free ‘contents’ parameter until you finished to process JSON. Desired order: 1. Read JSON file 2. Pass the contents to json\_parse() 3. Process/Handle JSON 4. free JSON document with json\_free() 5. free contents Parameters: *[in]* **contents** JSON string Returns: json document which contains json object as root object void `json_free`(json\_doc\_t \* \_\_restrict*jsondoc*)[¶](#c.json_free "Permalink to this definition") frees json document and its allocated memory Parameters: *[in]* **jsondoc** JSON document const json\_t\* `json_get`(const json\_t \* \_\_restrict*object*, const char \* \_\_restrict*key*)[¶](#c.json_get "Permalink to this definition") gets value for key You should only use this for DEBUG or if you only need to only specific key. Desired usage is iterative way: > > You must iterate through json’s next and value links. Parameters: *[in]* **object** json object *[in]* **key** key to find value Returns: value found for the key or NULL const json\_array\_t\* `json_array`(const json\_t \* \_\_restrict*object*)[¶](#c.json_array "Permalink to this definition") contenient function to cast object’s child/value to array Parameters: *[in]* **object** json object Returns: json array or NULL ### utils / helpers[¶](#utils-helpers "Permalink to this headline") Header: json/util.h Inline helpers to make things easier while process JSON. Most of uitl functions expects default value, so if it fails to convert string to a number or boolean then that default value will be returned. #### Table of contents (click to go):[¶](#table-of-contents-click-to-go "Permalink to this headline") Functions: 1. [`json\_int32()`](#c.json_int32 "json_int32") 2. [`json\_uint32()`](#c.json_uint32 "json_uint32") 3. [`json\_int64()`](#c.json_int64 "json_int64") 4. [`json\_uint64()`](#c.json_uint64 "json_uint64") 5. [`json\_float()`](#c.json_float "json_float") 6. [`json\_double()`](#c.json_double "json_double") 7. [`json\_bool()`](#c.json_bool "json_bool") 8. [`json\_string()`](#c.json_string "json_string") 9. [`json\_string\_dup()`](#c.json_string_dup "json_string_dup") 10. [`json\_key\_eq()`](#c.json_key_eq "json_key_eq") #### Functions documentation[¶](#functions-documentation "Permalink to this headline") int32\_t `json_int32`(const json\_t \* \_\_restrict*object*, int32\_t*defaultValue*)[¶](#c.json_int32 "Permalink to this definition") creates number (int32) from string value Parameters: *[in]* **object** json object *[in]* **defaultValue** default value if operation fails Returns: number uint32\_t `json_uint32`(const json\_t \* \_\_restrict*object*, uint32\_t*defaultValue*)[¶](#c.json_uint32 "Permalink to this definition") creates number (uint32) from string value Parameters: *[in]* **object** json object *[in]* **defaultValue** default value if operation fails Returns: number int64\_t `json_int64`(const json\_t \* \_\_restrict*object*, int64\_t*defaultValue*)[¶](#c.json_int64 "Permalink to this definition") creates number (int64) from string value Parameters: *[in]* **object** json object *[in]* **defaultValue** default value if operation fails Returns: number int64\_t `json_uint64`(const json\_t \* \_\_restrict*object*, uint64\_t*defaultValue*)[¶](#c.json_uint64 "Permalink to this definition") creates number (uint64) from string value Parameters: *[in]* **object** json object *[in]* **defaultValue** default value if operation fails Returns: number float `json_float`(const json\_t \* \_\_restrict*object*, float*defaultValue*)[¶](#c.json_float "Permalink to this definition") creates number (float) from string value Parameters: *[in]* **object** json object *[in]* **defaultValue** default value if operation fails Returns: number double `json_double`(const json\_t \* \_\_restrict*object*, double*defaultValue*)[¶](#c.json_double "Permalink to this definition") creates number (double) from string value Parameters: *[in]* **object** json object *[in]* **defaultValue** default value if operation fails Returns: number int `json_bool`(const json\_t \* \_\_restrict*object*, int*defaultValue*)[¶](#c.json_bool "Permalink to this definition") creates boolean from string value it returns integer to separate default value from true or false Parameters: *[in]* **object** json object *[in]* **defaultValue** default value if operation fails Returns: boolean values as integer: 1 true, 0 false, error: defaultValue const char\* `json_string`(const json\_t \* \_\_restrict*object*)[¶](#c.json_string "Permalink to this definition") return non-NULL terminated string value you must use object->valSize to copy, compare … string Parameters: *[in]* **object** json object Returns: non-NULL terminated string value (pointer only) char\* `json_string_dup`(const json\_t \* \_\_restrict*object*)[¶](#c.json_string_dup "Permalink to this definition") return non-NULL terminated string value you must use object->valSize to copy, compare … string Parameters: *[in]* **object** json object Returns: NULL terminated duplicated string value bool `json_key_eq`(const json\_t \* \_\_restrict*obj*, const char \* \_\_restrict*str*)[¶](#c.json_key_eq "Permalink to this definition") compares json key with a string| Parameters: *[in]* **obj** json object *[in]* **str** string to compare Returns: true if str is equals to json’s key ### print[¶](#print "Permalink to this headline") Header: json/print.h Print functions #### Table of contents (click to go):[¶](#table-of-contents-click-to-go "Permalink to this headline") Functions: 1. [`json\_print()`](#c.json_print "json_print") 2. [`json\_print\_pad()`](#c.json_print_pad "json_print_pad") #### Functions documentation[¶](#functions-documentation "Permalink to this headline") void `json_print`(const json\_t \* \_\_restrict*json*)[¶](#c.json_print "Permalink to this definition") print json Parameters: *[in]* **json** json object with title and zero padding void `json_print_pad`(const json\_t \* \_\_restrict*json*, int*pad*)[¶](#c.json_print_pad "Permalink to this definition") print json Parameters: *[in]* **json** json object *[in]* **pad** padding Indices and tables[¶](#indices-and-tables "Permalink to this headline") ======================================================================= * [Index](genindex.html) * [Module Index](py-modindex.html) * [Search Page](search.html)

namespaces

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namespaces 4.2.0 documentation namespaces[¶](#namespaces "Permalink to this headline") ======================================================= > > Namespaces are one honking great idea – let’s do more of those! > > > —[PEP 20: The Zen of Python](https://www.python.org/dev/peps/pep-0020/) > > > Namespaces are: * Pythonic structs * Python dict with dot-notation access * Javascript-like objects for Python Basic Usage[¶](#basic-usage "Permalink to this headline") --------------------------------------------------------- ``` import namespaces as ns ab = ns.Namespace(a=1, b=2) frozen\_ab = ns.FrozenNamespace(ab) frozen\_ab.b # => 2 ab.c # => AttributeError ab.c = 3 ab.c # => 3 frozen\_ab.c = 3 # => AttributeError ``` Installation[¶](#installation "Permalink to this headline") ----------------------------------------------------------- ``` $ pip install namespaces ``` Navigation[¶](#navigation "Permalink to this headline") ------------------------------------------------------- ### Design[¶](#design "Permalink to this headline") The goal of namespaces is to improve code readability by leveraging *beautiful* dot-notation instead of relying on `str` when describing object attributes. namespaces was born out of a desire for a mutable `namedtuple`. #### Before & after[¶](#before-after "Permalink to this headline") Before: ``` joey = {'name': 'joey', 'height': 70, 'age': 24} joey['species'] = 'kangaroo' print(joey['name'], 'is a', joey['species']) ``` After: ``` import namespaces as ns joey = ns.Namespace(name='joey', height=70, age=24) joey.species = 'kangaroo' print(joey.name, 'is a', joey.species) ``` #### namespaces vs dict[¶](#namespaces-vs-dict "Permalink to this headline") Namespaces is **not** meant as a replacement for dict. Use a namespace if: * you will be accessing items individually by name * you can enumerate and name items as you write your code * you want the semantics of a Javascript object * you want a dynamic, mutable `namedtuple` Use a `dict` if: * you will be accessing items in batches programmatically * you *cannot* enumerate or name items as you write your code * you need keys that are not `str` ### namespaces API[¶](#namespaces-api "Permalink to this headline") #### Namespace[¶](#namespace "Permalink to this headline") *class* `namespaces.``Namespace`(*\*args*, *\*\*kwargs*)[¶](#namespaces.Namespace "Permalink to this definition") Pythonic structs | Python dict with dot-notation access | Javascript-like objects for Python. Creation is equivalent to creating a `dict` with the positional arguments args and keyword arguments kwargs. In other words, if it works for the `dict` constructor, it will work for the `Namespace` constructor. | Parameters: | * **args** – Items as positional arguments. * **kwargs** – Items as keyword arguments. | Usage: ``` >>> import namespaces as ns >>> foo = ns.Namespace(a=1, b=2, c=3) >>> foo Namespace(a=1, b=2, c=3) >>> bar = ns.Namespace({'a': 1, 'b': 2, 'c': 3}) >>> bar Namespace(a=1, b=2, c=3) >>> baz = ns.Namespace() >>> baz.a = 1 >>> baz.a 1 >>> baz['a'] 1 >>> baz.a is baz['a'] True >>> baz['b'] = 2 >>> baz['b'] 2 >>> baz.b 2 >>> baz['b'] is baz.b True ``` `__delitem__`(*name*)[¶](#namespaces.Namespace.__delitem__ "Permalink to this definition") Delete item via bracket-notation. | Parameters: | **name** (*str*) – Key | Usage: ``` >>> import namespaces as ns >>> foo = ns.Namespace(a=1) >>> del foo['a'] # calls foo.\_\_delitem\_\_('a') >>> foo Namespace() ``` `__getattr__`(*name*)[¶](#namespaces.Namespace.__getattr__ "Permalink to this definition") Look up item via dot-notation. | Parameters: | **name** (*str*) – Key | | Returns: | Associated value | Usage: ``` >>> import namespaces as ns >>> foo = ns.Namespace(a=1) >>> foo.a # calls foo.\_\_getattr\_\_('a') 1 >>> foo.b AttributeError: 'Namespace' object has no attribute 'b' ``` `__getitem__`(*name*)[¶](#namespaces.Namespace.__getitem__ "Permalink to this definition") Get item via bracket-notation. | Parameters: | **name** (*str*) – Key | | Returns: | Associated value | Usage: ``` >>> import namespaces as ns >>> foo = ns.Namespace(a=1) >>> foo['a'] # calls foo.\_\_getitem\_\_('a') 1 ``` `__iter__`()[¶](#namespaces.Namespace.__iter__ "Permalink to this definition") Iterator over item keys. | Returns: | Key iterator | | Return type: | collections.KeysView | Usage: ``` >>> import namespaces as ns >>> foo = ns.Namespace(a=1, b=2, c=3) >>> for key in foo: # calls foo.\_\_iter\_\_() >>> print(key) a b c ``` `__len__`()[¶](#namespaces.Namespace.__len__ "Permalink to this definition") Calculates length of this Namespace. | Returns: | Length of this Namespace | | Return type: | int | Usage: ``` >>> import namespaces as ns >>> foo = ns.Namespace(a=1, b=2, c=3) >>> len(foo) 3 ``` `__setattr__`(*name*, *value*)[¶](#namespaces.Namespace.__setattr__ "Permalink to this definition") Set item via dot-notation. | Parameters: | * **name** (*str*) – Key * **value** – New associated value | Usage: ``` >>> import namespaces as ns >>> foo = ns.Namespace() >>> foo.a = 1 # calls foo.\_\_setattr\_\_('a', 1) >>> foo Namespace(a=1) ``` `__setitem__`(*name*, *value*)[¶](#namespaces.Namespace.__setitem__ "Permalink to this definition") Set item via bracket-notation. | Parameters: | * **name** (*str*) – Key * **value** – New associated value | Usage: ``` >>> import namespaces as ns >>> foo = ns.Namespace() >>> foo['a'] = 1 # calls foo.\_\_setitem\_\_('a', 1) >>> foo Namespace(a=1) ``` #### FrozenNamespace[¶](#frozennamespace "Permalink to this headline") *class* `namespaces.``FrozenNamespace`(*\*args*, *\*\*kwargs*)[¶](#namespaces.FrozenNamespace "Permalink to this definition") Immutable, hashable dictionary whose items are also available via dot-notation. `__getattr__`(*name*)[¶](#namespaces.FrozenNamespace.__getattr__ "Permalink to this definition") Look up item via dot-notation. | Parameters: | **name** (*str*) – Key | | Returns: | Associated value | Usage:: ``` >>> import namespaces as ns >>> foo = ns.FrozenNamespace(a=1) >>> foo.a # calls foo.\_\_getattr\_\_('a') 1 >>> foo.b AttributeError: 'Namespace' object has no attribute 'b' ``` `__getitem__`(*name*)[¶](#namespaces.FrozenNamespace.__getitem__ "Permalink to this definition") Get item via bracket-notation. | Parameters: | **name** (*str*) – Key | | Returns: | Corresponding value | Usage:: ``` >>> import namespaces as ns >>> foo = ns.FrozenNamespace(a=1) >>> foo['a'] # calls foo.\_\_getitem\_\_('a') 1 ``` `__hash__`()[¶](#namespaces.FrozenNamespace.__hash__ "Permalink to this definition") Caches lazily-evaluated hash for performance. | Returns: | Hash value for this FrozenNamespace | | Return type: | int | Usage:: ``` >>> import namespaces as ns >>> foo = ns.FrozenNamespace(a=1) >>> hash(foo) # calls foo.\_\_hash\_\_() -2550060783245333914 ``` `__iter__`()[¶](#namespaces.FrozenNamespace.__iter__ "Permalink to this definition") Iterator over item keys. | Returns: | Key iterator | | Return type: | collections.KeysView | Usage: ``` >>> import namespaces as ns >>> foo = ns.FrozenNamespace(a=1, b=2, c=3) >>> for key in foo: # calls foo.\_\_iter\_\_() ... print(key) a b c ``` `__len__`()[¶](#namespaces.FrozenNamespace.__len__ "Permalink to this definition") Calculates length of this FrozenNamespace | Returns: | Length of this FrozenNamespace | | Return type: | int | Usage: ``` >>> import namespaces as ns >>> foo = ns.FrozenNamespace(a=1, b=2, c=3) >>> len(foo) # calls foo.\_\_len\_\_() 3 ``` #### utils[¶](#module-namespaces.utils "Permalink to this headline") *class* `namespaces.utils.``NamespaceEncoder`(*skipkeys=False*, *ensure\_ascii=True*, *check\_circular=True*, *allow\_nan=True*, *sort\_keys=False*, *indent=None*, *separators=None*, *encoding='utf-8'*, *default=None*)[¶](#namespaces.utils.NamespaceEncoder "Permalink to this definition") Custom JSON encoder that converts both FrozenNamespace and Namespace to a dict. #### Reserved keys[¶](#reserved-keys "Permalink to this headline") `Namespace` reserves the key `\_dict`. `FrozenNamespace` reserves the keys `\_dict` and `\_hash`. In general, any and all reserved keys will be prefixed with an `\_`. Warning If you override the values for the reserved keys, all bets are off. ### Support[¶](#support "Permalink to this headline") If you are in need of assistance, please leave an issue on the namespaces github repository: <https://github.com/pcattori/namespaces/issues> ### Contribute[¶](#contribute "Permalink to this headline") Pull requests welcome! The namespaces github repository can be found [here](https://github.com/pcattori/namespaces). It is recommended that you follow the [Forking Workflow](https://www.atlassian.com/git/tutorials/comparing-workflows/forking-workflow) when contributing. #### Tests[¶](#tests "Permalink to this headline") * pass current tests python setup.py test * codecov * python 2/3 compatibility + caniusepython3 + pylint –py3k + travis w/ py2.7 and py3.5 ### License[¶](#license "Permalink to this headline") The MIT License (MIT) Copyright (c) 2016 Pedro Cattori Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. [](index.html#document-index) ### [Table Of Contents](index.html#document-index) * [Design](index.html#document-design) + [Before & after](index.html#before-after) + [namespaces vs dict](index.html#namespaces-vs-dict) * [namespaces API](index.html#document-api) + [Namespace](index.html#namespace) + [FrozenNamespace](index.html#frozennamespace) + [utils](index.html#module-namespaces.utils) + [Reserved keys](index.html#reserved-keys) * [Support](index.html#document-support) * [Contribute](index.html#document-contribute) + [Tests](index.html#tests) * [License](index.html#document-license) ### Related Topics * [Documentation overview](index.html#document-index) ### Quick search Enter search terms or a module, class or function name. ©2016, Pedro Cattori. | Powered by [Sphinx 1.3.5](http://sphinx-doc.org/) & [Alabaster 0.7.9](https://github.com/bitprophet/alabaster)

lab

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Lab documentation [Lab](index.html#document-index) stable Tutorials * [Lab tutorial](index.html#document-lab.tutorial) * [Downward Lab tutorial](index.html#document-downward.tutorial) How-to Guides * [Frequently asked questions](index.html#document-faq) * [Parse output](index.html#document-lab.parser) * [Run other planners](index.html#document-ff) * [Run Singularity images](index.html#document-singularity) Reference * [`lab.experiment` — Create experiments](index.html#document-lab.experiment) * [`lab.reports` – Make reports](index.html#document-lab.reports) * [`downward.experiment` — Fast Downward experiment](index.html#document-downward.experiment) * [`downward.reports` — Fast Downward reports](index.html#document-downward.reports) General * [Concepts](index.html#document-lab.concepts) * [Changelog](index.html#document-news) [Lab](index.html#document-index) * [Docs](index.html#document-index) » * Lab documentation * [Edit on GitHub](https://github.com/aibasel/lab/blob/b6f80cefe1c3745282cf4432c2989d0a03ddafe3/docs/index.rst) --- Lab and Downward Lab[¶](#lab-and-downward-lab "Permalink to this headline") =========================================================================== **Lab** is a Python package for evaluating solvers on benchmark sets. Experiments can run on a single machine or on a computer cluster. The package also contains code for parsing results and creating reports. The **Downward Lab** Python package facilitates running experiments for the [Fast Downward](http://www.fast-downward.org) planning system. It uses the generic experimentation package **Lab**. Currently, Lab and Downward Lab are distributed together. **Code**: <https://github.com/aibasel/lab> **Documentation**: <https://lab.readthedocs.io> **Cite**: please cite Downward Lab by using ``` @Misc{seipp-et-al-zenodo2017, author = "Jendrik Seipp and Florian Pommerening and Silvan Sievers and Malte Helmert", title = "{Downward} {Lab}", publisher = "Zenodo", year = "2017", howpublished = "\url{https://doi.org/10.5281/zenodo.790461}" } ``` Lab tutorial[¶](#lab-tutorial "Permalink to this headline") ----------------------------------------------------------- Note During [ICAPS 2020](https://icaps20.icaps-conference.org/), we gave an [online talk about Lab and Downward Lab](https://icaps20subpages.icaps-conference.org/tutorials/evaluating-planners-with-downward-lab/) (version 6.2). The first half of the presentation shows how to use Lab to run experiments for a solver. You can find the recording [here](https://www.youtube.com/watch?v=39tIUsxbh9w). ### Install Lab[¶](#install-lab "Permalink to this headline") Lab requires Python 3.7+ and Linux (e.g., Ubuntu). We recommend installing Lab in a [Python virtual environment](https://docs.python.org/3/tutorial/venv.html). This has the advantage that there are no modifications to the system-wide configuration, and that you can create multiple environments with different Lab versions (e.g., for different papers) without conflicts: ``` # Install required packages, including virtualenv. sudo apt install python3 python3-venv # Create a new directory for your experiments. mkdir experiments-for-my-paper cd experiments-for-my-paper # If PYTHONPATH is set, unset it to obtain a clean environment. unset PYTHONPATH # Create and activate a Python virtual environment for Lab. python3 -m venv --prompt my-paper .venv source .venv/bin/activate # Install Lab in the virtual environment. pip install -U pip wheel pip install lab # or preferably a specific version with lab==x.y # Store installed packages and exact versions for reproducibility. # Ignore pkg-resources package (https://github.com/pypa/pip/issues/4022). pip freeze | grep -v "pkg-resources" > requirements.txt ``` Please note that before running an experiment script you need to activate the virtual environment with: ``` source .venv/bin/activate ``` We recommend clearing the `PYTHONPATH` variable before activating the virtual environment. ### Run tutorial experiment[¶](#run-tutorial-experiment "Permalink to this headline") The following script shows a simple experiment that runs a naive vertex cover solver on a set of benchmarks. ../examples/vertex-cover/exp.py[¶](#id2 "Permalink to this code") ``` #! /usr/bin/env python """ Example experiment using a simple vertex cover solver. """ import glob import os import platform from downward.reports.absolute import AbsoluteReport from lab.environments import BaselSlurmEnvironment, LocalEnvironment from lab.experiment import Experiment from lab.reports import Attribute # Create custom report class with suitable info and error attributes. class BaseReport(AbsoluteReport): INFO\_ATTRIBUTES = ["time\_limit", "memory\_limit", "seed"] ERROR\_ATTRIBUTES = [ "domain", "problem", "algorithm", "unexplained\_errors", "error", "node", ] NODE = platform.node() REMOTE = NODE.endswith(".scicore.unibas.ch") or NODE.endswith(".cluster.bc2.ch") SCRIPT\_DIR = os.path.dirname(os.path.abspath(\_\_file\_\_)) BENCHMARKS\_DIR = os.path.join(SCRIPT\_DIR, "benchmarks") BHOSLIB\_GRAPHS = sorted(glob.glob(os.path.join(BENCHMARKS\_DIR, "bhoslib", "\*.mis"))) RANDOM\_GRAPHS = sorted(glob.glob(os.path.join(BENCHMARKS\_DIR, "random", "\*.txt"))) ALGORITHMS = ["2approx", "greedy"] SEED = 2018 TIME\_LIMIT = 1800 MEMORY\_LIMIT = 2048 if REMOTE: ENV = BaselSlurmEnvironment(email="my.name@unibas.ch") SUITE = BHOSLIB\_GRAPHS + RANDOM\_GRAPHS else: ENV = LocalEnvironment(processes=2) # Use smaller suite for local tests. SUITE = BHOSLIB\_GRAPHS[:1] + RANDOM\_GRAPHS[:1] ATTRIBUTES = [ "cover", "cover\_size", "error", "solve\_time", "solver\_exit\_code", Attribute("solved", absolute=True), ] # Create a new experiment. exp = Experiment(environment=ENV) # Add solver to experiment and make it available to all runs. exp.add\_resource("solver", os.path.join(SCRIPT\_DIR, "solver.py")) # Add custom parser. exp.add\_parser("parser.py") for algo in ALGORITHMS: for task in SUITE: run = exp.add\_run() # Create a symbolic link and an alias. This is optional. We # could also use absolute paths in add\_command(). run.add\_resource("task", task, symlink=True) run.add\_command( "solve", ["{solver}", "--seed", str(SEED), "{task}", algo], time\_limit=TIME\_LIMIT, memory\_limit=MEMORY\_LIMIT, ) # AbsoluteReport needs the following attributes: # 'domain', 'problem' and 'algorithm'. domain = os.path.basename(os.path.dirname(task)) task\_name = os.path.basename(task) run.set\_property("domain", domain) run.set\_property("problem", task\_name) run.set\_property("algorithm", algo) # BaseReport needs the following properties: # 'time\_limit', 'memory\_limit', 'seed'. run.set\_property("time\_limit", TIME\_LIMIT) run.set\_property("memory\_limit", MEMORY\_LIMIT) run.set\_property("seed", SEED) # Every run has to have a unique id in the form of a list. run.set\_property("id", [algo, domain, task\_name]) # Add step that writes experiment files to disk. exp.add\_step("build", exp.build) # Add step that executes all runs. exp.add\_step("start", exp.start\_runs) # Add step that collects properties from run directories and # writes them to \*-eval/properties. exp.add\_fetcher(name="fetch") # Make a report. exp.add\_report(BaseReport(attributes=ATTRIBUTES), outfile="report.html") # Parse the commandline and run the given steps. exp.run\_steps() ``` You can see the available steps with ``` ./exp.py ``` Select steps by name or index: ``` ./exp.py build ./exp.py 2 ./exp.py 3 4 ``` Here is the parser that the experiment uses: ../examples/vertex-cover/parser.py[¶](#id3 "Permalink to this code") ``` #! /usr/bin/env python """ Solver example output: Algorithm: 2approx Cover: set([1, 3, 5, 6, 7, 8, 9]) Cover size: 7 Solve time: 0.000771s """ from lab.parser import Parser def solved(content, props): props["solved"] = int("cover" in props) def error(content, props): if props["solved"]: props["error"] = "cover-found" else: props["error"] = "unsolved" if \_\_name\_\_ == "\_\_main\_\_": parser = Parser() parser.add\_pattern( "node", r"node: (.+)\n", type=str, file="driver.log", required=True ) parser.add\_pattern( "solver\_exit\_code", r"solve exit code: (.+)\n", type=int, file="driver.log" ) parser.add\_pattern("cover", r"Cover: (\{.\*\})", type=str) parser.add\_pattern("cover\_size", r"Cover size: (\d+)\n", type=int) parser.add\_pattern("solve\_time", r"Solve time: (.+)s", type=float) parser.add\_function(solved) parser.add\_function(error) parser.parse() ``` Find out how to create your own experiments by browsing the [Lab API](lab.experiment.html). Downward Lab tutorial[¶](#downward-lab-tutorial "Permalink to this headline") ----------------------------------------------------------------------------- This tutorial shows you how to install Downward Lab and how to create a simple experiment for Fast Downward that compares two heuristics, the causal graph (CG) heuristic and the FF heuristic. There are many ways for setting up your experiments. This tutorial gives you an opinionated alternative that has proven to work well in practice. Note During [ICAPS 2020](https://icaps20.icaps-conference.org/), we gave an online [Downward Lab presentation](https://icaps20subpages.icaps-conference.org/tutorials/evaluating-planners-with-downward-lab/) (version 6.2). The second half of the presentation covers this tutorial and you can find the recording [here](https://www.youtube.com/watch?v=39tIUsxbh9w). ### Installation[¶](#installation "Permalink to this headline") Lab requires **Python 3.7+** and **Linux**. To run Fast Downward experiments, you’ll need a **Fast Downward** repository, planning **benchmarks** and a plan **validator**. ``` # Install required packages. sudo apt install bison cmake flex g++ git make python3 python3-venv # Create directory for holding binaries and scripts. mkdir --parents ~/bin # Make directory for all projects related to Fast Downward. mkdir downward-projects cd downward-projects # Install the plan validator VAL. git clone https://github.com/KCL-Planning/VAL.git cd VAL # Newer VAL versions need time stamps, so we use an old version # (https://github.com/KCL-Planning/VAL/issues/46). git checkout a556539 make clean # Remove old binaries. sed -i 's/-Werror //g' Makefile # Ignore warnings. make cp validate ~/bin/ # Add binary to a directory on your ``$PATH``. # Return to projects directory. cd ../ # Download planning tasks. git clone https://github.com/aibasel/downward-benchmarks.git benchmarks # Clone Fast Downward and let it solve an example task. git clone https://github.com/aibasel/downward.git cd downward ./build.py ./fast-downward.py ../benchmarks/grid/prob01.pddl --search "astar(lmcut())" ``` If Fast Downward doesn’t compile, see <http://www.fast-downward.org/ObtainingAndRunningFastDownward> and <http://www.fast-downward.org/LPBuildInstructions>. We now create a new directory for our CG-vs-FF project. By putting it into the Fast Downward repo under `experiments/`, it’s easy to share both the code and experiment scripts with your collaborators. ``` # Create new branch. git checkout -b cg-vs-ff main # Create a new directory for your experiments in Fast Downward repo. cd experiments mkdir cg-vs-ff cd cg-vs-ff ``` Now it’s time to install Lab. We install it in a [Python virtual environment](https://docs.python.org/3/tutorial/venv.html) specific to the cg-vs-ff project. This has the advantage that there are no modifications to the system-wide configuration, and that you can have multiple projects with different Lab versions (e.g., for different papers). ``` # Create and activate a Python 3 virtual environment for Lab. python3 -m venv --prompt cg-vs-ff .venv source .venv/bin/activate # Install Lab in the virtual environment. pip install -U pip wheel # It's good to have new versions of these. pip install lab # or preferably a specific version with lab==x.y # Store installed packages and exact versions for reproducibility. # Ignore pkg-resources package (https://github.com/pypa/pip/issues/4022). pip freeze | grep -v "pkg-resources" > requirements.txt git add requirements.txt git commit -m "Store requirements for experiments." ``` To use the same versions of your requirements on a different computer, use `pip install -r requirements.txt` instead of the `pip install` commands above. Add to your `~/.bashrc` file: ``` # Make executables in ~/bin directory available globally. export PATH="${PATH}:${HOME}/bin" # Some example experiments need these two environment variables. export DOWNWARD\_BENCHMARKS=/path/to/downward-projects/benchmarks # Adapt path export DOWNWARD\_REPO=/path/to/downward-projects/downward # Adapt path ``` Add to your `~/.bash\_aliases` file: ``` # Activate virtualenv and unset PYTHONPATH to obtain isolated virtual environments. alias venv="unset PYTHONPATH; source .venv/bin/activate" ``` Finally, reload `.bashrc` (which usually also reloads `~/.bash\_aliases`): ``` source ~/.bashrc ``` You can activate virtual environments now by running `venv` in directories containing a `.venv` subdirectory. ### Run tutorial experiment[¶](#run-tutorial-experiment "Permalink to this headline") The files below are two experiment scripts, a `project.py` module that bundles common functionality for all experiments related to the project, a parser script, and a script for collecting results and making reports. You can use the files as a basis for your own experiments. They are available in the [Lab repo](https://github.com/aibasel/lab/tree/main/examples/downward). Copy the files into `experiments/my-exp-dir`. Make sure the experiment script and the parser are executable. Then you can see the available steps with ``` ./2020-09-11-A-cg-vs-ff.py ``` Run all steps with ``` ./2020-09-11-A-cg-vs-ff.py --all ``` Run individual steps with ``` ./2020-09-11-A-cg-vs-ff.py build ./2020-09-11-A-cg-vs-ff.py 2 ./2020-09-11-A-cg-vs-ff.py 3 6 7 ``` The `2020-09-11-A-cg-vs-ff.py` script uses the [`downward.experiment.FastDownwardExperiment`](index.html#downward.experiment.FastDownwardExperiment "downward.experiment.FastDownwardExperiment") class, which reduces the amount of code you need to write, but assumes a rigid structure of the experiment: it only allows you to run each added algorithm on each added task, and individual runs cannot be customized. If you need more flexibility, you can employ the [`lab.experiment.Experiment`](index.html#lab.experiment.Experiment "lab.experiment.Experiment") class instead and fill it by using [`FastDownwardAlgorithm`](index.html#downward.experiment.FastDownwardAlgorithm "downward.experiment.FastDownwardAlgorithm"), [`FastDownwardRun`](index.html#downward.experiment.FastDownwardRun "downward.experiment.FastDownwardRun"), [`CachedFastDownwardRevision`](index.html#downward.cached_revision.CachedFastDownwardRevision "downward.cached_revision.CachedFastDownwardRevision"), and [`Task`](index.html#downward.suites.Task "downward.suites.Task") objects. The `2020-09-11-A-cg-vs-ff.py` script shows an example. See the [Downward Lab API](downward.experiment.html) for a reference on all Downward Lab classes. ../examples/downward/2020-09-11-A-cg-vs-ff.py[¶](#id2 "Permalink to this code") ``` #! /usr/bin/env python import os import shutil import project REPO = project.get\_repo\_base() BENCHMARKS\_DIR = os.environ["DOWNWARD\_BENCHMARKS"] SCP\_LOGIN = "myname@myserver.com" REMOTE\_REPOS\_DIR = "/infai/seipp/projects" # If REVISION\_CACHE is None, the default "./data/revision-cache/" is used. REVISION\_CACHE = os.environ.get("DOWNWARD\_REVISION\_CACHE") if project.REMOTE: SUITE = project.SUITE\_SATISFICING ENV = project.BaselSlurmEnvironment(email="my.name@myhost.ch") else: SUITE = ["depot:p01.pddl", "grid:prob01.pddl", "gripper:prob01.pddl"] ENV = project.LocalEnvironment(processes=2) CONFIGS = [ (f"{index:02d}-{h\_nick}", ["--search", f"eager\_greedy([{h}])"]) for index, (h\_nick, h) in enumerate( [ ("cg", "cg(transform=adapt\_costs(one))"), ("ff", "ff(transform=adapt\_costs(one))"), ], start=1, ) ] BUILD\_OPTIONS = [] DRIVER\_OPTIONS = ["--overall-time-limit", "5m"] REV\_NICKS = [ ("main", ""), ] ATTRIBUTES = [ "error", "run\_dir", "search\_start\_time", "search\_start\_memory", "total\_time", "h\_values", "coverage", "expansions", "memory", project.EVALUATIONS\_PER\_TIME, ] exp = project.FastDownwardExperiment(environment=ENV, revision\_cache=REVISION\_CACHE) for config\_nick, config in CONFIGS: for rev, rev\_nick in REV\_NICKS: algo\_name = f"{rev\_nick}:{config\_nick}" if rev\_nick else config\_nick exp.add\_algorithm( algo\_name, REPO, rev, config, build\_options=BUILD\_OPTIONS, driver\_options=DRIVER\_OPTIONS, ) exp.add\_suite(BENCHMARKS\_DIR, SUITE) exp.add\_parser(exp.EXITCODE\_PARSER) exp.add\_parser(exp.TRANSLATOR\_PARSER) exp.add\_parser(exp.SINGLE\_SEARCH\_PARSER) exp.add\_parser(project.DIR / "parser.py") exp.add\_parser(exp.PLANNER\_PARSER) exp.add\_step("build", exp.build) exp.add\_step("start", exp.start\_runs) exp.add\_fetcher(name="fetch") if not project.REMOTE: exp.add\_step("remove-eval-dir", shutil.rmtree, exp.eval\_dir, ignore\_errors=True) project.add\_scp\_step(exp, SCP\_LOGIN, REMOTE\_REPOS\_DIR) project.add\_absolute\_report( exp, attributes=ATTRIBUTES, filter=[project.add\_evaluations\_per\_time] ) attributes = ["expansions"] pairs = [ ("01-cg", "02-ff"), ] suffix = "-rel" if project.RELATIVE else "" for algo1, algo2 in pairs: for attr in attributes: exp.add\_report( project.ScatterPlotReport( relative=project.RELATIVE, get\_category=None if project.TEX else lambda run1, run2: run1["domain"], attributes=[attr], filter\_algorithm=[algo1, algo2], filter=[project.add\_evaluations\_per\_time], format="tex" if project.TEX else "png", ), name=f"{exp.name}-{algo1}-vs-{algo2}-{attr}{suffix}", ) exp.run\_steps() ``` ../examples/downward/2020-09-11-B-bounded-cost.py[¶](#id3 "Permalink to this code") ``` #! /usr/bin/env python import json import os import shutil from downward import suites from downward.cached\_revision import CachedFastDownwardRevision from downward.experiment import FastDownwardAlgorithm, FastDownwardRun from lab.experiment import Experiment import project REPO = project.get\_repo\_base() BENCHMARKS\_DIR = os.environ["DOWNWARD\_BENCHMARKS"] SCP\_LOGIN = "myname@myserver.com" REMOTE\_REPOS\_DIR = "/infai/seipp/projects" BOUNDS\_FILE = "bounds.json" SUITE = ["depot:p01.pddl", "grid:prob01.pddl", "gripper:prob01.pddl"] # If REVISION\_CACHE is None, the default "./data/revision-cache/" is used. REVISION\_CACHE = os.environ.get("DOWNWARD\_REVISION\_CACHE") if project.REMOTE: # ENV = project.BaselSlurmEnvironment(email="my.name@myhost.ch") ENV = project.TetralithEnvironment( email="first.last@liu.se", extra\_options="#SBATCH --account=snic2022-5-341" ) SUITE = project.SUITE\_OPTIMAL\_STRIPS else: ENV = project.LocalEnvironment(processes=2) CONFIGS = [ ("ff", ["--search", "lazy\_greedy([ff()], bound=BOUND)"]), ] BUILD\_OPTIONS = [] DRIVER\_OPTIONS = [ "--validate", "--overall-time-limit", "5m", "--overall-memory-limit", "3584M", ] # Pairs of revision identifier and optional revision nick. REV\_NICKS = [ ("main", ""), ] ATTRIBUTES = [ "error", "run\_dir", "search\_start\_time", "search\_start\_memory", "total\_time", "h\_values", "coverage", "expansions", "memory", project.EVALUATIONS\_PER\_TIME, ] exp = Experiment(environment=ENV) for rev, rev\_nick in REV\_NICKS: cached\_rev = CachedFastDownwardRevision(REVISION\_CACHE, REPO, rev, BUILD\_OPTIONS) cached\_rev.cache() exp.add\_resource("", cached\_rev.path, cached\_rev.get\_relative\_exp\_path()) for config\_nick, config in CONFIGS: algo\_name = f"{rev\_nick}-{config\_nick}" if rev\_nick else config\_nick bounds = {} with open(BOUNDS\_FILE) as f: bounds = json.load(f) for task in suites.build\_suite(BENCHMARKS\_DIR, SUITE): upper\_bound = bounds[f"{task.domain}:{task.problem}"] if upper\_bound is None: upper\_bound = "infinity" config\_with\_bound = config.copy() config\_with\_bound[-1] = config\_with\_bound[-1].replace( "bound=BOUND", f"bound={upper\_bound}" ) algo = FastDownwardAlgorithm( algo\_name, cached\_rev, DRIVER\_OPTIONS, config\_with\_bound, ) run = FastDownwardRun(exp, algo, task) exp.add\_run(run) exp.add\_parser(project.FastDownwardExperiment.EXITCODE\_PARSER) exp.add\_parser(project.FastDownwardExperiment.TRANSLATOR\_PARSER) exp.add\_parser(project.FastDownwardExperiment.SINGLE\_SEARCH\_PARSER) exp.add\_parser(project.DIR / "parser.py") exp.add\_parser(project.FastDownwardExperiment.PLANNER\_PARSER) exp.add\_step("build", exp.build) exp.add\_step("start", exp.start\_runs) exp.add\_fetcher(name="fetch") if not project.REMOTE: exp.add\_step("remove-eval-dir", shutil.rmtree, exp.eval\_dir, ignore\_errors=True) project.add\_scp\_step(exp, SCP\_LOGIN, REMOTE\_REPOS\_DIR) project.add\_absolute\_report( exp, attributes=ATTRIBUTES, filter=[project.add\_evaluations\_per\_time, project.group\_domains], ) exp.run\_steps() ``` ../examples/downward/project.py[¶](#id4 "Permalink to this code") ``` from pathlib import Path import platform import re import subprocess import sys from downward.experiment import FastDownwardExperiment from downward.reports.absolute import AbsoluteReport from downward.reports.scatter import ScatterPlotReport from downward.reports.taskwise import TaskwiseReport from lab.environments import ( BaselSlurmEnvironment, LocalEnvironment, TetralithEnvironment, ) from lab.experiment import ARGPARSER from lab.reports import Attribute, geometric\_mean # Silence import-unused messages. Experiment scripts may use these imports. assert ( BaselSlurmEnvironment and FastDownwardExperiment and LocalEnvironment and ScatterPlotReport and TaskwiseReport and TetralithEnvironment ) DIR = Path(\_\_file\_\_).resolve().parent SCRIPT = Path(sys.argv[0]).resolve() NODE = platform.node() # Cover both the Basel and Linköping clusters for simplicity. REMOTE = NODE.endswith((".scicore.unibas.ch", ".cluster.bc2.ch")) or re.match( r"tetralith\d+\.nsc\.liu\.se|n\d+", NODE ) def parse\_args(): ARGPARSER.add\_argument("--tex", action="store\_true", help="produce LaTeX output") ARGPARSER.add\_argument( "--relative", action="store\_true", help="make relative scatter plots" ) return ARGPARSER.parse\_args() ARGS = parse\_args() TEX = ARGS.tex RELATIVE = ARGS.relative EVALUATIONS\_PER\_TIME = Attribute( "evaluations\_per\_time", min\_wins=False, function=geometric\_mean, digits=1 ) # Generated by "./suites.py satisficing" in aibasel/downward-benchmarks repo. # fmt: off SUITE\_SATISFICING = [ "agricola-sat18-strips", "airport", "assembly", "barman-sat11-strips", "barman-sat14-strips", "blocks", "caldera-sat18-adl", "caldera-split-sat18-adl", "cavediving-14-adl", "childsnack-sat14-strips", "citycar-sat14-adl", "data-network-sat18-strips", "depot", "driverlog", "elevators-sat08-strips", "elevators-sat11-strips", "flashfill-sat18-adl", "floortile-sat11-strips", "floortile-sat14-strips", "freecell", "ged-sat14-strips", "grid", "gripper", "hiking-sat14-strips", "logistics00", "logistics98", "maintenance-sat14-adl", "miconic", "miconic-fulladl", "miconic-simpleadl", "movie", "mprime", "mystery", "nomystery-sat11-strips", "nurikabe-sat18-adl", "openstacks", "openstacks-sat08-adl", "openstacks-sat08-strips", "openstacks-sat11-strips", "openstacks-sat14-strips", "openstacks-strips", "optical-telegraphs", "organic-synthesis-sat18-strips", "organic-synthesis-split-sat18-strips", "parcprinter-08-strips", "parcprinter-sat11-strips", "parking-sat11-strips", "parking-sat14-strips", "pathways", "pegsol-08-strips", "pegsol-sat11-strips", "philosophers", "pipesworld-notankage", "pipesworld-tankage", "psr-large", "psr-middle", "psr-small", "rovers", "satellite", "scanalyzer-08-strips", "scanalyzer-sat11-strips", "schedule", "settlers-sat18-adl", "snake-sat18-strips", "sokoban-sat08-strips", "sokoban-sat11-strips", "spider-sat18-strips", "storage", "termes-sat18-strips", "tetris-sat14-strips", "thoughtful-sat14-strips", "tidybot-sat11-strips", "tpp", "transport-sat08-strips", "transport-sat11-strips", "transport-sat14-strips", "trucks", "trucks-strips", "visitall-sat11-strips", "visitall-sat14-strips", "woodworking-sat08-strips", "woodworking-sat11-strips", "zenotravel", ] SUITE\_OPTIMAL\_STRIPS = [ "agricola-opt18-strips", "airport", "barman-opt11-strips", "barman-opt14-strips", "blocks", "childsnack-opt14-strips", "data-network-opt18-strips", "depot", "driverlog", "elevators-opt08-strips", "elevators-opt11-strips", "floortile-opt11-strips", "floortile-opt14-strips", "freecell", "ged-opt14-strips", "grid", "gripper", "hiking-opt14-strips", "logistics00", "logistics98", "miconic", "movie", "mprime", "mystery", "nomystery-opt11-strips", "openstacks-opt08-strips", "openstacks-opt11-strips", "openstacks-opt14-strips", "openstacks-strips", "organic-synthesis-opt18-strips", "organic-synthesis-split-opt18-strips", "parcprinter-08-strips", "parcprinter-opt11-strips", "parking-opt11-strips", "parking-opt14-strips", "pathways", "pegsol-08-strips", "pegsol-opt11-strips", "petri-net-alignment-opt18-strips", "pipesworld-notankage", "pipesworld-tankage", "psr-small", "rovers", "satellite", "scanalyzer-08-strips", "scanalyzer-opt11-strips", "snake-opt18-strips", "sokoban-opt08-strips", "sokoban-opt11-strips", "spider-opt18-strips", "storage", "termes-opt18-strips", "tetris-opt14-strips", "tidybot-opt11-strips", "tidybot-opt14-strips", "tpp", "transport-opt08-strips", "transport-opt11-strips", "transport-opt14-strips", "trucks-strips", "visitall-opt11-strips", "visitall-opt14-strips", "woodworking-opt08-strips", "woodworking-opt11-strips", "zenotravel", ] DOMAIN\_GROUPS = { "airport": ["airport"], "assembly": ["assembly"], "barman": [ "barman", "barman-opt11-strips", "barman-opt14-strips", "barman-sat11-strips", "barman-sat14-strips"], "blocksworld": ["blocks", "blocksworld"], "cavediving": ["cavediving-14-adl"], "childsnack": ["childsnack-opt14-strips", "childsnack-sat14-strips"], "citycar": ["citycar-opt14-adl", "citycar-sat14-adl"], "depots": ["depot", "depots"], "driverlog": ["driverlog"], "elevators": [ "elevators-opt08-strips", "elevators-opt11-strips", "elevators-sat08-strips", "elevators-sat11-strips"], "floortile": [ "floortile-opt11-strips", "floortile-opt14-strips", "floortile-sat11-strips", "floortile-sat14-strips"], "freecell": ["freecell"], "ged": ["ged-opt14-strips", "ged-sat14-strips"], "grid": ["grid"], "gripper": ["gripper"], "hiking": ["hiking-opt14-strips", "hiking-sat14-strips"], "logistics": ["logistics98", "logistics00"], "maintenance": ["maintenance-opt14-adl", "maintenance-sat14-adl"], "miconic": ["miconic", "miconic-strips"], "miconic-fulladl": ["miconic-fulladl"], "miconic-simpleadl": ["miconic-simpleadl"], "movie": ["movie"], "mprime": ["mprime"], "mystery": ["mystery"], "nomystery": ["nomystery-opt11-strips", "nomystery-sat11-strips"], "openstacks": [ "openstacks", "openstacks-strips", "openstacks-opt08-strips", "openstacks-opt11-strips", "openstacks-opt14-strips", "openstacks-sat08-adl", "openstacks-sat08-strips", "openstacks-sat11-strips", "openstacks-sat14-strips", "openstacks-opt08-adl", "openstacks-sat08-adl"], "optical-telegraphs": ["optical-telegraphs"], "parcprinter": [ "parcprinter-08-strips", "parcprinter-opt11-strips", "parcprinter-sat11-strips"], "parking": [ "parking-opt11-strips", "parking-opt14-strips", "parking-sat11-strips", "parking-sat14-strips"], "pathways": ["pathways"], "pathways-noneg": ["pathways-noneg"], "pegsol": ["pegsol-08-strips", "pegsol-opt11-strips", "pegsol-sat11-strips"], "philosophers": ["philosophers"], "pipes-nt": ["pipesworld-notankage"], "pipes-t": ["pipesworld-tankage"], "psr": ["psr-middle", "psr-large", "psr-small"], "rovers": ["rover", "rovers"], "satellite": ["satellite"], "scanalyzer": [ "scanalyzer-08-strips", "scanalyzer-opt11-strips", "scanalyzer-sat11-strips"], "schedule": ["schedule"], "sokoban": [ "sokoban-opt08-strips", "sokoban-opt11-strips", "sokoban-sat08-strips", "sokoban-sat11-strips"], "storage": ["storage"], "tetris": ["tetris-opt14-strips", "tetris-sat14-strips"], "thoughtful": ["thoughtful-sat14-strips"], "tidybot": [ "tidybot-opt11-strips", "tidybot-opt14-strips", "tidybot-sat11-strips", "tidybot-sat14-strips"], "tpp": ["tpp"], "transport": [ "transport-opt08-strips", "transport-opt11-strips", "transport-opt14-strips", "transport-sat08-strips", "transport-sat11-strips", "transport-sat14-strips"], "trucks": ["trucks", "trucks-strips"], "visitall": [ "visitall-opt11-strips", "visitall-opt14-strips", "visitall-sat11-strips", "visitall-sat14-strips"], "woodworking": [ "woodworking-opt08-strips", "woodworking-opt11-strips", "woodworking-sat08-strips", "woodworking-sat11-strips"], "zenotravel": ["zenotravel"], # IPC 2018: "agricola": ["agricola", "agricola-opt18-strips", "agricola-sat18-strips"], "caldera": ["caldera-opt18-adl", "caldera-sat18-adl"], "caldera-split": ["caldera-split-opt18-adl", "caldera-split-sat18-adl"], "data-network": [ "data-network", "data-network-opt18-strips", "data-network-sat18-strips"], "flashfill": ["flashfill-sat18-adl"], "nurikabe": ["nurikabe-opt18-adl", "nurikabe-sat18-adl"], "organic-split": [ "organic-synthesis-split", "organic-synthesis-split-opt18-strips", "organic-synthesis-split-sat18-strips"], "organic" : [ "organic-synthesis", "organic-synthesis-opt18-strips", "organic-synthesis-sat18-strips"], "petri-net": [ "petri-net-alignment", "petri-net-alignment-opt18-strips", "petri-net-alignment-sat18-strips"], "settlers": ["settlers-opt18-adl", "settlers-sat18-adl"], "snake": ["snake", "snake-opt18-strips", "snake-sat18-strips"], "spider": ["spider", "spider-opt18-strips", "spider-sat18-strips"], "termes": ["termes", "termes-opt18-strips", "termes-sat18-strips"], } # fmt: on DOMAIN\_RENAMINGS = {} for group\_name, domains in DOMAIN\_GROUPS.items(): for domain in domains: DOMAIN\_RENAMINGS[domain] = group\_name for group\_name in DOMAIN\_GROUPS: DOMAIN\_RENAMINGS[group\_name] = group\_name def group\_domains(run): old\_domain = run["domain"] run["domain"] = DOMAIN\_RENAMINGS[old\_domain] run["problem"] = old\_domain + "-" + run["problem"] run["id"][2] = run["problem"] return run def get\_repo\_base() -> Path: """Get base directory of the repository, as an absolute path. Search upwards in the directory tree from the main script until a directory with a subdirectory named ".git" is found. Abort if the repo base cannot be found.""" path = Path(SCRIPT) while path.parent != path: if (path / ".git").is\_dir(): return path path = path.parent sys.exit("repo base could not be found") def remove\_file(path: Path): try: path.unlink() except FileNotFoundError: pass def add\_evaluations\_per\_time(run): evaluations = run.get("evaluations") time = run.get("search\_time") if evaluations is not None and evaluations >= 100 and time: run["evaluations\_per\_time"] = evaluations / time return run def \_get\_exp\_dir\_relative\_to\_repo(): repo\_name = get\_repo\_base().name script = Path(SCRIPT) script\_dir = script.parent rel\_script\_dir = script\_dir.relative\_to(get\_repo\_base()) expname = script.stem return repo\_name / rel\_script\_dir / "data" / expname def add\_scp\_step(exp, login, repos\_dir): remote\_exp = Path(repos\_dir) / \_get\_exp\_dir\_relative\_to\_repo() exp.add\_step( "scp-eval-dir", subprocess.call, [ "scp", "-r", # Copy recursively. "-C", # Compress files. f"{login}:{remote\_exp}-eval", f"{exp.path}-eval", ], ) def fetch\_algorithm(exp, expname, algo, \*, new\_algo=None): """Fetch (and possibly rename) a single algorithm from \*expname\*.""" new\_algo = new\_algo or algo def rename\_and\_filter(run): if run["algorithm"] == algo: run["algorithm"] = new\_algo run["id"][0] = new\_algo return run return False exp.add\_fetcher( f"data/{expname}-eval", filter=rename\_and\_filter, name=f"fetch-{new\_algo}-from-{expname}", merge=True, ) def fetch\_algorithms(exp, expname, \*, algos=None, name=None, filters=None): """ Fetch multiple or all algorithms. """ assert not expname.rstrip("/").endswith("-eval") algos = set(algos or []) filters = filters or [] if algos: def algo\_filter(run): return run["algorithm"] in algos filters.append(algo\_filter) exp.add\_fetcher( f"data/{expname}-eval", filter=filters, name=name or f"fetch-from-{expname}", merge=True, ) def add\_absolute\_report(exp, \*, name=None, outfile=None, \*\*kwargs): report = AbsoluteReport(\*\*kwargs) if name and not outfile: outfile = f"{name}.{report.output\_format}" elif outfile and not name: name = Path(outfile).name elif not name and not outfile: name = f"{exp.name}-abs" outfile = f"{name}.{report.output\_format}" if not Path(outfile).is\_absolute(): outfile = Path(exp.eval\_dir) / outfile exp.add\_report(report, name=name, outfile=outfile) if not REMOTE: exp.add\_step(f"open-{name}", subprocess.call, ["xdg-open", outfile]) exp.add\_step(f"publish-{name}", subprocess.call, ["publish", outfile]) ``` ../examples/downward/parser.py[¶](#id5 "Permalink to this code") ``` #! /usr/bin/env python import logging import re from lab.parser import Parser class CommonParser(Parser): def add\_repeated\_pattern( self, name, regex, file="run.log", required=False, type=int ): def find\_all\_occurences(content, props): matches = re.findall(regex, content) if required and not matches: logging.error(f"Pattern {regex} not found in file {file}") props[name] = [type(m) for m in matches] self.add\_function(find\_all\_occurences, file=file) def add\_bottom\_up\_pattern( self, name, regex, file="run.log", required=False, type=int ): def search\_from\_bottom(content, props): reversed\_content = "\n".join(reversed(content.splitlines())) match = re.search(regex, reversed\_content) if required and not match: logging.error(f"Pattern {regex} not found in file {file}") if match: props[name] = type(match.group(1)) self.add\_function(search\_from\_bottom, file=file) def main(): parser = CommonParser() parser.add\_bottom\_up\_pattern( "search\_start\_time", r"\[t=(.+)s, \d+ KB\] g=0, 1 evaluated, 0 expanded", type=float, ) parser.add\_bottom\_up\_pattern( "search\_start\_memory", r"\[t=.+s, (\d+) KB\] g=0, 1 evaluated, 0 expanded", type=int, ) parser.add\_pattern( "initial\_h\_value", r"f = (\d+) \[1 evaluated, 0 expanded, t=.+s, \d+ KB\]", type=int, ) parser.add\_repeated\_pattern( "h\_values", r"New best heuristic value for .+: (\d+)\n", type=int, ) parser.parse() if \_\_name\_\_ == "\_\_main\_\_": main() ``` ../examples/downward/01-evaluation.py[¶](#id6 "Permalink to this code") ``` #! /usr/bin/env python from pathlib import Path from lab.experiment import Experiment import project ATTRIBUTES = [ "error", "run\_dir", "planner\_time", "initial\_h\_value", "coverage", "cost", "evaluations", "memory", project.EVALUATIONS\_PER\_TIME, ] exp = Experiment() exp.add\_step( "remove-combined-properties", project.remove\_file, Path(exp.eval\_dir) / "properties" ) project.fetch\_algorithm(exp, "2020-09-11-A-cg-vs-ff", "01-cg", new\_algo="cg") project.fetch\_algorithms(exp, "2020-09-11-B-bounded-cost") filters = [project.add\_evaluations\_per\_time] project.add\_absolute\_report( exp, attributes=ATTRIBUTES, filter=filters, name=f"{exp.name}" ) exp.run\_steps() ``` The [Downward Lab API](downward.experiment.html) shows you how to adjust this example to your needs. You may also find the [other example experiments](https://github.com/aibasel/lab/tree/main/examples/) useful. Frequently asked questions[¶](#frequently-asked-questions "Permalink to this headline") --------------------------------------------------------------------------------------- ### How can I parse and report my own attributes?[¶](#how-can-i-parse-and-report-my-own-attributes "Permalink to this headline") See the [parsing documentation](lab.parser.html). ### How can I combine the results from multiple experiments?[¶](#how-can-i-combine-the-results-from-multiple-experiments "Permalink to this headline") ``` exp = Experiment('/new/path/to/combined-results') exp.add\_fetcher('path/to/first/eval/dir') exp.add\_fetcher('path/to/second/eval/dir') exp.add\_fetcher('path/to/experiment/dir') exp.add\_report(AbsoluteReport()) ``` ### Some runs failed. How can I rerun them?[¶](#some-runs-failed-how-can-i-rerun-them "Permalink to this headline") If the failed runs were never started, for example, due to grid node failures, you can simply run the “start” experiment step again. It will skip all runs that have already been started. Afterwards, run “fetch” and make reports as usual. Lab detects which runs have already been started by checking if the `driver.log` file exists. So if you have failed runs that were already started, but you want to rerun them anyway, go to their run directories, remove the `driver.log` files and then run the “start” experiment step again as above. ### I forgot to parse something. How can I run only the parsers again?[¶](#i-forgot-to-parse-something-how-can-i-run-only-the-parsers-again "Permalink to this headline") See the [parsing documentation](lab.parser.html) for how to write parsers. Once you have fixed your existing parsers or added new parsers, add `exp.add\_parse\_again\_step()` to your experiment script `my-exp.py` and then call ``` ./my-exp.py parse-again ``` ### How can I compute a new attribute from multiple runs?[¶](#how-can-i-compute-a-new-attribute-from-multiple-runs "Permalink to this headline") Consider for example the IPC quality score. It is often computed over the list of runs for each task. Since filters only work on individual runs, we can’t compute the score with a single filter, but it is possible by using two filters as shown below: *store\_costs* saves the list of costs per task in a dictionary whereas *add\_quality* uses the stored costs to compute IPC quality scores and adds them to the runs. ``` class QualityFilters: """Compute the IPC quality score. >>> from downward.reports.absolute import AbsoluteReport >>> filters = QualityFilters() >>> report = AbsoluteReport(filter=[filters.store\_costs, filters.add\_quality]) """ def \_\_init\_\_(self): self.tasks\_to\_costs = defaultdict(list) def \_get\_task(self, run): return (run["domain"], run["problem"]) def \_compute\_quality(self, cost, all\_costs): if cost is None: return 0.0 assert all\_costs min\_cost = min(all\_costs) if cost == 0: assert min\_cost == 0 return 1.0 return min\_cost / cost def store\_costs(self, run): cost = run.get("cost") if cost is not None: assert run["coverage"] self.tasks\_to\_costs[self.\_get\_task(run)].append(cost) return True def add\_quality(self, run): run["quality"] = self.\_compute\_quality( run.get("cost"), self.tasks\_to\_costs[self.\_get\_task(run)] ) return run ``` ### How can I make reports and plots for results obtained without Lab?[¶](#how-can-i-make-reports-and-plots-for-results-obtained-without-lab "Permalink to this headline") See [report-external-results.py](https://github.com/aibasel/lab/blob/main/examples/report-external-results.py) for an example. ### Which experiment class should I use for which Fast Downward revision?[¶](#which-experiment-class-should-i-use-for-which-fast-downward-revision "Permalink to this headline") * Before CMake: use DownwardExperiment in Lab 1.x * With CMake and optional validation: use FastDownwardExperiment in Lab 1.x * With CMake and mandatory validation: use FastDownwardExperiment in Lab 2.x * New translator exit codes (issue739): use FastDownwardExperiment in Lab >= 3.x ### How can I contribute to Lab?[¶](#how-can-i-contribute-to-lab "Permalink to this headline") If you’d like to contribute a feature or a bugfix to Lab or Downward Lab, please see [CONTRIBUTING.md](https://github.com/aibasel/lab/blob/main/CONTRIBUTING.md). ### How can I customize Lab?[¶](#how-can-i-customize-lab "Permalink to this headline") Lab tries to be easily customizable. That means that you shouldn’t have to make any changes to the Lab code itself, but rather you should be able to inherit from Lab classes and implement custom behaviour in your subclasses. If this doesn’t work in your case, let’s discuss how we can improve things in a [GitHub issue](https://github.com/aibasel/lab/issues). That said, it can sometimes be easiest to quickly patch Lab. In this case, or when you want to run the latest Lab development version, you can clone the Lab repo and install it (preferable in a virtual environment): ``` git clone https://github.com/aibasel/lab.git /path/to/lab pip install --editable /path/to/lab ``` The `--editable` flag installs the project in “editable mode”, which makes any changes under `/path/to/lab` appear immediately in the virtual environment. ### Which best practices do you recommend for working with Lab?[¶](#which-best-practices-do-you-recommend-for-working-with-lab "Permalink to this headline") * automate as much as possible but not too much * use fixed solver revisions (“3a27ea77f” instead of “main”) * use Python virtual environments * pin versions of all Python dependencies in `requirements.txt` * collect common experiment code in project module * copy experiment scripts for new experiments, don’t change them * make evaluation locally rather than on remote cluster * collect exploratory results from multiple experiments * rerun experiments for camera-ready copy in single experiment and with single code revision Parse output[¶](#module-lab.parser "Permalink to this headline") ---------------------------------------------------------------- A parser can be any program that analyzes files in the run’s directory (e.g. `run.log`) and manipulates the `properties` file in the same directory. To make parsing easier, however, you can use the `Parser` class. Here is an example parser for the FF planner: ../examples/ff/ff-parser.py[¶](#id1 "Permalink to this code") ``` #! /usr/bin/env python """ FF example output: [...] ff: found legal plan as follows step 0: UP F0 F1 1: BOARD F1 P0 2: DOWN F1 F0 3: DEPART F0 P0 time spent: 0.00 seconds instantiating 4 easy, 0 hard action templates 0.00 seconds reachability analysis, yielding 4 facts and 4 actions 0.00 seconds creating final representation with 4 relevant facts 0.00 seconds building connectivity graph 0.00 seconds searching, evaluating 5 states, to a max depth of 2 0.00 seconds total time """ import re from lab.parser import Parser def error(content, props): if props["planner\_exit\_code"] == 0: props["error"] = "plan-found" else: props["error"] = "unsolvable-or-error" def coverage(content, props): props["coverage"] = int(props["planner\_exit\_code"] == 0) def get\_plan(content, props): # All patterns are parsed before functions are called. if props.get("evaluations") is not None: props["plan"] = re.findall(r"^(?:step)?\s\*\d+: (.+)$", content, re.M) def get\_times(content, props): props["times"] = re.findall(r"(\d+\.\d+) seconds", content) def trivially\_unsolvable(content, props): props["trivially\_unsolvable"] = int( "ff: goal can be simplified to FALSE. No plan will solve it" in content ) parser = Parser() parser.add\_pattern("node", r"node: (.+)\n", type=str, file="driver.log", required=True) parser.add\_pattern( "planner\_exit\_code", r"run-planner exit code: (.+)\n", type=int, file="driver.log" ) parser.add\_pattern("evaluations", r"evaluating (\d+) states") parser.add\_function(error) parser.add\_function(coverage) parser.add\_function(get\_plan) parser.add\_function(get\_times) parser.add\_function(trivially\_unsolvable) parser.parse() ``` You can add this parser to all runs by using [`add\_parser()`](index.html#lab.experiment.Experiment.add_parser "lab.experiment.Experiment.add_parser"): ``` >>> from pathlib import Path >>> from lab.experiment import Experiment >>> exp = Experiment() >>> # The path can be absolute or relative to the working directory at build time. >>> parser = Path(\_\_file\_\_).resolve().parents[1] / "examples/ff/ff-parser.py" >>> exp.add\_parser(parser) ``` All added parsers will be run in the order in which they were added after executing the run’s commands. If you need to change your parsers and execute them again, use the [`add\_parse\_again\_step()`](index.html#lab.experiment.Experiment.add_parse_again_step "lab.experiment.Experiment.add_parse_again_step") method to re-parse your results. Run other planners[¶](#run-other-planners "Permalink to this headline") ----------------------------------------------------------------------- The script below shows how to run the [FF planner](http://fai.cs.uni-saarland.de/hoffmann/ff.html) on a number of classical planning benchmarks. You can see the available steps with ``` ./ff.py ``` Select steps by name or index: ``` ./ff.py build ./ff.py 2 ./ff.py 3 4 ``` You can use this file as a basis for your own experiments. For Fast Downward experiments, we recommend taking a look at the <downward.tutorial>. ../examples/ff/ff.py[¶](#id1 "Permalink to this code") ``` #! /usr/bin/env python """ Example experiment for the FF planner (http://fai.cs.uni-saarland.de/hoffmann/ff.html). """ import os import platform from downward import suites from downward.reports.absolute import AbsoluteReport from lab.environments import BaselSlurmEnvironment, LocalEnvironment from lab.experiment import Experiment from lab.reports import Attribute, geometric\_mean # Create custom report class with suitable info and error attributes. class BaseReport(AbsoluteReport): INFO\_ATTRIBUTES = ["time\_limit", "memory\_limit"] ERROR\_ATTRIBUTES = [ "domain", "problem", "algorithm", "unexplained\_errors", "error", "node", ] NODE = platform.node() REMOTE = NODE.endswith(".scicore.unibas.ch") or NODE.endswith(".cluster.bc2.ch") BENCHMARKS\_DIR = os.environ["DOWNWARD\_BENCHMARKS"] if REMOTE: ENV = BaselSlurmEnvironment(email="my.name@unibas.ch") else: ENV = LocalEnvironment(processes=2) SUITE = ["grid", "gripper:prob01.pddl", "miconic:s1-0.pddl", "mystery:prob07.pddl"] ATTRIBUTES = [ "error", "plan", "times", Attribute("coverage", absolute=True, min\_wins=False, scale="linear"), Attribute("evaluations", function=geometric\_mean), Attribute("trivially\_unsolvable", min\_wins=False), ] TIME\_LIMIT = 1800 MEMORY\_LIMIT = 2048 # Create a new experiment. exp = Experiment(environment=ENV) # Add custom parser for FF. exp.add\_parser("ff-parser.py") for task in suites.build\_suite(BENCHMARKS\_DIR, SUITE): run = exp.add\_run() # Create symbolic links and aliases. This is optional. We # could also use absolute paths in add\_command(). run.add\_resource("domain", task.domain\_file, symlink=True) run.add\_resource("problem", task.problem\_file, symlink=True) # 'ff' binary has to be on the PATH. # We could also use exp.add\_resource(). run.add\_command( "run-planner", ["ff", "-o", "{domain}", "-f", "{problem}"], time\_limit=TIME\_LIMIT, memory\_limit=MEMORY\_LIMIT, ) # AbsoluteReport needs the following properties: # 'domain', 'problem', 'algorithm', 'coverage'. run.set\_property("domain", task.domain) run.set\_property("problem", task.problem) run.set\_property("algorithm", "ff") # BaseReport needs the following properties: # 'time\_limit', 'memory\_limit'. run.set\_property("time\_limit", TIME\_LIMIT) run.set\_property("memory\_limit", MEMORY\_LIMIT) # Every run has to have a unique id in the form of a list. # The algorithm name is only really needed when there are # multiple algorithms. run.set\_property("id", ["ff", task.domain, task.problem]) # Add step that writes experiment files to disk. exp.add\_step("build", exp.build) # Add step that executes all runs. exp.add\_step("start", exp.start\_runs) # Add step that collects properties from run directories and # writes them to \*-eval/properties. exp.add\_fetcher(name="fetch") # Make a report. exp.add\_report(BaseReport(attributes=ATTRIBUTES), outfile="report.html") # Parse the commandline and run the specified steps. exp.run\_steps() ``` Here is a simple parser for FF: ../examples/ff/ff-parser.py[¶](#id2 "Permalink to this code") ``` #! /usr/bin/env python """ FF example output: [...] ff: found legal plan as follows step 0: UP F0 F1 1: BOARD F1 P0 2: DOWN F1 F0 3: DEPART F0 P0 time spent: 0.00 seconds instantiating 4 easy, 0 hard action templates 0.00 seconds reachability analysis, yielding 4 facts and 4 actions 0.00 seconds creating final representation with 4 relevant facts 0.00 seconds building connectivity graph 0.00 seconds searching, evaluating 5 states, to a max depth of 2 0.00 seconds total time """ import re from lab.parser import Parser def error(content, props): if props["planner\_exit\_code"] == 0: props["error"] = "plan-found" else: props["error"] = "unsolvable-or-error" def coverage(content, props): props["coverage"] = int(props["planner\_exit\_code"] == 0) def get\_plan(content, props): # All patterns are parsed before functions are called. if props.get("evaluations") is not None: props["plan"] = re.findall(r"^(?:step)?\s\*\d+: (.+)$", content, re.M) def get\_times(content, props): props["times"] = re.findall(r"(\d+\.\d+) seconds", content) def trivially\_unsolvable(content, props): props["trivially\_unsolvable"] = int( "ff: goal can be simplified to FALSE. No plan will solve it" in content ) parser = Parser() parser.add\_pattern("node", r"node: (.+)\n", type=str, file="driver.log", required=True) parser.add\_pattern( "planner\_exit\_code", r"run-planner exit code: (.+)\n", type=int, file="driver.log" ) parser.add\_pattern("evaluations", r"evaluating (\d+) states") parser.add\_function(error) parser.add\_function(coverage) parser.add\_function(get\_plan) parser.add\_function(get\_times) parser.add\_function(trivially\_unsolvable) parser.parse() ``` Run Singularity images[¶](#run-singularity-images "Permalink to this headline") ------------------------------------------------------------------------------- The script below shows how to run Singularity planner images using Downward Lab. ../examples/singularity/singularity-exp.py[¶](#id1 "Permalink to this code") ``` #! /usr/bin/env python """ Example experiment for running Singularity/Apptainer planner images. The time and memory limits set with Lab can be circumvented by solvers that fork child processes. Their resource usage is not checked. If you're running solvers that don't check their resource usage like Fast Downward, we recommend using cgroups or the "runsolver" tool to enforce resource limits. Since setting time limits for solvers with cgroups is difficult, the experiment below uses the ``runsolver`` tool, which has been used in multiple SAT competitions to enforce resource limits. For the experiment to run, the runsolver binary needs to be on the PATH. You can obtain a runsolver copy from https://github.com/jendrikseipp/runsolver. Since Singularity (and Apptainer) reserve 1-2 GiB of \*virtual\* memory when starting the container, we recommend either enforcing a higher virtual memory limit with ``runsolver`` or limiting RSS memory with ``runsolver`` (like below). For limiting RSS memory, you can also use `runlim <https://github.com/arminbiere/runlim>`\_, which is more actively maintained than runsolver. A note on running Singularity on clusters: reading large Singularity files over the network is not optimal, so we recommend copying the images to a local filesystem (e.g., /tmp/) before running experiments. """ import os from pathlib import Path import platform import sys from downward import suites from downward.reports.absolute import AbsoluteReport from lab.environments import BaselSlurmEnvironment, LocalEnvironment from lab.experiment import Experiment # Create custom report class with suitable info and error attributes. class BaseReport(AbsoluteReport): INFO\_ATTRIBUTES = [] ERROR\_ATTRIBUTES = [ "domain", "problem", "algorithm", "unexplained\_errors", "error", "node", ] NODE = platform.node() RUNNING\_ON\_CLUSTER = NODE.endswith((".scicore.unibas.ch", ".cluster.bc2.ch")) DIR = Path(\_\_file\_\_).resolve().parent REPO = DIR.parent IMAGES\_DIR = Path(os.environ["SINGULARITY\_IMAGES"]) assert IMAGES\_DIR.is\_dir(), IMAGES\_DIR BENCHMARKS\_DIR = os.environ["DOWNWARD\_BENCHMARKS"] MEMORY\_LIMIT = 3584 # MiB if RUNNING\_ON\_CLUSTER: SUITE = ["depot", "freecell", "gripper", "zenotravel"] ENVIRONMENT = BaselSlurmEnvironment( partition="infai\_1", email="my.name@unibas.ch", memory\_per\_cpu="3872M", export=["PATH"], setup=BaselSlurmEnvironment.DEFAULT\_SETUP, # Until recently, we had to load the Singularity module here # by adding "module load Singularity/2.6.1 2> /dev/null". ) TIME\_LIMIT = 1800 else: SUITE = ["depot:p01.pddl", "gripper:prob01.pddl", "mystery:prob07.pddl"] ENVIRONMENT = LocalEnvironment(processes=2) TIME\_LIMIT = 5 ATTRIBUTES = [ "cost", "coverage", "error", "g\_values\_over\_time", "run\_dir", "raw\_memory", "runtime", "virtual\_memory", ] exp = Experiment(environment=ENVIRONMENT) exp.add\_step("build", exp.build) exp.add\_step("start", exp.start\_runs) exp.add\_fetcher(name="fetch") exp.add\_parser(DIR / "singularity-parser.py") def get\_image(name): planner = name.replace("-", "\_") image = IMAGES\_DIR / (name + ".img") assert image.is\_file(), image return planner, image IMAGES = [get\_image("fd1906-lama-first")] for planner, image in IMAGES: exp.add\_resource(planner, image, symlink=True) exp.add\_resource("run\_singularity", DIR / "run-singularity.sh") exp.add\_resource("filter\_stderr", DIR / "filter-stderr.py") for planner, \_ in IMAGES: for task in suites.build\_suite(BENCHMARKS\_DIR, SUITE): run = exp.add\_run() run.add\_resource("domain", task.domain\_file, "domain.pddl") run.add\_resource("problem", task.problem\_file, "problem.pddl") # Use runsolver to limit time and memory. It must be on the system # PATH. Important: we cannot use time\_limit and memory\_limit of # Lab's add\_command() because setting the same memory limit with # runsolver again using setrlimit fails. run.add\_command( "run-planner", [ "runsolver", "--cpu-limit", TIME\_LIMIT, "--rss-swap-limit", MEMORY\_LIMIT, "--watcher-data", "watch.log", "--var", "values.log", "{run\_singularity}", f"{{{planner}}}", "{domain}", "{problem}", "sas\_plan", ], ) # Remove temporary files from old Fast Downward versions. run.add\_command("rm-tmp-files", ["rm", "-f", "output.sas", "output"]) run.add\_command("filter-stderr", [sys.executable, "{filter\_stderr}"]) run.set\_property("domain", task.domain) run.set\_property("problem", task.problem) run.set\_property("algorithm", planner) run.set\_property("id", [planner, task.domain, task.problem]) report = Path(exp.eval\_dir) / f"{exp.name}.html" exp.add\_report(BaseReport(attributes=ATTRIBUTES), outfile=report) exp.run\_steps() ``` The experiment script needs a parser and a helper script: ../examples/singularity/singularity-parser.py[¶](#id2 "Permalink to this code") ``` #! /usr/bin/env python import re import sys from lab.parser import Parser def coverage(content, props): props["coverage"] = int("cost" in props) def unsolvable(content, props): # Note that this naive test may easily generate false positives. props["unsolvable"] = int( not props["coverage"] and "Completely explored state space -- no solution!" in content ) def parse\_g\_value\_over\_time(content, props): """Example line: "[g=6, 16 evaluated, 15 expanded, t=0.00328561s, 22300 KB]" """ matches = re.findall( r"\[g=(\d+), \d+ evaluated, \d+ expanded, t=(.+)s, \d+ KB\]\n", content ) props["g\_values\_over\_time"] = [(float(t), int(g)) for g, t in matches] def set\_outcome(content, props): lines = content.splitlines() solved = props["coverage"] unsolvable = props["unsolvable"] out\_of\_time = int("TIMEOUT=true" in lines) out\_of\_memory = int("MEMOUT=true" in lines) # runsolver decides "out of time" based on CPU rather than (cumulated) # WCTIME. if ( not solved and not unsolvable and not out\_of\_time and not out\_of\_memory and props["runtime"] > props["time\_limit"] ): out\_of\_time = 1 # In cases where CPU time is very slightly above the threshold so that # runsolver didn't kill the planner yet and the planner solved a task # just within the limit, runsolver will still record an "out of time". # We remove this record. This case also applies to iterative planners. # If such planners solve the task, we don't treat them as running out # of time. if (solved or unsolvable) and (out\_of\_time or out\_of\_memory): print("task solved however runsolver recorded an out\_of\_\*") print(props) out\_of\_time = 0 out\_of\_memory = 0 if not solved and not unsolvable: props["runtime"] = None if solved ^ unsolvable ^ out\_of\_time ^ out\_of\_memory: if solved: props["error"] = "solved" elif unsolvable: props["error"] = "unsolvable" elif out\_of\_time: props["error"] = "out\_of\_time" elif out\_of\_memory: props["error"] = "out\_of\_memory" else: print(f"unexpected error: {props}", file=sys.stderr) props["error"] = "unexpected-error" def main(): print("Running singularity parser") parser = Parser() parser.add\_pattern( "planner\_exit\_code", r"run-planner exit code: (.+)\n", type=int, file="driver.log", required=True, ) parser.add\_pattern( "node", r"node: (.+)\n", type=str, file="driver.log", required=True ) parser.add\_pattern( "planner\_wall\_clock\_time", r"run-planner wall-clock time: (.+)s", type=float, file="driver.log", required=True, ) parser.add\_pattern("runtime", r"Singularity runtime: (.+?)s", type=float) parser.add\_pattern( "time\_limit", r"Enforcing CPUTime limit \(soft limit, will send " r"SIGTERM then SIGKILL\): (\d+) seconds", type=int, file="watch.log", required=True, ) # Cumulative runtime and virtual memory of the solver and all child processes. parser.add\_pattern( "runtime", r"WCTIME=(.+)", type=float, file="values.log", required=True ) parser.add\_pattern( "virtual\_memory", r"MAXVM=(\d+)", type=int, file="values.log", required=True ) parser.add\_pattern("raw\_memory", r"Peak memory: (\d+) KB", type=int) parser.add\_pattern("cost", r"\nFinal value: (.+)\n", type=int) parser.add\_function(coverage) parser.add\_function(unsolvable) parser.add\_function(parse\_g\_value\_over\_time) parser.add\_function(set\_outcome, file="values.log") parser.parse() if \_\_name\_\_ == "\_\_main\_\_": main() ``` ../examples/singularity/run-singularity.sh[¶](#id3 "Permalink to this code") ``` #!/bin/bash set -euo pipefail if [[ $# != 4 ]]; then echo "usage: $(basename "$0") image domain_file problem_file plan_file" 1>&2 exit 2 fi if [ -f $PWD/$4 ]; then echo "Error: remove $PWD/$4" 1>&2 exit 2 fi # Ensure that strings like "CPU time limit exceeded" and "Killed" are in English. export LANG=C set +e singularity run -C -H "$PWD" "$1" "$PWD/$2" "$PWD/$3" "$4" set -e printf "\nRun VAL\n\n" if [ -f $PWD/$4 ]; then echo "Found plan file." validate -v "$PWD/$2" "$PWD/$3" "$PWD/$4" exit 0 else echo "No plan file." validate -v "$PWD/$2" "$PWD/$3" exit 99 fi ``` [`lab.experiment`](#module-lab.experiment "lab.experiment") — Create experiments[¶](#module-lab.experiment "Permalink to this headline") ---------------------------------------------------------------------------------------------------------------------------------------- ### [`Experiment`](#lab.experiment.Experiment "lab.experiment.Experiment")[¶](#experiment "Permalink to this headline") *class* `lab.experiment.``Experiment`(*path=None*, *environment=None*)[[source]](_modules/lab/experiment.html#Experiment)[¶](#lab.experiment.Experiment "Permalink to this definition") Base class for Lab experiments. See [Concepts](index.html#concepts) for a description of how Lab experiments are structured. The experiment will be built at *path*. It defaults to `<scriptdir>/data/<scriptname>/`. E.g., for the script `experiments/myexp.py`, the default *path* will be `experiments/data/myexp/`. *environment* must be an [Environment](#environments) instance. You can use [`LocalEnvironment`](#lab.environments.LocalEnvironment "lab.environments.LocalEnvironment") to run your experiment on a single computer (default). If you have access to the computer grid in Basel you can use the predefined grid environment [`BaselSlurmEnvironment`](#lab.environments.BaselSlurmEnvironment "lab.environments.BaselSlurmEnvironment"). Alternatively, you can derive your own class from [Environment](#environments). `add_command`(*name*, *command*, *time\_limit=None*, *memory\_limit=None*, *soft\_stdout\_limit=1024*, *hard\_stdout\_limit=10240*, *soft\_stderr\_limit=64*, *hard\_stderr\_limit=10240*, *\*\*kwargs*)[¶](#lab.experiment.Experiment.add_command "Permalink to this definition") Call an executable. If invoked on a *run*, this method adds the command to the **specific** run. If invoked on the experiment, the command is appended to the list of commands of **all** runs. *name* is a string describing the command. It must start with a letter and consist exclusively of letters, numbers, underscores and hyphens. *command* has to be a list of strings where the first item is the executable. After *time\_limit* seconds the signal SIGXCPU is sent to the command. The process can catch this signal and exit gracefully. If it doesn’t catch the SIGXCPU signal, the command is aborted with SIGKILL after five additional seconds. The time spent by a command is the sum of time spent across all threads of the process. The command is aborted with SIGKILL when it uses more than *memory\_limit* MiB. You can limit the log size (in KiB) with a soft and hard limit for both stdout and stderr. When the soft limit is hit, an unexplained error is registered for this run, but the command is allowed to continue running. When the hard limit is hit, the command is killed with SIGTERM. This signal can be caught and handled by the process. By default, there are limits for the log and error output, but time and memory are not restricted. All *kwargs* (except `stdin`) are passed to [subprocess.Popen](http://docs.python.org/library/subprocess.html). Instead of file handles you can also pass filenames for the `stdout` and `stderr` keyword arguments. Specifying the `stdin` kwarg is not supported. ``` >>> exp = Experiment() >>> run = exp.add\_run() >>> # Add commands to a \*specific\* run. >>> run.add\_command("solver", ["mysolver", "input-file"], time\_limit=60) >>> # Add a command to \*all\* runs. >>> exp.add\_command("cleanup", ["rm", "my-temp-file"]) ``` Make sure to call all Python programs from the currently active Python interpreter, i.e., `sys.executable`. Otherwise, the system Python version might be used instead of the Python version from the virtual environment. ``` >>> run.add\_command("myplanner", [sys.executable, "planner.py", "input-file"]) ``` `add_fetcher`(*src=None*, *dest=None*, *merge=None*, *name=None*, *filter=None*, *\*\*kwargs*)[[source]](_modules/lab/experiment.html#Experiment.add_fetcher)[¶](#lab.experiment.Experiment.add_fetcher "Permalink to this definition") Add a step that fetches results from experiment or evaluation directories into a new or existing evaluation directory. You can use this method to combine results from multiple experiments. *src* can be an experiment or evaluation directory. It defaults to `exp.path`. *dest* must be a new or existing evaluation directory. It defaults to `exp.eval\_dir`. If *dest* already contains data and *merge* is set to None, the user will be prompted whether to override the existing data or to merge the old and new data. Setting *merge* to True or to False has the effect that the old data is merged or replaced (and the user will not be prompted). If no *name* is given, call this step “fetch-`basename(src)`”. You can fetch only a subset of runs (e.g., runs for specific domains or algorithms) by passing [`filters`](index.html#lab.reports.Report "lab.reports.Report") with the *filter* argument. Example setup: ``` >>> exp = Experiment("/tmp/exp") ``` Fetch all results and write a single combined properties file to the default evaluation directory (this step is added by default): ``` >>> exp.add\_fetcher(name="fetch") ``` Merge the results from “other-exp” into this experiment’s results: ``` >>> exp.add\_fetcher(src="/path/to/other-exp-eval") ``` Fetch only the runs for certain algorithms: ``` >>> exp.add\_fetcher(filter\_algorithm=["algo\_1", "algo\_5"]) ``` `add_new_file`(*name*, *dest*, *content*, *permissions=420*)[¶](#lab.experiment.Experiment.add_new_file "Permalink to this definition") Write *content* to /path/to/exp-or-run/*dest* and make the new file available to the commands as *name*. *name* is an alias for the resource in commands. It must start with a letter and consist exclusively of letters, numbers and underscores. ``` >>> exp = Experiment() >>> run = exp.add\_run() >>> run.add\_new\_file("learn", "learn.txt", "a = 5; b = 2; c = 5") >>> run.add\_command("print-trainingset", ["cat", "{learn}"]) ``` `add_parse_again_step`()[[source]](_modules/lab/experiment.html#Experiment.add_parse_again_step)[¶](#lab.experiment.Experiment.add_parse_again_step "Permalink to this definition") Add a step that copies the parsers from their originally specified locations to the experiment directory and runs all of them again. This step overwrites the existing properties file in each run dir. Do not forget to run the default fetch step again to overwrite existing data in the -eval dir of the experiment. `add_parser`(*path\_to\_parser*)[[source]](_modules/lab/experiment.html#Experiment.add_parser)[¶](#lab.experiment.Experiment.add_parser "Permalink to this definition") Add a parser to each run of the experiment. Add the parser as a resource to the experiment and add a command that executes the parser to each run. Since commands are executed in the order they are added, parsers should be added after all other commands. If you need to change your parsers and execute them again you can use the [`add\_parse\_again\_step()`](#lab.experiment.Experiment.add_parse_again_step "lab.experiment.Experiment.add_parse_again_step") method. *path\_to\_parser* must be the path to a Python script. The script is executed in the run directory and manipulates the run’s “properties” file. The last part of the filename (without the extension) is used as a resource name. Therefore, it must be unique among all parsers and other resources. Also, it must start with a letter and contain only letters, numbers, underscores and dashes (which are converted to underscores automatically). For information about how to write parsers see [Parser](#parsing). `add_report`(*report*, *name=''*, *eval\_dir=''*, *outfile=''*)[[source]](_modules/lab/experiment.html#Experiment.add_report)[¶](#lab.experiment.Experiment.add_report "Permalink to this definition") Add *report* to the list of experiment steps. This method is a shortcut for `add\_step(name, report, eval\_dir, outfile)` and uses sensible defaults for omitted arguments. If no *name* is given, use *outfile* or the *report*’s class name. By default, use the experiment’s standard *eval\_dir*. If *outfile* is omitted, compose a filename from *name* and the *report*’s format. If *outfile* is a relative path, put it under *eval\_dir*. ``` >>> from downward.reports.absolute import AbsoluteReport >>> exp = Experiment("/tmp/exp") >>> exp.add\_report(AbsoluteReport(attributes=["coverage"])) ``` `add_resource`(*name*, *source*, *dest=''*, *symlink=False*)[¶](#lab.experiment.Experiment.add_resource "Permalink to this definition") Include the file or directory *source* in the experiment or run. *name* is an alias for the resource in commands. It must start with a letter and consist exclusively of letters, numbers and underscores. If you don’t need an alias for the resource, set name=’’. *source* is copied to /path/to/exp-or-run/*dest*. If *dest* is omitted, the last part of the path to *source* will be taken as the destination filename. If you only want an alias for your resource, but don’t want to copy or link it, set *dest* to None. Example: ``` >>> exp = Experiment() >>> exp.add\_resource("planner", "path/to/my-planner") ``` includes my-planner in the experiment directory. You can use `{planner}` to reference my-planner in a run’s commands: ``` >>> run = exp.add\_run() >>> run.add\_resource("domain", "path-to/gripper/domain.pddl") >>> run.add\_resource("task", "path-to/gripper/prob01.pddl") >>> run.add\_command("plan", ["{planner}", "{domain}", "{task}"]) ``` `add_run`(*run=None*)[[source]](_modules/lab/experiment.html#Experiment.add_run)[¶](#lab.experiment.Experiment.add_run "Permalink to this definition") Schedule *run* to be part of the experiment. If *run* is None, create a new run, add it to the experiment and return it. `add_step`(*name*, *function*, *\*args*, *\*\*kwargs*)[[source]](_modules/lab/experiment.html#Experiment.add_step)[¶](#lab.experiment.Experiment.add_step "Permalink to this definition") Add a step to the list of experiment steps. Use this method to add experiment steps like writing the experiment file to disk, removing directories and publishing results. To add fetch and report steps, use the convenience methods [`add\_fetcher()`](#lab.experiment.Experiment.add_fetcher "lab.experiment.Experiment.add_fetcher") and [`add\_report()`](#lab.experiment.Experiment.add_report "lab.experiment.Experiment.add_report"). *name* is a descriptive name for the step. When selecting steps on the command line, you may either use step names or their indices. *function* must be a callable Python object, e.g., a function or a class implementing \_\_call\_\_. *args* and *kwargs* will be passed to *function* when the step is executed. ``` >>> import shutil >>> import subprocess >>> from lab.experiment import Experiment >>> exp = Experiment("/tmp/myexp") >>> exp.add\_step("build", exp.build) >>> exp.add\_step("start", exp.start\_runs) >>> exp.add\_step("rm-eval-dir", shutil.rmtree, exp.eval\_dir) >>> exp.add\_step("greet", subprocess.call, ["echo", "Hello"]) ``` `build`(*write\_to\_disk=True*)[[source]](_modules/lab/experiment.html#Experiment.build)[¶](#lab.experiment.Experiment.build "Permalink to this definition") Finalize the internal data structures, then write all files needed for the experiment to disk. If *write\_to\_disk* is False, only compute the internal data structures. This is only needed on grids for FastDownwardExperiments.build() which turns the added algorithms and benchmarks into Runs. `eval_dir`[¶](#lab.experiment.Experiment.eval_dir "Permalink to this definition") Return the name of the default evaluation directory. This is the directory where the fetched and parsed results will land by default. `name`[¶](#lab.experiment.Experiment.name "Permalink to this definition") Return the directory name of the experiment’s `path`. `run_steps`()[[source]](_modules/lab/experiment.html#Experiment.run_steps)[¶](#lab.experiment.Experiment.run_steps "Permalink to this definition") Parse the commandline and run selected steps. `set_property`(*name*, *value*)[¶](#lab.experiment.Experiment.set_property "Permalink to this definition") Add a key-value property. These can be used later, for example, in reports. ``` >>> exp = Experiment() >>> exp.set\_property("suite", ["gripper", "grid"]) >>> run = exp.add\_run() >>> run.set\_property("domain", "gripper") >>> run.set\_property("problem", "prob01.pddl") ``` Each run must have the property *id* which must be a *unique* list of strings. They determine where the results for this run will land in the combined properties file. ``` >>> run.set\_property("id", ["algo1", "task1"]) >>> run.set\_property("id", ["algo2", "domain1", "problem1"]) ``` `start_runs`()[[source]](_modules/lab/experiment.html#Experiment.start_runs)[¶](#lab.experiment.Experiment.start_runs "Permalink to this definition") Execute all runs that were added to the experiment. Depending on the selected environment this method will start the runs locally or on a computer grid. #### Custom command line arguments[¶](#custom-command-line-arguments "Permalink to this headline") `lab.experiment.``ARGPARSER`[¶](#lab.experiment.ARGPARSER "Permalink to this definition") [ArgumentParser](http://docs.python.org/library/argparse.html) instance that can be used to add custom command line arguments. You can import it, add your arguments and call its `parse\_args()` method to retrieve the argument values. To avoid confusion with step names you shouldn’t use positional arguments. Note Custom command line arguments are only passed to locally executed steps. ``` from lab.experiment import ARGPARSER ARGPARSER.add\_argument( "--test", choices=["yes", "no"], required=True, dest="test\_run", help="run experiment on small suite locally") args = ARGPARSER.parse\_args() if args.test\_run: print "perform test run" else: print "run real experiment" ``` ### [`Run`](#lab.experiment.Run "lab.experiment.Run")[¶](#run "Permalink to this headline") *class* `lab.experiment.``Run`(*experiment*)[[source]](_modules/lab/experiment.html#Run)[¶](#lab.experiment.Run "Permalink to this definition") An experiment consists of multiple runs. There should be one run for each (algorithm, benchmark) pair. A run consists of one or more commands. *experiment* must be an [`Experiment`](#lab.experiment.Experiment "lab.experiment.Experiment") instance. `add_command`(*name*, *command*, *time\_limit=None*, *memory\_limit=None*, *soft\_stdout\_limit=1024*, *hard\_stdout\_limit=10240*, *soft\_stderr\_limit=64*, *hard\_stderr\_limit=10240*, *\*\*kwargs*)[¶](#lab.experiment.Run.add_command "Permalink to this definition") Call an executable. If invoked on a *run*, this method adds the command to the **specific** run. If invoked on the experiment, the command is appended to the list of commands of **all** runs. *name* is a string describing the command. It must start with a letter and consist exclusively of letters, numbers, underscores and hyphens. *command* has to be a list of strings where the first item is the executable. After *time\_limit* seconds the signal SIGXCPU is sent to the command. The process can catch this signal and exit gracefully. If it doesn’t catch the SIGXCPU signal, the command is aborted with SIGKILL after five additional seconds. The time spent by a command is the sum of time spent across all threads of the process. The command is aborted with SIGKILL when it uses more than *memory\_limit* MiB. You can limit the log size (in KiB) with a soft and hard limit for both stdout and stderr. When the soft limit is hit, an unexplained error is registered for this run, but the command is allowed to continue running. When the hard limit is hit, the command is killed with SIGTERM. This signal can be caught and handled by the process. By default, there are limits for the log and error output, but time and memory are not restricted. All *kwargs* (except `stdin`) are passed to [subprocess.Popen](http://docs.python.org/library/subprocess.html). Instead of file handles you can also pass filenames for the `stdout` and `stderr` keyword arguments. Specifying the `stdin` kwarg is not supported. ``` >>> exp = Experiment() >>> run = exp.add\_run() >>> # Add commands to a \*specific\* run. >>> run.add\_command("solver", ["mysolver", "input-file"], time\_limit=60) >>> # Add a command to \*all\* runs. >>> exp.add\_command("cleanup", ["rm", "my-temp-file"]) ``` Make sure to call all Python programs from the currently active Python interpreter, i.e., `sys.executable`. Otherwise, the system Python version might be used instead of the Python version from the virtual environment. ``` >>> run.add\_command("myplanner", [sys.executable, "planner.py", "input-file"]) ``` `add_new_file`(*name*, *dest*, *content*, *permissions=420*)[¶](#lab.experiment.Run.add_new_file "Permalink to this definition") Write *content* to /path/to/exp-or-run/*dest* and make the new file available to the commands as *name*. *name* is an alias for the resource in commands. It must start with a letter and consist exclusively of letters, numbers and underscores. ``` >>> exp = Experiment() >>> run = exp.add\_run() >>> run.add\_new\_file("learn", "learn.txt", "a = 5; b = 2; c = 5") >>> run.add\_command("print-trainingset", ["cat", "{learn}"]) ``` `add_resource`(*name*, *source*, *dest=''*, *symlink=False*)[¶](#lab.experiment.Run.add_resource "Permalink to this definition") Include the file or directory *source* in the experiment or run. *name* is an alias for the resource in commands. It must start with a letter and consist exclusively of letters, numbers and underscores. If you don’t need an alias for the resource, set name=’’. *source* is copied to /path/to/exp-or-run/*dest*. If *dest* is omitted, the last part of the path to *source* will be taken as the destination filename. If you only want an alias for your resource, but don’t want to copy or link it, set *dest* to None. Example: ``` >>> exp = Experiment() >>> exp.add\_resource("planner", "path/to/my-planner") ``` includes my-planner in the experiment directory. You can use `{planner}` to reference my-planner in a run’s commands: ``` >>> run = exp.add\_run() >>> run.add\_resource("domain", "path-to/gripper/domain.pddl") >>> run.add\_resource("task", "path-to/gripper/prob01.pddl") >>> run.add\_command("plan", ["{planner}", "{domain}", "{task}"]) ``` `set_property`(*name*, *value*)[¶](#lab.experiment.Run.set_property "Permalink to this definition") Add a key-value property. These can be used later, for example, in reports. ``` >>> exp = Experiment() >>> exp.set\_property("suite", ["gripper", "grid"]) >>> run = exp.add\_run() >>> run.set\_property("domain", "gripper") >>> run.set\_property("problem", "prob01.pddl") ``` Each run must have the property *id* which must be a *unique* list of strings. They determine where the results for this run will land in the combined properties file. ``` >>> run.set\_property("id", ["algo1", "task1"]) >>> run.set\_property("id", ["algo2", "domain1", "problem1"]) ``` ### `CachedRevision`[¶](#cachedrevision "Permalink to this headline") *class* `lab.cached_revision.``CachedRevision`(*revision\_cache*, *repo*, *rev*, *build\_cmd*, *exclude=None*, *subdir=''*)[[source]](_modules/lab/cached_revision.html#CachedRevision)[¶](#lab.cached_revision.CachedRevision "Permalink to this definition") Cache compiled revisions of a solver for quick reuse. * *revision\_cache*: path to revision cache directory. * *repo*: path to solver repository. * *rev*: solver revision. * *build\_cmd*: list with build script and any build options (e.g., `["./build.py", "release"]`, `["make"]`). Will be executed under *subdir*. * *exclude*: list of relative paths under *subdir* that are not needed for building and running the solver. Instead of this parameter, you can also use a `.gitattributes` file for Git repositories. * *subdir*: relative path from *repo* to solver subdir. The following example caches a Fast Downward revision. When you use the [`FastDownwardExperiment`](index.html#downward.experiment.FastDownwardExperiment "downward.experiment.FastDownwardExperiment") class, you don’t need to cache revisions yourself since the class will do it transparently for you. ``` >>> import os >>> repo = os.environ["DOWNWARD\_REPO"] >>> revision\_cache = os.environ.get("DOWNWARD\_REVISION\_CACHE") >>> if revision\_cache: ... rev = "main" ... cr = CachedRevision( ... revision\_cache, repo, rev, ["./build.py"], exclude=["experiments"] ... ) ... # cr.cache() # Uncomment to actually cache the code. ... ``` You can now copy the cached repo to your experiment: ``` ... from lab.experiment import Experiment ... exp = Experiment() ... dest\_path = os.path.join(exp.path, f"code-{cr.name}") ... exp.add\_resource(f"solver\_{cr.name}", cr.path, dest\_path) ``` `cache`()[[source]](_modules/lab/cached_revision.html#CachedRevision.cache)[¶](#lab.cached_revision.CachedRevision.cache "Permalink to this definition") Check out the solver revision to *self.path* and compile the solver. `get_relative_exp_path`(*relpath=''*)[[source]](_modules/lab/cached_revision.html#CachedRevision.get_relative_exp_path)[¶](#lab.cached_revision.CachedRevision.get_relative_exp_path "Permalink to this definition") Return a path relative to the experiment directory. Use this function to find out where files from the cache will be put in the experiment directory. ### `Parser`[¶](#parser "Permalink to this headline") *class* `lab.parser.``Parser`[[source]](_modules/lab/parser.html#Parser)[¶](#lab.parser.Parser "Permalink to this definition") Parse files in the current directory and write results into the run’s `properties` file. `add_function`(*function*, *file='run.log'*)[[source]](_modules/lab/parser.html#Parser.add_function)[¶](#lab.parser.Parser.add_function "Permalink to this definition") Call `function(open(file).read(), properties)` during parsing. Functions are applied **after** all patterns have been evaluated and in the order in which they are added to the parser. The function is passed the file contents and the properties dictionary. It must manipulate the passed properties dictionary. The return value is ignored. Example: ``` >>> import re >>> from lab.parser import Parser >>> def parse\_states\_over\_time(content, props): ... matches = re.findall(r"(.+)s: (\d+) states\n", content) ... props["states\_over\_time"] = [(float(t), int(s)) for t, s in matches] ... >>> parser = Parser() >>> parser.add\_function(parse\_states\_over\_time) ``` You can use `props.add\_unexplained\_error("message")` when your parsing function detects that something went wrong during the run. `add_pattern`(*attribute*, *regex*, *file='run.log'*, *type=<class 'int'>*, *flags=''*, *required=False*)[[source]](_modules/lab/parser.html#Parser.add_pattern)[¶](#lab.parser.Parser.add_pattern "Permalink to this definition") Look for *regex* in *file*, cast what is found in brackets to *type* and store it in the properties dictionary under *attribute*. During parsing roughly the following code will be executed: ``` contents = open(file).read() match = re.compile(regex).search(contents) properties[attribute] = type(match.group(1)) ``` *flags* must be a string of Python regular expression flags (see <https://docs.python.org/3/library/re.html>). E.g., `flags="M"` lets “^” and “$” match at the beginning and end of each line, respectively. If *required* is True and the pattern is not found in *file*, an error message is printed to stderr. ``` >>> parser = Parser() >>> parser.add\_pattern("facts", r"Facts: (\d+)", type=int) ``` `parse`()[[source]](_modules/lab/parser.html#Parser.parse)[¶](#lab.parser.Parser.parse "Permalink to this definition") Search all patterns and apply all functions. The found values are written to the run’s `properties` file. ### `Environment`[¶](#environment "Permalink to this headline") *class* `lab.environments.``Environment`(*randomize\_task\_order=True*)[[source]](_modules/lab/environments.html#Environment)[¶](#lab.environments.Environment "Permalink to this definition") Abstract base class for all environments. If *randomize\_task\_order* is True (default), tasks for runs are started in a random order. This is useful to avoid systematic noise due to, e.g., one of the algorithms being run on a machine with heavy load. Note that due to the randomization, run directories may be pristine while the experiment is running even though the logs say the runs are finished. *class* `lab.environments.``LocalEnvironment`(*processes=None*, *\*\*kwargs*)[[source]](_modules/lab/environments.html#LocalEnvironment)[¶](#lab.environments.LocalEnvironment "Permalink to this definition") Environment for running experiments locally on a single machine. If given, *processes* must be between 1 and #CPUs. If omitted, it will be set to #CPUs. See [`Environment`](#lab.environments.Environment "lab.environments.Environment") for inherited parameters. *class* `lab.environments.``SlurmEnvironment`(*email=None*, *extra\_options=None*, *partition=None*, *qos=None*, *time\_limit\_per\_task=None*, *memory\_per\_cpu=None*, *cpus\_per\_task=1*, *export=None*, *setup=None*, *\*\*kwargs*)[[source]](_modules/lab/environments.html#SlurmEnvironment)[¶](#lab.environments.SlurmEnvironment "Permalink to this definition") Abstract base class for Slurm environments. If the main experiment step is part of the selected steps, the selected steps are submitted to Slurm. Otherwise, the selected steps are run locally. Note If the steps are run by Slurm, this class writes job files to the directory `<exppath>-grid-steps` and makes them depend on one another. Please inspect the \*.log and \*.err files in this directory if something goes wrong. Since the job files call the experiment script during execution, it mustn’t be changed during the experiment. If *email* is provided and the steps run on the grid, a message will be sent when the last experiment step finishes. Use *extra\_options* to pass additional options. The *extra\_options* string may contain newlines. The first example below uses only a given set of nodes (additional nodes will be used if the given ones don’t satisfy the resource constraints). The second example shows show to specify a project account (needed on NSC if you’re part of multiple projects). ``` extra\_options="#SBATCH --nodelist=ase[1-5,7,10]" extra\_options="#SBATCH --account=snic2021-5-330" ``` *partition* must be a valid Slurm partition name. In Basel you can choose from * “infai\_1”: 24 nodes with 16 cores, 64GB memory, 500GB Sata (default) * “infai\_2”: 24 nodes with 20 cores, 128GB memory, 240GB SSD *qos* must be a valid Slurm QOS name. In Basel this must be “normal”. *time\_limit\_per\_task* sets the wall-clock time limit for each Slurm task. The BaselSlurmEnvironment subclass uses a default of “0”, i.e., no limit. (Note that there may still be an external limit set in slurm.conf.) The TetralithEnvironment class uses a default of “24:00:00”, i.e., 24 hours. This is because in certain situations, the scheduler prefers to schedule tasks shorter than 24 hours. *memory\_per\_cpu* must be a string specifying the memory allocated for each core. The string must end with one of the letters K, M or G. The default is “3872M”. The value for *memory\_per\_cpu* should not surpass the amount of memory that is available per core, which is “3872M” for infai\_1 and “6354M” for infai\_2. Processes that surpass the *memory\_per\_cpu* limit are terminated with SIGKILL. To impose a soft limit that can be caught from within your programs, you can use the `memory\_limit` kwarg of [`add\_command()`](#lab.experiment.Run.add_command "lab.experiment.Run.add_command"). Fast Downward users should set memory limits via the `driver\_options`. Slurm limits the memory with cgroups. Unfortunately, this often fails on our nodes, so we set our own soft memory limit for all Slurm jobs. We derive the soft memory limit by multiplying the value denoted by the *memory\_per\_cpu* parameter with 0.98 (the Slurm config file contains “AllowedRAMSpace=99” and we add some slack). We use a soft instead of a hard limit so that child processes can raise the limit. *cpus\_per\_task* sets the number of cores to be allocated per Slurm task (default: 1). Examples that reserve the maximum amount of memory available per core: ``` >>> env1 = BaselSlurmEnvironment(partition="infai\_1", memory\_per\_cpu="3872M") >>> env2 = BaselSlurmEnvironment(partition="infai\_2", memory\_per\_cpu="6354M") ``` Example that reserves 12 GiB of memory on infai\_1: ``` >>> # 12 \* 1024 / 3872 = 3.17 -> round to next int -> 4 cores per task >>> # 12G / 4 = 3G per core >>> env = BaselSlurmEnvironment( ... partition="infai\_1", ... memory\_per\_cpu="3G", ... cpus\_per\_task=4, ... ) ``` Example that reserves 12 GiB of memory on infai\_2: ``` >>> # 12 \* 1024 / 6354 = 1.93 -> round to next int -> 2 cores per task >>> # 12G / 2 = 6G per core >>> env = BaselSlurmEnvironment( ... partition="infai\_2", ... memory\_per\_cpu="6G", ... cpus\_per\_task=2, ... ) ``` Use *export* to specify a list of environment variables that should be exported from the login node to the compute nodes (default: [“PATH”]). You can alter the environment in which the experiment runs with the *setup* argument. If given, it must be a string of Bash commands. Example: ``` # Load Singularity module. setup="module load Singularity/2.6.1 2> /dev/null" ``` Slurm limits the number of job array tasks. You must set the appropriate value for your cluster in the *MAX\_TASKS* class variable. Lab groups ceil(runs/MAX\_TASKS) runs in one array task. See [`Environment`](#lab.environments.Environment "lab.environments.Environment") for inherited parameters. *class* `lab.environments.``BaselSlurmEnvironment`(*email=None*, *extra\_options=None*, *partition=None*, *qos=None*, *time\_limit\_per\_task=None*, *memory\_per\_cpu=None*, *cpus\_per\_task=1*, *export=None*, *setup=None*, *\*\*kwargs*)[[source]](_modules/lab/environments.html#BaselSlurmEnvironment)[¶](#lab.environments.BaselSlurmEnvironment "Permalink to this definition") Environment for Basel’s AI group. *class* `lab.environments.``TetralithEnvironment`(*email=None*, *extra\_options=None*, *partition=None*, *qos=None*, *time\_limit\_per\_task=None*, *memory\_per\_cpu=None*, *cpus\_per\_task=1*, *export=None*, *setup=None*, *\*\*kwargs*)[[source]](_modules/lab/environments.html#TetralithEnvironment)[¶](#lab.environments.TetralithEnvironment "Permalink to this definition") Environment for the NSC Tetralith cluster in Linköping. ### Various[¶](#various "Permalink to this headline") `lab.``__version__`[¶](#lab.__version__ "Permalink to this definition") Lab version number. A “+” is appended to all non-tagged revisions. `lab.reports` – Make reports[¶](#lab-reports-make-reports "Permalink to this headline") --------------------------------------------------------------------------------------- `lab.reports.``arithmetic_mean`(*values*)[[source]](_modules/lab/reports.html#arithmetic_mean)[¶](#lab.reports.arithmetic_mean "Permalink to this definition") Compute the arithmetic mean of a sequence of numbers. ``` >>> arithmetic\_mean([20, 30, 70]) 40.0 ``` `lab.reports.``geometric_mean`(*values*)[[source]](_modules/lab/reports.html#geometric_mean)[¶](#lab.reports.geometric_mean "Permalink to this definition") Compute the geometric mean of a sequence of numbers. ``` >>> round(geometric\_mean([2, 8]), 2) 4.0 ``` *class* `lab.reports.``Attribute`(*name*, *absolute=False*, *min\_wins=True*, *function=None*, *functions=None*, *scale=None*, *digits=2*)[[source]](_modules/lab/reports.html#Attribute)[¶](#lab.reports.Attribute "Permalink to this definition") A string subclass for attributes in reports. Use this class if your **custom** attribute needs a non-default value for: * *absolute*: if False, only include tasks for which all task runs have values in a per-domain table (e.g. `coverage` is absolute, whereas `expansions` is not, because we can’t compare algorithms A and B for task X if B has no value for `expansions`). * *min\_wins*: set to True if a smaller value for this attribute is better, to False if a higher value is better and to None if values can’t be compared. (E.g., *min\_wins* is False for `coverage`, but it is True for `expansions`). * *function*: the function used to aggregate values of multiple runs for this attribute, for example, in domain reports. Defaults to `sum()`. * *functions*: deprecated. Pass a single *function* instead. * *scale*: default scaling. Can be one of “linear”, “log” and “symlog”. If *scale* is None (default), the reports will choose the scaling. * *digits*: number of digits after the decimal point. The `downward` package automatically uses appropriate settings for most attributes. ``` >>> avg\_h = Attribute("avg\_h", min\_wins=False) >>> abstraction\_done = Attribute( ... "abstraction\_done", absolute=True, min\_wins=False ... ) ``` *class* `lab.reports.``Report`(*attributes=None*, *format='html'*, *filter=None*, *\*\*kwargs*)[[source]](_modules/lab/reports.html#Report)[¶](#lab.reports.Report "Permalink to this definition") Base class for all reports. Inherit from this or a child class to implement a custom report. Depending on the type of output you want to make, you will have to overwrite the [`write()`](#lab.reports.Report.write "lab.reports.Report.write"), [`get\_text()`](#lab.reports.Report.get_text "lab.reports.Report.get_text") or [`get\_markup()`](#lab.reports.Report.get_markup "lab.reports.Report.get_markup") method. *attributes* is the list of attributes you want to include in your report. If omitted, use all numerical attributes. Globbing characters \* and ? are allowed. Example: ``` >>> report = Report(attributes=["coverage", "translator\_\*"]) ``` When a report is made, both the available and the selected attributes are printed on the commandline. *format* can be one of e.g. html, tex, wiki (MediaWiki), doku (DokuWiki), pmw (PmWiki), moin (MoinMoin) and txt (Plain text). Subclasses may allow additional formats. If given, *filter* must be a function or a list of functions that are passed a dictionary of a run’s attribute keys and values. Filters must return True, False or a new dictionary. Depending on the returned value, the run is included or excluded from the report, or replaced by the new dictionary, respectively. Filters for properties can be given in shorter form without defining a function. To include only runs where attribute `foo` has value v, use `filter\_foo=v`. To include only runs where attribute `foo` has value v1, v2 or v3, use `filter\_foo=[v1, v2, v3]`. Filters are applied sequentially, i.e., the first filter is applied to all runs before the second filter is executed. Filters given as `filter\_\*` kwargs are applied *after* all filters passed via the `filter` kwarg. Examples: Include only the “cost” attribute in a LaTeX report: ``` >>> report = Report(attributes=["cost"], format="tex") ``` Only include successful runs in the report: ``` >>> report = Report(filter\_coverage=1) ``` Only include runs in the report where the initial h value is at most 100: ``` >>> def low\_init\_h(run): ... return run["initial\_h\_value"] <= 100 ... >>> report = Report(filter=low\_init\_h) ``` Only include runs from “blocks” and “barman” with a timeout: ``` >>> report = Report(filter\_domain=["blocks", "barman"], filter\_search\_timeout=1) ``` Add a new attribute: ``` >>> def add\_expansions\_per\_time(run): ... expansions = run.get("expansions") ... time = run.get("search\_time") ... if expansions is not None and time: ... run["expansions\_per\_time"] = expansions / time ... return run ... >>> report = Report( ... attributes=["expansions\_per\_time"], filter=[add\_expansions\_per\_time] ... ) ``` Rename, filter and sort algorithms: ``` >>> def rename\_algorithms(run): ... name = run["algorithm"] ... paper\_names = {"lama11": "LAMA 2011", "fdss\_sat1": "FDSS 1"} ... run["algorithm"] = paper\_names[name] ... return run ... ``` ``` >>> # We want LAMA 2011 to be the leftmost column. >>> # filter\_\* filters are evaluated last, so we use the updated >>> # algorithm names here. >>> algorithms = ["LAMA 2011", "FDSS 1"] >>> report = Report(filter=rename\_algorithms, filter\_algorithm=algorithms) ``` `__call__`(*eval\_dir*, *outfile*)[[source]](_modules/lab/reports.html#Report.__call__)[¶](#lab.reports.Report.__call__ "Permalink to this definition") Make the report. This method is called automatically when the report step is executed. It loads the data and calls [`write()`](#lab.reports.Report.write "lab.reports.Report.write"). *eval\_dir* must be a path to an evaluation directory containing a `properties` file. The report will be written to *outfile*. `get_markup`()[[source]](_modules/lab/reports.html#Report.get_markup)[¶](#lab.reports.Report.get_markup "Permalink to this definition") Return [txt2tags](http://txt2tags.org/) markup for the report. `get_text`()[[source]](_modules/lab/reports.html#Report.get_text)[¶](#lab.reports.Report.get_text "Permalink to this definition") Return text (e.g., HTML, LaTeX, etc.) for the report. By default this method calls [`get\_markup()`](#lab.reports.Report.get_markup "lab.reports.Report.get_markup") and converts the markup to the desired output *format*. `write`()[[source]](_modules/lab/reports.html#Report.write)[¶](#lab.reports.Report.write "Permalink to this definition") Write the report files. By default this method calls [`get\_text()`](#lab.reports.Report.get_text "lab.reports.Report.get_text") and writes the obtained text to *outfile*. Overwrite this method if you want to write the report file(s) directly. You should write them to *self.outfile*. *class* `lab.reports.filter.``FilterReport`(*\*\*kwargs*)[[source]](_modules/lab/reports/filter.html#FilterReport)[¶](#lab.reports.filter.FilterReport "Permalink to this definition") Filter properties files. This report only applies the given filter and writes a new properties file to the given output destination. ``` >>> def remove\_openstacks(run): ... return "openstacks" not in run["domain"] ... ``` ``` >>> from lab.experiment import Experiment >>> report = FilterReport(filter=remove\_openstacks) >>> exp = Experiment() >>> exp.add\_report(report, outfile="path/to/new/properties") ``` `downward.experiment` — Fast Downward experiment[¶](#downward-experiment-fast-downward-experiment "Permalink to this headline") ------------------------------------------------------------------------------------------------------------------------------- Note The [`FastDownwardExperiment`](#downward.experiment.FastDownwardExperiment "downward.experiment.FastDownwardExperiment") class makes it easy to write “standard” experiments with little boilerplate code, but it assumes a rigid experiment structure: it only allows you to run each added algorithm on each added task, and individual runs cannot easily be customized. An example for this is the [2020-09-11-A-cg-vs-ff.py](https://github.com/aibasel/lab/tree/main/examples/downward/2020-09-11-A-cg-vs-ff.py) experiment. If you need more flexibility, you can use the [`lab.experiment.Experiment`](index.html#lab.experiment.Experiment "lab.experiment.Experiment") class instead and fill it by using [`FastDownwardAlgorithm`](#downward.experiment.FastDownwardAlgorithm "downward.experiment.FastDownwardAlgorithm"), [`FastDownwardRun`](#downward.experiment.FastDownwardRun "downward.experiment.FastDownwardRun"), [`CachedFastDownwardRevision`](#downward.cached_revision.CachedFastDownwardRevision "downward.cached_revision.CachedFastDownwardRevision"), and [`Task`](#downward.suites.Task "downward.suites.Task") objects. The [2020-09-11-B-bounded-cost.py](https://github.com/aibasel/lab/tree/main/examples/downward/2020-09-11-B-bounded-cost.py) script shows an example. All of these classes are documented below. *class* `downward.experiment.``FastDownwardExperiment`(*path=None*, *environment=None*, *revision\_cache=None*)[[source]](_modules/downward/experiment.html#FastDownwardExperiment)[¶](#downward.experiment.FastDownwardExperiment "Permalink to this definition") Conduct a Fast Downward experiment. The most important methods for customizing an experiment are [`add\_algorithm()`](#downward.experiment.FastDownwardExperiment.add_algorithm "downward.experiment.FastDownwardExperiment.add_algorithm"), [`add\_suite()`](#downward.experiment.FastDownwardExperiment.add_suite "downward.experiment.FastDownwardExperiment.add_suite"), [`add\_parser()`](index.html#lab.experiment.Experiment.add_parser "lab.experiment.Experiment.add_parser"), [`add\_step()`](index.html#lab.experiment.Experiment.add_step "lab.experiment.Experiment.add_step") and [`add\_report()`](index.html#lab.experiment.Experiment.add_report "lab.experiment.Experiment.add_report"). Note To build the experiment, execute its runs and fetch the results, add the following steps: ``` >>> exp = FastDownwardExperiment() >>> exp.add\_step("build", exp.build) >>> exp.add\_step("start", exp.start\_runs) >>> exp.add\_fetcher(name="fetch") ``` See [`lab.experiment.Experiment`](index.html#lab.experiment.Experiment "lab.experiment.Experiment") for an explanation of the *path* and *environment* parameters. *revision\_cache* is the directory for caching Fast Downward revisions. It defaults to `<scriptdir>/data/revision-cache`. This directory can become very large since each revision uses about 30 MB. ``` >>> from lab.environments import BaselSlurmEnvironment >>> env = BaselSlurmEnvironment(email="my.name@unibas.ch") >>> exp = FastDownwardExperiment(environment=env) ``` You can add parsers with [`add\_parser()`](index.html#lab.experiment.Experiment.add_parser "lab.experiment.Experiment.add_parser"). See [Parser](index.html#parsing) for how to write custom parsers and [Bundled parsers](#downward-parsers) for the list of built-in parsers. Which parsers you should use depends on the algorithms you’re running. For single-search experiments, we recommend adding the following parsers in this order: ``` >>> exp.add\_parser(exp.EXITCODE\_PARSER) >>> exp.add\_parser(exp.TRANSLATOR\_PARSER) >>> exp.add\_parser(exp.SINGLE\_SEARCH\_PARSER) >>> exp.add\_parser(exp.PLANNER\_PARSER) ``` `add_algorithm`(*name*, *repo*, *rev*, *component\_options*, *build\_options=None*, *driver\_options=None*)[[source]](_modules/downward/experiment.html#FastDownwardExperiment.add_algorithm)[¶](#downward.experiment.FastDownwardExperiment.add_algorithm "Permalink to this definition") Add a Fast Downward algorithm to the experiment, i.e., a planner configuration in a given repository at a given revision. *name* is a string describing the algorithm (e.g. `"issue123-lmcut"`). *repo* must be a path to a Fast Downward repository. *rev* must be a valid revision in the given repository (e.g., `"e9c2370e6"`, `"my-branch"`, `"issue123"`). *component\_options* must be a list of strings. By default these options are passed to the search component. Use `"--translate-options"`, `"--preprocess-options"` or `"--search-options"` within the component options to override the default for the following options, until overridden again. If given, *build\_options* must be a list of strings. They will be passed to the `build.py` script. Options can be build names (e.g., `"releasenolp"`), `build.py` options (e.g., `"--debug"`) or options for Make. If *build\_options* is omitted, the `"release"` version is built. If given, *driver\_options* must be a list of strings. They will be passed to the `fast-downward.py` script. See `fast-downward.py --help` for available options. The list is always prepended with `["--validate", "--overall-time-limit", "30m", "--overall-memory-limit', "3584M"]`. Specifying custom limits overrides the default limits. Example experiment setup: ``` >>> import os >>> exp = FastDownwardExperiment() >>> repo = os.environ["DOWNWARD\_REPO"] >>> rev = "main" ``` Run iPDB using the latest revision on the main branch: ``` >>> exp.add\_algorithm("ipdb", repo, rev, ["--search", "astar(ipdb())"]) ``` Run blind search in debug mode: ``` >>> exp.add\_algorithm( ... "blind", ... repo, ... rev, ... ["--search", "astar(blind())"], ... build\_options=["--debug"], ... driver\_options=["--debug"], ... ) ``` Run LAMA-2011 with custom planner time limit: ``` >>> exp.add\_algorithm( ... "lama", ... repo, ... rev, ... [], ... driver\_options=[ ... "--alias", ... "seq-saq-lama-2011", ... "--overall-time-limit", ... "5m", ... ], ... ) ``` `add_suite`(*benchmarks\_dir*, *suite*)[[source]](_modules/downward/experiment.html#FastDownwardExperiment.add_suite)[¶](#downward.experiment.FastDownwardExperiment.add_suite "Permalink to this definition") Add PDDL or SAS+ benchmarks to the experiment. *benchmarks\_dir* must be a path to a benchmark directory. It must contain domain directories, which in turn hold PDDL or SAS+ files (ending with “.pddl” or “.sas”). *suite* must be a list of domain or domain:task names. ``` >>> benchmarks\_dir = os.environ["DOWNWARD\_BENCHMARKS"] >>> exp = FastDownwardExperiment() >>> exp.add\_suite(benchmarks\_dir, ["depot", "gripper"]) >>> exp.add\_suite(benchmarks\_dir, ["gripper:prob01.pddl"]) >>> exp.add\_suite(benchmarks\_dir, ["rubiks-cube:p01.sas"]) ``` One source for benchmarks is <https://github.com/aibasel/downward-benchmarks>. After cloning the repo, you can generate suites with the `suites.py` script. We recommend using the suite `optimal\_strips` for optimal STRIPS planners and `satisficing` for satisficing planners: ``` # Create standard optimal planning suite. $ path/to/downward-benchmarks/suites.py optimal\_strips ['airport', ..., 'zenotravel'] ``` Then you can copy the generated list into your experiment script: ``` >>> exp.add\_suite(benchmarks\_dir, ["airport", "zenotravel"]) ``` ### Bundled parsers[¶](#bundled-parsers "Permalink to this headline") The following constants are paths to default parsers that can be passed to [`exp.add\_parser()`](index.html#lab.experiment.Experiment.add_parser "lab.experiment.Experiment.add_parser"). The “Used attributes” and “Parsed attributes” lists describe the dependencies between the parsers. `FastDownwardExperiment.``EXITCODE_PARSER`[¶](#downward.experiment.FastDownwardExperiment.EXITCODE_PARSER "Permalink to this definition") Parsed attributes: “error”, “planner\_exit\_code”, “unsolvable”. `FastDownwardExperiment.``TRANSLATOR_PARSER`[¶](#downward.experiment.FastDownwardExperiment.TRANSLATOR_PARSER "Permalink to this definition") Parsed attributes: “translator\_peak\_memory”, “translator\_time\_done”, etc. `FastDownwardExperiment.``SINGLE_SEARCH_PARSER`[¶](#downward.experiment.FastDownwardExperiment.SINGLE_SEARCH_PARSER "Permalink to this definition") Parsed attributes: “coverage”, “memory”, “total\_time”, etc. `FastDownwardExperiment.``ANYTIME_SEARCH_PARSER`[¶](#downward.experiment.FastDownwardExperiment.ANYTIME_SEARCH_PARSER "Permalink to this definition") Parsed attributes: “cost”, “cost:all”, “coverage”. `FastDownwardExperiment.``PLANNER_PARSER`[¶](#downward.experiment.FastDownwardExperiment.PLANNER_PARSER "Permalink to this definition") Used attributes: “memory”, “total\_time”, “translator\_peak\_memory”, “translator\_time\_done”. Parsed attributes: “node”, “planner\_memory”, “planner\_time”, “planner\_wall\_clock\_time”, “score\_planner\_memory”, “score\_planner\_time”. *class* `downward.experiment.``FastDownwardAlgorithm`(*name: str*, *cached\_revision: downward.cached\_revision.CachedFastDownwardRevision*, *driver\_options*, *component\_options*)[[source]](_modules/downward/experiment.html#FastDownwardAlgorithm)[¶](#downward.experiment.FastDownwardAlgorithm "Permalink to this definition") A Fast Downward algorithm is the combination of revision, driver options and component options. `cached_revision` *= None*[¶](#downward.experiment.FastDownwardAlgorithm.cached_revision "Permalink to this definition") An instance of [`CachedFastDownwardRevision`](#downward.cached_revision.CachedFastDownwardRevision "downward.cached_revision.CachedFastDownwardRevision"). `component_options` *= None*[¶](#downward.experiment.FastDownwardAlgorithm.component_options "Permalink to this definition") Component options, e.g., `["--search", "astar(lmcut())"]`. `driver_options` *= None*[¶](#downward.experiment.FastDownwardAlgorithm.driver_options "Permalink to this definition") Driver options, e.g., `["--build", "debug"]`. `name` *= None*[¶](#downward.experiment.FastDownwardAlgorithm.name "Permalink to this definition") Algorithm name, e.g., `"rev123:astar-lmcut"`. *class* `downward.experiment.``FastDownwardRun`(*exp: lab.experiment.Experiment*, *algo: downward.experiment.FastDownwardAlgorithm*, *task: downward.suites.Task*)[[source]](_modules/downward/experiment.html#FastDownwardRun)[¶](#downward.experiment.FastDownwardRun "Permalink to this definition") An experiment run that uses *algo* to solve *task*. See [`Run`](index.html#lab.experiment.Run "lab.experiment.Run") for inherited methods. *class* `downward.cached_revision.``CachedFastDownwardRevision`(*revision\_cache*, *repo*, *rev*, *build\_options*, *subdir=''*)[[source]](_modules/downward/cached_revision.html#CachedFastDownwardRevision)[¶](#downward.cached_revision.CachedFastDownwardRevision "Permalink to this definition") This class represents Fast Downward checkouts. It provides methods for caching compiled revisions, so they can be reused quickly in different experiments. * *revision\_cache*: Path to revision cache. * *repo*: Path to Fast Downward repository. * *rev*: Fast Downward revision. * *build\_options*: List of build.py options. * *subdir*: relative path from *repo* to Fast Downward subdir. `cache`()[¶](#downward.cached_revision.CachedFastDownwardRevision.cache "Permalink to this definition") Check out the solver revision to *self.path* and compile the solver. `get_relative_exp_path`(*relpath=''*)[¶](#downward.cached_revision.CachedFastDownwardRevision.get_relative_exp_path "Permalink to this definition") Return a path relative to the experiment directory. Use this function to find out where files from the cache will be put in the experiment directory. ### `downward.suites` — Select benchmarks[¶](#downward-suites-select-benchmarks "Permalink to this headline") *class* `downward.suites.``Task`(*domain: str*, *problem: str*, *problem\_file*, *domain\_file=None*, *properties=None*)[[source]](_modules/downward/suites.html#Task)[¶](#downward.suites.Task "Permalink to this definition") *domain* and *problem* are the display names of the domain and problem, *domain\_file* and *problem\_file* are paths to the respective files on the disk. If *domain\_file* is not given, assume that *problem\_file* is a SAS task. *properties* may be a dictionary of entries that should be added to the properties file of each run that uses this problem. ``` >>> task = Task( ... "gripper", ... "p01.pddl", ... problem\_file="/path/to/prob01.pddl", ... domain\_file="/path/to/domain.pddl", ... properties={"relaxed": False}, ... ) ``` `downward.suites.``build_suite`(*benchmarks\_dir*, *descriptions*)[[source]](_modules/downward/suites.html#build_suite)[¶](#downward.suites.build_suite "Permalink to this definition") Compute a list of [`Task`](#downward.suites.Task "downward.suites.Task") objects. The path *benchmarks\_dir* must contain a subdir for each domain. *descriptions* must be a list of domain or problem descriptions: ``` build\_suite(benchmarks\_dir, ["gripper", "grid:prob01.pddl"]) ``` `downward.reports` — Fast Downward reports[¶](#downward-reports-fast-downward-reports "Permalink to this headline") ------------------------------------------------------------------------------------------------------------------- ### Tables[¶](#tables "Permalink to this headline") *class* `downward.reports.``PlanningReport`(*\*\*kwargs*)[[source]](_modules/downward/reports.html#PlanningReport)[¶](#downward.reports.PlanningReport "Permalink to this definition") Base class for planner reports. See [`Report`](index.html#lab.reports.Report "lab.reports.Report") for inherited parameters. You can filter and modify runs for a report with [`filters`](index.html#lab.reports.Report "lab.reports.Report"). For example, you can include only a subset of algorithms or compute new attributes. If you provide a list for *filter\_algorithm*, it will be used to determine the order of algorithms in the report. ``` >>> # Use a filter function to select algorithms. >>> def only\_blind\_and\_lmcut(run): ... return run["algorithm"] in ["blind", "lmcut"] ... >>> report = PlanningReport(filter=only\_blind\_and\_lmcut) ``` ``` >>> # Use "filter\_algorithm" to select and \*order\* algorithms. >>> report = PlanningReport(filter\_algorithm=["lmcut", "blind"]) ``` [`Filters`](index.html#lab.reports.Report "lab.reports.Report") can be very helpful so we recommend reading up on them to use their full potential. Subclasses can use the member variable `problem\_runs` to access the experiment data. It is a dictionary mapping from tasks (i.e., `(domain, problem)` pairs) to the runs for that task. Each run is a dictionary that maps from attribute names to values. ``` >>> class MinRuntimePerTask(PlanningReport): ... def get\_text(self): ... map = {} ... for (domain, problem), runs in self.problem\_runs.items(): ... times = [run.get("planner\_time") for run in runs] ... times = [t for t in times if t is not None] ... map[(domain, problem)] = min(times) if times else None ... return str(map) ... ``` You may want to override the following class attributes in subclasses: `PREDEFINED_ATTRIBUTES` *= ['cost', 'coverage', 'dead\_ends', 'evaluations', 'expansions', 'generated', 'initial\_h\_value', 'plan\_length', 'planner\_time', 'quality', 'score\_\*', 'search\_time', 'total\_time', 'unsolvable']*[¶](#downward.reports.PlanningReport.PREDEFINED_ATTRIBUTES "Permalink to this definition") List of predefined `Attribute` instances. For example, if PlanningReport receives `attributes=['coverage']`, it converts the plain string `'coverage'` to the attribute instance `Attribute('coverage', absolute=True, min\_wins=False, scale='linear')`. `ERROR_ATTRIBUTES` *= ['domain', 'problem', 'algorithm', 'unexplained\_errors', 'error', 'planner\_wall\_clock\_time', 'raw\_memory', 'node']*[¶](#downward.reports.PlanningReport.ERROR_ATTRIBUTES "Permalink to this definition") Attributes shown in the unexplained-errors table. `INFO_ATTRIBUTES` *= ['local\_revision', 'global\_revision', 'build\_options', 'driver\_options', 'component\_options']*[¶](#downward.reports.PlanningReport.INFO_ATTRIBUTES "Permalink to this definition") Attributes shown in the algorithm info table. *class* `downward.reports.absolute.``AbsoluteReport`(*\*\*kwargs*)[[source]](_modules/downward/reports/absolute.html#AbsoluteReport)[¶](#downward.reports.absolute.AbsoluteReport "Permalink to this definition") Report absolute values for the selected attributes. This report should be part of all your Fast Downward experiments as it includes a table of unexplained errors, e.g. invalid solutions, segmentation faults, etc. ``` >>> from downward.experiment import FastDownwardExperiment >>> exp = FastDownwardExperiment() >>> exp.add\_report(AbsoluteReport(attributes=["expansions"]), outfile="report.html") ``` Example output: > > > > > > > > | expansions | hFF | hCEA | > | --- | --- | --- | > | gripper | 118 | 72 | > | zenotravel | 21 | 17 | > > > *class* `downward.reports.taskwise.``TaskwiseReport`(*\*\*kwargs*)[[source]](_modules/downward/reports/taskwise.html#TaskwiseReport)[¶](#downward.reports.taskwise.TaskwiseReport "Permalink to this definition") For each task report all selected attributes in a single row. If the experiment contains more than one algorithm, use `filter\_algorithm=["my\_algorithm"]` to select exactly one algorithm for the report. ``` >>> from downward.experiment import FastDownwardExperiment >>> exp = FastDownwardExperiment() >>> exp.add\_report( ... TaskwiseReport( ... attributes=["expansions", "search\_time"], filter\_algorithm=["lmcut"] ... ) ... ) ``` Example output: > > > > > > > > | | expansions | search\_time | > | --- | --- | --- | > | grid:prob01.pddl | 118234 | 20.02 | > | gripper:prob01.pddl | 21938 | 17.58 | > > > *class* `downward.reports.compare.``ComparativeReport`(*algorithm\_pairs*, *\*\*kwargs*)[[source]](_modules/downward/reports/compare.html#ComparativeReport)[¶](#downward.reports.compare.ComparativeReport "Permalink to this definition") Compare pairs of algorithms. See [`AbsoluteReport`](#downward.reports.absolute.AbsoluteReport "downward.reports.absolute.AbsoluteReport") for inherited parameters. *algorithm\_pairs* is the list of algorithm pairs you want to compare. All columns in the report will be arranged such that the compared algorithms appear next to each other. After the two columns containing absolute values for the compared algorithms, a third column (“Diff”) is added showing the difference between the two values. Algorithms may appear in multiple comparisons. Algorithms not mentioned in *algorithm\_pairs* are not included in the report. If you want to compare algorithms A and B, instead of a pair `('A', 'B')` you may pass a triple `('A', 'B', 'A vs. B')`. The third entry of the triple will be used as the name of the corresponding “Diff” column. For example, if the properties file contains algorithms A, B, C and D and *algorithm\_pairs* is `[('A', 'B', 'Diff BA'), ('A', 'C')]` the resulting columns will be A, B, Diff BA (contains B - A), A, C , Diff (contains C - A). Example: ``` >>> from downward.experiment import FastDownwardExperiment >>> exp = FastDownwardExperiment() >>> algorithm\_pairs = [("default-lmcut", "issue123-lmcut", "Diff lmcut")] >>> exp.add\_report(ComparativeReport(algorithm\_pairs, attributes=["coverage"])) ``` Example output: > > > > > > > > > | coverage | default-lmcut | issue123-lmcut | Diff lmcut | > | --- | --- | --- | --- | > | depot | 15 | 17 | 2 | > | gripper | 7 | 6 | -1 | > > > ### Plots[¶](#plots "Permalink to this headline") *class* `downward.reports.scatter.``ScatterPlotReport`(*relative=False*, *show\_missing=True*, *get\_category=None*, *title=None*, *scale=None*, *xlabel=''*, *ylabel=''*, *matplotlib\_options=None*, *\*\*kwargs*)[[source]](_modules/downward/reports/scatter.html#ScatterPlotReport)[¶](#downward.reports.scatter.ScatterPlotReport "Permalink to this definition") Generate a scatter plot for an attribute. If *relative* is False, create a “standard” scatter plot with a diagonal line. If *relative* is True, create a relative scatter plot where each point *(x, y)* corresponds to a task for which the first algorithm yields a value of *x* and the second algorithm yields *x \* y*. Relative scatter plots are less common in the literature, but often show small differences between algorithms better than “standard” scatter plots. The keyword argument *attributes* must contain exactly one attribute. Use the *filter\_algorithm* keyword argument to select exactly two algorithms (see example below). If *show\_missing* is False, we only draw a point for an algorithm pair if both algorithms have a value. *get\_category* can be a function that takes **two** runs (dictionaries of properties) and returns a category name. This name is used to group the points in the plot. If there is more than one group, a legend is automatically added. Runs for which this function returns None are shown in a default category and are not contained in the legend. For example, to group by domain: ``` >>> def domain\_as\_category(run1, run2): ... # run2['domain'] has the same value, because we always ... # compare two runs of the same problem. ... return run1["domain"] ... ``` Example grouping by difficulty: ``` >>> def improvement(run1, run2): ... time1 = run1.get("search\_time", 1800) ... time2 = run2.get("search\_time", 1800) ... if time1 > time2: ... return "better" ... if time1 == time2: ... return "equal" ... return "worse" ... ``` ``` >>> from downward.experiment import FastDownwardExperiment >>> exp = FastDownwardExperiment() >>> exp.add\_report( ... ScatterPlotReport(attributes=["search\_time"], get\_category=improvement) ... ) ``` Example comparing the number of expanded states for two algorithms: ``` >>> exp.add\_report( ... ScatterPlotReport( ... attributes=["expansions\_until\_last\_jump"], ... filter\_algorithm=["algorithm-1", "algorithm-2"], ... get\_category=domain\_as\_category, ... format="png", # Use "tex" for pgfplots output. ... ), ... name="scatterplot-expansions", ... ) ``` The inherited *format* parameter can be set to ‘png’ (default), ‘eps’, ‘pdf’, ‘pgf’ (needs matplotlib 1.2) or ‘tex’. For the latter a pgfplots plot is created. If *title* is given it will be used for the name of the plot. Otherwise, the only given attribute will be the title. If none is given, there will be no title. *scale* can have the values ‘linear’, ‘log’ or ‘symlog’. If omitted, a sensible default will be used for some standard attributes and ‘log’ otherwise. Relative scatter plots always use a logarithmic scaling for the *y* axis. *xlabel* and *ylabel* are the axis labels. *matplotlib\_options* may be a dictionary of matplotlib rc parameters (see <http://matplotlib.org/users/customizing.html>): ``` >>> from downward.reports.scatter import ScatterPlotReport >>> matplotlib\_options = { ... "font.family": "serif", ... "font.weight": "normal", ... # Used if more specific sizes not set. ... "font.size": 20, ... "axes.labelsize": 20, ... "axes.titlesize": 30, ... "legend.fontsize": 22, ... "xtick.labelsize": 10, ... "ytick.labelsize": 10, ... "lines.markersize": 10, ... "lines.markeredgewidth": 0.25, ... "lines.linewidth": 1, ... # Width and height in inches. ... "figure.figsize": [8, 8], ... "savefig.dpi": 100, ... } >>> report = ScatterPlotReport( ... attributes=["initial\_h\_value"], matplotlib\_options=matplotlib\_options ... ) ``` You can see the full list of matplotlib options and their defaults by executing ``` import matplotlib print(matplotlib.rcParamsDefault) ``` [](_images/example-scatter-plot.png) Concepts[¶](#concepts "Permalink to this headline") --------------------------------------------------- An **experiment** consists of multiple **steps**. Most experiments will have steps for building and executing the experiment: ``` >>> from lab.experiment import Experiment >>> exp = Experiment() >>> exp.add\_step("build", exp.build) >>> exp.add\_step("start", exp.start\_runs) ``` Moreover, there are usually steps for **fetching** the results and making **reports**: ``` >>> from lab.reports import Report >>> exp.add\_fetcher(name="fetch") >>> exp.add\_report(Report(attributes=["error"])) ``` The “build” step creates all necessary files for running the experiment in the **experiment directory**. After the “start” step has finished running the experiment, we can fetch the result from the experiment directory to the **evaluation directory**. All reports only operate on evaluation directories. An experiment usually also has multiple **runs**, one for each pair of algorithm and benchmark. When calling [`start\_runs()`](index.html#lab.experiment.Experiment.start_runs "lab.experiment.Experiment.start_runs"), all **runs** part of the experiment are executed. You can add runs with the [`add\_run()`](index.html#lab.experiment.Experiment.add_run "lab.experiment.Experiment.add_run") method. Each run needs a unique ID and at least one **command**: ``` >>> for algo in ["algo1", "algo2"]: ... for value in range(10): ... run = exp.add\_run() ... run.set\_property("id", [algo, str(value)]) ... run.add\_command("solve", [algo, str(value)]) ``` You can pass the names of selected steps to your experiment script or use `--all` to execute all steps. At the end of your script, call `exp.run\_steps()` to parse the commandline and execute the selected steps. Changelog[¶](#changelog "Permalink to this headline") ----------------------------------------------------- ### v7.4 (2023-08-18)[¶](#v7-4-2023-08-18 "Permalink to this headline") #### Lab[¶](#lab "Permalink to this headline") * Require *revision\_cache* parameter in [`CachedRevision`](index.html#lab.cached_revision.CachedRevision "lab.cached_revision.CachedRevision") constructor (Jendrik Seipp). * Add *subdir* option for [`CachedRevision`](index.html#lab.cached_revision.CachedRevision "lab.cached_revision.CachedRevision") to support solvers at deeper levels of a repo (Jendrik Seipp). * Add [`CachedRevision.get\_relative\_exp\_path()`](index.html#lab.cached_revision.CachedRevision.get_relative_exp_path "lab.cached_revision.CachedRevision.get_relative_exp_path") method to query where cache artefacts will land in the experiment directory (Jendrik Seipp). * Document [`CachedRevision`](index.html#lab.cached_revision.CachedRevision "lab.cached_revision.CachedRevision") class and stabilize its API (Jendrik Seipp). * Only use documented classes and functions in example experiments (Jendrik Seipp). #### Downward Lab[¶](#downward-lab "Permalink to this headline") * Add *subdir* option for [`CachedFastDownwardRevision`](index.html#downward.cached_revision.CachedFastDownwardRevision "downward.cached_revision.CachedFastDownwardRevision") to support Fast Downward checkouts at deeper levels of a repo (Jendrik Seipp). * Make [`FastDownwardAlgorithm`](index.html#downward.experiment.FastDownwardAlgorithm "downward.experiment.FastDownwardAlgorithm"), [`FastDownwardRun`](index.html#downward.experiment.FastDownwardRun "downward.experiment.FastDownwardRun") and [`CachedFastDownwardRevision`](index.html#downward.cached_revision.CachedFastDownwardRevision "downward.cached_revision.CachedFastDownwardRevision") classes part of the documented, stable API (Jendrik Seipp). * Describe [two main alternatives](index.html#downward-experiment) for running Fast Downward experiments (Jendrik Seipp). ### v7.3 (2023-03-03)[¶](#v7-3-2023-03-03 "Permalink to this headline") #### Lab[¶](#id1 "Permalink to this headline") * Transparently handle xz-compressed properties files (Jendrik Seipp). * Add CI tests for Python 3.11 (Jendrik Seipp). #### Downward Lab[¶](#id2 "Permalink to this headline") * Adapt code for Matplotlib version 3.7 (Jendrik Seipp). ### v7.2 (2022-10-09)[¶](#v7-2-2022-10-09 "Permalink to this headline") #### Lab[¶](#id3 "Permalink to this headline") * Raise minimum supported Python version to 3.7 (Jendrik Seipp). * Add support for Python 3.10 (Jendrik Seipp). * Apply parsing functions in the order in which they were added (Jendrik Seipp). * For contributors: document pre-commit hook in `CONTRIBUTING.md` file (Jendrik Seipp). #### Downward Lab[¶](#id4 "Permalink to this headline") * Parse peak memory in anytime search parser (Jendrik Seipp). * Only store “planner\_memory” and “planner\_time” attributes for successful planner runs (Jendrik Seipp). * Add fully customizable example planner experiment without `FastDownwardExperiment` class (Jendrik Seipp). * Show how to group domain directories in example Fast Downward experiment (Jendrik Seipp). ### v7.1 (2022-06-20)[¶](#v7-1-2022-06-20 "Permalink to this headline") #### Lab[¶](#id5 "Permalink to this headline") * Revamp Singularity example experiment: use `runsolver` to limit resource usage (Silvan Sievers and Jendrik Seipp). #### Downward Lab[¶](#id6 "Permalink to this headline") * Fix header sizes in HTML reports (Jendrik Seipp). * Include domains in attribute overview tables even if none of their tasks has an attribute value for all algorithms (Jendrik Seipp). * Compute “score\_planner\_time” and “score\_planner\_memory” attributes in planner parser (Jendrik Seipp). * Only consider files ending with “.pddl” and “.sas” when building suites (Jendrik Seipp). * Explicitly left-align non-numeric cells to avoid \multicolumn entries in Latex output (Jendrik Seipp). ### v7.0 (2021-10-24)[¶](#v7-0-2021-10-24 "Permalink to this headline") #### Lab[¶](#id7 "Permalink to this headline") * Remove support for Mercurial repositories (Jendrik Seipp). #### Downward Lab[¶](#id8 "Permalink to this headline") * Fix rules for finding domain files for airport and psr-small domains (Silvan Sievers). * Add more ticks on y axis in relative plots (Jendrik Seipp). ### v6.5 (2021-09-27)[¶](#v6-5-2021-09-27 "Permalink to this headline") #### Lab[¶](#id9 "Permalink to this headline") * Allow rerunning experiments. This is useful if some runs were never started, for example, due to grid node failures. All runs that have already been started are skipped. For more information see the corresponding [FAQ](index.html#faq) (Jendrik Seipp). #### Downward Lab[¶](#id10 "Permalink to this headline") * Slightly generalize rules for finding domain files, adapted from Fast Downward (Silvan Sievers). ### v6.4 (2021-07-06)[¶](#v6-4-2021-07-06 "Permalink to this headline") #### Lab[¶](#id11 "Permalink to this headline") * Add `TetralithEnvironment` for the NSC cluster in Linköping (Jendrik Seipp). * Automatically group multiple runs into one Slurm task when the number of runs exceeds the maximum number of Slurm tasks (Jendrik Seipp). * Add `time\_limit\_per\_task` parameter to `SlurmEnvironment` (Jendrik Seipp). * Add `cpus\_per\_task` parameter to `SlurmEnvironment` (#98, Lucas Galery Käser). * Catch OverflowError when casting large ints to floats (#95, Silvan Sievers). #### Downward Lab[¶](#id12 "Permalink to this headline") * None. ### v6.3 (2021-02-14)[¶](#v6-3-2021-02-14 "Permalink to this headline") #### Lab[¶](#id13 "Permalink to this headline") * Use long Git revision hashes for revision cache. The short ones differ in length between Git versions (Jendrik Seipp). * Run continuous integration tests for Python 3.9 (Jendrik Seipp). #### Downward Lab[¶](#id14 "Permalink to this headline") * Remove “revision\_summary” column from info table (Jendrik Seipp). ### v6.2 (2020-10-20)[¶](#v6-2-2020-10-20 "Permalink to this headline") #### Lab[¶](#id15 "Permalink to this headline") * Reports: round values to desired precision before determining colors (Jendrik Seipp). * Restructure and extend documentation (Jendrik Seipp). * For developers: run CI tests on Ubuntu 20.04 in addition to 18.04 (Jendrik Seipp). #### Downward Lab[¶](#id16 "Permalink to this headline") * Allow adding SAS+ files with `FastDownwardExperiment.add\_suite()` (Jendrik Seipp). ### v6.1 (2020-09-15)[¶](#v6-1-2020-09-15 "Permalink to this headline") #### Lab[¶](#id17 "Permalink to this headline") * Take float precision into account when highlighting table cells (Jendrik Seipp). * Allow serializing pathlib.Path objects into JSON files (Jendrik Seipp). * For developers: add `.github/CONTRIBUTING.md` file (Jendrik Seipp). * For developers: separate tests for Singularity and FF example experiments from other tests (Jendrik Seipp). * For developers: skip `cached\_revision` doctests if `DOWNWARD\_REVISION\_CACHE` variable is not set (Jendrik Seipp). * For developers: use f-strings in code (Jendrik Seipp). #### Downward Lab[¶](#id18 "Permalink to this headline") * Print number of tasks above and below separator lines in scatter plots (Jendrik Seipp). * Ignore tasks for which runs have been filtered out in aggregate reports (Jendrik Seipp). * Fix order of bracketed task counts per domain in table reports (Jendrik Seipp). * Gracefully handle empty scatter plots (Jendrik Seipp). * Make `score\_\*` attributes absolute, i.e., include tasks for which not all algorithms have a value in aggregations (Jendrik Seipp). ### v6.0 (2020-04-05)[¶](#v6-0-2020-04-05 "Permalink to this headline") #### Lab[¶](#id19 "Permalink to this headline") * Bump minimum Python version to 3.6. * Move `CachedRevision` from `downward` to `lab` package (Thomas Keller). Please note that the interface to the class is experimental and may change in the future. Feedback is welcome! * Let tests fail if any example experiment produces unexplained errors. #### Downward Lab[¶](#id20 "Permalink to this headline") * No changes. ### v5.5 (2020-03-13)[¶](#v5-5-2020-03-13 "Permalink to this headline") #### Lab[¶](#id21 "Permalink to this headline") * Sort numbers with suffixes (5K, 2M, 8G) and “infinity” correctly in tables. * Gracefully handle missing “info” or “summary” tables in HTML reports. * Abort if a function is passed to a `filter\_\*` kwarg. * Abort if a filter checks missing attribute names (e.g., when passing `filter\_algorithms` instead of `filter\_algorithm`). #### Downward Lab[¶](#id22 "Permalink to this headline") * Add example experiment for running Singularity planner images. ### v5.4 (2020-03-01)[¶](#v5-4-2020-03-01 "Permalink to this headline") #### Lab[¶](#id23 "Permalink to this headline") * Use newer txt2tags version and remove bundled copy. * Call parsers with active Python interpreter. * Don’t call deprecated `time.clock()` (removed in Python 3.8). * Don’t add Lab to `PYTHONPATH` in `BaselSlurmEnvironment`. #### Downward Lab[¶](#id24 "Permalink to this headline") * Revision cache: only delete “misc” and “experiments” dirs if they exist (Maximilian Fickert). ### v5.3 (2020-02-03)[¶](#v5-3-2020-02-03 "Permalink to this headline") #### Lab[¶](#id25 "Permalink to this headline") * Format source code with black (<https://github.com/psf/black>). * Fix filters: retrieve new run ID from modified runs (Silvan Sievers). #### Downward Lab[¶](#id26 "Permalink to this headline") * Remove call to `rm -f output.sas`. Newer Fast Downward versions remove the temporary file automatically. If you want to keep the file, add `"--keep-sas-file"` to the `driver\_options`. * Fix ScatterPlotReport: skip None values in max() computation (Silvan Sievers). * Fix ScatterPlotReport: place diagonal line correctly even if axis scales differ. ### v5.2 (2020-01-07)[¶](#v5-2-2020-01-07 "Permalink to this headline") #### Lab[¶](#id27 "Permalink to this headline") * Use line buffering for run.err files. #### Downward Lab[¶](#id28 "Permalink to this headline") * Preserve line breaks for error logs in tables. * If an error log in a table has more than 100 lines, omit surplus lines from the middle of the log. * Always print the number of runs with unexplained errors when generating any type of report. ### v5.1 (2019-12-10)[¶](#v5-1-2019-12-10 "Permalink to this headline") #### Lab[¶](#id29 "Permalink to this headline") * Test Lab on Python 3.8. * Use active Python version to call run files in local experiments. #### Downward Lab[¶](#id30 "Permalink to this headline") * Support Fast Downward Git repos (Patrick Ferber). ### v5.0 (2019-12-04)[¶](#v5-0-2019-12-04 "Permalink to this headline") #### Lab[¶](#id31 "Permalink to this headline") * Deprecate support for Python versions 2.7 to 3.5. * Allow only a single aggregation function for `Attribute` objects. * If there is only a single HTML table, show it when the page loads. * Remove broken `--log-level` command line parameter. You can call `tools.configure\_logging(logging.DEBUG)` to enable debug messages instead. * Pass old hard memory limit when setting soft memory limit. #### Downward Lab[¶](#id32 "Permalink to this headline") * Scatter plots: + Add *relative* parameter for drawing relative scatter plots. + Draw points for algorithm pairs with missing values on axis boundaries. + Allow drawing negative values on linear and symlog axes. + Remove *xscale* and *yscale* parameters in favor of a new *scale* parameter. + Fold `PlotReport` class into `ScatterPlotReport`. + Simplify code by letting Matplotlib compute axis limits automatically. ### v4.2 (2019-09-27)[¶](#v4-2-2019-09-27 "Permalink to this headline") #### Lab[¶](#id33 "Permalink to this headline") * Upload to PyPI. Install Lab and Downward Lab with `pip install lab`. * Add support for running Lab in Python virtual environments (Guillem). * Parser scripts don’t have to be executable anymore, but they must be Python scripts. #### Downward Lab[¶](#id34 "Permalink to this headline") * Abort if two algorithms are identical, i.e., use the same revision, build config and commandline options. * Scatter plot report: include tasks for which both algorithms have no data if `show\_missing=True`. ### v4.1 (2019-06-03)[¶](#v4-1-2019-06-03 "Permalink to this headline") * Add support for Python 3. Lab now supports Python 2.7 and Python >= 3.5. ### v4.0 (2019-02-19)[¶](#v4-0-2019-02-19 "Permalink to this headline") #### Lab[¶](#id35 "Permalink to this headline") * Parser: don’t try to parse missing files. Print message to stdout instead. * Add soft memory limit of “memory\_per\_cpu \* 0.98” for Slurm runs to safeguard against cgroup failures. * Abort if report contains duplicate attribute names. * Make reports even if fetcher detects unexplained errors. * Use `flags=''` for [`lab.parser.Parser.add\_pattern()`](index.html#lab.parser.Parser.add_pattern "lab.parser.Parser.add_pattern") by default again. * Include node names in standard reports and warn if report mixes runs from different partitions. * Add new example experiment using a simple vertex cover solver. * `BaselSlurmEnvironment`: don’t load Python 2.7.11 since it might conflict with an already loaded module. * Raise default `nice` value to 5000. #### Downward Lab[¶](#id36 "Permalink to this headline") * Support new Fast Downward exitcodes (Silvan). * Parse “planner\_wall\_clock\_time” attribute in planner parser. * Include “planner\_wall\_clock\_time” and “raw\_memory” attributes in unexplained errors table. * Make PlanningReport more generic by letting derived classes override the new `PREDEFINED\_ATTRIBUTES`, `INFO\_ATTRIBUTES` and `ERROR\_ATTRIBUTES` class members (Augusto). * Don’t compute the “quality” attribute automatically. The docs and `showcase-options.py` show how to add the two filters that together add the IPC quality score to each run. ### v3.0 (2018-07-10)[¶](#v3-0-2018-07-10 "Permalink to this headline") #### Lab[¶](#id37 "Permalink to this headline") * Add [`exp.add\_parser()`](index.html#lab.experiment.Experiment.add_parser "lab.experiment.Experiment.add_parser") method. See also [Parser](index.html#parsing) (Silvan). * Add [`exp.add\_parse\_again\_step()`](index.html#lab.experiment.Experiment.add_parse_again_step "lab.experiment.Experiment.add_parse_again_step") method for running parsers again (Silvan). * Require that the `build`, `start\_runs` and `fetch` steps are added explicitly (see [`Experiment`](index.html#lab.experiment.Experiment "lab.experiment.Experiment")). * Remove *required* argument from `add\_resource()`. All resources are now required. * Use stricter naming rules for commands and resources. See respective `add\_\*` methods for details. * Use `required=False` and `flags='M'` by default for [`lab.parser.Parser.add\_pattern()`](index.html#lab.parser.Parser.add_pattern "lab.parser.Parser.add_pattern"). * Only support custom command line arguments for locally executed steps. * Log errors to stderr. * Log exit codes and wall-clock times of commands to driver.log. * Add unexplained error if driver.log is empty. * Let fetcher fetch `properties` and `static-properties` files. * Remove deprecated possibility of passing Step objects to `add\_step()`. * Remove deprecated `exp.\_\_call\_\_()` method. #### Downward Lab[¶](#id38 "Permalink to this headline") * Add “planner\_timer” and “planner\_memory” attributes. * Reorganize parsers and don’t add any parser implicitly. See [Bundled parsers](index.html#downward-parsers). * Add anytime-search parser that parses only “cost”, “cost:all” and “coverage”. * Revise and simplify single-search parser. * Parse new Fast Downward exit codes (<http://issues.fast-downward.org/issue739>). * Don’t exclude (obsolete) “benchmarks” directory when caching revisions. * Only copy “raw\_memory” value to “memory” when “total\_time” is present. * Rename “fast-downward” command to “planner”. * Make “error” attribute optional for reports. ### v2.3 (2018-04-12)[¶](#v2-3-2018-04-12 "Permalink to this headline") #### Lab[¶](#id39 "Permalink to this headline") * BaselSlurmEnvironment: Use `infai\_1` and `normal` as default Slurm partition and QOS. * Remove `OracleGridEngineEnvironment`. #### Downward Lab[¶](#id40 "Permalink to this headline") * Use `--overall-time-limit=30m` and `--overall-memory-limit=3584M` for all Fast Downward runs by default. * Don’t add `-j` option to build options (`build.py` now uses all CPUs automatically). ### v2.2 (2018-03-16)[¶](#v2-2-2018-03-16 "Permalink to this headline") #### Lab[¶](#id41 "Permalink to this headline") * Print run and task IDs during local experiments. * Make warnings and error messages more informative. * Abort after fetch step if fetcher finds unexplained errors. * Improve examples and docs. #### Downward Lab[¶](#id42 "Permalink to this headline") * Don’t parse preprocessor logs anymore. * Make regular expressions stricter in parsers. * Don’t complain if SAS file is missing. ### v2.1 (2017-11-27)[¶](#v2-1-2017-11-27 "Permalink to this headline") #### Lab[¶](#id43 "Permalink to this headline") * Add BaselSlurmEnvironment (Florian). * Support running experiments in virtualenv (Shuwa). * Redirect output to `driver.log` and `driver.err` as soon as possible. * Store all observed unexplained errors instead of a single one (Silvan). * Report unexplained error if `run.err` or `driver.err` contain output. * Report unexplained error if “error” attribute is missing. * Add configurable soft and hard limits for output to `run.log` and `run.err`. * Record grid node for each run and add it to warnings table. * Omit toprule and bottomrule in LaTeX tables. * Add `lab.reports.Table.set\_row\_order()` method. * Only escape text in table cells if it doesn’t contain LaTeX or HTML markup. * Allow run filters to change a run’s ID (needed for renaming algorithms). * Add `merge` kwarg to `add\_fetcher()` (Silvan). * Exit with returncode 1 if fetcher finds unexplained errors. * Let fetcher show warning if `slurm.err` is not empty. * Include content of `slurm.err` in reports if it contains text. * Add continuous integration testing. * Add `--skip-experiments` option for `tests/run-tests` script. * Clean up code. * Polish documentation. #### Downward Lab[¶](#id44 "Permalink to this headline") * For each error outcome show number of runs with that outcome in summary table and dedicated tables. * Add standalone exit code parser. Allow removing translate and search parsers (Silvan). * Allow passing `Problem` instances to `FastDownwardExperiment.add\_suite()` (Florian). * Don’t filter duplicate coordinates in scatter plots. * Don’t round scatter plot coordinates. * Remove output.sas instead of compressing it. * Fix scatter plots for multiple categories **and** the default `None` category (Silvan). ### v2.0 (2017-01-09)[¶](#v2-0-2017-01-09 "Permalink to this headline") #### Lab[¶](#id45 "Permalink to this headline") * Show warning and ask for action when evaluation dir already exists. * Add `scale` parameter to Attribute. It is used by the plot reports. * Add `digits` parameter to Attribute for specifying the number of digits after the decimal point. * Pass name, function, args and kwargs to `exp.add\_step()`. Deprecate passing Step objects. * After calling `add\_resource("mynick", ...)`, use resource in commands with “{mynick}”. * Call: make `name` parameter mandatory, rename `mem\_limit` kwarg to `memory\_limit`. * Store grid job files in `<exp-dir>-grid-steps`. * Use common `run-dispatcher` script for local and remote experiments. * LocalEnvironment: support randomizing task order (enabled by default). * Make `path` parameter optional for all experiments. * Warn if steps are listed explicitly and `--all` is used. * Change main experiment step name from “start” to “run”. * Deprecate `exp()`. Use `exp.run\_steps()` instead. * Don’t filter `None` values in `lab.reports` helper functions. * Make logging clearer. * Add example FF experiment. * Remove deprecated code (e.g. predefined Step objects, `tools.sendmail()`). * Remove `Run.require\_resource()`. All resources have always been available for all runs. * Fetcher: remove `write\_combined\_props` parameter. * Remove `Sequence` class. * Parser: remove `key\_value\_patterns` parameter. A better solution is in the works. * Remove `tools.overwrite\_dir()` and `tools.get\_command\_output()`. * Remove `lab.reports.minimum()`, `lab.reports.maximum()`, `lab.reports.stddev()`. * Move `lab.reports.prod()` to `lab.tools.product()`. * Rename `lab.reports.gm()` to `lab.reports.geometric\_mean()` and `lab.reports.avg()` to `lab.reports.arithmetic\_mean()`. * Many speed improvements and better error messages. * Rewrite docs. #### Downward Lab[¶](#id46 "Permalink to this headline") * Always validate plans. Previous Lab versions don’t add `--validate` since older Fast Downward versions don’t support it. * HTML reports: hide tables by default, add buttons for toggling visibility. * Unify “score\_\*”, “quality” and “coverage” attributes: assign values in range [0, 1] and compute only sum and no average. * Don’t print tables on commandline. * Remove DownwardExperiment and other deprecated code. * Move `FastDownwardExperiment` into `downward/experiment.py`. * Rename `config` attribute to `algorithm`. Remove `config\_nick` attribute. * Change call name from “search” to “fast-downward”. * Remove “memory\_capped”, and “id\_string” attributes. * Report raw memory in “unexplained errors” table. * Parser: remove `group` argument from `add\_pattern()`, and always use group 1. * Remove `cache\_dir` parameter. Add `revision\_cache` parameter to `FastDownwardExperiment`. * Fetcher: remove `copy\_all` option. * Remove predefined benchmark suites. * Remove IpcReport, ProblemPlotReport, RelativeReport, SuiteReport and TimeoutReport. * Rename CompareConfigsReport to ComparativeReport. * Remove possibility to add `\_relative` to an attribute to obtain relative results. * Apply filters sequentially instead of interleaved. * PlanningReport: remove `derived\_properties` parameter. Use two filters instead: one for caching results, the other for adding new properties (see `QualityFilters` in `downward/reports/\_\_init\_\_.py`). * PlotReport: use fixed legend location, remove `category\_styles` option. * AbsoluteReport: remove `colored` parameter and always color HTML reports. * Don’t use domain links in Latex reports. * AbsoluteReport: Remove `resolution` parameter and always use `combined` resolution. * Rewrite docs. ### v1.12 (2017-01-09)[¶](#v1-12-2017-01-09 "Permalink to this headline") #### Downward Lab[¶](#id47 "Permalink to this headline") * Only compress “output” file if it exists. * Preprocess parser: make legacy preprocessor output optional. ### v1.11 (2016-12-15)[¶](#v1-11-2016-12-15 "Permalink to this headline") #### Lab[¶](#id48 "Permalink to this headline") * Add bitbucket-pipelines.yml for continuous integration testing. #### Downward Lab[¶](#id49 "Permalink to this headline") * Add IPC 2014 benchmark suites (Silvan). * Set `min\_wins=False` for `dead\_ends` attribute. * Fit coordinates better into plots. * Add finite\_sum() function and use it for `initial\_h\_value` (Silvan). * Update example scripts for repos without benchmarks. * Update docs. ### v1.10 (2015-12-11)[¶](#v1-10-2015-12-11 "Permalink to this headline") #### Lab[¶](#id50 "Permalink to this headline") * Add `permissions` parameter to [`lab.experiment.Experiment.add\_new\_file()`](index.html#lab.experiment.Experiment.add_new_file "lab.experiment.Experiment.add_new_file"). * Add default parser which checks that log files are not bigger than 100 MB. Maybe we’ll make this configurable in the future. * Ensure that resource names are not shared between runs and experiment. * Show error message if resource names are not unique. * Table: don’t format list items. This allows us to keep the quotes for configuration lists. #### Downward Lab[¶](#id51 "Permalink to this headline") * Cleanup `downward.suites`: update suite names, add STRIPS and ADL versions of all IPCs. We recommend selecting a subset of domains manually to only run your code on “interesting” benchmarks. As a starting point you can use the suites `suite\_optimal\_strips` or `suite\_satisficing`. ### v1.9.1 (2015-11-12)[¶](#v1-9-1-2015-11-12 "Permalink to this headline") #### Downward Lab[¶](#id52 "Permalink to this headline") * Always prepend build options with `-j<num\_cpus>`. * Fix: Use correct revisions in `FastDownwardExperiment`. * Don’t abort parser if resource limits can’t be found (support old planner versions). ### v1.9 (2015-11-07)[¶](#v1-9-2015-11-07 "Permalink to this headline") #### Lab[¶](#id53 "Permalink to this headline") * Add [`lab.experiment.Experiment.add\_command()`](index.html#lab.experiment.Experiment.add_command "lab.experiment.Experiment.add_command") method. * Add [`lab.\_\_version\_\_`](index.html#lab.__version__ "lab.__version__") string. * Explicitly remove support for Python 2.6. #### Downward Lab[¶](#id54 "Permalink to this headline") * Add [`downward.experiment.FastDownwardExperiment`](index.html#downward.experiment.FastDownwardExperiment "downward.experiment.FastDownwardExperiment") class for whole-planner experiments. * Deprecate `downward.experiments.DownwardExperiment` class. * Repeat headers between domains in [`downward.reports.taskwise.TaskwiseReport`](index.html#downward.reports.taskwise.TaskwiseReport "downward.reports.taskwise.TaskwiseReport"). ### v1.8 (2015-10-02)[¶](#v1-8-2015-10-02 "Permalink to this headline") #### Lab[¶](#id55 "Permalink to this headline") * Deprecate predefined experiment steps (`remove\_exp\_dir`, `zip\_exp\_dir`, `unzip\_exp\_dir`). * Docs: add FAQs, update docs. * Add more regression and style tests. #### Downward Lab[¶](#id56 "Permalink to this headline") * Parse both evaluated states (evaluated) and evaluations (evaluations). * Add example experiment showing how to make reports for data obtained without Lab. * Add suite\_sat\_strips(). * Parse negative initial h values. * Support CMake builds. ### v1.7 (2015-08-19)[¶](#v1-7-2015-08-19 "Permalink to this headline") #### Lab[¶](#id57 "Permalink to this headline") * Automatically determine whether to queue steps sequentially on the grid. * Reports: right-align headers (except the left-most one). * Reports: let `lab.reports.gm()` return 0 if any of the numbers is 0. * Add test that checks for dead code with vulture. * Remove Step.remove\_exp\_dir step. * Remove default time and memory limits for commands. You can now pass `mem\_limit=None` and `time\_limit=None` to disable limits for a command. * Pass `extra\_options` kwarg to `lab.environments.OracleGridEngineEnvironment` to set additional options like parallel environments. * Sort `properties` files by keys. #### Downward Lab[¶](#id58 "Permalink to this headline") * Add support for new python driver script `fast-downward.py`. * Use booktabs package for latex tables. * Remove vertical lines from Latex tables (recommended by booktabs docs). * Capitalize attribute names and remove underscores for Latex reports. * Allow fractional plan costs. * Set search\_time and total\_time to 0.01 instead of 0.1 if they are 0.0 in the log. * Parse initial h-value for aborted searches (Florian). * Use EXIT\_UNSOLVABLE instead of logs to determine unsolvability. Currently, this exit code is only returned by EHC. * Exit with warning if search parser is not executable. * Deprecate `downward/configs.py` module. * Deprecate `examples/standard\_exp.py` module. * Remove `preprocess-all.py` script. * By default, use all CPUs for compiling Fast Downward. ### v1.6[¶](#v1-6 "Permalink to this headline") #### Lab[¶](#id59 "Permalink to this headline") * Restore earlier default behavior for grid jobs by passing all environment variables (e.g. `PYTHONPATH`) to the job environments. #### Downward Lab[¶](#id60 "Permalink to this headline") * Use write-once revision cache: instead of *cloning* the full FD repo into the revision cache only *copy* the `src` directory. This greatly reduces the time and space needed to cache revisions. As a consequence you cannot specify the destination for the clone anymore (the `dest` keyword argument is removed from the `Translator`, `Preprocessor` and `Planner` classes) and only local FD repositories are supported (see `downward.checkouts.HgCheckout`). After the files have been copied into the cache and FD has been compiled, a special file (`build\_successful`) is written in the cache directory. When the cached revision is requested later an error is shown if this file is missing. * Only use exit codes to reason about error reasons. Merge from FD main if your FD version does not produce meaningful exit codes. * Preprocess parser: only parse logs and never output files. * Never copy `all.groups` and `test.groups` files. Old Fast Downward branches need to merge from main. * Always compress `output.sas` (also for `compact=False`). Use `xz` for compressing. ### v1.5[¶](#v1-5 "Permalink to this headline") #### Lab[¶](#id61 "Permalink to this headline") * Add `Experiment.add\_fetcher()` method. * If all columns have the same value in an uncolored table row, make all values bold, not grey. * In `Experiment.add\_resource()` and `Run.add\_resource()` set `dest=None` if you don’t want to copy or link the resource, but only need an alias to reference it in a command. * Write and keep all logfiles only if they actually have content. * Don’t log time and memory consumption of process groups. It is still an unexplained error if too much wall-clock time is used. * Randomize task order for grid experiments by default. Use `randomize\_task\_order=False` to disable this. * Save wall-clock times in properties file. * Do not replace underscores by dashes in table headers. Instead allow browsers to break lines after underscores. * Left-justify string and list values in tables. #### Downward Lab[¶](#id62 "Permalink to this headline") * Add optional *nick* parameter to Translator, Preprocessor and Planner classes. It defaults to the revision name *rev*. * Save `hg id` output for each checkout and include it in reports. * Add *timeout* parameter to `DownwardExperiment.add\_config()`. * Count malformed-logs as unexplained errors. * Pass `legend\_location=None` if you don’t need a legend in your plot. * Pass custom benchmark directories in `DownwardExperiment.add\_suite()` by using the *benchmarks\_dir* keyword argument. * Do not copy logs from preprocess runs into search runs. * Reference preprocessed files in run scripts instead of creating links if `compact=True` is given in the experiment constructor (default). * Remove `unexplained\_error` attribute. Errors are unexplained if `run['error']` starts with ‘unexplained’. * Remove `\*\_error` attributes. It is preferrable to inspect `\*\_returncode` attributes instead (e.g. `search\_returncode`). * Make report generation faster (10-fold speedup for big reports). * Add `DownwardExperiment.add\_search\_parser()` method. * Run `make clean` in revision-cache after compiling preprocessor and search code. * Strip executables after compilation in revision-cache. * Do not copy Lab into experiment directories and grid-steps. Use the global Lab version instead. ### v1.4[¶](#v1-4 "Permalink to this headline") #### Lab[¶](#id63 "Permalink to this headline") * Add [`exp.add\_report()`](index.html#lab.experiment.Experiment.add_report "lab.experiment.Experiment.add_report") method to simplify adding reports. * Use simplejson when available to make loading properties more than twice as fast. * Raise default check-interval in Calls to 5s. This should reduce Lab’s overhead. * Send mail when grid experiment finishes. Usage: `MaiaEnvironment(email='mymail@example.com')`. * Remove `steps.Step.publish\_reports()` method. * Allow creating nested new files in experiment directory (e.g. `exp.add\_new\_file('path/to/file.txt')`). * Remove duplicate attributes from reports. * Make commandline parser available globally as [`lab.experiment.ARGPARSER`](index.html#lab.experiment.ARGPARSER "lab.experiment.ARGPARSER") so users can add custom arguments. * Add `cache\_dir` parameter in [`Experiment`](index.html#lab.experiment.Experiment "lab.experiment.Experiment") for specifying where Lab stores temporary data. #### Downward Lab[¶](#id64 "Permalink to this headline") * Move `downward.experiment.DownwardExperiment` to `downward.experiments.DownwardExperiment`, but keep both import locations indefinitely. * Flag invalid plans in absolute reports. * PlanningReport: When you append ‘\_relative’ to an attribute, you will get a table containing the attribute’s values of each configuration relative to the leftmost column. * Use bzip2 for compressing output.sas files instead of tar+gzip to save space and make opening the files easier. * Use bzip2 instead of gzip for compressing experiment directories to save space. * Color absolute reports by default. * Use log-scale instead of symlog-scale for plots. This produces equidistant grid lines. * By default place legend right of scatter plots. * Remove `--dereference` option from tar command. * Copy (instead of linking) PDDL files into preprocessed-tasks dir. * Add table with Fast Downward commandline strings and revisions to AbsoluteReport. ### v1.3[¶](#v1-3 "Permalink to this headline") #### Lab[¶](#id65 "Permalink to this headline") * For Latex tables only keep the first two and last two hlines. #### Downward Lab[¶](#id66 "Permalink to this headline") * Plots: Make category\_styles a dictionary mapping from names to dictionaries of matplotlib plotting parameters to allow for more and simpler customization. This means e.g. that you can now change the line style in plots. * Produce a combined domain- and problem-wise AbsoluteReport if `resolution=combined`. * Include info in AbsoluteReport if a table has no entries. * Plots: Add `params` argument for specifying matplotlib parameters like font-family, label sizes, line width, etc. * AbsoluteReport: If a non-numerical attribute is included in a domain-wise report, include some info in the table instead of aborting. * Add [`Attribute`](index.html#lab.reports.Attribute "lab.reports.Attribute") class for wrapping custom attributes that need non-default report options and aggregation functions. * Parse `expansions\_until\_last\_jump` attribute. * Tex reports: Add number of tasks per domain with new `\numtasks{x}` command that can be cutomized in the exported texts. * Add pgfplots backend for plots. ### v1.2[¶](#v1-2 "Permalink to this headline") #### Lab[¶](#id67 "Permalink to this headline") * Fetcher: Only copy the link not the content for symbolic links. * Make properties files more compact by using an indent of 2 instead of 4. * Nicer format for commandline help for experiments. * Reports: Only print available attributes if none have been set. * Fetcher: Pass custom parsers to fetcher to parse values from a finished experiment. * For geometric mean calculation substitute 0.1 for values <= 0. * Only show warning if not all attributes for the report are found in the evaluation dir, don’t abort if at least one attribute is found. * If an attribute is None for all runs, do not conclude it is not numeric. * Abort if experiment path contains a colon. * Abort with warning if all runs have been filtered for a report. * Reports: Allow specifying a *single* attribute as a string instead of a list of one string (e.g. attributes=’coverage’). #### Downward Lab[¶](#id68 "Permalink to this headline") * If compact=True for a DownwardExperiment, link to the benchmarks instead of copying them. * Do not call ./build-all script, but build components only if needed. * Fetch and compile sources only when needed: Only prepare translator and preprocessor for preprocessing experiments and only prepare planners for search experiments. Do it in a grid job if possible. * Save space by deleting the benchmarks directories and omitting the search directory and validator for preprocess experiments. * Only support using ‘src’ directory, not the old ‘downward’ dir. * Use `downward` script regardless of other binaries found in the search directory. * Do not try to set parent-revision property. It cannot be determined without fetching the code first. * Make ProblemPlotReport class more general by allowing the get\_points() method to return an arbitrary number of points and categories. * Specify xscale and yscale (linear, log, symlog) in PlotReports. * Fix removing downward.tmp.\* files (use bash for globbing). This greatly reduces the needed space for an experiment. * Label axes in ProblemPlots with `xlabel` and `ylabel`. * If a grid environment is selected, use all CPUs for compiling Fast Downward. * Do not use the same plot style again if it has already been assigned by the user. * Only write plot if valid points have been added. * DownwardExperiment: Add member `include\_preprocess\_results\_in\_search\_runs`. * Colored reports: If all configs have the same value in a row and some are None, highlight the values in green instead of making them grey. * Never set ‘error’ to ‘none’ if ‘search\_error’ is true. * PlotReport: Add `legend\_location` parameter. * Plots: Sort coordinates by x-value for correct connections between points. * Plots: Filter duplicate coordinates for nicer drawing. * Use less padding for linear scatterplots. * Scatterplots: Add `show\_missing` parameter. * Absolute reports: For absolute attributes (e.g. coverage) print a list of numbers of problems behind the domain name if not all configs have a value for the same number of problems. * Make ‘unsolvable’ an absolute attribute, i.e. one where we consider problem runs for which not all configs have a value. * If a non-numeric attribute is present in a domain-wise report, state its type in the error message. * Let plots use the `format` parameter given in constructor. * Allow generation of pgf plot files (only available in matplotlib 1.2). * Allow generation of pdf and eps plots. * DownwardReport: Allow passing a single function for `derived\_properties`. * Plots: Remove code that sets parameters explicitly, sort items in legend. * Add parameters to PlotReport that set the axes’ limits. * Add more items to Downward Lab FAQ. ### v1.1[¶](#v1-1 "Permalink to this headline") #### Lab[¶](#id69 "Permalink to this headline") * Add filter shortcuts: `filter\_config\_nick=['lama', 'hcea'], filter\_domain=['depot']` (see [`Report`](index.html#lab.reports.Report "lab.reports.Report")) (Florian) * Ability to use more than one filter function (Florian) * Pass an optional filter to `Fetcher` to fetch only a subset of results (Florian) * Better handling of timeouts and memory-outs (Florian) * Try to guess error reason when run was killed because of resource limits (Florian) * Do not abort after failed commands by default * Grid: When –all is passed only run all steps if none are supplied * Environments: Support Uni Basel maia cluster (Malte) * Add “pi” example * Add example showing how to parse custom attributes * Do not add resources and files again if they are already added to the experiment * Abort if no runs have been added to the experiment * Round all float values for the tables * Add function `lab.tools.sendmail()` for sending e-mails * Many bugfixes * Added more tests * Improved documentation #### Downward Lab[¶](#id70 "Permalink to this headline") * Make the files output.sas, domain.pddl and problem.pddl optional for search experiments * Use more compact table of contents for AbsoluteReports * Use named anchors in AbsoluteReport (`report.html#expansions`, `report.html#expansions-gripper`) * Add colored absolute tables (see [`AbsoluteReport`](index.html#downward.reports.absolute.AbsoluteReport "downward.reports.absolute.AbsoluteReport")) * Do not add summary functions in problem-wise reports * New report class `ProblemPlotReport` * Save more properties about experiments in the experiments’s properties file for easy lookup (suite, configs, portfolios, etc.) * Use separate table for each domain in problem-wise reports * Sort table columns based on given config filters if given (Florian) * Do not add VAL source files to experiment * Parse number of reopened states * Remove temporary Fast Downward files even if planner was killed * Divide scatter-plot points into categories and lable them (see [`ScatterPlotReport`](index.html#downward.reports.scatter.ScatterPlotReport "downward.reports.scatter.ScatterPlotReport")) (Florian) * Only add a highlighting and summary functions for numeric attributes in AbsoluteReports * Compile validator if it isn’t compiled already * Downward suites: Allow writing SUITE\_NAME\_FIRST to run the first instance of all domains in SUITE\_NAME * LocalEnvironment: If `processes` is given, use as many jobs to compile the planner in parallel * Check python version before creating preprocess experiment * Add avg, min, max and stddev rows to relative reports * Add RelativeReport * Add `DownwardExperiment.set\_path\_to\_python()` * Many bugfixes * Improved documentation

datastore

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| | | | --- | --- | | [appendDatastore() (datastore.core.basic.DatastoreCollection method)](package/datastore.core.html#datastore.core.basic.DatastoreCollection.appendDatastore) [apply\_filter() (datastore.core.query.Cursor method)](package/datastore.core.html#datastore.core.query.Cursor.apply_filter) [apply\_limit() (datastore.core.query.Cursor method)](package/datastore.core.html#datastore.core.query.Cursor.apply_limit) | [apply\_offset() (datastore.core.query.Cursor method)](package/datastore.core.html#datastore.core.query.Cursor.apply_offset) [apply\_order() (datastore.core.query.Cursor method)](package/datastore.core.html#datastore.core.query.Cursor.apply_order) | C - | | | | --- | --- | | [CacheShimDatastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.CacheShimDatastore) [chain\_gen() (in module datastore.core.query)](package/datastore.core.html#datastore.core.query.chain_gen) [child() (datastore.core.key.Key method)](package/datastore.core.html#datastore.core.key.Key.child) [conditional\_operators (datastore.core.query.Filter attribute)](package/datastore.core.html#datastore.core.query.Filter.conditional_operators) | [contains() (datastore.core.basic.CacheShimDatastore method)](package/datastore.core.html#datastore.core.basic.CacheShimDatastore.contains) [(datastore.core.basic.Datastore method)](package/datastore.core.html#datastore.core.basic.Datastore.contains) [(datastore.core.basic.DictDatastore method)](package/datastore.core.html#datastore.core.basic.DictDatastore.contains) [(datastore.core.basic.KeyTransformDatastore method)](package/datastore.core.html#datastore.core.basic.KeyTransformDatastore.contains) [(datastore.core.basic.LoggingDatastore method)](package/datastore.core.html#datastore.core.basic.LoggingDatastore.contains) [(datastore.core.basic.ShardedDatastore method)](package/datastore.core.html#datastore.core.basic.ShardedDatastore.contains) [(datastore.core.basic.TieredDatastore method)](package/datastore.core.html#datastore.core.basic.TieredDatastore.contains) [(datastore.filesystem.FileSystemDatastore method)](package/datastore.filesystem.html#datastore.filesystem.FileSystemDatastore.contains) [copy() (datastore.core.query.Query method)](package/datastore.core.html#datastore.core.query.Query.copy) [Cursor (class in datastore.core.query)](package/datastore.core.html#datastore.core.query.Cursor) | D - | | | | --- | --- | | [Datastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.Datastore) [datastore() (datastore.core.basic.DatastoreCollection method)](package/datastore.core.html#datastore.core.basic.DatastoreCollection.datastore) [datastore.core.\_\_init\_\_ (module)](package/datastore.core.html#module-datastore.core.__init__) [datastore.core.basic (module)](package/datastore.core.html#module-datastore.core.basic) [datastore.core.key (module)](package/datastore.core.html#module-datastore.core.key) [datastore.core.query (module)](package/datastore.core.html#module-datastore.core.query) [datastore.core.serialize (module)](package/datastore.core.html#module-datastore.core.serialize) [datastore.filesystem (module)](package/datastore.filesystem.html#module-datastore.filesystem) [DatastoreCollection (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.DatastoreCollection) | [delete() (datastore.core.basic.CacheShimDatastore method)](package/datastore.core.html#datastore.core.basic.CacheShimDatastore.delete) [(datastore.core.basic.Datastore method)](package/datastore.core.html#datastore.core.basic.Datastore.delete) [(datastore.core.basic.DictDatastore method)](package/datastore.core.html#datastore.core.basic.DictDatastore.delete) [(datastore.core.basic.DirectoryDatastore method)](package/datastore.core.html#datastore.core.basic.DirectoryDatastore.delete) [(datastore.core.basic.InterfaceMappingDatastore method)](package/datastore.core.html#datastore.core.basic.InterfaceMappingDatastore.delete) [(datastore.core.basic.KeyTransformDatastore method)](package/datastore.core.html#datastore.core.basic.KeyTransformDatastore.delete) [(datastore.core.basic.LoggingDatastore method)](package/datastore.core.html#datastore.core.basic.LoggingDatastore.delete) [(datastore.core.basic.NullDatastore method)](package/datastore.core.html#datastore.core.basic.NullDatastore.delete) [(datastore.core.basic.ShardedDatastore method)](package/datastore.core.html#datastore.core.basic.ShardedDatastore.delete) [(datastore.core.basic.ShimDatastore method)](package/datastore.core.html#datastore.core.basic.ShimDatastore.delete) [(datastore.core.basic.TieredDatastore method)](package/datastore.core.html#datastore.core.basic.TieredDatastore.delete) [(datastore.filesystem.FileSystemDatastore method)](package/datastore.filesystem.html#datastore.filesystem.FileSystemDatastore.delete) [deserialized\_gen() (in module datastore.core.serialize)](package/datastore.core.html#datastore.core.serialize.deserialized_gen) [deserializedValue() (datastore.core.serialize.SerializerShimDatastore method)](package/datastore.core.html#datastore.core.serialize.SerializerShimDatastore.deserializedValue) [dict() (datastore.core.query.Query method)](package/datastore.core.html#datastore.core.query.Query.dict) [DictDatastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.DictDatastore) [directory() (datastore.core.basic.DirectoryDatastore method)](package/datastore.core.html#datastore.core.basic.DirectoryDatastore.directory) [directory\_values\_generator() (datastore.core.basic.DirectoryDatastore method)](package/datastore.core.html#datastore.core.basic.DirectoryDatastore.directory_values_generator) [DirectoryDatastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.DirectoryDatastore) [dumps() (datastore.core.serialize.map\_serializer class method)](package/datastore.core.html#datastore.core.serialize.map_serializer.dumps) [(datastore.core.serialize.NonSerializer class method)](package/datastore.core.html#datastore.core.serialize.NonSerializer.dumps) [(datastore.core.serialize.Serializer class method)](package/datastore.core.html#datastore.core.serialize.Serializer.dumps) [(datastore.core.serialize.Stack method)](package/datastore.core.html#datastore.core.serialize.Stack.dumps) [(datastore.core.serialize.prettyjson class method)](package/datastore.core.html#datastore.core.serialize.prettyjson.dumps) | E - | | | --- | | [ensure\_directory\_exists() (in module datastore.filesystem)](package/datastore.filesystem.html#datastore.filesystem.ensure_directory_exists) | F - | | | | --- | --- | | [field (datastore.core.key.Namespace attribute)](package/datastore.core.html#datastore.core.key.Namespace.field) [FileSystemDatastore (class in datastore.filesystem)](package/datastore.filesystem.html#datastore.filesystem.FileSystemDatastore) [Filter (class in datastore.core.query)](package/datastore.core.html#datastore.core.query.Filter) | [filter() (datastore.core.query.Filter class method)](package/datastore.core.html#datastore.core.query.Filter.filter) [(datastore.core.query.Query method)](package/datastore.core.html#datastore.core.query.Query.filter) [from\_dict() (datastore.core.query.Query class method)](package/datastore.core.html#datastore.core.query.Query.from_dict) | G - | | | | --- | --- | | [generator() (datastore.core.query.Filter method)](package/datastore.core.html#datastore.core.query.Filter.generator) | [get() (datastore.core.basic.CacheShimDatastore method)](package/datastore.core.html#datastore.core.basic.CacheShimDatastore.get) [(datastore.core.basic.Datastore method)](package/datastore.core.html#datastore.core.basic.Datastore.get) [(datastore.core.basic.DictDatastore method)](package/datastore.core.html#datastore.core.basic.DictDatastore.get) [(datastore.core.basic.InterfaceMappingDatastore method)](package/datastore.core.html#datastore.core.basic.InterfaceMappingDatastore.get) [(datastore.core.basic.KeyTransformDatastore method)](package/datastore.core.html#datastore.core.basic.KeyTransformDatastore.get) [(datastore.core.basic.LoggingDatastore method)](package/datastore.core.html#datastore.core.basic.LoggingDatastore.get) [(datastore.core.basic.NullDatastore method)](package/datastore.core.html#datastore.core.basic.NullDatastore.get) [(datastore.core.basic.ShardedDatastore method)](package/datastore.core.html#datastore.core.basic.ShardedDatastore.get) [(datastore.core.basic.ShimDatastore method)](package/datastore.core.html#datastore.core.basic.ShimDatastore.get) [(datastore.core.basic.SymlinkDatastore method)](package/datastore.core.html#datastore.core.basic.SymlinkDatastore.get) [(datastore.core.basic.TieredDatastore method)](package/datastore.core.html#datastore.core.basic.TieredDatastore.get) [(datastore.core.serialize.SerializerShimDatastore method)](package/datastore.core.html#datastore.core.serialize.SerializerShimDatastore.get) [(datastore.filesystem.FileSystemDatastore method)](package/datastore.filesystem.html#datastore.filesystem.FileSystemDatastore.get) | I - | | | | --- | --- | | [ignore\_list (datastore.filesystem.FileSystemDatastore attribute)](package/datastore.filesystem.html#datastore.filesystem.FileSystemDatastore.ignore_list) [implements\_serializer\_interface() (datastore.core.serialize.Serializer static method)](package/datastore.core.html#datastore.core.serialize.Serializer.implements_serializer_interface) [insertDatastore() (datastore.core.basic.DatastoreCollection method)](package/datastore.core.html#datastore.core.basic.DatastoreCollection.insertDatastore) [instance() (datastore.core.key.Key method)](package/datastore.core.html#datastore.core.key.Key.instance) [InterfaceMappingDatastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.InterfaceMappingDatastore) [is\_iterable() (in module datastore.core.query)](package/datastore.core.html#datastore.core.query.is_iterable) | [isAncestorOf() (datastore.core.key.Key method)](package/datastore.core.html#datastore.core.key.Key.isAncestorOf) [isAscending() (datastore.core.query.Order method)](package/datastore.core.html#datastore.core.query.Order.isAscending) [isDescendantOf() (datastore.core.key.Key method)](package/datastore.core.html#datastore.core.key.Key.isDescendantOf) [isDescending() (datastore.core.query.Order method)](package/datastore.core.html#datastore.core.query.Order.isDescending) [isTopLevel() (datastore.core.key.Key method)](package/datastore.core.html#datastore.core.key.Key.isTopLevel) | K - | | | | --- | --- | | [Key (class in datastore.core.key)](package/datastore.core.html#datastore.core.key.Key) [keyfn() (datastore.core.query.Order method)](package/datastore.core.html#datastore.core.query.Order.keyfn) | [KeyTransformDatastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.KeyTransformDatastore) | L - | | | | --- | --- | | [limit\_gen() (in module datastore.core.query)](package/datastore.core.html#datastore.core.query.limit_gen) [link() (datastore.core.basic.SymlinkDatastore method)](package/datastore.core.html#datastore.core.basic.SymlinkDatastore.link) [list (datastore.core.key.Key attribute)](package/datastore.core.html#datastore.core.key.Key.list) [loads() (datastore.core.serialize.map\_serializer class method)](package/datastore.core.html#datastore.core.serialize.map_serializer.loads) [(datastore.core.serialize.NonSerializer class method)](package/datastore.core.html#datastore.core.serialize.NonSerializer.loads) [(datastore.core.serialize.Serializer class method)](package/datastore.core.html#datastore.core.serialize.Serializer.loads) [(datastore.core.serialize.Stack method)](package/datastore.core.html#datastore.core.serialize.Stack.loads) [(datastore.core.serialize.prettyjson class method)](package/datastore.core.html#datastore.core.serialize.prettyjson.loads) | [LoggingDatastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.LoggingDatastore) [lowercaseKey() (datastore.core.basic.LowercaseKeyDatastore class method)](package/datastore.core.html#datastore.core.basic.LowercaseKeyDatastore.lowercaseKey) [LowercaseKeyDatastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.LowercaseKeyDatastore) | M - | | | | --- | --- | | [map\_serializer (class in datastore.core.serialize)](package/datastore.core.html#datastore.core.serialize.map_serializer) [monkey\_patch\_bson() (in module datastore.core.serialize)](package/datastore.core.html#datastore.core.serialize.monkey_patch_bson) | [multipleOrderComparison() (datastore.core.query.Order class method)](package/datastore.core.html#datastore.core.query.Order.multipleOrderComparison) | N - | | | | --- | --- | | [name (datastore.core.key.Key attribute)](package/datastore.core.html#datastore.core.key.Key.name) [Namespace (class in datastore.core.key)](package/datastore.core.html#datastore.core.key.Namespace) [namespace\_delimiter (datastore.core.key.Namespace attribute)](package/datastore.core.html#datastore.core.key.Namespace.namespace_delimiter) [NamespaceDatastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.NamespaceDatastore) [namespaceKey() (datastore.core.basic.NamespaceDatastore method)](package/datastore.core.html#datastore.core.basic.NamespaceDatastore.namespaceKey) [namespaces (datastore.core.key.Key attribute)](package/datastore.core.html#datastore.core.key.Key.namespaces) | [nestedPath() (datastore.core.basic.NestedPathDatastore static method)](package/datastore.core.html#datastore.core.basic.NestedPathDatastore.nestedPath) [NestedPathDatastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.NestedPathDatastore) [nestKey() (datastore.core.basic.NestedPathDatastore method)](package/datastore.core.html#datastore.core.basic.NestedPathDatastore.nestKey) [next() (datastore.core.query.Cursor method)](package/datastore.core.html#datastore.core.query.Cursor.next) [NonSerializer (class in datastore.core.serialize)](package/datastore.core.html#datastore.core.serialize.NonSerializer) [NullDatastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.NullDatastore) | O - | | | | --- | --- | | [object\_extension (datastore.filesystem.FileSystemDatastore attribute)](package/datastore.filesystem.html#datastore.filesystem.FileSystemDatastore.object_extension) [object\_getattr() (datastore.core.query.Filter static method)](package/datastore.core.html#datastore.core.query.Filter.object_getattr) [(datastore.core.query.Order static method)](package/datastore.core.html#datastore.core.query.Order.object_getattr) [(datastore.core.query.Query static method)](package/datastore.core.html#datastore.core.query.Query.object_getattr) [object\_path() (datastore.filesystem.FileSystemDatastore method)](package/datastore.filesystem.html#datastore.filesystem.FileSystemDatastore.object_path) [offset\_gen() (in module datastore.core.query)](package/datastore.core.html#datastore.core.query.offset_gen) | [Order (class in datastore.core.query)](package/datastore.core.html#datastore.core.query.Order) [order() (datastore.core.query.Query method)](package/datastore.core.html#datastore.core.query.Query.order) [order\_operators (datastore.core.query.Order attribute)](package/datastore.core.html#datastore.core.query.Order.order_operators) | P - | | | | --- | --- | | [parent (datastore.core.key.Key attribute)](package/datastore.core.html#datastore.core.key.Key.parent) [path (datastore.core.key.Key attribute)](package/datastore.core.html#datastore.core.key.Key.path) [path() (datastore.filesystem.FileSystemDatastore method)](package/datastore.filesystem.html#datastore.filesystem.FileSystemDatastore.path) | [prettyjson (class in datastore.core.serialize)](package/datastore.core.html#datastore.core.serialize.prettyjson) [put() (datastore.core.basic.CacheShimDatastore method)](package/datastore.core.html#datastore.core.basic.CacheShimDatastore.put) [(datastore.core.basic.Datastore method)](package/datastore.core.html#datastore.core.basic.Datastore.put) [(datastore.core.basic.DictDatastore method)](package/datastore.core.html#datastore.core.basic.DictDatastore.put) [(datastore.core.basic.DirectoryDatastore method)](package/datastore.core.html#datastore.core.basic.DirectoryDatastore.put) [(datastore.core.basic.InterfaceMappingDatastore method)](package/datastore.core.html#datastore.core.basic.InterfaceMappingDatastore.put) [(datastore.core.basic.KeyTransformDatastore method)](package/datastore.core.html#datastore.core.basic.KeyTransformDatastore.put) [(datastore.core.basic.LoggingDatastore method)](package/datastore.core.html#datastore.core.basic.LoggingDatastore.put) [(datastore.core.basic.NullDatastore method)](package/datastore.core.html#datastore.core.basic.NullDatastore.put) [(datastore.core.basic.ShardedDatastore method)](package/datastore.core.html#datastore.core.basic.ShardedDatastore.put) [(datastore.core.basic.ShimDatastore method)](package/datastore.core.html#datastore.core.basic.ShimDatastore.put) [(datastore.core.basic.SymlinkDatastore method)](package/datastore.core.html#datastore.core.basic.SymlinkDatastore.put) [(datastore.core.basic.TieredDatastore method)](package/datastore.core.html#datastore.core.basic.TieredDatastore.put) [(datastore.core.serialize.SerializerShimDatastore method)](package/datastore.core.html#datastore.core.serialize.SerializerShimDatastore.put) [(datastore.filesystem.FileSystemDatastore method)](package/datastore.filesystem.html#datastore.filesystem.FileSystemDatastore.put) | Q - | | | | --- | --- | | [Query (class in datastore.core.query)](package/datastore.core.html#datastore.core.query.Query) [query (datastore.core.query.Cursor attribute)](package/datastore.core.html#datastore.core.query.Cursor.query) | [query() (datastore.core.basic.Datastore method)](package/datastore.core.html#datastore.core.basic.Datastore.query) [(datastore.core.basic.DictDatastore method)](package/datastore.core.html#datastore.core.basic.DictDatastore.query) [(datastore.core.basic.DirectoryDatastore method)](package/datastore.core.html#datastore.core.basic.DirectoryDatastore.query) [(datastore.core.basic.KeyTransformDatastore method)](package/datastore.core.html#datastore.core.basic.KeyTransformDatastore.query) [(datastore.core.basic.LoggingDatastore method)](package/datastore.core.html#datastore.core.basic.LoggingDatastore.query) [(datastore.core.basic.NestedPathDatastore method)](package/datastore.core.html#datastore.core.basic.NestedPathDatastore.query) [(datastore.core.basic.NullDatastore method)](package/datastore.core.html#datastore.core.basic.NullDatastore.query) [(datastore.core.basic.ShardedDatastore method)](package/datastore.core.html#datastore.core.basic.ShardedDatastore.query) [(datastore.core.basic.ShimDatastore method)](package/datastore.core.html#datastore.core.basic.ShimDatastore.query) [(datastore.core.basic.SymlinkDatastore method)](package/datastore.core.html#datastore.core.basic.SymlinkDatastore.query) [(datastore.core.basic.TieredDatastore method)](package/datastore.core.html#datastore.core.basic.TieredDatastore.query) [(datastore.core.serialize.SerializerShimDatastore method)](package/datastore.core.html#datastore.core.serialize.SerializerShimDatastore.query) [(datastore.filesystem.FileSystemDatastore method)](package/datastore.filesystem.html#datastore.filesystem.FileSystemDatastore.query) | R - | | | | --- | --- | | [randomKey() (datastore.core.key.Key class method)](package/datastore.core.html#datastore.core.key.Key.randomKey) [relative\_object\_path() (datastore.filesystem.FileSystemDatastore method)](package/datastore.filesystem.html#datastore.filesystem.FileSystemDatastore.relative_object_path) [relative\_path() (datastore.filesystem.FileSystemDatastore method)](package/datastore.filesystem.html#datastore.filesystem.FileSystemDatastore.relative_path) [removeDatastore() (datastore.core.basic.DatastoreCollection method)](package/datastore.core.html#datastore.core.basic.DatastoreCollection.removeDatastore) | [removeDuplicateSlashes() (datastore.core.key.Key class method)](package/datastore.core.html#datastore.core.key.Key.removeDuplicateSlashes) [returned (datastore.core.query.Cursor attribute)](package/datastore.core.html#datastore.core.query.Cursor.returned) [reverse (datastore.core.key.Key attribute)](package/datastore.core.html#datastore.core.key.Key.reverse) | S - | | | | --- | --- | | [sentinel (datastore.core.basic.SymlinkDatastore attribute)](package/datastore.core.html#datastore.core.basic.SymlinkDatastore.sentinel) [(datastore.core.serialize.map\_serializer attribute)](package/datastore.core.html#datastore.core.serialize.map_serializer.sentinel) [serialized\_gen() (in module datastore.core.serialize)](package/datastore.core.html#datastore.core.serialize.serialized_gen) [serializedValue() (datastore.core.serialize.SerializerShimDatastore method)](package/datastore.core.html#datastore.core.serialize.SerializerShimDatastore.serializedValue) [Serializer (class in datastore.core.serialize)](package/datastore.core.html#datastore.core.serialize.Serializer) [serializer (datastore.core.serialize.SerializerShimDatastore attribute)](package/datastore.core.html#datastore.core.serialize.SerializerShimDatastore.serializer) [SerializerShimDatastore (class in datastore.core.serialize)](package/datastore.core.html#datastore.core.serialize.SerializerShimDatastore) [shard() (datastore.core.basic.ShardedDatastore method)](package/datastore.core.html#datastore.core.basic.ShardedDatastore.shard) [shard\_query\_generator() (datastore.core.basic.ShardedDatastore method)](package/datastore.core.html#datastore.core.basic.ShardedDatastore.shard_query_generator) | [shardDatastore() (datastore.core.basic.ShardedDatastore method)](package/datastore.core.html#datastore.core.basic.ShardedDatastore.shardDatastore) [ShardedDatastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.ShardedDatastore) [shim() (in module datastore.core.serialize)](package/datastore.core.html#datastore.core.serialize.shim) [ShimDatastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.ShimDatastore) [skipped (datastore.core.query.Cursor attribute)](package/datastore.core.html#datastore.core.query.Cursor.skipped) [sorted() (datastore.core.query.Order class method)](package/datastore.core.html#datastore.core.query.Order.sorted) [Stack (class in datastore.core.serialize)](package/datastore.core.html#datastore.core.serialize.Stack) [SymlinkDatastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.SymlinkDatastore) | T - | | | | --- | --- | | [TieredDatastore (class in datastore.core.basic)](package/datastore.core.html#datastore.core.basic.TieredDatastore) | [type (datastore.core.key.Key attribute)](package/datastore.core.html#datastore.core.key.Key.type) | V - | | | | --- | --- | | [value (datastore.core.key.Namespace attribute)](package/datastore.core.html#datastore.core.key.Namespace.value) | [valuePasses() (datastore.core.query.Filter method)](package/datastore.core.html#datastore.core.query.Filter.valuePasses) | ### This Page * [Show on GitHub](https://github.com/jbenet/datastore/blob/master/docs/genindex.rst) * [Edit on GitHub](https://github.com/jbenet/datastore/edit/master/docs/genindex.rst) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](# "General Index") * [modules](py-modindex.html "Python Module Index") | * [datastore 0.3.0 documentation](index.html) » © Copyright 2011, Juan Batiz-Benet. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3+. .rtd-badge { position: fixed; display: block; bottom: 5px; height: 40px; text-indent: -9999em; border-radius: 3px; -moz-border-radius: 3px; -webkit-border-radius: 3px; box-shadow: 0 1px 0 rgba(0, 0, 0, 0.2), 0 1px 0 rgba(255, 255, 255, 0.2) inset; -moz-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.2), 0 1px 0 rgba(255, 255, 255, 0.2) inset; -webkit-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.2), 0 1px 0 rgba(255, 255, 255, 0.2) inset; } #version\_menu { position: fixed; display: none; bottom: 11px; right: 166px; list-style-type: none; margin: 0; } .footer\_popout:hover #version\_menu { display: block; } #version\_menu li { display: block; float: right; } #version\_menu li a { display: block; padding: 6px 10px 4px 10px; margin: 7px 7px 0 0; font-weight: bold; font-size: 14px; height: 20px; line-height: 17px; text-decoration: none; color: #fff; background: #8ca1af url(../images/gradient-light.png) bottom left repeat-x; border-radius: 3px; -moz-border-radius: 3px; -webkit-border-radius: 3px; box-shadow: 0 1px 1px #465158; -moz-box-shadow: 0 1px 1px #465158; -webkit-box-shadow: 0 1px 1px #465158; text-shadow: 0 1px 1px rgba(0, 0, 0, 0.5); } #version\_menu li a:hover { text-decoration: none; background-color: #697983; box-shadow: 0 1px 0px #465158; -moz-box-shadow: 0 1px 0px #465158; -webkit-box-shadow: 0 1px 0px #465158; } .rtd-badge.rtd { background: #3b4449 url(//media.readthedocs.org//images/badge-rtd.png) scroll 0px -46px no-repeat; border: 1px solid #282E32; width: 41px; right: 5px; } .footer\_popout:hover .rtd-badge.rtd { background-position: top left; width: 160px; } .rtd-badge.revsys { background: #465158 url(//media.readthedocs.org//images/badge-revsys.png) top left no-repeat; border: 1px solid #1C5871; width: 290px; right: 173px; } .rtd-badge.revsys-inline-sponsored { position: inherit; margin-left: auto; margin-right: 175px; margin-bottom: 5px; background: #465158 url(//media.readthedocs.org//images/badge-revsys.png) top left no-repeat; border: 1px solid #1C5871; width: 290px; right: 173px; } .rtd-badge.revsys-inline { position: inherit; margin-left: auto; margin-right: 175px; margin-bottom: 5px; background: #465158 url(//media.readthedocs.org//images/badge-revsys-sm.png) top left no-repeat; border: 1px solid #1C5871; width: 205px; right: 173px; } [Brought to you by Read the Docs](//readthedocs.org/projects/datastore/?fromdocs=datastore) * [latest](/en/latest/) datastore - simple, unified API for multiple data stores — datastore 0.3.0 documentation ### Navigation * [index](genindex.html "General Index") * [modules](py-modindex.html "Python Module Index") | * [next](api/index.html "Core datastore API") | * [datastore 0.3.0 documentation](#) » datastore - simple, unified API for multiple data stores[¶](#datastore-simple-unified-api-for-multiple-data-stores "Permalink to this headline") ================================================================================================================================================ Overview[¶](#overview "Permalink to this headline") --------------------------------------------------- datastore is a generic layer of abstraction for data store and database access. It is a **simple** API with the aim to enable application development in a datastore-agnostic way, allowing datastores to be swapped seamlessly without changing application code. Thus, one can leverage different datastores with different strengths without committing the application to one datastore throughout its lifetime. In addition, grouped datastores significantly simplify complex data access patterns, such as caching and sharding. Documentation[¶](#documentation "Permalink to this headline") ------------------------------------------------------------- The [*Core datastore API*](api/index.html#api) contains documentation of the core library. * [Core datastore API](api/index.html) + [Keys](api/key.html) + [Queries](api/query.html) + [Basic Datastores](api/basic.html) + [Shims](api/shims.html) + [Collections](api/collections.html) + [Serialize](api/serialize.html) + [datastore base class](api/index.html#datastore-base-class) + [Examples](api/index.html#examples) Package Hierarchy: * [datastore Package](package/datastore.html) + [Subpackages](package/datastore.html#subpackages) - [datastore.core](package/datastore.core.html) * [datastore.basic](package/datastore.core.html#module-datastore.core.basic) * [datastore.key](package/datastore.core.html#module-datastore.core.key) * [datastore.query](package/datastore.core.html#module-datastore.core.query) * [datastore.serialize](package/datastore.core.html#module-datastore.core.serialize) - [datastore.core.util](package/datastore.util.html) * [datastore.core.util.fasthash](package/datastore.util.html#datastore-core-util-fasthash) - [datastore.filesystem](package/datastore.filesystem.html) Install[¶](#install "Permalink to this headline") ------------------------------------------------- From pypi (using pip): ``` sudo pip install datastore ``` From pypi (using setuptools): ``` sudo easy_install datastore ``` From source: ``` git clone https://github.com/jbenet/datastore/ cd datastore sudo python setup.py install ``` ### License[¶](#license "Permalink to this headline") datastore is under the MIT Licence ### Contact[¶](#contact "Permalink to this headline") datastore is written by [Juan Batiz-Benet](<https://github.com/jbenet>). It was originally part of [`py-dronestore<https://github.com/jbenet/py-dronestore>`\_](#id1) On December 2011, it was re-written as a standalone project. Project Homepage: <https://github.com/jbenet/datastore> Feel free to contact me. But please file issues in github first. Cheers! Indices and tables[¶](#indices-and-tables "Permalink to this headline") ----------------------------------------------------------------------- * [*Index*](genindex.html) * [*Module Index*](py-modindex.html) * [*Search Page*](search.html) Hello Worlds[¶](#hello-worlds "Permalink to this headline") ----------------------------------------------------------- To illustrate the api and how it works across different data storage systems, here is Hello World in various datastores. Note the common code. ### Hello Dict[¶](#hello-dict "Permalink to this headline") ``` >>> import datastore.core >>> >>> ds = datastore.DictDatastore() >>> >>> hello = datastore.Key('hello') >>> ds.put(hello, 'world') >>> ds.contains(hello) True >>> ds.get(hello) 'world' >>> ds.delete(hello) >>> ds.get(hello) None ``` ### Hello filesystem[¶](#hello-filesystem "Permalink to this headline") ``` >>> import datastore.filesystem >>> >>> ds = datastore.filesystem.FileSystemDatastore('/tmp/.test\_datastore') >>> >>> hello = datastore.Key('hello') >>> ds.put(hello, 'world') >>> ds.contains(hello) True >>> ds.get(hello) 'world' >>> ds.delete(hello) >>> ds.get(hello) None ``` ### Hello Serialization[¶](#hello-serialization "Permalink to this headline") ``` >>> import datastore.core >>> import json >>> >>> ds\_child = datastore.DictDatastore() >>> ds = datastore.serialize.shim(ds\_child, json) >>> >>> hello = datastore.Key('hello') >>> ds.put(hello, 'world') >>> ds.contains(hello) True >>> ds.get(hello) 'world' >>> ds.delete(hello) >>> ds.get(hello) None ``` ### Hello Tiered Access[¶](#hello-tiered-access "Permalink to this headline") ``` >>> import pymongo >>> import datastore.core >>> >>> from datastore.mongo import MongoDatastore >>> from datastore.pylru import LRUCacheDatastore >>> from datastore.filesystem import FileSystemDatastore >>> >>> conn = pymongo.Connection() >>> mongo = MongoDatastore(conn.test\_db) >>> >>> cache = LRUCacheDatastore(1000) >>> fs = FileSystemDatastore('/tmp/.test\_db') >>> >>> ds = datastore.TieredDatastore([cache, mongo, fs]) >>> >>> hello = datastore.Key('hello') >>> ds.put(hello, 'world') >>> ds.contains(hello) True >>> ds.get(hello) 'world' >>> ds.delete(hello) >>> ds.get(hello) None ``` ### Hello Sharding[¶](#hello-sharding "Permalink to this headline") ``` >>> import datastore.core >>> >>> shards = [datastore.DictDatastore() for i in range(0, 10)] >>> >>> ds = datastore.ShardedDatastore(shards) >>> >>> hello = datastore.Key('hello') >>> ds.put(hello, 'world') >>> ds.contains(hello) True >>> ds.get(hello) 'world' >>> ds.delete(hello) >>> ds.get(hello) None ``` ### [Table Of Contents](#) * [datastore - simple, unified API for multiple data stores](#) + [Overview](#overview) + [Documentation](#documentation) + [Install](#install) - [License](#license) - [Contact](#contact) + [Indices and tables](#indices-and-tables) + [Hello Worlds](#hello-worlds) - [Hello Dict](#hello-dict) - [Hello filesystem](#hello-filesystem) - [Hello Serialization](#hello-serialization) - [Hello Tiered Access](#hello-tiered-access) - [Hello Sharding](#hello-sharding) #### Next topic [Core datastore API](api/index.html "next chapter") ### This Page * [Show Source](_sources/index.txt) * [Show on GitHub](https://github.com/jbenet/datastore/blob/master/docs/index.rst) * [Edit on GitHub](https://github.com/jbenet/datastore/edit/master/docs/index.rst) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](genindex.html "General Index") * [modules](py-modindex.html "Python Module Index") | * [next](api/index.html "Core datastore API") | * [datastore 0.3.0 documentation](#) » © Copyright 2011, Juan Batiz-Benet. 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Tested with: * Journaled HFS+ (Mac OS X 10.7.2) *class* datastore.filesystem.FileSystemDatastore(*root*, *case\_sensitive=True*)[¶](#datastore.filesystem.FileSystemDatastore "Permalink to this definition") Bases: [datastore.core.basic.Datastore](datastore.core.html#datastore.core.basic.Datastore "datastore.core.basic.Datastore") Simple flat-file datastore. FileSystemDatastore will store objects in independent files in the host’s filesystem. The FileSystemDatastore is initialized with a root path, under which to store all objects. Each object will be stored under its own file: root/key.obj The key portion also replaces namespace parameter delimiters (:) with slashes, creating several nested directories. For example, storing objects under root path ‘/data’ with the following keys: ``` Key('/Comedy:MontyPython/Actor:JohnCleese') Key('/Comedy:MontyPython/Sketch:ArgumentClinic') Key('/Comedy:MontyPython/Sketch:CheeseShop') Key('/Comedy:MontyPython/Sketch:CheeseShop/Character:Mousebender') ``` will yield the file structure: ``` /data/Comedy/MontyPython/Actor/JohnCleese.obj /data/Comedy/MontyPython/Sketch/ArgumentClinic.obj /data/Comedy/MontyPython/Sketch/CheeseShop.obj /data/Comedy/MontyPython/Sketch/CheeseShop/Character/Mousebender.obj ``` Implementation Notes: > > Separating key namespaces (and their parameters) within directories allows > granular querying for under a specific key. For example, a query with key: > > > > ``` > Key('/data/Comedy:MontyPython/Sketch:CheeseShop') > > ``` > > > will query for all objects under Sketch:CheeseShop independently of > queries for: > > > > ``` > Key('/data/Comedy:MontyPython/Sketch') > > ``` > > > Also, using the .obj extension gets around the ambiguity of having both a > CheeseShop object and directory: > > > > ``` > /data/Comedy/MontyPython/Sketch/CheeseShop.obj > /data/Comedy/MontyPython/Sketch/CheeseShop/ > ``` > > > Hello World: ``` >>> import datastore.filesystem >>> >>> ds = datastore.filesystem.FileSystemDatastore('/tmp/.test\_datastore') >>> >>> hello = datastore.Key('hello') >>> ds.put(hello, 'world') >>> ds.contains(hello) True >>> ds.get(hello) 'world' >>> ds.delete(hello) >>> ds.get(hello) None ``` contains(*key*)[¶](#datastore.filesystem.FileSystemDatastore.contains "Permalink to this definition") Returns whether the object named by key exists. Optimized to only check whether the file object exists. Args: key: Key naming the object to check. Returns: boalean whether the object exists delete(*key*)[¶](#datastore.filesystem.FileSystemDatastore.delete "Permalink to this definition") Removes the object named by key. Args: key: Key naming the object to remove. get(*key*)[¶](#datastore.filesystem.FileSystemDatastore.get "Permalink to this definition") Return the object named by key or None if it does not exist. Args: key: Key naming the object to retrieve Returns: object or None ignore\_list *= []*[¶](#datastore.filesystem.FileSystemDatastore.ignore_list "Permalink to this definition") object\_extension *= '.obj'*[¶](#datastore.filesystem.FileSystemDatastore.object_extension "Permalink to this definition") object\_path(*key*)[¶](#datastore.filesystem.FileSystemDatastore.object_path "Permalink to this definition") return the object path for key. path(*key*)[¶](#datastore.filesystem.FileSystemDatastore.path "Permalink to this definition") Returns the path for given key put(*key*, *value*)[¶](#datastore.filesystem.FileSystemDatastore.put "Permalink to this definition") Stores the object value named by key. Args: key: Key naming value value: the object to store. query(*query*)[¶](#datastore.filesystem.FileSystemDatastore.query "Permalink to this definition") Returns an iterable of objects matching criteria expressed in query FSDatastore.query queries all the .obj files within the directory specified by the query.key. Args: query: Query object describing the objects to return. Raturns: Cursor with all objects matching criteria relative\_object\_path(*key*)[¶](#datastore.filesystem.FileSystemDatastore.relative_object_path "Permalink to this definition") Returns the relative path for object pointed by key. relative\_path(*key*)[¶](#datastore.filesystem.FileSystemDatastore.relative_path "Permalink to this definition") Returns the relative path for given key datastore.filesystem.ensure\_directory\_exists(*directory*)[¶](#datastore.filesystem.ensure_directory_exists "Permalink to this definition") Ensures directory exists. May make directory and intermediate dirs. Raises RuntimeError if directory is a file. #### Previous topic [datastore.core.util](datastore.util.html "previous chapter") ### This Page * [Show Source](../_sources/package/datastore.filesystem.txt) * [Show on GitHub](https://github.com/jbenet/datastore/blob/master/docs/package/datastore.filesystem.rst) * [Edit on GitHub](https://github.com/jbenet/datastore/edit/master/docs/package/datastore.filesystem.rst) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../genindex.html "General Index") * [modules](../py-modindex.html "Python Module Index") | * [previous](datastore.util.html "datastore.core.util") | * [datastore 0.3.0 documentation](../index.html) » * [datastore Package](datastore.html) » © Copyright 2011, Juan Batiz-Benet. 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It is a **simple** API with the aim to enable application development in a datastore-agnostic way, allowing datastores to be swapped seamlessly without changing application code. Thus, one can leverage different datastores with different strengths without committing the application to one datastore throughout its lifetime. datastore.basic[¶](#module-datastore.core.basic "Permalink to this headline") ----------------------------------------------------------------------------- *class* datastore.core.basic.CacheShimDatastore(*\*args*, *\*\*kwargs*)[¶](#datastore.core.basic.CacheShimDatastore "Permalink to this definition") Bases: [datastore.core.basic.ShimDatastore](#datastore.core.basic.ShimDatastore "datastore.core.basic.ShimDatastore") Wraps a datastore with a caching shim optimizes some calls. contains(*key*)[¶](#datastore.core.basic.CacheShimDatastore.contains "Permalink to this definition") Returns whether the object named by key exists. First checks cache\_datastore. delete(*key*)[¶](#datastore.core.basic.CacheShimDatastore.delete "Permalink to this definition") Removes the object named by key. Writes to both cache\_datastore and child\_datastore. get(*key*)[¶](#datastore.core.basic.CacheShimDatastore.get "Permalink to this definition") Return the object named by key or None if it does not exist. CacheShimDatastore first checks its cache\_datastore. put(*key*, *value*)[¶](#datastore.core.basic.CacheShimDatastore.put "Permalink to this definition") Stores the object value named by key`self. Writes to both ``cache\_datastore` and child\_datastore. *class* datastore.core.basic.Datastore[¶](#datastore.core.basic.Datastore "Permalink to this definition") Bases: object A Datastore represents storage for any key-value pair. Datastores are general enough to be backed by all kinds of different storage: in-memory caches, databases, a remote datastore, flat files on disk, etc. The general idea is to wrap a more complicated storage facility in a simple, uniform interface, keeping the freedom of using the right tools for the job. In particular, a Datastore can aggregate other datastores in interesting ways, like sharded (to distribute load) or tiered access (caches before databases). While Datastores should be written general enough to accept all sorts of values, some implementations will undoubtedly have to be specific (e.g. SQL databases where fields should be decomposed into columns), particularly to support queries efficiently. contains(*key*)[¶](#datastore.core.basic.Datastore.contains "Permalink to this definition") Returns whether the object named by key exists. The default implementation pays the cost of a get. Some datastore implementations may optimize this. Args: key: Key naming the object to check. Returns: boalean whether the object exists delete(*key*)[¶](#datastore.core.basic.Datastore.delete "Permalink to this definition") Removes the object named by key. Args: key: Key naming the object to remove. get(*key*)[¶](#datastore.core.basic.Datastore.get "Permalink to this definition") Return the object named by key or None if it does not exist. None takes the role of default value, so no KeyError exception is raised. Args: key: Key naming the object to retrieve Returns: object or None put(*key*, *value*)[¶](#datastore.core.basic.Datastore.put "Permalink to this definition") Stores the object value named by key. How to serialize and store objects is up to the underlying datastore. It is recommended to use simple objects (strings, numbers, lists, dicts). Args: key: Key naming value value: the object to store. query(*query*)[¶](#datastore.core.basic.Datastore.query "Permalink to this definition") Returns an iterable of objects matching criteria expressed in query Implementations of query will be the largest differentiating factor amongst datastores. All datastores **must** implement query, even using query’s worst case scenario, see [:ref:class:`Query`](#id1) for details. Args: query: Query object describing the objects to return. Raturns: iterable cursor with all objects matching criteria *class* datastore.core.basic.DatastoreCollection(*stores=*[])[¶](#datastore.core.basic.DatastoreCollection "Permalink to this definition") Bases: [datastore.core.basic.ShimDatastore](#datastore.core.basic.ShimDatastore "datastore.core.basic.ShimDatastore") Represents a collection of datastores. appendDatastore(*store*)[¶](#datastore.core.basic.DatastoreCollection.appendDatastore "Permalink to this definition") Appends datastore store to this collection. datastore(*index*)[¶](#datastore.core.basic.DatastoreCollection.datastore "Permalink to this definition") Returns the datastore at index. insertDatastore(*index*, *store*)[¶](#datastore.core.basic.DatastoreCollection.insertDatastore "Permalink to this definition") Inserts datastore store into this collection at index. removeDatastore(*store*)[¶](#datastore.core.basic.DatastoreCollection.removeDatastore "Permalink to this definition") Removes datastore store from this collection. *class* datastore.core.basic.DictDatastore[¶](#datastore.core.basic.DictDatastore "Permalink to this definition") Bases: [datastore.core.basic.Datastore](#datastore.core.basic.Datastore "datastore.core.basic.Datastore") Simple straw-man in-memory datastore backed by nested dicts. contains(*key*)[¶](#datastore.core.basic.DictDatastore.contains "Permalink to this definition") Returns whether the object named by key exists. Checks for the object in the collection corresponding to key.path. Args: key: Key naming the object to check. Returns: boalean whether the object exists delete(*key*)[¶](#datastore.core.basic.DictDatastore.delete "Permalink to this definition") Removes the object named by key. Removes the object from the collection corresponding to key.path. Args: key: Key naming the object to remove. get(*key*)[¶](#datastore.core.basic.DictDatastore.get "Permalink to this definition") Return the object named by key or None. Retrieves the object from the collection corresponding to key.path. Args: key: Key naming the object to retrieve. Returns: object or None put(*key*, *value*)[¶](#datastore.core.basic.DictDatastore.put "Permalink to this definition") Stores the object value named by key. Stores the object in the collection corresponding to key.path. Args: key: Key naming value value: the object to store. query(*query*)[¶](#datastore.core.basic.DictDatastore.query "Permalink to this definition") Returns an iterable of objects matching criteria expressed in query Naively applies the query operations on the objects within the namespaced collection corresponding to query.key.path. Args: query: Query object describing the objects to return. Raturns: iterable cursor with all objects matching criteria *class* datastore.core.basic.DirectoryDatastore(*datastore*)[¶](#datastore.core.basic.DirectoryDatastore "Permalink to this definition") Bases: [datastore.core.basic.ShimDatastore](#datastore.core.basic.ShimDatastore "datastore.core.basic.ShimDatastore") Datastore that tracks directory entries, like in a filesystem. All key changes cause changes in a collection-like directory. For example: ``` >>> import datastore.core >>> >>> dds = datastore.DictDatastore() >>> rds = datastore.DirectoryDatastore(dds) >>> >>> a = datastore.Key('/A') >>> b = datastore.Key('/A/B') >>> c = datastore.Key('/A/C') >>> >>> rds.get(a) [] >>> rds.put(b, 1) >>> rds.get(b) 1 >>> rds.get(a) ['/A/B'] >>> rds.put(c, 1) >>> rds.get(c) 1 >>> rds.get(a) ['/A/B', '/A/C'] >>> rds.delete(b) >>> rds.get(a) ['/A/C'] >>> rds.delete(c) >>> rds.get(a) [] ``` delete(*key*)[¶](#datastore.core.basic.DirectoryDatastore.delete "Permalink to this definition") Removes the object named by key. DirectoryDatastore removes the directory entry. directory(*key*)[¶](#datastore.core.basic.DirectoryDatastore.directory "Permalink to this definition") Retrieves directory entries for given key. directory\_values\_generator(*key*)[¶](#datastore.core.basic.DirectoryDatastore.directory_values_generator "Permalink to this definition") Retrieve directory values for given key. put(*key*, *value*)[¶](#datastore.core.basic.DirectoryDatastore.put "Permalink to this definition") Stores the object value named by [`](#id3)key`self. DirectoryDatastore stores a directory entry. query(*query*)[¶](#datastore.core.basic.DirectoryDatastore.query "Permalink to this definition") Returns objects matching criteria expressed in query. DirectoryDatastore uses directory entries. *class* datastore.core.basic.InterfaceMappingDatastore(*service*, *get='get'*, *put='put'*, *delete='delete'*, *key=<type 'str'>*)[¶](#datastore.core.basic.InterfaceMappingDatastore "Permalink to this definition") Bases: [datastore.core.basic.Datastore](#datastore.core.basic.Datastore "datastore.core.basic.Datastore") Represents simple wrapper datastore around an object that, though not a Datastore, implements data storage through a similar interface. For example, memcached and redis both implement a get, set, delete interface. delete(*key*)[¶](#datastore.core.basic.InterfaceMappingDatastore.delete "Permalink to this definition") Removes the object named by key in service. Args: key: Key naming the object to remove. get(*key*)[¶](#datastore.core.basic.InterfaceMappingDatastore.get "Permalink to this definition") Return the object in service named by key or None. Args: key: Key naming the object to retrieve. Returns: object or None put(*key*, *value*)[¶](#datastore.core.basic.InterfaceMappingDatastore.put "Permalink to this definition") Stores the object value named by key in service. Args: key: Key naming value. value: the object to store. *class* datastore.core.basic.KeyTransformDatastore(*\*args*, *\*\*kwargs*)[¶](#datastore.core.basic.KeyTransformDatastore "Permalink to this definition") Bases: [datastore.core.basic.ShimDatastore](#datastore.core.basic.ShimDatastore "datastore.core.basic.ShimDatastore") Represents a simple ShimDatastore that applies a transform on all incoming keys. For example: ``` >>> import datastore.core >>> def transform(key): ... return key.reverse ... >>> ds = datastore.DictDatastore() >>> kt = datastore.KeyTransformDatastore(ds, keytransform=transform) None >>> ds.put(datastore.Key('/a/b/c'), 'abc') >>> ds.get(datastore.Key('/a/b/c')) 'abc' >>> kt.get(datastore.Key('/a/b/c')) None >>> kt.get(datastore.Key('/c/b/a')) 'abc' >>> ds.get(datastore.Key('/c/b/a')) None ``` contains(*key*)[¶](#datastore.core.basic.KeyTransformDatastore.contains "Permalink to this definition") Returns whether the object named by key is in this datastore. delete(*key*)[¶](#datastore.core.basic.KeyTransformDatastore.delete "Permalink to this definition") Removes the object named by keytransform(key). get(*key*)[¶](#datastore.core.basic.KeyTransformDatastore.get "Permalink to this definition") Return the object named by keytransform(key). put(*key*, *value*)[¶](#datastore.core.basic.KeyTransformDatastore.put "Permalink to this definition") Stores the object names by keytransform(key). query(*query*)[¶](#datastore.core.basic.KeyTransformDatastore.query "Permalink to this definition") Returns a sequence of objects matching criteria expressed in query *class* datastore.core.basic.LoggingDatastore(*child\_datastore*, *logger=None*)[¶](#datastore.core.basic.LoggingDatastore "Permalink to this definition") Bases: [datastore.core.basic.ShimDatastore](#datastore.core.basic.ShimDatastore "datastore.core.basic.ShimDatastore") Wraps a datastore with a logging shim. contains(*key*)[¶](#datastore.core.basic.LoggingDatastore.contains "Permalink to this definition") Returns whether the object named by key exists. LoggingDatastore logs the access. delete(*key*)[¶](#datastore.core.basic.LoggingDatastore.delete "Permalink to this definition") Removes the object named by key. LoggingDatastore logs the access. get(*key*)[¶](#datastore.core.basic.LoggingDatastore.get "Permalink to this definition") Return the object named by key or None if it does not exist. LoggingDatastore logs the access. put(*key*, *value*)[¶](#datastore.core.basic.LoggingDatastore.put "Permalink to this definition") Stores the object value named by [`](#id5)key`self. LoggingDatastore logs the access. query(*query*)[¶](#datastore.core.basic.LoggingDatastore.query "Permalink to this definition") Returns an iterable of objects matching criteria expressed in query. LoggingDatastore logs the access. *class* datastore.core.basic.LowercaseKeyDatastore(*\*args*, *\*\*kwargs*)[¶](#datastore.core.basic.LowercaseKeyDatastore "Permalink to this definition") Bases: [datastore.core.basic.KeyTransformDatastore](#datastore.core.basic.KeyTransformDatastore "datastore.core.basic.KeyTransformDatastore") Represents a simple ShimDatastore that lowercases all incoming keys. For example: ``` >>> import datastore.core >>> ds = datastore.DictDatastore() >>> ds.put(datastore.Key('hello'), 'world') >>> ds.put(datastore.Key('HELLO'), 'WORLD') >>> ds.get(datastore.Key('hello')) 'world' >>> ds.get(datastore.Key('HELLO')) 'WORLD' >>> ds.get(datastore.Key('HeLlO')) None >>> lds = datastore.LowercaseKeyDatastore(ds) >>> lds.get(datastore.Key('HeLlO')) 'world' >>> lds.get(datastore.Key('HeLlO')) 'world' >>> lds.get(datastore.Key('HeLlO')) 'world' ``` *classmethod* lowercaseKey(*key*)[¶](#datastore.core.basic.LowercaseKeyDatastore.lowercaseKey "Permalink to this definition") Returns a lowercased key. *class* datastore.core.basic.NamespaceDatastore(*namespace*, *\*args*, *\*\*kwargs*)[¶](#datastore.core.basic.NamespaceDatastore "Permalink to this definition") Bases: [datastore.core.basic.KeyTransformDatastore](#datastore.core.basic.KeyTransformDatastore "datastore.core.basic.KeyTransformDatastore") Represents a simple ShimDatastore that namespaces all incoming keys. For example: ``` >>> import datastore.core >>> >>> ds = datastore.DictDatastore() >>> ds.put(datastore.Key('/a/b'), 'ab') >>> ds.put(datastore.Key('/c/d'), 'cd') >>> ds.put(datastore.Key('/a/b/c/d'), 'abcd') >>> >>> nd = datastore.NamespaceDatastore('/a/b', ds) >>> nd.get(datastore.Key('/a/b')) None >>> nd.get(datastore.Key('/c/d')) 'abcd' >>> nd.get(datastore.Key('/a/b/c/d')) None >>> nd.put(datastore.Key('/c/d'), 'cd') >>> ds.get(datastore.Key('/a/b/c/d')) 'cd' ``` namespaceKey(*key*)[¶](#datastore.core.basic.NamespaceDatastore.namespaceKey "Permalink to this definition") Returns a namespaced key: namespace.child(key). *class* datastore.core.basic.NestedPathDatastore(*\*args*, *\*\*kwargs*)[¶](#datastore.core.basic.NestedPathDatastore "Permalink to this definition") Bases: [datastore.core.basic.KeyTransformDatastore](#datastore.core.basic.KeyTransformDatastore "datastore.core.basic.KeyTransformDatastore") Represents a simple ShimDatastore that shards/namespaces incoming keys. Incoming keys are sharded into nested namespaces. The idea is to use the key name to separate into nested namespaces. This is akin to the directory structure that git uses for objects. For example: ``` >>> import datastore.core >>> >>> ds = datastore.DictDatastore() >>> np = datastore.NestedPathDatastore(ds, depth=3, length=2) >>> >>> np.put(datastore.Key('/abcdefghijk'), 1) >>> np.get(datastore.Key('/abcdefghijk')) 1 >>> ds.get(datastore.Key('/abcdefghijk')) None >>> ds.get(datastore.Key('/ab/cd/ef/abcdefghijk')) 1 >>> np.put(datastore.Key('abc'), 2) >>> np.get(datastore.Key('abc')) 2 >>> ds.get(datastore.Key('/ab/ca/bc/abc')) 2 ``` nestKey(*key*)[¶](#datastore.core.basic.NestedPathDatastore.nestKey "Permalink to this definition") Returns a nested key. *static* nestedPath(*path*, *depth*, *length*)[¶](#datastore.core.basic.NestedPathDatastore.nestedPath "Permalink to this definition") returns a nested version of basename, using the starting characters. For example: ``` >>> NestedPathDatastore.nested\_path('abcdefghijk', 3, 2) 'ab/cd/ef' >>> NestedPathDatastore.nested\_path('abcdefghijk', 4, 2) 'ab/cd/ef/gh' >>> NestedPathDatastore.nested\_path('abcdefghijk', 3, 4) 'abcd/efgh/ijk' >>> NestedPathDatastore.nested\_path('abcdefghijk', 1, 4) 'abcd' >>> NestedPathDatastore.nested\_path('abcdefghijk', 3, 10) 'abcdefghij/k' ``` query(*query*)[¶](#datastore.core.basic.NestedPathDatastore.query "Permalink to this definition") *class* datastore.core.basic.NullDatastore[¶](#datastore.core.basic.NullDatastore "Permalink to this definition") Bases: [datastore.core.basic.Datastore](#datastore.core.basic.Datastore "datastore.core.basic.Datastore") Stores nothing, but conforms to the API. Useful to test with. delete(*key*)[¶](#datastore.core.basic.NullDatastore.delete "Permalink to this definition") Remove the object named by key (does nothing). get(*key*)[¶](#datastore.core.basic.NullDatastore.get "Permalink to this definition") Return the object named by key or None if it does not exist (None). put(*key*, *value*)[¶](#datastore.core.basic.NullDatastore.put "Permalink to this definition") Store the object value named by key (does nothing). query(*query*)[¶](#datastore.core.basic.NullDatastore.query "Permalink to this definition") Returns an iterable of objects matching criteria in query (empty). *class* datastore.core.basic.ShardedDatastore(*stores=*[], *shardingfn=<built-in function hash>*)[¶](#datastore.core.basic.ShardedDatastore "Permalink to this definition") Bases: [datastore.core.basic.DatastoreCollection](#datastore.core.basic.DatastoreCollection "datastore.core.basic.DatastoreCollection") Represents a collection of datastore shards. A datastore is selected based on a sharding function. Sharding functions should take a Key and return an integer. WARNING: adding or removing datastores while mid-use may severely affect consistency. Also ensure the order is correct upon initialization. While this is not as important for caches, it is crucial for persistent datastores. contains(*key*)[¶](#datastore.core.basic.ShardedDatastore.contains "Permalink to this definition") Returns whether the object is in this datastore. delete(*key*)[¶](#datastore.core.basic.ShardedDatastore.delete "Permalink to this definition") Removes the object from the corresponding datastore. get(*key*)[¶](#datastore.core.basic.ShardedDatastore.get "Permalink to this definition") Return the object named by key from the corresponding datastore. put(*key*, *value*)[¶](#datastore.core.basic.ShardedDatastore.put "Permalink to this definition") Stores the object to the corresponding datastore. query(*query*)[¶](#datastore.core.basic.ShardedDatastore.query "Permalink to this definition") Returns a sequence of objects matching criteria expressed in query shard(*key*)[¶](#datastore.core.basic.ShardedDatastore.shard "Permalink to this definition") Returns the shard index to handle key, according to sharding fn. shardDatastore(*key*)[¶](#datastore.core.basic.ShardedDatastore.shardDatastore "Permalink to this definition") Returns the shard to handle key. shard\_query\_generator(*query*)[¶](#datastore.core.basic.ShardedDatastore.shard_query_generator "Permalink to this definition") A generator that queries each shard in sequence. *class* datastore.core.basic.ShimDatastore(*datastore*)[¶](#datastore.core.basic.ShimDatastore "Permalink to this definition") Bases: [datastore.core.basic.Datastore](#datastore.core.basic.Datastore "datastore.core.basic.Datastore") Represents a non-concrete datastore that adds functionality between the client and a lower level datastore. Shim datastores do not actually store data themselves; instead, they delegate storage to an underlying child datastore. The default implementation just passes all calls to the child. delete(*key*)[¶](#datastore.core.basic.ShimDatastore.delete "Permalink to this definition") Removes the object named by key. Default shim implementation simply calls child\_datastore.delete(key) Override to provide different functionality. Args: key: Key naming the object to remove. get(*key*)[¶](#datastore.core.basic.ShimDatastore.get "Permalink to this definition") Return the object named by key or None if it does not exist. Default shim implementation simply returns child\_datastore.get(key) Override to provide different functionality, for example: ``` def get(self, key): value = self.child\_datastore.get(key) return json.loads(value) ``` Args: key: Key naming the object to retrieve Returns: object or None put(*key*, *value*)[¶](#datastore.core.basic.ShimDatastore.put "Permalink to this definition") Stores the object value named by key. Default shim implementation simply calls child\_datastore.put(key, value) Override to provide different functionality, for example: ``` def put(self, key, value): value = json.dumps(value) self.child\_datastore.put(key, value) ``` Args: key: Key naming value. value: the object to store. query(*query*)[¶](#datastore.core.basic.ShimDatastore.query "Permalink to this definition") Returns an iterable of objects matching criteria expressed in query. Default shim implementation simply returns child\_datastore.query(query) Override to provide different functionality, for example: ``` def query(self, query): cursor = self.child\_datastore.query(query) cursor.\_iterable = deserialized(cursor.\_iterable) return cursor ``` Args: query: Query object describing the objects to return. Raturns: iterable cursor with all objects matching criteria *class* datastore.core.basic.SymlinkDatastore(*datastore*)[¶](#datastore.core.basic.SymlinkDatastore "Permalink to this definition") Bases: [datastore.core.basic.ShimDatastore](#datastore.core.basic.ShimDatastore "datastore.core.basic.ShimDatastore") Datastore that creates filesystem-like symbolic link keys. A symbolic link key is a way of naming the same value with multiple keys. For example: ``` >>> import datastore.core >>> >>> dds = datastore.DictDatastore() >>> sds = datastore.SymlinkDatastore(dds) >>> >>> a = datastore.Key('/A') >>> b = datastore.Key('/B') >>> >>> sds.put(a, 1) >>> sds.get(a) 1 >>> sds.link(a, b) >>> sds.get(b) 1 >>> sds.put(b, 2) >>> sds.get(b) 2 >>> sds.get(a) 2 >>> sds.delete(a) >>> sds.get(a) None >>> sds.get(b) None >>> sds.put(a, 3) >>> sds.get(a) 3 >>> sds.get(b) 3 >>> sds.delete(b) >>> sds.get(b) None >>> sds.get(a) 3 ``` get(*key*)[¶](#datastore.core.basic.SymlinkDatastore.get "Permalink to this definition") Return the object named by [`](#id7)key. Follows links. link(*source\_key*, *target\_key*)[¶](#datastore.core.basic.SymlinkDatastore.link "Permalink to this definition") Creates a symbolic link key pointing from target\_key to source\_key put(*key*, *value*)[¶](#datastore.core.basic.SymlinkDatastore.put "Permalink to this definition") Stores the object named by key. Follows links. query(*query*)[¶](#datastore.core.basic.SymlinkDatastore.query "Permalink to this definition") Returns objects matching criteria expressed in query. Follows links. sentinel *= 'datastore\_link'*[¶](#datastore.core.basic.SymlinkDatastore.sentinel "Permalink to this definition") *class* datastore.core.basic.TieredDatastore(*stores=*[])[¶](#datastore.core.basic.TieredDatastore "Permalink to this definition") Bases: [datastore.core.basic.DatastoreCollection](#datastore.core.basic.DatastoreCollection "datastore.core.basic.DatastoreCollection") Represents a hierarchical collection of datastores. Each datastore is queried in order. This is helpful to organize access order in terms of speed (i.e. read caches first). Datastores should be arranged in order of completeness, with the most complete datastore last, as it will handle query calls. Semantics: * get : returns first found value * put : writes through to all * delete : deletes through to all * contains : returns first found value * query : queries bottom (most complete) datastore contains(*key*)[¶](#datastore.core.basic.TieredDatastore.contains "Permalink to this definition") Returns whether the object is in this datastore. delete(*key*)[¶](#datastore.core.basic.TieredDatastore.delete "Permalink to this definition") Removes the object from all underlying datastores. get(*key*)[¶](#datastore.core.basic.TieredDatastore.get "Permalink to this definition") Return the object named by key. Checks each datastore in order. put(*key*, *value*)[¶](#datastore.core.basic.TieredDatastore.put "Permalink to this definition") Stores the object in all underlying datastores. query(*query*)[¶](#datastore.core.basic.TieredDatastore.query "Permalink to this definition") Returns a sequence of objects matching criteria expressed in query. The last datastore will handle all query calls, as it has a (if not the only) complete record of all objects. datastore.key[¶](#module-datastore.core.key "Permalink to this headline") ------------------------------------------------------------------------- *class* datastore.core.key.Key(*key*)[¶](#datastore.core.key.Key "Permalink to this definition") Bases: object A Key represents the unique identifier of an object. Our Key scheme is inspired by file systems and the Google App Engine key model. Keys are meant to be unique across a system. Keys are hierarchical, incorporating more and more specific namespaces. Thus keys can be deemed ‘children’ or ‘ancestors’ of other keys: ``` Key('/Comedy') Key('/Comedy/MontyPython') ``` Also, every namespace can be parametrized to embed relevant object information. For example, the Key name (most specific namespace) could include the object type: ``` Key('/Comedy/MontyPython/Actor:JohnCleese') Key('/Comedy/MontyPython/Sketch:CheeseShop') Key('/Comedy/MontyPython/Sketch:CheeseShop/Character:Mousebender') ``` child(*other*)[¶](#datastore.core.key.Key.child "Permalink to this definition") Returns the child Key by appending namespace other. ``` >>> Key('/Comedy/MontyPython').child('Actor:JohnCleese') Key('/Comedy/MontyPython/Actor:JohnCleese') ``` instance(*other*)[¶](#datastore.core.key.Key.instance "Permalink to this definition") Returns an instance Key, by appending a name to the namespace. isAncestorOf(*other*)[¶](#datastore.core.key.Key.isAncestorOf "Permalink to this definition") Returns whether this Key is an ancestor of other. ``` >>> john = Key('/Comedy/MontyPython/Actor:JohnCleese') >>> Key('/Comedy').isAncestorOf(john) True ``` isDescendantOf(*other*)[¶](#datastore.core.key.Key.isDescendantOf "Permalink to this definition") Returns whether this Key is a descendant of other. ``` >>> Key('/Comedy/MontyPython').isDescendantOf(Key('/Comedy')) True ``` isTopLevel()[¶](#datastore.core.key.Key.isTopLevel "Permalink to this definition") Returns whether this Key is top-level (one namespace). list[¶](#datastore.core.key.Key.list "Permalink to this definition") Returns the list representation of this Key. Note that this method assumes the key is immutable. name[¶](#datastore.core.key.Key.name "Permalink to this definition") Returns the name of this Key, the value of the last namespace. namespaces[¶](#datastore.core.key.Key.namespaces "Permalink to this definition") Returns the list of namespaces of this Key. parent[¶](#datastore.core.key.Key.parent "Permalink to this definition") Returns the parent Key (all namespaces except the last). ``` >>> Key('/Comedy/MontyPython/Actor:JohnCleese').parent Key('/Comedy/MontyPython') ``` path[¶](#datastore.core.key.Key.path "Permalink to this definition") Returns the path of this Key, the parent and the type. *classmethod* randomKey()[¶](#datastore.core.key.Key.randomKey "Permalink to this definition") Returns a random Key *classmethod* removeDuplicateSlashes(*path*)[¶](#datastore.core.key.Key.removeDuplicateSlashes "Permalink to this definition") Returns the path string path without duplicate slashes. reverse[¶](#datastore.core.key.Key.reverse "Permalink to this definition") Returns the reverse of this Key. ``` >>> Key('/Comedy/MontyPython/Actor:JohnCleese').reverse Key('/Actor:JohnCleese/MontyPython/Comedy') ``` type[¶](#datastore.core.key.Key.type "Permalink to this definition") Returns the type of this Key, the field of the last namespace. *class* datastore.core.key.Namespace[¶](#datastore.core.key.Namespace "Permalink to this definition") Bases: str A Key Namespace is a string identifier. A namespace can optionally include a field (delimited by ‘:’) Example namespaces: ``` Namespace('Bruces') Namespace('Song:PhilosopherSong') ``` field[¶](#datastore.core.key.Namespace.field "Permalink to this definition") returns the field part of this namespace, if any. namespace\_delimiter *= ':'*[¶](#datastore.core.key.Namespace.namespace_delimiter "Permalink to this definition") value[¶](#datastore.core.key.Namespace.value "Permalink to this definition") returns the value part of this namespace. datastore.query[¶](#module-datastore.core.query "Permalink to this headline") ----------------------------------------------------------------------------- *class* datastore.core.query.Cursor(*query*, *iterable*)[¶](#datastore.core.query.Cursor "Permalink to this definition") Bases: object Represents a query result generator. apply\_filter()[¶](#datastore.core.query.Cursor.apply_filter "Permalink to this definition") Naively apply query filters. apply\_limit()[¶](#datastore.core.query.Cursor.apply_limit "Permalink to this definition") Naively apply query limit. apply\_offset()[¶](#datastore.core.query.Cursor.apply_offset "Permalink to this definition") Naively apply query offset. apply\_order()[¶](#datastore.core.query.Cursor.apply_order "Permalink to this definition") Naively apply query orders. next()[¶](#datastore.core.query.Cursor.next "Permalink to this definition") Iterator next. Build up count of returned elements during iteration. query[¶](#datastore.core.query.Cursor.query "Permalink to this definition") returned[¶](#datastore.core.query.Cursor.returned "Permalink to this definition") skipped[¶](#datastore.core.query.Cursor.skipped "Permalink to this definition") *class* datastore.core.query.Filter(*field*, *op*, *value*)[¶](#datastore.core.query.Filter "Permalink to this definition") Bases: object Represents a Filter for a specific field and its value. Filters are used on queries to narrow down the set of matching objects. Args: field: the attribute name (string) on which to apply the filter. op: the conditional operator to apply (one of [‘<’, ‘<=’, ‘=’, ‘!=’, ‘>=’, ‘>’]). value: the attribute value to compare against. Examples: ``` Filter('name', '=', 'John Cleese') Filter('age', '>=', 18) ``` conditional\_operators *= ['<', '<=', '=', '!=', '>=', '>']*[¶](#datastore.core.query.Filter.conditional_operators "Permalink to this definition") Conditional operators that Filters support. *classmethod* filter(*filters*, *iterable*)[¶](#datastore.core.query.Filter.filter "Permalink to this definition") Returns the elements in iterable that pass given filters generator(*iterable*)[¶](#datastore.core.query.Filter.generator "Permalink to this definition") Generator function that iteratively filters given items. *static* object\_getattr(*obj*, *field*)[¶](#datastore.core.query.Filter.object_getattr "Permalink to this definition") Object attribute getter. Can be overridden to match client data model. See datastore.query.\_object\_getattr(). valuePasses(*value*)[¶](#datastore.core.query.Filter.valuePasses "Permalink to this definition") Returns whether this value passes this filter *class* datastore.core.query.Order(*order*)[¶](#datastore.core.query.Order "Permalink to this definition") Bases: object Represents an Order upon a specific field, and a direction. Orders are used on queries to define how they operate on objects Args: order: an order in string form. This follows the format: [+-]name where + is ascending, - is descending, and name is the name of the field to order by. Note: if no ordering operator is specified, + is default. Examples: ``` Order('+name') # ascending order by name Order('-age') # descending order by age Order('score') # ascending order by score ``` isAscending()[¶](#datastore.core.query.Order.isAscending "Permalink to this definition") isDescending()[¶](#datastore.core.query.Order.isDescending "Permalink to this definition") keyfn(*obj*)[¶](#datastore.core.query.Order.keyfn "Permalink to this definition") A key function to be used in pythonic sort operations. *classmethod* multipleOrderComparison(*orders*)[¶](#datastore.core.query.Order.multipleOrderComparison "Permalink to this definition") Returns a function that will compare two items according to orders *static* object\_getattr(*obj*, *field*)[¶](#datastore.core.query.Order.object_getattr "Permalink to this definition") Object attribute getter. Can be overridden to match client data model. See datastore.query.\_object\_getattr(). order\_operators *= ['-', '+']*[¶](#datastore.core.query.Order.order_operators "Permalink to this definition") Ordering operators: + is ascending, - is descending. *classmethod* sorted(*items*, *orders*)[¶](#datastore.core.query.Order.sorted "Permalink to this definition") Returns the elements in items sorted according to orders *class* datastore.core.query.Query(*key*, *limit=None*, *offset=0*, *object\_getattr=None*)[¶](#datastore.core.query.Query "Permalink to this definition") Bases: object A Query describes a set of objects. Queries are used to retrieve objects and instances matching a set of criteria from Datastores. Query objects themselves are simply descriptions, the actual Query implementations are left up to the Datastores. copy()[¶](#datastore.core.query.Query.copy "Permalink to this definition") Returns a copy of this query. dict()[¶](#datastore.core.query.Query.dict "Permalink to this definition") Returns a dictionary representing this query. filter(*\*args*)[¶](#datastore.core.query.Query.filter "Permalink to this definition") Adds a Filter to this query. Args: see Filter constructor Returns self for JS-like method chaining: ``` query.filter('age', '>', 18).filter('sex', '=', 'Female') ``` *classmethod* from\_dict(*dictionary*)[¶](#datastore.core.query.Query.from_dict "Permalink to this definition") Constructs a query from a dictionary. *static* object\_getattr(*obj*, *field*)[¶](#datastore.core.query.Query.object_getattr "Permalink to this definition") Attribute getter for the objects to operate on. This function can be overridden in classes or instances of Query, Filter, and Order. Thus, a custom function to extract values to attributes can be specified, and the system can remain agnostic to the client’s data model, without loosing query power. For example, the default implementation works with attributes and items: ``` def \_object\_getattr(obj, field): # check whether this key is an attribute if hasattr(obj, field): value = getattr(obj, field) # if not, perhaps it is an item (raw dicts, etc) elif field in obj: value = obj[field] # return whatever we've got. return value ``` Or consider a more complex, application-specific structure: ``` def \_object\_getattr(version, field): if field in ['key', 'committed', 'created', 'hash']: return getattr(version, field) else: return version.attributes[field]['value'] ``` order(*order*)[¶](#datastore.core.query.Query.order "Permalink to this definition") Adds an Order to this query. Args: see Order constructor Returns self for JS-like method chaining: ``` query.order('+age').order('-home') ``` datastore.core.query.chain\_gen(*iterables*)[¶](#datastore.core.query.chain_gen "Permalink to this definition") A generator that chains iterables. datastore.core.query.is\_iterable(*obj*)[¶](#datastore.core.query.is_iterable "Permalink to this definition") datastore.core.query.limit\_gen(*limit*, *iterable*)[¶](#datastore.core.query.limit_gen "Permalink to this definition") A generator that applies a count limit. datastore.core.query.offset\_gen(*offset*, *iterable*, *skip\_signal=None*)[¶](#datastore.core.query.offset_gen "Permalink to this definition") A generator that applies an offset, skipping offset elements from iterable. If skip\_signal is a callable, it will be called with every skipped element. datastore.serialize[¶](#module-datastore.core.serialize "Permalink to this headline") ------------------------------------------------------------------------------------- *class* datastore.core.serialize.NonSerializer[¶](#datastore.core.serialize.NonSerializer "Permalink to this definition") Bases: [datastore.core.serialize.Serializer](#datastore.core.serialize.Serializer "datastore.core.serialize.Serializer") Implements serializing protocol but does not serialize at all. If only storing strings (or already-serialized values). *classmethod* dumps(*value*)[¶](#datastore.core.serialize.NonSerializer.dumps "Permalink to this definition") returns value. *classmethod* loads(*value*)[¶](#datastore.core.serialize.NonSerializer.loads "Permalink to this definition") returns value. *class* datastore.core.serialize.Serializer[¶](#datastore.core.serialize.Serializer "Permalink to this definition") Bases: object Serializing protocol. Serialized data must be a string. *classmethod* dumps(*value*)[¶](#datastore.core.serialize.Serializer.dumps "Permalink to this definition") returns serialized value. *static* implements\_serializer\_interface()[¶](#datastore.core.serialize.Serializer.implements_serializer_interface "Permalink to this definition") *classmethod* loads(*value*)[¶](#datastore.core.serialize.Serializer.loads "Permalink to this definition") returns deserialized value. *class* datastore.core.serialize.SerializerShimDatastore(*datastore*, *serializer=None*)[¶](#datastore.core.serialize.SerializerShimDatastore "Permalink to this definition") Bases: [datastore.core.basic.ShimDatastore](#datastore.core.basic.ShimDatastore "datastore.core.basic.ShimDatastore") Represents a Datastore that serializes and deserializes values. As data is put, the serializer shim serializes it and put``s it into the underlying ``child\_datastore. Correspondingly, on the way out (through get or query) the data is retrieved from the child\_datastore and deserialized. Args: datastore: a child datastore for the ShimDatastore superclass. serializer: a serializer object (responds to loads and dumps). deserializedValue(*value*)[¶](#datastore.core.serialize.SerializerShimDatastore.deserializedValue "Permalink to this definition") Returns deserialized value or None. get(*key*)[¶](#datastore.core.serialize.SerializerShimDatastore.get "Permalink to this definition") Return the object named by key or None if it does not exist. Retrieves the value from the child\_datastore, and de-serializes it on the way out. Args: key: Key naming the object to retrieve Returns: object or None put(*key*, *value*)[¶](#datastore.core.serialize.SerializerShimDatastore.put "Permalink to this definition") Stores the object value named by key. Serializes values on the way in, and stores the serialized data into the child\_datastore. Args: key: Key naming value value: the object to store. query(*query*)[¶](#datastore.core.serialize.SerializerShimDatastore.query "Permalink to this definition") Returns an iterable of objects matching criteria expressed in query De-serializes values on the way out, using a *deserialized\_gen* to avoid incurring the cost of de-serializing all data at once, or ever, if iteration over results does not finish (subject to order generator constraint). Args: query: Query object describing the objects to return. Raturns: iterable cursor with all objects matching criteria serializedValue(*value*)[¶](#datastore.core.serialize.SerializerShimDatastore.serializedValue "Permalink to this definition") Returns serialized value or None. serializer *= <module 'json' from '/usr/lib/python2.7/json/\_\_init\_\_.pyc'>*[¶](#datastore.core.serialize.SerializerShimDatastore.serializer "Permalink to this definition") *class* datastore.core.serialize.Stack[¶](#datastore.core.serialize.Stack "Permalink to this definition") Bases: [datastore.core.serialize.Serializer](#datastore.core.serialize.Serializer "datastore.core.serialize.Serializer"), list represents a stack of serializers, applying each serializer in sequence. dumps(*value*)[¶](#datastore.core.serialize.Stack.dumps "Permalink to this definition") returns serialized value. loads(*value*)[¶](#datastore.core.serialize.Stack.loads "Permalink to this definition") Returns deserialized value. datastore.core.serialize.deserialized\_gen(*serializer*, *iterable*)[¶](#datastore.core.serialize.deserialized_gen "Permalink to this definition") Generator that yields deserialized objects from iterable. *class* datastore.core.serialize.map\_serializer[¶](#datastore.core.serialize.map_serializer "Permalink to this definition") Bases: [datastore.core.serialize.Serializer](#datastore.core.serialize.Serializer "datastore.core.serialize.Serializer") map serializer that ensures the serialized value is a mapping type. *classmethod* dumps(*value*)[¶](#datastore.core.serialize.map_serializer.dumps "Permalink to this definition") returns mapping typed serialized value. *classmethod* loads(*value*)[¶](#datastore.core.serialize.map_serializer.loads "Permalink to this definition") Returns mapping type deserialized value. sentinel *= '@wrapped'*[¶](#datastore.core.serialize.map_serializer.sentinel "Permalink to this definition") datastore.core.serialize.monkey\_patch\_bson(*bson=None*)[¶](#datastore.core.serialize.monkey_patch_bson "Permalink to this definition") Patch bson in pymongo to use loads and dumps interface. *class* datastore.core.serialize.prettyjson[¶](#datastore.core.serialize.prettyjson "Permalink to this definition") Bases: [datastore.core.serialize.Serializer](#datastore.core.serialize.Serializer "datastore.core.serialize.Serializer") json wrapper serializer that pretty-prints. Useful for human readable values and versioning. *classmethod* dumps(*value*)[¶](#datastore.core.serialize.prettyjson.dumps "Permalink to this definition") returns json serialized value (pretty-printed). *classmethod* loads(*value*)[¶](#datastore.core.serialize.prettyjson.loads "Permalink to this definition") returns json deserialized value. datastore.core.serialize.serialized\_gen(*serializer*, *iterable*)[¶](#datastore.core.serialize.serialized_gen "Permalink to this definition") Generator that yields serialized objects from iterable. datastore.core.serialize.shim(*datastore*, *serializer=None*)[¶](#datastore.core.serialize.shim "Permalink to this definition") Return a SerializerShimDatastore wrapping datastore. Can be used as a syntacticly-nicer eay to wrap a datastore with a serializer: ``` my\_store = datastore.serialize.shim(my\_store, json) ``` ### [Table Of Contents](../index.html) * [datastore.core](#) + [datastore.basic](#module-datastore.core.basic) + [datastore.key](#module-datastore.core.key) + [datastore.query](#module-datastore.core.query) + [datastore.serialize](#module-datastore.core.serialize) #### Previous topic [datastore Package](datastore.html "previous chapter") #### Next topic [datastore.core.util](datastore.util.html "next chapter") ### This Page * [Show Source](../_sources/package/datastore.core.txt) * [Show on GitHub](https://github.com/jbenet/datastore/blob/master/docs/package/datastore.core.rst) * [Edit on GitHub](https://github.com/jbenet/datastore/edit/master/docs/package/datastore.core.rst) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../genindex.html "General Index") * [modules](../py-modindex.html "Python Module Index") | * [next](datastore.util.html "datastore.core.util") | * [previous](datastore.html "datastore Package") | * [datastore 0.3.0 documentation](../index.html) » * [datastore Package](datastore.html) » © Copyright 2011, Juan Batiz-Benet. 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The datastore Query model gleans a common set of operations performed when querying. To avoid pasting here years of database research, let’s summarize the operations datastore supports. Query Operations: > > * namespace - scope the query, usually by object type > * filters - select a subset of values by applying constraints > * orders - sort the results by applying sort conditions > * limit - impose a numeric limit on the number of results > * offset - skip a number of results (for efficient pagination) > > > datastore combines these operations into a simple Query class that allows applications to define their constraints in a simple, generic, and pythonic way without introducing datastore specific calls, languages, etc. Of course, different datastores provide relational query support across a wide spectrum, from full support in traditional databases to none at all in key-value stores. Datastore aims to provide a common, simple interface for the sake of application evolution over time and keeping large code bases free of tool-specific code. It would be ridiculous to claim to support high-performance queries on architectures that obviously do not. Instead, datastore provides the interface, ideally translating queries to their native form (e.g. into SQL for MySQL or a MongoDB query). However, on the wrong datastore, queries can potentially incur the high cost of performing the aforemantioned [*query operations*](#api-query-operations) on the data set directly in python. It is the client’s responsibility to select the right tool for the job: pick a data storage solution that fits the application’s needs now, and wrap it with a datastore implementation. Some applications, particularly in early development stages, can afford to incurr the cost of queries on non-relational databases (e.g. using a [FileSystemDatastore](../package/datastore.filesystem.html#datastore.filesystem.FileSystemDatastore "datastore.filesystem.FileSystemDatastore") and not worry about a database at all). When it comes time to switch the tool for performance, updating the application code can be as simple as swapping the datastore in one place, not all over the application code base. This gain in engineering time, both at initial development and during later iterations, can significantly offest the cost of the layer of abstraction. **tl;dr:** queries are supported across datastores. They are very cheap on top of relational databases, and very expensive otherwise. Pick the right tool for the job! Query classes[¶](#query-classes "Permalink to this headline") ------------------------------------------------------------- ### Query[¶](#query "Permalink to this headline") ### Filter[¶](#filter "Permalink to this headline") ### Order[¶](#order "Permalink to this headline") ### Cursor[¶](#cursor "Permalink to this headline") ### Generators[¶](#generators "Permalink to this headline") Note on generators: naive datastore queries use generators to delay performing work (such as filtering). Thus, no up-front cost is paid, but rather the cost comes at iteration. This is particularly useful in that even when working on large datasets, the naive query implementation can work as generators do not require having loading the entire dataset in memory upfront. When I say they can work, I do not imply quickly, just that they can work at all. The **crucial exception**, of course, is orders. If any order is placed on a query, the naive query implementation loses the benefit of delaying the work. That is because one cannot properly sort an entire dataset using a generator (sure, a generator could still be used to avoid paying the cost of sorting the *entire* dataset upfront, but that could still require putting the entire dataset in memory in the worst case). Specific datastore implementations should keep this in mind, performing as much work as possible in low-level, storage engine specific ways, and do the rest using generators. In particular, always try to push ordering into the underlying layer. ### Other[¶](#other "Permalink to this headline") Examples[¶](#examples "Permalink to this headline") --------------------------------------------------- TODO ### [Table Of Contents](../index.html) * [Queries](#) + [Query classes](#query-classes) - [Query](#query) - [Filter](#filter) - [Order](#order) - [Cursor](#cursor) - [Generators](#generators) - [Other](#other) + [Examples](#examples) #### Previous topic [Keys](key.html "previous chapter") #### Next topic [Basic Datastores](basic.html "next chapter") ### This Page * [Show Source](../_sources/api/query.txt) * [Show on GitHub](https://github.com/jbenet/datastore/blob/master/docs/api/query.rst) * [Edit on GitHub](https://github.com/jbenet/datastore/edit/master/docs/api/query.rst) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../genindex.html "General Index") * [modules](../py-modindex.html "Python Module Index") | * [next](basic.html "Basic Datastores") | * [previous](key.html "Keys") | * [datastore 0.3.0 documentation](../index.html) » * [Core datastore API](index.html) » © Copyright 2011, Juan Batiz-Benet. 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True >>> mc\_ds.get(Key('/Hello')) 'Goodbye!' ``` ### [Table Of Contents](../index.html) * [Basic Datastores](#) + [DictDatastore](#dictdatastore) + [InterfaceMappingDatastore](#interfacemappingdatastore) #### Previous topic [Queries](query.html "previous chapter") #### Next topic [Shims](shims.html "next chapter") ### This Page * [Show Source](../_sources/api/basic.txt) * [Show on GitHub](https://github.com/jbenet/datastore/blob/master/docs/api/basic.rst) * [Edit on GitHub](https://github.com/jbenet/datastore/edit/master/docs/api/basic.rst) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../genindex.html "General Index") * [modules](../py-modindex.html "Python Module Index") | * [next](shims.html "Shims") | * [previous](query.html "Queries") | * [datastore 0.3.0 documentation](../index.html) » * [Core datastore API](index.html) » © Copyright 2011, Juan Batiz-Benet. 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This key uniquely identifies a particular object, and provides namespacing for queries. The datastore.Key class below provides the required functionality. One can define another Key class with a different format that conforms to the same interface (particularly stringifying, hashes, namespacing, and ancestry). Key[¶](#key "Permalink to this headline") ----------------------------------------- Namespace[¶](#namespace "Permalink to this headline") ----------------------------------------------------- ### [Table Of Contents](../index.html) * [Keys](#) + [Key](#key) + [Namespace](#namespace) #### Previous topic [Core datastore API](index.html "previous chapter") #### Next topic [Queries](query.html "next chapter") ### This Page * [Show Source](../_sources/api/key.txt) * [Show on GitHub](https://github.com/jbenet/datastore/blob/master/docs/api/key.rst) * [Edit on GitHub](https://github.com/jbenet/datastore/edit/master/docs/api/key.rst) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../genindex.html "General Index") * [modules](../py-modindex.html "Python Module Index") | * [next](query.html "Queries") | * [previous](index.html "Core datastore API") | * [datastore 0.3.0 documentation](../index.html) » * [Core datastore API](index.html) » © Copyright 2011, Juan Batiz-Benet. 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At the same time, it would be ideal if a variety of serializers were available and trivially pluggable into the data storage pattern through a simple interface. The [pickle](http://docs.python.org/library/pickle.html#pickle "(in Python v2.7)") protocol has established the pickle.loads() and pickle.dumps() serialization interface, which other serializers (like [json](http://docs.python.org/library/json.html#json "(in Python v2.7)")) have adopted. This significantly simplifies things, but specific calls to serializers need to be added whenever inserting of extracting data with data storage libraries. To flexibly solve this issue, datastore defines a datastore.SerializerShimDatastore which can be layered on top of any other datastore. As data is put, the serializer shim serializes it and put``s it into the underlying ``child\_datastore. Correspondingly, on the way out (through get or query) the data is retrieved from the child\_datastore and deserialized. SerializerShimDatastore[¶](#serializershimdatastore "Permalink to this headline") --------------------------------------------------------------------------------- ### datastore.serialize.shim[¶](#datastore-serialize-shim "Permalink to this headline") Serializers[¶](#serializers "Permalink to this headline") --------------------------------------------------------- The serializers that datastore.SerializerShimDatastore accepts must respond to the protocol outlined in datastore.serialize.Serializer (the [pickle](http://docs.python.org/library/pickle.html#pickle "(in Python v2.7)") protocol). ### serialize.Serializer[¶](#serialize-serializer "Permalink to this headline") ### serialize.NonSerializer[¶](#serialize-nonserializer "Permalink to this headline") ### serialize.prettyjson[¶](#serialize-prettyjson "Permalink to this headline") ### serialize.Stack[¶](#serialize-stack "Permalink to this headline") ### serialize.map\_serializer[¶](#serialize-map-serializer "Permalink to this headline") ### Generators[¶](#generators "Permalink to this headline") Examples[¶](#examples "Permalink to this headline") --------------------------------------------------- TODO ### [Table Of Contents](../index.html) * [Serialize](#) + [SerializerShimDatastore](#serializershimdatastore) - [datastore.serialize.shim](#datastore-serialize-shim) + [Serializers](#serializers) - [serialize.Serializer](#serialize-serializer) - [serialize.NonSerializer](#serialize-nonserializer) - [serialize.prettyjson](#serialize-prettyjson) - [serialize.Stack](#serialize-stack) - [serialize.map\_serializer](#serialize-map-serializer) - [Generators](#generators) + [Examples](#examples) #### Previous topic [Collections](collections.html "previous chapter") #### Next topic [datastore Package](../package/datastore.html "next chapter") ### This Page * [Show Source](../_sources/api/serialize.txt) * [Show on GitHub](https://github.com/jbenet/datastore/blob/master/docs/api/serialize.rst) * [Edit on GitHub](https://github.com/jbenet/datastore/edit/master/docs/api/serialize.rst) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../genindex.html "General Index") * [modules](../py-modindex.html "Python Module Index") | * [next](../package/datastore.html "datastore Package") | * [previous](collections.html "Collections") | * [datastore 0.3.0 documentation](../index.html) » * [Core datastore API](index.html) » © Copyright 2011, Juan Batiz-Benet. 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For example, caching datastores can be checked before accessing more costly datastores, or a group of equivalent datastores can act as shards containing large data sets. As [*shims*](shims.html#api-shims), datastore collections also derive from *datastore*, and must implement the four datastore operations (get, put, delete, query). DatastoreCollection[¶](#datastorecollection "Permalink to this headline") ------------------------------------------------------------------------- Collections may derive from DatastoreCollection TieredDatastore[¶](#tiereddatastore "Permalink to this headline") ----------------------------------------------------------------- Example: ``` >>> import pymongo >>> import datastore.core >>> >>> from datastore.mongo import MongoDatastore >>> from datastore.pylru import LRUCacheDatastore >>> from datastore.filesystem import FileSystemDatastore >>> >>> conn = pymongo.Connection() >>> mongo = MongoDatastore(conn.test\_db) >>> >>> cache = LRUCacheDatastore(1000) >>> fs = FileSystemDatastore('/tmp/.test\_db') >>> >>> ds = datastore.TieredDatastore([cache, mongo, fs]) >>> >>> hello = datastore.Key('hello') >>> ds.put(hello, 'world') >>> ds.contains(hello) True >>> ds.get(hello) 'world' >>> ds.delete(hello) >>> ds.get(hello) None ``` ShardedDatastore[¶](#shardeddatastore "Permalink to this headline") ------------------------------------------------------------------- Example: ``` >>> import datastore.core >>> >>> shards = [datastore.DictDatastore() for i in range(0, 10)] >>> >>> ds = datastore.ShardedDatastore(shards) >>> >>> hello = datastore.Key('hello') >>> ds.put(hello, 'world') >>> ds.contains(hello) True >>> ds.get(hello) 'world' >>> ds.delete(hello) >>> ds.get(hello) None ``` ### [Table Of Contents](../index.html) * [Collections](#) + [DatastoreCollection](#datastorecollection) + [TieredDatastore](#tiereddatastore) + [ShardedDatastore](#shardeddatastore) #### Previous topic [Shims](shims.html "previous chapter") #### Next topic [Serialize](serialize.html "next chapter") ### This Page * [Show Source](../_sources/api/collections.txt) * [Show on GitHub](https://github.com/jbenet/datastore/blob/master/docs/api/collections.rst) * [Edit on GitHub](https://github.com/jbenet/datastore/edit/master/docs/api/collections.rst) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../genindex.html "General Index") * [modules](../py-modindex.html "Python Module Index") | * [next](serialize.html "Serialize") | * [previous](shims.html "Shims") | * [datastore 0.3.0 documentation](../index.html) » * [Core datastore API](index.html) » © Copyright 2011, Juan Batiz-Benet. 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border: 1px solid #1C5871; width: 290px; right: 173px; } .rtd-badge.revsys-inline { position: inherit; margin-left: auto; margin-right: 175px; margin-bottom: 5px; background: #465158 url(//media.readthedocs.org//images/badge-revsys-sm.png) top left no-repeat; border: 1px solid #1C5871; width: 205px; right: 173px; } [Brought to you by Read the Docs](//readthedocs.org/projects/datastore/?fromdocs=datastore) * [latest](/en/latest/) Core datastore API — datastore 0.3.0 documentation ### Navigation * [index](../genindex.html "General Index") * [modules](../py-modindex.html "Python Module Index") | * [next](key.html "Keys") | * [previous](../index.html "datastore - simple, unified API for multiple data stores") | * [datastore 0.3.0 documentation](../index.html) » Core datastore API[¶](#core-datastore-api "Permalink to this headline") ======================================================================= * [Keys](key.html) + [Key](key.html#key) + [Namespace](key.html#namespace) * [Queries](query.html) + [Query classes](query.html#query-classes) + [Examples](query.html#examples) * [Basic Datastores](basic.html) + [DictDatastore](basic.html#dictdatastore) + [InterfaceMappingDatastore](basic.html#interfacemappingdatastore) * [Shims](shims.html) + [ShimDatastore](shims.html#shimdatastore) + [KeyTransformDatastore](shims.html#keytransformdatastore) + [LowercaseKeyDatastore](shims.html#lowercasekeydatastore) + [NamespaceDatastore](shims.html#namespacedatastore) + [SymlinkDatastore](shims.html#symlinkdatastore) * [Collections](collections.html) + [DatastoreCollection](collections.html#datastorecollection) + [TieredDatastore](collections.html#tiereddatastore) + [ShardedDatastore](collections.html#shardeddatastore) * [Serialize](serialize.html) + [SerializerShimDatastore](serialize.html#serializershimdatastore) + [Serializers](serialize.html#serializers) + [Examples](serialize.html#examples) datastore base class[¶](#datastore-base-class "Permalink to this headline") --------------------------------------------------------------------------- Examples[¶](#examples "Permalink to this headline") --------------------------------------------------- ### Hello World[¶](#hello-world "Permalink to this headline") ``` >>> import datastore.core >>> ds = datastore.DictDatastore() >>> hello = datastore.Key('hello') >>> ds.put(hello, 'world') >>> ds.contains(hello) True >>> ds.get(hello) 'world' >>> ds.delete(hello) >>> ds.get(hello) None ``` ### [Table Of Contents](../index.html) * [Core datastore API](#) + [datastore base class](#datastore-base-class) + [Examples](#examples) - [Hello World](#hello-world) #### Previous topic [datastore - simple, unified API for multiple data stores](../index.html "previous chapter") #### Next topic [Keys](key.html "next chapter") ### This Page * [Show Source](../_sources/api/index.txt) * [Show on GitHub](https://github.com/jbenet/datastore/blob/master/docs/api/index.rst) * [Edit on GitHub](https://github.com/jbenet/datastore/edit/master/docs/api/index.rst) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../genindex.html "General Index") * [modules](../py-modindex.html "Python Module Index") | * [next](key.html "Keys") | * [previous](../index.html "datastore - simple, unified API for multiple data stores") | * [datastore 0.3.0 documentation](../index.html) » © Copyright 2011, Juan Batiz-Benet. 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For example, serializing and deserializing data as it is stored or extracted is a very common operation. Likewise, applications may need to perform routine operations as data makes its way from the top-level logic to the underlying storage. To address this use case in an elegant way, datastore uses the notion of a shim datastore, which implements all four main *datastore operations* in terms of an underlying child datastore. For example, a json serializer datastore could implement get and put as: ``` def get(self, key): value = self.child\_datastore.get(key) return json.loads(value) def put(self, key, value): value = json.dumps(value) self.child\_datastore.put(key, value) ``` ShimDatastore[¶](#shimdatastore "Permalink to this headline") ------------------------------------------------------------- To implement a shim datastore, derive from datastore.ShimDatastore and override any of the operations. KeyTransformDatastore[¶](#keytransformdatastore "Permalink to this headline") ----------------------------------------------------------------------------- LowercaseKeyDatastore[¶](#lowercasekeydatastore "Permalink to this headline") ----------------------------------------------------------------------------- NamespaceDatastore[¶](#namespacedatastore "Permalink to this headline") ----------------------------------------------------------------------- SymlinkDatastore[¶](#symlinkdatastore "Permalink to this headline") ------------------------------------------------------------------- ### [Table Of Contents](../index.html) * [Shims](#) + [ShimDatastore](#shimdatastore) + [KeyTransformDatastore](#keytransformdatastore) + [LowercaseKeyDatastore](#lowercasekeydatastore) + [NamespaceDatastore](#namespacedatastore) + [SymlinkDatastore](#symlinkdatastore) #### Previous topic [Basic Datastores](basic.html "previous chapter") #### Next topic [Collections](collections.html "next chapter") ### This Page * [Show Source](../_sources/api/shims.txt) * [Show on GitHub](https://github.com/jbenet/datastore/blob/master/docs/api/shims.rst) * [Edit on GitHub](https://github.com/jbenet/datastore/edit/master/docs/api/shims.rst) ### Quick search Enter search terms or a module, class or function name. ### Navigation * [index](../genindex.html "General Index") * [modules](../py-modindex.html "Python Module Index") | * [next](collections.html "Collections") | * [previous](basic.html "Basic Datastores") | * [datastore 0.3.0 documentation](../index.html) » * [Core datastore API](index.html) » © Copyright 2011, Juan Batiz-Benet. Created using [Sphinx](http://sphinx.pocoo.org/) 1.1.3+. .rtd-badge { position: fixed; display: block; bottom: 5px; height: 40px; text-indent: -9999em; border-radius: 3px; -moz-border-radius: 3px; -webkit-border-radius: 3px; box-shadow: 0 1px 0 rgba(0, 0, 0, 0.2), 0 1px 0 rgba(255, 255, 255, 0.2) inset; -moz-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.2), 0 1px 0 rgba(255, 255, 255, 0.2) inset; -webkit-box-shadow: 0 1px 0 rgba(0, 0, 0, 0.2), 0 1px 0 rgba(255, 255, 255, 0.2) inset; } #version\_menu { position: fixed; display: none; bottom: 11px; right: 166px; list-style-type: none; margin: 0; } .footer\_popout:hover #version\_menu { display: block; } #version\_menu li { display: block; float: right; } #version\_menu li a { display: block; padding: 6px 10px 4px 10px; margin: 7px 7px 0 0; font-weight: bold; font-size: 14px; height: 20px; line-height: 17px; text-decoration: none; color: #fff; background: #8ca1af url(../images/gradient-light.png) bottom left repeat-x; border-radius: 3px; -moz-border-radius: 3px; -webkit-border-radius: 3px; box-shadow: 0 1px 1px #465158; -moz-box-shadow: 0 1px 1px #465158; -webkit-box-shadow: 0 1px 1px #465158; text-shadow: 0 1px 1px rgba(0, 0, 0, 0.5); } #version\_menu li a:hover { text-decoration: none; background-color: #697983; box-shadow: 0 1px 0px #465158; -moz-box-shadow: 0 1px 0px #465158; -webkit-box-shadow: 0 1px 0px #465158; } .rtd-badge.rtd { background: #3b4449 url(//media.readthedocs.org//images/badge-rtd.png) scroll 0px -46px no-repeat; border: 1px solid #282E32; width: 41px; right: 5px; } .footer\_popout:hover .rtd-badge.rtd { background-position: top left; width: 160px; } .rtd-badge.revsys { background: #465158 url(//media.readthedocs.org//images/badge-revsys.png) top left no-repeat; border: 1px solid #1C5871; width: 290px; right: 173px; } .rtd-badge.revsys-inline-sponsored { position: inherit; margin-left: auto; margin-right: 175px; margin-bottom: 5px; background: #465158 url(//media.readthedocs.org//images/badge-revsys.png) top left no-repeat; border: 1px solid #1C5871; width: 290px; right: 173px; } .rtd-badge.revsys-inline { position: inherit; margin-left: auto; margin-right: 175px; margin-bottom: 5px; background: #465158 url(//media.readthedocs.org//images/badge-revsys-sm.png) top left no-repeat; border: 1px solid #1C5871; width: 205px; right: 173px; } [Brought to you by Read the Docs](//readthedocs.org/projects/datastore/?fromdocs=datastore) * [latest](/en/latest/)

q

go

q 0 documentation [q](index.html#document-index) latest [q](index.html#document-index) * [Docs](index.html#document-index) » * q 0 documentation * [Edit on GitHub](https://github.com/di-wu/q-docs/blob/master/docs/source/index.rst) --- Welcome to q’s documentation ============================================================================================

weave

go

Weave 0.14.0 documentation [Weave](index.html#document-index) stable * [Blockchain](index.html#document-basics/blockchain) * [Consensus](index.html#document-basics/consensus) * [Authentication](index.html#document-basics/authentication) * [State Machine](index.html#document-basics/state) * [Prepare Requirements](index.html#document-mycoind/setup) * [Installation](index.html#document-mycoind/installation) * [Using IOV-Core Client](index.html#document-mycoind/iovcore) * [Configuring Tendermint](index.html#document-configuration/tendermint) * [Configuring the Application](index.html#document-configuration/application) * [Setting the Validators](index.html#document-configuration/validators) * [Guiding Design Principles](index.html#document-design/overview) [Weave](index.html#document-index) * [Docs](index.html#document-index) » * Weave 0.14.0 documentation * [Edit on GitHub](https://github.com/iov-one/weave/blob/dfdb7cda77c7d3152eecf0478d3a91fe2a0edd5e/docs/index.rst) --- Welcome to IOV Weave’s documentation ============================================================================================================ [](_images/weave-logo.jpg) [IOV Weave](https://github.com/iov-one/weave) is a framework to quickly build your custom [ABCI application](https://github.com/tendermint/abci) to power a blockchain based on the best-of-class BFT Proof-of-stake [Tendermint consensus engine](https://tendermint.com). It provides much commonly used functionality that can quickly be imported in your custom chain, as well as a simple framework for adding the custom functionality unique to your project. Some of the highlights of Weave include a Merkle-tree backed data store, a highly configurable extension system that also applies to the core logic such as fees and signature validation. Weave also brings powerful customizations initialised from the genesis file. In addition there is a simple ORM which sits on top of a key-value store that also has proveable secondary indexes. There is a flexible permissioning system to use contracts as first-class actors, “No empty blocks” for quick synchronizing on quiet chains, and the ability to introduce “product fees” for transactions that need to charge more than the basic anti-spam fees. We have also added support for “migrations” that can switch on modules, or enable logic updates, via on-chain feature switch transactions. Existing Modules[¶](#existing-modules "Permalink to this headline") ------------------------------------------------------------------- | Module | Description | | --- | --- | | [Cash](https://github.com/iov-one/weave/tree/master/x/cash) | Wallets that support fungible tokens and fee deduction functionality | | [Sigs](https://github.com/iov-one/weave/tree/master/x/sigs) | Validate ed25519 signatures | | [Multisig](https://github.com/iov-one/weave/tree/master/x/multisig) | Supports first-class multiple signature contracts, and allow modification of membership | | [AtomicSwap](https://github.com/iov-one/weave/tree/master/x/aswap) | Supports HTLC for cross-chain atomic swaps, according to the [IOV Atomic Swap Spec](https://github.com/iov-one/iov-core/blob/master/docs/atomic-swap-protocol-v1.md) | | [Escrow](https://github.com/iov-one/weave/tree/master/x/escrow) | The arbiter can safely hold tokens, or use with timeouts to release on vesting schedule | | [Governance](https://github.com/iov-one/weave/tree/master/x/gov) | Hold on-chain elections for text proposals, or directly modify application parameters | | [PaymentChannels](https://github.com/iov-one/weave/tree/master/x/paychan) | Unidirectional payment channels, combine micro-payments with one on-chain settlement | | [Distribution](https://github.com/iov-one/weave/tree/master/x/distribution) | Allows the safe distribution of income among multiple participants using configurations. This can be used to distribute fee income. | | [Batch](https://github.com/iov-one/weave/tree/master/x/batch) | Used for combining multiple transactions into one atomic operation. A powerful example is in creating single-chain swaps. | | [Validators\_](#id1) | Used in a PoA context to update the validator set using either multisig or the on-chain elections module | | [NFT](https://github.com/iov-one/weave/tree/master/x/nft) | A generic Non Fungible Token module | | NFT/[Username](https://github.com/iov-one/weave/tree/master/cmd/bnsd/x/nft/username) | Example nft used by bnsd. Maps usernames to multiple chain addresses, including reverse lookups | | [MessageFee](https://github.com/iov-one/weave/tree/master/x/msgfee) | Validator-subjective minimum fee module, designed as an anti-spam measure. | | [Utils](https://github.com/iov-one/weave/tree/master/x/utils) | A range of utility functions such as KeyTagger which is designed to enable subscriptions to database. | **In Progress** Light client proofs, custom token issuance and support for IBC (Inter Blockchain Communication) are currently being designed. Basic Blockchain Terminology[¶](#basic-blockchain-terminology "Permalink to this headline") ------------------------------------------------------------------------------------------- ### Blockchain[¶](#blockchain "Permalink to this headline") A “blockchain” in the simplest sense is a chain of blocks. By chain, we each block is cryptographically linked to the proceeding block, and through recursion we can securely query the entire history from any block back to the genesis. A block is a set of transactions, along with this link, and some optional metadata that varies depending on the blockchain. #### Immutable Event Log[¶](#immutable-event-log "Permalink to this headline") If you are coming from working on typical databases, you can think of the blockchain as an immutable [transaction log](https://en.wikipedia.org/wiki/Transaction_log) . If you have worked with [Event Sourcing](https://martinfowler.com/eaaDev/EventSourcing.html) you can consider a block as a set of events that can always be replayed to create a [materialized view](https://docs.microsoft.com/en-us/azure/architecture/patterns/materialized-view) . Maybe you have a more theoretical background and recognize that a blockchain is a fault tolerant form of [state machine replication](https://en.wikipedia.org/wiki/State_machine_replication#Ordering_Inputs) . In any case, the point is that given a node knows a block is valid (more on that in [consensus](./consensus.html)), it can cryptographically prove it has the valid history of that block, and then replay that sequence of blocks to reproduce the current state. Many nodes performing this simultaneously create a [Byzantine Fault Tolerant](https://en.wikipedia.org/wiki/Byzantine_fault_tolerance) state machine. Since (most) computer programs can be mapped to state machines, we end up with an [unstoppable world computer](https://www.ethereum.org/) . This means we can trust that the blockchain and state represent the proper functioning of whatever program we run on it. This allows for extremely high levels of trust in a program, levels that were previously reserved for highly controlled, centralized systems, such as banks or governments. The first generation of blockchain, Bitcoin, proved it was possible to run a system with many unknown and mutually untrusting parties, yet produce a system that is harder to hack than any bank (bitcoin hacks involve grabbing someone’s wallet, not manipulating the blockchain). This was a true marvel of vision and engineering and laid the stage for all future development, and many other projects tried to fork bitcoin to create a custom blockchain. #### General Purpose Computer[¶](#general-purpose-computer "Permalink to this headline") Ethereum pioneered the second generation of blockchain, where they realized that we didn’t have to limit ourselves to handling payments, but actually have a general purpose state machine. They wanted to allow experimentation at a rate orders of magnitude faster than forking bitcoin, and produced the EVM (Ethereum Virtual Machine) that can run sandboxed code uploaded by any user. Since then, hundred of projects have experimented with porting other types of logic to the blockchain, and have demonstrated its utility for [decentralized governance](https://aragon.one/), [currency trading](https://0xproject.com/), [prediction markets](https://gnosis.pm/), even [collectible trading games](https://www.cryptokitties.co/) and much more… While Ethereum demonstrated the potential of blockchain technology in many areas, we it also provided some [high profile examples](https://www.cryptocompare.com/coins/guides/the-dao-the-hack-the-soft-fork-and-the-hard-fork/) of how [hard it is to write secure contracts](https://medium.com/chain-cloud-company-blog/parity-multisig-hack-again-b46771eaa838) . As it became more popular, it also showed a popular application can [overload the capacity of the network](https://dealbreaker.com/2017/12/ethereum-the-crypto-network-that-will-transform-everything-struggles-to-handle-digital-beanie-babies/) . #### Next Generation[¶](#next-generation "Permalink to this headline") Since that time, many groups are working on “next generation” solutions that take the learnings of Ethereum and attempt to build a highly scalable and secure blockchain that can run general purpose programs. One pioneering project is [Tendermint](https://tendermint.com/), which provides a highly efficient, Byzantine Fault Tolerant blockchain engine offering guaranteed finality in the order of 1-5 seconds. It was designed from the ground up to allow many projects to easily [plug their application logic](https://tendermint.readthedocs.io/en/master/app-development.html#abci-design) into the engine. [Weave](https://github.com/iov-one/weave) is a framework that provides many common tools to help you build ABCI apps rapidly. You can just focus on writing the application logic and the interface and rely on high quality and extensible libraries to solve most of the difficult problems with building a blockchain. ### Consensus[¶](#consensus "Permalink to this headline") Consensus is the algorithm by which a set of computers come to agreement on which possible state is correct, and thus guarantee one consistent, global view of the state of the system. #### Eventual finality[¶](#eventual-finality "Permalink to this headline") All PoW systems use eventual finality, where the resource cost of creating a block is extremely high. After many blocks are gossiped, the longest chain of blocks has the most work invested in it, and thus is the true chain. The “true” head of the chain can switch, in a process called “chain reorganization”. But the probability of such a reorganization decreases exponentially the more blocks are built on top of it. Thus, in Bitcoin, the “6 block rule” means that if there are 6 blocks build on top of the block with your transaction, you can be extremely confident that no chain reorganization will ever generate a new true chain that does not include that block. Note this is not a guarantee that it cannot happen, just that the cost of doing so becomes so prohibitively high that is very unlikely to ever happen. Many early PoS systems, such as BitShares, used voting instead of work to mine blocks, but still used the “longest chain wins” consensus algorithm. However, this has the critical [nothing at stake](https://github.com/ethereum/wiki/wiki/Problems#8-proof-of-stake) problem, since the cost of “mining” blocks on 2, 3, or even 100 alternate chains is quite low. Another issue here is that any state may have to be reverted, and the data store must maintain an “undo history” to undo several blocks and apply others. And clients must wait several blocks (minutes to hours) before they can take off-chain actions based on the transaction (eg. give you goods for a blockchain payment). #### Immediate finality[¶](#immediate-finality "Permalink to this headline") An alternative approach used to guarantee constency comes out of academic research into Byzantine Fault Tolerance from the 80s and 90s, which “culminated” in [PBFT](http://pmg.csail.mit.edu/papers/osdi99.pdf) . [Tendermint](https://tendermint.com/) uses an algorithm very similar to PBFT with optimizations learned from blockchain developments to create an extremely secure consensus algorithm. All nodes vote in multiple rounds, and only produce blocks when they are guaranteed that the block is the “correct” globally consensus. Even in the case of omnipotent network manipulation, this algorithm will never produce to blocks at the same height (a fork) if less than one third of the nodes are actively collaborating to break the system. This is possibly the strongest guarantee of any production blockchain consensus algorithm. The benefit of this approach is that any block that has over two thirds of the signature is [provably correct by light clients](https://blog.cosmos.network/light-clients-in-tendermint-consensus-1237cfbda104) The state is never rolled back and clients can take actions based on that state. This opens the possibility of blockchain payments to be settled in the order of a second or two, similar latency with using a credit card in a store. It also allows reasonably responsive applications to be built on a blockchain. ### Authentication[¶](#authentication "Permalink to this headline") One interesting attribute of blockchains is that there are no trusted nodes, and all transactions are publicly visible and can be copied. This naturally provides problem for traditional means of authentication like passwords and cookies. If you use your password to authorize one transaction, someone can copy it to run any other. Or a node in the middle can even change your transaction before writing to a block. Thus, all authentication on blockchains is based on [public key cryptography](https://arstechnica.com/information-technology/2013/10/a-relatively-easy-to-understand-primer-on-elliptic-curve-cryptography/), in particularly cryptographic signatures based on [elliptic curves](https://hackernoon.com/eliptic-curve-crypto-the-basics-e8eb1e934dc5). A client can locally generate a public-private key pair, and share the public key with the world as his/her identity (like a fingerprint). The client can then take any message (text or binary) and generate a unique signature with the private key. The signature can only be validated by the corresponding message and public key and cannot be forged. Any changes to the message will invalidate the signature and no information is leaked to allow a malicious actor to impersonate that client with a different message. #### Main Algorithms[¶](#main-algorithms "Permalink to this headline") * RSA - the gold standard from 1977-2014, still secure and the most widely supported. not used for blockchains as signatures are 1-4KB * secp256k1 - elliptic curve used in bitcoin and ethereum, signatures at 65-67 bytes * ed25519 - popularized with libsodium and most standardized elliptic curve, signatures at 64 bytes * bn256 - maybe the next curve… used by [zcash](https://blog.z.cash/new-snark-curve/) for pairing cryptography and [dfinity](https://medium.com/on-the-origin-of-smart-contract-platforms/on-the-origin-of-dfinity-526b4222eb4c#02dd) for BLS threshold signatures. in other words, they can do crazy magic math on this particular curve. If you want to go deeper than what you can find on wikipedia and google, I highly recommend buying a copy of `Serious Cryptography` by Jean-Philippe Aumasson. ### State Machine[¶](#state-machine "Permalink to this headline") Inside each block is a sequence of transactions to be applied to a state machine (ran by a program). There is also the state (database) representing the materialized view of all transactions included in all blocks up to this point, as executed by the state machine. #### Upgrading the state machine[¶](#upgrading-the-state-machine "Permalink to this headline") Of course, during the lifetime of the blockchain, we will want to update the software and expand functionality. However, the new software must also be able to re-run all transactions since genesis (the birth of the chain) and produce the same state as the active network that keeps updating software over time. That means all historical blocks must be interpreted the same by new and old clients. Bitcoin classifies various approaches to upgrading as [soft forks](https://en.bitcoin.it/wiki/Softfork) or [hard forks](https://en.bitcoin.it/wiki/Hardfork). Ethereum has a table defining the block height at which [various functionality changes](https://github.com/ethereum/go-ethereum/blob/master/params/config.go#L33-L45) and add checks for the [currently activated behavior](https://github.com/ethereum/go-ethereum/blob/master/core/vm/evm.go#L157-L166) based on block height at various places. This allows one server to handle multiple historical behaviors, but it can also add lots of dead code over time… #### UTXO vs Account Model[¶](#utxo-vs-account-model "Permalink to this headline") There are two main models used to store the current state. The main model for bitcoin and similar chains is called UTXO, or Unspent transaction output. Every transaction has an input and an output, and the system must just check if the inputs have been used yet. If they have not, they are marked spent and the outputs created. If any have been spent, then the transaction fails. This provides interesting ways to obfuscate identity (but not secure against sophisticated network analysis like ZCash), and allows easy parallelization of the transaction processing. However, it is quite hard to map non-payment systems (like voting or breeding crypto-kitties) to such a system. It is used mainly for focused payment networks. The account model creates one account per public key address and stores the information there. Sending money becomes reducing the balance on one account and incrementing on another. And many other more complex logic become easy to express, using logic that many developers are used to from interacting with databases or key-value stores. The downside is that the account allows an observer to easy view all activity by one key. Sure you have pseudoanonymity, but if you make one payment to me, I now can see your entire investment and voting history with little effort. Another downside is that it become harder to parallelize transaction processing, as sending from one account and receiving payments will conflict with each other. In practice, no production chains use optimistic concurrency on account based systems. #### Merkle Proofs[¶](#merkle-proofs "Permalink to this headline") Weave uses an account model much like Ethereum, and leaves anonymity to other developments like [mixnets](https://en.wikipedia.org/wiki/Mix_network) and [zkSNARKs](https://z.cash/technology/zksnarks.html). Under the hood, we use a key-value store, where different modules write their data to different key-spaces. This is not a normal key-value store (like redis or leveldb), but rather [merkle trees](https://www.codeproject.com/Articles/1176140/Understanding-Merkle-Trees-Why-use-them-who-uses-t). Merkle trees are like binary trees, but hash the children at each level. This allows us to provide a [proof as a chain of hashes](https://www.certificate-transparency.org/log-proofs-work) the same height as the tree. This proof can guarantee that a given key-value pair is in the tree with a given root hash. This root hash is then added to a block header after running the transactions, and validated by [consensus](./consensus.rst). If a client [follows the headers](https://blog.cosmos.network/light-clients-in-tendermint-consensus-1237cfbda104), they can securely verify if a node if providing them the correct data for eg. their account balance. In practice, the block header can maintain multiple hashes, each one the merkle root of another tree. Thus, a client can use a header to prove, state, presence of a transaction, or current validator set. If you are new to blockchains (or Tendermint), this is a crash course in just enough theory to follow the rest of the setup. [Read all](basics/blockchain.html) ### Immutable Event Log[¶](#immutable-event-log "Permalink to this headline") If you are coming from working on typical databases, you can think of the blockchain as an immutable [transaction log](https://en.wikipedia.org/wiki/Transaction_log) . If you have worked with [Event Sourcing](https://martinfowler.com/eaaDev/EventSourcing.html) you can consider a block as a set of events that can always be replayed to create a [materialized view](https://docs.microsoft.com/en-us/azure/architecture/patterns/materialized-view) . Maybe you have a more theoretical background and recognize that a blockchain is a fault tolerant form of [state machine replication](https://en.wikipedia.org/wiki/State_machine_replication#Ordering_Inputs) . [Read more](basics/blockchain.html#immutable-event-log) ### General Purpose Computer[¶](#general-purpose-computer "Permalink to this headline") Ethereum pioneered the second generation of blockchain, where they realized that we didn’t have to limit ourselves to handling payments, but actually have a general purpose state machine. [Read more](basics/blockchain.html#general-purpose-computer) ### Next Generation[¶](#next-generation "Permalink to this headline") Since that time, many groups are working on “next generation” solutions that take the learnings of Ethereum and attempt to build a highly scalable and secure blockchain that can run general purpose programs. [Read more](basics/blockchain.html#next-generation) ### Eventual finality[¶](#eventual-finality "Permalink to this headline") All Proof-of-Work systems use eventual finality, where the resource cost of creating a block is extremely high. After many blocks are gossiped, the longest chain of blocks has the most work invested in it, and thus is the true chain. [Read more](basics/consensus.html#eventual-finality) ### Immediate finality[¶](#immediate-finality "Permalink to this headline") An alternative approach used to guarantee constency comes out of academic research into Byzantine Fault Tolerance from the 80s and 90s, which “culminated” in [PBFT](http://pmg.csail.mit.edu/papers/osdi99.pdf) . [Read more](basics/consensus.html#immediate-finality) ### Authentication[¶](#authentication "Permalink to this headline") One interesting attribute of blockchains is that there are no trusted nodes, and all transactions are publically visible and can be copied. [Read more](basics/authentication.html) ### Upgrading the state machine[¶](#upgrading-the-state-machine "Permalink to this headline") Of course, during the lifetime of the blockchain, we will want to update the software and expand functionality. However, the new software must also be able to re-run all transactions since genesis. [Read more](basics/state.html#upgrading-the-state-machine) ### UTXO vs Account Model[¶](#utxo-vs-account-model "Permalink to this headline") There are two main models used to store the current state. The main model for bitcoin and similar chains is called UTXO, or Unspent transaction output. The account model creates one account per public key address and stores the information there. [Read more](basics/state.html#utxo-vs-account-model) ### Merkle Proofs[¶](#merkle-proofs "Permalink to this headline") Merkle trees are like binary trees, but hash the children at each level. This allows us to provide a [proof as a chain of hashes](https://www.certificate-transparency.org/log-proofs-work). [Read more](basics/state.html#merkle-proofs) Running an Existing Application[¶](#running-an-existing-application "Permalink to this headline") ------------------------------------------------------------------------------------------------- ### Prepare Requirements[¶](#prepare-requirements "Permalink to this headline") Before you can run this code, you need to have a number of programs set up on your machine. In particular, you will need a bash shell (or similar), and development tooling for both go and node. **WARNING** This is only tested under Linux and OSX. If you want to run under Windows, the only supported *development* environment is using WSL (Windows Subsytem for Linux) under Windows 10. Follow [these directions](https://docs.microsoft.com/en-us/windows/wsl/install-win10) to setup Ubuntu in WSL, then try the rest in your Ubuntu shell #### Install Go[¶](#install-go "Permalink to this headline") You will need to have the Go tooling installed, version 1.11.4+ (or 1.12). If you do not already have it, please [download](https://golang.org/dl/) and [follow the instructions](https://golang.org/doc/install) from the official Go language homepage. Make sure to read down to [Test Your Installation](https://golang.org/doc/install#testing). (Note this is not included in Ubuntu apt tooling until 19.04) We assume a standard setup in the Makefiles, especially to build tendermint nicely. With `go mod` much of the go configuration is unnecessary, but make sure to have the default “install” directory in your `PATH`, so you can run the binaries after compilation. ``` # this line should be in .bashrc or similar export PATH="$PATH:$HOME/go/bin" # this must report 1.11.4+ go version # this will properly place the code in $HOME/go/src/github.com/iov-one/weave go get github.com/iov-one/weave ``` ##### Go related tools[¶](#go-related-tools "Permalink to this headline") You must also make sure to have a few other developer tools installed. If you are a developer in any language, they are probably there. Just double check. If not, a simple `sudo apt get` should provide them. * git * make * curl * jq ### Installation[¶](#installation "Permalink to this headline") To run our system, we need two components: * `mycoind`, our custom ABCI application * `tendermint`, a powerful blockchain consensus engine If you have never used tendermint before, you should read the [ABCI Overview](https://tendermint.com/docs/introduction/introduction.html#abci-overview) and ideally through to the bottom of the page. The end result is that we have three programs communicating: ``` +---------+ +------------+ +----------+ | mycoind | <- (local) ABCI -> | Tendermint | <- websocket -> | client | +---------+ +------------+ +----------+ ``` `mycoind` and `tendermint` run on the same computer and communicate via a binary protocol over localhost or a unix socket. Together they form a “blockchain”. In a real setup, you would have dozens (or hundreds) of computers running this backend communicating over a self-adjusting p2p gossip network to replicate the state. For application development (and demos) one copy will work, but has none of the fault tolerance of a real blockchain. You can connect to tendermint rpc via various client libraries. We recommend [IOV Core](iovcore.html) which has very good support for weave-based apps, as well as different blockchains (such as Ethereum and Lisk). #### Install backend programs[¶](#install-backend-programs "Permalink to this headline") You should have a proper go development environment, as explained in the [last section](installation.html). Now, check out the most recent version of iov-one/weave and build `mycoind` then get the version 0.31.5 for `tendermint` from [here](https://github.com/tendermint/tendermint/releases/tag/v0.31.5). You can also build `tendermint` from source following the instructions [there](https://github.com/tendermint/tendermint/blob/master/docs/introduction/install.md) but make sure to use the tag **v0.31.5** as other versions might not be compatible. **Note** we use `go mod` for dependency management. This is enabled by default in go 1.12+. If you are running go 1.11.4+, you must run the following in the terminal (or add to `~/.bashrc`): `export GO111MODULE=on` Those were the most recent versions as of the time of the writing, your code should be a similar version. If you have an old version of the code, you may have to delete it to force go to rebuild: ``` rm `which tendermint` rm `which mycoind` ``` #### Initialize the Blockchain[¶](#initialize-the-blockchain "Permalink to this headline") Before we start the blockchain, we need to set up the initial state. This is defined in a genesis block. Both `tendermint` and `mycoind` have a directory to store configuration and internal database state. By default those are `~/.tendermint` and `~/.mycoind`. However, to make things simpler, we will ask them both to put everything in the same directory. First, we create a default genesis file, the private key for the validator to sign blocks, and a default config file. ``` # make sure you really don't care what was in this directory and... rm -rf ~/.mycoind tendermint init --home ~/.mycoind ``` You can take a look in this directory if you are curious. The most important piece for us is `~/.mycoind/config/genesis.json`. You may also notice `~/.mycoind/config/config.toml` with lots of [options to set](https://tendermint.com/docs/tendermint-core/configuration.html#options) for power users. We want to add a bunch of tokens to the account we just made before launching the blockchain. And we’d also like to enable the indexer, so we can search for our transactions by id (default state is off). But rather than have you fiddle with the config files by hand, you can just run this to do the setup: ``` mycoind init CASH bech32:tiov1qrw95py2x7fzjw25euuqlj6dq6t0jahe7rh8wp ``` Make sure you enter the same hex address, this account gets the tokens. You can take another look at `~/.mycoind/config/genesis.json` after running this command. The important change was to “app\_state”. You can also create this by hand later to give many people starting balances, but let’s keep it simple for now and get something working. Feel free to wipe out the directory later and reinitialize another blockchain with custom configuration to experiment. You may ask where this address comes from. It is a demo account derived from our test mnemonic: `dad kiss slogan offer outer bomb usual dream awkward jeans enlist mansion` using the hd derivation path: `m/44'/234'/0'`. This is the path used by our wallet, so you can enter your mnemonic in our web-wallet and see this account. Note that you can define the addresses both in *hex:* and *bech32:* formats (if prefix is ommitted, hex is assumed) #### Start the Blockchain[¶](#start-the-blockchain "Permalink to this headline") We have a private key and setup all the configuration. The only thing left is to start this blockchain running. ``` tendermint node --home ~/.mycoind > ~/.mycoind/tendermint.log & mycoind start ``` This connects over <tcp://localhost:26658> by default, to use unix sockets (arguably more secure), try the following: ``` tendermint node --home ~/.mycoind --proxy_app=unix://$HOME/abci.socket > ~/.mycoind/tendermint.log & mycoind start -bind=unix://$HOME/abci.socket ``` Open a new window and type in `tail -f  ~/.mycoind/tendermint.log` and you will be able to see the output. That means the blockchain is working away and producing new blocks, one a second. [](_images/tail-log.png) Note: if you did anything funky during setup and managed to get yourself a rogue tendermint node running in the background, you might encounter errors like panic: Error initializing DB: resource temporarily unavailable. A quick `killall tendermint` should get you back on track. ### Using IOV-Core Client[¶](#using-iov-core-client "Permalink to this headline") While the blockchain code is in the Go language, we have developed a TypeScript (javascript-compatible) client side sdk in order to access the functionality of the blockchain. Iov-Core works for many blockchains, not just weave (mycoind and bnsd), so take a look, it is useful for more than this demo #### Installing Tooling[¶](#installing-tooling "Permalink to this headline") You will need node 8+ to run the example client. Unless you know what you are doing, stick to even numbered versions (6, 8, 10, …), the odd numbers are unstable and get deprecated every few weeks it seems. For ease of updating later, I advise you to install [nvm](https://github.com/creationix/nvm#installation) and then add the most recent stable version ``` # this install most recent v8 version, use lts/dubnium for v10 track nvm install lts/carbon # test it out node --version node > let {x, y} = {x: 10, y:10} ``` ##### Node related tools[¶](#node-related-tools "Permalink to this headline") Yarn is a faster alternative to npm for installing modules, so we use that as default. ``` npm install -g yarn ``` #### Using Iov-Core[¶](#using-iov-core "Permalink to this headline") Please refer to the offical [iov-core documentation](https://github.com/iov-one/iov-core/blob/master/packages/iov-core/README.md) Note that you can use the `BnsConnection` to connect to a `mycoind` blockchain, as long as you restrict it to just sending tokens and querying balances and nonces (it is a subset of `bnsd`). You may also find [iov-cli](https://github.com/iov-one/iov-core/blob/master/packages/iov-cli/README.md) a useful debug tool. It is an enhanced version of the standard node REPL (interactive coding shell), but with support for top-level `await` and type-checks on all function calls (you can code in typescript). The [iov-core](https://iov-one.github.io/iov-core-docs/latest/iov-core/index.html) library supports the concept of user profiles and identities. An identity is a [BIP39](https://github.com/bitcoin/bips/tree/master/bip-0039) derived key. Please refer to those docs and tutorials for a deeper dive, it is out of the scope of the weave documentation. A good way to get familiar with setting up and running an application is to follow the steps in the [mycoin](mycoind/installation.html) sample application. You can run this on your local machine. If you don’t have a modern Go development environment already set up, please [follow these instructions](mycoind/setup.html). To connect a node to the BNS testnet on a cloud server, the steps to set up an instance on Digital Ocean are explored in this [blog post](https://medium.com/iov-internet-of-values/a-guide-to-deploy-a-validator-on-hugnet-3335192e11d5). Once you can run the blockchain, you will probably want to connect with it. You can view a sample wallet app for the BNS testnet at <https://wallet.hugnet.iov.one> Those that are comfortable with Javascript, should check out our [IOV Core Library](mycoind/iovcore.html) which allows easy access to the blockchain from a browser or node environment. Configuring your Blockchain[¶](#configuring-your-blockchain "Permalink to this headline") ----------------------------------------------------------------------------------------- ### Configuring Tendermint[¶](#configuring-tendermint "Permalink to this headline") Tendermint docs provide a [brief introduction](https://tendermint.com/docs/introduction/) to the tendermint cli. By default all files are writen to the `~/.tendermint` directory, unless you override that with a different “HOME” directory by providing `TMHOME=xyz` or `tendermint --home=xyz`. When you call `tendermint init`, it generates a `config` and `data` directory under the “HOME” dir. `data` will contain all blockchain state as well as the application state. `config` will contain configuration files. There are three main files to look at: * `genesis.json` must be shared by all validators on a chain and is used to initialize the first block. We discuss this more in [Application Config](#application_config) * `config.toml` is used to configure your local server, and can be configured much in the way the config for apache or postgres, to tune to your local system. * `priv\_validator.json` is used by any validating node to sign the blocks, and must be kept secret. We discuss this more in the [next section](./validators.html). #### Overriding Options[¶](#overriding-options "Permalink to this headline") In general, any option you see in [the configuration file](https://tendermint.readthedocs.io/en/master/specification/configuration.html) can also be provided via command-line or environmental variable. It is a simple conversion: Config: ``` [rpc] laddr = "tcp://0.0.0.0:8080" ``` Environment: `export TM\_RPC\_LADDR=tcp://0.0.0.0:8080` or `export TMRPC\_LADDR=tcp://0.0.0.0:8080` (optional \_ after TM) Command line: `tendermint --rpc.laddr=tcp://0.0.0.0:8080 ...` #### Important Options[¶](#important-options "Permalink to this headline") There are many options to tune tendermint, but a few are quite useful when configuring and deploying dev environements or testnets. I will cover them here, but please take a longer look at [all available options](https://github.com/tendermint/tendermint/blob/master/config/config.go). I use the command line format for these options, as it seems the most readable, but most of these should be writen to the `config.toml` file or stored in environmental options in the service ini (if using 12-factor style). Dev: - `--p2p.upnp --proxy\_app noop`: Don’t try to determine external address > > (noop for local testing) * `--log\_level=p2p:info,evidence:debug,consensus:info,\*:error`: Set the log levels for different subsystems (debug, info, error) * `--tx\_index.index\_all\_tags=true` to enable indexing for search and subscriptions. Should be on for public services, off for validators to conserve resources. * `--prof\_laddr=tcp://127.0.0.1:7777` to open up a profiling server at this port for debugging Testnet: - `--moniker=billy-bob` chooses a name to display on the node list > > to help understand the p2p network connections * `--mempool.recheck=false` and `--mempool.recheck\_empty=false` limit rechecking all leftover tx in mempool, which can help throughput at the expense of possibly invalid tx making it into blocks * `--rpc.laddr=tcp://0.0.0.0:46657` to change the interface or port we expose the rpc server (what we expose to the world) * `--p2p.laddr=tcp://0.0.0.0:46656` to change the interface or port we expose the p2p server (what we use to connect to other nodes) * `--p2p.seeds=tcp://12.34.56.78:46656,tcp://33.44.55.66:46656` to set the seed nodes we connect to on startup to discover the rest of the p2p network * `p2p.pex=true` turns on peer exchange, to allow us to dynamically update the network * `--consensus.create\_empty\_blocks=false` to only create a block when there are tx (otherwise blockchain grows fast even with no activity) * `--consensus.create\_empty\_blocks\_interval=300` to create a block every 300s even if no tx * `--consensus.timeout\_commit=5000` to set block interval to 5s (5000ms) + time it takes to achieve consensus (which is generally quite small with < 20 or so well-connected validators) Production: - `p2p.persistent\_peers=tcp://77.77.77.77:46656` contains peers we > > always remain connected to, regardless of peer exchange * `p2p.private\_peer\_ids=...` contains peers we do not gossip. this is essential if we have a non-validating node acting as a buffer for a validating node * `--priv\_validator\_laddr=???` to use a socket to connect to an hsm instead of using the priv\_validator.json file There are quite a few more options, but this is a good place to get started, and you can dig in deeper once you see how these numbers affect blockchains in practice. ### Configuring the Application[¶](#configuring-the-application "Permalink to this headline") The application is fed `genesis.json` the first time it starts up via the `InitChain` ABCI message. There are three fields that the application cares about: * `chain\_id` must be consistent on all nodes and distinct from all other blockchains. This is used in the tx signatures to provide replay protection from one chain and another * `validators` are the initial set and should be stored if the app wishes to dynamically adjust the validator set * `app\_state` contains a map of data, to set up the initial blockchain state, such as initial balances and any accounts with special permissions. #### App State[¶](#app-state "Permalink to this headline") If the backend ABCI app is weave-based, such as `mycoind` or `bns`, the app\_state contains one key for each extension that it wishes to initialize. Each element is an array of an extension-specific format, which is fed into `Initialized.FromGenesis` from the given extension. Sample to set the balances of a few accounts: ``` "app_state": { "cash": [ { "address": "849f0f5d8796f30fa95e8057f0ca596049112977", "coins": [ "88888888 BNS" ] }, { "address": "9729455c431911c8da3f5745a251a6a399ccd6ed", "coins": [ "7777777.666666 IOV" ] } ] } ``` This format is application-specific and extremely important to set the initial conditions of a blockchain, as the data is one of the largest distinguishing factors of a chain and a fork. `mycoind init` will set up one account with a lot of tokens of one name. For anything more complex, you will want to set this up by hand. Note that you should make sure someone has saved the private keys for all addresses or the tokens will never be usable. Also, for cash, ticker must be 3 or 4 upper-case letters. ### Setting the Validators[¶](#setting-the-validators "Permalink to this headline") Since Tendermint uses a traditional BFT algorithm to reach consensus on blocks, signatures from specified validator keys replace hashes used to mine blocks in typical PoW chains. This also means that the selection of validators is an extremely important part of the blockchain security, and every validator should have strong security in place to avoid their private keys being copied or stolen. #### Static Validators[¶](#static-validators "Permalink to this headline") In the simplest setup, every node can generate a private key with `tendermint init`. Note that this is stored as a clear-text file on the harddrive, so the machine should be well locked-down, and file permissions double-checked. This file not only contains the private key itself, but also information on the last block proposal signed, to avoid double-signing blocks, even in the even of a restart during one round. Every validator can find their validator public key, which is different than the public keys / addresses that are assigned tokens, via: ``` cat ~/.mycoind/config/priv\_validator.json | jq .pub\_key ``` If you still have the default genesis file from tendermint init, this public key should match the one validator registered for this blockchain, so it can mint blocks all by itself. ``` cat ~/.mycoind/config/genesis.json | jq .validators ``` In a multi-node network, all validators would have to generate their validator key separately, then share the public keys, and forge a genesis file will all the public keys present. Over two-thirds of these nodes must be online, connected to the p2p network, and acting correctly to mint new blocks. Up to one-third faulty nodes can be tolerated without any problems, and larger numbers of nodes usually halt the network, rather than fork it of mint incorrect blocks. The Tendermint dev team has produced [a simple utility](https://github.com/tendermint/alpha) to help gather these keys. Note that this liveness requirement means that after initializing the genesis and starting up tendermint on every node, they must set proper `--p2p.seeds` in order to connect all the nodes and get enough signatures gathered to mint the first block. #### HSMs[¶](#hsms "Permalink to this headline") If we really care about security, clearly a plaintext file on our machine is not the best solution, regardless of the firewall we put on it. For this reason, the tendermint team is working on integrating Hardware Security Modules (HSM) that will maintain the private key secret on specialized hardware, much like people use the Ledger Nano hardware wallet for cryptocurrencies. This is under active development, but please check the following repos to see the current state: * [Signatory](https://github.com/tendermint/signatory) provides a rust api exposing many curves to sign with * [YubiHSM](https://github.com/tendermint/yubihsm-rs) provides bindings to a YubiKey HSM * [KMS](https://github.com/tendermint/kms) is a work in progress to connect these crates via sockets to a tendermint node. **TODO** Update with current docs, now that cosmos mainnet is live and some people are actually using this. #### Dynamic Validators[¶](#dynamic-validators "Permalink to this headline") A static validator set in the genesis file is quite useless for a real network that is not just a testnet. Tendermint allows the ABCI application to send back messages to update the validator set at the end of every block. Weave-based applications can take advantage of this and implement any algorithm they want to select the validators, such as: * [PoA](https://github.com/iov-one/weave/issues/32) where a set of keys (held by clients) can appoint the validators. This allows them to bring up and down machines, but the authority of the chain rests in a fixed group of individuals. * `PoS` or proof-of-stake, where any individual can bond some of their tokens to an escrow for the right to select a validator. Each validator has a voting power proportional to how much is staked. These staked tokens also receive some share of the block rewards as compensation for the work and risk. * `DPoS` where users can either bond tokens to their own validator, or “delegate” their tokens to a validator run by someone else. Everyone gets some share of the block rewards, but the people running the validator nodes typically take a commission on the delegated rewards, as they must perform real work. For each of these general approaches there is a wide range of tuning of incentives and punishments in order to achieve the desired level of usability and security. The only current implementation shipping with weave is a [POA implementation](https://godoc.org/github.com/iov-one/weave/x/validators#ApplyDiffMsg) allowing some master key (can be a multisig or even an election) update the validator set. This can support systems from testnets to those with strong on-chain governance, but doesn’t work for the PoS fluid market-based solution. If you wish to build an extension supporting PoS, previous related work from cosmos-sdk can be found in their [simple stake](https://github.com/cosmos/cosmos-sdk/tree/v0.15.1/x/simplestake) implementation and the [more complicated DPoS implementation](https://github.com/cosmos/cosmos-sdk/tree/master/x/staking) with incentive mechanisms. When you ran the `mycoind` tutorial, you ran the following lines to configure the blockchain: ``` tendermint init --home ~/.mycoind mycoind init CASH bech32:tiov1qrw95py2x7fzjw25euuqlj6dq6t0jahe7rh8wp ``` This is nice for automatic initialization for dev mode, but for deploying a real network, we need to look under the hood and figure out how to configure it manually. ### Tendermint Configuration[¶](#tendermint-configuration "Permalink to this headline") Tendermint docs provide a brief introduction to the tendermint cli. Here we highlight some of the more important options and explain the interplay between cli flags, environmental variables, and config files, which all provide a way to customize the behavior of the tendermint daemon. [Read More](configuration/tendermint.html) ### Application State Configuration[¶](#application-state-configuration "Permalink to this headline") The application is fed `genesis.json` the first time it starts up via the `InitChain` ABCI message. There are three fields that the application cares about: `chain\_id`, `app\_state`, and `validators`. To learn more about these fields [Read More](configuration/application.html) ### Setting the Validators[¶](#setting-the-validators "Permalink to this headline") Since Tendermint uses a traditional BFT algorithm to reach consensus on blocks, signatures from specified validator keys replace hashes used to mine blocks in typical PoW chains. This also means that the selection of validators is an extremely important part of the blockchain security. [Read More](configuration/validators.html) Building your own Application[¶](#building-your-own-application "Permalink to this headline") --------------------------------------------------------------------------------------------- ### Guiding Design Principles[¶](#guiding-design-principles "Permalink to this headline") Before we get into the structure of the application, there are a few design principles for weave (but also tendermint apps in general) that we must keep in mind. If you are coming from developing web servers or microservices, some of these are counter-intuitive. (Eg. you cannot make external API calls and concurrency is limited) #### Determinism[¶](#determinism "Permalink to this headline") The big key to blockchain development is determinism. Two binaries with the same state must **ALWAYS** produce the same result when passed a given transaction. This seems obvious, but this also occurs when the transactions are replayed weeks, months, or years by a new version, attempting to replay the blockchain. * You cannot relay on walltime (just the timestamp in the header) * No usage of floating point math * No random numbers! * No network calls (especially external APIs)! * No concurrency (unless you **really** know what you are doing) * JSON encoding in the storage is questionable, as the key order may change with newer JSON libraries. * Etc…. The summary is that everything is executed sequentially and deterministically, and thus we require extremely fast transaction processing to enable high throughput. Aim for 1-2 ms per transaction, including committing to disk at the end of the block. Thus, attention to performance is very important. #### ABCI[¶](#abci "Permalink to this headline") To understand this design, you should first understand what an ABCI application is and how that level blockchain abstraction works. ABCI is the interface between the tendermint daemon and the state machine that processes the transactions, something akin to wsgi as the interface between apache/nginx and a django application. > > There is an [in-depth reference](https://tendermint.readthedocs.io/en/master/app-development.html) to the ABCI protocol, but in short, an ABCI application is a state machine that responds to messages sent from one client (the tendermint consensus engine). It is run in parallel on every node, and they must all run the same set of transactions (what was included in the blocks), and then verify they have the same result (merkle root). The main messages that you need to be concerned with are: * Validation - CheckTx Before including a transaction, or gossiping it to peers, every node will call `CheckTx` to check if it is valid. This should be a best-attempt filter, we may reject transactions that are included in the block, but this should eliminate much spam * Execution of Blocks After a block is written to the chain, the tendermint engine makes a number of calls to process it. These are our hooks to make any *writes* to the datastore. + BeginBlock BeginBlock provides the new header and block height. You can also use this as a hook to trigger any delayed tasks that will execute at a given height. (see `Ticker` below) + DeliverTx - once per transaction DeliverTx is passed the raw bytes, just like CheckTx, but it is expected to process the transactions and write the state changes to the key-value store. This is the most important call to trigger any state change. + EndBlock After all transactions have been processed, EndBlock is a place to communicate any configuration changes the application wishes to make on the tendermint engine. This can be changes to the validator set that signs the next block, or changes to the consensus parameters, like max block size, max numbers of transactions per block, etc. + Commit After all results are returned, a Commit message is sent to flush all data to disk. This is an atomic operation, and after a crash, the state should be that after executing block `H` entirely, or block `H+1` entirely, never somewhere in between (or else you are punished by rebuilding the blockchain state by replaying the entire chain from genesis…) * Query A client also wishes to *read* the state. To do so, they may query arbitrary keys in the datastore, and get the current value stored there. They may also fix a recent height to query, so they can guarantee to get a consistent snapshot between multiple queries even if a block was committed in the meantime. A client may also request that the node returns a merkle proof for the key-value pair. This proof is a series of hashes, and produces a unique root hash after passing the key-value pair through the list. If this root hash matches the `AppHash` stored in a blockheader, we know that this value was agreed upon by consensus, and we can trust this is the true value of the chain, regardless of whether we trust the node we connect to. If you are interested, you can read more about [using validating light clients with tendermint](https://blog.cosmos.network/light-clients-in-tendermint-consensus-1237cfbda104) #### Persistence[¶](#persistence "Permalink to this headline") All data structures that go over the wire (passed on any external interface, or saved to the key value store), must be able to be serialized and de-serialized. An application may have any custom binary format it wants, and to support this flexibility, we provide a `Persistent` interface to handle marshaling similar to the `encoding/json` library. ``` type Persistent interface { Marshal() ([]byte, error) Unmarshal([]byte) error } ``` Note that Marshal can work with a struct, while Unmarshal (almost) always requires a pointer to work properly. You may define these two functions for every persistent data structure in your code, using any codec you want. However, for simplicity and cross-language parsing on the client size, we recommend to define `.proto` files and compile them with protobuf. [gogo protobuf](https://github.com/gogo/protobuf) will autogenerate Marshal and Unmarshal functions requiring no reflection. See the [Makefile](https://github.com/iov-one/weave/blob/master/Makefile) for `tools` and `protoc` which show how to automate installing the protobuf compiler and compiling the files. However, if you have another favorite codec, feel free to use that. Or mix and match. Each struct can use it’s own Marshaller. Before we get into the strucutre of the application, there are a few design principles for weave (but also tendermint apps in general) that we must keep in mind. ### Determinism[¶](#determinism "Permalink to this headline") The big key to blockchain development is determinism. Two binaries with the same state must **ALWAYS** produce the same result when passed a given transaction. [Read More](design/overview.html#determinism) ### Abstract Block Chain Interface (ABCI)[¶](#abstract-block-chain-interface-abci "Permalink to this headline") To understand this design, you should first understand what an ABCI application is and how that level blockchain abstraction works. ABCI is the interface between the tendermint daemon and the state machine that processes the transactions, something akin to wsgi as the interface between apache/nginx and a django application. [Read More](design/overview.html#abci) ### Persistence[¶](#persistence "Permalink to this headline") All data structures that go over the wire (passed on any external interface, or saved to the key value store), must be able to be serialized and deserialized. An application may have any custom binary format it wants, although all standard weave extensions use protobuf. [Read More](design/overview.html#persistence) Additional Reading[¶](#additional-reading "Permalink to this headline") ----------------------------------------------------------------------- We are in the process of doing a large overhaul on the docs. Until we are finished, please look at the [older version of the docs](index_old.html) for more complete (if outdated) information

erpc

go

eRPC documentation eRPC documentation[¶](#erpc-documentation "Permalink to this headline") ======================================================================= [eRPC](index.html#document-index) ================================= ### Navigation ### Related Topics * [Documentation overview](index.html#document-index) ### Quick search ©2018, Anuj Kalia. | Powered by [Sphinx 1.7.9](http://sphinx-doc.org/) & [Alabaster 0.7.11](https://github.com/bitprophet/alabaster)

nodes

go

nodes alpha documentation [nodes](index.html#document-index)   [nodes](index.html#document-index) * [Docs](index.html#document-index) » * nodes alpha documentation * [Edit on GitHub](https://github.com/adamdonahue/nodes/blob/master/docs/index.rst) --- nodes[¶](#nodes "Permalink to this headline") ============================================= An easy-to-use graph-oriented object model for Python. Overview[¶](#overview "Permalink to this headline") --------------------------------------------------- Graph-oriented programming, a functional reactive model in which changes to function inputs trigger the need for reevaluation of those functions, is generally a feature reserved for languages with strong functional programming support, particular those that perform strong type checking and that can enforce function purity at compile time. This is too bad. A graph-oriented programming model, in addition to providing a useful model for the relationships between objects in your system, can be an extremely productivity-enhancing tool, freeing the developer from a lot of the coding that would typically be needed to perform such things as memoization, lazy evaluation, dependency tracking, subscriptions and callbacks, temporary changes, report building, and so forth. It doesn’t have to be this way. A programmer can still leverage a graph-oriented model in a less strictly typed language as long as she adheres to the semantics required by such a model (mainly that on-graph functions must be pure and side-effect free). The goal of nodes is to bridge this gap by providing Python developers with a simple, elegant way to put their classes on a graph. Features[¶](#features "Permalink to this headline") --------------------------------------------------- * Ease of use. * Dependency tracking and invalidation for on-graph nodes. * Lazy evaluation. * Memoization. * Change delegation. * Contextual evaluation. (What-if scenario building.) Current Limitations[¶](#current-limitations "Permalink to this headline") ------------------------------------------------------------------------- * Runtime overhead. The current version focuses on the developer interface, and is not tuned for high performance. So there is overhead associated with each on-graph method that will impact programs that require high performance. * Single threaded. The graph is single threaded, and use within a multiple threaded environment is not yet supported. * No object persistence. The graph must be constructed in memory each time a program is launched; there is no object persistence layer yet. *This is a feature under development, namely, an object-oriented database of Python objects allowing one to save and read back on-graph objects.* * Dynamic graph construction. There are two approaches I could have taken to building the graph. One involves using a AST to determine the full structure of the graph upfront. The other involves dynamically discovering the graph as graph methods are called. At present I use the dynamic route, which means that graph edges are added and updated as on-graph functions are called. I plan to abstract the way the graph is discovered into a separate class and allow a user to perform static graph discovery vs dynamic, if desired. (One benefit to static discovery is that it makes it possible to query the graph about its relationships without having to had evaluated its functions, and even in that case, in a dynamic graph that has been evaluated with some set of inputs, one still doesn’t necessarily get a full picture of the graph, but instead sees the relationships as of a state in time - thatis, how the graph was used before the time at which you asked it for its structure.) Using nodes: Requirements[¶](#using-nodes-requirements "Permalink to this headline") ------------------------------------------------------------------------------------ Putting an object on the graph is easy, as this example illustrates. There are three things a developer must do. Two of these are technical: * His class must be a subclass of GraphObject. * On-graph methods in this class must be decorated with @graphMethod. The third is has to do with the semantics of the methods he has decorated with @graphMethod. All graph methods must be *pure and side-effect free* (with some exceptions that we need not delve into here). A function is *pure* if given the same inputs it returns the same output: ``` def cubed(self, x): return x \* x \* x ``` is a pure function, whereas: ``` file.readline ``` is not. A function has *side-effects* if it modifies global state in any way. cubed is side-effect free, but file.readline is not, as it would update state indicating where in the file its next read should occur. Purity and lack of side-effects often go hand-in-hand, and vice-versa. Why is purity so important? Because without purity we cannot know when a function’s value needs to be recomputed; if that value is determined by factors other than the inputs to the function then we lose control over when a function needs to be invalidated. And a huge benefit of the graph is its support for automatic node invalidation, node memoization, and lazy evaluation of node functions. (The documentation will contain more detail on the model used and the patterns one can leverage to perform common operations in a graph-consistent manner. I don’t want to get too Haskell-y on you because this is a Python module, not a Haskell library.) So you may be thinking, jeez, this all sounds quite mathy and it sounds as if it’ll be a pain to write proper on-graph methods. That’s a fair initial reaction. But the reality is that developing with nodes is not that diffcult, and if you follow its practices you end up with cleaner code and maybe even a new way of thinking about problems. Using nodes: An Example[¶](#using-nodes-an-example "Permalink to this headline") -------------------------------------------------------------------------------- The following example illustrates how you might use nodes to put a simple example class on the graph. It doesn’t cover all of nodes’ features but will give you an idea of its flavor. The comments below indicate the status of each graph method after a given calculation. At this point I’m going to switch to using the term “node” instead of method, as in reality a method may map to multiple nodes (for example, in the case where the method has arguments in addition to self). * invalid: The node is not set and the method body will run when its value is next requested. * calced: The node is valid and its value was calculated by running the function body and memoizing the result. As long as the node remains valid its memoized output will be returned with no recomputation required. * set: The node was set to a specific value by the user. This setting is non-contextual (global) to the graph. * overlaid: The node was overlaid to a specific value by the user within a GraphContext. The overlay is active only within the context, and upon exiting the context the node’s state is reverted to its prior value. (This is not strictly true; if global dependencies changed that were hidden by the context the node might have been invalidated outside the context and thus require computation the next time it’s valid is requested.) That said, here is the code: ``` class Example(nodes.GraphObject): @nodes.graphMethod def X(self): return 'X:%s:%s' % (self.Y(), self.Z()) @nodes.graphMethod(nodes.Settable) def Y(self): return 'Y' @nodes.graphMethod(nodes.Settable) def Z(self): return 'Z' def main(): example = Example() # example.X <invalid> # example.Y <invalid> # example.Z <invalid> example.X() # example.X == 'X:Y:Z' <calced> # example.Y == 'Y' <calced> # example.Z == 'Z' <calced> example.Y = 'y' # example.X <invalid> # example.Y == 'y' <set> # example.Z == 'Z' <calced> example.X() # example.X == 'X:y:Z' <calced> # example.Y == 'y' <set> # example.Z == 'Z' <calced> example.Y.clearValue() # example.X <invalid> # example.Y <invalid> (maybe) # example.Z == 'Z' <calced> example.X() # example.X == 'X:Y:Z' <calced> # example.Y == 'Y' <calced> # example.Z == 'Z' <calced> with nodes.GraphContext(): example.Y.overlayValue('y') # example.X <invalid> # example.Y == 'Y' <overlaid> # example.Z == 'z' <calced> example.X() # example.X == 'X:Y:z' <calced> # example.Y == 'Y' <overlaid> # example.Z == 'z' <calced> # example.X <invalid> (maybe) # example.Y == 'Y' <invalid> (maybe) # example.Z == 'Z' <calced> with nodes.GraphContext() as savedContext: example.Y.overlayValue('y') # example.X <invalid> # example.Y == 'y' <overlaid> # example.Z == 'Z' <calced> # example.X <invalid (maybe)> # example.Y <invalid (maybe)> # example.Z == 'Z' <calced> example.X() # example.X == 'X:Y:Z' <calced> # example.Y == 'Y' <calced> # example.Z == 'Z' <calced> with savedContext: # example.X <invalid> # example.Y == 'y' <overlaid> # example.Z == 'Z' <calced> example.X() # example.X == 'X:y:Z' <calced> # example.Y == 'y' <overlaid> # example.Z == 'Z' <calced> with nodes.GraphContext(): example.Z.overlayValue('z') # example.X <invalid> # example.Y == 'y' <overlaid> # example.Z == 'z' <overlaid> example.X() # example.X == 'X:y:z' <calced> # example.Y == 'y' <overlaid> # example.Z == 'z' <overlaid> # example.X <invalid> # example.Y == 'y' <overlaid> # example.Z == 'Z' <invalid (maybe)> ```

utils

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utils 0.1.1 documentation [utils](index.html#document-index) * [List](index.html#document-list) * [Stat](index.html#document-stat) * [String](index.html#document-string) * [Bio](index.html#document-bio)   [utils](index.html#document-index) * [Docs](index.html#document-index) » * utils 0.1.1 documentation * [Edit on GitHub](https://github.com/sefakilic/utils/blob/master/docs/index.rst) --- Welcome to utils’s documentation ==================================================================================================== Contents: List[¶](#module-utils.list "Permalink to this headline") -------------------------------------------------------- List module implements list processing functions, mostly as named in Data.List module of Haskell. utils.list.concat(*xss*)[¶](#utils.list.concat "Permalink to this definition") Concatenate a list of lists ``` >>> concat([[1,2], [3,4], [5]]) [1, 2, 3, 4, 5] ``` utils.list.concat\_map(*f*, *xs*)[¶](#utils.list.concat_map "Permalink to this definition") Map a function over a list and concatenate the results. ``` >>> concat\_map(lambda x: [x]\*3, [1, 2, 3]) [1, 1, 1, 2, 2, 2, 3, 3, 3] ``` utils.list.cycle(*xs*)[¶](#utils.list.cycle "Permalink to this definition") Given a list, return a circular list, the infinite repetition of the original list. ``` >>> list(itertools.islice(cycle([1, 2, 3]), 10)) [1, 2, 3, 1, 2, 3, 1, 2, 3, 1] ``` utils.list.delete(*x*, *ys*)[¶](#utils.list.delete "Permalink to this definition") Remove the first occurrence of x from its list argument. ``` >>> delete(1, [0, 1, 2, 3, 4]) [0, 2, 3, 4] ``` utils.list.diff(*xs*, *ys*)[¶](#utils.list.diff "Permalink to this definition") List difference. In the result of diff(xs, ys) the first occurrence of each element of ys in turn (if any) has been removed from xs. Thus diff(xs+ys, xs) = ys ``` >>> diff([1, 2, 3, 1, 4, 2], [1, 2]) [3, 1, 4, 2] >>> diff([], [1, 2]) [] >>> diff([1, 2, 3], []) [1, 2, 3] ``` utils.list.drop(*n*, *xs*)[¶](#utils.list.drop "Permalink to this definition") Return the suffix of xs after first n elements, or [] if n > len(xs) ``` >>> drop(6, 'Hello world!') 'world!' >>> drop(3, [1, 2]) [] >>> drop(-1, [1, 2]) [1, 2] ``` utils.list.drop\_while(*pred*, *xs*)[¶](#utils.list.drop_while "Permalink to this definition") Return the suffix remaining after take\_while(p, xs). ``` >>> drop\_while(lambda x: operator.lt(x, 3), [1, 2, 3, 4, 5, 1, 2, 3]) [3, 4, 5, 1, 2, 3] >>> drop\_while(lambda x: operator.lt(x, 9), [1, 2, 3]) [] >>> drop\_while(lambda x: operator.lt(x, 0), [1, 2, 3]) [1, 2, 3] ``` utils.list.drop\_while\_end(*pred*, *xs*)[¶](#utils.list.drop_while_end "Permalink to this definition") Drop the largest suffix of the list in which the given predicate holds for all elements. ``` >>> drop\_while\_end(lambda x: x>3, [1, 2, 3, 4, 5]) [1, 2, 3] ``` utils.list.elem(*x*, *xs*)[¶](#utils.list.elem "Permalink to this definition") The list membership predicate. ``` >>> elem(1, [1, 2, 3]) True >>> elem(4, [1, 2, 3]) False ``` utils.list.elem\_index(*p*, *xs*)[¶](#utils.list.elem_index "Permalink to this definition") Return the index of the first element in the given list which is equal to the query element, or None if there is no such element. ``` >>> elem\_index(2, [1, 2, 3, 2]) 1 >>> elem\_index(4, [1, 2, 3, 2]) is None True ``` utils.list.elem\_indices(*p*, *xs*)[¶](#utils.list.elem_indices "Permalink to this definition") Return the indices of all elements equal to the query element, in ascending order. ``` >>> elem\_indices(2, [1, 2, 3, 2]) [1, 3] >>> elem\_indices(4, [1, 2, 3, 2]) == [] True ``` utils.list.find(*pred*, *xs*)[¶](#utils.list.find "Permalink to this definition") Return the first element in the list matching the predicate and None if there is no such element. ``` >>> find(lambda x: operator.lt(x, 3), [3, 2, 4]) 1 >>> find(lambda x: operator.lt(x, 1), [3, 2, 4]) is None True ``` utils.list.find\_index(*pred*, *xs*)[¶](#utils.list.find_index "Permalink to this definition") Return the index of the first element in the list satisfying the predicate, None if there is no such element. ``` >>> find\_index(lambda x: x==2, [1, 2, 3, 2]) 1 >>> find\_index(lambda x: x==4, [1, 2, 3, 2]) is None True ``` utils.list.find\_indices(*pred*, *xs*)[¶](#utils.list.find_indices "Permalink to this definition") Return the indices of all elements satisfying the predicate, in ascending order. ``` >>> find\_indices(lambda x: x==2, [1, 2, 3, 2]) [1, 3] >>> find\_indices(lambda x: x==4, [1, 2, 3, 2]) [] ``` utils.list.foldl(*f*, *z*, *xs*)[¶](#utils.list.foldl "Permalink to this definition") foldl, applied to a binary operator, a starting value (typically the left-identity of the operator), and a list, reduces the list using the binary operator, from left to right. foldl(f, z, [x1, x2, x3]) == f(f(f(z, x1), x2), x3) ``` >>> foldl (operator.div, 64, [4, 2, 4]) 2 ``` utils.list.foldl1(*f*, *xs*)[¶](#utils.list.foldl1 "Permalink to this definition") foldl1 is a variant of foldl that has no starting value argument, and thus must be applied to non-empty lists. ``` >>> foldl1 (operator.div, [64, 4, 2, 4]) 2 ``` utils.list.foldr(*f*, *z*, *xs*)[¶](#utils.list.foldr "Permalink to this definition") foldr, applied to a binary operator, a starting value (typically the right-identity of the operator), and a list, reduces the list using the binary operator, from right to left: foldr(f, z, [x1,x2,x3]) = f(x1, f(x2, f(x3, z))) ``` >>> foldr(operator.div, 2, [8, 12, 24, 4]) 8 ``` utils.list.foldr1(*f*, *xs*)[¶](#utils.list.foldr1 "Permalink to this definition") foldr1 is a variant of foldr that has no starting value argument, thus must be applied to non-empty lists. ``` >>> foldr1(operator.div, [8, 12, 24, 4]) 4 ``` utils.list.group(*xs*)[¶](#utils.list.group "Permalink to this definition") The group function takes a list and returns a list of lists such that the concatenation of the result is equal to the argument. Moreover, each sublist in the result contains only equal elements. ``` >>> group([1, 2, 2, 3, 3, 3, 1, 2, 2]) [[1], [2, 2], [3, 3, 3], [1], [2, 2]] ``` utils.list.head(*xs*)[¶](#utils.list.head "Permalink to this definition") Extract the first element of a list, which must be non-empty. ``` >>> head([1, 2, 3]) 1 ``` utils.list.init(*xs*)[¶](#utils.list.init "Permalink to this definition") Return all the elements of a list except the last one. The list must be non-empty. ``` >>> init([1, 2, 3]) [1, 2] ``` utils.list.inits(*xs*)[¶](#utils.list.inits "Permalink to this definition") Return all initial segments of the argument, shortest first. ``` >>> inits('abc') [[], 'a', 'ab', 'abc'] >>> inits([1,2,3]) [[], [1], [1, 2], [1, 2, 3]] ``` utils.list.intercalate(*xs*, *xss*)[¶](#utils.list.intercalate "Permalink to this definition") intercalate(xs, xss) is equivalent to concat(intersperse xs xss). It inserts the list xs in between the lists in xss and concatenates the result. ``` >>> intercalate([0, 0], [[1, 2, 3], [4, 5, 6], [7, 8]]) [1, 2, 3, 0, 0, 4, 5, 6, 0, 0, 7, 8] ``` utils.list.intersect(*xs*, *ys*)[¶](#utils.list.intersect "Permalink to this definition") Return the intersection of two lists. If the first list contains duplicates, so will the result. ``` >>> intersect([1, 2, 3, 4], [5, 4, 3]) [3, 4] >>> intersect([1, 1, 2, 2, 3, 3, 4, 4], [5, 4, 3]) [3, 3, 4, 4] ``` utils.list.intersperse(*sep*, *xs*)[¶](#utils.list.intersperse "Permalink to this definition") Take an element and a list and ‘intersperse’ that element between the elements of the list. ``` >>> intersperse(0, [1,2,3]) [1, 0, 2, 0, 3] >>> intersperse(0, []) [] ``` utils.list.is\_infix\_of(*xs*, *ys*)[¶](#utils.list.is_infix_of "Permalink to this definition") Take two lists and return True if the first is the infix of the second. ``` >>> is\_infix\_of([2, 3, 4], [1, 2, 3, 4, 5]) True >>> is\_infix\_of([1, 2, 5], [1, 2, 3, 4, 5]) False ``` utils.list.is\_prefix\_of(*xs*, *ys*)[¶](#utils.list.is_prefix_of "Permalink to this definition") Take two lists and return True if the first list is the prefix of the second. ``` >>> is\_prefix\_of([1, 2, 3], [1, 2, 3, 4, 5]) True >>> is\_prefix\_of([2, 3, 4], [1, 2, 3, 4, 5]) False ``` utils.list.is\_suffix\_of(*xs*, *ys*)[¶](#utils.list.is_suffix_of "Permalink to this definition") Take two lists and return True if the first is the suffix of the second. ``` >>> is\_suffix\_of([3, 4, 5], [1, 2, 3, 4, 5]) True >>> is\_suffix\_of([2, 3, 4], [1, 2, 3, 4, 5]) False ``` utils.list.iterate(*f*, *x*)[¶](#utils.list.iterate "Permalink to this definition") Return an infinite list of repeated applications of f to x. iterate(f, x) = [x, f(x), f(f(x)), f(f(f(x))), ...] ``` >>> list(itertools.islice(iterate(lambda x: 2\*x, 1), 3)) [2, 4, 8] ``` utils.list.lall(*f*, *xs*)[¶](#utils.list.lall "Permalink to this definition") Applied to a predicate and a list, ‘all’ determines if all elements of the list satisfy the predicate. ``` >>> lall(lambda x: operator.le(x, 5), [3, 4, 5, 6]) False >>> lall(lambda x: operator.le(x, 9), [3, 4, 5, 6]) True ``` utils.list.land(*bs*)[¶](#utils.list.land "Permalink to this definition") land returns the conjunction of a Boolean list. ``` >>> land([True, False, True]) False >>> land([True, True]) True >>> land([]) True ``` utils.list.lany(*f*, *xs*)[¶](#utils.list.lany "Permalink to this definition") Applied to a predicate and a list, ‘any’ determines if any element of the list satisfies the predicate. ``` >>> lany(lambda x: operator.ge(x, 5), [3, 4, 5, 6]) True >>> lany(lambda x: operator.ge(x, 9), [3, 4, 5, 6]) False ``` utils.list.last(*xs*)[¶](#utils.list.last "Permalink to this definition") Extract the last element of a list, which must be non-empty. ``` >>> last([1, 2, 3]) 3 ``` utils.list.lbreak(*pred*, *xs*)[¶](#utils.list.lbreak "Permalink to this definition") lbreak, applied to a predicate pred and a list xs, returns a tuple where first element is the longest prefix (possibly empty) of xs of elements that do \_not\_ satisfy p and the second element is the remainder of the list. ``` >>> lbreak(lambda x: x>3, [1, 2, 3, 4, 1, 2, 3, 4]) ([1, 2, 3], [4, 1, 2, 3, 4]) >>> lbreak(lambda x: x<9, [1, 2, 3]) ([], [1, 2, 3]) >>> lbreak(lambda x: x>9, [1, 2, 3]) ([1, 2, 3], []) ``` utils.list.length(*xs*)[¶](#utils.list.length "Permalink to this definition") Return the length of the list. ``` >>> length([1, 2, 3]) 3 ``` utils.list.lookup(*key*, *assocs*)[¶](#utils.list.lookup "Permalink to this definition") Look up a key in an association list. ``` >>> lookup('c', [('a', 0), ('b', 1), ('c', 2)]) 2 >>> lookup('f', [('a', 0), ('b', 1), ('c', 2)]) is None True ``` utils.list.lor(*bs*)[¶](#utils.list.lor "Permalink to this definition") lor returns the disjunction of a Boolean list. ``` >>> lor([True, False, True]) True >>> lor([True, True]) True >>> lor([]) False ``` utils.list.lsum(*nums*)[¶](#utils.list.lsum "Permalink to this definition") Compute the sum of a list of numbers. >>> lsum([1, 2, 3]) 6 utils.list.map\_accum\_l(*f*, *z*, *xs*)[¶](#utils.list.map_accum_l "Permalink to this definition") map\_accum\_l behaves like a combination of map and foldl; it applies a function to each element of a list, passing an accumulating parameter from left to right, and returning a final value for this accumulator together with the new list. ``` >>> map\_accum\_l(lambda x, y: (x, x\*y), 5, [9, 6, 3]) (5, [45, 30, 15]) >>> map\_accum\_l(lambda x, y: (x+y, x\*y), 5, [2, 4, 8]) (19, [10, 28, 88]) >>> map\_accum\_l(lambda x, y: (x+y, y), 5, [2, 4, 8]) (19, [2, 4, 8]) >>> map\_accum\_l(lambda x, y: (y, y), 5, [2, 4, 8]) (8, [2, 4, 8]) >>> map\_accum\_l(lambda x, y: (x, x), 5, [2, 4, 8]) (5, [5, 5, 5]) ``` utils.list.map\_accum\_r(*f*, *z*, *xs*)[¶](#utils.list.map_accum_r "Permalink to this definition") map\_accum\_r function behaves like a combination of ‘map’ and ‘foldr’; it applies a function to each element of a list, passing an accumulating parameter from right to left, and returning a final value of this accumulator together with the new list. ``` >>> map\_accum\_r(lambda x,y: (x, x\*y), 5, [9, 6, 3]) (5, [45, 30, 15]) >>> map\_accum\_r(lambda x,y: (x+y, x\*y), 5, [2, 4, 8]) (19, [34, 52, 40]) >>> map\_accum\_r(lambda x,y: (x+y, y), 5, [2, 4, 8]) (19, [2, 4, 8]) ``` utils.list.maximum(*xs*)[¶](#utils.list.maximum "Permalink to this definition") Return the maximum value from the list, which must be non-empty and of an ordered type. ``` >>> maximum([1, 3, 4, 2]) 4 ``` utils.list.minimum(*xs*)[¶](#utils.list.minimum "Permalink to this definition") Return the minimum value from the list, which must be non-empty and of an ordered type. ``` >>> minimum([1, 3, 4, 2]) 1 ``` utils.list.not\_elem(*x*, *xs*)[¶](#utils.list.not_elem "Permalink to this definition") Negation of elem. ``` >>> not\_elem(1, [1, 2, 3]) False >>> not\_elem(4, [1, 2, 3]) True ``` utils.list.nub(*xs*)[¶](#utils.list.nub "Permalink to this definition") Remove duplicates from a list. ``` >>> nub([1, 2, 1, 3, 2, 1, 2, 3]) [1, 2, 3] ``` utils.list.nub\_by(*eqtest*, *xs*)[¶](#utils.list.nub_by "Permalink to this definition") Behave just like nub, except use a user-supplied equality predicate instead of == function. ``` >>> nub\_by(lambda x, y: (x>0) == (y>0), [1, 2, -1, 2, -3, 4]) [1, -1] ``` utils.list.null(*xs*)[¶](#utils.list.null "Permalink to this definition") Test whether a list is empty. ``` >>> null([1, 2, 3]) False >>> null([]) True ``` utils.list.partition(*pred*, *xs*)[¶](#utils.list.partition "Permalink to this definition") Return a pair of lists of elements which do and don’t satisfy the predicate. ``` >>> partition(lambda x: x<3, [1, 2, 3, 4, 5]) ([1, 2], [3, 4, 5]) ``` utils.list.permutations(*xs*, *r=None*)[¶](#utils.list.permutations "Permalink to this definition") The permutations function returns the list of all permutations of the argument of length r If r is not specified or is None, it defaults to the length of the list. ``` >>> permutations([1,2,3]) [[1, 2, 3], [1, 3, 2], [2, 1, 3], [2, 3, 1], [3, 1, 2], [3, 2, 1]] >>> permutations([1,2,3], 2) [[1, 2], [1, 3], [2, 1], [2, 3], [3, 1], [3, 2]] ``` utils.list.product(*nums*)[¶](#utils.list.product "Permalink to this definition") Compute the product of a list of numbers. ``` >>> product([1, 2, 3, 4]) 24 ``` utils.list.repeat(*x*)[¶](#utils.list.repeat "Permalink to this definition") Return an infinite list, with x the value of every element. ``` >>> list(itertools.islice(repeat(3), 4)) [3, 3, 3, 3] ``` utils.list.replicate(*n*, *x*)[¶](#utils.list.replicate "Permalink to this definition") Return a list of length given by the first argument and the items having value of the second argument. ``` >>> replicate(3, 5) [5, 5, 5] ``` utils.list.reverse(*xs*)[¶](#utils.list.reverse "Permalink to this definition") Return the elements of xs in reverse order. ``` >>> reverse([1, 2, 3]) [3, 2, 1] ``` utils.list.scanl(*f*, *z*, *xs*)[¶](#utils.list.scanl "Permalink to this definition") scanl is similar to foldl, but returns a list of successive reduced values from the left. scanl(f, z, [x1,x2,x3]) == [z, f(z, x1), f(f(z, x1), x2), f(f(f(z, x1), x2), x3)] ``` >>> scanl(operator.div, 64, [4, 2, 4]) [64, 16, 8, 2] ``` utils.list.scanl1(*f*, *xs*)[¶](#utils.list.scanl1 "Permalink to this definition") scanl1 is a variant of scanl that has no starting value argument ``` >>> scanl1(operator.div, [64, 4, 2, 4]) [64, 16, 8, 2] ``` utils.list.scanr(*f*, *z*, *xs*)[¶](#utils.list.scanr "Permalink to this definition") scanr is the right-to-left dual of scanl. ``` >>> scanr(operator.add, 5, [1, 2, 3, 4]) [15, 14, 12, 9, 5] >>> scanr(operator.div, 2, [8, 12, 24, 4]) [8, 1, 12, 2, 2] ``` utils.list.scanr1(*f*, *xs*)[¶](#utils.list.scanr1 "Permalink to this definition") scanr1 is a variant of scanr that has no starting value argument. ``` >>> scanr1(operator.add, [1, 2, 3, 4]) [10, 9, 7, 4] >>> scanr1(operator.div, [8, 12, 24, 2]) [8, 1, 12, 2] ``` utils.list.shuffle(*xs*)[¶](#utils.list.shuffle "Permalink to this definition") Return a shuffled copy of the list. ``` >>> random.seed(1); shuffle([1, 2, 3, 4, 5]) [2, 5, 3, 4, 1] ``` utils.list.span(*pred*, *xs*)[¶](#utils.list.span "Permalink to this definition") span, applied to a predicate pred and a list xs, returns a tuple where the first element is longest prefix (possibly empty) of xs of elements that satisfy pred and the second element is the remainder of the list. ``` >>> span(lambda x: x<3, [1, 2, 3, 4, 1, 2, 3, 4]) ([1, 2], [3, 4, 1, 2, 3, 4]) >>> span(lambda x: x<9, [1, 2, 3]) ([1, 2, 3], []) >>> span(lambda x: x<0, [1, 2, 3]) ([], [1, 2, 3]) ``` utils.list.split\_at(*n*, *xs*)[¶](#utils.list.split_at "Permalink to this definition") Return a tuple where first element is xs prefix of length n and the second element is the remainder of the list. ``` >>> split\_at(3, [1,2,3,4,5]) ([1, 2, 3], [4, 5]) ``` utils.list.strip\_prefix(*prefix*, *xs*)[¶](#utils.list.strip_prefix "Permalink to this definition") Drop the given prefix from the list. ``` >>> strip\_prefix('foo', 'foobar') 'bar' >>> strip\_prefix('foo', 'foo') '' >>> strip\_prefix('barfoo', 'foo') 'foo' ``` utils.list.subsequences(*xs*)[¶](#utils.list.subsequences "Permalink to this definition") Return the list of all subsequences of the argument. ``` >>> subsequences([1,2,3]) [[], [1], [2], [3], [1, 2], [1, 3], [2, 3], [1, 2, 3]] ``` utils.list.tail(*xs*)[¶](#utils.list.tail "Permalink to this definition") Extract the elements after the head of a list, which must be non-empty. ``` >>> tail([1, 2, 3]) [2, 3] ``` utils.list.tails(*xs*)[¶](#utils.list.tails "Permalink to this definition") Return all tails of the argument, longest first. ``` >>> tails([1, 2, 3]) [[1, 2, 3], [2, 3], [3], []] ``` utils.list.take(*n*, *xs*)[¶](#utils.list.take "Permalink to this definition") Return the prefix of xs of length n, or xs itself if n > len(xs) ``` >>> take(5, 'hello world!') 'hello' >>> take(3, [1, 2, 3, 4, 5]) [1, 2, 3] >>> take(3, [1]) [1] ``` utils.list.take\_while(*pred*, *xs*)[¶](#utils.list.take_while "Permalink to this definition") Return the longest prefix (possibly empty) of xs of elements that satisfy pred. ``` >>> take\_while(lambda x: operator.lt(x, 3), [1, 2, 3, 4, 1, 2, 3, 4]) [1, 2] >>> take\_while(lambda x: operator.lt(x, 9), [1, 2, 3]) [1, 2, 3] >>> take\_while(lambda x: operator.lt(x, 0), [1, 2, 3]) [] ``` utils.list.transpose(*xss*)[¶](#utils.list.transpose "Permalink to this definition") Transpose the rows and columns of xss. ``` >>> transpose([[1,2,3], [4,5,6]]) [[1, 4], [2, 5], [3, 6]] ``` utils.list.union(*xs*, *ys*)[¶](#utils.list.union "Permalink to this definition") Return the list union of the two lists. Duplicates, and elements of the first list, are removed from the second list, but if the first list contains duplicates, so will the result. ``` >>> union([1, 2, 3], [4, 5]) [1, 2, 3, 4, 5] >>> union([1, 2, 3], [3, 2, 1, 0]) [1, 2, 3, 0] >>> union([1, 2, 2], [1, 2, 3]) [1, 2, 2, 3] ``` utils.list.unzip(*zipped*)[¶](#utils.list.unzip "Permalink to this definition") Transform a list of tuples into a list of first components, second components, and so on. ``` >>> unzip(zip([1, 2, 3], [4, 5, 6])) [[1, 2, 3], [4, 5, 6]] >>> unzip(zip([1, 2, 3], [4, 5, 6], [7, 8, 9])) [[1, 2, 3], [4, 5, 6], [7, 8, 9]] ``` utils.list.zip\_with(*f*, *\*lists*)[¶](#utils.list.zip_with "Permalink to this definition") Generalize zip by zipping with the function given as the first argument, instead of tupling function. ``` >>> zip\_with(operator.add, [1, 2, 3], [4, 5, 6]) [5, 7, 9] >>> zip\_with(lambda x,y,z: x\*y\*z, [1, 2, 3], [4, 5, 6], [7, 8, 9]) [28, 80, 162] ``` Stat[¶](#module-utils.stat "Permalink to this headline") -------------------------------------------------------- Math and statistics related utility functions utils.stat.log2(*x*)[¶](#utils.stat.log2 "Permalink to this definition") Binary logarithm ``` >>> log2(2) 1.0 ``` utils.stat.mean(*xs*)[¶](#utils.stat.mean "Permalink to this definition") Mean of a list of numbers. ``` >>> mean([1,2,3,4]) 2.5 ``` utils.stat.mean\_and\_sd(*xs*)[¶](#utils.stat.mean_and_sd "Permalink to this definition") Mean and standard deviation as tuple. ``` >>> map(lambda x: round(x, 2), mean\_and\_sd([1, 2, 3, 4, 5])) [3.0, 1.58] ``` utils.stat.median(*xs*)[¶](#utils.stat.median "Permalink to this definition") Median of a list of numbers ``` >>> median([3, 2, 1, 4, 5]) 3 >>> median([3, 2, 1, 4]) 2.5 ``` utils.stat.normalize(*xs*)[¶](#utils.stat.normalize "Permalink to this definition") utils.stat.pearson\_correlation(*x*, *y*)[¶](#utils.stat.pearson_correlation "Permalink to this definition") Pearson product-moment correlation coefficient utils.stat.sample(*xs*)[¶](#utils.stat.sample "Permalink to this definition") utils.stat.se(*xs*, *correct=True*)[¶](#utils.stat.se "Permalink to this definition") Standard error of a list of numbers. ``` >>> round(se([1, 2, 3, 4, 5]), 2) 0.71 >>> round(se([1, 2, 3, 4, 5], correct=False), 2) 0.63 ``` utils.stat.std(*xs*, *correct=True*)[¶](#utils.stat.std "Permalink to this definition") Standard deviation of a list of numbers. ``` >>> round(sd([1, 2, 3, 4, 5]), 2) 1.58 >>> round(sd([1, 2, 3, 4, 5], correct=False), 2) 1.41 ``` utils.stat.variance(*xs*, *correct=True*)[¶](#utils.stat.variance "Permalink to this definition") Variance of a list of numbers. ``` >>> variance([1, 2, 3, 4, 5]) 2.5 >>> variance([1, 2, 3, 4, 5], correct=False) 2.0 ``` String[¶](#string "Permalink to this headline") ----------------------------------------------- utils.string.lines(*s*)[¶](#utils.string.lines "Permalink to this definition") Break a string up into a list of strings at newline characters. The resulting strings do not contain newlines. ``` >>> lines("string\nwith\nnewline") ['string', 'with', 'newline'] ``` utils.string.unlines(*ls*)[¶](#utils.string.unlines "Permalink to this definition") Inverse operation to lines. ``` >>> unlines(['string', 'with', 'newline']) 'string\nwith\nnewline' ``` utils.string.unwords(*ws*)[¶](#utils.string.unwords "Permalink to this definition") Inverse operation to words. Join words with separating spaces. ``` >>> unwords(['two', 'words']) 'two words' ``` utils.string.words(*s*)[¶](#utils.string.words "Permalink to this definition") Break a string into a list of words, which were delimited by whitespace. ``` >>> words("two words") ['two', 'words'] ``` Bio[¶](#module-utils.bio "Permalink to this headline") ------------------------------------------------------ Utility functions, mostly using biopython utils.bio.base\_freq(*seq*)[¶](#utils.bio.base_freq "Permalink to this definition") Base frequencies of a sequence utils.bio.build\_motif(*sites*, *bg=None*)[¶](#utils.bio.build_motif "Permalink to this definition") Given a collection of sites, return a Biopython motif object. utils.bio.complement(*seq*)[¶](#utils.bio.complement "Permalink to this definition") Return the complement of a sequence utils.bio.get\_genome(*accession*, *data\_dir*)[¶](#utils.bio.get_genome "Permalink to this definition") Read the genome from file if available, otherwise retrieve genome record from NCBI. utils.bio.get\_motif\_sequences(*motif*)[¶](#utils.bio.get_motif_sequences "Permalink to this definition") Return sequences of a motif utils.bio.get\_org\_name(*rec*)[¶](#utils.bio.get_org_name "Permalink to this definition") Return the organism name, given the genome record. utils.bio.get\_protein(*accession*)[¶](#utils.bio.get_protein "Permalink to this definition") Get protein record from NCBI utils.bio.get\_pubmed(*pmid*)[¶](#utils.bio.get_pubmed "Permalink to this definition") Retrieve pubmed publication from NCBI database utils.bio.pssm\_threshold(*motif*, *alpha\_n=0.05*, *n=500*, *background=None*)[¶](#utils.bio.pssm_threshold "Permalink to this definition") Given a motif, return the pssm scoring threshold th such that the probability of observing a site in a n (e.g. 500) bp sequence from background distribution having a score at least th is alpha\_n (e.g. 0.05). utils.bio.read\_fasta(*fasta\_file*)[¶](#utils.bio.read_fasta "Permalink to this definition") Read fasta file and return the list of records, consisting of description and sequence utils.bio.read\_seqs\_from\_fasta(*fasta\_file*)[¶](#utils.bio.read_seqs_from_fasta "Permalink to this definition") Read sequences from the file utils.bio.read\_seqs\_from\_text\_file(*text\_file*)[¶](#utils.bio.read_seqs_from_text_file "Permalink to this definition") Read sequences from a text file, one sequence per line. utils.bio.reverse\_complement(*seq*)[¶](#utils.bio.reverse_complement "Permalink to this definition") Reverse complement of a sequence utils.bio.run\_meme(*seqs*, *outdir*, *meme\_settings*)[¶](#utils.bio.run_meme "Permalink to this definition") Run MEME for motif discovery. meme\_settings is a dictionary that contains all MEME parameters. Return a MEME record object as defined in Bio-python module. utils.bio.weblogo(*motif*, *fname=None*, *\*\*kwargs*)[¶](#utils.bio.weblogo "Permalink to this definition") Build the weblogo given a motif. If fname argument is None, just display the weblogo. Otherwise, write the logo to the file.

units

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[Unit Strings](index.html#unit-strings) - [Measurement strings](index.html#measurement-strings) - [Uncertain Measurements](index.html#uncertain-measurements) + [Units To Strings](index.html#document-user-guide/to_string) - [Advanced Usage](index.html#advanced-usage) - [Stream Operators](index.html#stream-operators) - [Underlying Conversion Map Access](index.html#underlying-conversion-map-access) + [Math Operations](index.html#document-user-guide/math_operations) - [Type traits](index.html#type-traits) - [Rounding and Truncation](index.html#rounding-and-truncation) - [Trigonometric functions](index.html#trigonometric-functions) - [Multiplies and divides](index.html#multiplies-and-divides) - [Others](index.html#others) + [Commodities](index.html#document-user-guide/commodities) - [Methods](index.html#methods) * [Custom Commodities](index.html#custom-commodities) - [Commodities to names](index.html#commodities-to-names) - [String To Commodities](index.html#string-to-commodities) - [Defined Commodities](index.html#defined-commodities) + [User defined units](index.html#document-user-guide/user_defined_units) - [Defining a custom unit](index.html#defining-a-custom-unit) - [Input File](index.html#input-file) - [Other Library Operations](index.html#other-library-operations) - [Notes on units and threads](index.html#notes-on-units-and-threads) + [Physical constants](index.html#document-user-guide/Physical_constants) - [Defined constants](index.html#defined-constants) - [Planck Units](index.html#planck-units) - [Atomic units](index.html#atomic-units) - [Numbers](index.html#numbers) - [Planetary masses](index.html#planetary-masses) - [From Strings](index.html#from-strings) - [Uncertainties](index.html#uncertainties) + [Defined Units](index.html#document-user-guide/defined_units) - [Listing of Units](index.html#listing-of-units) * [Base Units](index.html#base-units) * [Specialized units](index.html#specialized-units) * [Numerical Units](index.html#numerical-units) * [Derived SI Units](index.html#derived-si-units) * [Extra SI related units](index.html#extra-si-related-units) * [Centimeter-Gram-Second system](index.html#centimeter-gram-second-system) * [Conventional Electrical Units](index.html#conventional-electrical-units) * [Meter Gram Force System](index.html#meter-gram-force-system) * [Meter Tonne Second system](index.html#meter-tonne-second-system) * [Additional Time units](index.html#additional-time-units) * [International customary Units](index.html#international-customary-units) * [Avoirdupois units](index.html#avoirdupois-units) * [Troy Units](index.html#troy-units) * [United States Customary Units](index.html#united-states-customary-units) * [FDA and metric measures](index.html#fda-and-metric-measures) * [Canadian Units](index.html#canadian-units) * [Australia Units](index.html#australia-units) * [Imperial or British Units](index.html#imperial-or-british-units) * [Apothecaries System](index.html#apothecaries-system) * [Nautical Units](index.html#nautical-units) * [Japanese traditional Units](index.html#japanese-traditional-units) * [Chinese Traditional Units](index.html#chinese-traditional-units) * [Typographic units](index.html#typographic-units) * [Distance Units](index.html#distance-units) * [Area Units](index.html#area-units) * [Mass Units](index.html#mass-units) * [Volume Units](index.html#volume-units) * [Angle Units](index.html#angle-units) * [Directional Units](index.html#directional-units) * [Temperature Units](index.html#temperature-units) * [Pressure Units](index.html#pressure-units) * [Power Units](index.html#power-units) * [Energy Units](index.html#energy-units) * [Power system Units](index.html#power-system-units) * [Equation type Units](index.html#equation-type-units) * [Textile related Units](index.html#textile-related-units) * [Clinical Units](index.html#clinical-units) * [Laboratory Units](index.html#laboratory-units) * [Data Units](index.html#data-units) * [Computation units](index.html#computation-units) * [Special units](index.html#special-units) * [Other Units](index.html#other-units) * [Climate Units](index.html#climate-units) * [Speed Units](index.html#speed-units) - [Units in the units namespace](index.html#units-in-the-units-namespace) + [Custom Units](index.html#document-user-guide/custom_units) - [Custom units in Use](index.html#custom-units-in-use) - [Implementation Details](index.html#implementation-details) + [Custom Counting Units](index.html#document-user-guide/custom_count_units) - [Custom count units in Use](index.html#custom-count-units-in-use) - [Implementation details](index.html#implementation-details) + [Unit Domains](index.html#document-user-guide/Unit_domains) - [Available Domains](index.html#available-domains) - [Domain Specific Units](index.html#domain-specific-units) * [UCUM](index.html#ucum) * [Astronomy](index.html#astronomy) * [Cooking](index.html#cooking) * [Surveying](index.html#surveying) * [Nuclear](index.html#nuclear) * [Climate](index.html#climate) * [US customary](index.html#us-customary) * [All domains](index.html#all-domains) - [Specifying the domain](index.html#specifying-the-domain) + [Conversion Flags](index.html#document-user-guide/conversion_flags) - [Unit\_from\_string flags](index.html#unit-from-string-flags) * [Indications for use](index.html#indications-for-use) - [to\_string Flags](index.html#to-string-flags) * [Default flags](index.html#default-flags) + [Special Units](index.html#document-user-guide/special_units) - [Default Unit](index.html#default-unit) - [Error Unit](index.html#error-unit) - [Invalid Unit](index.html#invalid-unit) - [one](index.html#one) + [Equation Units](index.html#document-user-guide/equation_units) - [Equation Value conversions](index.html#equation-value-conversions) * [Application Notes](index.html#document-application_notes/index) + [Strain](index.html#document-application_notes/strain) - [Method 1](index.html#method-1) - [Method 2](index.html#method-2) - [Method 3](index.html#method-3) - [Discussion](index.html#discussion) * [The Low Level Details of the Units library](index.html#document-details/index) + [Unit base class](index.html#document-details/unit_base) - [Math operations](index.html#math-operations) * [Power and Root and Inv functions](index.html#power-and-root-and-inv-functions) - [Getters](index.html#getters) - [Modifiers](index.html#modifiers) - [Comparisons](index.html#comparisons) + [Commodity Details](index.html#document-details/commodities) * [Units on the Web](index.html#document-web/index) + [Rest API](index.html#rest-api) [Units](#) * * Units documentation * [Edit on GitHub](https://github.com/LLNL/units/blob/7917f5f2cfefdcc90b5085ade91761d06df74e59/docs/index) --- Welcome to The units library user guide and documentation ====================================================================================================================================================== The Units library provides a means of working with units of measurement at runtime, including conversion to and from strings. It provides a small number of types for working with units and measurements and operations necessary for user input and output with units. This software was developed for use in [LLNL/GridDyn](https://github.com/LLNL/GridDyn), and [HELICS](https://github.com/GMLC-TDC/HELICS) and is currently a work in progress (though getting close). Namespaces, function names, and code organization is subject to change though is fairly stable at this point, input is welcome. [](https://codecov.io/gh/LLNL/units) [](https://dev.azure.com/phlptp/units/_build/latest?definitionId=1&branchName=main) [](https://circleci.com/gh/LLNL/units) [](https://github.com/GMLC-TDC/HELICS-src/blob/main/LICENSE) The [Introduction](index.html#introduction) is a discussion about the why? and How the library came together and a generally what it does and how it was tested. The [Installation and Linking](index.html#installation-and-linking) guide is a discussion about linking and using the library, and the [User Guide](index.html#user-guide) is the how-to about how to use the software library. For the details see Details and to try out some of the string conversions check out [Units on the Web](index.html#units-on-the-web) Finally [Application Notes](index.html#application-notes) contains some discussions on particular applications and usages. Introduction[¶](#introduction "Permalink to this headline") ----------------------------------------------------------- ### Why?[¶](#why "Permalink to this headline") So why have another units library? This was something we poked at for a while before writing the library. There are number of other well designed C++ libraries but none of them met our needs. Some needs are pretty general, and others specific to power systems and electrical engineering. #### Design Requirements[¶](#design-requirements "Permalink to this headline") * have a units type that can be used in virtual function calls * Handle unit conversions * handle Per Unit operations and unit conversions * handle complex units easily like $/puMw/hr * Operate on strings with conversion to and from strings Previously the library we were using met these requirements but only for a very limited set of units. This library functioned but was nearing the limits of maintainability and operation as new units were needed and other conversions were required which required adding direct conversions between the classes of units and the code was getting to be a mess. So, looking around, many of the existing unit libraries in C++ represent individual units as an individual type. This works wonderfully if you can know all the types you want to use ahead of time. In our case, many of the conversions depended on configuration or input files so the units being converted were not known at compile time. A few were but in general they were not. That led to an issue of how to do you pass that unit to a function that is meant to hide the internal unit in use, so having a type per unit did not seem functional from a coding or structural perspective. Many of the dimensional analysis libraries, actually all as far as I can tell in C++, do not support string conversions. There are a few examples in Java or Python, but our code is written in C++ so that didn’t seem workable either. Which led to the conclusion that this library is needed and some additional design considerations. * A small single compact type to represent all units (no bigger than a double) if we wanted to use it in a number of contexts. * Another measurement type that was interoperable with doubles and numbers. Many numerical calculations came from a real array and numeric solver libraries so there is no opportunity to modify those types. Which means, we need double operations with measurements if we want to use them. * constexpr as much as possible since many units are known at compile time and we need to be able to generate complex units from other simpler units. * The library should be compatible with a broad range of compilers including some older ones back to GCC 4.7. * a fairly expansive list of predefined units to simplify operation Speaking with others, a few items and contexts came up such as recipes, trade documents, other software package unit representations, standardized string representation. Sometimes a lot of precision is needed, other times this is not the case. And it would be nice to be able to deal with uncertainties in measurements, commodities, and containers. ##### How It Works[¶](#how-it-works "Permalink to this headline") Given the design requirements the choice was how to make a class that could represent physical units. The desire for it to be a compact class drove the decision to somewhat limit what could be represented to physically realizable units. ###### Unit Representation[¶](#unit-representation "Permalink to this headline") The unit class consists of a multiplier and a representation of base units. The seven SI units + radians + currency units + count units. In addition a unit has 4 flags, per-unit for per unit or ratio units. One flag[i\_flag] that is a representation of imaginary units, one flags for a variety of purposes and to differentiate otherwise similar units[e\_flag]. And a flag to indicate an equation unit. Due to the requirement that the base units fit into a 4 byte type the represented powers of the units are limited. The list below shows the bit representation range and observed range of use in equations and observed usage * meter:[-8,+7] :normal range [-4,+4], intermediate ops [-6,+6] * kilogram:[-4,+3] :normal range [-1,+1], intermediate ops [-2,+2] * second:[-8,+7] :normal range [-4,+4], intermediate ops [-6,+6] * ampere:[-4,+3] :normal range [-2,+2] * kelvin:[-4,+3] :normal range [-4,+1] * mole:[-2,+1] :normal range [-1,+1] * candela:[-2,+1] :normal range [-1,+1] * currency:[-2,+1] :normal range [-1,+1] * count:[-2,+1] :normal range [-1,+1] * radians:[-4,+3] :normal range [-2,+2] These ranges were chosen to represent nearly all physical quantities that could be found in various disciplines we have encountered. * [SI units Publication guidelines](https://physics.nist.gov/cuu/pdf/sp811.pdf) ##### Testing[¶](#testing "Permalink to this headline") The Code for the units library is put through a series of CI tests before being merged. Current tests include running on CI systems. ###### Unit Tests[¶](#unit-tests "Permalink to this headline") The units library has a series of units tests that are executed as part of the CI builds and when developing. They are built using Google test module. The module is downloaded if the tests are built. It uses Release 1.10 from the google test github repository 1. examples\_test A simple executable that loads up some different types of measurements and does a few checks directly, it is mainly to test linking and has some useful features for helping with the code coverage measures. 2. fuzz\_issue\_tests tests a set of past fuzzing failures, including, errors, glitches, timeouts, round trip failures, and some round trip failures with particular flags. 3. test\_all\_unit\_base **DO NOT RUN THIS TEST** it will take a very long time it does an exhaustive test of all possible unit bases to make sure the string conversion round trip works. I haven’t actually executed it all yet. 4. test\_commodities Run test using the commodity related functions and operations on precise\_unit’s 5. test\_conversions1 a series of tests about specific conversions, such as temperature, SI prefixes, extended SI units, and some other general operations about conversions 6. test\_conversions2 run through a series of test units and conversion from one of the converter websites, there are number of files that get used that contain known conversions 7. test\_equation\_units direct testing of the established equation units 8. test\_leadingNumbers run a bunch of checks on the leading number processing for units and measurements, convert a leading string into a numerical value 9. test\_measurement a series of tests on measurement objects including operations and comparisons, and construction 10. test\_measurement\_strings a few tests on the basic to and from string operations for measurements 11. test\_pu tests of pu units and conversions 12. test\_random\_round\_trip randomly pick a few 32 bit number, assume they are a unit and do a string conversion and interpretation on them and make sure they produce the same thing 13. test\_ucum a series of tests coming from [UCUM](https://github.com/lhncbc/ucum-lhc) the units library tries to handle all official strings and a majority of the full names, and aliases 14. test\_udunits a series of tests and test files coming from [UDUNITS-2](https://github.com/Unidata/UDUNITS-2) Not all the units convert, some never will since they are ambiguous but we will probably allow a few more over time 15. test\_uncertain\_measurements test uncertain measurement operations using examples taken from web sources 16. test\_unit\_ops test operations on units, including mathematical expressions and comparison operators 17. test\_unit\_strings Unit strings test conversion to and from strings 18. test\_defined\_units tests checking all the unit string maps for duplicates and conflicts and correct sizing 19. test\_google\_units run some checks to ensure support for many units supported by google unit translation 20. test\_math run some tests on the extra mathematical operations found in units\_math.hpp 21. test\_siunits run some tests of SI specific units and prefixes ###### CI systems[¶](#ci-systems "Permalink to this headline") ####### Azure[¶](#azure "Permalink to this headline") 1. GCC 7.4 C++14 (Azure native Linux) 1. GCC 7.4 C++14 (Azure native Linux) with shared library build 1. AppleClang 11.0 (Xcode 11.3) C++17 1. AppleClang 11.0 (Xcode 11.3) C++11 1. MSVC 2019 C++17 1. MSVC 2019 C++11 1. MSVC 2022 C++20 1. GCC 4.8 C++11 1. GCC 7 C++11 1. GCC 7 C++14 1. GCC 8 C++17 1. GCC 9 C++17 1. GCC 12 C++20 1. Clang 3.4 C++11 1. Clang 3.5 C++11 1. Clang 8 C++14 1. Clang 9 C++17 1. Clang 14 C++20 1. Clang-tidy (both main library and tests) ####### Circle-CI[¶](#circle-ci "Permalink to this headline") 1. Clang 14, Thread Sanitizer 2. Clang 14, Address, undefined behavior sanitizer 3. Clang 14, Memory Sanitizer 4. Clang 8, Fuzzing library – run a couple of defined fuzzing tests from scratch to check for any anomalous situations. There are currently two fuzzers, the first test the units\_from\_string, and the second tests the measurement\_from string. It first converts the fuzzing sequence, then if it is a valid sequence, converts it to a string, then converts that string back to a measurement or unit and makes sure the two measurements or units are identical. Any string sequence which doesn’t work is captured and tested. ####### GitHub Actions[¶](#github-actions "Permalink to this headline") 1. CodeQl 1. Coverage (ubuntu 22.04 image C++11, C++14, C++17, C++20, 32 and 64 bit unit base) 1. CPPLINT 1. Quick CMAKE checks for all supported versions of cmake ####### Codecov[¶](#codecov "Permalink to this headline") Try to maintain the library at 100% coverage. ####### Pre-commit[¶](#pre-commit "Permalink to this headline") Runs clang-format and many other checks for repo cleanliness ##### Sources of Unit String Definitions[¶](#sources-of-unit-string-definitions "Permalink to this headline") The string processing tests and strings supported came from a number of different sources. ##### Converter App[¶](#converter-app "Permalink to this headline") As a simple example and potentially useful tool we made the converter app. It is a command line application that can be built as part of the units library to convert units given on the command line. ``` $ ./unit_convert 10 m ft 32.8084 $ ./unit_convert ten meters per second mph 22.3694 $ ./unit_convert --full ten meters per second mph ten meters per second = 22.3694 mph $ ./unit_convert --simplified ten meters per second miles/hour 10 m/s = 22.3694 mph $ ./unit_convert -s four hundred seventy-three kilograms per hour pounds/min 473 kg/hr = 17.3798 lb/min $ ./unit_convert -s 22 british fathoms * 10 british fathoms = 18.288 m ``` basically there are two options –full,-f and –simplified,-s a measurement which will take an arbitrary number of strings and a final string as a unit to convert to. It outputs the conversion and if specified the surrounding measurement and units either simplified or in the original. Using \* or <base> in place of the unit string will result in converting the measurement to base units. ``` $ ./unit_convert --help application to perform a conversion of a value from one unit to another Usage: unit_convert [OPTIONS] measure... convert Positionals: measure [TEXT ...] ... REQUIRED measurement to convert .e.g '57.4 m', 'two thousand GB' '45.7\*22.2 feet^3/s^2' convert TEXT REQUIRED the units to convert the measurement to Options: -h,--help Print this help message and exit -f,--full specify that the output should include the measurement and units -s,--simplified simplify the units using the units library to_string functions and print the conversion string like full. This option will take precedence over --full --measurement [TEXT ...] ... REQUIRED measurement to convert .e.g '57.4 m', 'two thousand GB' '45.7\*22.2 feet^3/s^2' --convert TEXT REQUIRED the units to convert the measurement to ``` Installation and Linking[¶](#installation-and-linking "Permalink to this headline") ----------------------------------------------------------------------------------- The units library supports a header only mode and a compiled mode. One of the strengths of the library is the string processing which is only available in the compiled mode. Other additions from the compiled mode are the root operations on units and measurements. The header only mode includes the unit and measurement classes conversions between them and the definition library. ### Header Only Use[¶](#header-only-use "Permalink to this headline") The header only portion of the library can simply be copied and used. There are 3 headers units\_decl.hpp declares the underlying classes. unit\_defintions.hpp declares constants for many of the units, and units.hpp which is the primary public interface to units. If units.hpp is included in another file and the variable UNITS\_HEADER\_ONLY is defined then none of the functions that require the cpp files are defined. These header files can simply be included in your project and used with no additional building required. The UNITS\_HEADER\_ONLY definition is needed otherwise linking errors will result. ### Compiled Usage[¶](#compiled-usage "Permalink to this headline") The second part is a few cpp files that can add some additional functionality. The primary additions from the cpp file are an ability to take roots of units and measurements and convert to and from strings. These files can be built as a standalone static library, a shared library or an object library, or included in the source code of whatever project want to use them. The code should build with an C++11 compiler. C++14 is recommended if possible to allow some additional function to be constexpr. Most of the library is tagged with constexpr so can be run at compile time to link units that are known at compile time. General Unit numerical conversions are not at compile time, so will have a run-time cost. A quick\_convert function is available to do simple conversions. with a requirement that the units have the same base and not be an equation unit. The cpp code also includes some functions for commodities and will eventually have r20 and x12 conversions, though this is not complete yet. ### Standalone Library[¶](#standalone-library "Permalink to this headline") The units library can be built as a standalone library with either the static or shared library and installed like a typical package. #### Unit Library CMake Reference[¶](#unit-library-cmake-reference "Permalink to this headline") There are a few CMake variables that control the build process, they can be altered to change how the units library is built and what exactly is built. ##### CMake variables[¶](#cmake-variables "Permalink to this headline") * BUILD\_TESTING : Generate CMake Variable controlling whether to build the tests or not * UNITS\_ENABLE\_TESTS : Does the same thing as BUILD\_TESTING * UNITS\_BUILD\_STATIC\_LIBRARY: Controls whether a static library should be built or not * UNITS\_BUILD\_SHARED\_LIBRARY: Controls whether to build a shared library or not, only one or none of UNITS\_BUILD\_STATIC\_LIBRARY and UNITS\_BUILD\_SHARED\_LIBRARY can be enabled at one time. * BUILD\_SHARED\_LIBS: Controls the defaults for the previous two options, overriding them takes precedence * UNITS\_BUILD\_FUZZ\_TARGETS: If set to ON, the library will try to compile the fuzzing targets for clang libFuzzer * UNITS\_BUILD\_WEB\_SERVER: If set to ON, build a webserver, This uses boost::beast and requires boost 1.70 or greater to build it also requires CMake 3.12 or greater * UNITS\_BUILD\_CONVERTER\_APP: enables building a simple command line converter application that can convert units from the command line * UNITS\_ENABLE\_EXTRA\_COMPILER\_WARNINGS: Turn on bunch of extra compiler warnings, on by default * UNITS\_ENABLE\_ERROR\_ON\_WARNINGS: Mostly useful in some testing contexts but will turn on Werror so any normal warnings generate an error. * CMAKE\_CXX\_STANDARD: Compile with a particular C++ standard, valid values are 11, 14, 17, 20, and likely 23 though that isn’t broadly supported. Will set to 14 by default if not otherwise specified * UNITS\_BINARY\_ONLY\_INSTALL: Just install shared libraries and executables, no headers or static libs or packaging information * UNITS\_CLANG\_TIDY: Enable the clang tidy tests as part of the build * UNITS\_CLANG\_TIDY\_OPTIONS: options that get passed to clang tidy when enabled * UNITS\_BASE\_TYPE: Set to uint64\_t for expanded base-unit power support. This increases the size of a unit by 4 Bytes. * UNITS\_DOMAIN: Specify a default domain to use for string conversions. Can be either a name from the domains namespace such as domains::surveying or one of ‘COOKING’, ‘ASTRONOMY’, ‘NUCLEAR’, ‘SURVEYING’, ‘USE\_CUSTOMARY’, ‘CLIMATE’, or ‘UCUM’. * UNITS\_DEFAULT\_MATCH\_FLAGS: Specify an integer value for the default match flags to be used for conversion * UNITS\_DISABLE\_NON\_ENGLISH\_UNITS: the library includes a number of non-english units that can be converted from strings, these can be disabled by setting UNITS\_DISABLE\_NON\_ENGLISH\_UNITS to ON or setting the definition in the C++ code. * UNITS\_NAMESPACE: The top level namespace of the library, defaults to units. When compiling with C++17 (or higher), this can be set to, e.g., mynamespace::units to avoid name clashes with other libraries defining units. * UNITS\_INSTALL: This is set to ON normally but defaults to OFF if used as a subproject. This controls whether anything gets installed by the install target. * UNITS\_CMAKE\_PROJECT\_NAME: This is set to UNITS by default. If using this in a package manager or wish to rename the project this variable can be set to another name to change the name of the package. This will change the install path and cmake target names. For example setting -DUNITS\_CMAKE\_PROJECT\_NAME=LLNL-UNITS will create cmake project llnl-units::units, and llnl-units::header\_only and will install in a llnl-units directory with appropriate cmake files. If compiling as part of a subproject then a few other options are useful * UNITS\_HEADER\_ONLY: Only generate the header only target, sets UNITS\_BUILD\_STATIC\_LIBRARY and UNITS\_BUILD\_SHARED\_LIBRARY to OFF * UNITS\_INSTALL: enable the install instructions of the library * UNITS\_BUILD\_OBJECT\_LIBRARY: Generate an object library that can be used as part of other builds. Only one of UNITS\_BUILD\_SHARED\_LIBRARY, UNITS\_BUILD\_STATIC\_LIBRARY, or UNITS\_BUILD\_OBJECT\_LIBRARY can be set to ON. If more than one are set, the shared library and object library settings take precedence over the static library. * UNITS\_LIBRARY\_EXPORT\_COMMAND: If desired the targets for the units library can be merged into an root project target list by modifying this variable. The use cases for this are rare, but if this is something you want to do this variable should be set to something like EXPORT rootProjectTargets. It defaults to “EXPORT unitsTargets” ##### CMake Targets[¶](#cmake-targets "Permalink to this headline") If you are using the library as a submodule or importing the package there are a couple targets that can be used depending on the build. NOTE: these can be changed using UNITS\_CMAKE\_PROJECT\_NAME. * units::units will be set to the library being built, either the shared, static, or object * units::header\_only is a target for the headers if UNITS\_HEADER\_ONLY CMake variable is set, then only this target is generated. This target is always created. ##### Example[¶](#example "Permalink to this headline") As part of the [HELICS](https://github.com/GMLC-TDC/HELICS) library the units library is used as a submodule it is included by the following code ``` # so units cpp exports to the correct target export set(UNITS\_INSTALL OFF CACHE INTERNAL "") if(NOT CMAKE\_CXX\_STANDARD) set(CMAKE\_CXX\_STANDARD 17) # Supported values are ``11``, ``14``, and ``17``. endif() set(UNITS\_BUILD\_OBJECT\_LIBRARY OFF CACHE INTERNAL "") set(UNITS\_BUILD\_STATIC\_LIBRARY ON CACHE INTERNAL "") set(UNITS\_BUILD\_SHARED\_LIBRARY OFF CACHE INTERNAL "") set(UNITS\_BUILD\_CONVERTER\_APP OFF CACHE INTERNAL "") set(UNITS\_BUILD\_WEBSERVER OFF CACHE INTERNAL "") set(UNITS\_CLANG\_TIDY\_OPTIONS "" CACHE INTERNAL "") set(UNITS\_BUILD\_FUZZ\_TARGETS OFF CACHE INTERNAL "") add\_subdirectory( "${PROJECT\_SOURCE\_DIR}/ThirdParty/units" "${PROJECT\_BINARY\_DIR}/ThirdParty/units" ) set\_target\_properties(units PROPERTIES FOLDER Extern) hide\_variable(UNITS\_HEADER\_ONLY) hide\_variable(UNITS\_BUILD\_OBJECT\_LIBRARY) hide\_variable(UNITS\_NAMESPACE) ``` Then the target linked by ``` target\_link\_libraries(helics\_common PUBLIC HELICS::utilities units::units) ``` User Guide[¶](#user-guide "Permalink to this headline") ------------------------------------------------------- The Units library user guide is an in depth look at how to use the C++ library and its functionality, covering the basic types in the library and operations with them. The guide covers the basic types and what operations are available on them, as well as a lot of details on how to use the library. ### Units[¶](#units "Permalink to this headline") #### Basic Unit Types[¶](#basic-unit-types "Permalink to this headline") There are two basic units classes units and precise\_units They both include a units\_base see [Unit base class](index.html#unit-base-class) for the details. units has a single precision floating point multiplier and the units\_base object. The precise\_unit type uses a double precision floating point multiplier and includes commodity. The commodity is represented by a 32-bit code. See [Commodities](index.html#commodities) for more details on how that is used and defined. The simplest way to start is by using one of the [Defined Units](index.html#defined-units) All standard units are defined and many non-standard ones as well. The Basics of units are the seven SI base units: * the kilogram (kg), for mass. * the second (s), for time. * the kelvin (K), for temperature. * the ampere (A), for electric current. * the mole (mol), for the amount of a substance. * the candela (cd), for luminous intensity. * the meter (m), for distance. In addition to the base SI units a couple additional bases are defined: * radian(rad), for angular measurement * Currency ($), for monetary values * Count (cnt), for single object counting Currency may seem like a unusual choice in units but numbers involving prices are encountered often enough in various disciplines that it is useful to include as part of a unit. Technically count and radians are not units, they are representations of real things. A radian is a representation of rotation around a circle and is therefore distinct from a true unitless quantity even though there are no physical measurements associated with either. And count and mole are theoretically equivalent though as a practical matter using moles for counts of things is a bit odd for example 1 GB of data is ~1.6605\*10^-15 mol of data. So they are used in different context and don’t mix very often, the convert functions can convert between them if necessary. The structure also defines some flags: * per-unit, indicating per unit units * i\_flag, general flag and complex quantity * e\_flag, general unit discriminant * equation, indicator that the unit is an equation unit. ##### Derived Units[¶](#derived-units "Permalink to this headline") A vast majority of physical units can be constructed using these bases, as well as many non-physical units. The entire structure for the units fits into 4 bytes to meet the design requirement for a compact type. This required a detailed evaluation of what physical units and combinations of them were in use in different scientific and commercial disciplines, The following list represents the range of allowed values chosen as the representation and those required by known and observed physical quantities. * meter:[-8,+7] :normal range [-4,+4], intermediate ops [-6,+6] * kilogram:[-4,+3] :normal range [-1,+1], intermediate ops [-2,+2] * second:[-8,+7] :normal range [-4,+4], intermediate ops [-6,+6] * ampere:[-4,+3] :normal range [-2,+2] * kelvin:[-4,+3] :normal range [-4,+1] * mole:[-2,+1] :normal range [-1,+1] * candela:[-2,+1] :normal range [-1,+1] * currency:[-2,+1] :normal range [-1,+1] * count:[-2,+1] :normal range [-1,+1] * radians:[-4,+3] :normal range [-2,+2] For example the kilogram is rarely used in a squared context, so it has a normal range of between -1 and 1. But in intermediate mathematical operations it is squared on occasion, so we needed to be able represent that without overflow. Since without getting extraordinary complex we are limited to whole bit representation that infers a two’s complement notation of 2 bits is [-2,-1] for 3 bits [-4,+3], and for 4 bits [-8,+7]. So for kilograms 3 bits were used. The pu flag was determined to be required by the initial design considerations, and a flag value also turned out to be required by library design requirements. The equation and e\_flag flags came a little later in the library development but turned out to be very useful in representing other kinds of units and discriminating between some units. #### Basic operations[¶](#basic-operations "Permalink to this headline") Some mathematical operations between units are supported. \* and / with units produce a new unit. ``` auto new\_unit=m/s; auto another=new\_unit\*s; //another == m ``` produces a new\_unit equivalent to meters/second. #### Comparison Operators[¶](#comparison-operators "Permalink to this headline") Units also support the comparison operators ==, and !=.The other comparison operators are not supported as it is somewhat undefined whether m > kg or many other comparison like that. The inequality is the inverse of equality, but the equality operator is an interesting subject. The unit component is relatively straightforward that part is the base units, if those are not equivalent then the answer is false. However there is a floating point component to the unit representing a multiplier. And floating point equality is treacherous. What is done is a rounding operation with a range. Basically units are assuming to have 6 decimal digits of precision, while precise\_units have 13. So units will result in equality as long as the first X significant digits in the multiplier are equivalent and the unit\_base is equal. this can’t be a specific range since the power of the multiplier is wide ranging this parsecs to picometers and all the base of meters. #### Methods[¶](#methods "Permalink to this headline") Frequently units need to raised to some power. Units have a pow(int) method to accomplish this. ``` auto area\_unit=m.pow(2); ``` The ^ will not work due to precedence rules in C++. If an operator for ‘^’ were defined an operation such as m/s^2 would produce meters squared per second squared which is probably not what is expected. Therefore best not to define the operator and use a function instead. ##### Special Units[¶](#special-units "Permalink to this headline") There are a few defined units that are special in some fashion, and can be used as sentinel values or have special operations associated with them. ###### Default Unit[¶](#default-unit "Permalink to this headline") The defunit unit is allowed to be converted to any other unit. it is equivalent of per-unit\*i\_flag The main use case is in the convert functions and makes a good ###### Error Unit[¶](#error-unit "Permalink to this headline") ``` auto error\_unit=unit(detail::unit\_data(nullptr)); ``` ###### Invalid Unit[¶](#invalid-unit "Permalink to this headline") An invalid unit is any unit that is either the error unit or has a NaN in the multiplier. This is the unit returned from a string conversion if the string does not describe a unit or measurement. ###### one[¶](#one "Permalink to this headline") The default constructor for unit and precise\_unit is empty unit data and 1.0 in the multiplier. There are also precise versions of these values in the precise namespace ##### Custom Units[¶](#custom-units "Permalink to this headline") The units library defines 1023 special custom units. These are custom units intended to specify a specific type of unit which doesn’t have a normal unit base definition. The key idea behind the custom units is that they can be multiplied, divided by some normal powers of distance, mass, or time units and can be inverted In strings these can be represented by “CXUN[X]” Where X is some number between 0 and 1023. In C++ code they can be generated by ``` precise\_unit new\_cxc\_unit=generate\_custom\_unit(code); ``` A set of checks and queries is available to check for custom\_units. * bool precise::custom::is\_custom\_unit(detail::unit\_data udata); * bool precise::custom::is\_custom\_unit\_inverted(detail::unit\_data udata); * unsigned short precise::custom::custom\_unit\_number(detail::unit\_data udata); These checks will operate regardless of any m/kg/s unit combination or inverted units. ###### Custom units in Use[¶](#custom-units-in-use "Permalink to this headline") there are a few custom count units in use for specific clinical units Many of these units defy conversion to other known units but are used in pharmacological contexts So there is no translation to other units and cannot be converted except to multiple of the same unit. There are often well established tests for these units but no good way to convert them to other units. Many of these units come from [UCUM](https://unitsofmeasure.org/ucum.html). * custom\_unit(37): is [hounsfield units](https://radiopaedia.org/articles/hounsfield-unit?lang=us) used it CT and radiology * many units in UCUM are defined like [MPL’U] or [mclg’U] for this context they define some unit which doesn’t interact with other units in any known fashion. The notion used in the units library for string translations is that these define custom units. Rather than individually define the library takes a hash of the part of the unit coming before the ‘U]’ and generates a 10 bit hash. That 10 bit hash is used as the custom code for the units. * custom\_unit(77): is global warming potential related to climate operations * custom\_unit(78): is global temperature change potential The other custom units are available for use or the one with known definition can be use if there is no domain conflicts. The primary usage of these is for units that are procedurally defined and often used in the context of per mass or per volume or per time. ###### Implementation Details[¶](#implementation-details "Permalink to this headline") Custom units use a combination of nearly all the different fields in the unit\_base class, with the exception of count and radians. Based on the definitions used the custom units can be taken as per length/area/volume/mass/second with no issues. Some of the unit fields are used for defining an index and others are used purely for identification purposes. ##### Equation Units[¶](#equation-units "Permalink to this headline") The use of an equation flag in the unit\_base defines a set of equation units. These are specific units where the relationship with other units is defined through an equation rather than a specific multiplier. There are 31 available equation units. Equation units use up the flags, count, and radian fields. All other units are left alone for defining the underlying units of the equation unit. So the equation specifier defines and equation rather than a specific unit. equation types 0-15 deal with logarithms in some way, 16-31 are undefined or represent some common scale type units to extract the equation type - unsigned short precise::custom::eq\_type(detail::unit\_data udata); Current equation definitions * 0: log10(x) * 1: nepers * 2: bels * 3: decibels * 4: -log10(x) * 5: -log10(x)/2.0 * 6: -log10(x)/3.0 * 7: -log10(x)/log10(50000) * 8: log2(x) * 9: ln(x) * 10: log10(x) * 11: 10\*log10(x) * 12: 2\*log10(x) * 13: 20\*log10(x) * 14: log10(x)/log10(3) * 15: 0.5\*ln(x) * 16: UNDEFINED * 17: UNDEFINED * 18: UNDEFINED * 19: UNDEFINED * 20: UNDEFINED * 21: UNDEFINED * 22: saffir-simpson hurricane wind scale * 23: Beaufort wind scale * 24: Fujita scale * 25: UNDEFINED * 26: UNDEFINED * 27: Prism diopter-100.0\*tan(x) * 28: UNDEFINED * 29: Moment magnitude scale for earthquakes (richter) * 30: Energy magnitude scale for earthquakes * 31: UNDEFINED The wind scales are not very accurate since they match up a slightly fuzzier notion to actual wind speed. There are general charts and the equations in use utilize a polynomial to approximate them to a continuous scale. So the units when used are generally convertible to a velocity unit such as m/s. There are currently 10 undefined equation units available if needed. ###### Equation Value conversions[¶](#equation-value-conversions "Permalink to this headline") The actual definitions of the equations are found in the unit::precise::equation namespace. Two functions are provided that convert values from equation values to units and vice versa. * double convert\_equnit\_to\_value(double val, detail::unit\_data UT) * double convert\_value\_to\_equnit(double val, detail::unit\_data UT) also since some equation unit definitions depend on whether the actual units are power or magnitude values, there is a helper function to help determine this. bool is\_power\_unit(detail::unit\_data UT) This applies in the neper, bel, and decibel units. ##### Custom Counting Units[¶](#custom-counting-units "Permalink to this headline") The units library defines 16 special counting units. These are custom counting units intended to specify a specific type of event. The key idea behind the custom counting units is that they can be multiplied, divided by any powers of distance, mass, currency, or time units and can be inverted. The primary usage of these is for units that are procedurally defined and often used in the context of per mass or per volume or per time or per $. In strings these can be represented by “CXCUN[X]” Where X is some number between 0 and 15. In C++ code they can be generated by ``` precise\_unit new\_cxc\_unit=generate\_custom\_count\_unit(code); ``` A set of checks and queries is available to check for custom\_count\_units. * bool precise::custom::is\_custom\_count\_unit(detail::unit\_data udata); * bool precise::custom::is\_custom\_count\_unit\_inverted(detail::unit\_data udata); * unsigned short precise::custom::custom\_count\_unit\_number(detail::unit\_data udata); These checks will operate regardless of any m/kg/s unit combination or inverted units. Underlying this is a set of codes and unit powers that would be extremely odd to encounter in normal use. ###### Custom count units in Use[¶](#custom-count-units-in-use "Permalink to this headline") there are a few custom count units in use for specific clinical units Many of these units defy conversion to other known units but are used in pharmacological contexts So there is no translation to other units and cannot be converted except to multiple of the same unit. There are often well established tests for these units but no good way to convert them to other units. Many of these units come from [UCUM](https://unitsofmeasure.org/ucum.html). * custom\_count\_unit(0): is used for specific count units with commodities of some kind for string translation * custom\_count\_unit(1): is Arbitrary Unit which has a clinical definition of some kind * custom\_count\_unit(2): is [International Unit](https://en.wikipedia.org/wiki/International_unit) * custom\_count\_unit(3): is [Index of reactivity](http://finto.fi/ucum/en/page/r394) which has a clinical definition * custom\_count\_unit(4): is [limit of flocculation](http://finto.fi/ucum/en/page/r404) which has a clinical definition * custom\_count\_unit(5): is [HPF](https://medical-dictionary.thefreedictionary.com/high-power+field) or High Power field which is related to microscopy * 6-15 are not currently in use. The other custom units are available for use or the one with known definition can be use if there is no domain conflicts. ###### Implementation details[¶](#implementation-details "Permalink to this headline") Custom count units utilizes the flags, candela, ampere, and Kelvin fields to make use of some non-physical unit definitions for a more useful purpose. ### Measurements[¶](#measurements "Permalink to this headline") The combination of a value and unit is known as a measurement. In the units library they are constructed by multiplying or dividing a unit by a numerical value. ``` measurement meas=10.0\*m; measurement meas2=5.3/s; ``` They can also be constructed via the constructor ``` measurement meas(10.0, kg); measurement meas2(2.7, MW); ``` There are two kinds of measurements the regular measurement which uses a double precision floating point for the value and a precise\_measurement which uses a double and a precise\_unit. In terms of size the measurement class is 16 Bytes and the precise\_measurement is 24 bytes. #### Precise measurements[¶](#precise-measurements "Permalink to this headline") A precise measurement includes a double for the value and a precise\_unit to represent the unit. Most of the string conversion routines to measurement produce a precise\_measurement. the measurement\_cast operation will convert a precise\_measurement into a regular measurements. ``` precise\_measurement mp(10.0, precise::kg); measurement meas2=measurement\_cast(mp); ``` ### Fixed Measurements[¶](#fixed-measurements "Permalink to this headline") The primary difference between fixed\_measurement and measurement is the idea that in a fixed\_measurement the unit part is a constant. It does not change. Therefore any addition or subtraction operation will produce another measurement with the same units. It also allows for interaction and comparisons with numerical types since the unit is known. This is unlike measurements where comparison and addition and subtraction operations with numbers are not allowed. Otherwise the behavior and operations are identical between measurement and fixed\_measurement and likewise between fixed\_precise\_measurement and precise\_measurement #### Relationship with numbers[¶](#relationship-with-numbers "Permalink to this headline") Because the unit associated with a fixed measurement is fixed. It becomes legitimate to work with singular real valued numbers. ``` fixed\_measurement dist(10, m); if (dist>10.0) //this has meaning because the units of dist is known. { //all other operators are defined with doubles } dist=dist+3.0; // dist is now 13 meters dist-=2.0; // dist is now 11 meters dist=5.0; // dist is now 5 meters ``` #### Interactions with measurement[¶](#interactions-with-measurement "Permalink to this headline") Fixed measurements have an implicit conversion to [Measurements](index.html#measurements), so all the methods that work with measurement work with fixed\_measurements. The construction of a fixed\_measurement from measurement is explicit. Likewise fixed\_precise\_measurement` have an implicit conversion to precise\_measurement, so all the methods that work with precise\_measurement work with fixed\_precise\_measurements. The construction of a fixed\_measurement from measurement is explicit. ### Uncertain Measurements[¶](#uncertain-measurements "Permalink to this headline") The units library supports a class of measurements including an uncertainty measurement. For Example 3.0±0.2m would indicate a measurement of 3.0 meters with an uncertainty of 0.2 m. All operations are supported The propagation of uncertainty follow the root sum of squares methods outlined [Here](http://lectureonline.cl.msu.edu/~mmp/labs/error/e2.htm). There are methods available such as simple\_divide, simple\_product, simple\_sum and simple\_subtract that just sum the uncertainties. The method in use in the regular operators assume that the measurements used in the mathematical operation are independent, and should use the sum of squares methods. A more thorough explanation can be found [at this location](http://web.mit.edu/fluids-modules/www/exper_techniques/2.Propagation_of_Uncertaint.pdf). The structure of an uncertain measurement consists of a float for the measurement value and a float for the uncertainty, and unit for the unit of the measurement. #### Constructors[¶](#constructors "Permalink to this headline") There are a number of different constructors for an uncertain measurement aimed at specify the uncertainty and measurement in different ways. * constexpr uncertain\_measurement() default constructor with 0 values for the value and uncertainty and a one for the unit. * constexpr uncertain\_measurement(<float|double> val, <float|double> uncertainty, unit base) : specify the parameters with values. * constexpr uncertain\_measurement(<float|double> val, unit base): Just specify the value and unit, assume 0.0 uncertainty. * constexpr uncertain\_measurement(measurement val, float uncertainty) noexcept : construct from a measurement and uncertainty value. * uncertain\_measurement(measurement val, measurement uncertainty) noexcept: construct from a measurement value and uncertainty measurement. The uncertainty is converted to the same units as the value measurement. #### Additional operators[¶](#additional-operators "Permalink to this headline") Beyond the operations used in [Measurements](index.html#measurements), there are some specific functions related to getting and setting the uncertainty. * uncertain\_measurement& uncertainty(<double|float> newUncertainty) : Will set the uncertainty value as a numerical value. * uncertain\_measurement& uncertainty(const measurement &newUncerrtainty): will set the uncertainty as a measurement in specific units. * double uncertainty(): Will get the current numerical value of the uncertainty * double uncertainty\_as(units): will get the value of the uncertainty in specific units. * float uncertainty\_f(): will get the value of the uncertainty as a single precision floating point value. * constexpr measurement uncertainty\_measurement(): will return a measurement containing the uncertainty. * double fractional\_uncertainty(): will get the fractional uncertainty value. which is uncertainty/|value|. #### String operations[¶](#string-operations "Permalink to this headline") The units library has some functions to extract an uncertain\_measurement from a string - uncertain\_measurement\_from\_string(const std::string &ustring, std::uint64\_t match\_flags=0) The from string operation searches for an uncertainty marker then splits the string into two parts. It then uses the measurement from string operation on both halves of the string and forms an uncertain measurement from them depending on whether both halves have units and or values. Allowed uncertainty marker strings include [“+/-”, “±”, “&plusmn;”, “+-”, “<u>+</u>”, “&#xB1;”, “&pm;”, ” \pm “]. These possibilities include unicode and ascii values and some sequences used in latex and html. For Example all the following string will produce the same uncertain\_measurement * “3.1±0.3 m/s” * “3.1 +/- 0.3 m/s” * “3.1 &pm; 0.3 m/s” * “3.1 m/s ±0.3 m/s” * “3.1 m/s ±0.3” * “3.1 meters per second ±0.3 m/s” * “3.1 m/s +- 0.3\*60 meters per minute” * “3.1(3) m/s” The last form is known as [concise notation](https://physics.nist.gov/cgi-bin/cuu/Info/Constants/definitions.html). The match flags are the same as would be used for converting [Measurements](index.html#measurements) ### Units From Strings[¶](#units-from-strings "Permalink to this headline") The units library contains a few functions to generate various types from string representations * precise\_unit unit\_from\_string(const std::string& ustring, std::uint32\_t flags=0) : will generate a precise\_unit based on the data in the string * unit unit\_cast\_from\_string(const std::string& ustring, std::uint32\_t flags=0): will generate a unit based on the data in the string * precise\_measurement measurement\_from\_string(const std::string& ustring, std::uint32\_t flags=0): will generate a precise\_measurement from the data in the string * measurement measurement\_cast\_from\_string(const std::string& ustring, std::uint32\_t flags=0): will generate a measurement from the data in the string * uncertain\_measurement uncertain\_measurement\_from\_string(const std::string& ustring, std::uint32\_t flags=0): will generate an uncertain\_measurement from the data in the string The general form is to take a string and optionally a flag object. See [Conversion Flags](index.html#conversion-flags) for a detailed description of the flags. Generally it is fine to leave off the flag argument. #### Unit Strings[¶](#unit-strings "Permalink to this headline") in general the unit string conversion is intended to be as flexible as possible. just about any unit normally written can be converted. For example * “m/s” * “meter/second” * “meter/s” * “metre/s” * “meters/s” * “meters per second” * “metres per second” * “m per sec” * “meterpersecond” * “METERS/s” * “m\*s^-1” * “meters\*seconds^(-1)” * “(second/meter)^(-1)” * “100 centimeters / 1000 ms” Will all produce the unit of meters per second. As a note there are quite a few more units that can be converted from strings than are listed in the [Defined Units](index.html#defined-units). Numbers are supported and become part of the unit. “99 feet” would create a new unit with a definition of 99 ft. The multiplier stored would include the conversion from meters to feet\*99. This allows for arbitrary unit definitions. The + operator also works if the units on both sides have the same base for example 3 ft + 2 in would be the equivalent of 38 inches. If the units do not have the same base + is interpreted as a multiplication. #### Measurement strings[¶](#measurement-strings "Permalink to this headline") The conversion from a string to measurement looks for a leading number before the unit. The “99 feet” in the previous example would then get a measurement value of 99 and the unit would be feet. The measurement from string function also can interpret written numbers such as “three thousand four hundred and twenty-seven miles” This should get correctly read as 3427 miles. The conversion function also handles a few cases where the unit symbol is written before the value such as currency $27.92 would be a value of 27.92 with the currency unit. #### Uncertain Measurements[¶](#uncertain-measurements "Permalink to this headline") Similarly to Measurement strings, uncertain measurements can also be converted from strings see [Uncertain Measurements](index.html#uncertain-measurements) for additional details on the formats supported. ### Units To Strings[¶](#units-to-strings "Permalink to this headline") All the class in the units library can be given as an argument to a to\_string function. This function converts the units or value into a std::string that is representative of the unit or measurement. In all cases the primary aim of the to\_string is to generate a string that the corresponding \*\_from\_string function will recognize and convert back to the original unit. The secondary aim is to generate string that is human readable in standard notation. While this is achieved for many common units there is some work left to do to make it better. For example ``` measurement density=10.0\*kg/m.pow(3); measurement meas2=2.7\*puMW; auto str1=to\_string(density); auto str2=to\_string(meas2); // from google tests EXPECT\_EQ(str1, "10 kg/m^3"); EXPECT\_EQ(str2, "2.7 puMW"); ``` ``` uncertain\_measurement um1(10.0, 0.4, m); auto str = to\_string(um1); EXPECT\_EQ(str, "10+/-0.4 m"); ``` Uncertain measurement string conversions make some attempt to honor significant digits based on the uncertainty. #### Advanced Usage[¶](#advanced-usage "Permalink to this headline") The to\_string function also takes a second argument which is a std::uint64\_t match\_flags in all cases this default to 0, it is currently unused though will be used in the future to allow some fine tuning of the output in specific cases. In the near future a flag to allow utf 8 output strings will convert certain units to more common utf8 symbols such as unit Powers and degree symbols, and a few others. The output string would default to ascii only characters. #### Stream Operators[¶](#stream-operators "Permalink to this headline") Output stream operators are NOT included in the library. It was debatable to include them or not but there would be a lot of additional overloads that would add quite a bit of code to the header files, that in most cases is not necessary so the decision was made to exclude them. The to\_string operations provide most of the capabilities with some additional flexibility, and if needed for a particular use case can be added to the user code in a simple fashion ``` namespace units{ std::ostream& operator<<(std::ostream& os, const precise\_unit& u) { os << to\_string(u); return os; } } // namespace units ``` Any of the types in the units library with a to\_string operation can be handled in the same way. Depending on the compiler, placing the operator in the namespace may or may not be necessary. #### Underlying Conversion Map Access[¶](#underlying-conversion-map-access "Permalink to this headline") The underlying conversion maps may be accessed by users if desired. To access them a compile time definition needs to be added to the build ENABLE\_UNIT\_MAP\_ACCESS ``` #ifdef ENABLE\_UNIT\_MAP\_ACCESS namespace detail { const std::unordered\_map<std::string, precise\_unit>& getUnitStringMap(); const std::unordered\_map<unit, const char\*>& getUnitNameMap(); } #endif ``` These may be useful for building a GUI or smart lookup or some other operations. getUnitStringMap() returns a map of known unit strings, and getUnitNameMap() is a mapping of common units back to strings as a building block for the to\_string operation. ### Math Operations[¶](#math-operations "Permalink to this headline") Additional mathematical operations on measurements are available in the unit\_math.hpp header these are header only so no additional compilation is required. The intention of this header is to match operations from the cmath header available in the standard library. These are all template functions which will work for any measurement type. #### Type traits[¶](#type-traits "Permalink to this headline") The header includes a few type traits used in the header file and potentially useful elsewhere including * is\_unit : true if the type is a unit (unit or precise\_unit) * is\_measurement : true if the type is one of the defined measurement types * is\_precise\_measurement : true if the type is one of the defined precise measurement type #### Rounding and Truncation[¶](#rounding-and-truncation "Permalink to this headline") These operations will effect only the value part of the measurement * round * trunc * ceil * floor #### Trigonometric functions[¶](#trigonometric-functions "Permalink to this headline") Trigonometric operations will operate only if the measurement is convertible to radians * sin * cos * tan #### Multiplies and divides[¶](#multiplies-and-divides "Permalink to this headline") Division and multiplication operators for measurements that have support for per\_unit measurement * multiplies : works like \* except when one of the measurements is per\_unit and they have the same unit base, then they remove the per unit * divides : works like / except if both measurements have the same base then the result has a per\_unit unit See [Strain](index.html#strain) for examples on usage #### Others[¶](#others "Permalink to this headline") Other common mathematical expressions found in <cmath> * fmod : return the floating point modulus of a division operation as long as division is a valid operation * hypot : works for two and three measurements or floating point values as long as addition is a valid operation. * cbrt : works similarly to the sqrt operation ### Commodities[¶](#commodities "Permalink to this headline") the precise\_unit class can represent commodities as well as units. The commodity is represented by a 32 bit unsigned number that codes a variety of commodities. The actual representation is still undergoing some change so expect this to change going forward. See [Commodity Details](index.html#commodity-details) for more details. #### Methods[¶](#methods "Permalink to this headline") There are a few available methods for dealing with commodity codes and string translation * std::uint32\_t getCommodity(std::string comm) - will get a commodity from a string. * std::string getCommodityName(std::uint32\_t commodity) - will translate a commodity code into a string ##### Custom Commodities[¶](#custom-commodities "Permalink to this headline") * void addCustomCommodity(std::string comm, std::uint32\_t code) - Add a custom commodity code using a string and code * void clearCustomCommodities() - clear all current user defined commodities * void disableCustomCommodities() - Turn off the use of custom commodities * void enableCustomCommodities() - Turn on the ability to add and check custom commodities for later access #### Commodities to names[¶](#commodities-to-names "Permalink to this headline") The getCommodityName methods has 4 stages and will return from any successful stage. 1. Check custom commodities if allowed 2. Check standardized commodity names 3. Check for special codes for name storage (short names <=5 ascii lower case characters are stored directly in the code) 4. generate string “CXCOMM[<code>]” #### String To Commodities[¶](#string-to-commodities "Permalink to this headline") The getCommodity method works nearly the opposite of getCommodityName. 1. Check custom commodities if allowed 2. Check standardized commodity codes 3. Check for string “CXCOMM[<code>]” 4. Check for special codes for name storage (short names <=5 ascii lower case characters are stored directly in the code) 5. Generate a hash code of the string and if allowed store it as a custom commodity #### Defined Commodities[¶](#defined-commodities "Permalink to this headline") The list of commodities is still in development. Generally [`traded commodities<https://en.wikipedia.org/wiki/List\_of\_traded\_commodities>`\_](#id1) are available as well as a few others that are used in clinical definitions or other uses as part of unit definition standards. In the future this list will more generally expand to match international trade tables. See [`commodities.cpp<https://github.com/LLNL/units/blob/master/units/commodities.cpp>`\_](#id3) for details on the exact list. ### User defined units[¶](#user-defined-units "Permalink to this headline") The units library has support for user defined units and [Commodities](index.html#commodities). These interact with the \*\_from\_string and to\_string operations to allow custom conversions and definitions. #### Defining a custom unit[¶](#defining-a-custom-unit "Permalink to this headline") The basic function for adding a custom unit is addUserDefinedUnit(string name, precise\_unit un) For example from a test in the library ``` precise\_unit idgit(4.754, mol/m.pow(2)); addUserDefinedUnit("idgit", idgit); auto ipm=unit\_from\_string("idgit/min"); EXPECT\_EQ(ipm, idgit / min); auto str = to\_string(ipm); EXPECT\_EQ(str, "idgit/min"); str = to\_string(ipm.inv()); EXPECT\_EQ(str, "min/idgit"); ``` Basically user defined units can interact with the string conversion functions just like any other unit defined in the library. A user defined unit gets priority when converting to a string as well including when squared or cubed as part of a compound unit. For example from the test cases: ``` addUserDefinedUnit("angstrom", units::precise::distance::angstrom); auto str = to\_string(units::unit\_from\_string("us / angstrom^2")); EXPECT\_EQ(str, "us/angstrom^2"); str = to\_string(units::unit\_from\_string("us / angstrom")); EXPECT\_EQ(str, "us/angstrom"); ``` If only an ability to interpret strings is needed the addUserDefinedInputUnit can be used ``` precise\_unit idgit(4.754, mol/m.pow(2)); addUserDefinedInputUnit("idgit", idgit); auto ipm = unit\_from\_string("idgit/min"); EXPECT\_EQ(ipm, idgit / min); auto str = to\_string(ipm); EXPECT\_EQ(str.find("idgit"), std::string::npos); EXPECT\_NE(str.find("kat") , std::string::npos); ``` If only output strings are needed the addUserDefinedOutputUnit can be used ``` precise\_unit idgit(4.754, mol / m.pow(2)); addUserDefinedOutputUnit("idgit", idgit); auto ipm = unit\_from\_string("idgit/min"); //this is not able to be read since idgit is undefined as an input EXPECT\_NE(ipm, idgit / min); auto str = to\_string(idgit/min); /\*\* output only should make this work\*/ EXPECT\_EQ(str,"idgit/min"); ``` The output unit can be used when the interpreter works fine but the string output doesn’t do what you want it to do. A unit can be removed from the user defined unit set via removeUserDefinedUnit ``` auto ipm=unit\_from\_string("idgit/min"); EXPECT\_EQ(ipm, idgit / min); auto str = to\_string(ipm); EXPECT\_EQ(str, "idgit/min"); str = to\_string(ipm.inv()); EXPECT\_EQ(str, "min/idgit"); removeUserDefinedUnit("idgit"); EXPECT\_FALSE(is\_valid(unit\_from\_string("idgit/min"))); ``` The removal also works for user defined units specified via addUserDefinedInputUnit or addUserDefinedOutputUnit #### Input File[¶](#input-file "Permalink to this headline") Sometimes it is useful to have a larger library of units in this case the std::string definedUnitsFromFile(const std::string& filename) can be used to load a number of units at once. The file format is quite simple. # at the beginning of a line indicates a comment other wise ``` # comment meeter == meter meh == meeter per hour # => indicates input only unit mehmeh => meh/s # <= indicates output only unit hemhem => s/meh ``` or ``` # comment yodles=73 counts # comment "yeedles", 19 yodles yimdles; dozen yeedles ``` or ``` # test the quotes for inclusion "bl==p"=18.7 cups # test single quotes for inclusion 'y,,p',9 tons # ignore just one quote 'np==14 kg # escaped quotes "j\"\""= 13.5 W # escaped quotes 'q""'= 15.5 W ``` The basic rule is that one of [<=,;] will separate a definition name from a unit definition. If the next character after the separator is an ‘=’ it is ignored. If it is a ‘>’ it implies input only definition. If the separator is an ‘<=’ then it is output only. Otherwise it calls addUserDefinedUnit for each definition. The function is declared noexcept and will return a string with each error separated by a newline. So if the result string is empty() there were no errors. #### Other Library Operations[¶](#other-library-operations "Permalink to this headline") * clearUserDefinedUnits() will erase all previously defined units * disableUserDefinedUnits() will disable the use of user defined units * enableUserDefinedUnits() will enable their use if they had been disabled, they are enabled by default. #### Notes on units and threads[¶](#notes-on-units-and-threads "Permalink to this headline") The user defined units usage flag is an atomic variable but the modification of the user defined library are not thread safe, so if threads are needed make all the changes in one thread before using it in other threads, or protect the calls with a separate mutex. The disable and enable functions trigger an atomic variable that enables the use of user defined units in the string translation functions. disableUserDefinedUnits() also turns off the ability to specify new user defined units but does not erase those already defined. ### Physical constants[¶](#physical-constants "Permalink to this headline") The units library comes with a number of physical constants with appropriate units defined. All the physical constants are specified as [Precise measurements](index.html#precise-measurements) and in the namespace units::constants In general the most recent definition was chosen which includes the 2019 redefinition of some SI units this matches with the rest of the library and the defined units. Inspiration for the different constants was taken from [wikipedia](https://en.wikipedia.org/wiki/List_of_physical_constants) and [NIST](https://physics.nist.gov/cuu/Constants/index.html). Defined constants. The [2019 redefinition](https://www.nist.gov/si-redefinition/meet-constants) of the SI system was used where applicable. All [common constants](https://physics.nist.gov/cgi-bin/cuu/Category?view=html&Frequently+used+constants.x=87&Frequently+used+constants.y=18) listed from NIST are included #### Defined constants[¶](#defined-constants "Permalink to this headline") Values are taken from [NIST 2018 CODATA](https://physics.nist.gov/cuu/Constants/Table/allascii.txt) unless otherwise noted * Standard gravity - g0 * Gravitational constant - G * Speed of light - c * Elementary Charge (2019 redefinition) - e * hyperfine structure transition frequency of the caesium-133 atom - fCs * fine structure constant - alpha * Planck constant (2019 redefinition) - h * Reduced Planck constant (2019 redefinition) - hbar * Boltzman constant (2019 redefinition) - k * Avogadros constant (2019 redefinition) - Na * Luminous efficiency - kcd * Permittivity of free space - eps0 * Permeability of free space - mu0 * Gas Constant - R * Stephan Boltzmann constant -s * Hubble constant 69.8 km/s/Mpc - H0 * Mass of an electron - me * Mass of a proton - mp * Bohr Radius - a0 * Faraday’s constant - F * Atomic mass constant - mu * Conductance quantum - G0 * Josephson constant - Kj * Magnetic flux quantum - phi0 * von Kiltzing constant - Rk * Rydberg constant - Rinf #### Planck Units[¶](#planck-units "Permalink to this headline") These units are found in the units::constants::planck namespace and include length, mass, time, charge, and temperature. #### Atomic units[¶](#atomic-units "Permalink to this headline") These physical constants are values related to an electron or [atomic measurements](https://www.bipm.org/en/publications/si-brochure/table7.html) They include length, mass, time, charge- same as e above, energy, and action. The atomic constants are defined in the units::constants::atomic namespace. #### Numbers[¶](#numbers "Permalink to this headline") There are a few numbers that are used in the library and include definitions in the units::constants namespace. They are represented as doubles and are defined as constexpr * pi (3.14159265358979323846) * tau (2.0\*pi) * invalid\_conversion (signaling NaN) * infinity * standard\_gravity the numerical value of g0, earth standard gravity in m/s/sec * speed\_of\_light The numerical value of the speed of light in m/s The last two are used in several other units and some conversions so it seemed better to just define the numerical value and use that rather than use the same number in several places. #### Planetary masses[¶](#planetary-masses "Permalink to this headline") The masses of some of the solar system bodies are included in units::constants::Planetary::mass * solar * earth * moon * jupiter * mars #### From Strings[¶](#from-strings "Permalink to this headline") All constants listed here are available for conversion from strings by wrapping in brackets For example the luminous efficiency would be converted to a unit by using [kcd] The planck constants are available as [planck::XXXXX] or planckXXXXXX and the atomic constants are available as `[atomic::XXXX] #### Uncertainties[¶](#uncertainties "Permalink to this headline") Certain physical constants have uncertainties associated with them and have an additional uncertain\_measurement associated with them see uncertain\_measurments. These can be found in the units::constants::uncertain namespace and include: * Gravitational constant - G * Permittivity of free space - eps0 * Permeability of free space - u0 * Hubble constant 69.8 km/s/Mpc - H0 * Mass of an electron - me * Mass of a proton - mp * Atomic mass constant - mu * mass of nuetron - mn * Rydberg constant - Rinf * fine structure constant - alpha *NOTE: A few of the uncertain constants have more precision than supported in uncertain\_measurments but were included for completeness* ### Defined Units[¶](#defined-units "Permalink to this headline") The units library comes with two sets of units predefined. They are all located in [src/units/unit\_definitions.hpp](https://github.com/LLNL/units/blob/master/units/unit_definitions.hpp). The definitions come in two flavors a precise\_unit and a regular unit. All the precise units are defined in the namespace units::precise All the units are defined as a constexpr values. The choice of which units to define is somewhat arbitrary and guided by the authors experience and the origins of the library in power systems and electrical engineering in the US. Units that the author has actually encountered in work or life are included and in cases where there might be conflicts depending on the location preference was given to the US customary definition, though international systems take priority. #### Listing of Units[¶](#listing-of-units "Permalink to this headline") The most common units are defined in the namespace units::precise and others are defined in subnamespaces. ##### Base Units[¶](#base-units "Permalink to this headline") Most base units have two definitions that are equivalent * meter, m * kilogram, kg * second, s * Ampere, A * Kelvin, K * mol * candela, cd * currency * count * pu * iflag * eflag * radian, rad ##### Specialized units[¶](#specialized-units "Permalink to this headline") Some specialized units are defined for use in conversion applications or for making handling string conversions a little easier * defunit - special unit that signifies conversion to any other units is possible * invalid - special unit that conversion has failed * error - an error unit ##### Numerical Units[¶](#numerical-units "Permalink to this headline") Sometimes it is useful to have pure numerical units, often for multiplication with other units such as hundred\*kg or something like that which becomes a single unit with 100 kg. * one * ten * hundred * percent (0.01) * infinite * nan Also included in this category are [SI prefixes](https://physics.nist.gov/cuu/Units/prefixes.html) deci, centi,milli, micro, nano, pico, femto, atto, zepto, yocto, ronto, quecto, deka, hecto, kilo, mega, giga, tera, peta, exa, hecto, zetta, yotta,rotta, quetta. and SI data prefixes kibi, mebi, gibi, tebi, pebi, exbi, zebi, yobi ##### Derived SI Units[¶](#derived-si-units "Permalink to this headline") There are many units that used in combination with the SI system that are derived from the base units * hertz, Hz * volt, V * Pa (pascal, on some systems this is defined as something else so the definition(pascal) is skipped) * newton, N * joule, J * watt, W * coulomb, C * farad, F * siemens, S * weber, Wb * tesla, T * henry, H * lumen, lm * lux, lx * becquerel, Bq * gray, Gy * sievert, Sv * katal, kat * sr ##### Extra SI related units[¶](#extra-si-related-units "Permalink to this headline") A few units that are not officially part of the SI but are [accepted](https://physics.nist.gov/cuu/Units/outside.html) for use with the SI system, along with a few other SI units with prefixes that are commonly used. * mg * g * mL * L * nm * mm * km * cm * bar ##### Centimeter-Gram-Second system[¶](#centimeter-gram-second-system "Permalink to this headline") The [CGS](https://en.wikipedia.org/wiki/Centimetre%E2%80%93gram%E2%80%93second_system_of_units) system is a variant on the metric system. Units from the CGS system are included under the namespace units::precise::cgs. * eng * dyn * barye * gal * poise * stokes * kayser * oersted * gauss * debye * maxwell * biot * gilbert * stilb * lambert * phot * curie * roentgen * REM * RAD * emu * langley * unitpole * statC\_charge * statC\_flux * abOhm * abFarad * abHenry * abVolt * statV * statT * statHenry * statOhm * statFarad ##### Conventional Electrical Units[¶](#conventional-electrical-units "Permalink to this headline") defined in namespace units::precise::conventional * volt90 * ampere90 * watt90 * henry90 * coulomb90 * farad90 * ohm90 ##### Meter Gram Force System[¶](#meter-gram-force-system "Permalink to this headline") defined in namespace units::precise::gm * pond * hyl * at * poncelet * PS ##### Meter Tonne Second system[¶](#meter-tonne-second-system "Permalink to this headline") Defined in namespace units::precise::MTS * sthene * pieze * thermie ##### Additional Time units[¶](#additional-time-units "Permalink to this headline") Defined in namespace units::precise::time, units marked with \* are also defined in the units::precise. * min\* * ms\* * ns\* * hr\* * h\* * day\* * week * yr\* (8760 hr) * fortnight * sday - sidereal day * syr - sidereal year * at - mean tropical year * aj - mean julian year * ag - mean gregorian year * year - aliased to median calendar year (365 days) which is the standard for SI * mos - synodal (lunar) month * moj - mean julian month * mog - mean gregorian month ##### International customary Units[¶](#international-customary-units "Permalink to this headline") These are traditional units that have some level of international definition Defined in namespace units::precise::i * grain * point * pica * inch * foot * yard * mile * league * hand * cord * board\_foot * mil * circ\_mil A few units have short symbols defined in unit::precise in, ft, yd, mile. These alias to the international definition. ##### Avoirdupois units[¶](#avoirdupois-units "Permalink to this headline") Avoirdupois units are another common international standard of units for weight and volumes. The units are defined in units::precise::av * dram * ounce * pound * hundredweight * longhundredweight * ton * longton * stone * lbf * ozf * slug * poundal A few common units have symbols defined in units::precise lb, ton, oz, lbf and these alias to the Avoirdupois equivalent. ##### Troy Units[¶](#troy-units "Permalink to this headline") Most commonly for precious metals a few units are defined in units::precise::troy, with a basis in the international grain. * pennyweight * oz * pound ##### United States Customary Units[¶](#united-states-customary-units "Permalink to this headline") These are traditional units defined in the United States, for survey or common usage. Defined in unit::precise::us. * foot * inch * mil * yard * rod * chain * link * furlong * mile * league * acre\* * homestead * section * township * minim * dram * floz * tbsp * tsp * pinch * dash * shot * gill * cup * pint * quart * gallon * flbarrel - liquid barrel * barrel * hogshead * cord * fifth A few US customary units are defined in specific namespaces to distinguish them from other forms US customary dry measurements are defined in units::precise::us::dry * pint * quart * gallon * peck * bushel * barrel * sack * strike Some grain measures used in markets and commodities are defined in units::precise::us::grain. When commodities are a little more developed this will be defined with appropriate commodity included. * bushel\_corn * bushel\_wheat * bushel\_barley * bushel\_oats Some survey units are defined in units::precise::us::engineers to distinguish them from others * chain * link The unit gal (gallon) is also defined in units::precise since that is pretty common to use. ##### FDA and metric measures[¶](#fda-and-metric-measures "Permalink to this headline") The food and drug administration has defined some customary units in metric terms for use in medicine. These are defined in units::precise::metric Also included are some other customary units that have a metric definition. * tbsp * tsp * floz * cup * cup\_uslegal * carat ##### Canadian Units[¶](#canadian-units "Permalink to this headline") Some Canadian definitions of customary units defined in units::precise::canada * tbsp * tsp * cup * cup\_trad * gallon * grain::bushel\_oats ##### Australia Units[¶](#australia-units "Permalink to this headline") Traditional Australian units defined in units::precise::australia * tbsp * tsp * cup ##### Imperial or British Units[¶](#imperial-or-british-units "Permalink to this headline") Traditional british or imperial units, defined in units::precise::imp. * inch * foot * thou * barleycorn * rod * chain * link * pace * yard * furlong * league * mile * nautical\_mile * knot * acre * perch * rood * gallon * quart * pint * gill * cup * floz * tbsp * tsp * barrel * peck * bushel * dram * minim * drachm * stone * hundredweight * ton * slug ##### Apothecaries System[¶](#apothecaries-system "Permalink to this headline") Used in pharmaceutical contexts the apothecaries system of units is defined in units::precise::apothecaries. * floz ( same as imperial version ) * minim * scruple * drachm * ounce * pound * pint * gallon * metric\_ounce ##### Nautical Units[¶](#nautical-units "Permalink to this headline") Some units defined in context of marine travel defined in units::precise::nautical * fathom * cable * mile * knot * league ##### Japanese traditional Units[¶](#japanese-traditional-units "Permalink to this headline") Some traditional Japanese units are included for historical interest in units::precise::japan * shaku * sun * ken * tsubo * sho * kan * go * cup ##### Chinese Traditional Units[¶](#chinese-traditional-units "Permalink to this headline") Some traditional Chinese units are included for historical interest in units::precise::chinese * jin * liang * qian * li * cun * chi * zhang ##### Typographic units[¶](#typographic-units "Permalink to this headline") Units used in typesetting and typography are included in units::precise::typographic Subsets of the units depending on the location are in subnamespaces ###### units::precise::typographic::american[¶](#units-precise-typographic-american "Permalink to this headline") * line * point * pica * twip ###### units::precise::typographic::printers[¶](#units-precise-typographic-printers "Permalink to this headline") * point * pica ###### units::precise::typographic::french[¶](#units-precise-typographic-french "Permalink to this headline") * point * ligne * pouce * didot * cicero * pied * toise ###### units::precise::typographic::metric[¶](#units-precise-typographic-metric "Permalink to this headline") * point * quart ###### units::precise::typographic::IN[¶](#units-precise-typographic-in "Permalink to this headline") l’Imprimerie nationale * point * pica ###### units::precise::typographic::tex[¶](#units-precise-typographic-tex "Permalink to this headline") * point * pica ###### units::precise::typographic::postscript[¶](#units-precise-typographic-postscript "Permalink to this headline") * point * pica ###### units::precise::typographic::dtp[¶](#units-precise-typographic-dtp "Permalink to this headline") This is the modern standard or as close to such a thing as exists * point * pica * twip * line ###### units::precise::typographic[¶](#units-precise-typographic "Permalink to this headline") Values taken from dtp namespace * point * pica ##### Distance Units[¶](#distance-units "Permalink to this headline") Some additional distance units are defined in units::precise::distance * ly * au * au\_old * angstrom * parsec * smoot * cubit * longcubit * arpent\_us * arpent\_fr * xu ##### Area Units[¶](#area-units "Permalink to this headline") Some additional units defining an area units::precise::area * are * hectare * barn * arpent ##### Mass Units[¶](#mass-units "Permalink to this headline") Some additional units defining a mass units::precise::mass * quintal * ton\_assay * longton\_assay * Da * u * tonne t is included in the units::precise namespace as a metric tonne ##### Volume Units[¶](#volume-units "Permalink to this headline") Some additional units defining a volume units::precise::volume * stere * acre\_foot * drum ##### Angle Units[¶](#angle-units "Permalink to this headline") A few units defining angles are defined in units::precise::angle. * deg\* * gon * grad * arcmin * arcsec * brad - binary radian (1/256 of a circle) ##### Directional Units[¶](#directional-units "Permalink to this headline") A few cardinal directional units are defined in units::precise::direction, these make use of the i\_flag and a numerical value * east * north * south * west ##### Temperature Units[¶](#temperature-units "Permalink to this headline") A few units related to temperature systems, defined in units::precise::temperature * celsius, degC\* * fahrenheit, degF\* * rankine, degR * reaumur ##### Pressure Units[¶](#pressure-units "Permalink to this headline") Some units related to pressure are defined in units::precise::pressure * psi * inHg * mmHg * torr * inH2O * mmH2O * atm - standard atmosphere * att - technical atmosphere ##### Power Units[¶](#power-units "Permalink to this headline") Some units related to power are defined in units::precise::power * hpE - electric Horsepower * hpI - international horsepower * hpS - steam horesepower * hpM - mechanical horsepower the unit hp is aliased in the units::precise namespace to power::hpI ##### Energy Units[¶](#energy-units "Permalink to this headline") Some units related to energy are defined in units::precise::energy * kWh * MWh * eV * kcal * cal\_4 - calorie at 4 deg C * cal\_15 - calorie at 15 deg C * cal\_28 - calorie at 28 deg C * cal\_mean - mean calorie * cal\_it - international table calorie * cal\_th - thermochemical calorie * btu\_th - thermochemical BTU * btu\_39 - BTU at 39 deg C * btu\_59 - BTU at 59 deg C * btu\_60 - BTU at 60 deg C * btu\_mean - mean BTU * btu\_it - international table BTU * btu\_iso - rounded btu\_it * quad * tonc - cooling ton * therm\_us * therm\_br * therm\_ec * EER - energy efficiency ratio * SG - specific gravity * ton\_tnt * boe - barrel of oil equivalent * foeb * hartree * tonhour * tce - ton of coal equivalent * lge - liter of gasoline equivalent in the units::precise namespace * btu = energy::but\_it * cal = energy::cal\_th * kWh = energy::kWh * MWh = energy::MWh ##### Power system Units[¶](#power-system-units "Permalink to this headline") Some additional units related to power systems and electrical engineering in units::precise::electrical namespace * MW * VAR - W\*i\_flag * MVAR * kW * kVAR * mW * puMW * puV * puHz * MJ * puOhm * puA * kV * mV * mA ##### Equation type Units[¶](#equation-type-units "Permalink to this headline") Equation units are explained more thoroughly in [Equation Units](index.html#equation-units) Some of the specific common equation units are defined in the namespace units::precise::log. * neper * logE - natural logarithm * neperA - neper of amplitude unit * neperP - neper of a power unit * logbase10 * bel * belP - bel of a power based unit * dBP * belA - bel of an amplitude based unit * dBA - dB of an amplitude based unit * logbase2 * dB * neglog10 * neglog100 * neglog1000 * neglog50000 * B\_SPL * B\_V * B\_mV * B\_uV * B\_10nV * B\_W * B\_kW * dB\_SPL * dB\_V * dB\_mV * dB\_uV * dB\_10nV * dB\_W * dB\_kW * dB\_Z - radar reflectivity * BZ - radar reflectivity ##### Textile related Units[¶](#textile-related-units "Permalink to this headline") Units related to the textile industry in namespace units::precise::textile. * tex * denier * span * finger * nail ##### Clinical Units[¶](#clinical-units "Permalink to this headline") Units related to clinical medicine in namespace units::precise::clinical. * pru * woodu * diopter * prism\_diopter * mesh * charriere * drop * met * hounsfield ##### Laboratory Units[¶](#laboratory-units "Permalink to this headline") Units used in laboratory settings in namespace units::precise::laboratory. * svedberg * HPF * LPF * enzyme\_unit * IU * arbU - arbitrary unit * IR - index of reactivity * Lf - Limit of flocculation * PFU * pH * molarity * molality ##### Data Units[¶](#data-units "Permalink to this headline") Units related to computer data and storage in units::precise::data * bit\* * nibble * byte * kB\* * MB\* * GB\* * kiB * MiB * GiB * bit\_s - Shannon bit for information theory * shannon * hartley * ban * dit * deciban * nat * trit * digits B is defined as byte in units::precise ##### Computation units[¶](#computation-units "Permalink to this headline") Units related to computation units::precise::computation. * flop * flops * mips ##### Special units[¶](#special-units "Permalink to this headline") Some special units that were not otherwise characterized in namespace units::precise::special. * ASD - amplitude spectral density * moment\_magnitude - moment magnitude for earthquake scales (related to richter scale) * moment\_energy * sshws - saffir simpson hurricane wind scale * beaufort - Beaufort wind scale * fujita - Fujita scale for tornados * mach - mach number(multiplier of the speed of sound) * rootHertz - square root of Hertz, this is a special handling unit that triggers some specific behavior to handle it. * rootMeter - square root of meter, this is a special handling unit that triggers some specific behavior to handle it. ##### Other Units[¶](#other-units "Permalink to this headline") General purpose other units not otherwise categorical in namespace units::precise::other * ppm - part per million * ppb - part per billion * candle * faraday * rpm\* - revolution per minute * CFM - cubic feet per minute * MegaBuck - $1,000,000 * GigaBuck - $1,000,000,000 ##### Climate Units[¶](#climate-units "Permalink to this headline") Units related to climate in namespace units::precise::climate * gwp - global warming potential * gtp - global temperature potential ##### Speed Units[¶](#speed-units "Permalink to this headline") mph and mps are defined in units::precise since they are pretty common #### Units in the units namespace[¶](#units-in-the-units-namespace "Permalink to this headline") Regular units are defined in the units namespace. The general rule is that any units with a definition directly in units::precise has an analog nonprecise unit in the units namespace. One addition is that any unit defined in precise::electrical also is defined in units this has to do with the origins of the library in power systems. ### Unit Domains[¶](#unit-domains "Permalink to this headline") There are some ambiguous unit symbols. Different fields use the same symbol to mean different things. In the units library the definition has defaulted to SI standard definition with two known ambiguities. the symbol ‘a’ is used for are, the symbol rad refers to radians. However there are occasions where the units from one field or another are desired. The units library applies the notion of a unit domain which can be passed to the unit\_flags argument for any string conversion, for a few select units this will change the resulting from a string. #### Available Domains[¶](#available-domains "Permalink to this headline") thus far 5 specific unit domains have been defined they are in the units::domains namespace. * ucum – THE UNIFIED CODE FOR UNITS OF MEASURE * cooking – units and symbols commonly used for recipes * astronomy – units and symbols used in astronomy * nuclear – units and symbols used in nuclear or particle physics * surveying – units and symbols used in surveying in the United states * us\_customary – units and symbols traditionally used in the us(combination of cooking and surveying) * climate – units and symbols used in climate science * allDomains – this domain does all the above domains where not mutually exclusive. So mostly a combination of ucum and astronomy/nuclear with a few us customary units IT is not recommended to use this but provided for the combinations The only units and symbols using the domain are those that might be ambiguous or contradictory to the ST definition. The specific units affected are defined in the next section. #### Domain Specific Units[¶](#domain-specific-units "Permalink to this headline") These are unit definitions affected by specifying a specific unit domain ##### UCUM[¶](#ucum "Permalink to this headline") * B – bel vs Byte * a – julian year vs are ##### Astronomy[¶](#astronomy "Permalink to this headline") * am – arc minute vs attometer * as – arc second vs attosecond * year – mean tropical year vs median calendar year ##### Cooking[¶](#cooking "Permalink to this headline") * C – cup vs coulomb * T – Tablespoon vs Tesla * c – cup vs speed of light * t – teaspoon vs metric tonne * TB – Tablespoon vs TeraByte * smi – smidgen vs square mile * scruple – slightly different definition when used in cooking context * ds – dessertspoon vs deci second ##### Surveying[¶](#surveying "Permalink to this headline") * ‘ and all variants refer to feet vs arcmin * ‘’ and all variants refer to inches vs arcsec ##### Nuclear[¶](#nuclear "Permalink to this headline") * rad radiation absorbed dose vs radian * rd same as rad vs rod ##### Climate[¶](#climate "Permalink to this headline") * kt kilo-tonne vs karat ##### US customary[¶](#us-customary "Permalink to this headline") Combination of surveying and cooking ##### All domains[¶](#all-domains "Permalink to this headline") Combination of all of the above More units will likely be added to this as the need arises #### Specifying the domain[¶](#specifying-the-domain "Permalink to this headline") The domain can be specified in the unit\_flag string supplied to the unit\_from\_string operation. ``` auto unit1=units::unit\_from\_string(unitString,nuclear\_units) ``` when used as part of the flags argument the definitions are in the unit\_conversion\_flags enumeration * strict\_ucum * cooking\_units * astronomy\_units * surveying\_units * nuclear\_units * climate\_units * us\_customary\_units A default domain can also be specified though ``` setUnitsDomain(code); ``` with the code using one of those found in the units::domains namespace. this domain will be used unless another is specified through the match flags. This function return the previous domain which can be used if only setting the value temporarily. The default domain can be set at compile time through the UNITS\_DEFAULT\_DOMAIN definition ``` #define UNITS\_DEFAULT\_DOMAIN units::domains::astronomy #include "units/units.hpp" ``` In CMake this field can be defined and will be directly translated. The UNITS\_DOMAIN CMake variable can also be used to specify a domain as a string like UCUM or COOKING and have it appropriately translate. See [Unit Library CMake Reference](index.html#unit-library-cmake-reference) for more details. ### Conversion Flags[¶](#conversion-flags "Permalink to this headline") The units\_from\_string and to\_string operations take an optional flags argument. This controls certain aspects of the conversion process. In most cases this can be left to default unless very specific needs arise. #### Unit\_from\_string flags[¶](#unit-from-string-flags "Permalink to this headline") * default\_conversions – no\_flags, so using the default operations * case\_insensitive –perform case insensitive matching for UCUM case insensitive matching * single\_slash –specify that there is a single numerator and denominator only a single slash in the unit operations * strict\_si –input units are strict SI * strict\_ucum –input units are matching UCUM standard * cooking\_units –input units for cooking and recipes are prioritized * astronomy\_units –input units for astronomy are prioritized * surveying\_units –input units for surveying are prioritized * nuclear\_units –input units for nuclear physics and radiation are prioritized * climate\_units –input units for climate sciences * us\_customary\_units – input units for us customary measurements are prioritized(same as cooking\_units | surveying\_units) * numbers\_only –indicate that only numbers should be matched in the first segments, mostly applies only to power operations * no\_recursion –don’t recurse through the string * not\_first\_pass –indicate that is not the first pass * no\_per\_operators –skip matching “per” * no\_locality\_modifiers –skip locality modifiers * no\_of\_operator –skip dealing with “of” * no\_addition – skip trying unit addition * no\_commodities –skip commodity checks * skip\_partition\_check –skip the partition check algorithm * skip\_si\_prefix\_check –skip checking for SI prefixes * skip\_code\_replacements –don’t do some code and sequence replacements * minimum\_partition\_size2 –specify that any unit partitions must be greater or equal to 2 characters * minimum\_partition\_size3 –specify that any unit partitions must be greater or equal to 3 characters * minimum\_partition\_size4 –specify that any unit partitions must be greater or equal to 4 characters * minimum\_partition\_size5 –specify that any unit partitions must be greater or equal to 5 characters * minimum\_partition\_size6 –specify that any unit partitions must be greater or equal to 6 characters * minimum\_partition\_size7 –specify that any unit partitions must be greater or equal to 7 characters ##### Indications for use[¶](#indications-for-use "Permalink to this headline") The case\_insensitive flag should be used to ignore capitalization completely. It is targeted at the UCUM upper case specification but is effective for all situations where case should be ignored. The library is by nature somewhat flexible in capitalization patterns, because of this some strings are allowed that otherwise would not be if SI were strictly followed. For example: Um would match to micro meters which should not if being exacting to the SI standard. The strict\_si flag prevents some not all of these instances, and whether others can be disabled is being investigated. The single\_slash flag is targeted at a few specific programs which use the format of a single slash marking the separation of numerator from denominator. strict\_ucum, cooking\_units, astronomy\_units, surveying\_units, nuclear\_units, climate\_units and us\_customary\_units are part of the domain system and can change the unit matched. The remainder of the flags are somewhat self explanatory and are primarily used as part of the string conversion program to prevent infinite recursion. The no\_commodities or no\_per\_operator may be used if it is known those do not apply for a slight increase in performance. The no\_recursion or skip\_partition\_check can be use if only simple strings are passed to speed up the process somewhat. The minimum partition size flags can be used to restrict how much partitioning it does which can reduce the possibility of “false positives” or unwanted unit matches on the strings All the flags can be “or”ed to make combinations such as minimum\_partition\_size4|astronomy\_units|no\_of\_operator #### to\_string Flags[¶](#to-string-flags "Permalink to this headline") * disable\_large\_power\_strings - if the units definition allows large powers this flag can disable the use of them in the output string. The to\_string flags can be combined with the other conversion flags without issue. ##### Default flags[¶](#default-flags "Permalink to this headline") Flags will normally default to 0U however they can be modified through setDefaultFlags. This function returns the previous value in case it is needed to swap them temporarily. The flags can be retrieved via getDefaultFlags() This function is automatically called if no flag argument is passed. The initial value can be set through a compile time or build time option UNITS\_DEFAULT\_MATCH\_FLAGS. Application Notes[¶](#application-notes "Permalink to this headline") --------------------------------------------------------------------- This folder is a collection of some example code snippets and discussions applied to various situations ### Strain[¶](#strain "Permalink to this headline") Strain is an interesting unit in that it is a dimensionless unit. The most common expression is in in/in or mm/mm often defined as ε. The effects of strain are pretty small so µε is also pretty common. It is the fractional change in length. There are a several ways to represent this in the units library depending on the needs for a particular situation. #### Method 1[¶](#method-1 "Permalink to this headline") The first way is simply as a ratio. ``` measurement deltaLength=0.00001\*m; measurement length=1\*m; auto strain=deltaLength/length; EXPECT\_EQ(to\_string(strain), "1e-05"); //applied to a 10 ft bar auto distortion=strain\*(10\*ft); EXPECT\_EQ(to\_string(distortion), "0.0001 ft"); ``` The main issue that there is no distinction between a strain measurement and any other ratio, but in many cases that is fine. #### Method 2[¶](#method-2 "Permalink to this headline") The default defined unit of strain in the units library uses per unit meters as a basis. The multiplies and divides methods in the units math library can take per unit flag into account when doing the multiplication to get the original units back. The advantages of this are that strain becomes a distinctive unit from all other ratio units. Volumetric or area strain can be represented in the same way. It does have the disadvantage of requiring the multiplies ``` #include <units/units\_math.hpp> //for multiplies precise\_measurement strain=1e-05\*default\_unit("strain"); EXPECT\_EQ(to\_string(strain), "1e-05 strain"); //applied to a 10 ft bar auto distortion=multiplies(strain,(10\*ft)); EXPECT\_EQ(to\_string(distortion), "0.0001 ft"); ``` ``` #include <units/units\_math.hpp> //for multiplies divides measurement deltaLength=0.00001\*m; measurement length=1\*m; auto strain=divides(deltaLength,length); EXPECT\_EQ(to\_string(strain), "1e-05 strain"); //applied to a 10 ft bar auto distortion=multiplies(strain,(10\*ft)); EXPECT\_EQ(to\_string(distortion), "0.0001 ft"); ``` #### Method 3[¶](#method-3 "Permalink to this headline") The third potential method is to use one of the indicator flags to define a unit for strain. This can work in cases where there is no other potential conflicts with the flag and you need the \* operator to work. ``` precise\_unit ustrain(1e-6,eflag); // microstrain addUserDefinedUnit("ustrain",ustrain); precise\_measurement strain=45.7\*ustrain; EXPECT\_EQ(to\_string(strain), "45.7 ustrain"); //applied to a 10 m bar auto distortion=strain\*(10\*m); EXPECT\_DOUBLE\_EQ(distortion.value\_as(precise::mm),0.457); ``` The advantages of this are that the there is no per unit values to handle. The disadvantage is that the eflag needs to be handled particularly when dealing with strings. If it is just dealing with computations this is less of an issue. So this method can work fine in some cases. #### Discussion[¶](#discussion "Permalink to this headline") There are several ways to represent strain or any other ratio unit that is derived from particular unit cancellations. All have advantages and disadvantages in particular situations and the method of choice will come down to the expected use cases. The library chooses the per unit method as it maintains the source units, but other choices are free to choose if they work better in particular situations. The Low Level Details of the Units library[¶](#the-low-level-details-of-the-units-library "Permalink to this headline") ----------------------------------------------------------------------------------------------------------------------- ### Unit base class[¶](#unit-base-class "Permalink to this headline") The unit base class is a bitmap comprising segments of a 32 bit number. all the bits are defined the underlying definition is a set of bit fields that cover a full 32 bit unsigned integer ``` // needs to be defined for the full 32 bits signed int meter\_ : 4; signed int second\_ : 4; // 8 signed int kilogram\_ : 3; signed int ampere\_ : 3; signed int candela\_ : 2; // 16 signed int kelvin\_ : 3; signed int mole\_ : 2; signed int radians\_ : 3; // 24 signed int currency\_ : 2; signed int count\_ : 2; // 28 unsigned int per\_unit\_ : 1; unsigned int i\_flag\_ : 1; // 30 unsigned int e\_flag\_ : 1; // unsigned int equation\_ : 1; // 32 ``` The default constructor sets all the fields to 0. But this is private and only accessible from friend classes like units. The main constructor looks like ``` constexpr unit\_data( int meter, int kilogram, int second, int ampere, int kelvin, int mole, int candela, int currency, int count, int radians, unsigned int per\_unit, unsigned int flag, unsigned int flag2, unsigned int equation) ``` an alternative constructor ``` explicit constexpr unit\_data(std::nullptr\_t); ``` sets all the fields to 1 #### Math operations[¶](#math-operations "Permalink to this headline") When multiplying two base units the powers are added. For the flags. The e\_flag and i\_flag are added, effectively an Xor while the pu and equation are ORed. For division the units are subtracted, while the operations on the flags are the same. ##### Power and Root and Inv functions[¶](#power-and-root-and-inv-functions "Permalink to this headline") For power operations all the individual powers of the base units are multiplied by the power number. The pu and equation flags are passed through. For even powers the i\_flag and e\_flag are set to 0, and odd powers they left as is. For root operations, First a check if the root is valid, if not the error unit is returned. If it is a valid root all the powers are divided by the root power. The Pu flag is left as is, the i\_flag and e\_flag are treated the same is in the pow function and the equations flag is set to 0. There is one exception to the above rules. There is a special unit for √Hz it is a combination of some i\_flag and e\_flag and a high power of the seconds unit. This unit is used in amplitude spectral density and comes up on occasion in some engineering contexts. There is some special logic in the power function that does the appropriate things such the square of √Hz= Hz. If a low power of seconds is multiplied or divided by the special unit it still does the appropriate thing. But √Hz\*√Hz will not generate the expected result. √Hz is a singular unique unit and the only coordinated result is a power operation to remove it. √Hz unit base itself uses a power of (-5) on the seconds and sets the i\_flag and e\_flag. The inverse function is equivalent to pow(-1), and just inverts the unit\_data. #### Getters[¶](#getters "Permalink to this headline") The unit data type supports getters for the different fields all these are constexpr methods * meter(): return the meter power * kg(): return the kilogram power * second(): return the seconds power * ampere(): return the ampere power * kelvin(): return the kelvin power * mole(): return the mole power * candela(): return the candela power * currency(): return the currency power * count(): return the count power * radian(): return the radian power * is\_per\_unit(): returns true if the unit\_base has the per\_unit flag set * is\_equation(): returns true if the unit\_base has the equation field set * has\_i\_flag(): returns true if the i\_flag is set * has\_e\_flag(): returns true if the e\_flag is set * empty(): will check if the unit\_data has any of the base units set, flags are ignored. * unit\_type\_count: will count the number of base units with a non-zero power #### Modifiers[¶](#modifiers "Permalink to this headline") there are a few methods will generate a new unit based on an existing one the methods are constexpr * add\_per\_unit(): will set the per\_unit flag * add\_i\_flag(): will set the i\_flag * add\_e\_flag(): will set the e\_flag The method clear\_flags is the only non-const method that modifies a unit\_data in place. #### Comparisons[¶](#comparisons "Permalink to this headline") Unit data support the == and != operators. these check all fields. There are a few additional comparison functions that are also available. * equivalent\_non\_counting(unit\_base other) : will return true if all the units but the counting units are equal, the counting units are mole, radian, and count. * has\_same\_base(unit\_base other): will return true if all the units bases are equivalent. So the flags can be different. ### Commodity Details[¶](#commodity-details "Permalink to this headline") The precise\_unit class includes an unsigned 32 bit field that represents a commodity of some kind. Units on the Web[¶](#units-on-the-web "Permalink to this headline") ------------------------------------------------------------------- You can try out the string conversions through the units [Webserver](../_static/convert.html) This page allows you to enter a measurement string and a unit string for conversion. The measurement string can be of any form with a number and units * 10 m * hundred pounds * 45.673 GB * dozen feet the unit string should be some unit that is convertible from the measurement units: * inches * troy oz * kiB * british fathoms The conversion also supports mathematical operations see [Units From Strings](index.html#units-from-strings) for additional details on string conversions. The units can also be set to \*`or `<base> to convert the measurement to base units. ### Rest API[¶](#rest-api "Permalink to this headline") The units web server does not serve files, it generates all responses on the fly. There are 3 URI indicators it responds to beyond the root page. * /convert : responds with an html page * /convert\_trivial : responds with the results as a simple text * /convert\_json : responds with a json string containing the requested conversions and the results. For example in Linux or anything with curl ``` $ curl -s "13.52.135.81/convert\_trivial?measurement=10%20tons&units=lb" 20000 $ curl -s "13.52.135.81/convert\_json?measurement=10%20tons&units=lb" { "request\_measurement":"", "request\_units":"lb"", "measurement":"", "units":"lb"", "value":"nan" } $ curl -s "13.52.135.81/convert\_json?measurement=ten%20meterspersecond&units=feetperminute&caction=to\_string" { "request\_measurement":"ten meterspersecond", "request\_units":"feetperminute", "measurement":"10 m/s", "units":"ft/min", "value":"1968.5" } ``` This works with POST or GET methods. The caction field can be set to “to\_string” this will “simplify” the units in the result or at least use the internal to\_string operations to convert to an interpretable string in more accessible units. Units Library String Conversions Units Library String Conversions ================================ [Source](https://github.com/LLNL/units) [Documentation](https://units.readthedocs.io/en/latest/index.html) ----------------------------------------------------------------------------------------------------------- Measurement string: E.g. "25 m", "ten tons", "3.252Tesla", "237uL" convert to: E.g. "inches", "lb", "Gauss", "tsp" "use '\*' to convert to base units

sqlc

go

sqlc 1.18.0 documentation [](#) v1.18.0 Overview * [Installing sqlc](index.html#document-overview/install) Tutorials * [Getting started with MySQL](index.html#document-tutorials/getting-started-mysql) * [Getting started with PostgreSQL](index.html#document-tutorials/getting-started-postgresql) * [Getting started with SQLite](index.html#document-tutorials/getting-started-sqlite) How-to Guides * [Retrieving rows](index.html#document-howto/select) * [Counting rows](index.html#document-howto/query_count) * [Inserting rows](index.html#document-howto/insert) * [Updating rows](index.html#document-howto/update) * [Deleting rows](index.html#document-howto/delete) * [Preparing queries](index.html#document-howto/prepared_query) * [Using transactions](index.html#document-howto/transactions) * [Naming parameters](index.html#document-howto/named_parameters) * [Modifying the database schema](index.html#document-howto/ddl) * [Configuring generated structs](index.html#document-howto/structs) * [Uploading projects](index.html#document-howto/upload) Reference * [CLI](index.html#document-reference/cli) * [Configuration](index.html#document-reference/config) * [Datatypes](index.html#document-reference/datatypes) * [Query annotations](index.html#document-reference/query-annotations) * [Database and language support](index.html#document-reference/language-support) * [Environment variables](index.html#document-reference/environment-variables) * [Changelog](index.html#document-reference/changelog) Conceptual Guides * [Using Go and pgx](index.html#document-guides/using-go-and-pgx) * [Developing sqlc](index.html#document-guides/development) * [Authoring plugins](index.html#document-guides/plugins) * [Privacy and data collection](index.html#document-guides/privacy) [sqlc](#) * » * sqlc 1.18.0 documentation * [Edit on GitHub](https://github.com/kyleconroy/sqlc/blob/v1.18.0/docs/index) --- sqlc Documentation[](#sqlc-documentation "Permalink to this headline") ======================================================================= > > > And lo, the Great One looked down upon the people and proclaimed:“SQL is actually pretty great” > > > > > sqlc generates **fully type-safe idiomatic Go code** from SQL. Here’s how it works: 1. You write SQL queries 2. You run sqlc to generate Go code that presents type-safe interfaces to those queries 3. You write application code that calls the methods sqlc generated Seriously, it’s that easy. You don’t have to write any boilerplate SQL querying code ever again. Installing sqlc[](#installing-sqlc "Permalink to this headline") ----------------------------------------------------------------- sqlc is distributed as a single binary with zero dependencies. ### macOS[](#macos "Permalink to this headline") ``` brew install sqlc ``` ### Ubuntu[](#ubuntu "Permalink to this headline") ``` sudo snap install sqlc ``` ### go install[](#go-install "Permalink to this headline") #### Go >= 1.17:[](#go-1-17 "Permalink to this headline") ``` go install github.com/kyleconroy/sqlc/cmd/sqlc@latest ``` #### Go < 1.17:[](#id1 "Permalink to this headline") ``` go get github.com/kyleconroy/sqlc/cmd/sqlc ``` ### Docker[](#docker "Permalink to this headline") ``` docker pull kjconroy/sqlc ``` Run `sqlc` using `docker run`: ``` docker run --rm -v $(pwd):/src -w /src kjconroy/sqlc generate ``` Run `sqlc` using `docker run` in the Command Prompt on Windows (`cmd`): ``` docker run --rm -v "%cd%:/src" -w /src kjconroy/sqlc generate ``` ### Downloads[](#downloads "Permalink to this headline") Get pre-built binaries for *v1.18.0*: * [Linux](https://github.com/kyleconroy/sqlc/releases/download/v1.18.0/sqlc_1.18.0_linux_amd64.tar.gz) * [macOS](https://github.com/kyleconroy/sqlc/releases/download/v1.18.0/sqlc_1.18.0_darwin_amd64.zip) * [Windows (MySQL only)](https://github.com/kyleconroy/sqlc/releases/download/v1.18.0/sqlc_1.18.0_windows_amd64.zip) See [downloads.sqlc.dev](https://downloads.sqlc.dev/) for older versions. Getting started with MySQL[](#getting-started-with-mysql "Permalink to this headline") --------------------------------------------------------------------------------------- This tutorial assumes that the latest version of sqlc is [installed](../overview/install.html) and ready to use. Create a new directory called `sqlc-tutorial` and open it up. Initialize a new Go module named `tutorial.sql.dev/app` ``` go mod init tutorial.sqlc.dev/app ``` sqlc looks for either a `sqlc.yaml` or `sqlc.json` file in the current directory. In our new directory, create a file named `sqlc.yaml` with the following contents: ``` version: 1 packages: - path: "tutorial" name: "tutorial" engine: "mysql" schema: "schema.sql" queries: "query.sql" ``` sqlc needs to know your database schema and queries. In the same directory, create a file named `schema.sql` with the following contents: ``` CREATE TABLE authors ( id BIGINT NOT NULL AUTO\_INCREMENT PRIMARY KEY, name text NOT NULL, bio text ); ``` Next, create a `query.sql` file with the following four queries: ``` -- name: GetAuthor :one SELECT \* FROM authors WHERE id = ? LIMIT 1; -- name: ListAuthors :many SELECT \* FROM authors ORDER BY name; -- name: CreateAuthor :execresult INSERT INTO authors ( name, bio ) VALUES ( ?, ? ); -- name: DeleteAuthor :exec DELETE FROM authors WHERE id = ?; ``` You are now ready to generate code. Run the `generate` command. You shouldn’t see any errors or output. ``` sqlc generate ``` You should now have a `tutorial` package containing three files. ``` ├── go.mod ├── query.sql ├── schema.sql ├── sqlc.yaml └── tutorial ├── db.go ├── models.go └── query.sql.go ``` You can use your newly generated queries in `app.go`. ``` package main import ( "context" "database/sql" "log" "reflect" "tutorial.sqlc.dev/app/tutorial" \_ "github.com/go-sql-driver/mysql" ) func run() error { ctx := context.Background() db, err := sql.Open("mysql", "user:password@/dbname") if err != nil { return err } queries := tutorial.New(db) // list all authors authors, err := queries.ListAuthors(ctx) if err != nil { return err } log.Println(authors) // create an author result, err := queries.CreateAuthor(ctx, tutorial.CreateAuthorParams{ Name: "Brian Kernighan", Bio: sql.NullString{String: "Co-author of The C Programming Language and The Go Programming Language", Valid: true}, }) if err != nil { return err } insertedAuthorID, err := result.LastInsertId() if err != nil { return err } log.Println(insertedAuthorID) // get the author we just inserted fetchedAuthor, err := queries.GetAuthor(ctx, insertedAuthorID) if err != nil { return err } // prints true log.Println(reflect.DeepEqual(insertedAuthorID, fetchedAuthor.ID)) return nil } func main() { if err := run(); err != nil { log.Fatal(err) } } ``` Before the code will compile, you’ll need to add the Go MySQL driver. ``` go get github.com/go-sql-driver/mysql go build ./... ``` To make that possible, sqlc generates readable, **idiomatic** Go code that you otherwise would have had to write yourself. Take a look in `tutorial/query.sql.go`. Getting started with PostgreSQL[](#getting-started-with-postgresql "Permalink to this headline") ------------------------------------------------------------------------------------------------- This tutorial assumes that the latest version of sqlc is [installed](../overview/install.html) and ready to use. Create a new directory called `sqlc-tutorial` and open it up. Initialize a new Go module named `tutorial.sqlc.dev/app` ``` go mod init tutorial.sqlc.dev/app ``` sqlc looks for either a `sqlc.yaml` or `sqlc.json` file in the current directory. In our new directory, create a file named `sqlc.yaml` with the following contents: ``` version: "2" sql: - engine: "postgresql" queries: "query.sql" schema: "schema.sql" gen: go: package: "tutorial" out: "tutorial" ``` sqlc needs to know your database schema and queries in order to generate code. In the same directory, create a file named `schema.sql` with the following content: ``` CREATE TABLE authors ( id BIGSERIAL PRIMARY KEY, name text NOT NULL, bio text ); ``` Next, create a `query.sql` file with the following four queries: ``` -- name: GetAuthor :one SELECT * FROM authors WHERE id = $1 LIMIT 1; -- name: ListAuthors :many SELECT * FROM authors ORDER BY name; -- name: CreateAuthor :one INSERT INTO authors ( name, bio ) VALUES ( $1, $2 ) RETURNING *; -- name: DeleteAuthor :exec DELETE FROM authors WHERE id = $1; ``` If you **do not** want your SQL `UPDATE` queries to return the updated record to the user, add this to `query.sql`: ``` -- name: UpdateAuthor :exec UPDATE authors set name = $2, bio = $3 WHERE id = $1; ``` Otherwise, to return the updated record to the user, add this to `query.sql`: ``` -- name: UpdateAuthor :one UPDATE authors set name = $2, bio = $3 WHERE id = $1 RETURNING *; ``` You are now ready to generate code. You shouldn’t see any errors or output. ``` sqlc generate ``` You should now have a `tutorial` package containing three files. ``` ├── go.mod ├── query.sql ├── schema.sql ├── sqlc.yaml └── tutorial ├── db.go ├── models.go └── query.sql.go ``` You can use your newly generated queries in `app.go`. ``` package main import ( "context" "database/sql" "log" "reflect" "tutorial.sqlc.dev/app/tutorial" \_ "github.com/lib/pq" ) func run() error { ctx := context.Background() db, err := sql.Open("postgres", "user=pqgotest dbname=pqgotest sslmode=verify-full") if err != nil { return err } queries := tutorial.New(db) // list all authors authors, err := queries.ListAuthors(ctx) if err != nil { return err } log.Println(authors) // create an author insertedAuthor, err := queries.CreateAuthor(ctx, tutorial.CreateAuthorParams{ Name: "Brian Kernighan", Bio: sql.NullString{String: "Co-author of The C Programming Language and The Go Programming Language", Valid: true}, }) if err != nil { return err } log.Println(insertedAuthor) // get the author we just inserted fetchedAuthor, err := queries.GetAuthor(ctx, insertedAuthor.ID) if err != nil { return err } // prints true log.Println(reflect.DeepEqual(insertedAuthor, fetchedAuthor)) return nil } func main() { if err := run(); err != nil { log.Fatal(err) } } ``` Before the code will compile, you’ll need to add the Go PostgreSQL driver. ``` go get github.com/lib/pq go build ./... ``` sqlc generates readable, **idiomatic** Go code that you otherwise would have had to write yourself. Take a look in the `tutorial` package to see what code sqlc generated. Getting started with SQLite[](#getting-started-with-sqlite "Permalink to this headline") ----------------------------------------------------------------------------------------- This tutorial assumes that the latest version of sqlc is [installed](../overview/install.html) and ready to use. Create a new directory called `sqlc-tutorial` and open it up. Initialize a new Go module named `tutorial.sql.dev/app` ``` go mod init tutorial.sqlc.dev/app ``` sqlc looks for either a `sqlc.yaml` or `sqlc.json` file in the current directory. In our new directory, create a file named `sqlc.yaml` with the following contents: ``` version: 2 sql: - engine: "sqlite" schema: "schema.sql" queries: "query.sql" gen: go: package: "tutorial" out: "tutorial" ``` sqlc needs to know your database schema and queries. In the same directory, create a file named `schema.sql` with the following contents: ``` CREATE TABLE authors ( id INTEGER PRIMARY KEY, name text NOT NULL, bio text ); ``` Next, create a `query.sql` file with the following four queries: ``` -- name: GetAuthor :one SELECT \* FROM authors WHERE id = ? LIMIT 1; -- name: ListAuthors :many SELECT \* FROM authors ORDER BY name; -- name: CreateAuthor :one INSERT INTO authors ( name, bio ) VALUES ( ?, ? ) RETURNING \*; -- name: DeleteAuthor :exec DELETE FROM authors WHERE id = ?; ``` For SQL UPDATE if you do not want to return the updated record to the user, add this to the `query.sql` file: ``` -- name: UpdateAuthor :exec UPDATE authors set name = ?, bio = ? WHERE id = ?; ``` Otherwise, to return the updated record back to the user, add this to the `query.sql` file: ``` -- name: UpdateAuthor :one UPDATE authors set name = ?, bio = ? WHERE id = ? RETURNING \*; ``` You are now ready to generate code. Run the `generate` command. You shouldn’t see any errors or output. ``` sqlc generate ``` You should now have a `tutorial` package containing three files. ``` ├── go.mod ├── query.sql ├── schema.sql ├── sqlc.yaml └── tutorial ├── db.go ├── models.go └── query.sql.go ``` You can use your newly generated queries in `app.go`. ``` package main import ( "context" "database/sql" "log" "reflect" "tutorial.sqlc.dev/app/tutorial" \_ "embed" \_ "github.com/mattn/go-sqlite3" ) //go:embed schema.sql var ddl string func run() error { ctx := context.Background() db, err := sql.Open("sqlite3", ":memory:") if err != nil { return err } // create tables if \_, err := db.ExecContext(ctx, ddl); err != nil { return err } queries := tutorial.New(db) // list all authors authors, err := queries.ListAuthors(ctx) if err != nil { return err } log.Println(authors) // create an author insertedAuthor, err := queries.CreateAuthor(ctx, tutorial.CreateAuthorParams{ Name: "Brian Kernighan", Bio: sql.NullString{String: "Co-author of The C Programming Language and The Go Programming Language", Valid: true}, }) if err != nil { return err } log.Println(insertedAuthor) // get the author we just inserted fetchedAuthor, err := queries.GetAuthor(ctx, insertedAuthor.ID) if err != nil { return err } // prints true log.Println(reflect.DeepEqual(insertedAuthor, fetchedAuthor)) return nil } func main() { if err := run(); err != nil { log.Fatal(err) } } ``` Before the code will compile, you’ll need to add the Go SQLite driver. ``` go mod tidy go build ./... ``` To make that possible, sqlc generates readable, **idiomatic** Go code that you otherwise would have had to write yourself. Take a look in `tutorial/query.sql.go`. Retrieving rows[](#retrieving-rows "Permalink to this headline") ----------------------------------------------------------------- To generate a database access method, annotate a query with a specific comment. ``` CREATE TABLE authors ( id SERIAL PRIMARY KEY, bio text NOT NULL, birth_year int NOT NULL ); -- name: GetAuthor :one SELECT * FROM authors WHERE id = $1; -- name: ListAuthors :many SELECT * FROM authors ORDER BY id; ``` A few new pieces of code are generated beyond the `Author` struct. An interface for the underlying database is generated. The `\*sql.DB` and `\*sql.Tx` types satisfy this interface. The database access methods are added to a `Queries` struct, which is created using the `New` method. Note that the `\*` in our query has been replaced with explicit column names. This change ensures that the query will never return unexpected data. Our query was annotated with `:one`, meaning that it should only return a single row. We scan the data from that one into a `Author` struct. Since the get query has a single parameter, the `GetAuthor` method takes a single `int` as an argument. Since the list query has no parameters, the `ListAuthors` method accepts no arguments. ``` package db import ( "context" "database/sql" ) type Author struct { ID int Bio string BirthYear int } type DBTX interface { QueryContext(context.Context, string, ...interface{}) (\*sql.Rows, error) QueryRowContext(context.Context, string, ...interface{}) \*sql.Row } func New(db DBTX) \*Queries { return &Queries{db: db} } type Queries struct { db DBTX } const getAuthor = `-- name: GetAuthor :one SELECT id, bio, birth\_year FROM authors WHERE id = $1 ` func (q \*Queries) GetAuthor(ctx context.Context, id int) (Author, error) { row := q.db.QueryRowContext(ctx, getAuthor, id) var i Author err := row.Scan(&i.ID, &i.Bio, &i.BirthYear) return i, err } const listAuthors = `-- name: ListAuthors :many SELECT id, bio, birth\_year FROM authors ORDER BY id ` func (q \*Queries) ListAuthors(ctx context.Context) ([]Author, error) { rows, err := q.db.QueryContext(ctx, listAuthors) if err != nil { return nil, err } defer rows.Close() var items []Author for rows.Next() { var i Author if err := rows.Scan(&i.ID, &i.Bio, &i.BirthYear); err != nil { return nil, err } items = append(items, i) } if err := rows.Close(); err != nil { return nil, err } if err := rows.Err(); err != nil { return nil, err } return items, nil } ``` ### Selecting columns[](#selecting-columns "Permalink to this headline") ``` CREATE TABLE authors ( id SERIAL PRIMARY KEY, bio text NOT NULL, birth_year int NOT NULL ); -- name: GetBioForAuthor :one SELECT bio FROM authors WHERE id = $1; -- name: GetInfoForAuthor :one SELECT bio, birth_year FROM authors WHERE id = $1; ``` When selecting a single column, only that value that returned. The `GetBioForAuthor` method takes a single `int` as an argument and returns a `string` and an `error`. When selecting multiple columns, a row record (method-specific struct) is returned. In this case, `GetInfoForAuthor` returns a struct with two fields: `Bio` and `BirthYear`. ``` package db import ( "context" "database/sql" ) type DBTX interface { QueryRowContext(context.Context, string, ...interface{}) \*sql.Row } func New(db DBTX) \*Queries { return &Queries{db: db} } type Queries struct { db DBTX } const getBioForAuthor = `-- name: GetBioForAuthor :one SELECT bio FROM authors WHERE id = $1 ` func (q \*Queries) GetBioForAuthor(ctx context.Context, id int) (string, error) { row := q.db.QueryRowContext(ctx, getBioForAuthor, id) var i string err := row.Scan(&i) return i, err } const getInfoForAuthor = `-- name: GetInfoForAuthor :one SELECT bio, birth\_year FROM authors WHERE id = $1 ` type GetInfoForAuthorRow struct { Bio string BirthYear int } func (q \*Queries) GetInfoForAuthor(ctx context.Context, id int) (GetInfoForAuthorRow, error) { row := q.db.QueryRowContext(ctx, getInfoForAuthor, id) var i GetInfoForAuthorRow err := row.Scan(&i.Bio, &i.BirthYear) return i, err } ``` ### Passing a slice as a parameter to a query[](#passing-a-slice-as-a-parameter-to-a-query "Permalink to this headline") #### PostgreSQL[](#postgresql "Permalink to this headline") In PostgreSQL, [ANY](https://www.postgresql.org/docs/current/functions-comparisons.html#id-1.5.8.28.16) allows you to check if a value exists in an array expression. Queries using ANY with a single parameter will generate method signatures with slices as arguments. Use the postgres data types, eg: int, varchar, etc. ``` CREATE TABLE authors ( id SERIAL PRIMARY KEY, bio text NOT NULL, birth_year int NOT NULL ); -- name: ListAuthorsByIDs :many SELECT * FROM authors WHERE id = ANY($1::int[]); ``` The above SQL will generate the following code: ``` package db import ( "context" "database/sql" "github.com/lib/pq" ) type Author struct { ID int Bio string BirthYear int } type DBTX interface { QueryContext(context.Context, string, ...interface{}) (\*sql.Rows, error) QueryRowContext(context.Context, string, ...interface{}) \*sql.Row } func New(db DBTX) \*Queries { return &Queries{db: db} } type Queries struct { db DBTX } const listAuthors = `-- name: ListAuthorsByIDs :many SELECT id, bio, birth\_year FROM authors WHERE id = ANY($1::int[]) ` func (q \*Queries) ListAuthorsByIDs(ctx context.Context, ids []int) ([]Author, error) { rows, err := q.db.QueryContext(ctx, listAuthors, pq.Array(ids)) if err != nil { return nil, err } defer rows.Close() var items []Author for rows.Next() { var i Author if err := rows.Scan(&i.ID, &i.Bio, &i.BirthYear); err != nil { return nil, err } items = append(items, i) } if err := rows.Close(); err != nil { return nil, err } if err := rows.Err(); err != nil { return nil, err } return items, nil } ``` #### MySQL[](#mysql "Permalink to this headline") MySQL differs from PostgreSQL in that placeholders must be generated based on the number of elements in the slice you pass in. Though trivial it is still something of a nuisance. The passed in slice must not be nil or empty or an error will be returned (ie not a panic). The placeholder insertion location is marked by the meta-function `sqlc.slice()` (which is similar to `sqlc.arg()` that you see documented under [Naming parameters](named_parameters)). To rephrase, the `sqlc.slice('param')` behaves identically to `sqlc.arg()` it terms of how it maps the explicit argument to the function signature, eg: * `sqlc.slice('ids')` maps to `ids []GoType` in the function signature * `sqlc.slice(cust\_ids)` maps to `custIds []GoType` in the function signature (like `sqlc.arg()`, the parameter does not have to be quoted) This feature is not compatible with `emit\_prepared\_queries` statement found in the [Configuration file](index.html#document-reference/config). ``` CREATE TABLE authors ( id SERIAL PRIMARY KEY, bio text NOT NULL, birth\_year int NOT NULL ); -- name: ListAuthorsByIDs :many SELECT \* FROM authors WHERE id IN (sqlc.slice('ids')); ``` The above SQL will generate the following code: ``` package db import ( "context" "database/sql" "fmt" "strings" ) type Author struct { ID int Bio string BirthYear int } type DBTX interface { ExecContext(context.Context, string, ...interface{}) (sql.Result, error) PrepareContext(context.Context, string) (\*sql.Stmt, error) QueryContext(context.Context, string, ...interface{}) (\*sql.Rows, error) QueryRowContext(context.Context, string, ...interface{}) \*sql.Row } func New(db DBTX) \*Queries { return &Queries{db: db} } type Queries struct { db DBTX } func (q \*Queries) WithTx(tx \*sql.Tx) \*Queries { return &Queries{ db: tx, } } const listAuthorsByIDs = `-- name: ListAuthorsByIDs :many SELECT id, bio, birth\_year FROM authors WHERE id IN (/\*SLICE:ids\*/?) ` func (q \*Queries) ListAuthorsByIDs(ctx context.Context, ids []int64) ([]Author, error) { sql := listAuthorsByIDs var queryParams []interface{} if len(ids) == 0 { return nil, fmt.Errorf("slice ids must have at least one element") } for \_, v := range ids { queryParams = append(queryParams, v) } sql = strings.Replace(sql, "/\*SLICE:ids\*/?", strings.Repeat(",?", len(ids))[1:], 1) rows, err := q.db.QueryContext(ctx, sql, queryParams...) if err != nil { return nil, err } defer rows.Close() var items []Author for rows.Next() { var i Author if err := rows.Scan(&i.ID, &i.Bio, &i.BirthYear); err != nil { return nil, err } items = append(items, i) } if err := rows.Close(); err != nil { return nil, err } if err := rows.Err(); err != nil { return nil, err } return items, nil } ``` Counting rows[](#counting-rows "Permalink to this headline") ------------------------------------------------------------- ``` CREATE TABLE authors ( id SERIAL PRIMARY KEY, hometown text NOT NULL ); -- name: CountAuthors :one SELECT count(\*) FROM authors; -- name: CountAuthorsByTown :many SELECT hometown, count(\*) FROM authors GROUP BY 1 ORDER BY 1; ``` ``` package db import ( "context" "database/sql" ) type DBTX interface { QueryContext(context.Context, string, ...interface{}) (\*sql.Rows, error) QueryRowContext(context.Context, string, ...interface{}) \*sql.Row } func New(db DBTX) \*Queries { return &Queries{db: db} } type Queries struct { db DBTX } const countAuthors = `-- name: CountAuthors :one SELECT count(\*) FROM authors ` func (q \*Queries) CountAuthors(ctx context.Context) (int, error) { row := q.db.QueryRowContext(ctx, countAuthors) var i int err := row.Scan(&i) return i, err } const countAuthorsByTown = `-- name: CountAuthorsByTown :many SELECT hometown, count(\*) FROM authors GROUP BY 1 ORDER BY 1 ` type CountAuthorsByTownRow struct { Hometown string Count int } func (q \*Queries) CountAuthorsByTown(ctx context.Context) ([]CountAuthorsByTownRow, error) { rows, err := q.db.QueryContext(ctx, countAuthorsByTown) if err != nil { return nil, err } defer rows.Close() items := []CountAuthorsByTownRow{} for rows.Next() { var i CountAuthorsByTownRow if err := rows.Scan(&i.Hometown, &i.Count); err != nil { return nil, err } items = append(items, i) } if err := rows.Close(); err != nil { return nil, err } if err := rows.Err(); err != nil { return nil, err } return items, nil } ``` Inserting rows[](#inserting-rows "Permalink to this headline") --------------------------------------------------------------- ``` CREATE TABLE authors ( id SERIAL PRIMARY KEY, bio text NOT NULL ); -- name: CreateAuthor :exec INSERT INTO authors (bio) VALUES ($1); ``` ``` package db import ( "context" "database/sql" ) type DBTX interface { ExecContext(context.Context, string, ...interface{}) (sql.Result, error) } func New(db DBTX) \*Queries { return &Queries{db: db} } type Queries struct { db DBTX } const createAuthor = `-- name: CreateAuthor :exec INSERT INTO authors (bio) VALUES ($1) ` func (q \*Queries) CreateAuthor(ctx context.Context, bio string) error { \_, err := q.db.ExecContext(ctx, createAuthor, bio) return err } ``` ### Returning columns from inserted rows[](#returning-columns-from-inserted-rows "Permalink to this headline") sqlc has full support for the `RETURNING` statement. ``` -- Example queries for sqlc CREATE TABLE authors ( id BIGSERIAL PRIMARY KEY, name text NOT NULL, bio text ); -- name: CreateAuthor :one INSERT INTO authors ( name, bio ) VALUES ( $1, $2 ) RETURNING *; -- name: CreateAuthorAndReturnId :one INSERT INTO authors ( name, bio ) VALUES ( $1, $2 ) RETURNING id; ``` ``` package db import ( "context" "database/sql" ) const createAuthor = `-- name: CreateAuthor :one INSERT INTO authors ( name, bio ) VALUES ( $1, $2 ) RETURNING id, name, bio ` type CreateAuthorParams struct { Name string Bio sql.NullString } func (q \*Queries) CreateAuthor(ctx context.Context, arg CreateAuthorParams) (Author, error) { row := q.db.QueryRowContext(ctx, createAuthor, arg.Name, arg.Bio) var i Author err := row.Scan(&i.ID, &i.Name, &i.Bio) return i, err } const createAuthorAndReturnId = `-- name: CreateAuthorAndReturnId :one INSERT INTO authors ( name, bio ) VALUES ( $1, $2 ) RETURNING id ` type CreateAuthorAndReturnIdParams struct { Name string Bio sql.NullString } func (q \*Queries) CreateAuthorAndReturnId(ctx context.Context, arg CreateAuthorAndReturnIdParams) (int64, error) { row := q.db.QueryRowContext(ctx, createAuthorAndReturnId, arg.Name, arg.Bio) var id int64 err := row.Scan(&id) return id, err } ``` ### Using CopyFrom[](#using-copyfrom "Permalink to this headline") PostgreSQL supports the Copy Protocol that can insert rows a lot faster than sequential inserts. You can use this easily with sqlc: ``` CREATE TABLE authors ( id SERIAL PRIMARY KEY, name text NOT NULL, bio text NOT NULL ); -- name: CreateAuthors :copyfrom INSERT INTO authors (name, bio) VALUES ($1, $2); ``` ``` type CreateAuthorsParams struct { Name string Bio string } func (q \*Queries) CreateAuthors(ctx context.Context, arg []CreateAuthorsParams) (int64, error) { return q.db.CopyFrom(ctx, []string{"authors"}, []string{"name", "bio"}, &iteratorForCreateAuthors{rows: arg}) } ``` Updating rows[](#updating-rows "Permalink to this headline") ------------------------------------------------------------- ``` CREATE TABLE authors ( id SERIAL PRIMARY KEY, bio text NOT NULL ); ``` ### Single parameter[](#single-parameter "Permalink to this headline") If your query has a single parameter, your Go method will also have a single parameter. ``` -- name: UpdateAuthorBios :exec UPDATE authors SET bio = $1; ``` ``` package db import ( "context" "database/sql" ) type DBTX interface { ExecContext(context.Context, string, ...interface{}) (sql.Result, error) } func New(db DBTX) \*Queries { return &Queries{db: db} } type Queries struct { db DBTX } const updateAuthorBios = `-- name: UpdateAuthorBios :exec UPDATE authors SET bio = $1 ` func (q \*Queries) UpdateAuthorBios(ctx context.Context, bio string) error { \_, err := q.db.ExecContext(ctx, updateAuthorBios, bio) return err } ``` ### Multiple parameters[](#multiple-parameters "Permalink to this headline") If your query has more than one parameter, your Go method will accept a `Params` struct. ``` -- name: UpdateAuthor :exec UPDATE authors SET bio = $2 WHERE id = $1; ``` ``` package db import ( "context" "database/sql" ) type DBTX interface { ExecContext(context.Context, string, ...interface{}) (sql.Result, error) } func New(db DBTX) \*Queries { return &Queries{db: db} } type Queries struct { db DBTX } const updateAuthor = `-- name: UpdateAuthor :exec UPDATE authors SET bio = $2 WHERE id = $1 ` type UpdateAuthorParams struct { ID int32 Bio string } func (q \*Queries) UpdateAuthor(ctx context.Context, arg UpdateAuthorParams) error { \_, err := q.db.ExecContext(ctx, updateAuthor, arg.ID, arg.Bio) return err } ``` Deleting rows[](#deleting-rows "Permalink to this headline") ------------------------------------------------------------- ``` CREATE TABLE authors ( id SERIAL PRIMARY KEY, bio text NOT NULL ); -- name: DeleteAuthor :exec DELETE FROM authors WHERE id = $1; ``` ``` package db import ( "context" "database/sql" ) type DBTX interface { ExecContext(context.Context, string, ...interface{}) (sql.Result, error) } func New(db DBTX) \*Queries { return &Queries{db: db} } type Queries struct { db DBTX } const deleteAuthor = `-- name: DeleteAuthor :exec DELETE FROM authors WHERE id = $1 ` func (q \*Queries) DeleteAuthor(ctx context.Context, id int) error { \_, err := q.db.ExecContext(ctx, deleteAuthor, id) return err } ``` Preparing queries[](#preparing-queries "Permalink to this headline") --------------------------------------------------------------------- ``` CREATE TABLE records ( id SERIAL PRIMARY KEY ); -- name: GetRecord :one SELECT * FROM records WHERE id = $1; ``` sqlc has an option to use prepared queries. These prepared queries also work with transactions. ``` package db import ( "context" "database/sql" "fmt" ) type Record struct { ID int32 } type DBTX interface { PrepareContext(context.Context, string) (\*sql.Stmt, error) QueryRowContext(context.Context, string, ...interface{}) \*sql.Row } func New(db DBTX) \*Queries { return &Queries{db: db} } func Prepare(ctx context.Context, db DBTX) (\*Queries, error) { q := Queries{db: db} var err error if q.getRecordStmt, err = db.PrepareContext(ctx, getRecord); err != nil { return nil, fmt.Errorf("error preparing query GetRecord: %w", err) } return &q, nil } func (q \*Queries) queryRow(ctx context.Context, stmt \*sql.Stmt, query string, args ...interface{}) \*sql.Row { switch { case stmt != nil && q.tx != nil: return q.tx.StmtContext(ctx, stmt).QueryRowContext(ctx, args...) case stmt != nil: return stmt.QueryRowContext(ctx, args...) default: return q.db.QueryRowContext(ctx, query, args...) } } type Queries struct { db DBTX tx \*sql.Tx getRecordStmt \*sql.Stmt } func (q \*Queries) WithTx(tx \*sql.Tx) \*Queries { return &Queries{ db: tx, tx: tx, getRecordStmt: q.getRecordStmt, } } const getRecord = `-- name: GetRecord :one SELECT id FROM records WHERE id = $1 ` func (q \*Queries) GetRecord(ctx context.Context, id int32) (int32, error) { row := q.queryRow(ctx, q.getRecordStmt, getRecord, id) err := row.Scan(&id) return id, err } ``` Using transactions[](#using-transactions "Permalink to this headline") ----------------------------------------------------------------------- In the code generated by sqlc, the `WithTx` method allows a `Queries` instance to be associated with a transaction. For example, with the following SQL structure: `schema.sql`: ``` CREATE TABLE records ( id SERIAL PRIMARY KEY, counter INT NOT NULL ); ``` `query.sql` ``` -- name: GetRecord :one SELECT * FROM records WHERE id = $1; -- name: UpdateRecord :exec UPDATE records SET counter = $2 WHERE id = $1; ``` And the generated code from sqlc in `db.go`: ``` package tutorial import ( "context" "database/sql" ) type DBTX interface { ExecContext(context.Context, string, ...interface{}) (sql.Result, error) PrepareContext(context.Context, string) (\*sql.Stmt, error) QueryContext(context.Context, string, ...interface{}) (\*sql.Rows, error) QueryRowContext(context.Context, string, ...interface{}) \*sql.Row } func New(db DBTX) \*Queries { return &Queries{db: db} } type Queries struct { db DBTX } func (q \*Queries) WithTx(tx \*sql.Tx) \*Queries { return &Queries{ db: tx, } } ``` You’d use it like this: ``` func bumpCounter(ctx context.Context, db \*sql.DB, queries \*tutorial.Queries, id int32) error { tx, err := db.Begin() if err != nil { return err } defer tx.Rollback() qtx := queries.WithTx(tx) r, err := qtx.GetRecord(ctx, id) if err != nil { return err } if err := qtx.UpdateRecord(ctx, tutorial.UpdateRecordParams{ ID: r.ID, Counter: r.Counter + 1, }); err != nil { return err } return tx.Commit() } ``` Naming parameters[](#naming-parameters "Permalink to this headline") --------------------------------------------------------------------- sqlc tried to generate good names for positional parameters, but sometimes it lacks enough context. The following SQL generates parameters with less than ideal names: ``` -- name: UpsertAuthorName :one UPDATE author SET name = CASE WHEN $1::bool THEN $2::text ELSE name END RETURNING *; ``` ``` type UpdateAuthorNameParams struct { Column1 bool `json:""` Column2\_2 string `json:"\_2"` } ``` In these cases, named parameters give you the control over field names on the Params struct. ``` -- name: UpsertAuthorName :one UPDATE author SET name = CASE WHEN sqlc.arg(set\_name)::bool THEN sqlc.arg(name)::text ELSE name END RETURNING \*; ``` ``` type UpdateAuthorNameParams struct { SetName bool `json:"set\_name"` Name string `json:"name"` } ``` If the `sqlc.arg()` syntax is too verbose for your taste, you can use the `@` operator as a shortcut. ``` -- name: UpsertAuthorName :one UPDATE author SET name = CASE WHEN @set\_name::bool THEN @name::text ELSE name END RETURNING \*; ``` ### Nullable parameters[](#nullable-parameters "Permalink to this headline") sqlc infers the nullability of any specified parameters, and often does exactly what you want. If you want finer control over the nullability of your parameters, you may use `sqlc.narg()` (**n**ullable arg) to override the default behavior. Using `sqlc.narg` tells sqlc to ignore whatever nullability it has inferred and generate a nullable parameter instead. There is no nullable equivalent of the `@` syntax. Here is an example that uses a single query to allow updating an author’s name, bio or both. ``` -- name: UpdateAuthor :one UPDATE author SET name = coalesce(sqlc.narg('name'), name), bio = coalesce(sqlc.narg('bio'), bio) WHERE id = sqlc.arg('id') RETURNING \*; ``` The following code is generated: ``` type UpdateAuthorParams struct { Name sql.NullString Bio sql.NullString ID int64 } ``` Modifying the database schema[](#modifying-the-database-schema "Permalink to this headline") --------------------------------------------------------------------------------------------- sqlc parses `CREATE TABLE` and `ALTER TABLE` statements in order to generate the necessary code. ``` CREATE TABLE authors ( id SERIAL PRIMARY KEY, birth\_year int NOT NULL ); ALTER TABLE authors ADD COLUMN bio text NOT NULL; ALTER TABLE authors DROP COLUMN birth\_year; ALTER TABLE authors RENAME TO writers; ``` ``` package db type Writer struct { ID int Bio string } ``` ### Handling SQL migrations[](#handling-sql-migrations "Permalink to this headline") sqlc does not perform database migrations for you. However, sqlc is able to differentiate between up and down migrations. sqlc ignores down migrations when parsing SQL files. sqlc supports parsing migrations from the following tools: * [dbmate](https://github.com/amacneil/dbmate) * [golang-migrate](https://github.com/golang-migrate/migrate) * [goose](https://github.com/pressly/goose) * [sql-migrate](https://github.com/rubenv/sql-migrate) * [tern](https://github.com/jackc/tern) #### goose[](#goose "Permalink to this headline") ``` -- +goose Up CREATE TABLE post ( id int NOT NULL, title text, body text, PRIMARY KEY(id) ); -- +goose Down DROP TABLE post; ``` ``` package db type Post struct { ID int Title sql.NullString Body sql.NullString } ``` #### sql-migrate[](#sql-migrate "Permalink to this headline") ``` -- +migrate Up -- SQL in section 'Up' is executed when this migration is applied CREATE TABLE people (id int); -- +migrate Down -- SQL section 'Down' is executed when this migration is rolled back DROP TABLE people; ``` ``` package db type People struct { ID int32 } ``` #### tern[](#tern "Permalink to this headline") ``` CREATE TABLE comment (id int NOT NULL, text text NOT NULL); ---- create above / drop below ---- DROP TABLE comment; ``` ``` package db type Comment struct { ID int32 Text string } ``` #### golang-migrate[](#golang-migrate "Permalink to this headline") **Warning:** [golang-migrate interprets](https://github.com/golang-migrate/migrate/blob/master/MIGRATIONS.md#migration-filename-format) migration filenames numerically. However, sqlc parses migration files in lexicographic order. If you choose to have sqlc enumerate your migration files, make sure their numeric ordering matches their lexicographic ordering to avoid unexpected behavior. This can be done by prepending enough zeroes to the migration filenames. This doesn’t work as intended. ``` 1\_initial.up.sql ... 9\_foo.up.sql # this migration file will be parsed BEFORE 9\_foo 10\_bar.up.sql ``` This worked as intended. ``` 001\_initial.up.sql ... 009\_foo.up.sql 010\_bar.up.sql ``` In `20060102.up.sql`: ``` CREATE TABLE post ( id int NOT NULL, title text, body text, PRIMARY KEY(id) ); ``` In `20060102.down.sql`: ``` DROP TABLE post; ``` ``` package db type Post struct { ID int Title sql.NullString Body sql.NullString } ``` #### dbmate[](#dbmate "Permalink to this headline") ``` -- migrate:up CREATE TABLE foo (bar INT NOT NULL); -- migrate:down DROP TABLE foo; ``` ``` package db type Foo struct { Bar int32 } ``` Configuring generated structs[](#configuring-generated-structs "Permalink to this headline") --------------------------------------------------------------------------------------------- ### Naming scheme[](#naming-scheme "Permalink to this headline") Structs generated from tables will attempt to use the singular form of a table name if the table name is pluralized. ``` CREATE TABLE authors ( id SERIAL PRIMARY KEY, name text NOT NULL ); ``` ``` package db // Struct names use the singular form of table names type Author struct { ID int Name string } ``` ### JSON tags[](#json-tags "Permalink to this headline") ``` CREATE TABLE authors ( id SERIAL PRIMARY KEY, created\_at timestamp NOT NULL ); ``` sqlc can generate structs with JSON tags. The JSON name for a field matches the column name in the database. ``` package db import ( "time" ) type Author struct { ID int `json:"id"` CreatedAt time.Time `json:"created\_at"` } ``` ### More control[](#more-control "Permalink to this headline") See the Type Overrides section of the Configuration File docs for fine-grained control over struct field types and tags. Uploading projects[](#uploading-projects "Permalink to this headline") ----------------------------------------------------------------------- *This feature requires signing up for [sqlc Cloud](https://app.sqlc.dev), which is currently in beta.* Uploading your project ensures that future releases of sqlc do not break your existing code. Similar to Rust’s [crater](https://github.com/rust-lang/crater) project, uploaded projects are tested against development releases of sqlc to verify correctness. ### Add configuration[](#add-configuration "Permalink to this headline") After creating a project, add the project ID to your sqlc configuration file. ``` version: "1" project: id: "<PROJECT-ID>" packages: [] ``` ``` { "version": "1", "project": { "id": "<PROJECT-ID>" }, "packages": [ ] } ``` You’ll also need to create an API token and make it available via the `SQLC\_AUTH\_TOKEN` environment variable. ``` export SQLC\_AUTH\_TOKEN=sqlc_xxxxxxxx ``` ### Dry run[](#dry-run "Permalink to this headline") You can see what’s included when uploading your project by using using the `--dry-run` flag: ``` sqlc upload --dry-run ``` The output will be the exact HTTP request sent by `sqlc`. ### Upload[](#upload "Permalink to this headline") Once you’re ready to upload, remove the `--dry-run` flag. ``` sqlc upload ``` By uploading your project, you’re making sqlc more stable and reliable. Thanks! CLI[](#cli "Permalink to this headline") ----------------------------------------- ``` Usage: sqlc [command] Available Commands: compile Statically check SQL for syntax and type errors completion Generate the autocompletion script for the specified shell generate Generate Go code from SQL help Help about any command init Create an empty sqlc.yaml settings file upload Upload the schema, queries, and configuration for this project version Print the sqlc version number Flags: -x, --experimental enable experimental features (default: false) -f, --file string specify an alternate config file (default: sqlc.yaml) -h, --help help for sqlc Use "sqlc [command] --help" for more information about a command. ``` Configuration[](#configuration "Permalink to this headline") ------------------------------------------------------------- The `sqlc` tool is configured via a `sqlc.yaml` or `sqlc.json` file. This file must be in the directory where the `sqlc` command is run. ### Version 2[](#version-2 "Permalink to this headline") ``` version: "2" sql: - schema: "postgresql/schema.sql" queries: "postgresql/query.sql" engine: "postgresql" gen: go: package: "authors" out: "postgresql" - schema: "mysql/schema.sql" queries: "mysql/query.sql" engine: "mysql" gen: go: package: "authors" out: "mysql" ``` #### sql[](#sql "Permalink to this headline") Each mapping in the `sql` collection has the following keys: * `engine`: + One of `postgresql`, `mysql` or `sqlite`. * `schema`: + Directory of SQL migrations or path to single SQL file; or a list of paths. * `queries`: + Directory of SQL queries or path to single SQL file; or a list of paths. * `codegen`: + A colleciton of mappings to configure code generators. See [codegen](#codegen) for the supported keys. * `gen`: + A mapping to configure built-in code generators. See [gen](#gen) for the supported keys. * `strict\_function\_checks` + If true, return an error if a called SQL function does not exist. Defaults to `false`. + #### codegen[](#codegen "Permalink to this headline") The `codegen` mapping supports the following keys: * `out`: + Output directory for generated code. * `plugin`: + The name of the plugin. Must be defined in the `plugins` collection. * `options`: + A mapping of plugin-specific options. ``` version: '2' plugins: - name: py wasm: url: https://github.com/tabbed/sqlc-gen-python/releases/download/v0.16.0-alpha/sqlc-gen-python.wasm sha256: 428476c7408fd4c032da4ec74e8a7344f4fa75e0f98a5a3302f238283b9b95f2 sql: - schema: "schema.sql" queries: "query.sql" engine: postgresql codegen: - out: src/authors plugin: py options: package: authors emit\_sync\_querier: true emit\_async\_querier: true query\_parameter\_limit: 5 ``` #### gen[](#gen "Permalink to this headline") The `gen` mapping supports the following keys: ##### go[](#go "Permalink to this headline") * `package`: + The package name to use for the generated code. Defaults to `out` basename. * `out`: + Output directory for generated code. * `sql\_package`: + Either `pgx/v4`, `pgx/v5` or `database/sql`. Defaults to `database/sql`. * `emit\_db\_tags`: + If true, add DB tags to generated structs. Defaults to `false`. * `emit\_prepared\_queries`: + If true, include support for prepared queries. Defaults to `false`. * `emit\_interface`: + If true, output a `Querier` interface in the generated package. Defaults to `false`. * `emit\_exact\_table\_names`: + If true, struct names will mirror table names. Otherwise, sqlc attempts to singularize plural table names. Defaults to `false`. * `emit\_empty\_slices`: + If true, slices returned by `:many` queries will be empty instead of `nil`. Defaults to `false`. * `emit\_exported\_queries`: + If true, autogenerated SQL statement can be exported to be accessed by another package. * `emit\_json\_tags`: + If true, add JSON tags to generated structs. Defaults to `false`. * `emit\_result\_struct\_pointers`: + If true, query results are returned as pointers to structs. Queries returning multiple results are returned as slices of pointers. Defaults to `false`. * `emit\_params\_struct\_pointers`: + If true, parameters are passed as pointers to structs. Defaults to `false`. * `emit\_methods\_with\_db\_argument`: + If true, generated methods will accept a DBTX argument instead of storing a DBTX on the `\*Queries` struct. Defaults to `false`. * `emit\_enum\_valid\_method`: + If true, generate a Valid method on enum types, indicating whether a string is a valid enum value. * `emit\_all\_enum\_values`: + If true, emit a function per enum type that returns all valid enum values. * `json\_tags\_case\_style`: + `camel` for camelCase, `pascal` for PascalCase, `snake` for snake\_case or `none` to use the column name in the DB. Defaults to `none`. * `output\_batch\_file\_name`: + Customize the name of the batch file. Defaults to `batch.go`. * `output\_db\_file\_name`: + Customize the name of the db file. Defaults to `db.go`. * `output\_models\_file\_name`: + Customize the name of the models file. Defaults to `models.go`. * `output\_querier\_file\_name`: + Customize the name of the querier file. Defaults to `querier.go`. * `output\_files\_suffix`: + If specified the suffix will be added to the name of the generated files. * `query\_parameter\_limit`: + Positional arguments that will be generated in Go functions (>= `1` or `-1`). To always emit a parameter struct, you would need to set it to `-1`. `0` is invalid. Defaults to `1`. `rename`: + Customize the name of generated struct fields. Explained in detail on the `Renaming fields` section. * `overrides`: + It is a collection of definitions that dictates which types are used to map a database types. Explained in detail on the `Type overriding` section. ###### Renaming fields[](#renaming-fields "Permalink to this headline") Struct field names are generated from column names using a simple algorithm: split the column name on underscores and capitalize the first letter of each part. ``` account -> Account spotify\_url -> SpotifyUrl app\_id -> AppID ``` If you’re not happy with a field’s generated name, use the `rename` mapping to pick a new name. The keys are column names and the values are the struct field name to use. ``` version: "2" sql: - schema: "postgresql/schema.sql" queries: "postgresql/query.sql" engine: "postgresql" gen: go: package: "authors" out: "postgresql" rename: spotify\_url: "SpotifyURL" ``` ###### Type overriding[](#type-overriding "Permalink to this headline") The default mapping of PostgreSQL/MySQL types to Go types only uses packages outside the standard library when it must. For example, the `uuid` PostgreSQL type is mapped to `github.com/google/uuid`. If a different Go package for UUIDs is required, specify the package in the `overrides` array. In this case, I’m going to use the `github.com/gofrs/uuid` instead. ``` version: "2" sql: - schema: "postgresql/schema.sql" queries: "postgresql/query.sql" engine: "postgresql" gen: go: package: "authors" out: "postgresql" overrides: - db\_type: "uuid" go\_type: "github.com/gofrs/uuid.UUID" ``` Each mapping of the `overrides` collection has the following keys: * `db\_type`: + The PostgreSQL or MySQL type to override. Find the full list of supported types in [postgresql\_type.go](https://github.com/kyleconroy/sqlc/blob/main/internal/codegen/golang/postgresql_type.go#L12) or [mysql\_type.go](https://github.com/kyleconroy/sqlc/blob/main/internal/codegen/golang/mysql_type.go#L12). Note that for Postgres you must use the pg\_catalog prefixed names where available. Can’t be used if the `column` key is defined. * `column` + In case the type overriding should be done on specific a column of a table instead of a type. `column` should be of the form `table.column` but you can be even more specific by specifying `schema.table.column` or `catalog.schema.table.column`. Can’t be used if the `db\_type` key is defined. * `go\_type`: + A fully qualified name to a Go type to use in the generated code. * `go\_struct\_tag`: + A reflect-style struct tag to use in the generated code, e.g. `a:"b" x:"y,z"`. If you want general json/db tags for all fields, use `emit\_db\_tags` and/or `emit\_json\_tags` instead. * `nullable`: + If true, use this type when a column is nullable. Defaults to `false`. For more complicated import paths, the `go\_type` can also be an object. ``` version: "2" sql: - schema: "postgresql/schema.sql" queries: "postgresql/query.sql" engine: "postgresql" gen: go: package: "authors" out: "postgresql" overrides: - db\_type: "uuid" go\_type: import: "a/b/v2" package: "b" type: "MyType" pointer: true ``` When generating code, entries using the `column` key will always have preference over entries using the `db\_type` key in order to generate the struct. ##### kotlin[](#kotlin "Permalink to this headline") * `package`: + The package name to use for the generated code. * `out`: + Output directory for generated code. * `emit\_exact\_table\_names`: + If true, use the exact table name for generated models. Otherwise, guess a singular form. Defaults to `false`. ##### python[](#python "Permalink to this headline") * `package`: + The package name to use for the generated code. * `out`: + Output directory for generated code. * `emit\_exact\_table\_names`: + If true, use the exact table name for generated models. Otherwise, guess a singular form. Defaults to `false`. * `emit\_sync\_querier`: + If true, generate a class with synchronous methods. Defaults to `false`. * `emit\_async\_querier`: + If true, generate a class with asynchronous methods. Defaults to `false`. * `emit\_pydantic\_models`: + If true, generate classes that inherit from `pydantic.BaseModel`. Otherwise, define classes using the `dataclass` decorator. Defaults to `false`. ##### json[](#json "Permalink to this headline") * `out`: + Output directory for the generated JSON. * `filename`: + Filename for the generated JSON document. Defaults to `codegen\_request.json`. * `indent`: + Indent string to use in the JSON document. Defaults to . #### plugins[](#plugins "Permalink to this headline") Each mapping in the `plugins` collection has the following keys: * `name`: + The name of this plugin. Required * `process`: A mapping with a single `cmd` key + `cmd`: - The executable to call when using this plugin * `wasm`: A mapping with a two keys `url` and `sha256` + `url`: - The URL to fetch the WASM file. Supports the `https://` or `file://` schemes. + `sha256` - The SHA256 checksum for the downloaded file. ``` version: 2 plugins: - name: "py" wasm: url: "https://github.com/tabbed/sqlc-gen-python/releases/download/v0.16.0-alpha/sqlc-gen-python.wasm" sha256: "428476c7408fd4c032da4ec74e8a7344f4fa75e0f98a5a3302f238283b9b95f2" - name: "js" process: cmd: "sqlc-gen-json" ``` #### global overrides[](#global-overrides "Permalink to this headline") Sometimes, the same configuration must be done across various specfications of code generation. Then a global definition for type overriding and field renaming can be done using the `overrides` mapping the following manner: ``` version: "2" overrides: go: rename: id: "Identifier" overrides: - db\_type: "timestamptz" nullable: true engine: "postgresql" go\_type: import: "gopkg.in/guregu/null.v4" package: "null" type: "Time" sql: - schema: "postgresql/schema.sql" queries: "postgresql/query.sql" engine: "postgresql" gen: go: package: "authors" out: "postgresql" - schema: "mysql/schema.sql" queries: "mysql/query.sql" engine: "mysql" gen: go: package: "authors" out: "mysql ``` With the previous configuration, whenever a struct field is generated from a table column that is called `id`, it will generated as `Identifier`. Also, whenever there is a nullable `timestamp with time zone` column in a Postgres table, it will be generated as `null.Time`. Note that, the mapping for global type overrides has a field called `engine` that is absent in the regular type overrides. This field is only used when there are multiple definitions using multiple engines. Otherwise, the value of the `engine` key will be defaulted to the engine that is currently being used. Currently, type overrides and field renaming, both global and regular, are only fully supported in Go. ### Version 1[](#version-1 "Permalink to this headline") ``` version: "1" packages: - name: "db" path: "internal/db" queries: "./sql/query/" schema: "./sql/schema/" engine: "postgresql" emit\_prepared\_queries: true emit\_interface: false emit\_exact\_table\_names: false emit\_empty\_slices: false emit\_exported\_queries: false emit\_json\_tags: true emit\_result\_struct\_pointers: false emit\_params\_struct\_pointers: false emit\_methods\_with\_db\_argument: false emit\_pointers\_for\_null\_types: false emit\_enum\_valid\_method: false emit\_all\_enum\_values: false json\_tags\_case\_style: "camel" output\_batch\_file\_name: "batch.go" output\_db\_file\_name: "db.go" output\_models\_file\_name: "models.go" output\_querier\_file\_name: "querier.go" ``` #### packages[](#packages "Permalink to this headline") Each mapping in the `packages` collection has the following keys: * `name`: + The package name to use for the generated code. Defaults to `path` basename. * `path`: + Output directory for generated code. * `queries`: + Directory of SQL queries or path to single SQL file; or a list of paths. * `schema`: + Directory of SQL migrations or path to single SQL file; or a list of paths. * `engine`: + Either `postgresql` or `mysql`. Defaults to `postgresql`. * `sql\_package`: + Either `pgx/v4`, `pgx/v5` or `database/sql`. Defaults to `database/sql`. * `emit\_db\_tags`: + If true, add DB tags to generated structs. Defaults to `false`. * `emit\_prepared\_queries`: + If true, include support for prepared queries. Defaults to `false`. * `emit\_interface`: + If true, output a `Querier` interface in the generated package. Defaults to `false`. * `emit\_exact\_table\_names`: + If true, struct names will mirror table names. Otherwise, sqlc attempts to singularize plural table names. Defaults to `false`. * `emit\_empty\_slices`: + If true, slices returned by `:many` queries will be empty instead of `nil`. Defaults to `false`. * `emit\_exported\_queries`: + If true, autogenerated SQL statement can be exported to be accessed by another package. * `emit\_json\_tags`: + If true, add JSON tags to generated structs. Defaults to `false`. * `emit\_result\_struct\_pointers`: + If true, query results are returned as pointers to structs. Queries returning multiple results are returned as slices of pointers. Defaults to `false`. * `emit\_params\_struct\_pointers`: + If true, parameters are passed as pointers to structs. Defaults to `false`. * `emit\_methods\_with\_db\_argument`: + If true, generated methods will accept a DBTX argument instead of storing a DBTX on the `\*Queries` struct. Defaults to `false`. * `emit\_pointers\_for\_null\_types`: + If true and `sql\_package` is set to `pgx/v4`, generated types for nullable columns are emitted as pointers (ie. `\*string`) instead of `database/sql` null types (ie. `NullString`). Defaults to `false`. * `emit\_enum\_valid\_method`: + If true, generate a Valid method on enum types, indicating whether a string is a valid enum value. * `emit\_all\_enum\_values`: + If true, emit a function per enum type that returns all valid enum values. * `json\_tags\_case\_style`: + `camel` for camelCase, `pascal` for PascalCase, `snake` for snake\_case or `none` to use the column name in the DB. Defaults to `none`. * `output\_batch\_file\_name`: + Customize the name of the batch file. Defaults to `batch.go`. * `output\_db\_file\_name`: + Customize the name of the db file. Defaults to `db.go`. * `output\_models\_file\_name`: + Customize the name of the models file. Defaults to `models.go`. * `output\_querier\_file\_name`: + Customize the name of the querier file. Defaults to `querier.go`. * `output\_files\_suffix`: + If specified the suffix will be added to the name of the generated files. * `query\_parameter\_limit`: + Positional arguments that will be generated in Go functions (`>= 0`). To always emit a parameter struct, you would need to set it to `0`. Defaults to `1`. #### overrides[](#overrides "Permalink to this headline") The default mapping of PostgreSQL/MySQL types to Go types only uses packages outside the standard library when it must. For example, the `uuid` PostgreSQL type is mapped to `github.com/google/uuid`. If a different Go package for UUIDs is required, specify the package in the `overrides` array. In this case, I’m going to use the `github.com/gofrs/uuid` instead. ``` version: "1" packages: [...] overrides: - go\_type: "github.com/gofrs/uuid.UUID" db\_type: "uuid" ``` Each override document has the following keys: * `db\_type`: + The PostgreSQL or MySQL type to override. Find the full list of supported types in [postgresql\_type.go](https://github.com/kyleconroy/sqlc/blob/main/internal/codegen/golang/postgresql_type.go#L12) or [mysql\_type.go](https://github.com/kyleconroy/sqlc/blob/main/internal/codegen/golang/mysql_type.go#L12). Note that for Postgres you must use the pg\_catalog prefixed names where available. * `go\_type`: + A fully qualified name to a Go type to use in the generated code. * `go\_struct\_tag`: + A reflect-style struct tag to use in the generated code, e.g. `a:"b" x:"y,z"`. If you want general json/db tags for all fields, use `emit\_db\_tags` and/or `emit\_json\_tags` instead. * `nullable`: + If true, use this type when a column is nullable. Defaults to `false`. For more complicated import paths, the `go\_type` can also be an object. ``` version: "1" packages: [...] overrides: - db\_type: "uuid" go\_type: import: "a/b/v2" package: "b" type: "MyType" ``` ##### Per-Column Type Overrides[](#per-column-type-overrides "Permalink to this headline") Sometimes you would like to override the Go type used in model or query generation for a specific field of a table and not on a type basis as described in the previous section. This may be configured by specifying the `column` property in the override definition. `column` should be of the form `table.column` but you can be even more specific by specifying `schema.table.column` or `catalog.schema.table.column`. ``` version: "1" packages: [...] overrides: - column: "authors.id" go\_type: "github.com/segmentio/ksuid.KSUID" ``` ##### Package Level Overrides[](#package-level-overrides "Permalink to this headline") Overrides can be configured globally, as demonstrated in the previous sections, or they can be configured on a per-package which scopes the override behavior to just a single package: ``` version: "1" packages: - overrides: [...] ``` #### rename[](#rename "Permalink to this headline") Struct field names are generated from column names using a simple algorithm: split the column name on underscores and capitalize the first letter of each part. ``` account -> Account spotify\_url -> SpotifyUrl app\_id -> AppID ``` If you’re not happy with a field’s generated name, use the `rename` mapping to pick a new name. The keys are column names and the values are the struct field name to use. ``` version: "1" packages: [...] rename: spotify\_url: "SpotifyURL" ``` Datatypes[](#datatypes "Permalink to this headline") ----------------------------------------------------- ### Arrays[](#arrays "Permalink to this headline") PostgreSQL [arrays](https://www.postgresql.org/docs/current/arrays.html) are materialized as Go slices. Currently, the `pgx/v5` sql package only supports multidimensional arrays. ``` CREATE TABLE places ( name text not null, tags text[] ); ``` ``` package db type Place struct { Name string Tags []string } ``` ### Dates and Time[](#dates-and-time "Permalink to this headline") All PostgreSQL time and date types are returned as `time.Time` structs. For null time or date values, the `NullTime` type from `database/sql` is used. The `pgx/v5` sql package uses the appropriate pgx types. ``` CREATE TABLE authors ( id SERIAL PRIMARY KEY, created\_at timestamp NOT NULL DEFAULT NOW(), updated\_at timestamp ); ``` ``` package db import ( "database/sql" "time" ) type Author struct { ID int CreatedAt time.Time UpdatedAt sql.NullTime } ``` ### Enums[](#enums "Permalink to this headline") PostgreSQL [enums](https://www.postgresql.org/docs/current/arrays.html) are mapped to an aliased string type. ``` CREATE TYPE status AS ENUM ( 'open', 'closed' ); CREATE TABLE stores ( name text PRIMARY KEY, status status NOT NULL ); ``` ``` package db type Status string const ( StatusOpen Status = "open" StatusClosed Status = "closed" ) type Store struct { Name string Status Status } ``` ### Null[](#null "Permalink to this headline") For structs, null values are represented using the appropriate type from the `database/sql` or `pgx` package. ``` CREATE TABLE authors ( id SERIAL PRIMARY KEY, name text NOT NULL, bio text ); ``` ``` package db import ( "database/sql" ) type Author struct { ID int Name string Bio sql.NullString } ``` ### UUIDs[](#uuids "Permalink to this headline") The Go standard library does not come with a `uuid` package. For UUID support, sqlc uses the excellent `github.com/google/uuid` package. ``` CREATE TABLE records ( id uuid PRIMARY KEY ); ``` ``` package db import ( "github.com/google/uuid" ) type Author struct { ID uuid.UUID } ``` For MySQL, there is no native `uuid` data type. When using `UUID\_TO\_BIN` to store a `UUID()`, the underlying field type is `BINARY(16)` which by default sqlc would interpret this to `sql.NullString`. To have sqlc automatically convert these fields to a `uuid.UUID` type, use an overide on the column storing the `uuid`. ``` { "overrides": [ { "column": "\*.uuid", "go\_type": "github.com/google/uuid.UUID" } ] } ``` ### JSON[](#json "Permalink to this headline") By default, sqlc will generate the `[]byte`, `pgtype.JSON` or `json.RawMessage` for JSON column type. But if you use the `pgx/v5` sql package then you can specify a some struct instead of default type. The `pgx` implementation will marshall/unmarshall the struct automatically. ``` package dto type BookData struct { Genres []string `json:"genres"` Title string `json:"title"` Published bool `json:"published"` } ``` ``` CREATE TABLE books ( data jsonb ); ``` ``` { "overrides": [ { "column": "books.data", "go\_type": { "import":"example/db", "package": "dto", "type":"BookData" } } ] } ``` ``` package db import ( "example.com/db/dto" ) type Book struct { Data \*dto.BookData } ``` Query annotations[](#query-annotations "Permalink to this headline") --------------------------------------------------------------------- sqlc requires each query to have a small comment indicating the name and command. The format of this comment is as follows: ``` -- name: <name> <command> ``` ### `:exec`[](#exec "Permalink to this headline") The generated method will return the error from [ExecContext](https://golang.org/pkg/database/sql/#DB.ExecContext). ``` -- name: DeleteAuthor :exec DELETE FROM authors WHERE id = $1; ``` ``` func (q \*Queries) DeleteAuthor(ctx context.Context, id int64) error { \_, err := q.db.ExecContext(ctx, deleteAuthor, id) return err } ``` ### `:execresult`[](#execresult "Permalink to this headline") The generated method will return the [sql.Result](https://golang.org/pkg/database/sql/#Result) returned by [ExecContext](https://golang.org/pkg/database/sql/#DB.ExecContext). ``` -- name: DeleteAllAuthors :execresult DELETE FROM authors; ``` ``` func (q \*Queries) DeleteAllAuthors(ctx context.Context) (sql.Result, error) { return q.db.ExecContext(ctx, deleteAllAuthors) } ``` ### `:execrows`[](#execrows "Permalink to this headline") The generated method will return the number of affected rows from the [result](https://golang.org/pkg/database/sql/#Result) returned by [ExecContext](https://golang.org/pkg/database/sql/#DB.ExecContext). ``` -- name: DeleteAllAuthors :execrows DELETE FROM authors; ``` ``` func (q \*Queries) DeleteAllAuthors(ctx context.Context) (int64, error) { \_, err := q.db.ExecContext(ctx, deleteAllAuthors) // ... } ``` ### `:execlastid`[](#execlastid "Permalink to this headline") The generated method will return the number generated by the database from the [result](https://golang.org/pkg/database/sql/#Result) returned by [ExecContext](https://golang.org/pkg/database/sql/#DB.ExecContext). ``` -- name: InsertAuthor :execlastid INSERT INTO authors (name) VALUES (?); ``` ``` func (q \*Queries) InsertAuthor(ctx context.Context, name string) (int64, error) { \_, err := q.db.ExecContext(ctx, insertAuthor, name) // ... } ``` ### `:many`[](#many "Permalink to this headline") The generated method will return a slice of records via [QueryContext](https://golang.org/pkg/database/sql/#DB.QueryContext). ``` -- name: ListAuthors :many SELECT \* FROM authors ORDER BY name; ``` ``` func (q \*Queries) ListAuthors(ctx context.Context) ([]Author, error) { rows, err := q.db.QueryContext(ctx, listAuthors) // ... } ``` ### `:one`[](#one "Permalink to this headline") The generated method will return a single record via [QueryRowContext](https://golang.org/pkg/database/sql/#DB.QueryRowContext). ``` -- name: GetAuthor :one SELECT * FROM authors WHERE id = $1 LIMIT 1; ``` ``` func (q \*Queries) GetAuthor(ctx context.Context, id int64) (Author, error) { row := q.db.QueryRowContext(ctx, getAuthor, id) // ... } ``` ### `:batchexec`[](#batchexec "Permalink to this headline") **NOTE: This command only works with PostgreSQL using the `pgx/v4` and `pgx/v5` drivers and outputting Go code.** The generated method will return a batch object. The batch object will have the following methods: * `Exec`, that takes a `func(int, error)` parameter, * `Close`, to close the batch operation early. ``` -- name: DeleteBook :batchexec DELETE FROM books WHERE book_id = $1; ``` ``` type DeleteBookBatchResults struct { br pgx.BatchResults ind int } func (q \*Queries) DeleteBook(ctx context.Context, bookID []int32) \*DeleteBookBatchResults { //... } func (b \*DeleteBookBatchResults) Exec(f func(int, error)) { //... } func (b \*DeleteBookBatchResults) Close() error { //... } ``` ### `:batchmany`[](#batchmany "Permalink to this headline") **NOTE: This command only works with PostgreSQL using the `pgx/v4` and `pgx/v5` drivers and outputting Go code.** The generated method will return a batch object. The batch object will have the following methods: * `Query`, that takes a `func(int, []T, error)` parameter, where `T` is your query’s return type * `Close`, to close the batch operation early. ``` -- name: BooksByTitleYear :batchmany SELECT * FROM books WHERE title = $1 AND year = $2; ``` ``` type BooksByTitleYearBatchResults struct { br pgx.BatchResults ind int } type BooksByTitleYearParams struct { Title string `json:"title"` Year int32 `json:"year"` } func (q \*Queries) BooksByTitleYear(ctx context.Context, arg []BooksByTitleYearParams) \*BooksByTitleYearBatchResults { //... } func (b \*BooksByTitleYearBatchResults) Query(f func(int, []Book, error)) { //... } func (b \*BooksByTitleYearBatchResults) Close() error { //... } ``` ### `:batchone`[](#batchone "Permalink to this headline") **NOTE: This command only works with PostgreSQL using the `pgx/v4` and `pgx/v5` drivers and outputting Go code.** The generated method will return a batch object. The batch object will have the following methods: * `QueryRow`, that takes a `func(int, T, error)` parameter, where `T` is your query’s return type * `Close`, to close the batch operation early. ``` -- name: CreateBook :batchone INSERT INTO books ( author_id, isbn ) VALUES ( $1, $2 ) RETURNING book_id, author_id, isbn ``` ``` type CreateBookBatchResults struct { br pgx.BatchResults ind int } type CreateBookParams struct { AuthorID int32 `json:"author\_id"` Isbn string `json:"isbn"` } func (q \*Queries) CreateBook(ctx context.Context, arg []CreateBookParams) \*CreateBookBatchResults { //... } func (b \*CreateBookBatchResults) QueryRow(f func(int, Book, error)) { //... } func (b \*CreateBookBatchResults) Close() error { //... } ``` Database and language support[](#database-and-language-support "Permalink to this headline") --------------------------------------------------------------------------------------------- | Language | Plugin | MySQL | PostgreSQL | SQLite | | --- | --- | --- | --- | --- | | Go | (built-in) | Stable | Stable | Beta | | Kotlin | sqlc-gen-kotlin | Beta | Beta | Not implemented | | Python | sqlc-gen-python | Beta | Beta | Not implemented | ### Future Language Support[](#future-language-support "Permalink to this headline") * [C#](https://github.com/kyleconroy/sqlc/issues/373) * [TypeScript](https://github.com/kyleconroy/sqlc/issues/296) Environment variables[](#environment-variables "Permalink to this headline") ----------------------------------------------------------------------------- ### SQLCCACHE[](#sqlccache "Permalink to this headline") The `SQLCCACHE` environment variable dictates where `sqlc` will store cached WASM-based plugins and modules. By default `sqlc` follows the [XDG Base Directory Specification](https://specifications.freedesktop.org/basedir-spec/basedir-spec-latest.html). ### SQLCDEBUG[](#sqlcdebug "Permalink to this headline") The `SQLCDEBUG` variable controls debugging variables within the runtime. It is a comma-separated list of name=val pairs settings. #### dumpast[](#dumpast "Permalink to this headline") The `dumpast` command shows the SQL AST that was generated by the parser. Note that this is the generic SQL AST, not the engine-specific SQL AST. ``` SQLCDEBUG=dumpast=1 ``` ``` ([]interface {}) (len=1 cap=1) { (\*catalog.Catalog)(0xc0004f48c0)({ Comment: (string) "", DefaultSchema: (string) (len=6) "public", Name: (string) "", Schemas: ([]\*catalog.Schema) (len=3 cap=4) { (\*catalog.Schema)(0xc0004f4930)({ Name: (string) (len=6) "public", Tables: ([]\*catalog.Table) (len=1 cap=1) { (\*catalog.Table)(0xc00052ff20)({ Rel: (\*ast.TableName)(0xc00052fda0)({ Catalog: (string) "", Schema: (string) "", Name: (string) (len=7) "authors" }), ``` #### dumpcatalog[](#dumpcatalog "Permalink to this headline") The `dumpcatalog` command outputs the entire catalog. If you’re using MySQL or PostgreSQL, this can be a bit overwhelming. Expect this output to change in future versions. ``` SQLCDEBUG=dumpcatalog=1 ``` ``` ([]interface {}) (len=1 cap=1) { (\*catalog.Catalog)(0xc00050d1f0)({ Comment: (string) "", DefaultSchema: (string) (len=6) "public", Name: (string) "", Schemas: ([]\*catalog.Schema) (len=3 cap=4) { (\*catalog.Schema)(0xc00050d260)({ Name: (string) (len=6) "public", Tables: ([]\*catalog.Table) (len=1 cap=1) { (\*catalog.Table)(0xc0000c0840)({ Rel: (\*ast.TableName)(0xc0000c06c0)({ Catalog: (string) "", Schema: (string) "", Name: (string) (len=7) "authors" }), ``` #### trace[](#trace "Permalink to this headline") The `trace` command is helpful for tracking down performance issues. `SQLCDEBUG=trace=1` By default, the trace output is written to `trace.out` in the current working directory. You can configure a different path if needed. `SQLCDEBUG=trace=name.out` View the execution trace using the Go `trace` tool. ``` go tool trace trace.out ``` There’s a ton of different views for the trace output, but here’s an example log showing the execution time for each package. ``` 0.000043897 . 1 task sqlc (id 1, parent 0) created 0.000144923 . 101026 1 region generate started (duration: 47.619781ms) 0.001048975 . 904052 1 region package started (duration: 14.588456ms) 0.001054616 . 5641 1 name=authors dir=/Users/kyle/projects/sqlc/examples/python language=python 0.001071257 . 16641 1 region parse started (duration: 7.966549ms) 0.009043960 . 7972703 1 region codegen started (duration: 6.587086ms) 0.009171704 . 127744 1 new goroutine 35: text/template/parse.lex·dwrap·1 0.010361654 . 1189950 1 new goroutine 36: text/template/parse.lex·dwrap·1 0.015641815 . 5280161 1 region package started (duration: 10.904938ms) 0.015644943 . 3128 1 name=booktest dir=/Users/kyle/projects/sqlc/examples/python language=python 0.015647431 . 2488 1 region parse started (duration: 4.207749ms) 0.019860308 . 4212877 1 region codegen started (duration: 6.681624ms) 0.020028488 . 168180 1 new goroutine 37: text/template/parse.lex·dwrap·1 0.021020310 . 991822 1 new goroutine 8: text/template/parse.lex·dwrap·1 0.026551163 . 5530853 1 region package started (duration: 9.217294ms) 0.026554368 . 3205 1 name=jets dir=/Users/kyle/projects/sqlc/examples/python language=python 0.026556804 . 2436 1 region parse started (duration: 3.491005ms) 0.030051911 . 3495107 1 region codegen started (duration: 5.711931ms) 0.030213937 . 162026 1 new goroutine 20: text/template/parse.lex·dwrap·1 0.031099938 . 886001 1 new goroutine 38: text/template/parse.lex·dwrap·1 0.035772637 . 4672699 1 region package started (duration: 10.267039ms) 0.035775688 . 3051 1 name=ondeck dir=/Users/kyle/projects/sqlc/examples/python language=python 0.035778150 . 2462 1 region parse started (duration: 4.094518ms) 0.039877181 . 4099031 1 region codegen started (duration: 6.156341ms) 0.040010771 . 133590 1 new goroutine 39: text/template/parse.lex·dwrap·1 0.040894567 . 883796 1 new goroutine 40: text/template/parse.lex·dwrap·1 0.046042779 . 5148212 1 region writefiles started (duration: 1.718259ms) 0.047767781 . 1725002 1 task end ``` #### processplugins[](#processplugins "Permalink to this headline") Setting this value to `0` disables process-based plugins. If a process-based plugin is declared in the configuration file, running any `sqlc` command will return an error. `SQLCDEBUG=processplugins=0` ### SQLCTMPDIR[](#sqlctmpdir "Permalink to this headline") If specified, use the given directory as the base for temporary folders. Only applies when using WASM-based codegen plugins. When not specified, this defaults to passing an empty string to [`os.MkdirTemp`](https://pkg.go.dev/os#MkdirTemp). Changelog[](#changelog "Permalink to this headline") ----------------------------------------------------- All notable changes to this project will be documented in this file. ### [1.18.0](https://github.com/kyleconroy/sqlc/releases/tag/1.18.0)[](#id1 "Permalink to this headline") Released 2023-04-27 #### Release notes[](#release-notes "Permalink to this headline") ##### Remote code generation[](#remote-code-generation "Permalink to this headline") *Developed by [@andrewmbenton](https://github.com/andrewmbenton)* At its core, sqlc is powered by SQL engines, which include parsers, formatters, analyzers and more. While our goal is to support each engine on each operating system, it’s not always possible. For example, the PostgreSQL engine does not work on Windows. To bridge that gap, we’re announcing remote code generation, currently in private alpha. To join the private alpha, [sign up for the waitlist](https://docs.google.com/forms/d/e/1FAIpQLScDWrGtTgZWKt3mdlF5R2XCX6tL1pMkB4yuZx5yq684tTNN1Q/viewform?usp=sf_link). To configure remote generation, configure a `cloud` block in `sqlc.json`. ``` { "version": "2", "cloud": { "organization": "<org-id>", "project": "<project-id>", }, ... } ``` You’ll also need to the `SQLC\_AUTH\_TOKEN` environment variable. ``` export SQLC\_AUTH\_TOKEN=<token> ``` When the cloud configuration exists, `sqlc generate` will default to remote generation. If you’d like to generate code locally, pass the `--no-remote` option. ``` sqlc generate --no-remote ``` Remote generation is off by default and requires an opt-in to use. ##### sqlc.embed[](#sqlc-embed "Permalink to this headline") *Developed by [@nickjackson](https://github.com/nickjackson)* Embedding allows you to reuse existing model structs in more queries, resulting in less manual serilization work. First, imagine we have the following schema with students and test scores. ``` CREATE TABLE students ( id bigserial PRIMARY KEY, name text, age integer ) CREATE TABLE test\_scores ( student\_id bigint, score integer, grade text ) ``` We want to select the student record and the highest score they got on a test. Here’s how we’d usually do that: ``` -- name: HighScore :many WITH high\_scores AS ( SELECT student\_id, max(score) as high\_score FROM test\_scores GROUP BY 1 ) SELECT students.\*, high\_score::integer FROM students JOIN high\_scores ON high\_scores.student\_id = students.id; ``` When using Go, sqlc will produce a struct like this: ``` type HighScoreRow struct { ID int64 Name sql.NullString Age sql.NullInt32 HighScore int32 } ``` With embedding, the struct will contain a model for the table instead of a flattened list of columns. ``` -- name: HighScoreEmbed :many WITH high\_scores AS ( SELECT student\_id, max(score) as high\_score FROM test\_scores GROUP BY 1 ) SELECT sqlc.embed(students), high\_score::integer FROM students JOIN high\_scores ON high\_scores.student\_id = students.id; ``` ``` type HighScoreRow struct { Student Student HighScore int32 } ``` ##### sqlc.slice[](#sqlc-slice "Permalink to this headline") *Developed by Paul Cameron and Jille Timmermans* The MySQL Go driver does not support passing slices to the IN operator. The `sqlc.slice` function generates a dynamic query at runtime with the correct number of parameters. ``` /\* name: SelectStudents :many \*/ SELECT \* FROM students WHERE age IN (sqlc.slice("ages")) ``` ``` func (q \*Queries) SelectStudents(ctx context.Context, arges []int32) ([]Student, error) { ``` This feature is only supported in MySQL and cannot be used with prepared queries. ##### Batch operation improvements[](#batch-operation-improvements "Permalink to this headline") When using batches with pgx, the error returned when a batch is closed is exported by the generated package. This change allows for cleaner error handling using `errors.Is`. ``` errors.Is(err, generated\_package.ErrBatchAlreadyClosed) ``` Previously, you would have had to check match on the error message itself. ``` err.Error() == "batch already closed" ``` The generated code for batch operations always lived in `batch.go`. This file name can now be configured via the `output\_batch\_file\_name` configuration option. ##### Configurable query parameter limits for Go[](#configurable-query-parameter-limits-for-go "Permalink to this headline") By default, sqlc will limit Go functions to a single parameter. If a query includes more than one parameter, the generated method will use an argument struct instead of positional arguments. This behavior can now be changed via the `query\_parameter\_limit` configuration option. If set to `0`, every genreated method will use a argument struct. #### Changes[](#changes "Permalink to this headline") ##### Bug Fixes[](#bug-fixes "Permalink to this headline") * Prevent variable redeclaration in single param conflict for pgx (#2058) * Retrieve Larg/Rarg join query after inner join (#2051) * Rename argument when conflicted to imported package (#2048) * Pgx closed batch return pointer if need #1959 (#1960) * Correct singularization of “waves” (#2194) * Honor Package level renames in v2 yaml config (#2001) * (mysql) Prevent UPDATE … JOIN panic #1590 (#2154) * Mysql delete join panic (#2197) * Missing import with pointer overrides, solves #2168 #2125 (#2217) ##### Documentation[](#documentation "Permalink to this headline") * (config.md) Add `sqlite` as engine option (#2164) * Add first pass at pgx documentation (#2174) * Add missed configuration option (#2188) * `specifies parameter ":one" without containing a RETURNING clause` (#2173) ##### Features[](#features "Permalink to this headline") * Add `sqlc.embed` to allow model re-use (#1615) * (Go) Add query\_parameter\_limit conf to codegen (#1558) * Add remote execution for codegen (#2214) ##### Testing[](#testing "Permalink to this headline") * Skip tests if required plugins are missing (#2104) * Add tests for reanme fix in v2 (#2196) * Regenerate batch output for filename tests * Remove remote test (#2232) * Regenerate test output ##### Bin/sqlc[](#bin-sqlc "Permalink to this headline") * Add SQLCTMPDIR environment variable (#2189) ##### Build[](#build "Permalink to this headline") * (deps) Bump github.com/antlr/antlr4/runtime/Go/antlr (#2109) * (deps) Bump github.com/jackc/pgx/v4 from 4.18.0 to 4.18.1 (#2119) * (deps) Bump golang from 1.20.1 to 1.20.2 (#2135) * (deps) Bump google.golang.org/protobuf from 1.28.1 to 1.29.0 (#2137) * (deps) Bump google.golang.org/protobuf from 1.29.0 to 1.29.1 (#2143) * (deps) Bump golang from 1.20.2 to 1.20.3 (#2192) * (deps) Bump actions/setup-go from 3 to 4 (#2150) * (deps) Bump google.golang.org/protobuf from 1.29.1 to 1.30.0 (#2151) * (deps) Bump github.com/spf13/cobra from 1.6.1 to 1.7.0 (#2193) * (deps) Bump github.com/lib/pq from 1.10.7 to 1.10.8 (#2211) * (deps) Bump github.com/lib/pq from 1.10.8 to 1.10.9 (#2229) * (deps) Bump github.com/go-sql-driver/mysql from 1.7.0 to 1.7.1 (#2228) ##### Cmd/sqlc[](#cmd-sqlc "Permalink to this headline") * Remove –experimental flag (#2170) * Add option to disable process-based plugins (#2180) * Bump version to v1.18.0 ##### Codegen[](#codegen "Permalink to this headline") * Correctly generate CopyFrom columns for single-column copyfroms (#2185) ##### Config[](#config "Permalink to this headline") * Add top-level cloud configuration (#2204) ##### Engine/postgres[](#engine-postgres "Permalink to this headline") * Upgrade to pg\_query\_go/v4 (#2114) ##### Ext/wasm[](#ext-wasm "Permalink to this headline") * Check exit code on returned error (#2223) ##### Parser[](#parser "Permalink to this headline") * Generate correct types for `SELECT NOT EXISTS` (#1972) ##### Sqlite[](#sqlite "Permalink to this headline") * Add support for CREATE TABLE … STRICT (#2175) ##### Wasm[](#wasm "Permalink to this headline") * Upgrade to wasmtime v8.0.0 (#2222) ### [1.17.2](https://github.com/kyleconroy/sqlc/releases/tag/1.17.2)[](#id2 "Permalink to this headline") Released 2023-02-22 #### Bug Fixes[](#id3 "Permalink to this headline") * Fix build on Windows (#2102) ### [1.17.1](https://github.com/kyleconroy/sqlc/releases/tag/1.17.1)[](#id4 "Permalink to this headline") Released 2023-02-22 #### Bug Fixes[](#id5 "Permalink to this headline") * Prefer to use []T over pgype.Array[T] (#2090) * Revert changes to Dockerfile (#2091) * Do not throw error when IF NOT EXISTS is used on ADD COLUMN (#2092) #### MySQL[](#mysql "Permalink to this headline") * Add `float` support to MySQL (#2097) #### Build[](#id6 "Permalink to this headline") * (deps) Bump golang from 1.20.0 to 1.20.1 (#2082) ### [1.17.0](https://github.com/kyleconroy/sqlc/releases/tag/1.17.0)[](#id7 "Permalink to this headline") Released 2023-02-13 #### Bug Fixes[](#id8 "Permalink to this headline") * Initialize generated code outside function (#1850) * (engine/mysql) Take into account column’s charset to distinguish text/blob, (var)char/(var)binary (#776) (#1895) * The enum Value method returns correct type (#1996) * Documentation for Inserting Rows (#2034) * Add import statements even if only pointer types exist (#2046) * Search from Rexpr if not found from Lexpr (#2056) #### Documentation[](#id9 "Permalink to this headline") * Change ENTRYPOINT to CMD (#1943) * Update samples for HOW-TO GUIDES (#1953) #### Features[](#id10 "Permalink to this headline") * Add the diff command (#1963) #### Build[](#id11 "Permalink to this headline") * (deps) Bump github.com/mattn/go-sqlite3 from 1.14.15 to 1.14.16 (#1913) * (deps) Bump github.com/spf13/cobra from 1.6.0 to 1.6.1 (#1909) * Fix devcontainer (#1942) * Run sqlc-pg-gen via GitHub Actions (#1944) * Move large arrays out of functions (#1947) * Fix conflicts from pointer configuration (#1950) * (deps) Bump github.com/go-sql-driver/mysql from 1.6.0 to 1.7.0 (#1988) * (deps) Bump github.com/jackc/pgtype from 1.12.0 to 1.13.0 (#1978) * (deps) Bump golang from 1.19.3 to 1.19.4 (#1992) * (deps) Bump certifi from 2020.12.5 to 2022.12.7 in /docs (#1993) * (deps) Bump golang from 1.19.4 to 1.19.5 (#2016) * (deps) Bump golang from 1.19.5 to 1.20.0 (#2045) * (deps) Bump github.com/jackc/pgtype from 1.13.0 to 1.14.0 (#2062) * (deps) Bump github.com/jackc/pgx/v4 from 4.17.2 to 4.18.0 (#2063) #### Cmd[](#cmd "Permalink to this headline") * Generate packages in parallel (#2026) #### Cmd/sqlc[](#id12 "Permalink to this headline") * Bump version to v1.17.0 #### Codegen[](#id13 "Permalink to this headline") * Remove built-in Kotlin support (#1935) * Remove built-in Python support (#1936) #### Internal/codegen[](#internal-codegen "Permalink to this headline") * Cache pattern matching compilations (#2028) #### Mysql[](#id14 "Permalink to this headline") * Add datatype tests (#1948) * Fix blob tests (#1949) #### Plugins[](#plugins "Permalink to this headline") * Upgrade to wasmtime 3.0.1 (#2009) #### Sqlite[](#id15 "Permalink to this headline") * Supported between expr (#1958) (#1967) #### Tools[](#tools "Permalink to this headline") * Regenerate scripts skips dirs that contains diff exec command (#1987) #### Wasm[](#id16 "Permalink to this headline") * Upgrade to wasmtime 5.0.0 (#2065) ### [1.16.0](https://github.com/kyleconroy/sqlc/releases/tag/1.16.0)[](#id17 "Permalink to this headline") Released 2022-11-09 #### Bug Fixes[](#id18 "Permalink to this headline") * (validate) Sqlc.arg & sqlc.narg are not “missing” (#1814) * Emit correct comment for nullable enums (#1819) * 🐛 Correctly switch `coalesce()` result `.NotNull` value (#1664) * Prevent batch infinite loop with arg length (#1794) * Support version 2 in error message (#1839) * Handle empty column list in postgresql (#1843) * Batch imports filter queries, update cmds having ret type (#1842) * Named params contribute to batch parameter count (#1841) #### Documentation[](#id19 "Permalink to this headline") * Add a getting started guide for SQLite (#1798) * Various readability improvements (#1854) * Add documentation for codegen plugins (#1904) * Update migration guides with links (#1933) #### Features[](#id20 "Permalink to this headline") * Add HAVING support to MySQL (#1806) #### Miscellaneous Tasks[](#miscellaneous-tasks "Permalink to this headline") * Upgrade wasmtime version (#1827) * Bump wasmtime version to v1.0.0 (#1869) #### Build[](#id21 "Permalink to this headline") * (deps) Bump github.com/jackc/pgconn from 1.12.1 to 1.13.0 (#1785) * (deps) Bump github.com/mattn/go-sqlite3 from 1.14.13 to 1.14.15 (#1799) * (deps) Bump github.com/jackc/pgx/v4 from 4.16.1 to 4.17.0 (#1786) * (deps) Bump github.com/jackc/pgx/v4 from 4.17.0 to 4.17.1 (#1825) * (deps) Bump github.com/bytecodealliance/wasmtime-go (#1826) * (deps) Bump github.com/jackc/pgx/v4 from 4.17.1 to 4.17.2 (#1831) * (deps) Bump golang from 1.19.0 to 1.19.1 (#1834) * (deps) Bump github.com/google/go-cmp from 0.5.8 to 0.5.9 (#1838) * (deps) Bump github.com/lib/pq from 1.10.6 to 1.10.7 (#1835) * (deps) Bump github.com/bytecodealliance/wasmtime-go (#1857) * (deps) Bump github.com/spf13/cobra from 1.5.0 to 1.6.0 (#1893) * (deps) Bump golang from 1.19.1 to 1.19.3 (#1920) #### Cmd/sqlc[](#id22 "Permalink to this headline") * Bump to v1.16.0 #### Codgen[](#codgen "Permalink to this headline") * Include serialized codegen options (#1890) #### Compiler[](#compiler "Permalink to this headline") * Move Kotlin parameter logic into codegen (#1910) #### Examples[](#examples "Permalink to this headline") * Port Python examples to WASM plugin (#1903) #### Pg-gen[](#pg-gen "Permalink to this headline") * Make sqlc-pg-gen the complete source of truth for pg\_catalog.go (#1809) * Implement information\_schema shema (#1815) #### Python[](#python "Permalink to this headline") * Port all Python tests to sqlc-gen-python (#1907) * Upgrade to sqlc-gen-python v1.0.0 (#1932) ### [1.15.0](https://github.com/kyleconroy/sqlc/releases/tag/1.15.0)[](#id23 "Permalink to this headline") Released 2022-08-07 #### Bug Fixes[](#id24 "Permalink to this headline") * (mysql) Typo (#1700) * (postgresql) Add quotes for CamelCase columns (#1729) * Cannot parse SQLite upsert statement (#1732) * (sqlite) Regenerate test output for builtins (#1735) * (wasm) Version modules by wasmtime version (#1734) * Missing imports (#1637) * Missing slice import for querier (#1773) #### Documentation[](#id25 "Permalink to this headline") * Add process-based plugin docs (#1669) * Add links to downloads.sqlc.dev (#1681) * Update transactions how to example (#1775) #### Features[](#id26 "Permalink to this headline") * More SQL Syntax Support for SQLite (#1687) * (sqlite) Promote SQLite support to beta (#1699) * Codegen plugins, powered by WASM (#1684) * Set user-agent for plugin downloads (#1707) * Null enums types (#1485) * (sqlite) Support stdlib functions (#1712) * (sqlite) Add support for returning (#1741) #### Miscellaneous Tasks[](#id27 "Permalink to this headline") * Add tests for quoting columns (#1733) * Remove catalog tests (#1762) #### Testing[](#id28 "Permalink to this headline") * Add tests for fixing slice imports (#1736) * Add test cases for returning (#1737) #### Build[](#id29 "Permalink to this headline") * Upgrade to Go 1.19 (#1780) * Upgrade to go-wasmtime 0.39.0 (#1781) #### Plugins[](#id30 "Permalink to this headline") * (wasm) Change default cache location (#1709) * (wasm) Change the SHA-256 config key (#1710) ### [1.14.0](https://github.com/kyleconroy/sqlc/releases/tag/1.14.0)[](#id31 "Permalink to this headline") Released 2022-06-09 #### Bug Fixes[](#id32 "Permalink to this headline") * (postgresql) Remove extra newline with db argument (#1417) * (sqlite) Fix DROP TABLE (#1443) * (compiler) Fix left join nullability with table aliases (#1491) * Regenerate testdata for CREATE TABLE AS (#1516) * (bundler) Only close multipart writer once (#1528) * (endtoend) Regenerate testdata for exex\_lastid * (pgx) Copyfrom imports (#1626) * Validate sqlc function arguments (#1633) * Fixed typo `sql.narg` in doc (#1668) #### Features[](#id33 "Permalink to this headline") * (golang) Add Enum.Valid and AllEnumValues (#1613) * (sqlite) Start expanding support (#1410) * (pgx) Add support for batch operations (#1437) * (sqlite) Add support for delete statements (#1447) * (codegen) Insert comments in interfaces (#1458) * (sdk) Add the plugin SDK package (#1463) * Upload projects (#1436) * Add sqlc version to generated Kotlin code (#1512) * Add sqlc version to generated Go code (#1513) * Pass sqlc version in codegen request (#1514) * (postgresql) Add materialized view support (#1509) * (python) Graduate Python support to beta (#1520) * Run sqlc with docker on windows cmd (#1557) * Add JSON “codegen” output (#1565) * Add sqlc.narg() for nullable named params (#1536) * Process-based codegen plugins (#1578) #### Miscellaneous Tasks[](#id34 "Permalink to this headline") * Fix extra newline in comments for copyfrom (#1438) * Generate marshal/unmarshal with vtprotobuf (#1467) #### Refactor[](#refactor "Permalink to this headline") * (codegen) Port Kotlin codegen package to use plugin types (#1416) * (codegen) Port Go to plugin types (#1460) * (cmd) Simplify codegen selection logic (#1466) * (sql/catalog) Improve Readability (#1595) * Add basic fuzzing for config / overrides (#1500) ### [1.13.0](https://github.com/kyleconroy/sqlc/releases/tag/v1.13.0)[](#id35 "Permalink to this headline") Released 2022-03-31 #### Bug Fixes[](#id36 "Permalink to this headline") * (compiler) Fix left join nullability with table aliases (#1491) * (postgresql) Remove extra newline with db argument (#1417) * (sqlite) Fix DROP TABLE (#1443) #### Features[](#id37 "Permalink to this headline") * (cli) Upload projects (#1436) * (codegen) Add sqlc version to generated Go code (#1513) * (codegen) Add sqlc version to generated Kotlin code (#1512) * (codegen) Insert comments in interfaces (#1458) * (codegen) Pass sqlc version in codegen request (#1514) * (pgx) Add support for batch operations (#1437) * (postgresql) Add materialized view support (#1509) * (python) Graduate Python support to beta (#1520) * (sdk) Add the plugin SDK package (#1463) * (sqlite) Add support for delete statements (#1447) * (sqlite) Start expanding support (#1410) #### Miscellaneous Tasks[](#id38 "Permalink to this headline") * Fix extra newline in comments for copyfrom (#1438) * Generate marshal/unmarshal with vtprotobuf (#1467) #### Refactor[](#id39 "Permalink to this headline") * (codegen) Port Kotlin codegen package to use plugin types (#1416) * (codegen) Port Go to plugin types (#1460) * (cmd) Simplify codegen selection logic (#1466) #### Config[](#id40 "Permalink to this headline") * Add basic fuzzing for config / overrides (#1500) ### [1.12.0](https://github.com/kyleconroy/sqlc/releases/tag/v1.12.0)[](#id41 "Permalink to this headline") Released 2022-02-05 #### Bug[](#bug "Permalink to this headline") * ALTER TABLE SET SCHEMA (#1409) #### Bug Fixes[](#id42 "Permalink to this headline") * Update ANTLR v4 go.mod entry (#1336) * Check delete statements for CTEs (#1329) * Fix validation of GROUP BY on field aliases (#1348) * Fix imports when non-copyfrom queries needed imports that copyfrom queries didn’t (#1386) * Remove extra comment newline (#1395) * Enable strict function checking (#1405) #### Documentation[](#id43 "Permalink to this headline") * Bump version to 1.11.0 (#1308) #### Features[](#id44 "Permalink to this headline") * Inheritance (#1339) * Generate query code using ASTs instead of templates (#1338) * Add support for CREATE TABLE a ( LIKE b ) (#1355) * Add support for sql.NullInt16 (#1376) #### Miscellaneous Tasks[](#id45 "Permalink to this headline") * Add tests for :exec{result,rows} (#1344) * Delete template-based codegen (#1345) #### Build[](#id46 "Permalink to this headline") * Bump github.com/jackc/pgx/v4 from 4.14.0 to 4.14.1 (#1316) * Bump golang from 1.17.3 to 1.17.4 (#1331) * Bump golang from 1.17.4 to 1.17.5 (#1337) * Bump github.com/spf13/cobra from 1.2.1 to 1.3.0 (#1343) * Remove devel Docker build * Bump golang from 1.17.5 to 1.17.6 (#1369) * Bump github.com/google/go-cmp from 0.5.6 to 0.5.7 (#1382) * Format all Go code (#1387) ### [1.11.0](https://github.com/kyleconroy/sqlc/releases/tag/v1.11.0)[](#id47 "Permalink to this headline") Released 2021-11-24 #### Bug Fixes[](#id48 "Permalink to this headline") * Update incorrect signatures (#1180) * Correct aggregate func sig (#1182) * Jsonb\_build\_object (#1211) * Case-insensitive identifiers (#1216) * Incorrect handling of meta (#1228) * Detect invalid INSERT expression (#1231) * Respect alias name for coalesce (#1232) * Mark nullable when casting NULL (#1233) * Support nullable fields in joins for MySQL engine (#1249) * Fix between expression handling of table references (#1268) * Support nullable fields in joins on same table (#1270) * Fix missing binds in ORDER BY (#1273) * Set RV for TargetList items on updates (#1252) * Fix MySQL parser for query without trailing semicolon (#1282) * Validate table alias references (#1283) * Add support for MySQL ON DUPLICATE KEY UPDATE (#1286) * Support references to columns in joined tables in UPDATE statements (#1289) * Add validation for GROUP BY clause column references (#1285) * Prevent variable redeclaration in single param conflict (#1298) * Use common params struct field for same named params (#1296) #### Documentation[](#id49 "Permalink to this headline") * Replace deprecated go get with go install (#1181) * Fix package name referenced in tutorial (#1202) * Add environment variables (#1264) * Add go.17+ install instructions (#1280) * Warn about golang-migrate file order (#1302) #### Features[](#id50 "Permalink to this headline") * Instrument compiler via runtime/trace (#1258) * Add MySQL support for BETWEEN arguments (#1265) #### Refactor[](#id51 "Permalink to this headline") * Move from io/ioutil to io and os package (#1164) #### Styling[](#styling "Permalink to this headline") * Apply gofmt to sample code (#1261) #### Build[](#id52 "Permalink to this headline") * Bump golang from 1.17.0 to 1.17.1 (#1173) * Bump eskatos/gradle-command-action from 1 to 2 (#1220) * Bump golang from 1.17.1 to 1.17.2 (#1227) * Bump github.com/pganalyze/pg\_query\_go/v2 (#1234) * Bump actions/checkout from 2.3.4 to 2.3.5 (#1238) * Bump babel from 2.9.0 to 2.9.1 in /docs (#1245) * Bump golang from 1.17.2 to 1.17.3 (#1272) * Bump actions/checkout from 2.3.5 to 2.4.0 (#1267) * Bump github.com/lib/pq from 1.10.3 to 1.10.4 (#1278) * Bump github.com/jackc/pgx/v4 from 4.13.0 to 4.14.0 (#1303) #### Cmd/sqlc[](#id53 "Permalink to this headline") * Bump version to v1.11.0 ### [1.10.0](https://github.com/kyleconroy/sqlc/releases/tag/v1.10.0)[](#id54 "Permalink to this headline") Released 2021-09-07 #### Documentation[](#id55 "Permalink to this headline") * Fix invalid language support table (#1161) * Add a getting started guide for MySQL (#1163) #### Build[](#id56 "Permalink to this headline") * Bump golang from 1.16.7 to 1.17.0 (#1129) * Bump github.com/lib/pq from 1.10.2 to 1.10.3 (#1160) #### Ci[](#ci "Permalink to this headline") * Upgrade Go to 1.17 (#1130) #### Cmd/sqlc[](#id57 "Permalink to this headline") * Bump version to v1.10.0 (#1165) #### Codegen/golang[](#codegen-golang "Permalink to this headline") * Consolidate import logic (#1139) * Add pgx support for range types (#1146) * Use pgtype for hstore when using pgx (#1156) #### Codgen/golang[](#codgen-golang "Permalink to this headline") * Use p[gq]type for network address types (#1142) #### Endtoend[](#endtoend "Permalink to this headline") * Run `go test` in CI (#1134) #### Engine/mysql[](#engine-mysql "Permalink to this headline") * Add support for LIKE (#1162) #### Golang[](#golang "Permalink to this headline") * Output NullUUID when necessary (#1137) ### [1.9.0](https://github.com/kyleconroy/sqlc/releases/tag/v1.9.0)[](#id58 "Permalink to this headline") Released 2021-08-13 #### Documentation[](#id59 "Permalink to this headline") * Update documentation (a bit) for v1.9.0 (#1117) #### Build[](#id60 "Permalink to this headline") * Bump golang from 1.16.6 to 1.16.7 (#1107) #### Cmd/sqlc[](#id61 "Permalink to this headline") * Bump version to v1.9.0 (#1121) #### Compiler[](#id62 "Permalink to this headline") * Add tests for COALESCE behavior (#1112) * Handle subqueries in SELECT statements (#1113) ### [1.8.0](https://github.com/kyleconroy/sqlc/releases/tag/v1.8.0)[](#id63 "Permalink to this headline") Released 2021-05-03 #### Documentation[](#id64 "Permalink to this headline") * Add language support Matrix (#920) #### Features[](#id65 "Permalink to this headline") * Add case style config option (#905) #### Python[](#id66 "Permalink to this headline") * Eliminate runtime package and use sqlalchemy (#939) #### Build[](#id67 "Permalink to this headline") * Bump github.com/google/go-cmp from 0.5.4 to 0.5.5 (#926) * Bump github.com/lib/pq from 1.9.0 to 1.10.0 (#931) * Bump golang from 1.16.0 to 1.16.1 (#935) * Bump golang from 1.16.1 to 1.16.2 (#942) * Bump github.com/jackc/pgx/v4 from 4.10.1 to 4.11.0 (#956) * Bump github.com/go-sql-driver/mysql from 1.5.0 to 1.6.0 (#961) * Bump github.com/pganalyze/pg\_query\_go/v2 (#965) * Bump urllib3 from 1.26.3 to 1.26.4 in /docs (#968) * Bump golang from 1.16.2 to 1.16.3 (#963) * Bump github.com/lib/pq from 1.10.0 to 1.10.1 (#980) #### Cmd[](#id68 "Permalink to this headline") * Add the –experimental flag (#929) * Fix sqlc init (#959) #### Cmd/sqlc[](#id69 "Permalink to this headline") * Bump version to v1.7.1-devel (#913) * Bump version to v1.8.0 #### Codegen[](#id70 "Permalink to this headline") * Generate valid enum names for symbols (#972) #### Postgresql[](#postgresql "Permalink to this headline") * Support generated columns * Add test for PRIMARY KEY INCLUDE * Add tests for CREATE TABLE PARTITION OF * CREATE TRIGGER EXECUTE FUNCTION * Add support for renaming types (#971) #### Sql/ast[](#sql-ast "Permalink to this headline") * Resolve return values from functions (#964) #### Workflows[](#workflows "Permalink to this headline") * Only run tests once (#924) ### [1.7.0](https://github.com/kyleconroy/sqlc/releases/tag/v1.7.0)[](#id71 "Permalink to this headline") Released 2021-02-28 #### Bug Fixes[](#id72 "Permalink to this headline") * Struct tag formatting (#833) #### Documentation[](#id73 "Permalink to this headline") * Include all the existing Markdown files (#877) * Split docs into four sections (#882) * Reorganize and consolidate documentation * Add link to Windows download (#888) * Shorten the README (#889) #### Features[](#id74 "Permalink to this headline") * Adding support for pgx/v4 * Adding support for pgx/v4 #### README[](#readme "Permalink to this headline") * Add Go Report Card badge (#891) #### Build[](#id75 "Permalink to this headline") * Bump github.com/google/go-cmp from 0.5.3 to 0.5.4 (#813) * Bump github.com/lib/pq from 1.8.0 to 1.9.0 (#820) * Bump golang from 1.15.5 to 1.15.6 (#822) * Bump github.com/jackc/pgx/v4 from 4.9.2 to 4.10.0 (#823) * Bump github.com/jackc/pgx/v4 from 4.10.0 to 4.10.1 (#839) * Bump golang from 1.15.6 to 1.15.7 (#855) * Bump golang from 1.15.7 to 1.15.8 (#881) * Bump github.com/spf13/cobra from 1.1.1 to 1.1.2 (#892) * Bump golang from 1.15.8 to 1.16.0 (#897) * Bump github.com/lfittl/pg\_query\_go from 1.0.1 to 1.0.2 (#901) * Bump github.com/spf13/cobra from 1.1.2 to 1.1.3 (#893) #### Catalog[](#catalog "Permalink to this headline") * Improve alter column type (#818) #### Ci[](#id76 "Permalink to this headline") * Uprade to Go 1.15 (#887) #### Cmd[](#id77 "Permalink to this headline") * Allow config file location to be specified (#863) #### Cmd/sqlc[](#id78 "Permalink to this headline") * Bump to version v1.6.1-devel (#807) * Bump version to v1.7.0 (#912) #### Codegen/golang[](#id79 "Permalink to this headline") * Make sure to import net package (#858) #### Compiler[](#id80 "Permalink to this headline") * Support UNION query #### Dolphin[](#dolphin "Permalink to this headline") * Generate bools for tinyint(1) * Support joins in update statements (#883) * Add support for union query #### Endtoend[](#id81 "Permalink to this headline") * Add tests for INTERSECT and EXCEPT #### Go.mod[](#go-mod "Permalink to this headline") * Update to go 1.15 and run ‘go mod tidy’ (#808) #### Mysql[](#id82 "Permalink to this headline") * Compile tinyint(1) to bool (#873) #### Sql/ast[](#id83 "Permalink to this headline") * Add enum values for SetOperation ### [1.6.0](https://github.com/kyleconroy/sqlc/releases/tag/v1.6.0)[](#id84 "Permalink to this headline") Released 2020-11-23 #### Dolphin[](#id85 "Permalink to this headline") * Implement Rename (#651) * Skip processing view drops (#653) #### README[](#id86 "Permalink to this headline") * Update language / database support (#698) #### Astutils[](#astutils "Permalink to this headline") * Fix Params rewrite call (#674) #### Build[](#id87 "Permalink to this headline") * Bump golang from 1.14 to 1.15.3 (#765) * Bump docker/build-push-action from v1 to v2.1.0 (#764) * Bump github.com/google/go-cmp from 0.4.0 to 0.5.2 (#766) * Bump github.com/spf13/cobra from 1.0.0 to 1.1.1 (#767) * Bump github.com/jackc/pgx/v4 from 4.6.0 to 4.9.2 (#768) * Bump github.com/lfittl/pg\_query\_go from 1.0.0 to 1.0.1 (#773) * Bump github.com/google/go-cmp from 0.5.2 to 0.5.3 (#783) * Bump golang from 1.15.3 to 1.15.5 (#782) * Bump github.com/lib/pq from 1.4.0 to 1.8.0 (#769) #### Catalog[](#id88 "Permalink to this headline") * Improve variadic argument support (#804) #### Cmd/sqlc[](#id89 "Permalink to this headline") * Bump to version v1.6.0 (#806) #### Codegen[](#id90 "Permalink to this headline") * Fix errant database/sql imports (#789) #### Compiler[](#id91 "Permalink to this headline") * Use engine-specific reserved keywords (#677) #### Dolphi[](#dolphi "Permalink to this headline") * Add list of builtin functions (#795) #### Dolphin[](#id92 "Permalink to this headline") * Update to the latest MySQL parser (#665) * Add ENUM() support (#676) * Add test for table aliasing (#684) * Add MySQL ddl\_create\_table test (#685) * Implete TRUNCATE table (#697) * Represent tinyint as int32 (#797) * Add support for coalesce (#802) * Add function signatures (#796) #### Endtoend[](#id93 "Permalink to this headline") * Add MySQL json test (#692) * Add MySQL update set multiple test (#696) #### Examples[](#id94 "Permalink to this headline") * Use generated enum constants in db\_test (#678) * Port ondeck to MySQL (#680) * Add MySQL authors example (#682) #### Internal/cmd[](#internal-cmd "Permalink to this headline") * Print correct config file on parse failure (#749) #### Kotlin[](#kotlin "Permalink to this headline") * Remove runtime dependency (#774) #### Metadata[](#metadata "Permalink to this headline") * Support multiple comment prefixes (#683) #### Postgresql[](#id95 "Permalink to this headline") * Support string concat operator (#701) #### Sql/catalog[](#sql-catalog "Permalink to this headline") * Add support for variadic functions (#798) ### [1.5.0](https://github.com/kyleconroy/sqlc/releases/tag/v1.5.0)[](#id96 "Permalink to this headline") Released 2020-08-05 #### Documentation[](#id97 "Permalink to this headline") * Build sqlc using Go 1.14 (#549) #### Cmd[](#id98 "Permalink to this headline") * Add debugging support (#573) #### Cmd/sqlc[](#id99 "Permalink to this headline") * Bump version to v1.4.1-devel (#548) * Bump version to v1.5.0 #### Compiler[](#id100 "Permalink to this headline") * Support calling functions with defaults (#635) * Skip func args without a paramRef (#636) * Return a single column from coalesce (#639) #### Config[](#id101 "Permalink to this headline") * Add emit\_empty\_slices to version one (#552) #### Contrib[](#contrib "Permalink to this headline") * Add generated code for contrib #### Dinosql[](#dinosql "Permalink to this headline") * Remove deprecated package (#554) #### Dolphin[](#id102 "Permalink to this headline") * Add support for column aliasing (#566) * Implement star expansion for subqueries (#619) * Implement exapansion with reserved words (#620) * Implement parameter refs (#621) * Implement limit and offest (#622) * Implement inserts (#623) * Implement delete (#624) * Implement simple update statements (#625) * Implement INSERT … SELECT (#626) * Use test driver instead of TiDB driver (#629) * Implement named parameters via sqlc.arg() (#632) #### Endtoend[](#id103 "Permalink to this headline") * Add MySQL test for SELECT \* JOIN (#565) * Add MySQL test for inflection (#567) #### Engine[](#engine "Permalink to this headline") * Create engine package (#556) #### Equinox[](#equinox "Permalink to this headline") * Use the new equinox-io/setup action (#586) #### Examples[](#id104 "Permalink to this headline") * Run tests for MySQL booktest (#627) #### Golang[](#id105 "Permalink to this headline") * Add support for the money type (#561) * Generate correct types for int2 and int8 (#579) #### Internal[](#internal "Permalink to this headline") * Rm catalog, pg, postgres packages (#555) #### Mod[](#mod "Permalink to this headline") * Downgrade TiDB package to fix build (#603) #### Mysql[](#id106 "Permalink to this headline") * Upgrade to the latest vitess commit (#562) * Support to infer type of a duplicated arg (#615) * Allow some builtin functions to be nullable (#616) #### Postgresql[](#id107 "Permalink to this headline") * Generate all functions in pg\_catalog (#550) * Remove pg\_catalog schema from tests (#638) * Move contrib code to a package #### Sql/catalog[](#id108 "Permalink to this headline") * Fix comparison of pg\_catalog types (#637) #### Tools[](#id109 "Permalink to this headline") * Generate functions for all of contrib #### Workflow[](#workflow "Permalink to this headline") * Migrate to equinox-io/setup-release-tool (#614) ### [1.4.0](https://github.com/kyleconroy/sqlc/releases/tag/v1.4.0)[](#id110 "Permalink to this headline") Released 2020-06-17 #### Dockerfile[](#dockerfile "Permalink to this headline") * Add version build argument (#487) #### MySQL[](#id111 "Permalink to this headline") * Prevent Panic when WHERE clause contains parenthesis. (#531) #### README[](#id112 "Permalink to this headline") * Document emit\_exact\_table\_names (#486) #### All[](#all "Permalink to this headline") * Remove the exp build tag (#507) #### Catalog[](#id113 "Permalink to this headline") * Support functions with table parameters (#541) #### Cmd[](#id114 "Permalink to this headline") * Bump to version 1.3.1-devel (#485) #### Cmd/sqlc[](#id115 "Permalink to this headline") * Bump version to v1.4.0 (#547) #### Codegen[](#id116 "Permalink to this headline") * Add the new codegen packages (#513) * Add the :execresult query annotation (#542) #### Compiler[](#id117 "Permalink to this headline") * Validate function calls (#505) * Port bottom of parseQuery (#510) * Don’t mutate table name (#517) * Enable experimental parser by default (#518) * Apply rename rules to enum constants (#523) * Temp fix for typecast function parameters (#530) #### Endtoend[](#id118 "Permalink to this headline") * Standardize JSON formatting (#490) * Add per-test configuration files (#521) * Read expected stderr failures from disk (#527) #### Internal/dinosql[](#internal-dinosql "Permalink to this headline") * Check parameter style before ref (#488) * Remove unneeded column suffix (#492) * Support named function arguments (#494) #### Internal/postgresql[](#internal-postgresql "Permalink to this headline") * Fix NamedArgExpr rewrite (#491) #### Multierr[](#multierr "Permalink to this headline") * Move dinosql.ParserErr to a new package (#496) #### Named[](#named "Permalink to this headline") * Port parameter style validation to SQL (#504) #### Parser[](#id119 "Permalink to this headline") * Support columns from subselect statements (#489) #### Rewrite[](#rewrite "Permalink to this headline") * Move parameter rewrite to package (#499) #### Sqlite[](#id120 "Permalink to this headline") * Use convert functions instead of the listener (#519) #### Sqlpath[](#sqlpath "Permalink to this headline") * Move ReadSQLFiles into a separate package (#495) #### Validation[](#validation "Permalink to this headline") * Move query validation to separate package (#498) ### [1.3.0](https://github.com/kyleconroy/sqlc/releases/tag/v1.3.0)[](#id121 "Permalink to this headline") Released 2020-05-12 #### Makefile[](#makefile "Permalink to this headline") * Update target (#449) #### README[](#id122 "Permalink to this headline") * Add Myles as a sponsor (#469) #### Testing[](#id123 "Permalink to this headline") * Make sure all Go examples build (#480) #### Cmd[](#id124 "Permalink to this headline") * Bump version to v1.3.0 (#484) #### Cmd/sqlc[](#id125 "Permalink to this headline") * Bump version to v1.2.1-devel (#442) #### Dinosql[](#id126 "Permalink to this headline") * Inline addFile (#446) * Add PostgreSQL support for TRUNCATE (#448) #### Gen[](#gen "Permalink to this headline") * Emit json.RawMessage for JSON columns (#461) #### Go.mod[](#id127 "Permalink to this headline") * Use latest lib/pq (#471) #### Parser[](#id128 "Permalink to this headline") * Use same function to load SQL files (#483) #### Postgresql[](#id129 "Permalink to this headline") * Fix panic walking CreateTableAsStmt (#475) ### [1.2.0](https://github.com/kyleconroy/sqlc/releases/tag/v1.2.0)[](#id130 "Permalink to this headline") Released 2020-04-07 #### Documentation[](#id131 "Permalink to this headline") * Publish to Docker Hub (#422) #### README[](#id132 "Permalink to this headline") * Docker installation docs (#424) #### Cmd/sqlc[](#id133 "Permalink to this headline") * Bump version to v1.1.1-devel (#407) * Bump version to v1.2.0 (#441) #### Gen[](#id134 "Permalink to this headline") * Add special case for “campus” (#435) * Properly quote reserved keywords on expansion (#436) #### Migrations[](#migrations "Permalink to this headline") * Move migration parsing to new package (#427) #### Parser[](#id135 "Permalink to this headline") * Generate correct types for SELECT EXISTS (#411) ### [1.1.0](https://github.com/kyleconroy/sqlc/releases/tag/v1.1.0)[](#id136 "Permalink to this headline") Released 2020-03-17 #### README[](#id137 "Permalink to this headline") * Add installation instructions (#350) * Add section on running tests (#357) * Fix typo (#371) #### Ast[](#ast "Permalink to this headline") * Add AST for ALTER TABLE ADD / DROP COLUMN (#376) * Add support for CREATE TYPE as ENUM (#388) * Add support for CREATE / DROP SCHEMA (#389) #### Astutils[](#id138 "Permalink to this headline") * Apply changes to the ValuesList slice (#372) #### Cmd[](#id139 "Permalink to this headline") * Return v1.0.0 (#348) * Return next bug fix version (#349) #### Cmd/sqlc[](#id140 "Permalink to this headline") * Bump version to v1.1.0 (#406) #### Compiler[](#id141 "Permalink to this headline") * Wire up the experimental parsers #### Config[](#id142 "Permalink to this headline") * Remove “emit\_single\_file” option (#367) #### Dolphin[](#id143 "Permalink to this headline") * Add experimental parser for MySQL #### Gen[](#id144 "Permalink to this headline") * Add option to emit single file for Go (#366) * Add support for the ltree extension (#385) #### Go.mod[](#id145 "Permalink to this headline") * Add packages for MySQL and SQLite parsers #### Internal/dinosql[](#id146 "Permalink to this headline") * Support Postgres macaddr type in Go (#358) #### Internal/endtoend[](#internal-endtoend "Permalink to this headline") * Remove %w (#354) #### Kotlin[](#id147 "Permalink to this headline") * Add Query class to support timeout and cancellation (#368) #### Postgresql[](#id148 "Permalink to this headline") * Add experimental parser for MySQL #### Sql[](#sql "Permalink to this headline") * Add generic SQL AST #### Sql/ast[](#id149 "Permalink to this headline") * Port support for COMMENT ON (#391) * Implement DROP TYPE (#397) * Implement ALTER TABLE RENAME (#398) * Implement ALTER TABLE RENAME column (#399) * Implement ALTER TABLE SET SCHEMA (#400) #### Sql/catalog[](#id150 "Permalink to this headline") * Port tests over from catalog pkg (#402) #### Sql/errors[](#sql-errors "Permalink to this headline") * Add a new errors package (#390) #### Sqlite[](#id151 "Permalink to this headline") * Add experimental parser for SQLite ### [1.0.0](https://github.com/kyleconroy/sqlc/releases/tag/v1.0.0)[](#id152 "Permalink to this headline") Released 2020-02-18 #### Documentation[](#id153 "Permalink to this headline") * Add documentation for query commands (#270) * Add named parameter documentation (#332) #### README[](#id154 "Permalink to this headline") * Add sponsors section (#333) #### Cmd[](#id155 "Permalink to this headline") * Remove parse subcommand (#322) #### Config[](#id156 "Permalink to this headline") * Parse V2 config format * Add support for YAML (#336) #### Examples[](#id157 "Permalink to this headline") * Add the jets and booktest examples (#237) * Move sqlc.json into examples folder (#238) * Add the authors example (#241) * Add build tag to authors tests (#319) #### Internal[](#id158 "Permalink to this headline") * Allow CTE to be used with UPDATE (#268) * Remove the PackageMap from settings (#295) #### Internal/config[](#internal-config "Permalink to this headline") * Create new config package (#313) #### Internal/dinosql[](#id159 "Permalink to this headline") * Emit Querier interface (#240) * Strip leading “go-“ or trailing “-go” from import (#262) * Overrides can now be basic types (#271) * Import needed types for Querier (#285) * Handle schema-scoped enums (#310) * Ignore golang-migrate rollbacks (#320) #### Internal/endtoend[](#id160 "Permalink to this headline") * Move more tests to the record/replay framework * Add update test for named params (#329) #### Internal/mysql[](#internal-mysql "Permalink to this headline") * Fix flaky test (#242) * Port tests to endtoend package (#315) #### Internal/parser[](#internal-parser "Permalink to this headline") * Resolve nested CTEs (#324) * Error if last query is missing (#325) * Support joins with aliases (#326) * Remove print statement (#327) #### Internal/sqlc[](#internal-sqlc "Permalink to this headline") * Add support for composite types (#311) #### Kotlin[](#id161 "Permalink to this headline") * Support primitives * Arrays, enums, and dates * Generate examples * README for examples * Factor out db setup extension * Fix enums, use List instead of Array * Port Go tests for examples * Rewrite numbered params to positional params * Always use use, fix indents * Unbox query params #### Parser[](#id162 "Permalink to this headline") * Attach range vars to insert params * Attach range vars to insert params (#342) * Remove dead code (#343) ### [0.1.0](https://github.com/kyleconroy/sqlc/releases/tag/v0.1.0)[](#id163 "Permalink to this headline") Released 2020-01-07 #### Documentation[](#id164 "Permalink to this headline") * Replace remaining references to DinoSQL with sqlc (#149) #### README[](#id165 "Permalink to this headline") * Fix download links (#66) * Add LIMIT 1 to query that should return one (#99) #### Catalog[](#id166 "Permalink to this headline") * Support “ALTER TABLE … DROP CONSTRAINT …” (#34) * Differentiate functions with different argument types (#51) #### Ci[](#id167 "Permalink to this headline") * Enable tests on pull requests #### Cmd[](#id168 "Permalink to this headline") * Include filenames in error messages (#69) * Do not output any changes on error (#72) #### Dinosql/internal[](#dinosql-internal "Permalink to this headline") * Add lower and upper functions (#215) * Ignore alter sequence commands (#219) #### Gen[](#id169 "Permalink to this headline") * Add DO NOT EDIT comments to generated code (#50) * Include all schemas when generating models (#90) * Prefix structs with schema name (#91) * Generate single import for uuid package (#98) * Use same import logic for all Go files * Pick correct struct to return for queries (#107) * Create consistent JSON tags (#110) * Add Close method to Queries struct (#127) * Ignore empty override settings (#128) * Turn SQL comments into Go comments (#136) #### Internal/catalog[](#internal-catalog "Permalink to this headline") * Parse unnamed function arguments (#166) #### Internal/dinosql[](#id170 "Permalink to this headline") * Prepare() with no GoQueries still valid (#95) * Fix multiline comment rendering (#142) * Dereference alias nodes on walk (#158) * Ignore sql-migrate rollbacks (#160) * Sort imported packages (#165) * Add support for timestamptz (#169) * Error on missing queries (#180) * Use more database/sql null types (#182) * Support the pg\_temp schema (#183) * Override columns with array type (#184) * Implement robust expansion * Implement robust expansion (#186) * Add COMMENT ON support (#191) * Add DATE support * Add DATE support (#196) * Filter out invalid characters (#198) * Quote reserved keywords (#205) * Return parser errors first (#207) * Implement advisory locks (#212) * Error on duplicate query names (#221) * Fix incorrect enum names (#223) * Add support for numeric types * Add support for numeric types (#228) #### Internal/dinosql/testdata/ondeck[](#internal-dinosql-testdata-ondeck "Permalink to this headline") * Add Makefile (#156) #### Ondeck[](#ondeck "Permalink to this headline") * Move all tests to GitHub CI (#58) #### ParseQuery[](#parsequery "Permalink to this headline") * Return either a query or an error (#178) #### Parser[](#id171 "Permalink to this headline") * Use schema when resolving catalog refs (#82) * Support function calls in expressions (#104) * Correctly handle single files (#119) * Return error if missing RETURNING (#131) * Add support for mathmatical operators (#132) * Add support for simple case expressions (#134) * Error on mismatched INSERT input (#135) * Set IsArray on joined columns (#139) #### Pg[](#pg "Permalink to this headline") * Store functions in the catalog (#41) * Add location to errors (#73) Using Go and pgx[](#using-go-and-pgx "Permalink to this headline") ------------------------------------------------------------------- Note Experimental support for `pgx/v5` was added in v1.17.2. Full support will be included in v1.18.0. Until then, you’ll need to pass the `--experimental` flag to `sqlc generate`. pgx is a pure Go driver and toolkit for PostgreSQL. It’s become the default PostgreSQL package for many Gophers since lib/pq was put into maitience mode. ### Getting started[](#getting-started "Permalink to this headline") To start generating code that uses pgx, set the `sql\_package` field in your `sqlc.yaml` configuration file. Valid options are `pgx/v4` or `pgx/v5` ``` version: "2" sql: - engine: "postgresql" queries: "query.sql" schema: "query.sql" gen: go: package: "db" sql\_package: "pgx/v5" out: "db" ``` If you don’t have an existing sqlc project on hand, create a directory with the configuration file above and the following `query.sql` file. ``` CREATE TABLE authors ( id BIGSERIAL PRIMARY KEY, name text NOT NULL, bio text ); -- name: GetAuthor :one SELECT * FROM authors WHERE id = $1 LIMIT 1; -- name: ListAuthors :many SELECT * FROM authors ORDER BY name; -- name: CreateAuthor :one INSERT INTO authors ( name, bio ) VALUES ( $1, $2 ) RETURNING *; -- name: DeleteAuthor :exec DELETE FROM authors WHERE id = $1; ``` Generating the code will now give you pgx-compatible database access methods. ``` sqlc generate --experimental ``` ### Generated code walkthrough[](#generated-code-walkthrough "Permalink to this headline") The generated code is very similar to the code generated when using `lib/pq`. However, instead of using `database/sql`, the code uses pgx types directly. ``` package main import ( "context" "fmt" "os" "github.com/jackc/pgx/v5" "example.com/sqlc-tutorial/db" ) func main() { // urlExample := "postgres://username:password@localhost:5432/database\_name" conn, err := pgx.Connect(context.Background(), os.Getenv("DATABASE\_URL")) if err != nil { fmt.Fprintf(os.Stderr, "Unable to connect to database: %v\n", err) os.Exit(1) } defer conn.Close(context.Background()) q := db.New(conn) author, err := q.GetAuthor(context.Background(), 1) if err != nil { fmt.Fprintf(os.Stderr, "GetAuthor failed: %v\n", err) os.Exit(1) } fmt.Println(author.Name) } ``` Developing sqlc[](#developing-sqlc "Permalink to this headline") ----------------------------------------------------------------- ### Building[](#building "Permalink to this headline") For local development, install `sqlc` under an alias. We suggest `sqlc-dev`. ``` go build -o ~/go/bin/sqlc-dev ./cmd/sqlc ``` ### Running Tests[](#running-tests "Permalink to this headline") ``` go test ./... ``` To run the tests in the examples folder, use the `examples` tag. ``` go test --tags=examples ./... ``` These tests require locally-running database instances. Run these databases using [Docker Compose](https://docs.docker.com/compose/). ``` docker-compose up -d ``` The tests use the following environment variables to connect to the database #### For PostgreSQL[](#for-postgresql "Permalink to this headline") ``` Variable Default Value ------------------------- PG\_HOST 127.0.0.1 PG\_PORT 5432 PG\_USER postgres PG\_PASSWORD mysecretpassword PG\_DATABASE dinotest ``` #### For MySQL[](#for-mysql "Permalink to this headline") ``` Variable Default Value ------------------------- MYSQL\_HOST 127.0.0.1 MYSQL\_PORT 3306 MYSQL\_USER root MYSQL\_ROOT\_PASSWORD mysecretpassword MYSQL\_DATABASE dinotest ``` ### Regenerate expected test output[](#regenerate-expected-test-output "Permalink to this headline") If you need to update a large number of expected test output in the `internal/endtoend/testdata` directory, run the `regenerate` script. ``` go build -o ~/go/bin/sqlc-dev ./cmd/sqlc go run scripts/regenerate/main.go ``` Note that this uses the `sqlc-dev` binary, not `sqlc` so make sure you have an up to date `sqlc-dev` binary. Authoring plugins[](#authoring-plugins "Permalink to this headline") --------------------------------------------------------------------- To use plugins, you must be using [Version 2](../reference/config.html) of the configuration file. The top-level `plugins` array defines the available plugins. ### WASM plugins[](#wasm-plugins "Permalink to this headline") > > WASM plugins are fully sandboxed. Plugins do not have access to the network, > filesystem, or environment variables. > > > In the `codegen` section, the `out` field dictates what directory will contain the new files. The `plugin` key must reference a plugin defined in the top-level `plugins` map. The `options` are serialized to a string and passed on to the plugin itself. ``` { "version": "2", "plugins": [ { "name": "greeter", "wasm": { "url": "https://github.com/kyleconroy/sqlc-gen-greeter/releases/download/v0.1.0/sqlc-gen-greeter.wasm", "sha256": "afc486dac2068d741d7a4110146559d12a013fd0286f42a2fc7dcd802424ad07" } } ], "sql": [ { "schema": "schema.sql", "queries": "query.sql", "engine": "postgresql", "codegen": [ { "out": "gen", "plugin": "greeter" } ] } ] } ``` For a complete working example see the following files: * [sqlc-gen-greeter](https://github.com/kyleconroy/sqlc-gen-greeter) + A WASM plugin (written in Rust) that outputs a friendly message * [wasm\_plugin\_sqlc\_gen\_greeter](https://github.com/kyleconroy/sqlc/tree/main/internal/endtoend/testdata/wasm_plugin_sqlc_gen_greeter) + An example project showing how to use a WASM plugin ### Process plugins[](#process-plugins "Permalink to this headline") > > Process-based plugins offer minimal security. Only use plugins that you > trust. Better yet, only use plugins that you’ve written yourself. > > > In the `codegen` section, the `out` field dictates what directory will contain the new files. The `plugin` key must reference a plugin defined in the top-level `plugins` map. The `options` are serialized to a string and passed on to the plugin itself. ``` { "version": "2", "plugins": [ { "name": "jsonb", "process": { "cmd": "sqlc-gen-json" } } ], "sql": [ { "schema": "schema.sql", "queries": "query.sql", "engine": "postgresql", "codegen": [ { "out": "gen", "plugin": "jsonb", "options": { "indent": " ", "filename": "codegen.json" } } ] } ] } ``` For a complete working example see the following files: * [sqlc-gen-json](https://github.com/kyleconroy/sqlc/tree/main/cmd/sqlc-gen-json) + A process-based plugin that serializes the CodeGenRequest to JSON * [process\_plugin\_sqlc\_gen\_json](https://github.com/kyleconroy/sqlc/tree/main/internal/endtoend/testdata/process_plugin_sqlc_gen_json) + An example project showing how to use a process-based plugin Privacy and data collection[](#privacy-and-data-collection "Permalink to this headline") ----------------------------------------------------------------------------------------- These days, it feels like every piece of software is tracking you. From your browser, to your phone, to your terminal, programs collect as much data about you as possible and send it off to the cloud for analysis. We believe the best way to keep data safe is to never collect it in the first place. ### Our Privacy Pledge[](#our-privacy-pledge "Permalink to this headline") The `sqlc` command line tool does not collect any information. It does not send crash reports to a third-party. It does not gather anonymous aggregate user behaviour analytics. No analytics. No finger-printing. No tracking. Not now and not in the future. #### Distribution Channels[](#distribution-channels "Permalink to this headline") We distribute sqlc using popular package managers such as [Homebrew](https://brew.sh/) and [Snapcraft](https://snapcraft.io/). These package managers and their associated command-line tools do collect usage metrics. We use these services to make it easy to for users to install sqlc. There will always be an option to download sqlc from a stable URL. ### Hosted Services[](#hosted-services "Permalink to this headline") We provide a few hosted services in addition to the sqlc command line tool. #### sqlc.dev[](#sqlc-dev "Permalink to this headline") * Hosted on [GitHub Pages](https://pages.github.com/) * Analytics with [Plausible](https://plausible.io/privacy-focused-web-analytics) #### docs.sqlc.dev[](#docs-sqlc-dev "Permalink to this headline") * Hosted on [Read the Docs](https://readthedocs.org/) * Analytics with [Plausible](https://plausible.io/privacy-focused-web-analytics) #### play.sqlc.dev[](#play-sqlc-dev "Permalink to this headline") * Hosted on [Heroku](https://heroku.com) * Playground data stored in [Google Cloud Storage](https://cloud.google.com/storage) + Automatically deleted after 30 days #### app.sqlc.dev / api.sqlc.dev[](#app-sqlc-dev-api-sqlc-dev "Permalink to this headline") * Hosted on [Heroku](https://heroku.com) * Error tracking and tracing with [Sentry](https://sentry.io)

watcher

go

watcher 0.2.0 documentation [watcher](#) stable First steps * [Prerequisites](index.html#document-prerreq) * [Quick Instalation](index.html#document-setup) * [Quick Start](index.html#document-quickstart) Features * [File Watcher Features](index.html#document-features) [watcher](#) * » * watcher 0.2.0 documentation * [Edit on GitHub](https://github.com/racherb/watcher/blob/e6c5e8f107da332b36bfbad2ed9b1704e7bda8d5/docs/source/index) --- Watcher for Watch everything ============================================================================================ [Watcher](https://watcher.readthedocs.io/en/latest/) simplifies the integration of non-connected systems by detecting changes in data and facilitates the development of monitoring, security and process automation applications. Think of Watcher as an intercom or a bridge between different servers or between different applications on the same server. Or you can simply take advantage of Watcher’s capabilities to develop your project. “Watch everything”Currently the functionality of detecting changes in the file system is implemented. However, the project has a larger scope and we invite you to collaborate with us to achieve the goal of “Watch Everything”. One step at a time! Come on and join us. Starting with the file system 🗂️Yes, we have started implementing watcher to observe and detect changes in the file system. You can use watcher to discover changes related to file creation, file deletion and file alteration. You can find out more about our all the [File Watcher Features](index.html#document-features) in these pages. Watcher is Free, Open Source and User Focused 💓Our code is free and [open source](https://github.com/racherb/watcher). We like open source but we like socially responsible software even more. Watcher is distributed under MIT license. First steps[](#first-steps "Permalink to this headline") --------------------------------------------------------- Your project needs to process inputs that trigger your business logic but those inputs are out of your control? Do you want to integrate your project based on detection of file system changes? Learn about the great options Watcher offers for advanced change detection that you can leverage for your project development. * **Getting started**: [Installation Prerequisites](index.html#document-prerreq) | [Quick Installation](index.html#document-setup) | [Feature Overview](index.html#document-features) File Watcher features[](#file-watcher-features "Permalink to this headline") ----------------------------------------------------------------------------- Currently **Watcher** comprises the following features: [Single File & Folders](index.html#single-file-folders), [Multiples File Groups](index.html#multiples-file-groups), [File Patterns](index.html#file-patterns), [Non-Bloking Execution](index.html#non-bloking-execution), [Blocking Execution](index.html#bloking-execution), [Bulk File Processing](index.html#bulk-file-processing), [Advanced File Deletion](index.html#advanced-file-deletion), [Advanced File Creation](index.html#advanced-file-creation), [Advanced File Alteration](index.html#advanced-file-alteration), [Watcher for Any Alteration](index.html#watcher-for-any-alteration), [Watcher for Specific Alteration](index.html#watcher-for-specific-alteration), [Decoupled Execution](index.html#decoupled-execution), [Novelty Detection](index.html#novelty-detection), [Qualitative Response](index.html#qualitative-response), [Check File Stability](index.html#check-file-stability), [Big Amounts of Files](index.html#big-amounts-of-files), [Atomic Function Injection](index.html#atomic-function-injection), [Folder Recursion](index.html#folder-recursion), [Selective Path Level](index.html#selective-path-level), [Watcher Monitoring](index.html#watcher-monitoring) ### Prerequisites[](#prerequisites "Permalink to this headline") If you like containers we have one ready in **DocketHub** ([watcher](https://hub.docker.com/r/racherb/watcher)) but if you want to install **Watcher** you should consider the following requirements: #### Tarantool[](#tarantool "Permalink to this headline") **Watcher runs on Tarantool**. Tarantool is an In-memory computing platform. For installation follow the instructions on the [tarantool.io](https://www.tarantool.io/en/download/os-installation/) website. Before you begin, ensure you have met the following requirements: * **Tarantool**: `>= 1.7`. Note If you already have Tarantool installed you can skip this step. #### Supported Platforms[](#supported-platforms "Permalink to this headline") * **POSIX Compliant**: `Unix`, `MacOsx`, `Linux`, `Freebsd`. * **POSIX for Windows**: `Cygwin`, `Microsoft POSIX Subsystem`, `Windows Services for UNIX`, `MKS Toolkit`. Warning Watcher has not been tested on POSIX Windows Systems. ### Quick Instalation[](#quick-instalation "Permalink to this headline") There are several ways to install **Watcher** on your server. Choose the option that suits you best and go ahead! #### From Docker[](#from-docker "Permalink to this headline") Get **Watcher** container from a docker image: ``` 1docker pull racherb/watcher:latest 2docker run -i -t racherb/watcher ``` Note **Use docker volumes**. If you want to look at the host or remote machine’s file system then start a container with a `volume`. The following example enables a volume on the temporary folder `/tmp` of the host at path `/opt/watcher/host/` of the container. `docker run -i -t -v /tmp/:/opt/watcher/host/tmp racherb/watcher` #### From DEB Package[](#from-deb-package "Permalink to this headline") Quick installation from DEB Package: ``` 1curl -s https://packagecloud.io/install/repositories/iamio/watcher/script.deb.sh | sudo bash 2sudo apt-get install watcher ``` Note *DEB Quick install* is available for the following distributions: * **Debian**: `Lenny`, `Trixie`, `Bookworm`, `Bullseye`, `Buster`, `Stretch`, `Jessie`. * **Ubuntu**: `Cosmic`, `Disco`, `Hirsute`, `Groovy`, `Focal`. * **ElementaryOS**: `Freya`, `Loki`, `Juno`, `Hera`. #### From RPM Package[](#from-rpm-package "Permalink to this headline") First install the repository: ``` curl -s https://packagecloud.io/install/repositories/iamio/watcher/script.rpm.sh | sudo bash ``` And install the package: * For **RHEL** and **Fedora** distros: `sudo yum install watcher-0.2.1-1.noarch`. * For **Opensuse** and **Suse Linux Enterprise**: `sudo zypper install watcher-0.2.1-1.noarch`. Note *RPM Quick install* is available for the following distributions: * **RHEL**: `7`, `6`, `8`. * **Fedora**: `29`, `30`, `31`, `32`, `33`. * **OpenSuse**: `15.1`, `15.2`, `15.3`, `42.1`, `42.2`, `42.3`. * **Suse Linux Enterprise**: `12.4`, `12.5`, `15.0`, `15.1`, `15.2`, `15.3`. #### From Tarantool[](#from-tarantool "Permalink to this headline") Quick installation from **Utility Tarantool**: Install watcher through Tarantool’s `tarantoolctl` command: ``` 1tarantoolctl rocks install avro-schema 2tarantoolctl rocks install https://raw.githubusercontent.com/racherb/watcher/master/watcher-scm-1.rockspec ``` #### From LuaRocks[](#from-luarocks "Permalink to this headline") Make sure you have Luarocks installed first. From the `terminal` run the following command: ``` luarocks install https://raw.githubusercontent.com/racherb/watcher/master/watcher-scm-1.rockspec ``` ### Quick Start[](#quick-start "Permalink to this headline") Detection of `creation`, `deletion` and `alteration` of **single files** or **single folders** in the file system. ``` 1fwa = require('watcher').file --for file-watcher 2fwa.creation({'/path/to/single\_file'}) --watching file creation 3fwa.deletion({'/path/to/single\_folder/'}) --watching folder deletion 4fwa.alteration('/path/to/single\_folder/\*') --watching file alteration ``` ### File Watcher Features[](#file-watcher-features "Permalink to this headline") The **Watcher** module has been designed with the typical use cases of the Banking and Telecommunications industry in mind for *IT Batch Processing*. If you know of a use case that is not covered by watcher, please tell us about it in the [GitHub Discussions Section](https://github.com/racherb/watcher/discussions/categories/ideas/) . Currently **Watcher** comprises the following features: [Single File & Folders](#single-file-folders), [Multiples File Groups](#multiples-file-groups), [File Patterns](#file-patterns), [Non-Bloking Execution](#non-bloking-execution), [Blocking Execution](#bloking-execution), [Bulk File Processing](#bulk-file-processing), [Advanced File Deletion](#advanced-file-deletion), [Advanced File Creation](#advanced-file-creation), [Advanced File Alteration](#advanced-file-alteration), [Watcher for Any Alteration](#watcher-for-any-alteration), [Watcher for Specific Alteration](#watcher-for-specific-alteration), [Decoupled Execution](#decoupled-execution), [Novelty Detection](#novelty-detection), [Qualitative Response](#qualitative-response), [Check File Stability](#check-file-stability), [Big Amounts of Files](#big-amounts-of-files), [Atomic Function Injection](#atomic-function-injection), [Folder Recursion](#folder-recursion), [Selective Path Level](#selective-path-level), [Watcher Monitoring](#watcher-monitoring) Note The lines of code used to exemplify each feature of watcher assume the following: ``` 1fwa = require('watcher').file --for file-watcher 2mon = require('watcher').monit --for watcher monitoring ``` #### Single File & Folders[](#single-file-folders "Permalink to this headline") Detection of `creation`, `deletion` and `alteration` of **single files** or **single folders** in the file system. ``` 1fwa.creation({'/path/to/single\_file'}) --watching file creation 2fwa.creation({'/path/to/single\_folder/'}) --watching folder creation ``` #### Multiples File Groups[](#multiples-file-groups "Permalink to this headline") Multiple groups of different files can be watched at the same time. The input list of watchable files is a Lua table type parameter. ``` 1fwa.deletion( 2 { 3 '/path\_1/to/group\_file\_a/\*', --folder 4 '/path\_2/to/group\_file\_b/\*' --another 5 } 6 ) ``` #### File Patterns[](#file-patterns "Permalink to this headline") ``` fwa.creation({'/path/to/files\_\*.txt'}) ``` Note The *watch-list* is constructed with a single flag that controls the behavior of the function: **GLOB\_NOESCAPE**. For details type `man 3 glob`. #### Non-Bloking Execution[](#non-bloking-execution "Permalink to this headline") By default the **Watcher** run is executed in non-blocking mode through tarantool fibers. Fibers are a unique Tarantool feature *“green threads”* or coroutines that run independently of operating system threads. #### Blocking Execution[](#blocking-execution "Permalink to this headline") The `waitfor` function blocks the code and waits for a watcher to finish. ``` waitfor(fwa.creation({'/path/to/file'}).wid) --wait for watcher ``` #### Bulk File Processing[](#bulk-file-processing "Permalink to this headline") **Watcher** has an internal mechanism to allocate fibers for every certain amount of files in the watcher list. This amount is determined by the `BULK\_CAPACITY` configuration value in order to optimize performance. #### Advanced File Deletion[](#advanced-file-deletion "Permalink to this headline") ##### Inputs[](#inputs "Permalink to this headline") File Watcher Deletion Parameters[](#id31 "Permalink to this table") | Param | Type | Description | | --- | --- | --- | | wlist | `table`, `required` | Watch List | | maxwait | `number`, `otional`, `default-value: 60` | Maximum wait time in seconds | | interval | `number`, `otional`, `default-value: 0.5` | Verification interval for watcher in seconds | | options | `table`, `optional`, `default-value: {'NS', 0, 0}` | List of search options | | recursion | `table`, `optional`, `default-value: nil` or `{false, {0}, false}` | Recursion paramaters | ##### wlist[](#wlist "Permalink to this headline") It is the list of files, directories or file patterns to be observed. The data type is a Lua table and the size of tables is already limited to `2.147.483.647` elements. An example definition is the following: ``` wlist = {'path/file', 'path', 'pattern\*', ...} --arbitrary code ``` ##### maxwait[](#maxwait "Permalink to this headline") Maxwait is a numeric value that represents the maximum time to wait for the watcher. Watcher will terminate as soon as possible and as long as the search conditions are met. The default value is `60 seconds`. ##### interval[](#interval "Permalink to this headline") Interval is a numerical value that determines how often the watcher checks the search conditions. This value must be less than the maxwait value. The default value is `0.5` seconds. ##### options[](#options "Permalink to this headline") The options parameter is a Lua table containing 3 elements: `sort`, `cases` and `match`. * The first one `sort` contains the ordering method of the `wlist`. * The second element `cases` contains the number of cases to observe from the wlist. * and the third element `match` indicates the number of cases expected to satisfy the search. By default, the value of the option table is `{sort = 'NS', cases = 0, match = 0}`. The list of possible values for `sort`[](#id32 "Permalink to this table") | Value | Description | | --- | --- | | `'NS'` | No sort | | `'AA'` | Sorted alphabetically ascending | | `'AD'` | Sorted alphabetically descending | | `'MA'` | Sorted by date of modification ascending | | `'MD'` | Sorted for date of modification descending | Note The value `'NS'` treats the list in the same order in which the elements are passed to the list `wlist`. ##### recursion[](#recursion "Permalink to this headline") To enable directory recursion you must define the recursion parameter. The recursion works only for an observable of type directory. The recursion value is a Lua table type composed of the following elements `{recursive\_mode, {deep\_levels}, hidden\_files}`: * **recursive\_mode**: Boolean indicating whether or not to activate the recursive mode on the root directory. The default value is `false`. * **deep\_levels**: Numerical table indicating the levels of depth to be evaluated in the directory structure. The default value is `{0}` * **hidden\_files**: Boolean indicating whether hidden files will be evaluated in the recursion. The default value is `false`. ##### How do the recursion levels work?[](#how-do-the-recursion-levels-work "Permalink to this headline") To understand how levels work in recursion, let’s look at the following example. Imagine you have the following directory structure and you want to observe the deletion of files from the path **‘/folder\_A/folder\_B/’**. [](_images/recursive-levels.png) The levels are determined from the object path or root path that will be used as input in the watcher expression. In this case the path **‘/folder\_A/folder\_B/’** has level zero and, for each folder node a level will be added according to its depth. The result is shown in the following summary table, which contains the list of files for each level. Identification of the levels of recursion[](#id33 "Permalink to this table") | | [Input] Level 0 `{0}` | Level 1 `{1}` | Level 2 `{2}` | Level 3 `{3}` | Level 4 `{4}` | | --- | --- | --- | --- | --- | --- | | **folder** | `'/folder\_A/folder\_B/'` | `'folder\_C'` | `'folder\_D'` | `'folder\_E'` | `'folder\_N'` | | **files** | `{A1}` `{B1, B2, .B3}` | `{C1, C2}` | `{.D1}` | `{E1, E2, .E3}` | `{N1, N2}` | Note The files, `.B3`, `.D1` and `.E3` are hidden files. Now that we know how to set the recursion level, let’s see an example of the observable files depending on different values of the **recursion** parameter for the above mentioned example. Observable files depending on the recursion level[](#id34 "Permalink to this table") | `recursion` value | Composition of the list of observable files `wlist` | | --- | --- | | `{true, {0}, false}` | `{A1, B1, B2}` | | `{true, {0}, true}` | `{A1, B1, B2, .B3}` | | `{true, {0, 1}, false}` | `{A1, B1, B2, C1, C2}` | | `{true, {0, 1}, true}` | `{A1, B1, B2, .B3, C1, C2}` | | `{true, {2}, false}` | `nil` | | `{true, {2}, true}` | `{.D1}` | | `{true, {0, 1, 2, 3, 4}, false}` | `{A1, B1, B2, C1, C2, E1, E2, N1, N2}` | | `{true, {0, 1, 2, 3, 4}, true}` | `{A1, B1, B2, .B3, C1, C2, .D1, E1, E2, .E3, N1, N2}` | ##### Output[](#output "Permalink to this headline") #### Advanced File Creation[](#advanced-file-creation "Permalink to this headline") ##### Inputs[](#id9 "Permalink to this headline") File Watcher Creation Parameters[](#id35 "Permalink to this table") | Param | Type | Description | | --- | --- | --- | | wlist | `table`, `required` | Watch List | | maxwait | `number`, `otional`, `default-value: 60` | Maximum wait time in seconds | | interval | `number`, `otional`, `default-value: 0.5` | Verification interval for watcher in seconds | | minsize | `number`, `optional`, `default-value: 0` | Value of the minimum expected file size | | stability | `table`, `optional`, `default-value: {1, 15}` | Minimum criteria for measuring file stability | | novelty | `table`, `optional`, `default-value: {0, 0}` | Time interval that determines the validity of the file’s novelty | | nmatch | `number`, `optional`, `default-value: 0` | Number of expected files as a search sufficiency condition | ##### wlist[](#id10 "Permalink to this headline") It is the list of files, directories or file patterns to be observed. The data type is a Lua table and the size of tables is already limited to `2.147.483.647` elements. An example definition is the following: ``` wlist = {'path/file', 'path', 'pattern\*', ...} --arbitrary code ``` ##### maxwait[](#id11 "Permalink to this headline") Maxwait is a numeric value that represents the maximum time to wait for the watcher. Watcher will terminate as soon as possible and as long as the search conditions are met. The default value is `60 seconds`. ##### interval[](#id12 "Permalink to this headline") Interval is a numerical value that determines how often the watcher checks the search conditions. This value must be less than the maxwait value. The default value is `0.5` seconds. ##### minsize[](#minsize "Permalink to this headline") Minsize is a numerical value representing the minimum expected file size. The default value is `0`, which means that it is sufficient to just generate the file when the minimum size is unknown. Important Regardless of whether the expected file size is `0 Bytes`, watcher will not terminate until the file arrives in its entirety, avoiding edge cases where a file is consumed before the data transfer is complete. ##### stability[](#stability "Permalink to this headline") The `stability` parameter contains the elements that allow to evaluate the stability of a file. It is a Lua table containing two elements: * The `interval` that defines the frequency of checking the file once it has arrived. * The number of `iterations` used to determine the stability of the file. The default value is: `{1, 15}`. ##### novelty[](#novelty "Permalink to this headline") The `novelty` parameter is a two-element Lua table that contains the time interval that determines the validity of the file’s novelty. The default value is `{0, 0}` which indicates that the novelty of the file will not be evaluated. ##### nmatch[](#nmatch "Permalink to this headline") `nmatch` is a number of expected files as a search sufficiency condition. #### Advanced File Alteration[](#advanced-file-alteration "Permalink to this headline") ##### Inputs[](#id15 "Permalink to this headline") File Watcher Alteration Parameters[](#id36 "Permalink to this table") | Param | Type | Description | | --- | --- | --- | | wlist | `table`, `required` | Watch List | | maxwait | `numeric`, `otional`, `default-value: 60` | Maximum wait time in seconds | | interval | `numeric`, `otional`, `default-value: 0.5` | Verification interval for watcher in seconds | | awhat | `string`, `optional`, `default-value: '1'` | Type of file alteration to be observed | | nmatch | `number`, `optional`, `default-value: 0` | Number of expected files as a search sufficiency condition | ##### wlist[](#id16 "Permalink to this headline") It is the list of files, directories or file patterns to be observed. The data type is a Lua table and the size of tables is already limited to `2.147.483.647` elements. An example definition is the following: ``` wlist = {'path/file', 'path', 'pattern\*', ...} --arbitrary code ``` ##### maxwait[](#id17 "Permalink to this headline") Maxwait is a numeric value that represents the maximum time to wait for the watcher. Watcher will terminate as soon as possible and as long as the search conditions are met. The default value is `60 seconds`. ##### interval[](#id18 "Permalink to this headline") Interval is a numerical value that determines how often the watcher checks the search conditions. This value must be less than the maxwait value. The default value is `0.5` seconds. ##### awhat[](#awhat "Permalink to this headline") Type of file alteration to be observed. See [File Watcher Alteration Parameters](#file-watcher-alteration-parameters). File Watcher Alteration Parameters[](#id37 "Permalink to this table") | Type | Value | Description | | --- | --- | --- | | `ANY\_ALTERATION` | `'1'` | Search for any alteration | | `CONTENT\_ALTERATION` | `'2'` | Search for content file alteration | | `SIZE\_ALTERATION` | `'3'` | Search for file size alteration | | `CHANGE\_TIME\_ALTERATION` | `'4'` | Search for file `ctime` alteration | | `MODIFICATION\_TIME\_ALTERATION` | `'5'` | Search for file `mtime` alteration | | `INODE\_ALTERATION` | `'6'` | Search for file `inode` alteration | | `OWNER\_ALTERATION` | `'7'` | Search for file `owner` alteration | | `GROUP\_ALTERATION` | `'8'` | Search for file `group` alteration | ##### nmatch[](#id19 "Permalink to this headline") `nmatch` is a number of expected files as a search sufficiency condition. #### Watcher for Any Alteration[](#watcher-for-any-alteration "Permalink to this headline") ``` fwa.alteration({'/path/to/file'}, nil, nil, '1') ``` #### Watcher for Specific Alteration[](#watcher-for-specific-alteration "Permalink to this headline") ``` 1fwa.alteration({'/path/to/file'}, nil, nil, '2') --Watcher for content file alteration 2fwa.alteration({'/path/to/file'}, nil, nil, '3') --Watcher for content file size alteration 3fwa.alteration({'/path/to/file'}, nil, nil, '4') --Watcher for content file ctime alteration 4--explore other options for 'awhat' values ``` See table [File Watcher Alteration Parameters](#file-watcher-alteration-parameters) for more options. #### Decoupled Execution[](#decoupled-execution "Permalink to this headline") The `create`, `run` function and the `monit` options have been decoupled for better behavior, overhead relief and versatility of use. #### Novelty Detection[](#novelty-detection "Permalink to this headline") **Watcher** implements the detection of the newness of a file based on the `mtime` modification date. This is useful to know if file system items have been created in an expected time window. Warning Note that the creation of the files may have been done preserving the attributes of the original file. In that case you should consider the novelty rank accordingly. ``` 1 date\_from = os.time() - 24\*60\*60 --One day before the current date 2 date\_to = os.time() + 24\*60\*60 --One day after the current date 3 os.execute('touch /tmp/novelty\_file.txt') --The file is created on the current date 4 fwt.creation({'/tmp/novelty\_file.txt'}, 10, nil, 0, nil, {date\_from, date\_to}) ``` Note > > For known dates you can use the Lua function **os.time()** as follows: > > > ``` 1 date\_from = os.time( 2 { 3 year = 2020, 4 month = 6, 5 day = 4, 6 hour = 23, 7 min = 48, 8 sec = 10 9 } 10 ) ``` #### Qualitative Response[](#qualitative-response "Permalink to this headline") Watcher leaves a record for each watchable file where it provides qualitative nformation about the search result for each of them. To explore this information see the [Watcher Monitoring](#watcher-monitoring) `match` and `nomatch` functions. ``` 1 NOT\_YET\_CREATED = '\_' --The file has not yet been created 2 FILE\_PATTERN = 'P' --This is a file pattern 3 HAS\_BEEN\_CREATED = 'C' --The file has been created 4 IS\_NOT\_NOVELTY = 'N' --The file is not an expected novelty 5 UNSTABLE\_SIZE = 'U' --The file has an unstable file size 6 UNEXPECTED\_SIZE = 'S' --The file size is unexpected 7 DISAPPEARED\_UNEXPECTEDLY = 'D' --The file has disappeared unexpectedly 8 DELETED = 'X' --The file has been deleted 9 NOT\_EXISTS = 'T' --The file does not exist 10 NOT\_YET\_DELETED = 'E' --The file has not been deleted yet 11 NO\_ALTERATION = '0' --The file has not been modified 12 ANY\_ALTERATION = '1' --The file has been modified 13 CONTENT\_ALTERATION = '2' --The content of the file has been altered 14 SIZE\_ALTERATION = '3' --The file size has been altered 15 CHANGE\_TIME\_ALTERATION = '4' --The ctime of the file has been altered 16 MODIFICATION\_TIME\_ALTERATION = '5' --The mtime of the file has been altered 17 INODE\_ALTERATION = '6' --The number of inodes has been altered 18 OWNER\_ALTERATION = '7' --The owner of the file has changed 19 GROUP\_ALTERATION = '8' --The group of the file has changed ``` #### Check File Stability[](#check-file-stability "Permalink to this headline") Enabled only for file creation. This feature ensures that the **watcher** terminates once the file creation is completely finished. This criterion is independent of the file size. See usage for parameter [stability](#stability) #### Big Amounts of Files[](#big-amounts-of-files "Permalink to this headline") In the following example, watching the file deletion from the path “/” recursively down to depth level 3 (`levels={0,1,2,3}`) yields a total of **163,170 watchable files**. Note that the execution takes 85 seconds (on a typical desktop machine) but the maximum timeout of the watcher has been specified as low as 10 seconds. This means that 88% of the time is consumed in creating the watcher due to recursion. > > > ``` > 1 tarantool> test=function() local ini=os.time() local fwa=fw.deletion({'/'}, 10, nil, {'NS', nil, 2}, {true, {0,1,2,3}, false}) print(os.time()-ini) print(fwa.wid) end > 2 tarantool> test() > 3 85 > 4 1620701962375155ULL > 5 --- > 6 tarantool> mon.info(1620701962375155ULL) > 7 --- > 8 - ans: true > 9 match: 72 > 10 what: '{"/"}' > 11 wid: 1620701962375155 > 12 type: FWD > 13 nomatch: 163098 > 14 status: completed > 15 ... > > ``` > > > #### Atomic Function Injection[](#atomic-function-injection "Permalink to this headline") Atomic function injection allows you to perform specific tasks on each element of the watchable list separately. In the example, the atomic function afu creates a backup copy for each element of the watchlist. ``` 1afu = function(file) os.execute('cp '..file..' '..file..'\_backup') end --Atomic Funcion 2cor = require('watcher').core 3wat = cor.create({'/tmp/original.txt'}, 'FWD', afu) --afu is passed as parameter 4res = run\_watcher(wat) ``` #### Folder Recursion[](#folder-recursion "Permalink to this headline") You can enable recursion on directories to detect changes in the file system. Recursion is enabled based on a directory entry as a parameter that is considered as a root directory. Starting from this root directory, considered as level zero, you can selectively activate the observation of successive directory levels. ``` 1 fwa.deletion( 2 {'/tmp/folder\_1'}, --Observed directory is considered a zero level root directory 3 nil, --Maxwait, nil to take the value by omission 4 nil, --Interval, nil to take the value by omission 5 nil, --Options, nil to take the value by omission 6 { 7 true, --Activate recursion 8 {0, 1, 2}, --Levels of directories to be observed (root and levels 1 & 2) 9 false --Includes hidden files 10 } 11 ) ``` For more info see [How do the recursion levels work?](#how-do-the-recursion-levels-work). #### Selective Path Level[](#selective-path-level "Permalink to this headline") The recursion levels is a list of numerical values so you can specify (selectively) the directory level you want to observe and ignore others. This is useful in situations where the full path to the file is unknown but the depth or level of the file is known. ``` 1 fwa.deletion( 2 {'/bac/invoices'}, 3 nil, 4 nil, 5 nil, 6 { 7 true, --Activate recursion 8 {3}, --Selective level 3 9 false --Includes hidden files 10 } 11 ) ``` See use case … #### Watcher Monitoring[](#watcher-monitoring "Permalink to this headline") `monit` for Watcher monitoring allows you to monitor and explore the running status of a watcher. ##### info[](#info "Permalink to this headline") The output is a Lua table containing the following elements: * **ans** is a boolean value containing the response of the watcher. `true` means that the watcher has detected the expected changes that are defined in the parameters. * **match** is the number of cases that match the `true` value of **ans**. * **nomatch** is the number of cases that do not belong to the set of `true` **ans**. * **what** is a string containing the obserbables parameter. * **wid** is the unique identifier of the watcher. * **type** is the type of the watcher * **status** is the execution status of the watcher. ``` 1 mon.info(1620701962375155ULL) 2 3 { 4 ans: true 5 match: 72 6 what: '{"/"}' 7 wid: 1620701962375155 8 type: 'FWD' 9 nomatch: 163098 10 status: 'completed' 11 } ``` ##### match[](#match "Permalink to this headline") ##### nomatch[](#nomatch "Permalink to this headline")

auth

go

auth stable documentation [auth](index.html#document-index) stable [auth](index.html#document-index) * [Docs](index.html#document-index) » * auth stable documentation * [Edit on GitHub](https://github.com/ourway/auth/blob/f676ac54dd11dbd5468c41d7d9f7d925a784acdf/docs/index.rst) --- Auth | Authorization for Humans[¶](#auth-authorization-for-humans "Permalink to this headline") =============================================================================================== RESTful, Simple Authorization system with ZERO configuration. [](https://badge.fury.io/py/auth) [](https://pypi.python.org/pypi/auth) [](https://travis-ci.org/ourway/auth) [](https://codecov.io/github/ourway/auth?branch=master) What is Auth?[¶](#what-is-auth "Permalink to this headline") ------------------------------------------------------------ Auth is a module that makes authorization simple and also scalable and powerful. It also has a beautiful RESTful API for use in micro-service architectures and platforms. It is originally desinged to use in Appido, a scalable media market in Iran. It supports Python2.6+ and if you have a mongodb backbone, you need ZERO configurations steps. Just type `auth-server` and press enter! I use Travis and Codecov to keep myself honest. requirements[¶](#requirements "Permalink to this headline") ----------------------------------------------------------- You need to access to **mongodb**. If you are using a remote mongodb, provide these environment variables: `MONGO\_HOST` and `MONGO\_PORT` Installation[¶](#installation "Permalink to this headline") ----------------------------------------------------------- ``` pip install auth ``` Show me an example[¶](#show-me-an-example "Permalink to this headline") ----------------------------------------------------------------------- ok, lets image you have two users, **Jack** and **Sara**. Sara can cook and Jack can dance. Both can laugh. You also need to choose a secret key for your application. Because you may want to use Auth in various tools and each must have a secret key for seperating their scope. ``` my\_secret\_key = "pleaSeDoN0tKillMyC\_at" from auth import Authorization cas = Authorization(my\_secret\_key) ``` Now, Lets add 3 groups, Cookers, Dancers and Laughers. Remember that groups are Roles. So when we create a group, indeed we create a role: ``` cas.add\_group('cookers') cas.add\_group('dancers') cas.add\_group('laughers') ``` Ok, great. You have 3 groups and you need to authorize them to do special things. ``` cas.add\_permission('cookers', 'cook') cas.add\_permission('dancers', 'dance') cas.add\_permission('laughers', 'laugh') ``` Good. You let cookers to cook and dancers to dance etc... The final part is to set memberships for Sara and Jack: ``` cas.add\_membership('sara', 'cookers') cas.add\_membership('sara', 'laughers') cas.add\_membership('jack', 'dancers') cas.add\_membership('jack', 'laughers') ``` That’s all we need. Now lets ensure that jack can dance: ``` if cas.user\_has\_permission('jack', 'dance'): print('YES!!! Jack can dance.') ``` Authirization Methods[¶](#authirization-methods "Permalink to this headline") ----------------------------------------------------------------------------- use pydoc to see all methods: ``` pydoc auth.Authorization ``` RESTful API[¶](#restful-api "Permalink to this headline") --------------------------------------------------------- Lets run the server on port 4000: ``` from auth import api, serve serve('localhost', 4000, api) ``` Or, from version 0.1.2+ you can use this command: ``` auth-server ``` Simple! Authorization server is ready to use.  You can use it via simple curl or using mighty Requests module. So in you remote application, you can do something like this: ``` import requests secret\_key = "pleaSeDoN0tKillMyC\_at" auth\_api = "http://127.0.0.1:4000/api" ``` Lets create admin group: ``` requests.post(auth\_api+'/role/'+secret\_key+'/admin') ``` And lets make Jack an admin: ``` requests.post(auth\_api+'/permission/'+secret\_key+'/jack/admin') ``` And finally let’s check if Sara still can cook: ``` requests.get(auth\_api+'/has\_permission/'+secret\_key+'/sara/cook') ``` RESTful API helpers[¶](#restful-api-helpers "Permalink to this headline") ------------------------------------------------------------------------- auth comes with a helper class that makes your life easy. ``` from auth.client import Client service = Client('srv201', 'http://192.168.99.100:4000') print(service) service.get\_roles() service.add\_role(role='admin') ``` API Methods[¶](#api-methods "Permalink to this headline") --------------------------------------------------------- ``` pydoc auth.CAS.REST.service ``` * `/ping` [GET] > > Ping API, useful for your monitoring tools * `/api/membership/{KEY}/{user}/{role}` [GET/POST/DELETE] > > Adding, removing and getting membership information. * `/api/permission/{KEY}/{role}/{name}` [GET/POST/DELETE] > > Adding, removing and getting permissions * `/api/has\_permission/{KEY}/{user}/{name}` [GET] > > Getting user permission info * `/api/role/{KEY}/{role}` [GET/POST/DELETE] Adding, removing and getting roles * `/api/which\_roles\_can/{KEY}/{name}` [GET] For example: Which roles can send\_mail? * `/api/which\_users\_can/{KEY}/{name}` [GET] For example: Which users can send\_mail? * `/api/user\_permissions/{KEY}/{user}` [GET] Get all permissions that a user has * `/api/role\_permissions/{KEY}/{role}` [GET] Get all permissions that a role has * `/api/user\_roles/{KEY}/{user}` [GET] > > Get roles that user assinged to > > > * `/api/roles/{KEY}` [GET] > > Get all available roles > > > Deployment[¶](#deployment "Permalink to this headline") ------------------------------------------------------- Deploying Auth module in production environment is easy: ``` gunicorn auth:api ``` Dockerizing[¶](#dockerizing "Permalink to this headline") --------------------------------------------------------- It’s simple: ``` docker build -t python/auth-server https://raw.githubusercontent.com/ourway/auth/master/Dockerfile docker run --name=auth -e MONGO\_HOST='192.168.99.100' -p 4000:4000 -d --restart=always --link=mongodb-server python/auth-server ``` Copyright[¶](#copyright "Permalink to this headline") ----------------------------------------------------- * Farsheed Ashouri [@](mailto:rodmena%40me.com) Documentation[¶](#documentation "Permalink to this headline") ------------------------------------------------------------- Feel free to dig into source code. If you think you can improve the documentation, please do so and send me a pull request. Unit Tests and Coverage[¶](#unit-tests-and-coverage "Permalink to this headline") --------------------------------------------------------------------------------- I am trying to add tests as much as I can, but still there are areas that need improvement. To DO[¶](#to-do "Permalink to this headline") --------------------------------------------- * Add Authentication features * Improve Code Coverage

lxd

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lxd latest documentation [lxd](#) latest [lxd](#) * » * lxd latest documentation * [Edit on GitHub](https://github.com/cap-ai/lxd/blob/main/index) --- Under Maintenance…….[](#under-maintenance "Permalink to this headline") ========================================================================

pipe

go

Warp Pipe 0.1.0 documentation [](index.html#document-index) latest * [Installation](index.html#document-installation) + [Stable release](index.html#stable-release) + [From sources](index.html#from-sources) * [Usage](index.html#document-usage) * [Contributing](index.html#document-contributing) + [Types of Contributions](index.html#types-of-contributions) + [Get Started!](index.html#get-started) + [Pull Request Guidelines](index.html#pull-request-guidelines) + [Tips](index.html#tips) * [Credits](index.html#document-authors) + [Development Lead](index.html#development-lead) + [Contributors](index.html#contributors) * [History](index.html#document-history) + [0.1.0 (2019-05-17)](index.html#id1) [Warp Pipe](index.html#document-index) * [Docs](index.html#document-index) » * Warp Pipe 0.1.0 documentation * [Edit on GitHub](https://github.com/yngtodd/pipe/blob/master/docs/index.rst) --- Warp Pipe Documentation[¶](#warp-pipe-documentation "Permalink to this headline") ================================================================================= Installation[¶](#installation "Permalink to this headline") ----------------------------------------------------------- ### Stable release[¶](#stable-release "Permalink to this headline") To install pipe, run this command in your terminal: ``` pip install pipe ``` This is the preferred method to install pipe, as it will always install the most recent stable release. If you don’t have [pip](https://pip.pypa.io) installed, this [Python installation guide](http://docs.python-guide.org/en/latest/starting/installation/) can guide you through the process. ### From sources[¶](#from-sources "Permalink to this headline") The sources for pipe can be downloaded from the [Github repo](https://github.com/yngtodd/pipe). You can either clone the public repository: ``` git clone git://github.com/yngtodd/pipe ``` Or download the [tarball](https://github.com/yngtodd/pipe/tarball/master): ``` curl -OL https://github.com/yngtodd/pipe/tarball/master ``` Once you have a copy of the source, you can install it with: ``` python setup.py install ``` Usage[¶](#usage "Permalink to this headline") --------------------------------------------- To use Warp Pipe in a project: ``` import pipe ``` Contributing[¶](#contributing "Permalink to this headline") ----------------------------------------------------------- Contributions are welcome, and they are greatly appreciated! Every little bit helps, and credit will always be given. You can contribute in many ways: ### Types of Contributions[¶](#types-of-contributions "Permalink to this headline") #### Report Bugs[¶](#report-bugs "Permalink to this headline") Report bugs at <https://github.com/yngtodd/pipe/issues>. If you are reporting a bug, please include: * Your operating system name and version. * Any details about your local setup that might be helpful in troubleshooting. * Detailed steps to reproduce the bug. #### Fix Bugs[¶](#fix-bugs "Permalink to this headline") Look through the GitHub issues for bugs. Anything tagged with “bug” is open to whoever wants to implement it. #### Implement Features[¶](#implement-features "Permalink to this headline") Look through the GitHub issues for features. Anything tagged with “feature” is open to whoever wants to implement it. #### Write Documentation[¶](#write-documentation "Permalink to this headline") Warp Pipe could always use more documentation, whether as part of the official Warp Pipe docs, in docstrings, or even on the web in blog posts, articles, and such. #### Submit Feedback[¶](#submit-feedback "Permalink to this headline") The best way to send feedback is to file an issue at <https://github.com/yngtodd/pipe/issues>. If you are proposing a feature: * Explain in detail how it would work. * Keep the scope as narrow as possible, to make it easier to implement. * Remember that this is a volunteer-driven project, and that contributions are welcome :) ### Get Started Ready to contribute? Here’s how to set up pipe for local development. 1. [Fork](https://github.com/Nekroze/pipe/fork) the pipe repo on GitHub. 2. Clone your fork locally: ``` git clone git@github.com:your\_name\_here/pipe.git ``` 3. Create a branch for local development: ``` git checkout -b name-of-your-bugfix-or-feature ``` Now you can make your changes locally. 4. When you’re done making changes, check that your changes pass style and unit tests, including testing other Python versions with tox: ``` tox ``` To get tox, just pip install it. 5. Commit your changes and push your branch to GitHub: ``` git add . git commit -m "Your detailed description of your changes." git push origin name-of-your-bugfix-or-feature ``` 6. Submit a pull request through the GitHub website. ### Pull Request Guidelines[¶](#pull-request-guidelines "Permalink to this headline") Before you submit a pull request, check that it meets these guidelines: 1. The pull request should include tests. 2. If the pull request adds functionality, the docs should be updated. Put your new functionality into a function with a docstring, and add the feature to the list in README.rst. 3. The pull request should work for Python >= 3.6 and for PyPy. Check <https://travis-ci.org/yngtodd/pipe> under pull requests for active pull requests or run the `tox` command and make sure that the tests pass for all supported Python versions. ### Tips[¶](#tips "Permalink to this headline") To run a subset of tests: ``` py.test test/test\_pipe.py ``` Credits[¶](#credits "Permalink to this headline") ------------------------------------------------- ### Development Lead[¶](#development-lead "Permalink to this headline") * Todd Young GitHub: [yngtodd](https://github.com/yngtodd) ### Contributors[¶](#contributors "Permalink to this headline") None yet. Why not be the first? History[¶](#history "Permalink to this headline") ------------------------------------------------- ### 0.1.0 (2019-05-17)[¶](#id1 "Permalink to this headline") * First release on PyPI. Feedback[¶](#feedback "Permalink to this headline") =================================================== If you have any suggestions or questions about **Warp Pipe** feel free to email me at [ygx@ornl.gov](mailto:ygx%40ornl.gov). If you encounter any errors or problems with **Warp Pipe**, please let me know! Open an Issue at the GitHub <http://github.com/yngtodd/pipe> main repository.

gi

go

GObject Introspection [GObject Introspection](#) * [Changelog](index.html#document-changelog) * [Goals](index.html#document-goals) * [Architecture](index.html#document-architecture) * [Users](index.html#document-users) * [Build & Test](index.html#document-build_test) * [Writing Bindable APIs](index.html#document-writingbindableapis) * [Build System Integration](index.html#document-buildsystems/index) * [Annotations](index.html#document-annotations/index) * [Writing Bindings](index.html#document-writingbindings/index) * [Command Line Tools](index.html#document-tools/index) [GObject Introspection](#) * * GObject Introspection * --- GObject Introspection[](#gobject-introspection "Permalink to this heading") ============================================================================ Changelog[](#changelog "Permalink to this heading") ---------------------------------------------------- Versions with an odd minor version are unstable releases (e.g. 1.57.x) while versions with even minor version are stable releases (e.g. 1.58.x). This list is sorted by release date. For more details see the GIT log: <https://gitlab.gnome.org/GNOME/gobject-introspection> ### 1.78.1 - 2023-09-16[](#id1 "Permalink to this heading") * Avoid undefined behaviour in the Regress test suite [#458] ### 1.78.0 - 2023-09-08[](#id2 "Permalink to this heading") * Update the GIR data for GLib, GObject, and GIO * Add GObject as a dependency for the Cairo GIR * Add more tests for GI marshalling * Update regression test suite * Fix build on Windows for paths using ‘' as a separator * Support different prefix for finding GIR data * Add GI\_GIR\_PATH environment variable for controlling GIR paths ### 1.76.1 - 2023-03-22[](#id3 "Permalink to this heading") * Handle null default values [#457] * Documentation fixes ### 1.76.0 - 2023-03-13[](#id4 "Permalink to this heading") * Documentation fixes * Update the GIR data for GLib ### 1.75.6 - 2023-02-13[](#id5 "Permalink to this heading") * Documentation fixes * Fix build when using GLib as a subproject * Update the GIR data for GLib ### 1.75.4 - 2023-01-09[](#id6 "Permalink to this heading") * Brown-paper bag release to fix the GLib dependency. ### 1.75.2 - 2023-01-09[](#id7 "Permalink to this heading") * Split ‘disguised’ attribute into two separate attributes [#101] * Add copy/free function annotations for plain-old data types [#14] * Include the default value of GObject properties in the GIR data [#4] * Drop wrap files for recursive dependencies * Add more marshalling tests [Marco Trevisan] * Update the GIR data for GLib, GObject, GModule, and GIO [Sebastian Dröge, Rico Tzschichholz] ### 1.74.0 - 2022-09-17[](#id8 "Permalink to this heading") * Update the GIR data for GLib, GObject, GModule, and GIO ### 1.73.1 - 2022-09-03[](#id9 "Permalink to this heading") * Update the GIR data for GLib, GObject, GModule, and GIO * Disable rpath on Windows [Christoph Reiter] * Add llvm/mingw support on Windows [Christoph Reiter] * Fix annotations in libgirepository [Philip Chimento] * Support C99 designated initializers when parsing C declarations [Jan Tojnar] * Add some more types to win32 GIR [Marc-André Lureau] * Let doctool prepend emitting objects in GJS signals [Andy Holmes] * Require a C99 toolchain like GLib ### 1.73.0 - 2022-07-13[](#id10 "Permalink to this heading") * Update the GIR data for GLib, GObject, GModule, and GIO * scanner: Support pre-processor macros with zero arguments [Philip Withnall] * scanner: Support ISO C varargs in macros [Philip Withnall] * Fix subproject build [Andoni Morales Alastruey] ### 1.72.0 - 2022-03-18[](#id11 "Permalink to this heading") * Add new utility API to libgirepository for bindings implementing an argument cache [Philip Chimento] * Update the GIR data for GLib, GObject, GModule, and GIO ### 1.71.0 - 2022-02-14[](#id12 "Permalink to this heading") * Create new API for libffi closures [Sergei Trofimovich, Cimbali] * Treat @-prefixed shlib paths as absolute on macOS [Rok Mandeljc] * Add new forever scope [#49] * Build fixes with newer Meson [#414] * Improve regression test suite [Marco Trevisan (Treviño)] * Avoid a segfault when using an invalid GType [Lukas Oberhuber] * Build fixes on Windows when using g-i as a subproject [Andoni Morales Alastruey] * Warn about property name collisions [#386] * Add “strict” warnings to g-ir-scanner * Add the “emitter” annotation for signal emitters * Add a command line option to g-ir-scanner to specify the compiler * Add new convenience API to libgirepository [Philip Chimento] * Build fixes on Windows when using MSVC [Chun-wei Fan] * Documentation fixes [#211, #327] * Update the GIR data for GLib, GObject, and GIO ### 1.70.0 - 2021-09-17[](#id13 "Permalink to this heading") * Update the GIR data for GLib, GObject, and GIO ### 1.69.0 - 2021-08-24[](#id14 "Permalink to this heading") * Fix build when gobject-introspection is a subproject [!266](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/266) * Add more float types [#384](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/384), [!269](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/269) * Make test suite work with cross-related options [#227](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/227) * Fix several leaks found by Coverity [!272](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/272) * Fix enum member c:identifier [!264](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/264) * Add g-ir-doc-tool man page [!284](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/284) * Export warnlib sources as variables [!287](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/287) * Update the GLib annotations [!288](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/288) * Add “final” class attribute [!257](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/257), [!291](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/291) * Add option to make .gir files installation paths configurable [!63](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/63) * Handle constructors with mismatched GTypes [#399](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/399), [!292](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/292) * Add property accessors annotations [#13](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/13), [!279](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/279) ### 1.68.0 - 2021-03-19[](#id15 "Permalink to this heading") * Update GLib annotations [!262](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/262) * docs: cleanup [!261](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/261) * Fix syntax errors in gir-1.2.rnc [!256](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/256) ### 1.67.1 - 2021-03-12[](#id16 "Permalink to this heading") * Requires Python 3.6+ * Update GLib annotations * Fix compatibility with Python 3.10 [#358](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/358) * Fix build with GIR data disabled [!248](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/248) * Add test object for signal marshallers [!259](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/259) ### 1.66.1 - 2020-10-03[](#id17 "Permalink to this heading") * Update glib annotations * Windows: Fix running on different drives [!239](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/239) * gimarshallingtests: Add more tests for flags [!235](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/235) * Revert “giscanner: Fix section matching for documentation [!237](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/237)” see [#360](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/360) ### 1.66.0 - 2020-09-12[](#id18 "Permalink to this heading") * Support the gtk-doc action syntax [!203](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/203) * Meson fixes with glib and/or g-i is a subproject [!206](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/206) [!208](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/208) * GITypeInfo storage type utility API [!205](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/205) * Meson: Fix build as subproject [!214](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/214) * Fixing XDG\_DATA\_DIRS logic [!215](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/215) * libgirepository: Add a couple missing nullable annotations [!217](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/217) [!225](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/225) * dumper: Fix missing symbols in LTO case or with overridden symbol visibility settings [!216](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/216) * Documentation improvements: [!220](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/220) [!232](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/232) * Remove old autoconf fallback code for the python tools [!221](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/221) * meson: Rename option gi\_cross\_use\_{host -> prebuilt}\_gi mr:211 * meson: Don’t override finding executables when using pre-built tools. [!212](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/212) * meson: gir: add a dependency for g-ir-compiler for building .girs [!228](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/228) * meson: Use pkgconfig generator [!207](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/207) * Fix gi-dump-types.c to build on Windows [!218](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/218) * giscanner: parse block comments for members and fields [!230](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/230) * Add the notion of standalone doc sections [!226](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/226) * giscanner: Add support for using clang-cl [!234](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/234) * giscanner: Fix section matching for documentation [!237](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/237) ### 1.64.1 - 2020-04-05[](#id19 "Permalink to this heading") * Replace calls to deprecated xml.etree.cElementTree removed in Python 3.9 [!202](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/202) ([Stephen Gallagher](https://gitlab.gnome.org/sgallagher)) * gimarshallingtests: Use g\_assert\_cmpfloat\_with\_epsilon. Fixes tests on some architectures [!200](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/200) ([Iain Lane](https://gitlab.gnome.org/iainl)) ### 1.64.0 - 2020-03-07[](#id20 "Permalink to this heading") * Update glib annotations ([Rico Tzschichholz](https://gitlab.gnome.org/ricotz)) * Fix regress scanner tests for non-gcc/clang compilers [!197](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/197) ([Chun-wei Fan](https://gitlab.gnome.org/fanc999)) * Document how to update glib GIR [!199](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/199) ([Bastien Nocera](https://gitlab.gnome.org/hadess)) ### 1.63.2 - 2020-01-17[](#id21 "Permalink to this heading") * Update glib annotations ([Rico Tzschichholz](https://gitlab.gnome.org/ricotz)) * Add GMemoryMonitor to glib annotations [!193](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/193) ([Bastien Nocera](https://gitlab.gnome.org/hadess)) * Fix build reproducibility [!192](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/192) ([Joshua Watt](https://gitlab.gnome.org/jpewhacker)) * Drop deprecated xml.etree.ElementTree.Element.getchildren() calls [!194](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/194) ([Miro Hrončok](https://gitlab.gnome.org/hroncok)) * Support Python 3.8.x+ on Windows [!195](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/195) ([Chun-wei Fan](https://gitlab.gnome.org/fanc999)) * Cross compile support [!64](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/64) ([Alexander Kanavin](https://gitlab.gnome.org/alex.kanavin)) * meson: Visual Studio builds: Use -utf-8 where available [!196](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/196) ([Chun-wei Fan](https://gitlab.gnome.org/fanc999)) ### 1.63.1 - 2019-11-24[](#id22 "Permalink to this heading") * Update glib annotations ([Rico Tzschichholz](https://gitlab.gnome.org/ricotz)) * build: require meson 0.50.1 * build: use proper dylib versioning on macOS [!177](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/177) ([Tom Schoonjans](https://gitlab.gnome.org/tschoonj)) * scanner: Support array arguments with static keyword [!176](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/176) ([Emmanuele Bassi](https://gitlab.gnome.org/ebassi)) * website: Add Ruby-GNOME to user list [!178](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/178) ([kojix2](https://gitlab.gnome.org/kojix2)) * Fix non-libtool code being run with no nob-libtool dependencies [!179](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/179) ([Alistair Buxton](https://gitlab.gnome.org/ali1234)) * meson: change “cairo” from a boolean to a feature option [!180](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/180) * meson: change “doctool” from a boolean to a feature option [!181](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/181) * Fix a memory leak in g\_irepository\_get\_object\_gtype\_interfaces() [!182](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/182) ([Philip Chimento](https://gitlab.gnome.org/ptomato)) * ccompiler.py: Fix macro defines with quotes on MSVC [!183](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/183) ([Chun-wei Fan](https://gitlab.gnome.org/fanc999)) * tests: Actually test libregress by specifying the LD\_LIBRARY\_PATH [!174](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/174) ([Corentin Noël](https://gitlab.gnome.org/tintou)) * examples: Make self contained and add build system integration examples [!189](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/189) * autotools: Make INTROSPECTION\_GIRDIR/INTROSPECTION\_TYPELIBDIR respect prefix/datadir/libdir [!190](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/190) * girepository: Also store GType cache misses [!191](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/191) ([Carlos Garnacho](https://gitlab.gnome.org/carlosg)) * docs: Document GI\_CROSS\_LAUNCHER envvar [!175](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/175) ([Emmanuele Bassi](https://gitlab.gnome.org/ebassi)) ### 1.62.0 - 2019-09-09[](#id23 "Permalink to this heading") * No changes since 1.61.2 ### 1.61.2 - 2019-08-17[](#id24 "Permalink to this heading") * dumper: Use the distutils linker [!170](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/170) ([David Demelier](https://gitlab.gnome.org/markand)) * structinfo: Fix offset in find\_method() [!171](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/171) ([Florian Müllner](https://gitlab.gnome.org/fmuellner)) * tests: Don’t include “config.h” in installed files [!172](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/172) ([Philip Chimento](https://gitlab.gnome.org/ptomato)) * meson: Make meson.override\_find\_program working on more complex use cases [!173](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/173) ([Thibault Saunier](https://gitlab.gnome.org/thiblahute)) ### 1.61.1 - 2019-08-07[](#id25 "Permalink to this heading") * Drop autotools build system [!143](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/143) * meson: require 0.49.2 * Update glib annotations ([Rico Tzschichholz](https://gitlab.gnome.org/ricotz)) * Add documentation to the RelaxNG schema [!139](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/139) ([David Bellot](https://gitlab.gnome.org/yimyom)) * Unused variable fixes [!147](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/147) ([Philip Chimento](https://gitlab.gnome.org/ptomato)) * cachestore: handle cache getting deleted while loading it [!148](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/148) * Visual Studio builds: Use msvc\_recommended\_pragmas.h from GLib [!150](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/150) ([Chun-wei Fan](https://gitlab.gnome.org/fanc999)) * Add Vulkan gir [!155](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/155) ([Matthew Waters](https://gitlab.gnome.org/ystreet00)) * Make g\_irepository\_get\_object\_gtype\_interfaces actually work [!157](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/157) ([Philip Chimento](https://gitlab.gnome.org/ptomato)) * gimarshallingtests: Add a marshalling test case for GPtrArrays and GArrays of structures [!160](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/160) ([Stéphane Seng](https://gitlab.gnome.org/stephaneseng)) * scanner: parse and expose function macros [!159](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/159) ([Mathieu Duponchelle](https://gitlab.gnome.org/mathieudu)) * meson: use pkg-config directly for libffi cflags and libs [!162](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/162) ([Aaron Boxer](https://gitlab.gnome.org/boxerab)) * meson: Fix wrong dependency type check for gio-unix [#166](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/166) ([Brook Milligan](https://gitlab.gnome.org/brook-milligan)) * regress: Add regression test for signal with GError param [!169](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/169) ([Philip Chimento](https://gitlab.gnome.org/ptomato)) ### 1.60.2 - 2019-06-15[](#id26 "Permalink to this heading") * docwriter: Fix Exception message attribute [!146](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/146) ([Philip Chimento](https://gitlab.gnome.org/ptomato)) * meson: fix default cairo DLL name on Windows * scanner: Fix error on Windows in case source files are on different drives * gi-test: Fix gir file tests with MSVC [!151](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/151) ([Chun-wei Fan](https://gitlab.gnome.org/fanc999)) * MSVC.README.rst: Update VS 2008/x64 build notes [!152](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/152) ([Chun-wei Fan](https://gitlab.gnome.org/fanc999)) * giscanner/scannerlexer.l: Include io.h on Windows ([Chun-wei Fan](https://gitlab.gnome.org/fanc999)) * build: Force-include msvc\_recommended\_pragmas.h on Visual Studio [!152](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/152) ([Chun-wei Fan](https://gitlab.gnome.org/fanc999)) * Update glib annotations (2.60.4) ### 1.60.1 - 2019-04-07[](#id27 "Permalink to this heading") * Update glib annotations (glib-2-60) * shlibs: fall back to basename on macOS for relative paths [#222](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/222) * meson: always pass –quiet to g-ir-scanner * docs: include ‘–c-include’ in g-ir-scanner man page [#275](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/275) * tests: Fix compatibility with Python 3.5 [#274](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/274) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) ### 1.60.0 - 2019-03-10[](#id28 "Permalink to this heading") NOTE: This is the last release supporting autotools. Please try building with meson instead and report any problems. This does not affect projects using autotools + g-i, only the build of g-i itself. * gir: Update glib annotations [!142](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/142) [!141](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/141) ([Andrea Azzarone](https://gitlab.gnome.org/azzaronea)) ### 1.59.5 - 2019-03-04[](#id29 "Permalink to this heading") * gir: Include C header in cairo gir file [!138](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/138) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) * ccompiler: restore customize\_compiler() setup for macOS. [#268](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/268) * gir: skip glib-enumtypes.h for GObject-2.0 [!140](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/140) ### 1.59.4 - 2019-02-04[](#id30 "Permalink to this heading") * tests: Add functions using flat struct arrays. [!130](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/130) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) * Clean shebangs out of non-executable scripts and drop exec perm from xmlwriter.py. [!131](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/131) ([Dominique Leuenberger](https://gitlab.gnome.org/DimStar77)) * maintransformer: parse deprecation annotations for section blocks. [#213](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/213) [!132](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/132) * repository: g\_irepository\_get\_object\_gtype\_interfaces. [!30](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/30) ([Colin Walters](https://gitlab.gnome.org/walters), [Philip Chimento](https://gitlab.gnome.org/ptomato)) * message: handle fatal errors even if warnings are disabled. [#229](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/229) [!126](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/126) * autotools: Fix build with `-Wl,--as-needed`. [#226](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/226) [!134](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/134) * maintransformer: Don’t warn on (optional) annotations on (inout) [!135](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/135) ([Guido Günther](https://gitlab.gnome.org/guidog)) * girepository: Fix a possible use-after-free if g\_mapped\_file\_new() fails and fix possible leak of transitive dependency names. [!136](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/136) ([Elliott Sales de Andrade](https://gitlab.gnome.org/QuLogic)) ### 1.59.3 - 2019-01-08[](#id31 "Permalink to this heading") * meson: use underscore as a separator in build options (gtk-doc -> gtk\_doc etc) [!129](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/129) * website: add cppgir C++ binding [!124](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/124) ([Mark Nauwelaerts](https://gitlab.gnome.org/mnauw)) * scanner: Merge specifiers and qualifiers when merging basic types. Fixes “unsigned char” being wrongly parsed as “unsigned” etc. [!125](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/125) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) * meson: warn that not all tests will be run if building without cairo/doctool * scanner: rework source root directory guessing code to not depend on the build directory * scanner: Remove incorrect c:type generated for array of synthesized unions [!127](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/127) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) * scanner: Flatten multi-dimensional arrays fields [!128](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/128) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) ### 1.59.2 - 2019-01-04[](#id32 "Permalink to this heading") * Everything included in 1.58.3 * meson: Various fixes and all tests have been ported (0.47+ is required now) [!114](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/114) [!110](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/110) etc. ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko), [Emmanuele Bassi](https://gitlab.gnome.org/ebassi), [Christoph Reiter](https://gitlab.gnome.org/creiter)) * scanner: Save preprocessor input and output files with `save-temps` [!107](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/107) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) * automake: Use the wildcard function where needed [!100](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/100) ([William Hua](https://gitlab.gnome.org/williamhua)) * build: extend `PYTHONPATH` instead of replacing it [!101](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/101) * gir/cairo: add `cairo\_rectangle\_t` [#74](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/74) [!103](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/103) (Yeti) * Add a `--version` option to g-ir-compiler and g-ir-generate [#55](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/55) [!106](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/106) * tests: various test improvements [!111](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/111) [!117](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/117) [!119](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/119) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) * ccompiler: don’t use Python compiler flags [#150](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/150) [!118](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/118) [!120](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/120) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko), [Christoph Reiter](https://gitlab.gnome.org/creiter)) * parser: Do not bail out when parsing GIR files without doc positions [!121](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/121) ([Emmanuele Bassi](https://gitlab.gnome.org/ebassi)) * gimarshallingtests: Remove declarations of nonexistent functions [!123](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/123) ([Philip Chimento](https://gitlab.gnome.org/ptomato)) ### 1.58.3 - 2018-12-30[](#id33 "Permalink to this heading") * docwriter: Support python-markdown 3.x [#250](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/250) * scanner: Define grefcount and gatomicrefcount as aliases to gint [#254](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/254) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) * scanner: make using bool without stdbool include work again [#247](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/247) * gir: Update glib annotations for glib 2.58.2 ### 1.59.1 - 2018-12-16[](#id34 "Permalink to this heading") * Everything included in 1.58.2 * build: Drop Python 2 support, require Python 3.4+ [!69](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/69) * build: Add option to make .gir files installation paths configurable [!63](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/63) ([Kai Kang](https://gitlab.gnome.org/kai.7.kang)) * build: Skip gobject/gvaluecollector.h when constructing GObject GIR [!20](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/20) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) * build: Port various tests to work with meson [!92](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/92) [!94](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/94) [!95](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/95) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) * regress: Add test for write-only property [!67](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/67) ([Philip Chimento](https://gitlab.gnome.org/ptomato)) * regress: Implement interface and override properties [!59](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/59) ([Philip Chimento](https://gitlab.gnome.org/ptomato)) * writer: Include documentation and symbol position in source files [!75](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/75) ([Thibault Saunier](https://gitlab.gnome.org/thiblahute)) * giscanner: Print relative filename paths when warning [!74](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/74) ([Jonas Ådahl](https://gitlab.gnome.org/jadahl)) * giscanner: Define grefcount and gatomicrefcount as aliases to gint [!76](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/76) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) * giscanner: Allow empty declarations. Fixes warnings with mingw headers. [#216](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/216) [!89](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/89) ([Christoph Reiter](https://gitlab.gnome.org/creiter)) * giscanner: Replace linked lists with arrays in source scanner [!90](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/90) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) * girepository: Various docs cleanups and fixes [!96](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/96) [!97](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/97) [#66](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/66) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko), [Christoph Reiter](https://gitlab.gnome.org/creiter), Jasper St. Pierre) * girepository: Don’t abort when calling g\_base\_info\_get\_name() on a GITypeInfo [#96](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/96) [!99](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/99) ([Christoph Reiter](https://gitlab.gnome.org/creiter)) * girepository: Add version macros and functions [#200](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/200) [!98](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/98) ([Christoph Reiter](https://gitlab.gnome.org/creiter)) ### 1.58.2 - 2018-12-09[](#id35 "Permalink to this heading") * meson: Fix random build errors (mostly MSVC) [!88](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/88) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) * scanner: Fix parsing of \_\_typeof\_\_ that is part of a cast expression [!78](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/78) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) * scanner: Ignore \_\_pragma keyword used by MSVC [!87](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/87) ([Tomasz Miąsko](https://gitlab.gnome.org/tmiasko)) ### 1.58.1 - 2018-11-17[](#id36 "Permalink to this heading") * meson: Install warnlib [!62](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/62) ([Jan Tojnar](https://gitlab.gnome.org/jtojnar)) * scanner: Parse \_\_typeof\_\_ and discard it [!71](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/71) ([Jan Alexander Steffens](https://gitlab.gnome.org/heftig)) * meson: add back /usr/bin/env to the python-cmd [#237](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/237) [!70](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/70) ([Håvard Graff](https://gitlab.gnome.org/hgr)) * Fix non libtool build [!72](https://gitlab.gnome.org/GNOME/gobject-introspection/merge_requests/72) ([Olivier Crête](https://gitlab.gnome.org/ocrete)) ### 1.58.0 - 2018-08-31[](#id37 "Permalink to this heading") ``` • Changes: - Update annotations imported from GLib, and require GLib 2.58 - Ensure that G-I builds on macOS - Add a --version argument to the Python-based tools - Allow selecting the output format for g-ir-doc-tool - Drop the Visual Studio templates - Use Sphinx to generate the user documentation; gtk-doc is still required for the girepository-1.0 C API reference - Support all _Float* C types from ISO/IEC TS 18661-3:2015 - The autotools build now uses autoconf-archive - g-ir-doc-tool: Add DevDocs formatting for GJS (--format=devdocs) This adds a dependency on the Python markdown package • Issues resolved on gitlab.gnome.org: - #139 - make check fails for gobject-introspection 1.44.0 on OS X 10.10.4 - #184 - Default element-type not set for GByteArray object properties - #189 - c:type missing pointer/array information in GIR for C array parameters - #134 - Allow multiple output formats - #218 - gtk2 hits unreachable code after enable -Wswitch-default - #113 - Memory leaks in GI regress and marshalling tests property setters • Bugs resolved on bugzilla.gnome.org: - #702788 - The girepository DLL is acquiring pointers incorrectly on Windows/x64 • Contributors: Tomasz Miąsko, Emmanuele Bassi, Rico Tzschichholz, Chun-wei Fan, Philip Chimento, Tom Schoonjans, Christoph Reiter, Ray Donnelly, Marcus Calhoun-Lopez, Florian Müllner, Evan Welsh, Mathieu Duponchelle ``` ### 1.56 - 2018-03-13[](#id38 "Permalink to this heading") ``` • Changes: - Add _Float128 to the base C types - Update annotations imported from GLib, and require GLib 2.56 • Issues resolved on gitlab.gnome.org: - #173 - Fails to parse flag-constants - #175 - writer: Include documentation and symbol position in source files - #120 - adding subdir-objects to AM_INIT_AUTOMAKE - #190 - const qualifier dropped from c:type on (type filename) parameters - #188 - giscanner: don't print "suppressed N warnings" if --quiet was specified • Bugs resolved on bugzilla.gnome.org: - #764791 - gitypelib.c: increase #define MAX_NAME_LEN from 200 to 255 - #756921 - g-ir-scanner does not recognize _Thread_local - #791991 - Broken shared-library value in gir files on *BSD when builddir includes the name of the library - #791902 - Building using non-libtool (e.g., meson) on MinGW - #629667 - MY_ENUM_VALUE = <constant> only works for last member - #699354 - g-ir-compiler man page for --shared-library incorrectly says "lib" and ".so" should be removed - #761985 - os.name can be wrong in some context - #699328 - GI_TYPELIB_PATH is undocumented • Contributors: Ting-Wei Lan, Leslie Giles, Rico Tzschichholz, Christoph Reiter, Nicola Fontana, Tomasz Miąsko, Emmanuele Bassi, Philip Chimento, Karl-Philipp Richter ``` Note For older releases, see the Git log Goals[](#goals "Permalink to this heading") -------------------------------------------- The introspection project has two major goals, and a variety of more minor ones. ### Two level applications - C and <your favorite runtime>[](#two-level-applications-c-and-your-favorite-runtime "Permalink to this heading") It makes sense to build many kinds of applications using (at least) two different levels and languages — one for the low level elements, interfacing with the OS and/or the hardware; and one for the high level application logic. C is good for graphics, multimedia, and lower level systems work. However, writing complex software in C is difficult and error-prone. A managed runtime such as [JavaScript](https://wiki.gnome.org/JavaScript), Python, Perl, Java, Lua, .NET, Scheme etc. makes a lot of sense for non-fast-path application logic such as configuration, layout, dialogs, etc. Note To achieve this goal you need to write your code using GObject convention. For more information about that, see the [GObject tutorial](https://docs.gtk.org/gobject/tutorial.html) Thus, one of the major goals of the GObject introspection project is to be a convenient bridge between these two worlds, and allow you to choose the right tool for the job, rather than being limited inside one or the other. With the introspection project, you can write for example a ClutterActor or GtkWidget subclass in C, and then without any additional work use that class inside the high level language of your choice. ### Sharing binding infrastructure work, and making the platform even more binding-friendly[](#sharing-binding-infrastructure-work-and-making-the-platform-even-more-binding-friendly "Permalink to this heading") Historically in GNOME, the core platform has been relatively binding-friendly, but there are several details not captured in the C+GObject layer that bindings have needed. For example, reference counting semantics and the item type inside GList’s. Up until now various language bindings such as Python, Mono, java-gnome etc. had duplicated copies of hand-maintained metadata, and this led to a situation where bindings tended to lag behind until these manual fixups were done, or were simply wrong, and your application would crash when calling a more obscure function. The introspection project solves this by putting all of the metadata inside the GObject library itself, using annotations in the comments. This will lead to less duplicate work from binding authors, and a more reliable experience for binding consumers. Additionally, because the introspection build process will occur inside the GObject libraries themselves, a goal is to encourage GObject authors to consider shaping their APIs to be more binding friendly from the start, rather than as an afterthought. ### Additional goals and uses[](#additional-goals-and-uses "Permalink to this heading") * API verification - Sometimes the API of a library in our stack changes by accident. Usually by a less experienced developer making a change without realizing it will break applications. Introspecting the available API in each release of the library and comparing it to the last one makes it easy to see what changed * Documentation tools - The tools written inside of the GObjectIntrospection can easily be reused to improve that problem. Essentially; replacing gtk-doc. We want to document what we export so it makes sense to glue this together with API verification mentioned above * UI Designer infrastructure * Serialization/RPC/DBus Architecture[](#architecture "Permalink to this heading") ----------------------------------------------------------  ``` BUILD TIME: +-----------------------------------------------------------+ | foo.c | | foo.h | | | | Library sources, with type annotations | +-----------------------------------------------------------+ | | gcc g-ir-scanner | | | V | +------------------------+ | | Foo.gir | | | | | | <GI-name>.gir | | | | | | XML file | | | | | | Invocation information | | | Required .gir files | | | API docs | | | | | +------------------------+ | | | g-ir-compiler | | DEPLOYMENT TIME: | | | V V +-----------------------------+ +---------------------------+ | libfoo.so | | Foo.typelib | | | | | | | | Binary version of the | | ELF file | | invocation info and | | | | required .typelib files | | Machine code, plus | +---------------------------+ | dynamic linkage information | A | (DWARF debug data, etc) | | +-----------------------------+ | A | | +---------------------------+ | | libgirepository.so | +-----------+ | | | libffi.so | | Can read typelibs and | | |<-------+------>| present them in a | +-----------+ | | libffi-based way | | | | | +---------------------------+ | | +----------------------------+ | Specific language bindings | +----------------------------+ ``` Users[](#users "Permalink to this heading") -------------------------------------------- ### Bindings based on GObject-Introspection[](#bindings-based-on-gobject-introspection "Permalink to this heading") * [Vala](https://wiki.gnome.org/Projects/Vala) - Compiler for the GObject type system (compile time) * [Genie](https://wiki.gnome.org/Projects/Genie) - Genie Language (compile time) * [PyGObject](https://wiki.gnome.org/Projects/PyGObject) - Python bindings (runtime) * [pygir-ctypes](http://code.google.com/p/pygir-ctypes/) - Pure Python GObject Introspection Repository (GIR) wrapper using ctypes (runtime) * [pgi](http://github.com/lazka/pgi) - Pure Python GObject Introspection Bindings (runtime) * [GTK2-Perl/Introspection](https://wiki.gnome.org/GTK2-Perl/Introspection) - Perl bindings (runtime) * [JGIR](https://wiki.gnome.org/Projects/JGIR) - Java/JVM bindings (compile time, using typelib) * [GJS](https://wiki.gnome.org/Projects/Gjs) - Javascript (spidermonkey) bindings (runtime) * [Seed](https://wiki.gnome.org/Projects/Seed) - Javascript (JSCore, WebKit JS engine) bindings (runtime) * [sbank](https://wiki.gnome.org/sbank) - Scheme binding for gobject-introspection (runtime) * [GObjectIntrospection/GObjectConsume](https://wiki.gnome.org/Projects/GObjectIntrospection/GObjectConsume) - Qt bindings (compile time) * [GirFFI](http://github.com/mvz/ruby-gir-ffi) - Ruby bindings (runtime) * [Ruby-GNOME](https://github.com/ruby-gnome/ruby-gnome) - Ruby bindings (runtime) * [lgob](http://oproj.tuxfamily.org/wiki/doku.php?id=lgob) - Lua bindings (compile time?) * [guile-gir](http://gitorious.org/guile-gir) - Guile bindings (runtime) * [factor-gir](http://github.com/ex-rzr/factor-gir) - Factor bindings (runtime) * [lgi](http://www.github.com/pavouk/lgi) - Lua bindings (runtime) * [GObject for PHP](https://github.com/megous/gobject-for-php) * [cl-gir](http://bazaar.launchpad.net/~scymtym/+junk/cl-gir/files) GIR for Common Lisp (work in progress) * [GNU Smalltalk](http://www.gitorious.org/gst-gobject-introspection) - A branch of GNU Smalltalk which adds GObject Introspection bindings. * [node-gir](https://github.com/creationix/node-gir) - Node.js bindings to the girepository * [go-gir-generator](https://github.com/linuxdeepin/go-gir-generator) - Go bindings (compile time) (Forked from [gogobject](https://github.com/nsf/gogobject/) which is unmaintained) * [haskell-gi](http://www.haskell.org/haskellwiki/GObjectIntrospection) - a Haskell binding for the GIRepository C library, and a Haskell code generator built upon it. It is very much a work in progress. * [cl-gobject-introspection](https://github.com/andy128k/cl-gobject-introspection) - A bridge between Common Lisp and GObject. * [ocaml-gir](http://git.ocamlcore.org/cgi-bin/gitweb.cgi?p=ocaml-gir/ocaml-gir.git) - An automatic binding generator for gtk-related libraries * [gir2pascal](http://wiki.freepascal.org/gir2pascal) - gir2pascal is a program to convert gir files into into pascal files * [PLGI](https://github.com/keriharris/plgi) - Prolog bindings (runtime) * [hbgi](https://github.com/tuffnatty/hbgi) - Harbour bindings for GObject Introspection (runtime) * [cppgir](https://www.gitlab.com/mnauw/cppgir) - C++ bindings (compile time, using typelib) * [crystal-gobject](https://github.com/jhass/crystal-gobject) - gobject-introspection for Crystal (compile time) ### Projects using GObject Introspection[](#projects-using-gobject-introspection "Permalink to this heading") > > * [Folks](http://telepathy.freedesktop.org/wiki/Folks) - the Gnome contact aggregator > * [GnomeShell](https://wiki.gnome.org/Projects/GnomeShell) - prototyping the future gnome shell > * [Midgard2](http://www.midgard2.org/) - language bindings to the Midgard content repository > * [libpeas](http://git.gnome.org/browse/libpeas/tree/) - library providing a generic plugin framework > * [telepathy-glib](http://telepathy.freedesktop.org/wiki/Telepathy%20GLib) - GLib bindings for Telepathy > * [gir2xmi](https://github.com/jralls/gir2xmi) - UML model generator for GObject-Introspection Gir files. > * [playerctl](https://github.com/acrisci/playerctl) - a library and cli for controlling media players that implement the MPRIS DBus interface > * [i3ipc-glib](https://github.com/acrisci/i3ipc-glib) - a library for extensions to the i3 window manager > * [gabi](https://gitlab.gnome.org/tmiasko/gabi) - a C/typelib ABI cross-validator > > > ### Projects that could use GObject-Introspection[](#projects-that-could-use-gobject-introspection "Permalink to this heading") > > * [Mono GAPI](http://www.mono-project.com/GAPI) could replace its gapi2-parser by using GOject-Introspection. > * [gtkmm](http://www.gtkmm.org/) could use GObject-Introspection in its [gmmproc](http://www.gtkmm.org/docs/gtkmm-2.4/docs/tutorial/html/chapter-wrapping-c-libraries.html) to generate C++ library bindings > > > Build & Test[](#build-test "Permalink to this heading") -------------------------------------------------------- Clone gobject-introspection with git: ``` git clone https://gitlab.gnome.org/GNOME/gobject-introspection.git cd gobject-introspection ``` ### Meson[](#meson "Permalink to this heading") Build: ``` meson setup _build cd _build # To see the build options run "meson configure" meson compile ``` Test: ``` meson test # run tests flake8 .. # run code quality checks ``` ### Dependencies[](#dependencies "Permalink to this heading") gobject-introspection depends on a row of other packages, either strictly, optionally or only for testing. The following installation instructions should over all cases for some common Distributions. Debian/Ubuntu: ``` sudo apt install pkg-config python3-dev flex bison libglib2.0-dev \ libcairo2-dev libffi-dev python3-mako \ python3-markdown python3-distutils meson build-essential \ gtk-doc-tools ``` Fedora: ``` sudo dnf install pkg-config flex bison cairo-devel cairo-gobject-devel python3-mako gcc \ python3-markdown meson libffi-devel python3-devel \ python3 gtk-doc ``` Writing Bindable APIs[](#writing-bindable-apis "Permalink to this heading") ---------------------------------------------------------------------------- ### Things to avoid[](#things-to-avoid "Permalink to this heading") #### Structures with custom memory management[](#structures-with-custom-memory-management "Permalink to this heading") Avoid creating C structures with custom memory management unless they are registered as a [boxed type](https://docs.gtk.org/gobject/boxed.html). If you don’t register them as a boxed type bindings will fall back to simple memory copying, which might not be what you want. Also consider using a full [GObject](https://docs.gtk.org/gobject/class.Object.html) as that allows bindings to better integrate those objects with the binding language, like for example preserve user defined state across language boundaries. Example to avoid: ``` struct \_GstMiniObject { GTypeInstance instance; /\*< public >\*/ /\* with COW \*/ gint refcount; guint flags; ``` #### Functionality only accessible through a C macro or inline function[](#functionality-only-accessible-through-a-c-macro-or-inline-function "Permalink to this heading") The scanner does not support C macros as API. Solution - add a function accessor rather than a macro. This also has the side effect of making debugging in C code easier. Example: ``` #define GTK\_WIDGET\_FLAGS(wid) (GTK\_OBJECT\_FLAGS (wid)) GtkWidgetFlags gtk\_widget\_get\_flags (GtkWidget \*widget); /\* Actually, see http://bugzilla.gnome.org/show\_bug.cgi?id=69872 \*/ ``` Likewise, inline functions cannot be loaded from a dynamic library. Make sure to provide a non-inline equivalent. #### Direct C structure access for objects[](#direct-c-structure-access-for-objects "Permalink to this heading") Having GObjects also have fields can be difficult to bind. Create accessor functions. Example: ``` struct \_SoupMessage { GObject parent; /\*< public >\*/ const char \*method; guint status\_code; ... } const char \* soup\_message\_get\_method (SoupMessage \*message); /\* Or use a GObject property \*/ ``` #### va\_list[](#va-list "Permalink to this heading") Using varargs can be convenient for C, but they are difficult to bind. Solution: Keep the C function for the convenience of C programmers, but add an another function which takes an array (either zero terminated or with a length parameter). **Good** example: ``` GtkListStore \*gtk\_list\_store\_new (gint n\_columns, ...); GtkListStore \*gtk\_list\_store\_newv (gint n\_columns, GType \*types); ``` You can also expose the array variant under the name of the varargs variant using the `rename-to` annotation: `gtk\_list\_store\_newv: (rename-to gtk\_list\_store\_new)` Also consider using C99’s compound literals and designated initializers to avoid `va\_list` even in the C API, which is more type-safe. #### Multiple out parameters[](#multiple-out-parameters "Permalink to this heading") Multiple out parameters are supported by introspection, but not all languages have an obvious mapping for multiple out values. A boxed structure could serve as an alternative. Example to think about (here, there could be a boxed `struct GtkCoordinate { gint x; gint y; }` structure). ``` void gtk\_widget\_get\_pointer (GtkWidget \*widget, gint \*x, gint \*y); ``` #### In-out parameters[](#in-out-parameters "Permalink to this heading") Don’t use in-out arguments, especially not for non-scalar values. It’s difficult to enforce or validate the conventions for in-out arguments, which can easily lead to crashes. Instead, pass the input as an in argument, and receive the output as either a return value or an out argument. ``` FooBoxed \*foo\_bar\_scale\_boxed(FooBar \*self, FooBoxed \*boxed); void foo\_bar\_scale\_boxed(FooBar \*self, FooBoxed \*boxed\_in, FooBoxed \*\*boxed\_out); ``` In particular, do not require the caller to pass an initialized `GValue` to avoid the in-out annotation; instead, pass a `GValue` as an out argument, and have the function initialize it. #### Arrays[](#arrays "Permalink to this heading") For reference types, zero-terminated arrays are the easiest to work with. Arrays of primitive type such as “int” will require length metadata. In a general-purpose library, it’s best not to expose GLib array and hash types such as `GArray`, `GPtrArray`, `GByteArray`, `GList`, `GSList`, `GQueue`, and `GHashTable` in the public API. They are fine for internal libraries, but difficult in general for consumers of introspected libraries to deal with. #### Strings[](#strings "Permalink to this heading") C treats strings as zero-terminated arrays of bytes, but many other languages do not. So don’t write APIs that treat `const char \*` parameters as arrays that need an `array length` annotation. Treat all `const char \*` parameters as zero-terminated strings. Don’t use the same entry point for zero-terminated strings as for byte arrays which may contain embedded zeroes. ``` void foo\_bar\_snarf\_string(FooBar \*self, const char \*str); void foo\_bar\_snarf\_bytes(FooBar \*self, const uint8\_t \*bytes, size\_t length); ``` In particular, avoid functions taking a `const char \*` with a signed length that can be set to a negative value to let the function compute the string length in bytes. These functions are hard to bind, and require manual overrides. #### Callbacks[](#callbacks "Permalink to this heading") Callbacks are hard to support for introspection bindings because of their complex life-cycle. Try to avoid having more than one callback in the same function, and consider using GClosure when you need more. #### Using a different name for error domain quarks from the enum name[](#using-a-different-name-for-error-domain-quarks-from-the-enum-name "Permalink to this heading") Error domain quarks should always be named in the form <namespace>\_<module>\_error\_quark() for an error enum called <Namespace><Module>Error. Example to avoid: ``` typedef enum FooBarError { FOO\_BAR\_ERROR\_MOO, FOO\_BAR\_ERROR\_BLEAT }; GQuark foo\_bar\_errors\_quark(); ``` #### Don’t have properties and methods with the same name[](#don-t-have-properties-and-methods-with-the-same-name "Permalink to this heading") Some bindings for dynamic languages expose GObject properties and methods in the same way, as properties on an object instance. So don’t make a GObject property with the same name as a method, e.g. a property named `add-feature` on a class named `SoupSession` which also has a method `soup\_session\_add\_feature()`. #### Custom code in constructors[](#custom-code-in-constructors "Permalink to this heading") Creating an object via `foo\_bar\_new()` shouldn’t execute any code differently than creating the same object via `g\_object\_new()`, since many bindings (and also GtkBuilder/Glade) create objects using `g\_object\_new()`. That is, don’t do this: ``` FooBar \* foo\_bar\_new (void) { FooBar \*retval = FOO\_BAR (g\_object\_new (FOO\_TYPE\_BAR, NULL)); retval->priv->some\_variable = 5; /\* Don't do this! \*/ return retval; } ``` Instead, put initialization code in the `foo\_bar\_init()` function or the `foo\_bar\_constructed()` virtual function. #### Transfer-none return values from the binding[](#transfer-none-return-values-from-the-binding "Permalink to this heading") If your library expects to call a function from C which may be implemented in another language and exposed through the binding (for example, a signal handler, or a GObject vfunc), it’s best not to return transfer-none values, because what you assume about storage lifetime in C may not apply in other languages. For example, ``` typedef struct { GTypeInterface iface; const char \* (\*my\_vfunc) (FooBaz \*self); /\* Don't do this! \*/ char \* (\*my\_better\_vfunc) (FooBaz \*self); /\* Do this instead! \*/ } FooBazIface; ``` A class that implements `FooBazIface` in another programming language may not be able to return a static string here, because the language may not have a concept of static storage lifetime, or it may not store strings as zero-terminated UTF-8 bytes as C code would expect. This can cause memory leaks. Instead, duplicate the string before returning it, and use transfer-full. This recommendation applies to any data type with an ownership, including boxed and object types. Build System Integration[](#build-system-integration "Permalink to this heading") ---------------------------------------------------------------------------------- ### Meson Integration[](#meson-integration "Permalink to this heading") Support for generating GObject introspection data is included in Meson directly and accessible through the `gnome.generate\_gir()` function. See the [meson documentation](https://mesonbuild.com/Gnome-module.html#gnomegenerate_gir) for details. For some real examples, see the meson build definitions of various GNOME modules: Pango:<https://gitlab.gnome.org/GNOME/pango/blob/master/pango/meson.build> ``` pango\_gir = gnome.generate\_gir( libpango, sources: pango\_sources + pango\_headers + [ pango\_enum\_h ], namespace: 'Pango', nsversion: pango\_api\_version, identifier\_prefix: 'Pango', symbol\_prefix: 'pango', export\_packages: 'pango', includes: [ 'GObject-2.0', 'cairo-1.0', ], header: 'pango/pango.h', install: true, extra\_args: gir\_args, ) ``` json-glib:<https://gitlab.gnome.org/GNOME/json-glib/blob/master/json-glib/meson.build> ``` json\_glib\_gir = gnome.generate\_gir( json\_lib, sources: source\_c + source\_h + json\_glib\_enums + [ json\_version\_h ], namespace: 'Json', nsversion: json\_api\_version, identifier\_prefix: 'Json', symbol\_prefix: 'json', export\_packages: json\_api\_name, includes: [ 'GObject-2.0', 'Gio-2.0', ], header: 'json-glib/json-glib.h', install: true, extra\_args: gir\_args, ) ``` ### Autotools Integration[](#autotools-integration "Permalink to this heading") The gobject-introspection package provides the following two macros for use in your configure.ac file: GOBJECT\_INTROSPECTION\_CHECK([version])This macro adds a `--enable-introspection=yes|no|auto` configure option which defaults to `auto`. * `auto` - Will set `HAVE\_INTROSPECTION` if gobject-introspection is available and the version requirement is satisfied. * `yes` - Will error out if gobject-introspection is missing or the version requirement is not satisfied. `HAVE\_INTROSPECTION` will always be true. * `no` - Will never error out and `HAVE\_INTROSPECTION` will always be false. GOBJECT\_INTROSPECTION\_REQUIRE([version])This macro does not add a configure option and behaves as if `--enable-introspection=yes`. #### Example[](#example "Permalink to this heading") You’ll need to adapt this for the library you’re adding introspection support to. * configure.ac (or configure.in if no .ac file is used) ``` GOBJECT_INTROSPECTION_CHECK([1.40.0]) ``` * Makefile.am ``` DISTCHECK_CONFIGURE_FLAGS = --enable-introspection ``` or just add to the existing DISTCHECK\_CONFIGURE\_FLAGS * foo/Makefile.am - must be near the end (after CLEANFILES has been set) ``` -include $(INTROSPECTION_MAKEFILE) INTROSPECTION_GIRS = INTROSPECTION_SCANNER_ARGS = --add-include-path=$(srcdir) --warn-all INTROSPECTION_COMPILER_ARGS = --includedir=$(srcdir) if HAVE_INTROSPECTION introspection_sources = $(libfoo_1_0_la_SOURCES) Foo-1.0.gir: libfoo-1.0.la Foo_1_0_gir_INCLUDES = GObject-2.0 Foo_1_0_gir_CFLAGS = $(INCLUDES) Foo_1_0_gir_LIBS = libfoo-1.0.la Foo_1_0_gir_FILES = $(introspection_sources) INTROSPECTION_GIRS += Foo-1.0.gir girdir = $(datadir)/gir-1.0 gir_DATA = $(INTROSPECTION_GIRS) typelibdir = $(libdir)/girepository-1.0 typelib_DATA = $(INTROSPECTION_GIRS:.gir=.typelib) CLEANFILES += $(gir_DATA) $(typelib_DATA) endif ``` You can also check out a complete example at <https://gitlab.gnome.org/GNOME/gtk/blob/c0ba041c73214f82d2c32b2ca1fa8f3c388c6170/gtk/Makefile.am#L1571> #### Makefile variable documentation[](#makefile-variable-documentation "Permalink to this heading") `INTROSPECTION\_GIRS` is the entry point, you should list all the gir files you want to build there in the XXX-Y.gir format where X is the name of the gir (for example Gtk) and Y is the version (for example 2.0). If output is Gtk-3.0.gir then you should name the variables like `Gtk\_3\_0\_gir\_NAMESPACE`, `Gtk\_3\_0\_gir\_VERSION` etc. * Required variables: + `FILES` - C sources and headers which should be scanned * One of these variables are required: + `LIBS` - Library where the symbol represented in the gir can be found + `PROGRAM` - Program where the symbol represented in the gir can be found * Optional variables, commonly used: + `INCLUDES` - Gir files to include without the .gir suffix, for instance GLib-2.0, Gtk-3.0. This is needed for all libraries which you depend on that provides introspection information. + `SCANNERFLAGS` - Flags to pass in to the scanner, see g-ir-scanner(1) for a list + `PACKAGES` - list of pkg-config names which cflags are required to parse the headers of this gir. Note that `INCLUDES` will already fetch the packages and thus the cflags for all dependencies. + `EXPORT\_PACKAGES` - List of pkg-config names which are provided by this Gir. + `CFLAGS` - Flags to pass in to the parser when scanning headers. Normally `INCLUDES` and `PACKAGES` will fetch the cflags for all dependencies. This is normally used for project specific CFLAGS. + `LDFLAGS` - Linker flags used by the scanner. Normally `INCLUDES` and `PACKAGES` will fetch the ldflags for all dependencies. This is normally used for project-specific LDFLAGS (for instance if you’re building several libraries and typelibs). * Optional variables, seldomly used: + `NAMESPACE` - Namespace of the gir, first letter capital, rest should be lower case, for instance: ‘Gtk’, ‘Clutter’, ‘ClutterGtk’. If not present the namespace will be fetched from the gir filename, the part before the first dash. For ‘Gtk-3.0’, namespace will be ‘Gtk’. + `VERSION` - Version of the gir, if not present, will be fetched from gir filename, the part after the first dash. For ‘Gtk-3.0’, version will be ‘3.0’. Annotations[](#annotations "Permalink to this heading") -------------------------------------------------------- ### GTK-Doc Format Primer[](#gtk-doc-format-primer "Permalink to this heading") GObject-Introspection annotations are built on top of GTK-Doc comment blocks. These are plain old C comment blocks, but formatted in a special way. Each GTK-Doc comment block starts with a `/\*\*` on its own line end ends with `\*/`, again on its own line. The basic format of a GTK-Doc comment block looks like this: ``` /\*\* \* identifier\_name: (annotations) \* @parameter\_name: (annotations): description \* \* symbol description \* \* tag\_name: (annotations): description \*/ ``` As we can see, a GTK-Doc comment block can be broken down into a couple of parts. Each part is built out of one or more fields, separated by a `:` character. Each part has to start on its own line. Fields cannot span multiple lines except the various `description` fields. The order in which parts are written is important. For example, putting a `tag` part before the `symbol description` part is invalid as it would result in the symbol description to be mistaken for the tag description. In the above example we have: * the start of a GTK-Doc comment block on line 1 * the identifier part on line 2 * a parameter part on line 3 * the symbol description on line 5 * a tag part on line 7 * the end of the comment block on line 8 #### identifier part[](#identifier-part "Permalink to this heading") ``` /\*\* \* identifier\_name: (annotations) \* ... \*/ ``` The identifier part is required as it identifies the symbol you want to annotate. It is always written on the line immediately following the start of your GTK-Doc comment block (`/\*\*`). The `identifier` part is constructed from: * a required `identifier\_name` field + different kinds of symbols that can be documented and annotated are described in the GTK-Doc manual. * an optional `annotations` field #### parameter part[](#parameter-part "Permalink to this heading") ``` /\*\* \* ... \* @parameter\_name: (annotations): description \* ... \*/ ``` The `parameter` part is optional. This means that there can be 0 or more parameters, depending on the symbol you are annotating. `parameter` parts are constructed from: * a required `parameter\_name` which starts with a `@` character + this name should correspond with the parameter name of you function’s signature. * an optional `annotations` field * a required description field (can be “empty”) + can contain a single paragraph (multiple lines but no empty lines) of text. Note that multiple `parameter` parts are never separated by an empty line. #### symbol description part[](#symbol-description-part "Permalink to this heading") ``` /\*\* \* ... \* \* symbol description \* ... \*/ ``` The `symbol description` part is optional. When present, it must always be preceded with an empty line. It can contain multiple paragraphs (multiple lines and empty lines) describing what the function, property, signal, enum or constant does. #### tag part[](#tag-part "Permalink to this heading") ``` /\*\* \* ... \* tag\_name: (annotations)||value: description \* ... \*/ ``` The `tag` part is optional. There can be 0 or more tags, depending on the symbol you are annotating. `tag` parts are constructed from: * a required `tag\_name` + There are only four valid tags: `Returns`, `Since`, `Deprecated`, and `Stability`. * an optional `annotations` field (`Returns`) **OR** an optional `value` field (`Since`, `Deprecated`, and `Stability`) * a required description field (can be “empty”) + can contain multiple paragraphs (multiple lines and empty lines) of text. `tag` parts can safely be preceded or followed by an empty line. Tags taking an optional `value` field accept the following values: | Tag | Value field | Description | | --- | --- | --- | | `Since` | `VERSION` | This symbol was added in version `VERSION`. | | `Deprecated` | `VERSION` | This symbol has been deprecated since version `VERSION`. | | `Stability` | `Stable`, `Unstable`, or `Private` | An informal description of the stability level of this symbol. | ##### GTK-Doc support[](#gtk-doc-support "Permalink to this heading") If GTK-Doc doesn’t seem to understand your introspection annotations, you may need to do two things: 1. make sure you are running GTK-Doc >= v1.12 (also try latest version from git) 2. add `<xi:include href="xml/annotation-glossary.xml"><xi:fallback/></xi:include>` to your master GTK-Doc document; e.g. see the end of [tester-docs.xml](https://gitlab.gnome.org/GNOME/gtk-doc/blob/master/tests/gobject/docs/tester-docs.xml) ### GObject-Introspection annotations[](#gobject-introspection-annotations "Permalink to this heading") #### Symbol visibility[](#symbol-visibility "Permalink to this heading") | Annotation | Applies to | Description | Since | | --- | --- | --- | --- | | `(skip)` | identifier | Omit the symbol from the introspected output. | [v0.6.4](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/7549c8053d0229a12d9196cc8abae54a01a555d0) [bz#556628](https://bugzilla.gnome.org/show_bug.cgi?id=556628) | | | paremeters, return value | Indicate that the parameter or return value is only useful in C and should be skipped. | [v1.29.0](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/9c6797e0478b5025c3f2f37b1331c1328cf34f4d) [bz#649657](https://bugzilla.gnome.org/show_bug.cgi?id=649657) | | `(rename-to SYMBOL)` | identifier | Rename the original symbol’s name to `SYMBOL`. If `SYMBOL` resolves to a symbol name that is already used, the original binding for that name is removed. | [v0.6.3](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/23e6fa6993c046de032598127ea48d4a7ee00935) [bz#556475](https://bugzilla.gnome.org/show_bug.cgi?id=556475) | #### Memory and lifecycle management[](#memory-and-lifecycle-management "Permalink to this heading") | Annotation | Applies to | Description | Since | | --- | --- | --- | --- | | `(transfer MODE)` | identifier (only properties) | Transfer ownership for the property, (see below) | [v0.9.0](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/22ae017ffd3052c0b81822b2ca6e41626b76b9c4) [bz#620484](https://bugzilla.gnome.org/show_bug.cgi?id=620484) | | | parameters, return value | Transfer mode for the parameter or return value (see below). | v0.5.0 unknown | Transfer modes: * `none`: the recipient does not own the value * `container`: the recipient owns the container, but not the elements. (Only meaningful for container types.) * `full`: the recipient owns the entire value. For a refcounted type, this means the recipient owns a ref on the value. For a container type, this means the recipient owns both container and elements. * `floating`: alias for none, can be used for floating objects. `container` is usually a pointer to a list or hash table, eg GList, GSList, GHashTable etc. `elements` is what is contained inside the list: integers, strings, GObjects etc. #### Support for GObject objects[](#support-for-gobject-objects "Permalink to this heading") | Annotation | Applies to | Description | Since | | --- | --- | --- | --- | | `(constructor)` | identifier | The annotated symbol should not become available as a static methods but as a constructor. | [v0.10.2](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/2c36790c) [bz#561264](https://bugzilla.gnome.org/show_bug.cgi?id=561264) | | `(method)` | identifier | This function is a method. | [v0.10.2](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/09bca85d) [bz#639945](https://bugzilla.gnome.org/show_bug.cgi?id=639945) | | `(virtual SLOT)` | identifier | This function is the invoker for a virtual method. | [v0.6.3](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/fdbe3cc3) [bz#557383](https://bugzilla.gnome.org/show_bug.cgi?id=557383) | | `(set-property NAME)` | identifier (only applies to methods) | This function is the setter method for the given GObject property. A setter function is defined as being the public function that is called by the `GObjectClass.set\_property` implementation in a class. | [#13](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/13) | | `(get-property NAME)` | identifier (only applies to methods) | This function is the getter method for the given GObject property. A getter function is defined as being the public function that is called by the `GObjectClass.get\_property` implementation in a class. | [#13](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/13) | | `(setter SYMBOL)` | identifier (only applies to properties) | This GObject property is accessed by the given setter function. A setter function is defined as being the public function that is called by the `GObjectClass.set\_property` implementation in a class. | [#13](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/13) | | `(getter SYMBOL)` | identifier (only applies to properties) | This GObject property is accessed by the given getter function. A getter function is defined as being the public function that is called by the `GObjectClass.get\_property` implementation in a class. | [#13](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/13) | | `(emitter METHOD)` | identifier (only applies to methods) | This signal is emitted by the given method | | | `(default-value VALUE)` | identifier (only applies to properties) | The default value of a GObject property, as a freeform string | [#4](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/4) | #### Support for GObject closures[](#support-for-gobject-closures "Permalink to this heading") | Annotation | Applies to | Description | Since | | --- | --- | --- | --- | | `(destroy)` | parameters | The parameter is part of a callback type and containing the `destroy\_data`. | [v0.6.3](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/cf7621f3) [bz#574284](https://bugzilla.gnome.org/show_bug.cgi?id=574284) | | `(destroy DESTROY)` | parameters | The parameter defines the `destroy\_data` for a given callback. The `DESTROY` option points to the parameter that is the actual callback. | | | `(closure)` | parameters | The parameter is part of a callback type and containing the `user\_data`. | | | `(closure CLOSURE)` | parameters | The parameter defines the `user\_data` for a given callback. The `CLOSURE` option points to the parameter that is the actual callback. Many bindings can pass `NULL` here. | | #### Support for non-GObject fundamental objects[](#support-for-non-gobject-fundamental-objects "Permalink to this heading") | Annotation | Applies to | Description | Since | | --- | --- | --- | --- | | `(ref-func FUNC)` | identifier | `FUNC` is the function used to ref a struct, must be a GTypeInstance | [v0.9.2](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/1e9822c7) [bz#568913](https://bugzilla.gnome.org/show_bug.cgi?id=568913) | | `(unref-func FUNC)` | identifier | `FUNC` is the function used to unref a struct, must be a GTypeInstance | | | `(get-value-func FUNC)` | identifier | `FUNC` is the function used to convert a struct from a GValue, must be a GTypeInstance | | | `(set-value-func FUNC)` | identifier | `FUNC` is the function used to convert from a struct to a GValue, must be a GTypeInstance | | | `(copy-func FUNC)` | identifier | `FUNC` is the function used to copy a struct or a union | 1.76 | | `(free-func FUNC)` | identifier | `FUNC` is the function used to free a struct or a union | 1.76 | #### Type signature[](#type-signature "Permalink to this heading") | Annotation | Applies to | Description | Since | | --- | --- | --- | --- | | `(nullable)` | parameters, return value | Indicates that `NULL` may be a valid value for a parameter (in, out, inout), or return value (though note that return and out values which are only `NULL` when throwing an error should not be annotated as `(nullable)`). | [1.42](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/1459ff3e) [bz#660879](https://bugzilla.gnome.org/show_bug.cgi?id=660879) | | `(not nullable)` | parameters, return value | Indicates that `NULL` is not a valid value for a parameter (in, out, inout), or return value. | [1.48](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/10cb665f) [bz#729660](https://bugzilla.gnome.org/show_bug.cgi?id=729660) | | `(optional)` | parameters | For `(out)` or `(inout)` parameters, signifies that the caller can pass `NULL` to ignore this output parameter. | [1.42](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/1459ff3e) [bz#660879](https://bugzilla.gnome.org/show_bug.cgi?id=660879) | | `(in)` | parameters | In parameter. | v0.5.0 unknown | | `(out)` | parameters | Out parameter (automatically determine allocation). | v0.5.0 unknown | | `(out caller-allocates)` | parameters | Out parameter, where the calling code must allocate storage. | [v0.6.13](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/5589687a) [bz#604749](https://bugzilla.gnome.org/show_bug.cgi?id=604749) | | `(out callee-allocates)` | parameters | Out parameter, where the receiving function must allocate storage. | | | `(inout)` | parameters | In/out parameter. | v0.5.0 unknown | | `(type TYPE)` | identifier | Override the default type, used for properties | [v0.6.2](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/6de1b296) [bz#546739](https://bugzilla.gnome.org/show_bug.cgi?id=546739) | | | parameters, return value | override the parsed C type with given type | | | `(array)` | parameters, return value | Arrays. | v0.5.0 unknown | | `(array fixed-size=N)` | parameters, return value | array of fixed length N | v0.5.0 unknown | | `(array length=PARAM)` | parameters, return value | array, fetch the length from parameter PARAM | v0.5.0 unknown | | `(array zero-terminated=1)` | parameters, return value | array which is NULL terminated | [v0.6.0](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/d15f8cde) [bz#557786](https://bugzilla.gnome.org/show_bug.cgi?id=557786) | | `(element-type TYPE)` | parameters, return value | Specify the type of the element inside a container. Can be used in combination with (array). | v0.5.0 unknown | | `(element-type KTYPE VTYPE)` | parameters, return value | Specify the types of the keys and values in a dictionary-like container (eg, `GHashTable`). | v0.5.0 unknown | | `(foreign)` | identifier | The annotated symbol is a foreign struct, meaning it is not available in a g-i supported library. | [v0.6.12](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/1edeccd2) [bz#619450](https://bugzilla.gnome.org/show_bug.cgi?id=619450) | | `(scope TYPE)` | parameters | The parameter is a callback, the `TYPE` option indicates the lifetime of the call. It is mainly used by language bindings wanting to know when the resources required to do the call (for instance ffi closures) can be freed. | [v0.6.2](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/bc88ef7b) [bz#556489](https://bugzilla.gnome.org/show_bug.cgi?id=556489) | Scope types: * `call` (default) - Only valid for the duration of the call. Can be called multiple times during the call. * `async` - Only valid for the duration of the first callback invocation. Can only be called once. * `notified` - valid until the GDestroyNotify argument is called. Can be called multiple times before the GDestroyNotify is called. * `forever` - valid until the process terminates. An example of a function using the `call` scope is `g\_slist\_foreach()`. For `async` there is `g\_file\_read\_async()` and for notified `g\_idle\_add\_full()`. Default Annotations: To avoid having the developers annotate everything the introspection framework is providing sane default annotation values for a couple of situations: * `(in)` parameters: `(transfer none)` * `(inout)` and `(out)` parameters: `(transfer full)` + if `(caller allocates)` is set: `(transfer none)` * `gchar\*` means `(type utf8)` * return values: `(transfer full)` + `gchar\*` means `(type utf8) (transfer full)` + `const gchar\*` means `(type utf8) (transfer none)` + `GObject\*` defaults to `(transfer full)` #### Data annotations[](#data-annotations "Permalink to this heading") | Annotation | Applies to | Description | Since | | --- | --- | --- | --- | | `(value VALUE)` | identifier | Used to override constants for defined values, VALUE contains the evaluated value | v0.5.0 unknown | | `(attributes my.key=val my.key2)` | identifier, parameters, return value | Attributes are free-form “key=value” annotations. When present, at least one key has to be specified. Assigning values to keys is optional. | [v0.9.0](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/11cfe386) [bz#571548](https://bugzilla.gnome.org/show_bug.cgi?id=571548) | #### Deprecated GObject-Introspection annotations[](#deprecated-gobject-introspection-annotations "Permalink to this heading") | Annotation | Description | Since | | --- | --- | --- | | `(null-ok)` | Replaced by `(allow-none)` | [v0.6.0](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/dc651812) [bz#557405](https://bugzilla.gnome.org/show_bug.cgi?id=557405) | | `(in-out)` | Replaced by `(inout)` | [1.39.0](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/a2b22ce7) [bz#688897](https://bugzilla.gnome.org/show_bug.cgi?id=688897) | | `(allow-none)` | Replaced by `(nullable)` and `(optional)` | [1.42](https://gitlab.gnome.org/GNOME/gobject-introspection/commit/1459ff3e) [bz#660879](https://bugzilla.gnome.org/show_bug.cgi?id=660879) | #### Possible future GObject-Introspection annotations[](#possible-future-gobject-introspection-annotations "Permalink to this heading") These proposed additions are currently being discussed and in various stages of development. | Annotation | Applies to | Description | Since | | --- | --- | --- | --- | | `(default VALUE)` | parameters | Default value for a parameter. | [bz#558620](https://bugzilla.gnome.org/show_bug.cgi?id=558620) | | `(error-domains DOM1 DOM2)` | parameters | Typed errors, similar to `throws` in Java. | unknown | ### Default Basic Types[](#default-basic-types "Permalink to this heading") Basic types: * gpointer: pointer to anything * gboolean:boolean * gint[8,16,32,64]: integer * guint[8,16,32,64]: unsigned integer * glong: long * gulong: unsigned long * GType: a gtype * gfloat: float * gdouble: double * utf8: string encoded in UTF-8, not containing any embedded nuls * filename: filename string (see below) * guint8 array: binary data Filename type: The filename type represents an utf-8 string on Windows and a zero terminated guint8 array on Unix. It should be used for filenames, environment variables and process arguments. ### Reference to Object Instances[](#reference-to-object-instances "Permalink to this heading") Instances: * Object: a GObject instance * Gtk.Button: a Gtk.Button instance ### Examples[](#examples "Permalink to this heading") #### Transfer[](#transfer "Permalink to this heading") ``` /\*\* \* mylib\_get\_constant1: \* \* Returns: (transfer full): a constant, free when you used it \*/ gchar \* mylib\_get\_constant1 (void) { return g\_strdup("a constant"); } ``` ``` /\*\* \* mylib\_get\_constant2: \* \* Returns: (transfer none): another constant \*/ const gchar \* mylib\_get\_string2 (void) { return "another constant"; } ``` ``` /\*\* \* mylib\_get\_string\_list1: \* \* Returns: (element-type utf8) (transfer full): list of constants, \* free the list with g\_slist\_free and the elements with g\_free when done. \*/ GSList \* mylib\_get\_string\_list1 (void) { GSList \*l = NULL; l = g\_slist\_append (l, g\_strdup ("foo")); l = g\_slist\_append (l, g\_strdup ("bar")); return l; } ``` ``` /\*\* \* mylib\_get\_string\_list2: \* \* Returns: (element-type utf8) (transfer container): list of constants \* free the list with g\_slist\_free when done. \*/ GSList \* mylib\_get\_string\_list2 (void) { GSList \*l = NULL; l = g\_slist\_append (l, "foo"); l = g\_slist\_append (l, "bar"); return l; } ``` #### Array length[](#array-length "Permalink to this heading") ``` /\*\* \* gtk\_list\_store\_set\_column\_types: \* @store: a #GtkListStore \* @n\_columns: Length of @types \* @types: (array length=n\_columns): List of types \*/ void gtk\_list\_store\_set\_column\_types (GtkListStore \*list\_store, gint n\_columns, GType \*types); ``` #### Nullable parameters[](#nullable-parameters "Permalink to this heading") A number of things are nullable by convention, which means that you do not have to add a `(nullable)` annotation to your code for them to be marked as nullable in a GIR file. If you need to mark a parameter or return value as not nullable, use `(not nullable)` to override the convention. Conventionally, the following are automatically nullable: * `(closure)` parameters and their corresponding user data parameters * `gpointer` parameters and return types, unless also annotated with `(type)` ``` /\*\* \* gtk\_link\_button\_new\_with\_label: \* @uri: A URI \* @label: (nullable): A piece of text or NULL \*/ GtkWidget \* gtk\_link\_button\_new\_with\_label (const gchar \*uri, const gchar \*label); ``` ``` /\*\* \* g\_source\_add\_unix\_fd: \* @source: a #GSource \* @fd: the fd to monitor \* @events: an event mask \* \* Returns: (not nullable): an opaque tag \*/ gpointer g\_source\_add\_unix\_fd (GSource \*source, gint fd, GIOCondition events); /\*\* \* g\_source\_remove\_unix\_fd: \* @source: a #GSource \* @tag: (not nullable): the tag from g\_source\_add\_unix\_fd() \*/ void g\_source\_remove\_unix\_fd (GSource \*source, gpointer tag); ``` #### G(S)List contained types[](#g-s-list-contained-types "Permalink to this heading") ``` /\*\* \* gtk\_container\_get\_children: \* @container: A #GtkContainer \* \* Returns: (element-type Gtk.Widget) (transfer container): List of #GtkWidget \*/ GList\* gtk\_container\_get\_children (GtkContainer \*container); ``` ``` /\*\* \* FooBar:alist: (type GSList(NiceObj)) \* \* This property is a GSList of NiceObj GOjects. \*/ g\_object\_class\_install\_property (object\_class, FOO\_BAR\_PROP\_ALIST, g\_param\_spec\_pointer ("alist", "Alist", "A list of nice objects", G\_PARAM\_READWRITE)); ``` #### Direction[](#direction "Permalink to this heading") ``` /\*\* \* gtk\_widget\_get\_size\_request: \* @width: (out): Int to store width in \* @height: (out): Int to store height in \*/ ``` #### Out parameters[](#out-parameters "Permalink to this heading") This is a callee-allocates example; the (out) annotation automatically infers this from the fact that there’s a double indirection on a structure parameter. ``` typedef struct \_FooSubObj FooSubObj /\*\* \* foo\_obj\_get\_sub\_obj: \* @obj: A #FooObj \* @subobj: (out): A #FooSubObj \* \* Get a sub object. \*/ void foo\_obj\_get\_sub\_obj (FooObj \*obj, FooSubObj \*\*subobj) { \*subobj = foo\_sub\_object\_new (); } ``` This is a caller-allocates example; the (out) annotation automatically infers this from the fact that there’s only a single indirection on a structure parameter. ``` typedef struct \_FooIter FooIter; /\*\* \* foo\_obj\_get\_iter: \* @obj: A #FooObj \* @iter: (out): An iterator, will be initialized \* \* Get an iterator. \*/ void foo\_obj\_get\_iter (FooObj \*obj, FooIter \*iter) { iter->state = 0; } ``` An example which demonstrates an (optional) parameter: an (out) parameter where the caller can pass NULL if they don’t want to receive the (out) value. Note that if the GError is returned set, the value of contents and length might be unspecified and should therefore not be used or freed. ``` /\*\* \* g\_file\_get\_contents: \* @filename: name of a file to read contents from, in the GLib file name encoding \* @contents: (out): location to store an allocated string, use g\_free() to free the returned string \* @length: (out) (optional): location to store length in bytes of the contents, or NULL \* @error: return location for a GError, or NULL \* \* [...] \* \* Returns: TRUE on success, FALSE if an error occurred \*/ gboolean g\_file\_get\_contents (const gchar \*filename, gchar \*\*contents, gsize \*length, GError \*\*error); /\* this is valid because length has (optional) \*/ g\_file\_get\_contents ("/etc/motd", &motd, NULL, &error); // VALID /\* but this is not valid, according to those annotations \*/ g\_file\_get\_contents ("/etc/motd", NULL, NULL, &error); // NOT VALID ``` mylib\_hash\_table\_iter\_next() demonstrates the difference between (nullable) and (optional) for (out) parameters. For an (out) parameter, (optional) indicates that NULL may be passed by the caller to indicate they don’t want to receive the (out) value. (nullable) indicates that NULL may be passed out by the callee as the returned value. ``` /\*\* \* mylib\_hash\_table\_iter\_next: \* @iter: an initialized #MylibHashTableIter \* @key: (out) (optional): a location to store the key \* @value: (out) (optional) (nullable): a location to store the value \* \* [...] \* \* Returns: %FALSE if the end of the #MylibHashTable has been reached. \*/ gboolean mylib\_hash\_table\_iter\_next (MylibHashTableIter \*iter, gpointer \*key, gpointer \*value); /\* this is valid because value and key have (optional) \*/ mylib\_hash\_table\_iter\_next (iter, NULL, NULL); gpointer key, value; mylib\_hash\_table\_iter\_next (iter, &key, &value); if (value == NULL) /\* this is valid because value has (nullable) \*/ if (key == NULL) /\* this is NOT VALID because key does not have (nullable) \*/ ``` #### Rename to[](#rename-to "Permalink to this heading") Rename to is an advisory annotation. It’s not required to fulfill the advisory when generating or making a language binding. The way it is currently implemented, if you rename a function to a name already in use, it will remove the other binding. This is useful to eliminate unwanted/deprecated functions from the binding. Another (currently unimplemented) use for the rename annotation would be overloading; for example, overloading of constructors or, like in this example, overloading a method to be both an asynchronous and a synchronous one (depending on the amount and what kind of parameters). ``` /\*\* \* my\_type\_perform\_async: (rename-to my\_type\_perform) \* @self: The this ptr \* @data: data \* @callback: callback when async operation finished \* @user\_data: user\_data for @callback \* \* Asynchronously perform \*\*/ void my\_type\_perform\_async (MyType \*self, gpointer data, GFunc callback, gpointer user\_data); /\*\* \* my\_type\_perform: \* @self: The this ptr \* @data: data \* \* Perform \*\*/ void my\_type\_perform (MyType \*self, gpointer data); ``` In a language supporting method overloading, because we advised to rename to perform, and because we have another perform already, this could be bound like this: ``` class MyType { public void perform (Pointer data) { } public void perform (Pointer data, GFunc callback, Pointer user\_data) { } } ``` However, currently the generated gir/typelib will only contain information about my\_type\_perform\_async, which will shadow (ie, remove) the binding of my\_type\_perform. #### Attributes[](#attributes "Permalink to this heading") Attributes are arbitrary key/value pairs that can be attached to almost any item including classes, methods, signals, properties, parameters and return values. These attributes appear in both the .gir and the .typelib files. Attributes can serve as a mechanism for software higher in the toolchain. Attributes are name-spaced using dot as a separator. At least one dot must appear in the key name. ``` /\*\* \* my\_frobnicator\_poke\_path: (attributes gdbus.method PokePath) \* @frobnicator: A #MyFrobnicator \* @object\_path: (gdbus.signature o): An object path. \* \* Manipulate an object path. \* \* Returns: (gdbus.signature o): A new object path. Free with g\_free(). \*/ gchar \* my\_frobnicator\_poke\_path (MyFrobnicator \*frobnicator, const gchar \*object\_path) ``` #### Constants[](#constants "Permalink to this heading") ``` /\*\* \* MY\_CONSTANT: (value 100) \* A constant. \*/ #define MY\_CONSTANT 10 \* 10 ``` Writing Bindings[](#writing-bindings "Permalink to this heading") ------------------------------------------------------------------ ### Guidelines[](#guidelines "Permalink to this heading") This page is intended as a guide describing the process of writing a language binding for the GObject Introspection framework. * Decide if you want to make a language binding which is implementation agnostic or implementation specific. Some languages such as Python have libraries which are available across implementations. The Python module `ctypes` is a binding for the libffi language binding, which is implemented in a couple of different Python implementations. It’s usually easier to target a specific interpreter or compiler implementation so if you unsure, write a specific one. * For interpreted language implementations, construct the language binding on top of the [libgirepository](index.html#document-writingbindings/libgirepository) library instead of writing a code generator. It’ll make it easier for developers to use your language binding as they will be able to read the typelibs in runtime without having an extra intermediate step to generate the language specific metadata. The libgirepository library can also be used for bindings based upon code generators as an optimization, its a lot faster to read metadata from the typelib than it is to extract the metadata from the GIR XML files. * Use the Everything library in your unittests, aim at testing all functions there. Do testing as early as possible in the development of the binding, as the code is likely to be more complex than you anticipate. * Use the same coding style as your language. If libraries for your language normally uses underscores do that as well. For instance, Java bindings should have a method on it’s GtkButton wrapper called `setLabel` and not `set\_label`. * If there are existing GObject bindings, reuse them for improved compatibility. It’s a nice feature being able to use static bindings and introspected bindings together. The Perl & Python bindings does that. * Try to stay close to the C language semantics, for instance GObject should be mapped to a class and gtk\_button\_set\_label to a method of that class: > > > + java: `button.setLabel("foo")` > + javascript/python/vala: `button.set\_label("foo")` > + perl: `$button->set\_label("foo")` > + scheme: `(send button (set-label "foo"))` > ### libgirepository[](#libgirepository "Permalink to this heading") libgirepository is a C library which provides a C API for accessing the typelib data and for interacting with the corresponding GObject based libraries. For more information about libgirepository see the [API documentation](https://gnome.pages.gitlab.gnome.org/gobject-introspection/girepository/). The following example shows how to call the `g\_assertion\_message()` function from libglib-2.0: ``` #include <girepository.h> int main (void) { GIRepository \*repository; GError \*error = NULL; GIBaseInfo \*base\_info; GIArgument in\_args[5]; GIArgument retval; repository = g\_irepository\_get\_default (); g\_irepository\_require (repository, "GLib", "2.0", 0, &error); if (error) { g\_error ("ERROR: %s\n", error->message); return 1; } base\_info = g\_irepository\_find\_by\_name (repository, "GLib", "assertion\_message"); if (!base\_info) { g\_error ("ERROR: %s\n", "Could not find GLib.assertion\_message"); return 1; } in\_args[0].v\_pointer = (gpointer)"domain"; in\_args[1].v\_pointer = (gpointer)"glib-print.c"; in\_args[2].v\_int = 42; in\_args[3].v\_pointer = (gpointer)"main"; in\_args[4].v\_pointer = (gpointer)"hello world"; if (!g\_function\_info\_invoke ((GIFunctionInfo \*) base\_info, (const GIArgument \*) &in\_args, 5, NULL, 0, &retval, &error)) { g\_error ("ERROR: %s\n", error->message); return 1; } g\_base\_info\_unref (base\_info); return 0; } ``` Command Line Tools[](#command-line-tools "Permalink to this heading") ---------------------------------------------------------------------- ### g-ir-compiler[](#g-ir-compiler "Permalink to this heading") #### Typelib compiler[](#typelib-compiler "Permalink to this heading") Manual section: 1 ##### SYNOPSIS[](#synopsis "Permalink to this heading") **g-ir-compiler** [OPTION…] GIRFILE ##### DESCRIPTION[](#description "Permalink to this heading") g-ir-compiler converts one or more GIR files into one or more typelib. The output will be written to standard output unless the `--output` is specified. ##### OPTIONS[](#options "Permalink to this heading") `--help` Show help options `--output=FILENAME` Save the resulting output in FILENAME. `--verbose` Show verbose messages `--debug` Show debug messages `--includedir=DIRECTORY` Adds a directory which will be used to find includes inside the GIR format. `--module=MODULE` FIXME `--shared-library=FILENAME` Specifies the shared library where the symbols in the typelib can be found. The name of the library should not contain the ending shared library suffix. `--version` Show program’s version number and exit ##### BUGS[](#bugs "Permalink to this heading") Report bugs at <https://gitlab.gnome.org/GNOME/gobject-introspection/issues> ##### HOMEPAGE and CONTACT[](#homepage-and-contact "Permalink to this heading") <https://gi.readthedocs.io/> ##### AUTHORS[](#authors "Permalink to this heading") Mattias Clasen ### g-ir-doc-tool[](#g-ir-doc-tool "Permalink to this heading") #### Documentation builder[](#documentation-builder "Permalink to this heading") Manual section: 1 ##### SYNOPSIS[](#synopsis "Permalink to this heading") **g-ir-doc-tool** [OPTION…] GIRFILE ##### DESCRIPTION[](#description "Permalink to this heading") g-ir-doc-tool builds library documentation directly from .gir files. The output is adjusted according to which programming language you’re generating docs for. ##### OPTIONS[](#options "Permalink to this heading") `--help` Show help options `--output=DIRECTORY` Save the resulting output in DIRECTORY. `--format=FORMAT` Output format. One of devdocs, mallard or sections. `--language=LANGUAGE` Output language. One of c, python, or gjs. `--add-include-path=DIRECTORY` Adds a directory which will be used to find includes inside the GIR format. `--version` Show program’s version number and exit `--write-sections-file` Backwards-compatible equivalent to -f sections. ##### BUGS[](#bugs "Permalink to this heading") Report bugs at <https://gitlab.gnome.org/GNOME/gobject-introspection/issues> ##### HOMEPAGE and CONTACT[](#homepage-and-contact "Permalink to this heading") <https://gi.readthedocs.io/> ##### AUTHORS[](#authors "Permalink to this heading") David King ### g-ir-generate[](#g-ir-generate "Permalink to this heading") #### Typelib generator[](#typelib-generator "Permalink to this heading") Manual section: 1 ##### SYNOPSIS[](#synopsis "Permalink to this heading") **g-ir-generate** [OPTION…] FILES… ##### DESCRIPTION[](#description "Permalink to this heading") g-ir-generate is an GIR generator, using the repository API. It generates GIR files from a raw typelib or in a shared library (`--shlib`). The output will be written to standard output unless the `--output` is specified. ##### OPTIONS[](#options "Permalink to this heading") `--help` Show help options `--shlib=FILENAME` The shared library to read the symbols from. `--output=FILENAME` Save the resulting output in FILENAME. `--version` Show program’s version number and exit ##### BUGS[](#bugs "Permalink to this heading") Report bugs at <https://gitlab.gnome.org/GNOME/gobject-introspection/issues> ##### HOMEPAGE and CONTACT[](#homepage-and-contact "Permalink to this heading") <https://gi.readthedocs.io/> ##### AUTHORS[](#authors "Permalink to this heading") Mattias Clasen ### g-ir-scanner[](#g-ir-scanner "Permalink to this heading") #### Extracting C metadata from sources and headers[](#extracting-c-metadata-from-sources-and-headers "Permalink to this heading") Manual section: 1 ##### SYNOPSIS[](#synopsis "Permalink to this heading") **g-ir-scanner** [OPTION…] FILES… ##### DESCRIPTION[](#description "Permalink to this heading") g-ir-scanner is a tool which generates GIR XML files by parsing headers and introspecting GObject based libraries. It is usually invoked during the normal build step for a project and the information is saved to disk and later installed, so that language bindings and other applications can use it. Header files and source files are passed in as arguments on the command line. The suffix determines whether a file be treated as a source file (.c) or a header file (.h). Currently only C based libraries are supported by the scanner. ##### OPTIONS[](#options "Permalink to this heading") `--help` Show help options `--quiet` If passed, do not print details of normal operation. `--warn-all` Display warnings for public API which is not introspectable. `--warn-error` Make warnings be fatal errors. `--strict` Display warnings for introspectable API that may present issues when consumed by known language bindings. `--format=FORMAT` This parameters decides which the resulting format will be used. The default value is gir. `--include=NAME` Add the specified introspection dependency to the scanned namespace. NAME is of the form NAMESPACE-VERSION, like Gtk-3.0. `--include-uninstalled=PATH` Add the specified introspection dependency to the scanned namespace. This differs from `--include` in that it takes a file path, and does not process the pkg-config dependencies (since they may not be installed yet). `--add-include-path=PATH` Add a directory to the path which the scanner uses to find GIR files. Can be used multiple times to specify multiple directories `-i, --library=LIBRARY` Specifies a library that will be introspected. This means that the \*\_get\_type() functions in it will be called for GObject data types. The name of the library should not contain the leading lib prefix nor the ending shared library suffix. `-L, --library-path=PATH` Include this directory when searching for a library. This option can be specified multiple times to include more than one directory to look for libraries in. `-Idirectory` Include this directory in the list of directories to be searched for header files. You need to pass to the scanner all the directories you’d normally pass to the compiler when using the specified source files. `--c-include=C\_INCLUDES` Headers which should be included in C programs. This option can be specified multiple times to include more than one header. `-n, --namespace=NAME` The namespace name. This name should be capitalized, eg the first letter should be upper case. Examples: Gtk, Clutter, WebKit. `--no-libtool` Disable usage of libtool for compiling stub introspection binary. Use this if your build system does not require libtool. `--libtool` Full path to libtool executable. Typically used for Automake systems. `--nsversion=VERSION` The namespace version. For instance 1.0. This is usually the platform version, eg 2.0 for Gtk+, not 2.12.7. `-p, --program=PROGRAM` Specifies a binary that will be introspected. This means that the \*\_get\_type() functions in it will be called for GObject data types. The binary must be modified to take a `--introspect-dump=` option, and to pass the argument to this function to g\_irepository\_dump. `--program-arg=ARG` Additional argument to pass to program for introspection. `--identifier-prefix=PREFIX` This option may be specified multiple times. Each one gives a prefix that will be stripped from all C identifiers. If none specified, the namespace will be used. Eg, an identifier prefix of Foo will export the identifier typdef struct \_FooBar FooBar; as Foo.Bar. `--symbol-prefix=PREFIX` This option may be specified multiple times. Each one gives a prefix that will be stripped from all C symbols. Eg, an symbol prefix of foo will export the symbol foo\_bar\_do\_something as Foo.Bar.do\_something. `--accept-unprefixed` If specified, the scanner will accept identifiers and symbols which do not match the namespace prefix. Try to avoid using this if possible. `--output=FILENAME` Name of the file to output. Normally namespace + format extension. Eg, GLib-2.0.gir. `--pkg=PACKAGE` List of pkg-config packages to get compiler and linker flags from. This option can be specified multiple times to include flags from several pkg-config packages. `--pkg-export=PACKAGE` List of pkg-config packages that are provided by the generated gir. This option can be specified multiple times if the gir provides more packages. If not specified, the packages specified with `--pkg=` will be used. `--compiler=COMPILER` The C compiler to be used internally by g-ir-scanner when introspecting the run time type information, like properties, signals, ancestors, and implemented interfaces. It has the same semantics as the `CC` environment variable. `--verbose` Be verbose, include some debugging information. ##### ENVIRONMENT VARIABLES[](#environment-variables "Permalink to this heading") The g-ir-scanner uses the `XDG\_DATA\_DIRS` variable to check for dirs, the girs are located in `XDG\_DATA\_DIRS/gir-1.0`. It is normally set on a distribution so you shouldn’t need to set it yourself. The variable `GI\_SCANNER\_DISABLE\_CACHE` ensures that the scanner will not write cache data to `$HOME`. The variable `GI\_SCANNER\_DEBUG` can be used to debug issues in the build-system that involve g-ir-scanner. When it is set to `save-temps`, then g-ir-scanner will not remove temporary files and directories after it terminates. The variable `GI\_HOST\_OS` can be used to control the OS name on the host that runs the scanner. It has the same semantics as the Python `os.name` property. ##### BUGS[](#bugs "Permalink to this heading") Report bugs at <https://gitlab.gnome.org/GNOME/gobject-introspection/issues> ##### HOMEPAGE and CONTACT[](#homepage-and-contact "Permalink to this heading") <https://gi.readthedocs.io/> ##### AUTHORS[](#authors "Permalink to this heading") Johan Dahlin [g-ir-compiler](index.html#document-tools/g-ir-compiler)Typelib compiler [g-ir-doc-tool](index.html#document-tools/g-ir-doc-tool)Documentation builder [g-ir-generate](index.html#document-tools/g-ir-generate)Typelib generator [g-ir-scanner](index.html#document-tools/g-ir-scanner)Extracting C metadata from sources and headers GObject introspection is a middleware layer between C libraries (using GObject) and language bindings. The C library can be scanned at compile time and generate metadata files, in addition to the actual native C library. Then language bindings can read this metadata and automatically provide bindings to call into the C library. [](_images/overview.svg) The GI project consists of: * an XML format called GIR containing introspection information in a machine parseable format * a Python package to create and parse the GIR format * a scanner to generate GIR format from C source and headers * a typelib similar to xpcom/msole which stores the information on disk in a binary format * a compiler to compile the typelib from a xml format (and vice versa) * C library to read the typelib, [libgirepository](index.html#document-writingbindings/libgirepository). Getting the code[](#getting-the-code "Permalink to this heading") ------------------------------------------------------------------ The latest stable release is available from <https://download.gnome.org/sources/gobject-introspection> GObject Introspection is stored in git and can be fetched: ``` git clone https://gitlab.gnome.org/GNOME/gobject-introspection.git ``` You can browse the repository online [here](https://gitlab.gnome.org/GNOME/gobject-introspection/). Reporting bugs[](#reporting-bugs "Permalink to this heading") -------------------------------------------------------------- For a list of existing bugs and feature requests, see the [issues page](https://gitlab.gnome.org/GNOME/gobject-introspection/issues). You can also [open an issue](https://gitlab.gnome.org/GNOME/gobject-introspection/issues/new). Contact[](#contact "Permalink to this heading") ------------------------------------------------ For questions or additional information, please use: * Discourse: <https://discourse.gnome.org/tag/introspection> * IRC: #introspection on irc.gnome.org

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React from \"react\";\nimport PropTypes from \"prop-types\";\nimport Link from \"gatsby-link\";\nimport Helmet from \"react-helmet\";\nimport GrapheneLogo from \"./graphene-logo.svg\";\nimport Arrow from \"./arrow.svg\";\nimport LogoOnWhite from \"./logo-on-white.svg\";\nimport GrapheneLogoWhite from \"./graphene-logo-white.svg\";\nimport { FaGithub, FaBars } from \"react-icons/fa\";\n\nimport \"docsearch.js/dist/cdn/docsearch.min.css\";\nimport \"./index.css\";\n\nconst SEARCH_DOCS = true;\n\n// let docsearch;\n// if (typeof window !== \"undefined\" && SEARCH_DOCS) {\n//   docsearch = require(\"docsearch.js/dist/cdn/docsearch.min\");\n//   try {\n//     docsearch({\n//       apiKey: \"4b6d0afa80197db35886555b5ef4721f\",\n//       inputSelector: \"#search-docs\",\n//       indexName: \"graphene_python\",\n//       transformData: function(suggestions) {\n//         return suggestions.map(function(suggestion) {\n//           suggestion.url.replace(\"http:\", \"https:\");\n//           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suggestion.url = suggestion.url.replace(\"http:\", \"https:\");\n              return suggestion;\n            });\n          }\n          // debug: true\n        });\n      }\n    } catch (e) {}\n  }\n  render() {\n    let { docs } = this.props;\n    return (\n      <div>\n        <header\n          className={`navbar-header  ${docs ? \"navbar-header-contrast\" : \"\"}`}\n        >\n          <div className=\"container\">\n            <HeaderLink to=\"/\" className=\"logo-link\">\n              {docs ? <GrapheneLogoWhite /> : <LogoOnWhite />}\n            </HeaderLink>\n            <a id=\"menu\" />\n            <a href=\"#menu\" className=\"mobile-menu\">\n              <FaBars />\n            </a>\n            <a className=\"background-mobile-menu\" href=\"#\" />\n            <nav>\n              {SEARCH_DOCS ? 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         right: 20px;\n              font-size: 20px;\n            }\n            .navbar-header-contrast .mobile-menu {\n              color: white;\n            }\n            .tagline .hide-mobile {\n              display: none;\n            }\n            .navbar-header nav {\n              height: auto;\n              color: black;\n              position: fixed;\n              top: 0;\n              bottom: 0;\n              right: 0;\n              width: 280px;\n              display: block;\n              background: white;\n              z-index: 10000;\n              transform: translateX(305px);\n              box-shadow: 0px 2px 25px rgba(0, 0, 0, 0.15);\n              transition: all 0.5s cubic-bezier(0.55, 0, 0.1, 1);\n            }\n            .navbar-header-contrast :global(.nav-link) {\n              color: black;\n            }\n            .navbar-header-contrast :global(.nav-link):hover {\n              color: #333;\n            }\n            .mobile-menu {\n     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.algolia-docsearch-suggestion--highlight{box-shadow:inset 0 -2px 0 0 #e55234}.algolia-autocomplete .ds-dropdown-menu .ds-suggestion.ds-cursor .algolia-docsearch-suggestion.suggestion-layout-simple,.algolia-autocomplete .ds-dropdown-menu .ds-suggestion.ds-cursor .algolia-docsearch-suggestion:not(.suggestion-layout-simple) .algolia-docsearch-suggestion--content{background:rgba(230,93,29,.1)}.algolia-autocomplete .algolia-docsearch-suggestion--highlight{color:#e65d1d;background:rgba(230,93,29,.08);padding:.1em .05em}[GroupCreated with Sketch.](https://graphene-python.org/)[Documentation](https://docs.graphene-python.org/)[Team](https://graphene-python.org/team) [Github](https://github.com/graphql-python/graphene)### [Table Of Contents](index.html#document-index) * [Getting started](index.html#document-quickstart) * [Types Reference](index.html#document-types/index) * [Execution](index.html#document-execution/index) * [Relay](index.html#document-relay/index) * [Testing in Graphene](index.html#document-testing/index) * [API Reference](index.html#document-api/index) [Older versions](https://readthedocs.org/projects/graphene-python/versions/) * [Docs](index.html#document-index) » * Graphene 1.0 documentation Graphene[¶](#graphene "Permalink to this headline") =================================================== --- The documentation below is for the `dev` (prerelease) version of Graphene. To view the documentation for the latest stable Graphene version go to the [v2 docs](https://docs.graphene-python.org/en/stable/). --- Contents: Getting started[¶](#getting-started "Permalink to this headline") ----------------------------------------------------------------- ### Introduction[¶](#introduction "Permalink to this headline") #### What is GraphQL?[¶](#what-is-graphql "Permalink to this headline") GraphQL is a query language for your API. It provides a standard way to: * *describe data provided by a server* in a statically typed **Schema** * *request data* in a **Query** which exactly describes your data requirements and * *receive data* in a **Response** containing only the data you requested. For an introduction to GraphQL and an overview of its concepts, please refer to [the official GraphQL documentation](http://graphql.org/learn/). #### What is Graphene?[¶](#what-is-graphene "Permalink to this headline") Graphene is a library that provides tools to implement a GraphQL API in Python using a *code-first* approach. Compare Graphene’s *code-first* approach to building a GraphQL API with *schema-first* approaches like [Apollo Server](https://www.apollographql.com/docs/apollo-server/) (JavaScript) or [Ariadne](https://ariadnegraphql.org/) (Python). Instead of writing GraphQL **Schema Definition Language (SDL)**, we write Python code to describe the data provided by your server. Graphene is fully featured with integrations for the most popular web frameworks and ORMs. Graphene produces schemas that are fully compliant with the GraphQL spec and provides tools and patterns for building a Relay-Compliant API as well. ### An example in Graphene[¶](#an-example-in-graphene "Permalink to this headline") Let’s build a basic GraphQL schema to say “hello” and “goodbye” in Graphene. When we send a **Query** requesting only one **Field**, `hello`, and specify a value for the `firstName` **Argument**... ``` { hello(firstName: "friend") } ``` ...we would expect the following Response containing only the data requested (the `goodbye` field is not resolved). ``` { "data": { "hello": "Hello friend!" } } ``` #### Requirements[¶](#requirements "Permalink to this headline") * Python (3.6, 3.7, 3.8, 3.9, 3.10, pypy) * Graphene (3.0) #### Project setup[¶](#project-setup "Permalink to this headline") ``` pip install "graphene>=3.0" ``` #### Creating a basic Schema[¶](#creating-a-basic-schema "Permalink to this headline") In Graphene, we can define a simple schema using the following code: ``` from graphene import ObjectType, String, Schema class Query(ObjectType): # this defines a Field `hello` in our Schema with a single Argument `first\_name` # By default, the argument name will automatically be camel-based into firstName in the generated schema hello = String(first\_name=String(default\_value="stranger")) goodbye = String() # our Resolver method takes the GraphQL context (root, info) as well as # Argument (first\_name) for the Field and returns data for the query Response def resolve\_hello(root, info, first\_name): return f'Hello {first\_name}!' def resolve\_goodbye(root, info): return 'See ya!' schema = Schema(query=Query) ``` A GraphQL **Schema** describes each **Field** in the data model provided by the server using scalar types like *String*, *Int* and *Enum* and compound types like *List* and *Object*. For more details refer to the Graphene [Types Reference](index.html#typesreference). Our schema can also define any number of **Arguments** for our **Fields**. This is a powerful way for a **Query** to describe the exact data requirements for each **Field**. For each **Field** in our **Schema**, we write a **Resolver** method to fetch data requested by a client’s **Query** using the current context and **Arguments**. For more details, refer to this section on [Resolvers](index.html#resolvers). #### Schema Definition Language (SDL)[¶](#schema-definition-language-sdl "Permalink to this headline") In the [GraphQL Schema Definition Language](https://graphql.org/learn/schema/), we could describe the fields defined by our example code as shown below. ``` type Query { hello(firstName: String = "stranger"): String goodbye: String } ``` Further examples in this documentation will use SDL to describe schema created by ObjectTypes and other fields. #### Querying[¶](#querying "Permalink to this headline") Then we can start querying our **Schema** by passing a GraphQL query string to `execute`: ``` # we can query for our field (with the default argument) query\_string = '{ hello }' result = schema.execute(query\_string) print(result.data['hello']) # "Hello stranger!" # or passing the argument in the query query\_with\_argument = '{ hello(firstName: "GraphQL") }' result = schema.execute(query\_with\_argument) print(result.data['hello']) # "Hello GraphQL!" ``` #### Next steps[¶](#next-steps "Permalink to this headline") Congrats! You got your first Graphene schema working! Normally, we don’t need to directly execute a query string against our schema as Graphene provides many useful Integrations with popular web frameworks like Flask and Django. Check out [Integrations](index.html#integrations) for more information on how to get started serving your GraphQL API. Types Reference[¶](#types-reference "Permalink to this headline") ----------------------------------------------------------------- ### Schema[¶](#schema "Permalink to this headline") A GraphQL **Schema** defines the types and relationships between **Fields** in your API. A Schema is created by supplying the root [ObjectType](index.html#objecttype) of each operation, query (mandatory), mutation and subscription. Schema will collect all type definitions related to the root operations and then supply them to the validator and executor. ``` my\_schema = Schema( query=MyRootQuery, mutation=MyRootMutation, subscription=MyRootSubscription ) ``` A Root Query is just a special [ObjectType](index.html#objecttype) that defines the fields that are the entrypoint for your API. Root Mutation and Root Subscription are similar to Root Query, but for different operation types: * Query fetches data * Mutation changes data and retrieves the changes * Subscription sends changes to clients in real-time Review the [GraphQL documentation on Schema](https://graphql.org/learn/schema/) for a brief overview of fields, schema and operations. #### Querying[¶](#querying "Permalink to this headline") To query a schema, call the `execute` method on it. See [Executing a query](index.html#schemaexecute) for more details. ``` query\_string = 'query whoIsMyBestFriend { myBestFriend { lastName } }' my\_schema.execute(query\_string) ``` #### Types[¶](#types "Permalink to this headline") There are some cases where the schema cannot access all of the types that we plan to have. For example, when a field returns an `Interface`, the schema doesn’t know about any of the implementations. In this case, we need to use the `types` argument when creating the Schema: ``` my\_schema = Schema( query=MyRootQuery, types=[SomeExtraObjectType, ] ) ``` #### Auto camelCase field names[¶](#auto-camelcase-field-names "Permalink to this headline") By default all field and argument names (that are not explicitly set with the `name` arg) will be converted from `snake\_case` to `camelCase` (as the API is usually being consumed by a js/mobile client) For example with the ObjectType the `last\_name` field name is converted to `lastName`: ``` class Person(graphene.ObjectType): last\_name = graphene.String() other\_name = graphene.String(name='\_other\_Name') ``` In case you don’t want to apply this transformation, provide a `name` argument to the field constructor. `other\_name` converts to `\_other\_Name` (without further transformations). Your query should look like: ``` { lastName \_other\_Name } ``` To disable this behavior, set the `auto\_camelcase` to `False` upon schema instantiation: ``` my\_schema = Schema( query=MyRootQuery, auto\_camelcase=False, ) ``` ### Scalars[¶](#scalars "Permalink to this headline") Scalar types represent concrete values at the leaves of a query. There are several built in types that Graphene provides out of the box which represent common values in Python. You can also create your own Scalar types to better express values that you might have in your data model. All Scalar types accept the following arguments. All are optional: `name`: *string* > > Override the name of the Field. `description`: *string* > > A description of the type to show in the GraphiQL browser. `required`: *boolean* > > If `True`, the server will enforce a value for this field. See [NonNull](../list-and-nonnull.html#nonnull). Default is `False`. `deprecation\_reason`: *string* > > Provide a deprecation reason for the Field. `default\_value`: *any* > > Provide a default value for the Field. #### Built in scalars[¶](#built-in-scalars "Permalink to this headline") Graphene defines the following base Scalar Types that match the default [GraphQL types](https://graphql.org/learn/schema/#scalar-types): ##### `graphene.String`[¶](#graphene-string "Permalink to this headline") > > Represents textual data, represented as UTF-8 > character sequences. The String type is most often used by GraphQL to > represent free-form human-readable text. ##### `graphene.Int`[¶](#graphene-int "Permalink to this headline") > > Represents non-fractional signed whole numeric > values. Int is a signed 32‐bit integer per the > [GraphQL spec](https://facebook.github.io/graphql/June2018/#sec-Int) ##### `graphene.Float`[¶](#graphene-float "Permalink to this headline") > > Represents signed double-precision fractional > values as specified by > [IEEE 754](http://en.wikipedia.org/wiki/IEEE_floating_point). ##### `graphene.Boolean`[¶](#graphene-boolean "Permalink to this headline") > > Represents true or false. ##### `graphene.ID`[¶](#graphene-id "Permalink to this headline") > > Represents a unique identifier, often used to > refetch an object or as key for a cache. The ID type appears in a JSON > response as a String; however, it is not intended to be human-readable. > When expected as an input type, any string (such as “4”) or integer > (such as 4) input value will be accepted as an ID. --- Graphene also provides custom scalars for common values: ##### `graphene.Date`[¶](#graphene-date "Permalink to this headline") > > Represents a Date value as specified by [iso8601](https://en.wikipedia.org/wiki/ISO_8601). ``` import datetime from graphene import Schema, ObjectType, Date class Query(ObjectType): one\_week\_from = Date(required=True, date\_input=Date(required=True)) def resolve\_one\_week\_from(root, info, date\_input): assert date\_input == datetime.date(2006, 1, 2) return date\_input + datetime.timedelta(weeks=1) schema = Schema(query=Query) results = schema.execute(""" query { oneWeekFrom(dateInput: "2006-01-02") } """) assert results.data == {"oneWeekFrom": "2006-01-09"} ``` ##### `graphene.DateTime`[¶](#graphene-datetime "Permalink to this headline") > > Represents a DateTime value as specified by [iso8601](https://en.wikipedia.org/wiki/ISO_8601). ``` import datetime from graphene import Schema, ObjectType, DateTime class Query(ObjectType): one\_hour\_from = DateTime(required=True, datetime\_input=DateTime(required=True)) def resolve\_one\_hour\_from(root, info, datetime\_input): assert datetime\_input == datetime.datetime(2006, 1, 2, 15, 4, 5) return datetime\_input + datetime.timedelta(hours=1) schema = Schema(query=Query) results = schema.execute(""" query { oneHourFrom(datetimeInput: "2006-01-02T15:04:05") } """) assert results.data == {"oneHourFrom": "2006-01-02T16:04:05"} ``` ##### `graphene.Time`[¶](#graphene-time "Permalink to this headline") > > Represents a Time value as specified by [iso8601](https://en.wikipedia.org/wiki/ISO_8601). ``` import datetime from graphene import Schema, ObjectType, Time class Query(ObjectType): one\_hour\_from = Time(required=True, time\_input=Time(required=True)) def resolve\_one\_hour\_from(root, info, time\_input): assert time\_input == datetime.time(15, 4, 5) tmp\_time\_input = datetime.datetime.combine(datetime.date(1, 1, 1), time\_input) return (tmp\_time\_input + datetime.timedelta(hours=1)).time() schema = Schema(query=Query) results = schema.execute(""" query { oneHourFrom(timeInput: "15:04:05") } """) assert results.data == {"oneHourFrom": "16:04:05"} ``` ##### `graphene.Decimal`[¶](#graphene-decimal "Permalink to this headline") > > Represents a Python Decimal value. ``` import decimal from graphene import Schema, ObjectType, Decimal class Query(ObjectType): add\_one\_to = Decimal(required=True, decimal\_input=Decimal(required=True)) def resolve\_add\_one\_to(root, info, decimal\_input): assert decimal\_input == decimal.Decimal("10.50") return decimal\_input + decimal.Decimal("1") schema = Schema(query=Query) results = schema.execute(""" query { addOneTo(decimalInput: "10.50") } """) assert results.data == {"addOneTo": "11.50"} ``` ##### `graphene.JSONString`[¶](#graphene-jsonstring "Permalink to this headline") > > Represents a JSON string. ``` from graphene import Schema, ObjectType, JSONString, String class Query(ObjectType): update\_json\_key = JSONString( required=True, json\_input=JSONString(required=True), key=String(required=True), value=String(required=True) ) def resolve\_update\_json\_key(root, info, json\_input, key, value): assert json\_input == {"name": "Jane"} json\_input[key] = value return json\_input schema = Schema(query=Query) results = schema.execute(""" query { updateJsonKey(jsonInput: "{\\"name\\": \\"Jane\\"}", key: "name", value: "Beth") } """) assert results.data == {"updateJsonKey": "{\"name\": \"Beth\"}"} ``` ##### `graphene.Base64`[¶](#graphene-base64 "Permalink to this headline") > > Represents a Base64 encoded string. ``` from graphene import Schema, ObjectType, Base64 class Query(ObjectType): increment\_encoded\_id = Base64( required=True, base64\_input=Base64(required=True), ) def resolve\_increment\_encoded\_id(root, info, base64\_input): assert base64\_input == "4" return int(base64\_input) + 1 schema = Schema(query=Query) results = schema.execute(""" query { incrementEncodedId(base64Input: "NA==") } """) assert results.data == {"incrementEncodedId": "NQ=="} ``` #### Custom scalars[¶](#custom-scalars "Permalink to this headline") You can create custom scalars for your schema. The following is an example for creating a DateTime scalar: ``` import datetime from graphene.types import Scalar from graphql.language import ast class DateTime(Scalar): '''DateTime Scalar Description''' @staticmethod def serialize(dt): return dt.isoformat() @staticmethod def parse\_literal(node, \_variables=None): if isinstance(node, ast.StringValue): return datetime.datetime.strptime( node.value, "%Y-%m-%dT%H:%M:%S.%f") @staticmethod def parse\_value(value): return datetime.datetime.strptime(value, "%Y-%m-%dT%H:%M:%S.%f") ``` #### Mounting Scalars[¶](#mounting-scalars "Permalink to this headline") Scalars mounted in a `ObjectType`, `Interface` or `Mutation` act as `Field`s. ``` class Person(graphene.ObjectType): name = graphene.String() # Is equivalent to: class Person(graphene.ObjectType): name = graphene.Field(graphene.String) ``` **Note:** when using the `Field` constructor directly, pass the type and not an instance. Types mounted in a `Field` act as `Argument`s. ``` graphene.Field(graphene.String, to=graphene.String()) # Is equivalent to: graphene.Field(graphene.String, to=graphene.Argument(graphene.String)) ``` ### Lists and Non-Null[¶](#lists-and-non-null "Permalink to this headline") Object types, scalars, and enums are the only kinds of types you can define in Graphene. But when you use the types in other parts of the schema, or in your query variable declarations, you can apply additional type modifiers that affect validation of those values. #### NonNull[¶](#nonnull "Permalink to this headline") ``` import graphene class Character(graphene.ObjectType): name = graphene.NonNull(graphene.String) ``` Here, we’re using a `String` type and marking it as Non-Null by wrapping it using the `NonNull` class. This means that our server always expects to return a non-null value for this field, and if it ends up getting a null value that will actually trigger a GraphQL execution error, letting the client know that something has gone wrong. The previous `NonNull` code snippet is also equivalent to: ``` import graphene class Character(graphene.ObjectType): name = graphene.String(required=True) ``` #### List[¶](#list "Permalink to this headline") ``` import graphene class Character(graphene.ObjectType): appears\_in = graphene.List(graphene.String) ``` Lists work in a similar way: We can use a type modifier to mark a type as a `List`, which indicates that this field will return a list of that type. It works the same for arguments, where the validation step will expect a list for that value. #### NonNull Lists[¶](#nonnull-lists "Permalink to this headline") By default items in a list will be considered nullable. To define a list without any nullable items the type needs to be marked as `NonNull`. For example: ``` import graphene class Character(graphene.ObjectType): appears\_in = graphene.List(graphene.NonNull(graphene.String)) ``` The above results in the type definition: ``` type Character { appearsIn: [String!] } ``` ### ObjectType[¶](#objecttype "Permalink to this headline") A Graphene *ObjectType* is the building block used to define the relationship between **Fields** in your **Schema** and how their data is retrieved. The basics: * Each ObjectType is a Python class that inherits from `graphene.ObjectType`. * Each attribute of the ObjectType represents a `Field`. * Each `Field` has a [resolver method](#resolvers) to fetch data (or [Default Resolver](#defaultresolver)). #### Quick example[¶](#quick-example "Permalink to this headline") This example model defines a Person, with a first and a last name: ``` from graphene import ObjectType, String class Person(ObjectType): first\_name = String() last\_name = String() full\_name = String() def resolve\_full\_name(parent, info): return f"{parent.first\_name} {parent.last\_name}" ``` This *ObjectType* defines the field **first\_name**, **last\_name**, and **full\_name**. Each field is specified as a class attribute, and each attribute maps to a Field. Data is fetched by our `resolve\_full\_name` [resolver method](#resolvers) for `full\_name` field and the [Default Resolver](#defaultresolver) for other fields. The above `Person` ObjectType has the following schema representation: ``` type Person { firstName: String lastName: String fullName: String } ``` #### Resolvers[¶](#resolvers "Permalink to this headline") A **Resolver** is a method that helps us answer **Queries** by fetching data for a **Field** in our **Schema**. Resolvers are lazily executed, so if a field is not included in a query, its resolver will not be executed. Each field on an *ObjectType* in Graphene should have a corresponding resolver method to fetch data. This resolver method should match the field name. For example, in the `Person` type above, the `full\_name` field is resolved by the method `resolve\_full\_name`. Each resolver method takes the parameters: * [Parent Value Object (parent)](#resolverparamparent) for the value object use to resolve most fields * [GraphQL Execution Info (info)](#resolverparaminfo) for query and schema meta information and per-request context * [GraphQL Arguments (\*\*kwargs)](#resolverparamgraphqlarguments) as defined on the **Field**. ##### Resolver Parameters[¶](#resolver-parameters "Permalink to this headline") ###### Parent Value Object (*parent*)[¶](#parent-value-object-parent "Permalink to this headline") This parameter is typically used to derive the values for most fields on an *ObjectType*. The first parameter of a resolver method (*parent*) is the value object returned from the resolver of the parent field. If there is no parent field, such as a root Query field, then the value for *parent* is set to the `root\_value` configured while executing the query (default `None`). See [Executing a query](index.html#schemaexecute) for more details on executing queries. ####### Resolver example[¶](#resolver-example "Permalink to this headline") If we have a schema with Person type and one field on the root query. ``` from graphene import ObjectType, String, Field class Person(ObjectType): full\_name = String() def resolve\_full\_name(parent, info): return f"{parent.first\_name} {parent.last\_name}" class Query(ObjectType): me = Field(Person) def resolve\_me(parent, info): # returns an object that represents a Person return get\_human(name="Luke Skywalker") ``` When we execute a query against that schema. ``` schema = Schema(query=Query) query\_string = "{ me { fullName } }" result = schema.execute(query\_string) assert result.data["me"] == {"fullName": "Luke Skywalker"} ``` Then we go through the following steps to resolve this query: * `parent` is set with the root\_value from query execution (None). * `Query.resolve\_me` called with `parent` None which returns a value object `Person("Luke", "Skywalker")`. * This value object is then used as `parent` while calling `Person.resolve\_full\_name` to resolve the scalar String value “Luke Skywalker”. * The scalar value is serialized and sent back in the query response. Each resolver returns the next [Parent Value Object (parent)](#resolverparamparent) to be used in executing the following resolver in the chain. If the Field is a Scalar type, that value will be serialized and sent in the **Response**. Otherwise, while resolving Compound types like *ObjectType*, the value be passed forward as the next [Parent Value Object (parent)](#resolverparamparent). ####### Naming convention[¶](#naming-convention "Permalink to this headline") This [Parent Value Object (parent)](#resolverparamparent) is sometimes named `obj`, `parent`, or `source` in other GraphQL documentation. It can also be named after the value object being resolved (ex. `root` for a root Query or Mutation, and `person` for a Person value object). Sometimes this argument will be named `self` in Graphene code, but this can be misleading due to [Implicit staticmethod](#resolverimplicitstaticmethod) while executing queries in Graphene. ###### GraphQL Execution Info (*info*)[¶](#graphql-execution-info-info "Permalink to this headline") The second parameter provides two things: * reference to meta information about the execution of the current GraphQL Query (fields, schema, parsed query, etc.) * access to per-request `context` which can be used to store user authentication, data loader instances or anything else useful for resolving the query. Only context will be required for most applications. See [Context](index.html#schemaexecutecontext) for more information about setting context. ###### GraphQL Arguments (*\*\*kwargs*)[¶](#graphql-arguments-kwargs "Permalink to this headline") Any arguments that a field defines gets passed to the resolver function as keyword arguments. For example: ``` from graphene import ObjectType, Field, String class Query(ObjectType): human\_by\_name = Field(Human, name=String(required=True)) def resolve\_human\_by\_name(parent, info, name): return get\_human(name=name) ``` You can then execute the following query: ``` query { humanByName(name: "Luke Skywalker") { firstName lastName } } ``` *Note:* There are several arguments to a field that are “reserved” by Graphene (see [Fields (Mounted Types)](index.html#fields-mounted-types)). You can still define an argument that clashes with one of these fields by using the `args` parameter like so: ``` from graphene import ObjectType, Field, String class Query(ObjectType): answer = String(args={'description': String()}) def resolve\_answer(parent, info, description): return description ``` ##### Convenience Features of Graphene Resolvers[¶](#convenience-features-of-graphene-resolvers "Permalink to this headline") ###### Implicit staticmethod[¶](#implicit-staticmethod "Permalink to this headline") One surprising feature of Graphene is that all resolver methods are treated implicitly as staticmethods. This means that, unlike other methods in Python, the first argument of a resolver is *never* `self` while it is being executed by Graphene. Instead, the first argument is always [Parent Value Object (parent)](#resolverparamparent). In practice, this is very convenient as, in GraphQL, we are almost always more concerned with the using the parent value object to resolve queries than attributes on the Python object itself. The two resolvers in this example are effectively the same. ``` from graphene import ObjectType, String class Person(ObjectType): first\_name = String() last\_name = String() @staticmethod def resolve\_first\_name(parent, info): ''' Decorating a Python method with `staticmethod` ensures that `self` will not be provided as an argument. However, Graphene does not need this decorator for this behavior. ''' return parent.first\_name def resolve\_last\_name(parent, info): ''' Normally the first argument for this method would be `self`, but Graphene executes this as a staticmethod implicitly. ''' return parent.last\_name # ... ``` If you prefer your code to be more explicit, feel free to use `@staticmethod` decorators. Otherwise, your code may be cleaner without them! ###### Default Resolver[¶](#default-resolver "Permalink to this headline") If a resolver method is not defined for a **Field** attribute on our *ObjectType*, Graphene supplies a default resolver. If the [Parent Value Object (parent)](#resolverparamparent) is a dictionary, the resolver will look for a dictionary key matching the field name. Otherwise, the resolver will get the attribute from the parent value object matching the field name. ``` from collections import namedtuple from graphene import ObjectType, String, Field, Schema PersonValueObject = namedtuple("Person", ["first\_name", "last\_name"]) class Person(ObjectType): first\_name = String() last\_name = String() class Query(ObjectType): me = Field(Person) my\_best\_friend = Field(Person) def resolve\_me(parent, info): # always pass an object for `me` field return PersonValueObject(first\_name="Luke", last\_name="Skywalker") def resolve\_my\_best\_friend(parent, info): # always pass a dictionary for `my\_best\_fiend\_field` return {"first\_name": "R2", "last\_name": "D2"} schema = Schema(query=Query) result = schema.execute(''' { me { firstName lastName } myBestFriend { firstName lastName } } ''') # With default resolvers we can resolve attributes from an object.. assert result.data["me"] == {"firstName": "Luke", "lastName": "Skywalker"} # With default resolvers, we can also resolve keys from a dictionary.. assert result.data["myBestFriend"] == {"firstName": "R2", "lastName": "D2"} ``` ##### Advanced[¶](#advanced "Permalink to this headline") ###### GraphQL Argument defaults[¶](#graphql-argument-defaults "Permalink to this headline") If you define an argument for a field that is not required (and in a query execution it is not provided as an argument) it will not be passed to the resolver function at all. This is so that the developer can differentiate between a `undefined` value for an argument and an explicit `null` value. For example, given this schema: ``` from graphene import ObjectType, String class Query(ObjectType): hello = String(required=True, name=String()) def resolve\_hello(parent, info, name): return name if name else 'World' ``` And this query: ``` query { hello } ``` An error will be thrown: ``` TypeError: resolve\_hello() missing 1 required positional argument: 'name' ``` You can fix this error in several ways. Either by combining all keyword arguments into a dict: ``` from graphene import ObjectType, String class Query(ObjectType): hello = String(required=True, name=String()) def resolve\_hello(parent, info, \*\*kwargs): name = kwargs.get('name', 'World') return f'Hello, {name}!' ``` Or by setting a default value for the keyword argument: ``` from graphene import ObjectType, String class Query(ObjectType): hello = String(required=True, name=String()) def resolve\_hello(parent, info, name='World'): return f'Hello, {name}!' ``` One can also set a default value for an Argument in the GraphQL schema itself using Graphene! ``` from graphene import ObjectType, String class Query(ObjectType): hello = String( required=True, name=String(default\_value='World') ) def resolve\_hello(parent, info, name): return f'Hello, {name}!' ``` ###### Resolvers outside the class[¶](#resolvers-outside-the-class "Permalink to this headline") A field can use a custom resolver from outside the class: ``` from graphene import ObjectType, String def resolve\_full\_name(person, info): return f"{person.first\_name} {person.last\_name}" class Person(ObjectType): first\_name = String() last\_name = String() full\_name = String(resolver=resolve\_full\_name) ``` ###### Instances as value objects[¶](#instances-as-value-objects "Permalink to this headline") Graphene `ObjectType`s can act as value objects too. So with the previous example you could use `Person` to capture data for each of the *ObjectType*‘s fields. ``` peter = Person(first\_name='Peter', last\_name='Griffin') peter.first\_name # prints "Peter" peter.last\_name # prints "Griffin" ``` ###### Field camelcasing[¶](#field-camelcasing "Permalink to this headline") Graphene automatically camelcases fields on *ObjectType* from `field\_name` to `fieldName` to conform with GraphQL standards. See [Auto camelCase field names](index.html#schemaautocamelcase) for more information. #### *ObjectType* Configuration - Meta class[¶](#objecttype-configuration-meta-class "Permalink to this headline") Graphene uses a Meta inner class on *ObjectType* to set different options. ##### GraphQL type name[¶](#graphql-type-name "Permalink to this headline") By default the type name in the GraphQL schema will be the same as the class name that defines the `ObjectType`. This can be changed by setting the `name` property on the `Meta` class: ``` from graphene import ObjectType class MyGraphQlSong(ObjectType): class Meta: name = 'Song' ``` ##### GraphQL Description[¶](#graphql-description "Permalink to this headline") The schema description of an *ObjectType* can be set as a docstring on the Python object or on the Meta inner class. ``` from graphene import ObjectType class MyGraphQlSong(ObjectType): ''' We can set the schema description for an Object Type here on a docstring ''' class Meta: description = 'But if we set the description in Meta, this value is used instead' ``` ##### Interfaces & Possible Types[¶](#interfaces-possible-types "Permalink to this headline") Setting `interfaces` in Meta inner class specifies the GraphQL Interfaces that this Object implements. Providing `possible\_types` helps Graphene resolve ambiguous types such as interfaces or Unions. See [Interfaces](index.html#interfaces) for more information. ``` from graphene import ObjectType, Node Song = namedtuple('Song', ('title', 'artist')) class MyGraphQlSong(ObjectType): class Meta: interfaces = (Node, ) possible\_types = (Song, ) ``` ### Enums[¶](#enums "Permalink to this headline") An `Enum` is a special `GraphQL` type that represents a set of symbolic names (members) bound to unique, constant values. #### Definition[¶](#definition "Permalink to this headline") You can create an `Enum` using classes: ``` import graphene class Episode(graphene.Enum): NEWHOPE = 4 EMPIRE = 5 JEDI = 6 ``` But also using instances of Enum: ``` Episode = graphene.Enum('Episode', [('NEWHOPE', 4), ('EMPIRE', 5), ('JEDI', 6)]) ``` #### Value descriptions[¶](#value-descriptions "Permalink to this headline") It’s possible to add a description to an enum value, for that the enum value needs to have the `description` property on it. ``` class Episode(graphene.Enum): NEWHOPE = 4 EMPIRE = 5 JEDI = 6 @property def description(self): if self == Episode.NEWHOPE: return 'New Hope Episode' return 'Other episode' ``` #### Usage with Python Enums[¶](#usage-with-python-enums "Permalink to this headline") In case the Enums are already defined it’s possible to reuse them using the `Enum.from\_enum` function. ``` graphene.Enum.from\_enum(AlreadyExistingPyEnum) ``` `Enum.from\_enum` supports a `description` and `deprecation\_reason` lambdas as input so you can add description etc. to your enum without changing the original: ``` graphene.Enum.from\_enum( AlreadyExistingPyEnum, description=lambda v: return 'foo' if v == AlreadyExistingPyEnum.Foo else 'bar' ) ``` #### Notes[¶](#notes "Permalink to this headline") `graphene.Enum` uses [`enum.Enum`](https://docs.python.org/3/library/enum.html) internally (or a backport if that’s not available) and can be used in a similar way, with the exception of member getters. In the Python `Enum` implementation you can access a member by initing the Enum. ``` from enum import Enum class Color(Enum): RED = 1 GREEN = 2 BLUE = 3 assert Color(1) == Color.RED ``` However, in Graphene `Enum` you need to call .get to have the same effect: ``` from graphene import Enum class Color(Enum): RED = 1 GREEN = 2 BLUE = 3 assert Color.get(1) == Color.RED ``` ### Interfaces[¶](#interfaces "Permalink to this headline") An *Interface* is an abstract type that defines a certain set of fields that a type must include to implement the interface. For example, you can define an Interface `Character` that represents any character in the Star Wars trilogy: ``` import graphene class Character(graphene.Interface): id = graphene.ID(required=True) name = graphene.String(required=True) friends = graphene.List(lambda: Character) ``` Any ObjectType that implements `Character` will have these exact fields, with these arguments and return types. For example, here are some types that might implement `Character`: ``` class Human(graphene.ObjectType): class Meta: interfaces = (Character, ) starships = graphene.List(Starship) home\_planet = graphene.String() class Droid(graphene.ObjectType): class Meta: interfaces = (Character, ) primary\_function = graphene.String() ``` Both of these types have all of the fields from the `Character` interface, but also bring in extra fields, `home\_planet`, `starships` and `primary\_function`, that are specific to that particular type of character. The full GraphQL schema definition will look like this: ``` interface Character { id: ID! name: String! friends: [Character] } type Human implements Character { id: ID! name: String! friends: [Character] starships: [Starship] homePlanet: String } type Droid implements Character { id: ID! name: String! friends: [Character] primaryFunction: String } ``` Interfaces are useful when you want to return an object or set of objects, which might be of several different types. For example, you can define a field `hero` that resolves to any `Character`, depending on the episode, like this: ``` class Query(graphene.ObjectType): hero = graphene.Field( Character, required=True, episode=graphene.Int(required=True) ) def resolve\_hero(root, info, episode): # Luke is the hero of Episode V if episode == 5: return get\_human(name='Luke Skywalker') return get\_droid(name='R2-D2') schema = graphene.Schema(query=Query, types=[Human, Droid]) ``` This allows you to directly query for fields that exist on the Character interface as well as selecting specific fields on any type that implements the interface using [inline fragments](https://graphql.org/learn/queries/#inline-fragments). For example, the following query: ``` query HeroForEpisode($episode: Int!) { hero(episode: $episode) { __typename name ... on Droid { primaryFunction } ... on Human { homePlanet } } } ``` Will return the following data with variables `{ "episode": 4 }`: ``` { "data": { "hero": { "\_\_typename": "Droid", "name": "R2-D2", "primaryFunction": "Astromech" } } } ``` And different data with the variables `{ "episode": 5 }`: ``` { "data": { "hero": { "\_\_typename": "Human", "name": "Luke Skywalker", "homePlanet": "Tatooine" } } } ``` #### Resolving data objects to types[¶](#resolving-data-objects-to-types "Permalink to this headline") As you build out your schema in Graphene it’s common for your resolvers to return objects that represent the data backing your GraphQL types rather than instances of the Graphene types (e.g. Django or SQLAlchemy models). This works well with `ObjectType` and `Scalar` fields, however when you start using Interfaces you might come across this error: ``` "Abstract type Character must resolve to an Object type at runtime for field Query.hero ..." ``` This happens because Graphene doesn’t have enough information to convert the data object into a Graphene type needed to resolve the `Interface`. To solve this you can define a `resolve\_type` class method on the `Interface` which maps a data object to a Graphene type: ``` class Character(graphene.Interface): id = graphene.ID(required=True) name = graphene.String(required=True) @classmethod def resolve\_type(cls, instance, info): if instance.type == 'DROID': return Droid return Human ``` ### Unions[¶](#unions "Permalink to this headline") Union types are very similar to interfaces, but they don’t get to specify any common fields between the types. The basics: * Each Union is a Python class that inherits from `graphene.Union`. * Unions don’t have any fields on it, just links to the possible ObjectTypes. #### Quick example[¶](#quick-example "Permalink to this headline") This example model defines several ObjectTypes with their own fields. `SearchResult` is the implementation of `Union` of this object types. ``` import graphene class Human(graphene.ObjectType): name = graphene.String() born\_in = graphene.String() class Droid(graphene.ObjectType): name = graphene.String() primary\_function = graphene.String() class Starship(graphene.ObjectType): name = graphene.String() length = graphene.Int() class SearchResult(graphene.Union): class Meta: types = (Human, Droid, Starship) ``` Wherever we return a SearchResult type in our schema, we might get a Human, a Droid, or a Starship. Note that members of a union type need to be concrete object types; you can’t create a union type out of interfaces or other unions. The above types have the following representation in a schema: ``` type Droid { name: String primaryFunction: String } type Human { name: String bornIn: String } type Ship { name: String length: Int } union SearchResult = Human | Droid | Starship ``` ### Mutations[¶](#mutations "Permalink to this headline") A Mutation is a special ObjectType that also defines an Input. #### Quick example[¶](#quick-example "Permalink to this headline") This example defines a Mutation: ``` import graphene class CreatePerson(graphene.Mutation): class Arguments: name = graphene.String() ok = graphene.Boolean() person = graphene.Field(lambda: Person) def mutate(root, info, name): person = Person(name=name) ok = True return CreatePerson(person=person, ok=ok) ``` **person** and **ok** are the output fields of the Mutation when it is resolved. **Arguments** attributes are the arguments that the Mutation `CreatePerson` needs for resolving, in this case **name** will be the only argument for the mutation. **mutate** is the function that will be applied once the mutation is called. This method is just a special resolver that we can change data within. It takes the same arguments as the standard query [Resolver Parameters](index.html#resolverarguments). So, we can finish our schema like this: ``` # ... the Mutation Class class Person(graphene.ObjectType): name = graphene.String() age = graphene.Int() class MyMutations(graphene.ObjectType): create\_person = CreatePerson.Field() # We must define a query for our schema class Query(graphene.ObjectType): person = graphene.Field(Person) schema = graphene.Schema(query=Query, mutation=MyMutations) ``` #### Executing the Mutation[¶](#executing-the-mutation "Permalink to this headline") Then, if we query (`schema.execute(query\_str)`) the following: ``` mutation myFirstMutation { createPerson(name:"Peter") { person { name } ok } } ``` We should receive: ``` { "createPerson": { "person" : { "name": "Peter" }, "ok": true } } ``` #### InputFields and InputObjectTypes[¶](#inputfields-and-inputobjecttypes "Permalink to this headline") InputFields are used in mutations to allow nested input data for mutations. To use an InputField you define an InputObjectType that specifies the structure of your input data: ``` import graphene class PersonInput(graphene.InputObjectType): name = graphene.String(required=True) age = graphene.Int(required=True) class CreatePerson(graphene.Mutation): class Arguments: person\_data = PersonInput(required=True) person = graphene.Field(Person) def mutate(root, info, person\_data=None): person = Person( name=person\_data.name, age=person\_data.age ) return CreatePerson(person=person) ``` Note that **name** and **age** are part of **person\_data** now. Using the above mutation your new query would look like this: ``` mutation myFirstMutation { createPerson(personData: {name:"Peter", age: 24}) { person { name, age } } } ``` InputObjectTypes can also be fields of InputObjectTypes allowing you to have as complex of input data as you need: ``` import graphene class LatLngInput(graphene.InputObjectType): lat = graphene.Float() lng = graphene.Float() #A location has a latlng associated to it class LocationInput(graphene.InputObjectType): name = graphene.String() latlng = graphene.InputField(LatLngInput) ``` #### Output type example[¶](#output-type-example "Permalink to this headline") To return an existing ObjectType instead of a mutation-specific type, set the **Output** attribute to the desired ObjectType: ``` import graphene class CreatePerson(graphene.Mutation): class Arguments: name = graphene.String() Output = Person def mutate(root, info, name): return Person(name=name) ``` Then, if we query (`schema.execute(query\_str)`) with the following: ``` mutation myFirstMutation { createPerson(name:"Peter") { name \_\_typename } } ``` We should receive: ``` { "createPerson": { "name": "Peter", "\_\_typename": "Person" } } ``` Execution[¶](#execution "Permalink to this headline") ----------------------------------------------------- ### Executing a query[¶](#executing-a-query "Permalink to this headline") For executing a query against a schema, you can directly call the `execute` method on it. ``` from graphene import Schema schema = Schema(...) result = schema.execute('{ name }') ``` `result` represents the result of execution. `result.data` is the result of executing the query, `result.errors` is `None` if no errors occurred, and is a non-empty list if an error occurred. #### Context[¶](#context "Permalink to this headline") You can pass context to a query via `context`. ``` from graphene import ObjectType, String, Schema class Query(ObjectType): name = String() def resolve\_name(root, info): return info.context.get('name') schema = Schema(Query) result = schema.execute('{ name }', context={'name': 'Syrus'}) assert result.data['name'] == 'Syrus' ``` #### Variables[¶](#variables "Permalink to this headline") You can pass variables to a query via `variables`. ``` from graphene import ObjectType, Field, ID, Schema class Query(ObjectType): user = Field(User, id=ID(required=True)) def resolve\_user(root, info, id): return get\_user\_by\_id(id) schema = Schema(Query) result = schema.execute( ''' query getUser($id: ID) { user(id: $id) { id firstName lastName } } ''', variables={'id': 12}, ) ``` #### Root Value[¶](#root-value "Permalink to this headline") Value used for [Parent Value Object (parent)](index.html#resolverparamparent) in root queries and mutations can be overridden using `root` parameter. ``` from graphene import ObjectType, Field, Schema class Query(ObjectType): me = Field(User) def resolve\_user(root, info): return {'id': root.id, 'firstName': root.name} schema = Schema(Query) user\_root = User(id=12, name='bob') result = schema.execute( ''' query getUser { user { id firstName lastName } } ''', root=user\_root ) assert result.data['user']['id'] == user\_root.id ``` #### Operation Name[¶](#operation-name "Permalink to this headline") If there are multiple operations defined in a query string, `operation\_name` should be used to indicate which should be executed. ``` from graphene import ObjectType, Field, Schema class Query(ObjectType): user = Field(User) def resolve\_user(root, info): return get\_user\_by\_id(12) schema = Schema(Query) query\_string = ''' query getUserWithFirstName { user { id firstName lastName } } query getUserWithFullName { user { id fullName } } ''' result = schema.execute( query\_string, operation\_name='getUserWithFullName' ) assert result.data['user']['fullName'] ``` ### Middleware[¶](#middleware "Permalink to this headline") You can use `middleware` to affect the evaluation of fields in your schema. A middleware is any object or function that responds to `resolve(next\_middleware, \*args)`. Inside that method, it should either: * Send `resolve` to the next middleware to continue the evaluation; or * Return a value to end the evaluation early. #### Resolve arguments[¶](#resolve-arguments "Permalink to this headline") Middlewares `resolve` is invoked with several arguments: * `next` represents the execution chain. Call `next` to continue evaluation. * `root` is the root value object passed throughout the query. * `info` is the resolver info. * `args` is the dict of arguments passed to the field. #### Example[¶](#example "Permalink to this headline") This middleware only continues evaluation if the `field\_name` is not `'user'` ``` class AuthorizationMiddleware(object): def resolve(self, next, root, info, \*\*args): if info.field\_name == 'user': return None return next(root, info, \*\*args) ``` And then execute it with: ``` result = schema.execute('THE QUERY', middleware=[AuthorizationMiddleware()]) ``` If the `middleware` argument includes multiple middlewares, these middlewares will be executed bottom-up, i.e. from last to first. #### Functional example[¶](#functional-example "Permalink to this headline") Middleware can also be defined as a function. Here we define a middleware that logs the time it takes to resolve each field: ``` from time import time as timer def timing\_middleware(next, root, info, \*\*args): start = timer() return\_value = next(root, info, \*\*args) duration = round((timer() - start) \* 1000, 2) parent\_type\_name = root.\_meta.name if root and hasattr(root, '\_meta') else '' logger.debug(f"{parent\_type\_name}.{info.field\_name}: {duration} ms") return return\_value ``` And then execute it with: ``` result = schema.execute('THE QUERY', middleware=[timing\_middleware]) ``` ### Dataloader[¶](#dataloader "Permalink to this headline") DataLoader is a generic utility to be used as part of your application’s data fetching layer to provide a simplified and consistent API over various remote data sources such as databases or web services via batching and caching. It is provided by a separate package aiodataloader <https://pypi.org/project/aiodataloader/>. #### Batching[¶](#batching "Permalink to this headline") Batching is not an advanced feature, it’s DataLoader’s primary feature. Create loaders by providing a batch loading function. ``` from aiodataloader import DataLoader class UserLoader(DataLoader): async def batch\_load\_fn(self, keys): # Here we call a function to return a user for each key in keys return [get\_user(id=key) for key in keys] ``` A batch loading async function accepts a list of keys, and returns a list of `values`. `DataLoader` will coalesce all individual loads which occur within a single frame of execution (executed once the wrapping event loop is resolved) and then call your batch function with all requested keys. ``` user\_loader = UserLoader() user1 = await user\_loader.load(1) user1\_best\_friend = await user\_loader.load(user1.best\_friend\_id)) user2 = await user\_loader.load(2) user2\_best\_friend = await user\_loader.load(user2.best\_friend\_id)) ``` A naive application may have issued *four* round-trips to a backend for the required information, but with `DataLoader` this application will make at most *two*. Note that loaded values are one-to-one with the keys and must have the same order. This means that if you load all values from a single query, you must make sure that you then order the query result for the results to match the keys: ``` class UserLoader(DataLoader): async def batch\_load\_fn(self, keys): users = {user.id: user for user in User.objects.filter(id\_\_in=keys)} return [users.get(user\_id) for user\_id in keys] ``` `DataLoader` allows you to decouple unrelated parts of your application without sacrificing the performance of batch data-loading. While the loader presents an API that loads individual values, all concurrent requests will be coalesced and presented to your batch loading function. This allows your application to safely distribute data fetching requirements throughout your application and maintain minimal outgoing data requests. #### Using with Graphene[¶](#using-with-graphene "Permalink to this headline") DataLoader pairs nicely well with Graphene/GraphQL. GraphQL fields are designed to be stand-alone functions. Without a caching or batching mechanism, it’s easy for a naive GraphQL server to issue new database requests each time a field is resolved. Consider the following GraphQL request: ``` { me { name bestFriend { name } friends(first: 5) { name bestFriend { name } } } } ``` If `me`, `bestFriend` and `friends` each need to send a request to the backend, there could be at most 13 database requests! When using DataLoader, we could define the User type using our previous example with leaner code and at most 4 database requests, and possibly fewer if there are cache hits. ``` class User(graphene.ObjectType): name = graphene.String() best\_friend = graphene.Field(lambda: User) friends = graphene.List(lambda: User) async def resolve\_best\_friend(root, info): return await user\_loader.load(root.best\_friend\_id) async def resolve\_friends(root, info): return await user\_loader.load\_many(root.friend\_ids) ``` ### File uploading[¶](#file-uploading "Permalink to this headline") File uploading is not part of the official GraphQL spec yet and is not natively implemented in Graphene. If your server needs to support file uploading then you can use the library: [graphene-file-upload](https://github.com/lmcgartland/graphene-file-upload) which enhances Graphene to add file uploads and conforms to the unoffical GraphQL [multipart request spec](https://github.com/jaydenseric/graphql-multipart-request-spec). ### Subscriptions[¶](#subscriptions "Permalink to this headline") To create a subscription, you can directly call the `subscribe` method on the schema. This method is async and must be awaited. ``` import asyncio from datetime import datetime from graphene import ObjectType, String, Schema, Field # Every schema requires a query. class Query(ObjectType): hello = String() def resolve\_hello(root, info): return "Hello, world!" class Subscription(ObjectType): time\_of\_day = String() async def subscribe\_time\_of\_day(root, info): while True: yield datetime.now().isoformat() await asyncio.sleep(1) schema = Schema(query=Query, subscription=Subscription) async def main(schema): subscription = 'subscription { timeOfDay }' result = await schema.subscribe(subscription) async for item in result: print(item.data['timeOfDay']) asyncio.run(main(schema)) ``` The `result` is an async iterator which yields items in the same manner as a query. ### Query Validation[¶](#query-validation "Permalink to this headline") GraphQL uses query validators to check if Query AST is valid and can be executed. Every GraphQL server implements standard query validators. For example, there is an validator that tests if queried field exists on queried type, that makes query fail with “Cannot query field on type” error if it doesn’t. To help with common use cases, graphene provides a few validation rules out of the box. #### Depth limit Validator[¶](#depth-limit-validator "Permalink to this headline") The depth limit validator helps to prevent execution of malicious queries. It takes in the following arguments. * `max\_depth` is the maximum allowed depth for any operation in a GraphQL document. * `ignore` Stops recursive depth checking based on a field name. Either a string or regexp to match the name, or a function that returns a boolean * `callback` Called each time validation runs. Receives an Object which is a map of the depths for each operation. #### Usage[¶](#usage "Permalink to this headline") Here is how you would implement depth-limiting on your schema. ``` from graphql import validate, parse from graphene import ObjectType, Schema, String from graphene.validation import depth\_limit\_validator class MyQuery(ObjectType): name = String(required=True) schema = Schema(query=MyQuery) # queries which have a depth more than 20 # will not be executed. validation\_errors = validate( schema=schema.graphql\_schema, document\_ast=parse('THE QUERY'), rules=( depth\_limit\_validator( max\_depth=20 ), ) ) ``` #### Disable Introspection[¶](#disable-introspection "Permalink to this headline") the disable introspection validation rule ensures that your schema cannot be introspected. This is a useful security measure in production environments. #### Usage[¶](#id1 "Permalink to this headline") Here is how you would disable introspection for your schema. ``` from graphql import validate, parse from graphene import ObjectType, Schema, String from graphene.validation import DisableIntrospection class MyQuery(ObjectType): name = String(required=True) schema = Schema(query=MyQuery) # introspection queries will not be executed. validation\_errors = validate( schema=schema.graphql\_schema, document\_ast=parse('THE QUERY'), rules=( DisableIntrospection, ) ) ``` #### Implementing custom validators[¶](#implementing-custom-validators "Permalink to this headline") All custom query validators should extend the [ValidationRule](https://github.com/graphql-python/graphql-core/blob/v3.0.5/src/graphql/validation/rules/__init__.py#L37) base class importable from the graphql.validation.rules module. Query validators are visitor classes. They are instantiated at the time of query validation with one required argument (context: ASTValidationContext). In order to perform validation, your validator class should define one or more of enter\_\* and leave\_\* methods. For possible enter/leave items as well as details on function documentation, please see contents of the visitor module. To make validation fail, you should call validator’s report\_error method with the instance of GraphQLError describing failure reason. Here is an example query validator that visits field definitions in GraphQL query and fails query validation if any of those fields are blacklisted: ``` from graphql import GraphQLError from graphql.language import FieldNode from graphql.validation import ValidationRule my\_blacklist = ( "disallowed\_field", ) def is\_blacklisted\_field(field\_name: str): return field\_name.lower() in my\_blacklist class BlackListRule(ValidationRule): def enter\_field(self, node: FieldNode, \*\_args): field\_name = node.name.value if not is\_blacklisted\_field(field\_name): return self.report\_error( GraphQLError( f"Cannot query '{field\_name}': field is blacklisted.", node, ) ) ``` Relay[¶](#relay "Permalink to this headline") --------------------------------------------- Graphene has complete support for [Relay](https://relay.dev/docs/guides/graphql-server-specification/) and offers some utils to make integration from Python easy. ### Nodes[¶](#nodes "Permalink to this headline") A `Node` is an Interface provided by `graphene.relay` that contains a single field `id` (which is a `ID!`). Any object that inherits from it has to implement a `get\_node` method for retrieving a `Node` by an *id*. #### Quick example[¶](#quick-example "Permalink to this headline") Example usage (taken from the [Starwars Relay example](https://github.com/graphql-python/graphene/blob/master/examples/starwars_relay/schema.py)): ``` class Ship(graphene.ObjectType): '''A ship in the Star Wars saga''' class Meta: interfaces = (relay.Node, ) name = graphene.String(description='The name of the ship.') @classmethod def get\_node(cls, info, id): return get\_ship(id) ``` The `id` returned by the `Ship` type when you query it will be a scalar which contains enough info for the server to know its type and its id. For example, the instance `Ship(id=1)` will return `U2hpcDox` as the id when you query it (which is the base64 encoding of `Ship:1`), and which could be useful later if we want to query a node by its id. #### Custom Nodes[¶](#custom-nodes "Permalink to this headline") You can use the predefined `relay.Node` or you can subclass it, defining custom ways of how a node id is encoded (using the `to\_global\_id` method in the class) or how we can retrieve a Node given a encoded id (with the `get\_node\_from\_global\_id` method). Example of a custom node: ``` class CustomNode(Node): class Meta: name = 'Node' @staticmethod def to\_global\_id(type\_, id): return f"{type\_}:{id}" @staticmethod def get\_node\_from\_global\_id(info, global\_id, only\_type=None): type\_, id = global\_id.split(':') if only\_type: # We assure that the node type that we want to retrieve # is the same that was indicated in the field type assert type\_ == only\_type.\_meta.name, 'Received not compatible node.' if type\_ == 'User': return get\_user(id) elif type\_ == 'Photo': return get\_photo(id) ``` The `get\_node\_from\_global\_id` method will be called when `CustomNode.Field` is resolved. #### Accessing node types[¶](#accessing-node-types "Permalink to this headline") If we want to retrieve node instances from a `global\_id` (scalar that identifies an instance by it’s type name and id), we can simply do `Node.get\_node\_from\_global\_id(info, global\_id)`. In the case we want to restrict the instance retrieval to a specific type, we can do: `Node.get\_node\_from\_global\_id(info, global\_id, only\_type=Ship)`. This will raise an error if the `global\_id` doesn’t correspond to a Ship type. #### Node Root field[¶](#node-root-field "Permalink to this headline") As is required in the [Relay specification](https://facebook.github.io/relay/docs/graphql-relay-specification.html), the server must implement a root field called `node` that returns a `Node` Interface. For this reason, `graphene` provides the field `relay.Node.Field`, which links to any type in the Schema which implements `Node`. Example usage: ``` class Query(graphene.ObjectType): # Should be CustomNode.Field() if we want to use our custom Node node = relay.Node.Field() ``` ### Connection[¶](#connection "Permalink to this headline") A connection is a vitaminized version of a List that provides ways of slicing and paginating through it. The way you create Connection types in `graphene` is using `relay.Connection` and `relay.ConnectionField`. #### Quick example[¶](#quick-example "Permalink to this headline") If we want to create a custom Connection on a given node, we have to subclass the `Connection` class. In the following example, `extra` will be an extra field in the connection, and `other` an extra field in the Connection Edge. ``` class ShipConnection(Connection): extra = String() class Meta: node = Ship class Edge: other = String() ``` The `ShipConnection` connection class, will have automatically a `pageInfo` field, and a `edges` field (which is a list of `ShipConnection.Edge`). This `Edge` will have a `node` field linking to the specified node (in `ShipConnection.Meta`) and the field `other` that we defined in the class. #### Connection Field[¶](#connection-field "Permalink to this headline") You can create connection fields in any Connection, in case any ObjectType that implements `Node` will have a default Connection. ``` class Faction(graphene.ObjectType): name = graphene.String() ships = relay.ConnectionField(ShipConnection) def resolve\_ships(root, info): return [] ``` ### Mutations[¶](#mutations "Permalink to this headline") Most APIs don’t just allow you to read data, they also allow you to write. In GraphQL, this is done using mutations. Just like queries, Relay puts some additional requirements on mutations, but Graphene nicely manages that for you. All you need to do is make your mutation a subclass of `relay.ClientIDMutation`. ``` class IntroduceShip(relay.ClientIDMutation): class Input: ship\_name = graphene.String(required=True) faction\_id = graphene.String(required=True) ship = graphene.Field(Ship) faction = graphene.Field(Faction) @classmethod def mutate\_and\_get\_payload(cls, root, info, \*\*input): ship\_name = input.ship\_name faction\_id = input.faction\_id ship = create\_ship(ship\_name, faction\_id) faction = get\_faction(faction\_id) return IntroduceShip(ship=ship, faction=faction) ``` #### Accepting Files[¶](#accepting-files "Permalink to this headline") Mutations can also accept files, that’s how it will work with different integrations: ``` class UploadFile(graphene.ClientIDMutation): class Input: pass # nothing needed for uploading file # your return fields success = graphene.String() @classmethod def mutate\_and\_get\_payload(cls, root, info, \*\*input): # When using it in Django, context will be the request files = info.context.FILES # Or, if used in Flask, context will be the flask global request # files = context.files # do something with files return UploadFile(success=True) ``` ### Useful links[¶](#useful-links "Permalink to this headline") * [Getting started with Relay](https://relay.dev/docs/getting-started/step-by-step-guide/) * [Relay Global Identification Specification](https://relay.dev/graphql/objectidentification.htm) * [Relay Cursor Connection Specification](https://relay.dev/graphql/connections.htm) Testing in Graphene[¶](#testing-in-graphene "Permalink to this headline") ------------------------------------------------------------------------- Automated testing is an extremely useful bug-killing tool for the modern developer. You can use a collection of tests – a test suite – to solve, or avoid, a number of problems: * When you’re writing new code, you can use tests to validate your code works as expected. * When you’re refactoring or modifying old code, you can use tests to ensure your changes haven’t affected your application’s behavior unexpectedly. Testing a GraphQL application is a complex task, because a GraphQL application is made of several layers of logic – schema definition, schema validation, permissions and field resolution. With Graphene test-execution framework and assorted utilities, you can simulate GraphQL requests, execute mutations, inspect your application’s output and generally verify your code is doing what it should be doing. ### Testing tools[¶](#testing-tools "Permalink to this headline") Graphene provides a small set of tools that come in handy when writing tests. #### Test Client[¶](#test-client "Permalink to this headline") The test client is a Python class that acts as a dummy GraphQL client, allowing you to test your views and interact with your Graphene-powered application programmatically. Some of the things you can do with the test client are: * Simulate Queries and Mutations and observe the response. * Test that a given query request is rendered by a given Django template, with a template context that contains certain values. #### Overview and a quick example[¶](#overview-and-a-quick-example "Permalink to this headline") To use the test client, instantiate `graphene.test.Client` and retrieve GraphQL responses: ``` from graphene.test import Client def test\_hey(): client = Client(my\_schema) executed = client.execute('''{ hey }''') assert executed == { 'data': { 'hey': 'hello!' } } ``` #### Execute parameters[¶](#execute-parameters "Permalink to this headline") You can also add extra keyword arguments to the `execute` method, such as `context`, `root`, `variables`, ...: ``` from graphene.test import Client def test\_hey(): client = Client(my\_schema) executed = client.execute('''{ hey }''', context={'user': 'Peter'}) assert executed == { 'data': { 'hey': 'hello Peter!' } } ``` #### Snapshot testing[¶](#snapshot-testing "Permalink to this headline") As our APIs evolve, we need to know when our changes introduce any breaking changes that might break some of the clients of our GraphQL app. However, writing tests and replicating the same response we expect from our GraphQL application can be a tedious and repetitive task, and sometimes it’s easier to skip this process. Because of that, we recommend the usage of [SnapshotTest](https://github.com/syrusakbary/snapshottest/). SnapshotTest lets us write all these tests in a breeze, as it automatically creates the `snapshots` for us the first time the test are executed. Here is a simple example on how our tests will look if we use `pytest`: ``` def test\_hey(snapshot): client = Client(my\_schema) # This will create a snapshot dir and a snapshot file # the first time the test is executed, with the response # of the execution. snapshot.assert\_match(client.execute('''{ hey }''')) ``` If we are using `unittest`: ``` from snapshottest import TestCase class APITestCase(TestCase): def test\_api\_me(self): """Testing the API for /me""" client = Client(my\_schema) self.assertMatchSnapshot(client.execute('''{ hey }''')) ``` API Reference[¶](#api-reference "Permalink to this headline") ------------------------------------------------------------- ### Schema[¶](#schema "Permalink to this headline") ### Object types[¶](#object-types "Permalink to this headline") ### Fields (Mounted Types)[¶](#fields-mounted-types "Permalink to this headline") ### Fields (Unmounted Types)[¶](#fields-unmounted-types "Permalink to this headline") ### GraphQL Scalars[¶](#graphql-scalars "Permalink to this headline") ### Graphene Scalars[¶](#graphene-scalars "Permalink to this headline") ### Enum[¶](#enum "Permalink to this headline") ### Structures[¶](#structures "Permalink to this headline") ### Type Extension[¶](#type-extension "Permalink to this headline") ### Execution Metadata[¶](#execution-metadata "Permalink to this headline") Integrations[¶](#integrations "Permalink to this headline") ----------------------------------------------------------- * [Graphene-Django](http://docs.graphene-python.org/projects/django/en/latest/) ([source](https://github.com/graphql-python/graphene-django/)) * Flask-Graphql ([source](https://github.com/graphql-python/flask-graphql)) * [Graphene-SQLAlchemy](http://docs.graphene-python.org/projects/sqlalchemy/en/latest/) ([source](https://github.com/graphql-python/graphene-sqlalchemy/)) * [Graphene-GAE](http://docs.graphene-python.org/projects/gae/en/latest/) ([source](https://github.com/graphql-python/graphene-gae/)) * [Graphene-Mongo](http://graphene-mongo.readthedocs.io/en/latest/) ([source](https://github.com/graphql-python/graphene-mongo)) * [Starlette](https://www.starlette.io/graphql/) ([source](https://github.com/encode/starlette)) * [FastAPI](https://fastapi.tiangolo.com/advanced/graphql/) ([source](https://github.com/tiangolo/fastapi))

daemon

go

Comodojo daemon 1.0.0 documentation [Comodojo daemon](index.html#document-index) stable * [General concepts](index.html#document-general) + [The big picture](index.html#the-big-picture) + [Daemon loop](index.html#daemon-loop) + [Socket communication](index.html#socket-communication) + [POSIX signals and signal-to-event bridge](index.html#posix-signals-and-signal-to-event-bridge) + [Workers and Worker management](index.html#workers-and-worker-management) * [Installation](index.html#document-install) + [Requirements](index.html#requirements) * [Using the library](index.html#document-usage) + [Defining the daemon](index.html#defining-the-daemon) + [Creating the exec script](index.html#creating-the-exec-script) + [Running the daemon](index.html#running-the-daemon) + [Interacting with the daemon](index.html#interacting-with-the-daemon) * [Daemon configuration](index.html#document-config) + [Configuration parameters](index.html#configuration-parameters) * [Using Workers](index.html#document-workers) + [Creating a worker](index.html#creating-a-worker) + [Adding a worker to the daemon](index.html#adding-a-worker-to-the-daemon) + [The forever switch](index.html#the-forever-switch) + [Communicating with the worker](index.html#communicating-with-the-worker) [Comodojo daemon](index.html#document-index) * [Docs](index.html#document-index) » * Comodojo daemon 1.0.0 documentation * [Edit on GitHub](https://github.com/comodojo/daemon/blob/c6b37638378968ccaa5fe7ae8319ea43280fab99/docs/source/index.rst) --- Comodojo daemon docs[¶](#comodojo-daemon-docs "Permalink to this headline") =========================================================================== This library provides tools to create, control and interact with complex, multi-process PHP daemons. Table of Contents: General concepts[¶](#general-concepts "Permalink to this headline") ------------------------------------------------------------------- This library provides basic tools to create solid PHP daemons that can: * spawn and control multiple workers, * communicate via unix/inet sockets using structured RPC calls, * receive and handle POSIX signals using a signal-to-event bridge, and * maintain small memory footprint. The following picture shows the high level architecture of the [comodojo/daemon](https://github.com/comodojo/daemon) package.  comodojo/daemon v1.X architecture ### The big picture[¶](#the-big-picture "Permalink to this headline") According to [wikipedia](https://en.wikipedia.org/wiki/Daemon_(computing)): > > […] a daemon is a computer program that runs as a background process, rather than being under the direct control of an interactive user. Starting from the ground up, the structure of this library reflects the above definition: the `\Comodojo\Daemon\Process` abstract class provides all the basic methods to create a standard \*nix process that can handle OS signals and set its own niceness. The `\Comodojo\Daemon\Daemon` abstract class extends the previous one with all the fancy daemon features. When extended and instantiated, this class, basically: * forks itself and close the parent process (to became an orphaned process) * detaches from STDOUT, STDERR, STDIN and became a session leader * creates and inject event listeners to react to common \*nix signals (SIGTERM, SIGINT, SIGCHLD) * creates a communication socket * start the internal daemon loop Creating a simple echo daemon this way requires just a couple of lines: | | | | --- | --- | | ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 ``` | ``` <?php namespace DaemonExamples; use \Comodojo\Daemon\Daemon as AbstractDaemon; use \Comodojo\RpcServer\RpcMethod; class EchoDaemon extends AbstractDaemon { public function setup() { // define the echo method using lambda function $echo = RpcMethod::create("examples.echo", function($params, $daemon) { $message = $params->get('message'); return $message; }, $this) ->setDescription("I'm here to reply your data") ->addParameter('string','message') ->setReturnType('string'); // inject the method to the daemon internal RPC server $this->getSocket() ->getRpcServer() ->methods() ->add($echo); } } ``` | Note This code is available in the [daemon-examples github repository](https://github.com/marcogiovinazzi/daemon-examples). ### Daemon loop[¶](#daemon-loop "Permalink to this headline") The daemon itself is designed to handle communication via socket or at the OS level. That’s why the main loop in [comodojo/daemon](https://github.com/comodojo/daemon) is implemented ad the socket level, i.e. the daemon loop endlessly waiting for incoming connections. Once received, the socket calls the internal RPC server to execute the command (if any). This behaviour can not be changed. Note See [comodojo/rpcserver github repo](https://github.com/comodojo/rpcserver) for more information about RPC server. ### Socket communication[¶](#socket-communication "Permalink to this headline") TBW ### POSIX signals and signal-to-event bridge[¶](#posix-signals-and-signal-to-event-bridge "Permalink to this headline") Once received, a POSIX signal is automatically converted into a `\Comodojo\Daemon\Events\PosixEvent` event that will fire hooked listeners. In this way the framework can be customized to react to specific events according to user needs. Predefined listeners are in place to handle most common system events; the `\Comodojo\Daemon\Listeners\StopDaemon`, for example, is designed to react on SIGTERM and to close the daemon gracefully. ### Workers and Worker management[¶](#workers-and-worker-management "Permalink to this headline") Workers are the standard way to create extended logic inside a project based on [comodojo/daemon](https://github.com/comodojo/daemon). A worker is a child process, forked from the daemon, that implements another kind of loop; the daemon itself constantly monitors the status of the worker and keeps an always open bidirectional communication channel using [shared memory segments (SHMOP)](http://php.net/manual/en/book.shmop.php). In other words, a worker can actually do a “specialized work” independently from the parent process, without exposing another socket, relying on the daemon for external communications. Installation[¶](#installation "Permalink to this headline") ----------------------------------------------------------- First [install composer](https://getcomposer.org/doc/00-intro.md), then: ``` composer require comodojo/daemon ``` ### Requirements[¶](#requirements "Permalink to this headline") To work properly, comodojo/daemon requires PHP >=5.6.0. Following PHP extension are also required: * ext-posix: PHP interface to \*nix Process Control Extensions * ext-pcntl: process Control support in PHP * ext-shmop: read, write, create and delete Unix shared memory segments * ext-sockets: low-level interface to the socket communication functions Using the library[¶](#using-the-library "Permalink to this headline") --------------------------------------------------------------------- Creating a daemon using this library requires at least two steps: 1. create your own daemon class, defining methods to be exposed via RPC socket, 2. create the daemon exec file, that will init the above mentioned class providing basic configuration. Workers can be also injected to the daemon in the second step. ### Defining the daemon[¶](#defining-the-daemon "Permalink to this headline") Your new daemon should extend the `\Comodojo\Daemon\Daemon` abstract class, implementing the abstract `setup` method. The main purpose of this method is to define all the commands that the daemon will accept from the input socket. Let’s take as an example the dummy *echo* daemon mentioned in [General concepts](index.html#general) section: | | | | --- | --- | | ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 ``` | ``` <?php namespace DaemonExamples; use \Comodojo\Daemon\Daemon as AbstractDaemon; use \Comodojo\RpcServer\RpcMethod; class EchoDaemon extends AbstractDaemon { public function setup() { // define the echo method using lambda function $echo = RpcMethod::create("examples.echo", function($params, $daemon) { $message = $params->get('message'); return $message; }, $this) ->setDescription("I'm here to reply your data") ->addParameter('string','message') ->setReturnType('string'); // inject the method to the daemon internal RPC server $this->getSocket() ->getRpcServer() ->methods() ->add($echo); } } ``` | Note This code is available in the [daemon-examples github repository](https://github.com/marcogiovinazzi/daemon-examples). The *examples.echo* RPC method expects a string parameter *message* that will be replied by the server. Now that we have our first daemon, let’s figure out how to start it. ### Creating the exec script[¶](#creating-the-exec-script "Permalink to this headline") The exec script typically provides only the basic configuration to the daemon class. Following an example exec script that init the daemon using an inet/tpc socket on port 10042. | | | | --- | --- | | ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 ``` | ``` #!/usr/bin/env php <?php $base\_path = realpath(dirname(\_\_FILE\_\_)."/../"); require "$base\_path/vendor/autoload.php"; use \DaemonExamples\EchoDaemon; $configuration = [ 'description' => 'Echo Daemon', 'sockethandler' => 'tcp://127.0.0.1:10042' ]; // Create a new instance of EchoDaemon $daemon = new EchoDaemon($configuration); // Start the daemon! $daemon->init(); ``` | Note This code is available in the [daemon-examples github repository](https://github.com/marcogiovinazzi/daemon-examples). Note for a complete list of configuration parameters, refer to the [Daemon configuration](index.html#configuration) section. Once saved and made executable, the daemon is ready start. ### Running the daemon[¶](#running-the-daemon "Permalink to this headline") If called with no arguments, the exec script will present the default daemon console:  comodojo/daemon default console The *-d* (run as a daemon) and the *-f* (run in foreground) arguments are the most important to understand. If *-d* is selected, the script will act as a daemon (forking itself, detaching from IO, …), while the *-f* keeps the script in foreground and the standard shell IO. So, it’s trivial to understand that the main purpose of the *-f* argument is to enable the debug at run-time. Two typical combination of arguments are the following: * run the daemon, (eventually) cleaning the socket and the locker: *./daemon -d -s* * run the daemon in foreground, enabling debug: *./daemon -f -v* ### Interacting with the daemon[¶](#interacting-with-the-daemon "Permalink to this headline") TBW Daemon configuration[¶](#daemon-configuration "Permalink to this headline") --------------------------------------------------------------------------- A daemon created using this package can be configured using an array of parameters provided as the first input argument to the `\Comodojo\Daemon\Daemon` abstract class. As an example: | | | | --- | --- | | ``` 1 2 3 4 5 6 7 8 9 10 11 12 ``` | ``` #!/usr/bin/env php <?php use \DaemonExamples\EchoDaemon; $configuration = [ 'description' => 'Echo Daemon', 'sockethandler' => 'tcp://127.0.0.1:10042' ]; // Create a new instance of EchoDaemon $daemon = new EchoDaemon($configuration); ``` | Note This code is available in the [`daemon-examples github repository`\_](#id1). ### Configuration parameters[¶](#configuration-parameters "Permalink to this headline") Following a list of accepted configuration parameters. #### sockethandler[¶](#sockethandler "Permalink to this headline") Address and type of the socket handler (see the [PHP socket documentation](http://php.net/manual/en/book.sockets.php)). *Example*: ‘sockethandler’ => ‘<tcp://127.0.0.1:60001>’ *Default*: ‘sockethandler’ => ‘unix://daemon.sock’ #### pidfile[¶](#pidfile "Permalink to this headline") Location (relative to the base path) of the daemon’s pid file. *Default*: ‘pidfile’ => ‘daemon.pid’ Note Prepend a slash to the file loaction to make it absolute (e.g. /tmp/daemon.pid). #### socketbuffer[¶](#socketbuffer "Permalink to this headline") Size of the socket buffer (see the [PHP socket documentation](http://php.net/manual/en/book.sockets.php)). *Default*: ‘socketbuffer’ => 1024 #### sockettimeout[¶](#sockettimeout "Permalink to this headline") Timeout for the select() system call (see the [PHP socket documentation](http://php.net/manual/en/book.sockets.php)). *Default*: ‘sockettimeout’ => 2 #### socketmaxconnections[¶](#socketmaxconnections "Permalink to this headline") Maximum number of connection accepted by the socket. *Default*: ‘socketmaxconnections’ => 10 #### niceness[¶](#niceness "Permalink to this headline") Define the nice value of the daemon process (see the [nice unix command on wikipedia](https://en.wikipedia.org/wiki/Nice_(Unix))). *Default*: ‘niceness’ => 0 #### arguments[¶](#arguments "Permalink to this headline") Definition of command line arguments, in the climate format (see [climate documentation](https://climate.thephpleague.com/)). *Default*: ‘arguments’ => ‘\Comodojo\Daemon\Console\DaemonArguments’ #### description[¶](#description "Permalink to this headline") Description banner in the daemon command line. *Default*: ‘description’ => ‘Comodojo Daemon’ Using Workers[¶](#using-workers "Permalink to this headline") ------------------------------------------------------------- In comodojo/daemon workers are, essentially, child processes that run in parallel maintaining a communication channel with the master daemon. Each worker has its own loop that can be configured from the daemon. ### Creating a worker[¶](#creating-a-worker "Permalink to this headline") The simplest way to create a worker, is to extend the `\Comodojo\Daemon\Worker\AbstractWorker` abstract class implementing the `loop()` method. There are two other optional methods, `spinup()` and `spindown` that can be used to control the worker startup and execute action before shutting down. As an example, let’s consider the following *CopyWorker*: it’s job is to check if a specific *test.txt” file exists in the \*tmp* directory and, if it’s there, duplicate the file. | | | | --- | --- | | ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 ``` | ``` <?php namespace DaemonExamples; use \Comodojo\Daemon\Worker\AbstractWorker; class CopyWorker extends AbstractWorker { protected $path; // Source file protected $file = 'test.txt'; // Destination file protected $copy = 'copy\_test.txt'; public function spinup() { $this->logger->info("CopyWorker ".$this->getName()." spinning up..."); $this->path = realpath(dirname(\_\_FILE\_\_)."/../../tmp/"); } public function loop() { $filename = $this->path."/".$this->file; if ( file\_exists($filename) ) { copy($filename, $this->path."/".$this->copy); } } public function spindown() { $this->logger->info("CopyWorker ".$this->getName()." spinning down."); unlink($this->path."/".$this->copy); } } ``` | Note This code is available in the [daemon-examples github repository](https://github.com/marcogiovinazzi/daemon-examples). ### Adding a worker to the daemon[¶](#adding-a-worker-to-the-daemon "Permalink to this headline") In order to run, a worker should be installed in the daemon before calling the `init()` method. The internal workers stack `Comodojo\Daemon\Worker\Manager` can be accessed using the `$daemon::getWorkers()` getter. The `install()` method can be used to push a worker into the stack, specifying the looptime: | | | | --- | --- | | ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 ``` | ``` #!/usr/bin/env php <?php $base\_path = realpath(dirname(\_\_FILE\_\_)."/../"); require "$base\_path/vendor/autoload.php"; use \DaemonExamples\CopyDaemon; use \DaemonExamples\CopyWorker; $configuration = [ 'description' => 'Copy Daemon', 'sockethandler' => 'tcp://127.0.0.1:10042' ]; $daemon = new CopyDaemon($configuration); // Create a CopyWorker with name: handyman $handyman = new CopyWorker("handyman"); // Install the worker into the stack configuring a 10 secs looptime and enabling the forever watchdog $daemon->getWorkers()->install($handyman, 10, true); $daemon->init(); ``` | Note This code is available in the [daemon-examples github repository](https://github.com/marcogiovinazzi/daemon-examples). ### The forever switch[¶](#the-forever-switch "Permalink to this headline") The `install()` method allows also to enable the *forever* mode for the worker. When the third argument is set to *true*, the internal watchdog of the daemon will restart the worker in case of crash, with no need to restart the whole daemon. On the contrary, in case of *false* a controlled shutdown of the whole daemon will be triggered if one worker goes down. ### Communicating with the worker[¶](#communicating-with-the-worker "Permalink to this headline") When a worker is created, the daemon will open a bidirectional communication channel using standard Unix shared memory segments. This channel will be kept opened for the entire life of the process. Using this channel: 1. the daemon is able to pool the worker to konw its state (running, paused, …) and trigger actions if the daemon crashes (worker watchdog); 2. the user can send commands to the worker using the daemon RPC socket. While the first point is totally automated, the second one requires a user interaction. #### Using default commands[¶](#using-default-commands "Permalink to this headline") By default, the RPC socket expose a couple of method to manage workers: 1. `worker.list()` - get the list of the currently installed workers 2. `worker.status(worker\_name)` - get the status of the worker * 0 => SPINUP * 1 => LOOPING * 2 => PAUSED * 3 => SPINDOWN 3. `worker.pause(worker\_name\*)` - pause the worker 4. `worker.resume(worker\_name\*)` - resume the worker These commands are automatically sent to the communication channel (using shmop), trapped by the worker loop and then propagated as `\Comodojo\Daemon\Events\WorkerEvent`. A listener on the worker side is responsible for executing the related action. For example, this RPC request can be used to request the status of all workers: | | | | --- | --- | | ``` 1 ``` | ``` $request = \Comodojo\RpcClient\RpcRequest::create("worker.status", []); ``` | And the following one to pause the *handyman* worker: | | | | --- | --- | | ``` 1 ``` | ``` $request = RpcRequest::create("worker.pause", ["handyman"]); ``` | #### Defining custom actions[¶](#defining-custom-actions "Permalink to this headline") Custom actions can be defined in the worker to trap user defined commands, using the same mechanism described in the previous section. As an example, let’s customize the CopyDaemon/CopyWorker to change the output filename if a *handyman.changename* request is received. To create this custom action, first step is to create a custom listener to handle the WorkerEvent: | | | | --- | --- | | ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 ``` | ``` <?php namespace DaemonExamples; use \League\Event\AbstractListener; use \League\Event\EventInterface; class ChangeNameListener extends AbstractListener { public function handle(EventInterface $event) { // get the current worker instance $worker = $event->getWorker()->getInstance(); // invoke the changeName method $worker->changeName(); return true; } } ``` | This listener should be hooked to a custom event at the worker level. The modified version of the CopyWorker is: | | | | --- | --- | | ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 ``` | ``` <?php namespace DaemonExamples; use \Comodojo\Daemon\Worker\AbstractWorker; class CopyWorker extends AbstractWorker { protected $path; protected $file = 'test.txt'; protected $copy = 'copy\_test.txt'; public function spinup() { $this->logger->info("CopyWorker ".$this->getName()." spinning up..."); $this->path = realpath(dirname(\_\_FILE\_\_)."/../../tmp/"); // Hook on daemon.worker.changename event to change the output file name $this->getEvents() ->subscribe('daemon.worker.changename', '\DaemonExamples\ChangeNameListener'); } public function loop() { $filename = $this->path."/".$this->file; if ( file\_exists($filename) ) { $this->logger->info("Copying file ".$this->file." to ".$this->copy); copy($filename, $this->path."/".$this->copy); } } public function spindown() { $this->logger->info("CopyWorker ".$this->getName()." spinning down."); unlink($this->path."/".$this->copy); } // this method will be invoked by the listener for daemon.worker.changename event public function changeName() { $this->logger->info("Changing filename..."); $this->copy = 'copy\_test\_2.txt'; } } ``` | Note This code is available in the [daemon-examples github repository](https://github.com/marcogiovinazzi/daemon-examples). The last step is to create a custom RPC Method in the daemon that can handle the *handyman.changename* request translating it to a message *changename* propagated in the communication channel (output side): | | | | --- | --- | | ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 ``` | ``` <?php namespace DaemonExamples; use \Comodojo\Daemon\Daemon as AbstractDaemon; use \Comodojo\RpcServer\RpcMethod; class CopyDaemon extends AbstractDaemon { public function setup() { // define the changename method using lambda function $change = RpcMethod::create("handyman.changename", function($params, $daemon) { return $daemon->getWorkers() ->get("handyman") ->getOutputChannel() ->send('changename') > 0; }, $this) ->setDescription("Change the output file name") ->setReturnType('string'); // inject the method to the daemon internal RPC server $this->getSocket() ->getRpcServer() ->methods() ->add($change); } } ``` | Note This code is available in the [daemon-examples github repository](https://github.com/marcogiovinazzi/daemon-examples).

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} .glyphicon-usd:before { content: "\e148"; } .glyphicon-gbp:before { content: "\e149"; } .glyphicon-sort:before { content: "\e150"; } .glyphicon-sort-by-alphabet:before { content: "\e151"; } .glyphicon-sort-by-alphabet-alt:before { content: "\e152"; } .glyphicon-sort-by-order:before { content: "\e153"; } .glyphicon-sort-by-order-alt:before { content: "\e154"; } .glyphicon-sort-by-attributes:before { content: "\e155"; } .glyphicon-sort-by-attributes-alt:before { content: "\e156"; } .glyphicon-unchecked:before { content: "\e157"; } .glyphicon-expand:before { content: "\e158"; } .glyphicon-collapse-down:before { content: "\e159"; } .glyphicon-collapse-up:before { content: "\e160"; } .glyphicon-log-in:before { content: "\e161"; } .glyphicon-flash:before { content: "\e162"; } .glyphicon-log-out:before { content: "\e163"; } .glyphicon-new-window:before { content: "\e164"; } .glyphicon-record:before { content: "\e165"; } .glyphicon-save:before { content: "\e166"; } .glyphicon-open:before { content: "\e167"; } .glyphicon-saved:before { content: "\e168"; } .glyphicon-import:before { content: "\e169"; } .glyphicon-export:before { content: "\e170"; } .glyphicon-send:before { content: "\e171"; } .glyphicon-floppy-disk:before { content: "\e172"; } .glyphicon-floppy-saved:before { content: "\e173"; } .glyphicon-floppy-remove:before { content: "\e174"; } .glyphicon-floppy-save:before { content: "\e175"; } .glyphicon-floppy-open:before { content: "\e176"; } .glyphicon-credit-card:before { content: "\e177"; } .glyphicon-transfer:before { content: "\e178"; } .glyphicon-cutlery:before { content: "\e179"; } .glyphicon-header:before { content: "\e180"; } .glyphicon-compressed:before { content: "\e181"; } .glyphicon-earphone:before { content: "\e182"; } .glyphicon-phone-alt:before { content: "\e183"; } .glyphicon-tower:before { content: "\e184"; } .glyphicon-stats:before { content: "\e185"; } .glyphicon-sd-video:before { content: "\e186"; } .glyphicon-hd-video:before { content: "\e187"; } .glyphicon-subtitles:before { content: "\e188"; } .glyphicon-sound-stereo:before { content: "\e189"; } .glyphicon-sound-dolby:before { content: "\e190"; } .glyphicon-sound-5-1:before { content: "\e191"; } .glyphicon-sound-6-1:before { content: "\e192"; } .glyphicon-sound-7-1:before { content: "\e193"; } .glyphicon-copyright-mark:before { content: "\e194"; } .glyphicon-registration-mark:before { content: "\e195"; } .glyphicon-cloud-download:before { content: "\e197"; } .glyphicon-cloud-upload:before { content: "\e198"; } .glyphicon-tree-conifer:before { content: "\e199"; } .glyphicon-tree-deciduous:before { content: "\e200"; } .glyphicon-cd:before { content: "\e201"; } .glyphicon-save-file:before { content: "\e202"; } .glyphicon-open-file:before { content: "\e203"; } .glyphicon-level-up:before { content: "\e204"; } .glyphicon-copy:before { content: "\e205"; } .glyphicon-paste:before { content: "\e206"; } .glyphicon-alert:before { content: "\e209"; } .glyphicon-equalizer:before { content: "\e210"; } .glyphicon-king:before { content: "\e211"; } .glyphicon-queen:before { content: "\e212"; } .glyphicon-pawn:before { content: "\e213"; } .glyphicon-bishop:before { content: "\e214"; } .glyphicon-knight:before { content: "\e215"; } .glyphicon-baby-formula:before { content: "\e216"; } .glyphicon-tent:before { content: "\26fa"; } .glyphicon-blackboard:before { content: "\e218"; } .glyphicon-bed:before { content: "\e219"; } .glyphicon-apple:before { content: "\f8ff"; } .glyphicon-erase:before { content: "\e221"; } .glyphicon-hourglass:before { content: "\231b"; } .glyphicon-lamp:before { content: "\e223"; } .glyphicon-duplicate:before { content: "\e224"; } .glyphicon-piggy-bank:before { content: "\e225"; } .glyphicon-scissors:before { content: "\e226"; } .glyphicon-bitcoin:before { content: "\e227"; } .glyphicon-btc:before { content: "\e227"; } .glyphicon-xbt:before { content: "\e227"; } .glyphicon-yen:before { content: "\00a5"; } .glyphicon-jpy:before { content: "\00a5"; } .glyphicon-ruble:before { content: "\20bd"; } .glyphicon-rub:before { content: "\20bd"; } .glyphicon-scale:before { content: "\e230"; } .glyphicon-ice-lolly:before { content: "\e231"; } .glyphicon-ice-lolly-tasted:before { content: "\e232"; } .glyphicon-education:before { content: "\e233"; } .glyphicon-option-horizontal:before { content: "\e234"; } .glyphicon-option-vertical:before { content: "\e235"; } .glyphicon-menu-hamburger:before { content: "\e236"; } .glyphicon-modal-window:before { content: "\e237"; } .glyphicon-oil:before { content: "\e238"; } .glyphicon-grain:before { content: "\e239"; } .glyphicon-sunglasses:before { content: "\e240"; } .glyphicon-text-size:before { content: "\e241"; } .glyphicon-text-color:before { content: "\e242"; } .glyphicon-text-background:before { content: "\e243"; } .glyphicon-object-align-top:before { content: "\e244"; } .glyphicon-object-align-bottom:before { content: "\e245"; } .glyphicon-object-align-horizontal:before { content: "\e246"; } .glyphicon-object-align-left:before { content: "\e247"; } .glyphicon-object-align-vertical:before { content: "\e248"; } .glyphicon-object-align-right:before { content: "\e249"; } .glyphicon-triangle-right:before { content: "\e250"; } .glyphicon-triangle-left:before { content: "\e251"; } .glyphicon-triangle-bottom:before { content: "\e252"; } .glyphicon-triangle-top:before { content: "\e253"; } .glyphicon-console:before { content: "\e254"; } .glyphicon-superscript:before { content: "\e255"; } .glyphicon-subscript:before { content: "\e256"; } .glyphicon-menu-left:before { content: "\e257"; } .glyphicon-menu-right:before { content: "\e258"; } .glyphicon-menu-down:before { content: "\e259"; } .glyphicon-menu-up:before { content: "\e260"; } \* { -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; } \*:before, \*:after { -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; } html { font-size: 10px; -webkit-tap-highlight-color: rgba(0, 0, 0, 0); } body { font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 13px; line-height: 1.42857143; color: #000; background-color: #fff; } input, button, select, textarea { font-family: inherit; font-size: inherit; line-height: inherit; } a { color: #337ab7; text-decoration: none; } a:hover, a:focus { color: #23527c; text-decoration: underline; } a:focus { outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } figure { margin: 0; } img { vertical-align: middle; } .img-responsive, .thumbnail > img, .thumbnail a > img, .carousel-inner > .item > img, .carousel-inner > .item > a > img { display: block; max-width: 100%; height: auto; } .img-rounded { border-radius: 3px; } .img-thumbnail { padding: 4px; line-height: 1.42857143; background-color: #fff; border: 1px solid #ddd; border-radius: 2px; -webkit-transition: all 0.2s ease-in-out; -o-transition: all 0.2s ease-in-out; transition: all 0.2s ease-in-out; display: inline-block; max-width: 100%; height: auto; } .img-circle { border-radius: 50%; } hr { margin-top: 18px; margin-bottom: 18px; border: 0; border-top: 1px solid #eeeeee; } .sr-only { position: absolute; width: 1px; height: 1px; margin: -1px; padding: 0; overflow: hidden; clip: rect(0, 0, 0, 0); border: 0; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } [role="button"] { cursor: pointer; } h1, h2, h3, h4, h5, h6, .h1, .h2, .h3, .h4, .h5, .h6 { font-family: inherit; font-weight: 500; line-height: 1.1; color: inherit; } h1 small, h2 small, h3 small, h4 small, h5 small, h6 small, .h1 small, .h2 small, .h3 small, .h4 small, .h5 small, .h6 small, h1 .small, h2 .small, h3 .small, h4 .small, h5 .small, h6 .small, .h1 .small, .h2 .small, .h3 .small, .h4 .small, .h5 .small, .h6 .small { font-weight: normal; line-height: 1; color: #777777; } h1, .h1, h2, .h2, h3, .h3 { margin-top: 18px; margin-bottom: 9px; } h1 small, .h1 small, h2 small, .h2 small, h3 small, .h3 small, h1 .small, .h1 .small, h2 .small, .h2 .small, h3 .small, .h3 .small { font-size: 65%; } h4, .h4, h5, .h5, h6, .h6 { margin-top: 9px; margin-bottom: 9px; } h4 small, .h4 small, h5 small, .h5 small, h6 small, .h6 small, h4 .small, .h4 .small, h5 .small, .h5 .small, h6 .small, .h6 .small { font-size: 75%; } h1, .h1 { font-size: 33px; } h2, .h2 { font-size: 27px; } h3, .h3 { font-size: 23px; } h4, .h4 { font-size: 17px; } h5, .h5 { font-size: 13px; } h6, .h6 { font-size: 12px; } p { margin: 0 0 9px; } .lead { margin-bottom: 18px; font-size: 14px; font-weight: 300; line-height: 1.4; } @media (min-width: 768px) { .lead { font-size: 19.5px; } } small, .small { font-size: 92%; } mark, .mark { background-color: #fcf8e3; padding: .2em; } .text-left { text-align: left; } .text-right { text-align: right; } .text-center { text-align: center; } .text-justify { text-align: justify; } .text-nowrap { white-space: nowrap; } .text-lowercase { text-transform: lowercase; } .text-uppercase { text-transform: uppercase; } .text-capitalize { text-transform: capitalize; } .text-muted { color: #777777; } .text-primary { color: #337ab7; } a.text-primary:hover, a.text-primary:focus { color: #286090; } .text-success { color: #3c763d; } a.text-success:hover, a.text-success:focus { color: #2b542c; } .text-info { color: #31708f; } a.text-info:hover, a.text-info:focus { color: #245269; } .text-warning { color: #8a6d3b; } a.text-warning:hover, a.text-warning:focus { color: #66512c; } .text-danger { color: #a94442; } a.text-danger:hover, a.text-danger:focus { color: #843534; } .bg-primary { color: #fff; background-color: #337ab7; } a.bg-primary:hover, a.bg-primary:focus { background-color: #286090; } .bg-success { background-color: #dff0d8; } a.bg-success:hover, a.bg-success:focus { background-color: #c1e2b3; } .bg-info { background-color: #d9edf7; } a.bg-info:hover, a.bg-info:focus { background-color: #afd9ee; } .bg-warning { background-color: #fcf8e3; } a.bg-warning:hover, a.bg-warning:focus { background-color: #f7ecb5; } .bg-danger { background-color: #f2dede; } a.bg-danger:hover, a.bg-danger:focus { background-color: #e4b9b9; } .page-header { padding-bottom: 8px; margin: 36px 0 18px; border-bottom: 1px solid #eeeeee; } ul, ol { margin-top: 0; margin-bottom: 9px; } ul ul, ol ul, ul ol, ol ol { margin-bottom: 0; } .list-unstyled { padding-left: 0; list-style: none; } .list-inline { padding-left: 0; list-style: none; margin-left: -5px; } .list-inline > li { display: inline-block; padding-left: 5px; padding-right: 5px; } dl { margin-top: 0; margin-bottom: 18px; } dt, dd { line-height: 1.42857143; } dt { font-weight: bold; } dd { margin-left: 0; } @media (min-width: 541px) { .dl-horizontal dt { float: left; width: 160px; clear: left; text-align: right; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; } .dl-horizontal dd { margin-left: 180px; } } abbr[title], abbr[data-original-title] { cursor: help; border-bottom: 1px dotted #777777; } .initialism { font-size: 90%; text-transform: uppercase; } blockquote { padding: 9px 18px; margin: 0 0 18px; font-size: inherit; border-left: 5px solid #eeeeee; } blockquote p:last-child, blockquote ul:last-child, blockquote ol:last-child { margin-bottom: 0; } blockquote footer, blockquote small, blockquote .small { display: block; font-size: 80%; line-height: 1.42857143; color: #777777; } blockquote footer:before, blockquote small:before, blockquote .small:before { content: '\2014 \00A0'; } .blockquote-reverse, blockquote.pull-right { padding-right: 15px; padding-left: 0; border-right: 5px solid #eeeeee; border-left: 0; text-align: right; } .blockquote-reverse footer:before, blockquote.pull-right footer:before, .blockquote-reverse small:before, blockquote.pull-right small:before, .blockquote-reverse .small:before, blockquote.pull-right .small:before { content: ''; } .blockquote-reverse footer:after, blockquote.pull-right footer:after, .blockquote-reverse small:after, blockquote.pull-right small:after, .blockquote-reverse .small:after, blockquote.pull-right .small:after { content: '\00A0 \2014'; } address { margin-bottom: 18px; font-style: normal; line-height: 1.42857143; } code, kbd, pre, samp { font-family: monospace; } code { padding: 2px 4px; font-size: 90%; color: #c7254e; background-color: #f9f2f4; border-radius: 2px; } kbd { padding: 2px 4px; font-size: 90%; color: #888; background-color: transparent; border-radius: 1px; box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); } kbd kbd { padding: 0; font-size: 100%; font-weight: bold; box-shadow: none; } pre { display: block; padding: 8.5px; margin: 0 0 9px; font-size: 12px; line-height: 1.42857143; word-break: break-all; word-wrap: break-word; color: #333333; background-color: #f5f5f5; border: 1px solid #ccc; border-radius: 2px; } pre code { padding: 0; font-size: inherit; color: inherit; white-space: pre-wrap; background-color: transparent; border-radius: 0; } .pre-scrollable { max-height: 340px; overflow-y: scroll; } .container { margin-right: auto; margin-left: auto; padding-left: 0px; padding-right: 0px; } @media (min-width: 768px) { .container { width: 768px; } } @media (min-width: 992px) { .container { width: 940px; } } @media (min-width: 1200px) { .container { width: 1140px; } } .container-fluid { margin-right: auto; margin-left: auto; padding-left: 0px; padding-right: 0px; } .row { margin-left: 0px; margin-right: 0px; } .col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 { position: relative; min-height: 1px; padding-left: 0px; padding-right: 0px; } .col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 { float: left; } .col-xs-12 { width: 100%; } .col-xs-11 { width: 91.66666667%; } .col-xs-10 { width: 83.33333333%; } .col-xs-9 { width: 75%; } .col-xs-8 { width: 66.66666667%; } .col-xs-7 { width: 58.33333333%; } .col-xs-6 { width: 50%; } .col-xs-5 { width: 41.66666667%; } .col-xs-4 { width: 33.33333333%; } .col-xs-3 { width: 25%; } .col-xs-2 { width: 16.66666667%; } .col-xs-1 { width: 8.33333333%; } .col-xs-pull-12 { right: 100%; } .col-xs-pull-11 { right: 91.66666667%; } .col-xs-pull-10 { right: 83.33333333%; } .col-xs-pull-9 { right: 75%; } .col-xs-pull-8 { right: 66.66666667%; } .col-xs-pull-7 { right: 58.33333333%; } .col-xs-pull-6 { right: 50%; } .col-xs-pull-5 { right: 41.66666667%; } .col-xs-pull-4 { right: 33.33333333%; } .col-xs-pull-3 { right: 25%; } .col-xs-pull-2 { right: 16.66666667%; } .col-xs-pull-1 { right: 8.33333333%; } .col-xs-pull-0 { right: auto; } .col-xs-push-12 { left: 100%; } .col-xs-push-11 { left: 91.66666667%; } .col-xs-push-10 { left: 83.33333333%; } .col-xs-push-9 { left: 75%; } .col-xs-push-8 { left: 66.66666667%; } .col-xs-push-7 { left: 58.33333333%; } .col-xs-push-6 { left: 50%; } .col-xs-push-5 { left: 41.66666667%; } .col-xs-push-4 { left: 33.33333333%; } .col-xs-push-3 { left: 25%; } .col-xs-push-2 { left: 16.66666667%; } .col-xs-push-1 { left: 8.33333333%; } .col-xs-push-0 { left: auto; } .col-xs-offset-12 { margin-left: 100%; } .col-xs-offset-11 { margin-left: 91.66666667%; } .col-xs-offset-10 { margin-left: 83.33333333%; } .col-xs-offset-9 { margin-left: 75%; } .col-xs-offset-8 { margin-left: 66.66666667%; } .col-xs-offset-7 { margin-left: 58.33333333%; } .col-xs-offset-6 { margin-left: 50%; } .col-xs-offset-5 { margin-left: 41.66666667%; } .col-xs-offset-4 { margin-left: 33.33333333%; } .col-xs-offset-3 { margin-left: 25%; } .col-xs-offset-2 { margin-left: 16.66666667%; } .col-xs-offset-1 { margin-left: 8.33333333%; } .col-xs-offset-0 { margin-left: 0%; } @media (min-width: 768px) { .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 { float: left; } .col-sm-12 { width: 100%; } .col-sm-11 { width: 91.66666667%; } .col-sm-10 { width: 83.33333333%; } .col-sm-9 { width: 75%; } .col-sm-8 { width: 66.66666667%; } .col-sm-7 { width: 58.33333333%; } .col-sm-6 { width: 50%; } .col-sm-5 { width: 41.66666667%; } .col-sm-4 { width: 33.33333333%; } .col-sm-3 { width: 25%; } .col-sm-2 { width: 16.66666667%; } .col-sm-1 { width: 8.33333333%; } .col-sm-pull-12 { right: 100%; } .col-sm-pull-11 { right: 91.66666667%; } .col-sm-pull-10 { right: 83.33333333%; } .col-sm-pull-9 { right: 75%; } .col-sm-pull-8 { right: 66.66666667%; } .col-sm-pull-7 { right: 58.33333333%; } .col-sm-pull-6 { right: 50%; } .col-sm-pull-5 { right: 41.66666667%; } .col-sm-pull-4 { right: 33.33333333%; } .col-sm-pull-3 { right: 25%; } .col-sm-pull-2 { right: 16.66666667%; } .col-sm-pull-1 { right: 8.33333333%; } .col-sm-pull-0 { right: auto; } .col-sm-push-12 { left: 100%; } .col-sm-push-11 { left: 91.66666667%; } .col-sm-push-10 { left: 83.33333333%; } .col-sm-push-9 { left: 75%; } .col-sm-push-8 { left: 66.66666667%; } .col-sm-push-7 { left: 58.33333333%; } .col-sm-push-6 { left: 50%; } .col-sm-push-5 { left: 41.66666667%; } .col-sm-push-4 { left: 33.33333333%; } .col-sm-push-3 { left: 25%; } .col-sm-push-2 { left: 16.66666667%; } .col-sm-push-1 { left: 8.33333333%; } .col-sm-push-0 { left: auto; } .col-sm-offset-12 { margin-left: 100%; } .col-sm-offset-11 { margin-left: 91.66666667%; } .col-sm-offset-10 { margin-left: 83.33333333%; } .col-sm-offset-9 { margin-left: 75%; } .col-sm-offset-8 { margin-left: 66.66666667%; } .col-sm-offset-7 { margin-left: 58.33333333%; } .col-sm-offset-6 { margin-left: 50%; } .col-sm-offset-5 { margin-left: 41.66666667%; } .col-sm-offset-4 { margin-left: 33.33333333%; } .col-sm-offset-3 { margin-left: 25%; } .col-sm-offset-2 { margin-left: 16.66666667%; } .col-sm-offset-1 { margin-left: 8.33333333%; } .col-sm-offset-0 { margin-left: 0%; } } @media (min-width: 992px) { .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 { float: left; } .col-md-12 { width: 100%; } .col-md-11 { width: 91.66666667%; } .col-md-10 { width: 83.33333333%; } .col-md-9 { width: 75%; } .col-md-8 { width: 66.66666667%; } .col-md-7 { width: 58.33333333%; } .col-md-6 { width: 50%; } .col-md-5 { width: 41.66666667%; } .col-md-4 { width: 33.33333333%; } .col-md-3 { width: 25%; } .col-md-2 { width: 16.66666667%; } .col-md-1 { width: 8.33333333%; } .col-md-pull-12 { right: 100%; } .col-md-pull-11 { right: 91.66666667%; } .col-md-pull-10 { right: 83.33333333%; } .col-md-pull-9 { right: 75%; } .col-md-pull-8 { right: 66.66666667%; } .col-md-pull-7 { right: 58.33333333%; } .col-md-pull-6 { right: 50%; } .col-md-pull-5 { right: 41.66666667%; } .col-md-pull-4 { right: 33.33333333%; } .col-md-pull-3 { right: 25%; } .col-md-pull-2 { right: 16.66666667%; } .col-md-pull-1 { right: 8.33333333%; } .col-md-pull-0 { right: auto; } .col-md-push-12 { left: 100%; } .col-md-push-11 { left: 91.66666667%; } .col-md-push-10 { left: 83.33333333%; } .col-md-push-9 { left: 75%; } .col-md-push-8 { left: 66.66666667%; } .col-md-push-7 { left: 58.33333333%; } .col-md-push-6 { left: 50%; } .col-md-push-5 { left: 41.66666667%; } .col-md-push-4 { left: 33.33333333%; } .col-md-push-3 { left: 25%; } .col-md-push-2 { left: 16.66666667%; } .col-md-push-1 { left: 8.33333333%; } .col-md-push-0 { left: auto; } .col-md-offset-12 { margin-left: 100%; } .col-md-offset-11 { margin-left: 91.66666667%; } .col-md-offset-10 { margin-left: 83.33333333%; } .col-md-offset-9 { margin-left: 75%; } .col-md-offset-8 { margin-left: 66.66666667%; } .col-md-offset-7 { margin-left: 58.33333333%; } .col-md-offset-6 { margin-left: 50%; } .col-md-offset-5 { margin-left: 41.66666667%; } .col-md-offset-4 { margin-left: 33.33333333%; } .col-md-offset-3 { margin-left: 25%; } .col-md-offset-2 { margin-left: 16.66666667%; } .col-md-offset-1 { margin-left: 8.33333333%; } .col-md-offset-0 { margin-left: 0%; } } @media (min-width: 1200px) { .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 { float: left; } .col-lg-12 { width: 100%; } .col-lg-11 { width: 91.66666667%; } .col-lg-10 { width: 83.33333333%; } .col-lg-9 { width: 75%; } .col-lg-8 { width: 66.66666667%; } .col-lg-7 { width: 58.33333333%; } .col-lg-6 { width: 50%; } .col-lg-5 { width: 41.66666667%; } .col-lg-4 { width: 33.33333333%; } .col-lg-3 { width: 25%; } .col-lg-2 { width: 16.66666667%; } .col-lg-1 { width: 8.33333333%; } .col-lg-pull-12 { right: 100%; } .col-lg-pull-11 { right: 91.66666667%; } .col-lg-pull-10 { right: 83.33333333%; } .col-lg-pull-9 { right: 75%; } .col-lg-pull-8 { right: 66.66666667%; } .col-lg-pull-7 { right: 58.33333333%; } .col-lg-pull-6 { right: 50%; } .col-lg-pull-5 { right: 41.66666667%; } .col-lg-pull-4 { right: 33.33333333%; } .col-lg-pull-3 { right: 25%; } .col-lg-pull-2 { right: 16.66666667%; } .col-lg-pull-1 { right: 8.33333333%; } .col-lg-pull-0 { right: auto; } .col-lg-push-12 { left: 100%; } .col-lg-push-11 { left: 91.66666667%; } .col-lg-push-10 { left: 83.33333333%; } .col-lg-push-9 { left: 75%; } .col-lg-push-8 { left: 66.66666667%; } .col-lg-push-7 { left: 58.33333333%; } .col-lg-push-6 { left: 50%; } .col-lg-push-5 { left: 41.66666667%; } .col-lg-push-4 { left: 33.33333333%; } .col-lg-push-3 { left: 25%; } .col-lg-push-2 { left: 16.66666667%; } .col-lg-push-1 { left: 8.33333333%; } .col-lg-push-0 { left: auto; } .col-lg-offset-12 { margin-left: 100%; } .col-lg-offset-11 { margin-left: 91.66666667%; } .col-lg-offset-10 { margin-left: 83.33333333%; } .col-lg-offset-9 { margin-left: 75%; } .col-lg-offset-8 { margin-left: 66.66666667%; } .col-lg-offset-7 { margin-left: 58.33333333%; } .col-lg-offset-6 { margin-left: 50%; } .col-lg-offset-5 { margin-left: 41.66666667%; } .col-lg-offset-4 { margin-left: 33.33333333%; } .col-lg-offset-3 { margin-left: 25%; } .col-lg-offset-2 { margin-left: 16.66666667%; } .col-lg-offset-1 { margin-left: 8.33333333%; } .col-lg-offset-0 { margin-left: 0%; } } table { background-color: transparent; } caption { padding-top: 8px; padding-bottom: 8px; color: #777777; text-align: left; } th { text-align: left; } .table { width: 100%; max-width: 100%; margin-bottom: 18px; } .table > thead > tr > th, .table > tbody > tr > th, .table > tfoot > tr > th, .table > thead > tr > td, .table > tbody > tr > td, .table > tfoot > tr > td { padding: 8px; line-height: 1.42857143; vertical-align: top; border-top: 1px solid #ddd; } .table > thead > tr > th { vertical-align: bottom; border-bottom: 2px solid #ddd; } .table > caption + thead > tr:first-child > th, .table > colgroup + thead > tr:first-child > th, .table > thead:first-child > tr:first-child > th, .table > caption + thead > tr:first-child > td, .table > colgroup + thead > tr:first-child > td, .table > thead:first-child > tr:first-child > td { border-top: 0; } .table > tbody + tbody { border-top: 2px solid #ddd; } .table .table { background-color: #fff; } .table-condensed > thead > tr > th, .table-condensed > tbody > tr > th, .table-condensed > tfoot > tr > th, .table-condensed > thead > tr > td, .table-condensed > tbody > tr > td, .table-condensed > tfoot > tr > td { padding: 5px; } .table-bordered { border: 1px solid #ddd; } .table-bordered > thead > tr > th, .table-bordered > tbody > tr > th, .table-bordered > tfoot > tr > th, .table-bordered > thead > tr > td, .table-bordered > tbody > tr > td, .table-bordered > tfoot > tr > td { border: 1px solid #ddd; } .table-bordered > thead > tr > th, .table-bordered > thead > tr > td { border-bottom-width: 2px; } .table-striped > tbody > tr:nth-of-type(odd) { background-color: #f9f9f9; } .table-hover > tbody > tr:hover { background-color: #f5f5f5; } table col[class\*="col-"] { position: static; float: none; display: table-column; } table td[class\*="col-"], table th[class\*="col-"] { position: static; float: none; display: table-cell; } .table > thead > tr > td.active, .table > tbody > tr > td.active, .table > tfoot > tr > td.active, .table > thead > tr > th.active, .table > tbody > tr > th.active, .table > tfoot > tr > th.active, .table > thead > tr.active > td, .table > tbody > tr.active > td, .table > tfoot > tr.active > td, .table > thead > tr.active > th, .table > tbody > tr.active > th, .table > tfoot > tr.active > th { background-color: #f5f5f5; } .table-hover > tbody > tr > td.active:hover, .table-hover > tbody > tr > th.active:hover, .table-hover > tbody > tr.active:hover > td, .table-hover > tbody > tr:hover > .active, .table-hover > tbody > tr.active:hover > th { background-color: #e8e8e8; } .table > thead > tr > td.success, .table > tbody > tr > td.success, .table > tfoot > tr > td.success, .table > thead > tr > th.success, .table > tbody > tr > th.success, .table > tfoot > tr > th.success, .table > thead > tr.success > td, .table > tbody > tr.success > td, .table > tfoot > tr.success > td, .table > thead > tr.success > th, .table > tbody > tr.success > th, .table > tfoot > tr.success > th { background-color: #dff0d8; } .table-hover > tbody > tr > td.success:hover, .table-hover > tbody > tr > th.success:hover, .table-hover > tbody > tr.success:hover > td, .table-hover > tbody > tr:hover > .success, .table-hover > tbody > tr.success:hover > th { background-color: #d0e9c6; } .table > thead > tr > td.info, .table > tbody > tr > td.info, .table > tfoot > tr > td.info, .table > thead > tr > th.info, .table > tbody > tr > th.info, .table > tfoot > tr > th.info, .table > thead > tr.info > td, .table > tbody > tr.info > td, .table > tfoot > tr.info > td, .table > thead > tr.info > th, .table > tbody > tr.info > th, .table > tfoot > tr.info > th { background-color: #d9edf7; } .table-hover > tbody > tr > td.info:hover, .table-hover > tbody > tr > th.info:hover, .table-hover > tbody > tr.info:hover > td, .table-hover > tbody > tr:hover > .info, .table-hover > tbody > tr.info:hover > th { background-color: #c4e3f3; } .table > thead > tr > td.warning, .table > tbody > tr > td.warning, .table > tfoot > tr > td.warning, .table > thead > tr > th.warning, .table > tbody > tr > th.warning, .table > tfoot > tr > th.warning, .table > thead > tr.warning > td, .table > tbody > tr.warning > td, .table > tfoot > tr.warning > td, .table > thead > tr.warning > th, .table > tbody > tr.warning > th, .table > tfoot > tr.warning > th { background-color: #fcf8e3; } .table-hover > tbody > tr > td.warning:hover, .table-hover > tbody > tr > th.warning:hover, .table-hover > tbody > tr.warning:hover > td, .table-hover > tbody > tr:hover > .warning, .table-hover > tbody > tr.warning:hover > th { background-color: #faf2cc; } .table > thead > tr > td.danger, .table > tbody > tr > td.danger, .table > tfoot > tr > td.danger, .table > thead > tr > th.danger, .table > tbody > tr > th.danger, .table > tfoot > tr > th.danger, .table > thead > tr.danger > td, .table > tbody > tr.danger > td, .table > tfoot > tr.danger > td, .table > thead > tr.danger > th, .table > tbody > tr.danger > th, .table > tfoot > tr.danger > th { background-color: #f2dede; } .table-hover > tbody > tr > td.danger:hover, .table-hover > tbody > tr > th.danger:hover, .table-hover > tbody > tr.danger:hover > td, .table-hover > tbody > tr:hover > .danger, .table-hover > tbody > tr.danger:hover > th { background-color: #ebcccc; } .table-responsive { overflow-x: auto; min-height: 0.01%; } @media screen and (max-width: 767px) { .table-responsive { width: 100%; margin-bottom: 13.5px; overflow-y: hidden; -ms-overflow-style: -ms-autohiding-scrollbar; border: 1px solid #ddd; } .table-responsive > .table { margin-bottom: 0; } .table-responsive > .table > thead > tr > th, .table-responsive > .table > tbody > tr > th, .table-responsive > .table > tfoot > tr > th, .table-responsive > .table > thead > tr > td, .table-responsive > .table > tbody > tr > td, .table-responsive > .table > tfoot > tr > td { white-space: nowrap; } .table-responsive > .table-bordered { border: 0; } .table-responsive > .table-bordered > thead > tr > th:first-child, .table-responsive > .table-bordered > tbody > tr > th:first-child, .table-responsive > .table-bordered > tfoot > tr > th:first-child, .table-responsive > .table-bordered > thead > tr > td:first-child, .table-responsive > .table-bordered > tbody > tr > td:first-child, .table-responsive > .table-bordered > tfoot > tr > td:first-child { border-left: 0; } .table-responsive > .table-bordered > thead > tr > th:last-child, .table-responsive > .table-bordered > tbody > tr > th:last-child, .table-responsive > .table-bordered > tfoot > tr > th:last-child, .table-responsive > .table-bordered > thead > tr > td:last-child, .table-responsive > .table-bordered > tbody > tr > td:last-child, .table-responsive > .table-bordered > tfoot > tr > td:last-child { border-right: 0; } .table-responsive > .table-bordered > tbody > tr:last-child > th, .table-responsive > .table-bordered > tfoot > tr:last-child > th, .table-responsive > .table-bordered > tbody > tr:last-child > td, .table-responsive > .table-bordered > tfoot > tr:last-child > td { border-bottom: 0; } } fieldset { padding: 0; margin: 0; border: 0; min-width: 0; } legend { display: block; width: 100%; padding: 0; margin-bottom: 18px; font-size: 19.5px; line-height: inherit; color: #333333; border: 0; border-bottom: 1px solid #e5e5e5; } label { display: inline-block; max-width: 100%; margin-bottom: 5px; font-weight: bold; } input[type="search"] { -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; } input[type="radio"], input[type="checkbox"] { margin: 4px 0 0; margin-top: 1px \9; line-height: normal; } input[type="file"] { display: block; } input[type="range"] { display: block; width: 100%; } select[multiple], select[size] { height: auto; } input[type="file"]:focus, input[type="radio"]:focus, input[type="checkbox"]:focus { outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } output { display: block; padding-top: 7px; font-size: 13px; line-height: 1.42857143; color: #555555; } .form-control { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; } .form-control:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .form-control::-moz-placeholder { color: #999; opacity: 1; } .form-control:-ms-input-placeholder { color: #999; } .form-control::-webkit-input-placeholder { color: #999; } .form-control::-ms-expand { border: 0; background-color: transparent; } .form-control[disabled], .form-control[readonly], fieldset[disabled] .form-control { background-color: #eeeeee; opacity: 1; } .form-control[disabled], fieldset[disabled] .form-control { cursor: not-allowed; } textarea.form-control { height: auto; } input[type="search"] { -webkit-appearance: none; } @media screen and (-webkit-min-device-pixel-ratio: 0) { input[type="date"].form-control, input[type="time"].form-control, input[type="datetime-local"].form-control, input[type="month"].form-control { line-height: 32px; } input[type="date"].input-sm, input[type="time"].input-sm, input[type="datetime-local"].input-sm, input[type="month"].input-sm, .input-group-sm input[type="date"], .input-group-sm input[type="time"], .input-group-sm input[type="datetime-local"], .input-group-sm input[type="month"] { line-height: 30px; } input[type="date"].input-lg, input[type="time"].input-lg, input[type="datetime-local"].input-lg, input[type="month"].input-lg, .input-group-lg input[type="date"], .input-group-lg input[type="time"], .input-group-lg input[type="datetime-local"], .input-group-lg input[type="month"] { line-height: 45px; } } .form-group { margin-bottom: 15px; } .radio, .checkbox { position: relative; display: block; margin-top: 10px; margin-bottom: 10px; } .radio label, .checkbox label { min-height: 18px; padding-left: 20px; margin-bottom: 0; font-weight: normal; cursor: pointer; } .radio input[type="radio"], .radio-inline input[type="radio"], .checkbox input[type="checkbox"], .checkbox-inline input[type="checkbox"] { position: absolute; margin-left: -20px; margin-top: 4px \9; } .radio + .radio, .checkbox + .checkbox { margin-top: -5px; } .radio-inline, .checkbox-inline { position: relative; display: inline-block; padding-left: 20px; margin-bottom: 0; vertical-align: middle; font-weight: normal; cursor: pointer; } .radio-inline + .radio-inline, .checkbox-inline + .checkbox-inline { margin-top: 0; margin-left: 10px; } input[type="radio"][disabled], input[type="checkbox"][disabled], input[type="radio"].disabled, input[type="checkbox"].disabled, fieldset[disabled] input[type="radio"], fieldset[disabled] input[type="checkbox"] { cursor: not-allowed; } .radio-inline.disabled, .checkbox-inline.disabled, fieldset[disabled] .radio-inline, fieldset[disabled] .checkbox-inline { cursor: not-allowed; } .radio.disabled label, .checkbox.disabled label, fieldset[disabled] .radio label, fieldset[disabled] .checkbox label { cursor: not-allowed; } .form-control-static { padding-top: 7px; padding-bottom: 7px; margin-bottom: 0; min-height: 31px; } .form-control-static.input-lg, .form-control-static.input-sm { padding-left: 0; padding-right: 0; } .input-sm { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } select.input-sm { height: 30px; line-height: 30px; } textarea.input-sm, select[multiple].input-sm { height: auto; } .form-group-sm .form-control { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .form-group-sm select.form-control { height: 30px; line-height: 30px; } .form-group-sm textarea.form-control, .form-group-sm select[multiple].form-control { height: auto; } .form-group-sm .form-control-static { height: 30px; min-height: 30px; padding: 6px 10px; font-size: 12px; line-height: 1.5; } .input-lg { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } select.input-lg { height: 45px; line-height: 45px; } textarea.input-lg, select[multiple].input-lg { height: auto; } .form-group-lg .form-control { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } .form-group-lg select.form-control { height: 45px; line-height: 45px; } .form-group-lg textarea.form-control, .form-group-lg select[multiple].form-control { height: auto; } .form-group-lg .form-control-static { height: 45px; min-height: 35px; padding: 11px 16px; font-size: 17px; line-height: 1.3333333; } .has-feedback { position: relative; } .has-feedback .form-control { padding-right: 40px; } .form-control-feedback { position: absolute; top: 0; right: 0; z-index: 2; display: block; width: 32px; height: 32px; line-height: 32px; text-align: center; pointer-events: none; } .input-lg + .form-control-feedback, .input-group-lg + .form-control-feedback, .form-group-lg .form-control + .form-control-feedback { width: 45px; height: 45px; line-height: 45px; } .input-sm + .form-control-feedback, .input-group-sm + .form-control-feedback, .form-group-sm .form-control + .form-control-feedback { width: 30px; height: 30px; line-height: 30px; } .has-success .help-block, .has-success .control-label, .has-success .radio, .has-success .checkbox, .has-success .radio-inline, .has-success .checkbox-inline, .has-success.radio label, .has-success.checkbox label, .has-success.radio-inline label, .has-success.checkbox-inline label { color: #3c763d; } .has-success .form-control { border-color: #3c763d; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-success .form-control:focus { border-color: #2b542c; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; } .has-success .input-group-addon { color: #3c763d; border-color: #3c763d; background-color: #dff0d8; } .has-success .form-control-feedback { color: #3c763d; } .has-warning .help-block, .has-warning .control-label, .has-warning .radio, .has-warning .checkbox, .has-warning .radio-inline, .has-warning .checkbox-inline, .has-warning.radio label, .has-warning.checkbox label, .has-warning.radio-inline label, .has-warning.checkbox-inline label { color: #8a6d3b; } .has-warning .form-control { border-color: #8a6d3b; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-warning .form-control:focus { border-color: #66512c; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; } .has-warning .input-group-addon { color: #8a6d3b; border-color: #8a6d3b; background-color: #fcf8e3; } .has-warning .form-control-feedback { color: #8a6d3b; } .has-error .help-block, .has-error .control-label, .has-error .radio, .has-error .checkbox, .has-error .radio-inline, .has-error .checkbox-inline, .has-error.radio label, .has-error.checkbox label, .has-error.radio-inline label, .has-error.checkbox-inline label { color: #a94442; } .has-error .form-control { border-color: #a94442; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-error .form-control:focus { border-color: #843534; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; } .has-error .input-group-addon { color: #a94442; border-color: #a94442; background-color: #f2dede; } .has-error .form-control-feedback { color: #a94442; } .has-feedback label ~ .form-control-feedback { top: 23px; } .has-feedback label.sr-only ~ .form-control-feedback { top: 0; } .help-block { display: block; margin-top: 5px; margin-bottom: 10px; color: #404040; } @media (min-width: 768px) { .form-inline .form-group { display: inline-block; margin-bottom: 0; vertical-align: middle; } .form-inline .form-control { display: inline-block; width: auto; vertical-align: middle; } .form-inline .form-control-static { display: inline-block; } .form-inline .input-group { display: inline-table; vertical-align: middle; } .form-inline .input-group .input-group-addon, .form-inline .input-group .input-group-btn, .form-inline .input-group .form-control { width: auto; } .form-inline .input-group > .form-control { width: 100%; } .form-inline .control-label { margin-bottom: 0; vertical-align: middle; } .form-inline .radio, .form-inline .checkbox { display: inline-block; margin-top: 0; margin-bottom: 0; vertical-align: middle; } .form-inline .radio label, .form-inline .checkbox label { padding-left: 0; } .form-inline .radio input[type="radio"], .form-inline .checkbox input[type="checkbox"] { position: relative; margin-left: 0; } .form-inline .has-feedback .form-control-feedback { top: 0; } } .form-horizontal .radio, .form-horizontal .checkbox, .form-horizontal .radio-inline, .form-horizontal .checkbox-inline { margin-top: 0; margin-bottom: 0; padding-top: 7px; } .form-horizontal .radio, .form-horizontal .checkbox { min-height: 25px; } .form-horizontal .form-group { margin-left: 0px; margin-right: 0px; } @media (min-width: 768px) { .form-horizontal .control-label { text-align: right; margin-bottom: 0; padding-top: 7px; } } .form-horizontal .has-feedback .form-control-feedback { right: 0px; } @media (min-width: 768px) { .form-horizontal .form-group-lg .control-label { padding-top: 11px; font-size: 17px; } } @media (min-width: 768px) { .form-horizontal .form-group-sm .control-label { padding-top: 6px; font-size: 12px; } } .btn { display: inline-block; margin-bottom: 0; font-weight: normal; text-align: center; vertical-align: middle; touch-action: manipulation; cursor: pointer; background-image: none; border: 1px solid transparent; white-space: nowrap; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; border-radius: 2px; -webkit-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; } .btn:focus, .btn:active:focus, .btn.active:focus, .btn.focus, .btn:active.focus, .btn.active.focus { outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } .btn:hover, .btn:focus, .btn.focus { color: #333; text-decoration: none; } .btn:active, .btn.active { outline: 0; background-image: none; -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); } .btn.disabled, .btn[disabled], fieldset[disabled] .btn { cursor: not-allowed; opacity: 0.65; filter: alpha(opacity=65); -webkit-box-shadow: none; box-shadow: none; } a.btn.disabled, fieldset[disabled] a.btn { pointer-events: none; } .btn-default { color: #333; background-color: #fff; border-color: #ccc; } .btn-default:focus, .btn-default.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } .btn-default:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } .btn-default:active, .btn-default.active, .open > .dropdown-toggle.btn-default { color: #333; background-color: #e6e6e6; border-color: #adadad; } .btn-default:active:hover, .btn-default.active:hover, .open > .dropdown-toggle.btn-default:hover, .btn-default:active:focus, .btn-default.active:focus, .open > .dropdown-toggle.btn-default:focus, .btn-default:active.focus, .btn-default.active.focus, .open > .dropdown-toggle.btn-default.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } .btn-default:active, .btn-default.active, .open > .dropdown-toggle.btn-default { background-image: none; } .btn-default.disabled:hover, .btn-default[disabled]:hover, fieldset[disabled] .btn-default:hover, .btn-default.disabled:focus, .btn-default[disabled]:focus, fieldset[disabled] .btn-default:focus, .btn-default.disabled.focus, .btn-default[disabled].focus, fieldset[disabled] .btn-default.focus { background-color: #fff; border-color: #ccc; } .btn-default .badge { color: #fff; background-color: #333; } .btn-primary { color: #fff; background-color: #337ab7; border-color: #2e6da4; } .btn-primary:focus, .btn-primary.focus { color: #fff; background-color: #286090; border-color: #122b40; } .btn-primary:hover { color: #fff; background-color: #286090; border-color: #204d74; } .btn-primary:active, .btn-primary.active, .open > .dropdown-toggle.btn-primary { color: #fff; background-color: #286090; border-color: #204d74; } .btn-primary:active:hover, .btn-primary.active:hover, .open > .dropdown-toggle.btn-primary:hover, .btn-primary:active:focus, .btn-primary.active:focus, .open > .dropdown-toggle.btn-primary:focus, .btn-primary:active.focus, .btn-primary.active.focus, .open > .dropdown-toggle.btn-primary.focus { color: #fff; background-color: #204d74; border-color: #122b40; } .btn-primary:active, .btn-primary.active, .open > .dropdown-toggle.btn-primary { background-image: none; } .btn-primary.disabled:hover, .btn-primary[disabled]:hover, fieldset[disabled] .btn-primary:hover, .btn-primary.disabled:focus, .btn-primary[disabled]:focus, fieldset[disabled] .btn-primary:focus, .btn-primary.disabled.focus, .btn-primary[disabled].focus, fieldset[disabled] .btn-primary.focus { background-color: #337ab7; border-color: #2e6da4; } .btn-primary .badge { color: #337ab7; background-color: #fff; } .btn-success { color: #fff; background-color: #5cb85c; border-color: #4cae4c; } .btn-success:focus, .btn-success.focus { color: #fff; background-color: #449d44; border-color: #255625; } .btn-success:hover { color: #fff; background-color: #449d44; border-color: #398439; } .btn-success:active, .btn-success.active, .open > .dropdown-toggle.btn-success { color: #fff; background-color: #449d44; border-color: #398439; } .btn-success:active:hover, .btn-success.active:hover, .open > .dropdown-toggle.btn-success:hover, .btn-success:active:focus, .btn-success.active:focus, .open > .dropdown-toggle.btn-success:focus, .btn-success:active.focus, .btn-success.active.focus, .open > .dropdown-toggle.btn-success.focus { color: #fff; background-color: #398439; border-color: #255625; } .btn-success:active, .btn-success.active, .open > .dropdown-toggle.btn-success { background-image: none; } .btn-success.disabled:hover, .btn-success[disabled]:hover, fieldset[disabled] .btn-success:hover, .btn-success.disabled:focus, .btn-success[disabled]:focus, fieldset[disabled] .btn-success:focus, .btn-success.disabled.focus, .btn-success[disabled].focus, fieldset[disabled] .btn-success.focus { background-color: #5cb85c; border-color: #4cae4c; } .btn-success .badge { color: #5cb85c; background-color: #fff; } .btn-info { color: #fff; background-color: #5bc0de; border-color: #46b8da; } .btn-info:focus, .btn-info.focus { color: #fff; background-color: #31b0d5; border-color: #1b6d85; } .btn-info:hover { color: #fff; background-color: #31b0d5; border-color: #269abc; } .btn-info:active, .btn-info.active, .open > .dropdown-toggle.btn-info { color: #fff; background-color: #31b0d5; border-color: #269abc; } .btn-info:active:hover, .btn-info.active:hover, .open > .dropdown-toggle.btn-info:hover, .btn-info:active:focus, .btn-info.active:focus, .open > .dropdown-toggle.btn-info:focus, .btn-info:active.focus, .btn-info.active.focus, .open > .dropdown-toggle.btn-info.focus { color: #fff; background-color: #269abc; border-color: #1b6d85; } .btn-info:active, .btn-info.active, .open > .dropdown-toggle.btn-info { background-image: none; } .btn-info.disabled:hover, .btn-info[disabled]:hover, fieldset[disabled] .btn-info:hover, .btn-info.disabled:focus, .btn-info[disabled]:focus, fieldset[disabled] .btn-info:focus, .btn-info.disabled.focus, .btn-info[disabled].focus, fieldset[disabled] .btn-info.focus { background-color: #5bc0de; border-color: #46b8da; } .btn-info .badge { color: #5bc0de; background-color: #fff; } .btn-warning { color: #fff; background-color: #f0ad4e; border-color: #eea236; } .btn-warning:focus, .btn-warning.focus { color: #fff; background-color: #ec971f; border-color: #985f0d; } .btn-warning:hover { color: #fff; background-color: #ec971f; border-color: #d58512; } .btn-warning:active, .btn-warning.active, .open > .dropdown-toggle.btn-warning { color: #fff; background-color: #ec971f; border-color: #d58512; } .btn-warning:active:hover, .btn-warning.active:hover, .open > .dropdown-toggle.btn-warning:hover, .btn-warning:active:focus, .btn-warning.active:focus, .open > .dropdown-toggle.btn-warning:focus, .btn-warning:active.focus, .btn-warning.active.focus, .open > .dropdown-toggle.btn-warning.focus { color: #fff; background-color: #d58512; border-color: #985f0d; } .btn-warning:active, .btn-warning.active, .open > .dropdown-toggle.btn-warning { background-image: none; } .btn-warning.disabled:hover, .btn-warning[disabled]:hover, fieldset[disabled] .btn-warning:hover, .btn-warning.disabled:focus, .btn-warning[disabled]:focus, fieldset[disabled] .btn-warning:focus, .btn-warning.disabled.focus, .btn-warning[disabled].focus, fieldset[disabled] .btn-warning.focus { background-color: #f0ad4e; border-color: #eea236; } .btn-warning .badge { color: #f0ad4e; background-color: #fff; } .btn-danger { color: #fff; background-color: #d9534f; border-color: #d43f3a; } .btn-danger:focus, .btn-danger.focus { color: #fff; background-color: #c9302c; border-color: #761c19; } .btn-danger:hover { color: #fff; background-color: #c9302c; border-color: #ac2925; } .btn-danger:active, .btn-danger.active, .open > .dropdown-toggle.btn-danger { color: #fff; background-color: #c9302c; border-color: #ac2925; } .btn-danger:active:hover, .btn-danger.active:hover, .open > .dropdown-toggle.btn-danger:hover, .btn-danger:active:focus, .btn-danger.active:focus, .open > .dropdown-toggle.btn-danger:focus, .btn-danger:active.focus, .btn-danger.active.focus, .open > .dropdown-toggle.btn-danger.focus { color: #fff; background-color: #ac2925; border-color: #761c19; } .btn-danger:active, .btn-danger.active, .open > .dropdown-toggle.btn-danger { background-image: none; } .btn-danger.disabled:hover, .btn-danger[disabled]:hover, fieldset[disabled] .btn-danger:hover, .btn-danger.disabled:focus, .btn-danger[disabled]:focus, fieldset[disabled] .btn-danger:focus, .btn-danger.disabled.focus, .btn-danger[disabled].focus, fieldset[disabled] .btn-danger.focus { background-color: #d9534f; border-color: #d43f3a; } .btn-danger .badge { color: #d9534f; background-color: #fff; } .btn-link { color: #337ab7; font-weight: normal; border-radius: 0; } .btn-link, .btn-link:active, .btn-link.active, .btn-link[disabled], fieldset[disabled] .btn-link { background-color: transparent; -webkit-box-shadow: none; box-shadow: none; } .btn-link, .btn-link:hover, .btn-link:focus, .btn-link:active { border-color: transparent; } .btn-link:hover, .btn-link:focus { color: #23527c; text-decoration: underline; background-color: transparent; } .btn-link[disabled]:hover, fieldset[disabled] .btn-link:hover, .btn-link[disabled]:focus, fieldset[disabled] .btn-link:focus { color: #777777; text-decoration: none; } .btn-lg, .btn-group-lg > .btn { padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } .btn-sm, .btn-group-sm > .btn { padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .btn-xs, .btn-group-xs > .btn { padding: 1px 5px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .btn-block { display: block; width: 100%; } .btn-block + .btn-block { margin-top: 5px; } input[type="submit"].btn-block, input[type="reset"].btn-block, input[type="button"].btn-block { width: 100%; } .fade { opacity: 0; -webkit-transition: opacity 0.15s linear; -o-transition: opacity 0.15s linear; transition: opacity 0.15s linear; } .fade.in { opacity: 1; } .collapse { display: none; } .collapse.in { display: block; } tr.collapse.in { display: table-row; } tbody.collapse.in { display: table-row-group; } .collapsing { position: relative; height: 0; overflow: hidden; -webkit-transition-property: height, visibility; transition-property: height, visibility; -webkit-transition-duration: 0.35s; transition-duration: 0.35s; -webkit-transition-timing-function: ease; transition-timing-function: ease; } .caret { display: inline-block; width: 0; height: 0; margin-left: 2px; vertical-align: middle; border-top: 4px dashed; border-top: 4px solid \9; border-right: 4px solid transparent; border-left: 4px solid transparent; } .dropup, .dropdown { position: relative; } .dropdown-toggle:focus { outline: 0; } .dropdown-menu { position: absolute; top: 100%; left: 0; z-index: 1000; display: none; float: left; min-width: 160px; padding: 5px 0; margin: 2px 0 0; list-style: none; font-size: 13px; text-align: left; background-color: #fff; border: 1px solid #ccc; border: 1px solid rgba(0, 0, 0, 0.15); border-radius: 2px; -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); background-clip: padding-box; } .dropdown-menu.pull-right { right: 0; left: auto; } .dropdown-menu .divider { height: 1px; margin: 8px 0; overflow: hidden; background-color: #e5e5e5; } .dropdown-menu > li > a { display: block; padding: 3px 20px; clear: both; font-weight: normal; line-height: 1.42857143; color: #333333; white-space: nowrap; } .dropdown-menu > li > a:hover, .dropdown-menu > li > a:focus { text-decoration: none; color: #262626; background-color: #f5f5f5; } .dropdown-menu > .active > a, .dropdown-menu > .active > a:hover, .dropdown-menu > .active > a:focus { color: #fff; text-decoration: none; outline: 0; background-color: #337ab7; } .dropdown-menu > .disabled > a, .dropdown-menu > .disabled > a:hover, .dropdown-menu > .disabled > a:focus { color: #777777; } .dropdown-menu > .disabled > a:hover, .dropdown-menu > .disabled > a:focus { text-decoration: none; background-color: transparent; background-image: none; filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); cursor: not-allowed; } .open > .dropdown-menu { display: block; } .open > a { outline: 0; } .dropdown-menu-right { left: auto; right: 0; } .dropdown-menu-left { left: 0; right: auto; } .dropdown-header { display: block; padding: 3px 20px; font-size: 12px; line-height: 1.42857143; color: #777777; white-space: nowrap; } .dropdown-backdrop { position: fixed; left: 0; right: 0; bottom: 0; top: 0; z-index: 990; } .pull-right > .dropdown-menu { right: 0; left: auto; } .dropup .caret, .navbar-fixed-bottom .dropdown .caret { border-top: 0; border-bottom: 4px dashed; border-bottom: 4px solid \9; content: ""; } .dropup .dropdown-menu, .navbar-fixed-bottom .dropdown .dropdown-menu { top: auto; bottom: 100%; margin-bottom: 2px; } @media (min-width: 541px) { .navbar-right .dropdown-menu { left: auto; right: 0; } .navbar-right .dropdown-menu-left { left: 0; right: auto; } } .btn-group, .btn-group-vertical { position: relative; display: inline-block; vertical-align: middle; } .btn-group > .btn, .btn-group-vertical > .btn { position: relative; float: left; } .btn-group > .btn:hover, .btn-group-vertical > .btn:hover, .btn-group > .btn:focus, .btn-group-vertical > .btn:focus, .btn-group > .btn:active, .btn-group-vertical > .btn:active, .btn-group > .btn.active, .btn-group-vertical > .btn.active { z-index: 2; } .btn-group .btn + .btn, .btn-group .btn + .btn-group, .btn-group .btn-group + .btn, .btn-group .btn-group + .btn-group { margin-left: -1px; } .btn-toolbar { margin-left: -5px; } .btn-toolbar .btn, .btn-toolbar .btn-group, .btn-toolbar .input-group { float: left; } .btn-toolbar > .btn, .btn-toolbar > .btn-group, .btn-toolbar > .input-group { margin-left: 5px; } .btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) { border-radius: 0; } .btn-group > .btn:first-child { margin-left: 0; } .btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) { border-bottom-right-radius: 0; border-top-right-radius: 0; } .btn-group > .btn:last-child:not(:first-child), .btn-group > .dropdown-toggle:not(:first-child) { border-bottom-left-radius: 0; border-top-left-radius: 0; } .btn-group > .btn-group { float: left; } .btn-group > .btn-group:not(:first-child):not(:last-child) > .btn { border-radius: 0; } .btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child, .btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle { border-bottom-right-radius: 0; border-top-right-radius: 0; } .btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child { border-bottom-left-radius: 0; border-top-left-radius: 0; } .btn-group .dropdown-toggle:active, .btn-group.open .dropdown-toggle { outline: 0; } .btn-group > .btn + .dropdown-toggle { padding-left: 8px; padding-right: 8px; } .btn-group > .btn-lg + .dropdown-toggle { padding-left: 12px; padding-right: 12px; } .btn-group.open .dropdown-toggle { -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); } .btn-group.open .dropdown-toggle.btn-link { -webkit-box-shadow: none; box-shadow: none; } .btn .caret { margin-left: 0; } .btn-lg .caret { border-width: 5px 5px 0; border-bottom-width: 0; } .dropup .btn-lg .caret { border-width: 0 5px 5px; } .btn-group-vertical > .btn, .btn-group-vertical > .btn-group, .btn-group-vertical > .btn-group > .btn { display: block; float: none; width: 100%; max-width: 100%; } .btn-group-vertical > .btn-group > .btn { float: none; } .btn-group-vertical > .btn + .btn, .btn-group-vertical > .btn + .btn-group, .btn-group-vertical > .btn-group + .btn, .btn-group-vertical > .btn-group + .btn-group { margin-top: -1px; margin-left: 0; } .btn-group-vertical > .btn:not(:first-child):not(:last-child) { border-radius: 0; } .btn-group-vertical > .btn:first-child:not(:last-child) { border-top-right-radius: 2px; border-top-left-radius: 2px; border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .btn-group-vertical > .btn:last-child:not(:first-child) { border-top-right-radius: 0; border-top-left-radius: 0; border-bottom-right-radius: 2px; border-bottom-left-radius: 2px; } .btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn { border-radius: 0; } .btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child, .btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle { border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child { border-top-right-radius: 0; border-top-left-radius: 0; } .btn-group-justified { display: table; width: 100%; table-layout: fixed; border-collapse: separate; } .btn-group-justified > .btn, .btn-group-justified > .btn-group { float: none; display: table-cell; width: 1%; } .btn-group-justified > .btn-group .btn { width: 100%; } .btn-group-justified > .btn-group .dropdown-menu { left: auto; } [data-toggle="buttons"] > .btn input[type="radio"], [data-toggle="buttons"] > .btn-group > .btn input[type="radio"], [data-toggle="buttons"] > .btn input[type="checkbox"], [data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] { position: absolute; clip: rect(0, 0, 0, 0); pointer-events: none; } .input-group { position: relative; display: table; border-collapse: separate; } .input-group[class\*="col-"] { float: none; padding-left: 0; padding-right: 0; } .input-group .form-control { position: relative; z-index: 2; float: left; width: 100%; margin-bottom: 0; } .input-group .form-control:focus { z-index: 3; } .input-group-lg > .form-control, .input-group-lg > .input-group-addon, .input-group-lg > .input-group-btn > .btn { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } select.input-group-lg > .form-control, select.input-group-lg > .input-group-addon, select.input-group-lg > .input-group-btn > .btn { height: 45px; line-height: 45px; } textarea.input-group-lg > .form-control, textarea.input-group-lg > .input-group-addon, textarea.input-group-lg > .input-group-btn > .btn, select[multiple].input-group-lg > .form-control, select[multiple].input-group-lg > .input-group-addon, select[multiple].input-group-lg > .input-group-btn > .btn { height: auto; } .input-group-sm > .form-control, .input-group-sm > .input-group-addon, .input-group-sm > .input-group-btn > .btn { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } select.input-group-sm > .form-control, select.input-group-sm > .input-group-addon, select.input-group-sm > .input-group-btn > .btn { height: 30px; line-height: 30px; } textarea.input-group-sm > .form-control, textarea.input-group-sm > .input-group-addon, textarea.input-group-sm > .input-group-btn > .btn, select[multiple].input-group-sm > .form-control, select[multiple].input-group-sm > .input-group-addon, select[multiple].input-group-sm > .input-group-btn > .btn { height: auto; } .input-group-addon, .input-group-btn, .input-group .form-control { display: table-cell; } .input-group-addon:not(:first-child):not(:last-child), .input-group-btn:not(:first-child):not(:last-child), .input-group .form-control:not(:first-child):not(:last-child) { border-radius: 0; } .input-group-addon, .input-group-btn { width: 1%; white-space: nowrap; vertical-align: middle; } .input-group-addon { padding: 6px 12px; font-size: 13px; font-weight: normal; line-height: 1; color: #555555; text-align: center; background-color: #eeeeee; border: 1px solid #ccc; border-radius: 2px; } .input-group-addon.input-sm { padding: 5px 10px; font-size: 12px; border-radius: 1px; } .input-group-addon.input-lg { padding: 10px 16px; font-size: 17px; border-radius: 3px; } .input-group-addon input[type="radio"], .input-group-addon input[type="checkbox"] { margin-top: 0; } .input-group .form-control:first-child, .input-group-addon:first-child, .input-group-btn:first-child > .btn, .input-group-btn:first-child > .btn-group > .btn, .input-group-btn:first-child > .dropdown-toggle, .input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle), .input-group-btn:last-child > .btn-group:not(:last-child) > .btn { border-bottom-right-radius: 0; border-top-right-radius: 0; } .input-group-addon:first-child { border-right: 0; } .input-group .form-control:last-child, .input-group-addon:last-child, .input-group-btn:last-child > .btn, .input-group-btn:last-child > .btn-group > .btn, .input-group-btn:last-child > .dropdown-toggle, .input-group-btn:first-child > .btn:not(:first-child), .input-group-btn:first-child > .btn-group:not(:first-child) > .btn { border-bottom-left-radius: 0; border-top-left-radius: 0; } .input-group-addon:last-child { border-left: 0; } .input-group-btn { position: relative; font-size: 0; white-space: nowrap; } .input-group-btn > .btn { position: relative; } .input-group-btn > .btn + .btn { margin-left: -1px; } .input-group-btn > .btn:hover, .input-group-btn > .btn:focus, .input-group-btn > .btn:active { z-index: 2; } .input-group-btn:first-child > .btn, .input-group-btn:first-child > .btn-group { margin-right: -1px; } .input-group-btn:last-child > .btn, .input-group-btn:last-child > .btn-group { z-index: 2; margin-left: -1px; } .nav { margin-bottom: 0; padding-left: 0; list-style: none; } .nav > li { position: relative; display: block; } .nav > li > a { position: relative; display: block; padding: 10px 15px; } .nav > li > a:hover, .nav > li > a:focus { text-decoration: none; background-color: #eeeeee; } .nav > li.disabled > a { color: #777777; } .nav > li.disabled > a:hover, .nav > li.disabled > a:focus { color: #777777; text-decoration: none; background-color: transparent; cursor: not-allowed; } .nav .open > a, .nav .open > a:hover, .nav .open > a:focus { background-color: #eeeeee; border-color: #337ab7; } .nav .nav-divider { height: 1px; margin: 8px 0; overflow: hidden; background-color: #e5e5e5; } .nav > li > a > img { max-width: none; } .nav-tabs { border-bottom: 1px solid #ddd; } .nav-tabs > li { float: left; margin-bottom: -1px; } .nav-tabs > li > a { margin-right: 2px; line-height: 1.42857143; border: 1px solid transparent; border-radius: 2px 2px 0 0; } .nav-tabs > li > a:hover { border-color: #eeeeee #eeeeee #ddd; } .nav-tabs > li.active > a, .nav-tabs > li.active > a:hover, .nav-tabs > li.active > a:focus { color: #555555; background-color: #fff; border: 1px solid #ddd; border-bottom-color: transparent; cursor: default; } .nav-tabs.nav-justified { width: 100%; border-bottom: 0; } .nav-tabs.nav-justified > li { float: none; } .nav-tabs.nav-justified > li > a { text-align: center; margin-bottom: 5px; } .nav-tabs.nav-justified > .dropdown .dropdown-menu { top: auto; left: auto; } @media (min-width: 768px) { .nav-tabs.nav-justified > li { display: table-cell; width: 1%; } .nav-tabs.nav-justified > li > a { margin-bottom: 0; } } .nav-tabs.nav-justified > li > a { margin-right: 0; border-radius: 2px; } .nav-tabs.nav-justified > .active > a, .nav-tabs.nav-justified > .active > a:hover, .nav-tabs.nav-justified > .active > a:focus { border: 1px solid #ddd; } @media (min-width: 768px) { .nav-tabs.nav-justified > li > a { border-bottom: 1px solid #ddd; border-radius: 2px 2px 0 0; } .nav-tabs.nav-justified > .active > a, .nav-tabs.nav-justified > .active > a:hover, .nav-tabs.nav-justified > .active > a:focus { border-bottom-color: #fff; } } .nav-pills > li { float: left; } .nav-pills > li > a { border-radius: 2px; } .nav-pills > li + li { margin-left: 2px; } .nav-pills > li.active > a, .nav-pills > li.active > a:hover, .nav-pills > li.active > a:focus { color: #fff; background-color: #337ab7; } .nav-stacked > li { float: none; } .nav-stacked > li + li { margin-top: 2px; margin-left: 0; } .nav-justified { width: 100%; } .nav-justified > li { float: none; } .nav-justified > li > a { text-align: center; margin-bottom: 5px; } .nav-justified > .dropdown .dropdown-menu { top: auto; left: auto; } @media (min-width: 768px) { .nav-justified > li { display: table-cell; width: 1%; } .nav-justified > li > a { margin-bottom: 0; } } .nav-tabs-justified { border-bottom: 0; } .nav-tabs-justified > li > a { margin-right: 0; border-radius: 2px; } .nav-tabs-justified > .active > a, .nav-tabs-justified > .active > a:hover, .nav-tabs-justified > .active > a:focus { border: 1px solid #ddd; } @media (min-width: 768px) { .nav-tabs-justified > li > a { border-bottom: 1px solid #ddd; border-radius: 2px 2px 0 0; } .nav-tabs-justified > .active > a, .nav-tabs-justified > .active > a:hover, .nav-tabs-justified > .active > a:focus { border-bottom-color: #fff; } } .tab-content > .tab-pane { display: none; } .tab-content > .active { display: block; } .nav-tabs .dropdown-menu { margin-top: -1px; border-top-right-radius: 0; border-top-left-radius: 0; } .navbar { position: relative; min-height: 30px; margin-bottom: 18px; border: 1px solid transparent; } @media (min-width: 541px) { .navbar { border-radius: 2px; } } @media (min-width: 541px) { .navbar-header { float: left; } } .navbar-collapse { overflow-x: visible; padding-right: 0px; padding-left: 0px; border-top: 1px solid transparent; box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1); -webkit-overflow-scrolling: touch; } .navbar-collapse.in { overflow-y: auto; } @media (min-width: 541px) { .navbar-collapse { width: auto; border-top: 0; box-shadow: none; } .navbar-collapse.collapse { display: block !important; height: auto !important; padding-bottom: 0; overflow: visible !important; } .navbar-collapse.in { overflow-y: visible; } .navbar-fixed-top .navbar-collapse, .navbar-static-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { padding-left: 0; padding-right: 0; } } .navbar-fixed-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { max-height: 340px; } @media (max-device-width: 540px) and (orientation: landscape) { .navbar-fixed-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { max-height: 200px; } } .container > .navbar-header, .container-fluid > .navbar-header, .container > .navbar-collapse, .container-fluid > .navbar-collapse { margin-right: 0px; margin-left: 0px; } @media (min-width: 541px) { .container > .navbar-header, .container-fluid > .navbar-header, .container > .navbar-collapse, .container-fluid > .navbar-collapse { margin-right: 0; margin-left: 0; } } .navbar-static-top { z-index: 1000; border-width: 0 0 1px; } @media (min-width: 541px) { .navbar-static-top { border-radius: 0; } } .navbar-fixed-top, .navbar-fixed-bottom { position: fixed; right: 0; left: 0; z-index: 1030; } @media (min-width: 541px) { .navbar-fixed-top, .navbar-fixed-bottom { border-radius: 0; } } .navbar-fixed-top { top: 0; border-width: 0 0 1px; } .navbar-fixed-bottom { bottom: 0; margin-bottom: 0; border-width: 1px 0 0; } .navbar-brand { float: left; padding: 6px 0px; font-size: 17px; line-height: 18px; height: 30px; } .navbar-brand:hover, .navbar-brand:focus { text-decoration: none; } .navbar-brand > img { display: block; } @media (min-width: 541px) { .navbar > .container .navbar-brand, .navbar > .container-fluid .navbar-brand { margin-left: 0px; } } .navbar-toggle { position: relative; float: right; margin-right: 0px; padding: 9px 10px; margin-top: -2px; margin-bottom: -2px; background-color: transparent; background-image: none; border: 1px solid transparent; border-radius: 2px; } .navbar-toggle:focus { outline: 0; } .navbar-toggle .icon-bar { display: block; width: 22px; height: 2px; border-radius: 1px; } .navbar-toggle .icon-bar + .icon-bar { margin-top: 4px; } @media (min-width: 541px) { .navbar-toggle { display: none; } } .navbar-nav { margin: 3px 0px; } .navbar-nav > li > a { padding-top: 10px; padding-bottom: 10px; line-height: 18px; } @media (max-width: 540px) { .navbar-nav .open .dropdown-menu { position: static; float: none; width: auto; margin-top: 0; background-color: transparent; border: 0; box-shadow: none; } .navbar-nav .open .dropdown-menu > li > a, .navbar-nav .open .dropdown-menu .dropdown-header { padding: 5px 15px 5px 25px; } .navbar-nav .open .dropdown-menu > li > a { line-height: 18px; } .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-nav .open .dropdown-menu > li > a:focus { background-image: none; } } @media (min-width: 541px) { .navbar-nav { float: left; margin: 0; } .navbar-nav > li { float: left; } .navbar-nav > li > a { padding-top: 6px; padding-bottom: 6px; } } .navbar-form { margin-left: 0px; margin-right: 0px; padding: 10px 0px; border-top: 1px solid transparent; border-bottom: 1px solid transparent; -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); margin-top: -1px; margin-bottom: -1px; } @media (min-width: 768px) { .navbar-form .form-group { display: inline-block; margin-bottom: 0; vertical-align: middle; } .navbar-form .form-control { display: inline-block; width: auto; vertical-align: middle; } .navbar-form .form-control-static { display: inline-block; } .navbar-form .input-group { display: inline-table; vertical-align: middle; } .navbar-form .input-group .input-group-addon, .navbar-form .input-group .input-group-btn, .navbar-form .input-group .form-control { width: auto; } .navbar-form .input-group > .form-control { width: 100%; } .navbar-form .control-label { margin-bottom: 0; vertical-align: middle; } .navbar-form .radio, .navbar-form .checkbox { display: inline-block; margin-top: 0; margin-bottom: 0; vertical-align: middle; } .navbar-form .radio label, .navbar-form .checkbox label { padding-left: 0; } .navbar-form .radio input[type="radio"], .navbar-form .checkbox input[type="checkbox"] { position: relative; margin-left: 0; } .navbar-form .has-feedback .form-control-feedback { top: 0; } } @media (max-width: 540px) { .navbar-form .form-group { margin-bottom: 5px; } .navbar-form .form-group:last-child { margin-bottom: 0; } } @media (min-width: 541px) { .navbar-form { width: auto; border: 0; margin-left: 0; margin-right: 0; padding-top: 0; padding-bottom: 0; -webkit-box-shadow: none; box-shadow: none; } } .navbar-nav > li > .dropdown-menu { margin-top: 0; border-top-right-radius: 0; border-top-left-radius: 0; } .navbar-fixed-bottom .navbar-nav > li > .dropdown-menu { margin-bottom: 0; border-top-right-radius: 2px; border-top-left-radius: 2px; border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .navbar-btn { margin-top: -1px; margin-bottom: -1px; } .navbar-btn.btn-sm { margin-top: 0px; margin-bottom: 0px; } .navbar-btn.btn-xs { margin-top: 4px; margin-bottom: 4px; } .navbar-text { margin-top: 6px; margin-bottom: 6px; } @media (min-width: 541px) { .navbar-text { float: left; margin-left: 0px; margin-right: 0px; } } @media (min-width: 541px) { .navbar-left { float: left !important; float: left; } .navbar-right { float: right !important; float: right; margin-right: 0px; } .navbar-right ~ .navbar-right { margin-right: 0; } } .navbar-default { background-color: #f8f8f8; border-color: #e7e7e7; } .navbar-default .navbar-brand { color: #777; } .navbar-default .navbar-brand:hover, .navbar-default .navbar-brand:focus { color: #5e5e5e; background-color: transparent; } .navbar-default .navbar-text { color: #777; } .navbar-default .navbar-nav > li > a { color: #777; } .navbar-default .navbar-nav > li > a:hover, .navbar-default .navbar-nav > li > a:focus { color: #333; background-color: transparent; } .navbar-default .navbar-nav > .active > a, .navbar-default .navbar-nav > .active > a:hover, .navbar-default .navbar-nav > .active > a:focus { color: #555; background-color: #e7e7e7; } .navbar-default .navbar-nav > .disabled > a, .navbar-default .navbar-nav > .disabled > a:hover, .navbar-default .navbar-nav > .disabled > a:focus { color: #ccc; background-color: transparent; } .navbar-default .navbar-toggle { border-color: #ddd; } .navbar-default .navbar-toggle:hover, .navbar-default .navbar-toggle:focus { background-color: #ddd; } .navbar-default .navbar-toggle .icon-bar { background-color: #888; } .navbar-default .navbar-collapse, .navbar-default .navbar-form { border-color: #e7e7e7; } .navbar-default .navbar-nav > .open > a, .navbar-default .navbar-nav > .open > a:hover, .navbar-default .navbar-nav > .open > a:focus { background-color: #e7e7e7; color: #555; } @media (max-width: 540px) { .navbar-default .navbar-nav .open .dropdown-menu > li > a { color: #777; } .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus { color: #333; background-color: transparent; } .navbar-default .navbar-nav .open .dropdown-menu > .active > a, .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus { color: #555; background-color: #e7e7e7; } .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a, .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus { color: #ccc; background-color: transparent; } } .navbar-default .navbar-link { color: #777; } .navbar-default .navbar-link:hover { color: #333; } .navbar-default .btn-link { color: #777; } .navbar-default .btn-link:hover, .navbar-default .btn-link:focus { color: #333; } .navbar-default .btn-link[disabled]:hover, fieldset[disabled] .navbar-default .btn-link:hover, .navbar-default .btn-link[disabled]:focus, fieldset[disabled] .navbar-default .btn-link:focus { color: #ccc; } .navbar-inverse { background-color: #222; border-color: #080808; } .navbar-inverse .navbar-brand { color: #9d9d9d; } .navbar-inverse .navbar-brand:hover, .navbar-inverse .navbar-brand:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-text { color: #9d9d9d; } .navbar-inverse .navbar-nav > li > a { color: #9d9d9d; } .navbar-inverse .navbar-nav > li > a:hover, .navbar-inverse .navbar-nav > li > a:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-nav > .active > a, .navbar-inverse .navbar-nav > .active > a:hover, .navbar-inverse .navbar-nav > .active > a:focus { color: #fff; background-color: #080808; } .navbar-inverse .navbar-nav > .disabled > a, .navbar-inverse .navbar-nav > .disabled > a:hover, .navbar-inverse .navbar-nav > .disabled > a:focus { color: #444; background-color: transparent; } .navbar-inverse .navbar-toggle { border-color: #333; } .navbar-inverse .navbar-toggle:hover, .navbar-inverse .navbar-toggle:focus { background-color: #333; } .navbar-inverse .navbar-toggle .icon-bar { background-color: #fff; } .navbar-inverse .navbar-collapse, .navbar-inverse .navbar-form { border-color: #101010; } .navbar-inverse .navbar-nav > .open > a, .navbar-inverse .navbar-nav > .open > a:hover, .navbar-inverse .navbar-nav > .open > a:focus { background-color: #080808; color: #fff; } @media (max-width: 540px) { .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header { border-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu .divider { background-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu > li > a { color: #9d9d9d; } .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a, .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus { color: #fff; background-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a, .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus { color: #444; background-color: transparent; } } .navbar-inverse .navbar-link { color: #9d9d9d; } .navbar-inverse .navbar-link:hover { color: #fff; } .navbar-inverse .btn-link { color: #9d9d9d; } .navbar-inverse .btn-link:hover, .navbar-inverse .btn-link:focus { color: #fff; } .navbar-inverse .btn-link[disabled]:hover, fieldset[disabled] .navbar-inverse .btn-link:hover, .navbar-inverse .btn-link[disabled]:focus, fieldset[disabled] .navbar-inverse .btn-link:focus { color: #444; } .breadcrumb { padding: 8px 15px; margin-bottom: 18px; list-style: none; background-color: #f5f5f5; border-radius: 2px; } .breadcrumb > li { display: inline-block; } .breadcrumb > li + li:before { content: "/\00a0"; padding: 0 5px; color: #5e5e5e; } .breadcrumb > .active { color: #777777; } .pagination { display: inline-block; padding-left: 0; margin: 18px 0; border-radius: 2px; } .pagination > li { display: inline; } .pagination > li > a, .pagination > li > span { position: relative; float: left; padding: 6px 12px; line-height: 1.42857143; text-decoration: none; color: #337ab7; background-color: #fff; border: 1px solid #ddd; margin-left: -1px; } .pagination > li:first-child > a, .pagination > li:first-child > span { margin-left: 0; border-bottom-left-radius: 2px; border-top-left-radius: 2px; } .pagination > li:last-child > a, .pagination > li:last-child > span { border-bottom-right-radius: 2px; border-top-right-radius: 2px; } .pagination > li > a:hover, .pagination > li > span:hover, .pagination > li > a:focus, .pagination > li > span:focus { z-index: 2; color: #23527c; background-color: #eeeeee; border-color: #ddd; } .pagination > .active > a, .pagination > .active > span, .pagination > .active > a:hover, .pagination > .active > span:hover, .pagination > .active > a:focus, .pagination > .active > span:focus { z-index: 3; color: #fff; background-color: #337ab7; border-color: #337ab7; cursor: default; } .pagination > .disabled > span, .pagination > .disabled > span:hover, .pagination > .disabled > span:focus, .pagination > .disabled > a, .pagination > .disabled > a:hover, .pagination > .disabled > a:focus { color: #777777; background-color: #fff; border-color: #ddd; cursor: not-allowed; } .pagination-lg > li > a, .pagination-lg > li > span { padding: 10px 16px; font-size: 17px; line-height: 1.3333333; } .pagination-lg > li:first-child > a, .pagination-lg > li:first-child > span { border-bottom-left-radius: 3px; border-top-left-radius: 3px; } .pagination-lg > li:last-child > a, .pagination-lg > li:last-child > span { border-bottom-right-radius: 3px; border-top-right-radius: 3px; } .pagination-sm > li > a, .pagination-sm > li > span { padding: 5px 10px; font-size: 12px; line-height: 1.5; } .pagination-sm > li:first-child > a, .pagination-sm > li:first-child > span { border-bottom-left-radius: 1px; border-top-left-radius: 1px; } .pagination-sm > li:last-child > a, .pagination-sm > li:last-child > span { border-bottom-right-radius: 1px; border-top-right-radius: 1px; } .pager { padding-left: 0; margin: 18px 0; list-style: none; text-align: center; } .pager li { display: inline; } .pager li > a, .pager li > span { display: inline-block; padding: 5px 14px; background-color: #fff; border: 1px solid #ddd; border-radius: 15px; } .pager li > a:hover, .pager li > a:focus { text-decoration: none; background-color: #eeeeee; } .pager .next > a, .pager .next > span { float: right; } .pager .previous > a, .pager .previous > span { float: left; } .pager .disabled > a, .pager .disabled > a:hover, .pager .disabled > a:focus, .pager .disabled > span { color: #777777; background-color: #fff; cursor: not-allowed; } .label { display: inline; padding: .2em .6em .3em; font-size: 75%; font-weight: bold; line-height: 1; color: #fff; text-align: center; white-space: nowrap; vertical-align: baseline; border-radius: .25em; } a.label:hover, a.label:focus { color: #fff; text-decoration: none; cursor: pointer; } .label:empty { display: none; } .btn .label { position: relative; top: -1px; } .label-default { background-color: #777777; } .label-default[href]:hover, .label-default[href]:focus { background-color: #5e5e5e; } .label-primary { background-color: #337ab7; } .label-primary[href]:hover, .label-primary[href]:focus { background-color: #286090; } .label-success { background-color: #5cb85c; } .label-success[href]:hover, .label-success[href]:focus { background-color: #449d44; } .label-info { background-color: #5bc0de; } .label-info[href]:hover, .label-info[href]:focus { background-color: #31b0d5; } .label-warning { background-color: #f0ad4e; } .label-warning[href]:hover, .label-warning[href]:focus { background-color: #ec971f; } .label-danger { background-color: #d9534f; } .label-danger[href]:hover, .label-danger[href]:focus { background-color: #c9302c; } .badge { display: inline-block; min-width: 10px; padding: 3px 7px; font-size: 12px; font-weight: bold; color: #fff; line-height: 1; vertical-align: middle; white-space: nowrap; text-align: center; background-color: #777777; border-radius: 10px; } .badge:empty { display: none; } .btn .badge { position: relative; top: -1px; } .btn-xs .badge, .btn-group-xs > .btn .badge { top: 0; padding: 1px 5px; } a.badge:hover, a.badge:focus { color: #fff; text-decoration: none; cursor: pointer; } .list-group-item.active > .badge, .nav-pills > .active > a > .badge { color: #337ab7; background-color: #fff; } .list-group-item > .badge { float: right; } .list-group-item > .badge + .badge { margin-right: 5px; } .nav-pills > li > a > .badge { margin-left: 3px; } .jumbotron { padding-top: 30px; padding-bottom: 30px; margin-bottom: 30px; color: inherit; background-color: #eeeeee; } .jumbotron h1, .jumbotron .h1 { color: inherit; } .jumbotron p { margin-bottom: 15px; font-size: 20px; font-weight: 200; } .jumbotron > hr { border-top-color: #d5d5d5; } .container .jumbotron, .container-fluid .jumbotron { border-radius: 3px; padding-left: 0px; padding-right: 0px; } .jumbotron .container { max-width: 100%; } @media screen and (min-width: 768px) { .jumbotron { padding-top: 48px; padding-bottom: 48px; } .container .jumbotron, .container-fluid .jumbotron { padding-left: 60px; padding-right: 60px; } .jumbotron h1, .jumbotron .h1 { font-size: 59px; } } .thumbnail { display: block; padding: 4px; margin-bottom: 18px; line-height: 1.42857143; background-color: #fff; border: 1px solid #ddd; border-radius: 2px; -webkit-transition: border 0.2s ease-in-out; -o-transition: border 0.2s ease-in-out; transition: border 0.2s ease-in-out; } .thumbnail > img, .thumbnail a > img { margin-left: auto; margin-right: auto; } a.thumbnail:hover, a.thumbnail:focus, a.thumbnail.active { border-color: #337ab7; } .thumbnail .caption { padding: 9px; color: #000; } .alert { padding: 15px; margin-bottom: 18px; border: 1px solid transparent; border-radius: 2px; } .alert h4 { margin-top: 0; color: inherit; } .alert .alert-link { font-weight: bold; } .alert > p, .alert > ul { margin-bottom: 0; } .alert > p + p { margin-top: 5px; } .alert-dismissable, .alert-dismissible { padding-right: 35px; } .alert-dismissable .close, .alert-dismissible .close { position: relative; top: -2px; right: -21px; color: inherit; } .alert-success { background-color: #dff0d8; border-color: #d6e9c6; color: #3c763d; } .alert-success hr { border-top-color: #c9e2b3; } .alert-success .alert-link { color: #2b542c; } .alert-info { background-color: #d9edf7; border-color: #bce8f1; color: #31708f; } .alert-info hr { border-top-color: #a6e1ec; } .alert-info .alert-link { color: #245269; } .alert-warning { background-color: #fcf8e3; border-color: #faebcc; color: #8a6d3b; } .alert-warning hr { border-top-color: #f7e1b5; } .alert-warning .alert-link { color: #66512c; } .alert-danger { background-color: #f2dede; border-color: #ebccd1; color: #a94442; } .alert-danger hr { border-top-color: #e4b9c0; } .alert-danger .alert-link { color: #843534; } @-webkit-keyframes progress-bar-stripes { from { background-position: 40px 0; } to { background-position: 0 0; } } @keyframes progress-bar-stripes { from { background-position: 40px 0; } to { background-position: 0 0; } } .progress { overflow: hidden; height: 18px; margin-bottom: 18px; background-color: #f5f5f5; border-radius: 2px; -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); } .progress-bar { float: left; width: 0%; height: 100%; font-size: 12px; line-height: 18px; color: #fff; text-align: center; background-color: #337ab7; -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); -webkit-transition: width 0.6s ease; -o-transition: width 0.6s ease; transition: width 0.6s ease; } .progress-striped .progress-bar, .progress-bar-striped { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-size: 40px 40px; } .progress.active .progress-bar, .progress-bar.active { -webkit-animation: progress-bar-stripes 2s linear infinite; -o-animation: progress-bar-stripes 2s linear infinite; animation: progress-bar-stripes 2s linear infinite; } .progress-bar-success { background-color: #5cb85c; } .progress-striped .progress-bar-success { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-info { background-color: #5bc0de; } .progress-striped .progress-bar-info { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-warning { background-color: #f0ad4e; } .progress-striped .progress-bar-warning { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-danger { background-color: #d9534f; } .progress-striped .progress-bar-danger { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .media { margin-top: 15px; } .media:first-child { margin-top: 0; } .media, .media-body { zoom: 1; overflow: hidden; } .media-body { width: 10000px; } .media-object { display: block; } .media-object.img-thumbnail { max-width: none; } .media-right, .media > .pull-right { padding-left: 10px; } .media-left, .media > .pull-left { padding-right: 10px; } .media-left, .media-right, .media-body { display: table-cell; vertical-align: top; } .media-middle { vertical-align: middle; } .media-bottom { vertical-align: bottom; } .media-heading { margin-top: 0; margin-bottom: 5px; } .media-list { padding-left: 0; list-style: none; } .list-group { margin-bottom: 20px; padding-left: 0; } .list-group-item { position: relative; display: block; padding: 10px 15px; margin-bottom: -1px; background-color: #fff; border: 1px solid #ddd; } .list-group-item:first-child { border-top-right-radius: 2px; border-top-left-radius: 2px; } .list-group-item:last-child { margin-bottom: 0; border-bottom-right-radius: 2px; border-bottom-left-radius: 2px; } a.list-group-item, button.list-group-item { color: #555; } a.list-group-item .list-group-item-heading, button.list-group-item .list-group-item-heading { color: #333; } a.list-group-item:hover, button.list-group-item:hover, a.list-group-item:focus, button.list-group-item:focus { text-decoration: none; color: #555; background-color: #f5f5f5; } button.list-group-item { width: 100%; text-align: left; } .list-group-item.disabled, .list-group-item.disabled:hover, .list-group-item.disabled:focus { background-color: #eeeeee; color: #777777; cursor: not-allowed; } .list-group-item.disabled .list-group-item-heading, .list-group-item.disabled:hover .list-group-item-heading, .list-group-item.disabled:focus .list-group-item-heading { color: inherit; } .list-group-item.disabled .list-group-item-text, .list-group-item.disabled:hover .list-group-item-text, .list-group-item.disabled:focus .list-group-item-text { color: #777777; } .list-group-item.active, .list-group-item.active:hover, .list-group-item.active:focus { z-index: 2; color: #fff; background-color: #337ab7; border-color: #337ab7; } .list-group-item.active .list-group-item-heading, .list-group-item.active:hover .list-group-item-heading, .list-group-item.active:focus .list-group-item-heading, .list-group-item.active .list-group-item-heading > small, .list-group-item.active:hover .list-group-item-heading > small, .list-group-item.active:focus .list-group-item-heading > small, .list-group-item.active .list-group-item-heading > .small, .list-group-item.active:hover .list-group-item-heading > .small, .list-group-item.active:focus .list-group-item-heading > .small { color: inherit; } .list-group-item.active .list-group-item-text, .list-group-item.active:hover .list-group-item-text, .list-group-item.active:focus .list-group-item-text { color: #c7ddef; } .list-group-item-success { color: #3c763d; background-color: #dff0d8; } a.list-group-item-success, button.list-group-item-success { color: #3c763d; } a.list-group-item-success .list-group-item-heading, button.list-group-item-success .list-group-item-heading { color: inherit; } a.list-group-item-success:hover, button.list-group-item-success:hover, a.list-group-item-success:focus, button.list-group-item-success:focus { color: #3c763d; background-color: #d0e9c6; } a.list-group-item-success.active, button.list-group-item-success.active, a.list-group-item-success.active:hover, button.list-group-item-success.active:hover, a.list-group-item-success.active:focus, button.list-group-item-success.active:focus { color: #fff; background-color: #3c763d; border-color: #3c763d; } .list-group-item-info { color: #31708f; background-color: #d9edf7; } a.list-group-item-info, button.list-group-item-info { color: #31708f; } a.list-group-item-info .list-group-item-heading, button.list-group-item-info .list-group-item-heading { color: inherit; } a.list-group-item-info:hover, button.list-group-item-info:hover, a.list-group-item-info:focus, button.list-group-item-info:focus { color: #31708f; background-color: #c4e3f3; } a.list-group-item-info.active, button.list-group-item-info.active, a.list-group-item-info.active:hover, button.list-group-item-info.active:hover, a.list-group-item-info.active:focus, button.list-group-item-info.active:focus { color: #fff; background-color: #31708f; border-color: #31708f; } .list-group-item-warning { color: #8a6d3b; background-color: #fcf8e3; } a.list-group-item-warning, button.list-group-item-warning { color: #8a6d3b; } a.list-group-item-warning .list-group-item-heading, button.list-group-item-warning .list-group-item-heading { color: inherit; } a.list-group-item-warning:hover, button.list-group-item-warning:hover, a.list-group-item-warning:focus, button.list-group-item-warning:focus { color: #8a6d3b; background-color: #faf2cc; } a.list-group-item-warning.active, button.list-group-item-warning.active, a.list-group-item-warning.active:hover, button.list-group-item-warning.active:hover, a.list-group-item-warning.active:focus, button.list-group-item-warning.active:focus { color: #fff; background-color: #8a6d3b; border-color: #8a6d3b; } .list-group-item-danger { color: #a94442; background-color: #f2dede; } a.list-group-item-danger, button.list-group-item-danger { color: #a94442; } a.list-group-item-danger .list-group-item-heading, button.list-group-item-danger .list-group-item-heading { color: inherit; } a.list-group-item-danger:hover, button.list-group-item-danger:hover, a.list-group-item-danger:focus, button.list-group-item-danger:focus { color: #a94442; background-color: #ebcccc; } a.list-group-item-danger.active, button.list-group-item-danger.active, a.list-group-item-danger.active:hover, button.list-group-item-danger.active:hover, a.list-group-item-danger.active:focus, button.list-group-item-danger.active:focus { color: #fff; background-color: #a94442; border-color: #a94442; } .list-group-item-heading { margin-top: 0; margin-bottom: 5px; } .list-group-item-text { margin-bottom: 0; line-height: 1.3; } .panel { margin-bottom: 18px; background-color: #fff; border: 1px solid transparent; border-radius: 2px; -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); } .panel-body { padding: 15px; } .panel-heading { padding: 10px 15px; border-bottom: 1px solid transparent; border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel-heading > .dropdown .dropdown-toggle { color: inherit; } .panel-title { margin-top: 0; margin-bottom: 0; font-size: 15px; color: inherit; } .panel-title > a, .panel-title > small, .panel-title > .small, .panel-title > small > a, .panel-title > .small > a { color: inherit; } .panel-footer { padding: 10px 15px; background-color: #f5f5f5; border-top: 1px solid #ddd; border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .list-group, .panel > .panel-collapse > .list-group { margin-bottom: 0; } .panel > .list-group .list-group-item, .panel > .panel-collapse > .list-group .list-group-item { border-width: 1px 0; border-radius: 0; } .panel > .list-group:first-child .list-group-item:first-child, .panel > .panel-collapse > .list-group:first-child .list-group-item:first-child { border-top: 0; border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel > .list-group:last-child .list-group-item:last-child, .panel > .panel-collapse > .list-group:last-child .list-group-item:last-child { border-bottom: 0; border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child { border-top-right-radius: 0; border-top-left-radius: 0; } .panel-heading + .list-group .list-group-item:first-child { border-top-width: 0; } .list-group + .panel-footer { border-top-width: 0; } .panel > .table, .panel > .table-responsive > .table, .panel > .panel-collapse > .table { margin-bottom: 0; } .panel > .table caption, .panel > .table-responsive > .table caption, .panel > .panel-collapse > .table caption { padding-left: 15px; padding-right: 15px; } .panel > .table:first-child, .panel > .table-responsive:first-child > .table:first-child { border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child { border-top-left-radius: 1px; border-top-right-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child td:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child td:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child, .panel > .table:first-child > thead:first-child > tr:first-child th:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child th:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child { border-top-left-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child td:last-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child, .panel > .table:first-child > tbody:first-child > tr:first-child td:last-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child, .panel > .table:first-child > thead:first-child > tr:first-child th:last-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child, .panel > .table:first-child > tbody:first-child > tr:first-child th:last-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child { border-top-right-radius: 1px; } .panel > .table:last-child, .panel > .table-responsive:last-child > .table:last-child { border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child { border-bottom-left-radius: 1px; border-bottom-right-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child td:first-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child, .panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child, .panel > .table:last-child > tbody:last-child > tr:last-child th:first-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child, .panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child { border-bottom-left-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child td:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child, .panel > .table:last-child > tbody:last-child > tr:last-child th:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child { border-bottom-right-radius: 1px; } .panel > .panel-body + .table, .panel > .panel-body + .table-responsive, .panel > .table + .panel-body, .panel > .table-responsive + .panel-body { border-top: 1px solid #ddd; } .panel > .table > tbody:first-child > tr:first-child th, .panel > .table > tbody:first-child > tr:first-child td { border-top: 0; } .panel > .table-bordered, .panel > .table-responsive > .table-bordered { border: 0; } .panel > .table-bordered > thead > tr > th:first-child, .panel > .table-responsive > .table-bordered > thead > tr > th:first-child, .panel > .table-bordered > tbody > tr > th:first-child, .panel > .table-responsive > .table-bordered > tbody > tr > th:first-child, .panel > .table-bordered > tfoot > tr > th:first-child, .panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child, .panel > .table-bordered > thead > tr > td:first-child, .panel > .table-responsive > .table-bordered > thead > tr > td:first-child, .panel > .table-bordered > tbody > tr > td:first-child, .panel > .table-responsive > .table-bordered > tbody > tr > td:first-child, .panel > .table-bordered > tfoot > tr > td:first-child, .panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child { border-left: 0; } .panel > .table-bordered > thead > tr > th:last-child, .panel > .table-responsive > .table-bordered > thead > tr > th:last-child, .panel > .table-bordered > tbody > tr > th:last-child, .panel > .table-responsive > .table-bordered > tbody > tr > th:last-child, .panel > .table-bordered > tfoot > tr > th:last-child, .panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child, .panel > .table-bordered > thead > tr > td:last-child, .panel > .table-responsive > .table-bordered > thead > tr > td:last-child, .panel > .table-bordered > tbody > tr > td:last-child, .panel > .table-responsive > .table-bordered > tbody > tr > td:last-child, .panel > .table-bordered > tfoot > tr > td:last-child, .panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child { border-right: 0; } .panel > .table-bordered > thead > tr:first-child > td, .panel > .table-responsive > .table-bordered > thead > tr:first-child > td, .panel > .table-bordered > tbody > tr:first-child > td, .panel > .table-responsive > .table-bordered > tbody > tr:first-child > td, .panel > .table-bordered > thead > tr:first-child > th, .panel > .table-responsive > .table-bordered > thead > tr:first-child > th, .panel > .table-bordered > tbody > tr:first-child > th, .panel > .table-responsive > .table-bordered > tbody > tr:first-child > th { border-bottom: 0; } .panel > .table-bordered > tbody > tr:last-child > td, .panel > .table-responsive > .table-bordered > tbody > tr:last-child > td, .panel > .table-bordered > tfoot > tr:last-child > td, .panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td, .panel > .table-bordered > tbody > tr:last-child > th, .panel > .table-responsive > .table-bordered > tbody > tr:last-child > th, .panel > .table-bordered > tfoot > tr:last-child > th, .panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th { border-bottom: 0; } .panel > .table-responsive { border: 0; margin-bottom: 0; } .panel-group { margin-bottom: 18px; } .panel-group .panel { margin-bottom: 0; border-radius: 2px; } .panel-group .panel + .panel { margin-top: 5px; } .panel-group .panel-heading { border-bottom: 0; } .panel-group .panel-heading + .panel-collapse > .panel-body, .panel-group .panel-heading + .panel-collapse > .list-group { border-top: 1px solid #ddd; } .panel-group .panel-footer { border-top: 0; } .panel-group .panel-footer + .panel-collapse .panel-body { border-bottom: 1px solid #ddd; } .panel-default { border-color: #ddd; } .panel-default > .panel-heading { color: #333333; background-color: #f5f5f5; border-color: #ddd; } .panel-default > .panel-heading + .panel-collapse > .panel-body { border-top-color: #ddd; } .panel-default > .panel-heading .badge { color: #f5f5f5; background-color: #333333; } .panel-default > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #ddd; } .panel-primary { border-color: #337ab7; } .panel-primary > .panel-heading { color: #fff; background-color: #337ab7; border-color: #337ab7; } .panel-primary > .panel-heading + .panel-collapse > .panel-body { border-top-color: #337ab7; } .panel-primary > .panel-heading .badge { color: #337ab7; background-color: #fff; } .panel-primary > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #337ab7; } .panel-success { border-color: #d6e9c6; } .panel-success > .panel-heading { color: #3c763d; background-color: #dff0d8; border-color: #d6e9c6; } .panel-success > .panel-heading + .panel-collapse > .panel-body { border-top-color: #d6e9c6; } .panel-success > .panel-heading .badge { color: #dff0d8; background-color: #3c763d; } .panel-success > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #d6e9c6; } .panel-info { border-color: #bce8f1; } .panel-info > .panel-heading { color: #31708f; background-color: #d9edf7; border-color: #bce8f1; } .panel-info > .panel-heading + .panel-collapse > .panel-body { border-top-color: #bce8f1; } .panel-info > .panel-heading .badge { color: #d9edf7; background-color: #31708f; } .panel-info > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #bce8f1; } .panel-warning { border-color: #faebcc; } .panel-warning > .panel-heading { color: #8a6d3b; background-color: #fcf8e3; border-color: #faebcc; } .panel-warning > .panel-heading + .panel-collapse > .panel-body { border-top-color: #faebcc; } .panel-warning > .panel-heading .badge { color: #fcf8e3; background-color: #8a6d3b; } .panel-warning > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #faebcc; } .panel-danger { border-color: #ebccd1; } .panel-danger > .panel-heading { color: #a94442; background-color: #f2dede; border-color: #ebccd1; } .panel-danger > .panel-heading + .panel-collapse > .panel-body { border-top-color: #ebccd1; } .panel-danger > .panel-heading .badge { color: #f2dede; background-color: #a94442; } .panel-danger > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #ebccd1; } .embed-responsive { position: relative; display: block; height: 0; padding: 0; overflow: hidden; } .embed-responsive .embed-responsive-item, .embed-responsive iframe, .embed-responsive embed, .embed-responsive object, .embed-responsive video { position: absolute; top: 0; left: 0; bottom: 0; height: 100%; width: 100%; border: 0; } .embed-responsive-16by9 { padding-bottom: 56.25%; } .embed-responsive-4by3 { padding-bottom: 75%; } .well { min-height: 20px; padding: 19px; margin-bottom: 20px; background-color: #f5f5f5; border: 1px solid #e3e3e3; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); } .well blockquote { border-color: #ddd; border-color: rgba(0, 0, 0, 0.15); } .well-lg { padding: 24px; border-radius: 3px; } .well-sm { padding: 9px; border-radius: 1px; } .close { float: right; font-size: 19.5px; font-weight: bold; line-height: 1; color: #000; text-shadow: 0 1px 0 #fff; opacity: 0.2; filter: alpha(opacity=20); } .close:hover, .close:focus { color: #000; text-decoration: none; cursor: pointer; opacity: 0.5; filter: alpha(opacity=50); } button.close { padding: 0; cursor: pointer; background: transparent; border: 0; -webkit-appearance: none; } .modal-open { overflow: hidden; } .modal { display: none; overflow: hidden; position: fixed; top: 0; right: 0; bottom: 0; left: 0; z-index: 1050; -webkit-overflow-scrolling: touch; outline: 0; } .modal.fade .modal-dialog { -webkit-transform: translate(0, -25%); -ms-transform: translate(0, -25%); -o-transform: translate(0, -25%); transform: translate(0, -25%); -webkit-transition: -webkit-transform 0.3s ease-out; -moz-transition: -moz-transform 0.3s ease-out; -o-transition: -o-transform 0.3s ease-out; transition: transform 0.3s ease-out; } .modal.in .modal-dialog { -webkit-transform: translate(0, 0); -ms-transform: translate(0, 0); -o-transform: translate(0, 0); transform: translate(0, 0); } .modal-open .modal { overflow-x: hidden; overflow-y: auto; } .modal-dialog { position: relative; width: auto; margin: 10px; } .modal-content { position: relative; background-color: #fff; border: 1px solid #999; border: 1px solid rgba(0, 0, 0, 0.2); border-radius: 3px; -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); background-clip: padding-box; outline: 0; } .modal-backdrop { position: fixed; top: 0; right: 0; bottom: 0; left: 0; z-index: 1040; background-color: #000; } .modal-backdrop.fade { opacity: 0; filter: alpha(opacity=0); } .modal-backdrop.in { opacity: 0.5; filter: alpha(opacity=50); } .modal-header { padding: 15px; border-bottom: 1px solid #e5e5e5; } .modal-header .close { margin-top: -2px; } .modal-title { margin: 0; line-height: 1.42857143; } .modal-body { position: relative; padding: 15px; } .modal-footer { padding: 15px; text-align: right; border-top: 1px solid #e5e5e5; } .modal-footer .btn + .btn { margin-left: 5px; margin-bottom: 0; } .modal-footer .btn-group .btn + .btn { margin-left: -1px; } .modal-footer .btn-block + .btn-block { margin-left: 0; } .modal-scrollbar-measure { position: absolute; top: -9999px; width: 50px; height: 50px; overflow: scroll; } @media (min-width: 768px) { .modal-dialog { width: 600px; margin: 30px auto; } .modal-content { -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); } .modal-sm { width: 300px; } } @media (min-width: 992px) { .modal-lg { width: 900px; } } .tooltip { position: absolute; z-index: 1070; display: block; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-style: normal; font-weight: normal; letter-spacing: normal; line-break: auto; line-height: 1.42857143; text-align: left; text-align: start; text-decoration: none; text-shadow: none; text-transform: none; white-space: normal; word-break: normal; word-spacing: normal; word-wrap: normal; font-size: 12px; opacity: 0; filter: alpha(opacity=0); } .tooltip.in { opacity: 0.9; filter: alpha(opacity=90); } .tooltip.top { margin-top: -3px; padding: 5px 0; } .tooltip.right { margin-left: 3px; padding: 0 5px; } .tooltip.bottom { margin-top: 3px; padding: 5px 0; } .tooltip.left { margin-left: -3px; padding: 0 5px; } .tooltip-inner { max-width: 200px; padding: 3px 8px; color: #fff; text-align: center; background-color: #000; border-radius: 2px; } .tooltip-arrow { position: absolute; width: 0; height: 0; border-color: transparent; border-style: solid; } .tooltip.top .tooltip-arrow { bottom: 0; left: 50%; margin-left: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.top-left .tooltip-arrow { bottom: 0; right: 5px; margin-bottom: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.top-right .tooltip-arrow { bottom: 0; left: 5px; margin-bottom: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.right .tooltip-arrow { top: 50%; left: 0; margin-top: -5px; border-width: 5px 5px 5px 0; border-right-color: #000; } .tooltip.left .tooltip-arrow { top: 50%; right: 0; margin-top: -5px; border-width: 5px 0 5px 5px; border-left-color: #000; } .tooltip.bottom .tooltip-arrow { top: 0; left: 50%; margin-left: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .tooltip.bottom-left .tooltip-arrow { top: 0; right: 5px; margin-top: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .tooltip.bottom-right .tooltip-arrow { top: 0; left: 5px; margin-top: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .popover { position: absolute; top: 0; left: 0; z-index: 1060; display: none; max-width: 276px; padding: 1px; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-style: normal; font-weight: normal; letter-spacing: normal; line-break: auto; line-height: 1.42857143; text-align: left; text-align: start; text-decoration: none; text-shadow: none; text-transform: none; white-space: normal; word-break: normal; word-spacing: normal; word-wrap: normal; font-size: 13px; background-color: #fff; background-clip: padding-box; border: 1px solid #ccc; border: 1px solid rgba(0, 0, 0, 0.2); border-radius: 3px; -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); } .popover.top { margin-top: -10px; } .popover.right { margin-left: 10px; } .popover.bottom { margin-top: 10px; } .popover.left { margin-left: -10px; } .popover-title { margin: 0; padding: 8px 14px; font-size: 13px; background-color: #f7f7f7; border-bottom: 1px solid #ebebeb; border-radius: 2px 2px 0 0; } .popover-content { padding: 9px 14px; } .popover > .arrow, .popover > .arrow:after { position: absolute; display: block; width: 0; height: 0; border-color: transparent; border-style: solid; } .popover > .arrow { border-width: 11px; } .popover > .arrow:after { border-width: 10px; content: ""; } .popover.top > .arrow { left: 50%; margin-left: -11px; border-bottom-width: 0; border-top-color: #999999; border-top-color: rgba(0, 0, 0, 0.25); bottom: -11px; } .popover.top > .arrow:after { content: " "; bottom: 1px; margin-left: -10px; border-bottom-width: 0; border-top-color: #fff; } .popover.right > .arrow { top: 50%; left: -11px; margin-top: -11px; border-left-width: 0; border-right-color: #999999; border-right-color: rgba(0, 0, 0, 0.25); } .popover.right > .arrow:after { content: " "; left: 1px; bottom: -10px; border-left-width: 0; border-right-color: #fff; } .popover.bottom > .arrow { left: 50%; margin-left: -11px; border-top-width: 0; border-bottom-color: #999999; border-bottom-color: rgba(0, 0, 0, 0.25); top: -11px; } .popover.bottom > .arrow:after { content: " "; top: 1px; margin-left: -10px; border-top-width: 0; border-bottom-color: #fff; } .popover.left > .arrow { top: 50%; right: -11px; margin-top: -11px; border-right-width: 0; border-left-color: #999999; border-left-color: rgba(0, 0, 0, 0.25); } .popover.left > .arrow:after { content: " "; right: 1px; border-right-width: 0; border-left-color: #fff; bottom: -10px; } .carousel { position: relative; } .carousel-inner { position: relative; overflow: hidden; width: 100%; } .carousel-inner > .item { display: none; position: relative; -webkit-transition: 0.6s ease-in-out left; -o-transition: 0.6s ease-in-out left; transition: 0.6s ease-in-out left; } .carousel-inner > .item > img, .carousel-inner > .item > a > img { line-height: 1; } @media all and (transform-3d), (-webkit-transform-3d) { .carousel-inner > .item { -webkit-transition: -webkit-transform 0.6s ease-in-out; -moz-transition: -moz-transform 0.6s ease-in-out; -o-transition: -o-transform 0.6s ease-in-out; transition: transform 0.6s ease-in-out; -webkit-backface-visibility: hidden; -moz-backface-visibility: hidden; backface-visibility: hidden; -webkit-perspective: 1000px; -moz-perspective: 1000px; perspective: 1000px; } .carousel-inner > .item.next, .carousel-inner > .item.active.right { -webkit-transform: translate3d(100%, 0, 0); transform: translate3d(100%, 0, 0); left: 0; } .carousel-inner > .item.prev, .carousel-inner > .item.active.left { -webkit-transform: translate3d(-100%, 0, 0); transform: translate3d(-100%, 0, 0); left: 0; } .carousel-inner > .item.next.left, .carousel-inner > .item.prev.right, .carousel-inner > .item.active { -webkit-transform: translate3d(0, 0, 0); transform: translate3d(0, 0, 0); left: 0; } } .carousel-inner > .active, .carousel-inner > .next, .carousel-inner > .prev { display: block; } .carousel-inner > .active { left: 0; } .carousel-inner > .next, .carousel-inner > .prev { position: absolute; top: 0; width: 100%; } .carousel-inner > .next { left: 100%; } .carousel-inner > .prev { left: -100%; } .carousel-inner > .next.left, .carousel-inner > .prev.right { left: 0; } .carousel-inner > .active.left { left: -100%; } .carousel-inner > .active.right { left: 100%; } .carousel-control { position: absolute; top: 0; left: 0; bottom: 0; width: 15%; opacity: 0.5; filter: alpha(opacity=50); font-size: 20px; color: #fff; text-align: center; text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); background-color: rgba(0, 0, 0, 0); } .carousel-control.left { background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-repeat: repeat-x; filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1); } .carousel-control.right { left: auto; right: 0; background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-repeat: repeat-x; filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1); } .carousel-control:hover, .carousel-control:focus { outline: 0; color: #fff; text-decoration: none; opacity: 0.9; filter: alpha(opacity=90); } .carousel-control .icon-prev, .carousel-control .icon-next, .carousel-control .glyphicon-chevron-left, .carousel-control .glyphicon-chevron-right { position: absolute; top: 50%; margin-top: -10px; z-index: 5; display: inline-block; } .carousel-control .icon-prev, .carousel-control .glyphicon-chevron-left { left: 50%; margin-left: -10px; } .carousel-control .icon-next, .carousel-control .glyphicon-chevron-right { right: 50%; margin-right: -10px; } .carousel-control .icon-prev, .carousel-control .icon-next { width: 20px; height: 20px; line-height: 1; font-family: serif; } .carousel-control .icon-prev:before { content: '\2039'; } .carousel-control .icon-next:before { content: '\203a'; } .carousel-indicators { position: absolute; bottom: 10px; left: 50%; z-index: 15; width: 60%; margin-left: -30%; padding-left: 0; list-style: none; text-align: center; } .carousel-indicators li { display: inline-block; width: 10px; height: 10px; margin: 1px; text-indent: -999px; border: 1px solid #fff; border-radius: 10px; cursor: pointer; background-color: #000 \9; background-color: rgba(0, 0, 0, 0); } .carousel-indicators .active { margin: 0; width: 12px; height: 12px; background-color: #fff; } .carousel-caption { position: absolute; left: 15%; right: 15%; bottom: 20px; z-index: 10; padding-top: 20px; padding-bottom: 20px; color: #fff; text-align: center; text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); } .carousel-caption .btn { text-shadow: none; } @media screen and (min-width: 768px) { .carousel-control .glyphicon-chevron-left, .carousel-control .glyphicon-chevron-right, .carousel-control .icon-prev, .carousel-control .icon-next { width: 30px; height: 30px; margin-top: -10px; font-size: 30px; } .carousel-control .glyphicon-chevron-left, .carousel-control .icon-prev { margin-left: -10px; } .carousel-control .glyphicon-chevron-right, .carousel-control .icon-next { margin-right: -10px; } .carousel-caption { left: 20%; right: 20%; padding-bottom: 30px; } .carousel-indicators { bottom: 20px; } } .clearfix:before, .clearfix:after, .dl-horizontal dd:before, .dl-horizontal dd:after, .container:before, .container:after, .container-fluid:before, .container-fluid:after, .row:before, .row:after, .form-horizontal .form-group:before, .form-horizontal .form-group:after, .btn-toolbar:before, .btn-toolbar:after, .btn-group-vertical > .btn-group:before, .btn-group-vertical > .btn-group:after, .nav:before, .nav:after, .navbar:before, .navbar:after, .navbar-header:before, .navbar-header:after, .navbar-collapse:before, .navbar-collapse:after, .pager:before, .pager:after, .panel-body:before, .panel-body:after, .modal-header:before, .modal-header:after, .modal-footer:before, .modal-footer:after, .item\_buttons:before, .item\_buttons:after { content: " "; display: table; } .clearfix:after, .dl-horizontal dd:after, .container:after, .container-fluid:after, .row:after, .form-horizontal .form-group:after, .btn-toolbar:after, .btn-group-vertical > .btn-group:after, .nav:after, .navbar:after, .navbar-header:after, .navbar-collapse:after, .pager:after, .panel-body:after, .modal-header:after, .modal-footer:after, .item\_buttons:after { clear: both; } .center-block { display: block; margin-left: auto; margin-right: auto; } .pull-right { float: right !important; } .pull-left { float: left !important; } .hide { display: none !important; } .show { display: block !important; } .invisible { visibility: hidden; } .text-hide { font: 0/0 a; color: transparent; text-shadow: none; background-color: transparent; border: 0; } .hidden { display: none !important; } .affix { position: fixed; } @-ms-viewport { width: device-width; } .visible-xs, .visible-sm, .visible-md, .visible-lg { display: none !important; } .visible-xs-block, .visible-xs-inline, .visible-xs-inline-block, .visible-sm-block, .visible-sm-inline, .visible-sm-inline-block, .visible-md-block, .visible-md-inline, .visible-md-inline-block, .visible-lg-block, .visible-lg-inline, .visible-lg-inline-block { display: none !important; } @media (max-width: 767px) { .visible-xs { display: block !important; } table.visible-xs { display: table !important; } tr.visible-xs { display: table-row !important; } th.visible-xs, td.visible-xs { display: table-cell !important; } } @media (max-width: 767px) { .visible-xs-block { display: block !important; } } @media (max-width: 767px) { .visible-xs-inline { display: inline !important; } } @media (max-width: 767px) { .visible-xs-inline-block { display: inline-block !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm { display: block !important; } table.visible-sm { display: table !important; } tr.visible-sm { display: table-row !important; } th.visible-sm, td.visible-sm { display: table-cell !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-block { display: block !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-inline { display: inline !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-inline-block { display: inline-block !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md { display: block !important; } table.visible-md { display: table !important; } tr.visible-md { display: table-row !important; } th.visible-md, td.visible-md { display: table-cell !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-block { display: block !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-inline { display: inline !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-inline-block { display: inline-block !important; } } @media (min-width: 1200px) { .visible-lg { display: block !important; } table.visible-lg { display: table !important; } tr.visible-lg { display: table-row !important; } th.visible-lg, td.visible-lg { display: table-cell !important; } } @media (min-width: 1200px) { .visible-lg-block { display: block !important; } } @media (min-width: 1200px) { .visible-lg-inline { display: inline !important; } } @media (min-width: 1200px) { .visible-lg-inline-block { display: inline-block !important; } } @media (max-width: 767px) { .hidden-xs { display: none !important; } } @media (min-width: 768px) and (max-width: 991px) { .hidden-sm { display: none !important; } } @media (min-width: 992px) and (max-width: 1199px) { .hidden-md { display: none !important; } } @media (min-width: 1200px) { .hidden-lg { display: none !important; } } .visible-print { display: none !important; } @media print { .visible-print { display: block !important; } table.visible-print { display: table !important; } tr.visible-print { display: table-row !important; } th.visible-print, td.visible-print { display: table-cell !important; } } .visible-print-block { display: none !important; } @media print { .visible-print-block { display: block !important; } } .visible-print-inline { display: none !important; } @media print { .visible-print-inline { display: inline !important; } } .visible-print-inline-block { display: none !important; } @media print { .visible-print-inline-block { display: inline-block !important; } } @media print { .hidden-print { display: none !important; } } /\*! \* \* Font Awesome \* \*/ /\*! \* Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome \* License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License) \*/ /\* FONT PATH \* -------------------------- \*/ @font-face { font-family: 'FontAwesome'; src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0'); src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg'); font-weight: normal; font-style: normal; } .fa { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; } /\* makes the font 33% larger relative to the icon container \*/ .fa-lg { font-size: 1.33333333em; line-height: 0.75em; vertical-align: -15%; } .fa-2x { font-size: 2em; } .fa-3x { font-size: 3em; } .fa-4x { font-size: 4em; } .fa-5x { font-size: 5em; } .fa-fw { width: 1.28571429em; text-align: center; } .fa-ul { padding-left: 0; margin-left: 2.14285714em; list-style-type: none; } .fa-ul > li { position: relative; } .fa-li { position: absolute; left: -2.14285714em; width: 2.14285714em; top: 0.14285714em; text-align: center; } .fa-li.fa-lg { left: -1.85714286em; } .fa-border { padding: .2em .25em .15em; border: solid 0.08em #eee; border-radius: .1em; } .fa-pull-left { float: left; } .fa-pull-right { float: right; } .fa.fa-pull-left { margin-right: .3em; } .fa.fa-pull-right { margin-left: .3em; } /\* Deprecated as of 4.4.0 \*/ .pull-right { float: right; } .pull-left { float: left; } .fa.pull-left { margin-right: .3em; } .fa.pull-right { margin-left: .3em; } .fa-spin { -webkit-animation: fa-spin 2s infinite linear; animation: fa-spin 2s infinite linear; } .fa-pulse { -webkit-animation: fa-spin 1s infinite steps(8); animation: fa-spin 1s infinite steps(8); } @-webkit-keyframes fa-spin { 0% { -webkit-transform: rotate(0deg); transform: rotate(0deg); } 100% { -webkit-transform: rotate(359deg); transform: rotate(359deg); } } @keyframes fa-spin { 0% { -webkit-transform: rotate(0deg); transform: rotate(0deg); } 100% { -webkit-transform: rotate(359deg); transform: rotate(359deg); } } .fa-rotate-90 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)"; -webkit-transform: rotate(90deg); -ms-transform: rotate(90deg); transform: rotate(90deg); } .fa-rotate-180 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)"; -webkit-transform: rotate(180deg); -ms-transform: rotate(180deg); transform: rotate(180deg); } .fa-rotate-270 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)"; -webkit-transform: rotate(270deg); -ms-transform: rotate(270deg); transform: rotate(270deg); } .fa-flip-horizontal { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)"; -webkit-transform: scale(-1, 1); -ms-transform: scale(-1, 1); transform: scale(-1, 1); } .fa-flip-vertical { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)"; -webkit-transform: scale(1, -1); -ms-transform: scale(1, -1); transform: scale(1, -1); } :root .fa-rotate-90, :root .fa-rotate-180, :root .fa-rotate-270, :root .fa-flip-horizontal, :root .fa-flip-vertical { filter: none; } .fa-stack { position: relative; display: inline-block; width: 2em; height: 2em; line-height: 2em; vertical-align: middle; } .fa-stack-1x, .fa-stack-2x { position: absolute; left: 0; width: 100%; text-align: center; } .fa-stack-1x { line-height: inherit; } .fa-stack-2x { font-size: 2em; } .fa-inverse { color: #fff; } /\* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen readers do not read off random characters that represent icons \*/ .fa-glass:before { content: "\f000"; } .fa-music:before { content: "\f001"; } .fa-search:before { content: "\f002"; } .fa-envelope-o:before { content: "\f003"; } .fa-heart:before { content: "\f004"; } .fa-star:before { content: "\f005"; } .fa-star-o:before { content: "\f006"; } .fa-user:before { content: "\f007"; } .fa-film:before { content: "\f008"; } .fa-th-large:before { content: "\f009"; } .fa-th:before { content: "\f00a"; } .fa-th-list:before { content: "\f00b"; } .fa-check:before { content: "\f00c"; } .fa-remove:before, .fa-close:before, .fa-times:before { content: "\f00d"; } .fa-search-plus:before { content: "\f00e"; } .fa-search-minus:before { content: "\f010"; } .fa-power-off:before { content: "\f011"; } .fa-signal:before { content: "\f012"; } .fa-gear:before, .fa-cog:before { content: "\f013"; } .fa-trash-o:before { content: "\f014"; } .fa-home:before { content: "\f015"; } .fa-file-o:before { content: "\f016"; } .fa-clock-o:before { content: "\f017"; } .fa-road:before { content: "\f018"; } .fa-download:before { content: "\f019"; } .fa-arrow-circle-o-down:before { content: "\f01a"; } .fa-arrow-circle-o-up:before { content: "\f01b"; } .fa-inbox:before { content: "\f01c"; } .fa-play-circle-o:before { content: "\f01d"; } .fa-rotate-right:before, .fa-repeat:before { content: "\f01e"; } .fa-refresh:before { content: "\f021"; } .fa-list-alt:before { content: "\f022"; } .fa-lock:before { content: "\f023"; } .fa-flag:before { content: "\f024"; } .fa-headphones:before { content: "\f025"; } .fa-volume-off:before { content: "\f026"; } .fa-volume-down:before { content: "\f027"; } .fa-volume-up:before { content: "\f028"; } .fa-qrcode:before { content: "\f029"; } .fa-barcode:before { content: "\f02a"; } .fa-tag:before { content: "\f02b"; } .fa-tags:before { content: "\f02c"; } .fa-book:before { content: "\f02d"; } .fa-bookmark:before { content: "\f02e"; } .fa-print:before { content: "\f02f"; } .fa-camera:before { content: "\f030"; } .fa-font:before { content: "\f031"; } .fa-bold:before { content: "\f032"; } .fa-italic:before { content: "\f033"; } .fa-text-height:before { content: "\f034"; } .fa-text-width:before { content: "\f035"; } .fa-align-left:before { content: "\f036"; } .fa-align-center:before { content: "\f037"; } .fa-align-right:before { content: "\f038"; } .fa-align-justify:before { content: "\f039"; } .fa-list:before { content: "\f03a"; } .fa-dedent:before, .fa-outdent:before { content: "\f03b"; } .fa-indent:before { content: "\f03c"; } .fa-video-camera:before { content: "\f03d"; } .fa-photo:before, .fa-image:before, .fa-picture-o:before { content: "\f03e"; } .fa-pencil:before { content: "\f040"; } .fa-map-marker:before { content: "\f041"; } .fa-adjust:before { content: "\f042"; } .fa-tint:before { content: "\f043"; } .fa-edit:before, .fa-pencil-square-o:before { content: "\f044"; } .fa-share-square-o:before { content: "\f045"; } .fa-check-square-o:before { content: "\f046"; } .fa-arrows:before { content: "\f047"; } .fa-step-backward:before { content: "\f048"; } .fa-fast-backward:before { content: "\f049"; } .fa-backward:before { content: "\f04a"; } .fa-play:before { content: "\f04b"; } .fa-pause:before { content: "\f04c"; } .fa-stop:before { content: "\f04d"; } .fa-forward:before { content: "\f04e"; } .fa-fast-forward:before { content: "\f050"; } .fa-step-forward:before { content: "\f051"; } .fa-eject:before { content: "\f052"; } .fa-chevron-left:before { content: "\f053"; } .fa-chevron-right:before { content: "\f054"; } .fa-plus-circle:before { content: "\f055"; } .fa-minus-circle:before { content: "\f056"; } .fa-times-circle:before { content: "\f057"; } .fa-check-circle:before { content: "\f058"; } .fa-question-circle:before { content: "\f059"; } .fa-info-circle:before { content: "\f05a"; } .fa-crosshairs:before { content: "\f05b"; } .fa-times-circle-o:before { content: "\f05c"; } .fa-check-circle-o:before { content: "\f05d"; } .fa-ban:before { content: "\f05e"; } .fa-arrow-left:before { content: "\f060"; } .fa-arrow-right:before { content: "\f061"; } .fa-arrow-up:before { content: "\f062"; } .fa-arrow-down:before { content: "\f063"; } .fa-mail-forward:before, .fa-share:before { content: "\f064"; } .fa-expand:before { content: "\f065"; } .fa-compress:before { content: "\f066"; } .fa-plus:before { content: "\f067"; } .fa-minus:before { content: "\f068"; } .fa-asterisk:before { content: "\f069"; } .fa-exclamation-circle:before { content: "\f06a"; } .fa-gift:before { content: "\f06b"; } .fa-leaf:before { content: "\f06c"; } .fa-fire:before { content: "\f06d"; } .fa-eye:before { content: "\f06e"; } .fa-eye-slash:before { content: "\f070"; } .fa-warning:before, .fa-exclamation-triangle:before { content: "\f071"; } .fa-plane:before { content: "\f072"; } .fa-calendar:before { content: "\f073"; } .fa-random:before { content: "\f074"; } .fa-comment:before { content: "\f075"; } .fa-magnet:before { content: "\f076"; } .fa-chevron-up:before { content: "\f077"; } .fa-chevron-down:before { content: "\f078"; } .fa-retweet:before { content: "\f079"; } .fa-shopping-cart:before { content: "\f07a"; } .fa-folder:before { content: "\f07b"; } .fa-folder-open:before { content: "\f07c"; } .fa-arrows-v:before { content: "\f07d"; } .fa-arrows-h:before { content: "\f07e"; } .fa-bar-chart-o:before, .fa-bar-chart:before { content: "\f080"; } .fa-twitter-square:before { content: "\f081"; } .fa-facebook-square:before { content: "\f082"; } .fa-camera-retro:before { content: "\f083"; } .fa-key:before { content: "\f084"; } .fa-gears:before, .fa-cogs:before { content: "\f085"; } .fa-comments:before { content: "\f086"; } .fa-thumbs-o-up:before { content: "\f087"; } .fa-thumbs-o-down:before { content: "\f088"; } .fa-star-half:before { content: "\f089"; } .fa-heart-o:before { content: "\f08a"; } .fa-sign-out:before { content: "\f08b"; } .fa-linkedin-square:before { content: "\f08c"; } .fa-thumb-tack:before { content: "\f08d"; } .fa-external-link:before { content: "\f08e"; } .fa-sign-in:before { content: "\f090"; } .fa-trophy:before { content: "\f091"; } .fa-github-square:before { content: "\f092"; } .fa-upload:before { content: "\f093"; } .fa-lemon-o:before { content: "\f094"; } .fa-phone:before { content: "\f095"; } .fa-square-o:before { content: "\f096"; } .fa-bookmark-o:before { content: "\f097"; } .fa-phone-square:before { content: "\f098"; } .fa-twitter:before { content: "\f099"; } .fa-facebook-f:before, .fa-facebook:before { content: "\f09a"; } .fa-github:before { content: "\f09b"; } .fa-unlock:before { content: "\f09c"; } .fa-credit-card:before { content: "\f09d"; } .fa-feed:before, .fa-rss:before { content: "\f09e"; } .fa-hdd-o:before { content: "\f0a0"; } .fa-bullhorn:before { content: "\f0a1"; } .fa-bell:before { content: "\f0f3"; } .fa-certificate:before { content: "\f0a3"; } .fa-hand-o-right:before { content: "\f0a4"; } .fa-hand-o-left:before { content: "\f0a5"; } .fa-hand-o-up:before { content: "\f0a6"; } .fa-hand-o-down:before { content: "\f0a7"; } .fa-arrow-circle-left:before { content: "\f0a8"; } .fa-arrow-circle-right:before { content: "\f0a9"; } .fa-arrow-circle-up:before { content: "\f0aa"; } .fa-arrow-circle-down:before { content: "\f0ab"; } .fa-globe:before { content: "\f0ac"; } .fa-wrench:before { content: "\f0ad"; } .fa-tasks:before { content: "\f0ae"; } .fa-filter:before { content: "\f0b0"; } .fa-briefcase:before { content: "\f0b1"; } .fa-arrows-alt:before { content: "\f0b2"; } .fa-group:before, .fa-users:before { content: "\f0c0"; } .fa-chain:before, .fa-link:before { content: "\f0c1"; } .fa-cloud:before { content: "\f0c2"; } .fa-flask:before { content: "\f0c3"; } .fa-cut:before, .fa-scissors:before { content: "\f0c4"; } .fa-copy:before, .fa-files-o:before { content: "\f0c5"; } .fa-paperclip:before { content: "\f0c6"; } .fa-save:before, .fa-floppy-o:before { content: "\f0c7"; } .fa-square:before { content: "\f0c8"; } .fa-navicon:before, .fa-reorder:before, .fa-bars:before { content: "\f0c9"; } .fa-list-ul:before { content: "\f0ca"; } .fa-list-ol:before { content: "\f0cb"; } .fa-strikethrough:before { content: "\f0cc"; } .fa-underline:before { content: "\f0cd"; } .fa-table:before { content: "\f0ce"; } .fa-magic:before { content: "\f0d0"; } .fa-truck:before { content: "\f0d1"; } .fa-pinterest:before { content: "\f0d2"; } .fa-pinterest-square:before { content: "\f0d3"; } .fa-google-plus-square:before { content: "\f0d4"; } .fa-google-plus:before { content: "\f0d5"; } .fa-money:before { content: "\f0d6"; } .fa-caret-down:before { content: "\f0d7"; } .fa-caret-up:before { content: "\f0d8"; } .fa-caret-left:before { content: "\f0d9"; } .fa-caret-right:before { content: "\f0da"; } .fa-columns:before { content: "\f0db"; } .fa-unsorted:before, .fa-sort:before { content: "\f0dc"; } .fa-sort-down:before, .fa-sort-desc:before { content: "\f0dd"; } .fa-sort-up:before, .fa-sort-asc:before { content: "\f0de"; } .fa-envelope:before { content: "\f0e0"; } .fa-linkedin:before { content: "\f0e1"; } .fa-rotate-left:before, .fa-undo:before { content: "\f0e2"; } .fa-legal:before, .fa-gavel:before { content: "\f0e3"; } .fa-dashboard:before, .fa-tachometer:before { content: "\f0e4"; } .fa-comment-o:before { content: "\f0e5"; } .fa-comments-o:before { content: "\f0e6"; } .fa-flash:before, .fa-bolt:before { content: "\f0e7"; } .fa-sitemap:before { content: "\f0e8"; } .fa-umbrella:before { content: "\f0e9"; } .fa-paste:before, .fa-clipboard:before { content: "\f0ea"; } .fa-lightbulb-o:before { content: "\f0eb"; } .fa-exchange:before { content: "\f0ec"; } .fa-cloud-download:before { content: "\f0ed"; } .fa-cloud-upload:before { content: "\f0ee"; } .fa-user-md:before { content: "\f0f0"; } .fa-stethoscope:before { content: "\f0f1"; } .fa-suitcase:before { content: "\f0f2"; } .fa-bell-o:before { content: "\f0a2"; } .fa-coffee:before { content: "\f0f4"; } .fa-cutlery:before { content: "\f0f5"; } .fa-file-text-o:before { content: "\f0f6"; } .fa-building-o:before { content: "\f0f7"; } .fa-hospital-o:before { content: "\f0f8"; } .fa-ambulance:before { content: "\f0f9"; } .fa-medkit:before { content: "\f0fa"; } .fa-fighter-jet:before { content: "\f0fb"; } .fa-beer:before { content: "\f0fc"; } .fa-h-square:before { content: "\f0fd"; } .fa-plus-square:before { content: "\f0fe"; } .fa-angle-double-left:before { content: "\f100"; } .fa-angle-double-right:before { content: "\f101"; } .fa-angle-double-up:before { content: "\f102"; } .fa-angle-double-down:before { content: "\f103"; } .fa-angle-left:before { content: "\f104"; } .fa-angle-right:before { content: "\f105"; } .fa-angle-up:before { content: "\f106"; } .fa-angle-down:before { content: "\f107"; } .fa-desktop:before { content: "\f108"; } .fa-laptop:before { content: "\f109"; } .fa-tablet:before { content: "\f10a"; } .fa-mobile-phone:before, .fa-mobile:before { content: "\f10b"; } .fa-circle-o:before { content: "\f10c"; } .fa-quote-left:before { content: "\f10d"; } .fa-quote-right:before { content: "\f10e"; } .fa-spinner:before { content: "\f110"; } .fa-circle:before { content: "\f111"; } .fa-mail-reply:before, .fa-reply:before { content: "\f112"; } .fa-github-alt:before { content: "\f113"; } .fa-folder-o:before { content: "\f114"; } .fa-folder-open-o:before { content: "\f115"; } .fa-smile-o:before { content: "\f118"; } .fa-frown-o:before { content: "\f119"; } .fa-meh-o:before { content: "\f11a"; } .fa-gamepad:before { content: "\f11b"; } .fa-keyboard-o:before { content: "\f11c"; } .fa-flag-o:before { content: "\f11d"; } .fa-flag-checkered:before { content: "\f11e"; } .fa-terminal:before { content: "\f120"; } .fa-code:before { content: "\f121"; } .fa-mail-reply-all:before, .fa-reply-all:before { content: "\f122"; } .fa-star-half-empty:before, .fa-star-half-full:before, .fa-star-half-o:before { content: "\f123"; } .fa-location-arrow:before { content: "\f124"; } .fa-crop:before { content: "\f125"; } .fa-code-fork:before { content: "\f126"; } .fa-unlink:before, .fa-chain-broken:before { content: "\f127"; } .fa-question:before { content: "\f128"; } .fa-info:before { content: "\f129"; } .fa-exclamation:before { content: "\f12a"; } .fa-superscript:before { content: "\f12b"; } .fa-subscript:before { content: "\f12c"; } .fa-eraser:before { content: "\f12d"; } .fa-puzzle-piece:before { content: "\f12e"; } .fa-microphone:before { content: "\f130"; } .fa-microphone-slash:before { content: "\f131"; } .fa-shield:before { content: "\f132"; } .fa-calendar-o:before { content: "\f133"; } .fa-fire-extinguisher:before { content: "\f134"; } .fa-rocket:before { content: "\f135"; } .fa-maxcdn:before { content: "\f136"; } .fa-chevron-circle-left:before { content: "\f137"; } .fa-chevron-circle-right:before { content: "\f138"; } .fa-chevron-circle-up:before { content: "\f139"; } .fa-chevron-circle-down:before { content: "\f13a"; } .fa-html5:before { content: "\f13b"; } .fa-css3:before { content: "\f13c"; } .fa-anchor:before { content: "\f13d"; } .fa-unlock-alt:before { content: "\f13e"; } .fa-bullseye:before { content: "\f140"; } .fa-ellipsis-h:before { content: "\f141"; } .fa-ellipsis-v:before { content: "\f142"; } .fa-rss-square:before { content: "\f143"; } .fa-play-circle:before { content: "\f144"; } .fa-ticket:before { content: "\f145"; } .fa-minus-square:before { content: "\f146"; } .fa-minus-square-o:before { content: "\f147"; } .fa-level-up:before { content: "\f148"; } .fa-level-down:before { content: "\f149"; } .fa-check-square:before { content: "\f14a"; } .fa-pencil-square:before { content: "\f14b"; } .fa-external-link-square:before { content: "\f14c"; } .fa-share-square:before { content: "\f14d"; } .fa-compass:before { content: "\f14e"; } .fa-toggle-down:before, .fa-caret-square-o-down:before { content: "\f150"; } .fa-toggle-up:before, .fa-caret-square-o-up:before { content: "\f151"; } .fa-toggle-right:before, .fa-caret-square-o-right:before { content: "\f152"; } .fa-euro:before, .fa-eur:before { content: "\f153"; } .fa-gbp:before { content: "\f154"; } .fa-dollar:before, .fa-usd:before { content: "\f155"; } .fa-rupee:before, .fa-inr:before { content: "\f156"; } .fa-cny:before, .fa-rmb:before, .fa-yen:before, .fa-jpy:before { content: "\f157"; } .fa-ruble:before, .fa-rouble:before, .fa-rub:before { content: "\f158"; } .fa-won:before, .fa-krw:before { content: "\f159"; } .fa-bitcoin:before, .fa-btc:before { content: "\f15a"; } .fa-file:before { content: "\f15b"; } .fa-file-text:before { content: "\f15c"; } .fa-sort-alpha-asc:before { content: "\f15d"; } .fa-sort-alpha-desc:before { content: "\f15e"; } .fa-sort-amount-asc:before { content: "\f160"; } .fa-sort-amount-desc:before { content: "\f161"; } .fa-sort-numeric-asc:before { content: "\f162"; } .fa-sort-numeric-desc:before { content: "\f163"; } .fa-thumbs-up:before { content: "\f164"; } .fa-thumbs-down:before { content: "\f165"; } .fa-youtube-square:before { content: "\f166"; } .fa-youtube:before { content: "\f167"; } .fa-xing:before { content: "\f168"; } .fa-xing-square:before { content: "\f169"; } .fa-youtube-play:before { content: "\f16a"; } .fa-dropbox:before { content: "\f16b"; } .fa-stack-overflow:before { content: "\f16c"; } .fa-instagram:before { content: "\f16d"; } .fa-flickr:before { content: "\f16e"; } .fa-adn:before { content: "\f170"; } .fa-bitbucket:before { content: "\f171"; } .fa-bitbucket-square:before { content: "\f172"; } .fa-tumblr:before { content: "\f173"; } .fa-tumblr-square:before { content: "\f174"; } .fa-long-arrow-down:before { content: "\f175"; } .fa-long-arrow-up:before { content: "\f176"; } .fa-long-arrow-left:before { content: "\f177"; } .fa-long-arrow-right:before { content: "\f178"; } .fa-apple:before { content: "\f179"; } .fa-windows:before { content: "\f17a"; } .fa-android:before { content: "\f17b"; } .fa-linux:before { content: "\f17c"; } .fa-dribbble:before { content: "\f17d"; } .fa-skype:before { content: "\f17e"; } .fa-foursquare:before { content: "\f180"; } .fa-trello:before { content: "\f181"; } .fa-female:before { content: "\f182"; } .fa-male:before { content: "\f183"; } .fa-gittip:before, .fa-gratipay:before { content: "\f184"; } .fa-sun-o:before { content: "\f185"; } .fa-moon-o:before { content: "\f186"; } .fa-archive:before { content: "\f187"; } .fa-bug:before { content: "\f188"; } .fa-vk:before { content: "\f189"; } .fa-weibo:before { content: "\f18a"; } .fa-renren:before { content: "\f18b"; } .fa-pagelines:before { content: "\f18c"; } .fa-stack-exchange:before { content: "\f18d"; } .fa-arrow-circle-o-right:before { content: "\f18e"; } .fa-arrow-circle-o-left:before { content: "\f190"; } .fa-toggle-left:before, .fa-caret-square-o-left:before { content: "\f191"; } .fa-dot-circle-o:before { content: "\f192"; } .fa-wheelchair:before { content: "\f193"; } .fa-vimeo-square:before { content: "\f194"; } .fa-turkish-lira:before, .fa-try:before { content: "\f195"; } .fa-plus-square-o:before { content: "\f196"; } .fa-space-shuttle:before { content: "\f197"; } .fa-slack:before { content: "\f198"; } .fa-envelope-square:before { content: "\f199"; } .fa-wordpress:before { content: "\f19a"; } .fa-openid:before { content: "\f19b"; } .fa-institution:before, .fa-bank:before, .fa-university:before { content: "\f19c"; } .fa-mortar-board:before, .fa-graduation-cap:before { content: "\f19d"; } .fa-yahoo:before { content: "\f19e"; } .fa-google:before { content: "\f1a0"; } .fa-reddit:before { content: "\f1a1"; } .fa-reddit-square:before { content: "\f1a2"; } .fa-stumbleupon-circle:before { content: "\f1a3"; } .fa-stumbleupon:before { content: "\f1a4"; } .fa-delicious:before { content: "\f1a5"; } .fa-digg:before { content: "\f1a6"; } .fa-pied-piper-pp:before { content: "\f1a7"; } .fa-pied-piper-alt:before { content: "\f1a8"; } .fa-drupal:before { content: "\f1a9"; } .fa-joomla:before { content: "\f1aa"; } .fa-language:before { content: "\f1ab"; } .fa-fax:before { content: "\f1ac"; } .fa-building:before { content: "\f1ad"; } .fa-child:before { content: "\f1ae"; } .fa-paw:before { content: "\f1b0"; } .fa-spoon:before { content: "\f1b1"; } .fa-cube:before { content: "\f1b2"; } .fa-cubes:before { content: "\f1b3"; } .fa-behance:before { content: "\f1b4"; } .fa-behance-square:before { content: "\f1b5"; } .fa-steam:before { content: "\f1b6"; } .fa-steam-square:before { content: "\f1b7"; } .fa-recycle:before { content: "\f1b8"; } .fa-automobile:before, .fa-car:before { content: "\f1b9"; } .fa-cab:before, .fa-taxi:before { content: "\f1ba"; } .fa-tree:before { content: "\f1bb"; } .fa-spotify:before { content: "\f1bc"; } .fa-deviantart:before { content: "\f1bd"; } .fa-soundcloud:before { content: "\f1be"; } .fa-database:before { content: "\f1c0"; } .fa-file-pdf-o:before { content: "\f1c1"; } .fa-file-word-o:before { content: "\f1c2"; } .fa-file-excel-o:before { content: "\f1c3"; } .fa-file-powerpoint-o:before { content: "\f1c4"; } .fa-file-photo-o:before, .fa-file-picture-o:before, .fa-file-image-o:before { content: "\f1c5"; } .fa-file-zip-o:before, .fa-file-archive-o:before { content: "\f1c6"; } .fa-file-sound-o:before, .fa-file-audio-o:before { content: "\f1c7"; } .fa-file-movie-o:before, .fa-file-video-o:before { content: "\f1c8"; } .fa-file-code-o:before { content: "\f1c9"; } .fa-vine:before { content: "\f1ca"; } .fa-codepen:before { content: "\f1cb"; } .fa-jsfiddle:before { content: "\f1cc"; } .fa-life-bouy:before, .fa-life-buoy:before, .fa-life-saver:before, .fa-support:before, .fa-life-ring:before { content: "\f1cd"; } .fa-circle-o-notch:before { content: "\f1ce"; } .fa-ra:before, .fa-resistance:before, .fa-rebel:before { content: "\f1d0"; } .fa-ge:before, .fa-empire:before { content: "\f1d1"; } .fa-git-square:before { content: "\f1d2"; } .fa-git:before { content: "\f1d3"; } .fa-y-combinator-square:before, .fa-yc-square:before, .fa-hacker-news:before { content: "\f1d4"; } .fa-tencent-weibo:before { content: "\f1d5"; } .fa-qq:before { content: "\f1d6"; } .fa-wechat:before, .fa-weixin:before { content: "\f1d7"; } .fa-send:before, .fa-paper-plane:before { content: "\f1d8"; } .fa-send-o:before, .fa-paper-plane-o:before { content: "\f1d9"; } .fa-history:before { content: "\f1da"; } .fa-circle-thin:before { content: "\f1db"; } .fa-header:before { content: "\f1dc"; } .fa-paragraph:before { content: "\f1dd"; } .fa-sliders:before { content: "\f1de"; } .fa-share-alt:before { content: "\f1e0"; } .fa-share-alt-square:before { content: "\f1e1"; } .fa-bomb:before { content: "\f1e2"; } .fa-soccer-ball-o:before, .fa-futbol-o:before { content: "\f1e3"; } .fa-tty:before { content: "\f1e4"; } .fa-binoculars:before { content: "\f1e5"; } .fa-plug:before { content: "\f1e6"; } .fa-slideshare:before { content: "\f1e7"; } .fa-twitch:before { content: "\f1e8"; } .fa-yelp:before { content: "\f1e9"; } .fa-newspaper-o:before { content: "\f1ea"; } .fa-wifi:before { content: "\f1eb"; } .fa-calculator:before { content: "\f1ec"; } .fa-paypal:before { content: "\f1ed"; } .fa-google-wallet:before { content: "\f1ee"; } .fa-cc-visa:before { content: "\f1f0"; } .fa-cc-mastercard:before { content: "\f1f1"; } .fa-cc-discover:before { content: "\f1f2"; } .fa-cc-amex:before { content: "\f1f3"; } .fa-cc-paypal:before { content: "\f1f4"; } .fa-cc-stripe:before { content: "\f1f5"; } .fa-bell-slash:before { content: "\f1f6"; } .fa-bell-slash-o:before { content: "\f1f7"; } .fa-trash:before { content: "\f1f8"; } .fa-copyright:before { content: "\f1f9"; } .fa-at:before { content: "\f1fa"; } .fa-eyedropper:before { content: "\f1fb"; } .fa-paint-brush:before { content: "\f1fc"; } .fa-birthday-cake:before { content: "\f1fd"; } .fa-area-chart:before { content: "\f1fe"; } .fa-pie-chart:before { content: "\f200"; } .fa-line-chart:before { content: "\f201"; } .fa-lastfm:before { content: "\f202"; } .fa-lastfm-square:before { content: "\f203"; } .fa-toggle-off:before { content: "\f204"; } .fa-toggle-on:before { content: "\f205"; } .fa-bicycle:before { content: "\f206"; } .fa-bus:before { content: "\f207"; } .fa-ioxhost:before { content: "\f208"; } .fa-angellist:before { content: "\f209"; } .fa-cc:before { content: "\f20a"; } .fa-shekel:before, .fa-sheqel:before, .fa-ils:before { content: "\f20b"; } .fa-meanpath:before { content: "\f20c"; } .fa-buysellads:before { content: "\f20d"; } .fa-connectdevelop:before { content: "\f20e"; } .fa-dashcube:before { content: "\f210"; } .fa-forumbee:before { content: "\f211"; } .fa-leanpub:before { content: "\f212"; } .fa-sellsy:before { content: "\f213"; } .fa-shirtsinbulk:before { content: "\f214"; } .fa-simplybuilt:before { content: "\f215"; } .fa-skyatlas:before { content: "\f216"; } .fa-cart-plus:before { content: "\f217"; } .fa-cart-arrow-down:before { content: "\f218"; } .fa-diamond:before { content: "\f219"; } .fa-ship:before { content: "\f21a"; } .fa-user-secret:before { content: "\f21b"; } .fa-motorcycle:before { content: "\f21c"; } .fa-street-view:before { content: "\f21d"; } .fa-heartbeat:before { content: "\f21e"; } .fa-venus:before { content: "\f221"; } .fa-mars:before { content: "\f222"; } .fa-mercury:before { content: "\f223"; } .fa-intersex:before, .fa-transgender:before { content: "\f224"; } .fa-transgender-alt:before { content: "\f225"; } .fa-venus-double:before { content: "\f226"; } .fa-mars-double:before { content: "\f227"; } .fa-venus-mars:before { content: "\f228"; } .fa-mars-stroke:before { content: "\f229"; } .fa-mars-stroke-v:before { content: "\f22a"; } .fa-mars-stroke-h:before { content: "\f22b"; } .fa-neuter:before { content: "\f22c"; } .fa-genderless:before { content: "\f22d"; } .fa-facebook-official:before { content: "\f230"; } .fa-pinterest-p:before { content: "\f231"; } .fa-whatsapp:before { content: "\f232"; } .fa-server:before { content: "\f233"; } .fa-user-plus:before { content: "\f234"; } .fa-user-times:before { content: "\f235"; } .fa-hotel:before, .fa-bed:before { content: "\f236"; } .fa-viacoin:before { content: "\f237"; } .fa-train:before { content: "\f238"; } .fa-subway:before { content: "\f239"; } .fa-medium:before { content: "\f23a"; } .fa-yc:before, .fa-y-combinator:before { content: "\f23b"; } .fa-optin-monster:before { content: "\f23c"; } .fa-opencart:before { content: "\f23d"; } .fa-expeditedssl:before { content: "\f23e"; } .fa-battery-4:before, .fa-battery:before, .fa-battery-full:before { content: "\f240"; } .fa-battery-3:before, .fa-battery-three-quarters:before { content: "\f241"; } .fa-battery-2:before, .fa-battery-half:before { content: "\f242"; } .fa-battery-1:before, .fa-battery-quarter:before { content: "\f243"; } .fa-battery-0:before, .fa-battery-empty:before { content: "\f244"; } .fa-mouse-pointer:before { content: "\f245"; } .fa-i-cursor:before { content: "\f246"; } .fa-object-group:before { content: "\f247"; } .fa-object-ungroup:before { content: "\f248"; } .fa-sticky-note:before { content: "\f249"; } .fa-sticky-note-o:before { content: "\f24a"; } .fa-cc-jcb:before { content: "\f24b"; } .fa-cc-diners-club:before { content: "\f24c"; } .fa-clone:before { content: "\f24d"; } .fa-balance-scale:before { content: "\f24e"; } .fa-hourglass-o:before { content: "\f250"; } .fa-hourglass-1:before, .fa-hourglass-start:before { content: "\f251"; } .fa-hourglass-2:before, .fa-hourglass-half:before { content: "\f252"; } .fa-hourglass-3:before, .fa-hourglass-end:before { content: "\f253"; } .fa-hourglass:before { content: "\f254"; } .fa-hand-grab-o:before, .fa-hand-rock-o:before { content: "\f255"; } .fa-hand-stop-o:before, .fa-hand-paper-o:before { content: "\f256"; } .fa-hand-scissors-o:before { content: "\f257"; } .fa-hand-lizard-o:before { content: "\f258"; } .fa-hand-spock-o:before { content: "\f259"; } .fa-hand-pointer-o:before { content: "\f25a"; } .fa-hand-peace-o:before { content: "\f25b"; } .fa-trademark:before { content: "\f25c"; } .fa-registered:before { content: "\f25d"; } .fa-creative-commons:before { content: "\f25e"; } .fa-gg:before { content: "\f260"; } .fa-gg-circle:before { content: "\f261"; } .fa-tripadvisor:before { content: "\f262"; } .fa-odnoklassniki:before { content: "\f263"; } .fa-odnoklassniki-square:before { content: "\f264"; } .fa-get-pocket:before { content: "\f265"; } .fa-wikipedia-w:before { content: "\f266"; } .fa-safari:before { content: "\f267"; } .fa-chrome:before { content: "\f268"; } .fa-firefox:before { content: "\f269"; } .fa-opera:before { content: "\f26a"; } .fa-internet-explorer:before { content: "\f26b"; } .fa-tv:before, .fa-television:before { content: "\f26c"; } .fa-contao:before { content: "\f26d"; } .fa-500px:before { content: "\f26e"; } .fa-amazon:before { content: "\f270"; } .fa-calendar-plus-o:before { content: "\f271"; } .fa-calendar-minus-o:before { content: "\f272"; } .fa-calendar-times-o:before { content: "\f273"; } .fa-calendar-check-o:before { content: "\f274"; } .fa-industry:before { content: "\f275"; } .fa-map-pin:before { content: "\f276"; } .fa-map-signs:before { content: "\f277"; } .fa-map-o:before { content: "\f278"; } .fa-map:before { content: "\f279"; } .fa-commenting:before { content: "\f27a"; } .fa-commenting-o:before { content: "\f27b"; } .fa-houzz:before { content: "\f27c"; } .fa-vimeo:before { content: "\f27d"; } .fa-black-tie:before { content: "\f27e"; } .fa-fonticons:before { content: "\f280"; } .fa-reddit-alien:before { content: "\f281"; } .fa-edge:before { content: "\f282"; } .fa-credit-card-alt:before { content: "\f283"; } .fa-codiepie:before { content: "\f284"; } .fa-modx:before { content: "\f285"; } .fa-fort-awesome:before { content: "\f286"; } .fa-usb:before { content: "\f287"; } .fa-product-hunt:before { content: "\f288"; } .fa-mixcloud:before { content: "\f289"; } .fa-scribd:before { content: "\f28a"; } .fa-pause-circle:before { content: "\f28b"; } .fa-pause-circle-o:before { content: "\f28c"; } .fa-stop-circle:before { content: "\f28d"; } .fa-stop-circle-o:before { content: "\f28e"; } .fa-shopping-bag:before { content: "\f290"; } .fa-shopping-basket:before { content: "\f291"; } .fa-hashtag:before { content: "\f292"; } .fa-bluetooth:before { content: "\f293"; } .fa-bluetooth-b:before { content: "\f294"; } .fa-percent:before { content: "\f295"; } .fa-gitlab:before { content: "\f296"; } .fa-wpbeginner:before { content: "\f297"; } .fa-wpforms:before { content: "\f298"; } .fa-envira:before { content: "\f299"; } .fa-universal-access:before { content: "\f29a"; } .fa-wheelchair-alt:before { content: "\f29b"; } .fa-question-circle-o:before { content: "\f29c"; } .fa-blind:before { content: "\f29d"; } .fa-audio-description:before { content: "\f29e"; } .fa-volume-control-phone:before { content: "\f2a0"; } .fa-braille:before { content: "\f2a1"; } .fa-assistive-listening-systems:before { content: "\f2a2"; } .fa-asl-interpreting:before, .fa-american-sign-language-interpreting:before { content: "\f2a3"; } .fa-deafness:before, .fa-hard-of-hearing:before, .fa-deaf:before { content: "\f2a4"; } .fa-glide:before { content: "\f2a5"; } .fa-glide-g:before { content: "\f2a6"; } .fa-signing:before, .fa-sign-language:before { content: "\f2a7"; } .fa-low-vision:before { content: "\f2a8"; } .fa-viadeo:before { content: "\f2a9"; } .fa-viadeo-square:before { content: "\f2aa"; } .fa-snapchat:before { content: "\f2ab"; } .fa-snapchat-ghost:before { content: "\f2ac"; } .fa-snapchat-square:before { content: "\f2ad"; } .fa-pied-piper:before { content: "\f2ae"; } .fa-first-order:before { content: "\f2b0"; } .fa-yoast:before { content: "\f2b1"; } .fa-themeisle:before { content: "\f2b2"; } .fa-google-plus-circle:before, .fa-google-plus-official:before { content: "\f2b3"; } .fa-fa:before, .fa-font-awesome:before { content: "\f2b4"; } .fa-handshake-o:before { content: "\f2b5"; } .fa-envelope-open:before { content: "\f2b6"; } .fa-envelope-open-o:before { content: "\f2b7"; } .fa-linode:before { content: "\f2b8"; } .fa-address-book:before { content: "\f2b9"; } .fa-address-book-o:before { content: "\f2ba"; } .fa-vcard:before, .fa-address-card:before { content: "\f2bb"; } .fa-vcard-o:before, .fa-address-card-o:before { content: "\f2bc"; } .fa-user-circle:before { content: "\f2bd"; } .fa-user-circle-o:before { content: "\f2be"; } .fa-user-o:before { content: "\f2c0"; } .fa-id-badge:before { content: "\f2c1"; } .fa-drivers-license:before, .fa-id-card:before { content: "\f2c2"; } .fa-drivers-license-o:before, .fa-id-card-o:before { content: "\f2c3"; } .fa-quora:before { content: "\f2c4"; } .fa-free-code-camp:before { content: "\f2c5"; } .fa-telegram:before { content: "\f2c6"; } .fa-thermometer-4:before, .fa-thermometer:before, .fa-thermometer-full:before { content: "\f2c7"; } .fa-thermometer-3:before, .fa-thermometer-three-quarters:before { content: "\f2c8"; } .fa-thermometer-2:before, .fa-thermometer-half:before { content: "\f2c9"; } .fa-thermometer-1:before, .fa-thermometer-quarter:before { content: "\f2ca"; } .fa-thermometer-0:before, .fa-thermometer-empty:before { content: "\f2cb"; } .fa-shower:before { content: "\f2cc"; } .fa-bathtub:before, .fa-s15:before, .fa-bath:before { content: "\f2cd"; } .fa-podcast:before { content: "\f2ce"; } .fa-window-maximize:before { content: "\f2d0"; } .fa-window-minimize:before { content: "\f2d1"; } .fa-window-restore:before { content: "\f2d2"; } .fa-times-rectangle:before, .fa-window-close:before { content: "\f2d3"; } .fa-times-rectangle-o:before, .fa-window-close-o:before { content: "\f2d4"; } .fa-bandcamp:before { content: "\f2d5"; } .fa-grav:before { content: "\f2d6"; } .fa-etsy:before { content: "\f2d7"; } .fa-imdb:before { content: "\f2d8"; } .fa-ravelry:before { content: "\f2d9"; } .fa-eercast:before { content: "\f2da"; } .fa-microchip:before { content: "\f2db"; } .fa-snowflake-o:before { content: "\f2dc"; } .fa-superpowers:before { content: "\f2dd"; } .fa-wpexplorer:before { content: "\f2de"; } .fa-meetup:before { content: "\f2e0"; } .sr-only { position: absolute; width: 1px; height: 1px; padding: 0; margin: -1px; overflow: hidden; clip: rect(0, 0, 0, 0); border: 0; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } /\*! \* \* IPython base \* \*/ .modal.fade .modal-dialog { -webkit-transform: translate(0, 0); -ms-transform: translate(0, 0); -o-transform: translate(0, 0); transform: translate(0, 0); } code { color: #000; } pre { font-size: inherit; line-height: inherit; } label { font-weight: normal; } /\* Make the page background atleast 100% the height of the view port \*/ /\* Make the page itself atleast 70% the height of the view port \*/ .border-box-sizing { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } .corner-all { border-radius: 2px; } .no-padding { padding: 0px; } /\* Flexible box model classes \*/ /\* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ \*/ /\* This file is a compatability layer. It allows the usage of flexible box model layouts accross multiple browsers, including older browsers. The newest, universal implementation of the flexible box model is used when available (see `Modern browsers` comments below). Browsers that are known to implement this new spec completely include: Firefox 28.0+ Chrome 29.0+ Internet Explorer 11+ Opera 17.0+ Browsers not listed, including Safari, are supported via the styling under the `Old browsers` comments below. \*/ .hbox { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } .hbox > \* { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; } .vbox { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } .vbox > \* { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; } .hbox.reverse, .vbox.reverse, .reverse { /\* Old browsers \*/ -webkit-box-direction: reverse; -moz-box-direction: reverse; box-direction: reverse; /\* Modern browsers \*/ flex-direction: row-reverse; } .hbox.box-flex0, .vbox.box-flex0, .box-flex0 { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; width: auto; } .hbox.box-flex1, .vbox.box-flex1, .box-flex1 { /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } .hbox.box-flex, .vbox.box-flex, .box-flex { /\* Old browsers \*/ /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } .hbox.box-flex2, .vbox.box-flex2, .box-flex2 { /\* Old browsers \*/ -webkit-box-flex: 2; -moz-box-flex: 2; box-flex: 2; /\* Modern browsers \*/ flex: 2; } .box-group1 { /\* Deprecated \*/ -webkit-box-flex-group: 1; -moz-box-flex-group: 1; box-flex-group: 1; } .box-group2 { /\* Deprecated \*/ -webkit-box-flex-group: 2; -moz-box-flex-group: 2; box-flex-group: 2; } .hbox.start, .vbox.start, .start { /\* Old browsers \*/ -webkit-box-pack: start; -moz-box-pack: start; box-pack: start; /\* Modern browsers \*/ justify-content: flex-start; } .hbox.end, .vbox.end, .end { /\* Old browsers \*/ -webkit-box-pack: end; -moz-box-pack: end; box-pack: end; /\* Modern browsers \*/ justify-content: flex-end; } .hbox.center, .vbox.center, .center { /\* Old browsers \*/ -webkit-box-pack: center; -moz-box-pack: center; box-pack: center; /\* Modern browsers \*/ justify-content: center; } .hbox.baseline, .vbox.baseline, .baseline { /\* Old browsers \*/ -webkit-box-pack: baseline; -moz-box-pack: baseline; box-pack: baseline; /\* Modern browsers \*/ justify-content: baseline; } .hbox.stretch, .vbox.stretch, .stretch { /\* Old browsers \*/ -webkit-box-pack: stretch; -moz-box-pack: stretch; box-pack: stretch; /\* Modern browsers \*/ justify-content: stretch; } .hbox.align-start, .vbox.align-start, .align-start { /\* Old browsers \*/ -webkit-box-align: start; -moz-box-align: start; box-align: start; /\* Modern browsers \*/ align-items: flex-start; } .hbox.align-end, .vbox.align-end, .align-end { /\* Old browsers \*/ -webkit-box-align: end; -moz-box-align: end; box-align: end; /\* Modern browsers \*/ align-items: flex-end; } .hbox.align-center, .vbox.align-center, .align-center { /\* Old browsers \*/ -webkit-box-align: center; -moz-box-align: center; box-align: center; /\* Modern browsers \*/ align-items: center; } .hbox.align-baseline, .vbox.align-baseline, .align-baseline { /\* Old browsers \*/ -webkit-box-align: baseline; -moz-box-align: baseline; box-align: baseline; /\* Modern browsers \*/ align-items: baseline; } .hbox.align-stretch, .vbox.align-stretch, .align-stretch { /\* Old browsers \*/ -webkit-box-align: stretch; -moz-box-align: stretch; box-align: stretch; /\* Modern browsers \*/ align-items: stretch; } div.error { margin: 2em; text-align: center; } div.error > h1 { font-size: 500%; line-height: normal; } div.error > p { font-size: 200%; line-height: normal; } div.traceback-wrapper { text-align: left; max-width: 800px; margin: auto; } div.traceback-wrapper pre.traceback { max-height: 600px; overflow: auto; } /\*\* \* Primary styles \* \* Author: Jupyter Development Team \*/ body { background-color: #fff; /\* This makes sure that the body covers the entire window and needs to be in a different element than the display: box in wrapper below \*/ position: absolute; left: 0px; right: 0px; top: 0px; bottom: 0px; overflow: visible; } body > #header { /\* Initially hidden to prevent FLOUC \*/ display: none; background-color: #fff; /\* Display over codemirror \*/ position: relative; z-index: 100; } body > #header #header-container { display: flex; flex-direction: row; justify-content: space-between; padding: 5px; padding-bottom: 5px; padding-top: 5px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } body > #header .header-bar { width: 100%; height: 1px; background: #e7e7e7; margin-bottom: -1px; } @media print { body > #header { display: none !important; } } #header-spacer { width: 100%; visibility: hidden; } @media print { #header-spacer { display: none; } } #ipython\_notebook { padding-left: 0px; padding-top: 1px; padding-bottom: 1px; } [dir="rtl"] #ipython\_notebook { margin-right: 10px; margin-left: 0; } [dir="rtl"] #ipython\_notebook.pull-left { float: right !important; float: right; } .flex-spacer { flex: 1; } #noscript { width: auto; padding-top: 16px; padding-bottom: 16px; text-align: center; font-size: 22px; color: red; font-weight: bold; } #ipython\_notebook img { height: 28px; } #site { width: 100%; display: none; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; overflow: auto; } @media print { #site { height: auto !important; } } /\* Smaller buttons \*/ .ui-button .ui-button-text { padding: 0.2em 0.8em; font-size: 77%; } input.ui-button { padding: 0.3em 0.9em; } span#kernel\_logo\_widget { margin: 0 10px; } span#login\_widget { float: right; } [dir="rtl"] span#login\_widget { float: left; } span#login\_widget > .button, #logout { color: #333; background-color: #fff; border-color: #ccc; } span#login\_widget > .button:focus, #logout:focus, span#login\_widget > .button.focus, #logout.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } span#login\_widget > .button:hover, #logout:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } span#login\_widget > .button:active, #logout:active, span#login\_widget > .button.active, #logout.active, .open > .dropdown-togglespan#login\_widget > .button, .open > .dropdown-toggle#logout { color: #333; background-color: #e6e6e6; border-color: #adadad; } span#login\_widget > .button:active:hover, #logout:active:hover, span#login\_widget > .button.active:hover, #logout.active:hover, .open > .dropdown-togglespan#login\_widget > .button:hover, .open > .dropdown-toggle#logout:hover, span#login\_widget > .button:active:focus, #logout:active:focus, span#login\_widget > .button.active:focus, #logout.active:focus, .open > .dropdown-togglespan#login\_widget > .button:focus, .open > .dropdown-toggle#logout:focus, span#login\_widget > .button:active.focus, #logout:active.focus, span#login\_widget > .button.active.focus, #logout.active.focus, .open > .dropdown-togglespan#login\_widget > .button.focus, .open > .dropdown-toggle#logout.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } span#login\_widget > .button:active, #logout:active, span#login\_widget > .button.active, #logout.active, .open > .dropdown-togglespan#login\_widget > .button, .open > .dropdown-toggle#logout { background-image: none; } span#login\_widget > .button.disabled:hover, #logout.disabled:hover, span#login\_widget > .button[disabled]:hover, #logout[disabled]:hover, fieldset[disabled] span#login\_widget > .button:hover, fieldset[disabled] #logout:hover, span#login\_widget > .button.disabled:focus, #logout.disabled:focus, span#login\_widget > .button[disabled]:focus, #logout[disabled]:focus, fieldset[disabled] span#login\_widget > .button:focus, fieldset[disabled] #logout:focus, span#login\_widget > .button.disabled.focus, #logout.disabled.focus, span#login\_widget > .button[disabled].focus, #logout[disabled].focus, fieldset[disabled] span#login\_widget > .button.focus, fieldset[disabled] #logout.focus { background-color: #fff; border-color: #ccc; } span#login\_widget > .button .badge, #logout .badge { color: #fff; background-color: #333; } .nav-header { text-transform: none; } #header > span { margin-top: 10px; } .modal\_stretch .modal-dialog { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; min-height: 80vh; } .modal\_stretch .modal-dialog .modal-body { max-height: calc(100vh - 200px); overflow: auto; flex: 1; } .modal-header { cursor: move; } @media (min-width: 768px) { .modal .modal-dialog { width: 700px; } } @media (min-width: 768px) { select.form-control { margin-left: 12px; margin-right: 12px; } } /\*! \* \* IPython auth \* \*/ .center-nav { display: inline-block; margin-bottom: -4px; } [dir="rtl"] .center-nav form.pull-left { float: right !important; float: right; } [dir="rtl"] .center-nav .navbar-text { float: right; } [dir="rtl"] .navbar-inner { text-align: right; } [dir="rtl"] div.text-left { text-align: right; } /\*! \* \* IPython tree view \* \*/ /\* We need an invisible input field on top of the sentense\*/ /\* "Drag file onto the list ..." \*/ .alternate\_upload { background-color: none; display: inline; } .alternate\_upload.form { padding: 0; margin: 0; } .alternate\_upload input.fileinput { position: absolute; display: block; width: 100%; height: 100%; overflow: hidden; cursor: pointer; opacity: 0; z-index: 2; } .alternate\_upload .btn-xs > input.fileinput { margin: -1px -5px; } .alternate\_upload .btn-upload { position: relative; height: 22px; } ::-webkit-file-upload-button { cursor: pointer; } /\*\* \* Primary styles \* \* Author: Jupyter Development Team \*/ ul#tabs { margin-bottom: 4px; } ul#tabs a { padding-top: 6px; padding-bottom: 4px; } [dir="rtl"] ul#tabs.nav-tabs > li { float: right; } [dir="rtl"] ul#tabs.nav.nav-tabs { padding-right: 0; } ul.breadcrumb a:focus, ul.breadcrumb a:hover { text-decoration: none; } ul.breadcrumb i.icon-home { font-size: 16px; margin-right: 4px; } ul.breadcrumb span { color: #5e5e5e; } .list\_toolbar { padding: 4px 0 4px 0; vertical-align: middle; } .list\_toolbar .tree-buttons { padding-top: 1px; } [dir="rtl"] .list\_toolbar .tree-buttons .pull-right { float: left !important; float: left; } [dir="rtl"] .list\_toolbar .col-sm-4, [dir="rtl"] .list\_toolbar .col-sm-8 { float: right; } .dynamic-buttons { padding-top: 3px; display: inline-block; } .list\_toolbar [class\*="span"] { min-height: 24px; } .list\_header { font-weight: bold; background-color: #EEE; } .list\_placeholder { font-weight: bold; padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; } .list\_container { margin-top: 4px; margin-bottom: 20px; border: 1px solid #ddd; border-radius: 2px; } .list\_container > div { border-bottom: 1px solid #ddd; } .list\_container > div:hover .list-item { background-color: red; } .list\_container > div:last-child { border: none; } .list\_item:hover .list\_item { background-color: #ddd; } .list\_item a { text-decoration: none; } .list\_item:hover { background-color: #fafafa; } .list\_header > div, .list\_item > div { padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; line-height: 22px; } .list\_header > div input, .list\_item > div input { margin-right: 7px; margin-left: 14px; vertical-align: text-bottom; line-height: 22px; position: relative; top: -1px; } .list\_header > div .item\_link, .list\_item > div .item\_link { margin-left: -1px; vertical-align: baseline; line-height: 22px; } [dir="rtl"] .list\_item > div input { margin-right: 0; } .new-file input[type=checkbox] { visibility: hidden; } .item\_name { line-height: 22px; height: 24px; } .item\_icon { font-size: 14px; color: #5e5e5e; margin-right: 7px; margin-left: 7px; line-height: 22px; vertical-align: baseline; } .item\_modified { margin-right: 7px; margin-left: 7px; } [dir="rtl"] .item\_modified.pull-right { float: left !important; float: left; } .item\_buttons { line-height: 1em; margin-left: -5px; } .item\_buttons .btn, .item\_buttons .btn-group, .item\_buttons .input-group { float: left; } .item\_buttons > .btn, .item\_buttons > .btn-group, .item\_buttons > .input-group { margin-left: 5px; } .item\_buttons .btn { min-width: 13ex; } .item\_buttons .running-indicator { padding-top: 4px; color: #5cb85c; } .item\_buttons .kernel-name { padding-top: 4px; color: #5bc0de; margin-right: 7px; float: left; } [dir="rtl"] .item\_buttons.pull-right { float: left !important; float: left; } [dir="rtl"] .item\_buttons .kernel-name { margin-left: 7px; float: right; } .toolbar\_info { height: 24px; line-height: 24px; } .list\_item input:not([type=checkbox]) { padding-top: 3px; padding-bottom: 3px; height: 22px; line-height: 14px; margin: 0px; } .highlight\_text { color: blue; } #project\_name { display: inline-block; padding-left: 7px; margin-left: -2px; } #project\_name > .breadcrumb { padding: 0px; margin-bottom: 0px; background-color: transparent; font-weight: bold; } .sort\_button { display: inline-block; padding-left: 7px; } [dir="rtl"] .sort\_button.pull-right { float: left !important; float: left; } #tree-selector { padding-right: 0px; } #button-select-all { min-width: 50px; } [dir="rtl"] #button-select-all.btn { float: right ; } #select-all { margin-left: 7px; margin-right: 2px; margin-top: 2px; height: 16px; } [dir="rtl"] #select-all.pull-left { float: right !important; float: right; } .menu\_icon { margin-right: 2px; } .tab-content .row { margin-left: 0px; margin-right: 0px; } .folder\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f114"; } .folder\_icon:before.fa-pull-left { margin-right: .3em; } .folder\_icon:before.fa-pull-right { margin-left: .3em; } .folder\_icon:before.pull-left { margin-right: .3em; } .folder\_icon:before.pull-right { margin-left: .3em; } .notebook\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f02d"; position: relative; top: -1px; } .notebook\_icon:before.fa-pull-left { margin-right: .3em; } .notebook\_icon:before.fa-pull-right { margin-left: .3em; } .notebook\_icon:before.pull-left { margin-right: .3em; } .notebook\_icon:before.pull-right { margin-left: .3em; } .running\_notebook\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f02d"; position: relative; top: -1px; color: #5cb85c; } .running\_notebook\_icon:before.fa-pull-left { margin-right: .3em; } .running\_notebook\_icon:before.fa-pull-right { margin-left: .3em; } .running\_notebook\_icon:before.pull-left { margin-right: .3em; } .running\_notebook\_icon:before.pull-right { margin-left: .3em; } .file\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f016"; position: relative; top: -2px; } .file\_icon:before.fa-pull-left { margin-right: .3em; } .file\_icon:before.fa-pull-right { margin-left: .3em; } .file\_icon:before.pull-left { margin-right: .3em; } .file\_icon:before.pull-right { margin-left: .3em; } #notebook\_toolbar .pull-right { padding-top: 0px; margin-right: -1px; } ul#new-menu { left: auto; right: 0; } #new-menu .dropdown-header { font-size: 10px; border-bottom: 1px solid #e5e5e5; padding: 0 0 3px; margin: -3px 20px 0; } .kernel-menu-icon { padding-right: 12px; width: 24px; content: "\f096"; } .kernel-menu-icon:before { content: "\f096"; } .kernel-menu-icon-current:before { content: "\f00c"; } #tab\_content { padding-top: 20px; } #running .panel-group .panel { margin-top: 3px; margin-bottom: 1em; } #running .panel-group .panel .panel-heading { background-color: #EEE; padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; line-height: 22px; } #running .panel-group .panel .panel-heading a:focus, #running .panel-group .panel .panel-heading a:hover { text-decoration: none; } #running .panel-group .panel .panel-body { padding: 0px; } #running .panel-group .panel .panel-body .list\_container { margin-top: 0px; margin-bottom: 0px; border: 0px; border-radius: 0px; } #running .panel-group .panel .panel-body .list\_container .list\_item { border-bottom: 1px solid #ddd; } #running .panel-group .panel .panel-body .list\_container .list\_item:last-child { border-bottom: 0px; } .delete-button { display: none; } .duplicate-button { display: none; } .rename-button { display: none; } .move-button { display: none; } .download-button { display: none; } .shutdown-button { display: none; } .dynamic-instructions { display: inline-block; padding-top: 4px; } /\*! \* \* IPython text editor webapp \* \*/ .selected-keymap i.fa { padding: 0px 5px; } .selected-keymap i.fa:before { content: "\f00c"; } #mode-menu { overflow: auto; max-height: 20em; } .edit\_app #header { -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } .edit\_app #menubar .navbar { /\* Use a negative 1 bottom margin, so the border overlaps the border of the header \*/ margin-bottom: -1px; } .dirty-indicator { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator.fa-pull-left { margin-right: .3em; } .dirty-indicator.fa-pull-right { margin-left: .3em; } .dirty-indicator.pull-left { margin-right: .3em; } .dirty-indicator.pull-right { margin-left: .3em; } .dirty-indicator-dirty { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator-dirty.fa-pull-left { margin-right: .3em; } .dirty-indicator-dirty.fa-pull-right { margin-left: .3em; } .dirty-indicator-dirty.pull-left { margin-right: .3em; } .dirty-indicator-dirty.pull-right { margin-left: .3em; } .dirty-indicator-clean { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator-clean.fa-pull-left { margin-right: .3em; } .dirty-indicator-clean.fa-pull-right { margin-left: .3em; } .dirty-indicator-clean.pull-left { margin-right: .3em; } .dirty-indicator-clean.pull-right { margin-left: .3em; } .dirty-indicator-clean:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f00c"; } .dirty-indicator-clean:before.fa-pull-left { margin-right: .3em; } .dirty-indicator-clean:before.fa-pull-right { margin-left: .3em; } .dirty-indicator-clean:before.pull-left { margin-right: .3em; } .dirty-indicator-clean:before.pull-right { margin-left: .3em; } #filename { font-size: 16pt; display: table; padding: 0px 5px; } #current-mode { padding-left: 5px; padding-right: 5px; } #texteditor-backdrop { padding-top: 20px; padding-bottom: 20px; } @media not print { #texteditor-backdrop { background-color: #EEE; } } @media print { #texteditor-backdrop #texteditor-container .CodeMirror-gutter, #texteditor-backdrop #texteditor-container .CodeMirror-gutters { background-color: #fff; } } @media not print { #texteditor-backdrop #texteditor-container .CodeMirror-gutter, #texteditor-backdrop #texteditor-container .CodeMirror-gutters { background-color: #fff; } } @media not print { #texteditor-backdrop #texteditor-container { padding: 0px; background-color: #fff; -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } } .CodeMirror-dialog { background-color: #fff; } /\*! \* \* IPython notebook \* \*/ /\* CSS font colors for translated ANSI escape sequences \*/ /\* The color values are a mix of http://www.xcolors.net/dl/baskerville-ivorylight and http://www.xcolors.net/dl/euphrasia \*/ .ansi-black-fg { color: #3E424D; } .ansi-black-bg { background-color: #3E424D; } .ansi-black-intense-fg { color: #282C36; } .ansi-black-intense-bg { background-color: #282C36; } .ansi-red-fg { color: #E75C58; } .ansi-red-bg { background-color: #E75C58; } .ansi-red-intense-fg { color: #B22B31; } .ansi-red-intense-bg { background-color: #B22B31; } .ansi-green-fg { color: #00A250; } .ansi-green-bg { background-color: #00A250; } .ansi-green-intense-fg { color: #007427; } .ansi-green-intense-bg { background-color: #007427; } .ansi-yellow-fg { color: #DDB62B; } .ansi-yellow-bg { background-color: #DDB62B; } .ansi-yellow-intense-fg { color: #B27D12; } .ansi-yellow-intense-bg { background-color: #B27D12; } .ansi-blue-fg { color: #208FFB; } .ansi-blue-bg { background-color: #208FFB; } .ansi-blue-intense-fg { color: #0065CA; } .ansi-blue-intense-bg { background-color: #0065CA; } .ansi-magenta-fg { color: #D160C4; } .ansi-magenta-bg { background-color: #D160C4; } .ansi-magenta-intense-fg { color: #A03196; } .ansi-magenta-intense-bg { background-color: #A03196; } .ansi-cyan-fg { color: #60C6C8; } .ansi-cyan-bg { background-color: #60C6C8; } .ansi-cyan-intense-fg { color: #258F8F; } .ansi-cyan-intense-bg { background-color: #258F8F; } .ansi-white-fg { color: #C5C1B4; } .ansi-white-bg { background-color: #C5C1B4; } .ansi-white-intense-fg { color: #A1A6B2; } .ansi-white-intense-bg { background-color: #A1A6B2; } .ansi-default-inverse-fg { color: #FFFFFF; } .ansi-default-inverse-bg { background-color: #000000; } .ansi-bold { font-weight: bold; } .ansi-underline { text-decoration: underline; } /\* The following styles are deprecated an will be removed in a future version \*/ .ansibold { font-weight: bold; } .ansi-inverse { outline: 0.5px dotted; } /\* use dark versions for foreground, to improve visibility \*/ .ansiblack { color: black; } .ansired { color: darkred; } .ansigreen { color: darkgreen; } .ansiyellow { color: #c4a000; } .ansiblue { color: darkblue; } .ansipurple { color: darkviolet; } .ansicyan { color: steelblue; } .ansigray { color: gray; } /\* and light for background, for the same reason \*/ .ansibgblack { background-color: black; } .ansibgred { background-color: red; } .ansibggreen { background-color: green; } .ansibgyellow { background-color: yellow; } .ansibgblue { background-color: blue; } .ansibgpurple { background-color: magenta; } .ansibgcyan { background-color: cyan; } .ansibggray { background-color: gray; } div.cell { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; border-radius: 2px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; border-width: 1px; border-style: solid; border-color: transparent; width: 100%; padding: 5px; /\* This acts as a spacer between cells, that is outside the border \*/ margin: 0px; outline: none; position: relative; overflow: visible; } div.cell:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: transparent; } div.cell.jupyter-soft-selected { border-left-color: #E3F2FD; border-left-width: 1px; padding-left: 5px; border-right-color: #E3F2FD; border-right-width: 1px; background: #E3F2FD; } @media print { div.cell.jupyter-soft-selected { border-color: transparent; } } div.cell.selected, div.cell.selected.jupyter-soft-selected { border-color: #ababab; } div.cell.selected:before, div.cell.selected.jupyter-soft-selected:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: #42A5F5; } @media print { div.cell.selected, div.cell.selected.jupyter-soft-selected { border-color: transparent; } } .edit\_mode div.cell.selected { border-color: #66BB6A; } .edit\_mode div.cell.selected:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: #66BB6A; } @media print { .edit\_mode div.cell.selected { border-color: transparent; } } .prompt { /\* This needs to be wide enough for 3 digit prompt numbers: In[100]: \*/ min-width: 14ex; /\* This padding is tuned to match the padding on the CodeMirror editor. \*/ padding: 0.4em; margin: 0px; font-family: monospace; text-align: right; /\* This has to match that of the the CodeMirror class line-height below \*/ line-height: 1.21429em; /\* Don't highlight prompt number selection \*/ -webkit-touch-callout: none; -webkit-user-select: none; -khtml-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; /\* Use default cursor \*/ cursor: default; } @media (max-width: 540px) { .prompt { text-align: left; } } div.inner\_cell { min-width: 0; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } /\* input\_area and input\_prompt must match in top border and margin for alignment \*/ div.input\_area { border: 1px solid #cfcfcf; border-radius: 2px; background: #f7f7f7; line-height: 1.21429em; } /\* This is needed so that empty prompt areas can collapse to zero height when there is no content in the output\_subarea and the prompt. The main purpose of this is to make sure that empty JavaScript output\_subareas have no height. \*/ div.prompt:empty { padding-top: 0; padding-bottom: 0; } div.unrecognized\_cell { padding: 5px 5px 5px 0px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } div.unrecognized\_cell .inner\_cell { border-radius: 2px; padding: 5px; font-weight: bold; color: red; border: 1px solid #cfcfcf; background: #eaeaea; } div.unrecognized\_cell .inner\_cell a { color: inherit; text-decoration: none; } div.unrecognized\_cell .inner\_cell a:hover { color: inherit; text-decoration: none; } @media (max-width: 540px) { div.unrecognized\_cell > div.prompt { display: none; } } div.code\_cell { /\* avoid page breaking on code cells when printing \*/ } @media print { div.code\_cell { page-break-inside: avoid; } } /\* any special styling for code cells that are currently running goes here \*/ div.input { page-break-inside: avoid; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } @media (max-width: 540px) { div.input { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } } /\* input\_area and input\_prompt must match in top border and margin for alignment \*/ div.input\_prompt { color: #303F9F; border-top: 1px solid transparent; } div.input\_area > div.highlight { margin: 0.4em; border: none; padding: 0px; background-color: transparent; } div.input\_area > div.highlight > pre { margin: 0px; border: none; padding: 0px; background-color: transparent; } /\* The following gets added to the <head> if it is detected that the user has a \* monospace font with inconsistent normal/bold/italic height. See \* notebookmain.js. Such fonts will have keywords vertically offset with \* respect to the rest of the text. The user should select a better font. \* See: https://github.com/ipython/ipython/issues/1503 \* \* .CodeMirror span { \* vertical-align: bottom; \* } \*/ .CodeMirror { line-height: 1.21429em; /\* Changed from 1em to our global default \*/ font-size: 14px; height: auto; /\* Changed to auto to autogrow \*/ background: none; /\* Changed from white to allow our bg to show through \*/ } .CodeMirror-scroll { /\* The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.\*/ /\* We have found that if it is visible, vertical scrollbars appear with font size changes.\*/ overflow-y: hidden; overflow-x: auto; } .CodeMirror-lines { /\* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because \*/ /\* we have set a different line-height and want this to scale with that. \*/ /\* Note that this should set vertical padding only, since CodeMirror assumes that horizontal padding will be set on CodeMirror pre \*/ padding: 0.4em 0; } .CodeMirror-linenumber { padding: 0 8px 0 4px; } .CodeMirror-gutters { border-bottom-left-radius: 2px; border-top-left-radius: 2px; } .CodeMirror pre { /\* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only, use .CodeMirror-lines for vertical \*/ padding: 0 0.4em; border: 0; border-radius: 0; } .CodeMirror-cursor { border-left: 1.4px solid black; } @media screen and (min-width: 2138px) and (max-width: 4319px) { .CodeMirror-cursor { border-left: 2px solid black; } } @media screen and (min-width: 4320px) { .CodeMirror-cursor { border-left: 4px solid black; } } /\* Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org> Adapted from GitHub theme \*/ .highlight-base { color: #000; } .highlight-variable { color: #000; } .highlight-variable-2 { color: #1a1a1a; } .highlight-variable-3 { color: #333333; } .highlight-string { color: #BA2121; } .highlight-comment { color: #408080; font-style: italic; } .highlight-number { color: #080; } .highlight-atom { color: #88F; } .highlight-keyword { color: #008000; font-weight: bold; } .highlight-builtin { color: #008000; } .highlight-error { color: #f00; } .highlight-operator { color: #AA22FF; font-weight: bold; } .highlight-meta { color: #AA22FF; } /\* previously not defined, copying from default codemirror \*/ .highlight-def { color: #00f; } .highlight-string-2 { color: #f50; } .highlight-qualifier { color: #555; } .highlight-bracket { color: #997; } .highlight-tag { color: #170; } .highlight-attribute { color: #00c; } .highlight-header { color: blue; } .highlight-quote { color: #090; } .highlight-link { color: #00c; } /\* apply the same style to codemirror \*/ .cm-s-ipython span.cm-keyword { color: #008000; font-weight: bold; } .cm-s-ipython span.cm-atom { color: #88F; } .cm-s-ipython span.cm-number { color: #080; } .cm-s-ipython span.cm-def { color: #00f; } .cm-s-ipython span.cm-variable { color: #000; } .cm-s-ipython span.cm-operator { color: #AA22FF; font-weight: bold; } .cm-s-ipython span.cm-variable-2 { color: #1a1a1a; } .cm-s-ipython span.cm-variable-3 { color: #333333; } .cm-s-ipython span.cm-comment { color: #408080; font-style: italic; } .cm-s-ipython span.cm-string { color: #BA2121; } .cm-s-ipython span.cm-string-2 { color: #f50; } .cm-s-ipython span.cm-meta { color: #AA22FF; } .cm-s-ipython span.cm-qualifier { color: #555; } .cm-s-ipython span.cm-builtin { color: #008000; } .cm-s-ipython span.cm-bracket { color: #997; } .cm-s-ipython span.cm-tag { color: #170; } .cm-s-ipython span.cm-attribute { color: #00c; } .cm-s-ipython span.cm-header { color: blue; } .cm-s-ipython span.cm-quote { color: #090; } .cm-s-ipython span.cm-link { color: #00c; } .cm-s-ipython span.cm-error { color: #f00; } .cm-s-ipython span.cm-tab { background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=); background-position: right; background-repeat: no-repeat; } div.output\_wrapper { /\* this position must be relative to enable descendents to be absolute within it \*/ position: relative; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; z-index: 1; } /\* class for the output area when it should be height-limited \*/ div.output\_scroll { /\* ideally, this would be max-height, but FF barfs all over that \*/ height: 24em; /\* FF needs this \*and the wrapper\* to specify full width, or it will shrinkwrap \*/ width: 100%; overflow: auto; border-radius: 2px; -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8); box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8); display: block; } /\* output div while it is collapsed \*/ div.output\_collapsed { margin: 0px; padding: 0px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } div.out\_prompt\_overlay { height: 100%; padding: 0px 0.4em; position: absolute; border-radius: 2px; } div.out\_prompt\_overlay:hover { /\* use inner shadow to get border that is computed the same on WebKit/FF \*/ -webkit-box-shadow: inset 0 0 1px #000; box-shadow: inset 0 0 1px #000; background: rgba(240, 240, 240, 0.5); } div.output\_prompt { color: #D84315; } /\* This class is the outer container of all output sections. \*/ div.output\_area { padding: 0px; page-break-inside: avoid; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } div.output\_area .MathJax\_Display { text-align: left !important; } div.output\_area .rendered\_html table { margin-left: 0; margin-right: 0; } div.output\_area .rendered\_html img { margin-left: 0; margin-right: 0; } div.output\_area img, div.output\_area svg { max-width: 100%; height: auto; } div.output\_area img.unconfined, div.output\_area svg.unconfined { max-width: none; } div.output\_area .mglyph > img { max-width: none; } /\* This is needed to protect the pre formating from global settings such as that of bootstrap \*/ .output { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } @media (max-width: 540px) { div.output\_area { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } } div.output\_area pre { margin: 0; padding: 1px 0 1px 0; border: 0; vertical-align: baseline; color: black; background-color: transparent; border-radius: 0; } /\* This class is for the output subarea inside the output\_area and after the prompt div. \*/ div.output\_subarea { overflow-x: auto; padding: 0.4em; /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; max-width: calc(100% - 14ex); } div.output\_scroll div.output\_subarea { overflow-x: visible; } /\* The rest of the output\_\* classes are for special styling of the different output types \*/ /\* all text output has this class: \*/ div.output\_text { text-align: left; color: #000; /\* This has to match that of the the CodeMirror class line-height below \*/ line-height: 1.21429em; } /\* stdout/stderr are 'text' as well as 'stream', but execute\_result/error are \*not\* streams \*/ div.output\_stderr { background: #fdd; /\* very light red background for stderr \*/ } div.output\_latex { text-align: left; } /\* Empty output\_javascript divs should have no height \*/ div.output\_javascript:empty { padding: 0; } .js-error { color: darkred; } /\* raw\_input styles \*/ div.raw\_input\_container { line-height: 1.21429em; padding-top: 5px; } pre.raw\_input\_prompt { /\* nothing needed here. \*/ } input.raw\_input { font-family: monospace; font-size: inherit; color: inherit; width: auto; /\* make sure input baseline aligns with prompt \*/ vertical-align: baseline; /\* padding + margin = 0.5em between prompt and cursor \*/ padding: 0em 0.25em; margin: 0em 0.25em; } input.raw\_input:focus { box-shadow: none; } p.p-space { margin-bottom: 10px; } div.output\_unrecognized { padding: 5px; font-weight: bold; color: red; } div.output\_unrecognized a { color: inherit; text-decoration: none; } div.output\_unrecognized a:hover { color: inherit; text-decoration: none; } .rendered\_html { color: #000; /\* any extras will just be numbers: \*/ } .rendered\_html em { font-style: italic; } .rendered\_html strong { font-weight: bold; } .rendered\_html u { text-decoration: underline; } .rendered\_html :link { text-decoration: underline; } .rendered\_html :visited { text-decoration: underline; } .rendered\_html h1 { font-size: 185.7%; margin: 1.08em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h2 { font-size: 157.1%; margin: 1.27em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h3 { font-size: 128.6%; margin: 1.55em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h4 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h5 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; font-style: italic; } .rendered\_html h6 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; font-style: italic; } .rendered\_html h1:first-child { margin-top: 0.538em; } .rendered\_html h2:first-child { margin-top: 0.636em; } .rendered\_html h3:first-child { margin-top: 0.777em; } .rendered\_html h4:first-child { margin-top: 1em; } .rendered\_html h5:first-child { margin-top: 1em; } .rendered\_html h6:first-child { margin-top: 1em; } .rendered\_html ul:not(.list-inline), .rendered\_html ol:not(.list-inline) { padding-left: 2em; } .rendered\_html ul { list-style: disc; } .rendered\_html ul ul { list-style: square; margin-top: 0; } .rendered\_html ul ul ul { list-style: circle; } .rendered\_html ol { list-style: decimal; } .rendered\_html ol ol { list-style: upper-alpha; margin-top: 0; } .rendered\_html ol ol ol { list-style: lower-alpha; } .rendered\_html ol ol ol ol { list-style: lower-roman; } .rendered\_html ol ol ol ol ol { list-style: decimal; } .rendered\_html \* + ul { margin-top: 1em; } .rendered\_html \* + ol { margin-top: 1em; } .rendered\_html hr { color: black; background-color: black; } .rendered\_html pre { margin: 1em 2em; padding: 0px; background-color: #fff; } .rendered\_html code { background-color: #eff0f1; } .rendered\_html p code { padding: 1px 5px; } .rendered\_html pre code { background-color: #fff; } .rendered\_html pre, .rendered\_html code { border: 0; color: #000; font-size: 100%; } .rendered\_html blockquote { margin: 1em 2em; } .rendered\_html table { margin-left: auto; margin-right: auto; border: none; border-collapse: collapse; border-spacing: 0; color: black; font-size: 12px; table-layout: fixed; } .rendered\_html thead { border-bottom: 1px solid black; vertical-align: bottom; } .rendered\_html tr, .rendered\_html th, .rendered\_html td { text-align: right; vertical-align: middle; padding: 0.5em 0.5em; line-height: normal; white-space: normal; max-width: none; border: none; } .rendered\_html th { font-weight: bold; } .rendered\_html tbody tr:nth-child(odd) { background: #f5f5f5; } .rendered\_html tbody tr:hover { background: rgba(66, 165, 245, 0.2); } .rendered\_html \* + table { margin-top: 1em; } .rendered\_html p { text-align: left; } .rendered\_html \* + p { margin-top: 1em; } .rendered\_html img { display: block; margin-left: auto; margin-right: auto; } .rendered\_html \* + img { margin-top: 1em; } .rendered\_html img, .rendered\_html svg { max-width: 100%; height: auto; } .rendered\_html img.unconfined, .rendered\_html svg.unconfined { max-width: none; } .rendered\_html .alert { margin-bottom: initial; } .rendered\_html \* + .alert { margin-top: 1em; } [dir="rtl"] .rendered\_html p { text-align: right; } div.text\_cell { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } @media (max-width: 540px) { div.text\_cell > div.prompt { display: none; } } div.text\_cell\_render { /\*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;\*/ outline: none; resize: none; width: inherit; border-style: none; padding: 0.5em 0.5em 0.5em 0.4em; color: #000; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } a.anchor-link:link { text-decoration: none; padding: 0px 20px; visibility: hidden; } h1:hover .anchor-link, h2:hover .anchor-link, h3:hover .anchor-link, h4:hover .anchor-link, h5:hover .anchor-link, h6:hover .anchor-link { visibility: visible; } .text\_cell.rendered .input\_area { display: none; } .text\_cell.rendered .rendered\_html { overflow-x: auto; overflow-y: hidden; } .text\_cell.rendered .rendered\_html tr, .text\_cell.rendered .rendered\_html th, .text\_cell.rendered .rendered\_html td { max-width: none; } .text\_cell.unrendered .text\_cell\_render { display: none; } .text\_cell .dropzone .input\_area { border: 2px dashed #bababa; margin: -1px; } .cm-header-1, .cm-header-2, .cm-header-3, .cm-header-4, .cm-header-5, .cm-header-6 { font-weight: bold; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; } .cm-header-1 { font-size: 185.7%; } .cm-header-2 { font-size: 157.1%; } .cm-header-3 { font-size: 128.6%; } .cm-header-4 { font-size: 110%; } .cm-header-5 { font-size: 100%; font-style: italic; } .cm-header-6 { font-size: 100%; font-style: italic; } /\*! \* \* IPython notebook webapp \* \*/ @media (max-width: 767px) { .notebook\_app { padding-left: 0px; padding-right: 0px; } } #ipython-main-app { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; height: 100%; } div#notebook\_panel { margin: 0px; padding: 0px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; height: 100%; } div#notebook { font-size: 14px; line-height: 20px; overflow-y: hidden; overflow-x: auto; width: 100%; /\* This spaces the page away from the edge of the notebook area \*/ padding-top: 20px; margin: 0px; outline: none; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; min-height: 100%; } @media not print { #notebook-container { padding: 15px; background-color: #fff; min-height: 0; -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } } @media print { #notebook-container { width: 100%; } } div.ui-widget-content { border: 1px solid #ababab; outline: none; } pre.dialog { background-color: #f7f7f7; border: 1px solid #ddd; border-radius: 2px; padding: 0.4em; padding-left: 2em; } p.dialog { padding: 0.2em; } /\* Word-wrap output correctly. This is the CSS3 spelling, though Firefox seems to not honor it correctly. Webkit browsers (Chrome, rekonq, Safari) do. \*/ pre, code, kbd, samp { white-space: pre-wrap; } #fonttest { font-family: monospace; } p { margin-bottom: 0; } .end\_space { min-height: 100px; transition: height .2s ease; } .notebook\_app > #header { -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } @media not print { .notebook\_app { background-color: #EEE; } } kbd { border-style: solid; border-width: 1px; box-shadow: none; margin: 2px; padding-left: 2px; padding-right: 2px; padding-top: 1px; padding-bottom: 1px; } .jupyter-keybindings { padding: 1px; line-height: 24px; border-bottom: 1px solid gray; } .jupyter-keybindings input { margin: 0; padding: 0; border: none; } .jupyter-keybindings i { padding: 6px; } .well code { background-color: #ffffff; border-color: #ababab; border-width: 1px; border-style: solid; padding: 2px; padding-top: 1px; padding-bottom: 1px; } /\* CSS for the cell toolbar \*/ .celltoolbar { border: thin solid #CFCFCF; border-bottom: none; background: #EEE; border-radius: 2px 2px 0px 0px; width: 100%; height: 29px; padding-right: 4px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; /\* Old browsers \*/ -webkit-box-pack: end; -moz-box-pack: end; box-pack: end; /\* Modern browsers \*/ justify-content: flex-end; display: -webkit-flex; } @media print { .celltoolbar { display: none; } } .ctb\_hideshow { display: none; vertical-align: bottom; } /\* ctb\_show is added to the ctb\_hideshow div to show the cell toolbar. Cell toolbars are only shown when the ctb\_global\_show class is also set. \*/ .ctb\_global\_show .ctb\_show.ctb\_hideshow { display: block; } .ctb\_global\_show .ctb\_show + .input\_area, .ctb\_global\_show .ctb\_show + div.text\_cell\_input, .ctb\_global\_show .ctb\_show ~ div.text\_cell\_render { border-top-right-radius: 0px; border-top-left-radius: 0px; } .ctb\_global\_show .ctb\_show ~ div.text\_cell\_render { border: 1px solid #cfcfcf; } .celltoolbar { font-size: 87%; padding-top: 3px; } .celltoolbar select { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; width: inherit; font-size: inherit; height: 22px; padding: 0px; display: inline-block; } .celltoolbar select:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .celltoolbar select::-moz-placeholder { color: #999; opacity: 1; } .celltoolbar select:-ms-input-placeholder { color: #999; } .celltoolbar select::-webkit-input-placeholder { color: #999; } .celltoolbar select::-ms-expand { border: 0; background-color: transparent; } .celltoolbar select[disabled], .celltoolbar select[readonly], fieldset[disabled] .celltoolbar select { background-color: #eeeeee; opacity: 1; } .celltoolbar select[disabled], fieldset[disabled] .celltoolbar select { cursor: not-allowed; } textarea.celltoolbar select { height: auto; } select.celltoolbar select { height: 30px; line-height: 30px; } textarea.celltoolbar select, select[multiple].celltoolbar select { height: auto; } .celltoolbar label { margin-left: 5px; margin-right: 5px; } .tags\_button\_container { width: 100%; display: flex; } .tag-container { display: flex; flex-direction: row; flex-grow: 1; overflow: hidden; position: relative; } .tag-container > \* { margin: 0 4px; } .remove-tag-btn { margin-left: 4px; } .tags-input { display: flex; } .cell-tag:last-child:after { content: ""; position: absolute; right: 0; width: 40px; height: 100%; /\* Fade to background color of cell toolbar \*/ background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE); } .tags-input > \* { margin-left: 4px; } .cell-tag, .tags-input input, .tags-input button { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; box-shadow: none; width: inherit; font-size: inherit; height: 22px; line-height: 22px; padding: 0px 4px; display: inline-block; } .cell-tag:focus, .tags-input input:focus, .tags-input button:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .cell-tag::-moz-placeholder, .tags-input input::-moz-placeholder, .tags-input button::-moz-placeholder { color: #999; opacity: 1; } .cell-tag:-ms-input-placeholder, .tags-input input:-ms-input-placeholder, .tags-input button:-ms-input-placeholder { color: #999; } .cell-tag::-webkit-input-placeholder, .tags-input input::-webkit-input-placeholder, .tags-input button::-webkit-input-placeholder { color: #999; } .cell-tag::-ms-expand, .tags-input input::-ms-expand, .tags-input button::-ms-expand { border: 0; background-color: transparent; } .cell-tag[disabled], .tags-input input[disabled], .tags-input button[disabled], .cell-tag[readonly], .tags-input input[readonly], .tags-input button[readonly], fieldset[disabled] .cell-tag, fieldset[disabled] .tags-input input, fieldset[disabled] .tags-input button { background-color: #eeeeee; opacity: 1; } .cell-tag[disabled], .tags-input input[disabled], .tags-input button[disabled], fieldset[disabled] .cell-tag, fieldset[disabled] .tags-input input, fieldset[disabled] .tags-input button { cursor: not-allowed; } textarea.cell-tag, textarea.tags-input input, textarea.tags-input button { height: auto; } select.cell-tag, select.tags-input input, select.tags-input button { height: 30px; line-height: 30px; } textarea.cell-tag, textarea.tags-input input, textarea.tags-input button, select[multiple].cell-tag, select[multiple].tags-input input, select[multiple].tags-input button { height: auto; } .cell-tag, .tags-input button { padding: 0px 4px; } .cell-tag { background-color: #fff; white-space: nowrap; } .tags-input input[type=text]:focus { outline: none; box-shadow: none; border-color: #ccc; } .completions { position: absolute; z-index: 110; overflow: hidden; border: 1px solid #ababab; border-radius: 2px; -webkit-box-shadow: 0px 6px 10px -1px #adadad; box-shadow: 0px 6px 10px -1px #adadad; line-height: 1; } .completions select { background: white; outline: none; border: none; padding: 0px; margin: 0px; overflow: auto; font-family: monospace; font-size: 110%; color: #000; width: auto; } .completions select option.context { color: #286090; } #kernel\_logo\_widget .current\_kernel\_logo { display: none; margin-top: -1px; margin-bottom: -1px; width: 32px; height: 32px; } [dir="rtl"] #kernel\_logo\_widget { float: left !important; float: left; } .modal .modal-body .move-path { display: flex; flex-direction: row; justify-content: space; align-items: center; } .modal .modal-body .move-path .server-root { padding-right: 20px; } .modal .modal-body .move-path .path-input { flex: 1; } #menubar { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; margin-top: 1px; } #menubar .navbar { border-top: 1px; border-radius: 0px 0px 2px 2px; margin-bottom: 0px; } #menubar .navbar-toggle { float: left; padding-top: 7px; padding-bottom: 7px; border: none; } #menubar .navbar-collapse { clear: left; } [dir="rtl"] #menubar .navbar-toggle { float: right; } [dir="rtl"] #menubar .navbar-collapse { clear: right; } [dir="rtl"] #menubar .navbar-nav { float: right; } [dir="rtl"] #menubar .nav { padding-right: 0px; } [dir="rtl"] #menubar .navbar-nav > li { float: right; } [dir="rtl"] #menubar .navbar-right { float: left !important; } [dir="rtl"] ul.dropdown-menu { text-align: right; left: auto; } [dir="rtl"] ul#new-menu.dropdown-menu { right: auto; left: 0; } .nav-wrapper { border-bottom: 1px solid #e7e7e7; } i.menu-icon { padding-top: 4px; } [dir="rtl"] i.menu-icon.pull-right { float: left !important; float: left; } ul#help\_menu li a { overflow: hidden; padding-right: 2.2em; } ul#help\_menu li a i { margin-right: -1.2em; } [dir="rtl"] ul#help\_menu li a { padding-left: 2.2em; } [dir="rtl"] ul#help\_menu li a i { margin-right: 0; margin-left: -1.2em; } [dir="rtl"] ul#help\_menu li a i.pull-right { float: left !important; float: left; } .dropdown-submenu { position: relative; } .dropdown-submenu > .dropdown-menu { top: 0; left: 100%; margin-top: -6px; margin-left: -1px; } [dir="rtl"] .dropdown-submenu > .dropdown-menu { right: 100%; margin-right: -1px; } .dropdown-submenu:hover > .dropdown-menu { display: block; } .dropdown-submenu > a:after { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; display: block; content: "\f0da"; float: right; color: #333333; margin-top: 2px; margin-right: -10px; } .dropdown-submenu > a:after.fa-pull-left { margin-right: .3em; } .dropdown-submenu > a:after.fa-pull-right { margin-left: .3em; } .dropdown-submenu > a:after.pull-left { margin-right: .3em; } .dropdown-submenu > a:after.pull-right { margin-left: .3em; } [dir="rtl"] .dropdown-submenu > a:after { float: left; content: "\f0d9"; margin-right: 0; margin-left: -10px; } .dropdown-submenu:hover > a:after { color: #262626; } .dropdown-submenu.pull-left { float: none; } .dropdown-submenu.pull-left > .dropdown-menu { left: -100%; margin-left: 10px; } #notification\_area { float: right !important; float: right; z-index: 10; } [dir="rtl"] #notification\_area { float: left !important; float: left; } .indicator\_area { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; } [dir="rtl"] .indicator\_area { float: left !important; float: left; } #kernel\_indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; border-left: 1px solid; } #kernel\_indicator .kernel\_indicator\_name { padding-left: 5px; padding-right: 5px; } [dir="rtl"] #kernel\_indicator { float: left !important; float: left; border-left: 0; border-right: 1px solid; } #modal\_indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; } [dir="rtl"] #modal\_indicator { float: left !important; float: left; } #readonly-indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; margin-top: 2px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; display: none; } .modal\_indicator:before { width: 1.28571429em; text-align: center; } .edit\_mode .modal\_indicator:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f040"; } .edit\_mode .modal\_indicator:before.fa-pull-left { margin-right: .3em; } .edit\_mode .modal\_indicator:before.fa-pull-right { margin-left: .3em; } .edit\_mode .modal\_indicator:before.pull-left { margin-right: .3em; } .edit\_mode .modal\_indicator:before.pull-right { margin-left: .3em; } .command\_mode .modal\_indicator:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: ' '; } .command\_mode .modal\_indicator:before.fa-pull-left { margin-right: .3em; } .command\_mode .modal\_indicator:before.fa-pull-right { margin-left: .3em; } .command\_mode .modal\_indicator:before.pull-left { margin-right: .3em; } .command\_mode .modal\_indicator:before.pull-right { margin-left: .3em; } .kernel\_idle\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f10c"; } .kernel\_idle\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_idle\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_idle\_icon:before.pull-left { margin-right: .3em; } .kernel\_idle\_icon:before.pull-right { margin-left: .3em; } .kernel\_busy\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f111"; } .kernel\_busy\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_busy\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_busy\_icon:before.pull-left { margin-right: .3em; } .kernel\_busy\_icon:before.pull-right { margin-left: .3em; } .kernel\_dead\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f1e2"; } .kernel\_dead\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_dead\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_dead\_icon:before.pull-left { margin-right: .3em; } .kernel\_dead\_icon:before.pull-right { margin-left: .3em; } .kernel\_disconnected\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f127"; } .kernel\_disconnected\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_disconnected\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_disconnected\_icon:before.pull-left { margin-right: .3em; } .kernel\_disconnected\_icon:before.pull-right { margin-left: .3em; } .notification\_widget { color: #777; z-index: 10; background: rgba(240, 240, 240, 0.5); margin-right: 4px; color: #333; background-color: #fff; border-color: #ccc; } .notification\_widget:focus, .notification\_widget.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } .notification\_widget:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } .notification\_widget:active, .notification\_widget.active, .open > .dropdown-toggle.notification\_widget { color: #333; background-color: #e6e6e6; border-color: #adadad; } .notification\_widget:active:hover, .notification\_widget.active:hover, .open > .dropdown-toggle.notification\_widget:hover, .notification\_widget:active:focus, .notification\_widget.active:focus, .open > .dropdown-toggle.notification\_widget:focus, .notification\_widget:active.focus, .notification\_widget.active.focus, .open > .dropdown-toggle.notification\_widget.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } .notification\_widget:active, .notification\_widget.active, .open > .dropdown-toggle.notification\_widget { background-image: none; } .notification\_widget.disabled:hover, .notification\_widget[disabled]:hover, fieldset[disabled] .notification\_widget:hover, .notification\_widget.disabled:focus, .notification\_widget[disabled]:focus, fieldset[disabled] .notification\_widget:focus, .notification\_widget.disabled.focus, .notification\_widget[disabled].focus, fieldset[disabled] .notification\_widget.focus { background-color: #fff; border-color: #ccc; } .notification\_widget .badge { color: #fff; background-color: #333; } .notification\_widget.warning { color: #fff; background-color: #f0ad4e; border-color: #eea236; } .notification\_widget.warning:focus, .notification\_widget.warning.focus { color: #fff; background-color: #ec971f; border-color: #985f0d; } .notification\_widget.warning:hover { color: #fff; background-color: #ec971f; border-color: #d58512; } .notification\_widget.warning:active, .notification\_widget.warning.active, .open > .dropdown-toggle.notification\_widget.warning { color: #fff; background-color: #ec971f; border-color: #d58512; } .notification\_widget.warning:active:hover, .notification\_widget.warning.active:hover, .open > .dropdown-toggle.notification\_widget.warning:hover, .notification\_widget.warning:active:focus, .notification\_widget.warning.active:focus, .open > .dropdown-toggle.notification\_widget.warning:focus, .notification\_widget.warning:active.focus, .notification\_widget.warning.active.focus, .open > .dropdown-toggle.notification\_widget.warning.focus { color: #fff; background-color: #d58512; border-color: #985f0d; } .notification\_widget.warning:active, .notification\_widget.warning.active, .open > .dropdown-toggle.notification\_widget.warning { background-image: none; } .notification\_widget.warning.disabled:hover, .notification\_widget.warning[disabled]:hover, fieldset[disabled] .notification\_widget.warning:hover, .notification\_widget.warning.disabled:focus, .notification\_widget.warning[disabled]:focus, fieldset[disabled] .notification\_widget.warning:focus, .notification\_widget.warning.disabled.focus, .notification\_widget.warning[disabled].focus, fieldset[disabled] .notification\_widget.warning.focus { background-color: #f0ad4e; border-color: #eea236; } .notification\_widget.warning .badge { color: #f0ad4e; background-color: #fff; } .notification\_widget.success { color: #fff; background-color: #5cb85c; border-color: #4cae4c; } .notification\_widget.success:focus, .notification\_widget.success.focus { color: #fff; background-color: #449d44; 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border-top: none; }@media print { div.cell { display: block; page-break-inside: avoid; } div.output\_wrapper { display: block; page-break-inside: avoid; } div.output { display: block; page-break-inside: avoid; } } Introduction[¶](#Introduction) ------------------------------ This tutorial shows how to compute the power spectra of spin 0 and 2 fields. We will use the `CAR` pixellisation to pass through the different steps of generation. If you are interested on doing the same thing with `HEALPIX` pixellisation, you can have a look at this [file](tutorial_spectra_healpix_spin0and2.ipynb). The `CAR` survey mask is defined with coordinates `ra0, ra1, dec0, dec1` and has an angular resolution of 1 arcminute. We simulate 2 splits with 5 µK.arcmin noise and we also include a point source mask with 100 holes of size 10 arcminutes. We appodize the survey mask with an apodisation of 1 degree and the point source mask with an apodisation of 0.3 degree. We finally compute the spectra between the 2 splits of data up to `lmax=1000`. Preamble[¶](#Preamble) ---------------------- `matplotlib` magic In [1]: ``` %matplotlib inline ``` Versions used for this tutorial In [2]: ``` import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import pspy, pixell print(" Numpy :", np.\_\_version\_\_) print("Matplotlib :", mpl.\_\_version\_\_) print(" pixell :", pixell.\_\_version\_\_) print(" pspy :", pspy.\_\_version\_\_) ``` ``` Numpy : 1.18.0 Matplotlib : 3.1.2 pixell : 0.6.0+34.g23be32d pspy : 0+untagged.118.gbf1f0bc.dirty ``` Get default data dir from `pspy` and set Planck colormap as default In [3]: ``` from pspy.so\_config import DEFAULT\_DATA\_DIR pixell.colorize.mpl\_setdefault("planck") ``` Generation of the templates, mask and apodisation type[¶](#Generation-of-the-templates,-mask-and-apodisation-type) ------------------------------------------------------------------------------------------------------------------ We start by specifying the `CAR` survey parameters, it will go from right ascension `ra0` to `ra1` and from declination `dec0` to `dec1` (all in degrees) with a resolution of 1 arcminute. In [4]: ``` ra0, ra1, dec0, dec1 = -25, 25, -25, 25 res = 1 ``` For this example, we will make use of 3 components : Temperature (spin 0) and polarisation Q and U (spin 2) In [5]: ``` ncomp = 3 ``` Given the parameters, we can generate the `CAR` template as follow In [6]: ``` from pspy import so\_map template\_car = so\_map.car\_template(ncomp, ra0, ra1, dec0, dec1, res) ``` We also define a binary template for the window function: we set pixels inside the survey at 1 and at the border to be zero In [7]: ``` binary\_car = so\_map.car\_template(1, ra0, ra1, dec0, dec1, res) binary\_car.data[:] = 0 binary\_car.data[1:-1, 1:-1] = 1 ``` Generation of spectra[¶](#Generation-of-spectra) ------------------------------------------------ ### Generation of simulations[¶](#Generation-of-simulations) We first have to compute a set of theoretical power spectra $C\_\ell$ using a Boltzmann solver such as [CAMB](https://camb.readthedocs.io/en/latest/) and we need to install it since this is a prerequisite of `pspy`. We can do it within this notebook by executing the following command In [8]: ``` %pip install camb ``` ``` Requirement already satisfied: camb in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (1.1.0) Requirement already satisfied: scipy>=1.0 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.4.1) Requirement already satisfied: six in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.13.0) Requirement already satisfied: sympy>=1.0 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.5) Requirement already satisfied: numpy>=1.13.3 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from scipy>=1.0->camb) (1.18.0) Requirement already satisfied: mpmath>=0.19 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from sympy>=1.0->camb) (1.1.0) Note: you may need to restart the kernel to use updated packages. ``` To make sure everything goes well, we can import `CAMB` and check its version In [9]: ``` import camb print("CAMB version:", camb.\_\_version\_\_) ``` ``` CAMB version: 1.1.0 ``` Now that `CAMB` is properly installed, we will produce the $C\_\ell$s from $\ell$min=2 to $\ell$max=104 for the following set of $\Lambda$CDM parameters In [10]: ``` lmin, lmax = 2, 10\*\*4 l = np.arange(lmin, lmax) cosmo\_params = { "H0": 67.5, "As": 1e-10\*np.exp(3.044), "ombh2": 0.02237, "omch2": 0.1200, "ns": 0.9649, "Alens": 1.0, "tau": 0.0544 } pars = camb.set\_params(\*\*cosmo\_params) pars.set\_for\_lmax(lmax, lens\_potential\_accuracy=1) results = camb.get\_results(pars) powers = results.get\_cmb\_power\_spectra(pars, CMB\_unit="muK") ``` We finally have to write the $C\_\ell$s into a file to feed the `so_map.synfast` function In [11]: ``` import os output\_dir = "/tmp/tutorial\_spectra\_spin0and2" os.makedirs(output\_dir, exist\_ok=True) cl\_file = output\_dir + "/cl\_camb.dat" np.savetxt(cl\_file, np.hstack([l[:, np.newaxis], powers["total"][lmin:lmax]])) ``` Given the `CAMB` file, we generate a CMB realisation In [12]: ``` cmb = template\_car.synfast(cl\_file) ``` Then, we make 2 splits out of it, each with 5 µK.arcmin rms in temperature and 5xsqrt(2) µK.arcmin in polarisation In [13]: ``` nsplits = 2 splits = [cmb.copy() for i in range(nsplits)] for i in range(nsplits): noise = so\_map.white\_noise(cmb, rms\_uKarcmin\_T=5, rms\_uKarcmin\_pol=np.sqrt(2)\*5) splits[i].data += noise.data ``` We can plot each component T, Q, U for both splits and for the original CMB realisation In [14]: ``` fig, axes = plt.subplots(3, 3, figsize=(9, 9), sharex=True, sharey=True) fields = ["T", "Q", "U"] kwargs = dict(extent=[ra1, ra0, dec0, dec1], origin="lower") for i, field in enumerate(fields): if field=='T': vrange={"vmin": -300, "vmax": 300} else: vrange={"vmin": -20, "vmax": 20} axes[0, i].set\_title(fields[i]) axes[0, i].imshow(cmb.data[i], \*\*kwargs, \*\*vrange) axes[1, i].imshow(splits[0].data[i], \*\*kwargs, \*\*vrange) axes[2, i].imshow(splits[1].data[i], \*\*kwargs, \*\*vrange) axes[0, 0].set\_ylabel("original") axes[1, 0].set\_ylabel("split 0") axes[2, 0].set\_ylabel("split 1") plt.tight\_layout() ```  ### Generate window[¶](#Generate-window) We then create an apodisation for the survey mask. We use an apodisation routine designed for rectangle maps and an apodisation size of 1 degree In [15]: ``` from pspy import so\_window window = so\_window.create\_apodization(binary\_car, apo\_type="C1", apo\_radius\_degree=1) ``` We also create a point source mask made of 100 holes each with a 10 arcminutes size In [16]: ``` mask = so\_map.simulate\_source\_mask(binary\_car, n\_holes=100, hole\_radius\_arcmin=10) ``` and we apodize it In [17]: ``` mask = so\_window.create\_apodization(mask, apo\_type="C1", apo\_radius\_degree=0.3) ``` The window is given by the product of the survey window and the mask window In [18]: ``` window.data \*= mask.data plt.figure(figsize=(5, 5)) plt.imshow(window.data, \*\*kwargs, vmin=-1, vmax=+1) ``` Out[18]: ``` <matplotlib.image.AxesImage at 0x7fa14252e310> ```  ### Compute mode coupling matrix[¶](#Compute-mode-coupling-matrix) For spin 0 and 2 the window need to be a tuple made of two objects: the window used for spin 0 and the one used for spin 2 In [19]: ``` window = (window, window) ``` The windows (for `spin0` and `spin2`) are going to couple mode together, we compute a mode coupling matrix in order to undo this effect given a binning file (format: lmin, lmax, lmean) and a $\ell$max value of 1000 In [20]: ``` lmax = 1000 binning\_file = output\_dir + "/binning.dat" from pspy import pspy\_utils pspy\_utils.create\_binning\_file(bin\_size=40, n\_bins=100, file\_name=binning\_file) from pspy import so\_mcm mbb\_inv, Bbl = so\_mcm.mcm\_and\_bbl\_spin0and2(window, binning\_file, lmax=lmax, type="Dl", niter=0) ``` ### Compute alms and bin spectra[¶](#Compute-alms-and-bin-spectra) In [21]: ``` from pspy import sph\_tools alms = [sph\_tools.get\_alms(split, window, niter=0, lmax=lmax) for split in splits] ``` We need to specify the order of the spectra to be used by `pspy` In [22]: ``` spectra = ["TT", "TE", "TB", "ET", "BT", "EE", "EB", "BE", "BB"] ``` and we finally build a dictionary of cross split spectra In [23]: ``` Db\_dict = {} from itertools import combinations\_with\_replacement as cwr for (i1, alm1), (i2, alm2) in cwr(enumerate(alms), 2): from pspy import so\_spectra l, ps = so\_spectra.get\_spectra(alm1, alm2, spectra=spectra) lb, Db = so\_spectra.bin\_spectra(l, ps, binning\_file, lmax, type="Dl", mbb\_inv=mbb\_inv, spectra=spectra) Db\_dict.update({"split{}xsplit{}".format(i1, i2): Db}) ``` To compare with the input $C\_\ell$, we also compute the binned theory spectra In [24]: ``` from pspy import pspy\_utils l, ps\_theory = pspy\_utils.ps\_lensed\_theory\_to\_dict(cl\_file, 'Dl', lmax=lmax) ps\_theory\_b = so\_mcm.apply\_Bbl(Bbl, ps\_theory, spectra=spectra) ``` and we finally plot all the results In [25]: ``` fig, axes = plt.subplots(3, 3, figsize=(15, 12), sharex=True) ax = axes.flatten() for i, spec in enumerate(spectra): for k, v in Db\_dict.items(): ax[i].plot(lb, v[spec], "-o", label=k) ax[i].plot(lb, ps\_theory\_b[spec], "--", color="tab:red", label="binned theory") ax[i].plot(l, ps\_theory[spec], color="tab:red", label="theory") ax[i].set\_ylabel(r'$D^{%s}\_{\ell}$'%spec, fontsize=20) if i==0: fig.legend(loc="upper left", bbox\_to\_anchor=(1,1)) for ax in axes[-1]: ax.set\_xlabel(r'$\ell$',fontsize=20) plt.tight\_layout() ```  In [ ]: ``` ``` tutorial\_io /\*! 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} .glyphicon-queen:before { content: "\e212"; } .glyphicon-pawn:before { content: "\e213"; } .glyphicon-bishop:before { content: "\e214"; } .glyphicon-knight:before { content: "\e215"; } .glyphicon-baby-formula:before { content: "\e216"; } .glyphicon-tent:before { content: "\26fa"; } .glyphicon-blackboard:before { content: "\e218"; } .glyphicon-bed:before { content: "\e219"; } .glyphicon-apple:before { content: "\f8ff"; } .glyphicon-erase:before { content: "\e221"; } .glyphicon-hourglass:before { content: "\231b"; } .glyphicon-lamp:before { content: "\e223"; } .glyphicon-duplicate:before { content: "\e224"; } .glyphicon-piggy-bank:before { content: "\e225"; } .glyphicon-scissors:before { content: "\e226"; } .glyphicon-bitcoin:before { content: "\e227"; } .glyphicon-btc:before { content: "\e227"; } .glyphicon-xbt:before { content: "\e227"; } .glyphicon-yen:before { content: "\00a5"; } .glyphicon-jpy:before { content: "\00a5"; } .glyphicon-ruble:before { content: "\20bd"; } .glyphicon-rub:before { content: "\20bd"; } .glyphicon-scale:before { content: "\e230"; } .glyphicon-ice-lolly:before { content: "\e231"; } .glyphicon-ice-lolly-tasted:before { content: "\e232"; } .glyphicon-education:before { content: "\e233"; } .glyphicon-option-horizontal:before { content: "\e234"; } .glyphicon-option-vertical:before { content: "\e235"; } .glyphicon-menu-hamburger:before { content: "\e236"; } .glyphicon-modal-window:before { content: "\e237"; } .glyphicon-oil:before { content: "\e238"; } .glyphicon-grain:before { content: "\e239"; } .glyphicon-sunglasses:before { content: "\e240"; } .glyphicon-text-size:before { content: "\e241"; } .glyphicon-text-color:before { content: "\e242"; } .glyphicon-text-background:before { content: "\e243"; } .glyphicon-object-align-top:before { content: "\e244"; } .glyphicon-object-align-bottom:before { content: "\e245"; } .glyphicon-object-align-horizontal:before { content: "\e246"; } .glyphicon-object-align-left:before { content: "\e247"; } .glyphicon-object-align-vertical:before { content: "\e248"; } .glyphicon-object-align-right:before { content: "\e249"; } .glyphicon-triangle-right:before { content: "\e250"; } .glyphicon-triangle-left:before { content: "\e251"; } .glyphicon-triangle-bottom:before { content: "\e252"; } .glyphicon-triangle-top:before { content: "\e253"; } .glyphicon-console:before { content: "\e254"; } .glyphicon-superscript:before { content: "\e255"; } .glyphicon-subscript:before { content: "\e256"; } .glyphicon-menu-left:before { content: "\e257"; } .glyphicon-menu-right:before { content: "\e258"; } .glyphicon-menu-down:before { content: "\e259"; } .glyphicon-menu-up:before { content: "\e260"; } \* { -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; } \*:before, \*:after { -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; } html { font-size: 10px; -webkit-tap-highlight-color: rgba(0, 0, 0, 0); } body { font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-size: 13px; line-height: 1.42857143; color: #000; background-color: #fff; } input, button, select, textarea { font-family: inherit; font-size: inherit; line-height: inherit; } a { color: #337ab7; text-decoration: none; } a:hover, a:focus { color: #23527c; text-decoration: underline; } a:focus { outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } figure { margin: 0; } img { vertical-align: middle; } .img-responsive, .thumbnail > img, .thumbnail a > img, .carousel-inner > .item > img, .carousel-inner > .item > a > img { display: block; max-width: 100%; height: auto; } .img-rounded { border-radius: 3px; } .img-thumbnail { padding: 4px; line-height: 1.42857143; background-color: #fff; border: 1px solid #ddd; border-radius: 2px; -webkit-transition: all 0.2s ease-in-out; -o-transition: all 0.2s ease-in-out; transition: all 0.2s ease-in-out; display: inline-block; max-width: 100%; height: auto; } .img-circle { border-radius: 50%; } hr { margin-top: 18px; margin-bottom: 18px; border: 0; border-top: 1px solid #eeeeee; } .sr-only { position: absolute; width: 1px; height: 1px; margin: -1px; padding: 0; overflow: hidden; clip: rect(0, 0, 0, 0); border: 0; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } [role="button"] { cursor: pointer; } h1, h2, h3, h4, h5, h6, .h1, .h2, .h3, .h4, .h5, .h6 { font-family: inherit; font-weight: 500; line-height: 1.1; color: inherit; } h1 small, h2 small, h3 small, h4 small, h5 small, h6 small, .h1 small, .h2 small, .h3 small, .h4 small, .h5 small, .h6 small, h1 .small, h2 .small, h3 .small, h4 .small, h5 .small, h6 .small, .h1 .small, .h2 .small, .h3 .small, .h4 .small, .h5 .small, .h6 .small { font-weight: normal; line-height: 1; color: #777777; } h1, .h1, h2, .h2, h3, .h3 { margin-top: 18px; margin-bottom: 9px; } h1 small, .h1 small, h2 small, .h2 small, h3 small, .h3 small, h1 .small, .h1 .small, h2 .small, .h2 .small, h3 .small, .h3 .small { font-size: 65%; } h4, .h4, h5, .h5, h6, .h6 { margin-top: 9px; margin-bottom: 9px; } h4 small, .h4 small, h5 small, .h5 small, h6 small, .h6 small, h4 .small, .h4 .small, h5 .small, .h5 .small, h6 .small, .h6 .small { font-size: 75%; } h1, .h1 { font-size: 33px; } h2, .h2 { font-size: 27px; } h3, .h3 { font-size: 23px; } h4, .h4 { font-size: 17px; } h5, .h5 { font-size: 13px; } h6, .h6 { font-size: 12px; } p { margin: 0 0 9px; } .lead { margin-bottom: 18px; font-size: 14px; font-weight: 300; line-height: 1.4; } @media (min-width: 768px) { .lead { font-size: 19.5px; } } small, .small { font-size: 92%; } mark, .mark { background-color: #fcf8e3; padding: .2em; } .text-left { text-align: left; } .text-right { text-align: right; } .text-center { text-align: center; } .text-justify { text-align: justify; } .text-nowrap { white-space: nowrap; } .text-lowercase { text-transform: lowercase; } .text-uppercase { text-transform: uppercase; } .text-capitalize { text-transform: capitalize; } .text-muted { color: #777777; } .text-primary { color: #337ab7; } a.text-primary:hover, a.text-primary:focus { color: #286090; } .text-success { color: #3c763d; } a.text-success:hover, a.text-success:focus { color: #2b542c; } .text-info { color: #31708f; } a.text-info:hover, a.text-info:focus { color: #245269; } .text-warning { color: #8a6d3b; } a.text-warning:hover, a.text-warning:focus { color: #66512c; } .text-danger { color: #a94442; } a.text-danger:hover, a.text-danger:focus { color: #843534; } .bg-primary { color: #fff; background-color: #337ab7; } a.bg-primary:hover, a.bg-primary:focus { background-color: #286090; } .bg-success { background-color: #dff0d8; } a.bg-success:hover, a.bg-success:focus { background-color: #c1e2b3; } .bg-info { background-color: #d9edf7; } a.bg-info:hover, a.bg-info:focus { background-color: #afd9ee; } .bg-warning { background-color: #fcf8e3; } a.bg-warning:hover, a.bg-warning:focus { background-color: #f7ecb5; } .bg-danger { background-color: #f2dede; } a.bg-danger:hover, a.bg-danger:focus { background-color: #e4b9b9; } .page-header { padding-bottom: 8px; margin: 36px 0 18px; border-bottom: 1px solid #eeeeee; } ul, ol { margin-top: 0; margin-bottom: 9px; } ul ul, ol ul, ul ol, ol ol { margin-bottom: 0; } .list-unstyled { padding-left: 0; list-style: none; } .list-inline { padding-left: 0; list-style: none; margin-left: -5px; } .list-inline > li { display: inline-block; padding-left: 5px; padding-right: 5px; } dl { margin-top: 0; margin-bottom: 18px; } dt, dd { line-height: 1.42857143; } dt { font-weight: bold; } dd { margin-left: 0; } @media (min-width: 541px) { .dl-horizontal dt { float: left; width: 160px; clear: left; text-align: right; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; } .dl-horizontal dd { margin-left: 180px; } } abbr[title], abbr[data-original-title] { cursor: help; border-bottom: 1px dotted #777777; } .initialism { font-size: 90%; text-transform: uppercase; } blockquote { padding: 9px 18px; margin: 0 0 18px; font-size: inherit; border-left: 5px solid #eeeeee; } blockquote p:last-child, blockquote ul:last-child, blockquote ol:last-child { margin-bottom: 0; } blockquote footer, blockquote small, blockquote .small { display: block; font-size: 80%; line-height: 1.42857143; color: #777777; } blockquote footer:before, blockquote small:before, blockquote .small:before { content: '\2014 \00A0'; } .blockquote-reverse, blockquote.pull-right { padding-right: 15px; padding-left: 0; border-right: 5px solid #eeeeee; border-left: 0; text-align: right; } .blockquote-reverse footer:before, blockquote.pull-right footer:before, .blockquote-reverse small:before, blockquote.pull-right small:before, .blockquote-reverse .small:before, blockquote.pull-right .small:before { content: ''; } .blockquote-reverse footer:after, blockquote.pull-right footer:after, .blockquote-reverse small:after, blockquote.pull-right small:after, .blockquote-reverse .small:after, blockquote.pull-right .small:after { content: '\00A0 \2014'; } address { margin-bottom: 18px; font-style: normal; line-height: 1.42857143; } code, kbd, pre, samp { font-family: monospace; } code { padding: 2px 4px; font-size: 90%; color: #c7254e; background-color: #f9f2f4; border-radius: 2px; } kbd { padding: 2px 4px; font-size: 90%; color: #888; background-color: transparent; border-radius: 1px; box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); } kbd kbd { padding: 0; font-size: 100%; font-weight: bold; box-shadow: none; } pre { display: block; padding: 8.5px; margin: 0 0 9px; font-size: 12px; line-height: 1.42857143; word-break: break-all; word-wrap: break-word; color: #333333; background-color: #f5f5f5; border: 1px solid #ccc; border-radius: 2px; } pre code { padding: 0; font-size: inherit; color: inherit; white-space: pre-wrap; background-color: transparent; border-radius: 0; } .pre-scrollable { max-height: 340px; overflow-y: scroll; } .container { margin-right: auto; margin-left: auto; padding-left: 0px; padding-right: 0px; } @media (min-width: 768px) { .container { width: 768px; } } @media (min-width: 992px) { .container { width: 940px; } } @media (min-width: 1200px) { .container { width: 1140px; } } .container-fluid { margin-right: auto; margin-left: auto; padding-left: 0px; padding-right: 0px; } .row { margin-left: 0px; margin-right: 0px; } .col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 { position: relative; min-height: 1px; padding-left: 0px; padding-right: 0px; } .col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 { float: left; } .col-xs-12 { width: 100%; } .col-xs-11 { width: 91.66666667%; } .col-xs-10 { width: 83.33333333%; } .col-xs-9 { width: 75%; } .col-xs-8 { width: 66.66666667%; } .col-xs-7 { width: 58.33333333%; } .col-xs-6 { width: 50%; } .col-xs-5 { width: 41.66666667%; } .col-xs-4 { width: 33.33333333%; } .col-xs-3 { width: 25%; } .col-xs-2 { width: 16.66666667%; } .col-xs-1 { width: 8.33333333%; } .col-xs-pull-12 { right: 100%; } .col-xs-pull-11 { right: 91.66666667%; } .col-xs-pull-10 { right: 83.33333333%; } .col-xs-pull-9 { right: 75%; } .col-xs-pull-8 { right: 66.66666667%; } .col-xs-pull-7 { right: 58.33333333%; } .col-xs-pull-6 { right: 50%; } .col-xs-pull-5 { right: 41.66666667%; } .col-xs-pull-4 { right: 33.33333333%; } .col-xs-pull-3 { right: 25%; } .col-xs-pull-2 { right: 16.66666667%; } .col-xs-pull-1 { right: 8.33333333%; } .col-xs-pull-0 { right: auto; } .col-xs-push-12 { left: 100%; } .col-xs-push-11 { left: 91.66666667%; } .col-xs-push-10 { left: 83.33333333%; } .col-xs-push-9 { left: 75%; } .col-xs-push-8 { left: 66.66666667%; } .col-xs-push-7 { left: 58.33333333%; } .col-xs-push-6 { left: 50%; } .col-xs-push-5 { left: 41.66666667%; } .col-xs-push-4 { left: 33.33333333%; } .col-xs-push-3 { left: 25%; } .col-xs-push-2 { left: 16.66666667%; } .col-xs-push-1 { left: 8.33333333%; } .col-xs-push-0 { left: auto; } .col-xs-offset-12 { margin-left: 100%; } .col-xs-offset-11 { margin-left: 91.66666667%; } .col-xs-offset-10 { margin-left: 83.33333333%; } .col-xs-offset-9 { margin-left: 75%; } .col-xs-offset-8 { margin-left: 66.66666667%; } .col-xs-offset-7 { margin-left: 58.33333333%; } .col-xs-offset-6 { margin-left: 50%; } .col-xs-offset-5 { margin-left: 41.66666667%; } .col-xs-offset-4 { margin-left: 33.33333333%; } .col-xs-offset-3 { margin-left: 25%; } .col-xs-offset-2 { margin-left: 16.66666667%; } .col-xs-offset-1 { margin-left: 8.33333333%; } .col-xs-offset-0 { margin-left: 0%; } @media (min-width: 768px) { .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 { float: left; } .col-sm-12 { width: 100%; } .col-sm-11 { width: 91.66666667%; } .col-sm-10 { width: 83.33333333%; } .col-sm-9 { width: 75%; } .col-sm-8 { width: 66.66666667%; } .col-sm-7 { width: 58.33333333%; } .col-sm-6 { width: 50%; } .col-sm-5 { width: 41.66666667%; } .col-sm-4 { width: 33.33333333%; } .col-sm-3 { width: 25%; } .col-sm-2 { width: 16.66666667%; } .col-sm-1 { width: 8.33333333%; } .col-sm-pull-12 { right: 100%; } .col-sm-pull-11 { right: 91.66666667%; } .col-sm-pull-10 { right: 83.33333333%; } .col-sm-pull-9 { right: 75%; } .col-sm-pull-8 { right: 66.66666667%; } .col-sm-pull-7 { right: 58.33333333%; } .col-sm-pull-6 { right: 50%; } .col-sm-pull-5 { right: 41.66666667%; } .col-sm-pull-4 { right: 33.33333333%; } .col-sm-pull-3 { right: 25%; } .col-sm-pull-2 { right: 16.66666667%; } .col-sm-pull-1 { right: 8.33333333%; } .col-sm-pull-0 { right: auto; } .col-sm-push-12 { left: 100%; } .col-sm-push-11 { left: 91.66666667%; } .col-sm-push-10 { left: 83.33333333%; } .col-sm-push-9 { left: 75%; } .col-sm-push-8 { left: 66.66666667%; } .col-sm-push-7 { left: 58.33333333%; } .col-sm-push-6 { left: 50%; } .col-sm-push-5 { left: 41.66666667%; } .col-sm-push-4 { left: 33.33333333%; } .col-sm-push-3 { left: 25%; } .col-sm-push-2 { left: 16.66666667%; } .col-sm-push-1 { left: 8.33333333%; } .col-sm-push-0 { left: auto; } .col-sm-offset-12 { margin-left: 100%; } .col-sm-offset-11 { margin-left: 91.66666667%; } .col-sm-offset-10 { margin-left: 83.33333333%; } .col-sm-offset-9 { margin-left: 75%; } .col-sm-offset-8 { margin-left: 66.66666667%; } .col-sm-offset-7 { margin-left: 58.33333333%; } .col-sm-offset-6 { margin-left: 50%; } .col-sm-offset-5 { margin-left: 41.66666667%; } .col-sm-offset-4 { margin-left: 33.33333333%; } .col-sm-offset-3 { margin-left: 25%; } .col-sm-offset-2 { margin-left: 16.66666667%; } .col-sm-offset-1 { margin-left: 8.33333333%; } .col-sm-offset-0 { margin-left: 0%; } } @media (min-width: 992px) { .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 { float: left; } .col-md-12 { width: 100%; } .col-md-11 { width: 91.66666667%; } .col-md-10 { width: 83.33333333%; } .col-md-9 { width: 75%; } .col-md-8 { width: 66.66666667%; } .col-md-7 { width: 58.33333333%; } .col-md-6 { width: 50%; } .col-md-5 { width: 41.66666667%; } .col-md-4 { width: 33.33333333%; } .col-md-3 { width: 25%; } .col-md-2 { width: 16.66666667%; } .col-md-1 { width: 8.33333333%; } .col-md-pull-12 { right: 100%; } .col-md-pull-11 { right: 91.66666667%; } .col-md-pull-10 { right: 83.33333333%; } .col-md-pull-9 { right: 75%; } .col-md-pull-8 { right: 66.66666667%; } .col-md-pull-7 { right: 58.33333333%; } .col-md-pull-6 { right: 50%; } .col-md-pull-5 { right: 41.66666667%; } .col-md-pull-4 { right: 33.33333333%; } .col-md-pull-3 { right: 25%; } .col-md-pull-2 { right: 16.66666667%; } .col-md-pull-1 { right: 8.33333333%; } .col-md-pull-0 { right: auto; } .col-md-push-12 { left: 100%; } .col-md-push-11 { left: 91.66666667%; } .col-md-push-10 { left: 83.33333333%; } .col-md-push-9 { left: 75%; } .col-md-push-8 { left: 66.66666667%; } .col-md-push-7 { left: 58.33333333%; } .col-md-push-6 { left: 50%; } .col-md-push-5 { left: 41.66666667%; } .col-md-push-4 { left: 33.33333333%; } .col-md-push-3 { left: 25%; } .col-md-push-2 { left: 16.66666667%; } .col-md-push-1 { left: 8.33333333%; } .col-md-push-0 { left: auto; } .col-md-offset-12 { margin-left: 100%; } .col-md-offset-11 { margin-left: 91.66666667%; } .col-md-offset-10 { margin-left: 83.33333333%; } .col-md-offset-9 { margin-left: 75%; } .col-md-offset-8 { margin-left: 66.66666667%; } .col-md-offset-7 { margin-left: 58.33333333%; } .col-md-offset-6 { margin-left: 50%; } .col-md-offset-5 { margin-left: 41.66666667%; } .col-md-offset-4 { margin-left: 33.33333333%; } .col-md-offset-3 { margin-left: 25%; } .col-md-offset-2 { margin-left: 16.66666667%; } .col-md-offset-1 { margin-left: 8.33333333%; } .col-md-offset-0 { margin-left: 0%; } } @media (min-width: 1200px) { .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 { float: left; } .col-lg-12 { width: 100%; } .col-lg-11 { width: 91.66666667%; } .col-lg-10 { width: 83.33333333%; } .col-lg-9 { width: 75%; } .col-lg-8 { width: 66.66666667%; } .col-lg-7 { width: 58.33333333%; } .col-lg-6 { width: 50%; } .col-lg-5 { width: 41.66666667%; } .col-lg-4 { width: 33.33333333%; } .col-lg-3 { width: 25%; } .col-lg-2 { width: 16.66666667%; } .col-lg-1 { width: 8.33333333%; } .col-lg-pull-12 { right: 100%; } .col-lg-pull-11 { right: 91.66666667%; } .col-lg-pull-10 { right: 83.33333333%; } .col-lg-pull-9 { right: 75%; } .col-lg-pull-8 { right: 66.66666667%; } .col-lg-pull-7 { right: 58.33333333%; } .col-lg-pull-6 { right: 50%; } .col-lg-pull-5 { right: 41.66666667%; } .col-lg-pull-4 { right: 33.33333333%; } .col-lg-pull-3 { right: 25%; } .col-lg-pull-2 { right: 16.66666667%; } .col-lg-pull-1 { right: 8.33333333%; } .col-lg-pull-0 { right: auto; } .col-lg-push-12 { left: 100%; } .col-lg-push-11 { left: 91.66666667%; } .col-lg-push-10 { left: 83.33333333%; } .col-lg-push-9 { left: 75%; } .col-lg-push-8 { left: 66.66666667%; } .col-lg-push-7 { left: 58.33333333%; } .col-lg-push-6 { left: 50%; } .col-lg-push-5 { left: 41.66666667%; } .col-lg-push-4 { left: 33.33333333%; } .col-lg-push-3 { left: 25%; } .col-lg-push-2 { left: 16.66666667%; } .col-lg-push-1 { left: 8.33333333%; } .col-lg-push-0 { left: auto; } .col-lg-offset-12 { margin-left: 100%; } .col-lg-offset-11 { margin-left: 91.66666667%; } .col-lg-offset-10 { margin-left: 83.33333333%; } .col-lg-offset-9 { margin-left: 75%; } .col-lg-offset-8 { margin-left: 66.66666667%; } .col-lg-offset-7 { margin-left: 58.33333333%; } .col-lg-offset-6 { margin-left: 50%; } .col-lg-offset-5 { margin-left: 41.66666667%; } .col-lg-offset-4 { margin-left: 33.33333333%; } .col-lg-offset-3 { margin-left: 25%; } .col-lg-offset-2 { margin-left: 16.66666667%; } .col-lg-offset-1 { margin-left: 8.33333333%; } .col-lg-offset-0 { margin-left: 0%; } } table { background-color: transparent; } caption { padding-top: 8px; padding-bottom: 8px; color: #777777; text-align: left; } th { text-align: left; } .table { width: 100%; max-width: 100%; margin-bottom: 18px; } .table > thead > tr > th, .table > tbody > tr > th, .table > tfoot > tr > th, .table > thead > tr > td, .table > tbody > tr > td, .table > tfoot > tr > td { padding: 8px; line-height: 1.42857143; vertical-align: top; border-top: 1px solid #ddd; } .table > thead > tr > th { vertical-align: bottom; border-bottom: 2px solid #ddd; } .table > caption + thead > tr:first-child > th, .table > colgroup + thead > tr:first-child > th, .table > thead:first-child > tr:first-child > th, .table > caption + thead > tr:first-child > td, .table > colgroup + thead > tr:first-child > td, .table > thead:first-child > tr:first-child > td { border-top: 0; } .table > tbody + tbody { border-top: 2px solid #ddd; } .table .table { background-color: #fff; } .table-condensed > thead > tr > th, .table-condensed > tbody > tr > th, .table-condensed > tfoot > tr > th, .table-condensed > thead > tr > td, .table-condensed > tbody > tr > td, .table-condensed > tfoot > tr > td { padding: 5px; } .table-bordered { border: 1px solid #ddd; } .table-bordered > thead > tr > th, .table-bordered > tbody > tr > th, .table-bordered > tfoot > tr > th, .table-bordered > thead > tr > td, .table-bordered > tbody > tr > td, .table-bordered > tfoot > tr > td { border: 1px solid #ddd; } .table-bordered > thead > tr > th, .table-bordered > thead > tr > td { border-bottom-width: 2px; } .table-striped > tbody > tr:nth-of-type(odd) { background-color: #f9f9f9; } .table-hover > tbody > tr:hover { background-color: #f5f5f5; } table col[class\*="col-"] { position: static; float: none; display: table-column; } table td[class\*="col-"], table th[class\*="col-"] { position: static; float: none; display: table-cell; } .table > thead > tr > td.active, .table > tbody > tr > td.active, .table > tfoot > tr > td.active, .table > thead > tr > th.active, .table > tbody > tr > th.active, .table > tfoot > tr > th.active, .table > thead > tr.active > td, .table > tbody > tr.active > td, .table > tfoot > tr.active > td, .table > thead > tr.active > th, .table > tbody > tr.active > th, .table > tfoot > tr.active > th { background-color: #f5f5f5; } .table-hover > tbody > tr > td.active:hover, .table-hover > tbody > tr > th.active:hover, .table-hover > tbody > tr.active:hover > td, .table-hover > tbody > tr:hover > .active, .table-hover > tbody > tr.active:hover > th { background-color: #e8e8e8; } .table > thead > tr > td.success, .table > tbody > tr > td.success, .table > tfoot > tr > td.success, .table > thead > tr > th.success, .table > tbody > tr > th.success, .table > tfoot > tr > th.success, .table > thead > tr.success > td, .table > tbody > tr.success > td, .table > tfoot > tr.success > td, .table > thead > tr.success > th, .table > tbody > tr.success > th, .table > tfoot > tr.success > th { background-color: #dff0d8; } .table-hover > tbody > tr > td.success:hover, .table-hover > tbody > tr > th.success:hover, .table-hover > tbody > tr.success:hover > td, .table-hover > tbody > tr:hover > .success, .table-hover > tbody > tr.success:hover > th { background-color: #d0e9c6; } .table > thead > tr > td.info, .table > tbody > tr > td.info, .table > tfoot > tr > td.info, .table > thead > tr > th.info, .table > tbody > tr > th.info, .table > tfoot > tr > th.info, .table > thead > tr.info > td, .table > tbody > tr.info > td, .table > tfoot > tr.info > td, .table > thead > tr.info > th, .table > tbody > tr.info > th, .table > tfoot > tr.info > th { background-color: #d9edf7; } .table-hover > tbody > tr > td.info:hover, .table-hover > tbody > tr > th.info:hover, .table-hover > tbody > tr.info:hover > td, .table-hover > tbody > tr:hover > .info, .table-hover > tbody > tr.info:hover > th { background-color: #c4e3f3; } .table > thead > tr > td.warning, .table > tbody > tr > td.warning, .table > tfoot > tr > td.warning, .table > thead > tr > th.warning, .table > tbody > tr > th.warning, .table > tfoot > tr > th.warning, .table > thead > tr.warning > td, .table > tbody > tr.warning > td, .table > tfoot > tr.warning > td, .table > thead > tr.warning > th, .table > tbody > tr.warning > th, .table > tfoot > tr.warning > th { background-color: #fcf8e3; } .table-hover > tbody > tr > td.warning:hover, .table-hover > tbody > tr > th.warning:hover, .table-hover > tbody > tr.warning:hover > td, .table-hover > tbody > tr:hover > .warning, .table-hover > tbody > tr.warning:hover > th { background-color: #faf2cc; } .table > thead > tr > td.danger, .table > tbody > tr > td.danger, .table > tfoot > tr > td.danger, .table > thead > tr > th.danger, .table > tbody > tr > th.danger, .table > tfoot > tr > th.danger, .table > thead > tr.danger > td, .table > tbody > tr.danger > td, .table > tfoot > tr.danger > td, .table > thead > tr.danger > th, .table > tbody > tr.danger > th, .table > tfoot > tr.danger > th { background-color: #f2dede; } .table-hover > tbody > tr > td.danger:hover, .table-hover > tbody > tr > th.danger:hover, .table-hover > tbody > tr.danger:hover > td, .table-hover > tbody > tr:hover > .danger, .table-hover > tbody > tr.danger:hover > th { background-color: #ebcccc; } .table-responsive { overflow-x: auto; min-height: 0.01%; } @media screen and (max-width: 767px) { .table-responsive { width: 100%; margin-bottom: 13.5px; overflow-y: hidden; -ms-overflow-style: -ms-autohiding-scrollbar; border: 1px solid #ddd; } .table-responsive > .table { margin-bottom: 0; } .table-responsive > .table > thead > tr > th, .table-responsive > .table > tbody > tr > th, .table-responsive > .table > tfoot > tr > th, .table-responsive > .table > thead > tr > td, .table-responsive > .table > tbody > tr > td, .table-responsive > .table > tfoot > tr > td { white-space: nowrap; } .table-responsive > .table-bordered { border: 0; } .table-responsive > .table-bordered > thead > tr > th:first-child, .table-responsive > .table-bordered > tbody > tr > th:first-child, .table-responsive > .table-bordered > tfoot > tr > th:first-child, .table-responsive > .table-bordered > thead > tr > td:first-child, .table-responsive > .table-bordered > tbody > tr > td:first-child, .table-responsive > .table-bordered > tfoot > tr > td:first-child { border-left: 0; } .table-responsive > .table-bordered > thead > tr > th:last-child, .table-responsive > .table-bordered > tbody > tr > th:last-child, .table-responsive > .table-bordered > tfoot > tr > th:last-child, .table-responsive > .table-bordered > thead > tr > td:last-child, .table-responsive > .table-bordered > tbody > tr > td:last-child, .table-responsive > .table-bordered > tfoot > tr > td:last-child { border-right: 0; } .table-responsive > .table-bordered > tbody > tr:last-child > th, .table-responsive > .table-bordered > tfoot > tr:last-child > th, .table-responsive > .table-bordered > tbody > tr:last-child > td, .table-responsive > .table-bordered > tfoot > tr:last-child > td { border-bottom: 0; } } fieldset { padding: 0; margin: 0; border: 0; min-width: 0; } legend { display: block; width: 100%; padding: 0; margin-bottom: 18px; font-size: 19.5px; line-height: inherit; color: #333333; border: 0; border-bottom: 1px solid #e5e5e5; } label { display: inline-block; max-width: 100%; margin-bottom: 5px; font-weight: bold; } input[type="search"] { -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; } input[type="radio"], input[type="checkbox"] { margin: 4px 0 0; margin-top: 1px \9; line-height: normal; } input[type="file"] { display: block; } input[type="range"] { display: block; width: 100%; } select[multiple], select[size] { height: auto; } input[type="file"]:focus, input[type="radio"]:focus, input[type="checkbox"]:focus { outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } output { display: block; padding-top: 7px; font-size: 13px; line-height: 1.42857143; color: #555555; } .form-control { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; } .form-control:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .form-control::-moz-placeholder { color: #999; opacity: 1; } .form-control:-ms-input-placeholder { color: #999; } .form-control::-webkit-input-placeholder { color: #999; } .form-control::-ms-expand { border: 0; background-color: transparent; } .form-control[disabled], .form-control[readonly], fieldset[disabled] .form-control { background-color: #eeeeee; opacity: 1; } .form-control[disabled], fieldset[disabled] .form-control { cursor: not-allowed; } textarea.form-control { height: auto; } input[type="search"] { -webkit-appearance: none; } @media screen and (-webkit-min-device-pixel-ratio: 0) { input[type="date"].form-control, input[type="time"].form-control, input[type="datetime-local"].form-control, input[type="month"].form-control { line-height: 32px; } input[type="date"].input-sm, input[type="time"].input-sm, input[type="datetime-local"].input-sm, input[type="month"].input-sm, .input-group-sm input[type="date"], .input-group-sm input[type="time"], .input-group-sm input[type="datetime-local"], .input-group-sm input[type="month"] { line-height: 30px; } input[type="date"].input-lg, input[type="time"].input-lg, input[type="datetime-local"].input-lg, input[type="month"].input-lg, .input-group-lg input[type="date"], .input-group-lg input[type="time"], .input-group-lg input[type="datetime-local"], .input-group-lg input[type="month"] { line-height: 45px; } } .form-group { margin-bottom: 15px; } .radio, .checkbox { position: relative; display: block; margin-top: 10px; margin-bottom: 10px; } .radio label, .checkbox label { min-height: 18px; padding-left: 20px; margin-bottom: 0; font-weight: normal; cursor: pointer; } .radio input[type="radio"], .radio-inline input[type="radio"], .checkbox input[type="checkbox"], .checkbox-inline input[type="checkbox"] { position: absolute; margin-left: -20px; margin-top: 4px \9; } .radio + .radio, .checkbox + .checkbox { margin-top: -5px; } .radio-inline, .checkbox-inline { position: relative; display: inline-block; padding-left: 20px; margin-bottom: 0; vertical-align: middle; font-weight: normal; cursor: pointer; } .radio-inline + .radio-inline, .checkbox-inline + .checkbox-inline { margin-top: 0; margin-left: 10px; } input[type="radio"][disabled], input[type="checkbox"][disabled], input[type="radio"].disabled, input[type="checkbox"].disabled, fieldset[disabled] input[type="radio"], fieldset[disabled] input[type="checkbox"] { cursor: not-allowed; } .radio-inline.disabled, .checkbox-inline.disabled, fieldset[disabled] .radio-inline, fieldset[disabled] .checkbox-inline { cursor: not-allowed; } .radio.disabled label, .checkbox.disabled label, fieldset[disabled] .radio label, fieldset[disabled] .checkbox label { cursor: not-allowed; } .form-control-static { padding-top: 7px; padding-bottom: 7px; margin-bottom: 0; min-height: 31px; } .form-control-static.input-lg, .form-control-static.input-sm { padding-left: 0; padding-right: 0; } .input-sm { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } select.input-sm { height: 30px; line-height: 30px; } textarea.input-sm, select[multiple].input-sm { height: auto; } .form-group-sm .form-control { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .form-group-sm select.form-control { height: 30px; line-height: 30px; } .form-group-sm textarea.form-control, .form-group-sm select[multiple].form-control { height: auto; } .form-group-sm .form-control-static { height: 30px; min-height: 30px; padding: 6px 10px; font-size: 12px; line-height: 1.5; } .input-lg { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } select.input-lg { height: 45px; line-height: 45px; } textarea.input-lg, select[multiple].input-lg { height: auto; } .form-group-lg .form-control { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } .form-group-lg select.form-control { height: 45px; line-height: 45px; } .form-group-lg textarea.form-control, .form-group-lg select[multiple].form-control { height: auto; } .form-group-lg .form-control-static { height: 45px; min-height: 35px; padding: 11px 16px; font-size: 17px; line-height: 1.3333333; } .has-feedback { position: relative; } .has-feedback .form-control { padding-right: 40px; } .form-control-feedback { position: absolute; top: 0; right: 0; z-index: 2; display: block; width: 32px; height: 32px; line-height: 32px; text-align: center; pointer-events: none; } .input-lg + .form-control-feedback, .input-group-lg + .form-control-feedback, .form-group-lg .form-control + .form-control-feedback { width: 45px; height: 45px; line-height: 45px; } .input-sm + .form-control-feedback, .input-group-sm + .form-control-feedback, .form-group-sm .form-control + .form-control-feedback { width: 30px; height: 30px; line-height: 30px; } .has-success .help-block, .has-success .control-label, .has-success .radio, .has-success .checkbox, .has-success .radio-inline, .has-success .checkbox-inline, .has-success.radio label, .has-success.checkbox label, .has-success.radio-inline label, .has-success.checkbox-inline label { color: #3c763d; } .has-success .form-control { border-color: #3c763d; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-success .form-control:focus { border-color: #2b542c; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; } .has-success .input-group-addon { color: #3c763d; border-color: #3c763d; background-color: #dff0d8; } .has-success .form-control-feedback { color: #3c763d; } .has-warning .help-block, .has-warning .control-label, .has-warning .radio, .has-warning .checkbox, .has-warning .radio-inline, .has-warning .checkbox-inline, .has-warning.radio label, .has-warning.checkbox label, .has-warning.radio-inline label, .has-warning.checkbox-inline label { color: #8a6d3b; } .has-warning .form-control { border-color: #8a6d3b; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-warning .form-control:focus { border-color: #66512c; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; } .has-warning .input-group-addon { color: #8a6d3b; border-color: #8a6d3b; background-color: #fcf8e3; } .has-warning .form-control-feedback { color: #8a6d3b; } .has-error .help-block, .has-error .control-label, .has-error .radio, .has-error .checkbox, .has-error .radio-inline, .has-error .checkbox-inline, .has-error.radio label, .has-error.checkbox label, .has-error.radio-inline label, .has-error.checkbox-inline label { color: #a94442; } .has-error .form-control { border-color: #a94442; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-error .form-control:focus { border-color: #843534; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; } .has-error .input-group-addon { color: #a94442; border-color: #a94442; background-color: #f2dede; } .has-error .form-control-feedback { color: #a94442; } .has-feedback label ~ .form-control-feedback { top: 23px; } .has-feedback label.sr-only ~ .form-control-feedback { top: 0; } .help-block { display: block; margin-top: 5px; margin-bottom: 10px; color: #404040; } @media (min-width: 768px) { .form-inline .form-group { display: inline-block; margin-bottom: 0; vertical-align: middle; } .form-inline .form-control { display: inline-block; width: auto; vertical-align: middle; } .form-inline .form-control-static { display: inline-block; } .form-inline .input-group { display: inline-table; vertical-align: middle; } .form-inline .input-group .input-group-addon, .form-inline .input-group .input-group-btn, .form-inline .input-group .form-control { width: auto; } .form-inline .input-group > .form-control { width: 100%; } .form-inline .control-label { margin-bottom: 0; vertical-align: middle; } .form-inline .radio, .form-inline .checkbox { display: inline-block; margin-top: 0; margin-bottom: 0; vertical-align: middle; } .form-inline .radio label, .form-inline .checkbox label { padding-left: 0; } .form-inline .radio input[type="radio"], .form-inline .checkbox input[type="checkbox"] { position: relative; margin-left: 0; } .form-inline .has-feedback .form-control-feedback { top: 0; } } .form-horizontal .radio, .form-horizontal .checkbox, .form-horizontal .radio-inline, .form-horizontal .checkbox-inline { margin-top: 0; margin-bottom: 0; padding-top: 7px; } .form-horizontal .radio, .form-horizontal .checkbox { min-height: 25px; } .form-horizontal .form-group { margin-left: 0px; margin-right: 0px; } @media (min-width: 768px) { .form-horizontal .control-label { text-align: right; margin-bottom: 0; padding-top: 7px; } } .form-horizontal .has-feedback .form-control-feedback { right: 0px; } @media (min-width: 768px) { .form-horizontal .form-group-lg .control-label { padding-top: 11px; font-size: 17px; } } @media (min-width: 768px) { .form-horizontal .form-group-sm .control-label { padding-top: 6px; font-size: 12px; } } .btn { display: inline-block; margin-bottom: 0; font-weight: normal; text-align: center; vertical-align: middle; touch-action: manipulation; cursor: pointer; background-image: none; border: 1px solid transparent; white-space: nowrap; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; border-radius: 2px; -webkit-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; } .btn:focus, .btn:active:focus, .btn.active:focus, .btn.focus, .btn:active.focus, .btn.active.focus { outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } .btn:hover, .btn:focus, .btn.focus { color: #333; text-decoration: none; } .btn:active, .btn.active { outline: 0; background-image: none; -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); } .btn.disabled, .btn[disabled], fieldset[disabled] .btn { cursor: not-allowed; opacity: 0.65; filter: alpha(opacity=65); -webkit-box-shadow: none; box-shadow: none; } a.btn.disabled, fieldset[disabled] a.btn { pointer-events: none; } .btn-default { color: #333; background-color: #fff; border-color: #ccc; } .btn-default:focus, .btn-default.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } .btn-default:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } .btn-default:active, .btn-default.active, .open > .dropdown-toggle.btn-default { color: #333; background-color: #e6e6e6; border-color: #adadad; } .btn-default:active:hover, .btn-default.active:hover, .open > .dropdown-toggle.btn-default:hover, .btn-default:active:focus, .btn-default.active:focus, .open > .dropdown-toggle.btn-default:focus, .btn-default:active.focus, .btn-default.active.focus, .open > .dropdown-toggle.btn-default.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } .btn-default:active, .btn-default.active, .open > .dropdown-toggle.btn-default { background-image: none; } .btn-default.disabled:hover, .btn-default[disabled]:hover, fieldset[disabled] .btn-default:hover, .btn-default.disabled:focus, .btn-default[disabled]:focus, fieldset[disabled] .btn-default:focus, .btn-default.disabled.focus, .btn-default[disabled].focus, fieldset[disabled] .btn-default.focus { background-color: #fff; border-color: #ccc; } .btn-default .badge { color: #fff; background-color: #333; } .btn-primary { color: #fff; background-color: #337ab7; border-color: #2e6da4; } .btn-primary:focus, .btn-primary.focus { color: #fff; background-color: #286090; border-color: #122b40; } .btn-primary:hover { color: #fff; background-color: #286090; border-color: #204d74; } .btn-primary:active, .btn-primary.active, .open > .dropdown-toggle.btn-primary { color: #fff; background-color: #286090; border-color: #204d74; } .btn-primary:active:hover, .btn-primary.active:hover, .open > .dropdown-toggle.btn-primary:hover, .btn-primary:active:focus, .btn-primary.active:focus, .open > .dropdown-toggle.btn-primary:focus, .btn-primary:active.focus, .btn-primary.active.focus, .open > .dropdown-toggle.btn-primary.focus { color: #fff; background-color: #204d74; border-color: #122b40; } .btn-primary:active, .btn-primary.active, .open > .dropdown-toggle.btn-primary { background-image: none; } .btn-primary.disabled:hover, .btn-primary[disabled]:hover, fieldset[disabled] .btn-primary:hover, .btn-primary.disabled:focus, .btn-primary[disabled]:focus, fieldset[disabled] .btn-primary:focus, .btn-primary.disabled.focus, .btn-primary[disabled].focus, fieldset[disabled] .btn-primary.focus { background-color: #337ab7; border-color: #2e6da4; } .btn-primary .badge { color: #337ab7; background-color: #fff; } .btn-success { color: #fff; background-color: #5cb85c; border-color: #4cae4c; } .btn-success:focus, .btn-success.focus { color: #fff; background-color: #449d44; border-color: #255625; } .btn-success:hover { color: #fff; background-color: #449d44; border-color: #398439; } .btn-success:active, .btn-success.active, .open > .dropdown-toggle.btn-success { color: #fff; background-color: #449d44; border-color: #398439; } .btn-success:active:hover, .btn-success.active:hover, .open > .dropdown-toggle.btn-success:hover, .btn-success:active:focus, .btn-success.active:focus, .open > .dropdown-toggle.btn-success:focus, .btn-success:active.focus, .btn-success.active.focus, .open > .dropdown-toggle.btn-success.focus { color: #fff; background-color: #398439; border-color: #255625; } .btn-success:active, .btn-success.active, .open > .dropdown-toggle.btn-success { background-image: none; } .btn-success.disabled:hover, .btn-success[disabled]:hover, fieldset[disabled] .btn-success:hover, .btn-success.disabled:focus, .btn-success[disabled]:focus, fieldset[disabled] .btn-success:focus, .btn-success.disabled.focus, .btn-success[disabled].focus, fieldset[disabled] .btn-success.focus { background-color: #5cb85c; border-color: #4cae4c; } .btn-success .badge { color: #5cb85c; background-color: #fff; } .btn-info { color: #fff; background-color: #5bc0de; border-color: #46b8da; } .btn-info:focus, .btn-info.focus { color: #fff; background-color: #31b0d5; border-color: #1b6d85; } .btn-info:hover { color: #fff; background-color: #31b0d5; border-color: #269abc; } .btn-info:active, .btn-info.active, .open > .dropdown-toggle.btn-info { color: #fff; background-color: #31b0d5; border-color: #269abc; } .btn-info:active:hover, .btn-info.active:hover, .open > .dropdown-toggle.btn-info:hover, .btn-info:active:focus, .btn-info.active:focus, .open > .dropdown-toggle.btn-info:focus, .btn-info:active.focus, .btn-info.active.focus, .open > .dropdown-toggle.btn-info.focus { color: #fff; background-color: #269abc; border-color: #1b6d85; } .btn-info:active, .btn-info.active, .open > .dropdown-toggle.btn-info { background-image: none; } .btn-info.disabled:hover, .btn-info[disabled]:hover, fieldset[disabled] .btn-info:hover, .btn-info.disabled:focus, .btn-info[disabled]:focus, fieldset[disabled] .btn-info:focus, .btn-info.disabled.focus, .btn-info[disabled].focus, fieldset[disabled] .btn-info.focus { background-color: #5bc0de; border-color: #46b8da; } .btn-info .badge { color: #5bc0de; background-color: #fff; } .btn-warning { color: #fff; background-color: #f0ad4e; border-color: #eea236; } .btn-warning:focus, .btn-warning.focus { color: #fff; background-color: #ec971f; border-color: #985f0d; } .btn-warning:hover { color: #fff; background-color: #ec971f; border-color: #d58512; } .btn-warning:active, .btn-warning.active, .open > .dropdown-toggle.btn-warning { color: #fff; background-color: #ec971f; border-color: #d58512; } .btn-warning:active:hover, .btn-warning.active:hover, .open > .dropdown-toggle.btn-warning:hover, .btn-warning:active:focus, .btn-warning.active:focus, .open > .dropdown-toggle.btn-warning:focus, .btn-warning:active.focus, .btn-warning.active.focus, .open > .dropdown-toggle.btn-warning.focus { color: #fff; background-color: #d58512; border-color: #985f0d; } .btn-warning:active, .btn-warning.active, .open > .dropdown-toggle.btn-warning { background-image: none; } .btn-warning.disabled:hover, .btn-warning[disabled]:hover, fieldset[disabled] .btn-warning:hover, .btn-warning.disabled:focus, .btn-warning[disabled]:focus, fieldset[disabled] .btn-warning:focus, .btn-warning.disabled.focus, .btn-warning[disabled].focus, fieldset[disabled] .btn-warning.focus { background-color: #f0ad4e; border-color: #eea236; } .btn-warning .badge { color: #f0ad4e; background-color: #fff; } .btn-danger { color: #fff; background-color: #d9534f; border-color: #d43f3a; } .btn-danger:focus, .btn-danger.focus { color: #fff; background-color: #c9302c; border-color: #761c19; } .btn-danger:hover { color: #fff; background-color: #c9302c; border-color: #ac2925; } .btn-danger:active, .btn-danger.active, .open > .dropdown-toggle.btn-danger { color: #fff; background-color: #c9302c; border-color: #ac2925; } .btn-danger:active:hover, .btn-danger.active:hover, .open > .dropdown-toggle.btn-danger:hover, .btn-danger:active:focus, .btn-danger.active:focus, .open > .dropdown-toggle.btn-danger:focus, .btn-danger:active.focus, .btn-danger.active.focus, .open > .dropdown-toggle.btn-danger.focus { color: #fff; background-color: #ac2925; border-color: #761c19; } .btn-danger:active, .btn-danger.active, .open > .dropdown-toggle.btn-danger { background-image: none; } .btn-danger.disabled:hover, .btn-danger[disabled]:hover, fieldset[disabled] .btn-danger:hover, .btn-danger.disabled:focus, .btn-danger[disabled]:focus, fieldset[disabled] .btn-danger:focus, .btn-danger.disabled.focus, .btn-danger[disabled].focus, fieldset[disabled] .btn-danger.focus { background-color: #d9534f; border-color: #d43f3a; } .btn-danger .badge { color: #d9534f; background-color: #fff; } .btn-link { color: #337ab7; font-weight: normal; border-radius: 0; } .btn-link, .btn-link:active, .btn-link.active, .btn-link[disabled], fieldset[disabled] .btn-link { background-color: transparent; -webkit-box-shadow: none; box-shadow: none; } .btn-link, .btn-link:hover, .btn-link:focus, .btn-link:active { border-color: transparent; } .btn-link:hover, .btn-link:focus { color: #23527c; text-decoration: underline; background-color: transparent; } .btn-link[disabled]:hover, fieldset[disabled] .btn-link:hover, .btn-link[disabled]:focus, fieldset[disabled] .btn-link:focus { color: #777777; text-decoration: none; } .btn-lg, .btn-group-lg > .btn { padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } .btn-sm, .btn-group-sm > .btn { padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .btn-xs, .btn-group-xs > .btn { padding: 1px 5px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .btn-block { display: block; width: 100%; } .btn-block + .btn-block { margin-top: 5px; } input[type="submit"].btn-block, input[type="reset"].btn-block, input[type="button"].btn-block { width: 100%; } .fade { opacity: 0; -webkit-transition: opacity 0.15s linear; -o-transition: opacity 0.15s linear; transition: opacity 0.15s linear; } .fade.in { opacity: 1; } .collapse { display: none; } .collapse.in { display: block; } tr.collapse.in { display: table-row; } tbody.collapse.in { display: table-row-group; } .collapsing { position: relative; height: 0; overflow: hidden; -webkit-transition-property: height, visibility; transition-property: height, visibility; -webkit-transition-duration: 0.35s; transition-duration: 0.35s; -webkit-transition-timing-function: ease; transition-timing-function: ease; } .caret { display: inline-block; width: 0; height: 0; margin-left: 2px; vertical-align: middle; border-top: 4px dashed; border-top: 4px solid \9; border-right: 4px solid transparent; border-left: 4px solid transparent; } .dropup, .dropdown { position: relative; } .dropdown-toggle:focus { outline: 0; } .dropdown-menu { position: absolute; top: 100%; left: 0; z-index: 1000; display: none; float: left; min-width: 160px; padding: 5px 0; margin: 2px 0 0; list-style: none; font-size: 13px; text-align: left; background-color: #fff; border: 1px solid #ccc; border: 1px solid rgba(0, 0, 0, 0.15); border-radius: 2px; -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); background-clip: padding-box; } .dropdown-menu.pull-right { right: 0; left: auto; } .dropdown-menu .divider { height: 1px; margin: 8px 0; overflow: hidden; background-color: #e5e5e5; } .dropdown-menu > li > a { display: block; padding: 3px 20px; clear: both; font-weight: normal; line-height: 1.42857143; color: #333333; white-space: nowrap; } .dropdown-menu > li > a:hover, .dropdown-menu > li > a:focus { text-decoration: none; color: #262626; background-color: #f5f5f5; } .dropdown-menu > .active > a, .dropdown-menu > .active > a:hover, .dropdown-menu > .active > a:focus { color: #fff; text-decoration: none; outline: 0; background-color: #337ab7; } .dropdown-menu > .disabled > a, .dropdown-menu > .disabled > a:hover, .dropdown-menu > .disabled > a:focus { color: #777777; } .dropdown-menu > .disabled > a:hover, .dropdown-menu > .disabled > a:focus { text-decoration: none; background-color: transparent; background-image: none; filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); cursor: not-allowed; } .open > .dropdown-menu { display: block; } .open > a { outline: 0; } .dropdown-menu-right { left: auto; right: 0; } .dropdown-menu-left { left: 0; right: auto; } .dropdown-header { display: block; padding: 3px 20px; font-size: 12px; line-height: 1.42857143; color: #777777; white-space: nowrap; } .dropdown-backdrop { position: fixed; left: 0; right: 0; bottom: 0; top: 0; z-index: 990; } .pull-right > .dropdown-menu { right: 0; left: auto; } .dropup .caret, .navbar-fixed-bottom .dropdown .caret { border-top: 0; border-bottom: 4px dashed; border-bottom: 4px solid \9; content: ""; } .dropup .dropdown-menu, .navbar-fixed-bottom .dropdown .dropdown-menu { top: auto; bottom: 100%; margin-bottom: 2px; } @media (min-width: 541px) { .navbar-right .dropdown-menu { left: auto; right: 0; } .navbar-right .dropdown-menu-left { left: 0; right: auto; } } .btn-group, .btn-group-vertical { position: relative; display: inline-block; vertical-align: middle; } .btn-group > .btn, .btn-group-vertical > .btn { position: relative; float: left; } .btn-group > .btn:hover, .btn-group-vertical > .btn:hover, .btn-group > .btn:focus, .btn-group-vertical > .btn:focus, .btn-group > .btn:active, .btn-group-vertical > .btn:active, .btn-group > .btn.active, .btn-group-vertical > .btn.active { z-index: 2; } .btn-group .btn + .btn, .btn-group .btn + .btn-group, .btn-group .btn-group + .btn, .btn-group .btn-group + .btn-group { margin-left: -1px; } .btn-toolbar { margin-left: -5px; } .btn-toolbar .btn, .btn-toolbar .btn-group, .btn-toolbar .input-group { float: left; } .btn-toolbar > .btn, .btn-toolbar > .btn-group, .btn-toolbar > .input-group { margin-left: 5px; } .btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) { border-radius: 0; } .btn-group > .btn:first-child { margin-left: 0; } .btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) { border-bottom-right-radius: 0; border-top-right-radius: 0; } .btn-group > .btn:last-child:not(:first-child), .btn-group > .dropdown-toggle:not(:first-child) { border-bottom-left-radius: 0; border-top-left-radius: 0; } .btn-group > .btn-group { float: left; } .btn-group > .btn-group:not(:first-child):not(:last-child) > .btn { border-radius: 0; } .btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child, .btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle { border-bottom-right-radius: 0; border-top-right-radius: 0; } .btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child { border-bottom-left-radius: 0; border-top-left-radius: 0; } .btn-group .dropdown-toggle:active, .btn-group.open .dropdown-toggle { outline: 0; } .btn-group > .btn + .dropdown-toggle { padding-left: 8px; padding-right: 8px; } .btn-group > .btn-lg + .dropdown-toggle { padding-left: 12px; padding-right: 12px; } .btn-group.open .dropdown-toggle { -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); } .btn-group.open .dropdown-toggle.btn-link { -webkit-box-shadow: none; box-shadow: none; } .btn .caret { margin-left: 0; } .btn-lg .caret { border-width: 5px 5px 0; border-bottom-width: 0; } .dropup .btn-lg .caret { border-width: 0 5px 5px; } .btn-group-vertical > .btn, .btn-group-vertical > .btn-group, .btn-group-vertical > .btn-group > .btn { display: block; float: none; width: 100%; max-width: 100%; } .btn-group-vertical > .btn-group > .btn { float: none; } .btn-group-vertical > .btn + .btn, .btn-group-vertical > .btn + .btn-group, .btn-group-vertical > .btn-group + .btn, .btn-group-vertical > .btn-group + .btn-group { margin-top: -1px; margin-left: 0; } .btn-group-vertical > .btn:not(:first-child):not(:last-child) { border-radius: 0; } .btn-group-vertical > .btn:first-child:not(:last-child) { border-top-right-radius: 2px; border-top-left-radius: 2px; border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .btn-group-vertical > .btn:last-child:not(:first-child) { border-top-right-radius: 0; border-top-left-radius: 0; border-bottom-right-radius: 2px; border-bottom-left-radius: 2px; } .btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn { border-radius: 0; } .btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child, .btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle { border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child { border-top-right-radius: 0; border-top-left-radius: 0; } .btn-group-justified { display: table; width: 100%; table-layout: fixed; border-collapse: separate; } .btn-group-justified > .btn, .btn-group-justified > .btn-group { float: none; display: table-cell; width: 1%; } .btn-group-justified > .btn-group .btn { width: 100%; } .btn-group-justified > .btn-group .dropdown-menu { left: auto; } [data-toggle="buttons"] > .btn input[type="radio"], [data-toggle="buttons"] > .btn-group > .btn input[type="radio"], [data-toggle="buttons"] > .btn input[type="checkbox"], [data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] { position: absolute; clip: rect(0, 0, 0, 0); pointer-events: none; } .input-group { position: relative; display: table; border-collapse: separate; } .input-group[class\*="col-"] { float: none; padding-left: 0; padding-right: 0; } .input-group .form-control { position: relative; z-index: 2; float: left; width: 100%; margin-bottom: 0; } .input-group .form-control:focus { z-index: 3; } .input-group-lg > .form-control, .input-group-lg > .input-group-addon, .input-group-lg > .input-group-btn > .btn { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } select.input-group-lg > .form-control, select.input-group-lg > .input-group-addon, select.input-group-lg > .input-group-btn > .btn { height: 45px; line-height: 45px; } textarea.input-group-lg > .form-control, textarea.input-group-lg > .input-group-addon, textarea.input-group-lg > .input-group-btn > .btn, select[multiple].input-group-lg > .form-control, select[multiple].input-group-lg > .input-group-addon, select[multiple].input-group-lg > .input-group-btn > .btn { height: auto; } .input-group-sm > .form-control, .input-group-sm > .input-group-addon, .input-group-sm > .input-group-btn > .btn { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } select.input-group-sm > .form-control, select.input-group-sm > .input-group-addon, select.input-group-sm > .input-group-btn > .btn { height: 30px; line-height: 30px; } textarea.input-group-sm > .form-control, textarea.input-group-sm > .input-group-addon, textarea.input-group-sm > .input-group-btn > .btn, select[multiple].input-group-sm > .form-control, select[multiple].input-group-sm > .input-group-addon, select[multiple].input-group-sm > .input-group-btn > .btn { height: auto; } .input-group-addon, .input-group-btn, .input-group .form-control { display: table-cell; } .input-group-addon:not(:first-child):not(:last-child), .input-group-btn:not(:first-child):not(:last-child), .input-group .form-control:not(:first-child):not(:last-child) { border-radius: 0; } .input-group-addon, .input-group-btn { width: 1%; white-space: nowrap; vertical-align: middle; } .input-group-addon { padding: 6px 12px; font-size: 13px; font-weight: normal; line-height: 1; color: #555555; text-align: center; background-color: #eeeeee; border: 1px solid #ccc; border-radius: 2px; } .input-group-addon.input-sm { padding: 5px 10px; font-size: 12px; border-radius: 1px; } .input-group-addon.input-lg { padding: 10px 16px; font-size: 17px; border-radius: 3px; } .input-group-addon input[type="radio"], .input-group-addon input[type="checkbox"] { margin-top: 0; } .input-group .form-control:first-child, .input-group-addon:first-child, .input-group-btn:first-child > .btn, .input-group-btn:first-child > .btn-group > .btn, .input-group-btn:first-child > .dropdown-toggle, .input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle), .input-group-btn:last-child > .btn-group:not(:last-child) > .btn { border-bottom-right-radius: 0; border-top-right-radius: 0; } .input-group-addon:first-child { border-right: 0; } .input-group .form-control:last-child, .input-group-addon:last-child, .input-group-btn:last-child > .btn, .input-group-btn:last-child > .btn-group > .btn, .input-group-btn:last-child > .dropdown-toggle, .input-group-btn:first-child > .btn:not(:first-child), .input-group-btn:first-child > .btn-group:not(:first-child) > .btn { border-bottom-left-radius: 0; border-top-left-radius: 0; } .input-group-addon:last-child { border-left: 0; } .input-group-btn { position: relative; font-size: 0; white-space: nowrap; } .input-group-btn > .btn { position: relative; } .input-group-btn > .btn + .btn { margin-left: -1px; } .input-group-btn > .btn:hover, .input-group-btn > .btn:focus, .input-group-btn > .btn:active { z-index: 2; } .input-group-btn:first-child > .btn, .input-group-btn:first-child > .btn-group { margin-right: -1px; } .input-group-btn:last-child > .btn, .input-group-btn:last-child > .btn-group { z-index: 2; margin-left: -1px; } .nav { margin-bottom: 0; padding-left: 0; list-style: none; } .nav > li { position: relative; display: block; } .nav > li > a { position: relative; display: block; padding: 10px 15px; } .nav > li > a:hover, .nav > li > a:focus { text-decoration: none; background-color: #eeeeee; } .nav > li.disabled > a { color: #777777; } .nav > li.disabled > a:hover, .nav > li.disabled > a:focus { color: #777777; text-decoration: none; background-color: transparent; cursor: not-allowed; } .nav .open > a, .nav .open > a:hover, .nav .open > a:focus { background-color: #eeeeee; border-color: #337ab7; } .nav .nav-divider { height: 1px; margin: 8px 0; overflow: hidden; background-color: #e5e5e5; } .nav > li > a > img { max-width: none; } .nav-tabs { border-bottom: 1px solid #ddd; } .nav-tabs > li { float: left; margin-bottom: -1px; } .nav-tabs > li > a { margin-right: 2px; line-height: 1.42857143; border: 1px solid transparent; border-radius: 2px 2px 0 0; } .nav-tabs > li > a:hover { border-color: #eeeeee #eeeeee #ddd; } .nav-tabs > li.active > a, .nav-tabs > li.active > a:hover, .nav-tabs > li.active > a:focus { color: #555555; background-color: #fff; border: 1px solid #ddd; border-bottom-color: transparent; cursor: default; } .nav-tabs.nav-justified { width: 100%; border-bottom: 0; } .nav-tabs.nav-justified > li { float: none; } .nav-tabs.nav-justified > li > a { text-align: center; margin-bottom: 5px; } .nav-tabs.nav-justified > .dropdown .dropdown-menu { top: auto; left: auto; } @media (min-width: 768px) { .nav-tabs.nav-justified > li { display: table-cell; width: 1%; } .nav-tabs.nav-justified > li > a { margin-bottom: 0; } } .nav-tabs.nav-justified > li > a { margin-right: 0; border-radius: 2px; } .nav-tabs.nav-justified > .active > a, .nav-tabs.nav-justified > .active > a:hover, .nav-tabs.nav-justified > .active > a:focus { border: 1px solid #ddd; } @media (min-width: 768px) { .nav-tabs.nav-justified > li > a { border-bottom: 1px solid #ddd; border-radius: 2px 2px 0 0; } .nav-tabs.nav-justified > .active > a, .nav-tabs.nav-justified > .active > a:hover, .nav-tabs.nav-justified > .active > a:focus { border-bottom-color: #fff; } } .nav-pills > li { float: left; } .nav-pills > li > a { border-radius: 2px; } .nav-pills > li + li { margin-left: 2px; } .nav-pills > li.active > a, .nav-pills > li.active > a:hover, .nav-pills > li.active > a:focus { color: #fff; background-color: #337ab7; } .nav-stacked > li { float: none; } .nav-stacked > li + li { margin-top: 2px; margin-left: 0; } .nav-justified { width: 100%; } .nav-justified > li { float: none; } .nav-justified > li > a { text-align: center; margin-bottom: 5px; } .nav-justified > .dropdown .dropdown-menu { top: auto; left: auto; } @media (min-width: 768px) { .nav-justified > li { display: table-cell; width: 1%; } .nav-justified > li > a { margin-bottom: 0; } } .nav-tabs-justified { border-bottom: 0; } .nav-tabs-justified > li > a { margin-right: 0; border-radius: 2px; } .nav-tabs-justified > .active > a, .nav-tabs-justified > .active > a:hover, .nav-tabs-justified > .active > a:focus { border: 1px solid #ddd; } @media (min-width: 768px) { .nav-tabs-justified > li > a { border-bottom: 1px solid #ddd; border-radius: 2px 2px 0 0; } .nav-tabs-justified > .active > a, .nav-tabs-justified > .active > a:hover, .nav-tabs-justified > .active > a:focus { border-bottom-color: #fff; } } .tab-content > .tab-pane { display: none; } .tab-content > .active { display: block; } .nav-tabs .dropdown-menu { margin-top: -1px; border-top-right-radius: 0; border-top-left-radius: 0; } .navbar { position: relative; min-height: 30px; margin-bottom: 18px; border: 1px solid transparent; } @media (min-width: 541px) { .navbar { border-radius: 2px; } } @media (min-width: 541px) { .navbar-header { float: left; } } .navbar-collapse { overflow-x: visible; padding-right: 0px; padding-left: 0px; border-top: 1px solid transparent; box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1); -webkit-overflow-scrolling: touch; } .navbar-collapse.in { overflow-y: auto; } @media (min-width: 541px) { .navbar-collapse { width: auto; border-top: 0; box-shadow: none; } .navbar-collapse.collapse { display: block !important; height: auto !important; padding-bottom: 0; overflow: visible !important; } .navbar-collapse.in { overflow-y: visible; } .navbar-fixed-top .navbar-collapse, .navbar-static-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { padding-left: 0; padding-right: 0; } } .navbar-fixed-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { max-height: 340px; } @media (max-device-width: 540px) and (orientation: landscape) { .navbar-fixed-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { max-height: 200px; } } .container > .navbar-header, .container-fluid > .navbar-header, .container > .navbar-collapse, .container-fluid > .navbar-collapse { margin-right: 0px; margin-left: 0px; } @media (min-width: 541px) { .container > .navbar-header, .container-fluid > .navbar-header, .container > .navbar-collapse, .container-fluid > .navbar-collapse { margin-right: 0; margin-left: 0; } } .navbar-static-top { z-index: 1000; border-width: 0 0 1px; } @media (min-width: 541px) { .navbar-static-top { border-radius: 0; } } .navbar-fixed-top, .navbar-fixed-bottom { position: fixed; right: 0; left: 0; z-index: 1030; } @media (min-width: 541px) { .navbar-fixed-top, .navbar-fixed-bottom { border-radius: 0; } } .navbar-fixed-top { top: 0; border-width: 0 0 1px; } .navbar-fixed-bottom { bottom: 0; margin-bottom: 0; border-width: 1px 0 0; } .navbar-brand { float: left; padding: 6px 0px; font-size: 17px; line-height: 18px; height: 30px; } .navbar-brand:hover, .navbar-brand:focus { text-decoration: none; } .navbar-brand > img { display: block; } @media (min-width: 541px) { .navbar > .container .navbar-brand, .navbar > .container-fluid .navbar-brand { margin-left: 0px; } } .navbar-toggle { position: relative; float: right; margin-right: 0px; padding: 9px 10px; margin-top: -2px; margin-bottom: -2px; background-color: transparent; background-image: none; border: 1px solid transparent; border-radius: 2px; } .navbar-toggle:focus { outline: 0; } .navbar-toggle .icon-bar { display: block; width: 22px; height: 2px; border-radius: 1px; } .navbar-toggle .icon-bar + .icon-bar { margin-top: 4px; } @media (min-width: 541px) { .navbar-toggle { display: none; } } .navbar-nav { margin: 3px 0px; } .navbar-nav > li > a { padding-top: 10px; padding-bottom: 10px; line-height: 18px; } @media (max-width: 540px) { .navbar-nav .open .dropdown-menu { position: static; float: none; width: auto; margin-top: 0; background-color: transparent; border: 0; box-shadow: none; } .navbar-nav .open .dropdown-menu > li > a, .navbar-nav .open .dropdown-menu .dropdown-header { padding: 5px 15px 5px 25px; } .navbar-nav .open .dropdown-menu > li > a { line-height: 18px; } .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-nav .open .dropdown-menu > li > a:focus { background-image: none; } } @media (min-width: 541px) { .navbar-nav { float: left; margin: 0; } .navbar-nav > li { float: left; } .navbar-nav > li > a { padding-top: 6px; padding-bottom: 6px; } } .navbar-form { margin-left: 0px; margin-right: 0px; padding: 10px 0px; border-top: 1px solid transparent; border-bottom: 1px solid transparent; -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); margin-top: -1px; margin-bottom: -1px; } @media (min-width: 768px) { .navbar-form .form-group { display: inline-block; margin-bottom: 0; vertical-align: middle; } .navbar-form .form-control { display: inline-block; width: auto; vertical-align: middle; } .navbar-form .form-control-static { display: inline-block; } .navbar-form .input-group { display: inline-table; vertical-align: middle; } .navbar-form .input-group .input-group-addon, .navbar-form .input-group .input-group-btn, .navbar-form .input-group .form-control { width: auto; } .navbar-form .input-group > .form-control { width: 100%; } .navbar-form .control-label { margin-bottom: 0; vertical-align: middle; } .navbar-form .radio, .navbar-form .checkbox { display: inline-block; margin-top: 0; margin-bottom: 0; vertical-align: middle; } .navbar-form .radio label, .navbar-form .checkbox label { padding-left: 0; } .navbar-form .radio input[type="radio"], .navbar-form .checkbox input[type="checkbox"] { position: relative; margin-left: 0; } .navbar-form .has-feedback .form-control-feedback { top: 0; } } @media (max-width: 540px) { .navbar-form .form-group { margin-bottom: 5px; } .navbar-form .form-group:last-child { margin-bottom: 0; } } @media (min-width: 541px) { .navbar-form { width: auto; border: 0; margin-left: 0; margin-right: 0; padding-top: 0; padding-bottom: 0; -webkit-box-shadow: none; box-shadow: none; } } .navbar-nav > li > .dropdown-menu { margin-top: 0; border-top-right-radius: 0; border-top-left-radius: 0; } .navbar-fixed-bottom .navbar-nav > li > .dropdown-menu { margin-bottom: 0; border-top-right-radius: 2px; border-top-left-radius: 2px; border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .navbar-btn { margin-top: -1px; margin-bottom: -1px; } .navbar-btn.btn-sm { margin-top: 0px; margin-bottom: 0px; } .navbar-btn.btn-xs { margin-top: 4px; margin-bottom: 4px; } .navbar-text { margin-top: 6px; margin-bottom: 6px; } @media (min-width: 541px) { .navbar-text { float: left; margin-left: 0px; margin-right: 0px; } } @media (min-width: 541px) { .navbar-left { float: left !important; float: left; } .navbar-right { float: right !important; float: right; margin-right: 0px; } .navbar-right ~ .navbar-right { margin-right: 0; } } .navbar-default { background-color: #f8f8f8; border-color: #e7e7e7; } .navbar-default .navbar-brand { color: #777; } .navbar-default .navbar-brand:hover, .navbar-default .navbar-brand:focus { color: #5e5e5e; background-color: transparent; } .navbar-default .navbar-text { color: #777; } .navbar-default .navbar-nav > li > a { color: #777; } .navbar-default .navbar-nav > li > a:hover, .navbar-default .navbar-nav > li > a:focus { color: #333; background-color: transparent; } .navbar-default .navbar-nav > .active > a, .navbar-default .navbar-nav > .active > a:hover, .navbar-default .navbar-nav > .active > a:focus { color: #555; background-color: #e7e7e7; } .navbar-default .navbar-nav > .disabled > a, .navbar-default .navbar-nav > .disabled > a:hover, .navbar-default .navbar-nav > .disabled > a:focus { color: #ccc; background-color: transparent; } .navbar-default .navbar-toggle { border-color: #ddd; } .navbar-default .navbar-toggle:hover, .navbar-default .navbar-toggle:focus { background-color: #ddd; } .navbar-default .navbar-toggle .icon-bar { background-color: #888; } .navbar-default .navbar-collapse, .navbar-default .navbar-form { border-color: #e7e7e7; } .navbar-default .navbar-nav > .open > a, .navbar-default .navbar-nav > .open > a:hover, .navbar-default .navbar-nav > .open > a:focus { background-color: #e7e7e7; color: #555; } @media (max-width: 540px) { .navbar-default .navbar-nav .open .dropdown-menu > li > a { color: #777; } .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus { color: #333; background-color: transparent; } .navbar-default .navbar-nav .open .dropdown-menu > .active > a, .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus { color: #555; background-color: #e7e7e7; } .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a, .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus { color: #ccc; background-color: transparent; } } .navbar-default .navbar-link { color: #777; } .navbar-default .navbar-link:hover { color: #333; } .navbar-default .btn-link { color: #777; } .navbar-default .btn-link:hover, .navbar-default .btn-link:focus { color: #333; } .navbar-default .btn-link[disabled]:hover, fieldset[disabled] .navbar-default .btn-link:hover, .navbar-default .btn-link[disabled]:focus, fieldset[disabled] .navbar-default .btn-link:focus { color: #ccc; } .navbar-inverse { background-color: #222; border-color: #080808; } .navbar-inverse .navbar-brand { color: #9d9d9d; } .navbar-inverse .navbar-brand:hover, .navbar-inverse .navbar-brand:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-text { color: #9d9d9d; } .navbar-inverse .navbar-nav > li > a { color: #9d9d9d; } .navbar-inverse .navbar-nav > li > a:hover, .navbar-inverse .navbar-nav > li > a:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-nav > .active > a, .navbar-inverse .navbar-nav > .active > a:hover, .navbar-inverse .navbar-nav > .active > a:focus { color: #fff; background-color: #080808; } .navbar-inverse .navbar-nav > .disabled > a, .navbar-inverse .navbar-nav > .disabled > a:hover, .navbar-inverse .navbar-nav > .disabled > a:focus { color: #444; background-color: transparent; } .navbar-inverse .navbar-toggle { border-color: #333; } .navbar-inverse .navbar-toggle:hover, .navbar-inverse .navbar-toggle:focus { background-color: #333; } .navbar-inverse .navbar-toggle .icon-bar { background-color: #fff; } .navbar-inverse .navbar-collapse, .navbar-inverse .navbar-form { border-color: #101010; } .navbar-inverse .navbar-nav > .open > a, .navbar-inverse .navbar-nav > .open > a:hover, .navbar-inverse .navbar-nav > .open > a:focus { background-color: #080808; color: #fff; } @media (max-width: 540px) { .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header { border-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu .divider { background-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu > li > a { color: #9d9d9d; } .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a, .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus { color: #fff; background-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a, .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus { color: #444; background-color: transparent; } } .navbar-inverse .navbar-link { color: #9d9d9d; } .navbar-inverse .navbar-link:hover { color: #fff; } .navbar-inverse .btn-link { color: #9d9d9d; } .navbar-inverse .btn-link:hover, .navbar-inverse .btn-link:focus { color: #fff; } .navbar-inverse .btn-link[disabled]:hover, fieldset[disabled] .navbar-inverse .btn-link:hover, .navbar-inverse .btn-link[disabled]:focus, fieldset[disabled] .navbar-inverse .btn-link:focus { color: #444; } .breadcrumb { padding: 8px 15px; margin-bottom: 18px; list-style: none; background-color: #f5f5f5; border-radius: 2px; } .breadcrumb > li { display: inline-block; } .breadcrumb > li + li:before { content: "/\00a0"; padding: 0 5px; color: #5e5e5e; } .breadcrumb > .active { color: #777777; } .pagination { display: inline-block; padding-left: 0; margin: 18px 0; border-radius: 2px; } .pagination > li { display: inline; } .pagination > li > a, .pagination > li > span { position: relative; float: left; padding: 6px 12px; line-height: 1.42857143; text-decoration: none; color: #337ab7; background-color: #fff; border: 1px solid #ddd; margin-left: -1px; } .pagination > li:first-child > a, .pagination > li:first-child > span { margin-left: 0; border-bottom-left-radius: 2px; border-top-left-radius: 2px; } .pagination > li:last-child > a, .pagination > li:last-child > span { border-bottom-right-radius: 2px; border-top-right-radius: 2px; } .pagination > li > a:hover, .pagination > li > span:hover, .pagination > li > a:focus, .pagination > li > span:focus { z-index: 2; color: #23527c; background-color: #eeeeee; border-color: #ddd; } .pagination > .active > a, .pagination > .active > span, .pagination > .active > a:hover, .pagination > .active > span:hover, .pagination > .active > a:focus, .pagination > .active > span:focus { z-index: 3; color: #fff; background-color: #337ab7; border-color: #337ab7; cursor: default; } .pagination > .disabled > span, .pagination > .disabled > span:hover, .pagination > .disabled > span:focus, .pagination > .disabled > a, .pagination > .disabled > a:hover, .pagination > .disabled > a:focus { color: #777777; background-color: #fff; border-color: #ddd; cursor: not-allowed; } .pagination-lg > li > a, .pagination-lg > li > span { padding: 10px 16px; font-size: 17px; line-height: 1.3333333; } .pagination-lg > li:first-child > a, .pagination-lg > li:first-child > span { border-bottom-left-radius: 3px; border-top-left-radius: 3px; } .pagination-lg > li:last-child > a, .pagination-lg > li:last-child > span { border-bottom-right-radius: 3px; border-top-right-radius: 3px; } .pagination-sm > li > a, .pagination-sm > li > span { padding: 5px 10px; font-size: 12px; line-height: 1.5; } .pagination-sm > li:first-child > a, .pagination-sm > li:first-child > span { border-bottom-left-radius: 1px; border-top-left-radius: 1px; } .pagination-sm > li:last-child > a, .pagination-sm > li:last-child > span { border-bottom-right-radius: 1px; border-top-right-radius: 1px; } .pager { padding-left: 0; margin: 18px 0; list-style: none; text-align: center; } .pager li { display: inline; } .pager li > a, .pager li > span { display: inline-block; padding: 5px 14px; background-color: #fff; border: 1px solid #ddd; border-radius: 15px; } .pager li > a:hover, .pager li > a:focus { text-decoration: none; background-color: #eeeeee; } .pager .next > a, .pager .next > span { float: right; } .pager .previous > a, .pager .previous > span { float: left; } .pager .disabled > a, .pager .disabled > a:hover, .pager .disabled > a:focus, .pager .disabled > span { color: #777777; background-color: #fff; cursor: not-allowed; } .label { display: inline; padding: .2em .6em .3em; font-size: 75%; font-weight: bold; line-height: 1; color: #fff; text-align: center; white-space: nowrap; vertical-align: baseline; border-radius: .25em; } a.label:hover, a.label:focus { color: #fff; text-decoration: none; cursor: pointer; } .label:empty { display: none; } .btn .label { position: relative; top: -1px; } .label-default { background-color: #777777; } .label-default[href]:hover, .label-default[href]:focus { background-color: #5e5e5e; } .label-primary { background-color: #337ab7; } .label-primary[href]:hover, .label-primary[href]:focus { background-color: #286090; } .label-success { background-color: #5cb85c; } .label-success[href]:hover, .label-success[href]:focus { background-color: #449d44; } .label-info { background-color: #5bc0de; } .label-info[href]:hover, .label-info[href]:focus { background-color: #31b0d5; } .label-warning { background-color: #f0ad4e; } .label-warning[href]:hover, .label-warning[href]:focus { background-color: #ec971f; } .label-danger { background-color: #d9534f; } .label-danger[href]:hover, .label-danger[href]:focus { background-color: #c9302c; } .badge { display: inline-block; min-width: 10px; padding: 3px 7px; font-size: 12px; font-weight: bold; color: #fff; line-height: 1; vertical-align: middle; white-space: nowrap; text-align: center; background-color: #777777; border-radius: 10px; } .badge:empty { display: none; } .btn .badge { position: relative; top: -1px; } .btn-xs .badge, .btn-group-xs > .btn .badge { top: 0; padding: 1px 5px; } a.badge:hover, a.badge:focus { color: #fff; text-decoration: none; cursor: pointer; } .list-group-item.active > .badge, .nav-pills > .active > a > .badge { color: #337ab7; background-color: #fff; } .list-group-item > .badge { float: right; } .list-group-item > .badge + .badge { margin-right: 5px; } .nav-pills > li > a > .badge { margin-left: 3px; } .jumbotron { padding-top: 30px; padding-bottom: 30px; margin-bottom: 30px; color: inherit; background-color: #eeeeee; } .jumbotron h1, .jumbotron .h1 { color: inherit; } .jumbotron p { margin-bottom: 15px; font-size: 20px; font-weight: 200; } .jumbotron > hr { border-top-color: #d5d5d5; } .container .jumbotron, .container-fluid .jumbotron { border-radius: 3px; padding-left: 0px; padding-right: 0px; } .jumbotron .container { max-width: 100%; } @media screen and (min-width: 768px) { .jumbotron { padding-top: 48px; padding-bottom: 48px; } .container .jumbotron, .container-fluid .jumbotron { padding-left: 60px; padding-right: 60px; } .jumbotron h1, .jumbotron .h1 { font-size: 59px; } } .thumbnail { display: block; padding: 4px; margin-bottom: 18px; line-height: 1.42857143; background-color: #fff; border: 1px solid #ddd; border-radius: 2px; -webkit-transition: border 0.2s ease-in-out; -o-transition: border 0.2s ease-in-out; transition: border 0.2s ease-in-out; } .thumbnail > img, .thumbnail a > img { margin-left: auto; margin-right: auto; } a.thumbnail:hover, a.thumbnail:focus, a.thumbnail.active { border-color: #337ab7; } .thumbnail .caption { padding: 9px; color: #000; } .alert { padding: 15px; margin-bottom: 18px; border: 1px solid transparent; border-radius: 2px; } .alert h4 { margin-top: 0; color: inherit; } .alert .alert-link { font-weight: bold; } .alert > p, .alert > ul { margin-bottom: 0; } .alert > p + p { margin-top: 5px; } .alert-dismissable, .alert-dismissible { padding-right: 35px; } .alert-dismissable .close, .alert-dismissible .close { position: relative; top: -2px; right: -21px; color: inherit; } .alert-success { background-color: #dff0d8; border-color: #d6e9c6; color: #3c763d; } .alert-success hr { border-top-color: #c9e2b3; } .alert-success .alert-link { color: #2b542c; } .alert-info { background-color: #d9edf7; border-color: #bce8f1; color: #31708f; } .alert-info hr { border-top-color: #a6e1ec; } .alert-info .alert-link { color: #245269; } .alert-warning { background-color: #fcf8e3; border-color: #faebcc; color: #8a6d3b; } .alert-warning hr { border-top-color: #f7e1b5; } .alert-warning .alert-link { color: #66512c; } .alert-danger { background-color: #f2dede; border-color: #ebccd1; color: #a94442; } .alert-danger hr { border-top-color: #e4b9c0; } .alert-danger .alert-link { color: #843534; } @-webkit-keyframes progress-bar-stripes { from { background-position: 40px 0; } to { background-position: 0 0; } } @keyframes progress-bar-stripes { from { background-position: 40px 0; } to { background-position: 0 0; } } .progress { overflow: hidden; height: 18px; margin-bottom: 18px; background-color: #f5f5f5; border-radius: 2px; -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); } .progress-bar { float: left; width: 0%; height: 100%; font-size: 12px; line-height: 18px; color: #fff; text-align: center; background-color: #337ab7; -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); -webkit-transition: width 0.6s ease; -o-transition: width 0.6s ease; transition: width 0.6s ease; } .progress-striped .progress-bar, .progress-bar-striped { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-size: 40px 40px; } .progress.active .progress-bar, .progress-bar.active { -webkit-animation: progress-bar-stripes 2s linear infinite; -o-animation: progress-bar-stripes 2s linear infinite; animation: progress-bar-stripes 2s linear infinite; } .progress-bar-success { background-color: #5cb85c; } .progress-striped .progress-bar-success { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-info { background-color: #5bc0de; } .progress-striped .progress-bar-info { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-warning { background-color: #f0ad4e; } .progress-striped .progress-bar-warning { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-danger { background-color: #d9534f; } .progress-striped .progress-bar-danger { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .media { margin-top: 15px; } .media:first-child { margin-top: 0; } .media, .media-body { zoom: 1; overflow: hidden; } .media-body { width: 10000px; } .media-object { display: block; } .media-object.img-thumbnail { max-width: none; } .media-right, .media > .pull-right { padding-left: 10px; } .media-left, .media > .pull-left { padding-right: 10px; } .media-left, .media-right, .media-body { display: table-cell; vertical-align: top; } .media-middle { vertical-align: middle; } .media-bottom { vertical-align: bottom; } .media-heading { margin-top: 0; margin-bottom: 5px; } .media-list { padding-left: 0; list-style: none; } .list-group { margin-bottom: 20px; padding-left: 0; } .list-group-item { position: relative; display: block; padding: 10px 15px; margin-bottom: -1px; background-color: #fff; border: 1px solid #ddd; } .list-group-item:first-child { border-top-right-radius: 2px; border-top-left-radius: 2px; } .list-group-item:last-child { margin-bottom: 0; border-bottom-right-radius: 2px; border-bottom-left-radius: 2px; } a.list-group-item, button.list-group-item { color: #555; } a.list-group-item .list-group-item-heading, button.list-group-item .list-group-item-heading { color: #333; } a.list-group-item:hover, button.list-group-item:hover, a.list-group-item:focus, button.list-group-item:focus { text-decoration: none; color: #555; background-color: #f5f5f5; } button.list-group-item { width: 100%; text-align: left; } .list-group-item.disabled, .list-group-item.disabled:hover, .list-group-item.disabled:focus { background-color: #eeeeee; color: #777777; cursor: not-allowed; } .list-group-item.disabled .list-group-item-heading, .list-group-item.disabled:hover .list-group-item-heading, .list-group-item.disabled:focus .list-group-item-heading { color: inherit; } .list-group-item.disabled .list-group-item-text, .list-group-item.disabled:hover .list-group-item-text, .list-group-item.disabled:focus .list-group-item-text { color: #777777; } .list-group-item.active, .list-group-item.active:hover, .list-group-item.active:focus { z-index: 2; color: #fff; background-color: #337ab7; border-color: #337ab7; } .list-group-item.active .list-group-item-heading, .list-group-item.active:hover .list-group-item-heading, .list-group-item.active:focus .list-group-item-heading, .list-group-item.active .list-group-item-heading > small, .list-group-item.active:hover .list-group-item-heading > small, .list-group-item.active:focus .list-group-item-heading > small, .list-group-item.active .list-group-item-heading > .small, .list-group-item.active:hover .list-group-item-heading > .small, .list-group-item.active:focus .list-group-item-heading > .small { color: inherit; } .list-group-item.active .list-group-item-text, .list-group-item.active:hover .list-group-item-text, .list-group-item.active:focus .list-group-item-text { color: #c7ddef; } .list-group-item-success { color: #3c763d; background-color: #dff0d8; } a.list-group-item-success, button.list-group-item-success { color: #3c763d; } a.list-group-item-success .list-group-item-heading, button.list-group-item-success .list-group-item-heading { color: inherit; } a.list-group-item-success:hover, button.list-group-item-success:hover, a.list-group-item-success:focus, button.list-group-item-success:focus { color: #3c763d; background-color: #d0e9c6; } a.list-group-item-success.active, button.list-group-item-success.active, a.list-group-item-success.active:hover, button.list-group-item-success.active:hover, a.list-group-item-success.active:focus, button.list-group-item-success.active:focus { color: #fff; background-color: #3c763d; border-color: #3c763d; } .list-group-item-info { color: #31708f; background-color: #d9edf7; } a.list-group-item-info, button.list-group-item-info { color: #31708f; } a.list-group-item-info .list-group-item-heading, button.list-group-item-info .list-group-item-heading { color: inherit; } a.list-group-item-info:hover, button.list-group-item-info:hover, a.list-group-item-info:focus, button.list-group-item-info:focus { color: #31708f; background-color: #c4e3f3; } a.list-group-item-info.active, button.list-group-item-info.active, a.list-group-item-info.active:hover, button.list-group-item-info.active:hover, a.list-group-item-info.active:focus, button.list-group-item-info.active:focus { color: #fff; background-color: #31708f; border-color: #31708f; } .list-group-item-warning { color: #8a6d3b; background-color: #fcf8e3; } a.list-group-item-warning, button.list-group-item-warning { color: #8a6d3b; } a.list-group-item-warning .list-group-item-heading, button.list-group-item-warning .list-group-item-heading { color: inherit; } a.list-group-item-warning:hover, button.list-group-item-warning:hover, a.list-group-item-warning:focus, button.list-group-item-warning:focus { color: #8a6d3b; background-color: #faf2cc; } a.list-group-item-warning.active, button.list-group-item-warning.active, a.list-group-item-warning.active:hover, button.list-group-item-warning.active:hover, a.list-group-item-warning.active:focus, button.list-group-item-warning.active:focus { color: #fff; background-color: #8a6d3b; border-color: #8a6d3b; } .list-group-item-danger { color: #a94442; background-color: #f2dede; } a.list-group-item-danger, button.list-group-item-danger { color: #a94442; } a.list-group-item-danger .list-group-item-heading, button.list-group-item-danger .list-group-item-heading { color: inherit; } a.list-group-item-danger:hover, button.list-group-item-danger:hover, a.list-group-item-danger:focus, button.list-group-item-danger:focus { color: #a94442; background-color: #ebcccc; } a.list-group-item-danger.active, button.list-group-item-danger.active, a.list-group-item-danger.active:hover, button.list-group-item-danger.active:hover, a.list-group-item-danger.active:focus, button.list-group-item-danger.active:focus { color: #fff; background-color: #a94442; border-color: #a94442; } .list-group-item-heading { margin-top: 0; margin-bottom: 5px; } .list-group-item-text { margin-bottom: 0; line-height: 1.3; } .panel { margin-bottom: 18px; background-color: #fff; border: 1px solid transparent; border-radius: 2px; -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); } .panel-body { padding: 15px; } .panel-heading { padding: 10px 15px; border-bottom: 1px solid transparent; border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel-heading > .dropdown .dropdown-toggle { color: inherit; } .panel-title { margin-top: 0; margin-bottom: 0; font-size: 15px; color: inherit; } .panel-title > a, .panel-title > small, .panel-title > .small, .panel-title > small > a, .panel-title > .small > a { color: inherit; } .panel-footer { padding: 10px 15px; background-color: #f5f5f5; border-top: 1px solid #ddd; border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .list-group, .panel > .panel-collapse > .list-group { margin-bottom: 0; } .panel > .list-group .list-group-item, .panel > .panel-collapse > .list-group .list-group-item { border-width: 1px 0; border-radius: 0; } .panel > .list-group:first-child .list-group-item:first-child, .panel > .panel-collapse > .list-group:first-child .list-group-item:first-child { border-top: 0; border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel > .list-group:last-child .list-group-item:last-child, .panel > .panel-collapse > .list-group:last-child .list-group-item:last-child { border-bottom: 0; border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child { border-top-right-radius: 0; border-top-left-radius: 0; } .panel-heading + .list-group .list-group-item:first-child { border-top-width: 0; } .list-group + .panel-footer { border-top-width: 0; } .panel > .table, .panel > .table-responsive > .table, .panel > .panel-collapse > .table { margin-bottom: 0; } .panel > .table caption, .panel > .table-responsive > .table caption, .panel > .panel-collapse > .table caption { padding-left: 15px; padding-right: 15px; } .panel > .table:first-child, .panel > .table-responsive:first-child > .table:first-child { border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child { border-top-left-radius: 1px; border-top-right-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child td:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child td:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child, .panel > .table:first-child > thead:first-child > tr:first-child th:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child th:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child { border-top-left-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child td:last-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child, .panel > .table:first-child > tbody:first-child > tr:first-child td:last-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child, .panel > .table:first-child > thead:first-child > tr:first-child th:last-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child, .panel > .table:first-child > tbody:first-child > tr:first-child th:last-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child { border-top-right-radius: 1px; } .panel > .table:last-child, .panel > .table-responsive:last-child > .table:last-child { border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child { border-bottom-left-radius: 1px; border-bottom-right-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child td:first-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child, .panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child, .panel > .table:last-child > tbody:last-child > tr:last-child th:first-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child, .panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child { border-bottom-left-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child td:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child, .panel > .table:last-child > tbody:last-child > tr:last-child th:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child { border-bottom-right-radius: 1px; } .panel > .panel-body + .table, .panel > .panel-body + .table-responsive, .panel > .table + .panel-body, .panel > .table-responsive + .panel-body { border-top: 1px solid #ddd; } .panel > .table > tbody:first-child > tr:first-child th, .panel > .table > tbody:first-child > tr:first-child td { border-top: 0; } .panel > .table-bordered, .panel > .table-responsive > .table-bordered { border: 0; } .panel > .table-bordered > thead > tr > th:first-child, .panel > .table-responsive > .table-bordered > thead > tr > th:first-child, .panel > .table-bordered > tbody > tr > th:first-child, .panel > .table-responsive > .table-bordered > tbody > tr > th:first-child, .panel > .table-bordered > tfoot > tr > th:first-child, .panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child, .panel > .table-bordered > thead > tr > td:first-child, .panel > .table-responsive > .table-bordered > thead > tr > td:first-child, .panel > .table-bordered > tbody > tr > td:first-child, .panel > .table-responsive > .table-bordered > tbody > tr > td:first-child, .panel > .table-bordered > tfoot > tr > td:first-child, .panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child { border-left: 0; } .panel > .table-bordered > thead > tr > th:last-child, .panel > .table-responsive > .table-bordered > thead > tr > th:last-child, .panel > .table-bordered > tbody > tr > th:last-child, .panel > .table-responsive > .table-bordered > tbody > tr > th:last-child, .panel > .table-bordered > tfoot > tr > th:last-child, .panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child, .panel > .table-bordered > thead > tr > td:last-child, .panel > .table-responsive > .table-bordered > thead > tr > td:last-child, .panel > .table-bordered > tbody > tr > td:last-child, .panel > .table-responsive > .table-bordered > tbody > tr > td:last-child, .panel > .table-bordered > tfoot > tr > td:last-child, .panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child { border-right: 0; } .panel > .table-bordered > thead > tr:first-child > td, .panel > .table-responsive > .table-bordered > thead > tr:first-child > td, .panel > .table-bordered > tbody > tr:first-child > td, .panel > .table-responsive > .table-bordered > tbody > tr:first-child > td, .panel > .table-bordered > thead > tr:first-child > th, .panel > .table-responsive > .table-bordered > thead > tr:first-child > th, .panel > .table-bordered > tbody > tr:first-child > th, .panel > .table-responsive > .table-bordered > tbody > tr:first-child > th { border-bottom: 0; } .panel > .table-bordered > tbody > tr:last-child > td, .panel > .table-responsive > .table-bordered > tbody > tr:last-child > td, .panel > .table-bordered > tfoot > tr:last-child > td, .panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td, .panel > .table-bordered > tbody > tr:last-child > th, .panel > .table-responsive > .table-bordered > tbody > tr:last-child > th, .panel > .table-bordered > tfoot > tr:last-child > th, .panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th { border-bottom: 0; } .panel > .table-responsive { border: 0; margin-bottom: 0; } .panel-group { margin-bottom: 18px; } .panel-group .panel { margin-bottom: 0; border-radius: 2px; } .panel-group .panel + .panel { margin-top: 5px; } .panel-group .panel-heading { border-bottom: 0; } .panel-group .panel-heading + .panel-collapse > .panel-body, .panel-group .panel-heading + .panel-collapse > .list-group { border-top: 1px solid #ddd; } .panel-group .panel-footer { border-top: 0; } .panel-group .panel-footer + .panel-collapse .panel-body { border-bottom: 1px solid #ddd; } .panel-default { border-color: #ddd; } .panel-default > .panel-heading { color: #333333; background-color: #f5f5f5; border-color: #ddd; } .panel-default > .panel-heading + .panel-collapse > .panel-body { border-top-color: #ddd; } .panel-default > .panel-heading .badge { color: #f5f5f5; background-color: #333333; } .panel-default > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #ddd; } .panel-primary { border-color: #337ab7; } .panel-primary > .panel-heading { color: #fff; background-color: #337ab7; border-color: #337ab7; } .panel-primary > .panel-heading + .panel-collapse > .panel-body { border-top-color: #337ab7; } .panel-primary > .panel-heading .badge { color: #337ab7; background-color: #fff; } .panel-primary > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #337ab7; } .panel-success { border-color: #d6e9c6; } .panel-success > .panel-heading { color: #3c763d; background-color: #dff0d8; border-color: #d6e9c6; } .panel-success > .panel-heading + .panel-collapse > .panel-body { border-top-color: #d6e9c6; } .panel-success > .panel-heading .badge { color: #dff0d8; background-color: #3c763d; } .panel-success > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #d6e9c6; } .panel-info { border-color: #bce8f1; } .panel-info > .panel-heading { color: #31708f; background-color: #d9edf7; border-color: #bce8f1; } .panel-info > .panel-heading + .panel-collapse > .panel-body { border-top-color: #bce8f1; } .panel-info > .panel-heading .badge { color: #d9edf7; background-color: #31708f; } .panel-info > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #bce8f1; } .panel-warning { border-color: #faebcc; } .panel-warning > .panel-heading { color: #8a6d3b; background-color: #fcf8e3; border-color: #faebcc; } .panel-warning > .panel-heading + .panel-collapse > .panel-body { border-top-color: #faebcc; } .panel-warning > .panel-heading .badge { color: #fcf8e3; background-color: #8a6d3b; } .panel-warning > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #faebcc; } .panel-danger { border-color: #ebccd1; } .panel-danger > .panel-heading { color: #a94442; background-color: #f2dede; border-color: #ebccd1; } .panel-danger > .panel-heading + .panel-collapse > .panel-body { border-top-color: #ebccd1; } .panel-danger > .panel-heading .badge { color: #f2dede; background-color: #a94442; } .panel-danger > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #ebccd1; } .embed-responsive { position: relative; display: block; height: 0; padding: 0; overflow: hidden; } .embed-responsive .embed-responsive-item, .embed-responsive iframe, .embed-responsive embed, .embed-responsive object, .embed-responsive video { position: absolute; top: 0; left: 0; bottom: 0; height: 100%; width: 100%; border: 0; } .embed-responsive-16by9 { padding-bottom: 56.25%; } .embed-responsive-4by3 { padding-bottom: 75%; } .well { min-height: 20px; padding: 19px; margin-bottom: 20px; background-color: #f5f5f5; border: 1px solid #e3e3e3; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); } .well blockquote { border-color: #ddd; border-color: rgba(0, 0, 0, 0.15); } .well-lg { padding: 24px; border-radius: 3px; } .well-sm { padding: 9px; border-radius: 1px; } .close { float: right; font-size: 19.5px; font-weight: bold; line-height: 1; color: #000; text-shadow: 0 1px 0 #fff; opacity: 0.2; filter: alpha(opacity=20); } .close:hover, .close:focus { color: #000; text-decoration: none; cursor: pointer; opacity: 0.5; filter: alpha(opacity=50); } button.close { padding: 0; cursor: pointer; background: transparent; border: 0; -webkit-appearance: none; } .modal-open { overflow: hidden; } .modal { display: none; overflow: hidden; position: fixed; top: 0; right: 0; bottom: 0; left: 0; z-index: 1050; -webkit-overflow-scrolling: touch; outline: 0; } .modal.fade .modal-dialog { -webkit-transform: translate(0, -25%); -ms-transform: translate(0, -25%); -o-transform: translate(0, -25%); transform: translate(0, -25%); -webkit-transition: -webkit-transform 0.3s ease-out; -moz-transition: -moz-transform 0.3s ease-out; -o-transition: -o-transform 0.3s ease-out; transition: transform 0.3s ease-out; } .modal.in .modal-dialog { -webkit-transform: translate(0, 0); -ms-transform: translate(0, 0); -o-transform: translate(0, 0); transform: translate(0, 0); } .modal-open .modal { overflow-x: hidden; overflow-y: auto; } .modal-dialog { position: relative; width: auto; margin: 10px; } .modal-content { position: relative; background-color: #fff; border: 1px solid #999; border: 1px solid rgba(0, 0, 0, 0.2); border-radius: 3px; -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); background-clip: padding-box; outline: 0; } .modal-backdrop { position: fixed; top: 0; right: 0; bottom: 0; left: 0; z-index: 1040; background-color: #000; } .modal-backdrop.fade { opacity: 0; filter: alpha(opacity=0); } .modal-backdrop.in { opacity: 0.5; filter: alpha(opacity=50); } .modal-header { padding: 15px; border-bottom: 1px solid #e5e5e5; } .modal-header .close { margin-top: -2px; } .modal-title { margin: 0; line-height: 1.42857143; } .modal-body { position: relative; padding: 15px; } .modal-footer { padding: 15px; text-align: right; border-top: 1px solid #e5e5e5; } .modal-footer .btn + .btn { margin-left: 5px; margin-bottom: 0; } .modal-footer .btn-group .btn + .btn { margin-left: -1px; } .modal-footer .btn-block + .btn-block { margin-left: 0; } .modal-scrollbar-measure { position: absolute; top: -9999px; width: 50px; height: 50px; overflow: scroll; } @media (min-width: 768px) { .modal-dialog { width: 600px; margin: 30px auto; } .modal-content { -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); } .modal-sm { width: 300px; } } @media (min-width: 992px) { .modal-lg { width: 900px; } } .tooltip { position: absolute; z-index: 1070; display: block; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-style: normal; font-weight: normal; letter-spacing: normal; line-break: auto; line-height: 1.42857143; text-align: left; text-align: start; text-decoration: none; text-shadow: none; text-transform: none; white-space: normal; word-break: normal; word-spacing: normal; word-wrap: normal; font-size: 12px; opacity: 0; filter: alpha(opacity=0); } .tooltip.in { opacity: 0.9; filter: alpha(opacity=90); } .tooltip.top { margin-top: -3px; padding: 5px 0; } .tooltip.right { margin-left: 3px; padding: 0 5px; } .tooltip.bottom { margin-top: 3px; padding: 5px 0; } .tooltip.left { margin-left: -3px; padding: 0 5px; } .tooltip-inner { max-width: 200px; padding: 3px 8px; color: #fff; text-align: center; background-color: #000; border-radius: 2px; } .tooltip-arrow { position: absolute; width: 0; height: 0; border-color: transparent; border-style: solid; } .tooltip.top .tooltip-arrow { bottom: 0; left: 50%; margin-left: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.top-left .tooltip-arrow { bottom: 0; right: 5px; margin-bottom: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.top-right .tooltip-arrow { bottom: 0; left: 5px; margin-bottom: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.right .tooltip-arrow { top: 50%; left: 0; margin-top: -5px; border-width: 5px 5px 5px 0; border-right-color: #000; } .tooltip.left .tooltip-arrow { top: 50%; right: 0; margin-top: -5px; border-width: 5px 0 5px 5px; border-left-color: #000; } .tooltip.bottom .tooltip-arrow { top: 0; left: 50%; margin-left: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .tooltip.bottom-left .tooltip-arrow { top: 0; right: 5px; margin-top: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .tooltip.bottom-right .tooltip-arrow { top: 0; left: 5px; margin-top: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .popover { position: absolute; top: 0; left: 0; z-index: 1060; display: none; max-width: 276px; padding: 1px; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-style: normal; font-weight: normal; letter-spacing: normal; line-break: auto; line-height: 1.42857143; text-align: left; text-align: start; text-decoration: none; text-shadow: none; text-transform: none; white-space: normal; word-break: normal; word-spacing: normal; word-wrap: normal; font-size: 13px; background-color: #fff; background-clip: padding-box; border: 1px solid #ccc; border: 1px solid rgba(0, 0, 0, 0.2); border-radius: 3px; -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); } .popover.top { margin-top: -10px; } .popover.right { margin-left: 10px; } .popover.bottom { margin-top: 10px; } .popover.left { margin-left: -10px; } .popover-title { margin: 0; padding: 8px 14px; font-size: 13px; background-color: #f7f7f7; border-bottom: 1px solid #ebebeb; border-radius: 2px 2px 0 0; } .popover-content { padding: 9px 14px; } .popover > .arrow, .popover > .arrow:after { position: absolute; display: block; width: 0; height: 0; border-color: transparent; border-style: solid; } .popover > .arrow { border-width: 11px; } .popover > .arrow:after { border-width: 10px; content: ""; } .popover.top > .arrow { left: 50%; margin-left: -11px; border-bottom-width: 0; border-top-color: #999999; border-top-color: rgba(0, 0, 0, 0.25); bottom: -11px; } .popover.top > .arrow:after { content: " "; bottom: 1px; margin-left: -10px; border-bottom-width: 0; border-top-color: #fff; } .popover.right > .arrow { top: 50%; left: -11px; margin-top: -11px; border-left-width: 0; border-right-color: #999999; border-right-color: rgba(0, 0, 0, 0.25); } .popover.right > .arrow:after { content: " "; left: 1px; bottom: -10px; border-left-width: 0; border-right-color: #fff; } .popover.bottom > .arrow { left: 50%; margin-left: -11px; border-top-width: 0; border-bottom-color: #999999; border-bottom-color: rgba(0, 0, 0, 0.25); top: -11px; } .popover.bottom > .arrow:after { content: " "; top: 1px; margin-left: -10px; border-top-width: 0; border-bottom-color: #fff; } .popover.left > .arrow { top: 50%; right: -11px; margin-top: -11px; border-right-width: 0; border-left-color: #999999; border-left-color: rgba(0, 0, 0, 0.25); } .popover.left > .arrow:after { content: " "; right: 1px; border-right-width: 0; border-left-color: #fff; bottom: -10px; } .carousel { position: relative; } .carousel-inner { position: relative; overflow: hidden; width: 100%; } .carousel-inner > .item { display: none; position: relative; -webkit-transition: 0.6s ease-in-out left; -o-transition: 0.6s ease-in-out left; transition: 0.6s ease-in-out left; } .carousel-inner > .item > img, .carousel-inner > .item > a > img { line-height: 1; } @media all and (transform-3d), (-webkit-transform-3d) { .carousel-inner > .item { -webkit-transition: -webkit-transform 0.6s ease-in-out; -moz-transition: -moz-transform 0.6s ease-in-out; -o-transition: -o-transform 0.6s ease-in-out; transition: transform 0.6s ease-in-out; -webkit-backface-visibility: hidden; -moz-backface-visibility: hidden; backface-visibility: hidden; -webkit-perspective: 1000px; -moz-perspective: 1000px; perspective: 1000px; } .carousel-inner > .item.next, .carousel-inner > .item.active.right { -webkit-transform: translate3d(100%, 0, 0); transform: translate3d(100%, 0, 0); left: 0; } .carousel-inner > .item.prev, .carousel-inner > .item.active.left { -webkit-transform: translate3d(-100%, 0, 0); transform: translate3d(-100%, 0, 0); left: 0; } .carousel-inner > .item.next.left, .carousel-inner > .item.prev.right, .carousel-inner > .item.active { -webkit-transform: translate3d(0, 0, 0); transform: translate3d(0, 0, 0); left: 0; } } .carousel-inner > .active, .carousel-inner > .next, .carousel-inner > .prev { display: block; } .carousel-inner > .active { left: 0; } .carousel-inner > .next, .carousel-inner > .prev { position: absolute; top: 0; width: 100%; } .carousel-inner > .next { left: 100%; } .carousel-inner > .prev { left: -100%; } .carousel-inner > .next.left, .carousel-inner > .prev.right { left: 0; } .carousel-inner > .active.left { left: -100%; } .carousel-inner > .active.right { left: 100%; } .carousel-control { position: absolute; top: 0; left: 0; bottom: 0; width: 15%; opacity: 0.5; filter: alpha(opacity=50); font-size: 20px; color: #fff; text-align: center; text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); background-color: rgba(0, 0, 0, 0); } .carousel-control.left { background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-repeat: repeat-x; filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1); } .carousel-control.right { left: auto; right: 0; background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-repeat: repeat-x; filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1); } .carousel-control:hover, .carousel-control:focus { outline: 0; color: #fff; text-decoration: none; opacity: 0.9; filter: alpha(opacity=90); } .carousel-control .icon-prev, .carousel-control .icon-next, .carousel-control .glyphicon-chevron-left, .carousel-control .glyphicon-chevron-right { position: absolute; top: 50%; margin-top: -10px; z-index: 5; display: inline-block; } .carousel-control .icon-prev, .carousel-control .glyphicon-chevron-left { left: 50%; margin-left: -10px; } .carousel-control .icon-next, .carousel-control .glyphicon-chevron-right { right: 50%; margin-right: -10px; } .carousel-control .icon-prev, .carousel-control .icon-next { width: 20px; height: 20px; line-height: 1; font-family: serif; } .carousel-control .icon-prev:before { content: '\2039'; } .carousel-control .icon-next:before { content: '\203a'; } .carousel-indicators { position: absolute; bottom: 10px; left: 50%; z-index: 15; width: 60%; margin-left: -30%; padding-left: 0; list-style: none; text-align: center; } .carousel-indicators li { display: inline-block; width: 10px; height: 10px; margin: 1px; text-indent: -999px; border: 1px solid #fff; border-radius: 10px; cursor: pointer; background-color: #000 \9; background-color: rgba(0, 0, 0, 0); } .carousel-indicators .active { margin: 0; width: 12px; height: 12px; background-color: #fff; } .carousel-caption { position: absolute; left: 15%; right: 15%; bottom: 20px; z-index: 10; padding-top: 20px; padding-bottom: 20px; color: #fff; text-align: center; text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); } .carousel-caption .btn { text-shadow: none; } @media screen and (min-width: 768px) { .carousel-control .glyphicon-chevron-left, .carousel-control .glyphicon-chevron-right, .carousel-control .icon-prev, .carousel-control .icon-next { width: 30px; height: 30px; margin-top: -10px; font-size: 30px; } .carousel-control .glyphicon-chevron-left, .carousel-control .icon-prev { margin-left: -10px; } .carousel-control .glyphicon-chevron-right, .carousel-control .icon-next { margin-right: -10px; } .carousel-caption { left: 20%; right: 20%; padding-bottom: 30px; } .carousel-indicators { bottom: 20px; } } .clearfix:before, .clearfix:after, .dl-horizontal dd:before, .dl-horizontal dd:after, .container:before, .container:after, .container-fluid:before, .container-fluid:after, .row:before, .row:after, .form-horizontal .form-group:before, .form-horizontal .form-group:after, .btn-toolbar:before, .btn-toolbar:after, .btn-group-vertical > .btn-group:before, .btn-group-vertical > .btn-group:after, .nav:before, .nav:after, .navbar:before, .navbar:after, .navbar-header:before, .navbar-header:after, .navbar-collapse:before, .navbar-collapse:after, .pager:before, .pager:after, .panel-body:before, .panel-body:after, .modal-header:before, .modal-header:after, .modal-footer:before, .modal-footer:after, .item\_buttons:before, .item\_buttons:after { content: " "; display: table; } .clearfix:after, .dl-horizontal dd:after, .container:after, .container-fluid:after, .row:after, .form-horizontal .form-group:after, .btn-toolbar:after, .btn-group-vertical > .btn-group:after, .nav:after, .navbar:after, .navbar-header:after, .navbar-collapse:after, .pager:after, .panel-body:after, .modal-header:after, .modal-footer:after, .item\_buttons:after { clear: both; } .center-block { display: block; margin-left: auto; margin-right: auto; } .pull-right { float: right !important; } .pull-left { float: left !important; } .hide { display: none !important; } .show { display: block !important; } .invisible { visibility: hidden; } .text-hide { font: 0/0 a; color: transparent; text-shadow: none; background-color: transparent; border: 0; } .hidden { display: none !important; } .affix { position: fixed; } @-ms-viewport { width: device-width; } .visible-xs, .visible-sm, .visible-md, .visible-lg { display: none !important; } .visible-xs-block, .visible-xs-inline, .visible-xs-inline-block, .visible-sm-block, .visible-sm-inline, .visible-sm-inline-block, .visible-md-block, .visible-md-inline, .visible-md-inline-block, .visible-lg-block, .visible-lg-inline, .visible-lg-inline-block { display: none !important; } @media (max-width: 767px) { .visible-xs { display: block !important; } table.visible-xs { display: table !important; } tr.visible-xs { display: table-row !important; } th.visible-xs, td.visible-xs { display: table-cell !important; } } @media (max-width: 767px) { .visible-xs-block { display: block !important; } } @media (max-width: 767px) { .visible-xs-inline { display: inline !important; } } @media (max-width: 767px) { .visible-xs-inline-block { display: inline-block !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm { display: block !important; } table.visible-sm { display: table !important; } tr.visible-sm { display: table-row !important; } th.visible-sm, td.visible-sm { display: table-cell !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-block { display: block !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-inline { display: inline !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-inline-block { display: inline-block !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md { display: block !important; } table.visible-md { display: table !important; } tr.visible-md { display: table-row !important; } th.visible-md, td.visible-md { display: table-cell !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-block { display: block !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-inline { display: inline !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-inline-block { display: inline-block !important; } } @media (min-width: 1200px) { .visible-lg { display: block !important; } table.visible-lg { display: table !important; } tr.visible-lg { display: table-row !important; } th.visible-lg, td.visible-lg { display: table-cell !important; } } @media (min-width: 1200px) { .visible-lg-block { display: block !important; } } @media (min-width: 1200px) { .visible-lg-inline { display: inline !important; } } @media (min-width: 1200px) { .visible-lg-inline-block { display: inline-block !important; } } @media (max-width: 767px) { .hidden-xs { display: none !important; } } @media (min-width: 768px) and (max-width: 991px) { .hidden-sm { display: none !important; } } @media (min-width: 992px) and (max-width: 1199px) { .hidden-md { display: none !important; } } @media (min-width: 1200px) { .hidden-lg { display: none !important; } } .visible-print { display: none !important; } @media print { .visible-print { display: block !important; } table.visible-print { display: table !important; } tr.visible-print { display: table-row !important; } th.visible-print, td.visible-print { display: table-cell !important; } } .visible-print-block { display: none !important; } @media print { .visible-print-block { display: block !important; } } .visible-print-inline { display: none !important; } @media print { .visible-print-inline { display: inline !important; } } .visible-print-inline-block { display: none !important; } @media print { .visible-print-inline-block { display: inline-block !important; } } @media print { .hidden-print { display: none !important; } } /\*! \* \* Font Awesome \* \*/ /\*! \* Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome \* License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License) \*/ /\* FONT PATH \* -------------------------- \*/ @font-face { font-family: 'FontAwesome'; src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0'); src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg'); font-weight: normal; font-style: normal; } .fa { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; } /\* makes the font 33% larger relative to the icon container \*/ .fa-lg { font-size: 1.33333333em; line-height: 0.75em; vertical-align: -15%; } .fa-2x { font-size: 2em; } .fa-3x { font-size: 3em; } .fa-4x { font-size: 4em; } .fa-5x { font-size: 5em; } .fa-fw { width: 1.28571429em; text-align: center; } .fa-ul { padding-left: 0; margin-left: 2.14285714em; list-style-type: none; } .fa-ul > li { position: relative; } .fa-li { position: absolute; left: -2.14285714em; width: 2.14285714em; top: 0.14285714em; text-align: center; } .fa-li.fa-lg { left: -1.85714286em; } .fa-border { padding: .2em .25em .15em; border: solid 0.08em #eee; border-radius: .1em; } .fa-pull-left { float: left; } .fa-pull-right { float: right; } .fa.fa-pull-left { margin-right: .3em; } .fa.fa-pull-right { margin-left: .3em; } /\* Deprecated as of 4.4.0 \*/ .pull-right { float: right; } .pull-left { float: left; } .fa.pull-left { margin-right: .3em; } .fa.pull-right { margin-left: .3em; } .fa-spin { -webkit-animation: fa-spin 2s infinite linear; animation: fa-spin 2s infinite linear; } .fa-pulse { -webkit-animation: fa-spin 1s infinite steps(8); animation: fa-spin 1s infinite steps(8); } @-webkit-keyframes fa-spin { 0% { -webkit-transform: rotate(0deg); transform: rotate(0deg); } 100% { -webkit-transform: rotate(359deg); transform: rotate(359deg); } } @keyframes fa-spin { 0% { -webkit-transform: rotate(0deg); transform: rotate(0deg); } 100% { -webkit-transform: rotate(359deg); transform: rotate(359deg); } } .fa-rotate-90 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)"; -webkit-transform: rotate(90deg); -ms-transform: rotate(90deg); transform: rotate(90deg); } .fa-rotate-180 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)"; -webkit-transform: rotate(180deg); -ms-transform: rotate(180deg); transform: rotate(180deg); } .fa-rotate-270 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)"; -webkit-transform: rotate(270deg); -ms-transform: rotate(270deg); transform: rotate(270deg); } .fa-flip-horizontal { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)"; -webkit-transform: scale(-1, 1); -ms-transform: scale(-1, 1); transform: scale(-1, 1); } .fa-flip-vertical { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)"; -webkit-transform: scale(1, -1); -ms-transform: scale(1, -1); transform: scale(1, -1); } :root .fa-rotate-90, :root .fa-rotate-180, :root .fa-rotate-270, :root .fa-flip-horizontal, :root .fa-flip-vertical { filter: none; } .fa-stack { position: relative; display: inline-block; width: 2em; height: 2em; line-height: 2em; vertical-align: middle; } .fa-stack-1x, .fa-stack-2x { position: absolute; left: 0; width: 100%; text-align: center; } .fa-stack-1x { line-height: inherit; } .fa-stack-2x { font-size: 2em; } .fa-inverse { color: #fff; } /\* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen readers do not read off random characters that represent icons \*/ .fa-glass:before { content: "\f000"; } .fa-music:before { content: "\f001"; } .fa-search:before { content: "\f002"; } .fa-envelope-o:before { content: "\f003"; } .fa-heart:before { content: "\f004"; } .fa-star:before { content: "\f005"; } .fa-star-o:before { content: "\f006"; } .fa-user:before { content: "\f007"; } .fa-film:before { content: "\f008"; } .fa-th-large:before { content: "\f009"; } .fa-th:before { content: "\f00a"; } .fa-th-list:before { content: "\f00b"; } .fa-check:before { content: "\f00c"; } .fa-remove:before, .fa-close:before, .fa-times:before { content: "\f00d"; } .fa-search-plus:before { content: "\f00e"; } .fa-search-minus:before { content: "\f010"; } .fa-power-off:before { content: "\f011"; } .fa-signal:before { content: "\f012"; } .fa-gear:before, .fa-cog:before { content: "\f013"; } .fa-trash-o:before { content: "\f014"; } .fa-home:before { content: "\f015"; } .fa-file-o:before { content: "\f016"; } .fa-clock-o:before { content: "\f017"; } .fa-road:before { content: "\f018"; } .fa-download:before { content: "\f019"; } .fa-arrow-circle-o-down:before { content: "\f01a"; } .fa-arrow-circle-o-up:before { content: "\f01b"; } .fa-inbox:before { content: "\f01c"; } .fa-play-circle-o:before { content: "\f01d"; } .fa-rotate-right:before, .fa-repeat:before { content: "\f01e"; } .fa-refresh:before { content: "\f021"; } .fa-list-alt:before { content: "\f022"; } .fa-lock:before { content: "\f023"; } .fa-flag:before { content: "\f024"; } .fa-headphones:before { content: "\f025"; } .fa-volume-off:before { content: "\f026"; } .fa-volume-down:before { content: "\f027"; } .fa-volume-up:before { content: "\f028"; } .fa-qrcode:before { content: "\f029"; } .fa-barcode:before { content: "\f02a"; } .fa-tag:before { content: "\f02b"; } .fa-tags:before { content: "\f02c"; } .fa-book:before { content: "\f02d"; } .fa-bookmark:before { content: "\f02e"; } .fa-print:before { content: "\f02f"; } .fa-camera:before { content: "\f030"; } .fa-font:before { content: "\f031"; } .fa-bold:before { content: "\f032"; } .fa-italic:before { content: "\f033"; } .fa-text-height:before { content: "\f034"; } .fa-text-width:before { content: "\f035"; } .fa-align-left:before { content: "\f036"; } .fa-align-center:before { content: "\f037"; } .fa-align-right:before { content: "\f038"; } .fa-align-justify:before { content: "\f039"; } .fa-list:before { content: "\f03a"; } .fa-dedent:before, .fa-outdent:before { content: "\f03b"; } .fa-indent:before { content: "\f03c"; } .fa-video-camera:before { content: "\f03d"; } .fa-photo:before, .fa-image:before, .fa-picture-o:before { content: "\f03e"; } .fa-pencil:before { content: "\f040"; } .fa-map-marker:before { content: "\f041"; } .fa-adjust:before { content: "\f042"; } .fa-tint:before { content: "\f043"; } .fa-edit:before, .fa-pencil-square-o:before { content: "\f044"; } .fa-share-square-o:before { content: "\f045"; } .fa-check-square-o:before { content: "\f046"; } .fa-arrows:before { content: "\f047"; } .fa-step-backward:before { content: "\f048"; } .fa-fast-backward:before { content: "\f049"; } .fa-backward:before { content: "\f04a"; } .fa-play:before { content: "\f04b"; } .fa-pause:before { content: "\f04c"; } .fa-stop:before { content: "\f04d"; } .fa-forward:before { content: "\f04e"; } .fa-fast-forward:before { content: "\f050"; } .fa-step-forward:before { content: "\f051"; } .fa-eject:before { content: "\f052"; } .fa-chevron-left:before { content: "\f053"; } .fa-chevron-right:before { content: "\f054"; } .fa-plus-circle:before { content: "\f055"; } .fa-minus-circle:before { content: "\f056"; } .fa-times-circle:before { content: "\f057"; } .fa-check-circle:before { content: "\f058"; } .fa-question-circle:before { content: "\f059"; } .fa-info-circle:before { content: "\f05a"; } .fa-crosshairs:before { content: "\f05b"; } .fa-times-circle-o:before { content: "\f05c"; } .fa-check-circle-o:before { content: "\f05d"; } .fa-ban:before { content: "\f05e"; } .fa-arrow-left:before { content: "\f060"; } .fa-arrow-right:before { content: "\f061"; } .fa-arrow-up:before { content: "\f062"; } .fa-arrow-down:before { content: "\f063"; } .fa-mail-forward:before, .fa-share:before { content: "\f064"; } .fa-expand:before { content: "\f065"; } .fa-compress:before { content: "\f066"; } .fa-plus:before { content: "\f067"; } .fa-minus:before { content: "\f068"; } .fa-asterisk:before { content: "\f069"; } .fa-exclamation-circle:before { content: "\f06a"; } .fa-gift:before { content: "\f06b"; } .fa-leaf:before { content: "\f06c"; } .fa-fire:before { content: "\f06d"; } .fa-eye:before { content: "\f06e"; } .fa-eye-slash:before { content: "\f070"; } .fa-warning:before, .fa-exclamation-triangle:before { content: "\f071"; } .fa-plane:before { content: "\f072"; } .fa-calendar:before { content: "\f073"; } .fa-random:before { content: "\f074"; } .fa-comment:before { content: "\f075"; } .fa-magnet:before { content: "\f076"; } .fa-chevron-up:before { content: "\f077"; } .fa-chevron-down:before { content: "\f078"; } .fa-retweet:before { content: "\f079"; } .fa-shopping-cart:before { content: "\f07a"; } .fa-folder:before { content: "\f07b"; } .fa-folder-open:before { content: "\f07c"; } .fa-arrows-v:before { content: "\f07d"; } .fa-arrows-h:before { content: "\f07e"; } .fa-bar-chart-o:before, .fa-bar-chart:before { content: "\f080"; } .fa-twitter-square:before { content: "\f081"; } .fa-facebook-square:before { content: "\f082"; } .fa-camera-retro:before { content: "\f083"; } .fa-key:before { content: "\f084"; } .fa-gears:before, .fa-cogs:before { content: "\f085"; } .fa-comments:before { content: "\f086"; } .fa-thumbs-o-up:before { content: "\f087"; } .fa-thumbs-o-down:before { content: "\f088"; } .fa-star-half:before { content: "\f089"; } .fa-heart-o:before { content: "\f08a"; } .fa-sign-out:before { content: "\f08b"; } .fa-linkedin-square:before { content: "\f08c"; } .fa-thumb-tack:before { content: "\f08d"; } .fa-external-link:before { content: "\f08e"; } .fa-sign-in:before { content: "\f090"; } .fa-trophy:before { content: "\f091"; } .fa-github-square:before { content: "\f092"; } .fa-upload:before { content: "\f093"; } .fa-lemon-o:before { content: "\f094"; } .fa-phone:before { content: "\f095"; } .fa-square-o:before { content: "\f096"; } .fa-bookmark-o:before { content: "\f097"; } .fa-phone-square:before { content: "\f098"; } .fa-twitter:before { content: "\f099"; } .fa-facebook-f:before, .fa-facebook:before { content: "\f09a"; } .fa-github:before { content: "\f09b"; } .fa-unlock:before { content: "\f09c"; } .fa-credit-card:before { content: "\f09d"; } .fa-feed:before, .fa-rss:before { content: "\f09e"; } .fa-hdd-o:before { content: "\f0a0"; } .fa-bullhorn:before { content: "\f0a1"; } .fa-bell:before { content: "\f0f3"; } .fa-certificate:before { content: "\f0a3"; } .fa-hand-o-right:before { content: "\f0a4"; } .fa-hand-o-left:before { content: "\f0a5"; } .fa-hand-o-up:before { content: "\f0a6"; } .fa-hand-o-down:before { content: "\f0a7"; } .fa-arrow-circle-left:before { content: "\f0a8"; } .fa-arrow-circle-right:before { content: "\f0a9"; } .fa-arrow-circle-up:before { content: "\f0aa"; } .fa-arrow-circle-down:before { content: "\f0ab"; } .fa-globe:before { content: "\f0ac"; } .fa-wrench:before { content: "\f0ad"; } .fa-tasks:before { content: "\f0ae"; } .fa-filter:before { content: "\f0b0"; } .fa-briefcase:before { content: "\f0b1"; } .fa-arrows-alt:before { content: "\f0b2"; } .fa-group:before, .fa-users:before { content: "\f0c0"; } .fa-chain:before, .fa-link:before { content: "\f0c1"; } .fa-cloud:before { content: "\f0c2"; } .fa-flask:before { content: "\f0c3"; } .fa-cut:before, .fa-scissors:before { content: "\f0c4"; } .fa-copy:before, .fa-files-o:before { content: "\f0c5"; } .fa-paperclip:before { content: "\f0c6"; } .fa-save:before, .fa-floppy-o:before { content: "\f0c7"; } .fa-square:before { content: "\f0c8"; } .fa-navicon:before, .fa-reorder:before, .fa-bars:before { content: "\f0c9"; } .fa-list-ul:before { content: "\f0ca"; } .fa-list-ol:before { content: "\f0cb"; } .fa-strikethrough:before { content: "\f0cc"; } .fa-underline:before { content: "\f0cd"; } .fa-table:before { content: "\f0ce"; } .fa-magic:before { content: "\f0d0"; } .fa-truck:before { content: "\f0d1"; } .fa-pinterest:before { content: "\f0d2"; } .fa-pinterest-square:before { content: "\f0d3"; } .fa-google-plus-square:before { content: "\f0d4"; } .fa-google-plus:before { content: "\f0d5"; } .fa-money:before { content: "\f0d6"; } .fa-caret-down:before { content: "\f0d7"; } .fa-caret-up:before { content: "\f0d8"; } .fa-caret-left:before { content: "\f0d9"; } .fa-caret-right:before { content: "\f0da"; } .fa-columns:before { content: "\f0db"; } .fa-unsorted:before, .fa-sort:before { content: "\f0dc"; } .fa-sort-down:before, .fa-sort-desc:before { content: "\f0dd"; } .fa-sort-up:before, .fa-sort-asc:before { content: "\f0de"; } .fa-envelope:before { content: "\f0e0"; } .fa-linkedin:before { content: "\f0e1"; } .fa-rotate-left:before, .fa-undo:before { content: "\f0e2"; } .fa-legal:before, .fa-gavel:before { content: "\f0e3"; } .fa-dashboard:before, .fa-tachometer:before { content: "\f0e4"; } .fa-comment-o:before { content: "\f0e5"; } .fa-comments-o:before { content: "\f0e6"; } .fa-flash:before, .fa-bolt:before { content: "\f0e7"; } .fa-sitemap:before { content: "\f0e8"; } .fa-umbrella:before { content: "\f0e9"; } .fa-paste:before, .fa-clipboard:before { content: "\f0ea"; } .fa-lightbulb-o:before { content: "\f0eb"; } .fa-exchange:before { content: "\f0ec"; } .fa-cloud-download:before { content: "\f0ed"; } .fa-cloud-upload:before { content: "\f0ee"; } .fa-user-md:before { content: "\f0f0"; } .fa-stethoscope:before { content: "\f0f1"; } .fa-suitcase:before { content: "\f0f2"; } .fa-bell-o:before { content: "\f0a2"; } .fa-coffee:before { content: "\f0f4"; } .fa-cutlery:before { content: "\f0f5"; } .fa-file-text-o:before { content: "\f0f6"; } .fa-building-o:before { content: "\f0f7"; } .fa-hospital-o:before { content: "\f0f8"; } .fa-ambulance:before { content: "\f0f9"; } .fa-medkit:before { content: "\f0fa"; } .fa-fighter-jet:before { content: "\f0fb"; } .fa-beer:before { content: "\f0fc"; } .fa-h-square:before { content: "\f0fd"; } .fa-plus-square:before { content: "\f0fe"; } .fa-angle-double-left:before { content: "\f100"; } .fa-angle-double-right:before { content: "\f101"; } .fa-angle-double-up:before { content: "\f102"; } .fa-angle-double-down:before { content: "\f103"; } .fa-angle-left:before { content: "\f104"; } .fa-angle-right:before { content: "\f105"; } .fa-angle-up:before { content: "\f106"; } .fa-angle-down:before { content: "\f107"; } .fa-desktop:before { content: "\f108"; } .fa-laptop:before { content: "\f109"; } .fa-tablet:before { content: "\f10a"; } .fa-mobile-phone:before, .fa-mobile:before { content: "\f10b"; } .fa-circle-o:before { content: "\f10c"; } .fa-quote-left:before { content: "\f10d"; } .fa-quote-right:before { content: "\f10e"; } .fa-spinner:before { content: "\f110"; } .fa-circle:before { content: "\f111"; } .fa-mail-reply:before, .fa-reply:before { content: "\f112"; } .fa-github-alt:before { content: "\f113"; } .fa-folder-o:before { content: "\f114"; } .fa-folder-open-o:before { content: "\f115"; } .fa-smile-o:before { content: "\f118"; } .fa-frown-o:before { content: "\f119"; } .fa-meh-o:before { content: "\f11a"; } .fa-gamepad:before { content: "\f11b"; } .fa-keyboard-o:before { content: "\f11c"; } .fa-flag-o:before { content: "\f11d"; } .fa-flag-checkered:before { content: "\f11e"; } .fa-terminal:before { content: "\f120"; } .fa-code:before { content: "\f121"; } .fa-mail-reply-all:before, .fa-reply-all:before { content: "\f122"; } .fa-star-half-empty:before, .fa-star-half-full:before, .fa-star-half-o:before { content: "\f123"; } .fa-location-arrow:before { content: "\f124"; } .fa-crop:before { content: "\f125"; } .fa-code-fork:before { content: "\f126"; } .fa-unlink:before, .fa-chain-broken:before { content: "\f127"; } .fa-question:before { content: "\f128"; } .fa-info:before { content: "\f129"; } .fa-exclamation:before { content: "\f12a"; } .fa-superscript:before { content: "\f12b"; } .fa-subscript:before { content: "\f12c"; } .fa-eraser:before { content: "\f12d"; } .fa-puzzle-piece:before { content: "\f12e"; } .fa-microphone:before { content: "\f130"; } .fa-microphone-slash:before { content: "\f131"; } .fa-shield:before { content: "\f132"; } .fa-calendar-o:before { content: "\f133"; } .fa-fire-extinguisher:before { content: "\f134"; } .fa-rocket:before { content: "\f135"; } .fa-maxcdn:before { content: "\f136"; } .fa-chevron-circle-left:before { content: "\f137"; } .fa-chevron-circle-right:before { content: "\f138"; } .fa-chevron-circle-up:before { content: "\f139"; } .fa-chevron-circle-down:before { content: "\f13a"; } .fa-html5:before { content: "\f13b"; } .fa-css3:before { content: "\f13c"; } .fa-anchor:before { content: "\f13d"; } .fa-unlock-alt:before { content: "\f13e"; } .fa-bullseye:before { content: "\f140"; } .fa-ellipsis-h:before { content: "\f141"; } .fa-ellipsis-v:before { content: "\f142"; } .fa-rss-square:before { content: "\f143"; } .fa-play-circle:before { content: "\f144"; } .fa-ticket:before { content: "\f145"; } .fa-minus-square:before { content: "\f146"; } .fa-minus-square-o:before { content: "\f147"; } .fa-level-up:before { content: "\f148"; } .fa-level-down:before { content: "\f149"; } .fa-check-square:before { content: "\f14a"; } .fa-pencil-square:before { content: "\f14b"; } .fa-external-link-square:before { content: "\f14c"; } .fa-share-square:before { content: "\f14d"; } .fa-compass:before { content: "\f14e"; } .fa-toggle-down:before, .fa-caret-square-o-down:before { content: "\f150"; } .fa-toggle-up:before, .fa-caret-square-o-up:before { content: "\f151"; } .fa-toggle-right:before, .fa-caret-square-o-right:before { content: "\f152"; } .fa-euro:before, .fa-eur:before { content: "\f153"; } .fa-gbp:before { content: "\f154"; } .fa-dollar:before, .fa-usd:before { content: "\f155"; } .fa-rupee:before, .fa-inr:before { content: "\f156"; } .fa-cny:before, .fa-rmb:before, .fa-yen:before, .fa-jpy:before { content: "\f157"; } .fa-ruble:before, .fa-rouble:before, .fa-rub:before { content: "\f158"; } .fa-won:before, .fa-krw:before { content: "\f159"; } .fa-bitcoin:before, .fa-btc:before { content: "\f15a"; } .fa-file:before { content: "\f15b"; } .fa-file-text:before { content: "\f15c"; } .fa-sort-alpha-asc:before { content: "\f15d"; } .fa-sort-alpha-desc:before { content: "\f15e"; } .fa-sort-amount-asc:before { content: "\f160"; } .fa-sort-amount-desc:before { content: "\f161"; } .fa-sort-numeric-asc:before { content: "\f162"; } .fa-sort-numeric-desc:before { content: "\f163"; } .fa-thumbs-up:before { content: "\f164"; } .fa-thumbs-down:before { content: "\f165"; } .fa-youtube-square:before { content: "\f166"; } .fa-youtube:before { content: "\f167"; } .fa-xing:before { content: "\f168"; } .fa-xing-square:before { content: "\f169"; } .fa-youtube-play:before { content: "\f16a"; } .fa-dropbox:before { content: "\f16b"; } .fa-stack-overflow:before { content: "\f16c"; } .fa-instagram:before { content: "\f16d"; } .fa-flickr:before { content: "\f16e"; } .fa-adn:before { content: "\f170"; } .fa-bitbucket:before { content: "\f171"; } .fa-bitbucket-square:before { content: "\f172"; } .fa-tumblr:before { content: "\f173"; } .fa-tumblr-square:before { content: "\f174"; } .fa-long-arrow-down:before { content: "\f175"; } .fa-long-arrow-up:before { content: "\f176"; } .fa-long-arrow-left:before { content: "\f177"; } .fa-long-arrow-right:before { content: "\f178"; } .fa-apple:before { content: "\f179"; } .fa-windows:before { content: "\f17a"; } .fa-android:before { content: "\f17b"; } .fa-linux:before { content: "\f17c"; } .fa-dribbble:before { content: "\f17d"; } .fa-skype:before { content: "\f17e"; } .fa-foursquare:before { content: "\f180"; } .fa-trello:before { content: "\f181"; } .fa-female:before { content: "\f182"; } .fa-male:before { content: "\f183"; } .fa-gittip:before, .fa-gratipay:before { content: "\f184"; } .fa-sun-o:before { content: "\f185"; } .fa-moon-o:before { content: "\f186"; } .fa-archive:before { content: "\f187"; } .fa-bug:before { content: "\f188"; } .fa-vk:before { content: "\f189"; } .fa-weibo:before { content: "\f18a"; } .fa-renren:before { content: "\f18b"; } .fa-pagelines:before { content: "\f18c"; } .fa-stack-exchange:before { content: "\f18d"; } .fa-arrow-circle-o-right:before { content: "\f18e"; } .fa-arrow-circle-o-left:before { content: "\f190"; } .fa-toggle-left:before, .fa-caret-square-o-left:before { content: "\f191"; } .fa-dot-circle-o:before { content: "\f192"; } .fa-wheelchair:before { content: "\f193"; } .fa-vimeo-square:before { content: "\f194"; } .fa-turkish-lira:before, .fa-try:before { content: "\f195"; } .fa-plus-square-o:before { content: "\f196"; } .fa-space-shuttle:before { content: "\f197"; } .fa-slack:before { content: "\f198"; } .fa-envelope-square:before { content: "\f199"; } .fa-wordpress:before { content: "\f19a"; } .fa-openid:before { content: "\f19b"; } .fa-institution:before, .fa-bank:before, .fa-university:before { content: "\f19c"; } .fa-mortar-board:before, .fa-graduation-cap:before { content: "\f19d"; } .fa-yahoo:before { content: "\f19e"; } .fa-google:before { content: "\f1a0"; } .fa-reddit:before { content: "\f1a1"; } .fa-reddit-square:before { content: "\f1a2"; } .fa-stumbleupon-circle:before { content: "\f1a3"; } .fa-stumbleupon:before { content: "\f1a4"; } .fa-delicious:before { content: "\f1a5"; } .fa-digg:before { content: "\f1a6"; } .fa-pied-piper-pp:before { content: "\f1a7"; } .fa-pied-piper-alt:before { content: "\f1a8"; } .fa-drupal:before { content: "\f1a9"; } .fa-joomla:before { content: "\f1aa"; } .fa-language:before { content: "\f1ab"; } .fa-fax:before { content: "\f1ac"; } .fa-building:before { content: "\f1ad"; } .fa-child:before { content: "\f1ae"; } .fa-paw:before { content: "\f1b0"; } .fa-spoon:before { content: "\f1b1"; } .fa-cube:before { content: "\f1b2"; } .fa-cubes:before { content: "\f1b3"; } .fa-behance:before { content: "\f1b4"; } .fa-behance-square:before { content: "\f1b5"; } .fa-steam:before { content: "\f1b6"; } .fa-steam-square:before { content: "\f1b7"; } .fa-recycle:before { content: "\f1b8"; } .fa-automobile:before, .fa-car:before { content: "\f1b9"; } .fa-cab:before, .fa-taxi:before { content: "\f1ba"; } .fa-tree:before { content: "\f1bb"; } .fa-spotify:before { content: "\f1bc"; } .fa-deviantart:before { content: "\f1bd"; } .fa-soundcloud:before { content: "\f1be"; } .fa-database:before { content: "\f1c0"; } .fa-file-pdf-o:before { content: "\f1c1"; } .fa-file-word-o:before { content: "\f1c2"; } .fa-file-excel-o:before { content: "\f1c3"; } .fa-file-powerpoint-o:before { content: "\f1c4"; } .fa-file-photo-o:before, .fa-file-picture-o:before, .fa-file-image-o:before { content: "\f1c5"; } .fa-file-zip-o:before, .fa-file-archive-o:before { content: "\f1c6"; } .fa-file-sound-o:before, .fa-file-audio-o:before { content: "\f1c7"; } .fa-file-movie-o:before, .fa-file-video-o:before { content: "\f1c8"; } .fa-file-code-o:before { content: "\f1c9"; } .fa-vine:before { content: "\f1ca"; } .fa-codepen:before { content: "\f1cb"; } .fa-jsfiddle:before { content: "\f1cc"; } .fa-life-bouy:before, .fa-life-buoy:before, .fa-life-saver:before, .fa-support:before, .fa-life-ring:before { content: "\f1cd"; } .fa-circle-o-notch:before { content: "\f1ce"; } .fa-ra:before, .fa-resistance:before, .fa-rebel:before { content: "\f1d0"; } .fa-ge:before, .fa-empire:before { content: "\f1d1"; } .fa-git-square:before { content: "\f1d2"; } .fa-git:before { content: "\f1d3"; } .fa-y-combinator-square:before, .fa-yc-square:before, .fa-hacker-news:before { content: "\f1d4"; } .fa-tencent-weibo:before { content: "\f1d5"; } .fa-qq:before { content: "\f1d6"; } .fa-wechat:before, .fa-weixin:before { content: "\f1d7"; } .fa-send:before, .fa-paper-plane:before { content: "\f1d8"; } .fa-send-o:before, .fa-paper-plane-o:before { content: "\f1d9"; } .fa-history:before { content: "\f1da"; } .fa-circle-thin:before { content: "\f1db"; } .fa-header:before { content: "\f1dc"; } .fa-paragraph:before { content: "\f1dd"; } .fa-sliders:before { content: "\f1de"; } .fa-share-alt:before { content: "\f1e0"; } .fa-share-alt-square:before { content: "\f1e1"; } .fa-bomb:before { content: "\f1e2"; } .fa-soccer-ball-o:before, .fa-futbol-o:before { content: "\f1e3"; } .fa-tty:before { content: "\f1e4"; } .fa-binoculars:before { content: "\f1e5"; } .fa-plug:before { content: "\f1e6"; } .fa-slideshare:before { content: "\f1e7"; } .fa-twitch:before { content: "\f1e8"; } .fa-yelp:before { content: "\f1e9"; } .fa-newspaper-o:before { content: "\f1ea"; } .fa-wifi:before { content: "\f1eb"; } .fa-calculator:before { content: "\f1ec"; } .fa-paypal:before { content: "\f1ed"; } .fa-google-wallet:before { content: "\f1ee"; } .fa-cc-visa:before { content: "\f1f0"; } .fa-cc-mastercard:before { content: "\f1f1"; } .fa-cc-discover:before { content: "\f1f2"; } .fa-cc-amex:before { content: "\f1f3"; } .fa-cc-paypal:before { content: "\f1f4"; } .fa-cc-stripe:before { content: "\f1f5"; } .fa-bell-slash:before { content: "\f1f6"; } .fa-bell-slash-o:before { content: "\f1f7"; } .fa-trash:before { content: "\f1f8"; } .fa-copyright:before { content: "\f1f9"; } .fa-at:before { content: "\f1fa"; } .fa-eyedropper:before { content: "\f1fb"; } .fa-paint-brush:before { content: "\f1fc"; } .fa-birthday-cake:before { content: "\f1fd"; } .fa-area-chart:before { content: "\f1fe"; } .fa-pie-chart:before { content: "\f200"; } .fa-line-chart:before { content: "\f201"; } .fa-lastfm:before { content: "\f202"; } .fa-lastfm-square:before { content: "\f203"; } .fa-toggle-off:before { content: "\f204"; } .fa-toggle-on:before { content: "\f205"; } .fa-bicycle:before { content: "\f206"; } .fa-bus:before { content: "\f207"; } .fa-ioxhost:before { content: "\f208"; } .fa-angellist:before { content: "\f209"; } .fa-cc:before { content: "\f20a"; } .fa-shekel:before, .fa-sheqel:before, .fa-ils:before { content: "\f20b"; } .fa-meanpath:before { content: "\f20c"; } .fa-buysellads:before { content: "\f20d"; } .fa-connectdevelop:before { content: "\f20e"; } .fa-dashcube:before { content: "\f210"; } .fa-forumbee:before { content: "\f211"; } .fa-leanpub:before { content: "\f212"; } .fa-sellsy:before { content: "\f213"; } .fa-shirtsinbulk:before { content: "\f214"; } .fa-simplybuilt:before { content: "\f215"; } .fa-skyatlas:before { content: "\f216"; } .fa-cart-plus:before { content: "\f217"; } .fa-cart-arrow-down:before { content: "\f218"; } .fa-diamond:before { content: "\f219"; } .fa-ship:before { content: "\f21a"; } .fa-user-secret:before { content: "\f21b"; } .fa-motorcycle:before { content: "\f21c"; } .fa-street-view:before { content: "\f21d"; } .fa-heartbeat:before { content: "\f21e"; } .fa-venus:before { content: "\f221"; } .fa-mars:before { content: "\f222"; } .fa-mercury:before { content: "\f223"; } .fa-intersex:before, .fa-transgender:before { content: "\f224"; } .fa-transgender-alt:before { content: "\f225"; } .fa-venus-double:before { content: "\f226"; } .fa-mars-double:before { content: "\f227"; } .fa-venus-mars:before { content: "\f228"; } .fa-mars-stroke:before { content: "\f229"; } .fa-mars-stroke-v:before { content: "\f22a"; } .fa-mars-stroke-h:before { content: "\f22b"; } .fa-neuter:before { content: "\f22c"; } .fa-genderless:before { content: "\f22d"; } .fa-facebook-official:before { content: "\f230"; } .fa-pinterest-p:before { content: "\f231"; } .fa-whatsapp:before { content: "\f232"; } .fa-server:before { content: "\f233"; } .fa-user-plus:before { content: "\f234"; } .fa-user-times:before { content: "\f235"; } .fa-hotel:before, .fa-bed:before { content: "\f236"; } .fa-viacoin:before { content: "\f237"; } .fa-train:before { content: "\f238"; } .fa-subway:before { content: "\f239"; } .fa-medium:before { content: "\f23a"; } .fa-yc:before, .fa-y-combinator:before { content: "\f23b"; } .fa-optin-monster:before { content: "\f23c"; } .fa-opencart:before { content: "\f23d"; } .fa-expeditedssl:before { content: "\f23e"; } .fa-battery-4:before, .fa-battery:before, .fa-battery-full:before { content: "\f240"; } .fa-battery-3:before, .fa-battery-three-quarters:before { content: "\f241"; } .fa-battery-2:before, .fa-battery-half:before { content: "\f242"; } .fa-battery-1:before, .fa-battery-quarter:before { content: "\f243"; } .fa-battery-0:before, .fa-battery-empty:before { content: "\f244"; } .fa-mouse-pointer:before { content: "\f245"; } .fa-i-cursor:before { content: "\f246"; } .fa-object-group:before { content: "\f247"; } .fa-object-ungroup:before { content: "\f248"; } .fa-sticky-note:before { content: "\f249"; } .fa-sticky-note-o:before { content: "\f24a"; } .fa-cc-jcb:before { content: "\f24b"; } .fa-cc-diners-club:before { content: "\f24c"; } .fa-clone:before { content: "\f24d"; } .fa-balance-scale:before { content: "\f24e"; } .fa-hourglass-o:before { content: "\f250"; } .fa-hourglass-1:before, .fa-hourglass-start:before { content: "\f251"; } .fa-hourglass-2:before, .fa-hourglass-half:before { content: "\f252"; } .fa-hourglass-3:before, .fa-hourglass-end:before { content: "\f253"; } .fa-hourglass:before { content: "\f254"; } .fa-hand-grab-o:before, .fa-hand-rock-o:before { content: "\f255"; } .fa-hand-stop-o:before, .fa-hand-paper-o:before { content: "\f256"; } .fa-hand-scissors-o:before { content: "\f257"; } .fa-hand-lizard-o:before { content: "\f258"; } .fa-hand-spock-o:before { content: "\f259"; } .fa-hand-pointer-o:before { content: "\f25a"; } .fa-hand-peace-o:before { content: "\f25b"; } .fa-trademark:before { content: "\f25c"; } .fa-registered:before { content: "\f25d"; } .fa-creative-commons:before { content: "\f25e"; } .fa-gg:before { content: "\f260"; } .fa-gg-circle:before { content: "\f261"; } .fa-tripadvisor:before { content: "\f262"; } .fa-odnoklassniki:before { content: "\f263"; } .fa-odnoklassniki-square:before { content: "\f264"; } .fa-get-pocket:before { content: "\f265"; } .fa-wikipedia-w:before { content: "\f266"; } .fa-safari:before { content: "\f267"; } .fa-chrome:before { content: "\f268"; } .fa-firefox:before { content: "\f269"; } .fa-opera:before { content: "\f26a"; } .fa-internet-explorer:before { content: "\f26b"; } .fa-tv:before, .fa-television:before { content: "\f26c"; } .fa-contao:before { content: "\f26d"; } .fa-500px:before { content: "\f26e"; } .fa-amazon:before { content: "\f270"; } .fa-calendar-plus-o:before { content: "\f271"; } .fa-calendar-minus-o:before { content: "\f272"; } .fa-calendar-times-o:before { content: "\f273"; } .fa-calendar-check-o:before { content: "\f274"; } .fa-industry:before { content: "\f275"; } .fa-map-pin:before { content: "\f276"; } .fa-map-signs:before { content: "\f277"; } .fa-map-o:before { content: "\f278"; } .fa-map:before { content: "\f279"; } .fa-commenting:before { content: "\f27a"; } .fa-commenting-o:before { content: "\f27b"; } .fa-houzz:before { content: "\f27c"; } .fa-vimeo:before { content: "\f27d"; } .fa-black-tie:before { content: "\f27e"; } .fa-fonticons:before { content: "\f280"; } .fa-reddit-alien:before { content: "\f281"; } .fa-edge:before { content: "\f282"; } .fa-credit-card-alt:before { content: "\f283"; } .fa-codiepie:before { content: "\f284"; } .fa-modx:before { content: "\f285"; } .fa-fort-awesome:before { content: "\f286"; } .fa-usb:before { content: "\f287"; } .fa-product-hunt:before { content: "\f288"; } .fa-mixcloud:before { content: "\f289"; } .fa-scribd:before { content: "\f28a"; } .fa-pause-circle:before { content: "\f28b"; } .fa-pause-circle-o:before { content: "\f28c"; } .fa-stop-circle:before { content: "\f28d"; } .fa-stop-circle-o:before { content: "\f28e"; } .fa-shopping-bag:before { content: "\f290"; } .fa-shopping-basket:before { content: "\f291"; } .fa-hashtag:before { content: "\f292"; } .fa-bluetooth:before { content: "\f293"; } .fa-bluetooth-b:before { content: "\f294"; } .fa-percent:before { content: "\f295"; } .fa-gitlab:before { content: "\f296"; } .fa-wpbeginner:before { content: "\f297"; } .fa-wpforms:before { content: "\f298"; } .fa-envira:before { content: "\f299"; } .fa-universal-access:before { content: "\f29a"; } .fa-wheelchair-alt:before { content: "\f29b"; } .fa-question-circle-o:before { content: "\f29c"; } .fa-blind:before { content: "\f29d"; } .fa-audio-description:before { content: "\f29e"; } .fa-volume-control-phone:before { content: "\f2a0"; } .fa-braille:before { content: "\f2a1"; } .fa-assistive-listening-systems:before { content: "\f2a2"; } .fa-asl-interpreting:before, .fa-american-sign-language-interpreting:before { content: "\f2a3"; } .fa-deafness:before, .fa-hard-of-hearing:before, .fa-deaf:before { content: "\f2a4"; } .fa-glide:before { content: "\f2a5"; } .fa-glide-g:before { content: "\f2a6"; } .fa-signing:before, .fa-sign-language:before { content: "\f2a7"; } .fa-low-vision:before { content: "\f2a8"; } .fa-viadeo:before { content: "\f2a9"; } .fa-viadeo-square:before { content: "\f2aa"; } .fa-snapchat:before { content: "\f2ab"; } .fa-snapchat-ghost:before { content: "\f2ac"; } .fa-snapchat-square:before { content: "\f2ad"; } .fa-pied-piper:before { content: "\f2ae"; } .fa-first-order:before { content: "\f2b0"; } .fa-yoast:before { content: "\f2b1"; } .fa-themeisle:before { content: "\f2b2"; } .fa-google-plus-circle:before, .fa-google-plus-official:before { content: "\f2b3"; } .fa-fa:before, .fa-font-awesome:before { content: "\f2b4"; } .fa-handshake-o:before { content: "\f2b5"; } .fa-envelope-open:before { content: "\f2b6"; } .fa-envelope-open-o:before { content: "\f2b7"; } .fa-linode:before { content: "\f2b8"; } .fa-address-book:before { content: "\f2b9"; } .fa-address-book-o:before { content: "\f2ba"; } .fa-vcard:before, .fa-address-card:before { content: "\f2bb"; } .fa-vcard-o:before, .fa-address-card-o:before { content: "\f2bc"; } .fa-user-circle:before { content: "\f2bd"; } .fa-user-circle-o:before { content: "\f2be"; } .fa-user-o:before { content: "\f2c0"; } .fa-id-badge:before { content: "\f2c1"; } .fa-drivers-license:before, .fa-id-card:before { content: "\f2c2"; } .fa-drivers-license-o:before, .fa-id-card-o:before { content: "\f2c3"; } .fa-quora:before { content: "\f2c4"; } .fa-free-code-camp:before { content: "\f2c5"; } .fa-telegram:before { content: "\f2c6"; } .fa-thermometer-4:before, .fa-thermometer:before, .fa-thermometer-full:before { content: "\f2c7"; } .fa-thermometer-3:before, .fa-thermometer-three-quarters:before { content: "\f2c8"; } .fa-thermometer-2:before, .fa-thermometer-half:before { content: "\f2c9"; } .fa-thermometer-1:before, .fa-thermometer-quarter:before { content: "\f2ca"; } .fa-thermometer-0:before, .fa-thermometer-empty:before { content: "\f2cb"; } .fa-shower:before { content: "\f2cc"; } .fa-bathtub:before, .fa-s15:before, .fa-bath:before { content: "\f2cd"; } .fa-podcast:before { content: "\f2ce"; } .fa-window-maximize:before { content: "\f2d0"; } .fa-window-minimize:before { content: "\f2d1"; } .fa-window-restore:before { content: "\f2d2"; } .fa-times-rectangle:before, .fa-window-close:before { content: "\f2d3"; } .fa-times-rectangle-o:before, .fa-window-close-o:before { content: "\f2d4"; } .fa-bandcamp:before { content: "\f2d5"; } .fa-grav:before { content: "\f2d6"; } .fa-etsy:before { content: "\f2d7"; } .fa-imdb:before { content: "\f2d8"; } .fa-ravelry:before { content: "\f2d9"; } .fa-eercast:before { content: "\f2da"; } .fa-microchip:before { content: "\f2db"; } .fa-snowflake-o:before { content: "\f2dc"; } .fa-superpowers:before { content: "\f2dd"; } .fa-wpexplorer:before { content: "\f2de"; } .fa-meetup:before { content: "\f2e0"; } .sr-only { position: absolute; width: 1px; height: 1px; padding: 0; margin: -1px; overflow: hidden; clip: rect(0, 0, 0, 0); border: 0; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } /\*! \* \* IPython base \* \*/ .modal.fade .modal-dialog { -webkit-transform: translate(0, 0); -ms-transform: translate(0, 0); -o-transform: translate(0, 0); transform: translate(0, 0); } code { color: #000; } pre { font-size: inherit; line-height: inherit; } label { font-weight: normal; } /\* Make the page background atleast 100% the height of the view port \*/ /\* Make the page itself atleast 70% the height of the view port \*/ .border-box-sizing { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } .corner-all { border-radius: 2px; } .no-padding { padding: 0px; } /\* Flexible box model classes \*/ /\* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ \*/ /\* This file is a compatability layer. It allows the usage of flexible box model layouts accross multiple browsers, including older browsers. The newest, universal implementation of the flexible box model is used when available (see `Modern browsers` comments below). Browsers that are known to implement this new spec completely include: Firefox 28.0+ Chrome 29.0+ Internet Explorer 11+ Opera 17.0+ Browsers not listed, including Safari, are supported via the styling under the `Old browsers` comments below. \*/ .hbox { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } .hbox > \* { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; } .vbox { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } .vbox > \* { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; } .hbox.reverse, .vbox.reverse, .reverse { /\* Old browsers \*/ -webkit-box-direction: reverse; -moz-box-direction: reverse; box-direction: reverse; /\* Modern browsers \*/ flex-direction: row-reverse; } .hbox.box-flex0, .vbox.box-flex0, .box-flex0 { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; width: auto; } .hbox.box-flex1, .vbox.box-flex1, .box-flex1 { /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } .hbox.box-flex, .vbox.box-flex, .box-flex { /\* Old browsers \*/ /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } .hbox.box-flex2, .vbox.box-flex2, .box-flex2 { /\* Old browsers \*/ -webkit-box-flex: 2; -moz-box-flex: 2; box-flex: 2; /\* Modern browsers \*/ flex: 2; } .box-group1 { /\* Deprecated \*/ -webkit-box-flex-group: 1; -moz-box-flex-group: 1; box-flex-group: 1; } .box-group2 { /\* Deprecated \*/ -webkit-box-flex-group: 2; -moz-box-flex-group: 2; box-flex-group: 2; } .hbox.start, .vbox.start, .start { /\* Old browsers \*/ -webkit-box-pack: start; -moz-box-pack: start; box-pack: start; /\* Modern browsers \*/ justify-content: flex-start; } .hbox.end, .vbox.end, .end { /\* Old browsers \*/ -webkit-box-pack: end; -moz-box-pack: end; box-pack: end; /\* Modern browsers \*/ justify-content: flex-end; } .hbox.center, .vbox.center, .center { /\* Old browsers \*/ -webkit-box-pack: center; -moz-box-pack: center; box-pack: center; /\* Modern browsers \*/ justify-content: center; } .hbox.baseline, .vbox.baseline, .baseline { /\* Old browsers \*/ -webkit-box-pack: baseline; -moz-box-pack: baseline; box-pack: baseline; /\* Modern browsers \*/ justify-content: baseline; } .hbox.stretch, .vbox.stretch, .stretch { /\* Old browsers \*/ -webkit-box-pack: stretch; -moz-box-pack: stretch; box-pack: stretch; /\* Modern browsers \*/ justify-content: stretch; } .hbox.align-start, .vbox.align-start, .align-start { /\* Old browsers \*/ -webkit-box-align: start; -moz-box-align: start; box-align: start; /\* Modern browsers \*/ align-items: flex-start; } .hbox.align-end, .vbox.align-end, .align-end { /\* Old browsers \*/ -webkit-box-align: end; -moz-box-align: end; box-align: end; /\* Modern browsers \*/ align-items: flex-end; } .hbox.align-center, .vbox.align-center, .align-center { /\* Old browsers \*/ -webkit-box-align: center; -moz-box-align: center; box-align: center; /\* Modern browsers \*/ align-items: center; } .hbox.align-baseline, .vbox.align-baseline, .align-baseline { /\* Old browsers \*/ -webkit-box-align: baseline; -moz-box-align: baseline; box-align: baseline; /\* Modern browsers \*/ align-items: baseline; } .hbox.align-stretch, .vbox.align-stretch, .align-stretch { /\* Old browsers \*/ -webkit-box-align: stretch; -moz-box-align: stretch; box-align: stretch; /\* Modern browsers \*/ align-items: stretch; } div.error { margin: 2em; text-align: center; } div.error > h1 { font-size: 500%; line-height: normal; } div.error > p { font-size: 200%; line-height: normal; } div.traceback-wrapper { text-align: left; max-width: 800px; margin: auto; } div.traceback-wrapper pre.traceback { max-height: 600px; overflow: auto; } /\*\* \* Primary styles \* \* Author: Jupyter Development Team \*/ body { background-color: #fff; /\* This makes sure that the body covers the entire window and needs to be in a different element than the display: box in wrapper below \*/ position: absolute; left: 0px; right: 0px; top: 0px; bottom: 0px; overflow: visible; } body > #header { /\* Initially hidden to prevent FLOUC \*/ display: none; background-color: #fff; /\* Display over codemirror \*/ position: relative; z-index: 100; } body > #header #header-container { display: flex; flex-direction: row; justify-content: space-between; padding: 5px; padding-bottom: 5px; padding-top: 5px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } body > #header .header-bar { width: 100%; height: 1px; background: #e7e7e7; margin-bottom: -1px; } @media print { body > #header { display: none !important; } } #header-spacer { width: 100%; visibility: hidden; } @media print { #header-spacer { display: none; } } #ipython\_notebook { padding-left: 0px; padding-top: 1px; padding-bottom: 1px; } [dir="rtl"] #ipython\_notebook { margin-right: 10px; margin-left: 0; } [dir="rtl"] #ipython\_notebook.pull-left { float: right !important; float: right; } .flex-spacer { flex: 1; } #noscript { width: auto; padding-top: 16px; padding-bottom: 16px; text-align: center; font-size: 22px; color: red; font-weight: bold; } #ipython\_notebook img { height: 28px; } #site { width: 100%; display: none; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; overflow: auto; } @media print { #site { height: auto !important; } } /\* Smaller buttons \*/ .ui-button .ui-button-text { padding: 0.2em 0.8em; font-size: 77%; } input.ui-button { padding: 0.3em 0.9em; } span#kernel\_logo\_widget { margin: 0 10px; } span#login\_widget { float: right; } [dir="rtl"] span#login\_widget { float: left; } span#login\_widget > .button, #logout { color: #333; background-color: #fff; border-color: #ccc; } span#login\_widget > .button:focus, #logout:focus, span#login\_widget > .button.focus, #logout.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } span#login\_widget > .button:hover, #logout:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } span#login\_widget > .button:active, #logout:active, span#login\_widget > .button.active, #logout.active, .open > .dropdown-togglespan#login\_widget > .button, .open > .dropdown-toggle#logout { color: #333; background-color: #e6e6e6; border-color: #adadad; } span#login\_widget > .button:active:hover, #logout:active:hover, span#login\_widget > .button.active:hover, #logout.active:hover, .open > .dropdown-togglespan#login\_widget > .button:hover, .open > .dropdown-toggle#logout:hover, span#login\_widget > .button:active:focus, #logout:active:focus, span#login\_widget > .button.active:focus, #logout.active:focus, .open > .dropdown-togglespan#login\_widget > .button:focus, .open > .dropdown-toggle#logout:focus, span#login\_widget > .button:active.focus, #logout:active.focus, span#login\_widget > .button.active.focus, #logout.active.focus, .open > .dropdown-togglespan#login\_widget > .button.focus, .open > .dropdown-toggle#logout.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } span#login\_widget > .button:active, #logout:active, span#login\_widget > .button.active, #logout.active, .open > .dropdown-togglespan#login\_widget > .button, .open > .dropdown-toggle#logout { background-image: none; } span#login\_widget > .button.disabled:hover, #logout.disabled:hover, span#login\_widget > .button[disabled]:hover, #logout[disabled]:hover, fieldset[disabled] span#login\_widget > .button:hover, fieldset[disabled] #logout:hover, span#login\_widget > .button.disabled:focus, #logout.disabled:focus, span#login\_widget > .button[disabled]:focus, #logout[disabled]:focus, fieldset[disabled] span#login\_widget > .button:focus, fieldset[disabled] #logout:focus, span#login\_widget > .button.disabled.focus, #logout.disabled.focus, span#login\_widget > .button[disabled].focus, #logout[disabled].focus, fieldset[disabled] span#login\_widget > .button.focus, fieldset[disabled] #logout.focus { background-color: #fff; border-color: #ccc; } span#login\_widget > .button .badge, #logout .badge { color: #fff; background-color: #333; } .nav-header { text-transform: none; } #header > span { margin-top: 10px; } .modal\_stretch .modal-dialog { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; min-height: 80vh; } .modal\_stretch .modal-dialog .modal-body { max-height: calc(100vh - 200px); overflow: auto; flex: 1; } .modal-header { cursor: move; } @media (min-width: 768px) { .modal .modal-dialog { width: 700px; } } @media (min-width: 768px) { select.form-control { margin-left: 12px; margin-right: 12px; } } /\*! \* \* IPython auth \* \*/ .center-nav { display: inline-block; margin-bottom: -4px; } [dir="rtl"] .center-nav form.pull-left { float: right !important; float: right; } [dir="rtl"] .center-nav .navbar-text { float: right; } [dir="rtl"] .navbar-inner { text-align: right; } [dir="rtl"] div.text-left { text-align: right; } /\*! \* \* IPython tree view \* \*/ /\* We need an invisible input field on top of the sentense\*/ /\* "Drag file onto the list ..." \*/ .alternate\_upload { background-color: none; display: inline; } .alternate\_upload.form { padding: 0; margin: 0; } .alternate\_upload input.fileinput { position: absolute; display: block; width: 100%; height: 100%; overflow: hidden; cursor: pointer; opacity: 0; z-index: 2; } .alternate\_upload .btn-xs > input.fileinput { margin: -1px -5px; } .alternate\_upload .btn-upload { position: relative; height: 22px; } ::-webkit-file-upload-button { cursor: pointer; } /\*\* \* Primary styles \* \* Author: Jupyter Development Team \*/ ul#tabs { margin-bottom: 4px; } ul#tabs a { padding-top: 6px; padding-bottom: 4px; } [dir="rtl"] ul#tabs.nav-tabs > li { float: right; } [dir="rtl"] ul#tabs.nav.nav-tabs { padding-right: 0; } ul.breadcrumb a:focus, ul.breadcrumb a:hover { text-decoration: none; } ul.breadcrumb i.icon-home { font-size: 16px; margin-right: 4px; } ul.breadcrumb span { color: #5e5e5e; } .list\_toolbar { padding: 4px 0 4px 0; vertical-align: middle; } .list\_toolbar .tree-buttons { padding-top: 1px; } [dir="rtl"] .list\_toolbar .tree-buttons .pull-right { float: left !important; float: left; } [dir="rtl"] .list\_toolbar .col-sm-4, [dir="rtl"] .list\_toolbar .col-sm-8 { float: right; } .dynamic-buttons { padding-top: 3px; display: inline-block; } .list\_toolbar [class\*="span"] { min-height: 24px; } .list\_header { font-weight: bold; background-color: #EEE; } .list\_placeholder { font-weight: bold; padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; } .list\_container { margin-top: 4px; margin-bottom: 20px; border: 1px solid #ddd; border-radius: 2px; } .list\_container > div { border-bottom: 1px solid #ddd; } .list\_container > div:hover .list-item { background-color: red; } .list\_container > div:last-child { border: none; } .list\_item:hover .list\_item { background-color: #ddd; } .list\_item a { text-decoration: none; } .list\_item:hover { background-color: #fafafa; } .list\_header > div, .list\_item > div { padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; line-height: 22px; } .list\_header > div input, .list\_item > div input { margin-right: 7px; margin-left: 14px; vertical-align: text-bottom; line-height: 22px; position: relative; top: -1px; } .list\_header > div .item\_link, .list\_item > div .item\_link { margin-left: -1px; vertical-align: baseline; line-height: 22px; } [dir="rtl"] .list\_item > div input { margin-right: 0; } .new-file input[type=checkbox] { visibility: hidden; } .item\_name { line-height: 22px; height: 24px; } .item\_icon { font-size: 14px; color: #5e5e5e; margin-right: 7px; margin-left: 7px; line-height: 22px; vertical-align: baseline; } .item\_modified { margin-right: 7px; margin-left: 7px; } [dir="rtl"] .item\_modified.pull-right { float: left !important; float: left; } .item\_buttons { line-height: 1em; margin-left: -5px; } .item\_buttons .btn, .item\_buttons .btn-group, .item\_buttons .input-group { float: left; } .item\_buttons > .btn, .item\_buttons > .btn-group, .item\_buttons > .input-group { margin-left: 5px; } .item\_buttons .btn { min-width: 13ex; } .item\_buttons .running-indicator { padding-top: 4px; color: #5cb85c; } .item\_buttons .kernel-name { padding-top: 4px; color: #5bc0de; margin-right: 7px; float: left; } [dir="rtl"] .item\_buttons.pull-right { float: left !important; float: left; } [dir="rtl"] .item\_buttons .kernel-name { margin-left: 7px; float: right; } .toolbar\_info { height: 24px; line-height: 24px; } .list\_item input:not([type=checkbox]) { padding-top: 3px; padding-bottom: 3px; height: 22px; line-height: 14px; margin: 0px; } .highlight\_text { color: blue; } #project\_name { display: inline-block; padding-left: 7px; margin-left: -2px; } #project\_name > .breadcrumb { padding: 0px; margin-bottom: 0px; background-color: transparent; font-weight: bold; } .sort\_button { display: inline-block; padding-left: 7px; } [dir="rtl"] .sort\_button.pull-right { float: left !important; float: left; } #tree-selector { padding-right: 0px; } #button-select-all { min-width: 50px; } [dir="rtl"] #button-select-all.btn { float: right ; } #select-all { margin-left: 7px; margin-right: 2px; margin-top: 2px; height: 16px; } [dir="rtl"] #select-all.pull-left { float: right !important; float: right; } .menu\_icon { margin-right: 2px; } .tab-content .row { margin-left: 0px; margin-right: 0px; } .folder\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f114"; } .folder\_icon:before.fa-pull-left { margin-right: .3em; } .folder\_icon:before.fa-pull-right { margin-left: .3em; } .folder\_icon:before.pull-left { margin-right: .3em; } .folder\_icon:before.pull-right { margin-left: .3em; } .notebook\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f02d"; position: relative; top: -1px; } .notebook\_icon:before.fa-pull-left { margin-right: .3em; } .notebook\_icon:before.fa-pull-right { margin-left: .3em; } .notebook\_icon:before.pull-left { margin-right: .3em; } .notebook\_icon:before.pull-right { margin-left: .3em; } .running\_notebook\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f02d"; position: relative; top: -1px; color: #5cb85c; } .running\_notebook\_icon:before.fa-pull-left { margin-right: .3em; } .running\_notebook\_icon:before.fa-pull-right { margin-left: .3em; } .running\_notebook\_icon:before.pull-left { margin-right: .3em; } .running\_notebook\_icon:before.pull-right { margin-left: .3em; } .file\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f016"; position: relative; top: -2px; } .file\_icon:before.fa-pull-left { margin-right: .3em; } .file\_icon:before.fa-pull-right { margin-left: .3em; } .file\_icon:before.pull-left { margin-right: .3em; } .file\_icon:before.pull-right { margin-left: .3em; } #notebook\_toolbar .pull-right { padding-top: 0px; margin-right: -1px; } ul#new-menu { left: auto; right: 0; } #new-menu .dropdown-header { font-size: 10px; border-bottom: 1px solid #e5e5e5; padding: 0 0 3px; margin: -3px 20px 0; } .kernel-menu-icon { padding-right: 12px; width: 24px; content: "\f096"; } .kernel-menu-icon:before { content: "\f096"; } .kernel-menu-icon-current:before { content: "\f00c"; } #tab\_content { padding-top: 20px; } #running .panel-group .panel { margin-top: 3px; margin-bottom: 1em; } #running .panel-group .panel .panel-heading { background-color: #EEE; padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; line-height: 22px; } #running .panel-group .panel .panel-heading a:focus, #running .panel-group .panel .panel-heading a:hover { text-decoration: none; } #running .panel-group .panel .panel-body { padding: 0px; } #running .panel-group .panel .panel-body .list\_container { margin-top: 0px; margin-bottom: 0px; border: 0px; border-radius: 0px; } #running .panel-group .panel .panel-body .list\_container .list\_item { border-bottom: 1px solid #ddd; } #running .panel-group .panel .panel-body .list\_container .list\_item:last-child { border-bottom: 0px; } .delete-button { display: none; } .duplicate-button { display: none; } .rename-button { display: none; } .move-button { display: none; } .download-button { display: none; } .shutdown-button { display: none; } .dynamic-instructions { display: inline-block; padding-top: 4px; } /\*! \* \* IPython text editor webapp \* \*/ .selected-keymap i.fa { padding: 0px 5px; } .selected-keymap i.fa:before { content: "\f00c"; } #mode-menu { overflow: auto; max-height: 20em; } .edit\_app #header { -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } .edit\_app #menubar .navbar { /\* Use a negative 1 bottom margin, so the border overlaps the border of the header \*/ margin-bottom: -1px; } .dirty-indicator { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator.fa-pull-left { margin-right: .3em; } .dirty-indicator.fa-pull-right { margin-left: .3em; } .dirty-indicator.pull-left { margin-right: .3em; } .dirty-indicator.pull-right { margin-left: .3em; } .dirty-indicator-dirty { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator-dirty.fa-pull-left { margin-right: .3em; } .dirty-indicator-dirty.fa-pull-right { margin-left: .3em; } .dirty-indicator-dirty.pull-left { margin-right: .3em; } .dirty-indicator-dirty.pull-right { margin-left: .3em; } .dirty-indicator-clean { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator-clean.fa-pull-left { margin-right: .3em; } .dirty-indicator-clean.fa-pull-right { margin-left: .3em; } .dirty-indicator-clean.pull-left { margin-right: .3em; } .dirty-indicator-clean.pull-right { margin-left: .3em; } .dirty-indicator-clean:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f00c"; } .dirty-indicator-clean:before.fa-pull-left { margin-right: .3em; } .dirty-indicator-clean:before.fa-pull-right { margin-left: .3em; } .dirty-indicator-clean:before.pull-left { margin-right: .3em; } .dirty-indicator-clean:before.pull-right { margin-left: .3em; } #filename { font-size: 16pt; display: table; padding: 0px 5px; } #current-mode { padding-left: 5px; padding-right: 5px; } #texteditor-backdrop { padding-top: 20px; padding-bottom: 20px; } @media not print { #texteditor-backdrop { background-color: #EEE; } } @media print { #texteditor-backdrop #texteditor-container .CodeMirror-gutter, #texteditor-backdrop #texteditor-container .CodeMirror-gutters { background-color: #fff; } } @media not print { #texteditor-backdrop #texteditor-container .CodeMirror-gutter, #texteditor-backdrop #texteditor-container .CodeMirror-gutters { background-color: #fff; } } @media not print { #texteditor-backdrop #texteditor-container { padding: 0px; background-color: #fff; -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } } .CodeMirror-dialog { background-color: #fff; } /\*! \* \* IPython notebook \* \*/ /\* CSS font colors for translated ANSI escape sequences \*/ /\* The color values are a mix of http://www.xcolors.net/dl/baskerville-ivorylight and http://www.xcolors.net/dl/euphrasia \*/ .ansi-black-fg { color: #3E424D; } .ansi-black-bg { background-color: #3E424D; } .ansi-black-intense-fg { color: #282C36; } .ansi-black-intense-bg { background-color: #282C36; } .ansi-red-fg { color: #E75C58; } .ansi-red-bg { background-color: #E75C58; } .ansi-red-intense-fg { color: #B22B31; } .ansi-red-intense-bg { background-color: #B22B31; } .ansi-green-fg { color: #00A250; } .ansi-green-bg { background-color: #00A250; } .ansi-green-intense-fg { color: #007427; } .ansi-green-intense-bg { background-color: #007427; } .ansi-yellow-fg { color: #DDB62B; } .ansi-yellow-bg { background-color: #DDB62B; } .ansi-yellow-intense-fg { color: #B27D12; } .ansi-yellow-intense-bg { background-color: #B27D12; } .ansi-blue-fg { color: #208FFB; } .ansi-blue-bg { background-color: #208FFB; } .ansi-blue-intense-fg { color: #0065CA; } .ansi-blue-intense-bg { background-color: #0065CA; } .ansi-magenta-fg { color: #D160C4; } .ansi-magenta-bg { background-color: #D160C4; } .ansi-magenta-intense-fg { color: #A03196; } .ansi-magenta-intense-bg { background-color: #A03196; } .ansi-cyan-fg { color: #60C6C8; } .ansi-cyan-bg { background-color: #60C6C8; } .ansi-cyan-intense-fg { color: #258F8F; } .ansi-cyan-intense-bg { background-color: #258F8F; } .ansi-white-fg { color: #C5C1B4; } .ansi-white-bg { background-color: #C5C1B4; } .ansi-white-intense-fg { color: #A1A6B2; } .ansi-white-intense-bg { background-color: #A1A6B2; } .ansi-default-inverse-fg { color: #FFFFFF; } .ansi-default-inverse-bg { background-color: #000000; } .ansi-bold { font-weight: bold; } .ansi-underline { text-decoration: underline; } /\* The following styles are deprecated an will be removed in a future version \*/ .ansibold { font-weight: bold; } .ansi-inverse { outline: 0.5px dotted; } /\* use dark versions for foreground, to improve visibility \*/ .ansiblack { color: black; } .ansired { color: darkred; } .ansigreen { color: darkgreen; } .ansiyellow { color: #c4a000; } .ansiblue { color: darkblue; } .ansipurple { color: darkviolet; } .ansicyan { color: steelblue; } .ansigray { color: gray; } /\* and light for background, for the same reason \*/ .ansibgblack { background-color: black; } .ansibgred { background-color: red; } .ansibggreen { background-color: green; } .ansibgyellow { background-color: yellow; } .ansibgblue { background-color: blue; } .ansibgpurple { background-color: magenta; } .ansibgcyan { background-color: cyan; } .ansibggray { background-color: gray; } div.cell { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; border-radius: 2px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; border-width: 1px; border-style: solid; border-color: transparent; width: 100%; padding: 5px; /\* This acts as a spacer between cells, that is outside the border \*/ margin: 0px; outline: none; position: relative; overflow: visible; } div.cell:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: transparent; } div.cell.jupyter-soft-selected { border-left-color: #E3F2FD; border-left-width: 1px; padding-left: 5px; border-right-color: #E3F2FD; border-right-width: 1px; background: #E3F2FD; } @media print { div.cell.jupyter-soft-selected { border-color: transparent; } } div.cell.selected, div.cell.selected.jupyter-soft-selected { border-color: #ababab; } div.cell.selected:before, div.cell.selected.jupyter-soft-selected:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: #42A5F5; } @media print { div.cell.selected, div.cell.selected.jupyter-soft-selected { border-color: transparent; } } .edit\_mode div.cell.selected { border-color: #66BB6A; } .edit\_mode div.cell.selected:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: #66BB6A; } @media print { .edit\_mode div.cell.selected { border-color: transparent; } } .prompt { /\* This needs to be wide enough for 3 digit prompt numbers: In[100]: \*/ min-width: 14ex; /\* This padding is tuned to match the padding on the CodeMirror editor. \*/ padding: 0.4em; margin: 0px; font-family: monospace; text-align: right; /\* This has to match that of the the CodeMirror class line-height below \*/ line-height: 1.21429em; /\* Don't highlight prompt number selection \*/ -webkit-touch-callout: none; -webkit-user-select: none; -khtml-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; /\* Use default cursor \*/ cursor: default; } @media (max-width: 540px) { .prompt { text-align: left; } } div.inner\_cell { min-width: 0; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } /\* input\_area and input\_prompt must match in top border and margin for alignment \*/ div.input\_area { border: 1px solid #cfcfcf; border-radius: 2px; background: #f7f7f7; line-height: 1.21429em; } /\* This is needed so that empty prompt areas can collapse to zero height when there is no content in the output\_subarea and the prompt. The main purpose of this is to make sure that empty JavaScript output\_subareas have no height. \*/ div.prompt:empty { padding-top: 0; padding-bottom: 0; } div.unrecognized\_cell { padding: 5px 5px 5px 0px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } div.unrecognized\_cell .inner\_cell { border-radius: 2px; padding: 5px; font-weight: bold; color: red; border: 1px solid #cfcfcf; background: #eaeaea; } div.unrecognized\_cell .inner\_cell a { color: inherit; text-decoration: none; } div.unrecognized\_cell .inner\_cell a:hover { color: inherit; text-decoration: none; } @media (max-width: 540px) { div.unrecognized\_cell > div.prompt { display: none; } } div.code\_cell { /\* avoid page breaking on code cells when printing \*/ } @media print { div.code\_cell { page-break-inside: avoid; } } /\* any special styling for code cells that are currently running goes here \*/ div.input { page-break-inside: avoid; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } @media (max-width: 540px) { div.input { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } } /\* input\_area and input\_prompt must match in top border and margin for alignment \*/ div.input\_prompt { color: #303F9F; border-top: 1px solid transparent; } div.input\_area > div.highlight { margin: 0.4em; border: none; padding: 0px; background-color: transparent; } div.input\_area > div.highlight > pre { margin: 0px; border: none; padding: 0px; background-color: transparent; } /\* The following gets added to the <head> if it is detected that the user has a \* monospace font with inconsistent normal/bold/italic height. See \* notebookmain.js. Such fonts will have keywords vertically offset with \* respect to the rest of the text. The user should select a better font. \* See: https://github.com/ipython/ipython/issues/1503 \* \* .CodeMirror span { \* vertical-align: bottom; \* } \*/ .CodeMirror { line-height: 1.21429em; /\* Changed from 1em to our global default \*/ font-size: 14px; height: auto; /\* Changed to auto to autogrow \*/ background: none; /\* Changed from white to allow our bg to show through \*/ } .CodeMirror-scroll { /\* The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.\*/ /\* We have found that if it is visible, vertical scrollbars appear with font size changes.\*/ overflow-y: hidden; overflow-x: auto; } .CodeMirror-lines { /\* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because \*/ /\* we have set a different line-height and want this to scale with that. \*/ /\* Note that this should set vertical padding only, since CodeMirror assumes that horizontal padding will be set on CodeMirror pre \*/ padding: 0.4em 0; } .CodeMirror-linenumber { padding: 0 8px 0 4px; } .CodeMirror-gutters { border-bottom-left-radius: 2px; border-top-left-radius: 2px; } .CodeMirror pre { /\* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only, use .CodeMirror-lines for vertical \*/ padding: 0 0.4em; border: 0; border-radius: 0; } .CodeMirror-cursor { border-left: 1.4px solid black; } @media screen and (min-width: 2138px) and (max-width: 4319px) { .CodeMirror-cursor { border-left: 2px solid black; } } @media screen and (min-width: 4320px) { .CodeMirror-cursor { border-left: 4px solid black; } } /\* Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org> Adapted from GitHub theme \*/ .highlight-base { color: #000; } .highlight-variable { color: #000; } .highlight-variable-2 { color: #1a1a1a; } .highlight-variable-3 { color: #333333; } .highlight-string { color: #BA2121; } .highlight-comment { color: #408080; font-style: italic; } .highlight-number { color: #080; } .highlight-atom { color: #88F; } .highlight-keyword { color: #008000; font-weight: bold; } .highlight-builtin { color: #008000; } .highlight-error { color: #f00; } .highlight-operator { color: #AA22FF; font-weight: bold; } .highlight-meta { color: #AA22FF; } /\* previously not defined, copying from default codemirror \*/ .highlight-def { color: #00f; } .highlight-string-2 { color: #f50; } .highlight-qualifier { color: #555; } .highlight-bracket { color: #997; } .highlight-tag { color: #170; } .highlight-attribute { color: #00c; } .highlight-header { color: blue; } .highlight-quote { color: #090; } .highlight-link { color: #00c; } /\* apply the same style to codemirror \*/ .cm-s-ipython span.cm-keyword { color: #008000; font-weight: bold; } .cm-s-ipython span.cm-atom { color: #88F; } .cm-s-ipython span.cm-number { color: #080; } .cm-s-ipython span.cm-def { color: #00f; } .cm-s-ipython span.cm-variable { color: #000; } .cm-s-ipython span.cm-operator { color: #AA22FF; font-weight: bold; } .cm-s-ipython span.cm-variable-2 { color: #1a1a1a; } .cm-s-ipython span.cm-variable-3 { color: #333333; } .cm-s-ipython span.cm-comment { color: #408080; font-style: italic; } .cm-s-ipython span.cm-string { color: #BA2121; } .cm-s-ipython span.cm-string-2 { color: #f50; } .cm-s-ipython span.cm-meta { color: #AA22FF; } .cm-s-ipython span.cm-qualifier { color: #555; } .cm-s-ipython span.cm-builtin { color: #008000; } .cm-s-ipython span.cm-bracket { color: #997; } .cm-s-ipython span.cm-tag { color: #170; } .cm-s-ipython span.cm-attribute { color: #00c; } .cm-s-ipython span.cm-header { color: blue; } .cm-s-ipython span.cm-quote { color: #090; } .cm-s-ipython span.cm-link { color: #00c; } .cm-s-ipython span.cm-error { color: #f00; } .cm-s-ipython span.cm-tab { background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=); background-position: right; background-repeat: no-repeat; } div.output\_wrapper { /\* this position must be relative to enable descendents to be absolute within it \*/ position: relative; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; z-index: 1; } /\* class for the output area when it should be height-limited \*/ div.output\_scroll { /\* ideally, this would be max-height, but FF barfs all over that \*/ height: 24em; /\* FF needs this \*and the wrapper\* to specify full width, or it will shrinkwrap \*/ width: 100%; overflow: auto; border-radius: 2px; -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8); box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8); display: block; } /\* output div while it is collapsed \*/ div.output\_collapsed { margin: 0px; padding: 0px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } div.out\_prompt\_overlay { height: 100%; padding: 0px 0.4em; position: absolute; border-radius: 2px; } div.out\_prompt\_overlay:hover { /\* use inner shadow to get border that is computed the same on WebKit/FF \*/ -webkit-box-shadow: inset 0 0 1px #000; box-shadow: inset 0 0 1px #000; background: rgba(240, 240, 240, 0.5); } div.output\_prompt { color: #D84315; } /\* This class is the outer container of all output sections. \*/ div.output\_area { padding: 0px; page-break-inside: avoid; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } div.output\_area .MathJax\_Display { text-align: left !important; } div.output\_area .rendered\_html table { margin-left: 0; margin-right: 0; } div.output\_area .rendered\_html img { margin-left: 0; margin-right: 0; } div.output\_area img, div.output\_area svg { max-width: 100%; height: auto; } div.output\_area img.unconfined, div.output\_area svg.unconfined { max-width: none; } div.output\_area .mglyph > img { max-width: none; } /\* This is needed to protect the pre formating from global settings such as that of bootstrap \*/ .output { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } @media (max-width: 540px) { div.output\_area { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } } div.output\_area pre { margin: 0; padding: 1px 0 1px 0; border: 0; vertical-align: baseline; color: black; background-color: transparent; border-radius: 0; } /\* This class is for the output subarea inside the output\_area and after the prompt div. \*/ div.output\_subarea { overflow-x: auto; padding: 0.4em; /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; max-width: calc(100% - 14ex); } div.output\_scroll div.output\_subarea { overflow-x: visible; } /\* The rest of the output\_\* classes are for special styling of the different output types \*/ /\* all text output has this class: \*/ div.output\_text { text-align: left; color: #000; /\* This has to match that of the the CodeMirror class line-height below \*/ line-height: 1.21429em; } /\* stdout/stderr are 'text' as well as 'stream', but execute\_result/error are \*not\* streams \*/ div.output\_stderr { background: #fdd; /\* very light red background for stderr \*/ } div.output\_latex { text-align: left; } /\* Empty output\_javascript divs should have no height \*/ div.output\_javascript:empty { padding: 0; } .js-error { color: darkred; } /\* raw\_input styles \*/ div.raw\_input\_container { line-height: 1.21429em; padding-top: 5px; } pre.raw\_input\_prompt { /\* nothing needed here. \*/ } input.raw\_input { font-family: monospace; font-size: inherit; color: inherit; width: auto; /\* make sure input baseline aligns with prompt \*/ vertical-align: baseline; /\* padding + margin = 0.5em between prompt and cursor \*/ padding: 0em 0.25em; margin: 0em 0.25em; } input.raw\_input:focus { box-shadow: none; } p.p-space { margin-bottom: 10px; } div.output\_unrecognized { padding: 5px; font-weight: bold; color: red; } div.output\_unrecognized a { color: inherit; text-decoration: none; } div.output\_unrecognized a:hover { color: inherit; text-decoration: none; } .rendered\_html { color: #000; /\* any extras will just be numbers: \*/ } .rendered\_html em { font-style: italic; } .rendered\_html strong { font-weight: bold; } .rendered\_html u { text-decoration: underline; } .rendered\_html :link { text-decoration: underline; } .rendered\_html :visited { text-decoration: underline; } .rendered\_html h1 { font-size: 185.7%; margin: 1.08em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h2 { font-size: 157.1%; margin: 1.27em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h3 { font-size: 128.6%; margin: 1.55em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h4 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h5 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; font-style: italic; } .rendered\_html h6 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; font-style: italic; } .rendered\_html h1:first-child { margin-top: 0.538em; } .rendered\_html h2:first-child { margin-top: 0.636em; } .rendered\_html h3:first-child { margin-top: 0.777em; } .rendered\_html h4:first-child { margin-top: 1em; } .rendered\_html h5:first-child { margin-top: 1em; } .rendered\_html h6:first-child { margin-top: 1em; } .rendered\_html ul:not(.list-inline), .rendered\_html ol:not(.list-inline) { padding-left: 2em; } .rendered\_html ul { list-style: disc; } .rendered\_html ul ul { list-style: square; margin-top: 0; } .rendered\_html ul ul ul { list-style: circle; } .rendered\_html ol { list-style: decimal; } .rendered\_html ol ol { list-style: upper-alpha; margin-top: 0; } .rendered\_html ol ol ol { list-style: lower-alpha; } .rendered\_html ol ol ol ol { list-style: lower-roman; } .rendered\_html ol ol ol ol ol { list-style: decimal; } .rendered\_html \* + ul { margin-top: 1em; } .rendered\_html \* + ol { margin-top: 1em; } .rendered\_html hr { color: black; background-color: black; } .rendered\_html pre { margin: 1em 2em; padding: 0px; background-color: #fff; } .rendered\_html code { background-color: #eff0f1; } .rendered\_html p code { padding: 1px 5px; } .rendered\_html pre code { background-color: #fff; } .rendered\_html pre, .rendered\_html code { border: 0; color: #000; font-size: 100%; } .rendered\_html blockquote { margin: 1em 2em; } .rendered\_html table { margin-left: auto; margin-right: auto; border: none; border-collapse: collapse; border-spacing: 0; color: black; font-size: 12px; table-layout: fixed; } .rendered\_html thead { border-bottom: 1px solid black; vertical-align: bottom; } .rendered\_html tr, .rendered\_html th, .rendered\_html td { text-align: right; vertical-align: middle; padding: 0.5em 0.5em; line-height: normal; white-space: normal; max-width: none; border: none; } .rendered\_html th { font-weight: bold; } .rendered\_html tbody tr:nth-child(odd) { background: #f5f5f5; } .rendered\_html tbody tr:hover { background: rgba(66, 165, 245, 0.2); } .rendered\_html \* + table { margin-top: 1em; } .rendered\_html p { text-align: left; } .rendered\_html \* + p { margin-top: 1em; } .rendered\_html img { display: block; margin-left: auto; margin-right: auto; } .rendered\_html \* + img { margin-top: 1em; } .rendered\_html img, .rendered\_html svg { max-width: 100%; height: auto; } .rendered\_html img.unconfined, .rendered\_html svg.unconfined { max-width: none; } .rendered\_html .alert { margin-bottom: initial; } .rendered\_html \* + .alert { margin-top: 1em; } [dir="rtl"] .rendered\_html p { text-align: right; } div.text\_cell { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } @media (max-width: 540px) { div.text\_cell > div.prompt { display: none; } } div.text\_cell\_render { /\*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;\*/ outline: none; resize: none; width: inherit; border-style: none; padding: 0.5em 0.5em 0.5em 0.4em; color: #000; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } a.anchor-link:link { text-decoration: none; padding: 0px 20px; visibility: hidden; } h1:hover .anchor-link, h2:hover .anchor-link, h3:hover .anchor-link, h4:hover .anchor-link, h5:hover .anchor-link, h6:hover .anchor-link { visibility: visible; } .text\_cell.rendered .input\_area { display: none; } .text\_cell.rendered .rendered\_html { overflow-x: auto; overflow-y: hidden; } .text\_cell.rendered .rendered\_html tr, .text\_cell.rendered .rendered\_html th, .text\_cell.rendered .rendered\_html td { max-width: none; } .text\_cell.unrendered .text\_cell\_render { display: none; } .text\_cell .dropzone .input\_area { border: 2px dashed #bababa; margin: -1px; } .cm-header-1, .cm-header-2, .cm-header-3, .cm-header-4, .cm-header-5, .cm-header-6 { font-weight: bold; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; } .cm-header-1 { font-size: 185.7%; } .cm-header-2 { font-size: 157.1%; } .cm-header-3 { font-size: 128.6%; } .cm-header-4 { font-size: 110%; } .cm-header-5 { font-size: 100%; font-style: italic; } .cm-header-6 { font-size: 100%; font-style: italic; } /\*! \* \* IPython notebook webapp \* \*/ @media (max-width: 767px) { .notebook\_app { padding-left: 0px; padding-right: 0px; } } #ipython-main-app { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; height: 100%; } div#notebook\_panel { margin: 0px; padding: 0px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; height: 100%; } div#notebook { font-size: 14px; line-height: 20px; overflow-y: hidden; overflow-x: auto; width: 100%; /\* This spaces the page away from the edge of the notebook area \*/ padding-top: 20px; margin: 0px; outline: none; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; min-height: 100%; } @media not print { #notebook-container { padding: 15px; background-color: #fff; min-height: 0; -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } } @media print { #notebook-container { width: 100%; } } div.ui-widget-content { border: 1px solid #ababab; outline: none; } pre.dialog { background-color: #f7f7f7; border: 1px solid #ddd; border-radius: 2px; padding: 0.4em; padding-left: 2em; } p.dialog { padding: 0.2em; } /\* Word-wrap output correctly. This is the CSS3 spelling, though Firefox seems to not honor it correctly. Webkit browsers (Chrome, rekonq, Safari) do. \*/ pre, code, kbd, samp { white-space: pre-wrap; } #fonttest { font-family: monospace; } p { margin-bottom: 0; } .end\_space { min-height: 100px; transition: height .2s ease; } .notebook\_app > #header { -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } @media not print { .notebook\_app { background-color: #EEE; } } kbd { border-style: solid; border-width: 1px; box-shadow: none; margin: 2px; padding-left: 2px; padding-right: 2px; padding-top: 1px; padding-bottom: 1px; } .jupyter-keybindings { padding: 1px; line-height: 24px; border-bottom: 1px solid gray; } .jupyter-keybindings input { margin: 0; padding: 0; border: none; } .jupyter-keybindings i { padding: 6px; } .well code { background-color: #ffffff; border-color: #ababab; border-width: 1px; border-style: solid; padding: 2px; padding-top: 1px; padding-bottom: 1px; } /\* CSS for the cell toolbar \*/ .celltoolbar { border: thin solid #CFCFCF; border-bottom: none; background: #EEE; border-radius: 2px 2px 0px 0px; width: 100%; height: 29px; padding-right: 4px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; /\* Old browsers \*/ -webkit-box-pack: end; -moz-box-pack: end; box-pack: end; /\* Modern browsers \*/ justify-content: flex-end; display: -webkit-flex; } @media print { .celltoolbar { display: none; } } .ctb\_hideshow { display: none; vertical-align: bottom; } /\* ctb\_show is added to the ctb\_hideshow div to show the cell toolbar. Cell toolbars are only shown when the ctb\_global\_show class is also set. \*/ .ctb\_global\_show .ctb\_show.ctb\_hideshow { display: block; } .ctb\_global\_show .ctb\_show + .input\_area, .ctb\_global\_show .ctb\_show + div.text\_cell\_input, .ctb\_global\_show .ctb\_show ~ div.text\_cell\_render { border-top-right-radius: 0px; border-top-left-radius: 0px; } .ctb\_global\_show .ctb\_show ~ div.text\_cell\_render { border: 1px solid #cfcfcf; } .celltoolbar { font-size: 87%; padding-top: 3px; } .celltoolbar select { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; width: inherit; font-size: inherit; height: 22px; padding: 0px; display: inline-block; } .celltoolbar select:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .celltoolbar select::-moz-placeholder { color: #999; opacity: 1; } .celltoolbar select:-ms-input-placeholder { color: #999; } .celltoolbar select::-webkit-input-placeholder { color: #999; } .celltoolbar select::-ms-expand { border: 0; background-color: transparent; } .celltoolbar select[disabled], .celltoolbar select[readonly], fieldset[disabled] .celltoolbar select { background-color: #eeeeee; opacity: 1; } .celltoolbar select[disabled], fieldset[disabled] .celltoolbar select { cursor: not-allowed; } textarea.celltoolbar select { height: auto; } select.celltoolbar select { height: 30px; line-height: 30px; } textarea.celltoolbar select, select[multiple].celltoolbar select { height: auto; } .celltoolbar label { margin-left: 5px; margin-right: 5px; } .tags\_button\_container { width: 100%; display: flex; } .tag-container { display: flex; flex-direction: row; flex-grow: 1; overflow: hidden; position: relative; } .tag-container > \* { margin: 0 4px; } .remove-tag-btn { margin-left: 4px; } .tags-input { display: flex; } .cell-tag:last-child:after { content: ""; position: absolute; right: 0; width: 40px; height: 100%; /\* Fade to background color of cell toolbar \*/ background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE); } .tags-input > \* { margin-left: 4px; } .cell-tag, .tags-input input, .tags-input button { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; box-shadow: none; width: inherit; font-size: inherit; height: 22px; line-height: 22px; padding: 0px 4px; display: inline-block; } .cell-tag:focus, .tags-input input:focus, .tags-input button:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .cell-tag::-moz-placeholder, .tags-input input::-moz-placeholder, .tags-input button::-moz-placeholder { color: #999; opacity: 1; } .cell-tag:-ms-input-placeholder, .tags-input input:-ms-input-placeholder, .tags-input button:-ms-input-placeholder { color: #999; } .cell-tag::-webkit-input-placeholder, .tags-input input::-webkit-input-placeholder, .tags-input button::-webkit-input-placeholder { color: #999; } .cell-tag::-ms-expand, .tags-input input::-ms-expand, .tags-input button::-ms-expand { border: 0; background-color: transparent; } .cell-tag[disabled], .tags-input input[disabled], .tags-input button[disabled], .cell-tag[readonly], .tags-input input[readonly], .tags-input button[readonly], fieldset[disabled] .cell-tag, fieldset[disabled] .tags-input input, fieldset[disabled] .tags-input button { background-color: #eeeeee; opacity: 1; } .cell-tag[disabled], .tags-input input[disabled], .tags-input button[disabled], fieldset[disabled] .cell-tag, fieldset[disabled] .tags-input input, fieldset[disabled] .tags-input button { cursor: not-allowed; } textarea.cell-tag, textarea.tags-input input, textarea.tags-input button { height: auto; } select.cell-tag, select.tags-input input, select.tags-input button { height: 30px; line-height: 30px; } textarea.cell-tag, textarea.tags-input input, textarea.tags-input button, select[multiple].cell-tag, select[multiple].tags-input input, select[multiple].tags-input button { height: auto; } .cell-tag, .tags-input button { padding: 0px 4px; } .cell-tag { background-color: #fff; white-space: nowrap; } .tags-input input[type=text]:focus { outline: none; box-shadow: none; border-color: #ccc; } .completions { position: absolute; z-index: 110; overflow: hidden; border: 1px solid #ababab; border-radius: 2px; -webkit-box-shadow: 0px 6px 10px -1px #adadad; box-shadow: 0px 6px 10px -1px #adadad; line-height: 1; } .completions select { background: white; outline: none; border: none; padding: 0px; margin: 0px; overflow: auto; font-family: monospace; font-size: 110%; color: #000; width: auto; } .completions select option.context { color: #286090; } #kernel\_logo\_widget .current\_kernel\_logo { display: none; margin-top: -1px; margin-bottom: -1px; width: 32px; height: 32px; } [dir="rtl"] #kernel\_logo\_widget { float: left !important; float: left; } .modal .modal-body .move-path { display: flex; flex-direction: row; justify-content: space; align-items: center; } .modal .modal-body .move-path .server-root { padding-right: 20px; } .modal .modal-body .move-path .path-input { flex: 1; } #menubar { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; margin-top: 1px; } #menubar .navbar { border-top: 1px; border-radius: 0px 0px 2px 2px; margin-bottom: 0px; } #menubar .navbar-toggle { float: left; padding-top: 7px; padding-bottom: 7px; border: none; } #menubar .navbar-collapse { clear: left; } [dir="rtl"] #menubar .navbar-toggle { float: right; } [dir="rtl"] #menubar .navbar-collapse { clear: right; } [dir="rtl"] #menubar .navbar-nav { float: right; } [dir="rtl"] #menubar .nav { padding-right: 0px; } [dir="rtl"] #menubar .navbar-nav > li { float: right; } [dir="rtl"] #menubar .navbar-right { float: left !important; } [dir="rtl"] ul.dropdown-menu { text-align: right; left: auto; } [dir="rtl"] ul#new-menu.dropdown-menu { right: auto; left: 0; } .nav-wrapper { border-bottom: 1px solid #e7e7e7; } i.menu-icon { padding-top: 4px; } [dir="rtl"] i.menu-icon.pull-right { float: left !important; float: left; } ul#help\_menu li a { overflow: hidden; padding-right: 2.2em; } ul#help\_menu li a i { margin-right: -1.2em; } [dir="rtl"] ul#help\_menu li a { padding-left: 2.2em; } [dir="rtl"] ul#help\_menu li a i { margin-right: 0; margin-left: -1.2em; } [dir="rtl"] ul#help\_menu li a i.pull-right { float: left !important; float: left; } .dropdown-submenu { position: relative; } .dropdown-submenu > .dropdown-menu { top: 0; left: 100%; margin-top: -6px; margin-left: -1px; } [dir="rtl"] .dropdown-submenu > .dropdown-menu { right: 100%; margin-right: -1px; } .dropdown-submenu:hover > .dropdown-menu { display: block; } .dropdown-submenu > a:after { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; display: block; content: "\f0da"; float: right; color: #333333; margin-top: 2px; margin-right: -10px; } .dropdown-submenu > a:after.fa-pull-left { margin-right: .3em; } .dropdown-submenu > a:after.fa-pull-right { margin-left: .3em; } .dropdown-submenu > a:after.pull-left { margin-right: .3em; } .dropdown-submenu > a:after.pull-right { margin-left: .3em; } [dir="rtl"] .dropdown-submenu > a:after { float: left; content: "\f0d9"; margin-right: 0; margin-left: -10px; } .dropdown-submenu:hover > a:after { color: #262626; } .dropdown-submenu.pull-left { float: none; } .dropdown-submenu.pull-left > .dropdown-menu { left: -100%; margin-left: 10px; } #notification\_area { float: right !important; float: right; z-index: 10; } [dir="rtl"] #notification\_area { float: left !important; float: left; } .indicator\_area { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; } [dir="rtl"] .indicator\_area { float: left !important; float: left; } #kernel\_indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; border-left: 1px solid; } #kernel\_indicator .kernel\_indicator\_name { padding-left: 5px; padding-right: 5px; } [dir="rtl"] #kernel\_indicator { float: left !important; float: left; border-left: 0; border-right: 1px solid; } #modal\_indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; } [dir="rtl"] #modal\_indicator { float: left !important; float: left; } #readonly-indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; margin-top: 2px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; display: none; } .modal\_indicator:before { width: 1.28571429em; text-align: center; } .edit\_mode .modal\_indicator:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f040"; } .edit\_mode .modal\_indicator:before.fa-pull-left { margin-right: .3em; } .edit\_mode .modal\_indicator:before.fa-pull-right { margin-left: .3em; } .edit\_mode .modal\_indicator:before.pull-left { margin-right: .3em; } .edit\_mode .modal\_indicator:before.pull-right { margin-left: .3em; } .command\_mode .modal\_indicator:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: ' '; } .command\_mode .modal\_indicator:before.fa-pull-left { margin-right: .3em; } .command\_mode .modal\_indicator:before.fa-pull-right { margin-left: .3em; } .command\_mode .modal\_indicator:before.pull-left { margin-right: .3em; } .command\_mode .modal\_indicator:before.pull-right { margin-left: .3em; } .kernel\_idle\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f10c"; } .kernel\_idle\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_idle\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_idle\_icon:before.pull-left { margin-right: .3em; } .kernel\_idle\_icon:before.pull-right { margin-left: .3em; } .kernel\_busy\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f111"; } .kernel\_busy\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_busy\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_busy\_icon:before.pull-left { margin-right: .3em; } .kernel\_busy\_icon:before.pull-right { margin-left: .3em; } .kernel\_dead\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f1e2"; } .kernel\_dead\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_dead\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_dead\_icon:before.pull-left { margin-right: .3em; } .kernel\_dead\_icon:before.pull-right { margin-left: .3em; } .kernel\_disconnected\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f127"; } .kernel\_disconnected\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_disconnected\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_disconnected\_icon:before.pull-left { margin-right: .3em; } .kernel\_disconnected\_icon:before.pull-right { margin-left: .3em; } .notification\_widget { color: #777; z-index: 10; background: rgba(240, 240, 240, 0.5); margin-right: 4px; color: #333; background-color: #fff; border-color: #ccc; } .notification\_widget:focus, .notification\_widget.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } .notification\_widget:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } .notification\_widget:active, .notification\_widget.active, .open > .dropdown-toggle.notification\_widget { color: #333; background-color: #e6e6e6; border-color: #adadad; } .notification\_widget:active:hover, .notification\_widget.active:hover, .open > .dropdown-toggle.notification\_widget:hover, .notification\_widget:active:focus, .notification\_widget.active:focus, .open > .dropdown-toggle.notification\_widget:focus, .notification\_widget:active.focus, .notification\_widget.active.focus, .open > .dropdown-toggle.notification\_widget.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } .notification\_widget:active, .notification\_widget.active, .open > .dropdown-toggle.notification\_widget { background-image: none; } .notification\_widget.disabled:hover, .notification\_widget[disabled]:hover, fieldset[disabled] .notification\_widget:hover, .notification\_widget.disabled:focus, .notification\_widget[disabled]:focus, fieldset[disabled] .notification\_widget:focus, .notification\_widget.disabled.focus, .notification\_widget[disabled].focus, fieldset[disabled] .notification\_widget.focus { background-color: #fff; border-color: #ccc; } .notification\_widget .badge { color: #fff; background-color: #333; } .notification\_widget.warning { color: #fff; background-color: #f0ad4e; border-color: #eea236; } .notification\_widget.warning:focus, .notification\_widget.warning.focus { color: #fff; background-color: #ec971f; border-color: #985f0d; } .notification\_widget.warning:hover { color: #fff; background-color: #ec971f; border-color: #d58512; } .notification\_widget.warning:active, .notification\_widget.warning.active, .open > .dropdown-toggle.notification\_widget.warning { color: #fff; background-color: #ec971f; border-color: #d58512; } .notification\_widget.warning:active:hover, .notification\_widget.warning.active:hover, .open > .dropdown-toggle.notification\_widget.warning:hover, .notification\_widget.warning:active:focus, .notification\_widget.warning.active:focus, .open > .dropdown-toggle.notification\_widget.warning:focus, .notification\_widget.warning:active.focus, .notification\_widget.warning.active.focus, .open > .dropdown-toggle.notification\_widget.warning.focus { color: #fff; background-color: #d58512; border-color: #985f0d; } .notification\_widget.warning:active, .notification\_widget.warning.active, .open > .dropdown-toggle.notification\_widget.warning { background-image: none; } .notification\_widget.warning.disabled:hover, .notification\_widget.warning[disabled]:hover, fieldset[disabled] .notification\_widget.warning:hover, .notification\_widget.warning.disabled:focus, .notification\_widget.warning[disabled]:focus, fieldset[disabled] .notification\_widget.warning:focus, .notification\_widget.warning.disabled.focus, .notification\_widget.warning[disabled].focus, fieldset[disabled] .notification\_widget.warning.focus { background-color: #f0ad4e; border-color: #eea236; } .notification\_widget.warning .badge { color: #f0ad4e; background-color: #fff; } .notification\_widget.success { color: #fff; background-color: #5cb85c; border-color: #4cae4c; } .notification\_widget.success:focus, .notification\_widget.success.focus { color: #fff; background-color: #449d44; border-color: #255625; } .notification\_widget.success:hover { color: #fff; background-color: #449d44; border-color: #398439; } .notification\_widget.success:active, .notification\_widget.success.active, .open > .dropdown-toggle.notification\_widget.success { color: #fff; background-color: #449d44; border-color: #398439; } .notification\_widget.success:active:hover, .notification\_widget.success.active:hover, .open > .dropdown-toggle.notification\_widget.success:hover, .notification\_widget.success:active:focus, .notification\_widget.success.active:focus, .open > .dropdown-toggle.notification\_widget.success:focus, .notification\_widget.success:active.focus, .notification\_widget.success.active.focus, .open > .dropdown-toggle.notification\_widget.success.focus { color: #fff; background-color: #398439; border-color: #255625; } .notification\_widget.success:active, .notification\_widget.success.active, .open > .dropdown-toggle.notification\_widget.success { background-image: none; } .notification\_widget.success.disabled:hover, .notification\_widget.success[disabled]:hover, fieldset[disabled] .notification\_widget.success:hover, .notification\_widget.success.disabled:focus, .notification\_widget.success[disabled]:focus, fieldset[disabled] .notification\_widget.success:focus, .notification\_widget.success.disabled.focus, .notification\_widget.success[disabled].focus, fieldset[disabled] .notification\_widget.success.focus { background-color: #5cb85c; border-color: #4cae4c; } .notification\_widget.success .badge { color: #5cb85c; background-color: #fff; } .notification\_widget.info { color: #fff; background-color: #5bc0de; border-color: #46b8da; } .notification\_widget.info:focus, .notification\_widget.info.focus { color: #fff; background-color: #31b0d5; border-color: #1b6d85; } .notification\_widget.info:hover { color: #fff; background-color: #31b0d5; border-color: #269abc; } .notification\_widget.info:active, .notification\_widget.info.active, .open > .dropdown-toggle.notification\_widget.info { color: #fff; background-color: #31b0d5; border-color: #269abc; } .notification\_widget.info:active:hover, .notification\_widget.info.active:hover, .open > .dropdown-toggle.notification\_widget.info:hover, .notification\_widget.info:active:focus, .notification\_widget.info.active:focus, .open > .dropdown-toggle.notification\_widget.info:focus, .notification\_widget.info:active.focus, .notification\_widget.info.active.focus, .open > .dropdown-toggle.notification\_widget.info.focus { color: #fff; 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border-top: none; }@media print { div.cell { display: block; page-break-inside: avoid; } div.output\_wrapper { display: block; page-break-inside: avoid; } div.output { display: block; page-break-inside: avoid; } } Introduction[¶](#Introduction) ------------------------------ This tutorial shows how to read/write and plot SO maps. We will use both pixellisation *i.e.* `CAR` and `HEALPIX` with the same interface showing how `pspy` can deal with both data format. Preamble[¶](#Preamble) ---------------------- `matplotlib` magic In [1]: ``` %matplotlib inline ``` Versions used for this tutorial In [2]: ``` import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import pspy, pixell print(" Numpy :", np.\_\_version\_\_) print("Matplotlib :", mpl.\_\_version\_\_) print(" pixell :", pixell.\_\_version\_\_) print(" pspy :", pspy.\_\_version\_\_) ``` ``` Numpy : 1.18.0 Matplotlib : 3.1.2 pixell : 0.6.0+34.g23be32d pspy : 0+untagged.118.gbf1f0bc.dirty ``` Get default data dir from `pspy` and set Planck colormap as default In [3]: ``` from pspy.so\_config import DEFAULT\_DATA\_DIR pixell.colorize.mpl\_setdefault("planck") ``` Generation of CAR and HEALPIX templates[¶](#Generation-of-CAR-and-HEALPIX-templates) ------------------------------------------------------------------------------------ We start with the definition of the `CAR` template, it will go from right ascension `ra0` to `ra1` and from declination `dec0` to `dec1` (all in degrees). The resolution will be 1 arcminute and we will allow 3 components (stokes parameter in the case of CMB anisotropies) In [4]: ``` ra0, ra1, dec0, dec1 = -5, 5, -5, 5 res = 1 ncomp = 3 from pspy import so\_map template\_car= so\_map.car\_template(ncomp, ra0, ra1, dec0, dec1, res) ``` We do the same with `HEALPIX` In [5]: ``` template\_healpix = so\_map.healpix\_template(ncomp, nside=256, coordinate="equ") ``` Simulation of CMB data[¶](#Simulation-of-CMB-data) -------------------------------------------------- We first have to compute the power spectra $C\_\ell$s using a Boltzmann solver such as [CAMB](https://camb.readthedocs.io/en/latest/) and we need to install it since this is a prerequisite of `pspy`. We can do it within this notebook by executing the following command In [6]: ``` %pip install camb ``` ``` Requirement already satisfied: camb in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (1.1.0) Requirement already satisfied: scipy>=1.0 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.4.1) Requirement already satisfied: six in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.13.0) Requirement already satisfied: sympy>=1.0 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.5) Requirement already satisfied: numpy>=1.13.3 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from scipy>=1.0->camb) (1.18.0) Requirement already satisfied: mpmath>=0.19 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from sympy>=1.0->camb) (1.1.0) Note: you may need to restart the kernel to use updated packages. ``` To make sure everything goes well, we can import `CAMB` and check its version In [7]: ``` import camb print("CAMB version:", camb.\_\_version\_\_) ``` ``` CAMB version: 1.1.0 ``` Now that `CAMB` is properly installed, we will produce $C\_\ell$ data from $\ell$min=2 to $\ell$max=104 for the following set of $\Lambda$CDM parameters In [8]: ``` lmin, lmax = 2, 10\*\*4 cosmo\_params = { "H0": 67.5, "As": 1e-10\*np.exp(3.044), "ombh2": 0.02237, "omch2": 0.1200, "ns": 0.9649, "Alens": 1.0, "tau": 0.0544 } pars = camb.set\_params(\*\*cosmo\_params) pars.set\_for\_lmax(lmax, lens\_potential\_accuracy=1) results = camb.get\_results(pars) powers = results.get\_cmb\_power\_spectra(pars, CMB\_unit="muK") ``` We can plot the results for sanity check In [9]: ``` l = np.arange(lmin, lmax) cl\_dict = {spec: powers["total"][lmin:lmax, i] for i, spec in enumerate(["tt", "ee", "bb", "te"])} fig, axes = plt.subplots(2, 1, sharex=True, figsize=(6, 8)) axes[0].set\_yscale("log") for i, (k, v) in enumerate(cl\_dict.items()): ax = axes[1] if k == "te" else axes[0] ax.plot(l, v, "-C{}".format(i), label=k.upper()) for ax in axes: ax.set\_ylabel(r"$D\_\ell$") ax.legend() axes[1].set\_xlabel(r"$\ell$") plt.tight\_layout() ```  We finally have to write the $C\_\ell$s into a file to feed the `so_map.synfast` function for both pixellisation templates In [10]: ``` import os output\_dir = "/tmp/tutorial\_io" os.makedirs(output\_dir, exist\_ok=True) cl\_file = output\_dir + "/cl\_camb.dat" np.savetxt(cl\_file, np.hstack([l[:, np.newaxis], powers["total"][lmin:lmax]])) cmb\_car = template\_car.synfast(cl\_file) cmb\_healpix = template\_healpix.synfast(cl\_file) ``` We can plot both maps, first for the `CAR` pixellisation In [11]: ``` fig, axes = plt.subplots(1, 3, figsize=(10, 5), sharey=True) fields = ["T", "Q", "U"] kwargs = dict(extent=[ra1, ra0, dec0, dec1], origin="lower") for i, field in enumerate(fields): im = axes[i].imshow(cmb\_car.data[i], \*\*kwargs) axes[i].set\_title(fields[i]) fig.colorbar(im, ax=axes[i], orientation="horizontal", shrink=0.9) axes[0].set\_ylabel(r"$\delta$ [deg]") for ax in axes: ax.set\_xlabel(r"$\alpha$ [deg]") plt.tight\_layout() ```  then for the `HEALPIX` pixellisation In [12]: ``` import healpy as hp plt.figure(figsize=(12,8)) for i, field in enumerate(fields): hp.mollview(cmb\_healpix.data[i], title=field, sub=(1, ncomp, i+1)) ```  Actually, saving CMB maps can be done with the `so_map.plot` function which can be used interactively (maps will popup *via* an external image viewer program) but can also be used to store each CMB maps (T, Q, U) inside a directory as follow In [13]: ``` cmb\_car.plot(file\_name="{}/map\_car\_io".format(output\_dir)) cmb\_healpix.plot(file\_name="{}/map\_healpix\_io".format(output\_dir)) ``` ``` <Figure size 612x388.8 with 0 Axes> ``` ``` <Figure size 612x388.8 with 0 Axes> ``` ``` <Figure size 612x388.8 with 0 Axes> ``` Writing/reading SO maps[¶](#Writing/reading-SO-maps) ---------------------------------------------------- Maps can also be writen to disk in `fits` format with the `so_map.write_map` function In [14]: ``` cmb\_car.write\_map("{}/map\_car.fits".format(output\_dir)) cmb\_healpix.write\_map("{}/map\_healpix.fits".format(output\_dir)) ``` ``` /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages/healpy/fitsfunc.py:184: FutureWarning: The default dtype of write_map() will change in a future version: explicitly set the dtype if it is important to you warnings.warn( ``` We can read them back In [15]: ``` cmb\_car2 = so\_map.read\_map("{}/map\_car.fits".format(output\_dir)) cmb\_healpix2 = so\_map.read\_map("{}/map\_healpix.fits".format(output\_dir)) ``` ``` /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages/healpy/fitsfunc.py:351: UserWarning: If you are not specifying the input dtype and using the default np.float64 dtype of read_map(), please consider that it will change in a future version to None as to keep the same dtype of the input file: please explicitly set the dtype if it is important to you. warnings.warn( ``` We null them In [16]: ``` cmb\_car2.data -= cmb\_car.data cmb\_healpix2.data -= cmb\_healpix.data ``` and plot the nulls In [17]: ``` fig, axes = plt.subplots(1, 3, figsize=(10, 5), sharey=True) for i, field in enumerate(fields): im = axes[i].imshow(cmb\_car2.data[i], \*\*kwargs) axes[i].set\_title(fields[i]) fig.colorbar(im, ax=axes[i], orientation="horizontal", shrink=0.9) axes[0].set\_ylabel(r"$\delta$ [deg]") for ax in axes: ax.set\_xlabel(r"$\alpha$ [deg]") plt.tight\_layout() ```  In [18]: ``` plt.figure(figsize=(12,8)) for i, field in enumerate(fields): hp.mollview(cmb\_healpix2.data[i], title=field, sub=(1, ncomp, i+1)) ```  Note that while `CAR` is zero, some low amplitude numerical noise is there in `HEALPIX`. 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} .sr-only { position: absolute; width: 1px; height: 1px; margin: -1px; padding: 0; overflow: hidden; clip: rect(0, 0, 0, 0); border: 0; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } [role="button"] { cursor: pointer; } h1, h2, h3, h4, h5, h6, .h1, .h2, .h3, .h4, .h5, .h6 { font-family: inherit; font-weight: 500; line-height: 1.1; color: inherit; } h1 small, h2 small, h3 small, h4 small, h5 small, h6 small, .h1 small, .h2 small, .h3 small, .h4 small, .h5 small, .h6 small, h1 .small, h2 .small, h3 .small, h4 .small, h5 .small, h6 .small, .h1 .small, .h2 .small, .h3 .small, .h4 .small, .h5 .small, .h6 .small { font-weight: normal; line-height: 1; color: #777777; } h1, .h1, h2, .h2, h3, .h3 { margin-top: 18px; margin-bottom: 9px; } h1 small, .h1 small, h2 small, .h2 small, h3 small, .h3 small, h1 .small, .h1 .small, h2 .small, .h2 .small, h3 .small, .h3 .small { font-size: 65%; } h4, .h4, h5, .h5, h6, .h6 { margin-top: 9px; margin-bottom: 9px; } h4 small, .h4 small, h5 small, .h5 small, h6 small, .h6 small, h4 .small, .h4 .small, h5 .small, .h5 .small, h6 .small, .h6 .small { font-size: 75%; } h1, .h1 { font-size: 33px; } h2, .h2 { font-size: 27px; } h3, .h3 { font-size: 23px; } h4, .h4 { font-size: 17px; } h5, .h5 { font-size: 13px; } h6, .h6 { font-size: 12px; } p { margin: 0 0 9px; } .lead { margin-bottom: 18px; font-size: 14px; font-weight: 300; line-height: 1.4; } @media (min-width: 768px) { .lead { font-size: 19.5px; } } small, .small { font-size: 92%; } mark, .mark { background-color: #fcf8e3; padding: .2em; } .text-left { text-align: left; } .text-right { text-align: right; } .text-center { text-align: center; } .text-justify { text-align: justify; } .text-nowrap { white-space: nowrap; } .text-lowercase { text-transform: lowercase; } .text-uppercase { text-transform: uppercase; } .text-capitalize { text-transform: capitalize; } .text-muted { color: #777777; } .text-primary { color: #337ab7; } a.text-primary:hover, a.text-primary:focus { color: #286090; } .text-success { color: #3c763d; } a.text-success:hover, a.text-success:focus { color: #2b542c; } .text-info { color: #31708f; } a.text-info:hover, a.text-info:focus { color: #245269; } .text-warning { color: #8a6d3b; } a.text-warning:hover, a.text-warning:focus { color: #66512c; } .text-danger { color: #a94442; } a.text-danger:hover, a.text-danger:focus { color: #843534; } .bg-primary { color: #fff; background-color: #337ab7; } a.bg-primary:hover, a.bg-primary:focus { background-color: #286090; } .bg-success { background-color: #dff0d8; } a.bg-success:hover, a.bg-success:focus { background-color: #c1e2b3; } .bg-info { background-color: #d9edf7; } a.bg-info:hover, a.bg-info:focus { background-color: #afd9ee; } .bg-warning { background-color: #fcf8e3; } a.bg-warning:hover, a.bg-warning:focus { background-color: #f7ecb5; } .bg-danger { background-color: #f2dede; } a.bg-danger:hover, a.bg-danger:focus { background-color: #e4b9b9; } .page-header { padding-bottom: 8px; margin: 36px 0 18px; border-bottom: 1px solid #eeeeee; } ul, ol { margin-top: 0; margin-bottom: 9px; } ul ul, ol ul, ul ol, ol ol { margin-bottom: 0; } .list-unstyled { padding-left: 0; list-style: none; } .list-inline { padding-left: 0; list-style: none; margin-left: -5px; } .list-inline > li { display: inline-block; padding-left: 5px; padding-right: 5px; } dl { margin-top: 0; margin-bottom: 18px; } dt, dd { line-height: 1.42857143; } dt { font-weight: bold; } dd { margin-left: 0; } @media (min-width: 541px) { .dl-horizontal dt { float: left; width: 160px; clear: left; text-align: right; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; } .dl-horizontal dd { margin-left: 180px; } } abbr[title], abbr[data-original-title] { cursor: help; border-bottom: 1px dotted #777777; } .initialism { font-size: 90%; text-transform: uppercase; } blockquote { padding: 9px 18px; margin: 0 0 18px; font-size: inherit; border-left: 5px solid #eeeeee; } blockquote p:last-child, blockquote ul:last-child, blockquote ol:last-child { margin-bottom: 0; } blockquote footer, blockquote small, blockquote .small { display: block; font-size: 80%; line-height: 1.42857143; color: #777777; } blockquote footer:before, blockquote small:before, blockquote .small:before { content: '\2014 \00A0'; } .blockquote-reverse, blockquote.pull-right { padding-right: 15px; padding-left: 0; border-right: 5px solid #eeeeee; border-left: 0; text-align: right; } .blockquote-reverse footer:before, blockquote.pull-right footer:before, .blockquote-reverse small:before, blockquote.pull-right small:before, .blockquote-reverse .small:before, blockquote.pull-right .small:before { content: ''; } .blockquote-reverse footer:after, blockquote.pull-right footer:after, .blockquote-reverse small:after, blockquote.pull-right small:after, .blockquote-reverse .small:after, blockquote.pull-right .small:after { content: '\00A0 \2014'; } address { margin-bottom: 18px; font-style: normal; line-height: 1.42857143; } code, kbd, pre, samp { font-family: monospace; } code { padding: 2px 4px; font-size: 90%; color: #c7254e; background-color: #f9f2f4; border-radius: 2px; } kbd { padding: 2px 4px; font-size: 90%; color: #888; background-color: transparent; border-radius: 1px; box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); } kbd kbd { padding: 0; font-size: 100%; font-weight: bold; box-shadow: none; } pre { display: block; padding: 8.5px; margin: 0 0 9px; font-size: 12px; line-height: 1.42857143; word-break: break-all; word-wrap: break-word; color: #333333; background-color: #f5f5f5; border: 1px solid #ccc; border-radius: 2px; } pre code { padding: 0; font-size: inherit; color: inherit; white-space: pre-wrap; background-color: transparent; border-radius: 0; } .pre-scrollable { max-height: 340px; overflow-y: scroll; } .container { margin-right: auto; margin-left: auto; padding-left: 0px; padding-right: 0px; } @media (min-width: 768px) { .container { width: 768px; } } @media (min-width: 992px) { .container { width: 940px; } } @media (min-width: 1200px) { .container { width: 1140px; } } .container-fluid { margin-right: auto; margin-left: auto; padding-left: 0px; padding-right: 0px; } .row { margin-left: 0px; margin-right: 0px; } .col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 { position: relative; min-height: 1px; padding-left: 0px; padding-right: 0px; } .col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 { float: left; } .col-xs-12 { width: 100%; } .col-xs-11 { width: 91.66666667%; } .col-xs-10 { width: 83.33333333%; } .col-xs-9 { width: 75%; } .col-xs-8 { width: 66.66666667%; } .col-xs-7 { width: 58.33333333%; } .col-xs-6 { width: 50%; } .col-xs-5 { width: 41.66666667%; } .col-xs-4 { width: 33.33333333%; } .col-xs-3 { width: 25%; } .col-xs-2 { width: 16.66666667%; } .col-xs-1 { width: 8.33333333%; } .col-xs-pull-12 { right: 100%; } .col-xs-pull-11 { right: 91.66666667%; } .col-xs-pull-10 { right: 83.33333333%; } .col-xs-pull-9 { right: 75%; } .col-xs-pull-8 { right: 66.66666667%; } .col-xs-pull-7 { right: 58.33333333%; } .col-xs-pull-6 { right: 50%; } .col-xs-pull-5 { right: 41.66666667%; } .col-xs-pull-4 { right: 33.33333333%; } .col-xs-pull-3 { right: 25%; } .col-xs-pull-2 { right: 16.66666667%; } .col-xs-pull-1 { right: 8.33333333%; } .col-xs-pull-0 { right: auto; } .col-xs-push-12 { left: 100%; } .col-xs-push-11 { left: 91.66666667%; } .col-xs-push-10 { left: 83.33333333%; } .col-xs-push-9 { left: 75%; } .col-xs-push-8 { left: 66.66666667%; } .col-xs-push-7 { left: 58.33333333%; } .col-xs-push-6 { left: 50%; } .col-xs-push-5 { left: 41.66666667%; } .col-xs-push-4 { left: 33.33333333%; } .col-xs-push-3 { left: 25%; } .col-xs-push-2 { left: 16.66666667%; } .col-xs-push-1 { left: 8.33333333%; } .col-xs-push-0 { left: auto; } .col-xs-offset-12 { margin-left: 100%; } .col-xs-offset-11 { margin-left: 91.66666667%; } .col-xs-offset-10 { margin-left: 83.33333333%; } .col-xs-offset-9 { margin-left: 75%; } .col-xs-offset-8 { margin-left: 66.66666667%; } .col-xs-offset-7 { margin-left: 58.33333333%; } .col-xs-offset-6 { margin-left: 50%; } .col-xs-offset-5 { margin-left: 41.66666667%; } .col-xs-offset-4 { margin-left: 33.33333333%; } .col-xs-offset-3 { margin-left: 25%; } .col-xs-offset-2 { margin-left: 16.66666667%; } .col-xs-offset-1 { margin-left: 8.33333333%; } .col-xs-offset-0 { margin-left: 0%; } @media (min-width: 768px) { .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 { float: left; } .col-sm-12 { width: 100%; } .col-sm-11 { width: 91.66666667%; } .col-sm-10 { width: 83.33333333%; } .col-sm-9 { width: 75%; } .col-sm-8 { width: 66.66666667%; } .col-sm-7 { width: 58.33333333%; } .col-sm-6 { width: 50%; } .col-sm-5 { width: 41.66666667%; } .col-sm-4 { width: 33.33333333%; } .col-sm-3 { width: 25%; } .col-sm-2 { width: 16.66666667%; } .col-sm-1 { width: 8.33333333%; } .col-sm-pull-12 { right: 100%; } .col-sm-pull-11 { right: 91.66666667%; } .col-sm-pull-10 { right: 83.33333333%; } .col-sm-pull-9 { right: 75%; } .col-sm-pull-8 { right: 66.66666667%; } .col-sm-pull-7 { right: 58.33333333%; } .col-sm-pull-6 { right: 50%; } .col-sm-pull-5 { right: 41.66666667%; } .col-sm-pull-4 { right: 33.33333333%; } .col-sm-pull-3 { right: 25%; } .col-sm-pull-2 { right: 16.66666667%; } .col-sm-pull-1 { right: 8.33333333%; } .col-sm-pull-0 { right: auto; } .col-sm-push-12 { left: 100%; } .col-sm-push-11 { left: 91.66666667%; } .col-sm-push-10 { left: 83.33333333%; } .col-sm-push-9 { left: 75%; } .col-sm-push-8 { left: 66.66666667%; } .col-sm-push-7 { left: 58.33333333%; } .col-sm-push-6 { left: 50%; } .col-sm-push-5 { left: 41.66666667%; } .col-sm-push-4 { left: 33.33333333%; } .col-sm-push-3 { left: 25%; } .col-sm-push-2 { left: 16.66666667%; } .col-sm-push-1 { left: 8.33333333%; } .col-sm-push-0 { left: auto; } .col-sm-offset-12 { margin-left: 100%; } .col-sm-offset-11 { margin-left: 91.66666667%; } .col-sm-offset-10 { margin-left: 83.33333333%; } .col-sm-offset-9 { margin-left: 75%; } .col-sm-offset-8 { margin-left: 66.66666667%; } .col-sm-offset-7 { margin-left: 58.33333333%; } .col-sm-offset-6 { margin-left: 50%; } .col-sm-offset-5 { margin-left: 41.66666667%; } .col-sm-offset-4 { margin-left: 33.33333333%; } .col-sm-offset-3 { margin-left: 25%; } .col-sm-offset-2 { margin-left: 16.66666667%; } .col-sm-offset-1 { margin-left: 8.33333333%; } .col-sm-offset-0 { margin-left: 0%; } } @media (min-width: 992px) { .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 { float: left; } .col-md-12 { width: 100%; } .col-md-11 { width: 91.66666667%; } .col-md-10 { width: 83.33333333%; } .col-md-9 { width: 75%; } .col-md-8 { width: 66.66666667%; } .col-md-7 { width: 58.33333333%; } .col-md-6 { width: 50%; } .col-md-5 { width: 41.66666667%; } .col-md-4 { width: 33.33333333%; } .col-md-3 { width: 25%; } .col-md-2 { width: 16.66666667%; } .col-md-1 { width: 8.33333333%; } .col-md-pull-12 { right: 100%; } .col-md-pull-11 { right: 91.66666667%; } .col-md-pull-10 { right: 83.33333333%; } .col-md-pull-9 { right: 75%; } .col-md-pull-8 { right: 66.66666667%; } .col-md-pull-7 { right: 58.33333333%; } .col-md-pull-6 { right: 50%; } .col-md-pull-5 { right: 41.66666667%; } .col-md-pull-4 { right: 33.33333333%; } .col-md-pull-3 { right: 25%; } .col-md-pull-2 { right: 16.66666667%; } .col-md-pull-1 { right: 8.33333333%; } .col-md-pull-0 { right: auto; } .col-md-push-12 { left: 100%; } .col-md-push-11 { left: 91.66666667%; } .col-md-push-10 { left: 83.33333333%; } .col-md-push-9 { left: 75%; } .col-md-push-8 { left: 66.66666667%; } .col-md-push-7 { left: 58.33333333%; } .col-md-push-6 { left: 50%; } .col-md-push-5 { left: 41.66666667%; } .col-md-push-4 { left: 33.33333333%; } .col-md-push-3 { left: 25%; } .col-md-push-2 { left: 16.66666667%; } .col-md-push-1 { left: 8.33333333%; } .col-md-push-0 { left: auto; } .col-md-offset-12 { margin-left: 100%; } .col-md-offset-11 { margin-left: 91.66666667%; } .col-md-offset-10 { margin-left: 83.33333333%; } .col-md-offset-9 { margin-left: 75%; } .col-md-offset-8 { margin-left: 66.66666667%; } .col-md-offset-7 { margin-left: 58.33333333%; } .col-md-offset-6 { margin-left: 50%; } .col-md-offset-5 { margin-left: 41.66666667%; } .col-md-offset-4 { margin-left: 33.33333333%; } .col-md-offset-3 { margin-left: 25%; } .col-md-offset-2 { margin-left: 16.66666667%; } .col-md-offset-1 { margin-left: 8.33333333%; } .col-md-offset-0 { margin-left: 0%; } } @media (min-width: 1200px) { .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 { float: left; } .col-lg-12 { width: 100%; } .col-lg-11 { width: 91.66666667%; } .col-lg-10 { width: 83.33333333%; } .col-lg-9 { width: 75%; } .col-lg-8 { width: 66.66666667%; } .col-lg-7 { width: 58.33333333%; } .col-lg-6 { width: 50%; } .col-lg-5 { width: 41.66666667%; } .col-lg-4 { width: 33.33333333%; } .col-lg-3 { width: 25%; } .col-lg-2 { width: 16.66666667%; } .col-lg-1 { width: 8.33333333%; } .col-lg-pull-12 { right: 100%; } .col-lg-pull-11 { right: 91.66666667%; } .col-lg-pull-10 { right: 83.33333333%; } .col-lg-pull-9 { right: 75%; } .col-lg-pull-8 { right: 66.66666667%; } .col-lg-pull-7 { right: 58.33333333%; } .col-lg-pull-6 { right: 50%; } .col-lg-pull-5 { right: 41.66666667%; } .col-lg-pull-4 { right: 33.33333333%; } .col-lg-pull-3 { right: 25%; } .col-lg-pull-2 { right: 16.66666667%; } .col-lg-pull-1 { right: 8.33333333%; } .col-lg-pull-0 { right: auto; } .col-lg-push-12 { left: 100%; } .col-lg-push-11 { left: 91.66666667%; } .col-lg-push-10 { left: 83.33333333%; } .col-lg-push-9 { left: 75%; } .col-lg-push-8 { left: 66.66666667%; } .col-lg-push-7 { left: 58.33333333%; } .col-lg-push-6 { left: 50%; } .col-lg-push-5 { left: 41.66666667%; } .col-lg-push-4 { left: 33.33333333%; } .col-lg-push-3 { left: 25%; } .col-lg-push-2 { left: 16.66666667%; } .col-lg-push-1 { left: 8.33333333%; } .col-lg-push-0 { left: auto; } .col-lg-offset-12 { margin-left: 100%; } .col-lg-offset-11 { margin-left: 91.66666667%; } .col-lg-offset-10 { margin-left: 83.33333333%; } .col-lg-offset-9 { margin-left: 75%; } .col-lg-offset-8 { margin-left: 66.66666667%; } .col-lg-offset-7 { margin-left: 58.33333333%; } .col-lg-offset-6 { margin-left: 50%; } .col-lg-offset-5 { margin-left: 41.66666667%; } .col-lg-offset-4 { margin-left: 33.33333333%; } .col-lg-offset-3 { margin-left: 25%; } .col-lg-offset-2 { margin-left: 16.66666667%; } .col-lg-offset-1 { margin-left: 8.33333333%; } .col-lg-offset-0 { margin-left: 0%; } } table { background-color: transparent; } caption { padding-top: 8px; padding-bottom: 8px; color: #777777; text-align: left; } th { text-align: left; } .table { width: 100%; max-width: 100%; margin-bottom: 18px; } .table > thead > tr > th, .table > tbody > tr > th, .table > tfoot > tr > th, .table > thead > tr > td, .table > tbody > tr > td, .table > tfoot > tr > td { padding: 8px; line-height: 1.42857143; vertical-align: top; border-top: 1px solid #ddd; } .table > thead > tr > th { vertical-align: bottom; border-bottom: 2px solid #ddd; } .table > caption + thead > tr:first-child > th, .table > colgroup + thead > tr:first-child > th, .table > thead:first-child > tr:first-child > th, .table > caption + thead > tr:first-child > td, .table > colgroup + thead > tr:first-child > td, .table > thead:first-child > tr:first-child > td { border-top: 0; } .table > tbody + tbody { border-top: 2px solid #ddd; } .table .table { background-color: #fff; } .table-condensed > thead > tr > th, .table-condensed > tbody > tr > th, .table-condensed > tfoot > tr > th, .table-condensed > thead > tr > td, .table-condensed > tbody > tr > td, .table-condensed > tfoot > tr > td { padding: 5px; } .table-bordered { border: 1px solid #ddd; } .table-bordered > thead > tr > th, .table-bordered > tbody > tr > th, .table-bordered > tfoot > tr > th, .table-bordered > thead > tr > td, .table-bordered > tbody > tr > td, .table-bordered > tfoot > tr > td { border: 1px solid #ddd; } .table-bordered > thead > tr > th, .table-bordered > thead > tr > td { border-bottom-width: 2px; } .table-striped > tbody > tr:nth-of-type(odd) { background-color: #f9f9f9; } .table-hover > tbody > tr:hover { background-color: #f5f5f5; } table col[class\*="col-"] { position: static; float: none; display: table-column; } table td[class\*="col-"], table th[class\*="col-"] { position: static; float: none; display: table-cell; } .table > thead > tr > td.active, .table > tbody > tr > td.active, .table > tfoot > tr > td.active, .table > thead > tr > th.active, .table > tbody > tr > th.active, .table > tfoot > tr > th.active, .table > thead > tr.active > td, .table > tbody > tr.active > td, .table > tfoot > tr.active > td, .table > thead > tr.active > th, .table > tbody > tr.active > th, .table > tfoot > tr.active > th { background-color: #f5f5f5; } .table-hover > tbody > tr > td.active:hover, .table-hover > tbody > tr > th.active:hover, .table-hover > tbody > tr.active:hover > td, .table-hover > tbody > tr:hover > .active, .table-hover > tbody > tr.active:hover > th { background-color: #e8e8e8; } .table > thead > tr > td.success, .table > tbody > tr > td.success, .table > tfoot > tr > td.success, .table > thead > tr > th.success, .table > tbody > tr > th.success, .table > tfoot > tr > th.success, .table > thead > tr.success > td, .table > tbody > tr.success > td, .table > tfoot > tr.success > td, .table > thead > tr.success > th, .table > tbody > tr.success > th, .table > tfoot > tr.success > th { background-color: #dff0d8; } .table-hover > tbody > tr > td.success:hover, .table-hover > tbody > tr > th.success:hover, .table-hover > tbody > tr.success:hover > td, .table-hover > tbody > tr:hover > .success, .table-hover > tbody > tr.success:hover > th { background-color: #d0e9c6; } .table > thead > tr > td.info, .table > tbody > tr > td.info, .table > tfoot > tr > td.info, .table > thead > tr > th.info, .table > tbody > tr > th.info, .table > tfoot > tr > th.info, .table > thead > tr.info > td, .table > tbody > tr.info > td, .table > tfoot > tr.info > td, .table > thead > tr.info > th, .table > tbody > tr.info > th, .table > tfoot > tr.info > th { background-color: #d9edf7; } .table-hover > tbody > tr > td.info:hover, .table-hover > tbody > tr > th.info:hover, .table-hover > tbody > tr.info:hover > td, .table-hover > tbody > tr:hover > .info, .table-hover > tbody > tr.info:hover > th { background-color: #c4e3f3; } .table > thead > tr > td.warning, .table > tbody > tr > td.warning, .table > tfoot > tr > td.warning, .table > thead > tr > th.warning, .table > tbody > tr > th.warning, .table > tfoot > tr > th.warning, .table > thead > tr.warning > td, .table > tbody > tr.warning > td, .table > tfoot > tr.warning > td, .table > thead > tr.warning > th, .table > tbody > tr.warning > th, .table > tfoot > tr.warning > th { background-color: #fcf8e3; } .table-hover > tbody > tr > td.warning:hover, .table-hover > tbody > tr > th.warning:hover, .table-hover > tbody > tr.warning:hover > td, .table-hover > tbody > tr:hover > .warning, .table-hover > tbody > tr.warning:hover > th { background-color: #faf2cc; } .table > thead > tr > td.danger, .table > tbody > tr > td.danger, .table > tfoot > tr > td.danger, .table > thead > tr > th.danger, .table > tbody > tr > th.danger, .table > tfoot > tr > th.danger, .table > thead > tr.danger > td, .table > tbody > tr.danger > td, .table > tfoot > tr.danger > td, .table > thead > tr.danger > th, .table > tbody > tr.danger > th, .table > tfoot > tr.danger > th { background-color: #f2dede; } .table-hover > tbody > tr > td.danger:hover, .table-hover > tbody > tr > th.danger:hover, .table-hover > tbody > tr.danger:hover > td, .table-hover > tbody > tr:hover > .danger, .table-hover > tbody > tr.danger:hover > th { background-color: #ebcccc; } .table-responsive { overflow-x: auto; min-height: 0.01%; } @media screen and (max-width: 767px) { .table-responsive { width: 100%; margin-bottom: 13.5px; overflow-y: hidden; -ms-overflow-style: -ms-autohiding-scrollbar; border: 1px solid #ddd; } .table-responsive > .table { margin-bottom: 0; } .table-responsive > .table > thead > tr > th, .table-responsive > .table > tbody > tr > th, .table-responsive > .table > tfoot > tr > th, .table-responsive > .table > thead > tr > td, .table-responsive > .table > tbody > tr > td, .table-responsive > .table > tfoot > tr > td { white-space: nowrap; } .table-responsive > .table-bordered { border: 0; } .table-responsive > .table-bordered > thead > tr > th:first-child, .table-responsive > .table-bordered > tbody > tr > th:first-child, .table-responsive > .table-bordered > tfoot > tr > th:first-child, .table-responsive > .table-bordered > thead > tr > td:first-child, .table-responsive > .table-bordered > tbody > tr > td:first-child, .table-responsive > .table-bordered > tfoot > tr > td:first-child { border-left: 0; } .table-responsive > .table-bordered > thead > tr > th:last-child, .table-responsive > .table-bordered > tbody > tr > th:last-child, .table-responsive > .table-bordered > tfoot > tr > th:last-child, .table-responsive > .table-bordered > thead > tr > td:last-child, .table-responsive > .table-bordered > tbody > tr > td:last-child, .table-responsive > .table-bordered > tfoot > tr > td:last-child { border-right: 0; } .table-responsive > .table-bordered > tbody > tr:last-child > th, .table-responsive > .table-bordered > tfoot > tr:last-child > th, .table-responsive > .table-bordered > tbody > tr:last-child > td, .table-responsive > .table-bordered > tfoot > tr:last-child > td { border-bottom: 0; } } fieldset { padding: 0; margin: 0; border: 0; min-width: 0; } legend { display: block; width: 100%; padding: 0; margin-bottom: 18px; font-size: 19.5px; line-height: inherit; color: #333333; border: 0; border-bottom: 1px solid #e5e5e5; } label { display: inline-block; max-width: 100%; margin-bottom: 5px; font-weight: bold; } input[type="search"] { -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; } input[type="radio"], input[type="checkbox"] { margin: 4px 0 0; margin-top: 1px \9; line-height: normal; } input[type="file"] { display: block; } input[type="range"] { display: block; width: 100%; } select[multiple], select[size] { height: auto; } input[type="file"]:focus, input[type="radio"]:focus, input[type="checkbox"]:focus { outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } output { display: block; padding-top: 7px; font-size: 13px; line-height: 1.42857143; color: #555555; } .form-control { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; } .form-control:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .form-control::-moz-placeholder { color: #999; opacity: 1; } .form-control:-ms-input-placeholder { color: #999; } .form-control::-webkit-input-placeholder { color: #999; } .form-control::-ms-expand { border: 0; background-color: transparent; } .form-control[disabled], .form-control[readonly], fieldset[disabled] .form-control { background-color: #eeeeee; opacity: 1; } .form-control[disabled], fieldset[disabled] .form-control { cursor: not-allowed; } textarea.form-control { height: auto; } input[type="search"] { -webkit-appearance: none; } @media screen and (-webkit-min-device-pixel-ratio: 0) { input[type="date"].form-control, input[type="time"].form-control, input[type="datetime-local"].form-control, input[type="month"].form-control { line-height: 32px; } input[type="date"].input-sm, input[type="time"].input-sm, input[type="datetime-local"].input-sm, input[type="month"].input-sm, .input-group-sm input[type="date"], .input-group-sm input[type="time"], .input-group-sm input[type="datetime-local"], .input-group-sm input[type="month"] { line-height: 30px; } input[type="date"].input-lg, input[type="time"].input-lg, input[type="datetime-local"].input-lg, input[type="month"].input-lg, .input-group-lg input[type="date"], .input-group-lg input[type="time"], .input-group-lg input[type="datetime-local"], .input-group-lg input[type="month"] { line-height: 45px; } } .form-group { margin-bottom: 15px; } .radio, .checkbox { position: relative; display: block; margin-top: 10px; margin-bottom: 10px; } .radio label, .checkbox label { min-height: 18px; padding-left: 20px; margin-bottom: 0; font-weight: normal; cursor: pointer; } .radio input[type="radio"], .radio-inline input[type="radio"], .checkbox input[type="checkbox"], .checkbox-inline input[type="checkbox"] { position: absolute; margin-left: -20px; margin-top: 4px \9; } .radio + .radio, .checkbox + .checkbox { margin-top: -5px; } .radio-inline, .checkbox-inline { position: relative; display: inline-block; padding-left: 20px; margin-bottom: 0; vertical-align: middle; font-weight: normal; cursor: pointer; } .radio-inline + .radio-inline, .checkbox-inline + .checkbox-inline { margin-top: 0; margin-left: 10px; } input[type="radio"][disabled], input[type="checkbox"][disabled], input[type="radio"].disabled, input[type="checkbox"].disabled, fieldset[disabled] input[type="radio"], fieldset[disabled] input[type="checkbox"] { cursor: not-allowed; } .radio-inline.disabled, .checkbox-inline.disabled, fieldset[disabled] .radio-inline, fieldset[disabled] .checkbox-inline { cursor: not-allowed; } .radio.disabled label, .checkbox.disabled label, fieldset[disabled] .radio label, fieldset[disabled] .checkbox label { cursor: not-allowed; } .form-control-static { padding-top: 7px; padding-bottom: 7px; margin-bottom: 0; min-height: 31px; } .form-control-static.input-lg, .form-control-static.input-sm { padding-left: 0; padding-right: 0; } .input-sm { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } select.input-sm { height: 30px; line-height: 30px; } textarea.input-sm, select[multiple].input-sm { height: auto; } .form-group-sm .form-control { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .form-group-sm select.form-control { height: 30px; line-height: 30px; } .form-group-sm textarea.form-control, .form-group-sm select[multiple].form-control { height: auto; } .form-group-sm .form-control-static { height: 30px; min-height: 30px; padding: 6px 10px; font-size: 12px; line-height: 1.5; } .input-lg { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } select.input-lg { height: 45px; line-height: 45px; } textarea.input-lg, select[multiple].input-lg { height: auto; } .form-group-lg .form-control { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } .form-group-lg select.form-control { height: 45px; line-height: 45px; } .form-group-lg textarea.form-control, .form-group-lg select[multiple].form-control { height: auto; } .form-group-lg .form-control-static { height: 45px; min-height: 35px; padding: 11px 16px; font-size: 17px; line-height: 1.3333333; } .has-feedback { position: relative; } .has-feedback .form-control { padding-right: 40px; } .form-control-feedback { position: absolute; top: 0; right: 0; z-index: 2; display: block; width: 32px; height: 32px; line-height: 32px; text-align: center; pointer-events: none; } .input-lg + .form-control-feedback, .input-group-lg + .form-control-feedback, .form-group-lg .form-control + .form-control-feedback { width: 45px; height: 45px; line-height: 45px; } .input-sm + .form-control-feedback, .input-group-sm + .form-control-feedback, .form-group-sm .form-control + .form-control-feedback { width: 30px; height: 30px; line-height: 30px; } .has-success .help-block, .has-success .control-label, .has-success .radio, .has-success .checkbox, .has-success .radio-inline, .has-success .checkbox-inline, .has-success.radio label, .has-success.checkbox label, .has-success.radio-inline label, .has-success.checkbox-inline label { color: #3c763d; } .has-success .form-control { border-color: #3c763d; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-success .form-control:focus { border-color: #2b542c; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; } .has-success .input-group-addon { color: #3c763d; border-color: #3c763d; background-color: #dff0d8; } .has-success .form-control-feedback { color: #3c763d; } .has-warning .help-block, .has-warning .control-label, .has-warning .radio, .has-warning .checkbox, .has-warning .radio-inline, .has-warning .checkbox-inline, .has-warning.radio label, .has-warning.checkbox label, .has-warning.radio-inline label, .has-warning.checkbox-inline label { color: #8a6d3b; } .has-warning .form-control { border-color: #8a6d3b; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-warning .form-control:focus { border-color: #66512c; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; } .has-warning .input-group-addon { color: #8a6d3b; border-color: #8a6d3b; background-color: #fcf8e3; } .has-warning .form-control-feedback { color: #8a6d3b; } .has-error .help-block, .has-error .control-label, .has-error .radio, .has-error .checkbox, .has-error .radio-inline, .has-error .checkbox-inline, .has-error.radio label, .has-error.checkbox label, .has-error.radio-inline label, .has-error.checkbox-inline label { color: #a94442; } .has-error .form-control { border-color: #a94442; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-error .form-control:focus { border-color: #843534; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; } .has-error .input-group-addon { color: #a94442; border-color: #a94442; background-color: #f2dede; } .has-error .form-control-feedback { color: #a94442; } .has-feedback label ~ .form-control-feedback { top: 23px; } .has-feedback label.sr-only ~ .form-control-feedback { top: 0; } .help-block { display: block; margin-top: 5px; margin-bottom: 10px; color: #404040; } @media (min-width: 768px) { .form-inline .form-group { display: inline-block; margin-bottom: 0; vertical-align: middle; } .form-inline .form-control { display: inline-block; width: auto; vertical-align: middle; } .form-inline .form-control-static { display: inline-block; } .form-inline .input-group { display: inline-table; vertical-align: middle; } .form-inline .input-group .input-group-addon, .form-inline .input-group .input-group-btn, .form-inline .input-group .form-control { width: auto; } .form-inline .input-group > .form-control { width: 100%; } .form-inline .control-label { margin-bottom: 0; vertical-align: middle; } .form-inline .radio, .form-inline .checkbox { display: inline-block; margin-top: 0; margin-bottom: 0; vertical-align: middle; } .form-inline .radio label, .form-inline .checkbox label { padding-left: 0; } .form-inline .radio input[type="radio"], .form-inline .checkbox input[type="checkbox"] { position: relative; margin-left: 0; } .form-inline .has-feedback .form-control-feedback { top: 0; } } .form-horizontal .radio, .form-horizontal .checkbox, .form-horizontal .radio-inline, .form-horizontal .checkbox-inline { margin-top: 0; margin-bottom: 0; padding-top: 7px; } .form-horizontal .radio, .form-horizontal .checkbox { min-height: 25px; } .form-horizontal .form-group { margin-left: 0px; margin-right: 0px; } @media (min-width: 768px) { .form-horizontal .control-label { text-align: right; margin-bottom: 0; padding-top: 7px; } } .form-horizontal .has-feedback .form-control-feedback { right: 0px; } @media (min-width: 768px) { .form-horizontal .form-group-lg .control-label { padding-top: 11px; font-size: 17px; } } @media (min-width: 768px) { .form-horizontal .form-group-sm .control-label { padding-top: 6px; font-size: 12px; } } .btn { display: inline-block; margin-bottom: 0; font-weight: normal; text-align: center; vertical-align: middle; touch-action: manipulation; cursor: pointer; background-image: none; border: 1px solid transparent; white-space: nowrap; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; border-radius: 2px; -webkit-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; } .btn:focus, .btn:active:focus, .btn.active:focus, .btn.focus, .btn:active.focus, .btn.active.focus { outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } .btn:hover, .btn:focus, .btn.focus { color: #333; text-decoration: none; } .btn:active, .btn.active { outline: 0; background-image: none; -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); } .btn.disabled, .btn[disabled], fieldset[disabled] .btn { cursor: not-allowed; opacity: 0.65; filter: alpha(opacity=65); -webkit-box-shadow: none; box-shadow: none; } a.btn.disabled, fieldset[disabled] a.btn { pointer-events: none; } .btn-default { color: #333; background-color: #fff; border-color: #ccc; } .btn-default:focus, .btn-default.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } .btn-default:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } .btn-default:active, .btn-default.active, .open > .dropdown-toggle.btn-default { color: #333; background-color: #e6e6e6; border-color: #adadad; } .btn-default:active:hover, .btn-default.active:hover, .open > .dropdown-toggle.btn-default:hover, .btn-default:active:focus, .btn-default.active:focus, .open > .dropdown-toggle.btn-default:focus, .btn-default:active.focus, .btn-default.active.focus, .open > .dropdown-toggle.btn-default.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } .btn-default:active, .btn-default.active, .open > .dropdown-toggle.btn-default { background-image: none; } .btn-default.disabled:hover, .btn-default[disabled]:hover, fieldset[disabled] .btn-default:hover, .btn-default.disabled:focus, .btn-default[disabled]:focus, fieldset[disabled] .btn-default:focus, .btn-default.disabled.focus, .btn-default[disabled].focus, fieldset[disabled] .btn-default.focus { background-color: #fff; border-color: #ccc; } .btn-default .badge { color: #fff; background-color: #333; } .btn-primary { color: #fff; background-color: #337ab7; border-color: #2e6da4; } .btn-primary:focus, .btn-primary.focus { color: #fff; background-color: #286090; border-color: #122b40; } .btn-primary:hover { color: #fff; background-color: #286090; border-color: #204d74; } .btn-primary:active, .btn-primary.active, .open > .dropdown-toggle.btn-primary { color: #fff; background-color: #286090; border-color: #204d74; } .btn-primary:active:hover, .btn-primary.active:hover, .open > .dropdown-toggle.btn-primary:hover, .btn-primary:active:focus, .btn-primary.active:focus, .open > .dropdown-toggle.btn-primary:focus, .btn-primary:active.focus, .btn-primary.active.focus, .open > .dropdown-toggle.btn-primary.focus { color: #fff; background-color: #204d74; border-color: #122b40; } .btn-primary:active, .btn-primary.active, .open > .dropdown-toggle.btn-primary { background-image: none; } .btn-primary.disabled:hover, .btn-primary[disabled]:hover, fieldset[disabled] .btn-primary:hover, .btn-primary.disabled:focus, .btn-primary[disabled]:focus, fieldset[disabled] .btn-primary:focus, .btn-primary.disabled.focus, .btn-primary[disabled].focus, fieldset[disabled] .btn-primary.focus { background-color: #337ab7; border-color: #2e6da4; } .btn-primary .badge { color: #337ab7; background-color: #fff; } .btn-success { color: #fff; background-color: #5cb85c; border-color: #4cae4c; } .btn-success:focus, .btn-success.focus { color: #fff; background-color: #449d44; border-color: #255625; } .btn-success:hover { color: #fff; background-color: #449d44; border-color: #398439; } .btn-success:active, .btn-success.active, .open > .dropdown-toggle.btn-success { color: #fff; background-color: #449d44; border-color: #398439; } .btn-success:active:hover, .btn-success.active:hover, .open > .dropdown-toggle.btn-success:hover, .btn-success:active:focus, .btn-success.active:focus, .open > .dropdown-toggle.btn-success:focus, .btn-success:active.focus, .btn-success.active.focus, .open > .dropdown-toggle.btn-success.focus { color: #fff; background-color: #398439; border-color: #255625; } .btn-success:active, .btn-success.active, .open > .dropdown-toggle.btn-success { background-image: none; } .btn-success.disabled:hover, .btn-success[disabled]:hover, fieldset[disabled] .btn-success:hover, .btn-success.disabled:focus, .btn-success[disabled]:focus, fieldset[disabled] .btn-success:focus, .btn-success.disabled.focus, .btn-success[disabled].focus, fieldset[disabled] .btn-success.focus { background-color: #5cb85c; border-color: #4cae4c; } .btn-success .badge { color: #5cb85c; background-color: #fff; } .btn-info { color: #fff; background-color: #5bc0de; border-color: #46b8da; } .btn-info:focus, .btn-info.focus { color: #fff; background-color: #31b0d5; border-color: #1b6d85; } .btn-info:hover { color: #fff; background-color: #31b0d5; border-color: #269abc; } .btn-info:active, .btn-info.active, .open > .dropdown-toggle.btn-info { color: #fff; background-color: #31b0d5; border-color: #269abc; } .btn-info:active:hover, .btn-info.active:hover, .open > .dropdown-toggle.btn-info:hover, .btn-info:active:focus, .btn-info.active:focus, .open > .dropdown-toggle.btn-info:focus, .btn-info:active.focus, .btn-info.active.focus, .open > .dropdown-toggle.btn-info.focus { color: #fff; background-color: #269abc; border-color: #1b6d85; } .btn-info:active, .btn-info.active, .open > .dropdown-toggle.btn-info { background-image: none; } .btn-info.disabled:hover, .btn-info[disabled]:hover, fieldset[disabled] .btn-info:hover, .btn-info.disabled:focus, .btn-info[disabled]:focus, fieldset[disabled] .btn-info:focus, .btn-info.disabled.focus, .btn-info[disabled].focus, fieldset[disabled] .btn-info.focus { background-color: #5bc0de; border-color: #46b8da; } .btn-info .badge { color: #5bc0de; background-color: #fff; } .btn-warning { color: #fff; background-color: #f0ad4e; border-color: #eea236; } .btn-warning:focus, .btn-warning.focus { color: #fff; background-color: #ec971f; border-color: #985f0d; } .btn-warning:hover { color: #fff; background-color: #ec971f; border-color: #d58512; } .btn-warning:active, .btn-warning.active, .open > .dropdown-toggle.btn-warning { color: #fff; background-color: #ec971f; border-color: #d58512; } .btn-warning:active:hover, .btn-warning.active:hover, .open > .dropdown-toggle.btn-warning:hover, .btn-warning:active:focus, .btn-warning.active:focus, .open > .dropdown-toggle.btn-warning:focus, .btn-warning:active.focus, .btn-warning.active.focus, .open > .dropdown-toggle.btn-warning.focus { color: #fff; background-color: #d58512; border-color: #985f0d; } .btn-warning:active, .btn-warning.active, .open > .dropdown-toggle.btn-warning { background-image: none; } .btn-warning.disabled:hover, .btn-warning[disabled]:hover, fieldset[disabled] .btn-warning:hover, .btn-warning.disabled:focus, .btn-warning[disabled]:focus, fieldset[disabled] .btn-warning:focus, .btn-warning.disabled.focus, .btn-warning[disabled].focus, fieldset[disabled] .btn-warning.focus { background-color: #f0ad4e; border-color: #eea236; } .btn-warning .badge { color: #f0ad4e; background-color: #fff; } .btn-danger { color: #fff; background-color: #d9534f; border-color: #d43f3a; } .btn-danger:focus, .btn-danger.focus { color: #fff; background-color: #c9302c; border-color: #761c19; } .btn-danger:hover { color: #fff; background-color: #c9302c; border-color: #ac2925; } .btn-danger:active, .btn-danger.active, .open > .dropdown-toggle.btn-danger { color: #fff; background-color: #c9302c; border-color: #ac2925; } .btn-danger:active:hover, .btn-danger.active:hover, .open > .dropdown-toggle.btn-danger:hover, .btn-danger:active:focus, .btn-danger.active:focus, .open > .dropdown-toggle.btn-danger:focus, .btn-danger:active.focus, .btn-danger.active.focus, .open > .dropdown-toggle.btn-danger.focus { color: #fff; background-color: #ac2925; border-color: #761c19; } .btn-danger:active, .btn-danger.active, .open > .dropdown-toggle.btn-danger { background-image: none; } .btn-danger.disabled:hover, .btn-danger[disabled]:hover, fieldset[disabled] .btn-danger:hover, .btn-danger.disabled:focus, .btn-danger[disabled]:focus, fieldset[disabled] .btn-danger:focus, .btn-danger.disabled.focus, .btn-danger[disabled].focus, fieldset[disabled] .btn-danger.focus { background-color: #d9534f; border-color: #d43f3a; } .btn-danger .badge { color: #d9534f; background-color: #fff; } .btn-link { color: #337ab7; font-weight: normal; border-radius: 0; } .btn-link, .btn-link:active, .btn-link.active, .btn-link[disabled], fieldset[disabled] .btn-link { background-color: transparent; -webkit-box-shadow: none; box-shadow: none; } .btn-link, .btn-link:hover, .btn-link:focus, .btn-link:active { border-color: transparent; } .btn-link:hover, .btn-link:focus { color: #23527c; text-decoration: underline; background-color: transparent; } .btn-link[disabled]:hover, fieldset[disabled] .btn-link:hover, .btn-link[disabled]:focus, fieldset[disabled] .btn-link:focus { color: #777777; text-decoration: none; } .btn-lg, .btn-group-lg > .btn { padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } .btn-sm, .btn-group-sm > .btn { padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .btn-xs, .btn-group-xs > .btn { padding: 1px 5px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .btn-block { display: block; width: 100%; } .btn-block + .btn-block { margin-top: 5px; } input[type="submit"].btn-block, input[type="reset"].btn-block, input[type="button"].btn-block { width: 100%; } .fade { opacity: 0; -webkit-transition: opacity 0.15s linear; -o-transition: opacity 0.15s linear; transition: opacity 0.15s linear; } .fade.in { opacity: 1; } .collapse { display: none; } .collapse.in { display: block; } tr.collapse.in { display: table-row; } tbody.collapse.in { display: table-row-group; } .collapsing { position: relative; height: 0; overflow: hidden; -webkit-transition-property: height, visibility; transition-property: height, visibility; -webkit-transition-duration: 0.35s; transition-duration: 0.35s; -webkit-transition-timing-function: ease; transition-timing-function: ease; } .caret { display: inline-block; width: 0; height: 0; margin-left: 2px; vertical-align: middle; border-top: 4px dashed; border-top: 4px solid \9; border-right: 4px solid transparent; border-left: 4px solid transparent; } .dropup, .dropdown { position: relative; } .dropdown-toggle:focus { outline: 0; } .dropdown-menu { position: absolute; top: 100%; left: 0; z-index: 1000; display: none; float: left; min-width: 160px; padding: 5px 0; margin: 2px 0 0; list-style: none; font-size: 13px; text-align: left; background-color: #fff; border: 1px solid #ccc; border: 1px solid rgba(0, 0, 0, 0.15); border-radius: 2px; -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); background-clip: padding-box; } .dropdown-menu.pull-right { right: 0; left: auto; } .dropdown-menu .divider { height: 1px; margin: 8px 0; overflow: hidden; background-color: #e5e5e5; } .dropdown-menu > li > a { display: block; padding: 3px 20px; clear: both; font-weight: normal; line-height: 1.42857143; color: #333333; white-space: nowrap; } .dropdown-menu > li > a:hover, .dropdown-menu > li > a:focus { text-decoration: none; color: #262626; background-color: #f5f5f5; } .dropdown-menu > .active > a, .dropdown-menu > .active > a:hover, .dropdown-menu > .active > a:focus { color: #fff; text-decoration: none; outline: 0; background-color: #337ab7; } .dropdown-menu > .disabled > a, .dropdown-menu > .disabled > a:hover, .dropdown-menu > .disabled > a:focus { color: #777777; } .dropdown-menu > .disabled > a:hover, .dropdown-menu > .disabled > a:focus { text-decoration: none; background-color: transparent; background-image: none; filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); cursor: not-allowed; } .open > .dropdown-menu { display: block; } .open > a { outline: 0; } .dropdown-menu-right { left: auto; right: 0; } .dropdown-menu-left { left: 0; right: auto; } .dropdown-header { display: block; padding: 3px 20px; font-size: 12px; line-height: 1.42857143; color: #777777; white-space: nowrap; } .dropdown-backdrop { position: fixed; left: 0; right: 0; bottom: 0; top: 0; z-index: 990; } .pull-right > .dropdown-menu { right: 0; left: auto; } .dropup .caret, .navbar-fixed-bottom .dropdown .caret { border-top: 0; border-bottom: 4px dashed; border-bottom: 4px solid \9; content: ""; } .dropup .dropdown-menu, .navbar-fixed-bottom .dropdown .dropdown-menu { top: auto; bottom: 100%; margin-bottom: 2px; } @media (min-width: 541px) { .navbar-right .dropdown-menu { left: auto; right: 0; } .navbar-right .dropdown-menu-left { left: 0; right: auto; } } .btn-group, .btn-group-vertical { position: relative; display: inline-block; vertical-align: middle; } .btn-group > .btn, .btn-group-vertical > .btn { position: relative; float: left; } .btn-group > .btn:hover, .btn-group-vertical > .btn:hover, .btn-group > .btn:focus, .btn-group-vertical > .btn:focus, .btn-group > .btn:active, .btn-group-vertical > .btn:active, .btn-group > .btn.active, .btn-group-vertical > .btn.active { z-index: 2; } .btn-group .btn + .btn, .btn-group .btn + .btn-group, .btn-group .btn-group + .btn, .btn-group .btn-group + .btn-group { margin-left: -1px; } .btn-toolbar { margin-left: -5px; } .btn-toolbar .btn, .btn-toolbar .btn-group, .btn-toolbar .input-group { float: left; } .btn-toolbar > .btn, .btn-toolbar > .btn-group, .btn-toolbar > .input-group { margin-left: 5px; } .btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) { border-radius: 0; } .btn-group > .btn:first-child { margin-left: 0; } .btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) { border-bottom-right-radius: 0; border-top-right-radius: 0; } .btn-group > .btn:last-child:not(:first-child), .btn-group > .dropdown-toggle:not(:first-child) { border-bottom-left-radius: 0; border-top-left-radius: 0; } .btn-group > .btn-group { float: left; } .btn-group > .btn-group:not(:first-child):not(:last-child) > .btn { border-radius: 0; } .btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child, .btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle { border-bottom-right-radius: 0; border-top-right-radius: 0; } .btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child { border-bottom-left-radius: 0; border-top-left-radius: 0; } .btn-group .dropdown-toggle:active, .btn-group.open .dropdown-toggle { outline: 0; } .btn-group > .btn + .dropdown-toggle { padding-left: 8px; padding-right: 8px; } .btn-group > .btn-lg + .dropdown-toggle { padding-left: 12px; padding-right: 12px; } .btn-group.open .dropdown-toggle { -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); } .btn-group.open .dropdown-toggle.btn-link { -webkit-box-shadow: none; box-shadow: none; } .btn .caret { margin-left: 0; } .btn-lg .caret { border-width: 5px 5px 0; border-bottom-width: 0; } .dropup .btn-lg .caret { border-width: 0 5px 5px; } .btn-group-vertical > .btn, .btn-group-vertical > .btn-group, .btn-group-vertical > .btn-group > .btn { display: block; float: none; width: 100%; max-width: 100%; } .btn-group-vertical > .btn-group > .btn { float: none; } .btn-group-vertical > .btn + .btn, .btn-group-vertical > .btn + .btn-group, .btn-group-vertical > .btn-group + .btn, .btn-group-vertical > .btn-group + .btn-group { margin-top: -1px; margin-left: 0; } .btn-group-vertical > .btn:not(:first-child):not(:last-child) { border-radius: 0; } .btn-group-vertical > .btn:first-child:not(:last-child) { border-top-right-radius: 2px; border-top-left-radius: 2px; border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .btn-group-vertical > .btn:last-child:not(:first-child) { border-top-right-radius: 0; border-top-left-radius: 0; border-bottom-right-radius: 2px; border-bottom-left-radius: 2px; } .btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn { border-radius: 0; } .btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child, .btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle { border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child { border-top-right-radius: 0; border-top-left-radius: 0; } .btn-group-justified { display: table; width: 100%; table-layout: fixed; border-collapse: separate; } .btn-group-justified > .btn, .btn-group-justified > .btn-group { float: none; display: table-cell; width: 1%; } .btn-group-justified > .btn-group .btn { width: 100%; } .btn-group-justified > .btn-group .dropdown-menu { left: auto; } [data-toggle="buttons"] > .btn input[type="radio"], [data-toggle="buttons"] > .btn-group > .btn input[type="radio"], [data-toggle="buttons"] > .btn input[type="checkbox"], [data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] { position: absolute; clip: rect(0, 0, 0, 0); pointer-events: none; } .input-group { position: relative; display: table; border-collapse: separate; } .input-group[class\*="col-"] { float: none; padding-left: 0; padding-right: 0; } .input-group .form-control { position: relative; z-index: 2; float: left; width: 100%; margin-bottom: 0; } .input-group .form-control:focus { z-index: 3; } .input-group-lg > .form-control, .input-group-lg > .input-group-addon, .input-group-lg > .input-group-btn > .btn { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } select.input-group-lg > .form-control, select.input-group-lg > .input-group-addon, select.input-group-lg > .input-group-btn > .btn { height: 45px; line-height: 45px; } textarea.input-group-lg > .form-control, textarea.input-group-lg > .input-group-addon, textarea.input-group-lg > .input-group-btn > .btn, select[multiple].input-group-lg > .form-control, select[multiple].input-group-lg > .input-group-addon, select[multiple].input-group-lg > .input-group-btn > .btn { height: auto; } .input-group-sm > .form-control, .input-group-sm > .input-group-addon, .input-group-sm > .input-group-btn > .btn { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } select.input-group-sm > .form-control, select.input-group-sm > .input-group-addon, select.input-group-sm > .input-group-btn > .btn { height: 30px; line-height: 30px; } textarea.input-group-sm > .form-control, textarea.input-group-sm > .input-group-addon, textarea.input-group-sm > .input-group-btn > .btn, select[multiple].input-group-sm > .form-control, select[multiple].input-group-sm > .input-group-addon, select[multiple].input-group-sm > .input-group-btn > .btn { height: auto; } .input-group-addon, .input-group-btn, .input-group .form-control { display: table-cell; } .input-group-addon:not(:first-child):not(:last-child), .input-group-btn:not(:first-child):not(:last-child), .input-group .form-control:not(:first-child):not(:last-child) { border-radius: 0; } .input-group-addon, .input-group-btn { width: 1%; white-space: nowrap; vertical-align: middle; } .input-group-addon { padding: 6px 12px; font-size: 13px; font-weight: normal; line-height: 1; color: #555555; text-align: center; background-color: #eeeeee; border: 1px solid #ccc; border-radius: 2px; } .input-group-addon.input-sm { padding: 5px 10px; font-size: 12px; border-radius: 1px; } .input-group-addon.input-lg { padding: 10px 16px; font-size: 17px; border-radius: 3px; } .input-group-addon input[type="radio"], .input-group-addon input[type="checkbox"] { margin-top: 0; } .input-group .form-control:first-child, .input-group-addon:first-child, .input-group-btn:first-child > .btn, .input-group-btn:first-child > .btn-group > .btn, .input-group-btn:first-child > .dropdown-toggle, .input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle), .input-group-btn:last-child > .btn-group:not(:last-child) > .btn { border-bottom-right-radius: 0; border-top-right-radius: 0; } .input-group-addon:first-child { border-right: 0; } .input-group .form-control:last-child, .input-group-addon:last-child, .input-group-btn:last-child > .btn, .input-group-btn:last-child > .btn-group > .btn, .input-group-btn:last-child > .dropdown-toggle, .input-group-btn:first-child > .btn:not(:first-child), .input-group-btn:first-child > .btn-group:not(:first-child) > .btn { border-bottom-left-radius: 0; border-top-left-radius: 0; } .input-group-addon:last-child { border-left: 0; } .input-group-btn { position: relative; font-size: 0; white-space: nowrap; } .input-group-btn > .btn { position: relative; } .input-group-btn > .btn + .btn { margin-left: -1px; } .input-group-btn > .btn:hover, .input-group-btn > .btn:focus, .input-group-btn > .btn:active { z-index: 2; } .input-group-btn:first-child > .btn, .input-group-btn:first-child > .btn-group { margin-right: -1px; } .input-group-btn:last-child > .btn, .input-group-btn:last-child > .btn-group { z-index: 2; margin-left: -1px; } .nav { margin-bottom: 0; padding-left: 0; list-style: none; } .nav > li { position: relative; display: block; } .nav > li > a { position: relative; display: block; padding: 10px 15px; } .nav > li > a:hover, .nav > li > a:focus { text-decoration: none; background-color: #eeeeee; } .nav > li.disabled > a { color: #777777; } .nav > li.disabled > a:hover, .nav > li.disabled > a:focus { color: #777777; text-decoration: none; background-color: transparent; cursor: not-allowed; } .nav .open > a, .nav .open > a:hover, .nav .open > a:focus { background-color: #eeeeee; border-color: #337ab7; } .nav .nav-divider { height: 1px; margin: 8px 0; overflow: hidden; background-color: #e5e5e5; } .nav > li > a > img { max-width: none; } .nav-tabs { border-bottom: 1px solid #ddd; } .nav-tabs > li { float: left; margin-bottom: -1px; } .nav-tabs > li > a { margin-right: 2px; line-height: 1.42857143; border: 1px solid transparent; border-radius: 2px 2px 0 0; } .nav-tabs > li > a:hover { border-color: #eeeeee #eeeeee #ddd; } .nav-tabs > li.active > a, .nav-tabs > li.active > a:hover, .nav-tabs > li.active > a:focus { color: #555555; background-color: #fff; border: 1px solid #ddd; border-bottom-color: transparent; cursor: default; } .nav-tabs.nav-justified { width: 100%; border-bottom: 0; } .nav-tabs.nav-justified > li { float: none; } .nav-tabs.nav-justified > li > a { text-align: center; margin-bottom: 5px; } .nav-tabs.nav-justified > .dropdown .dropdown-menu { top: auto; left: auto; } @media (min-width: 768px) { .nav-tabs.nav-justified > li { display: table-cell; width: 1%; } .nav-tabs.nav-justified > li > a { margin-bottom: 0; } } .nav-tabs.nav-justified > li > a { margin-right: 0; border-radius: 2px; } .nav-tabs.nav-justified > .active > a, .nav-tabs.nav-justified > .active > a:hover, .nav-tabs.nav-justified > .active > a:focus { border: 1px solid #ddd; } @media (min-width: 768px) { .nav-tabs.nav-justified > li > a { border-bottom: 1px solid #ddd; border-radius: 2px 2px 0 0; } .nav-tabs.nav-justified > .active > a, .nav-tabs.nav-justified > .active > a:hover, .nav-tabs.nav-justified > .active > a:focus { border-bottom-color: #fff; } } .nav-pills > li { float: left; } .nav-pills > li > a { border-radius: 2px; } .nav-pills > li + li { margin-left: 2px; } .nav-pills > li.active > a, .nav-pills > li.active > a:hover, .nav-pills > li.active > a:focus { color: #fff; background-color: #337ab7; } .nav-stacked > li { float: none; } .nav-stacked > li + li { margin-top: 2px; margin-left: 0; } .nav-justified { width: 100%; } .nav-justified > li { float: none; } .nav-justified > li > a { text-align: center; margin-bottom: 5px; } .nav-justified > .dropdown .dropdown-menu { top: auto; left: auto; } @media (min-width: 768px) { .nav-justified > li { display: table-cell; width: 1%; } .nav-justified > li > a { margin-bottom: 0; } } .nav-tabs-justified { border-bottom: 0; } .nav-tabs-justified > li > a { margin-right: 0; border-radius: 2px; } .nav-tabs-justified > .active > a, .nav-tabs-justified > .active > a:hover, .nav-tabs-justified > .active > a:focus { border: 1px solid #ddd; } @media (min-width: 768px) { .nav-tabs-justified > li > a { border-bottom: 1px solid #ddd; border-radius: 2px 2px 0 0; } .nav-tabs-justified > .active > a, .nav-tabs-justified > .active > a:hover, .nav-tabs-justified > .active > a:focus { border-bottom-color: #fff; } } .tab-content > .tab-pane { display: none; } .tab-content > .active { display: block; } .nav-tabs .dropdown-menu { margin-top: -1px; border-top-right-radius: 0; border-top-left-radius: 0; } .navbar { position: relative; min-height: 30px; margin-bottom: 18px; border: 1px solid transparent; } @media (min-width: 541px) { .navbar { border-radius: 2px; } } @media (min-width: 541px) { .navbar-header { float: left; } } .navbar-collapse { overflow-x: visible; padding-right: 0px; padding-left: 0px; border-top: 1px solid transparent; box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1); -webkit-overflow-scrolling: touch; } .navbar-collapse.in { overflow-y: auto; } @media (min-width: 541px) { .navbar-collapse { width: auto; border-top: 0; box-shadow: none; } .navbar-collapse.collapse { display: block !important; height: auto !important; padding-bottom: 0; overflow: visible !important; } .navbar-collapse.in { overflow-y: visible; } .navbar-fixed-top .navbar-collapse, .navbar-static-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { padding-left: 0; padding-right: 0; } } .navbar-fixed-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { max-height: 340px; } @media (max-device-width: 540px) and (orientation: landscape) { .navbar-fixed-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { max-height: 200px; } } .container > .navbar-header, .container-fluid > .navbar-header, .container > .navbar-collapse, .container-fluid > .navbar-collapse { margin-right: 0px; margin-left: 0px; } @media (min-width: 541px) { .container > .navbar-header, .container-fluid > .navbar-header, .container > .navbar-collapse, .container-fluid > .navbar-collapse { margin-right: 0; margin-left: 0; } } .navbar-static-top { z-index: 1000; border-width: 0 0 1px; } @media (min-width: 541px) { .navbar-static-top { border-radius: 0; } } .navbar-fixed-top, .navbar-fixed-bottom { position: fixed; right: 0; left: 0; z-index: 1030; } @media (min-width: 541px) { .navbar-fixed-top, .navbar-fixed-bottom { border-radius: 0; } } .navbar-fixed-top { top: 0; border-width: 0 0 1px; } .navbar-fixed-bottom { bottom: 0; margin-bottom: 0; border-width: 1px 0 0; } .navbar-brand { float: left; padding: 6px 0px; font-size: 17px; line-height: 18px; height: 30px; } .navbar-brand:hover, .navbar-brand:focus { text-decoration: none; } .navbar-brand > img { display: block; } @media (min-width: 541px) { .navbar > .container .navbar-brand, .navbar > .container-fluid .navbar-brand { margin-left: 0px; } } .navbar-toggle { position: relative; float: right; margin-right: 0px; padding: 9px 10px; margin-top: -2px; margin-bottom: -2px; background-color: transparent; background-image: none; border: 1px solid transparent; border-radius: 2px; } .navbar-toggle:focus { outline: 0; } .navbar-toggle .icon-bar { display: block; width: 22px; height: 2px; border-radius: 1px; } .navbar-toggle .icon-bar + .icon-bar { margin-top: 4px; } @media (min-width: 541px) { .navbar-toggle { display: none; } } .navbar-nav { margin: 3px 0px; } .navbar-nav > li > a { padding-top: 10px; padding-bottom: 10px; line-height: 18px; } @media (max-width: 540px) { .navbar-nav .open .dropdown-menu { position: static; float: none; width: auto; margin-top: 0; background-color: transparent; border: 0; box-shadow: none; } .navbar-nav .open .dropdown-menu > li > a, .navbar-nav .open .dropdown-menu .dropdown-header { padding: 5px 15px 5px 25px; } .navbar-nav .open .dropdown-menu > li > a { line-height: 18px; } .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-nav .open .dropdown-menu > li > a:focus { background-image: none; } } @media (min-width: 541px) { .navbar-nav { float: left; margin: 0; } .navbar-nav > li { float: left; } .navbar-nav > li > a { padding-top: 6px; padding-bottom: 6px; } } .navbar-form { margin-left: 0px; margin-right: 0px; padding: 10px 0px; border-top: 1px solid transparent; border-bottom: 1px solid transparent; -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); margin-top: -1px; margin-bottom: -1px; } @media (min-width: 768px) { .navbar-form .form-group { display: inline-block; margin-bottom: 0; vertical-align: middle; } .navbar-form .form-control { display: inline-block; width: auto; vertical-align: middle; } .navbar-form .form-control-static { display: inline-block; } .navbar-form .input-group { display: inline-table; vertical-align: middle; } .navbar-form .input-group .input-group-addon, .navbar-form .input-group .input-group-btn, .navbar-form .input-group .form-control { width: auto; } .navbar-form .input-group > .form-control { width: 100%; } .navbar-form .control-label { margin-bottom: 0; vertical-align: middle; } .navbar-form .radio, .navbar-form .checkbox { display: inline-block; margin-top: 0; margin-bottom: 0; vertical-align: middle; } .navbar-form .radio label, .navbar-form .checkbox label { padding-left: 0; } .navbar-form .radio input[type="radio"], .navbar-form .checkbox input[type="checkbox"] { position: relative; margin-left: 0; } .navbar-form .has-feedback .form-control-feedback { top: 0; } } @media (max-width: 540px) { .navbar-form .form-group { margin-bottom: 5px; } .navbar-form .form-group:last-child { margin-bottom: 0; } } @media (min-width: 541px) { .navbar-form { width: auto; border: 0; margin-left: 0; margin-right: 0; padding-top: 0; padding-bottom: 0; -webkit-box-shadow: none; box-shadow: none; } } .navbar-nav > li > .dropdown-menu { margin-top: 0; border-top-right-radius: 0; border-top-left-radius: 0; } .navbar-fixed-bottom .navbar-nav > li > .dropdown-menu { margin-bottom: 0; border-top-right-radius: 2px; border-top-left-radius: 2px; border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .navbar-btn { margin-top: -1px; margin-bottom: -1px; } .navbar-btn.btn-sm { margin-top: 0px; margin-bottom: 0px; } .navbar-btn.btn-xs { margin-top: 4px; margin-bottom: 4px; } .navbar-text { margin-top: 6px; margin-bottom: 6px; } @media (min-width: 541px) { .navbar-text { float: left; margin-left: 0px; margin-right: 0px; } } @media (min-width: 541px) { .navbar-left { float: left !important; float: left; } .navbar-right { float: right !important; float: right; margin-right: 0px; } .navbar-right ~ .navbar-right { margin-right: 0; } } .navbar-default { background-color: #f8f8f8; border-color: #e7e7e7; } .navbar-default .navbar-brand { color: #777; } .navbar-default .navbar-brand:hover, .navbar-default .navbar-brand:focus { color: #5e5e5e; background-color: transparent; } .navbar-default .navbar-text { color: #777; } .navbar-default .navbar-nav > li > a { color: #777; } .navbar-default .navbar-nav > li > a:hover, .navbar-default .navbar-nav > li > a:focus { color: #333; background-color: transparent; } .navbar-default .navbar-nav > .active > a, .navbar-default .navbar-nav > .active > a:hover, .navbar-default .navbar-nav > .active > a:focus { color: #555; background-color: #e7e7e7; } .navbar-default .navbar-nav > .disabled > a, .navbar-default .navbar-nav > .disabled > a:hover, .navbar-default .navbar-nav > .disabled > a:focus { color: #ccc; background-color: transparent; } .navbar-default .navbar-toggle { border-color: #ddd; } .navbar-default .navbar-toggle:hover, .navbar-default .navbar-toggle:focus { background-color: #ddd; } .navbar-default .navbar-toggle .icon-bar { background-color: #888; } .navbar-default .navbar-collapse, .navbar-default .navbar-form { border-color: #e7e7e7; } .navbar-default .navbar-nav > .open > a, .navbar-default .navbar-nav > .open > a:hover, .navbar-default .navbar-nav > .open > a:focus { background-color: #e7e7e7; color: #555; } @media (max-width: 540px) { .navbar-default .navbar-nav .open .dropdown-menu > li > a { color: #777; } .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus { color: #333; background-color: transparent; } .navbar-default .navbar-nav .open .dropdown-menu > .active > a, .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus { color: #555; background-color: #e7e7e7; } .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a, .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus { color: #ccc; background-color: transparent; } } .navbar-default .navbar-link { color: #777; } .navbar-default .navbar-link:hover { color: #333; } .navbar-default .btn-link { color: #777; } .navbar-default .btn-link:hover, .navbar-default .btn-link:focus { color: #333; } .navbar-default .btn-link[disabled]:hover, fieldset[disabled] .navbar-default .btn-link:hover, .navbar-default .btn-link[disabled]:focus, fieldset[disabled] .navbar-default .btn-link:focus { color: #ccc; } .navbar-inverse { background-color: #222; border-color: #080808; } .navbar-inverse .navbar-brand { color: #9d9d9d; } .navbar-inverse .navbar-brand:hover, .navbar-inverse .navbar-brand:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-text { color: #9d9d9d; } .navbar-inverse .navbar-nav > li > a { color: #9d9d9d; } .navbar-inverse .navbar-nav > li > a:hover, .navbar-inverse .navbar-nav > li > a:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-nav > .active > a, .navbar-inverse .navbar-nav > .active > a:hover, .navbar-inverse .navbar-nav > .active > a:focus { color: #fff; background-color: #080808; } .navbar-inverse .navbar-nav > .disabled > a, .navbar-inverse .navbar-nav > .disabled > a:hover, .navbar-inverse .navbar-nav > .disabled > a:focus { color: #444; background-color: transparent; } .navbar-inverse .navbar-toggle { border-color: #333; } .navbar-inverse .navbar-toggle:hover, .navbar-inverse .navbar-toggle:focus { background-color: #333; } .navbar-inverse .navbar-toggle .icon-bar { background-color: #fff; } .navbar-inverse .navbar-collapse, .navbar-inverse .navbar-form { border-color: #101010; } .navbar-inverse .navbar-nav > .open > a, .navbar-inverse .navbar-nav > .open > a:hover, .navbar-inverse .navbar-nav > .open > a:focus { background-color: #080808; color: #fff; } @media (max-width: 540px) { .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header { border-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu .divider { background-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu > li > a { color: #9d9d9d; } .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a, .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus { color: #fff; background-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a, .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus { color: #444; background-color: transparent; } } .navbar-inverse .navbar-link { color: #9d9d9d; } .navbar-inverse .navbar-link:hover { color: #fff; } .navbar-inverse .btn-link { color: #9d9d9d; } .navbar-inverse .btn-link:hover, .navbar-inverse .btn-link:focus { color: #fff; } .navbar-inverse .btn-link[disabled]:hover, fieldset[disabled] .navbar-inverse .btn-link:hover, .navbar-inverse .btn-link[disabled]:focus, fieldset[disabled] .navbar-inverse .btn-link:focus { color: #444; } .breadcrumb { padding: 8px 15px; margin-bottom: 18px; list-style: none; background-color: #f5f5f5; border-radius: 2px; } .breadcrumb > li { display: inline-block; } .breadcrumb > li + li:before { content: "/\00a0"; padding: 0 5px; color: #5e5e5e; } .breadcrumb > .active { color: #777777; } .pagination { display: inline-block; padding-left: 0; margin: 18px 0; border-radius: 2px; } .pagination > li { display: inline; } .pagination > li > a, .pagination > li > span { position: relative; float: left; padding: 6px 12px; line-height: 1.42857143; text-decoration: none; color: #337ab7; background-color: #fff; border: 1px solid #ddd; margin-left: -1px; } .pagination > li:first-child > a, .pagination > li:first-child > span { margin-left: 0; border-bottom-left-radius: 2px; border-top-left-radius: 2px; } .pagination > li:last-child > a, .pagination > li:last-child > span { border-bottom-right-radius: 2px; border-top-right-radius: 2px; } .pagination > li > a:hover, .pagination > li > span:hover, .pagination > li > a:focus, .pagination > li > span:focus { z-index: 2; color: #23527c; background-color: #eeeeee; border-color: #ddd; } .pagination > .active > a, .pagination > .active > span, .pagination > .active > a:hover, .pagination > .active > span:hover, .pagination > .active > a:focus, .pagination > .active > span:focus { z-index: 3; color: #fff; background-color: #337ab7; border-color: #337ab7; cursor: default; } .pagination > .disabled > span, .pagination > .disabled > span:hover, .pagination > .disabled > span:focus, .pagination > .disabled > a, .pagination > .disabled > a:hover, .pagination > .disabled > a:focus { color: #777777; background-color: #fff; border-color: #ddd; cursor: not-allowed; } .pagination-lg > li > a, .pagination-lg > li > span { padding: 10px 16px; font-size: 17px; line-height: 1.3333333; } .pagination-lg > li:first-child > a, .pagination-lg > li:first-child > span { border-bottom-left-radius: 3px; border-top-left-radius: 3px; } .pagination-lg > li:last-child > a, .pagination-lg > li:last-child > span { border-bottom-right-radius: 3px; border-top-right-radius: 3px; } .pagination-sm > li > a, .pagination-sm > li > span { padding: 5px 10px; font-size: 12px; line-height: 1.5; } .pagination-sm > li:first-child > a, .pagination-sm > li:first-child > span { border-bottom-left-radius: 1px; border-top-left-radius: 1px; } .pagination-sm > li:last-child > a, .pagination-sm > li:last-child > span { border-bottom-right-radius: 1px; border-top-right-radius: 1px; } .pager { padding-left: 0; margin: 18px 0; list-style: none; text-align: center; } .pager li { display: inline; } .pager li > a, .pager li > span { display: inline-block; padding: 5px 14px; background-color: #fff; border: 1px solid #ddd; border-radius: 15px; } .pager li > a:hover, .pager li > a:focus { text-decoration: none; background-color: #eeeeee; } .pager .next > a, .pager .next > span { float: right; } .pager .previous > a, .pager .previous > span { float: left; } .pager .disabled > a, .pager .disabled > a:hover, .pager .disabled > a:focus, .pager .disabled > span { color: #777777; background-color: #fff; cursor: not-allowed; } .label { display: inline; padding: .2em .6em .3em; font-size: 75%; font-weight: bold; line-height: 1; color: #fff; text-align: center; white-space: nowrap; vertical-align: baseline; border-radius: .25em; } a.label:hover, a.label:focus { color: #fff; text-decoration: none; cursor: pointer; } .label:empty { display: none; } .btn .label { position: relative; top: -1px; } .label-default { background-color: #777777; } .label-default[href]:hover, .label-default[href]:focus { background-color: #5e5e5e; } .label-primary { background-color: #337ab7; } .label-primary[href]:hover, .label-primary[href]:focus { background-color: #286090; } .label-success { background-color: #5cb85c; } .label-success[href]:hover, .label-success[href]:focus { background-color: #449d44; } .label-info { background-color: #5bc0de; } .label-info[href]:hover, .label-info[href]:focus { background-color: #31b0d5; } .label-warning { background-color: #f0ad4e; } .label-warning[href]:hover, .label-warning[href]:focus { background-color: #ec971f; } .label-danger { background-color: #d9534f; } .label-danger[href]:hover, .label-danger[href]:focus { background-color: #c9302c; } .badge { display: inline-block; min-width: 10px; padding: 3px 7px; font-size: 12px; font-weight: bold; color: #fff; line-height: 1; vertical-align: middle; white-space: nowrap; text-align: center; background-color: #777777; border-radius: 10px; } .badge:empty { display: none; } .btn .badge { position: relative; top: -1px; } .btn-xs .badge, .btn-group-xs > .btn .badge { top: 0; padding: 1px 5px; } a.badge:hover, a.badge:focus { color: #fff; text-decoration: none; cursor: pointer; } .list-group-item.active > .badge, .nav-pills > .active > a > .badge { color: #337ab7; background-color: #fff; } .list-group-item > .badge { float: right; } .list-group-item > .badge + .badge { margin-right: 5px; } .nav-pills > li > a > .badge { margin-left: 3px; } .jumbotron { padding-top: 30px; padding-bottom: 30px; margin-bottom: 30px; color: inherit; background-color: #eeeeee; } .jumbotron h1, .jumbotron .h1 { color: inherit; } .jumbotron p { margin-bottom: 15px; font-size: 20px; font-weight: 200; } .jumbotron > hr { border-top-color: #d5d5d5; } .container .jumbotron, .container-fluid .jumbotron { border-radius: 3px; padding-left: 0px; padding-right: 0px; } .jumbotron .container { max-width: 100%; } @media screen and (min-width: 768px) { .jumbotron { padding-top: 48px; padding-bottom: 48px; } .container .jumbotron, .container-fluid .jumbotron { padding-left: 60px; padding-right: 60px; } .jumbotron h1, .jumbotron .h1 { font-size: 59px; } } .thumbnail { display: block; padding: 4px; margin-bottom: 18px; line-height: 1.42857143; background-color: #fff; border: 1px solid #ddd; border-radius: 2px; -webkit-transition: border 0.2s ease-in-out; -o-transition: border 0.2s ease-in-out; transition: border 0.2s ease-in-out; } .thumbnail > img, .thumbnail a > img { margin-left: auto; margin-right: auto; } a.thumbnail:hover, a.thumbnail:focus, a.thumbnail.active { border-color: #337ab7; } .thumbnail .caption { padding: 9px; color: #000; } .alert { padding: 15px; margin-bottom: 18px; border: 1px solid transparent; border-radius: 2px; } .alert h4 { margin-top: 0; color: inherit; } .alert .alert-link { font-weight: bold; } .alert > p, .alert > ul { margin-bottom: 0; } .alert > p + p { margin-top: 5px; } .alert-dismissable, .alert-dismissible { padding-right: 35px; } .alert-dismissable .close, .alert-dismissible .close { position: relative; top: -2px; right: -21px; color: inherit; } .alert-success { background-color: #dff0d8; border-color: #d6e9c6; color: #3c763d; } .alert-success hr { border-top-color: #c9e2b3; } .alert-success .alert-link { color: #2b542c; } .alert-info { background-color: #d9edf7; border-color: #bce8f1; color: #31708f; } .alert-info hr { border-top-color: #a6e1ec; } .alert-info .alert-link { color: #245269; } .alert-warning { background-color: #fcf8e3; border-color: #faebcc; color: #8a6d3b; } .alert-warning hr { border-top-color: #f7e1b5; } .alert-warning .alert-link { color: #66512c; } .alert-danger { background-color: #f2dede; border-color: #ebccd1; color: #a94442; } .alert-danger hr { border-top-color: #e4b9c0; } .alert-danger .alert-link { color: #843534; } @-webkit-keyframes progress-bar-stripes { from { background-position: 40px 0; } to { background-position: 0 0; } } @keyframes progress-bar-stripes { from { background-position: 40px 0; } to { background-position: 0 0; } } .progress { overflow: hidden; height: 18px; margin-bottom: 18px; background-color: #f5f5f5; border-radius: 2px; -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); } .progress-bar { float: left; width: 0%; height: 100%; font-size: 12px; line-height: 18px; color: #fff; text-align: center; background-color: #337ab7; -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); -webkit-transition: width 0.6s ease; -o-transition: width 0.6s ease; transition: width 0.6s ease; } .progress-striped .progress-bar, .progress-bar-striped { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-size: 40px 40px; } .progress.active .progress-bar, .progress-bar.active { -webkit-animation: progress-bar-stripes 2s linear infinite; -o-animation: progress-bar-stripes 2s linear infinite; animation: progress-bar-stripes 2s linear infinite; } .progress-bar-success { background-color: #5cb85c; } .progress-striped .progress-bar-success { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-info { background-color: #5bc0de; } .progress-striped .progress-bar-info { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-warning { background-color: #f0ad4e; } .progress-striped .progress-bar-warning { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-danger { background-color: #d9534f; } .progress-striped .progress-bar-danger { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .media { margin-top: 15px; } .media:first-child { margin-top: 0; } .media, .media-body { zoom: 1; overflow: hidden; } .media-body { width: 10000px; } .media-object { display: block; } .media-object.img-thumbnail { max-width: none; } .media-right, .media > .pull-right { padding-left: 10px; } .media-left, .media > .pull-left { padding-right: 10px; } .media-left, .media-right, .media-body { display: table-cell; vertical-align: top; } .media-middle { vertical-align: middle; } .media-bottom { vertical-align: bottom; } .media-heading { margin-top: 0; margin-bottom: 5px; } .media-list { padding-left: 0; list-style: none; } .list-group { margin-bottom: 20px; padding-left: 0; } .list-group-item { position: relative; display: block; padding: 10px 15px; margin-bottom: -1px; background-color: #fff; border: 1px solid #ddd; } .list-group-item:first-child { border-top-right-radius: 2px; border-top-left-radius: 2px; } .list-group-item:last-child { margin-bottom: 0; border-bottom-right-radius: 2px; border-bottom-left-radius: 2px; } a.list-group-item, button.list-group-item { color: #555; } a.list-group-item .list-group-item-heading, button.list-group-item .list-group-item-heading { color: #333; } a.list-group-item:hover, button.list-group-item:hover, a.list-group-item:focus, button.list-group-item:focus { text-decoration: none; color: #555; background-color: #f5f5f5; } button.list-group-item { width: 100%; text-align: left; } .list-group-item.disabled, .list-group-item.disabled:hover, .list-group-item.disabled:focus { background-color: #eeeeee; color: #777777; cursor: not-allowed; } .list-group-item.disabled .list-group-item-heading, .list-group-item.disabled:hover .list-group-item-heading, .list-group-item.disabled:focus .list-group-item-heading { color: inherit; } .list-group-item.disabled .list-group-item-text, .list-group-item.disabled:hover .list-group-item-text, .list-group-item.disabled:focus .list-group-item-text { color: #777777; } .list-group-item.active, .list-group-item.active:hover, .list-group-item.active:focus { z-index: 2; color: #fff; background-color: #337ab7; border-color: #337ab7; } .list-group-item.active .list-group-item-heading, .list-group-item.active:hover .list-group-item-heading, .list-group-item.active:focus .list-group-item-heading, .list-group-item.active .list-group-item-heading > small, .list-group-item.active:hover .list-group-item-heading > small, .list-group-item.active:focus .list-group-item-heading > small, .list-group-item.active .list-group-item-heading > .small, .list-group-item.active:hover .list-group-item-heading > .small, .list-group-item.active:focus .list-group-item-heading > .small { color: inherit; } .list-group-item.active .list-group-item-text, .list-group-item.active:hover .list-group-item-text, .list-group-item.active:focus .list-group-item-text { color: #c7ddef; } .list-group-item-success { color: #3c763d; background-color: #dff0d8; } a.list-group-item-success, button.list-group-item-success { color: #3c763d; } a.list-group-item-success .list-group-item-heading, button.list-group-item-success .list-group-item-heading { color: inherit; } a.list-group-item-success:hover, button.list-group-item-success:hover, a.list-group-item-success:focus, button.list-group-item-success:focus { color: #3c763d; background-color: #d0e9c6; } a.list-group-item-success.active, button.list-group-item-success.active, a.list-group-item-success.active:hover, button.list-group-item-success.active:hover, a.list-group-item-success.active:focus, button.list-group-item-success.active:focus { color: #fff; background-color: #3c763d; border-color: #3c763d; } .list-group-item-info { color: #31708f; background-color: #d9edf7; } a.list-group-item-info, button.list-group-item-info { color: #31708f; } a.list-group-item-info .list-group-item-heading, button.list-group-item-info .list-group-item-heading { color: inherit; } a.list-group-item-info:hover, button.list-group-item-info:hover, a.list-group-item-info:focus, button.list-group-item-info:focus { color: #31708f; background-color: #c4e3f3; } a.list-group-item-info.active, button.list-group-item-info.active, a.list-group-item-info.active:hover, button.list-group-item-info.active:hover, a.list-group-item-info.active:focus, button.list-group-item-info.active:focus { color: #fff; background-color: #31708f; border-color: #31708f; } .list-group-item-warning { color: #8a6d3b; background-color: #fcf8e3; } a.list-group-item-warning, button.list-group-item-warning { color: #8a6d3b; } a.list-group-item-warning .list-group-item-heading, button.list-group-item-warning .list-group-item-heading { color: inherit; } a.list-group-item-warning:hover, button.list-group-item-warning:hover, a.list-group-item-warning:focus, button.list-group-item-warning:focus { color: #8a6d3b; background-color: #faf2cc; } a.list-group-item-warning.active, button.list-group-item-warning.active, a.list-group-item-warning.active:hover, button.list-group-item-warning.active:hover, a.list-group-item-warning.active:focus, button.list-group-item-warning.active:focus { color: #fff; background-color: #8a6d3b; border-color: #8a6d3b; } .list-group-item-danger { color: #a94442; background-color: #f2dede; } a.list-group-item-danger, button.list-group-item-danger { color: #a94442; } a.list-group-item-danger .list-group-item-heading, button.list-group-item-danger .list-group-item-heading { color: inherit; } a.list-group-item-danger:hover, button.list-group-item-danger:hover, a.list-group-item-danger:focus, button.list-group-item-danger:focus { color: #a94442; background-color: #ebcccc; } a.list-group-item-danger.active, button.list-group-item-danger.active, a.list-group-item-danger.active:hover, button.list-group-item-danger.active:hover, a.list-group-item-danger.active:focus, button.list-group-item-danger.active:focus { color: #fff; background-color: #a94442; border-color: #a94442; } .list-group-item-heading { margin-top: 0; margin-bottom: 5px; } .list-group-item-text { margin-bottom: 0; line-height: 1.3; } .panel { margin-bottom: 18px; background-color: #fff; border: 1px solid transparent; border-radius: 2px; -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); } .panel-body { padding: 15px; } .panel-heading { padding: 10px 15px; border-bottom: 1px solid transparent; border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel-heading > .dropdown .dropdown-toggle { color: inherit; } .panel-title { margin-top: 0; margin-bottom: 0; font-size: 15px; color: inherit; } .panel-title > a, .panel-title > small, .panel-title > .small, .panel-title > small > a, .panel-title > .small > a { color: inherit; } .panel-footer { padding: 10px 15px; background-color: #f5f5f5; border-top: 1px solid #ddd; border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .list-group, .panel > .panel-collapse > .list-group { margin-bottom: 0; } .panel > .list-group .list-group-item, .panel > .panel-collapse > .list-group .list-group-item { border-width: 1px 0; border-radius: 0; } .panel > .list-group:first-child .list-group-item:first-child, .panel > .panel-collapse > .list-group:first-child .list-group-item:first-child { border-top: 0; border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel > .list-group:last-child .list-group-item:last-child, .panel > .panel-collapse > .list-group:last-child .list-group-item:last-child { border-bottom: 0; border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child { border-top-right-radius: 0; border-top-left-radius: 0; } .panel-heading + .list-group .list-group-item:first-child { border-top-width: 0; } .list-group + .panel-footer { border-top-width: 0; } .panel > .table, .panel > .table-responsive > .table, .panel > .panel-collapse > .table { margin-bottom: 0; } .panel > .table caption, .panel > .table-responsive > .table caption, .panel > .panel-collapse > .table caption { padding-left: 15px; padding-right: 15px; } .panel > .table:first-child, .panel > .table-responsive:first-child > .table:first-child { border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child { border-top-left-radius: 1px; border-top-right-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child td:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child td:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child, .panel > .table:first-child > thead:first-child > tr:first-child th:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child th:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child { border-top-left-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child td:last-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child, .panel > .table:first-child > tbody:first-child > tr:first-child td:last-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child, .panel > .table:first-child > thead:first-child > tr:first-child th:last-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child, .panel > .table:first-child > tbody:first-child > tr:first-child th:last-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child { border-top-right-radius: 1px; } .panel > .table:last-child, .panel > .table-responsive:last-child > .table:last-child { border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child { border-bottom-left-radius: 1px; border-bottom-right-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child td:first-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child, .panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child, .panel > .table:last-child > tbody:last-child > tr:last-child th:first-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child, .panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child { border-bottom-left-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child td:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child, .panel > .table:last-child > tbody:last-child > tr:last-child th:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child { border-bottom-right-radius: 1px; } .panel > .panel-body + .table, .panel > .panel-body + .table-responsive, .panel > .table + .panel-body, .panel > .table-responsive + .panel-body { border-top: 1px solid #ddd; } .panel > .table > tbody:first-child > tr:first-child th, .panel > .table > tbody:first-child > tr:first-child td { border-top: 0; } .panel > .table-bordered, .panel > .table-responsive > .table-bordered { border: 0; } .panel > .table-bordered > thead > tr > th:first-child, .panel > .table-responsive > .table-bordered > thead > tr > th:first-child, .panel > .table-bordered > tbody > tr > th:first-child, .panel > .table-responsive > .table-bordered > tbody > tr > th:first-child, .panel > .table-bordered > tfoot > tr > th:first-child, .panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child, .panel > .table-bordered > thead > tr > td:first-child, .panel > .table-responsive > .table-bordered > thead > tr > td:first-child, .panel > .table-bordered > tbody > tr > td:first-child, .panel > .table-responsive > .table-bordered > tbody > tr > td:first-child, .panel > .table-bordered > tfoot > tr > td:first-child, .panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child { border-left: 0; } .panel > .table-bordered > thead > tr > th:last-child, .panel > .table-responsive > .table-bordered > thead > tr > th:last-child, .panel > .table-bordered > tbody > tr > th:last-child, .panel > .table-responsive > .table-bordered > tbody > tr > th:last-child, .panel > .table-bordered > tfoot > tr > th:last-child, .panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child, .panel > .table-bordered > thead > tr > td:last-child, .panel > .table-responsive > .table-bordered > thead > tr > td:last-child, .panel > .table-bordered > tbody > tr > td:last-child, .panel > .table-responsive > .table-bordered > tbody > tr > td:last-child, .panel > .table-bordered > tfoot > tr > td:last-child, .panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child { border-right: 0; } .panel > .table-bordered > thead > tr:first-child > td, .panel > .table-responsive > .table-bordered > thead > tr:first-child > td, .panel > .table-bordered > tbody > tr:first-child > td, .panel > .table-responsive > .table-bordered > tbody > tr:first-child > td, .panel > .table-bordered > thead > tr:first-child > th, .panel > .table-responsive > .table-bordered > thead > tr:first-child > th, .panel > .table-bordered > tbody > tr:first-child > th, .panel > .table-responsive > .table-bordered > tbody > tr:first-child > th { border-bottom: 0; } .panel > .table-bordered > tbody > tr:last-child > td, .panel > .table-responsive > .table-bordered > tbody > tr:last-child > td, .panel > .table-bordered > tfoot > tr:last-child > td, .panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td, .panel > .table-bordered > tbody > tr:last-child > th, .panel > .table-responsive > .table-bordered > tbody > tr:last-child > th, .panel > .table-bordered > tfoot > tr:last-child > th, .panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th { border-bottom: 0; } .panel > .table-responsive { border: 0; margin-bottom: 0; } .panel-group { margin-bottom: 18px; } .panel-group .panel { margin-bottom: 0; border-radius: 2px; } .panel-group .panel + .panel { margin-top: 5px; } .panel-group .panel-heading { border-bottom: 0; } .panel-group .panel-heading + .panel-collapse > .panel-body, .panel-group .panel-heading + .panel-collapse > .list-group { border-top: 1px solid #ddd; } .panel-group .panel-footer { border-top: 0; } .panel-group .panel-footer + .panel-collapse .panel-body { border-bottom: 1px solid #ddd; } .panel-default { border-color: #ddd; } .panel-default > .panel-heading { color: #333333; background-color: #f5f5f5; border-color: #ddd; } .panel-default > .panel-heading + .panel-collapse > .panel-body { border-top-color: #ddd; } .panel-default > .panel-heading .badge { color: #f5f5f5; background-color: #333333; } .panel-default > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #ddd; } .panel-primary { border-color: #337ab7; } .panel-primary > .panel-heading { color: #fff; background-color: #337ab7; border-color: #337ab7; } .panel-primary > .panel-heading + .panel-collapse > .panel-body { border-top-color: #337ab7; } .panel-primary > .panel-heading .badge { color: #337ab7; background-color: #fff; } .panel-primary > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #337ab7; } .panel-success { border-color: #d6e9c6; } .panel-success > .panel-heading { color: #3c763d; background-color: #dff0d8; border-color: #d6e9c6; } .panel-success > .panel-heading + .panel-collapse > .panel-body { border-top-color: #d6e9c6; } .panel-success > .panel-heading .badge { color: #dff0d8; background-color: #3c763d; } .panel-success > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #d6e9c6; } .panel-info { border-color: #bce8f1; } .panel-info > .panel-heading { color: #31708f; background-color: #d9edf7; border-color: #bce8f1; } .panel-info > .panel-heading + .panel-collapse > .panel-body { border-top-color: #bce8f1; } .panel-info > .panel-heading .badge { color: #d9edf7; background-color: #31708f; } .panel-info > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #bce8f1; } .panel-warning { border-color: #faebcc; } .panel-warning > .panel-heading { color: #8a6d3b; background-color: #fcf8e3; border-color: #faebcc; } .panel-warning > .panel-heading + .panel-collapse > .panel-body { border-top-color: #faebcc; } .panel-warning > .panel-heading .badge { color: #fcf8e3; background-color: #8a6d3b; } .panel-warning > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #faebcc; } .panel-danger { border-color: #ebccd1; } .panel-danger > .panel-heading { color: #a94442; background-color: #f2dede; border-color: #ebccd1; } .panel-danger > .panel-heading + .panel-collapse > .panel-body { border-top-color: #ebccd1; } .panel-danger > .panel-heading .badge { color: #f2dede; background-color: #a94442; } .panel-danger > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #ebccd1; } .embed-responsive { position: relative; display: block; height: 0; padding: 0; overflow: hidden; } .embed-responsive .embed-responsive-item, .embed-responsive iframe, .embed-responsive embed, .embed-responsive object, .embed-responsive video { position: absolute; top: 0; left: 0; bottom: 0; height: 100%; width: 100%; border: 0; } .embed-responsive-16by9 { padding-bottom: 56.25%; } .embed-responsive-4by3 { padding-bottom: 75%; } .well { min-height: 20px; padding: 19px; margin-bottom: 20px; background-color: #f5f5f5; border: 1px solid #e3e3e3; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); } .well blockquote { border-color: #ddd; border-color: rgba(0, 0, 0, 0.15); } .well-lg { padding: 24px; border-radius: 3px; } .well-sm { padding: 9px; border-radius: 1px; } .close { float: right; font-size: 19.5px; font-weight: bold; line-height: 1; color: #000; text-shadow: 0 1px 0 #fff; opacity: 0.2; filter: alpha(opacity=20); } .close:hover, .close:focus { color: #000; text-decoration: none; cursor: pointer; opacity: 0.5; filter: alpha(opacity=50); } button.close { padding: 0; cursor: pointer; background: transparent; border: 0; -webkit-appearance: none; } .modal-open { overflow: hidden; } .modal { display: none; overflow: hidden; position: fixed; top: 0; right: 0; bottom: 0; left: 0; z-index: 1050; -webkit-overflow-scrolling: touch; outline: 0; } .modal.fade .modal-dialog { -webkit-transform: translate(0, -25%); -ms-transform: translate(0, -25%); -o-transform: translate(0, -25%); transform: translate(0, -25%); -webkit-transition: -webkit-transform 0.3s ease-out; -moz-transition: -moz-transform 0.3s ease-out; -o-transition: -o-transform 0.3s ease-out; transition: transform 0.3s ease-out; } .modal.in .modal-dialog { -webkit-transform: translate(0, 0); -ms-transform: translate(0, 0); -o-transform: translate(0, 0); transform: translate(0, 0); } .modal-open .modal { overflow-x: hidden; overflow-y: auto; } .modal-dialog { position: relative; width: auto; margin: 10px; } .modal-content { position: relative; background-color: #fff; border: 1px solid #999; border: 1px solid rgba(0, 0, 0, 0.2); border-radius: 3px; -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); background-clip: padding-box; outline: 0; } .modal-backdrop { position: fixed; top: 0; right: 0; bottom: 0; left: 0; z-index: 1040; background-color: #000; } .modal-backdrop.fade { opacity: 0; filter: alpha(opacity=0); } .modal-backdrop.in { opacity: 0.5; filter: alpha(opacity=50); } .modal-header { padding: 15px; border-bottom: 1px solid #e5e5e5; } .modal-header .close { margin-top: -2px; } .modal-title { margin: 0; line-height: 1.42857143; } .modal-body { position: relative; padding: 15px; } .modal-footer { padding: 15px; text-align: right; border-top: 1px solid #e5e5e5; } .modal-footer .btn + .btn { margin-left: 5px; margin-bottom: 0; } .modal-footer .btn-group .btn + .btn { margin-left: -1px; } .modal-footer .btn-block + .btn-block { margin-left: 0; } .modal-scrollbar-measure { position: absolute; top: -9999px; width: 50px; height: 50px; overflow: scroll; } @media (min-width: 768px) { .modal-dialog { width: 600px; margin: 30px auto; } .modal-content { -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); } .modal-sm { width: 300px; } } @media (min-width: 992px) { .modal-lg { width: 900px; } } .tooltip { position: absolute; z-index: 1070; display: block; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-style: normal; font-weight: normal; letter-spacing: normal; line-break: auto; line-height: 1.42857143; text-align: left; text-align: start; text-decoration: none; text-shadow: none; text-transform: none; white-space: normal; word-break: normal; word-spacing: normal; word-wrap: normal; font-size: 12px; opacity: 0; filter: alpha(opacity=0); } .tooltip.in { opacity: 0.9; filter: alpha(opacity=90); } .tooltip.top { margin-top: -3px; padding: 5px 0; } .tooltip.right { margin-left: 3px; padding: 0 5px; } .tooltip.bottom { margin-top: 3px; padding: 5px 0; } .tooltip.left { margin-left: -3px; padding: 0 5px; } .tooltip-inner { max-width: 200px; padding: 3px 8px; color: #fff; text-align: center; background-color: #000; border-radius: 2px; } .tooltip-arrow { position: absolute; width: 0; height: 0; border-color: transparent; border-style: solid; } .tooltip.top .tooltip-arrow { bottom: 0; left: 50%; margin-left: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.top-left .tooltip-arrow { bottom: 0; right: 5px; margin-bottom: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.top-right .tooltip-arrow { bottom: 0; left: 5px; margin-bottom: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.right .tooltip-arrow { top: 50%; left: 0; margin-top: -5px; border-width: 5px 5px 5px 0; border-right-color: #000; } .tooltip.left .tooltip-arrow { top: 50%; right: 0; margin-top: -5px; border-width: 5px 0 5px 5px; border-left-color: #000; } .tooltip.bottom .tooltip-arrow { top: 0; left: 50%; margin-left: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .tooltip.bottom-left .tooltip-arrow { top: 0; right: 5px; margin-top: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .tooltip.bottom-right .tooltip-arrow { top: 0; left: 5px; margin-top: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .popover { position: absolute; top: 0; left: 0; z-index: 1060; display: none; max-width: 276px; padding: 1px; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-style: normal; font-weight: normal; letter-spacing: normal; line-break: auto; line-height: 1.42857143; text-align: left; text-align: start; text-decoration: none; text-shadow: none; text-transform: none; white-space: normal; word-break: normal; word-spacing: normal; word-wrap: normal; font-size: 13px; background-color: #fff; background-clip: padding-box; border: 1px solid #ccc; border: 1px solid rgba(0, 0, 0, 0.2); border-radius: 3px; -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); } .popover.top { margin-top: -10px; } .popover.right { margin-left: 10px; } .popover.bottom { margin-top: 10px; } .popover.left { margin-left: -10px; } .popover-title { margin: 0; padding: 8px 14px; font-size: 13px; background-color: #f7f7f7; border-bottom: 1px solid #ebebeb; border-radius: 2px 2px 0 0; } .popover-content { padding: 9px 14px; } .popover > .arrow, .popover > .arrow:after { position: absolute; display: block; width: 0; height: 0; border-color: transparent; border-style: solid; } .popover > .arrow { border-width: 11px; } .popover > .arrow:after { border-width: 10px; content: ""; } .popover.top > .arrow { left: 50%; margin-left: -11px; border-bottom-width: 0; border-top-color: #999999; border-top-color: rgba(0, 0, 0, 0.25); bottom: -11px; } .popover.top > .arrow:after { content: " "; bottom: 1px; margin-left: -10px; border-bottom-width: 0; border-top-color: #fff; } .popover.right > .arrow { top: 50%; left: -11px; margin-top: -11px; border-left-width: 0; border-right-color: #999999; border-right-color: rgba(0, 0, 0, 0.25); } .popover.right > .arrow:after { content: " "; left: 1px; bottom: -10px; border-left-width: 0; border-right-color: #fff; } .popover.bottom > .arrow { left: 50%; margin-left: -11px; border-top-width: 0; border-bottom-color: #999999; border-bottom-color: rgba(0, 0, 0, 0.25); top: -11px; } .popover.bottom > .arrow:after { content: " "; top: 1px; margin-left: -10px; border-top-width: 0; border-bottom-color: #fff; } .popover.left > .arrow { top: 50%; right: -11px; margin-top: -11px; border-right-width: 0; border-left-color: #999999; border-left-color: rgba(0, 0, 0, 0.25); } .popover.left > .arrow:after { content: " "; right: 1px; border-right-width: 0; border-left-color: #fff; bottom: -10px; } .carousel { position: relative; } .carousel-inner { position: relative; overflow: hidden; width: 100%; } .carousel-inner > .item { display: none; position: relative; -webkit-transition: 0.6s ease-in-out left; -o-transition: 0.6s ease-in-out left; transition: 0.6s ease-in-out left; } .carousel-inner > .item > img, .carousel-inner > .item > a > img { line-height: 1; } @media all and (transform-3d), (-webkit-transform-3d) { .carousel-inner > .item { -webkit-transition: -webkit-transform 0.6s ease-in-out; -moz-transition: -moz-transform 0.6s ease-in-out; -o-transition: -o-transform 0.6s ease-in-out; transition: transform 0.6s ease-in-out; -webkit-backface-visibility: hidden; -moz-backface-visibility: hidden; backface-visibility: hidden; -webkit-perspective: 1000px; -moz-perspective: 1000px; perspective: 1000px; } .carousel-inner > .item.next, .carousel-inner > .item.active.right { -webkit-transform: translate3d(100%, 0, 0); transform: translate3d(100%, 0, 0); left: 0; } .carousel-inner > .item.prev, .carousel-inner > .item.active.left { -webkit-transform: translate3d(-100%, 0, 0); transform: translate3d(-100%, 0, 0); left: 0; } .carousel-inner > .item.next.left, .carousel-inner > .item.prev.right, .carousel-inner > .item.active { -webkit-transform: translate3d(0, 0, 0); transform: translate3d(0, 0, 0); left: 0; } } .carousel-inner > .active, .carousel-inner > .next, .carousel-inner > .prev { display: block; } .carousel-inner > .active { left: 0; } .carousel-inner > .next, .carousel-inner > .prev { position: absolute; top: 0; width: 100%; } .carousel-inner > .next { left: 100%; } .carousel-inner > .prev { left: -100%; } .carousel-inner > .next.left, .carousel-inner > .prev.right { left: 0; } .carousel-inner > .active.left { left: -100%; } .carousel-inner > .active.right { left: 100%; } .carousel-control { position: absolute; top: 0; left: 0; bottom: 0; width: 15%; opacity: 0.5; filter: alpha(opacity=50); font-size: 20px; color: #fff; text-align: center; text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); background-color: rgba(0, 0, 0, 0); } .carousel-control.left { background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-repeat: repeat-x; filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1); } .carousel-control.right { left: auto; right: 0; background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-repeat: repeat-x; filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1); } .carousel-control:hover, .carousel-control:focus { outline: 0; color: #fff; text-decoration: none; opacity: 0.9; filter: alpha(opacity=90); } .carousel-control .icon-prev, .carousel-control .icon-next, .carousel-control .glyphicon-chevron-left, .carousel-control .glyphicon-chevron-right { position: absolute; top: 50%; margin-top: -10px; z-index: 5; display: inline-block; } .carousel-control .icon-prev, .carousel-control .glyphicon-chevron-left { left: 50%; margin-left: -10px; } .carousel-control .icon-next, .carousel-control .glyphicon-chevron-right { right: 50%; margin-right: -10px; } .carousel-control .icon-prev, .carousel-control .icon-next { width: 20px; height: 20px; line-height: 1; font-family: serif; } .carousel-control .icon-prev:before { content: '\2039'; } .carousel-control .icon-next:before { content: '\203a'; } .carousel-indicators { position: absolute; bottom: 10px; left: 50%; z-index: 15; width: 60%; margin-left: -30%; padding-left: 0; list-style: none; text-align: center; } .carousel-indicators li { display: inline-block; width: 10px; height: 10px; margin: 1px; text-indent: -999px; border: 1px solid #fff; border-radius: 10px; cursor: pointer; background-color: #000 \9; background-color: rgba(0, 0, 0, 0); } .carousel-indicators .active { margin: 0; width: 12px; height: 12px; background-color: #fff; } .carousel-caption { position: absolute; left: 15%; right: 15%; bottom: 20px; z-index: 10; padding-top: 20px; padding-bottom: 20px; color: #fff; text-align: center; text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); } .carousel-caption .btn { text-shadow: none; } @media screen and (min-width: 768px) { .carousel-control .glyphicon-chevron-left, .carousel-control .glyphicon-chevron-right, .carousel-control .icon-prev, .carousel-control .icon-next { width: 30px; height: 30px; margin-top: -10px; font-size: 30px; } .carousel-control .glyphicon-chevron-left, .carousel-control .icon-prev { margin-left: -10px; } .carousel-control .glyphicon-chevron-right, .carousel-control .icon-next { margin-right: -10px; } .carousel-caption { left: 20%; right: 20%; padding-bottom: 30px; } .carousel-indicators { bottom: 20px; } } .clearfix:before, .clearfix:after, .dl-horizontal dd:before, .dl-horizontal dd:after, .container:before, .container:after, .container-fluid:before, .container-fluid:after, .row:before, .row:after, .form-horizontal .form-group:before, .form-horizontal .form-group:after, .btn-toolbar:before, .btn-toolbar:after, .btn-group-vertical > .btn-group:before, .btn-group-vertical > .btn-group:after, .nav:before, .nav:after, .navbar:before, .navbar:after, .navbar-header:before, .navbar-header:after, .navbar-collapse:before, .navbar-collapse:after, .pager:before, .pager:after, .panel-body:before, .panel-body:after, .modal-header:before, .modal-header:after, .modal-footer:before, .modal-footer:after, .item\_buttons:before, .item\_buttons:after { content: " "; display: table; } .clearfix:after, .dl-horizontal dd:after, .container:after, .container-fluid:after, .row:after, .form-horizontal .form-group:after, .btn-toolbar:after, .btn-group-vertical > .btn-group:after, .nav:after, .navbar:after, .navbar-header:after, .navbar-collapse:after, .pager:after, .panel-body:after, .modal-header:after, .modal-footer:after, .item\_buttons:after { clear: both; } .center-block { display: block; margin-left: auto; margin-right: auto; } .pull-right { float: right !important; } .pull-left { float: left !important; } .hide { display: none !important; } .show { display: block !important; } .invisible { visibility: hidden; } .text-hide { font: 0/0 a; color: transparent; text-shadow: none; background-color: transparent; border: 0; } .hidden { display: none !important; } .affix { position: fixed; } @-ms-viewport { width: device-width; } .visible-xs, .visible-sm, .visible-md, .visible-lg { display: none !important; } .visible-xs-block, .visible-xs-inline, .visible-xs-inline-block, .visible-sm-block, .visible-sm-inline, .visible-sm-inline-block, .visible-md-block, .visible-md-inline, .visible-md-inline-block, .visible-lg-block, .visible-lg-inline, .visible-lg-inline-block { display: none !important; } @media (max-width: 767px) { .visible-xs { display: block !important; } table.visible-xs { display: table !important; } tr.visible-xs { display: table-row !important; } th.visible-xs, td.visible-xs { display: table-cell !important; } } @media (max-width: 767px) { .visible-xs-block { display: block !important; } } @media (max-width: 767px) { .visible-xs-inline { display: inline !important; } } @media (max-width: 767px) { .visible-xs-inline-block { display: inline-block !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm { display: block !important; } table.visible-sm { display: table !important; } tr.visible-sm { display: table-row !important; } th.visible-sm, td.visible-sm { display: table-cell !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-block { display: block !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-inline { display: inline !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-inline-block { display: inline-block !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md { display: block !important; } table.visible-md { display: table !important; } tr.visible-md { display: table-row !important; } th.visible-md, td.visible-md { display: table-cell !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-block { display: block !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-inline { display: inline !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-inline-block { display: inline-block !important; } } @media (min-width: 1200px) { .visible-lg { display: block !important; } table.visible-lg { display: table !important; } tr.visible-lg { display: table-row !important; } th.visible-lg, td.visible-lg { display: table-cell !important; } } @media (min-width: 1200px) { .visible-lg-block { display: block !important; } } @media (min-width: 1200px) { .visible-lg-inline { display: inline !important; } } @media (min-width: 1200px) { .visible-lg-inline-block { display: inline-block !important; } } @media (max-width: 767px) { .hidden-xs { display: none !important; } } @media (min-width: 768px) and (max-width: 991px) { .hidden-sm { display: none !important; } } @media (min-width: 992px) and (max-width: 1199px) { .hidden-md { display: none !important; } } @media (min-width: 1200px) { .hidden-lg { display: none !important; } } .visible-print { display: none !important; } @media print { .visible-print { display: block !important; } table.visible-print { display: table !important; } tr.visible-print { display: table-row !important; } th.visible-print, td.visible-print { display: table-cell !important; } } .visible-print-block { display: none !important; } @media print { .visible-print-block { display: block !important; } } .visible-print-inline { display: none !important; } @media print { .visible-print-inline { display: inline !important; } } .visible-print-inline-block { display: none !important; } @media print { .visible-print-inline-block { display: inline-block !important; } } @media print { .hidden-print { display: none !important; } } /\*! \* \* Font Awesome \* \*/ /\*! \* Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome \* License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License) \*/ /\* FONT PATH \* -------------------------- \*/ @font-face { font-family: 'FontAwesome'; src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0'); src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg'); font-weight: normal; font-style: normal; } .fa { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; } /\* makes the font 33% larger relative to the icon container \*/ .fa-lg { font-size: 1.33333333em; line-height: 0.75em; vertical-align: -15%; } .fa-2x { font-size: 2em; } .fa-3x { font-size: 3em; } .fa-4x { font-size: 4em; } .fa-5x { font-size: 5em; } .fa-fw { width: 1.28571429em; text-align: center; } .fa-ul { padding-left: 0; margin-left: 2.14285714em; list-style-type: none; } .fa-ul > li { position: relative; } .fa-li { position: absolute; left: -2.14285714em; width: 2.14285714em; top: 0.14285714em; text-align: center; } .fa-li.fa-lg { left: -1.85714286em; } .fa-border { padding: .2em .25em .15em; border: solid 0.08em #eee; border-radius: .1em; } .fa-pull-left { float: left; } .fa-pull-right { float: right; } .fa.fa-pull-left { margin-right: .3em; } .fa.fa-pull-right { margin-left: .3em; } /\* Deprecated as of 4.4.0 \*/ .pull-right { float: right; } .pull-left { float: left; } .fa.pull-left { margin-right: .3em; } .fa.pull-right { margin-left: .3em; } .fa-spin { -webkit-animation: fa-spin 2s infinite linear; animation: fa-spin 2s infinite linear; } .fa-pulse { -webkit-animation: fa-spin 1s infinite steps(8); animation: fa-spin 1s infinite steps(8); } @-webkit-keyframes fa-spin { 0% { -webkit-transform: rotate(0deg); transform: rotate(0deg); } 100% { -webkit-transform: rotate(359deg); transform: rotate(359deg); } } @keyframes fa-spin { 0% { -webkit-transform: rotate(0deg); transform: rotate(0deg); } 100% { -webkit-transform: rotate(359deg); transform: rotate(359deg); } } .fa-rotate-90 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)"; -webkit-transform: rotate(90deg); -ms-transform: rotate(90deg); transform: rotate(90deg); } .fa-rotate-180 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)"; -webkit-transform: rotate(180deg); -ms-transform: rotate(180deg); transform: rotate(180deg); } .fa-rotate-270 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)"; -webkit-transform: rotate(270deg); -ms-transform: rotate(270deg); transform: rotate(270deg); } .fa-flip-horizontal { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)"; -webkit-transform: scale(-1, 1); -ms-transform: scale(-1, 1); transform: scale(-1, 1); } .fa-flip-vertical { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)"; -webkit-transform: scale(1, -1); -ms-transform: scale(1, -1); transform: scale(1, -1); } :root .fa-rotate-90, :root .fa-rotate-180, :root .fa-rotate-270, :root .fa-flip-horizontal, :root .fa-flip-vertical { filter: none; } .fa-stack { position: relative; display: inline-block; width: 2em; height: 2em; line-height: 2em; vertical-align: middle; } .fa-stack-1x, .fa-stack-2x { position: absolute; left: 0; width: 100%; text-align: center; } .fa-stack-1x { line-height: inherit; } .fa-stack-2x { font-size: 2em; } .fa-inverse { color: #fff; } /\* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen readers do not read off random characters that represent icons \*/ .fa-glass:before { content: "\f000"; } .fa-music:before { content: "\f001"; } .fa-search:before { content: "\f002"; } .fa-envelope-o:before { content: "\f003"; } .fa-heart:before { content: "\f004"; } .fa-star:before { content: "\f005"; } .fa-star-o:before { content: "\f006"; } .fa-user:before { content: "\f007"; } .fa-film:before { content: "\f008"; } .fa-th-large:before { content: "\f009"; } .fa-th:before { content: "\f00a"; } .fa-th-list:before { content: "\f00b"; } .fa-check:before { content: "\f00c"; } .fa-remove:before, .fa-close:before, .fa-times:before { content: "\f00d"; } .fa-search-plus:before { content: "\f00e"; } .fa-search-minus:before { content: "\f010"; } .fa-power-off:before { content: "\f011"; } .fa-signal:before { content: "\f012"; } .fa-gear:before, .fa-cog:before { content: "\f013"; } .fa-trash-o:before { content: "\f014"; } .fa-home:before { content: "\f015"; } .fa-file-o:before { content: "\f016"; } .fa-clock-o:before { content: "\f017"; } .fa-road:before { content: "\f018"; } .fa-download:before { content: "\f019"; } .fa-arrow-circle-o-down:before { content: "\f01a"; } .fa-arrow-circle-o-up:before { content: "\f01b"; } .fa-inbox:before { content: "\f01c"; } .fa-play-circle-o:before { content: "\f01d"; } .fa-rotate-right:before, .fa-repeat:before { content: "\f01e"; } .fa-refresh:before { content: "\f021"; } .fa-list-alt:before { content: "\f022"; } .fa-lock:before { content: "\f023"; } .fa-flag:before { content: "\f024"; } .fa-headphones:before { content: "\f025"; } .fa-volume-off:before { content: "\f026"; } .fa-volume-down:before { content: "\f027"; } .fa-volume-up:before { content: "\f028"; } .fa-qrcode:before { content: "\f029"; } .fa-barcode:before { content: "\f02a"; } .fa-tag:before { content: "\f02b"; } .fa-tags:before { content: "\f02c"; } .fa-book:before { content: "\f02d"; } .fa-bookmark:before { content: "\f02e"; } .fa-print:before { content: "\f02f"; } .fa-camera:before { content: "\f030"; } .fa-font:before { content: "\f031"; } .fa-bold:before { content: "\f032"; } .fa-italic:before { content: "\f033"; } .fa-text-height:before { content: "\f034"; } .fa-text-width:before { content: "\f035"; } .fa-align-left:before { content: "\f036"; } .fa-align-center:before { content: "\f037"; } .fa-align-right:before { content: "\f038"; } .fa-align-justify:before { content: "\f039"; } .fa-list:before { content: "\f03a"; } .fa-dedent:before, .fa-outdent:before { content: "\f03b"; } .fa-indent:before { content: "\f03c"; } .fa-video-camera:before { content: "\f03d"; } .fa-photo:before, .fa-image:before, .fa-picture-o:before { content: "\f03e"; } .fa-pencil:before { content: "\f040"; } .fa-map-marker:before { content: "\f041"; } .fa-adjust:before { content: "\f042"; } .fa-tint:before { content: "\f043"; } .fa-edit:before, .fa-pencil-square-o:before { content: "\f044"; } .fa-share-square-o:before { content: "\f045"; } .fa-check-square-o:before { content: "\f046"; } .fa-arrows:before { content: "\f047"; } .fa-step-backward:before { content: "\f048"; } .fa-fast-backward:before { content: "\f049"; } .fa-backward:before { content: "\f04a"; } .fa-play:before { content: "\f04b"; } .fa-pause:before { content: "\f04c"; } .fa-stop:before { content: "\f04d"; } .fa-forward:before { content: "\f04e"; } .fa-fast-forward:before { content: "\f050"; } .fa-step-forward:before { content: "\f051"; } .fa-eject:before { content: "\f052"; } .fa-chevron-left:before { content: "\f053"; } .fa-chevron-right:before { content: "\f054"; } .fa-plus-circle:before { content: "\f055"; } .fa-minus-circle:before { content: "\f056"; } .fa-times-circle:before { content: "\f057"; } .fa-check-circle:before { content: "\f058"; } .fa-question-circle:before { content: "\f059"; } .fa-info-circle:before { content: "\f05a"; } .fa-crosshairs:before { content: "\f05b"; } .fa-times-circle-o:before { content: "\f05c"; } .fa-check-circle-o:before { content: "\f05d"; } .fa-ban:before { content: "\f05e"; } .fa-arrow-left:before { content: "\f060"; } .fa-arrow-right:before { content: "\f061"; } .fa-arrow-up:before { content: "\f062"; } .fa-arrow-down:before { content: "\f063"; } .fa-mail-forward:before, .fa-share:before { content: "\f064"; } .fa-expand:before { content: "\f065"; } .fa-compress:before { content: "\f066"; } .fa-plus:before { content: "\f067"; } .fa-minus:before { content: "\f068"; } .fa-asterisk:before { content: "\f069"; } .fa-exclamation-circle:before { content: "\f06a"; } .fa-gift:before { content: "\f06b"; } .fa-leaf:before { content: "\f06c"; } .fa-fire:before { content: "\f06d"; } .fa-eye:before { content: "\f06e"; } .fa-eye-slash:before { content: "\f070"; } .fa-warning:before, .fa-exclamation-triangle:before { content: "\f071"; } .fa-plane:before { content: "\f072"; } .fa-calendar:before { content: "\f073"; } .fa-random:before { content: "\f074"; } .fa-comment:before { content: "\f075"; } .fa-magnet:before { content: "\f076"; } .fa-chevron-up:before { content: "\f077"; } .fa-chevron-down:before { content: "\f078"; } .fa-retweet:before { content: "\f079"; } .fa-shopping-cart:before { content: "\f07a"; } .fa-folder:before { content: "\f07b"; } .fa-folder-open:before { content: "\f07c"; } .fa-arrows-v:before { content: "\f07d"; } .fa-arrows-h:before { content: "\f07e"; } .fa-bar-chart-o:before, .fa-bar-chart:before { content: "\f080"; } .fa-twitter-square:before { content: "\f081"; } .fa-facebook-square:before { content: "\f082"; } .fa-camera-retro:before { content: "\f083"; } .fa-key:before { content: "\f084"; } .fa-gears:before, .fa-cogs:before { content: "\f085"; } .fa-comments:before { content: "\f086"; } .fa-thumbs-o-up:before { content: "\f087"; } .fa-thumbs-o-down:before { content: "\f088"; } .fa-star-half:before { content: "\f089"; } .fa-heart-o:before { content: "\f08a"; } .fa-sign-out:before { content: "\f08b"; } .fa-linkedin-square:before { content: "\f08c"; } .fa-thumb-tack:before { content: "\f08d"; } .fa-external-link:before { content: "\f08e"; } .fa-sign-in:before { content: "\f090"; } .fa-trophy:before { content: "\f091"; } .fa-github-square:before { content: "\f092"; } .fa-upload:before { content: "\f093"; } .fa-lemon-o:before { content: "\f094"; } .fa-phone:before { content: "\f095"; } .fa-square-o:before { content: "\f096"; } .fa-bookmark-o:before { content: "\f097"; } .fa-phone-square:before { content: "\f098"; } .fa-twitter:before { content: "\f099"; } .fa-facebook-f:before, .fa-facebook:before { content: "\f09a"; } .fa-github:before { content: "\f09b"; } .fa-unlock:before { content: "\f09c"; } .fa-credit-card:before { content: "\f09d"; } .fa-feed:before, .fa-rss:before { content: "\f09e"; } .fa-hdd-o:before { content: "\f0a0"; } .fa-bullhorn:before { content: "\f0a1"; } .fa-bell:before { content: "\f0f3"; } .fa-certificate:before { content: "\f0a3"; } .fa-hand-o-right:before { content: "\f0a4"; } .fa-hand-o-left:before { content: "\f0a5"; } .fa-hand-o-up:before { content: "\f0a6"; } .fa-hand-o-down:before { content: "\f0a7"; } .fa-arrow-circle-left:before { content: "\f0a8"; } .fa-arrow-circle-right:before { content: "\f0a9"; } .fa-arrow-circle-up:before { content: "\f0aa"; } .fa-arrow-circle-down:before { content: "\f0ab"; } .fa-globe:before { content: "\f0ac"; } .fa-wrench:before { content: "\f0ad"; } .fa-tasks:before { content: "\f0ae"; } .fa-filter:before { content: "\f0b0"; } .fa-briefcase:before { content: "\f0b1"; } .fa-arrows-alt:before { content: "\f0b2"; } .fa-group:before, .fa-users:before { content: "\f0c0"; } .fa-chain:before, .fa-link:before { content: "\f0c1"; } .fa-cloud:before { content: "\f0c2"; } .fa-flask:before { content: "\f0c3"; } .fa-cut:before, .fa-scissors:before { content: "\f0c4"; } .fa-copy:before, .fa-files-o:before { content: "\f0c5"; } .fa-paperclip:before { content: "\f0c6"; } .fa-save:before, .fa-floppy-o:before { content: "\f0c7"; } .fa-square:before { content: "\f0c8"; } .fa-navicon:before, .fa-reorder:before, .fa-bars:before { content: "\f0c9"; } .fa-list-ul:before { content: "\f0ca"; } .fa-list-ol:before { content: "\f0cb"; } .fa-strikethrough:before { content: "\f0cc"; } .fa-underline:before { content: "\f0cd"; } .fa-table:before { content: "\f0ce"; } .fa-magic:before { content: "\f0d0"; } .fa-truck:before { content: "\f0d1"; } .fa-pinterest:before { content: "\f0d2"; } .fa-pinterest-square:before { content: "\f0d3"; } .fa-google-plus-square:before { content: "\f0d4"; } .fa-google-plus:before { content: "\f0d5"; } .fa-money:before { content: "\f0d6"; } .fa-caret-down:before { content: "\f0d7"; } .fa-caret-up:before { content: "\f0d8"; } .fa-caret-left:before { content: "\f0d9"; } .fa-caret-right:before { content: "\f0da"; } .fa-columns:before { content: "\f0db"; } .fa-unsorted:before, .fa-sort:before { content: "\f0dc"; } .fa-sort-down:before, .fa-sort-desc:before { content: "\f0dd"; } .fa-sort-up:before, .fa-sort-asc:before { content: "\f0de"; } .fa-envelope:before { content: "\f0e0"; } .fa-linkedin:before { content: "\f0e1"; } .fa-rotate-left:before, .fa-undo:before { content: "\f0e2"; } .fa-legal:before, .fa-gavel:before { content: "\f0e3"; } .fa-dashboard:before, .fa-tachometer:before { content: "\f0e4"; } .fa-comment-o:before { content: "\f0e5"; } .fa-comments-o:before { content: "\f0e6"; } .fa-flash:before, .fa-bolt:before { content: "\f0e7"; } .fa-sitemap:before { content: "\f0e8"; } .fa-umbrella:before { content: "\f0e9"; } .fa-paste:before, .fa-clipboard:before { content: "\f0ea"; } .fa-lightbulb-o:before { content: "\f0eb"; } .fa-exchange:before { content: "\f0ec"; } .fa-cloud-download:before { content: "\f0ed"; } .fa-cloud-upload:before { content: "\f0ee"; } .fa-user-md:before { content: "\f0f0"; } .fa-stethoscope:before { content: "\f0f1"; } .fa-suitcase:before { content: "\f0f2"; } .fa-bell-o:before { content: "\f0a2"; } .fa-coffee:before { content: "\f0f4"; } .fa-cutlery:before { content: "\f0f5"; } .fa-file-text-o:before { content: "\f0f6"; } .fa-building-o:before { content: "\f0f7"; } .fa-hospital-o:before { content: "\f0f8"; } .fa-ambulance:before { content: "\f0f9"; } .fa-medkit:before { content: "\f0fa"; } .fa-fighter-jet:before { content: "\f0fb"; } .fa-beer:before { content: "\f0fc"; } .fa-h-square:before { content: "\f0fd"; } .fa-plus-square:before { content: "\f0fe"; } .fa-angle-double-left:before { content: "\f100"; } .fa-angle-double-right:before { content: "\f101"; } .fa-angle-double-up:before { content: "\f102"; } .fa-angle-double-down:before { content: "\f103"; } .fa-angle-left:before { content: "\f104"; } .fa-angle-right:before { content: "\f105"; } .fa-angle-up:before { content: "\f106"; } .fa-angle-down:before { content: "\f107"; } .fa-desktop:before { content: "\f108"; } .fa-laptop:before { content: "\f109"; } .fa-tablet:before { content: "\f10a"; } .fa-mobile-phone:before, .fa-mobile:before { content: "\f10b"; } .fa-circle-o:before { content: "\f10c"; } .fa-quote-left:before { content: "\f10d"; } .fa-quote-right:before { content: "\f10e"; } .fa-spinner:before { content: "\f110"; } .fa-circle:before { content: "\f111"; } .fa-mail-reply:before, .fa-reply:before { content: "\f112"; } .fa-github-alt:before { content: "\f113"; } .fa-folder-o:before { content: "\f114"; } .fa-folder-open-o:before { content: "\f115"; } .fa-smile-o:before { content: "\f118"; } .fa-frown-o:before { content: "\f119"; } .fa-meh-o:before { content: "\f11a"; } .fa-gamepad:before { content: "\f11b"; } .fa-keyboard-o:before { content: "\f11c"; } .fa-flag-o:before { content: "\f11d"; } .fa-flag-checkered:before { content: "\f11e"; } .fa-terminal:before { content: "\f120"; } .fa-code:before { content: "\f121"; } .fa-mail-reply-all:before, .fa-reply-all:before { content: "\f122"; } .fa-star-half-empty:before, .fa-star-half-full:before, .fa-star-half-o:before { content: "\f123"; } .fa-location-arrow:before { content: "\f124"; } .fa-crop:before { content: "\f125"; } .fa-code-fork:before { content: "\f126"; } .fa-unlink:before, .fa-chain-broken:before { content: "\f127"; } .fa-question:before { content: "\f128"; } .fa-info:before { content: "\f129"; } .fa-exclamation:before { content: "\f12a"; } .fa-superscript:before { content: "\f12b"; } .fa-subscript:before { content: "\f12c"; } .fa-eraser:before { content: "\f12d"; } .fa-puzzle-piece:before { content: "\f12e"; } .fa-microphone:before { content: "\f130"; } .fa-microphone-slash:before { content: "\f131"; } .fa-shield:before { content: "\f132"; } .fa-calendar-o:before { content: "\f133"; } .fa-fire-extinguisher:before { content: "\f134"; } .fa-rocket:before { content: "\f135"; } .fa-maxcdn:before { content: "\f136"; } .fa-chevron-circle-left:before { content: "\f137"; } .fa-chevron-circle-right:before { content: "\f138"; } .fa-chevron-circle-up:before { content: "\f139"; } .fa-chevron-circle-down:before { content: "\f13a"; } .fa-html5:before { content: "\f13b"; } .fa-css3:before { content: "\f13c"; } .fa-anchor:before { content: "\f13d"; } .fa-unlock-alt:before { content: "\f13e"; } .fa-bullseye:before { content: "\f140"; } .fa-ellipsis-h:before { content: "\f141"; } .fa-ellipsis-v:before { content: "\f142"; } .fa-rss-square:before { content: "\f143"; } .fa-play-circle:before { content: "\f144"; } .fa-ticket:before { content: "\f145"; } .fa-minus-square:before { content: "\f146"; } .fa-minus-square-o:before { content: "\f147"; } .fa-level-up:before { content: "\f148"; } .fa-level-down:before { content: "\f149"; } .fa-check-square:before { content: "\f14a"; } .fa-pencil-square:before { content: "\f14b"; } .fa-external-link-square:before { content: "\f14c"; } .fa-share-square:before { content: "\f14d"; } .fa-compass:before { content: "\f14e"; } .fa-toggle-down:before, .fa-caret-square-o-down:before { content: "\f150"; } .fa-toggle-up:before, .fa-caret-square-o-up:before { content: "\f151"; } .fa-toggle-right:before, .fa-caret-square-o-right:before { content: "\f152"; } .fa-euro:before, .fa-eur:before { content: "\f153"; } .fa-gbp:before { content: "\f154"; } .fa-dollar:before, .fa-usd:before { content: "\f155"; } .fa-rupee:before, .fa-inr:before { content: "\f156"; } .fa-cny:before, .fa-rmb:before, .fa-yen:before, .fa-jpy:before { content: "\f157"; } .fa-ruble:before, .fa-rouble:before, .fa-rub:before { content: "\f158"; } .fa-won:before, .fa-krw:before { content: "\f159"; } .fa-bitcoin:before, .fa-btc:before { content: "\f15a"; } .fa-file:before { content: "\f15b"; } .fa-file-text:before { content: "\f15c"; } .fa-sort-alpha-asc:before { content: "\f15d"; } .fa-sort-alpha-desc:before { content: "\f15e"; } .fa-sort-amount-asc:before { content: "\f160"; } .fa-sort-amount-desc:before { content: "\f161"; } .fa-sort-numeric-asc:before { content: "\f162"; } .fa-sort-numeric-desc:before { content: "\f163"; } .fa-thumbs-up:before { content: "\f164"; } .fa-thumbs-down:before { content: "\f165"; } .fa-youtube-square:before { content: "\f166"; } .fa-youtube:before { content: "\f167"; } .fa-xing:before { content: "\f168"; } .fa-xing-square:before { content: "\f169"; } .fa-youtube-play:before { content: "\f16a"; } .fa-dropbox:before { content: "\f16b"; } .fa-stack-overflow:before { content: "\f16c"; } .fa-instagram:before { content: "\f16d"; } .fa-flickr:before { content: "\f16e"; } .fa-adn:before { content: "\f170"; } .fa-bitbucket:before { content: "\f171"; } .fa-bitbucket-square:before { content: "\f172"; } .fa-tumblr:before { content: "\f173"; } .fa-tumblr-square:before { content: "\f174"; } .fa-long-arrow-down:before { content: "\f175"; } .fa-long-arrow-up:before { content: "\f176"; } .fa-long-arrow-left:before { content: "\f177"; } .fa-long-arrow-right:before { content: "\f178"; } .fa-apple:before { content: "\f179"; } .fa-windows:before { content: "\f17a"; } .fa-android:before { content: "\f17b"; } .fa-linux:before { content: "\f17c"; } .fa-dribbble:before { content: "\f17d"; } .fa-skype:before { content: "\f17e"; } .fa-foursquare:before { content: "\f180"; } .fa-trello:before { content: "\f181"; } .fa-female:before { content: "\f182"; } .fa-male:before { content: "\f183"; } .fa-gittip:before, .fa-gratipay:before { content: "\f184"; } .fa-sun-o:before { content: "\f185"; } .fa-moon-o:before { content: "\f186"; } .fa-archive:before { content: "\f187"; } .fa-bug:before { content: "\f188"; } .fa-vk:before { content: "\f189"; } .fa-weibo:before { content: "\f18a"; } .fa-renren:before { content: "\f18b"; } .fa-pagelines:before { content: "\f18c"; } .fa-stack-exchange:before { content: "\f18d"; } .fa-arrow-circle-o-right:before { content: "\f18e"; } .fa-arrow-circle-o-left:before { content: "\f190"; } .fa-toggle-left:before, .fa-caret-square-o-left:before { content: "\f191"; } .fa-dot-circle-o:before { content: "\f192"; } .fa-wheelchair:before { content: "\f193"; } .fa-vimeo-square:before { content: "\f194"; } .fa-turkish-lira:before, .fa-try:before { content: "\f195"; } .fa-plus-square-o:before { content: "\f196"; } .fa-space-shuttle:before { content: "\f197"; } .fa-slack:before { content: "\f198"; } .fa-envelope-square:before { content: "\f199"; } .fa-wordpress:before { content: "\f19a"; } .fa-openid:before { content: "\f19b"; } .fa-institution:before, .fa-bank:before, .fa-university:before { content: "\f19c"; } .fa-mortar-board:before, .fa-graduation-cap:before { content: "\f19d"; } .fa-yahoo:before { content: "\f19e"; } .fa-google:before { content: "\f1a0"; } .fa-reddit:before { content: "\f1a1"; } .fa-reddit-square:before { content: "\f1a2"; } .fa-stumbleupon-circle:before { content: "\f1a3"; } .fa-stumbleupon:before { content: "\f1a4"; } .fa-delicious:before { content: "\f1a5"; } .fa-digg:before { content: "\f1a6"; } .fa-pied-piper-pp:before { content: "\f1a7"; } .fa-pied-piper-alt:before { content: "\f1a8"; } .fa-drupal:before { content: "\f1a9"; } .fa-joomla:before { content: "\f1aa"; } .fa-language:before { content: "\f1ab"; } .fa-fax:before { content: "\f1ac"; } .fa-building:before { content: "\f1ad"; } .fa-child:before { content: "\f1ae"; } .fa-paw:before { content: "\f1b0"; } .fa-spoon:before { content: "\f1b1"; } .fa-cube:before { content: "\f1b2"; } .fa-cubes:before { content: "\f1b3"; } .fa-behance:before { content: "\f1b4"; } .fa-behance-square:before { content: "\f1b5"; } .fa-steam:before { content: "\f1b6"; } .fa-steam-square:before { content: "\f1b7"; } .fa-recycle:before { content: "\f1b8"; } .fa-automobile:before, .fa-car:before { content: "\f1b9"; } .fa-cab:before, .fa-taxi:before { content: "\f1ba"; } .fa-tree:before { content: "\f1bb"; } .fa-spotify:before { content: "\f1bc"; } .fa-deviantart:before { content: "\f1bd"; } .fa-soundcloud:before { content: "\f1be"; } .fa-database:before { content: "\f1c0"; } .fa-file-pdf-o:before { content: "\f1c1"; } .fa-file-word-o:before { content: "\f1c2"; } .fa-file-excel-o:before { content: "\f1c3"; } .fa-file-powerpoint-o:before { content: "\f1c4"; } .fa-file-photo-o:before, .fa-file-picture-o:before, .fa-file-image-o:before { content: "\f1c5"; } .fa-file-zip-o:before, .fa-file-archive-o:before { content: "\f1c6"; } .fa-file-sound-o:before, .fa-file-audio-o:before { content: "\f1c7"; } .fa-file-movie-o:before, .fa-file-video-o:before { content: "\f1c8"; } .fa-file-code-o:before { content: "\f1c9"; } .fa-vine:before { content: "\f1ca"; } .fa-codepen:before { content: "\f1cb"; } .fa-jsfiddle:before { content: "\f1cc"; } .fa-life-bouy:before, .fa-life-buoy:before, .fa-life-saver:before, .fa-support:before, .fa-life-ring:before { content: "\f1cd"; } .fa-circle-o-notch:before { content: "\f1ce"; } .fa-ra:before, .fa-resistance:before, .fa-rebel:before { content: "\f1d0"; } .fa-ge:before, .fa-empire:before { content: "\f1d1"; } .fa-git-square:before { content: "\f1d2"; } .fa-git:before { content: "\f1d3"; } .fa-y-combinator-square:before, .fa-yc-square:before, .fa-hacker-news:before { content: "\f1d4"; } .fa-tencent-weibo:before { content: "\f1d5"; } .fa-qq:before { content: "\f1d6"; } .fa-wechat:before, .fa-weixin:before { content: "\f1d7"; } .fa-send:before, .fa-paper-plane:before { content: "\f1d8"; } .fa-send-o:before, .fa-paper-plane-o:before { content: "\f1d9"; } .fa-history:before { content: "\f1da"; } .fa-circle-thin:before { content: "\f1db"; } .fa-header:before { content: "\f1dc"; } .fa-paragraph:before { content: "\f1dd"; } .fa-sliders:before { content: "\f1de"; } .fa-share-alt:before { content: "\f1e0"; } .fa-share-alt-square:before { content: "\f1e1"; } .fa-bomb:before { content: "\f1e2"; } .fa-soccer-ball-o:before, .fa-futbol-o:before { content: "\f1e3"; } .fa-tty:before { content: "\f1e4"; } .fa-binoculars:before { content: "\f1e5"; } .fa-plug:before { content: "\f1e6"; } .fa-slideshare:before { content: "\f1e7"; } .fa-twitch:before { content: "\f1e8"; } .fa-yelp:before { content: "\f1e9"; } .fa-newspaper-o:before { content: "\f1ea"; } .fa-wifi:before { content: "\f1eb"; } .fa-calculator:before { content: "\f1ec"; } .fa-paypal:before { content: "\f1ed"; } .fa-google-wallet:before { content: "\f1ee"; } .fa-cc-visa:before { content: "\f1f0"; } .fa-cc-mastercard:before { content: "\f1f1"; } .fa-cc-discover:before { content: "\f1f2"; } .fa-cc-amex:before { content: "\f1f3"; } .fa-cc-paypal:before { content: "\f1f4"; } .fa-cc-stripe:before { content: "\f1f5"; } .fa-bell-slash:before { content: "\f1f6"; } .fa-bell-slash-o:before { content: "\f1f7"; } .fa-trash:before { content: "\f1f8"; } .fa-copyright:before { content: "\f1f9"; } .fa-at:before { content: "\f1fa"; } .fa-eyedropper:before { content: "\f1fb"; } .fa-paint-brush:before { content: "\f1fc"; } .fa-birthday-cake:before { content: "\f1fd"; } .fa-area-chart:before { content: "\f1fe"; } .fa-pie-chart:before { content: "\f200"; } .fa-line-chart:before { content: "\f201"; } .fa-lastfm:before { content: "\f202"; } .fa-lastfm-square:before { content: "\f203"; } .fa-toggle-off:before { content: "\f204"; } .fa-toggle-on:before { content: "\f205"; } .fa-bicycle:before { content: "\f206"; } .fa-bus:before { content: "\f207"; } .fa-ioxhost:before { content: "\f208"; } .fa-angellist:before { content: "\f209"; } .fa-cc:before { content: "\f20a"; } .fa-shekel:before, .fa-sheqel:before, .fa-ils:before { content: "\f20b"; } .fa-meanpath:before { content: "\f20c"; } .fa-buysellads:before { content: "\f20d"; } .fa-connectdevelop:before { content: "\f20e"; } .fa-dashcube:before { content: "\f210"; } .fa-forumbee:before { content: "\f211"; } .fa-leanpub:before { content: "\f212"; } .fa-sellsy:before { content: "\f213"; } .fa-shirtsinbulk:before { content: "\f214"; } .fa-simplybuilt:before { content: "\f215"; } .fa-skyatlas:before { content: "\f216"; } .fa-cart-plus:before { content: "\f217"; } .fa-cart-arrow-down:before { content: "\f218"; } .fa-diamond:before { content: "\f219"; } .fa-ship:before { content: "\f21a"; } .fa-user-secret:before { content: "\f21b"; } .fa-motorcycle:before { content: "\f21c"; } .fa-street-view:before { content: "\f21d"; } .fa-heartbeat:before { content: "\f21e"; } .fa-venus:before { content: "\f221"; } .fa-mars:before { content: "\f222"; } .fa-mercury:before { content: "\f223"; } .fa-intersex:before, .fa-transgender:before { content: "\f224"; } .fa-transgender-alt:before { content: "\f225"; } .fa-venus-double:before { content: "\f226"; } .fa-mars-double:before { content: "\f227"; } .fa-venus-mars:before { content: "\f228"; } .fa-mars-stroke:before { content: "\f229"; } .fa-mars-stroke-v:before { content: "\f22a"; } .fa-mars-stroke-h:before { content: "\f22b"; } .fa-neuter:before { content: "\f22c"; } .fa-genderless:before { content: "\f22d"; } .fa-facebook-official:before { content: "\f230"; } .fa-pinterest-p:before { content: "\f231"; } .fa-whatsapp:before { content: "\f232"; } .fa-server:before { content: "\f233"; } .fa-user-plus:before { content: "\f234"; } .fa-user-times:before { content: "\f235"; } .fa-hotel:before, .fa-bed:before { content: "\f236"; } .fa-viacoin:before { content: "\f237"; } .fa-train:before { content: "\f238"; } .fa-subway:before { content: "\f239"; } .fa-medium:before { content: "\f23a"; } .fa-yc:before, .fa-y-combinator:before { content: "\f23b"; } .fa-optin-monster:before { content: "\f23c"; } .fa-opencart:before { content: "\f23d"; } .fa-expeditedssl:before { content: "\f23e"; } .fa-battery-4:before, .fa-battery:before, .fa-battery-full:before { content: "\f240"; } .fa-battery-3:before, .fa-battery-three-quarters:before { content: "\f241"; } .fa-battery-2:before, .fa-battery-half:before { content: "\f242"; } .fa-battery-1:before, .fa-battery-quarter:before { content: "\f243"; } .fa-battery-0:before, .fa-battery-empty:before { content: "\f244"; } .fa-mouse-pointer:before { content: "\f245"; } .fa-i-cursor:before { content: "\f246"; } .fa-object-group:before { content: "\f247"; } .fa-object-ungroup:before { content: "\f248"; } .fa-sticky-note:before { content: "\f249"; } .fa-sticky-note-o:before { content: "\f24a"; } .fa-cc-jcb:before { content: "\f24b"; } .fa-cc-diners-club:before { content: "\f24c"; } .fa-clone:before { content: "\f24d"; } .fa-balance-scale:before { content: "\f24e"; } .fa-hourglass-o:before { content: "\f250"; } .fa-hourglass-1:before, .fa-hourglass-start:before { content: "\f251"; } .fa-hourglass-2:before, .fa-hourglass-half:before { content: "\f252"; } .fa-hourglass-3:before, .fa-hourglass-end:before { content: "\f253"; } .fa-hourglass:before { content: "\f254"; } .fa-hand-grab-o:before, .fa-hand-rock-o:before { content: "\f255"; } .fa-hand-stop-o:before, .fa-hand-paper-o:before { content: "\f256"; } .fa-hand-scissors-o:before { content: "\f257"; } .fa-hand-lizard-o:before { content: "\f258"; } .fa-hand-spock-o:before { content: "\f259"; } .fa-hand-pointer-o:before { content: "\f25a"; } .fa-hand-peace-o:before { content: "\f25b"; } .fa-trademark:before { content: "\f25c"; } .fa-registered:before { content: "\f25d"; } .fa-creative-commons:before { content: "\f25e"; } .fa-gg:before { content: "\f260"; } .fa-gg-circle:before { content: "\f261"; } .fa-tripadvisor:before { content: "\f262"; } .fa-odnoklassniki:before { content: "\f263"; } .fa-odnoklassniki-square:before { content: "\f264"; } .fa-get-pocket:before { content: "\f265"; } .fa-wikipedia-w:before { content: "\f266"; } .fa-safari:before { content: "\f267"; } .fa-chrome:before { content: "\f268"; } .fa-firefox:before { content: "\f269"; } .fa-opera:before { content: "\f26a"; } .fa-internet-explorer:before { content: "\f26b"; } .fa-tv:before, .fa-television:before { content: "\f26c"; } .fa-contao:before { content: "\f26d"; } .fa-500px:before { content: "\f26e"; } .fa-amazon:before { content: "\f270"; } .fa-calendar-plus-o:before { content: "\f271"; } .fa-calendar-minus-o:before { content: "\f272"; } .fa-calendar-times-o:before { content: "\f273"; } .fa-calendar-check-o:before { content: "\f274"; } .fa-industry:before { content: "\f275"; } .fa-map-pin:before { content: "\f276"; } .fa-map-signs:before { content: "\f277"; } .fa-map-o:before { content: "\f278"; } .fa-map:before { content: "\f279"; } .fa-commenting:before { content: "\f27a"; } .fa-commenting-o:before { content: "\f27b"; } .fa-houzz:before { content: "\f27c"; } .fa-vimeo:before { content: "\f27d"; } .fa-black-tie:before { content: "\f27e"; } .fa-fonticons:before { content: "\f280"; } .fa-reddit-alien:before { content: "\f281"; } .fa-edge:before { content: "\f282"; } .fa-credit-card-alt:before { content: "\f283"; } .fa-codiepie:before { content: "\f284"; } .fa-modx:before { content: "\f285"; } .fa-fort-awesome:before { content: "\f286"; } .fa-usb:before { content: "\f287"; } .fa-product-hunt:before { content: "\f288"; } .fa-mixcloud:before { content: "\f289"; } .fa-scribd:before { content: "\f28a"; } .fa-pause-circle:before { content: "\f28b"; } .fa-pause-circle-o:before { content: "\f28c"; } .fa-stop-circle:before { content: "\f28d"; } .fa-stop-circle-o:before { content: "\f28e"; } .fa-shopping-bag:before { content: "\f290"; } .fa-shopping-basket:before { content: "\f291"; } .fa-hashtag:before { content: "\f292"; } .fa-bluetooth:before { content: "\f293"; } .fa-bluetooth-b:before { content: "\f294"; } .fa-percent:before { content: "\f295"; } .fa-gitlab:before { content: "\f296"; } .fa-wpbeginner:before { content: "\f297"; } .fa-wpforms:before { content: "\f298"; } .fa-envira:before { content: "\f299"; } .fa-universal-access:before { content: "\f29a"; } .fa-wheelchair-alt:before { content: "\f29b"; } .fa-question-circle-o:before { content: "\f29c"; } .fa-blind:before { content: "\f29d"; } .fa-audio-description:before { content: "\f29e"; } .fa-volume-control-phone:before { content: "\f2a0"; } .fa-braille:before { content: "\f2a1"; } .fa-assistive-listening-systems:before { content: "\f2a2"; } .fa-asl-interpreting:before, .fa-american-sign-language-interpreting:before { content: "\f2a3"; } .fa-deafness:before, .fa-hard-of-hearing:before, .fa-deaf:before { content: "\f2a4"; } .fa-glide:before { content: "\f2a5"; } .fa-glide-g:before { content: "\f2a6"; } .fa-signing:before, .fa-sign-language:before { content: "\f2a7"; } .fa-low-vision:before { content: "\f2a8"; } .fa-viadeo:before { content: "\f2a9"; } .fa-viadeo-square:before { content: "\f2aa"; } .fa-snapchat:before { content: "\f2ab"; } .fa-snapchat-ghost:before { content: "\f2ac"; } .fa-snapchat-square:before { content: "\f2ad"; } .fa-pied-piper:before { content: "\f2ae"; } .fa-first-order:before { content: "\f2b0"; } .fa-yoast:before { content: "\f2b1"; } .fa-themeisle:before { content: "\f2b2"; } .fa-google-plus-circle:before, .fa-google-plus-official:before { content: "\f2b3"; } .fa-fa:before, .fa-font-awesome:before { content: "\f2b4"; } .fa-handshake-o:before { content: "\f2b5"; } .fa-envelope-open:before { content: "\f2b6"; } .fa-envelope-open-o:before { content: "\f2b7"; } .fa-linode:before { content: "\f2b8"; } .fa-address-book:before { content: "\f2b9"; } .fa-address-book-o:before { content: "\f2ba"; } .fa-vcard:before, .fa-address-card:before { content: "\f2bb"; } .fa-vcard-o:before, .fa-address-card-o:before { content: "\f2bc"; } .fa-user-circle:before { content: "\f2bd"; } .fa-user-circle-o:before { content: "\f2be"; } .fa-user-o:before { content: "\f2c0"; } .fa-id-badge:before { content: "\f2c1"; } .fa-drivers-license:before, .fa-id-card:before { content: "\f2c2"; } .fa-drivers-license-o:before, .fa-id-card-o:before { content: "\f2c3"; } .fa-quora:before { content: "\f2c4"; } .fa-free-code-camp:before { content: "\f2c5"; } .fa-telegram:before { content: "\f2c6"; } .fa-thermometer-4:before, .fa-thermometer:before, .fa-thermometer-full:before { content: "\f2c7"; } .fa-thermometer-3:before, .fa-thermometer-three-quarters:before { content: "\f2c8"; } .fa-thermometer-2:before, .fa-thermometer-half:before { content: "\f2c9"; } .fa-thermometer-1:before, .fa-thermometer-quarter:before { content: "\f2ca"; } .fa-thermometer-0:before, .fa-thermometer-empty:before { content: "\f2cb"; } .fa-shower:before { content: "\f2cc"; } .fa-bathtub:before, .fa-s15:before, .fa-bath:before { content: "\f2cd"; } .fa-podcast:before { content: "\f2ce"; } .fa-window-maximize:before { content: "\f2d0"; } .fa-window-minimize:before { content: "\f2d1"; } .fa-window-restore:before { content: "\f2d2"; } .fa-times-rectangle:before, .fa-window-close:before { content: "\f2d3"; } .fa-times-rectangle-o:before, .fa-window-close-o:before { content: "\f2d4"; } .fa-bandcamp:before { content: "\f2d5"; } .fa-grav:before { content: "\f2d6"; } .fa-etsy:before { content: "\f2d7"; } .fa-imdb:before { content: "\f2d8"; } .fa-ravelry:before { content: "\f2d9"; } .fa-eercast:before { content: "\f2da"; } .fa-microchip:before { content: "\f2db"; } .fa-snowflake-o:before { content: "\f2dc"; } .fa-superpowers:before { content: "\f2dd"; } .fa-wpexplorer:before { content: "\f2de"; } .fa-meetup:before { content: "\f2e0"; } .sr-only { position: absolute; width: 1px; height: 1px; padding: 0; margin: -1px; overflow: hidden; clip: rect(0, 0, 0, 0); border: 0; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } /\*! \* \* IPython base \* \*/ .modal.fade .modal-dialog { -webkit-transform: translate(0, 0); -ms-transform: translate(0, 0); -o-transform: translate(0, 0); transform: translate(0, 0); } code { color: #000; } pre { font-size: inherit; line-height: inherit; } label { font-weight: normal; } /\* Make the page background atleast 100% the height of the view port \*/ /\* Make the page itself atleast 70% the height of the view port \*/ .border-box-sizing { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } .corner-all { border-radius: 2px; } .no-padding { padding: 0px; } /\* Flexible box model classes \*/ /\* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ \*/ /\* This file is a compatability layer. It allows the usage of flexible box model layouts accross multiple browsers, including older browsers. The newest, universal implementation of the flexible box model is used when available (see `Modern browsers` comments below). Browsers that are known to implement this new spec completely include: Firefox 28.0+ Chrome 29.0+ Internet Explorer 11+ Opera 17.0+ Browsers not listed, including Safari, are supported via the styling under the `Old browsers` comments below. \*/ .hbox { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } .hbox > \* { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; } .vbox { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } .vbox > \* { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; } .hbox.reverse, .vbox.reverse, .reverse { /\* Old browsers \*/ -webkit-box-direction: reverse; -moz-box-direction: reverse; box-direction: reverse; /\* Modern browsers \*/ flex-direction: row-reverse; } .hbox.box-flex0, .vbox.box-flex0, .box-flex0 { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; width: auto; } .hbox.box-flex1, .vbox.box-flex1, .box-flex1 { /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } .hbox.box-flex, .vbox.box-flex, .box-flex { /\* Old browsers \*/ /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } .hbox.box-flex2, .vbox.box-flex2, .box-flex2 { /\* Old browsers \*/ -webkit-box-flex: 2; -moz-box-flex: 2; box-flex: 2; /\* Modern browsers \*/ flex: 2; } .box-group1 { /\* Deprecated \*/ -webkit-box-flex-group: 1; -moz-box-flex-group: 1; box-flex-group: 1; } .box-group2 { /\* Deprecated \*/ -webkit-box-flex-group: 2; -moz-box-flex-group: 2; box-flex-group: 2; } .hbox.start, .vbox.start, .start { /\* Old browsers \*/ -webkit-box-pack: start; -moz-box-pack: start; box-pack: start; /\* Modern browsers \*/ justify-content: flex-start; } .hbox.end, .vbox.end, .end { /\* Old browsers \*/ -webkit-box-pack: end; -moz-box-pack: end; box-pack: end; /\* Modern browsers \*/ justify-content: flex-end; } .hbox.center, .vbox.center, .center { /\* Old browsers \*/ -webkit-box-pack: center; -moz-box-pack: center; box-pack: center; /\* Modern browsers \*/ justify-content: center; } .hbox.baseline, .vbox.baseline, .baseline { /\* Old browsers \*/ -webkit-box-pack: baseline; -moz-box-pack: baseline; box-pack: baseline; /\* Modern browsers \*/ justify-content: baseline; } .hbox.stretch, .vbox.stretch, .stretch { /\* Old browsers \*/ -webkit-box-pack: stretch; -moz-box-pack: stretch; box-pack: stretch; /\* Modern browsers \*/ justify-content: stretch; } .hbox.align-start, .vbox.align-start, .align-start { /\* Old browsers \*/ -webkit-box-align: start; -moz-box-align: start; box-align: start; /\* Modern browsers \*/ align-items: flex-start; } .hbox.align-end, .vbox.align-end, .align-end { /\* Old browsers \*/ -webkit-box-align: end; -moz-box-align: end; box-align: end; /\* Modern browsers \*/ align-items: flex-end; } .hbox.align-center, .vbox.align-center, .align-center { /\* Old browsers \*/ -webkit-box-align: center; -moz-box-align: center; box-align: center; /\* Modern browsers \*/ align-items: center; } .hbox.align-baseline, .vbox.align-baseline, .align-baseline { /\* Old browsers \*/ -webkit-box-align: baseline; -moz-box-align: baseline; box-align: baseline; /\* Modern browsers \*/ align-items: baseline; } .hbox.align-stretch, .vbox.align-stretch, .align-stretch { /\* Old browsers \*/ -webkit-box-align: stretch; -moz-box-align: stretch; box-align: stretch; /\* Modern browsers \*/ align-items: stretch; } div.error { margin: 2em; text-align: center; } div.error > h1 { font-size: 500%; line-height: normal; } div.error > p { font-size: 200%; line-height: normal; } div.traceback-wrapper { text-align: left; max-width: 800px; margin: auto; } div.traceback-wrapper pre.traceback { max-height: 600px; overflow: auto; } /\*\* \* Primary styles \* \* Author: Jupyter Development Team \*/ body { background-color: #fff; /\* This makes sure that the body covers the entire window and needs to be in a different element than the display: box in wrapper below \*/ position: absolute; left: 0px; right: 0px; top: 0px; bottom: 0px; overflow: visible; } body > #header { /\* Initially hidden to prevent FLOUC \*/ display: none; background-color: #fff; /\* Display over codemirror \*/ position: relative; z-index: 100; } body > #header #header-container { display: flex; flex-direction: row; justify-content: space-between; padding: 5px; padding-bottom: 5px; padding-top: 5px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } body > #header .header-bar { width: 100%; height: 1px; background: #e7e7e7; margin-bottom: -1px; } @media print { body > #header { display: none !important; } } #header-spacer { width: 100%; visibility: hidden; } @media print { #header-spacer { display: none; } } #ipython\_notebook { padding-left: 0px; padding-top: 1px; padding-bottom: 1px; } [dir="rtl"] #ipython\_notebook { margin-right: 10px; margin-left: 0; } [dir="rtl"] #ipython\_notebook.pull-left { float: right !important; float: right; } .flex-spacer { flex: 1; } #noscript { width: auto; padding-top: 16px; padding-bottom: 16px; text-align: center; font-size: 22px; color: red; font-weight: bold; } #ipython\_notebook img { height: 28px; } #site { width: 100%; display: none; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; overflow: auto; } @media print { #site { height: auto !important; } } /\* Smaller buttons \*/ .ui-button .ui-button-text { padding: 0.2em 0.8em; font-size: 77%; } input.ui-button { padding: 0.3em 0.9em; } span#kernel\_logo\_widget { margin: 0 10px; } span#login\_widget { float: right; } [dir="rtl"] span#login\_widget { float: left; } span#login\_widget > .button, #logout { color: #333; background-color: #fff; border-color: #ccc; } span#login\_widget > .button:focus, #logout:focus, span#login\_widget > .button.focus, #logout.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } span#login\_widget > .button:hover, #logout:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } span#login\_widget > .button:active, #logout:active, span#login\_widget > .button.active, #logout.active, .open > .dropdown-togglespan#login\_widget > .button, .open > .dropdown-toggle#logout { color: #333; background-color: #e6e6e6; border-color: #adadad; } span#login\_widget > .button:active:hover, #logout:active:hover, span#login\_widget > .button.active:hover, #logout.active:hover, .open > .dropdown-togglespan#login\_widget > .button:hover, .open > .dropdown-toggle#logout:hover, span#login\_widget > .button:active:focus, #logout:active:focus, span#login\_widget > .button.active:focus, #logout.active:focus, .open > .dropdown-togglespan#login\_widget > .button:focus, .open > .dropdown-toggle#logout:focus, span#login\_widget > .button:active.focus, #logout:active.focus, span#login\_widget > .button.active.focus, #logout.active.focus, .open > .dropdown-togglespan#login\_widget > .button.focus, .open > .dropdown-toggle#logout.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } span#login\_widget > .button:active, #logout:active, span#login\_widget > .button.active, #logout.active, .open > .dropdown-togglespan#login\_widget > .button, .open > .dropdown-toggle#logout { background-image: none; } span#login\_widget > .button.disabled:hover, #logout.disabled:hover, span#login\_widget > .button[disabled]:hover, #logout[disabled]:hover, fieldset[disabled] span#login\_widget > .button:hover, fieldset[disabled] #logout:hover, span#login\_widget > .button.disabled:focus, #logout.disabled:focus, span#login\_widget > .button[disabled]:focus, #logout[disabled]:focus, fieldset[disabled] span#login\_widget > .button:focus, fieldset[disabled] #logout:focus, span#login\_widget > .button.disabled.focus, #logout.disabled.focus, span#login\_widget > .button[disabled].focus, #logout[disabled].focus, fieldset[disabled] span#login\_widget > .button.focus, fieldset[disabled] #logout.focus { background-color: #fff; border-color: #ccc; } span#login\_widget > .button .badge, #logout .badge { color: #fff; background-color: #333; } .nav-header { text-transform: none; } #header > span { margin-top: 10px; } .modal\_stretch .modal-dialog { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; min-height: 80vh; } .modal\_stretch .modal-dialog .modal-body { max-height: calc(100vh - 200px); overflow: auto; flex: 1; } .modal-header { cursor: move; } @media (min-width: 768px) { .modal .modal-dialog { width: 700px; } } @media (min-width: 768px) { select.form-control { margin-left: 12px; margin-right: 12px; } } /\*! \* \* IPython auth \* \*/ .center-nav { display: inline-block; margin-bottom: -4px; } [dir="rtl"] .center-nav form.pull-left { float: right !important; float: right; } [dir="rtl"] .center-nav .navbar-text { float: right; } [dir="rtl"] .navbar-inner { text-align: right; } [dir="rtl"] div.text-left { text-align: right; } /\*! \* \* IPython tree view \* \*/ /\* We need an invisible input field on top of the sentense\*/ /\* "Drag file onto the list ..." \*/ .alternate\_upload { background-color: none; display: inline; } .alternate\_upload.form { padding: 0; margin: 0; } .alternate\_upload input.fileinput { position: absolute; display: block; width: 100%; height: 100%; overflow: hidden; cursor: pointer; opacity: 0; z-index: 2; } .alternate\_upload .btn-xs > input.fileinput { margin: -1px -5px; } .alternate\_upload .btn-upload { position: relative; height: 22px; } ::-webkit-file-upload-button { cursor: pointer; } /\*\* \* Primary styles \* \* Author: Jupyter Development Team \*/ ul#tabs { margin-bottom: 4px; } ul#tabs a { padding-top: 6px; padding-bottom: 4px; } [dir="rtl"] ul#tabs.nav-tabs > li { float: right; } [dir="rtl"] ul#tabs.nav.nav-tabs { padding-right: 0; } ul.breadcrumb a:focus, ul.breadcrumb a:hover { text-decoration: none; } ul.breadcrumb i.icon-home { font-size: 16px; margin-right: 4px; } ul.breadcrumb span { color: #5e5e5e; } .list\_toolbar { padding: 4px 0 4px 0; vertical-align: middle; } .list\_toolbar .tree-buttons { padding-top: 1px; } [dir="rtl"] .list\_toolbar .tree-buttons .pull-right { float: left !important; float: left; } [dir="rtl"] .list\_toolbar .col-sm-4, [dir="rtl"] .list\_toolbar .col-sm-8 { float: right; } .dynamic-buttons { padding-top: 3px; display: inline-block; } .list\_toolbar [class\*="span"] { min-height: 24px; } .list\_header { font-weight: bold; background-color: #EEE; } .list\_placeholder { font-weight: bold; padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; } .list\_container { margin-top: 4px; margin-bottom: 20px; border: 1px solid #ddd; border-radius: 2px; } .list\_container > div { border-bottom: 1px solid #ddd; } .list\_container > div:hover .list-item { background-color: red; } .list\_container > div:last-child { border: none; } .list\_item:hover .list\_item { background-color: #ddd; } .list\_item a { text-decoration: none; } .list\_item:hover { background-color: #fafafa; } .list\_header > div, .list\_item > div { padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; line-height: 22px; } .list\_header > div input, .list\_item > div input { margin-right: 7px; margin-left: 14px; vertical-align: text-bottom; line-height: 22px; position: relative; top: -1px; } .list\_header > div .item\_link, .list\_item > div .item\_link { margin-left: -1px; vertical-align: baseline; line-height: 22px; } [dir="rtl"] .list\_item > div input { margin-right: 0; } .new-file input[type=checkbox] { visibility: hidden; } .item\_name { line-height: 22px; height: 24px; } .item\_icon { font-size: 14px; color: #5e5e5e; margin-right: 7px; margin-left: 7px; line-height: 22px; vertical-align: baseline; } .item\_modified { margin-right: 7px; margin-left: 7px; } [dir="rtl"] .item\_modified.pull-right { float: left !important; float: left; } .item\_buttons { line-height: 1em; margin-left: -5px; } .item\_buttons .btn, .item\_buttons .btn-group, .item\_buttons .input-group { float: left; } .item\_buttons > .btn, .item\_buttons > .btn-group, .item\_buttons > .input-group { margin-left: 5px; } .item\_buttons .btn { min-width: 13ex; } .item\_buttons .running-indicator { padding-top: 4px; color: #5cb85c; } .item\_buttons .kernel-name { padding-top: 4px; color: #5bc0de; margin-right: 7px; float: left; } [dir="rtl"] .item\_buttons.pull-right { float: left !important; float: left; } [dir="rtl"] .item\_buttons .kernel-name { margin-left: 7px; float: right; } .toolbar\_info { height: 24px; line-height: 24px; } .list\_item input:not([type=checkbox]) { padding-top: 3px; padding-bottom: 3px; height: 22px; line-height: 14px; margin: 0px; } .highlight\_text { color: blue; } #project\_name { display: inline-block; padding-left: 7px; margin-left: -2px; } #project\_name > .breadcrumb { padding: 0px; margin-bottom: 0px; background-color: transparent; font-weight: bold; } .sort\_button { display: inline-block; padding-left: 7px; } [dir="rtl"] .sort\_button.pull-right { float: left !important; float: left; } #tree-selector { padding-right: 0px; } #button-select-all { min-width: 50px; } [dir="rtl"] #button-select-all.btn { float: right ; } #select-all { margin-left: 7px; margin-right: 2px; margin-top: 2px; height: 16px; } [dir="rtl"] #select-all.pull-left { float: right !important; float: right; } .menu\_icon { margin-right: 2px; } .tab-content .row { margin-left: 0px; margin-right: 0px; } .folder\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f114"; } .folder\_icon:before.fa-pull-left { margin-right: .3em; } .folder\_icon:before.fa-pull-right { margin-left: .3em; } .folder\_icon:before.pull-left { margin-right: .3em; } .folder\_icon:before.pull-right { margin-left: .3em; } .notebook\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f02d"; position: relative; top: -1px; } .notebook\_icon:before.fa-pull-left { margin-right: .3em; } .notebook\_icon:before.fa-pull-right { margin-left: .3em; } .notebook\_icon:before.pull-left { margin-right: .3em; } .notebook\_icon:before.pull-right { margin-left: .3em; } .running\_notebook\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f02d"; position: relative; top: -1px; color: #5cb85c; } .running\_notebook\_icon:before.fa-pull-left { margin-right: .3em; } .running\_notebook\_icon:before.fa-pull-right { margin-left: .3em; } .running\_notebook\_icon:before.pull-left { margin-right: .3em; } .running\_notebook\_icon:before.pull-right { margin-left: .3em; } .file\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f016"; position: relative; top: -2px; } .file\_icon:before.fa-pull-left { margin-right: .3em; } .file\_icon:before.fa-pull-right { margin-left: .3em; } .file\_icon:before.pull-left { margin-right: .3em; } .file\_icon:before.pull-right { margin-left: .3em; } #notebook\_toolbar .pull-right { padding-top: 0px; margin-right: -1px; } ul#new-menu { left: auto; right: 0; } #new-menu .dropdown-header { font-size: 10px; border-bottom: 1px solid #e5e5e5; padding: 0 0 3px; margin: -3px 20px 0; } .kernel-menu-icon { padding-right: 12px; width: 24px; content: "\f096"; } .kernel-menu-icon:before { content: "\f096"; } .kernel-menu-icon-current:before { content: "\f00c"; } #tab\_content { padding-top: 20px; } #running .panel-group .panel { margin-top: 3px; margin-bottom: 1em; } #running .panel-group .panel .panel-heading { background-color: #EEE; padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; line-height: 22px; } #running .panel-group .panel .panel-heading a:focus, #running .panel-group .panel .panel-heading a:hover { text-decoration: none; } #running .panel-group .panel .panel-body { padding: 0px; } #running .panel-group .panel .panel-body .list\_container { margin-top: 0px; margin-bottom: 0px; border: 0px; border-radius: 0px; } #running .panel-group .panel .panel-body .list\_container .list\_item { border-bottom: 1px solid #ddd; } #running .panel-group .panel .panel-body .list\_container .list\_item:last-child { border-bottom: 0px; } .delete-button { display: none; } .duplicate-button { display: none; } .rename-button { display: none; } .move-button { display: none; } .download-button { display: none; } .shutdown-button { display: none; } .dynamic-instructions { display: inline-block; padding-top: 4px; } /\*! \* \* IPython text editor webapp \* \*/ .selected-keymap i.fa { padding: 0px 5px; } .selected-keymap i.fa:before { content: "\f00c"; } #mode-menu { overflow: auto; max-height: 20em; } .edit\_app #header { -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } .edit\_app #menubar .navbar { /\* Use a negative 1 bottom margin, so the border overlaps the border of the header \*/ margin-bottom: -1px; } .dirty-indicator { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator.fa-pull-left { margin-right: .3em; } .dirty-indicator.fa-pull-right { margin-left: .3em; } .dirty-indicator.pull-left { margin-right: .3em; } .dirty-indicator.pull-right { margin-left: .3em; } .dirty-indicator-dirty { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator-dirty.fa-pull-left { margin-right: .3em; } .dirty-indicator-dirty.fa-pull-right { margin-left: .3em; } .dirty-indicator-dirty.pull-left { margin-right: .3em; } .dirty-indicator-dirty.pull-right { margin-left: .3em; } .dirty-indicator-clean { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator-clean.fa-pull-left { margin-right: .3em; } .dirty-indicator-clean.fa-pull-right { margin-left: .3em; } .dirty-indicator-clean.pull-left { margin-right: .3em; } .dirty-indicator-clean.pull-right { margin-left: .3em; } .dirty-indicator-clean:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f00c"; } .dirty-indicator-clean:before.fa-pull-left { margin-right: .3em; } .dirty-indicator-clean:before.fa-pull-right { margin-left: .3em; } .dirty-indicator-clean:before.pull-left { margin-right: .3em; } .dirty-indicator-clean:before.pull-right { margin-left: .3em; } #filename { font-size: 16pt; display: table; padding: 0px 5px; } #current-mode { padding-left: 5px; padding-right: 5px; } #texteditor-backdrop { padding-top: 20px; padding-bottom: 20px; } @media not print { #texteditor-backdrop { background-color: #EEE; } } @media print { #texteditor-backdrop #texteditor-container .CodeMirror-gutter, #texteditor-backdrop #texteditor-container .CodeMirror-gutters { background-color: #fff; } } @media not print { #texteditor-backdrop #texteditor-container .CodeMirror-gutter, #texteditor-backdrop #texteditor-container .CodeMirror-gutters { background-color: #fff; } } @media not print { #texteditor-backdrop #texteditor-container { padding: 0px; background-color: #fff; -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } } .CodeMirror-dialog { background-color: #fff; } /\*! \* \* IPython notebook \* \*/ /\* CSS font colors for translated ANSI escape sequences \*/ /\* The color values are a mix of http://www.xcolors.net/dl/baskerville-ivorylight and http://www.xcolors.net/dl/euphrasia \*/ .ansi-black-fg { color: #3E424D; } .ansi-black-bg { background-color: #3E424D; } .ansi-black-intense-fg { color: #282C36; } .ansi-black-intense-bg { background-color: #282C36; } .ansi-red-fg { color: #E75C58; } .ansi-red-bg { background-color: #E75C58; } .ansi-red-intense-fg { color: #B22B31; } .ansi-red-intense-bg { background-color: #B22B31; } .ansi-green-fg { color: #00A250; } .ansi-green-bg { background-color: #00A250; } .ansi-green-intense-fg { color: #007427; } .ansi-green-intense-bg { background-color: #007427; } .ansi-yellow-fg { color: #DDB62B; } .ansi-yellow-bg { background-color: #DDB62B; } .ansi-yellow-intense-fg { color: #B27D12; } .ansi-yellow-intense-bg { background-color: #B27D12; } .ansi-blue-fg { color: #208FFB; } .ansi-blue-bg { background-color: #208FFB; } .ansi-blue-intense-fg { color: #0065CA; } .ansi-blue-intense-bg { background-color: #0065CA; } .ansi-magenta-fg { color: #D160C4; } .ansi-magenta-bg { background-color: #D160C4; } .ansi-magenta-intense-fg { color: #A03196; } .ansi-magenta-intense-bg { background-color: #A03196; } .ansi-cyan-fg { color: #60C6C8; } .ansi-cyan-bg { background-color: #60C6C8; } .ansi-cyan-intense-fg { color: #258F8F; } .ansi-cyan-intense-bg { background-color: #258F8F; } .ansi-white-fg { color: #C5C1B4; } .ansi-white-bg { background-color: #C5C1B4; } .ansi-white-intense-fg { color: #A1A6B2; } .ansi-white-intense-bg { background-color: #A1A6B2; } .ansi-default-inverse-fg { color: #FFFFFF; } .ansi-default-inverse-bg { background-color: #000000; } .ansi-bold { font-weight: bold; } .ansi-underline { text-decoration: underline; } /\* The following styles are deprecated an will be removed in a future version \*/ .ansibold { font-weight: bold; } .ansi-inverse { outline: 0.5px dotted; } /\* use dark versions for foreground, to improve visibility \*/ .ansiblack { color: black; } .ansired { color: darkred; } .ansigreen { color: darkgreen; } .ansiyellow { color: #c4a000; } .ansiblue { color: darkblue; } .ansipurple { color: darkviolet; } .ansicyan { color: steelblue; } .ansigray { color: gray; } /\* and light for background, for the same reason \*/ .ansibgblack { background-color: black; } .ansibgred { background-color: red; } .ansibggreen { background-color: green; } .ansibgyellow { background-color: yellow; } .ansibgblue { background-color: blue; } .ansibgpurple { background-color: magenta; } .ansibgcyan { background-color: cyan; } .ansibggray { background-color: gray; } div.cell { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; border-radius: 2px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; border-width: 1px; border-style: solid; border-color: transparent; width: 100%; padding: 5px; /\* This acts as a spacer between cells, that is outside the border \*/ margin: 0px; outline: none; position: relative; overflow: visible; } div.cell:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: transparent; } div.cell.jupyter-soft-selected { border-left-color: #E3F2FD; border-left-width: 1px; padding-left: 5px; border-right-color: #E3F2FD; border-right-width: 1px; background: #E3F2FD; } @media print { div.cell.jupyter-soft-selected { border-color: transparent; } } div.cell.selected, div.cell.selected.jupyter-soft-selected { border-color: #ababab; } div.cell.selected:before, div.cell.selected.jupyter-soft-selected:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: #42A5F5; } @media print { div.cell.selected, div.cell.selected.jupyter-soft-selected { border-color: transparent; } } .edit\_mode div.cell.selected { border-color: #66BB6A; } .edit\_mode div.cell.selected:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: #66BB6A; } @media print { .edit\_mode div.cell.selected { border-color: transparent; } } .prompt { /\* This needs to be wide enough for 3 digit prompt numbers: In[100]: \*/ min-width: 14ex; /\* This padding is tuned to match the padding on the CodeMirror editor. \*/ padding: 0.4em; margin: 0px; font-family: monospace; text-align: right; /\* This has to match that of the the CodeMirror class line-height below \*/ line-height: 1.21429em; /\* Don't highlight prompt number selection \*/ -webkit-touch-callout: none; -webkit-user-select: none; -khtml-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; /\* Use default cursor \*/ cursor: default; } @media (max-width: 540px) { .prompt { text-align: left; } } div.inner\_cell { min-width: 0; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } /\* input\_area and input\_prompt must match in top border and margin for alignment \*/ div.input\_area { border: 1px solid #cfcfcf; border-radius: 2px; background: #f7f7f7; line-height: 1.21429em; } /\* This is needed so that empty prompt areas can collapse to zero height when there is no content in the output\_subarea and the prompt. The main purpose of this is to make sure that empty JavaScript output\_subareas have no height. \*/ div.prompt:empty { padding-top: 0; padding-bottom: 0; } div.unrecognized\_cell { padding: 5px 5px 5px 0px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } div.unrecognized\_cell .inner\_cell { border-radius: 2px; padding: 5px; font-weight: bold; color: red; border: 1px solid #cfcfcf; background: #eaeaea; } div.unrecognized\_cell .inner\_cell a { color: inherit; text-decoration: none; } div.unrecognized\_cell .inner\_cell a:hover { color: inherit; text-decoration: none; } @media (max-width: 540px) { div.unrecognized\_cell > div.prompt { display: none; } } div.code\_cell { /\* avoid page breaking on code cells when printing \*/ } @media print { div.code\_cell { page-break-inside: avoid; } } /\* any special styling for code cells that are currently running goes here \*/ div.input { page-break-inside: avoid; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } @media (max-width: 540px) { div.input { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } } /\* input\_area and input\_prompt must match in top border and margin for alignment \*/ div.input\_prompt { color: #303F9F; border-top: 1px solid transparent; } div.input\_area > div.highlight { margin: 0.4em; border: none; padding: 0px; background-color: transparent; } div.input\_area > div.highlight > pre { margin: 0px; border: none; padding: 0px; background-color: transparent; } /\* The following gets added to the <head> if it is detected that the user has a \* monospace font with inconsistent normal/bold/italic height. See \* notebookmain.js. Such fonts will have keywords vertically offset with \* respect to the rest of the text. The user should select a better font. \* See: https://github.com/ipython/ipython/issues/1503 \* \* .CodeMirror span { \* vertical-align: bottom; \* } \*/ .CodeMirror { line-height: 1.21429em; /\* Changed from 1em to our global default \*/ font-size: 14px; height: auto; /\* Changed to auto to autogrow \*/ background: none; /\* Changed from white to allow our bg to show through \*/ } .CodeMirror-scroll { /\* The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.\*/ /\* We have found that if it is visible, vertical scrollbars appear with font size changes.\*/ overflow-y: hidden; overflow-x: auto; } .CodeMirror-lines { /\* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because \*/ /\* we have set a different line-height and want this to scale with that. \*/ /\* Note that this should set vertical padding only, since CodeMirror assumes that horizontal padding will be set on CodeMirror pre \*/ padding: 0.4em 0; } .CodeMirror-linenumber { padding: 0 8px 0 4px; } .CodeMirror-gutters { border-bottom-left-radius: 2px; border-top-left-radius: 2px; } .CodeMirror pre { /\* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only, use .CodeMirror-lines for vertical \*/ padding: 0 0.4em; border: 0; border-radius: 0; } .CodeMirror-cursor { border-left: 1.4px solid black; } @media screen and (min-width: 2138px) and (max-width: 4319px) { .CodeMirror-cursor { border-left: 2px solid black; } } @media screen and (min-width: 4320px) { .CodeMirror-cursor { border-left: 4px solid black; } } /\* Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org> Adapted from GitHub theme \*/ .highlight-base { color: #000; } .highlight-variable { color: #000; } .highlight-variable-2 { color: #1a1a1a; } .highlight-variable-3 { color: #333333; } .highlight-string { color: #BA2121; } .highlight-comment { color: #408080; font-style: italic; } .highlight-number { color: #080; } .highlight-atom { color: #88F; } .highlight-keyword { color: #008000; font-weight: bold; } .highlight-builtin { color: #008000; } .highlight-error { color: #f00; } .highlight-operator { color: #AA22FF; font-weight: bold; } .highlight-meta { color: #AA22FF; } /\* previously not defined, copying from default codemirror \*/ .highlight-def { color: #00f; } .highlight-string-2 { color: #f50; } .highlight-qualifier { color: #555; } .highlight-bracket { color: #997; } .highlight-tag { color: #170; } .highlight-attribute { color: #00c; } .highlight-header { color: blue; } .highlight-quote { color: #090; } .highlight-link { color: #00c; } /\* apply the same style to codemirror \*/ .cm-s-ipython span.cm-keyword { color: #008000; font-weight: bold; } .cm-s-ipython span.cm-atom { color: #88F; } .cm-s-ipython span.cm-number { color: #080; } .cm-s-ipython span.cm-def { color: #00f; } .cm-s-ipython span.cm-variable { color: #000; } .cm-s-ipython span.cm-operator { color: #AA22FF; font-weight: bold; } .cm-s-ipython span.cm-variable-2 { color: #1a1a1a; } .cm-s-ipython span.cm-variable-3 { color: #333333; } .cm-s-ipython span.cm-comment { color: #408080; font-style: italic; } .cm-s-ipython span.cm-string { color: #BA2121; } .cm-s-ipython span.cm-string-2 { color: #f50; } .cm-s-ipython span.cm-meta { color: #AA22FF; } .cm-s-ipython span.cm-qualifier { color: #555; } .cm-s-ipython span.cm-builtin { color: #008000; } .cm-s-ipython span.cm-bracket { color: #997; } .cm-s-ipython span.cm-tag { color: #170; } .cm-s-ipython span.cm-attribute { color: #00c; } .cm-s-ipython span.cm-header { color: blue; } .cm-s-ipython span.cm-quote { color: #090; } .cm-s-ipython span.cm-link { color: #00c; } .cm-s-ipython span.cm-error { color: #f00; } .cm-s-ipython span.cm-tab { background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=); background-position: right; background-repeat: no-repeat; } div.output\_wrapper { /\* this position must be relative to enable descendents to be absolute within it \*/ position: relative; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; z-index: 1; } /\* class for the output area when it should be height-limited \*/ div.output\_scroll { /\* ideally, this would be max-height, but FF barfs all over that \*/ height: 24em; /\* FF needs this \*and the wrapper\* to specify full width, or it will shrinkwrap \*/ width: 100%; overflow: auto; border-radius: 2px; -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8); box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8); display: block; } /\* output div while it is collapsed \*/ div.output\_collapsed { margin: 0px; padding: 0px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } div.out\_prompt\_overlay { height: 100%; padding: 0px 0.4em; position: absolute; border-radius: 2px; } div.out\_prompt\_overlay:hover { /\* use inner shadow to get border that is computed the same on WebKit/FF \*/ -webkit-box-shadow: inset 0 0 1px #000; box-shadow: inset 0 0 1px #000; background: rgba(240, 240, 240, 0.5); } div.output\_prompt { color: #D84315; } /\* This class is the outer container of all output sections. \*/ div.output\_area { padding: 0px; page-break-inside: avoid; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } div.output\_area .MathJax\_Display { text-align: left !important; } div.output\_area .rendered\_html table { margin-left: 0; margin-right: 0; } div.output\_area .rendered\_html img { margin-left: 0; margin-right: 0; } div.output\_area img, div.output\_area svg { max-width: 100%; height: auto; } div.output\_area img.unconfined, div.output\_area svg.unconfined { max-width: none; } div.output\_area .mglyph > img { max-width: none; } /\* This is needed to protect the pre formating from global settings such as that of bootstrap \*/ .output { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } @media (max-width: 540px) { div.output\_area { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } } div.output\_area pre { margin: 0; padding: 1px 0 1px 0; border: 0; vertical-align: baseline; color: black; background-color: transparent; border-radius: 0; } /\* This class is for the output subarea inside the output\_area and after the prompt div. \*/ div.output\_subarea { overflow-x: auto; padding: 0.4em; /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; max-width: calc(100% - 14ex); } div.output\_scroll div.output\_subarea { overflow-x: visible; } /\* The rest of the output\_\* classes are for special styling of the different output types \*/ /\* all text output has this class: \*/ div.output\_text { text-align: left; color: #000; /\* This has to match that of the the CodeMirror class line-height below \*/ line-height: 1.21429em; } /\* stdout/stderr are 'text' as well as 'stream', but execute\_result/error are \*not\* streams \*/ div.output\_stderr { background: #fdd; /\* very light red background for stderr \*/ } div.output\_latex { text-align: left; } /\* Empty output\_javascript divs should have no height \*/ div.output\_javascript:empty { padding: 0; } .js-error { color: darkred; } /\* raw\_input styles \*/ div.raw\_input\_container { line-height: 1.21429em; padding-top: 5px; } pre.raw\_input\_prompt { /\* nothing needed here. \*/ } input.raw\_input { font-family: monospace; font-size: inherit; color: inherit; width: auto; /\* make sure input baseline aligns with prompt \*/ vertical-align: baseline; /\* padding + margin = 0.5em between prompt and cursor \*/ padding: 0em 0.25em; margin: 0em 0.25em; } input.raw\_input:focus { box-shadow: none; } p.p-space { margin-bottom: 10px; } div.output\_unrecognized { padding: 5px; font-weight: bold; color: red; } div.output\_unrecognized a { color: inherit; text-decoration: none; } div.output\_unrecognized a:hover { color: inherit; text-decoration: none; } .rendered\_html { color: #000; /\* any extras will just be numbers: \*/ } .rendered\_html em { font-style: italic; } .rendered\_html strong { font-weight: bold; } .rendered\_html u { text-decoration: underline; } .rendered\_html :link { text-decoration: underline; } .rendered\_html :visited { text-decoration: underline; } .rendered\_html h1 { font-size: 185.7%; margin: 1.08em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h2 { font-size: 157.1%; margin: 1.27em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h3 { font-size: 128.6%; margin: 1.55em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h4 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h5 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; font-style: italic; } .rendered\_html h6 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; font-style: italic; } .rendered\_html h1:first-child { margin-top: 0.538em; } .rendered\_html h2:first-child { margin-top: 0.636em; } .rendered\_html h3:first-child { margin-top: 0.777em; } .rendered\_html h4:first-child { margin-top: 1em; } .rendered\_html h5:first-child { margin-top: 1em; } .rendered\_html h6:first-child { margin-top: 1em; } .rendered\_html ul:not(.list-inline), .rendered\_html ol:not(.list-inline) { padding-left: 2em; } .rendered\_html ul { list-style: disc; } .rendered\_html ul ul { list-style: square; margin-top: 0; } .rendered\_html ul ul ul { list-style: circle; } .rendered\_html ol { list-style: decimal; } .rendered\_html ol ol { list-style: upper-alpha; margin-top: 0; } .rendered\_html ol ol ol { list-style: lower-alpha; } .rendered\_html ol ol ol ol { list-style: lower-roman; } .rendered\_html ol ol ol ol ol { list-style: decimal; } .rendered\_html \* + ul { margin-top: 1em; } .rendered\_html \* + ol { margin-top: 1em; } .rendered\_html hr { color: black; background-color: black; } .rendered\_html pre { margin: 1em 2em; padding: 0px; background-color: #fff; } .rendered\_html code { background-color: #eff0f1; } .rendered\_html p code { padding: 1px 5px; } .rendered\_html pre code { background-color: #fff; } .rendered\_html pre, .rendered\_html code { border: 0; color: #000; font-size: 100%; } .rendered\_html blockquote { margin: 1em 2em; } .rendered\_html table { margin-left: auto; margin-right: auto; border: none; border-collapse: collapse; border-spacing: 0; color: black; font-size: 12px; table-layout: fixed; } .rendered\_html thead { border-bottom: 1px solid black; vertical-align: bottom; } .rendered\_html tr, .rendered\_html th, .rendered\_html td { text-align: right; vertical-align: middle; padding: 0.5em 0.5em; line-height: normal; white-space: normal; max-width: none; border: none; } .rendered\_html th { font-weight: bold; } .rendered\_html tbody tr:nth-child(odd) { background: #f5f5f5; } .rendered\_html tbody tr:hover { background: rgba(66, 165, 245, 0.2); } .rendered\_html \* + table { margin-top: 1em; } .rendered\_html p { text-align: left; } .rendered\_html \* + p { margin-top: 1em; } .rendered\_html img { display: block; margin-left: auto; margin-right: auto; } .rendered\_html \* + img { margin-top: 1em; } .rendered\_html img, .rendered\_html svg { max-width: 100%; height: auto; } .rendered\_html img.unconfined, .rendered\_html svg.unconfined { max-width: none; } .rendered\_html .alert { margin-bottom: initial; } .rendered\_html \* + .alert { margin-top: 1em; } [dir="rtl"] .rendered\_html p { text-align: right; } div.text\_cell { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } @media (max-width: 540px) { div.text\_cell > div.prompt { display: none; } } div.text\_cell\_render { /\*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;\*/ outline: none; resize: none; width: inherit; border-style: none; padding: 0.5em 0.5em 0.5em 0.4em; color: #000; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } a.anchor-link:link { text-decoration: none; padding: 0px 20px; visibility: hidden; } h1:hover .anchor-link, h2:hover .anchor-link, h3:hover .anchor-link, h4:hover .anchor-link, h5:hover .anchor-link, h6:hover .anchor-link { visibility: visible; } .text\_cell.rendered .input\_area { display: none; } .text\_cell.rendered .rendered\_html { overflow-x: auto; overflow-y: hidden; } .text\_cell.rendered .rendered\_html tr, .text\_cell.rendered .rendered\_html th, .text\_cell.rendered .rendered\_html td { max-width: none; } .text\_cell.unrendered .text\_cell\_render { display: none; } .text\_cell .dropzone .input\_area { border: 2px dashed #bababa; margin: -1px; } .cm-header-1, .cm-header-2, .cm-header-3, .cm-header-4, .cm-header-5, .cm-header-6 { font-weight: bold; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; } .cm-header-1 { font-size: 185.7%; } .cm-header-2 { font-size: 157.1%; } .cm-header-3 { font-size: 128.6%; } .cm-header-4 { font-size: 110%; } .cm-header-5 { font-size: 100%; font-style: italic; } .cm-header-6 { font-size: 100%; font-style: italic; } /\*! \* \* IPython notebook webapp \* \*/ @media (max-width: 767px) { .notebook\_app { padding-left: 0px; padding-right: 0px; } } #ipython-main-app { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; height: 100%; } div#notebook\_panel { margin: 0px; padding: 0px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; height: 100%; } div#notebook { font-size: 14px; line-height: 20px; overflow-y: hidden; overflow-x: auto; width: 100%; /\* This spaces the page away from the edge of the notebook area \*/ padding-top: 20px; margin: 0px; outline: none; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; min-height: 100%; } @media not print { #notebook-container { padding: 15px; background-color: #fff; min-height: 0; -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } } @media print { #notebook-container { width: 100%; } } div.ui-widget-content { border: 1px solid #ababab; outline: none; } pre.dialog { background-color: #f7f7f7; border: 1px solid #ddd; border-radius: 2px; padding: 0.4em; padding-left: 2em; } p.dialog { padding: 0.2em; } /\* Word-wrap output correctly. This is the CSS3 spelling, though Firefox seems to not honor it correctly. Webkit browsers (Chrome, rekonq, Safari) do. \*/ pre, code, kbd, samp { white-space: pre-wrap; } #fonttest { font-family: monospace; } p { margin-bottom: 0; } .end\_space { min-height: 100px; transition: height .2s ease; } .notebook\_app > #header { -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } @media not print { .notebook\_app { background-color: #EEE; } } kbd { border-style: solid; border-width: 1px; box-shadow: none; margin: 2px; padding-left: 2px; padding-right: 2px; padding-top: 1px; padding-bottom: 1px; } .jupyter-keybindings { padding: 1px; line-height: 24px; border-bottom: 1px solid gray; } .jupyter-keybindings input { margin: 0; padding: 0; border: none; } .jupyter-keybindings i { padding: 6px; } .well code { background-color: #ffffff; border-color: #ababab; border-width: 1px; border-style: solid; padding: 2px; padding-top: 1px; padding-bottom: 1px; } /\* CSS for the cell toolbar \*/ .celltoolbar { border: thin solid #CFCFCF; border-bottom: none; background: #EEE; border-radius: 2px 2px 0px 0px; width: 100%; height: 29px; padding-right: 4px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; /\* Old browsers \*/ -webkit-box-pack: end; -moz-box-pack: end; box-pack: end; /\* Modern browsers \*/ justify-content: flex-end; display: -webkit-flex; } @media print { .celltoolbar { display: none; } } .ctb\_hideshow { display: none; vertical-align: bottom; } /\* ctb\_show is added to the ctb\_hideshow div to show the cell toolbar. Cell toolbars are only shown when the ctb\_global\_show class is also set. \*/ .ctb\_global\_show .ctb\_show.ctb\_hideshow { display: block; } .ctb\_global\_show .ctb\_show + .input\_area, .ctb\_global\_show .ctb\_show + div.text\_cell\_input, .ctb\_global\_show .ctb\_show ~ div.text\_cell\_render { border-top-right-radius: 0px; border-top-left-radius: 0px; } .ctb\_global\_show .ctb\_show ~ div.text\_cell\_render { border: 1px solid #cfcfcf; } .celltoolbar { font-size: 87%; padding-top: 3px; } .celltoolbar select { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; width: inherit; font-size: inherit; height: 22px; padding: 0px; display: inline-block; } .celltoolbar select:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .celltoolbar select::-moz-placeholder { color: #999; opacity: 1; } .celltoolbar select:-ms-input-placeholder { color: #999; } .celltoolbar select::-webkit-input-placeholder { color: #999; } .celltoolbar select::-ms-expand { border: 0; background-color: transparent; } .celltoolbar select[disabled], .celltoolbar select[readonly], fieldset[disabled] .celltoolbar select { background-color: #eeeeee; opacity: 1; } .celltoolbar select[disabled], fieldset[disabled] .celltoolbar select { cursor: not-allowed; } textarea.celltoolbar select { height: auto; } select.celltoolbar select { height: 30px; line-height: 30px; } textarea.celltoolbar select, select[multiple].celltoolbar select { height: auto; } .celltoolbar label { margin-left: 5px; margin-right: 5px; } .tags\_button\_container { width: 100%; display: flex; } .tag-container { display: flex; flex-direction: row; flex-grow: 1; overflow: hidden; position: relative; } .tag-container > \* { margin: 0 4px; } .remove-tag-btn { margin-left: 4px; } .tags-input { display: flex; } .cell-tag:last-child:after { content: ""; position: absolute; right: 0; width: 40px; height: 100%; /\* Fade to background color of cell toolbar \*/ background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE); } .tags-input > \* { margin-left: 4px; } .cell-tag, .tags-input input, .tags-input button { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; box-shadow: none; width: inherit; font-size: inherit; height: 22px; line-height: 22px; padding: 0px 4px; display: inline-block; } .cell-tag:focus, .tags-input input:focus, .tags-input button:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .cell-tag::-moz-placeholder, .tags-input input::-moz-placeholder, .tags-input button::-moz-placeholder { color: #999; opacity: 1; } .cell-tag:-ms-input-placeholder, .tags-input input:-ms-input-placeholder, .tags-input button:-ms-input-placeholder { color: #999; } .cell-tag::-webkit-input-placeholder, .tags-input input::-webkit-input-placeholder, .tags-input button::-webkit-input-placeholder { color: #999; } .cell-tag::-ms-expand, .tags-input input::-ms-expand, .tags-input button::-ms-expand { border: 0; background-color: transparent; } .cell-tag[disabled], .tags-input input[disabled], .tags-input button[disabled], .cell-tag[readonly], .tags-input input[readonly], .tags-input button[readonly], fieldset[disabled] .cell-tag, fieldset[disabled] .tags-input input, fieldset[disabled] .tags-input button { background-color: #eeeeee; opacity: 1; } .cell-tag[disabled], .tags-input input[disabled], .tags-input button[disabled], fieldset[disabled] .cell-tag, fieldset[disabled] .tags-input input, fieldset[disabled] .tags-input button { cursor: not-allowed; } textarea.cell-tag, textarea.tags-input input, textarea.tags-input button { height: auto; } select.cell-tag, select.tags-input input, select.tags-input button { height: 30px; line-height: 30px; } textarea.cell-tag, textarea.tags-input input, textarea.tags-input button, select[multiple].cell-tag, select[multiple].tags-input input, select[multiple].tags-input button { height: auto; } .cell-tag, .tags-input button { padding: 0px 4px; } .cell-tag { background-color: #fff; white-space: nowrap; } .tags-input input[type=text]:focus { outline: none; box-shadow: none; border-color: #ccc; } .completions { position: absolute; z-index: 110; overflow: hidden; border: 1px solid #ababab; border-radius: 2px; -webkit-box-shadow: 0px 6px 10px -1px #adadad; box-shadow: 0px 6px 10px -1px #adadad; line-height: 1; } .completions select { background: white; outline: none; border: none; padding: 0px; margin: 0px; overflow: auto; font-family: monospace; font-size: 110%; color: #000; width: auto; } .completions select option.context { color: #286090; } #kernel\_logo\_widget .current\_kernel\_logo { display: none; margin-top: -1px; margin-bottom: -1px; width: 32px; height: 32px; } [dir="rtl"] #kernel\_logo\_widget { float: left !important; float: left; } .modal .modal-body .move-path { display: flex; flex-direction: row; justify-content: space; align-items: center; } .modal .modal-body .move-path .server-root { padding-right: 20px; } .modal .modal-body .move-path .path-input { flex: 1; } #menubar { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; margin-top: 1px; } #menubar .navbar { border-top: 1px; border-radius: 0px 0px 2px 2px; margin-bottom: 0px; } #menubar .navbar-toggle { float: left; padding-top: 7px; padding-bottom: 7px; border: none; } #menubar .navbar-collapse { clear: left; } [dir="rtl"] #menubar .navbar-toggle { float: right; } [dir="rtl"] #menubar .navbar-collapse { clear: right; } [dir="rtl"] #menubar .navbar-nav { float: right; } [dir="rtl"] #menubar .nav { padding-right: 0px; } [dir="rtl"] #menubar .navbar-nav > li { float: right; } [dir="rtl"] #menubar .navbar-right { float: left !important; } [dir="rtl"] ul.dropdown-menu { text-align: right; left: auto; } [dir="rtl"] ul#new-menu.dropdown-menu { right: auto; left: 0; } .nav-wrapper { border-bottom: 1px solid #e7e7e7; } i.menu-icon { padding-top: 4px; } [dir="rtl"] i.menu-icon.pull-right { float: left !important; float: left; } ul#help\_menu li a { overflow: hidden; padding-right: 2.2em; } ul#help\_menu li a i { margin-right: -1.2em; } [dir="rtl"] ul#help\_menu li a { padding-left: 2.2em; } [dir="rtl"] ul#help\_menu li a i { margin-right: 0; margin-left: -1.2em; } [dir="rtl"] ul#help\_menu li a i.pull-right { float: left !important; float: left; } .dropdown-submenu { position: relative; } .dropdown-submenu > .dropdown-menu { top: 0; left: 100%; margin-top: -6px; margin-left: -1px; } [dir="rtl"] .dropdown-submenu > .dropdown-menu { right: 100%; margin-right: -1px; } .dropdown-submenu:hover > .dropdown-menu { display: block; } .dropdown-submenu > a:after { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; display: block; content: "\f0da"; float: right; color: #333333; margin-top: 2px; margin-right: -10px; } .dropdown-submenu > a:after.fa-pull-left { margin-right: .3em; } .dropdown-submenu > a:after.fa-pull-right { margin-left: .3em; } .dropdown-submenu > a:after.pull-left { margin-right: .3em; } .dropdown-submenu > a:after.pull-right { margin-left: .3em; } [dir="rtl"] .dropdown-submenu > a:after { float: left; content: "\f0d9"; margin-right: 0; margin-left: -10px; } .dropdown-submenu:hover > a:after { color: #262626; } .dropdown-submenu.pull-left { float: none; } .dropdown-submenu.pull-left > .dropdown-menu { left: -100%; margin-left: 10px; } #notification\_area { float: right !important; float: right; z-index: 10; } [dir="rtl"] #notification\_area { float: left !important; float: left; } .indicator\_area { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; } [dir="rtl"] .indicator\_area { float: left !important; float: left; } #kernel\_indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; border-left: 1px solid; } #kernel\_indicator .kernel\_indicator\_name { padding-left: 5px; padding-right: 5px; } [dir="rtl"] #kernel\_indicator { float: left !important; float: left; border-left: 0; border-right: 1px solid; } #modal\_indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; } [dir="rtl"] #modal\_indicator { float: left !important; float: left; } #readonly-indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; margin-top: 2px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; display: none; } .modal\_indicator:before { width: 1.28571429em; text-align: center; } .edit\_mode .modal\_indicator:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f040"; } .edit\_mode .modal\_indicator:before.fa-pull-left { margin-right: .3em; } .edit\_mode .modal\_indicator:before.fa-pull-right { margin-left: .3em; } .edit\_mode .modal\_indicator:before.pull-left { margin-right: .3em; } .edit\_mode .modal\_indicator:before.pull-right { margin-left: .3em; } .command\_mode .modal\_indicator:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: ' '; } .command\_mode .modal\_indicator:before.fa-pull-left { margin-right: .3em; } .command\_mode .modal\_indicator:before.fa-pull-right { margin-left: .3em; } .command\_mode .modal\_indicator:before.pull-left { margin-right: .3em; } .command\_mode .modal\_indicator:before.pull-right { margin-left: .3em; } .kernel\_idle\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f10c"; } .kernel\_idle\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_idle\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_idle\_icon:before.pull-left { margin-right: .3em; } .kernel\_idle\_icon:before.pull-right { margin-left: .3em; } .kernel\_busy\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f111"; } .kernel\_busy\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_busy\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_busy\_icon:before.pull-left { margin-right: .3em; } .kernel\_busy\_icon:before.pull-right { margin-left: .3em; } .kernel\_dead\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f1e2"; } .kernel\_dead\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_dead\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_dead\_icon:before.pull-left { margin-right: .3em; } .kernel\_dead\_icon:before.pull-right { margin-left: .3em; } .kernel\_disconnected\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f127"; } .kernel\_disconnected\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_disconnected\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_disconnected\_icon:before.pull-left { margin-right: .3em; } .kernel\_disconnected\_icon:before.pull-right { margin-left: .3em; } .notification\_widget { color: #777; z-index: 10; background: rgba(240, 240, 240, 0.5); margin-right: 4px; color: #333; background-color: #fff; border-color: #ccc; } .notification\_widget:focus, .notification\_widget.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } .notification\_widget:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } .notification\_widget:active, .notification\_widget.active, .open > .dropdown-toggle.notification\_widget { color: #333; background-color: #e6e6e6; border-color: #adadad; } .notification\_widget:active:hover, .notification\_widget.active:hover, .open > .dropdown-toggle.notification\_widget:hover, .notification\_widget:active:focus, .notification\_widget.active:focus, .open > .dropdown-toggle.notification\_widget:focus, .notification\_widget:active.focus, .notification\_widget.active.focus, .open > .dropdown-toggle.notification\_widget.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } .notification\_widget:active, .notification\_widget.active, .open > .dropdown-toggle.notification\_widget { background-image: none; } .notification\_widget.disabled:hover, .notification\_widget[disabled]:hover, fieldset[disabled] .notification\_widget:hover, .notification\_widget.disabled:focus, .notification\_widget[disabled]:focus, fieldset[disabled] .notification\_widget:focus, .notification\_widget.disabled.focus, .notification\_widget[disabled].focus, fieldset[disabled] .notification\_widget.focus { background-color: #fff; border-color: #ccc; } .notification\_widget .badge { color: #fff; background-color: #333; } .notification\_widget.warning { color: #fff; background-color: #f0ad4e; border-color: #eea236; } .notification\_widget.warning:focus, .notification\_widget.warning.focus { color: #fff; background-color: #ec971f; border-color: #985f0d; } .notification\_widget.warning:hover { color: #fff; background-color: #ec971f; border-color: #d58512; } .notification\_widget.warning:active, .notification\_widget.warning.active, .open > .dropdown-toggle.notification\_widget.warning { color: #fff; background-color: #ec971f; border-color: #d58512; } .notification\_widget.warning:active:hover, .notification\_widget.warning.active:hover, .open > .dropdown-toggle.notification\_widget.warning:hover, .notification\_widget.warning:active:focus, .notification\_widget.warning.active:focus, .open > .dropdown-toggle.notification\_widget.warning:focus, .notification\_widget.warning:active.focus, .notification\_widget.warning.active.focus, .open > .dropdown-toggle.notification\_widget.warning.focus { color: #fff; background-color: #d58512; border-color: #985f0d; } .notification\_widget.warning:active, .notification\_widget.warning.active, .open > .dropdown-toggle.notification\_widget.warning { background-image: none; } .notification\_widget.warning.disabled:hover, .notification\_widget.warning[disabled]:hover, fieldset[disabled] .notification\_widget.warning:hover, .notification\_widget.warning.disabled:focus, .notification\_widget.warning[disabled]:focus, fieldset[disabled] .notification\_widget.warning:focus, .notification\_widget.warning.disabled.focus, .notification\_widget.warning[disabled].focus, fieldset[disabled] .notification\_widget.warning.focus { background-color: #f0ad4e; border-color: #eea236; } .notification\_widget.warning .badge { color: #f0ad4e; background-color: #fff; } .notification\_widget.success { color: #fff; background-color: #5cb85c; border-color: #4cae4c; } .notification\_widget.success:focus, .notification\_widget.success.focus { color: #fff; background-color: #449d44; border-color: #255625; } .notification\_widget.success:hover { color: #fff; background-color: #449d44; border-color: #398439; } .notification\_widget.success:active, .notification\_widget.success.active, .open > .dropdown-toggle.notification\_widget.success { color: #fff; background-color: #449d44; border-color: #398439; } .notification\_widget.success:active:hover, .notification\_widget.success.active:hover, .open > .dropdown-toggle.notification\_widget.success:hover, .notification\_widget.success:active:focus, .notification\_widget.success.active:focus, .open > .dropdown-toggle.notification\_widget.success:focus, .notification\_widget.success:active.focus, .notification\_widget.success.active.focus, .open > .dropdown-toggle.notification\_widget.success.focus { color: #fff; background-color: #398439; border-color: #255625; } .notification\_widget.success:active, .notification\_widget.success.active, .open > .dropdown-toggle.notification\_widget.success { background-image: none; } .notification\_widget.success.disabled:hover, .notification\_widget.success[disabled]:hover, fieldset[disabled] .notification\_widget.success:hover, .notification\_widget.success.disabled:focus, .notification\_widget.success[disabled]:focus, fieldset[disabled] .notification\_widget.success:focus, .notification\_widget.success.disabled.focus, .notification\_widget.success[disabled].focus, fieldset[disabled] .notification\_widget.success.focus { background-color: #5cb85c; border-color: #4cae4c; } .notification\_widget.success .badge { color: #5cb85c; background-color: #fff; } .notification\_widget.info { color: #fff; background-color: #5bc0de; border-color: #46b8da; } .notification\_widget.info:focus, .notification\_widget.info.focus { color: #fff; background-color: #31b0d5; border-color: #1b6d85; } .notification\_widget.info:hover { color: #fff; background-color: #31b0d5; border-color: #269abc; } .notification\_widget.info:active, .notification\_widget.info.active, .open > .dropdown-toggle.notification\_widget.info { color: #fff; background-color: #31b0d5; border-color: #269abc; } .notification\_widget.info:active:hover, .notification\_widget.info.active:hover, .open > .dropdown-toggle.notification\_widget.info:hover, .notification\_widget.info:active:focus, .notification\_widget.info.active:focus, .open > .dropdown-toggle.notification\_widget.info:focus, .notification\_widget.info:active.focus, .notification\_widget.info.active.focus, .open > .dropdown-toggle.notification\_widget.info.focus { color: #fff; background-color: #269abc; border-color: #1b6d85; } .notification\_widget.info:active, .notification\_widget.info.active, .open > .dropdown-toggle.notification\_widget.info { background-image: none; } .notification\_widget.info.disabled:hover, .notification\_widget.info[disabled]:hover, fieldset[disabled] .notification\_widget.info:hover, .notification\_widget.info.disabled:focus, .notification\_widget.info[disabled]:focus, fieldset[disabled] .notification\_widget.info:focus, .notification\_widget.info.disabled.focus, .notification\_widget.info[disabled].focus, fieldset[disabled] .notification\_widget.info.focus { background-color: #5bc0de; border-color: #46b8da; } .notification\_widget.info .badge { color: #5bc0de; background-color: #fff; } .notification\_widget.danger { color: #fff; background-color: #d9534f; border-color: #d43f3a; } .notification\_widget.danger:focus, .notification\_widget.danger.focus { color: #fff; background-color: #c9302c; border-color: #761c19; } .notification\_widget.danger:hover { color: #fff; background-color: #c9302c; border-color: #ac2925; } .notification\_widget.danger:active, .notification\_widget.danger.active, .open > .dropdown-toggle.notification\_widget.danger { color: #fff; background-color: #c9302c; border-color: #ac2925; } .notification\_widget.danger:active:hover, .notification\_widget.danger.active:hover, .open > .dropdown-toggle.notification\_widget.danger:hover, .notification\_widget.danger:active:focus, .notification\_widget.danger.active:focus, .open > .dropdown-toggle.notification\_widget.danger:focus, .notification\_widget.danger:active.focus, .notification\_widget.danger.active.focus, .open > .dropdown-toggle.notification\_widget.danger.focus { color: #fff; background-color: #ac2925; border-color: #761c19; } .notification\_widget.danger:active, .notification\_widget.danger.active, .open > .dropdown-toggle.notification\_widget.danger { background-image: none; } .notification\_widget.danger.disabled:hover, .notification\_widget.danger[disabled]:hover, fieldset[disabled] .notification\_widget.danger:hover, .notification\_widget.danger.disabled:focus, .notification\_widget.danger[disabled]:focus, fieldset[disabled] .notification\_widget.danger:focus, .notification\_widget.danger.disabled.focus, .notification\_widget.danger[disabled].focus, fieldset[disabled] .notification\_widget.danger.focus { background-color: #d9534f; border-color: #d43f3a; } .notification\_widget.danger .badge { color: #d9534f; background-color: #fff; } div#pager { background-color: #fff; font-size: 14px; line-height: 20px; overflow: hidden; display: none; position: fixed; bottom: 0px; width: 100%; max-height: 50%; padding-top: 8px; -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); /\* Display over codemirror \*/ z-index: 100; /\* Hack which prevents jquery ui resizable from changing top. \*/ top: auto !important; } div#pager pre { line-height: 1.21429em; color: #000; background-color: #f7f7f7; padding: 0.4em; } div#pager #pager-button-area { position: absolute; top: 8px; right: 20px; } div#pager #pager-contents { position: relative; overflow: auto; width: 100%; height: 100%; } div#pager #pager-contents #pager-container { position: relative; padding: 15px 0px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } div#pager .ui-resizable-handle { top: 0px; 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border-top: none; }@media print { div.cell { display: block; page-break-inside: avoid; } div.output\_wrapper { display: block; page-break-inside: avoid; } div.output { display: block; page-break-inside: avoid; } } Introduction[¶](#Introduction) ------------------------------ This is a tutorial of generation of simulations and projection. We first specify two templates, one in equatorial coordinates with `CAR` pixellisation and one in equatorial coordinates with `HEALPIX` pixellisation. We generate alms from a `CAMB` lensed power spectrum file and use them to generate a random CMB realisation in both templates. We then project the `HEALPIX` simulation into the CAR template and plot both the native `CAR` simulation and the projected `HEALPIX` simulation. We chose a low resolution `nside` to emphasize the effect of resolution Preamble[¶](#Preamble) ---------------------- `matplotlib` magic In [1]: ``` %matplotlib inline ``` Print versions used In [2]: ``` import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import pspy, pixell print(" Numpy :", np.\_\_version\_\_) print("Matplotlib :", mpl.\_\_version\_\_) print(" pixell :", pixell.\_\_version\_\_) print(" pspy :", pspy.\_\_version\_\_) ``` ``` Numpy : 1.18.0 Matplotlib : 3.1.2 pixell : 0.6.0+34.g23be32d pspy : 0+untagged.118.gbf1f0bc.dirty ``` Get default data dir from `pspy` and set Planck colormap as default In [3]: ``` from pspy.so\_config import DEFAULT\_DATA\_DIR pixell.colorize.mpl\_setdefault("planck") ``` Generation of the templates[¶](#Generation-of-the-templates) ------------------------------------------------------------ The `CAR` template will go from right ascension `ra0` to `ra1` and from declination `dec0` to `dec1` (all in degrees). It will have a resolution of 1 arcminute and it allows 3 components (stokes parameter in the case of CMB anisotropies). In [4]: ``` ra0, ra1 = -5, 5 dec0, dec1 = -5, 5 res = 1 ncomp = 3 from pspy import so\_map template\_car = so\_map.car\_template(ncomp, ra0, ra1, dec0, dec1, res) ``` We also generate an `HEALPIX` template for which we choose `nside=256` so that the resolution of `HEALPIX` is much smaller In [5]: ``` template\_healpix = so\_map.healpix\_template(ncomp, nside=256, coordinate="equ") ``` Read power spectrum and alm generation[¶](#Read-power-spectrum-and-alm-generation) ---------------------------------------------------------------------------------- We first have to compute the power spectra $C\_\ell$s using a Boltzmann solver such as [CAMB](https://camb.readthedocs.io/en/latest/) and we need to install it since this is a prerequisite of `pspy`. We can do it within this notebook by executing the following command In [6]: ``` %pip install camb ``` ``` Requirement already satisfied: camb in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (1.1.0) Requirement already satisfied: scipy>=1.0 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.4.1) Requirement already satisfied: six in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.13.0) Requirement already satisfied: sympy>=1.0 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.5) Requirement already satisfied: numpy>=1.13.3 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from scipy>=1.0->camb) (1.18.0) Requirement already satisfied: mpmath>=0.19 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from sympy>=1.0->camb) (1.1.0) Note: you may need to restart the kernel to use updated packages. ``` To make sure everything goes well, we can import `CAMB` and check its version In [7]: ``` import camb print("CAMB version:", camb.\_\_version\_\_) ``` ``` CAMB version: 1.1.0 ``` Now that `CAMB` is properly installed, we will produce $C\_\ell$ data from $\ell$min=2 to $\ell$max=104 for the following set of $\Lambda$CDM parameters In [8]: ``` lmin, lmax = 2, 10\*\*4 l = np.arange(lmin, lmax) cosmo\_params = { "H0": 67.5, "As": 1e-10\*np.exp(3.044), "ombh2": 0.02237, "omch2": 0.1200, "ns": 0.9649, "Alens": 1.0, "tau": 0.0544 } pars = camb.set\_params(\*\*cosmo\_params) pars.set\_for\_lmax(lmax, lens\_potential\_accuracy=1) results = camb.get\_results(pars) powers = results.get\_cmb\_power\_spectra(pars, CMB\_unit="muK") ``` We finally have to write $C\_\ell$ into a file to read back using the `pixell.powspec` function In [9]: ``` import os output\_dir = "/tmp/tutorial\_projection" os.makedirs(output\_dir, exist\_ok=True) cl\_file = output\_dir + "/cl\_camb.dat" np.savetxt(cl\_file, np.hstack([l[:, np.newaxis], powers["total"][lmin:lmax]])) from pixell import powspec ps = powspec.read\_spectrum(cl\_file)[:ncomp,:ncomp] ``` and generate alms from the power spectrum up to `lmax = 5000` In [10]: ``` from pixell import curvedsky lmax = 5000 alms = curvedsky.rand\_alm(ps, lmax=lmax) ``` Computation of stokes parameters[¶](#Computation-of-stokes-parameters) ---------------------------------------------------------------------- We compute the stokes parameters from the alms in both templates In [11]: ``` from pspy import sph\_tools map\_healpix = sph\_tools.alm2map(alms, template\_healpix) map\_car = sph\_tools.alm2map(alms, template\_car) ``` and we project the `HEALPIX` map into the `CAR` template In [12]: ``` map\_healpix\_proj = so\_map.healpix2car(map\_healpix, map\_car, lmax=lmax) ``` ``` WARNING: your lmax is too large, setting it to 3*nside-1 now Preparing SHT T -> alm float64 complex128 P -> alm Projecting ``` Showing maps[¶](#Showing-maps) ------------------------------ We plot both the native `CAR` map and the `HEALPIX` projected to `CAR` map. They contain the same CMB but have different resolutions. In [13]: ``` fig, axes = plt.subplots(2, 3, figsize=(9, 6), sharex=True, sharey=True) fields = ["T", "Q", "U"] kwargs = dict(extent=[ra1, ra0, dec0, dec1], origin="lower") for i, field in enumerate(fields): kwargs["vmin"] = np.min([map\_car.data[i], map\_healpix\_proj.data[i]]) kwargs["vmax"] = np.max([map\_car.data[i], map\_healpix\_proj.data[i]]) axes[0, i].imshow(map\_car.data[i], \*\*kwargs) axes[1, i].imshow(map\_healpix\_proj.data[i], \*\*kwargs) axes[0, i].set\_title(fields[i]) axes[0, 0].set\_ylabel("CAR") axes[1, 0].set\_ylabel("HEALPIX") plt.tight\_layout() ```  We can also use the `plot` function from `pspy.so_map` and set the output path to get individual images for each component T, Q, U. 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margin-bottom: 9px; } h4 small, .h4 small, h5 small, .h5 small, h6 small, .h6 small, h4 .small, .h4 .small, h5 .small, .h5 .small, h6 .small, .h6 .small { font-size: 75%; } h1, .h1 { font-size: 33px; } h2, .h2 { font-size: 27px; } h3, .h3 { font-size: 23px; } h4, .h4 { font-size: 17px; } h5, .h5 { font-size: 13px; } h6, .h6 { font-size: 12px; } p { margin: 0 0 9px; } .lead { margin-bottom: 18px; font-size: 14px; font-weight: 300; line-height: 1.4; } @media (min-width: 768px) { .lead { font-size: 19.5px; } } small, .small { font-size: 92%; } mark, .mark { background-color: #fcf8e3; padding: .2em; } .text-left { text-align: left; } .text-right { text-align: right; } .text-center { text-align: center; } .text-justify { text-align: justify; } .text-nowrap { white-space: nowrap; } .text-lowercase { text-transform: lowercase; } .text-uppercase { text-transform: uppercase; } .text-capitalize { text-transform: capitalize; } .text-muted { color: #777777; } .text-primary { color: #337ab7; } a.text-primary:hover, a.text-primary:focus { color: #286090; } .text-success { color: #3c763d; } a.text-success:hover, a.text-success:focus { color: #2b542c; } .text-info { color: #31708f; } a.text-info:hover, a.text-info:focus { color: #245269; } .text-warning { color: #8a6d3b; } a.text-warning:hover, a.text-warning:focus { color: #66512c; } .text-danger { color: #a94442; } a.text-danger:hover, a.text-danger:focus { color: #843534; } .bg-primary { color: #fff; background-color: #337ab7; } a.bg-primary:hover, a.bg-primary:focus { background-color: #286090; } .bg-success { background-color: #dff0d8; } a.bg-success:hover, a.bg-success:focus { background-color: #c1e2b3; } .bg-info { background-color: #d9edf7; } a.bg-info:hover, a.bg-info:focus { background-color: #afd9ee; } .bg-warning { background-color: #fcf8e3; } a.bg-warning:hover, a.bg-warning:focus { background-color: #f7ecb5; } .bg-danger { background-color: #f2dede; } a.bg-danger:hover, a.bg-danger:focus { background-color: #e4b9b9; } .page-header { padding-bottom: 8px; margin: 36px 0 18px; border-bottom: 1px solid #eeeeee; } ul, ol { margin-top: 0; margin-bottom: 9px; } ul ul, ol ul, ul ol, ol ol { margin-bottom: 0; } .list-unstyled { padding-left: 0; list-style: none; } .list-inline { padding-left: 0; list-style: none; margin-left: -5px; } .list-inline > li { display: inline-block; padding-left: 5px; padding-right: 5px; } dl { margin-top: 0; margin-bottom: 18px; } dt, dd { line-height: 1.42857143; } dt { font-weight: bold; } dd { margin-left: 0; } @media (min-width: 541px) { .dl-horizontal dt { float: left; width: 160px; clear: left; text-align: right; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; } .dl-horizontal dd { margin-left: 180px; } } abbr[title], abbr[data-original-title] { cursor: help; border-bottom: 1px dotted #777777; } .initialism { font-size: 90%; text-transform: uppercase; } blockquote { padding: 9px 18px; margin: 0 0 18px; font-size: inherit; border-left: 5px solid #eeeeee; } blockquote p:last-child, blockquote ul:last-child, blockquote ol:last-child { margin-bottom: 0; } blockquote footer, blockquote small, blockquote .small { display: block; font-size: 80%; line-height: 1.42857143; color: #777777; } blockquote footer:before, blockquote small:before, blockquote .small:before { content: '\2014 \00A0'; } .blockquote-reverse, blockquote.pull-right { padding-right: 15px; padding-left: 0; border-right: 5px solid #eeeeee; border-left: 0; text-align: right; } .blockquote-reverse footer:before, blockquote.pull-right footer:before, .blockquote-reverse small:before, blockquote.pull-right small:before, .blockquote-reverse .small:before, blockquote.pull-right .small:before { content: ''; } .blockquote-reverse footer:after, blockquote.pull-right footer:after, .blockquote-reverse small:after, blockquote.pull-right small:after, .blockquote-reverse .small:after, blockquote.pull-right .small:after { content: '\00A0 \2014'; } address { margin-bottom: 18px; font-style: normal; line-height: 1.42857143; } code, kbd, pre, samp { font-family: monospace; } code { padding: 2px 4px; font-size: 90%; color: #c7254e; background-color: #f9f2f4; border-radius: 2px; } kbd { padding: 2px 4px; font-size: 90%; color: #888; background-color: transparent; border-radius: 1px; box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); } kbd kbd { padding: 0; font-size: 100%; font-weight: bold; box-shadow: none; } pre { display: block; padding: 8.5px; margin: 0 0 9px; font-size: 12px; line-height: 1.42857143; word-break: break-all; word-wrap: break-word; color: #333333; background-color: #f5f5f5; border: 1px solid #ccc; border-radius: 2px; } pre code { padding: 0; font-size: inherit; color: inherit; white-space: pre-wrap; background-color: transparent; border-radius: 0; } .pre-scrollable { max-height: 340px; overflow-y: scroll; } .container { margin-right: auto; margin-left: auto; padding-left: 0px; padding-right: 0px; } @media (min-width: 768px) { .container { width: 768px; } } @media (min-width: 992px) { .container { width: 940px; } } @media (min-width: 1200px) { .container { width: 1140px; } } .container-fluid { margin-right: auto; margin-left: auto; padding-left: 0px; padding-right: 0px; } .row { margin-left: 0px; margin-right: 0px; } .col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 { position: relative; min-height: 1px; padding-left: 0px; padding-right: 0px; } .col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 { float: left; } .col-xs-12 { width: 100%; } .col-xs-11 { width: 91.66666667%; } .col-xs-10 { width: 83.33333333%; } .col-xs-9 { width: 75%; } .col-xs-8 { width: 66.66666667%; } .col-xs-7 { width: 58.33333333%; } .col-xs-6 { width: 50%; } .col-xs-5 { width: 41.66666667%; } .col-xs-4 { width: 33.33333333%; } .col-xs-3 { width: 25%; } .col-xs-2 { width: 16.66666667%; } .col-xs-1 { width: 8.33333333%; } .col-xs-pull-12 { right: 100%; } .col-xs-pull-11 { right: 91.66666667%; } .col-xs-pull-10 { right: 83.33333333%; } .col-xs-pull-9 { right: 75%; } .col-xs-pull-8 { right: 66.66666667%; } .col-xs-pull-7 { right: 58.33333333%; } .col-xs-pull-6 { right: 50%; } .col-xs-pull-5 { right: 41.66666667%; } .col-xs-pull-4 { right: 33.33333333%; } .col-xs-pull-3 { right: 25%; } .col-xs-pull-2 { right: 16.66666667%; } .col-xs-pull-1 { right: 8.33333333%; } .col-xs-pull-0 { right: auto; } .col-xs-push-12 { left: 100%; } .col-xs-push-11 { left: 91.66666667%; } .col-xs-push-10 { left: 83.33333333%; } .col-xs-push-9 { left: 75%; } .col-xs-push-8 { left: 66.66666667%; } .col-xs-push-7 { left: 58.33333333%; } .col-xs-push-6 { left: 50%; } .col-xs-push-5 { left: 41.66666667%; } .col-xs-push-4 { left: 33.33333333%; } .col-xs-push-3 { left: 25%; } .col-xs-push-2 { left: 16.66666667%; } .col-xs-push-1 { left: 8.33333333%; } .col-xs-push-0 { left: auto; } .col-xs-offset-12 { margin-left: 100%; } .col-xs-offset-11 { margin-left: 91.66666667%; } .col-xs-offset-10 { margin-left: 83.33333333%; } .col-xs-offset-9 { margin-left: 75%; } .col-xs-offset-8 { margin-left: 66.66666667%; } .col-xs-offset-7 { margin-left: 58.33333333%; } .col-xs-offset-6 { margin-left: 50%; } .col-xs-offset-5 { margin-left: 41.66666667%; } .col-xs-offset-4 { margin-left: 33.33333333%; } .col-xs-offset-3 { margin-left: 25%; } .col-xs-offset-2 { margin-left: 16.66666667%; } .col-xs-offset-1 { margin-left: 8.33333333%; } .col-xs-offset-0 { margin-left: 0%; } @media (min-width: 768px) { .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 { float: left; } .col-sm-12 { width: 100%; } .col-sm-11 { width: 91.66666667%; } .col-sm-10 { width: 83.33333333%; } .col-sm-9 { width: 75%; } .col-sm-8 { width: 66.66666667%; } .col-sm-7 { width: 58.33333333%; } .col-sm-6 { width: 50%; } .col-sm-5 { width: 41.66666667%; } .col-sm-4 { width: 33.33333333%; } .col-sm-3 { width: 25%; } .col-sm-2 { width: 16.66666667%; } .col-sm-1 { width: 8.33333333%; } .col-sm-pull-12 { right: 100%; } .col-sm-pull-11 { right: 91.66666667%; } .col-sm-pull-10 { right: 83.33333333%; } .col-sm-pull-9 { right: 75%; } .col-sm-pull-8 { right: 66.66666667%; } .col-sm-pull-7 { right: 58.33333333%; } .col-sm-pull-6 { right: 50%; } .col-sm-pull-5 { right: 41.66666667%; } .col-sm-pull-4 { right: 33.33333333%; } .col-sm-pull-3 { right: 25%; } .col-sm-pull-2 { right: 16.66666667%; } .col-sm-pull-1 { right: 8.33333333%; } .col-sm-pull-0 { right: auto; } .col-sm-push-12 { left: 100%; } .col-sm-push-11 { left: 91.66666667%; } .col-sm-push-10 { left: 83.33333333%; } .col-sm-push-9 { left: 75%; } .col-sm-push-8 { left: 66.66666667%; } .col-sm-push-7 { left: 58.33333333%; } .col-sm-push-6 { left: 50%; } .col-sm-push-5 { left: 41.66666667%; } .col-sm-push-4 { left: 33.33333333%; } .col-sm-push-3 { left: 25%; } .col-sm-push-2 { left: 16.66666667%; } .col-sm-push-1 { left: 8.33333333%; } .col-sm-push-0 { left: auto; } .col-sm-offset-12 { margin-left: 100%; } .col-sm-offset-11 { margin-left: 91.66666667%; } .col-sm-offset-10 { margin-left: 83.33333333%; } .col-sm-offset-9 { margin-left: 75%; } .col-sm-offset-8 { margin-left: 66.66666667%; } .col-sm-offset-7 { margin-left: 58.33333333%; } .col-sm-offset-6 { margin-left: 50%; } .col-sm-offset-5 { margin-left: 41.66666667%; } .col-sm-offset-4 { margin-left: 33.33333333%; } .col-sm-offset-3 { margin-left: 25%; } .col-sm-offset-2 { margin-left: 16.66666667%; } .col-sm-offset-1 { margin-left: 8.33333333%; } .col-sm-offset-0 { margin-left: 0%; } } @media (min-width: 992px) { .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 { float: left; } .col-md-12 { width: 100%; } .col-md-11 { width: 91.66666667%; } .col-md-10 { width: 83.33333333%; } .col-md-9 { width: 75%; } .col-md-8 { width: 66.66666667%; } .col-md-7 { width: 58.33333333%; } .col-md-6 { width: 50%; } .col-md-5 { width: 41.66666667%; } .col-md-4 { width: 33.33333333%; } .col-md-3 { width: 25%; } .col-md-2 { width: 16.66666667%; } .col-md-1 { width: 8.33333333%; } .col-md-pull-12 { right: 100%; } .col-md-pull-11 { right: 91.66666667%; } .col-md-pull-10 { right: 83.33333333%; } .col-md-pull-9 { right: 75%; } .col-md-pull-8 { right: 66.66666667%; } .col-md-pull-7 { right: 58.33333333%; } .col-md-pull-6 { right: 50%; } .col-md-pull-5 { right: 41.66666667%; } .col-md-pull-4 { right: 33.33333333%; } .col-md-pull-3 { right: 25%; } .col-md-pull-2 { right: 16.66666667%; } .col-md-pull-1 { right: 8.33333333%; } .col-md-pull-0 { right: auto; } .col-md-push-12 { left: 100%; } .col-md-push-11 { left: 91.66666667%; } .col-md-push-10 { left: 83.33333333%; } .col-md-push-9 { left: 75%; } .col-md-push-8 { left: 66.66666667%; } .col-md-push-7 { left: 58.33333333%; } .col-md-push-6 { left: 50%; } .col-md-push-5 { left: 41.66666667%; } .col-md-push-4 { left: 33.33333333%; } .col-md-push-3 { left: 25%; } .col-md-push-2 { left: 16.66666667%; } .col-md-push-1 { left: 8.33333333%; } .col-md-push-0 { left: auto; } .col-md-offset-12 { margin-left: 100%; } .col-md-offset-11 { margin-left: 91.66666667%; } .col-md-offset-10 { margin-left: 83.33333333%; } .col-md-offset-9 { margin-left: 75%; } .col-md-offset-8 { margin-left: 66.66666667%; } .col-md-offset-7 { margin-left: 58.33333333%; } .col-md-offset-6 { margin-left: 50%; } .col-md-offset-5 { margin-left: 41.66666667%; } .col-md-offset-4 { margin-left: 33.33333333%; } .col-md-offset-3 { margin-left: 25%; } .col-md-offset-2 { margin-left: 16.66666667%; } .col-md-offset-1 { margin-left: 8.33333333%; } .col-md-offset-0 { margin-left: 0%; } } @media (min-width: 1200px) { .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 { float: left; } .col-lg-12 { width: 100%; } .col-lg-11 { width: 91.66666667%; } .col-lg-10 { width: 83.33333333%; } .col-lg-9 { width: 75%; } .col-lg-8 { width: 66.66666667%; } .col-lg-7 { width: 58.33333333%; } .col-lg-6 { width: 50%; } .col-lg-5 { width: 41.66666667%; } .col-lg-4 { width: 33.33333333%; } .col-lg-3 { width: 25%; } .col-lg-2 { width: 16.66666667%; } .col-lg-1 { width: 8.33333333%; } .col-lg-pull-12 { right: 100%; } .col-lg-pull-11 { right: 91.66666667%; } .col-lg-pull-10 { right: 83.33333333%; } .col-lg-pull-9 { right: 75%; } .col-lg-pull-8 { right: 66.66666667%; } .col-lg-pull-7 { right: 58.33333333%; } .col-lg-pull-6 { right: 50%; } .col-lg-pull-5 { right: 41.66666667%; } .col-lg-pull-4 { right: 33.33333333%; } .col-lg-pull-3 { right: 25%; } .col-lg-pull-2 { right: 16.66666667%; } .col-lg-pull-1 { right: 8.33333333%; } .col-lg-pull-0 { right: auto; } .col-lg-push-12 { left: 100%; } .col-lg-push-11 { left: 91.66666667%; } .col-lg-push-10 { left: 83.33333333%; } .col-lg-push-9 { left: 75%; } .col-lg-push-8 { left: 66.66666667%; } .col-lg-push-7 { left: 58.33333333%; } .col-lg-push-6 { left: 50%; } .col-lg-push-5 { left: 41.66666667%; } .col-lg-push-4 { left: 33.33333333%; } .col-lg-push-3 { left: 25%; } .col-lg-push-2 { left: 16.66666667%; } .col-lg-push-1 { left: 8.33333333%; } .col-lg-push-0 { left: auto; } .col-lg-offset-12 { margin-left: 100%; } .col-lg-offset-11 { margin-left: 91.66666667%; } .col-lg-offset-10 { margin-left: 83.33333333%; } .col-lg-offset-9 { margin-left: 75%; } .col-lg-offset-8 { margin-left: 66.66666667%; } .col-lg-offset-7 { margin-left: 58.33333333%; } .col-lg-offset-6 { margin-left: 50%; } .col-lg-offset-5 { margin-left: 41.66666667%; } .col-lg-offset-4 { margin-left: 33.33333333%; } .col-lg-offset-3 { margin-left: 25%; } .col-lg-offset-2 { margin-left: 16.66666667%; } .col-lg-offset-1 { margin-left: 8.33333333%; } .col-lg-offset-0 { margin-left: 0%; } } table { background-color: transparent; } caption { padding-top: 8px; padding-bottom: 8px; color: #777777; text-align: left; } th { text-align: left; } .table { width: 100%; max-width: 100%; margin-bottom: 18px; } .table > thead > tr > th, .table > tbody > tr > th, .table > tfoot > tr > th, .table > thead > tr > td, .table > tbody > tr > td, .table > tfoot > tr > td { padding: 8px; line-height: 1.42857143; vertical-align: top; border-top: 1px solid #ddd; } .table > thead > tr > th { vertical-align: bottom; border-bottom: 2px solid #ddd; } .table > caption + thead > tr:first-child > th, .table > colgroup + thead > tr:first-child > th, .table > thead:first-child > tr:first-child > th, .table > caption + thead > tr:first-child > td, .table > colgroup + thead > tr:first-child > td, .table > thead:first-child > tr:first-child > td { border-top: 0; } .table > tbody + tbody { border-top: 2px solid #ddd; } .table .table { background-color: #fff; } .table-condensed > thead > tr > th, .table-condensed > tbody > tr > th, .table-condensed > tfoot > tr > th, .table-condensed > thead > tr > td, .table-condensed > tbody > tr > td, .table-condensed > tfoot > tr > td { padding: 5px; } .table-bordered { border: 1px solid #ddd; } .table-bordered > thead > tr > th, .table-bordered > tbody > tr > th, .table-bordered > tfoot > tr > th, .table-bordered > thead > tr > td, .table-bordered > tbody > tr > td, .table-bordered > tfoot > tr > td { border: 1px solid #ddd; } .table-bordered > thead > tr > th, .table-bordered > thead > tr > td { border-bottom-width: 2px; } .table-striped > tbody > tr:nth-of-type(odd) { background-color: #f9f9f9; } .table-hover > tbody > tr:hover { background-color: #f5f5f5; } table col[class\*="col-"] { position: static; float: none; display: table-column; } table td[class\*="col-"], table th[class\*="col-"] { position: static; float: none; display: table-cell; } .table > thead > tr > td.active, .table > tbody > tr > td.active, .table > tfoot > tr > td.active, .table > thead > tr > th.active, .table > tbody > tr > th.active, .table > tfoot > tr > th.active, .table > thead > tr.active > td, .table > tbody > tr.active > td, .table > tfoot > tr.active > td, .table > thead > tr.active > th, .table > tbody > tr.active > th, .table > tfoot > tr.active > th { background-color: #f5f5f5; } .table-hover > tbody > tr > td.active:hover, .table-hover > tbody > tr > th.active:hover, .table-hover > tbody > tr.active:hover > td, .table-hover > tbody > tr:hover > .active, .table-hover > tbody > tr.active:hover > th { background-color: #e8e8e8; } .table > thead > tr > td.success, .table > tbody > tr > td.success, .table > tfoot > tr > td.success, .table > thead > tr > th.success, .table > tbody > tr > th.success, .table > tfoot > tr > th.success, .table > thead > tr.success > td, .table > tbody > tr.success > td, .table > tfoot > tr.success > td, .table > thead > tr.success > th, .table > tbody > tr.success > th, .table > tfoot > tr.success > th { background-color: #dff0d8; } .table-hover > tbody > tr > td.success:hover, .table-hover > tbody > tr > th.success:hover, .table-hover > tbody > tr.success:hover > td, .table-hover > tbody > tr:hover > .success, .table-hover > tbody > tr.success:hover > th { background-color: #d0e9c6; } .table > thead > tr > td.info, .table > tbody > tr > td.info, .table > tfoot > tr > td.info, .table > thead > tr > th.info, .table > tbody > tr > th.info, .table > tfoot > tr > th.info, .table > thead > tr.info > td, .table > tbody > tr.info > td, .table > tfoot > tr.info > td, .table > thead > tr.info > th, .table > tbody > tr.info > th, .table > tfoot > tr.info > th { background-color: #d9edf7; } .table-hover > tbody > tr > td.info:hover, .table-hover > tbody > tr > th.info:hover, .table-hover > tbody > tr.info:hover > td, .table-hover > tbody > tr:hover > .info, .table-hover > tbody > tr.info:hover > th { background-color: #c4e3f3; } .table > thead > tr > td.warning, .table > tbody > tr > td.warning, .table > tfoot > tr > td.warning, .table > thead > tr > th.warning, .table > tbody > tr > th.warning, .table > tfoot > tr > th.warning, .table > thead > tr.warning > td, .table > tbody > tr.warning > td, .table > tfoot > tr.warning > td, .table > thead > tr.warning > th, .table > tbody > tr.warning > th, .table > tfoot > tr.warning > th { background-color: #fcf8e3; } .table-hover > tbody > tr > td.warning:hover, .table-hover > tbody > tr > th.warning:hover, .table-hover > tbody > tr.warning:hover > td, .table-hover > tbody > tr:hover > .warning, .table-hover > tbody > tr.warning:hover > th { background-color: #faf2cc; } .table > thead > tr > td.danger, .table > tbody > tr > td.danger, .table > tfoot > tr > td.danger, .table > thead > tr > th.danger, .table > tbody > tr > th.danger, .table > tfoot > tr > th.danger, .table > thead > tr.danger > td, .table > tbody > tr.danger > td, .table > tfoot > tr.danger > td, .table > thead > tr.danger > th, .table > tbody > tr.danger > th, .table > tfoot > tr.danger > th { background-color: #f2dede; } .table-hover > tbody > tr > td.danger:hover, .table-hover > tbody > tr > th.danger:hover, .table-hover > tbody > tr.danger:hover > td, .table-hover > tbody > tr:hover > .danger, .table-hover > tbody > tr.danger:hover > th { background-color: #ebcccc; } .table-responsive { overflow-x: auto; min-height: 0.01%; } @media screen and (max-width: 767px) { .table-responsive { width: 100%; margin-bottom: 13.5px; overflow-y: hidden; -ms-overflow-style: -ms-autohiding-scrollbar; border: 1px solid #ddd; } .table-responsive > .table { margin-bottom: 0; } .table-responsive > .table > thead > tr > th, .table-responsive > .table > tbody > tr > th, .table-responsive > .table > tfoot > tr > th, .table-responsive > .table > thead > tr > td, .table-responsive > .table > tbody > tr > td, .table-responsive > .table > tfoot > tr > td { white-space: nowrap; } .table-responsive > .table-bordered { border: 0; } .table-responsive > .table-bordered > thead > tr > th:first-child, .table-responsive > .table-bordered > tbody > tr > th:first-child, .table-responsive > .table-bordered > tfoot > tr > th:first-child, .table-responsive > .table-bordered > thead > tr > td:first-child, .table-responsive > .table-bordered > tbody > tr > td:first-child, .table-responsive > .table-bordered > tfoot > tr > td:first-child { border-left: 0; } .table-responsive > .table-bordered > thead > tr > th:last-child, .table-responsive > .table-bordered > tbody > tr > th:last-child, .table-responsive > .table-bordered > tfoot > tr > th:last-child, .table-responsive > .table-bordered > thead > tr > td:last-child, .table-responsive > .table-bordered > tbody > tr > td:last-child, .table-responsive > .table-bordered > tfoot > tr > td:last-child { border-right: 0; } .table-responsive > .table-bordered > tbody > tr:last-child > th, .table-responsive > .table-bordered > tfoot > tr:last-child > th, .table-responsive > .table-bordered > tbody > tr:last-child > td, .table-responsive > .table-bordered > tfoot > tr:last-child > td { border-bottom: 0; } } fieldset { padding: 0; margin: 0; border: 0; min-width: 0; } legend { display: block; width: 100%; padding: 0; margin-bottom: 18px; font-size: 19.5px; line-height: inherit; color: #333333; border: 0; border-bottom: 1px solid #e5e5e5; } label { display: inline-block; max-width: 100%; margin-bottom: 5px; font-weight: bold; } input[type="search"] { -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; } input[type="radio"], input[type="checkbox"] { margin: 4px 0 0; margin-top: 1px \9; line-height: normal; } input[type="file"] { display: block; } input[type="range"] { display: block; width: 100%; } select[multiple], select[size] { height: auto; } input[type="file"]:focus, input[type="radio"]:focus, input[type="checkbox"]:focus { outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } output { display: block; padding-top: 7px; font-size: 13px; line-height: 1.42857143; color: #555555; } .form-control { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; } .form-control:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .form-control::-moz-placeholder { color: #999; opacity: 1; } .form-control:-ms-input-placeholder { color: #999; } .form-control::-webkit-input-placeholder { color: #999; } .form-control::-ms-expand { border: 0; background-color: transparent; } .form-control[disabled], .form-control[readonly], fieldset[disabled] .form-control { background-color: #eeeeee; opacity: 1; } .form-control[disabled], fieldset[disabled] .form-control { cursor: not-allowed; } textarea.form-control { height: auto; } input[type="search"] { -webkit-appearance: none; } @media screen and (-webkit-min-device-pixel-ratio: 0) { input[type="date"].form-control, input[type="time"].form-control, input[type="datetime-local"].form-control, input[type="month"].form-control { line-height: 32px; } input[type="date"].input-sm, input[type="time"].input-sm, input[type="datetime-local"].input-sm, input[type="month"].input-sm, .input-group-sm input[type="date"], .input-group-sm input[type="time"], .input-group-sm input[type="datetime-local"], .input-group-sm input[type="month"] { line-height: 30px; } input[type="date"].input-lg, input[type="time"].input-lg, input[type="datetime-local"].input-lg, input[type="month"].input-lg, .input-group-lg input[type="date"], .input-group-lg input[type="time"], .input-group-lg input[type="datetime-local"], .input-group-lg input[type="month"] { line-height: 45px; } } .form-group { margin-bottom: 15px; } .radio, .checkbox { position: relative; display: block; margin-top: 10px; margin-bottom: 10px; } .radio label, .checkbox label { min-height: 18px; padding-left: 20px; margin-bottom: 0; font-weight: normal; cursor: pointer; } .radio input[type="radio"], .radio-inline input[type="radio"], .checkbox input[type="checkbox"], .checkbox-inline input[type="checkbox"] { position: absolute; margin-left: -20px; margin-top: 4px \9; } .radio + .radio, .checkbox + .checkbox { margin-top: -5px; } .radio-inline, .checkbox-inline { position: relative; display: inline-block; padding-left: 20px; margin-bottom: 0; vertical-align: middle; font-weight: normal; cursor: pointer; } .radio-inline + .radio-inline, .checkbox-inline + .checkbox-inline { margin-top: 0; margin-left: 10px; } input[type="radio"][disabled], input[type="checkbox"][disabled], input[type="radio"].disabled, input[type="checkbox"].disabled, fieldset[disabled] input[type="radio"], fieldset[disabled] input[type="checkbox"] { cursor: not-allowed; } .radio-inline.disabled, .checkbox-inline.disabled, fieldset[disabled] .radio-inline, fieldset[disabled] .checkbox-inline { cursor: not-allowed; } .radio.disabled label, .checkbox.disabled label, fieldset[disabled] .radio label, fieldset[disabled] .checkbox label { cursor: not-allowed; } .form-control-static { padding-top: 7px; padding-bottom: 7px; margin-bottom: 0; min-height: 31px; } .form-control-static.input-lg, .form-control-static.input-sm { padding-left: 0; padding-right: 0; } .input-sm { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } select.input-sm { height: 30px; line-height: 30px; } textarea.input-sm, select[multiple].input-sm { height: auto; } .form-group-sm .form-control { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .form-group-sm select.form-control { height: 30px; line-height: 30px; } .form-group-sm textarea.form-control, .form-group-sm select[multiple].form-control { height: auto; } .form-group-sm .form-control-static { height: 30px; min-height: 30px; padding: 6px 10px; font-size: 12px; line-height: 1.5; } .input-lg { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } select.input-lg { height: 45px; line-height: 45px; } textarea.input-lg, select[multiple].input-lg { height: auto; } .form-group-lg .form-control { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } .form-group-lg select.form-control { height: 45px; line-height: 45px; } .form-group-lg textarea.form-control, .form-group-lg select[multiple].form-control { height: auto; } .form-group-lg .form-control-static { height: 45px; min-height: 35px; padding: 11px 16px; font-size: 17px; line-height: 1.3333333; } .has-feedback { position: relative; } .has-feedback .form-control { padding-right: 40px; } .form-control-feedback { position: absolute; top: 0; right: 0; z-index: 2; display: block; width: 32px; height: 32px; line-height: 32px; text-align: center; pointer-events: none; } .input-lg + .form-control-feedback, .input-group-lg + .form-control-feedback, .form-group-lg .form-control + .form-control-feedback { width: 45px; height: 45px; line-height: 45px; } .input-sm + .form-control-feedback, .input-group-sm + .form-control-feedback, .form-group-sm .form-control + .form-control-feedback { width: 30px; height: 30px; line-height: 30px; } .has-success .help-block, .has-success .control-label, .has-success .radio, .has-success .checkbox, .has-success .radio-inline, .has-success .checkbox-inline, .has-success.radio label, .has-success.checkbox label, .has-success.radio-inline label, .has-success.checkbox-inline label { color: #3c763d; } .has-success .form-control { border-color: #3c763d; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-success .form-control:focus { border-color: #2b542c; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; } .has-success .input-group-addon { color: #3c763d; border-color: #3c763d; background-color: #dff0d8; } .has-success .form-control-feedback { color: #3c763d; } .has-warning .help-block, .has-warning .control-label, .has-warning .radio, .has-warning .checkbox, .has-warning .radio-inline, .has-warning .checkbox-inline, .has-warning.radio label, .has-warning.checkbox label, .has-warning.radio-inline label, .has-warning.checkbox-inline label { color: #8a6d3b; } .has-warning .form-control { border-color: #8a6d3b; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-warning .form-control:focus { border-color: #66512c; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; } .has-warning .input-group-addon { color: #8a6d3b; border-color: #8a6d3b; background-color: #fcf8e3; } .has-warning .form-control-feedback { color: #8a6d3b; } .has-error .help-block, .has-error .control-label, .has-error .radio, .has-error .checkbox, .has-error .radio-inline, .has-error .checkbox-inline, .has-error.radio label, .has-error.checkbox label, .has-error.radio-inline label, .has-error.checkbox-inline label { color: #a94442; } .has-error .form-control { border-color: #a94442; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-error .form-control:focus { border-color: #843534; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; } .has-error .input-group-addon { color: #a94442; border-color: #a94442; background-color: #f2dede; } .has-error .form-control-feedback { color: #a94442; } .has-feedback label ~ .form-control-feedback { top: 23px; } .has-feedback label.sr-only ~ .form-control-feedback { top: 0; } .help-block { display: block; margin-top: 5px; margin-bottom: 10px; color: #404040; } @media (min-width: 768px) { .form-inline .form-group { display: inline-block; margin-bottom: 0; vertical-align: middle; } .form-inline .form-control { display: inline-block; width: auto; vertical-align: middle; } .form-inline .form-control-static { display: inline-block; } .form-inline .input-group { display: inline-table; vertical-align: middle; } .form-inline .input-group .input-group-addon, .form-inline .input-group .input-group-btn, .form-inline .input-group .form-control { width: auto; } .form-inline .input-group > .form-control { width: 100%; } .form-inline .control-label { margin-bottom: 0; vertical-align: middle; } .form-inline .radio, .form-inline .checkbox { display: inline-block; margin-top: 0; margin-bottom: 0; vertical-align: middle; } .form-inline .radio label, .form-inline .checkbox label { padding-left: 0; } .form-inline .radio input[type="radio"], .form-inline .checkbox input[type="checkbox"] { position: relative; margin-left: 0; } .form-inline .has-feedback .form-control-feedback { top: 0; } } .form-horizontal .radio, .form-horizontal .checkbox, .form-horizontal .radio-inline, .form-horizontal .checkbox-inline { margin-top: 0; margin-bottom: 0; padding-top: 7px; } .form-horizontal .radio, .form-horizontal .checkbox { min-height: 25px; } .form-horizontal .form-group { margin-left: 0px; margin-right: 0px; } @media (min-width: 768px) { .form-horizontal .control-label { text-align: right; margin-bottom: 0; padding-top: 7px; } } .form-horizontal .has-feedback .form-control-feedback { right: 0px; } @media (min-width: 768px) { .form-horizontal .form-group-lg .control-label { padding-top: 11px; font-size: 17px; } } @media (min-width: 768px) { .form-horizontal .form-group-sm .control-label { padding-top: 6px; font-size: 12px; } } .btn { display: inline-block; margin-bottom: 0; font-weight: normal; text-align: center; vertical-align: middle; touch-action: manipulation; cursor: pointer; background-image: none; border: 1px solid transparent; white-space: nowrap; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; border-radius: 2px; -webkit-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; } .btn:focus, .btn:active:focus, .btn.active:focus, .btn.focus, .btn:active.focus, .btn.active.focus { outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } .btn:hover, .btn:focus, .btn.focus { color: #333; text-decoration: none; } .btn:active, .btn.active { outline: 0; background-image: none; -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); } .btn.disabled, .btn[disabled], fieldset[disabled] .btn { cursor: not-allowed; opacity: 0.65; filter: alpha(opacity=65); -webkit-box-shadow: none; box-shadow: none; } a.btn.disabled, fieldset[disabled] a.btn { pointer-events: none; } .btn-default { color: #333; background-color: #fff; border-color: #ccc; } .btn-default:focus, .btn-default.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } .btn-default:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } .btn-default:active, .btn-default.active, .open > .dropdown-toggle.btn-default { color: #333; background-color: #e6e6e6; border-color: #adadad; } .btn-default:active:hover, .btn-default.active:hover, .open > .dropdown-toggle.btn-default:hover, .btn-default:active:focus, .btn-default.active:focus, .open > .dropdown-toggle.btn-default:focus, .btn-default:active.focus, .btn-default.active.focus, .open > .dropdown-toggle.btn-default.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } .btn-default:active, .btn-default.active, .open > .dropdown-toggle.btn-default { background-image: none; } .btn-default.disabled:hover, .btn-default[disabled]:hover, fieldset[disabled] .btn-default:hover, .btn-default.disabled:focus, .btn-default[disabled]:focus, fieldset[disabled] .btn-default:focus, .btn-default.disabled.focus, .btn-default[disabled].focus, fieldset[disabled] .btn-default.focus { background-color: #fff; border-color: #ccc; } .btn-default .badge { color: #fff; background-color: #333; } .btn-primary { color: #fff; background-color: #337ab7; border-color: #2e6da4; } .btn-primary:focus, .btn-primary.focus { color: #fff; background-color: #286090; border-color: #122b40; } .btn-primary:hover { color: #fff; background-color: #286090; border-color: #204d74; } .btn-primary:active, .btn-primary.active, .open > .dropdown-toggle.btn-primary { color: #fff; background-color: #286090; border-color: #204d74; } .btn-primary:active:hover, .btn-primary.active:hover, .open > .dropdown-toggle.btn-primary:hover, .btn-primary:active:focus, .btn-primary.active:focus, .open > .dropdown-toggle.btn-primary:focus, .btn-primary:active.focus, .btn-primary.active.focus, .open > .dropdown-toggle.btn-primary.focus { color: #fff; background-color: #204d74; border-color: #122b40; } .btn-primary:active, .btn-primary.active, .open > .dropdown-toggle.btn-primary { background-image: none; } .btn-primary.disabled:hover, .btn-primary[disabled]:hover, fieldset[disabled] .btn-primary:hover, .btn-primary.disabled:focus, .btn-primary[disabled]:focus, fieldset[disabled] .btn-primary:focus, .btn-primary.disabled.focus, .btn-primary[disabled].focus, fieldset[disabled] .btn-primary.focus { background-color: #337ab7; border-color: #2e6da4; } .btn-primary .badge { color: #337ab7; background-color: #fff; } .btn-success { color: #fff; background-color: #5cb85c; border-color: #4cae4c; } .btn-success:focus, .btn-success.focus { color: #fff; background-color: #449d44; border-color: #255625; } .btn-success:hover { color: #fff; background-color: #449d44; border-color: #398439; } .btn-success:active, .btn-success.active, .open > .dropdown-toggle.btn-success { color: #fff; background-color: #449d44; border-color: #398439; } .btn-success:active:hover, .btn-success.active:hover, .open > .dropdown-toggle.btn-success:hover, .btn-success:active:focus, .btn-success.active:focus, .open > .dropdown-toggle.btn-success:focus, .btn-success:active.focus, .btn-success.active.focus, .open > .dropdown-toggle.btn-success.focus { color: #fff; background-color: #398439; border-color: #255625; } .btn-success:active, .btn-success.active, .open > .dropdown-toggle.btn-success { background-image: none; } .btn-success.disabled:hover, .btn-success[disabled]:hover, fieldset[disabled] .btn-success:hover, .btn-success.disabled:focus, .btn-success[disabled]:focus, fieldset[disabled] .btn-success:focus, .btn-success.disabled.focus, .btn-success[disabled].focus, fieldset[disabled] .btn-success.focus { background-color: #5cb85c; border-color: #4cae4c; } .btn-success .badge { color: #5cb85c; background-color: #fff; } .btn-info { color: #fff; background-color: #5bc0de; border-color: #46b8da; } .btn-info:focus, .btn-info.focus { color: #fff; background-color: #31b0d5; border-color: #1b6d85; } .btn-info:hover { color: #fff; background-color: #31b0d5; border-color: #269abc; } .btn-info:active, .btn-info.active, .open > .dropdown-toggle.btn-info { color: #fff; background-color: #31b0d5; border-color: #269abc; } .btn-info:active:hover, .btn-info.active:hover, .open > .dropdown-toggle.btn-info:hover, .btn-info:active:focus, .btn-info.active:focus, .open > .dropdown-toggle.btn-info:focus, .btn-info:active.focus, .btn-info.active.focus, .open > .dropdown-toggle.btn-info.focus { color: #fff; background-color: #269abc; border-color: #1b6d85; } .btn-info:active, .btn-info.active, .open > .dropdown-toggle.btn-info { background-image: none; } .btn-info.disabled:hover, .btn-info[disabled]:hover, fieldset[disabled] .btn-info:hover, .btn-info.disabled:focus, .btn-info[disabled]:focus, fieldset[disabled] .btn-info:focus, .btn-info.disabled.focus, .btn-info[disabled].focus, fieldset[disabled] .btn-info.focus { background-color: #5bc0de; border-color: #46b8da; } .btn-info .badge { color: #5bc0de; background-color: #fff; } .btn-warning { color: #fff; background-color: #f0ad4e; border-color: #eea236; } .btn-warning:focus, .btn-warning.focus { color: #fff; background-color: #ec971f; border-color: #985f0d; } .btn-warning:hover { color: #fff; background-color: #ec971f; border-color: #d58512; } .btn-warning:active, .btn-warning.active, .open > .dropdown-toggle.btn-warning { color: #fff; background-color: #ec971f; border-color: #d58512; } .btn-warning:active:hover, .btn-warning.active:hover, .open > .dropdown-toggle.btn-warning:hover, .btn-warning:active:focus, .btn-warning.active:focus, .open > .dropdown-toggle.btn-warning:focus, .btn-warning:active.focus, .btn-warning.active.focus, .open > .dropdown-toggle.btn-warning.focus { color: #fff; background-color: #d58512; border-color: #985f0d; } .btn-warning:active, .btn-warning.active, .open > .dropdown-toggle.btn-warning { background-image: none; } .btn-warning.disabled:hover, .btn-warning[disabled]:hover, fieldset[disabled] .btn-warning:hover, .btn-warning.disabled:focus, .btn-warning[disabled]:focus, fieldset[disabled] .btn-warning:focus, .btn-warning.disabled.focus, .btn-warning[disabled].focus, fieldset[disabled] .btn-warning.focus { background-color: #f0ad4e; border-color: #eea236; } .btn-warning .badge { color: #f0ad4e; background-color: #fff; } .btn-danger { color: #fff; background-color: #d9534f; border-color: #d43f3a; } .btn-danger:focus, .btn-danger.focus { color: #fff; background-color: #c9302c; border-color: #761c19; } .btn-danger:hover { color: #fff; background-color: #c9302c; border-color: #ac2925; } .btn-danger:active, .btn-danger.active, .open > .dropdown-toggle.btn-danger { color: #fff; background-color: #c9302c; border-color: #ac2925; } .btn-danger:active:hover, .btn-danger.active:hover, .open > .dropdown-toggle.btn-danger:hover, .btn-danger:active:focus, .btn-danger.active:focus, .open > .dropdown-toggle.btn-danger:focus, .btn-danger:active.focus, .btn-danger.active.focus, .open > .dropdown-toggle.btn-danger.focus { color: #fff; background-color: #ac2925; border-color: #761c19; } .btn-danger:active, .btn-danger.active, .open > .dropdown-toggle.btn-danger { background-image: none; } .btn-danger.disabled:hover, .btn-danger[disabled]:hover, fieldset[disabled] .btn-danger:hover, .btn-danger.disabled:focus, .btn-danger[disabled]:focus, fieldset[disabled] .btn-danger:focus, .btn-danger.disabled.focus, .btn-danger[disabled].focus, fieldset[disabled] .btn-danger.focus { background-color: #d9534f; border-color: #d43f3a; } .btn-danger .badge { color: #d9534f; background-color: #fff; } .btn-link { color: #337ab7; font-weight: normal; border-radius: 0; } .btn-link, .btn-link:active, .btn-link.active, .btn-link[disabled], fieldset[disabled] .btn-link { background-color: transparent; -webkit-box-shadow: none; box-shadow: none; } .btn-link, .btn-link:hover, .btn-link:focus, .btn-link:active { border-color: transparent; } .btn-link:hover, .btn-link:focus { color: #23527c; text-decoration: underline; background-color: transparent; } .btn-link[disabled]:hover, fieldset[disabled] .btn-link:hover, .btn-link[disabled]:focus, fieldset[disabled] .btn-link:focus { color: #777777; text-decoration: none; } .btn-lg, .btn-group-lg > .btn { padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } .btn-sm, .btn-group-sm > .btn { padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .btn-xs, .btn-group-xs > .btn { padding: 1px 5px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .btn-block { display: block; width: 100%; } .btn-block + .btn-block { margin-top: 5px; } input[type="submit"].btn-block, input[type="reset"].btn-block, input[type="button"].btn-block { width: 100%; } .fade { opacity: 0; -webkit-transition: opacity 0.15s linear; -o-transition: opacity 0.15s linear; transition: opacity 0.15s linear; } .fade.in { opacity: 1; } .collapse { display: none; } .collapse.in { display: block; } tr.collapse.in { display: table-row; } tbody.collapse.in { display: table-row-group; } .collapsing { position: relative; height: 0; overflow: hidden; -webkit-transition-property: height, visibility; transition-property: height, visibility; -webkit-transition-duration: 0.35s; transition-duration: 0.35s; -webkit-transition-timing-function: ease; transition-timing-function: ease; } .caret { display: inline-block; width: 0; height: 0; margin-left: 2px; vertical-align: middle; border-top: 4px dashed; border-top: 4px solid \9; border-right: 4px solid transparent; border-left: 4px solid transparent; } .dropup, .dropdown { position: relative; } .dropdown-toggle:focus { outline: 0; } .dropdown-menu { position: absolute; top: 100%; left: 0; z-index: 1000; display: none; float: left; min-width: 160px; padding: 5px 0; margin: 2px 0 0; list-style: none; font-size: 13px; text-align: left; background-color: #fff; border: 1px solid #ccc; border: 1px solid rgba(0, 0, 0, 0.15); border-radius: 2px; -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); background-clip: padding-box; } .dropdown-menu.pull-right { right: 0; left: auto; } .dropdown-menu .divider { height: 1px; margin: 8px 0; overflow: hidden; background-color: #e5e5e5; } .dropdown-menu > li > a { display: block; padding: 3px 20px; clear: both; font-weight: normal; line-height: 1.42857143; color: #333333; white-space: nowrap; } .dropdown-menu > li > a:hover, .dropdown-menu > li > a:focus { text-decoration: none; color: #262626; background-color: #f5f5f5; } .dropdown-menu > .active > a, .dropdown-menu > .active > a:hover, .dropdown-menu > .active > a:focus { color: #fff; text-decoration: none; outline: 0; background-color: #337ab7; } .dropdown-menu > .disabled > a, .dropdown-menu > .disabled > a:hover, .dropdown-menu > .disabled > a:focus { color: #777777; } .dropdown-menu > .disabled > a:hover, .dropdown-menu > .disabled > a:focus { text-decoration: none; background-color: transparent; background-image: none; filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); cursor: not-allowed; } .open > .dropdown-menu { display: block; } .open > a { outline: 0; } .dropdown-menu-right { left: auto; right: 0; } .dropdown-menu-left { left: 0; right: auto; } .dropdown-header { display: block; padding: 3px 20px; font-size: 12px; line-height: 1.42857143; color: #777777; white-space: nowrap; } .dropdown-backdrop { position: fixed; left: 0; right: 0; bottom: 0; top: 0; z-index: 990; } .pull-right > .dropdown-menu { right: 0; left: auto; } .dropup .caret, .navbar-fixed-bottom .dropdown .caret { border-top: 0; border-bottom: 4px dashed; border-bottom: 4px solid \9; content: ""; } .dropup .dropdown-menu, .navbar-fixed-bottom .dropdown .dropdown-menu { top: auto; bottom: 100%; margin-bottom: 2px; } @media (min-width: 541px) { .navbar-right .dropdown-menu { left: auto; right: 0; } .navbar-right .dropdown-menu-left { left: 0; right: auto; } } .btn-group, .btn-group-vertical { position: relative; display: inline-block; vertical-align: middle; } .btn-group > .btn, .btn-group-vertical > .btn { position: relative; float: left; } .btn-group > .btn:hover, .btn-group-vertical > .btn:hover, .btn-group > .btn:focus, .btn-group-vertical > .btn:focus, .btn-group > .btn:active, .btn-group-vertical > .btn:active, .btn-group > .btn.active, .btn-group-vertical > .btn.active { z-index: 2; } .btn-group .btn + .btn, .btn-group .btn + .btn-group, .btn-group .btn-group + .btn, .btn-group .btn-group + .btn-group { margin-left: -1px; } .btn-toolbar { margin-left: -5px; } .btn-toolbar .btn, .btn-toolbar .btn-group, .btn-toolbar .input-group { float: left; } .btn-toolbar > .btn, .btn-toolbar > .btn-group, .btn-toolbar > .input-group { margin-left: 5px; } .btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) { border-radius: 0; } .btn-group > .btn:first-child { margin-left: 0; } .btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) { border-bottom-right-radius: 0; border-top-right-radius: 0; } .btn-group > .btn:last-child:not(:first-child), .btn-group > .dropdown-toggle:not(:first-child) { border-bottom-left-radius: 0; border-top-left-radius: 0; } .btn-group > .btn-group { float: left; } .btn-group > .btn-group:not(:first-child):not(:last-child) > .btn { border-radius: 0; } .btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child, .btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle { border-bottom-right-radius: 0; border-top-right-radius: 0; } .btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child { border-bottom-left-radius: 0; border-top-left-radius: 0; } .btn-group .dropdown-toggle:active, .btn-group.open .dropdown-toggle { outline: 0; } .btn-group > .btn + .dropdown-toggle { padding-left: 8px; padding-right: 8px; } .btn-group > .btn-lg + .dropdown-toggle { padding-left: 12px; padding-right: 12px; } .btn-group.open .dropdown-toggle { -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); } .btn-group.open .dropdown-toggle.btn-link { -webkit-box-shadow: none; box-shadow: none; } .btn .caret { margin-left: 0; } .btn-lg .caret { border-width: 5px 5px 0; border-bottom-width: 0; } .dropup .btn-lg .caret { border-width: 0 5px 5px; } .btn-group-vertical > .btn, .btn-group-vertical > .btn-group, .btn-group-vertical > .btn-group > .btn { display: block; float: none; width: 100%; max-width: 100%; } .btn-group-vertical > .btn-group > .btn { float: none; } .btn-group-vertical > .btn + .btn, .btn-group-vertical > .btn + .btn-group, .btn-group-vertical > .btn-group + .btn, .btn-group-vertical > .btn-group + .btn-group { margin-top: -1px; margin-left: 0; } .btn-group-vertical > .btn:not(:first-child):not(:last-child) { border-radius: 0; } .btn-group-vertical > .btn:first-child:not(:last-child) { border-top-right-radius: 2px; border-top-left-radius: 2px; border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .btn-group-vertical > .btn:last-child:not(:first-child) { border-top-right-radius: 0; border-top-left-radius: 0; border-bottom-right-radius: 2px; border-bottom-left-radius: 2px; } .btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn { border-radius: 0; } .btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child, .btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle { border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child { border-top-right-radius: 0; border-top-left-radius: 0; } .btn-group-justified { display: table; width: 100%; table-layout: fixed; border-collapse: separate; } .btn-group-justified > .btn, .btn-group-justified > .btn-group { float: none; display: table-cell; width: 1%; } .btn-group-justified > .btn-group .btn { width: 100%; } .btn-group-justified > .btn-group .dropdown-menu { left: auto; } [data-toggle="buttons"] > .btn input[type="radio"], [data-toggle="buttons"] > .btn-group > .btn input[type="radio"], [data-toggle="buttons"] > .btn input[type="checkbox"], [data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] { position: absolute; clip: rect(0, 0, 0, 0); pointer-events: none; } .input-group { position: relative; display: table; border-collapse: separate; } .input-group[class\*="col-"] { float: none; padding-left: 0; padding-right: 0; } .input-group .form-control { position: relative; z-index: 2; float: left; width: 100%; margin-bottom: 0; } .input-group .form-control:focus { z-index: 3; } .input-group-lg > .form-control, .input-group-lg > .input-group-addon, .input-group-lg > .input-group-btn > .btn { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } select.input-group-lg > .form-control, select.input-group-lg > .input-group-addon, select.input-group-lg > .input-group-btn > .btn { height: 45px; line-height: 45px; } textarea.input-group-lg > .form-control, textarea.input-group-lg > .input-group-addon, textarea.input-group-lg > .input-group-btn > .btn, select[multiple].input-group-lg > .form-control, select[multiple].input-group-lg > .input-group-addon, select[multiple].input-group-lg > .input-group-btn > .btn { height: auto; } .input-group-sm > .form-control, .input-group-sm > .input-group-addon, .input-group-sm > .input-group-btn > .btn { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } select.input-group-sm > .form-control, select.input-group-sm > .input-group-addon, select.input-group-sm > .input-group-btn > .btn { height: 30px; line-height: 30px; } textarea.input-group-sm > .form-control, textarea.input-group-sm > .input-group-addon, textarea.input-group-sm > .input-group-btn > .btn, select[multiple].input-group-sm > .form-control, select[multiple].input-group-sm > .input-group-addon, select[multiple].input-group-sm > .input-group-btn > .btn { height: auto; } .input-group-addon, .input-group-btn, .input-group .form-control { display: table-cell; } .input-group-addon:not(:first-child):not(:last-child), .input-group-btn:not(:first-child):not(:last-child), .input-group .form-control:not(:first-child):not(:last-child) { border-radius: 0; } .input-group-addon, .input-group-btn { width: 1%; white-space: nowrap; vertical-align: middle; } .input-group-addon { padding: 6px 12px; font-size: 13px; font-weight: normal; line-height: 1; color: #555555; text-align: center; background-color: #eeeeee; border: 1px solid #ccc; border-radius: 2px; } .input-group-addon.input-sm { padding: 5px 10px; font-size: 12px; border-radius: 1px; } .input-group-addon.input-lg { padding: 10px 16px; font-size: 17px; border-radius: 3px; } .input-group-addon input[type="radio"], .input-group-addon input[type="checkbox"] { margin-top: 0; } .input-group .form-control:first-child, .input-group-addon:first-child, .input-group-btn:first-child > .btn, .input-group-btn:first-child > .btn-group > .btn, .input-group-btn:first-child > .dropdown-toggle, .input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle), .input-group-btn:last-child > .btn-group:not(:last-child) > .btn { border-bottom-right-radius: 0; border-top-right-radius: 0; } .input-group-addon:first-child { border-right: 0; } .input-group .form-control:last-child, .input-group-addon:last-child, .input-group-btn:last-child > .btn, .input-group-btn:last-child > .btn-group > .btn, .input-group-btn:last-child > .dropdown-toggle, .input-group-btn:first-child > .btn:not(:first-child), .input-group-btn:first-child > .btn-group:not(:first-child) > .btn { border-bottom-left-radius: 0; border-top-left-radius: 0; } .input-group-addon:last-child { border-left: 0; } .input-group-btn { position: relative; font-size: 0; white-space: nowrap; } .input-group-btn > .btn { position: relative; } .input-group-btn > .btn + .btn { margin-left: -1px; } .input-group-btn > .btn:hover, .input-group-btn > .btn:focus, .input-group-btn > .btn:active { z-index: 2; } .input-group-btn:first-child > .btn, .input-group-btn:first-child > .btn-group { margin-right: -1px; } .input-group-btn:last-child > .btn, .input-group-btn:last-child > .btn-group { z-index: 2; margin-left: -1px; } .nav { margin-bottom: 0; padding-left: 0; list-style: none; } .nav > li { position: relative; display: block; } .nav > li > a { position: relative; display: block; padding: 10px 15px; } .nav > li > a:hover, .nav > li > a:focus { text-decoration: none; background-color: #eeeeee; } .nav > li.disabled > a { color: #777777; } .nav > li.disabled > a:hover, .nav > li.disabled > a:focus { color: #777777; text-decoration: none; background-color: transparent; cursor: not-allowed; } .nav .open > a, .nav .open > a:hover, .nav .open > a:focus { background-color: #eeeeee; border-color: #337ab7; } .nav .nav-divider { height: 1px; margin: 8px 0; overflow: hidden; background-color: #e5e5e5; } .nav > li > a > img { max-width: none; } .nav-tabs { border-bottom: 1px solid #ddd; } .nav-tabs > li { float: left; margin-bottom: -1px; } .nav-tabs > li > a { margin-right: 2px; line-height: 1.42857143; border: 1px solid transparent; border-radius: 2px 2px 0 0; } .nav-tabs > li > a:hover { border-color: #eeeeee #eeeeee #ddd; } .nav-tabs > li.active > a, .nav-tabs > li.active > a:hover, .nav-tabs > li.active > a:focus { color: #555555; background-color: #fff; border: 1px solid #ddd; border-bottom-color: transparent; cursor: default; } .nav-tabs.nav-justified { width: 100%; border-bottom: 0; } .nav-tabs.nav-justified > li { float: none; } .nav-tabs.nav-justified > li > a { text-align: center; margin-bottom: 5px; } .nav-tabs.nav-justified > .dropdown .dropdown-menu { top: auto; left: auto; } @media (min-width: 768px) { .nav-tabs.nav-justified > li { display: table-cell; width: 1%; } .nav-tabs.nav-justified > li > a { margin-bottom: 0; } } .nav-tabs.nav-justified > li > a { margin-right: 0; border-radius: 2px; } .nav-tabs.nav-justified > .active > a, .nav-tabs.nav-justified > .active > a:hover, .nav-tabs.nav-justified > .active > a:focus { border: 1px solid #ddd; } @media (min-width: 768px) { .nav-tabs.nav-justified > li > a { border-bottom: 1px solid #ddd; border-radius: 2px 2px 0 0; } .nav-tabs.nav-justified > .active > a, .nav-tabs.nav-justified > .active > a:hover, .nav-tabs.nav-justified > .active > a:focus { border-bottom-color: #fff; } } .nav-pills > li { float: left; } .nav-pills > li > a { border-radius: 2px; } .nav-pills > li + li { margin-left: 2px; } .nav-pills > li.active > a, .nav-pills > li.active > a:hover, .nav-pills > li.active > a:focus { color: #fff; background-color: #337ab7; } .nav-stacked > li { float: none; } .nav-stacked > li + li { margin-top: 2px; margin-left: 0; } .nav-justified { width: 100%; } .nav-justified > li { float: none; } .nav-justified > li > a { text-align: center; margin-bottom: 5px; } .nav-justified > .dropdown .dropdown-menu { top: auto; left: auto; } @media (min-width: 768px) { .nav-justified > li { display: table-cell; width: 1%; } .nav-justified > li > a { margin-bottom: 0; } } .nav-tabs-justified { border-bottom: 0; } .nav-tabs-justified > li > a { margin-right: 0; border-radius: 2px; } .nav-tabs-justified > .active > a, .nav-tabs-justified > .active > a:hover, .nav-tabs-justified > .active > a:focus { border: 1px solid #ddd; } @media (min-width: 768px) { .nav-tabs-justified > li > a { border-bottom: 1px solid #ddd; border-radius: 2px 2px 0 0; } .nav-tabs-justified > .active > a, .nav-tabs-justified > .active > a:hover, .nav-tabs-justified > .active > a:focus { border-bottom-color: #fff; } } .tab-content > .tab-pane { display: none; } .tab-content > .active { display: block; } .nav-tabs .dropdown-menu { margin-top: -1px; border-top-right-radius: 0; border-top-left-radius: 0; } .navbar { position: relative; min-height: 30px; margin-bottom: 18px; border: 1px solid transparent; } @media (min-width: 541px) { .navbar { border-radius: 2px; } } @media (min-width: 541px) { .navbar-header { float: left; } } .navbar-collapse { overflow-x: visible; padding-right: 0px; padding-left: 0px; border-top: 1px solid transparent; box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1); -webkit-overflow-scrolling: touch; } .navbar-collapse.in { overflow-y: auto; } @media (min-width: 541px) { .navbar-collapse { width: auto; border-top: 0; box-shadow: none; } .navbar-collapse.collapse { display: block !important; height: auto !important; padding-bottom: 0; overflow: visible !important; } .navbar-collapse.in { overflow-y: visible; } .navbar-fixed-top .navbar-collapse, .navbar-static-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { padding-left: 0; padding-right: 0; } } .navbar-fixed-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { max-height: 340px; } @media (max-device-width: 540px) and (orientation: landscape) { .navbar-fixed-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { max-height: 200px; } } .container > .navbar-header, .container-fluid > .navbar-header, .container > .navbar-collapse, .container-fluid > .navbar-collapse { margin-right: 0px; margin-left: 0px; } @media (min-width: 541px) { .container > .navbar-header, .container-fluid > .navbar-header, .container > .navbar-collapse, .container-fluid > .navbar-collapse { margin-right: 0; margin-left: 0; } } .navbar-static-top { z-index: 1000; border-width: 0 0 1px; } @media (min-width: 541px) { .navbar-static-top { border-radius: 0; } } .navbar-fixed-top, .navbar-fixed-bottom { position: fixed; right: 0; left: 0; z-index: 1030; } @media (min-width: 541px) { .navbar-fixed-top, .navbar-fixed-bottom { border-radius: 0; } } .navbar-fixed-top { top: 0; border-width: 0 0 1px; } .navbar-fixed-bottom { bottom: 0; margin-bottom: 0; border-width: 1px 0 0; } .navbar-brand { float: left; padding: 6px 0px; font-size: 17px; line-height: 18px; height: 30px; } .navbar-brand:hover, .navbar-brand:focus { text-decoration: none; } .navbar-brand > img { display: block; } @media (min-width: 541px) { .navbar > .container .navbar-brand, .navbar > .container-fluid .navbar-brand { margin-left: 0px; } } .navbar-toggle { position: relative; float: right; margin-right: 0px; padding: 9px 10px; margin-top: -2px; margin-bottom: -2px; background-color: transparent; background-image: none; border: 1px solid transparent; border-radius: 2px; } .navbar-toggle:focus { outline: 0; } .navbar-toggle .icon-bar { display: block; width: 22px; height: 2px; border-radius: 1px; } .navbar-toggle .icon-bar + .icon-bar { margin-top: 4px; } @media (min-width: 541px) { .navbar-toggle { display: none; } } .navbar-nav { margin: 3px 0px; } .navbar-nav > li > a { padding-top: 10px; padding-bottom: 10px; line-height: 18px; } @media (max-width: 540px) { .navbar-nav .open .dropdown-menu { position: static; float: none; width: auto; margin-top: 0; background-color: transparent; border: 0; box-shadow: none; } .navbar-nav .open .dropdown-menu > li > a, .navbar-nav .open .dropdown-menu .dropdown-header { padding: 5px 15px 5px 25px; } .navbar-nav .open .dropdown-menu > li > a { line-height: 18px; } .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-nav .open .dropdown-menu > li > a:focus { background-image: none; } } @media (min-width: 541px) { .navbar-nav { float: left; margin: 0; } .navbar-nav > li { float: left; } .navbar-nav > li > a { padding-top: 6px; padding-bottom: 6px; } } .navbar-form { margin-left: 0px; margin-right: 0px; padding: 10px 0px; border-top: 1px solid transparent; border-bottom: 1px solid transparent; -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); margin-top: -1px; margin-bottom: -1px; } @media (min-width: 768px) { .navbar-form .form-group { display: inline-block; margin-bottom: 0; vertical-align: middle; } .navbar-form .form-control { display: inline-block; width: auto; vertical-align: middle; } .navbar-form .form-control-static { display: inline-block; } .navbar-form .input-group { display: inline-table; vertical-align: middle; } .navbar-form .input-group .input-group-addon, .navbar-form .input-group .input-group-btn, .navbar-form .input-group .form-control { width: auto; } .navbar-form .input-group > .form-control { width: 100%; } .navbar-form .control-label { margin-bottom: 0; vertical-align: middle; } .navbar-form .radio, .navbar-form .checkbox { display: inline-block; margin-top: 0; margin-bottom: 0; vertical-align: middle; } .navbar-form .radio label, .navbar-form .checkbox label { padding-left: 0; } .navbar-form .radio input[type="radio"], .navbar-form .checkbox input[type="checkbox"] { position: relative; margin-left: 0; } .navbar-form .has-feedback .form-control-feedback { top: 0; } } @media (max-width: 540px) { .navbar-form .form-group { margin-bottom: 5px; } .navbar-form .form-group:last-child { margin-bottom: 0; } } @media (min-width: 541px) { .navbar-form { width: auto; border: 0; margin-left: 0; margin-right: 0; padding-top: 0; padding-bottom: 0; -webkit-box-shadow: none; box-shadow: none; } } .navbar-nav > li > .dropdown-menu { margin-top: 0; border-top-right-radius: 0; border-top-left-radius: 0; } .navbar-fixed-bottom .navbar-nav > li > .dropdown-menu { margin-bottom: 0; border-top-right-radius: 2px; border-top-left-radius: 2px; border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .navbar-btn { margin-top: -1px; margin-bottom: -1px; } .navbar-btn.btn-sm { margin-top: 0px; margin-bottom: 0px; } .navbar-btn.btn-xs { margin-top: 4px; margin-bottom: 4px; } .navbar-text { margin-top: 6px; margin-bottom: 6px; } @media (min-width: 541px) { .navbar-text { float: left; margin-left: 0px; margin-right: 0px; } } @media (min-width: 541px) { .navbar-left { float: left !important; float: left; } .navbar-right { float: right !important; float: right; margin-right: 0px; } .navbar-right ~ .navbar-right { margin-right: 0; } } .navbar-default { background-color: #f8f8f8; border-color: #e7e7e7; } .navbar-default .navbar-brand { color: #777; } .navbar-default .navbar-brand:hover, .navbar-default .navbar-brand:focus { color: #5e5e5e; background-color: transparent; } .navbar-default .navbar-text { color: #777; } .navbar-default .navbar-nav > li > a { color: #777; } .navbar-default .navbar-nav > li > a:hover, .navbar-default .navbar-nav > li > a:focus { color: #333; background-color: transparent; } .navbar-default .navbar-nav > .active > a, .navbar-default .navbar-nav > .active > a:hover, .navbar-default .navbar-nav > .active > a:focus { color: #555; background-color: #e7e7e7; } .navbar-default .navbar-nav > .disabled > a, .navbar-default .navbar-nav > .disabled > a:hover, .navbar-default .navbar-nav > .disabled > a:focus { color: #ccc; background-color: transparent; } .navbar-default .navbar-toggle { border-color: #ddd; } .navbar-default .navbar-toggle:hover, .navbar-default .navbar-toggle:focus { background-color: #ddd; } .navbar-default .navbar-toggle .icon-bar { background-color: #888; } .navbar-default .navbar-collapse, .navbar-default .navbar-form { border-color: #e7e7e7; } .navbar-default .navbar-nav > .open > a, .navbar-default .navbar-nav > .open > a:hover, .navbar-default .navbar-nav > .open > a:focus { background-color: #e7e7e7; color: #555; } @media (max-width: 540px) { .navbar-default .navbar-nav .open .dropdown-menu > li > a { color: #777; } .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus { color: #333; background-color: transparent; } .navbar-default .navbar-nav .open .dropdown-menu > .active > a, .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus { color: #555; background-color: #e7e7e7; } .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a, .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus { color: #ccc; background-color: transparent; } } .navbar-default .navbar-link { color: #777; } .navbar-default .navbar-link:hover { color: #333; } .navbar-default .btn-link { color: #777; } .navbar-default .btn-link:hover, .navbar-default .btn-link:focus { color: #333; } .navbar-default .btn-link[disabled]:hover, fieldset[disabled] .navbar-default .btn-link:hover, .navbar-default .btn-link[disabled]:focus, fieldset[disabled] .navbar-default .btn-link:focus { color: #ccc; } .navbar-inverse { background-color: #222; border-color: #080808; } .navbar-inverse .navbar-brand { color: #9d9d9d; } .navbar-inverse .navbar-brand:hover, .navbar-inverse .navbar-brand:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-text { color: #9d9d9d; } .navbar-inverse .navbar-nav > li > a { color: #9d9d9d; } .navbar-inverse .navbar-nav > li > a:hover, .navbar-inverse .navbar-nav > li > a:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-nav > .active > a, .navbar-inverse .navbar-nav > .active > a:hover, .navbar-inverse .navbar-nav > .active > a:focus { color: #fff; background-color: #080808; } .navbar-inverse .navbar-nav > .disabled > a, .navbar-inverse .navbar-nav > .disabled > a:hover, .navbar-inverse .navbar-nav > .disabled > a:focus { color: #444; background-color: transparent; } .navbar-inverse .navbar-toggle { border-color: #333; } .navbar-inverse .navbar-toggle:hover, .navbar-inverse .navbar-toggle:focus { background-color: #333; } .navbar-inverse .navbar-toggle .icon-bar { background-color: #fff; } .navbar-inverse .navbar-collapse, .navbar-inverse .navbar-form { border-color: #101010; } .navbar-inverse .navbar-nav > .open > a, .navbar-inverse .navbar-nav > .open > a:hover, .navbar-inverse .navbar-nav > .open > a:focus { background-color: #080808; color: #fff; } @media (max-width: 540px) { .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header { border-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu .divider { background-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu > li > a { color: #9d9d9d; } .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a, .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus { color: #fff; background-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a, .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus { color: #444; background-color: transparent; } } .navbar-inverse .navbar-link { color: #9d9d9d; } .navbar-inverse .navbar-link:hover { color: #fff; } .navbar-inverse .btn-link { color: #9d9d9d; } .navbar-inverse .btn-link:hover, .navbar-inverse .btn-link:focus { color: #fff; } .navbar-inverse .btn-link[disabled]:hover, fieldset[disabled] .navbar-inverse .btn-link:hover, .navbar-inverse .btn-link[disabled]:focus, fieldset[disabled] .navbar-inverse .btn-link:focus { color: #444; } .breadcrumb { padding: 8px 15px; margin-bottom: 18px; list-style: none; background-color: #f5f5f5; border-radius: 2px; } .breadcrumb > li { display: inline-block; } .breadcrumb > li + li:before { content: "/\00a0"; padding: 0 5px; color: #5e5e5e; } .breadcrumb > .active { color: #777777; } .pagination { display: inline-block; padding-left: 0; margin: 18px 0; border-radius: 2px; } .pagination > li { display: inline; } .pagination > li > a, .pagination > li > span { position: relative; float: left; padding: 6px 12px; line-height: 1.42857143; text-decoration: none; color: #337ab7; background-color: #fff; border: 1px solid #ddd; margin-left: -1px; } .pagination > li:first-child > a, .pagination > li:first-child > span { margin-left: 0; border-bottom-left-radius: 2px; border-top-left-radius: 2px; } .pagination > li:last-child > a, .pagination > li:last-child > span { border-bottom-right-radius: 2px; border-top-right-radius: 2px; } .pagination > li > a:hover, .pagination > li > span:hover, .pagination > li > a:focus, .pagination > li > span:focus { z-index: 2; color: #23527c; background-color: #eeeeee; border-color: #ddd; } .pagination > .active > a, .pagination > .active > span, .pagination > .active > a:hover, .pagination > .active > span:hover, .pagination > .active > a:focus, .pagination > .active > span:focus { z-index: 3; color: #fff; background-color: #337ab7; border-color: #337ab7; cursor: default; } .pagination > .disabled > span, .pagination > .disabled > span:hover, .pagination > .disabled > span:focus, .pagination > .disabled > a, .pagination > .disabled > a:hover, .pagination > .disabled > a:focus { color: #777777; background-color: #fff; border-color: #ddd; cursor: not-allowed; } .pagination-lg > li > a, .pagination-lg > li > span { padding: 10px 16px; font-size: 17px; line-height: 1.3333333; } .pagination-lg > li:first-child > a, .pagination-lg > li:first-child > span { border-bottom-left-radius: 3px; border-top-left-radius: 3px; } .pagination-lg > li:last-child > a, .pagination-lg > li:last-child > span { border-bottom-right-radius: 3px; border-top-right-radius: 3px; } .pagination-sm > li > a, .pagination-sm > li > span { padding: 5px 10px; font-size: 12px; line-height: 1.5; } .pagination-sm > li:first-child > a, .pagination-sm > li:first-child > span { border-bottom-left-radius: 1px; border-top-left-radius: 1px; } .pagination-sm > li:last-child > a, .pagination-sm > li:last-child > span { border-bottom-right-radius: 1px; border-top-right-radius: 1px; } .pager { padding-left: 0; margin: 18px 0; list-style: none; text-align: center; } .pager li { display: inline; } .pager li > a, .pager li > span { display: inline-block; padding: 5px 14px; background-color: #fff; border: 1px solid #ddd; border-radius: 15px; } .pager li > a:hover, .pager li > a:focus { text-decoration: none; background-color: #eeeeee; } .pager .next > a, .pager .next > span { float: right; } .pager .previous > a, .pager .previous > span { float: left; } .pager .disabled > a, .pager .disabled > a:hover, .pager .disabled > a:focus, .pager .disabled > span { color: #777777; background-color: #fff; cursor: not-allowed; } .label { display: inline; padding: .2em .6em .3em; font-size: 75%; font-weight: bold; line-height: 1; color: #fff; text-align: center; white-space: nowrap; vertical-align: baseline; border-radius: .25em; } a.label:hover, a.label:focus { color: #fff; text-decoration: none; cursor: pointer; } .label:empty { display: none; } .btn .label { position: relative; top: -1px; } .label-default { background-color: #777777; } .label-default[href]:hover, .label-default[href]:focus { background-color: #5e5e5e; } .label-primary { background-color: #337ab7; } .label-primary[href]:hover, .label-primary[href]:focus { background-color: #286090; } .label-success { background-color: #5cb85c; } .label-success[href]:hover, .label-success[href]:focus { background-color: #449d44; } .label-info { background-color: #5bc0de; } .label-info[href]:hover, .label-info[href]:focus { background-color: #31b0d5; } .label-warning { background-color: #f0ad4e; } .label-warning[href]:hover, .label-warning[href]:focus { background-color: #ec971f; } .label-danger { background-color: #d9534f; } .label-danger[href]:hover, .label-danger[href]:focus { background-color: #c9302c; } .badge { display: inline-block; min-width: 10px; padding: 3px 7px; font-size: 12px; font-weight: bold; color: #fff; line-height: 1; vertical-align: middle; white-space: nowrap; text-align: center; background-color: #777777; border-radius: 10px; } .badge:empty { display: none; } .btn .badge { position: relative; top: -1px; } .btn-xs .badge, .btn-group-xs > .btn .badge { top: 0; padding: 1px 5px; } a.badge:hover, a.badge:focus { color: #fff; text-decoration: none; cursor: pointer; } .list-group-item.active > .badge, .nav-pills > .active > a > .badge { color: #337ab7; background-color: #fff; } .list-group-item > .badge { float: right; } .list-group-item > .badge + .badge { margin-right: 5px; } .nav-pills > li > a > .badge { margin-left: 3px; } .jumbotron { padding-top: 30px; padding-bottom: 30px; margin-bottom: 30px; color: inherit; background-color: #eeeeee; } .jumbotron h1, .jumbotron .h1 { color: inherit; } .jumbotron p { margin-bottom: 15px; font-size: 20px; font-weight: 200; } .jumbotron > hr { border-top-color: #d5d5d5; } .container .jumbotron, .container-fluid .jumbotron { border-radius: 3px; padding-left: 0px; padding-right: 0px; } .jumbotron .container { max-width: 100%; } @media screen and (min-width: 768px) { .jumbotron { padding-top: 48px; padding-bottom: 48px; } .container .jumbotron, .container-fluid .jumbotron { padding-left: 60px; padding-right: 60px; } .jumbotron h1, .jumbotron .h1 { font-size: 59px; } } .thumbnail { display: block; padding: 4px; margin-bottom: 18px; line-height: 1.42857143; background-color: #fff; border: 1px solid #ddd; border-radius: 2px; -webkit-transition: border 0.2s ease-in-out; -o-transition: border 0.2s ease-in-out; transition: border 0.2s ease-in-out; } .thumbnail > img, .thumbnail a > img { margin-left: auto; margin-right: auto; } a.thumbnail:hover, a.thumbnail:focus, a.thumbnail.active { border-color: #337ab7; } .thumbnail .caption { padding: 9px; color: #000; } .alert { padding: 15px; margin-bottom: 18px; border: 1px solid transparent; border-radius: 2px; } .alert h4 { margin-top: 0; color: inherit; } .alert .alert-link { font-weight: bold; } .alert > p, .alert > ul { margin-bottom: 0; } .alert > p + p { margin-top: 5px; } .alert-dismissable, .alert-dismissible { padding-right: 35px; } .alert-dismissable .close, .alert-dismissible .close { position: relative; top: -2px; right: -21px; color: inherit; } .alert-success { background-color: #dff0d8; border-color: #d6e9c6; color: #3c763d; } .alert-success hr { border-top-color: #c9e2b3; } .alert-success .alert-link { color: #2b542c; } .alert-info { background-color: #d9edf7; border-color: #bce8f1; color: #31708f; } .alert-info hr { border-top-color: #a6e1ec; } .alert-info .alert-link { color: #245269; } .alert-warning { background-color: #fcf8e3; border-color: #faebcc; color: #8a6d3b; } .alert-warning hr { border-top-color: #f7e1b5; } .alert-warning .alert-link { color: #66512c; } .alert-danger { background-color: #f2dede; border-color: #ebccd1; color: #a94442; } .alert-danger hr { border-top-color: #e4b9c0; } .alert-danger .alert-link { color: #843534; } @-webkit-keyframes progress-bar-stripes { from { background-position: 40px 0; } to { background-position: 0 0; } } @keyframes progress-bar-stripes { from { background-position: 40px 0; } to { background-position: 0 0; } } .progress { overflow: hidden; height: 18px; margin-bottom: 18px; background-color: #f5f5f5; border-radius: 2px; -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); } .progress-bar { float: left; width: 0%; height: 100%; font-size: 12px; line-height: 18px; color: #fff; text-align: center; background-color: #337ab7; -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); -webkit-transition: width 0.6s ease; -o-transition: width 0.6s ease; transition: width 0.6s ease; } .progress-striped .progress-bar, .progress-bar-striped { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-size: 40px 40px; } .progress.active .progress-bar, .progress-bar.active { -webkit-animation: progress-bar-stripes 2s linear infinite; -o-animation: progress-bar-stripes 2s linear infinite; animation: progress-bar-stripes 2s linear infinite; } .progress-bar-success { background-color: #5cb85c; } .progress-striped .progress-bar-success { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-info { background-color: #5bc0de; } .progress-striped .progress-bar-info { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-warning { background-color: #f0ad4e; } .progress-striped .progress-bar-warning { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-danger { background-color: #d9534f; } .progress-striped .progress-bar-danger { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .media { margin-top: 15px; } .media:first-child { margin-top: 0; } .media, .media-body { zoom: 1; overflow: hidden; } .media-body { width: 10000px; } .media-object { display: block; } .media-object.img-thumbnail { max-width: none; } .media-right, .media > .pull-right { padding-left: 10px; } .media-left, .media > .pull-left { padding-right: 10px; } .media-left, .media-right, .media-body { display: table-cell; vertical-align: top; } .media-middle { vertical-align: middle; } .media-bottom { vertical-align: bottom; } .media-heading { margin-top: 0; margin-bottom: 5px; } .media-list { padding-left: 0; list-style: none; } .list-group { margin-bottom: 20px; padding-left: 0; } .list-group-item { position: relative; display: block; padding: 10px 15px; margin-bottom: -1px; background-color: #fff; border: 1px solid #ddd; } .list-group-item:first-child { border-top-right-radius: 2px; border-top-left-radius: 2px; } .list-group-item:last-child { margin-bottom: 0; border-bottom-right-radius: 2px; border-bottom-left-radius: 2px; } a.list-group-item, button.list-group-item { color: #555; } a.list-group-item .list-group-item-heading, button.list-group-item .list-group-item-heading { color: #333; } a.list-group-item:hover, button.list-group-item:hover, a.list-group-item:focus, button.list-group-item:focus { text-decoration: none; color: #555; background-color: #f5f5f5; } button.list-group-item { width: 100%; text-align: left; } .list-group-item.disabled, .list-group-item.disabled:hover, .list-group-item.disabled:focus { background-color: #eeeeee; color: #777777; cursor: not-allowed; } .list-group-item.disabled .list-group-item-heading, .list-group-item.disabled:hover .list-group-item-heading, .list-group-item.disabled:focus .list-group-item-heading { color: inherit; } .list-group-item.disabled .list-group-item-text, .list-group-item.disabled:hover .list-group-item-text, .list-group-item.disabled:focus .list-group-item-text { color: #777777; } .list-group-item.active, .list-group-item.active:hover, .list-group-item.active:focus { z-index: 2; color: #fff; background-color: #337ab7; border-color: #337ab7; } .list-group-item.active .list-group-item-heading, .list-group-item.active:hover .list-group-item-heading, .list-group-item.active:focus .list-group-item-heading, .list-group-item.active .list-group-item-heading > small, .list-group-item.active:hover .list-group-item-heading > small, .list-group-item.active:focus .list-group-item-heading > small, .list-group-item.active .list-group-item-heading > .small, .list-group-item.active:hover .list-group-item-heading > .small, .list-group-item.active:focus .list-group-item-heading > .small { color: inherit; } .list-group-item.active .list-group-item-text, .list-group-item.active:hover .list-group-item-text, .list-group-item.active:focus .list-group-item-text { color: #c7ddef; } .list-group-item-success { color: #3c763d; background-color: #dff0d8; } a.list-group-item-success, button.list-group-item-success { color: #3c763d; } a.list-group-item-success .list-group-item-heading, button.list-group-item-success .list-group-item-heading { color: inherit; } a.list-group-item-success:hover, button.list-group-item-success:hover, a.list-group-item-success:focus, button.list-group-item-success:focus { color: #3c763d; background-color: #d0e9c6; } a.list-group-item-success.active, button.list-group-item-success.active, a.list-group-item-success.active:hover, button.list-group-item-success.active:hover, a.list-group-item-success.active:focus, button.list-group-item-success.active:focus { color: #fff; background-color: #3c763d; border-color: #3c763d; } .list-group-item-info { color: #31708f; background-color: #d9edf7; } a.list-group-item-info, button.list-group-item-info { color: #31708f; } a.list-group-item-info .list-group-item-heading, button.list-group-item-info .list-group-item-heading { color: inherit; } a.list-group-item-info:hover, button.list-group-item-info:hover, a.list-group-item-info:focus, button.list-group-item-info:focus { color: #31708f; background-color: #c4e3f3; } a.list-group-item-info.active, button.list-group-item-info.active, a.list-group-item-info.active:hover, button.list-group-item-info.active:hover, a.list-group-item-info.active:focus, button.list-group-item-info.active:focus { color: #fff; background-color: #31708f; border-color: #31708f; } .list-group-item-warning { color: #8a6d3b; background-color: #fcf8e3; } a.list-group-item-warning, button.list-group-item-warning { color: #8a6d3b; } a.list-group-item-warning .list-group-item-heading, button.list-group-item-warning .list-group-item-heading { color: inherit; } a.list-group-item-warning:hover, button.list-group-item-warning:hover, a.list-group-item-warning:focus, button.list-group-item-warning:focus { color: #8a6d3b; background-color: #faf2cc; } a.list-group-item-warning.active, button.list-group-item-warning.active, a.list-group-item-warning.active:hover, button.list-group-item-warning.active:hover, a.list-group-item-warning.active:focus, button.list-group-item-warning.active:focus { color: #fff; background-color: #8a6d3b; border-color: #8a6d3b; } .list-group-item-danger { color: #a94442; background-color: #f2dede; } a.list-group-item-danger, button.list-group-item-danger { color: #a94442; } a.list-group-item-danger .list-group-item-heading, button.list-group-item-danger .list-group-item-heading { color: inherit; } a.list-group-item-danger:hover, button.list-group-item-danger:hover, a.list-group-item-danger:focus, button.list-group-item-danger:focus { color: #a94442; background-color: #ebcccc; } a.list-group-item-danger.active, button.list-group-item-danger.active, a.list-group-item-danger.active:hover, button.list-group-item-danger.active:hover, a.list-group-item-danger.active:focus, button.list-group-item-danger.active:focus { color: #fff; background-color: #a94442; border-color: #a94442; } .list-group-item-heading { margin-top: 0; margin-bottom: 5px; } .list-group-item-text { margin-bottom: 0; line-height: 1.3; } .panel { margin-bottom: 18px; background-color: #fff; border: 1px solid transparent; border-radius: 2px; -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); } .panel-body { padding: 15px; } .panel-heading { padding: 10px 15px; border-bottom: 1px solid transparent; border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel-heading > .dropdown .dropdown-toggle { color: inherit; } .panel-title { margin-top: 0; margin-bottom: 0; font-size: 15px; color: inherit; } .panel-title > a, .panel-title > small, .panel-title > .small, .panel-title > small > a, .panel-title > .small > a { color: inherit; } .panel-footer { padding: 10px 15px; background-color: #f5f5f5; border-top: 1px solid #ddd; border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .list-group, .panel > .panel-collapse > .list-group { margin-bottom: 0; } .panel > .list-group .list-group-item, .panel > .panel-collapse > .list-group .list-group-item { border-width: 1px 0; border-radius: 0; } .panel > .list-group:first-child .list-group-item:first-child, .panel > .panel-collapse > .list-group:first-child .list-group-item:first-child { border-top: 0; border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel > .list-group:last-child .list-group-item:last-child, .panel > .panel-collapse > .list-group:last-child .list-group-item:last-child { border-bottom: 0; border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child { border-top-right-radius: 0; border-top-left-radius: 0; } .panel-heading + .list-group .list-group-item:first-child { border-top-width: 0; } .list-group + .panel-footer { border-top-width: 0; } .panel > .table, .panel > .table-responsive > .table, .panel > .panel-collapse > .table { margin-bottom: 0; } .panel > .table caption, .panel > .table-responsive > .table caption, .panel > .panel-collapse > .table caption { padding-left: 15px; padding-right: 15px; } .panel > .table:first-child, .panel > .table-responsive:first-child > .table:first-child { border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child { border-top-left-radius: 1px; border-top-right-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child td:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child td:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child, .panel > .table:first-child > thead:first-child > tr:first-child th:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child th:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child { border-top-left-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child td:last-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child, .panel > .table:first-child > tbody:first-child > tr:first-child td:last-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child, .panel > .table:first-child > thead:first-child > tr:first-child th:last-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child, .panel > .table:first-child > tbody:first-child > tr:first-child th:last-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child { border-top-right-radius: 1px; } .panel > .table:last-child, .panel > .table-responsive:last-child > .table:last-child { border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child { border-bottom-left-radius: 1px; border-bottom-right-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child td:first-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child, .panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child, .panel > .table:last-child > tbody:last-child > tr:last-child th:first-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child, .panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child { border-bottom-left-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child td:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child, .panel > .table:last-child > tbody:last-child > tr:last-child th:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child { border-bottom-right-radius: 1px; } .panel > .panel-body + .table, .panel > .panel-body + .table-responsive, .panel > .table + .panel-body, .panel > .table-responsive + .panel-body { border-top: 1px solid #ddd; } .panel > .table > tbody:first-child > tr:first-child th, .panel > .table > tbody:first-child > tr:first-child td { border-top: 0; } .panel > .table-bordered, .panel > .table-responsive > .table-bordered { border: 0; } .panel > .table-bordered > thead > tr > th:first-child, .panel > .table-responsive > .table-bordered > thead > tr > th:first-child, .panel > .table-bordered > tbody > tr > th:first-child, .panel > .table-responsive > .table-bordered > tbody > tr > th:first-child, .panel > .table-bordered > tfoot > tr > th:first-child, .panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child, .panel > .table-bordered > thead > tr > td:first-child, .panel > .table-responsive > .table-bordered > thead > tr > td:first-child, .panel > .table-bordered > tbody > tr > td:first-child, .panel > .table-responsive > .table-bordered > tbody > tr > td:first-child, .panel > .table-bordered > tfoot > tr > td:first-child, .panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child { border-left: 0; } .panel > .table-bordered > thead > tr > th:last-child, .panel > .table-responsive > .table-bordered > thead > tr > th:last-child, .panel > .table-bordered > tbody > tr > th:last-child, .panel > .table-responsive > .table-bordered > tbody > tr > th:last-child, .panel > .table-bordered > tfoot > tr > th:last-child, .panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child, .panel > .table-bordered > thead > tr > td:last-child, .panel > .table-responsive > .table-bordered > thead > tr > td:last-child, .panel > .table-bordered > tbody > tr > td:last-child, .panel > .table-responsive > .table-bordered > tbody > tr > td:last-child, .panel > .table-bordered > tfoot > tr > td:last-child, .panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child { border-right: 0; } .panel > .table-bordered > thead > tr:first-child > td, .panel > .table-responsive > .table-bordered > thead > tr:first-child > td, .panel > .table-bordered > tbody > tr:first-child > td, .panel > .table-responsive > .table-bordered > tbody > tr:first-child > td, .panel > .table-bordered > thead > tr:first-child > th, .panel > .table-responsive > .table-bordered > thead > tr:first-child > th, .panel > .table-bordered > tbody > tr:first-child > th, .panel > .table-responsive > .table-bordered > tbody > tr:first-child > th { border-bottom: 0; } .panel > .table-bordered > tbody > tr:last-child > td, .panel > .table-responsive > .table-bordered > tbody > tr:last-child > td, .panel > .table-bordered > tfoot > tr:last-child > td, .panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td, .panel > .table-bordered > tbody > tr:last-child > th, .panel > .table-responsive > .table-bordered > tbody > tr:last-child > th, .panel > .table-bordered > tfoot > tr:last-child > th, .panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th { border-bottom: 0; } .panel > .table-responsive { border: 0; margin-bottom: 0; } .panel-group { margin-bottom: 18px; } .panel-group .panel { margin-bottom: 0; border-radius: 2px; } .panel-group .panel + .panel { margin-top: 5px; } .panel-group .panel-heading { border-bottom: 0; } .panel-group .panel-heading + .panel-collapse > .panel-body, .panel-group .panel-heading + .panel-collapse > .list-group { border-top: 1px solid #ddd; } .panel-group .panel-footer { border-top: 0; } .panel-group .panel-footer + .panel-collapse .panel-body { border-bottom: 1px solid #ddd; } .panel-default { border-color: #ddd; } .panel-default > .panel-heading { color: #333333; background-color: #f5f5f5; border-color: #ddd; } .panel-default > .panel-heading + .panel-collapse > .panel-body { border-top-color: #ddd; } .panel-default > .panel-heading .badge { color: #f5f5f5; background-color: #333333; } .panel-default > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #ddd; } .panel-primary { border-color: #337ab7; } .panel-primary > .panel-heading { color: #fff; background-color: #337ab7; border-color: #337ab7; } .panel-primary > .panel-heading + .panel-collapse > .panel-body { border-top-color: #337ab7; } .panel-primary > .panel-heading .badge { color: #337ab7; background-color: #fff; } .panel-primary > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #337ab7; } .panel-success { border-color: #d6e9c6; } .panel-success > .panel-heading { color: #3c763d; background-color: #dff0d8; border-color: #d6e9c6; } .panel-success > .panel-heading + .panel-collapse > .panel-body { border-top-color: #d6e9c6; } .panel-success > .panel-heading .badge { color: #dff0d8; background-color: #3c763d; } .panel-success > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #d6e9c6; } .panel-info { border-color: #bce8f1; } .panel-info > .panel-heading { color: #31708f; background-color: #d9edf7; border-color: #bce8f1; } .panel-info > .panel-heading + .panel-collapse > .panel-body { border-top-color: #bce8f1; } .panel-info > .panel-heading .badge { color: #d9edf7; background-color: #31708f; } .panel-info > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #bce8f1; } .panel-warning { border-color: #faebcc; } .panel-warning > .panel-heading { color: #8a6d3b; background-color: #fcf8e3; border-color: #faebcc; } .panel-warning > .panel-heading + .panel-collapse > .panel-body { border-top-color: #faebcc; } .panel-warning > .panel-heading .badge { color: #fcf8e3; background-color: #8a6d3b; } .panel-warning > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #faebcc; } .panel-danger { border-color: #ebccd1; } .panel-danger > .panel-heading { color: #a94442; background-color: #f2dede; border-color: #ebccd1; } .panel-danger > .panel-heading + .panel-collapse > .panel-body { border-top-color: #ebccd1; } .panel-danger > .panel-heading .badge { color: #f2dede; background-color: #a94442; } .panel-danger > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #ebccd1; } .embed-responsive { position: relative; display: block; height: 0; padding: 0; overflow: hidden; } .embed-responsive .embed-responsive-item, .embed-responsive iframe, .embed-responsive embed, .embed-responsive object, .embed-responsive video { position: absolute; top: 0; left: 0; bottom: 0; height: 100%; width: 100%; border: 0; } .embed-responsive-16by9 { padding-bottom: 56.25%; } .embed-responsive-4by3 { padding-bottom: 75%; } .well { min-height: 20px; padding: 19px; margin-bottom: 20px; background-color: #f5f5f5; border: 1px solid #e3e3e3; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); } .well blockquote { border-color: #ddd; border-color: rgba(0, 0, 0, 0.15); } .well-lg { padding: 24px; border-radius: 3px; } .well-sm { padding: 9px; border-radius: 1px; } .close { float: right; font-size: 19.5px; font-weight: bold; line-height: 1; color: #000; text-shadow: 0 1px 0 #fff; opacity: 0.2; filter: alpha(opacity=20); } .close:hover, .close:focus { color: #000; text-decoration: none; cursor: pointer; opacity: 0.5; filter: alpha(opacity=50); } button.close { padding: 0; cursor: pointer; background: transparent; border: 0; -webkit-appearance: none; } .modal-open { overflow: hidden; } .modal { display: none; overflow: hidden; position: fixed; top: 0; right: 0; bottom: 0; left: 0; z-index: 1050; -webkit-overflow-scrolling: touch; outline: 0; } .modal.fade .modal-dialog { -webkit-transform: translate(0, -25%); -ms-transform: translate(0, -25%); -o-transform: translate(0, -25%); transform: translate(0, -25%); -webkit-transition: -webkit-transform 0.3s ease-out; -moz-transition: -moz-transform 0.3s ease-out; -o-transition: -o-transform 0.3s ease-out; transition: transform 0.3s ease-out; } .modal.in .modal-dialog { -webkit-transform: translate(0, 0); -ms-transform: translate(0, 0); -o-transform: translate(0, 0); transform: translate(0, 0); } .modal-open .modal { overflow-x: hidden; overflow-y: auto; } .modal-dialog { position: relative; width: auto; margin: 10px; } .modal-content { position: relative; background-color: #fff; border: 1px solid #999; border: 1px solid rgba(0, 0, 0, 0.2); border-radius: 3px; -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); background-clip: padding-box; outline: 0; } .modal-backdrop { position: fixed; top: 0; right: 0; bottom: 0; left: 0; z-index: 1040; background-color: #000; } .modal-backdrop.fade { opacity: 0; filter: alpha(opacity=0); } .modal-backdrop.in { opacity: 0.5; filter: alpha(opacity=50); } .modal-header { padding: 15px; border-bottom: 1px solid #e5e5e5; } .modal-header .close { margin-top: -2px; } .modal-title { margin: 0; line-height: 1.42857143; } .modal-body { position: relative; padding: 15px; } .modal-footer { padding: 15px; text-align: right; border-top: 1px solid #e5e5e5; } .modal-footer .btn + .btn { margin-left: 5px; margin-bottom: 0; } .modal-footer .btn-group .btn + .btn { margin-left: -1px; } .modal-footer .btn-block + .btn-block { margin-left: 0; } .modal-scrollbar-measure { position: absolute; top: -9999px; width: 50px; height: 50px; overflow: scroll; } @media (min-width: 768px) { .modal-dialog { width: 600px; margin: 30px auto; } .modal-content { -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); } .modal-sm { width: 300px; } } @media (min-width: 992px) { .modal-lg { width: 900px; } } .tooltip { position: absolute; z-index: 1070; display: block; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-style: normal; font-weight: normal; letter-spacing: normal; line-break: auto; line-height: 1.42857143; text-align: left; text-align: start; text-decoration: none; text-shadow: none; text-transform: none; white-space: normal; word-break: normal; word-spacing: normal; word-wrap: normal; font-size: 12px; opacity: 0; filter: alpha(opacity=0); } .tooltip.in { opacity: 0.9; filter: alpha(opacity=90); } .tooltip.top { margin-top: -3px; padding: 5px 0; } .tooltip.right { margin-left: 3px; padding: 0 5px; } .tooltip.bottom { margin-top: 3px; padding: 5px 0; } .tooltip.left { margin-left: -3px; padding: 0 5px; } .tooltip-inner { max-width: 200px; padding: 3px 8px; color: #fff; text-align: center; background-color: #000; border-radius: 2px; } .tooltip-arrow { position: absolute; width: 0; height: 0; border-color: transparent; border-style: solid; } .tooltip.top .tooltip-arrow { bottom: 0; left: 50%; margin-left: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.top-left .tooltip-arrow { bottom: 0; right: 5px; margin-bottom: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.top-right .tooltip-arrow { bottom: 0; left: 5px; margin-bottom: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.right .tooltip-arrow { top: 50%; left: 0; margin-top: -5px; border-width: 5px 5px 5px 0; border-right-color: #000; } .tooltip.left .tooltip-arrow { top: 50%; right: 0; margin-top: -5px; border-width: 5px 0 5px 5px; border-left-color: #000; } .tooltip.bottom .tooltip-arrow { top: 0; left: 50%; margin-left: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .tooltip.bottom-left .tooltip-arrow { top: 0; right: 5px; margin-top: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .tooltip.bottom-right .tooltip-arrow { top: 0; left: 5px; margin-top: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .popover { position: absolute; top: 0; left: 0; z-index: 1060; display: none; max-width: 276px; padding: 1px; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-style: normal; font-weight: normal; letter-spacing: normal; line-break: auto; line-height: 1.42857143; text-align: left; text-align: start; text-decoration: none; text-shadow: none; text-transform: none; white-space: normal; word-break: normal; word-spacing: normal; word-wrap: normal; font-size: 13px; background-color: #fff; background-clip: padding-box; border: 1px solid #ccc; border: 1px solid rgba(0, 0, 0, 0.2); border-radius: 3px; -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); } .popover.top { margin-top: -10px; } .popover.right { margin-left: 10px; } .popover.bottom { margin-top: 10px; } .popover.left { margin-left: -10px; } .popover-title { margin: 0; padding: 8px 14px; font-size: 13px; background-color: #f7f7f7; border-bottom: 1px solid #ebebeb; border-radius: 2px 2px 0 0; } .popover-content { padding: 9px 14px; } .popover > .arrow, .popover > .arrow:after { position: absolute; display: block; width: 0; height: 0; border-color: transparent; border-style: solid; } .popover > .arrow { border-width: 11px; } .popover > .arrow:after { border-width: 10px; content: ""; } .popover.top > .arrow { left: 50%; margin-left: -11px; border-bottom-width: 0; border-top-color: #999999; border-top-color: rgba(0, 0, 0, 0.25); bottom: -11px; } .popover.top > .arrow:after { content: " "; bottom: 1px; margin-left: -10px; border-bottom-width: 0; border-top-color: #fff; } .popover.right > .arrow { top: 50%; left: -11px; margin-top: -11px; border-left-width: 0; border-right-color: #999999; border-right-color: rgba(0, 0, 0, 0.25); } .popover.right > .arrow:after { content: " "; left: 1px; bottom: -10px; border-left-width: 0; border-right-color: #fff; } .popover.bottom > .arrow { left: 50%; margin-left: -11px; border-top-width: 0; border-bottom-color: #999999; border-bottom-color: rgba(0, 0, 0, 0.25); top: -11px; } .popover.bottom > .arrow:after { content: " "; top: 1px; margin-left: -10px; border-top-width: 0; border-bottom-color: #fff; } .popover.left > .arrow { top: 50%; right: -11px; margin-top: -11px; border-right-width: 0; border-left-color: #999999; border-left-color: rgba(0, 0, 0, 0.25); } .popover.left > .arrow:after { content: " "; right: 1px; border-right-width: 0; border-left-color: #fff; bottom: -10px; } .carousel { position: relative; } .carousel-inner { position: relative; overflow: hidden; width: 100%; } .carousel-inner > .item { display: none; position: relative; -webkit-transition: 0.6s ease-in-out left; -o-transition: 0.6s ease-in-out left; transition: 0.6s ease-in-out left; } .carousel-inner > .item > img, .carousel-inner > .item > a > img { line-height: 1; } @media all and (transform-3d), (-webkit-transform-3d) { .carousel-inner > .item { -webkit-transition: -webkit-transform 0.6s ease-in-out; -moz-transition: -moz-transform 0.6s ease-in-out; -o-transition: -o-transform 0.6s ease-in-out; transition: transform 0.6s ease-in-out; -webkit-backface-visibility: hidden; -moz-backface-visibility: hidden; backface-visibility: hidden; -webkit-perspective: 1000px; -moz-perspective: 1000px; perspective: 1000px; } .carousel-inner > .item.next, .carousel-inner > .item.active.right { -webkit-transform: translate3d(100%, 0, 0); transform: translate3d(100%, 0, 0); left: 0; } .carousel-inner > .item.prev, .carousel-inner > .item.active.left { -webkit-transform: translate3d(-100%, 0, 0); transform: translate3d(-100%, 0, 0); left: 0; } .carousel-inner > .item.next.left, .carousel-inner > .item.prev.right, .carousel-inner > .item.active { -webkit-transform: translate3d(0, 0, 0); transform: translate3d(0, 0, 0); left: 0; } } .carousel-inner > .active, .carousel-inner > .next, .carousel-inner > .prev { display: block; } .carousel-inner > .active { left: 0; } .carousel-inner > .next, .carousel-inner > .prev { position: absolute; top: 0; width: 100%; } .carousel-inner > .next { left: 100%; } .carousel-inner > .prev { left: -100%; } .carousel-inner > .next.left, .carousel-inner > .prev.right { left: 0; } .carousel-inner > .active.left { left: -100%; } .carousel-inner > .active.right { left: 100%; } .carousel-control { position: absolute; top: 0; left: 0; bottom: 0; width: 15%; opacity: 0.5; filter: alpha(opacity=50); font-size: 20px; color: #fff; text-align: center; text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); background-color: rgba(0, 0, 0, 0); } .carousel-control.left { background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-repeat: repeat-x; filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1); } .carousel-control.right { left: auto; right: 0; background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-repeat: repeat-x; filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1); } .carousel-control:hover, .carousel-control:focus { outline: 0; color: #fff; text-decoration: none; opacity: 0.9; filter: alpha(opacity=90); } .carousel-control .icon-prev, .carousel-control .icon-next, .carousel-control .glyphicon-chevron-left, .carousel-control .glyphicon-chevron-right { position: absolute; top: 50%; margin-top: -10px; z-index: 5; display: inline-block; } .carousel-control .icon-prev, .carousel-control .glyphicon-chevron-left { left: 50%; margin-left: -10px; } .carousel-control .icon-next, .carousel-control .glyphicon-chevron-right { right: 50%; margin-right: -10px; } .carousel-control .icon-prev, .carousel-control .icon-next { width: 20px; height: 20px; line-height: 1; font-family: serif; } .carousel-control .icon-prev:before { content: '\2039'; } .carousel-control .icon-next:before { content: '\203a'; } .carousel-indicators { position: absolute; bottom: 10px; left: 50%; z-index: 15; width: 60%; margin-left: -30%; padding-left: 0; list-style: none; text-align: center; } .carousel-indicators li { display: inline-block; width: 10px; height: 10px; margin: 1px; text-indent: -999px; border: 1px solid #fff; border-radius: 10px; cursor: pointer; background-color: #000 \9; background-color: rgba(0, 0, 0, 0); } .carousel-indicators .active { margin: 0; width: 12px; height: 12px; background-color: #fff; } .carousel-caption { position: absolute; left: 15%; right: 15%; bottom: 20px; z-index: 10; padding-top: 20px; padding-bottom: 20px; color: #fff; text-align: center; text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); } .carousel-caption .btn { text-shadow: none; } @media screen and (min-width: 768px) { .carousel-control .glyphicon-chevron-left, .carousel-control .glyphicon-chevron-right, .carousel-control .icon-prev, .carousel-control .icon-next { width: 30px; height: 30px; margin-top: -10px; font-size: 30px; } .carousel-control .glyphicon-chevron-left, .carousel-control .icon-prev { margin-left: -10px; } .carousel-control .glyphicon-chevron-right, .carousel-control .icon-next { margin-right: -10px; } .carousel-caption { left: 20%; right: 20%; padding-bottom: 30px; } .carousel-indicators { bottom: 20px; } } .clearfix:before, .clearfix:after, .dl-horizontal dd:before, .dl-horizontal dd:after, .container:before, .container:after, .container-fluid:before, .container-fluid:after, .row:before, .row:after, .form-horizontal .form-group:before, .form-horizontal .form-group:after, .btn-toolbar:before, .btn-toolbar:after, .btn-group-vertical > .btn-group:before, .btn-group-vertical > .btn-group:after, .nav:before, .nav:after, .navbar:before, .navbar:after, .navbar-header:before, .navbar-header:after, .navbar-collapse:before, .navbar-collapse:after, .pager:before, .pager:after, .panel-body:before, .panel-body:after, .modal-header:before, .modal-header:after, .modal-footer:before, .modal-footer:after, .item\_buttons:before, .item\_buttons:after { content: " "; display: table; } .clearfix:after, .dl-horizontal dd:after, .container:after, .container-fluid:after, .row:after, .form-horizontal .form-group:after, .btn-toolbar:after, .btn-group-vertical > .btn-group:after, .nav:after, .navbar:after, .navbar-header:after, .navbar-collapse:after, .pager:after, .panel-body:after, .modal-header:after, .modal-footer:after, .item\_buttons:after { clear: both; } .center-block { display: block; margin-left: auto; margin-right: auto; } .pull-right { float: right !important; } .pull-left { float: left !important; } .hide { display: none !important; } .show { display: block !important; } .invisible { visibility: hidden; } .text-hide { font: 0/0 a; color: transparent; text-shadow: none; background-color: transparent; border: 0; } .hidden { display: none !important; } .affix { position: fixed; } @-ms-viewport { width: device-width; } .visible-xs, .visible-sm, .visible-md, .visible-lg { display: none !important; } .visible-xs-block, .visible-xs-inline, .visible-xs-inline-block, .visible-sm-block, .visible-sm-inline, .visible-sm-inline-block, .visible-md-block, .visible-md-inline, .visible-md-inline-block, .visible-lg-block, .visible-lg-inline, .visible-lg-inline-block { display: none !important; } @media (max-width: 767px) { .visible-xs { display: block !important; } table.visible-xs { display: table !important; } tr.visible-xs { display: table-row !important; } th.visible-xs, td.visible-xs { display: table-cell !important; } } @media (max-width: 767px) { .visible-xs-block { display: block !important; } } @media (max-width: 767px) { .visible-xs-inline { display: inline !important; } } @media (max-width: 767px) { .visible-xs-inline-block { display: inline-block !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm { display: block !important; } table.visible-sm { display: table !important; } tr.visible-sm { display: table-row !important; } th.visible-sm, td.visible-sm { display: table-cell !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-block { display: block !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-inline { display: inline !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-inline-block { display: inline-block !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md { display: block !important; } table.visible-md { display: table !important; } tr.visible-md { display: table-row !important; } th.visible-md, td.visible-md { display: table-cell !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-block { display: block !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-inline { display: inline !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-inline-block { display: inline-block !important; } } @media (min-width: 1200px) { .visible-lg { display: block !important; } table.visible-lg { display: table !important; } tr.visible-lg { display: table-row !important; } th.visible-lg, td.visible-lg { display: table-cell !important; } } @media (min-width: 1200px) { .visible-lg-block { display: block !important; } } @media (min-width: 1200px) { .visible-lg-inline { display: inline !important; } } @media (min-width: 1200px) { .visible-lg-inline-block { display: inline-block !important; } } @media (max-width: 767px) { .hidden-xs { display: none !important; } } @media (min-width: 768px) and (max-width: 991px) { .hidden-sm { display: none !important; } } @media (min-width: 992px) and (max-width: 1199px) { .hidden-md { display: none !important; } } @media (min-width: 1200px) { .hidden-lg { display: none !important; } } .visible-print { display: none !important; } @media print { .visible-print { display: block !important; } table.visible-print { display: table !important; } tr.visible-print { display: table-row !important; } th.visible-print, td.visible-print { display: table-cell !important; } } .visible-print-block { display: none !important; } @media print { .visible-print-block { display: block !important; } } .visible-print-inline { display: none !important; } @media print { .visible-print-inline { display: inline !important; } } .visible-print-inline-block { display: none !important; } @media print { .visible-print-inline-block { display: inline-block !important; } } @media print { .hidden-print { display: none !important; } } /\*! \* \* Font Awesome \* \*/ /\*! \* Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome \* License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License) \*/ /\* FONT PATH \* -------------------------- \*/ @font-face { font-family: 'FontAwesome'; src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0'); src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg'); font-weight: normal; font-style: normal; } .fa { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; } /\* makes the font 33% larger relative to the icon container \*/ .fa-lg { font-size: 1.33333333em; line-height: 0.75em; vertical-align: -15%; } .fa-2x { font-size: 2em; } .fa-3x { font-size: 3em; } .fa-4x { font-size: 4em; } .fa-5x { font-size: 5em; } .fa-fw { width: 1.28571429em; text-align: center; } .fa-ul { padding-left: 0; margin-left: 2.14285714em; list-style-type: none; } .fa-ul > li { position: relative; } .fa-li { position: absolute; left: -2.14285714em; width: 2.14285714em; top: 0.14285714em; text-align: center; } .fa-li.fa-lg { left: -1.85714286em; } .fa-border { padding: .2em .25em .15em; border: solid 0.08em #eee; border-radius: .1em; } .fa-pull-left { float: left; } .fa-pull-right { float: right; } .fa.fa-pull-left { margin-right: .3em; } .fa.fa-pull-right { margin-left: .3em; } /\* Deprecated as of 4.4.0 \*/ .pull-right { float: right; } .pull-left { float: left; } .fa.pull-left { margin-right: .3em; } .fa.pull-right { margin-left: .3em; } .fa-spin { -webkit-animation: fa-spin 2s infinite linear; animation: fa-spin 2s infinite linear; } .fa-pulse { -webkit-animation: fa-spin 1s infinite steps(8); animation: fa-spin 1s infinite steps(8); } @-webkit-keyframes fa-spin { 0% { -webkit-transform: rotate(0deg); transform: rotate(0deg); } 100% { -webkit-transform: rotate(359deg); transform: rotate(359deg); } } @keyframes fa-spin { 0% { -webkit-transform: rotate(0deg); transform: rotate(0deg); } 100% { -webkit-transform: rotate(359deg); transform: rotate(359deg); } } .fa-rotate-90 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)"; -webkit-transform: rotate(90deg); -ms-transform: rotate(90deg); transform: rotate(90deg); } .fa-rotate-180 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)"; -webkit-transform: rotate(180deg); -ms-transform: rotate(180deg); transform: rotate(180deg); } .fa-rotate-270 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)"; -webkit-transform: rotate(270deg); -ms-transform: rotate(270deg); transform: rotate(270deg); } .fa-flip-horizontal { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)"; -webkit-transform: scale(-1, 1); -ms-transform: scale(-1, 1); transform: scale(-1, 1); } .fa-flip-vertical { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)"; -webkit-transform: scale(1, -1); -ms-transform: scale(1, -1); transform: scale(1, -1); } :root .fa-rotate-90, :root .fa-rotate-180, :root .fa-rotate-270, :root .fa-flip-horizontal, :root .fa-flip-vertical { filter: none; } .fa-stack { position: relative; display: inline-block; width: 2em; height: 2em; line-height: 2em; vertical-align: middle; } .fa-stack-1x, .fa-stack-2x { position: absolute; left: 0; width: 100%; text-align: center; } .fa-stack-1x { line-height: inherit; } .fa-stack-2x { font-size: 2em; } .fa-inverse { color: #fff; } /\* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen readers do not read off random characters that represent icons \*/ .fa-glass:before { content: "\f000"; } .fa-music:before { content: "\f001"; } .fa-search:before { content: "\f002"; } .fa-envelope-o:before { content: "\f003"; } .fa-heart:before { content: "\f004"; } .fa-star:before { content: "\f005"; } .fa-star-o:before { content: "\f006"; } .fa-user:before { content: "\f007"; } .fa-film:before { content: "\f008"; } .fa-th-large:before { content: "\f009"; } .fa-th:before { content: "\f00a"; } .fa-th-list:before { content: "\f00b"; } .fa-check:before { content: "\f00c"; } .fa-remove:before, .fa-close:before, .fa-times:before { content: "\f00d"; } .fa-search-plus:before { content: "\f00e"; } .fa-search-minus:before { content: "\f010"; } .fa-power-off:before { content: "\f011"; } .fa-signal:before { content: "\f012"; } .fa-gear:before, .fa-cog:before { content: "\f013"; } .fa-trash-o:before { content: "\f014"; } .fa-home:before { content: "\f015"; } .fa-file-o:before { content: "\f016"; } .fa-clock-o:before { content: "\f017"; } .fa-road:before { content: "\f018"; } .fa-download:before { content: "\f019"; } .fa-arrow-circle-o-down:before { content: "\f01a"; } .fa-arrow-circle-o-up:before { content: "\f01b"; } .fa-inbox:before { content: "\f01c"; } .fa-play-circle-o:before { content: "\f01d"; } .fa-rotate-right:before, .fa-repeat:before { content: "\f01e"; } .fa-refresh:before { content: "\f021"; } .fa-list-alt:before { content: "\f022"; } .fa-lock:before { content: "\f023"; } .fa-flag:before { content: "\f024"; } .fa-headphones:before { content: "\f025"; } .fa-volume-off:before { content: "\f026"; } .fa-volume-down:before { content: "\f027"; } .fa-volume-up:before { content: "\f028"; } .fa-qrcode:before { content: "\f029"; } .fa-barcode:before { content: "\f02a"; } .fa-tag:before { content: "\f02b"; } .fa-tags:before { content: "\f02c"; } .fa-book:before { content: "\f02d"; } .fa-bookmark:before { content: "\f02e"; } .fa-print:before { content: "\f02f"; } .fa-camera:before { content: "\f030"; } .fa-font:before { content: "\f031"; } .fa-bold:before { content: "\f032"; } .fa-italic:before { content: "\f033"; } .fa-text-height:before { content: "\f034"; } .fa-text-width:before { content: "\f035"; } .fa-align-left:before { content: "\f036"; } .fa-align-center:before { content: "\f037"; } .fa-align-right:before { content: "\f038"; } .fa-align-justify:before { content: "\f039"; } .fa-list:before { content: "\f03a"; } .fa-dedent:before, .fa-outdent:before { content: "\f03b"; } .fa-indent:before { content: "\f03c"; } .fa-video-camera:before { content: "\f03d"; } .fa-photo:before, .fa-image:before, .fa-picture-o:before { content: "\f03e"; } .fa-pencil:before { content: "\f040"; } .fa-map-marker:before { content: "\f041"; } .fa-adjust:before { content: "\f042"; } .fa-tint:before { content: "\f043"; } .fa-edit:before, .fa-pencil-square-o:before { content: "\f044"; } .fa-share-square-o:before { content: "\f045"; } .fa-check-square-o:before { content: "\f046"; } .fa-arrows:before { content: "\f047"; } .fa-step-backward:before { content: "\f048"; } .fa-fast-backward:before { content: "\f049"; } .fa-backward:before { content: "\f04a"; } .fa-play:before { content: "\f04b"; } .fa-pause:before { content: "\f04c"; } .fa-stop:before { content: "\f04d"; } .fa-forward:before { content: "\f04e"; } .fa-fast-forward:before { content: "\f050"; } .fa-step-forward:before { content: "\f051"; } .fa-eject:before { content: "\f052"; } .fa-chevron-left:before { content: "\f053"; } .fa-chevron-right:before { content: "\f054"; } .fa-plus-circle:before { content: "\f055"; } .fa-minus-circle:before { content: "\f056"; } .fa-times-circle:before { content: "\f057"; } .fa-check-circle:before { content: "\f058"; } .fa-question-circle:before { content: "\f059"; } .fa-info-circle:before { content: "\f05a"; } .fa-crosshairs:before { content: "\f05b"; } .fa-times-circle-o:before { content: "\f05c"; } .fa-check-circle-o:before { content: "\f05d"; } .fa-ban:before { content: "\f05e"; } .fa-arrow-left:before { content: "\f060"; } .fa-arrow-right:before { content: "\f061"; } .fa-arrow-up:before { content: "\f062"; } .fa-arrow-down:before { content: "\f063"; } .fa-mail-forward:before, .fa-share:before { content: "\f064"; } .fa-expand:before { content: "\f065"; } .fa-compress:before { content: "\f066"; } .fa-plus:before { content: "\f067"; } .fa-minus:before { content: "\f068"; } .fa-asterisk:before { content: "\f069"; } .fa-exclamation-circle:before { content: "\f06a"; } .fa-gift:before { content: "\f06b"; } .fa-leaf:before { content: "\f06c"; } .fa-fire:before { content: "\f06d"; } .fa-eye:before { content: "\f06e"; } .fa-eye-slash:before { content: "\f070"; } .fa-warning:before, .fa-exclamation-triangle:before { content: "\f071"; } .fa-plane:before { content: "\f072"; } .fa-calendar:before { content: "\f073"; } .fa-random:before { content: "\f074"; } .fa-comment:before { content: "\f075"; } .fa-magnet:before { content: "\f076"; } .fa-chevron-up:before { content: "\f077"; } .fa-chevron-down:before { content: "\f078"; } .fa-retweet:before { content: "\f079"; } .fa-shopping-cart:before { content: "\f07a"; } .fa-folder:before { content: "\f07b"; } .fa-folder-open:before { content: "\f07c"; } .fa-arrows-v:before { content: "\f07d"; } .fa-arrows-h:before { content: "\f07e"; } .fa-bar-chart-o:before, .fa-bar-chart:before { content: "\f080"; } .fa-twitter-square:before { content: "\f081"; } .fa-facebook-square:before { content: "\f082"; } .fa-camera-retro:before { content: "\f083"; } .fa-key:before { content: "\f084"; } .fa-gears:before, .fa-cogs:before { content: "\f085"; } .fa-comments:before { content: "\f086"; } .fa-thumbs-o-up:before { content: "\f087"; } .fa-thumbs-o-down:before { content: "\f088"; } .fa-star-half:before { content: "\f089"; } .fa-heart-o:before { content: "\f08a"; } .fa-sign-out:before { content: "\f08b"; } .fa-linkedin-square:before { content: "\f08c"; } .fa-thumb-tack:before { content: "\f08d"; } .fa-external-link:before { content: "\f08e"; } .fa-sign-in:before { content: "\f090"; } .fa-trophy:before { content: "\f091"; } .fa-github-square:before { content: "\f092"; } .fa-upload:before { content: "\f093"; } .fa-lemon-o:before { content: "\f094"; } .fa-phone:before { content: "\f095"; } .fa-square-o:before { content: "\f096"; } .fa-bookmark-o:before { content: "\f097"; } .fa-phone-square:before { content: "\f098"; } .fa-twitter:before { content: "\f099"; } .fa-facebook-f:before, .fa-facebook:before { content: "\f09a"; } .fa-github:before { content: "\f09b"; } .fa-unlock:before { content: "\f09c"; } .fa-credit-card:before { content: "\f09d"; } .fa-feed:before, .fa-rss:before { content: "\f09e"; } .fa-hdd-o:before { content: "\f0a0"; } .fa-bullhorn:before { content: "\f0a1"; } .fa-bell:before { content: "\f0f3"; } .fa-certificate:before { content: "\f0a3"; } .fa-hand-o-right:before { content: "\f0a4"; } .fa-hand-o-left:before { content: "\f0a5"; } .fa-hand-o-up:before { content: "\f0a6"; } .fa-hand-o-down:before { content: "\f0a7"; } .fa-arrow-circle-left:before { content: "\f0a8"; } .fa-arrow-circle-right:before { content: "\f0a9"; } .fa-arrow-circle-up:before { content: "\f0aa"; } .fa-arrow-circle-down:before { content: "\f0ab"; } .fa-globe:before { content: "\f0ac"; } .fa-wrench:before { content: "\f0ad"; } .fa-tasks:before { content: "\f0ae"; } .fa-filter:before { content: "\f0b0"; } .fa-briefcase:before { content: "\f0b1"; } .fa-arrows-alt:before { content: "\f0b2"; } .fa-group:before, .fa-users:before { content: "\f0c0"; } .fa-chain:before, .fa-link:before { content: "\f0c1"; } .fa-cloud:before { content: "\f0c2"; } .fa-flask:before { content: "\f0c3"; } .fa-cut:before, .fa-scissors:before { content: "\f0c4"; } .fa-copy:before, .fa-files-o:before { content: "\f0c5"; } .fa-paperclip:before { content: "\f0c6"; } .fa-save:before, .fa-floppy-o:before { content: "\f0c7"; } .fa-square:before { content: "\f0c8"; } .fa-navicon:before, .fa-reorder:before, .fa-bars:before { content: "\f0c9"; } .fa-list-ul:before { content: "\f0ca"; } .fa-list-ol:before { content: "\f0cb"; } .fa-strikethrough:before { content: "\f0cc"; } .fa-underline:before { content: "\f0cd"; } .fa-table:before { content: "\f0ce"; } .fa-magic:before { content: "\f0d0"; } .fa-truck:before { content: "\f0d1"; } .fa-pinterest:before { content: "\f0d2"; } .fa-pinterest-square:before { content: "\f0d3"; } .fa-google-plus-square:before { content: "\f0d4"; } .fa-google-plus:before { content: "\f0d5"; } .fa-money:before { content: "\f0d6"; } .fa-caret-down:before { content: "\f0d7"; } .fa-caret-up:before { content: "\f0d8"; } .fa-caret-left:before { content: "\f0d9"; } .fa-caret-right:before { content: "\f0da"; } .fa-columns:before { content: "\f0db"; } .fa-unsorted:before, .fa-sort:before { content: "\f0dc"; } .fa-sort-down:before, .fa-sort-desc:before { content: "\f0dd"; } .fa-sort-up:before, .fa-sort-asc:before { content: "\f0de"; } .fa-envelope:before { content: "\f0e0"; } .fa-linkedin:before { content: "\f0e1"; } .fa-rotate-left:before, .fa-undo:before { content: "\f0e2"; } .fa-legal:before, .fa-gavel:before { content: "\f0e3"; } .fa-dashboard:before, .fa-tachometer:before { content: "\f0e4"; } .fa-comment-o:before { content: "\f0e5"; } .fa-comments-o:before { content: "\f0e6"; } .fa-flash:before, .fa-bolt:before { content: "\f0e7"; } .fa-sitemap:before { content: "\f0e8"; } .fa-umbrella:before { content: "\f0e9"; } .fa-paste:before, .fa-clipboard:before { content: "\f0ea"; } .fa-lightbulb-o:before { content: "\f0eb"; } .fa-exchange:before { content: "\f0ec"; } .fa-cloud-download:before { content: "\f0ed"; } .fa-cloud-upload:before { content: "\f0ee"; } .fa-user-md:before { content: "\f0f0"; } .fa-stethoscope:before { content: "\f0f1"; } .fa-suitcase:before { content: "\f0f2"; } .fa-bell-o:before { content: "\f0a2"; } .fa-coffee:before { content: "\f0f4"; } .fa-cutlery:before { content: "\f0f5"; } .fa-file-text-o:before { content: "\f0f6"; } .fa-building-o:before { content: "\f0f7"; } .fa-hospital-o:before { content: "\f0f8"; } .fa-ambulance:before { content: "\f0f9"; } .fa-medkit:before { content: "\f0fa"; } .fa-fighter-jet:before { content: "\f0fb"; } .fa-beer:before { content: "\f0fc"; } .fa-h-square:before { content: "\f0fd"; } .fa-plus-square:before { content: "\f0fe"; } .fa-angle-double-left:before { content: "\f100"; } .fa-angle-double-right:before { content: "\f101"; } .fa-angle-double-up:before { content: "\f102"; } .fa-angle-double-down:before { content: "\f103"; } .fa-angle-left:before { content: "\f104"; } .fa-angle-right:before { content: "\f105"; } .fa-angle-up:before { content: "\f106"; } .fa-angle-down:before { content: "\f107"; } .fa-desktop:before { content: "\f108"; } .fa-laptop:before { content: "\f109"; } .fa-tablet:before { content: "\f10a"; } .fa-mobile-phone:before, .fa-mobile:before { content: "\f10b"; } .fa-circle-o:before { content: "\f10c"; } .fa-quote-left:before { content: "\f10d"; } .fa-quote-right:before { content: "\f10e"; } .fa-spinner:before { content: "\f110"; } .fa-circle:before { content: "\f111"; } .fa-mail-reply:before, .fa-reply:before { content: "\f112"; } .fa-github-alt:before { content: "\f113"; } .fa-folder-o:before { content: "\f114"; } .fa-folder-open-o:before { content: "\f115"; } .fa-smile-o:before { content: "\f118"; } .fa-frown-o:before { content: "\f119"; } .fa-meh-o:before { content: "\f11a"; } .fa-gamepad:before { content: "\f11b"; } .fa-keyboard-o:before { content: "\f11c"; } .fa-flag-o:before { content: "\f11d"; } .fa-flag-checkered:before { content: "\f11e"; } .fa-terminal:before { content: "\f120"; } .fa-code:before { content: "\f121"; } .fa-mail-reply-all:before, .fa-reply-all:before { content: "\f122"; } .fa-star-half-empty:before, .fa-star-half-full:before, .fa-star-half-o:before { content: "\f123"; } .fa-location-arrow:before { content: "\f124"; } .fa-crop:before { content: "\f125"; } .fa-code-fork:before { content: "\f126"; } .fa-unlink:before, .fa-chain-broken:before { content: "\f127"; } .fa-question:before { content: "\f128"; } .fa-info:before { content: "\f129"; } .fa-exclamation:before { content: "\f12a"; } .fa-superscript:before { content: "\f12b"; } .fa-subscript:before { content: "\f12c"; } .fa-eraser:before { content: "\f12d"; } .fa-puzzle-piece:before { content: "\f12e"; } .fa-microphone:before { content: "\f130"; } .fa-microphone-slash:before { content: "\f131"; } .fa-shield:before { content: "\f132"; } .fa-calendar-o:before { content: "\f133"; } .fa-fire-extinguisher:before { content: "\f134"; } .fa-rocket:before { content: "\f135"; } .fa-maxcdn:before { content: "\f136"; } .fa-chevron-circle-left:before { content: "\f137"; } .fa-chevron-circle-right:before { content: "\f138"; } .fa-chevron-circle-up:before { content: "\f139"; } .fa-chevron-circle-down:before { content: "\f13a"; } .fa-html5:before { content: "\f13b"; } .fa-css3:before { content: "\f13c"; } .fa-anchor:before { content: "\f13d"; } .fa-unlock-alt:before { content: "\f13e"; } .fa-bullseye:before { content: "\f140"; } .fa-ellipsis-h:before { content: "\f141"; } .fa-ellipsis-v:before { content: "\f142"; } .fa-rss-square:before { content: "\f143"; } .fa-play-circle:before { content: "\f144"; } .fa-ticket:before { content: "\f145"; } .fa-minus-square:before { content: "\f146"; } .fa-minus-square-o:before { content: "\f147"; } .fa-level-up:before { content: "\f148"; } .fa-level-down:before { content: "\f149"; } .fa-check-square:before { content: "\f14a"; } .fa-pencil-square:before { content: "\f14b"; } .fa-external-link-square:before { content: "\f14c"; } .fa-share-square:before { content: "\f14d"; } .fa-compass:before { content: "\f14e"; } .fa-toggle-down:before, .fa-caret-square-o-down:before { content: "\f150"; } .fa-toggle-up:before, .fa-caret-square-o-up:before { content: "\f151"; } .fa-toggle-right:before, .fa-caret-square-o-right:before { content: "\f152"; } .fa-euro:before, .fa-eur:before { content: "\f153"; } .fa-gbp:before { content: "\f154"; } .fa-dollar:before, .fa-usd:before { content: "\f155"; } .fa-rupee:before, .fa-inr:before { content: "\f156"; } .fa-cny:before, .fa-rmb:before, .fa-yen:before, .fa-jpy:before { content: "\f157"; } .fa-ruble:before, .fa-rouble:before, .fa-rub:before { content: "\f158"; } .fa-won:before, .fa-krw:before { content: "\f159"; } .fa-bitcoin:before, .fa-btc:before { content: "\f15a"; } .fa-file:before { content: "\f15b"; } .fa-file-text:before { content: "\f15c"; } .fa-sort-alpha-asc:before { content: "\f15d"; } .fa-sort-alpha-desc:before { content: "\f15e"; } .fa-sort-amount-asc:before { content: "\f160"; } .fa-sort-amount-desc:before { content: "\f161"; } .fa-sort-numeric-asc:before { content: "\f162"; } .fa-sort-numeric-desc:before { content: "\f163"; } .fa-thumbs-up:before { content: "\f164"; } .fa-thumbs-down:before { content: "\f165"; } .fa-youtube-square:before { content: "\f166"; } .fa-youtube:before { content: "\f167"; } .fa-xing:before { content: "\f168"; } .fa-xing-square:before { content: "\f169"; } .fa-youtube-play:before { content: "\f16a"; } .fa-dropbox:before { content: "\f16b"; } .fa-stack-overflow:before { content: "\f16c"; } .fa-instagram:before { content: "\f16d"; } .fa-flickr:before { content: "\f16e"; } .fa-adn:before { content: "\f170"; } .fa-bitbucket:before { content: "\f171"; } .fa-bitbucket-square:before { content: "\f172"; } .fa-tumblr:before { content: "\f173"; } .fa-tumblr-square:before { content: "\f174"; } .fa-long-arrow-down:before { content: "\f175"; } .fa-long-arrow-up:before { content: "\f176"; } .fa-long-arrow-left:before { content: "\f177"; } .fa-long-arrow-right:before { content: "\f178"; } .fa-apple:before { content: "\f179"; } .fa-windows:before { content: "\f17a"; } .fa-android:before { content: "\f17b"; } .fa-linux:before { content: "\f17c"; } .fa-dribbble:before { content: "\f17d"; } .fa-skype:before { content: "\f17e"; } .fa-foursquare:before { content: "\f180"; } .fa-trello:before { content: "\f181"; } .fa-female:before { content: "\f182"; } .fa-male:before { content: "\f183"; } .fa-gittip:before, .fa-gratipay:before { content: "\f184"; } .fa-sun-o:before { content: "\f185"; } .fa-moon-o:before { content: "\f186"; } .fa-archive:before { content: "\f187"; } .fa-bug:before { content: "\f188"; } .fa-vk:before { content: "\f189"; } .fa-weibo:before { content: "\f18a"; } .fa-renren:before { content: "\f18b"; } .fa-pagelines:before { content: "\f18c"; } .fa-stack-exchange:before { content: "\f18d"; } .fa-arrow-circle-o-right:before { content: "\f18e"; } .fa-arrow-circle-o-left:before { content: "\f190"; } .fa-toggle-left:before, .fa-caret-square-o-left:before { content: "\f191"; } .fa-dot-circle-o:before { content: "\f192"; } .fa-wheelchair:before { content: "\f193"; } .fa-vimeo-square:before { content: "\f194"; } .fa-turkish-lira:before, .fa-try:before { content: "\f195"; } .fa-plus-square-o:before { content: "\f196"; } .fa-space-shuttle:before { content: "\f197"; } .fa-slack:before { content: "\f198"; } .fa-envelope-square:before { content: "\f199"; } .fa-wordpress:before { content: "\f19a"; } .fa-openid:before { content: "\f19b"; } .fa-institution:before, .fa-bank:before, .fa-university:before { content: "\f19c"; } .fa-mortar-board:before, .fa-graduation-cap:before { content: "\f19d"; } .fa-yahoo:before { content: "\f19e"; } .fa-google:before { content: "\f1a0"; } .fa-reddit:before { content: "\f1a1"; } .fa-reddit-square:before { content: "\f1a2"; } .fa-stumbleupon-circle:before { content: "\f1a3"; } .fa-stumbleupon:before { content: "\f1a4"; } .fa-delicious:before { content: "\f1a5"; } .fa-digg:before { content: "\f1a6"; } .fa-pied-piper-pp:before { content: "\f1a7"; } .fa-pied-piper-alt:before { content: "\f1a8"; } .fa-drupal:before { content: "\f1a9"; } .fa-joomla:before { content: "\f1aa"; } .fa-language:before { content: "\f1ab"; } .fa-fax:before { content: "\f1ac"; } .fa-building:before { content: "\f1ad"; } .fa-child:before { content: "\f1ae"; } .fa-paw:before { content: "\f1b0"; } .fa-spoon:before { content: "\f1b1"; } .fa-cube:before { content: "\f1b2"; } .fa-cubes:before { content: "\f1b3"; } .fa-behance:before { content: "\f1b4"; } .fa-behance-square:before { content: "\f1b5"; } .fa-steam:before { content: "\f1b6"; } .fa-steam-square:before { content: "\f1b7"; } .fa-recycle:before { content: "\f1b8"; } .fa-automobile:before, .fa-car:before { content: "\f1b9"; } .fa-cab:before, .fa-taxi:before { content: "\f1ba"; } .fa-tree:before { content: "\f1bb"; } .fa-spotify:before { content: "\f1bc"; } .fa-deviantart:before { content: "\f1bd"; } .fa-soundcloud:before { content: "\f1be"; } .fa-database:before { content: "\f1c0"; } .fa-file-pdf-o:before { content: "\f1c1"; } .fa-file-word-o:before { content: "\f1c2"; } .fa-file-excel-o:before { content: "\f1c3"; } .fa-file-powerpoint-o:before { content: "\f1c4"; } .fa-file-photo-o:before, .fa-file-picture-o:before, .fa-file-image-o:before { content: "\f1c5"; } .fa-file-zip-o:before, .fa-file-archive-o:before { content: "\f1c6"; } .fa-file-sound-o:before, .fa-file-audio-o:before { content: "\f1c7"; } .fa-file-movie-o:before, .fa-file-video-o:before { content: "\f1c8"; } .fa-file-code-o:before { content: "\f1c9"; } .fa-vine:before { content: "\f1ca"; } .fa-codepen:before { content: "\f1cb"; } .fa-jsfiddle:before { content: "\f1cc"; } .fa-life-bouy:before, .fa-life-buoy:before, .fa-life-saver:before, .fa-support:before, .fa-life-ring:before { content: "\f1cd"; } .fa-circle-o-notch:before { content: "\f1ce"; } .fa-ra:before, .fa-resistance:before, .fa-rebel:before { content: "\f1d0"; } .fa-ge:before, .fa-empire:before { content: "\f1d1"; } .fa-git-square:before { content: "\f1d2"; } .fa-git:before { content: "\f1d3"; } .fa-y-combinator-square:before, .fa-yc-square:before, .fa-hacker-news:before { content: "\f1d4"; } .fa-tencent-weibo:before { content: "\f1d5"; } .fa-qq:before { content: "\f1d6"; } .fa-wechat:before, .fa-weixin:before { content: "\f1d7"; } .fa-send:before, .fa-paper-plane:before { content: "\f1d8"; } .fa-send-o:before, .fa-paper-plane-o:before { content: "\f1d9"; } .fa-history:before { content: "\f1da"; } .fa-circle-thin:before { content: "\f1db"; } .fa-header:before { content: "\f1dc"; } .fa-paragraph:before { content: "\f1dd"; } .fa-sliders:before { content: "\f1de"; } .fa-share-alt:before { content: "\f1e0"; } .fa-share-alt-square:before { content: "\f1e1"; } .fa-bomb:before { content: "\f1e2"; } .fa-soccer-ball-o:before, .fa-futbol-o:before { content: "\f1e3"; } .fa-tty:before { content: "\f1e4"; } .fa-binoculars:before { content: "\f1e5"; } .fa-plug:before { content: "\f1e6"; } .fa-slideshare:before { content: "\f1e7"; } .fa-twitch:before { content: "\f1e8"; } .fa-yelp:before { content: "\f1e9"; } .fa-newspaper-o:before { content: "\f1ea"; } .fa-wifi:before { content: "\f1eb"; } .fa-calculator:before { content: "\f1ec"; } .fa-paypal:before { content: "\f1ed"; } .fa-google-wallet:before { content: "\f1ee"; } .fa-cc-visa:before { content: "\f1f0"; } .fa-cc-mastercard:before { content: "\f1f1"; } .fa-cc-discover:before { content: "\f1f2"; } .fa-cc-amex:before { content: "\f1f3"; } .fa-cc-paypal:before { content: "\f1f4"; } .fa-cc-stripe:before { content: "\f1f5"; } .fa-bell-slash:before { content: "\f1f6"; } .fa-bell-slash-o:before { content: "\f1f7"; } .fa-trash:before { content: "\f1f8"; } .fa-copyright:before { content: "\f1f9"; } .fa-at:before { content: "\f1fa"; } .fa-eyedropper:before { content: "\f1fb"; } .fa-paint-brush:before { content: "\f1fc"; } .fa-birthday-cake:before { content: "\f1fd"; } .fa-area-chart:before { content: "\f1fe"; } .fa-pie-chart:before { content: "\f200"; } .fa-line-chart:before { content: "\f201"; } .fa-lastfm:before { content: "\f202"; } .fa-lastfm-square:before { content: "\f203"; } .fa-toggle-off:before { content: "\f204"; } .fa-toggle-on:before { content: "\f205"; } .fa-bicycle:before { content: "\f206"; } .fa-bus:before { content: "\f207"; } .fa-ioxhost:before { content: "\f208"; } .fa-angellist:before { content: "\f209"; } .fa-cc:before { content: "\f20a"; } .fa-shekel:before, .fa-sheqel:before, .fa-ils:before { content: "\f20b"; } .fa-meanpath:before { content: "\f20c"; } .fa-buysellads:before { content: "\f20d"; } .fa-connectdevelop:before { content: "\f20e"; } .fa-dashcube:before { content: "\f210"; } .fa-forumbee:before { content: "\f211"; } .fa-leanpub:before { content: "\f212"; } .fa-sellsy:before { content: "\f213"; } .fa-shirtsinbulk:before { content: "\f214"; } .fa-simplybuilt:before { content: "\f215"; } .fa-skyatlas:before { content: "\f216"; } .fa-cart-plus:before { content: "\f217"; } .fa-cart-arrow-down:before { content: "\f218"; } .fa-diamond:before { content: "\f219"; } .fa-ship:before { content: "\f21a"; } .fa-user-secret:before { content: "\f21b"; } .fa-motorcycle:before { content: "\f21c"; } .fa-street-view:before { content: "\f21d"; } .fa-heartbeat:before { content: "\f21e"; } .fa-venus:before { content: "\f221"; } .fa-mars:before { content: "\f222"; } .fa-mercury:before { content: "\f223"; } .fa-intersex:before, .fa-transgender:before { content: "\f224"; } .fa-transgender-alt:before { content: "\f225"; } .fa-venus-double:before { content: "\f226"; } .fa-mars-double:before { content: "\f227"; } .fa-venus-mars:before { content: "\f228"; } .fa-mars-stroke:before { content: "\f229"; } .fa-mars-stroke-v:before { content: "\f22a"; } .fa-mars-stroke-h:before { content: "\f22b"; } .fa-neuter:before { content: "\f22c"; } .fa-genderless:before { content: "\f22d"; } .fa-facebook-official:before { content: "\f230"; } .fa-pinterest-p:before { content: "\f231"; } .fa-whatsapp:before { content: "\f232"; } .fa-server:before { content: "\f233"; } .fa-user-plus:before { content: "\f234"; } .fa-user-times:before { content: "\f235"; } .fa-hotel:before, .fa-bed:before { content: "\f236"; } .fa-viacoin:before { content: "\f237"; } .fa-train:before { content: "\f238"; } .fa-subway:before { content: "\f239"; } .fa-medium:before { content: "\f23a"; } .fa-yc:before, .fa-y-combinator:before { content: "\f23b"; } .fa-optin-monster:before { content: "\f23c"; } .fa-opencart:before { content: "\f23d"; } .fa-expeditedssl:before { content: "\f23e"; } .fa-battery-4:before, .fa-battery:before, .fa-battery-full:before { content: "\f240"; } .fa-battery-3:before, .fa-battery-three-quarters:before { content: "\f241"; } .fa-battery-2:before, .fa-battery-half:before { content: "\f242"; } .fa-battery-1:before, .fa-battery-quarter:before { content: "\f243"; } .fa-battery-0:before, .fa-battery-empty:before { content: "\f244"; } .fa-mouse-pointer:before { content: "\f245"; } .fa-i-cursor:before { content: "\f246"; } .fa-object-group:before { content: "\f247"; } .fa-object-ungroup:before { content: "\f248"; } .fa-sticky-note:before { content: "\f249"; } .fa-sticky-note-o:before { content: "\f24a"; } .fa-cc-jcb:before { content: "\f24b"; } .fa-cc-diners-club:before { content: "\f24c"; } .fa-clone:before { content: "\f24d"; } .fa-balance-scale:before { content: "\f24e"; } .fa-hourglass-o:before { content: "\f250"; } .fa-hourglass-1:before, .fa-hourglass-start:before { content: "\f251"; } .fa-hourglass-2:before, .fa-hourglass-half:before { content: "\f252"; } .fa-hourglass-3:before, .fa-hourglass-end:before { content: "\f253"; } .fa-hourglass:before { content: "\f254"; } .fa-hand-grab-o:before, .fa-hand-rock-o:before { content: "\f255"; } .fa-hand-stop-o:before, .fa-hand-paper-o:before { content: "\f256"; } .fa-hand-scissors-o:before { content: "\f257"; } .fa-hand-lizard-o:before { content: "\f258"; } .fa-hand-spock-o:before { content: "\f259"; } .fa-hand-pointer-o:before { content: "\f25a"; } .fa-hand-peace-o:before { content: "\f25b"; } .fa-trademark:before { content: "\f25c"; } .fa-registered:before { content: "\f25d"; } .fa-creative-commons:before { content: "\f25e"; } .fa-gg:before { content: "\f260"; } .fa-gg-circle:before { content: "\f261"; } .fa-tripadvisor:before { content: "\f262"; } .fa-odnoklassniki:before { content: "\f263"; } .fa-odnoklassniki-square:before { content: "\f264"; } .fa-get-pocket:before { content: "\f265"; } .fa-wikipedia-w:before { content: "\f266"; } .fa-safari:before { content: "\f267"; } .fa-chrome:before { content: "\f268"; } .fa-firefox:before { content: "\f269"; } .fa-opera:before { content: "\f26a"; } .fa-internet-explorer:before { content: "\f26b"; } .fa-tv:before, .fa-television:before { content: "\f26c"; } .fa-contao:before { content: "\f26d"; } .fa-500px:before { content: "\f26e"; } .fa-amazon:before { content: "\f270"; } .fa-calendar-plus-o:before { content: "\f271"; } .fa-calendar-minus-o:before { content: "\f272"; } .fa-calendar-times-o:before { content: "\f273"; } .fa-calendar-check-o:before { content: "\f274"; } .fa-industry:before { content: "\f275"; } .fa-map-pin:before { content: "\f276"; } .fa-map-signs:before { content: "\f277"; } .fa-map-o:before { content: "\f278"; } .fa-map:before { content: "\f279"; } .fa-commenting:before { content: "\f27a"; } .fa-commenting-o:before { content: "\f27b"; } .fa-houzz:before { content: "\f27c"; } .fa-vimeo:before { content: "\f27d"; } .fa-black-tie:before { content: "\f27e"; } .fa-fonticons:before { content: "\f280"; } .fa-reddit-alien:before { content: "\f281"; } .fa-edge:before { content: "\f282"; } .fa-credit-card-alt:before { content: "\f283"; } .fa-codiepie:before { content: "\f284"; } .fa-modx:before { content: "\f285"; } .fa-fort-awesome:before { content: "\f286"; } .fa-usb:before { content: "\f287"; } .fa-product-hunt:before { content: "\f288"; } .fa-mixcloud:before { content: "\f289"; } .fa-scribd:before { content: "\f28a"; } .fa-pause-circle:before { content: "\f28b"; } .fa-pause-circle-o:before { content: "\f28c"; } .fa-stop-circle:before { content: "\f28d"; } .fa-stop-circle-o:before { content: "\f28e"; } .fa-shopping-bag:before { content: "\f290"; } .fa-shopping-basket:before { content: "\f291"; } .fa-hashtag:before { content: "\f292"; } .fa-bluetooth:before { content: "\f293"; } .fa-bluetooth-b:before { content: "\f294"; } .fa-percent:before { content: "\f295"; } .fa-gitlab:before { content: "\f296"; } .fa-wpbeginner:before { content: "\f297"; } .fa-wpforms:before { content: "\f298"; } .fa-envira:before { content: "\f299"; } .fa-universal-access:before { content: "\f29a"; } .fa-wheelchair-alt:before { content: "\f29b"; } .fa-question-circle-o:before { content: "\f29c"; } .fa-blind:before { content: "\f29d"; } .fa-audio-description:before { content: "\f29e"; } .fa-volume-control-phone:before { content: "\f2a0"; } .fa-braille:before { content: "\f2a1"; } .fa-assistive-listening-systems:before { content: "\f2a2"; } .fa-asl-interpreting:before, .fa-american-sign-language-interpreting:before { content: "\f2a3"; } .fa-deafness:before, .fa-hard-of-hearing:before, .fa-deaf:before { content: "\f2a4"; } .fa-glide:before { content: "\f2a5"; } .fa-glide-g:before { content: "\f2a6"; } .fa-signing:before, .fa-sign-language:before { content: "\f2a7"; } .fa-low-vision:before { content: "\f2a8"; } .fa-viadeo:before { content: "\f2a9"; } .fa-viadeo-square:before { content: "\f2aa"; } .fa-snapchat:before { content: "\f2ab"; } .fa-snapchat-ghost:before { content: "\f2ac"; } .fa-snapchat-square:before { content: "\f2ad"; } .fa-pied-piper:before { content: "\f2ae"; } .fa-first-order:before { content: "\f2b0"; } .fa-yoast:before { content: "\f2b1"; } .fa-themeisle:before { content: "\f2b2"; } .fa-google-plus-circle:before, .fa-google-plus-official:before { content: "\f2b3"; } .fa-fa:before, .fa-font-awesome:before { content: "\f2b4"; } .fa-handshake-o:before { content: "\f2b5"; } .fa-envelope-open:before { content: "\f2b6"; } .fa-envelope-open-o:before { content: "\f2b7"; } .fa-linode:before { content: "\f2b8"; } .fa-address-book:before { content: "\f2b9"; } .fa-address-book-o:before { content: "\f2ba"; } .fa-vcard:before, .fa-address-card:before { content: "\f2bb"; } .fa-vcard-o:before, .fa-address-card-o:before { content: "\f2bc"; } .fa-user-circle:before { content: "\f2bd"; } .fa-user-circle-o:before { content: "\f2be"; } .fa-user-o:before { content: "\f2c0"; } .fa-id-badge:before { content: "\f2c1"; } .fa-drivers-license:before, .fa-id-card:before { content: "\f2c2"; } .fa-drivers-license-o:before, .fa-id-card-o:before { content: "\f2c3"; } .fa-quora:before { content: "\f2c4"; } .fa-free-code-camp:before { content: "\f2c5"; } .fa-telegram:before { content: "\f2c6"; } .fa-thermometer-4:before, .fa-thermometer:before, .fa-thermometer-full:before { content: "\f2c7"; } .fa-thermometer-3:before, .fa-thermometer-three-quarters:before { content: "\f2c8"; } .fa-thermometer-2:before, .fa-thermometer-half:before { content: "\f2c9"; } .fa-thermometer-1:before, .fa-thermometer-quarter:before { content: "\f2ca"; } .fa-thermometer-0:before, .fa-thermometer-empty:before { content: "\f2cb"; } .fa-shower:before { content: "\f2cc"; } .fa-bathtub:before, .fa-s15:before, .fa-bath:before { content: "\f2cd"; } .fa-podcast:before { content: "\f2ce"; } .fa-window-maximize:before { content: "\f2d0"; } .fa-window-minimize:before { content: "\f2d1"; } .fa-window-restore:before { content: "\f2d2"; } .fa-times-rectangle:before, .fa-window-close:before { content: "\f2d3"; } .fa-times-rectangle-o:before, .fa-window-close-o:before { content: "\f2d4"; } .fa-bandcamp:before { content: "\f2d5"; } .fa-grav:before { content: "\f2d6"; } .fa-etsy:before { content: "\f2d7"; } .fa-imdb:before { content: "\f2d8"; } .fa-ravelry:before { content: "\f2d9"; } .fa-eercast:before { content: "\f2da"; } .fa-microchip:before { content: "\f2db"; } .fa-snowflake-o:before { content: "\f2dc"; } .fa-superpowers:before { content: "\f2dd"; } .fa-wpexplorer:before { content: "\f2de"; } .fa-meetup:before { content: "\f2e0"; } .sr-only { position: absolute; width: 1px; height: 1px; padding: 0; margin: -1px; overflow: hidden; clip: rect(0, 0, 0, 0); border: 0; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } /\*! \* \* IPython base \* \*/ .modal.fade .modal-dialog { -webkit-transform: translate(0, 0); -ms-transform: translate(0, 0); -o-transform: translate(0, 0); transform: translate(0, 0); } code { color: #000; } pre { font-size: inherit; line-height: inherit; } label { font-weight: normal; } /\* Make the page background atleast 100% the height of the view port \*/ /\* Make the page itself atleast 70% the height of the view port \*/ .border-box-sizing { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } .corner-all { border-radius: 2px; } .no-padding { padding: 0px; } /\* Flexible box model classes \*/ /\* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ \*/ /\* This file is a compatability layer. It allows the usage of flexible box model layouts accross multiple browsers, including older browsers. The newest, universal implementation of the flexible box model is used when available (see `Modern browsers` comments below). Browsers that are known to implement this new spec completely include: Firefox 28.0+ Chrome 29.0+ Internet Explorer 11+ Opera 17.0+ Browsers not listed, including Safari, are supported via the styling under the `Old browsers` comments below. \*/ .hbox { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } .hbox > \* { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; } .vbox { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } .vbox > \* { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; } .hbox.reverse, .vbox.reverse, .reverse { /\* Old browsers \*/ -webkit-box-direction: reverse; -moz-box-direction: reverse; box-direction: reverse; /\* Modern browsers \*/ flex-direction: row-reverse; } .hbox.box-flex0, .vbox.box-flex0, .box-flex0 { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; width: auto; } .hbox.box-flex1, .vbox.box-flex1, .box-flex1 { /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } .hbox.box-flex, .vbox.box-flex, .box-flex { /\* Old browsers \*/ /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } .hbox.box-flex2, .vbox.box-flex2, .box-flex2 { /\* Old browsers \*/ -webkit-box-flex: 2; -moz-box-flex: 2; box-flex: 2; /\* Modern browsers \*/ flex: 2; } .box-group1 { /\* Deprecated \*/ -webkit-box-flex-group: 1; -moz-box-flex-group: 1; box-flex-group: 1; } .box-group2 { /\* Deprecated \*/ -webkit-box-flex-group: 2; -moz-box-flex-group: 2; box-flex-group: 2; } .hbox.start, .vbox.start, .start { /\* Old browsers \*/ -webkit-box-pack: start; -moz-box-pack: start; box-pack: start; /\* Modern browsers \*/ justify-content: flex-start; } .hbox.end, .vbox.end, .end { /\* Old browsers \*/ -webkit-box-pack: end; -moz-box-pack: end; box-pack: end; /\* Modern browsers \*/ justify-content: flex-end; } .hbox.center, .vbox.center, .center { /\* Old browsers \*/ -webkit-box-pack: center; -moz-box-pack: center; box-pack: center; /\* Modern browsers \*/ justify-content: center; } .hbox.baseline, .vbox.baseline, .baseline { /\* Old browsers \*/ -webkit-box-pack: baseline; -moz-box-pack: baseline; box-pack: baseline; /\* Modern browsers \*/ justify-content: baseline; } .hbox.stretch, .vbox.stretch, .stretch { /\* Old browsers \*/ -webkit-box-pack: stretch; -moz-box-pack: stretch; box-pack: stretch; /\* Modern browsers \*/ justify-content: stretch; } .hbox.align-start, .vbox.align-start, .align-start { /\* Old browsers \*/ -webkit-box-align: start; -moz-box-align: start; box-align: start; /\* Modern browsers \*/ align-items: flex-start; } .hbox.align-end, .vbox.align-end, .align-end { /\* Old browsers \*/ -webkit-box-align: end; -moz-box-align: end; box-align: end; /\* Modern browsers \*/ align-items: flex-end; } .hbox.align-center, .vbox.align-center, .align-center { /\* Old browsers \*/ -webkit-box-align: center; -moz-box-align: center; box-align: center; /\* Modern browsers \*/ align-items: center; } .hbox.align-baseline, .vbox.align-baseline, .align-baseline { /\* Old browsers \*/ -webkit-box-align: baseline; -moz-box-align: baseline; box-align: baseline; /\* Modern browsers \*/ align-items: baseline; } .hbox.align-stretch, .vbox.align-stretch, .align-stretch { /\* Old browsers \*/ -webkit-box-align: stretch; -moz-box-align: stretch; box-align: stretch; /\* Modern browsers \*/ align-items: stretch; } div.error { margin: 2em; text-align: center; } div.error > h1 { font-size: 500%; line-height: normal; } div.error > p { font-size: 200%; line-height: normal; } div.traceback-wrapper { text-align: left; max-width: 800px; margin: auto; } div.traceback-wrapper pre.traceback { max-height: 600px; overflow: auto; } /\*\* \* Primary styles \* \* Author: Jupyter Development Team \*/ body { background-color: #fff; /\* This makes sure that the body covers the entire window and needs to be in a different element than the display: box in wrapper below \*/ position: absolute; left: 0px; right: 0px; top: 0px; bottom: 0px; overflow: visible; } body > #header { /\* Initially hidden to prevent FLOUC \*/ display: none; background-color: #fff; /\* Display over codemirror \*/ position: relative; z-index: 100; } body > #header #header-container { display: flex; flex-direction: row; justify-content: space-between; padding: 5px; padding-bottom: 5px; padding-top: 5px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } body > #header .header-bar { width: 100%; height: 1px; background: #e7e7e7; margin-bottom: -1px; } @media print { body > #header { display: none !important; } } #header-spacer { width: 100%; visibility: hidden; } @media print { #header-spacer { display: none; } } #ipython\_notebook { padding-left: 0px; padding-top: 1px; padding-bottom: 1px; } [dir="rtl"] #ipython\_notebook { margin-right: 10px; margin-left: 0; } [dir="rtl"] #ipython\_notebook.pull-left { float: right !important; float: right; } .flex-spacer { flex: 1; } #noscript { width: auto; padding-top: 16px; padding-bottom: 16px; text-align: center; font-size: 22px; color: red; font-weight: bold; } #ipython\_notebook img { height: 28px; } #site { width: 100%; display: none; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; overflow: auto; } @media print { #site { height: auto !important; } } /\* Smaller buttons \*/ .ui-button .ui-button-text { padding: 0.2em 0.8em; font-size: 77%; } input.ui-button { padding: 0.3em 0.9em; } span#kernel\_logo\_widget { margin: 0 10px; } span#login\_widget { float: right; } [dir="rtl"] span#login\_widget { float: left; } span#login\_widget > .button, #logout { color: #333; background-color: #fff; border-color: #ccc; } span#login\_widget > .button:focus, #logout:focus, span#login\_widget > .button.focus, #logout.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } span#login\_widget > .button:hover, #logout:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } span#login\_widget > .button:active, #logout:active, span#login\_widget > .button.active, #logout.active, .open > .dropdown-togglespan#login\_widget > .button, .open > .dropdown-toggle#logout { color: #333; background-color: #e6e6e6; border-color: #adadad; } span#login\_widget > .button:active:hover, #logout:active:hover, span#login\_widget > .button.active:hover, #logout.active:hover, .open > .dropdown-togglespan#login\_widget > .button:hover, .open > .dropdown-toggle#logout:hover, span#login\_widget > .button:active:focus, #logout:active:focus, span#login\_widget > .button.active:focus, #logout.active:focus, .open > .dropdown-togglespan#login\_widget > .button:focus, .open > .dropdown-toggle#logout:focus, span#login\_widget > .button:active.focus, #logout:active.focus, span#login\_widget > .button.active.focus, #logout.active.focus, .open > .dropdown-togglespan#login\_widget > .button.focus, .open > .dropdown-toggle#logout.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } span#login\_widget > .button:active, #logout:active, span#login\_widget > .button.active, #logout.active, .open > .dropdown-togglespan#login\_widget > .button, .open > .dropdown-toggle#logout { background-image: none; } span#login\_widget > .button.disabled:hover, #logout.disabled:hover, span#login\_widget > .button[disabled]:hover, #logout[disabled]:hover, fieldset[disabled] span#login\_widget > .button:hover, fieldset[disabled] #logout:hover, span#login\_widget > .button.disabled:focus, #logout.disabled:focus, span#login\_widget > .button[disabled]:focus, #logout[disabled]:focus, fieldset[disabled] span#login\_widget > .button:focus, fieldset[disabled] #logout:focus, span#login\_widget > .button.disabled.focus, #logout.disabled.focus, span#login\_widget > .button[disabled].focus, #logout[disabled].focus, fieldset[disabled] span#login\_widget > .button.focus, fieldset[disabled] #logout.focus { background-color: #fff; border-color: #ccc; } span#login\_widget > .button .badge, #logout .badge { color: #fff; background-color: #333; } .nav-header { text-transform: none; } #header > span { margin-top: 10px; } .modal\_stretch .modal-dialog { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; min-height: 80vh; } .modal\_stretch .modal-dialog .modal-body { max-height: calc(100vh - 200px); overflow: auto; flex: 1; } .modal-header { cursor: move; } @media (min-width: 768px) { .modal .modal-dialog { width: 700px; } } @media (min-width: 768px) { select.form-control { margin-left: 12px; margin-right: 12px; } } /\*! \* \* IPython auth \* \*/ .center-nav { display: inline-block; margin-bottom: -4px; } [dir="rtl"] .center-nav form.pull-left { float: right !important; float: right; } [dir="rtl"] .center-nav .navbar-text { float: right; } [dir="rtl"] .navbar-inner { text-align: right; } [dir="rtl"] div.text-left { text-align: right; } /\*! \* \* IPython tree view \* \*/ /\* We need an invisible input field on top of the sentense\*/ /\* "Drag file onto the list ..." \*/ .alternate\_upload { background-color: none; display: inline; } .alternate\_upload.form { padding: 0; margin: 0; } .alternate\_upload input.fileinput { position: absolute; display: block; width: 100%; height: 100%; overflow: hidden; cursor: pointer; opacity: 0; z-index: 2; } .alternate\_upload .btn-xs > input.fileinput { margin: -1px -5px; } .alternate\_upload .btn-upload { position: relative; height: 22px; } ::-webkit-file-upload-button { cursor: pointer; } /\*\* \* Primary styles \* \* Author: Jupyter Development Team \*/ ul#tabs { margin-bottom: 4px; } ul#tabs a { padding-top: 6px; padding-bottom: 4px; } [dir="rtl"] ul#tabs.nav-tabs > li { float: right; } [dir="rtl"] ul#tabs.nav.nav-tabs { padding-right: 0; } ul.breadcrumb a:focus, ul.breadcrumb a:hover { text-decoration: none; } ul.breadcrumb i.icon-home { font-size: 16px; margin-right: 4px; } ul.breadcrumb span { color: #5e5e5e; } .list\_toolbar { padding: 4px 0 4px 0; vertical-align: middle; } .list\_toolbar .tree-buttons { padding-top: 1px; } [dir="rtl"] .list\_toolbar .tree-buttons .pull-right { float: left !important; float: left; } [dir="rtl"] .list\_toolbar .col-sm-4, [dir="rtl"] .list\_toolbar .col-sm-8 { float: right; } .dynamic-buttons { padding-top: 3px; display: inline-block; } .list\_toolbar [class\*="span"] { min-height: 24px; } .list\_header { font-weight: bold; background-color: #EEE; } .list\_placeholder { font-weight: bold; padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; } .list\_container { margin-top: 4px; margin-bottom: 20px; border: 1px solid #ddd; border-radius: 2px; } .list\_container > div { border-bottom: 1px solid #ddd; } .list\_container > div:hover .list-item { background-color: red; } .list\_container > div:last-child { border: none; } .list\_item:hover .list\_item { background-color: #ddd; } .list\_item a { text-decoration: none; } .list\_item:hover { background-color: #fafafa; } .list\_header > div, .list\_item > div { padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; line-height: 22px; } .list\_header > div input, .list\_item > div input { margin-right: 7px; margin-left: 14px; vertical-align: text-bottom; line-height: 22px; position: relative; top: -1px; } .list\_header > div .item\_link, .list\_item > div .item\_link { margin-left: -1px; vertical-align: baseline; line-height: 22px; } [dir="rtl"] .list\_item > div input { margin-right: 0; } .new-file input[type=checkbox] { visibility: hidden; } .item\_name { line-height: 22px; height: 24px; } .item\_icon { font-size: 14px; color: #5e5e5e; margin-right: 7px; margin-left: 7px; line-height: 22px; vertical-align: baseline; } .item\_modified { margin-right: 7px; margin-left: 7px; } [dir="rtl"] .item\_modified.pull-right { float: left !important; float: left; } .item\_buttons { line-height: 1em; margin-left: -5px; } .item\_buttons .btn, .item\_buttons .btn-group, .item\_buttons .input-group { float: left; } .item\_buttons > .btn, .item\_buttons > .btn-group, .item\_buttons > .input-group { margin-left: 5px; } .item\_buttons .btn { min-width: 13ex; } .item\_buttons .running-indicator { padding-top: 4px; color: #5cb85c; } .item\_buttons .kernel-name { padding-top: 4px; color: #5bc0de; margin-right: 7px; float: left; } [dir="rtl"] .item\_buttons.pull-right { float: left !important; float: left; } [dir="rtl"] .item\_buttons .kernel-name { margin-left: 7px; float: right; } .toolbar\_info { height: 24px; line-height: 24px; } .list\_item input:not([type=checkbox]) { padding-top: 3px; padding-bottom: 3px; height: 22px; line-height: 14px; margin: 0px; } .highlight\_text { color: blue; } #project\_name { display: inline-block; padding-left: 7px; margin-left: -2px; } #project\_name > .breadcrumb { padding: 0px; margin-bottom: 0px; background-color: transparent; font-weight: bold; } .sort\_button { display: inline-block; padding-left: 7px; } [dir="rtl"] .sort\_button.pull-right { float: left !important; float: left; } #tree-selector { padding-right: 0px; } #button-select-all { min-width: 50px; } [dir="rtl"] #button-select-all.btn { float: right ; } #select-all { margin-left: 7px; margin-right: 2px; margin-top: 2px; height: 16px; } [dir="rtl"] #select-all.pull-left { float: right !important; float: right; } .menu\_icon { margin-right: 2px; } .tab-content .row { margin-left: 0px; margin-right: 0px; } .folder\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f114"; } .folder\_icon:before.fa-pull-left { margin-right: .3em; } .folder\_icon:before.fa-pull-right { margin-left: .3em; } .folder\_icon:before.pull-left { margin-right: .3em; } .folder\_icon:before.pull-right { margin-left: .3em; } .notebook\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f02d"; position: relative; top: -1px; } .notebook\_icon:before.fa-pull-left { margin-right: .3em; } .notebook\_icon:before.fa-pull-right { margin-left: .3em; } .notebook\_icon:before.pull-left { margin-right: .3em; } .notebook\_icon:before.pull-right { margin-left: .3em; } .running\_notebook\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f02d"; position: relative; top: -1px; color: #5cb85c; } .running\_notebook\_icon:before.fa-pull-left { margin-right: .3em; } .running\_notebook\_icon:before.fa-pull-right { margin-left: .3em; } .running\_notebook\_icon:before.pull-left { margin-right: .3em; } .running\_notebook\_icon:before.pull-right { margin-left: .3em; } .file\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f016"; position: relative; top: -2px; } .file\_icon:before.fa-pull-left { margin-right: .3em; } .file\_icon:before.fa-pull-right { margin-left: .3em; } .file\_icon:before.pull-left { margin-right: .3em; } .file\_icon:before.pull-right { margin-left: .3em; } #notebook\_toolbar .pull-right { padding-top: 0px; margin-right: -1px; } ul#new-menu { left: auto; right: 0; } #new-menu .dropdown-header { font-size: 10px; border-bottom: 1px solid #e5e5e5; padding: 0 0 3px; margin: -3px 20px 0; } .kernel-menu-icon { padding-right: 12px; width: 24px; content: "\f096"; } .kernel-menu-icon:before { content: "\f096"; } .kernel-menu-icon-current:before { content: "\f00c"; } #tab\_content { padding-top: 20px; } #running .panel-group .panel { margin-top: 3px; margin-bottom: 1em; } #running .panel-group .panel .panel-heading { background-color: #EEE; padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; line-height: 22px; } #running .panel-group .panel .panel-heading a:focus, #running .panel-group .panel .panel-heading a:hover { text-decoration: none; } #running .panel-group .panel .panel-body { padding: 0px; } #running .panel-group .panel .panel-body .list\_container { margin-top: 0px; margin-bottom: 0px; border: 0px; border-radius: 0px; } #running .panel-group .panel .panel-body .list\_container .list\_item { border-bottom: 1px solid #ddd; } #running .panel-group .panel .panel-body .list\_container .list\_item:last-child { border-bottom: 0px; } .delete-button { display: none; } .duplicate-button { display: none; } .rename-button { display: none; } .move-button { display: none; } .download-button { display: none; } .shutdown-button { display: none; } .dynamic-instructions { display: inline-block; padding-top: 4px; } /\*! \* \* IPython text editor webapp \* \*/ .selected-keymap i.fa { padding: 0px 5px; } .selected-keymap i.fa:before { content: "\f00c"; } #mode-menu { overflow: auto; max-height: 20em; } .edit\_app #header { -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } .edit\_app #menubar .navbar { /\* Use a negative 1 bottom margin, so the border overlaps the border of the header \*/ margin-bottom: -1px; } .dirty-indicator { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator.fa-pull-left { margin-right: .3em; } .dirty-indicator.fa-pull-right { margin-left: .3em; } .dirty-indicator.pull-left { margin-right: .3em; } .dirty-indicator.pull-right { margin-left: .3em; } .dirty-indicator-dirty { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator-dirty.fa-pull-left { margin-right: .3em; } .dirty-indicator-dirty.fa-pull-right { margin-left: .3em; } .dirty-indicator-dirty.pull-left { margin-right: .3em; } .dirty-indicator-dirty.pull-right { margin-left: .3em; } .dirty-indicator-clean { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator-clean.fa-pull-left { margin-right: .3em; } .dirty-indicator-clean.fa-pull-right { margin-left: .3em; } .dirty-indicator-clean.pull-left { margin-right: .3em; } .dirty-indicator-clean.pull-right { margin-left: .3em; } .dirty-indicator-clean:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f00c"; } .dirty-indicator-clean:before.fa-pull-left { margin-right: .3em; } .dirty-indicator-clean:before.fa-pull-right { margin-left: .3em; } .dirty-indicator-clean:before.pull-left { margin-right: .3em; } .dirty-indicator-clean:before.pull-right { margin-left: .3em; } #filename { font-size: 16pt; display: table; padding: 0px 5px; } #current-mode { padding-left: 5px; padding-right: 5px; } #texteditor-backdrop { padding-top: 20px; padding-bottom: 20px; } @media not print { #texteditor-backdrop { background-color: #EEE; } } @media print { #texteditor-backdrop #texteditor-container .CodeMirror-gutter, #texteditor-backdrop #texteditor-container .CodeMirror-gutters { background-color: #fff; } } @media not print { #texteditor-backdrop #texteditor-container .CodeMirror-gutter, #texteditor-backdrop #texteditor-container .CodeMirror-gutters { background-color: #fff; } } @media not print { #texteditor-backdrop #texteditor-container { padding: 0px; background-color: #fff; -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } } .CodeMirror-dialog { background-color: #fff; } /\*! \* \* IPython notebook \* \*/ /\* CSS font colors for translated ANSI escape sequences \*/ /\* The color values are a mix of http://www.xcolors.net/dl/baskerville-ivorylight and http://www.xcolors.net/dl/euphrasia \*/ .ansi-black-fg { color: #3E424D; } .ansi-black-bg { background-color: #3E424D; } .ansi-black-intense-fg { color: #282C36; } .ansi-black-intense-bg { background-color: #282C36; } .ansi-red-fg { color: #E75C58; } .ansi-red-bg { background-color: #E75C58; } .ansi-red-intense-fg { color: #B22B31; } .ansi-red-intense-bg { background-color: #B22B31; } .ansi-green-fg { color: #00A250; } .ansi-green-bg { background-color: #00A250; } .ansi-green-intense-fg { color: #007427; } .ansi-green-intense-bg { background-color: #007427; } .ansi-yellow-fg { color: #DDB62B; } .ansi-yellow-bg { background-color: #DDB62B; } .ansi-yellow-intense-fg { color: #B27D12; } .ansi-yellow-intense-bg { background-color: #B27D12; } .ansi-blue-fg { color: #208FFB; } .ansi-blue-bg { background-color: #208FFB; } .ansi-blue-intense-fg { color: #0065CA; } .ansi-blue-intense-bg { background-color: #0065CA; } .ansi-magenta-fg { color: #D160C4; } .ansi-magenta-bg { background-color: #D160C4; } .ansi-magenta-intense-fg { color: #A03196; } .ansi-magenta-intense-bg { background-color: #A03196; } .ansi-cyan-fg { color: #60C6C8; } .ansi-cyan-bg { background-color: #60C6C8; } .ansi-cyan-intense-fg { color: #258F8F; } .ansi-cyan-intense-bg { background-color: #258F8F; } .ansi-white-fg { color: #C5C1B4; } .ansi-white-bg { background-color: #C5C1B4; } .ansi-white-intense-fg { color: #A1A6B2; } .ansi-white-intense-bg { background-color: #A1A6B2; } .ansi-default-inverse-fg { color: #FFFFFF; } .ansi-default-inverse-bg { background-color: #000000; } .ansi-bold { font-weight: bold; } .ansi-underline { text-decoration: underline; } /\* The following styles are deprecated an will be removed in a future version \*/ .ansibold { font-weight: bold; } .ansi-inverse { outline: 0.5px dotted; } /\* use dark versions for foreground, to improve visibility \*/ .ansiblack { color: black; } .ansired { color: darkred; } .ansigreen { color: darkgreen; } .ansiyellow { color: #c4a000; } .ansiblue { color: darkblue; } .ansipurple { color: darkviolet; } .ansicyan { color: steelblue; } .ansigray { color: gray; } /\* and light for background, for the same reason \*/ .ansibgblack { background-color: black; } .ansibgred { background-color: red; } .ansibggreen { background-color: green; } .ansibgyellow { background-color: yellow; } .ansibgblue { background-color: blue; } .ansibgpurple { background-color: magenta; } .ansibgcyan { background-color: cyan; } .ansibggray { background-color: gray; } div.cell { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; border-radius: 2px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; border-width: 1px; border-style: solid; border-color: transparent; width: 100%; padding: 5px; /\* This acts as a spacer between cells, that is outside the border \*/ margin: 0px; outline: none; position: relative; overflow: visible; } div.cell:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: transparent; } div.cell.jupyter-soft-selected { border-left-color: #E3F2FD; border-left-width: 1px; padding-left: 5px; border-right-color: #E3F2FD; border-right-width: 1px; background: #E3F2FD; } @media print { div.cell.jupyter-soft-selected { border-color: transparent; } } div.cell.selected, div.cell.selected.jupyter-soft-selected { border-color: #ababab; } div.cell.selected:before, div.cell.selected.jupyter-soft-selected:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: #42A5F5; } @media print { div.cell.selected, div.cell.selected.jupyter-soft-selected { border-color: transparent; } } .edit\_mode div.cell.selected { border-color: #66BB6A; } .edit\_mode div.cell.selected:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: #66BB6A; } @media print { .edit\_mode div.cell.selected { border-color: transparent; } } .prompt { /\* This needs to be wide enough for 3 digit prompt numbers: In[100]: \*/ min-width: 14ex; /\* This padding is tuned to match the padding on the CodeMirror editor. \*/ padding: 0.4em; margin: 0px; font-family: monospace; text-align: right; /\* This has to match that of the the CodeMirror class line-height below \*/ line-height: 1.21429em; /\* Don't highlight prompt number selection \*/ -webkit-touch-callout: none; -webkit-user-select: none; -khtml-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; /\* Use default cursor \*/ cursor: default; } @media (max-width: 540px) { .prompt { text-align: left; } } div.inner\_cell { min-width: 0; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } /\* input\_area and input\_prompt must match in top border and margin for alignment \*/ div.input\_area { border: 1px solid #cfcfcf; border-radius: 2px; background: #f7f7f7; line-height: 1.21429em; } /\* This is needed so that empty prompt areas can collapse to zero height when there is no content in the output\_subarea and the prompt. The main purpose of this is to make sure that empty JavaScript output\_subareas have no height. \*/ div.prompt:empty { padding-top: 0; padding-bottom: 0; } div.unrecognized\_cell { padding: 5px 5px 5px 0px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } div.unrecognized\_cell .inner\_cell { border-radius: 2px; padding: 5px; font-weight: bold; color: red; border: 1px solid #cfcfcf; background: #eaeaea; } div.unrecognized\_cell .inner\_cell a { color: inherit; text-decoration: none; } div.unrecognized\_cell .inner\_cell a:hover { color: inherit; text-decoration: none; } @media (max-width: 540px) { div.unrecognized\_cell > div.prompt { display: none; } } div.code\_cell { /\* avoid page breaking on code cells when printing \*/ } @media print { div.code\_cell { page-break-inside: avoid; } } /\* any special styling for code cells that are currently running goes here \*/ div.input { page-break-inside: avoid; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } @media (max-width: 540px) { div.input { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } } /\* input\_area and input\_prompt must match in top border and margin for alignment \*/ div.input\_prompt { color: #303F9F; border-top: 1px solid transparent; } div.input\_area > div.highlight { margin: 0.4em; border: none; padding: 0px; background-color: transparent; } div.input\_area > div.highlight > pre { margin: 0px; border: none; padding: 0px; background-color: transparent; } /\* The following gets added to the <head> if it is detected that the user has a \* monospace font with inconsistent normal/bold/italic height. See \* notebookmain.js. Such fonts will have keywords vertically offset with \* respect to the rest of the text. The user should select a better font. \* See: https://github.com/ipython/ipython/issues/1503 \* \* .CodeMirror span { \* vertical-align: bottom; \* } \*/ .CodeMirror { line-height: 1.21429em; /\* Changed from 1em to our global default \*/ font-size: 14px; height: auto; /\* Changed to auto to autogrow \*/ background: none; /\* Changed from white to allow our bg to show through \*/ } .CodeMirror-scroll { /\* The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.\*/ /\* We have found that if it is visible, vertical scrollbars appear with font size changes.\*/ overflow-y: hidden; overflow-x: auto; } .CodeMirror-lines { /\* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because \*/ /\* we have set a different line-height and want this to scale with that. \*/ /\* Note that this should set vertical padding only, since CodeMirror assumes that horizontal padding will be set on CodeMirror pre \*/ padding: 0.4em 0; } .CodeMirror-linenumber { padding: 0 8px 0 4px; } .CodeMirror-gutters { border-bottom-left-radius: 2px; border-top-left-radius: 2px; } .CodeMirror pre { /\* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only, use .CodeMirror-lines for vertical \*/ padding: 0 0.4em; border: 0; border-radius: 0; } .CodeMirror-cursor { border-left: 1.4px solid black; } @media screen and (min-width: 2138px) and (max-width: 4319px) { .CodeMirror-cursor { border-left: 2px solid black; } } @media screen and (min-width: 4320px) { .CodeMirror-cursor { border-left: 4px solid black; } } /\* Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org> Adapted from GitHub theme \*/ .highlight-base { color: #000; } .highlight-variable { color: #000; } .highlight-variable-2 { color: #1a1a1a; } .highlight-variable-3 { color: #333333; } .highlight-string { color: #BA2121; } .highlight-comment { color: #408080; font-style: italic; } .highlight-number { color: #080; } .highlight-atom { color: #88F; } .highlight-keyword { color: #008000; font-weight: bold; } .highlight-builtin { color: #008000; } .highlight-error { color: #f00; } .highlight-operator { color: #AA22FF; font-weight: bold; } .highlight-meta { color: #AA22FF; } /\* previously not defined, copying from default codemirror \*/ .highlight-def { color: #00f; } .highlight-string-2 { color: #f50; } .highlight-qualifier { color: #555; } .highlight-bracket { color: #997; } .highlight-tag { color: #170; } .highlight-attribute { color: #00c; } .highlight-header { color: blue; } .highlight-quote { color: #090; } .highlight-link { color: #00c; } /\* apply the same style to codemirror \*/ .cm-s-ipython span.cm-keyword { color: #008000; font-weight: bold; } .cm-s-ipython span.cm-atom { color: #88F; } .cm-s-ipython span.cm-number { color: #080; } .cm-s-ipython span.cm-def { color: #00f; } .cm-s-ipython span.cm-variable { color: #000; } .cm-s-ipython span.cm-operator { color: #AA22FF; font-weight: bold; } .cm-s-ipython span.cm-variable-2 { color: #1a1a1a; } .cm-s-ipython span.cm-variable-3 { color: #333333; } .cm-s-ipython span.cm-comment { color: #408080; font-style: italic; } .cm-s-ipython span.cm-string { color: #BA2121; } .cm-s-ipython span.cm-string-2 { color: #f50; } .cm-s-ipython span.cm-meta { color: #AA22FF; } .cm-s-ipython span.cm-qualifier { color: #555; } .cm-s-ipython span.cm-builtin { color: #008000; } .cm-s-ipython span.cm-bracket { color: #997; } .cm-s-ipython span.cm-tag { color: #170; } .cm-s-ipython span.cm-attribute { color: #00c; } .cm-s-ipython span.cm-header { color: blue; } .cm-s-ipython span.cm-quote { color: #090; } .cm-s-ipython span.cm-link { color: #00c; } .cm-s-ipython span.cm-error { color: #f00; } .cm-s-ipython span.cm-tab { background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=); background-position: right; background-repeat: no-repeat; } div.output\_wrapper { /\* this position must be relative to enable descendents to be absolute within it \*/ position: relative; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; z-index: 1; } /\* class for the output area when it should be height-limited \*/ div.output\_scroll { /\* ideally, this would be max-height, but FF barfs all over that \*/ height: 24em; /\* FF needs this \*and the wrapper\* to specify full width, or it will shrinkwrap \*/ width: 100%; overflow: auto; border-radius: 2px; -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8); box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8); display: block; } /\* output div while it is collapsed \*/ div.output\_collapsed { margin: 0px; padding: 0px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } div.out\_prompt\_overlay { height: 100%; padding: 0px 0.4em; position: absolute; border-radius: 2px; } div.out\_prompt\_overlay:hover { /\* use inner shadow to get border that is computed the same on WebKit/FF \*/ -webkit-box-shadow: inset 0 0 1px #000; box-shadow: inset 0 0 1px #000; background: rgba(240, 240, 240, 0.5); } div.output\_prompt { color: #D84315; } /\* This class is the outer container of all output sections. \*/ div.output\_area { padding: 0px; page-break-inside: avoid; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } div.output\_area .MathJax\_Display { text-align: left !important; } div.output\_area .rendered\_html table { margin-left: 0; margin-right: 0; } div.output\_area .rendered\_html img { margin-left: 0; margin-right: 0; } div.output\_area img, div.output\_area svg { max-width: 100%; height: auto; } div.output\_area img.unconfined, div.output\_area svg.unconfined { max-width: none; } div.output\_area .mglyph > img { max-width: none; } /\* This is needed to protect the pre formating from global settings such as that of bootstrap \*/ .output { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } @media (max-width: 540px) { div.output\_area { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } } div.output\_area pre { margin: 0; padding: 1px 0 1px 0; border: 0; vertical-align: baseline; color: black; background-color: transparent; border-radius: 0; } /\* This class is for the output subarea inside the output\_area and after the prompt div. \*/ div.output\_subarea { overflow-x: auto; padding: 0.4em; /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; max-width: calc(100% - 14ex); } div.output\_scroll div.output\_subarea { overflow-x: visible; } /\* The rest of the output\_\* classes are for special styling of the different output types \*/ /\* all text output has this class: \*/ div.output\_text { text-align: left; color: #000; /\* This has to match that of the the CodeMirror class line-height below \*/ line-height: 1.21429em; } /\* stdout/stderr are 'text' as well as 'stream', but execute\_result/error are \*not\* streams \*/ div.output\_stderr { background: #fdd; /\* very light red background for stderr \*/ } div.output\_latex { text-align: left; } /\* Empty output\_javascript divs should have no height \*/ div.output\_javascript:empty { padding: 0; } .js-error { color: darkred; } /\* raw\_input styles \*/ div.raw\_input\_container { line-height: 1.21429em; padding-top: 5px; } pre.raw\_input\_prompt { /\* nothing needed here. \*/ } input.raw\_input { font-family: monospace; font-size: inherit; color: inherit; width: auto; /\* make sure input baseline aligns with prompt \*/ vertical-align: baseline; /\* padding + margin = 0.5em between prompt and cursor \*/ padding: 0em 0.25em; margin: 0em 0.25em; } input.raw\_input:focus { box-shadow: none; } p.p-space { margin-bottom: 10px; } div.output\_unrecognized { padding: 5px; font-weight: bold; color: red; } div.output\_unrecognized a { color: inherit; text-decoration: none; } div.output\_unrecognized a:hover { color: inherit; text-decoration: none; } .rendered\_html { color: #000; /\* any extras will just be numbers: \*/ } .rendered\_html em { font-style: italic; } .rendered\_html strong { font-weight: bold; } .rendered\_html u { text-decoration: underline; } .rendered\_html :link { text-decoration: underline; } .rendered\_html :visited { text-decoration: underline; } .rendered\_html h1 { font-size: 185.7%; margin: 1.08em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h2 { font-size: 157.1%; margin: 1.27em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h3 { font-size: 128.6%; margin: 1.55em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h4 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h5 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; font-style: italic; } .rendered\_html h6 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; font-style: italic; } .rendered\_html h1:first-child { margin-top: 0.538em; } .rendered\_html h2:first-child { margin-top: 0.636em; } .rendered\_html h3:first-child { margin-top: 0.777em; } .rendered\_html h4:first-child { margin-top: 1em; } .rendered\_html h5:first-child { margin-top: 1em; } .rendered\_html h6:first-child { margin-top: 1em; } .rendered\_html ul:not(.list-inline), .rendered\_html ol:not(.list-inline) { padding-left: 2em; } .rendered\_html ul { list-style: disc; } .rendered\_html ul ul { list-style: square; margin-top: 0; } .rendered\_html ul ul ul { list-style: circle; } .rendered\_html ol { list-style: decimal; } .rendered\_html ol ol { list-style: upper-alpha; margin-top: 0; } .rendered\_html ol ol ol { list-style: lower-alpha; } .rendered\_html ol ol ol ol { list-style: lower-roman; } .rendered\_html ol ol ol ol ol { list-style: decimal; } .rendered\_html \* + ul { margin-top: 1em; } .rendered\_html \* + ol { margin-top: 1em; } .rendered\_html hr { color: black; background-color: black; } .rendered\_html pre { margin: 1em 2em; padding: 0px; background-color: #fff; } .rendered\_html code { background-color: #eff0f1; } .rendered\_html p code { padding: 1px 5px; } .rendered\_html pre code { background-color: #fff; } .rendered\_html pre, .rendered\_html code { border: 0; color: #000; font-size: 100%; } .rendered\_html blockquote { margin: 1em 2em; } .rendered\_html table { margin-left: auto; margin-right: auto; border: none; border-collapse: collapse; border-spacing: 0; color: black; font-size: 12px; table-layout: fixed; } .rendered\_html thead { border-bottom: 1px solid black; vertical-align: bottom; } .rendered\_html tr, .rendered\_html th, .rendered\_html td { text-align: right; vertical-align: middle; padding: 0.5em 0.5em; line-height: normal; white-space: normal; max-width: none; border: none; } .rendered\_html th { font-weight: bold; } .rendered\_html tbody tr:nth-child(odd) { background: #f5f5f5; } .rendered\_html tbody tr:hover { background: rgba(66, 165, 245, 0.2); } .rendered\_html \* + table { margin-top: 1em; } .rendered\_html p { text-align: left; } .rendered\_html \* + p { margin-top: 1em; } .rendered\_html img { display: block; margin-left: auto; margin-right: auto; } .rendered\_html \* + img { margin-top: 1em; } .rendered\_html img, .rendered\_html svg { max-width: 100%; height: auto; } .rendered\_html img.unconfined, .rendered\_html svg.unconfined { max-width: none; } .rendered\_html .alert { margin-bottom: initial; } .rendered\_html \* + .alert { margin-top: 1em; } [dir="rtl"] .rendered\_html p { text-align: right; } div.text\_cell { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } @media (max-width: 540px) { div.text\_cell > div.prompt { display: none; } } div.text\_cell\_render { /\*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;\*/ outline: none; resize: none; width: inherit; border-style: none; padding: 0.5em 0.5em 0.5em 0.4em; color: #000; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } a.anchor-link:link { text-decoration: none; padding: 0px 20px; visibility: hidden; } h1:hover .anchor-link, h2:hover .anchor-link, h3:hover .anchor-link, h4:hover .anchor-link, h5:hover .anchor-link, h6:hover .anchor-link { visibility: visible; } .text\_cell.rendered .input\_area { display: none; } .text\_cell.rendered .rendered\_html { overflow-x: auto; overflow-y: hidden; } .text\_cell.rendered .rendered\_html tr, .text\_cell.rendered .rendered\_html th, .text\_cell.rendered .rendered\_html td { max-width: none; } .text\_cell.unrendered .text\_cell\_render { display: none; } .text\_cell .dropzone .input\_area { border: 2px dashed #bababa; margin: -1px; } .cm-header-1, .cm-header-2, .cm-header-3, .cm-header-4, .cm-header-5, .cm-header-6 { font-weight: bold; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; } .cm-header-1 { font-size: 185.7%; } .cm-header-2 { font-size: 157.1%; } .cm-header-3 { font-size: 128.6%; } .cm-header-4 { font-size: 110%; } .cm-header-5 { font-size: 100%; font-style: italic; } .cm-header-6 { font-size: 100%; font-style: italic; } /\*! \* \* IPython notebook webapp \* \*/ @media (max-width: 767px) { .notebook\_app { padding-left: 0px; padding-right: 0px; } } #ipython-main-app { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; height: 100%; } div#notebook\_panel { margin: 0px; padding: 0px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; height: 100%; } div#notebook { font-size: 14px; line-height: 20px; overflow-y: hidden; overflow-x: auto; width: 100%; /\* This spaces the page away from the edge of the notebook area \*/ padding-top: 20px; margin: 0px; outline: none; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; min-height: 100%; } @media not print { #notebook-container { padding: 15px; background-color: #fff; min-height: 0; -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } } @media print { #notebook-container { width: 100%; } } div.ui-widget-content { border: 1px solid #ababab; outline: none; } pre.dialog { background-color: #f7f7f7; border: 1px solid #ddd; border-radius: 2px; padding: 0.4em; padding-left: 2em; } p.dialog { padding: 0.2em; } /\* Word-wrap output correctly. This is the CSS3 spelling, though Firefox seems to not honor it correctly. Webkit browsers (Chrome, rekonq, Safari) do. \*/ pre, code, kbd, samp { white-space: pre-wrap; } #fonttest { font-family: monospace; } p { margin-bottom: 0; } .end\_space { min-height: 100px; transition: height .2s ease; } .notebook\_app > #header { -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } @media not print { .notebook\_app { background-color: #EEE; } } kbd { border-style: solid; border-width: 1px; box-shadow: none; margin: 2px; padding-left: 2px; padding-right: 2px; padding-top: 1px; padding-bottom: 1px; } .jupyter-keybindings { padding: 1px; line-height: 24px; border-bottom: 1px solid gray; } .jupyter-keybindings input { margin: 0; padding: 0; border: none; } .jupyter-keybindings i { padding: 6px; } .well code { background-color: #ffffff; border-color: #ababab; border-width: 1px; border-style: solid; padding: 2px; padding-top: 1px; padding-bottom: 1px; } /\* CSS for the cell toolbar \*/ .celltoolbar { border: thin solid #CFCFCF; border-bottom: none; background: #EEE; border-radius: 2px 2px 0px 0px; width: 100%; height: 29px; padding-right: 4px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; /\* Old browsers \*/ -webkit-box-pack: end; -moz-box-pack: end; box-pack: end; /\* Modern browsers \*/ justify-content: flex-end; display: -webkit-flex; } @media print { .celltoolbar { display: none; } } .ctb\_hideshow { display: none; vertical-align: bottom; } /\* ctb\_show is added to the ctb\_hideshow div to show the cell toolbar. Cell toolbars are only shown when the ctb\_global\_show class is also set. \*/ .ctb\_global\_show .ctb\_show.ctb\_hideshow { display: block; } .ctb\_global\_show .ctb\_show + .input\_area, .ctb\_global\_show .ctb\_show + div.text\_cell\_input, .ctb\_global\_show .ctb\_show ~ div.text\_cell\_render { border-top-right-radius: 0px; border-top-left-radius: 0px; } .ctb\_global\_show .ctb\_show ~ div.text\_cell\_render { border: 1px solid #cfcfcf; } .celltoolbar { font-size: 87%; padding-top: 3px; } .celltoolbar select { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; width: inherit; font-size: inherit; height: 22px; padding: 0px; display: inline-block; } .celltoolbar select:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .celltoolbar select::-moz-placeholder { color: #999; opacity: 1; } .celltoolbar select:-ms-input-placeholder { color: #999; } .celltoolbar select::-webkit-input-placeholder { color: #999; } .celltoolbar select::-ms-expand { border: 0; background-color: transparent; } .celltoolbar select[disabled], .celltoolbar select[readonly], fieldset[disabled] .celltoolbar select { background-color: #eeeeee; opacity: 1; } .celltoolbar select[disabled], fieldset[disabled] .celltoolbar select { cursor: not-allowed; } textarea.celltoolbar select { height: auto; } select.celltoolbar select { height: 30px; line-height: 30px; } textarea.celltoolbar select, select[multiple].celltoolbar select { height: auto; } .celltoolbar label { margin-left: 5px; margin-right: 5px; } .tags\_button\_container { width: 100%; display: flex; } .tag-container { display: flex; flex-direction: row; flex-grow: 1; overflow: hidden; position: relative; } .tag-container > \* { margin: 0 4px; } .remove-tag-btn { margin-left: 4px; } .tags-input { display: flex; } .cell-tag:last-child:after { content: ""; position: absolute; right: 0; width: 40px; height: 100%; /\* Fade to background color of cell toolbar \*/ background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE); } .tags-input > \* { margin-left: 4px; } .cell-tag, .tags-input input, .tags-input button { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; box-shadow: none; width: inherit; font-size: inherit; height: 22px; line-height: 22px; padding: 0px 4px; display: inline-block; } .cell-tag:focus, .tags-input input:focus, .tags-input button:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .cell-tag::-moz-placeholder, .tags-input input::-moz-placeholder, .tags-input button::-moz-placeholder { color: #999; opacity: 1; } .cell-tag:-ms-input-placeholder, .tags-input input:-ms-input-placeholder, .tags-input button:-ms-input-placeholder { color: #999; } .cell-tag::-webkit-input-placeholder, .tags-input input::-webkit-input-placeholder, .tags-input button::-webkit-input-placeholder { color: #999; } .cell-tag::-ms-expand, .tags-input input::-ms-expand, .tags-input button::-ms-expand { border: 0; background-color: transparent; } .cell-tag[disabled], .tags-input input[disabled], .tags-input button[disabled], .cell-tag[readonly], .tags-input input[readonly], .tags-input button[readonly], fieldset[disabled] .cell-tag, fieldset[disabled] .tags-input input, fieldset[disabled] .tags-input button { background-color: #eeeeee; opacity: 1; } .cell-tag[disabled], .tags-input input[disabled], .tags-input button[disabled], fieldset[disabled] .cell-tag, fieldset[disabled] .tags-input input, fieldset[disabled] .tags-input button { cursor: not-allowed; } textarea.cell-tag, textarea.tags-input input, textarea.tags-input button { height: auto; } select.cell-tag, select.tags-input input, select.tags-input button { height: 30px; line-height: 30px; } textarea.cell-tag, textarea.tags-input input, textarea.tags-input button, select[multiple].cell-tag, select[multiple].tags-input input, select[multiple].tags-input button { height: auto; } .cell-tag, .tags-input button { padding: 0px 4px; } .cell-tag { background-color: #fff; white-space: nowrap; } .tags-input input[type=text]:focus { outline: none; box-shadow: none; border-color: #ccc; } .completions { position: absolute; z-index: 110; overflow: hidden; border: 1px solid #ababab; border-radius: 2px; -webkit-box-shadow: 0px 6px 10px -1px #adadad; box-shadow: 0px 6px 10px -1px #adadad; line-height: 1; } .completions select { background: white; outline: none; border: none; padding: 0px; margin: 0px; overflow: auto; font-family: monospace; font-size: 110%; color: #000; width: auto; } .completions select option.context { color: #286090; } #kernel\_logo\_widget .current\_kernel\_logo { display: none; margin-top: -1px; margin-bottom: -1px; width: 32px; height: 32px; } [dir="rtl"] #kernel\_logo\_widget { float: left !important; float: left; } .modal .modal-body .move-path { display: flex; flex-direction: row; justify-content: space; align-items: center; } .modal .modal-body .move-path .server-root { padding-right: 20px; } .modal .modal-body .move-path .path-input { flex: 1; } #menubar { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; margin-top: 1px; } #menubar .navbar { border-top: 1px; border-radius: 0px 0px 2px 2px; margin-bottom: 0px; } #menubar .navbar-toggle { float: left; padding-top: 7px; padding-bottom: 7px; border: none; } #menubar .navbar-collapse { clear: left; } [dir="rtl"] #menubar .navbar-toggle { float: right; } [dir="rtl"] #menubar .navbar-collapse { clear: right; } [dir="rtl"] #menubar .navbar-nav { float: right; } [dir="rtl"] #menubar .nav { padding-right: 0px; } [dir="rtl"] #menubar .navbar-nav > li { float: right; } [dir="rtl"] #menubar .navbar-right { float: left !important; } [dir="rtl"] ul.dropdown-menu { text-align: right; left: auto; } [dir="rtl"] ul#new-menu.dropdown-menu { right: auto; left: 0; } .nav-wrapper { border-bottom: 1px solid #e7e7e7; } i.menu-icon { padding-top: 4px; } [dir="rtl"] i.menu-icon.pull-right { float: left !important; float: left; } ul#help\_menu li a { overflow: hidden; padding-right: 2.2em; } ul#help\_menu li a i { margin-right: -1.2em; } [dir="rtl"] ul#help\_menu li a { padding-left: 2.2em; } [dir="rtl"] ul#help\_menu li a i { margin-right: 0; margin-left: -1.2em; } [dir="rtl"] ul#help\_menu li a i.pull-right { float: left !important; float: left; } .dropdown-submenu { position: relative; } .dropdown-submenu > .dropdown-menu { top: 0; left: 100%; margin-top: -6px; margin-left: -1px; } [dir="rtl"] .dropdown-submenu > .dropdown-menu { right: 100%; margin-right: -1px; } .dropdown-submenu:hover > .dropdown-menu { display: block; } .dropdown-submenu > a:after { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; display: block; content: "\f0da"; float: right; color: #333333; margin-top: 2px; margin-right: -10px; } .dropdown-submenu > a:after.fa-pull-left { margin-right: .3em; } .dropdown-submenu > a:after.fa-pull-right { margin-left: .3em; } .dropdown-submenu > a:after.pull-left { margin-right: .3em; } .dropdown-submenu > a:after.pull-right { margin-left: .3em; } [dir="rtl"] .dropdown-submenu > a:after { float: left; content: "\f0d9"; margin-right: 0; margin-left: -10px; } .dropdown-submenu:hover > a:after { color: #262626; } .dropdown-submenu.pull-left { float: none; } .dropdown-submenu.pull-left > .dropdown-menu { left: -100%; margin-left: 10px; } #notification\_area { float: right !important; float: right; z-index: 10; } [dir="rtl"] #notification\_area { float: left !important; float: left; } .indicator\_area { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; } [dir="rtl"] .indicator\_area { float: left !important; float: left; } #kernel\_indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; border-left: 1px solid; } #kernel\_indicator .kernel\_indicator\_name { padding-left: 5px; padding-right: 5px; } [dir="rtl"] #kernel\_indicator { float: left !important; float: left; border-left: 0; border-right: 1px solid; } #modal\_indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; } [dir="rtl"] #modal\_indicator { float: left !important; float: left; } #readonly-indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; margin-top: 2px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; display: none; } .modal\_indicator:before { width: 1.28571429em; text-align: center; } .edit\_mode .modal\_indicator:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f040"; } .edit\_mode .modal\_indicator:before.fa-pull-left { margin-right: .3em; } .edit\_mode .modal\_indicator:before.fa-pull-right { margin-left: .3em; } .edit\_mode .modal\_indicator:before.pull-left { margin-right: .3em; } .edit\_mode .modal\_indicator:before.pull-right { margin-left: .3em; } .command\_mode .modal\_indicator:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: ' '; } .command\_mode .modal\_indicator:before.fa-pull-left { margin-right: .3em; } .command\_mode .modal\_indicator:before.fa-pull-right { margin-left: .3em; } .command\_mode .modal\_indicator:before.pull-left { margin-right: .3em; } .command\_mode .modal\_indicator:before.pull-right { margin-left: .3em; } .kernel\_idle\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f10c"; } .kernel\_idle\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_idle\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_idle\_icon:before.pull-left { margin-right: .3em; } .kernel\_idle\_icon:before.pull-right { margin-left: .3em; } .kernel\_busy\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f111"; } .kernel\_busy\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_busy\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_busy\_icon:before.pull-left { margin-right: .3em; } .kernel\_busy\_icon:before.pull-right { margin-left: .3em; } .kernel\_dead\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f1e2"; } .kernel\_dead\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_dead\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_dead\_icon:before.pull-left { margin-right: .3em; } .kernel\_dead\_icon:before.pull-right { margin-left: .3em; } .kernel\_disconnected\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f127"; } .kernel\_disconnected\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_disconnected\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_disconnected\_icon:before.pull-left { margin-right: .3em; } .kernel\_disconnected\_icon:before.pull-right { margin-left: .3em; } .notification\_widget { color: #777; z-index: 10; background: rgba(240, 240, 240, 0.5); margin-right: 4px; color: #333; background-color: #fff; border-color: #ccc; } .notification\_widget:focus, .notification\_widget.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } .notification\_widget:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } .notification\_widget:active, .notification\_widget.active, .open > .dropdown-toggle.notification\_widget { color: #333; background-color: #e6e6e6; border-color: #adadad; } .notification\_widget:active:hover, .notification\_widget.active:hover, .open > .dropdown-toggle.notification\_widget:hover, .notification\_widget:active:focus, .notification\_widget.active:focus, .open > .dropdown-toggle.notification\_widget:focus, .notification\_widget:active.focus, .notification\_widget.active.focus, .open > .dropdown-toggle.notification\_widget.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } .notification\_widget:active, .notification\_widget.active, .open > .dropdown-toggle.notification\_widget { background-image: none; } .notification\_widget.disabled:hover, .notification\_widget[disabled]:hover, fieldset[disabled] .notification\_widget:hover, .notification\_widget.disabled:focus, .notification\_widget[disabled]:focus, fieldset[disabled] .notification\_widget:focus, .notification\_widget.disabled.focus, .notification\_widget[disabled].focus, fieldset[disabled] .notification\_widget.focus { background-color: #fff; border-color: #ccc; } .notification\_widget .badge { color: #fff; background-color: #333; } .notification\_widget.warning { color: #fff; background-color: #f0ad4e; border-color: #eea236; } .notification\_widget.warning:focus, .notification\_widget.warning.focus { color: #fff; background-color: #ec971f; border-color: #985f0d; } .notification\_widget.warning:hover { color: #fff; background-color: #ec971f; border-color: #d58512; } .notification\_widget.warning:active, .notification\_widget.warning.active, .open > .dropdown-toggle.notification\_widget.warning { color: #fff; background-color: #ec971f; border-color: #d58512; } .notification\_widget.warning:active:hover, .notification\_widget.warning.active:hover, .open > .dropdown-toggle.notification\_widget.warning:hover, .notification\_widget.warning:active:focus, .notification\_widget.warning.active:focus, .open > .dropdown-toggle.notification\_widget.warning:focus, .notification\_widget.warning:active.focus, .notification\_widget.warning.active.focus, .open > .dropdown-toggle.notification\_widget.warning.focus { color: #fff; background-color: #d58512; border-color: #985f0d; } .notification\_widget.warning:active, .notification\_widget.warning.active, .open > .dropdown-toggle.notification\_widget.warning { background-image: none; } .notification\_widget.warning.disabled:hover, .notification\_widget.warning[disabled]:hover, fieldset[disabled] .notification\_widget.warning:hover, .notification\_widget.warning.disabled:focus, .notification\_widget.warning[disabled]:focus, fieldset[disabled] .notification\_widget.warning:focus, .notification\_widget.warning.disabled.focus, .notification\_widget.warning[disabled].focus, fieldset[disabled] .notification\_widget.warning.focus { background-color: #f0ad4e; border-color: #eea236; } .notification\_widget.warning .badge { color: #f0ad4e; background-color: #fff; } .notification\_widget.success { color: #fff; background-color: #5cb85c; border-color: #4cae4c; } .notification\_widget.success:focus, .notification\_widget.success.focus { color: #fff; background-color: #449d44; border-color: #255625; } .notification\_widget.success:hover { color: #fff; background-color: #449d44; border-color: #398439; } .notification\_widget.success:active, .notification\_widget.success.active, .open > .dropdown-toggle.notification\_widget.success { color: #fff; background-color: #449d44; border-color: #398439; } .notification\_widget.success:active:hover, .notification\_widget.success.active:hover, .open > .dropdown-toggle.notification\_widget.success:hover, .notification\_widget.success:active:focus, .notification\_widget.success.active:focus, .open > .dropdown-toggle.notification\_widget.success:focus, .notification\_widget.success:active.focus, .notification\_widget.success.active.focus, .open > .dropdown-toggle.notification\_widget.success.focus { color: #fff; background-color: #398439; border-color: #255625; } .notification\_widget.success:active, .notification\_widget.success.active, .open > .dropdown-toggle.notification\_widget.success { background-image: none; } .notification\_widget.success.disabled:hover, .notification\_widget.success[disabled]:hover, fieldset[disabled] .notification\_widget.success:hover, .notification\_widget.success.disabled:focus, .notification\_widget.success[disabled]:focus, fieldset[disabled] .notification\_widget.success:focus, .notification\_widget.success.disabled.focus, .notification\_widget.success[disabled].focus, fieldset[disabled] .notification\_widget.success.focus { background-color: #5cb85c; border-color: #4cae4c; } .notification\_widget.success .badge { color: #5cb85c; background-color: #fff; } .notification\_widget.info { color: #fff; background-color: #5bc0de; border-color: #46b8da; } .notification\_widget.info:focus, .notification\_widget.info.focus { color: #fff; background-color: #31b0d5; border-color: #1b6d85; } .notification\_widget.info:hover { color: #fff; background-color: #31b0d5; border-color: #269abc; } .notification\_widget.info:active, .notification\_widget.info.active, .open > .dropdown-toggle.notification\_widget.info { color: #fff; background-color: #31b0d5; border-color: #269abc; } .notification\_widget.info:active:hover, .notification\_widget.info.active:hover, .open > .dropdown-toggle.notification\_widget.info:hover, .notification\_widget.info:active:focus, .notification\_widget.info.active:focus, .open > .dropdown-toggle.notification\_widget.info:focus, .notification\_widget.info:active.focus, .notification\_widget.info.active.focus, .open > .dropdown-toggle.notification\_widget.info.focus { color: #fff; background-color: #269abc; border-color: #1b6d85; } .notification\_widget.info:active, .notification\_widget.info.active, .open > .dropdown-toggle.notification\_widget.info { background-image: none; } .notification\_widget.info.disabled:hover, .notification\_widget.info[disabled]:hover, fieldset[disabled] .notification\_widget.info:hover, .notification\_widget.info.disabled:focus, .notification\_widget.info[disabled]:focus, fieldset[disabled] .notification\_widget.info:focus, .notification\_widget.info.disabled.focus, .notification\_widget.info[disabled].focus, fieldset[disabled] .notification\_widget.info.focus { background-color: #5bc0de; border-color: #46b8da; } .notification\_widget.info .badge { color: #5bc0de; background-color: #fff; } .notification\_widget.danger { color: #fff; background-color: #d9534f; border-color: #d43f3a; } .notification\_widget.danger:focus, .notification\_widget.danger.focus { color: #fff; background-color: #c9302c; border-color: #761c19; } .notification\_widget.danger:hover { color: #fff; background-color: #c9302c; border-color: #ac2925; } .notification\_widget.danger:active, .notification\_widget.danger.active, .open > .dropdown-toggle.notification\_widget.danger { color: #fff; background-color: #c9302c; border-color: #ac2925; } .notification\_widget.danger:active:hover, .notification\_widget.danger.active:hover, .open > .dropdown-toggle.notification\_widget.danger:hover, .notification\_widget.danger:active:focus, .notification\_widget.danger.active:focus, .open > .dropdown-toggle.notification\_widget.danger:focus, .notification\_widget.danger:active.focus, .notification\_widget.danger.active.focus, .open > .dropdown-toggle.notification\_widget.danger.focus { color: #fff; background-color: #ac2925; border-color: #761c19; } .notification\_widget.danger:active, .notification\_widget.danger.active, .open > .dropdown-toggle.notification\_widget.danger { background-image: none; } .notification\_widget.danger.disabled:hover, .notification\_widget.danger[disabled]:hover, fieldset[disabled] .notification\_widget.danger:hover, .notification\_widget.danger.disabled:focus, .notification\_widget.danger[disabled]:focus, fieldset[disabled] .notification\_widget.danger:focus, .notification\_widget.danger.disabled.focus, .notification\_widget.danger[disabled].focus, fieldset[disabled] .notification\_widget.danger.focus { background-color: #d9534f; border-color: #d43f3a; } .notification\_widget.danger .badge { color: #d9534f; background-color: #fff; } div#pager { background-color: #fff; font-size: 14px; line-height: 20px; overflow: hidden; display: none; position: fixed; bottom: 0px; width: 100%; max-height: 50%; padding-top: 8px; -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); /\* Display over codemirror \*/ z-index: 100; /\* Hack which prevents jquery ui resizable from changing top. \*/ top: auto !important; } div#pager pre { line-height: 1.21429em; color: #000; background-color: #f7f7f7; padding: 0.4em; } div#pager #pager-button-area { position: absolute; top: 8px; right: 20px; } div#pager #pager-contents { position: relative; overflow: auto; width: 100%; height: 100%; } div#pager #pager-contents #pager-container { position: relative; padding: 15px 0px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } div#pager .ui-resizable-handle { top: 0px; height: 8px; background: #f7f7f7; border-top: 1px solid #cfcfcf; border-bottom: 1px solid #cfcfcf; /\* This injects handle bars (a short, wide = symbol) for the resize handle. \*/ } div#pager .ui-resizable-handle::after { content: ''; top: 2px; left: 50%; height: 3px; width: 30px; margin-left: -15px; position: absolute; border-top: 1px solid #cfcfcf; } .quickhelp { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; line-height: 1.8em; } .shortcut\_key { display: inline-block; width: 21ex; text-align: right; font-family: monospace; } .shortcut\_descr { display: inline-block; /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } span.save\_widget { height: 30px; margin-top: 4px; display: flex; justify-content: flex-start; align-items: baseline; width: 50%; flex: 1; } span.save\_widget span.filename { height: 100%; line-height: 1em; margin-left: 16px; border: none; font-size: 146.5%; text-overflow: ellipsis; overflow: hidden; white-space: nowrap; border-radius: 2px; } span.save\_widget span.filename:hover { background-color: #e6e6e6; } [dir="rtl"] span.save\_widget.pull-left { float: right !important; float: right; } [dir="rtl"] span.save\_widget span.filename { margin-left: 0; margin-right: 16px; } span.checkpoint\_status, span.autosave\_status { font-size: small; white-space: nowrap; padding: 0 5px; } @media (max-width: 767px) { span.save\_widget { font-size: small; padding: 0 0 0 5px; } span.checkpoint\_status, span.autosave\_status { display: none; } } @media (min-width: 768px) and (max-width: 991px) { span.checkpoint\_status { display: none; } span.autosave\_status { font-size: x-small; } } .toolbar { padding: 0px; margin-left: -5px; margin-top: 2px; margin-bottom: 5px; 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border-top: none; }@media print { div.cell { display: block; page-break-inside: avoid; } div.output\_wrapper { display: block; page-break-inside: avoid; } div.output { display: block; page-break-inside: avoid; } } Introduction[¶](#Introduction) ------------------------------ This tutorial shows how to compute the power spectra of spin 0 and 2 fields. We will use the `HEALPIX` pixellisation to pass through the different steps of generation. If you are interested on doing the same thing with `CAR` pixellisation, you can have a look at this [file](tutorial_spectra_car_spin0and2.ipynb). The `HEALPIX` survey mask is defined will be a disk of radius 25 degree centered on longitude 30 degree and latitude 50 degree, It will have a resolution nside=1024. We simulate 2 splits with 5 µK.arcmin noise and we also include a point source mask with 100 holes of size 10 arcminutes. We appodize the survey mask with an apodisation of 1 degree and the point source mask with an apodisation of 0.3 degree. We finally compute the spectra between the 2 splits of data up to `lmax=1000`. Preamble[¶](#Preamble) ---------------------- `matplotlib` magic In [1]: ``` %matplotlib inline ``` Versions used for this tutorial In [2]: ``` import numpy as np import healpy as hp import matplotlib as mpl import matplotlib.pyplot as plt import pspy, pixell print(" Numpy :", np.\_\_version\_\_) print(" Healpy :", hp.\_\_version\_\_) print("Matplotlib :", mpl.\_\_version\_\_) print(" pixell :", pixell.\_\_version\_\_) print(" pspy :", pspy.\_\_version\_\_) ``` ``` Numpy : 1.18.0 Healpy : 1.13.0 Matplotlib : 3.1.2 pixell : 0.6.0+34.g23be32d pspy : 0+untagged.118.gbf1f0bc.dirty ``` Get default data dir from `pspy` and set Planck colormap as default In [3]: ``` from pspy.so\_config import DEFAULT\_DATA\_DIR pixell.colorize.mpl\_setdefault("planck") ``` If you face issue with `openmp` library, run the next code block In [4]: ``` import os os.environ["KMP\_DUPLICATE\_LIB\_OK"] = "True" #only used in this notebook to avoid a openmp bugs ``` Generation of the templates, mask and apodisation type[¶](#Generation-of-the-templates,-mask-and-apodisation-type) ------------------------------------------------------------------------------------------------------------------ We start by specifying the `HEALPIX` parameters for the window function. it will be a disk of radius 25 degree centered on longitude 30 degree and latitude 50 degree, It will have a resolution nside=1024. In [5]: ``` lon, lat = 30, 50 radius = 25 nside = 1024 ``` For this example, we will make use of 3 components : Temperature (spin 0) and polarisation Q and U (spin 2) In [6]: ``` ncomp = 3 ``` Given the parameters, we can generate the `HEALPIX` template as follow In [7]: ``` from pspy import so\_map template\_healpix = so\_map.healpix\_template(ncomp, nside=nside) ``` We also define a binary template for the window function: we set pixel inside the disk at 1 and pixel outside at zero In [8]: ``` binary\_healpix = so\_map.healpix\_template(ncomp=1, nside=nside) vec = hp.pixelfunc.ang2vec(lon, lat, lonlat=True) disc = hp.query\_disc(nside, vec, radius=radius\*np.pi/180) binary\_healpix.data[disc] = 1 ``` Generation of spectra[¶](#Generation-of-spectra) ------------------------------------------------ ### Generation of simulations[¶](#Generation-of-simulations) We first have to compute a set of theoretical power spectra $C\_\ell$ using a Boltzmann solver such as [CAMB](https://camb.readthedocs.io/en/latest/) and we need to install it since this is a prerequisite of `pspy`. We can do it within this notebook by executing the following command In [9]: ``` %pip install camb ``` ``` Requirement already satisfied: camb in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (1.1.0) Requirement already satisfied: scipy>=1.0 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.4.1) Requirement already satisfied: six in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.13.0) Requirement already satisfied: sympy>=1.0 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.5) Requirement already satisfied: numpy>=1.13.3 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from scipy>=1.0->camb) (1.18.0) Requirement already satisfied: mpmath>=0.19 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from sympy>=1.0->camb) (1.1.0) Note: you may need to restart the kernel to use updated packages. ``` To make sure everything goes well, we can import `CAMB` and check its version In [10]: ``` import camb print("CAMB version:", camb.\_\_version\_\_) ``` ``` CAMB version: 1.1.0 ``` Now that `CAMB` is properly installed, we will produce the $C\_\ell$s from $\ell$min=2 to $\ell$max=104 for the following set of $\Lambda$CDM parameters In [11]: ``` lmin, lmax = 2, 10\*\*4 l = np.arange(lmin, lmax) cosmo\_params = { "H0": 67.5, "As": 1e-10\*np.exp(3.044), "ombh2": 0.02237, "omch2": 0.1200, "ns": 0.9649, "Alens": 1.0, "tau": 0.0544 } pars = camb.set\_params(\*\*cosmo\_params) pars.set\_for\_lmax(lmax, lens\_potential\_accuracy=1) results = camb.get\_results(pars) powers = results.get\_cmb\_power\_spectra(pars, CMB\_unit="muK") ``` We finally have to write the $C\_\ell$s into a file to feed the `so_map.synfast` function In [12]: ``` import os output\_dir = "/tmp/tutorial\_spectra\_spin0and2" os.makedirs(output\_dir, exist\_ok=True) cl\_file = output\_dir + "/cl\_camb.dat" np.savetxt(cl\_file, np.hstack([l[:, np.newaxis], powers["total"][lmin:lmax]])) ``` Given the `CAMB` file, we generate a CMB realisation In [13]: ``` cmb = template\_healpix.synfast(cl\_file) ``` Then, we make 2 splits out of it, each with 5 µK.arcmin rms in temperature and 5xsqrt(2) µK.arcmin in polarisation In [14]: ``` nsplits = 2 splits = [cmb.copy() for i in range(nsplits)] for i in range(nsplits): noise = so\_map.white\_noise(cmb, rms\_uKarcmin\_T=5, rms\_uKarcmin\_pol=np.sqrt(2)\*5) splits[i].data += noise.data ``` We can plot each component T, Q, U for the original CMB realisation In [15]: ``` fields = ["T", "Q", "U"] plt.figure() for i, field in enumerate(fields): hp.mollview(cmb.data[i],title='%s'%field) ``` ``` <Figure size 432x288 with 0 Axes> ```    ### Generate window[¶](#Generate-window) We then create an apodisation for the survey mask. We use a C1 apodisation with an apodisation size of 1 degree In [16]: ``` from pspy import so\_window window = so\_window.create\_apodization(binary\_healpix, apo\_type="C1", apo\_radius\_degree=1) ``` We also create a point source mask made of 100 holes each with a 10 arcminutes size In [17]: ``` mask = so\_map.simulate\_source\_mask(binary\_healpix, n\_holes=100, hole\_radius\_arcmin=10) ``` and we apodize it In [18]: ``` mask = so\_window.create\_apodization(mask, apo\_type="C1", apo\_radius\_degree=0.3) ``` The window is given by the product of the survey window and the mask window In [19]: ``` window.data \*= mask.data plt.figure(figsize=(5, 5)) hp.mollview(window.data, min=0, max=1) ``` ``` <Figure size 360x360 with 0 Axes> ```  ### Compute mode coupling matrix[¶](#Compute-mode-coupling-matrix) For spin 0 and 2 the window need to be a tuple made of two objects: the window used for spin 0 and the one used for spin 2 In [20]: ``` window = (window, window) ``` The windows (for `spin0` and `spin2`) are going to couple mode together, we compute a mode coupling matrix in order to undo this effect given a binning file (format: lmin, lmax, lmean) and a $\ell$max value of 1000 In [21]: ``` lmax = 1000 binning\_file = output\_dir + "/binning.dat" from pspy import pspy\_utils pspy\_utils.create\_binning\_file(bin\_size=40, n\_bins=100, file\_name=binning\_file) from pspy import so\_mcm mbb\_inv, Bbl = so\_mcm.mcm\_and\_bbl\_spin0and2(window, binning\_file, lmax=lmax, type="Dl", niter=0) ``` ### Compute alms and bin spectra[¶](#Compute-alms-and-bin-spectra) In [22]: ``` from pspy import sph\_tools alms = [sph\_tools.get\_alms(split, window, niter=0, lmax=lmax) for split in splits] ``` We need to specify the order of the spectra to be used by `pspy` In [23]: ``` spectra = ["TT", "TE", "TB", "ET", "BT", "EE", "EB", "BE", "BB"] ``` and we finally build a dictionary of cross split spectra In [24]: ``` Db\_dict = {} from itertools import combinations\_with\_replacement as cwr for (i1, alm1), (i2, alm2) in cwr(enumerate(alms), 2): from pspy import so\_spectra l, ps = so\_spectra.get\_spectra(alm1, alm2, spectra=spectra) lb, Db = so\_spectra.bin\_spectra(l, ps, binning\_file, lmax, type="Dl", mbb\_inv=mbb\_inv, spectra=spectra) Db\_dict.update({"split{}xsplit{}".format(i1, i2): Db}) ``` To compare with the input $C\_\ell$, we also compute the binned theory spectra In [25]: ``` from pspy import pspy\_utils l, ps\_theory = pspy\_utils.ps\_lensed\_theory\_to\_dict(cl\_file, 'Dl', lmax=lmax) ps\_theory\_b = so\_mcm.apply\_Bbl(Bbl, ps\_theory, spectra=spectra) ``` and we finally plot all the results In [26]: ``` fig, axes = plt.subplots(3, 3, figsize=(15, 12), sharex=True) ax = axes.flatten() for i, spec in enumerate(spectra): for k, v in Db\_dict.items(): ax[i].plot(lb, v[spec], "-o", label=k) ax[i].plot(lb, ps\_theory\_b[spec], "--", color="tab:red", label="binned theory") ax[i].plot(l, ps\_theory[spec], color="tab:red", label="theory") ax[i].set\_ylabel(r'$D^{%s}\_{\ell}$'%spec, fontsize=20) if i==0: fig.legend(loc="upper left", bbox\_to\_anchor=(1,1)) for ax in axes[-1]: ax.set\_xlabel(r'$\ell$',fontsize=20) plt.tight\_layout() ```  tutorial\_purebb /\*! 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color: #000; background-color: #fff; } input, button, select, textarea { font-family: inherit; font-size: inherit; line-height: inherit; } a { color: #337ab7; text-decoration: none; } a:hover, a:focus { color: #23527c; text-decoration: underline; } a:focus { outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } figure { margin: 0; } img { vertical-align: middle; } .img-responsive, .thumbnail > img, .thumbnail a > img, .carousel-inner > .item > img, .carousel-inner > .item > a > img { display: block; max-width: 100%; height: auto; } .img-rounded { border-radius: 3px; } .img-thumbnail { padding: 4px; line-height: 1.42857143; background-color: #fff; border: 1px solid #ddd; border-radius: 2px; -webkit-transition: all 0.2s ease-in-out; -o-transition: all 0.2s ease-in-out; transition: all 0.2s ease-in-out; display: inline-block; max-width: 100%; height: auto; } .img-circle { border-radius: 50%; } hr { margin-top: 18px; margin-bottom: 18px; border: 0; border-top: 1px solid #eeeeee; } .sr-only { position: absolute; width: 1px; height: 1px; margin: -1px; padding: 0; overflow: hidden; clip: rect(0, 0, 0, 0); border: 0; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } [role="button"] { cursor: pointer; } h1, h2, h3, h4, h5, h6, .h1, .h2, .h3, .h4, .h5, .h6 { font-family: inherit; font-weight: 500; line-height: 1.1; color: inherit; } h1 small, h2 small, h3 small, h4 small, h5 small, h6 small, .h1 small, .h2 small, .h3 small, .h4 small, .h5 small, .h6 small, h1 .small, h2 .small, h3 .small, h4 .small, h5 .small, h6 .small, .h1 .small, .h2 .small, .h3 .small, .h4 .small, .h5 .small, .h6 .small { font-weight: normal; line-height: 1; color: #777777; } h1, .h1, h2, .h2, h3, .h3 { margin-top: 18px; margin-bottom: 9px; } h1 small, .h1 small, h2 small, .h2 small, h3 small, .h3 small, h1 .small, .h1 .small, h2 .small, .h2 .small, h3 .small, .h3 .small { font-size: 65%; } h4, .h4, h5, .h5, h6, .h6 { margin-top: 9px; margin-bottom: 9px; } h4 small, .h4 small, h5 small, .h5 small, h6 small, .h6 small, h4 .small, .h4 .small, h5 .small, .h5 .small, h6 .small, .h6 .small { font-size: 75%; } h1, .h1 { font-size: 33px; } h2, .h2 { font-size: 27px; } h3, .h3 { font-size: 23px; } h4, .h4 { font-size: 17px; } h5, .h5 { font-size: 13px; } h6, .h6 { font-size: 12px; } p { margin: 0 0 9px; } .lead { margin-bottom: 18px; font-size: 14px; font-weight: 300; line-height: 1.4; } @media (min-width: 768px) { .lead { font-size: 19.5px; } } small, .small { font-size: 92%; } mark, .mark { background-color: #fcf8e3; padding: .2em; } .text-left { text-align: left; } .text-right { text-align: right; } .text-center { text-align: center; } .text-justify { text-align: justify; } .text-nowrap { white-space: nowrap; } .text-lowercase { text-transform: lowercase; } .text-uppercase { text-transform: uppercase; } .text-capitalize { text-transform: capitalize; } .text-muted { color: #777777; } .text-primary { color: #337ab7; } a.text-primary:hover, a.text-primary:focus { color: #286090; } .text-success { color: #3c763d; } a.text-success:hover, a.text-success:focus { color: #2b542c; } .text-info { color: #31708f; } a.text-info:hover, a.text-info:focus { color: #245269; } .text-warning { color: #8a6d3b; } a.text-warning:hover, a.text-warning:focus { color: #66512c; } .text-danger { color: #a94442; } a.text-danger:hover, a.text-danger:focus { color: #843534; } .bg-primary { color: #fff; background-color: #337ab7; } a.bg-primary:hover, a.bg-primary:focus { background-color: #286090; } .bg-success { background-color: #dff0d8; } a.bg-success:hover, a.bg-success:focus { background-color: #c1e2b3; } .bg-info { background-color: #d9edf7; } a.bg-info:hover, a.bg-info:focus { background-color: #afd9ee; } .bg-warning { background-color: #fcf8e3; } a.bg-warning:hover, a.bg-warning:focus { background-color: #f7ecb5; } .bg-danger { background-color: #f2dede; } a.bg-danger:hover, a.bg-danger:focus { background-color: #e4b9b9; } .page-header { padding-bottom: 8px; margin: 36px 0 18px; border-bottom: 1px solid #eeeeee; } ul, ol { margin-top: 0; margin-bottom: 9px; } ul ul, ol ul, ul ol, ol ol { margin-bottom: 0; } .list-unstyled { padding-left: 0; list-style: none; } .list-inline { padding-left: 0; list-style: none; margin-left: -5px; } .list-inline > li { display: inline-block; padding-left: 5px; padding-right: 5px; } dl { margin-top: 0; margin-bottom: 18px; } dt, dd { line-height: 1.42857143; } dt { font-weight: bold; } dd { margin-left: 0; } @media (min-width: 541px) { .dl-horizontal dt { float: left; width: 160px; clear: left; text-align: right; overflow: hidden; text-overflow: ellipsis; white-space: nowrap; } .dl-horizontal dd { margin-left: 180px; } } abbr[title], abbr[data-original-title] { cursor: help; border-bottom: 1px dotted #777777; } .initialism { font-size: 90%; text-transform: uppercase; } blockquote { padding: 9px 18px; margin: 0 0 18px; font-size: inherit; border-left: 5px solid #eeeeee; } blockquote p:last-child, blockquote ul:last-child, blockquote ol:last-child { margin-bottom: 0; } blockquote footer, blockquote small, blockquote .small { display: block; font-size: 80%; line-height: 1.42857143; color: #777777; } blockquote footer:before, blockquote small:before, blockquote .small:before { content: '\2014 \00A0'; } .blockquote-reverse, blockquote.pull-right { padding-right: 15px; padding-left: 0; border-right: 5px solid #eeeeee; border-left: 0; text-align: right; } .blockquote-reverse footer:before, blockquote.pull-right footer:before, .blockquote-reverse small:before, blockquote.pull-right small:before, .blockquote-reverse .small:before, blockquote.pull-right .small:before { content: ''; } .blockquote-reverse footer:after, blockquote.pull-right footer:after, .blockquote-reverse small:after, blockquote.pull-right small:after, .blockquote-reverse .small:after, blockquote.pull-right .small:after { content: '\00A0 \2014'; } address { margin-bottom: 18px; font-style: normal; line-height: 1.42857143; } code, kbd, pre, samp { font-family: monospace; } code { padding: 2px 4px; font-size: 90%; color: #c7254e; background-color: #f9f2f4; border-radius: 2px; } kbd { padding: 2px 4px; font-size: 90%; color: #888; background-color: transparent; border-radius: 1px; box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); } kbd kbd { padding: 0; font-size: 100%; font-weight: bold; box-shadow: none; } pre { display: block; padding: 8.5px; margin: 0 0 9px; font-size: 12px; line-height: 1.42857143; word-break: break-all; word-wrap: break-word; color: #333333; background-color: #f5f5f5; border: 1px solid #ccc; border-radius: 2px; } pre code { padding: 0; font-size: inherit; color: inherit; white-space: pre-wrap; background-color: transparent; border-radius: 0; } .pre-scrollable { max-height: 340px; overflow-y: scroll; } .container { margin-right: auto; margin-left: auto; padding-left: 0px; padding-right: 0px; } @media (min-width: 768px) { .container { width: 768px; } } @media (min-width: 992px) { .container { width: 940px; } } @media (min-width: 1200px) { .container { width: 1140px; } } .container-fluid { margin-right: auto; margin-left: auto; padding-left: 0px; padding-right: 0px; } .row { margin-left: 0px; margin-right: 0px; } .col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 { position: relative; min-height: 1px; padding-left: 0px; padding-right: 0px; } .col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 { float: left; } .col-xs-12 { width: 100%; } .col-xs-11 { width: 91.66666667%; } .col-xs-10 { width: 83.33333333%; } .col-xs-9 { width: 75%; } .col-xs-8 { width: 66.66666667%; } .col-xs-7 { width: 58.33333333%; } .col-xs-6 { width: 50%; } .col-xs-5 { width: 41.66666667%; } .col-xs-4 { width: 33.33333333%; } .col-xs-3 { width: 25%; } .col-xs-2 { width: 16.66666667%; } .col-xs-1 { width: 8.33333333%; } .col-xs-pull-12 { right: 100%; } .col-xs-pull-11 { right: 91.66666667%; } .col-xs-pull-10 { right: 83.33333333%; } .col-xs-pull-9 { right: 75%; } .col-xs-pull-8 { right: 66.66666667%; } .col-xs-pull-7 { right: 58.33333333%; } .col-xs-pull-6 { right: 50%; } .col-xs-pull-5 { right: 41.66666667%; } .col-xs-pull-4 { right: 33.33333333%; } .col-xs-pull-3 { right: 25%; } .col-xs-pull-2 { right: 16.66666667%; } .col-xs-pull-1 { right: 8.33333333%; } .col-xs-pull-0 { right: auto; } .col-xs-push-12 { left: 100%; } .col-xs-push-11 { left: 91.66666667%; } .col-xs-push-10 { left: 83.33333333%; } .col-xs-push-9 { left: 75%; } .col-xs-push-8 { left: 66.66666667%; } .col-xs-push-7 { left: 58.33333333%; } .col-xs-push-6 { left: 50%; } .col-xs-push-5 { left: 41.66666667%; } .col-xs-push-4 { left: 33.33333333%; } .col-xs-push-3 { left: 25%; } .col-xs-push-2 { left: 16.66666667%; } .col-xs-push-1 { left: 8.33333333%; } .col-xs-push-0 { left: auto; } .col-xs-offset-12 { margin-left: 100%; } .col-xs-offset-11 { margin-left: 91.66666667%; } .col-xs-offset-10 { margin-left: 83.33333333%; } .col-xs-offset-9 { margin-left: 75%; } .col-xs-offset-8 { margin-left: 66.66666667%; } .col-xs-offset-7 { margin-left: 58.33333333%; } .col-xs-offset-6 { margin-left: 50%; } .col-xs-offset-5 { margin-left: 41.66666667%; } .col-xs-offset-4 { margin-left: 33.33333333%; } .col-xs-offset-3 { margin-left: 25%; } .col-xs-offset-2 { margin-left: 16.66666667%; } .col-xs-offset-1 { margin-left: 8.33333333%; } .col-xs-offset-0 { margin-left: 0%; } @media (min-width: 768px) { .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 { float: left; } .col-sm-12 { width: 100%; } .col-sm-11 { width: 91.66666667%; } .col-sm-10 { width: 83.33333333%; } .col-sm-9 { width: 75%; } .col-sm-8 { width: 66.66666667%; } .col-sm-7 { width: 58.33333333%; } .col-sm-6 { width: 50%; } .col-sm-5 { width: 41.66666667%; } .col-sm-4 { width: 33.33333333%; } .col-sm-3 { width: 25%; } .col-sm-2 { width: 16.66666667%; } .col-sm-1 { width: 8.33333333%; } .col-sm-pull-12 { right: 100%; } .col-sm-pull-11 { right: 91.66666667%; } .col-sm-pull-10 { right: 83.33333333%; } .col-sm-pull-9 { right: 75%; } .col-sm-pull-8 { right: 66.66666667%; } .col-sm-pull-7 { right: 58.33333333%; } .col-sm-pull-6 { right: 50%; } .col-sm-pull-5 { right: 41.66666667%; } .col-sm-pull-4 { right: 33.33333333%; } .col-sm-pull-3 { right: 25%; } .col-sm-pull-2 { right: 16.66666667%; } .col-sm-pull-1 { right: 8.33333333%; } .col-sm-pull-0 { right: auto; } .col-sm-push-12 { left: 100%; } .col-sm-push-11 { left: 91.66666667%; } .col-sm-push-10 { left: 83.33333333%; } .col-sm-push-9 { left: 75%; } .col-sm-push-8 { left: 66.66666667%; } .col-sm-push-7 { left: 58.33333333%; } .col-sm-push-6 { left: 50%; } .col-sm-push-5 { left: 41.66666667%; } .col-sm-push-4 { left: 33.33333333%; } .col-sm-push-3 { left: 25%; } .col-sm-push-2 { left: 16.66666667%; } .col-sm-push-1 { left: 8.33333333%; } .col-sm-push-0 { left: auto; } .col-sm-offset-12 { margin-left: 100%; } .col-sm-offset-11 { margin-left: 91.66666667%; } .col-sm-offset-10 { margin-left: 83.33333333%; } .col-sm-offset-9 { margin-left: 75%; } .col-sm-offset-8 { margin-left: 66.66666667%; } .col-sm-offset-7 { margin-left: 58.33333333%; } .col-sm-offset-6 { margin-left: 50%; } .col-sm-offset-5 { margin-left: 41.66666667%; } .col-sm-offset-4 { margin-left: 33.33333333%; } .col-sm-offset-3 { margin-left: 25%; } .col-sm-offset-2 { margin-left: 16.66666667%; } .col-sm-offset-1 { margin-left: 8.33333333%; } .col-sm-offset-0 { margin-left: 0%; } } @media (min-width: 992px) { .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 { float: left; } .col-md-12 { width: 100%; } .col-md-11 { width: 91.66666667%; } .col-md-10 { width: 83.33333333%; } .col-md-9 { width: 75%; } .col-md-8 { width: 66.66666667%; } .col-md-7 { width: 58.33333333%; } .col-md-6 { width: 50%; } .col-md-5 { width: 41.66666667%; } .col-md-4 { width: 33.33333333%; } .col-md-3 { width: 25%; } .col-md-2 { width: 16.66666667%; } .col-md-1 { width: 8.33333333%; } .col-md-pull-12 { right: 100%; } .col-md-pull-11 { right: 91.66666667%; } .col-md-pull-10 { right: 83.33333333%; } .col-md-pull-9 { right: 75%; } .col-md-pull-8 { right: 66.66666667%; } .col-md-pull-7 { right: 58.33333333%; } .col-md-pull-6 { right: 50%; } .col-md-pull-5 { right: 41.66666667%; } .col-md-pull-4 { right: 33.33333333%; } .col-md-pull-3 { right: 25%; } .col-md-pull-2 { right: 16.66666667%; } .col-md-pull-1 { right: 8.33333333%; } .col-md-pull-0 { right: auto; } .col-md-push-12 { left: 100%; } .col-md-push-11 { left: 91.66666667%; } .col-md-push-10 { left: 83.33333333%; } .col-md-push-9 { left: 75%; } .col-md-push-8 { left: 66.66666667%; } .col-md-push-7 { left: 58.33333333%; } .col-md-push-6 { left: 50%; } .col-md-push-5 { left: 41.66666667%; } .col-md-push-4 { left: 33.33333333%; } .col-md-push-3 { left: 25%; } .col-md-push-2 { left: 16.66666667%; } .col-md-push-1 { left: 8.33333333%; } .col-md-push-0 { left: auto; } .col-md-offset-12 { margin-left: 100%; } .col-md-offset-11 { margin-left: 91.66666667%; } .col-md-offset-10 { margin-left: 83.33333333%; } .col-md-offset-9 { margin-left: 75%; } .col-md-offset-8 { margin-left: 66.66666667%; } .col-md-offset-7 { margin-left: 58.33333333%; } .col-md-offset-6 { margin-left: 50%; } .col-md-offset-5 { margin-left: 41.66666667%; } .col-md-offset-4 { margin-left: 33.33333333%; } .col-md-offset-3 { margin-left: 25%; } .col-md-offset-2 { margin-left: 16.66666667%; } .col-md-offset-1 { margin-left: 8.33333333%; } .col-md-offset-0 { margin-left: 0%; } } @media (min-width: 1200px) { .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 { float: left; } .col-lg-12 { width: 100%; } .col-lg-11 { width: 91.66666667%; } .col-lg-10 { width: 83.33333333%; } .col-lg-9 { width: 75%; } .col-lg-8 { width: 66.66666667%; } .col-lg-7 { width: 58.33333333%; } .col-lg-6 { width: 50%; } .col-lg-5 { width: 41.66666667%; } .col-lg-4 { width: 33.33333333%; } .col-lg-3 { width: 25%; } .col-lg-2 { width: 16.66666667%; } .col-lg-1 { width: 8.33333333%; } .col-lg-pull-12 { right: 100%; } .col-lg-pull-11 { right: 91.66666667%; } .col-lg-pull-10 { right: 83.33333333%; } .col-lg-pull-9 { right: 75%; } .col-lg-pull-8 { right: 66.66666667%; } .col-lg-pull-7 { right: 58.33333333%; } .col-lg-pull-6 { right: 50%; } .col-lg-pull-5 { right: 41.66666667%; } .col-lg-pull-4 { right: 33.33333333%; } .col-lg-pull-3 { right: 25%; } .col-lg-pull-2 { right: 16.66666667%; } .col-lg-pull-1 { right: 8.33333333%; } .col-lg-pull-0 { right: auto; } .col-lg-push-12 { left: 100%; } .col-lg-push-11 { left: 91.66666667%; } .col-lg-push-10 { left: 83.33333333%; } .col-lg-push-9 { left: 75%; } .col-lg-push-8 { left: 66.66666667%; } .col-lg-push-7 { left: 58.33333333%; } .col-lg-push-6 { left: 50%; } .col-lg-push-5 { left: 41.66666667%; } .col-lg-push-4 { left: 33.33333333%; } .col-lg-push-3 { left: 25%; } .col-lg-push-2 { left: 16.66666667%; } .col-lg-push-1 { left: 8.33333333%; } .col-lg-push-0 { left: auto; } .col-lg-offset-12 { margin-left: 100%; } .col-lg-offset-11 { margin-left: 91.66666667%; } .col-lg-offset-10 { margin-left: 83.33333333%; } .col-lg-offset-9 { margin-left: 75%; } .col-lg-offset-8 { margin-left: 66.66666667%; } .col-lg-offset-7 { margin-left: 58.33333333%; } .col-lg-offset-6 { margin-left: 50%; } .col-lg-offset-5 { margin-left: 41.66666667%; } .col-lg-offset-4 { margin-left: 33.33333333%; } .col-lg-offset-3 { margin-left: 25%; } .col-lg-offset-2 { margin-left: 16.66666667%; } .col-lg-offset-1 { margin-left: 8.33333333%; } .col-lg-offset-0 { margin-left: 0%; } } table { background-color: transparent; } caption { padding-top: 8px; padding-bottom: 8px; color: #777777; text-align: left; } th { text-align: left; } .table { width: 100%; max-width: 100%; margin-bottom: 18px; } .table > thead > tr > th, .table > tbody > tr > th, .table > tfoot > tr > th, .table > thead > tr > td, .table > tbody > tr > td, .table > tfoot > tr > td { padding: 8px; line-height: 1.42857143; vertical-align: top; border-top: 1px solid #ddd; } .table > thead > tr > th { vertical-align: bottom; border-bottom: 2px solid #ddd; } .table > caption + thead > tr:first-child > th, .table > colgroup + thead > tr:first-child > th, .table > thead:first-child > tr:first-child > th, .table > caption + thead > tr:first-child > td, .table > colgroup + thead > tr:first-child > td, .table > thead:first-child > tr:first-child > td { border-top: 0; } .table > tbody + tbody { border-top: 2px solid #ddd; } .table .table { background-color: #fff; } .table-condensed > thead > tr > th, .table-condensed > tbody > tr > th, .table-condensed > tfoot > tr > th, .table-condensed > thead > tr > td, .table-condensed > tbody > tr > td, .table-condensed > tfoot > tr > td { padding: 5px; } .table-bordered { border: 1px solid #ddd; } .table-bordered > thead > tr > th, .table-bordered > tbody > tr > th, .table-bordered > tfoot > tr > th, .table-bordered > thead > tr > td, .table-bordered > tbody > tr > td, .table-bordered > tfoot > tr > td { border: 1px solid #ddd; } .table-bordered > thead > tr > th, .table-bordered > thead > tr > td { border-bottom-width: 2px; } .table-striped > tbody > tr:nth-of-type(odd) { background-color: #f9f9f9; } .table-hover > tbody > tr:hover { background-color: #f5f5f5; } table col[class\*="col-"] { position: static; float: none; display: table-column; } table td[class\*="col-"], table th[class\*="col-"] { position: static; float: none; display: table-cell; } .table > thead > tr > td.active, .table > tbody > tr > td.active, .table > tfoot > tr > td.active, .table > thead > tr > th.active, .table > tbody > tr > th.active, .table > tfoot > tr > th.active, .table > thead > tr.active > td, .table > tbody > tr.active > td, .table > tfoot > tr.active > td, .table > thead > tr.active > th, .table > tbody > tr.active > th, .table > tfoot > tr.active > th { background-color: #f5f5f5; } .table-hover > tbody > tr > td.active:hover, .table-hover > tbody > tr > th.active:hover, .table-hover > tbody > tr.active:hover > td, .table-hover > tbody > tr:hover > .active, .table-hover > tbody > tr.active:hover > th { background-color: #e8e8e8; } .table > thead > tr > td.success, .table > tbody > tr > td.success, .table > tfoot > tr > td.success, .table > thead > tr > th.success, .table > tbody > tr > th.success, .table > tfoot > tr > th.success, .table > thead > tr.success > td, .table > tbody > tr.success > td, .table > tfoot > tr.success > td, .table > thead > tr.success > th, .table > tbody > tr.success > th, .table > tfoot > tr.success > th { background-color: #dff0d8; } .table-hover > tbody > tr > td.success:hover, .table-hover > tbody > tr > th.success:hover, .table-hover > tbody > tr.success:hover > td, .table-hover > tbody > tr:hover > .success, .table-hover > tbody > tr.success:hover > th { background-color: #d0e9c6; } .table > thead > tr > td.info, .table > tbody > tr > td.info, .table > tfoot > tr > td.info, .table > thead > tr > th.info, .table > tbody > tr > th.info, .table > tfoot > tr > th.info, .table > thead > tr.info > td, .table > tbody > tr.info > td, .table > tfoot > tr.info > td, .table > thead > tr.info > th, .table > tbody > tr.info > th, .table > tfoot > tr.info > th { background-color: #d9edf7; } .table-hover > tbody > tr > td.info:hover, .table-hover > tbody > tr > th.info:hover, .table-hover > tbody > tr.info:hover > td, .table-hover > tbody > tr:hover > .info, .table-hover > tbody > tr.info:hover > th { background-color: #c4e3f3; } .table > thead > tr > td.warning, .table > tbody > tr > td.warning, .table > tfoot > tr > td.warning, .table > thead > tr > th.warning, .table > tbody > tr > th.warning, .table > tfoot > tr > th.warning, .table > thead > tr.warning > td, .table > tbody > tr.warning > td, .table > tfoot > tr.warning > td, .table > thead > tr.warning > th, .table > tbody > tr.warning > th, .table > tfoot > tr.warning > th { background-color: #fcf8e3; } .table-hover > tbody > tr > td.warning:hover, .table-hover > tbody > tr > th.warning:hover, .table-hover > tbody > tr.warning:hover > td, .table-hover > tbody > tr:hover > .warning, .table-hover > tbody > tr.warning:hover > th { background-color: #faf2cc; } .table > thead > tr > td.danger, .table > tbody > tr > td.danger, .table > tfoot > tr > td.danger, .table > thead > tr > th.danger, .table > tbody > tr > th.danger, .table > tfoot > tr > th.danger, .table > thead > tr.danger > td, .table > tbody > tr.danger > td, .table > tfoot > tr.danger > td, .table > thead > tr.danger > th, .table > tbody > tr.danger > th, .table > tfoot > tr.danger > th { background-color: #f2dede; } .table-hover > tbody > tr > td.danger:hover, .table-hover > tbody > tr > th.danger:hover, .table-hover > tbody > tr.danger:hover > td, .table-hover > tbody > tr:hover > .danger, .table-hover > tbody > tr.danger:hover > th { background-color: #ebcccc; } .table-responsive { overflow-x: auto; min-height: 0.01%; } @media screen and (max-width: 767px) { .table-responsive { width: 100%; margin-bottom: 13.5px; overflow-y: hidden; -ms-overflow-style: -ms-autohiding-scrollbar; border: 1px solid #ddd; } .table-responsive > .table { margin-bottom: 0; } .table-responsive > .table > thead > tr > th, .table-responsive > .table > tbody > tr > th, .table-responsive > .table > tfoot > tr > th, .table-responsive > .table > thead > tr > td, .table-responsive > .table > tbody > tr > td, .table-responsive > .table > tfoot > tr > td { white-space: nowrap; } .table-responsive > .table-bordered { border: 0; } .table-responsive > .table-bordered > thead > tr > th:first-child, .table-responsive > .table-bordered > tbody > tr > th:first-child, .table-responsive > .table-bordered > tfoot > tr > th:first-child, .table-responsive > .table-bordered > thead > tr > td:first-child, .table-responsive > .table-bordered > tbody > tr > td:first-child, .table-responsive > .table-bordered > tfoot > tr > td:first-child { border-left: 0; } .table-responsive > .table-bordered > thead > tr > th:last-child, .table-responsive > .table-bordered > tbody > tr > th:last-child, .table-responsive > .table-bordered > tfoot > tr > th:last-child, .table-responsive > .table-bordered > thead > tr > td:last-child, .table-responsive > .table-bordered > tbody > tr > td:last-child, .table-responsive > .table-bordered > tfoot > tr > td:last-child { border-right: 0; } .table-responsive > .table-bordered > tbody > tr:last-child > th, .table-responsive > .table-bordered > tfoot > tr:last-child > th, .table-responsive > .table-bordered > tbody > tr:last-child > td, .table-responsive > .table-bordered > tfoot > tr:last-child > td { border-bottom: 0; } } fieldset { padding: 0; margin: 0; border: 0; min-width: 0; } legend { display: block; width: 100%; padding: 0; margin-bottom: 18px; font-size: 19.5px; line-height: inherit; color: #333333; border: 0; border-bottom: 1px solid #e5e5e5; } label { display: inline-block; max-width: 100%; margin-bottom: 5px; font-weight: bold; } input[type="search"] { -webkit-box-sizing: border-box; -moz-box-sizing: border-box; box-sizing: border-box; } input[type="radio"], input[type="checkbox"] { margin: 4px 0 0; margin-top: 1px \9; line-height: normal; } input[type="file"] { display: block; } input[type="range"] { display: block; width: 100%; } select[multiple], select[size] { height: auto; } input[type="file"]:focus, input[type="radio"]:focus, input[type="checkbox"]:focus { outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } output { display: block; padding-top: 7px; font-size: 13px; line-height: 1.42857143; color: #555555; } .form-control { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; } .form-control:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .form-control::-moz-placeholder { color: #999; opacity: 1; } .form-control:-ms-input-placeholder { color: #999; } .form-control::-webkit-input-placeholder { color: #999; } .form-control::-ms-expand { border: 0; background-color: transparent; } .form-control[disabled], .form-control[readonly], fieldset[disabled] .form-control { background-color: #eeeeee; opacity: 1; } .form-control[disabled], fieldset[disabled] .form-control { cursor: not-allowed; } textarea.form-control { height: auto; } input[type="search"] { -webkit-appearance: none; } @media screen and (-webkit-min-device-pixel-ratio: 0) { input[type="date"].form-control, input[type="time"].form-control, input[type="datetime-local"].form-control, input[type="month"].form-control { line-height: 32px; } input[type="date"].input-sm, input[type="time"].input-sm, input[type="datetime-local"].input-sm, input[type="month"].input-sm, .input-group-sm input[type="date"], .input-group-sm input[type="time"], .input-group-sm input[type="datetime-local"], .input-group-sm input[type="month"] { line-height: 30px; } input[type="date"].input-lg, input[type="time"].input-lg, input[type="datetime-local"].input-lg, input[type="month"].input-lg, .input-group-lg input[type="date"], .input-group-lg input[type="time"], .input-group-lg input[type="datetime-local"], .input-group-lg input[type="month"] { line-height: 45px; } } .form-group { margin-bottom: 15px; } .radio, .checkbox { position: relative; display: block; margin-top: 10px; margin-bottom: 10px; } .radio label, .checkbox label { min-height: 18px; padding-left: 20px; margin-bottom: 0; font-weight: normal; cursor: pointer; } .radio input[type="radio"], .radio-inline input[type="radio"], .checkbox input[type="checkbox"], .checkbox-inline input[type="checkbox"] { position: absolute; margin-left: -20px; margin-top: 4px \9; } .radio + .radio, .checkbox + .checkbox { margin-top: -5px; } .radio-inline, .checkbox-inline { position: relative; display: inline-block; padding-left: 20px; margin-bottom: 0; vertical-align: middle; font-weight: normal; cursor: pointer; } .radio-inline + .radio-inline, .checkbox-inline + .checkbox-inline { margin-top: 0; margin-left: 10px; } input[type="radio"][disabled], input[type="checkbox"][disabled], input[type="radio"].disabled, input[type="checkbox"].disabled, fieldset[disabled] input[type="radio"], fieldset[disabled] input[type="checkbox"] { cursor: not-allowed; } .radio-inline.disabled, .checkbox-inline.disabled, fieldset[disabled] .radio-inline, fieldset[disabled] .checkbox-inline { cursor: not-allowed; } .radio.disabled label, .checkbox.disabled label, fieldset[disabled] .radio label, fieldset[disabled] .checkbox label { cursor: not-allowed; } .form-control-static { padding-top: 7px; padding-bottom: 7px; margin-bottom: 0; min-height: 31px; } .form-control-static.input-lg, .form-control-static.input-sm { padding-left: 0; padding-right: 0; } .input-sm { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } select.input-sm { height: 30px; line-height: 30px; } textarea.input-sm, select[multiple].input-sm { height: auto; } .form-group-sm .form-control { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .form-group-sm select.form-control { height: 30px; line-height: 30px; } .form-group-sm textarea.form-control, .form-group-sm select[multiple].form-control { height: auto; } .form-group-sm .form-control-static { height: 30px; min-height: 30px; padding: 6px 10px; font-size: 12px; line-height: 1.5; } .input-lg { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } select.input-lg { height: 45px; line-height: 45px; } textarea.input-lg, select[multiple].input-lg { height: auto; } .form-group-lg .form-control { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } .form-group-lg select.form-control { height: 45px; line-height: 45px; } .form-group-lg textarea.form-control, .form-group-lg select[multiple].form-control { height: auto; } .form-group-lg .form-control-static { height: 45px; min-height: 35px; padding: 11px 16px; font-size: 17px; line-height: 1.3333333; } .has-feedback { position: relative; } .has-feedback .form-control { padding-right: 40px; } .form-control-feedback { position: absolute; top: 0; right: 0; z-index: 2; display: block; width: 32px; height: 32px; line-height: 32px; text-align: center; pointer-events: none; } .input-lg + .form-control-feedback, .input-group-lg + .form-control-feedback, .form-group-lg .form-control + .form-control-feedback { width: 45px; height: 45px; line-height: 45px; } .input-sm + .form-control-feedback, .input-group-sm + .form-control-feedback, .form-group-sm .form-control + .form-control-feedback { width: 30px; height: 30px; line-height: 30px; } .has-success .help-block, .has-success .control-label, .has-success .radio, .has-success .checkbox, .has-success .radio-inline, .has-success .checkbox-inline, .has-success.radio label, .has-success.checkbox label, .has-success.radio-inline label, .has-success.checkbox-inline label { color: #3c763d; } .has-success .form-control { border-color: #3c763d; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-success .form-control:focus { border-color: #2b542c; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; } .has-success .input-group-addon { color: #3c763d; border-color: #3c763d; background-color: #dff0d8; } .has-success .form-control-feedback { color: #3c763d; } .has-warning .help-block, .has-warning .control-label, .has-warning .radio, .has-warning .checkbox, .has-warning .radio-inline, .has-warning .checkbox-inline, .has-warning.radio label, .has-warning.checkbox label, .has-warning.radio-inline label, .has-warning.checkbox-inline label { color: #8a6d3b; } .has-warning .form-control { border-color: #8a6d3b; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-warning .form-control:focus { border-color: #66512c; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; } .has-warning .input-group-addon { color: #8a6d3b; border-color: #8a6d3b; background-color: #fcf8e3; } .has-warning .form-control-feedback { color: #8a6d3b; } .has-error .help-block, .has-error .control-label, .has-error .radio, .has-error .checkbox, .has-error .radio-inline, .has-error .checkbox-inline, .has-error.radio label, .has-error.checkbox label, .has-error.radio-inline label, .has-error.checkbox-inline label { color: #a94442; } .has-error .form-control { border-color: #a94442; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); } .has-error .form-control:focus { border-color: #843534; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; } .has-error .input-group-addon { color: #a94442; border-color: #a94442; background-color: #f2dede; } .has-error .form-control-feedback { color: #a94442; } .has-feedback label ~ .form-control-feedback { top: 23px; } .has-feedback label.sr-only ~ .form-control-feedback { top: 0; } .help-block { display: block; margin-top: 5px; margin-bottom: 10px; color: #404040; } @media (min-width: 768px) { .form-inline .form-group { display: inline-block; margin-bottom: 0; vertical-align: middle; } .form-inline .form-control { display: inline-block; width: auto; vertical-align: middle; } .form-inline .form-control-static { display: inline-block; } .form-inline .input-group { display: inline-table; vertical-align: middle; } .form-inline .input-group .input-group-addon, .form-inline .input-group .input-group-btn, .form-inline .input-group .form-control { width: auto; } .form-inline .input-group > .form-control { width: 100%; } .form-inline .control-label { margin-bottom: 0; vertical-align: middle; } .form-inline .radio, .form-inline .checkbox { display: inline-block; margin-top: 0; margin-bottom: 0; vertical-align: middle; } .form-inline .radio label, .form-inline .checkbox label { padding-left: 0; } .form-inline .radio input[type="radio"], .form-inline .checkbox input[type="checkbox"] { position: relative; margin-left: 0; } .form-inline .has-feedback .form-control-feedback { top: 0; } } .form-horizontal .radio, .form-horizontal .checkbox, .form-horizontal .radio-inline, .form-horizontal .checkbox-inline { margin-top: 0; margin-bottom: 0; padding-top: 7px; } .form-horizontal .radio, .form-horizontal .checkbox { min-height: 25px; } .form-horizontal .form-group { margin-left: 0px; margin-right: 0px; } @media (min-width: 768px) { .form-horizontal .control-label { text-align: right; margin-bottom: 0; padding-top: 7px; } } .form-horizontal .has-feedback .form-control-feedback { right: 0px; } @media (min-width: 768px) { .form-horizontal .form-group-lg .control-label { padding-top: 11px; font-size: 17px; } } @media (min-width: 768px) { .form-horizontal .form-group-sm .control-label { padding-top: 6px; font-size: 12px; } } .btn { display: inline-block; margin-bottom: 0; font-weight: normal; text-align: center; vertical-align: middle; touch-action: manipulation; cursor: pointer; background-image: none; border: 1px solid transparent; white-space: nowrap; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; border-radius: 2px; -webkit-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; } .btn:focus, .btn:active:focus, .btn.active:focus, .btn.focus, .btn:active.focus, .btn.active.focus { outline: 5px auto -webkit-focus-ring-color; outline-offset: -2px; } .btn:hover, .btn:focus, .btn.focus { color: #333; text-decoration: none; } .btn:active, .btn.active { outline: 0; background-image: none; -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); } .btn.disabled, .btn[disabled], fieldset[disabled] .btn { cursor: not-allowed; opacity: 0.65; filter: alpha(opacity=65); -webkit-box-shadow: none; box-shadow: none; } a.btn.disabled, fieldset[disabled] a.btn { pointer-events: none; } .btn-default { color: #333; background-color: #fff; border-color: #ccc; } .btn-default:focus, .btn-default.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } .btn-default:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } .btn-default:active, .btn-default.active, .open > .dropdown-toggle.btn-default { color: #333; background-color: #e6e6e6; border-color: #adadad; } .btn-default:active:hover, .btn-default.active:hover, .open > .dropdown-toggle.btn-default:hover, .btn-default:active:focus, .btn-default.active:focus, .open > .dropdown-toggle.btn-default:focus, .btn-default:active.focus, .btn-default.active.focus, .open > .dropdown-toggle.btn-default.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } .btn-default:active, .btn-default.active, .open > .dropdown-toggle.btn-default { background-image: none; } .btn-default.disabled:hover, .btn-default[disabled]:hover, fieldset[disabled] .btn-default:hover, .btn-default.disabled:focus, .btn-default[disabled]:focus, fieldset[disabled] .btn-default:focus, .btn-default.disabled.focus, .btn-default[disabled].focus, fieldset[disabled] .btn-default.focus { background-color: #fff; border-color: #ccc; } .btn-default .badge { color: #fff; background-color: #333; } .btn-primary { color: #fff; background-color: #337ab7; border-color: #2e6da4; } .btn-primary:focus, .btn-primary.focus { color: #fff; background-color: #286090; border-color: #122b40; } .btn-primary:hover { color: #fff; background-color: #286090; border-color: #204d74; } .btn-primary:active, .btn-primary.active, .open > .dropdown-toggle.btn-primary { color: #fff; background-color: #286090; border-color: #204d74; } .btn-primary:active:hover, .btn-primary.active:hover, .open > .dropdown-toggle.btn-primary:hover, .btn-primary:active:focus, .btn-primary.active:focus, .open > .dropdown-toggle.btn-primary:focus, .btn-primary:active.focus, .btn-primary.active.focus, .open > .dropdown-toggle.btn-primary.focus { color: #fff; background-color: #204d74; border-color: #122b40; } .btn-primary:active, .btn-primary.active, .open > .dropdown-toggle.btn-primary { background-image: none; } .btn-primary.disabled:hover, .btn-primary[disabled]:hover, fieldset[disabled] .btn-primary:hover, .btn-primary.disabled:focus, .btn-primary[disabled]:focus, fieldset[disabled] .btn-primary:focus, .btn-primary.disabled.focus, .btn-primary[disabled].focus, fieldset[disabled] .btn-primary.focus { background-color: #337ab7; border-color: #2e6da4; } .btn-primary .badge { color: #337ab7; background-color: #fff; } .btn-success { color: #fff; background-color: #5cb85c; border-color: #4cae4c; } .btn-success:focus, .btn-success.focus { color: #fff; background-color: #449d44; border-color: #255625; } .btn-success:hover { color: #fff; background-color: #449d44; border-color: #398439; } .btn-success:active, .btn-success.active, .open > .dropdown-toggle.btn-success { color: #fff; background-color: #449d44; border-color: #398439; } .btn-success:active:hover, .btn-success.active:hover, .open > .dropdown-toggle.btn-success:hover, .btn-success:active:focus, .btn-success.active:focus, .open > .dropdown-toggle.btn-success:focus, .btn-success:active.focus, .btn-success.active.focus, .open > .dropdown-toggle.btn-success.focus { color: #fff; background-color: #398439; border-color: #255625; } .btn-success:active, .btn-success.active, .open > .dropdown-toggle.btn-success { background-image: none; } .btn-success.disabled:hover, .btn-success[disabled]:hover, fieldset[disabled] .btn-success:hover, .btn-success.disabled:focus, .btn-success[disabled]:focus, fieldset[disabled] .btn-success:focus, .btn-success.disabled.focus, .btn-success[disabled].focus, fieldset[disabled] .btn-success.focus { background-color: #5cb85c; border-color: #4cae4c; } .btn-success .badge { color: #5cb85c; background-color: #fff; } .btn-info { color: #fff; background-color: #5bc0de; border-color: #46b8da; } .btn-info:focus, .btn-info.focus { color: #fff; background-color: #31b0d5; border-color: #1b6d85; } .btn-info:hover { color: #fff; background-color: #31b0d5; border-color: #269abc; } .btn-info:active, .btn-info.active, .open > .dropdown-toggle.btn-info { color: #fff; background-color: #31b0d5; border-color: #269abc; } .btn-info:active:hover, .btn-info.active:hover, .open > .dropdown-toggle.btn-info:hover, .btn-info:active:focus, .btn-info.active:focus, .open > .dropdown-toggle.btn-info:focus, .btn-info:active.focus, .btn-info.active.focus, .open > .dropdown-toggle.btn-info.focus { color: #fff; background-color: #269abc; border-color: #1b6d85; } .btn-info:active, .btn-info.active, .open > .dropdown-toggle.btn-info { background-image: none; } .btn-info.disabled:hover, .btn-info[disabled]:hover, fieldset[disabled] .btn-info:hover, .btn-info.disabled:focus, .btn-info[disabled]:focus, fieldset[disabled] .btn-info:focus, .btn-info.disabled.focus, .btn-info[disabled].focus, fieldset[disabled] .btn-info.focus { background-color: #5bc0de; border-color: #46b8da; } .btn-info .badge { color: #5bc0de; background-color: #fff; } .btn-warning { color: #fff; background-color: #f0ad4e; border-color: #eea236; } .btn-warning:focus, .btn-warning.focus { color: #fff; background-color: #ec971f; border-color: #985f0d; } .btn-warning:hover { color: #fff; background-color: #ec971f; border-color: #d58512; } .btn-warning:active, .btn-warning.active, .open > .dropdown-toggle.btn-warning { color: #fff; background-color: #ec971f; border-color: #d58512; } .btn-warning:active:hover, .btn-warning.active:hover, .open > .dropdown-toggle.btn-warning:hover, .btn-warning:active:focus, .btn-warning.active:focus, .open > .dropdown-toggle.btn-warning:focus, .btn-warning:active.focus, .btn-warning.active.focus, .open > .dropdown-toggle.btn-warning.focus { color: #fff; background-color: #d58512; border-color: #985f0d; } .btn-warning:active, .btn-warning.active, .open > .dropdown-toggle.btn-warning { background-image: none; } .btn-warning.disabled:hover, .btn-warning[disabled]:hover, fieldset[disabled] .btn-warning:hover, .btn-warning.disabled:focus, .btn-warning[disabled]:focus, fieldset[disabled] .btn-warning:focus, .btn-warning.disabled.focus, .btn-warning[disabled].focus, fieldset[disabled] .btn-warning.focus { background-color: #f0ad4e; border-color: #eea236; } .btn-warning .badge { color: #f0ad4e; background-color: #fff; } .btn-danger { color: #fff; background-color: #d9534f; border-color: #d43f3a; } .btn-danger:focus, .btn-danger.focus { color: #fff; background-color: #c9302c; border-color: #761c19; } .btn-danger:hover { color: #fff; background-color: #c9302c; border-color: #ac2925; } .btn-danger:active, .btn-danger.active, .open > .dropdown-toggle.btn-danger { color: #fff; background-color: #c9302c; border-color: #ac2925; } .btn-danger:active:hover, .btn-danger.active:hover, .open > .dropdown-toggle.btn-danger:hover, .btn-danger:active:focus, .btn-danger.active:focus, .open > .dropdown-toggle.btn-danger:focus, .btn-danger:active.focus, .btn-danger.active.focus, .open > .dropdown-toggle.btn-danger.focus { color: #fff; background-color: #ac2925; border-color: #761c19; } .btn-danger:active, .btn-danger.active, .open > .dropdown-toggle.btn-danger { background-image: none; } .btn-danger.disabled:hover, .btn-danger[disabled]:hover, fieldset[disabled] .btn-danger:hover, .btn-danger.disabled:focus, .btn-danger[disabled]:focus, fieldset[disabled] .btn-danger:focus, .btn-danger.disabled.focus, .btn-danger[disabled].focus, fieldset[disabled] .btn-danger.focus { background-color: #d9534f; border-color: #d43f3a; } .btn-danger .badge { color: #d9534f; background-color: #fff; } .btn-link { color: #337ab7; font-weight: normal; border-radius: 0; } .btn-link, .btn-link:active, .btn-link.active, .btn-link[disabled], fieldset[disabled] .btn-link { background-color: transparent; -webkit-box-shadow: none; box-shadow: none; } .btn-link, .btn-link:hover, .btn-link:focus, .btn-link:active { border-color: transparent; } .btn-link:hover, .btn-link:focus { color: #23527c; text-decoration: underline; background-color: transparent; } .btn-link[disabled]:hover, fieldset[disabled] .btn-link:hover, .btn-link[disabled]:focus, fieldset[disabled] .btn-link:focus { color: #777777; text-decoration: none; } .btn-lg, .btn-group-lg > .btn { padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } .btn-sm, .btn-group-sm > .btn { padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .btn-xs, .btn-group-xs > .btn { padding: 1px 5px; font-size: 12px; line-height: 1.5; border-radius: 1px; } .btn-block { display: block; width: 100%; } .btn-block + .btn-block { margin-top: 5px; } input[type="submit"].btn-block, input[type="reset"].btn-block, input[type="button"].btn-block { width: 100%; } .fade { opacity: 0; -webkit-transition: opacity 0.15s linear; -o-transition: opacity 0.15s linear; transition: opacity 0.15s linear; } .fade.in { opacity: 1; } .collapse { display: none; } .collapse.in { display: block; } tr.collapse.in { display: table-row; } tbody.collapse.in { display: table-row-group; } .collapsing { position: relative; height: 0; overflow: hidden; -webkit-transition-property: height, visibility; transition-property: height, visibility; -webkit-transition-duration: 0.35s; transition-duration: 0.35s; -webkit-transition-timing-function: ease; transition-timing-function: ease; } .caret { display: inline-block; width: 0; height: 0; margin-left: 2px; vertical-align: middle; border-top: 4px dashed; border-top: 4px solid \9; border-right: 4px solid transparent; border-left: 4px solid transparent; } .dropup, .dropdown { position: relative; } .dropdown-toggle:focus { outline: 0; } .dropdown-menu { position: absolute; top: 100%; left: 0; z-index: 1000; display: none; float: left; min-width: 160px; padding: 5px 0; margin: 2px 0 0; list-style: none; font-size: 13px; text-align: left; background-color: #fff; border: 1px solid #ccc; border: 1px solid rgba(0, 0, 0, 0.15); border-radius: 2px; -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); background-clip: padding-box; } .dropdown-menu.pull-right { right: 0; left: auto; } .dropdown-menu .divider { height: 1px; margin: 8px 0; overflow: hidden; background-color: #e5e5e5; } .dropdown-menu > li > a { display: block; padding: 3px 20px; clear: both; font-weight: normal; line-height: 1.42857143; color: #333333; white-space: nowrap; } .dropdown-menu > li > a:hover, .dropdown-menu > li > a:focus { text-decoration: none; color: #262626; background-color: #f5f5f5; } .dropdown-menu > .active > a, .dropdown-menu > .active > a:hover, .dropdown-menu > .active > a:focus { color: #fff; text-decoration: none; outline: 0; background-color: #337ab7; } .dropdown-menu > .disabled > a, .dropdown-menu > .disabled > a:hover, .dropdown-menu > .disabled > a:focus { color: #777777; } .dropdown-menu > .disabled > a:hover, .dropdown-menu > .disabled > a:focus { text-decoration: none; background-color: transparent; background-image: none; filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); cursor: not-allowed; } .open > .dropdown-menu { display: block; } .open > a { outline: 0; } .dropdown-menu-right { left: auto; right: 0; } .dropdown-menu-left { left: 0; right: auto; } .dropdown-header { display: block; padding: 3px 20px; font-size: 12px; line-height: 1.42857143; color: #777777; white-space: nowrap; } .dropdown-backdrop { position: fixed; left: 0; right: 0; bottom: 0; top: 0; z-index: 990; } .pull-right > .dropdown-menu { right: 0; left: auto; } .dropup .caret, .navbar-fixed-bottom .dropdown .caret { border-top: 0; border-bottom: 4px dashed; border-bottom: 4px solid \9; content: ""; } .dropup .dropdown-menu, .navbar-fixed-bottom .dropdown .dropdown-menu { top: auto; bottom: 100%; margin-bottom: 2px; } @media (min-width: 541px) { .navbar-right .dropdown-menu { left: auto; right: 0; } .navbar-right .dropdown-menu-left { left: 0; right: auto; } } .btn-group, .btn-group-vertical { position: relative; display: inline-block; vertical-align: middle; } .btn-group > .btn, .btn-group-vertical > .btn { position: relative; float: left; } .btn-group > .btn:hover, .btn-group-vertical > .btn:hover, .btn-group > .btn:focus, .btn-group-vertical > .btn:focus, .btn-group > .btn:active, .btn-group-vertical > .btn:active, .btn-group > .btn.active, .btn-group-vertical > .btn.active { z-index: 2; } .btn-group .btn + .btn, .btn-group .btn + .btn-group, .btn-group .btn-group + .btn, .btn-group .btn-group + .btn-group { margin-left: -1px; } .btn-toolbar { margin-left: -5px; } .btn-toolbar .btn, .btn-toolbar .btn-group, .btn-toolbar .input-group { float: left; } .btn-toolbar > .btn, .btn-toolbar > .btn-group, .btn-toolbar > .input-group { margin-left: 5px; } .btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) { border-radius: 0; } .btn-group > .btn:first-child { margin-left: 0; } .btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) { border-bottom-right-radius: 0; border-top-right-radius: 0; } .btn-group > .btn:last-child:not(:first-child), .btn-group > .dropdown-toggle:not(:first-child) { border-bottom-left-radius: 0; border-top-left-radius: 0; } .btn-group > .btn-group { float: left; } .btn-group > .btn-group:not(:first-child):not(:last-child) > .btn { border-radius: 0; } .btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child, .btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle { border-bottom-right-radius: 0; border-top-right-radius: 0; } .btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child { border-bottom-left-radius: 0; border-top-left-radius: 0; } .btn-group .dropdown-toggle:active, .btn-group.open .dropdown-toggle { outline: 0; } .btn-group > .btn + .dropdown-toggle { padding-left: 8px; padding-right: 8px; } .btn-group > .btn-lg + .dropdown-toggle { padding-left: 12px; padding-right: 12px; } .btn-group.open .dropdown-toggle { -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); } .btn-group.open .dropdown-toggle.btn-link { -webkit-box-shadow: none; box-shadow: none; } .btn .caret { margin-left: 0; } .btn-lg .caret { border-width: 5px 5px 0; border-bottom-width: 0; } .dropup .btn-lg .caret { border-width: 0 5px 5px; } .btn-group-vertical > .btn, .btn-group-vertical > .btn-group, .btn-group-vertical > .btn-group > .btn { display: block; float: none; width: 100%; max-width: 100%; } .btn-group-vertical > .btn-group > .btn { float: none; } .btn-group-vertical > .btn + .btn, .btn-group-vertical > .btn + .btn-group, .btn-group-vertical > .btn-group + .btn, .btn-group-vertical > .btn-group + .btn-group { margin-top: -1px; margin-left: 0; } .btn-group-vertical > .btn:not(:first-child):not(:last-child) { border-radius: 0; } .btn-group-vertical > .btn:first-child:not(:last-child) { border-top-right-radius: 2px; border-top-left-radius: 2px; border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .btn-group-vertical > .btn:last-child:not(:first-child) { border-top-right-radius: 0; border-top-left-radius: 0; border-bottom-right-radius: 2px; border-bottom-left-radius: 2px; } .btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn { border-radius: 0; } .btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child, .btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle { border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child { border-top-right-radius: 0; border-top-left-radius: 0; } .btn-group-justified { display: table; width: 100%; table-layout: fixed; border-collapse: separate; } .btn-group-justified > .btn, .btn-group-justified > .btn-group { float: none; display: table-cell; width: 1%; } .btn-group-justified > .btn-group .btn { width: 100%; } .btn-group-justified > .btn-group .dropdown-menu { left: auto; } [data-toggle="buttons"] > .btn input[type="radio"], [data-toggle="buttons"] > .btn-group > .btn input[type="radio"], [data-toggle="buttons"] > .btn input[type="checkbox"], [data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] { position: absolute; clip: rect(0, 0, 0, 0); pointer-events: none; } .input-group { position: relative; display: table; border-collapse: separate; } .input-group[class\*="col-"] { float: none; padding-left: 0; padding-right: 0; } .input-group .form-control { position: relative; z-index: 2; float: left; width: 100%; margin-bottom: 0; } .input-group .form-control:focus { z-index: 3; } .input-group-lg > .form-control, .input-group-lg > .input-group-addon, .input-group-lg > .input-group-btn > .btn { height: 45px; padding: 10px 16px; font-size: 17px; line-height: 1.3333333; border-radius: 3px; } select.input-group-lg > .form-control, select.input-group-lg > .input-group-addon, select.input-group-lg > .input-group-btn > .btn { height: 45px; line-height: 45px; } textarea.input-group-lg > .form-control, textarea.input-group-lg > .input-group-addon, textarea.input-group-lg > .input-group-btn > .btn, select[multiple].input-group-lg > .form-control, select[multiple].input-group-lg > .input-group-addon, select[multiple].input-group-lg > .input-group-btn > .btn { height: auto; } .input-group-sm > .form-control, .input-group-sm > .input-group-addon, .input-group-sm > .input-group-btn > .btn { height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; } select.input-group-sm > .form-control, select.input-group-sm > .input-group-addon, select.input-group-sm > .input-group-btn > .btn { height: 30px; line-height: 30px; } textarea.input-group-sm > .form-control, textarea.input-group-sm > .input-group-addon, textarea.input-group-sm > .input-group-btn > .btn, select[multiple].input-group-sm > .form-control, select[multiple].input-group-sm > .input-group-addon, select[multiple].input-group-sm > .input-group-btn > .btn { height: auto; } .input-group-addon, .input-group-btn, .input-group .form-control { display: table-cell; } .input-group-addon:not(:first-child):not(:last-child), .input-group-btn:not(:first-child):not(:last-child), .input-group .form-control:not(:first-child):not(:last-child) { border-radius: 0; } .input-group-addon, .input-group-btn { width: 1%; white-space: nowrap; vertical-align: middle; } .input-group-addon { padding: 6px 12px; font-size: 13px; font-weight: normal; line-height: 1; color: #555555; text-align: center; background-color: #eeeeee; border: 1px solid #ccc; border-radius: 2px; } .input-group-addon.input-sm { padding: 5px 10px; font-size: 12px; border-radius: 1px; } .input-group-addon.input-lg { padding: 10px 16px; font-size: 17px; border-radius: 3px; } .input-group-addon input[type="radio"], .input-group-addon input[type="checkbox"] { margin-top: 0; } .input-group .form-control:first-child, .input-group-addon:first-child, .input-group-btn:first-child > .btn, .input-group-btn:first-child > .btn-group > .btn, .input-group-btn:first-child > .dropdown-toggle, .input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle), .input-group-btn:last-child > .btn-group:not(:last-child) > .btn { border-bottom-right-radius: 0; border-top-right-radius: 0; } .input-group-addon:first-child { border-right: 0; } .input-group .form-control:last-child, .input-group-addon:last-child, .input-group-btn:last-child > .btn, .input-group-btn:last-child > .btn-group > .btn, .input-group-btn:last-child > .dropdown-toggle, .input-group-btn:first-child > .btn:not(:first-child), .input-group-btn:first-child > .btn-group:not(:first-child) > .btn { border-bottom-left-radius: 0; border-top-left-radius: 0; } .input-group-addon:last-child { border-left: 0; } .input-group-btn { position: relative; font-size: 0; white-space: nowrap; } .input-group-btn > .btn { position: relative; } .input-group-btn > .btn + .btn { margin-left: -1px; } .input-group-btn > .btn:hover, .input-group-btn > .btn:focus, .input-group-btn > .btn:active { z-index: 2; } .input-group-btn:first-child > .btn, .input-group-btn:first-child > .btn-group { margin-right: -1px; } .input-group-btn:last-child > .btn, .input-group-btn:last-child > .btn-group { z-index: 2; margin-left: -1px; } .nav { margin-bottom: 0; padding-left: 0; list-style: none; } .nav > li { position: relative; display: block; } .nav > li > a { position: relative; display: block; padding: 10px 15px; } .nav > li > a:hover, .nav > li > a:focus { text-decoration: none; background-color: #eeeeee; } .nav > li.disabled > a { color: #777777; } .nav > li.disabled > a:hover, .nav > li.disabled > a:focus { color: #777777; text-decoration: none; background-color: transparent; cursor: not-allowed; } .nav .open > a, .nav .open > a:hover, .nav .open > a:focus { background-color: #eeeeee; border-color: #337ab7; } .nav .nav-divider { height: 1px; margin: 8px 0; overflow: hidden; background-color: #e5e5e5; } .nav > li > a > img { max-width: none; } .nav-tabs { border-bottom: 1px solid #ddd; } .nav-tabs > li { float: left; margin-bottom: -1px; } .nav-tabs > li > a { margin-right: 2px; line-height: 1.42857143; border: 1px solid transparent; border-radius: 2px 2px 0 0; } .nav-tabs > li > a:hover { border-color: #eeeeee #eeeeee #ddd; } .nav-tabs > li.active > a, .nav-tabs > li.active > a:hover, .nav-tabs > li.active > a:focus { color: #555555; background-color: #fff; border: 1px solid #ddd; border-bottom-color: transparent; cursor: default; } .nav-tabs.nav-justified { width: 100%; border-bottom: 0; } .nav-tabs.nav-justified > li { float: none; } .nav-tabs.nav-justified > li > a { text-align: center; margin-bottom: 5px; } .nav-tabs.nav-justified > .dropdown .dropdown-menu { top: auto; left: auto; } @media (min-width: 768px) { .nav-tabs.nav-justified > li { display: table-cell; width: 1%; } .nav-tabs.nav-justified > li > a { margin-bottom: 0; } } .nav-tabs.nav-justified > li > a { margin-right: 0; border-radius: 2px; } .nav-tabs.nav-justified > .active > a, .nav-tabs.nav-justified > .active > a:hover, .nav-tabs.nav-justified > .active > a:focus { border: 1px solid #ddd; } @media (min-width: 768px) { .nav-tabs.nav-justified > li > a { border-bottom: 1px solid #ddd; border-radius: 2px 2px 0 0; } .nav-tabs.nav-justified > .active > a, .nav-tabs.nav-justified > .active > a:hover, .nav-tabs.nav-justified > .active > a:focus { border-bottom-color: #fff; } } .nav-pills > li { float: left; } .nav-pills > li > a { border-radius: 2px; } .nav-pills > li + li { margin-left: 2px; } .nav-pills > li.active > a, .nav-pills > li.active > a:hover, .nav-pills > li.active > a:focus { color: #fff; background-color: #337ab7; } .nav-stacked > li { float: none; } .nav-stacked > li + li { margin-top: 2px; margin-left: 0; } .nav-justified { width: 100%; } .nav-justified > li { float: none; } .nav-justified > li > a { text-align: center; margin-bottom: 5px; } .nav-justified > .dropdown .dropdown-menu { top: auto; left: auto; } @media (min-width: 768px) { .nav-justified > li { display: table-cell; width: 1%; } .nav-justified > li > a { margin-bottom: 0; } } .nav-tabs-justified { border-bottom: 0; } .nav-tabs-justified > li > a { margin-right: 0; border-radius: 2px; } .nav-tabs-justified > .active > a, .nav-tabs-justified > .active > a:hover, .nav-tabs-justified > .active > a:focus { border: 1px solid #ddd; } @media (min-width: 768px) { .nav-tabs-justified > li > a { border-bottom: 1px solid #ddd; border-radius: 2px 2px 0 0; } .nav-tabs-justified > .active > a, .nav-tabs-justified > .active > a:hover, .nav-tabs-justified > .active > a:focus { border-bottom-color: #fff; } } .tab-content > .tab-pane { display: none; } .tab-content > .active { display: block; } .nav-tabs .dropdown-menu { margin-top: -1px; border-top-right-radius: 0; border-top-left-radius: 0; } .navbar { position: relative; min-height: 30px; margin-bottom: 18px; border: 1px solid transparent; } @media (min-width: 541px) { .navbar { border-radius: 2px; } } @media (min-width: 541px) { .navbar-header { float: left; } } .navbar-collapse { overflow-x: visible; padding-right: 0px; padding-left: 0px; border-top: 1px solid transparent; box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1); -webkit-overflow-scrolling: touch; } .navbar-collapse.in { overflow-y: auto; } @media (min-width: 541px) { .navbar-collapse { width: auto; border-top: 0; box-shadow: none; } .navbar-collapse.collapse { display: block !important; height: auto !important; padding-bottom: 0; overflow: visible !important; } .navbar-collapse.in { overflow-y: visible; } .navbar-fixed-top .navbar-collapse, .navbar-static-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { padding-left: 0; padding-right: 0; } } .navbar-fixed-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { max-height: 340px; } @media (max-device-width: 540px) and (orientation: landscape) { .navbar-fixed-top .navbar-collapse, .navbar-fixed-bottom .navbar-collapse { max-height: 200px; } } .container > .navbar-header, .container-fluid > .navbar-header, .container > .navbar-collapse, .container-fluid > .navbar-collapse { margin-right: 0px; margin-left: 0px; } @media (min-width: 541px) { .container > .navbar-header, .container-fluid > .navbar-header, .container > .navbar-collapse, .container-fluid > .navbar-collapse { margin-right: 0; margin-left: 0; } } .navbar-static-top { z-index: 1000; border-width: 0 0 1px; } @media (min-width: 541px) { .navbar-static-top { border-radius: 0; } } .navbar-fixed-top, .navbar-fixed-bottom { position: fixed; right: 0; left: 0; z-index: 1030; } @media (min-width: 541px) { .navbar-fixed-top, .navbar-fixed-bottom { border-radius: 0; } } .navbar-fixed-top { top: 0; border-width: 0 0 1px; } .navbar-fixed-bottom { bottom: 0; margin-bottom: 0; border-width: 1px 0 0; } .navbar-brand { float: left; padding: 6px 0px; font-size: 17px; line-height: 18px; height: 30px; } .navbar-brand:hover, .navbar-brand:focus { text-decoration: none; } .navbar-brand > img { display: block; } @media (min-width: 541px) { .navbar > .container .navbar-brand, .navbar > .container-fluid .navbar-brand { margin-left: 0px; } } .navbar-toggle { position: relative; float: right; margin-right: 0px; padding: 9px 10px; margin-top: -2px; margin-bottom: -2px; background-color: transparent; background-image: none; border: 1px solid transparent; border-radius: 2px; } .navbar-toggle:focus { outline: 0; } .navbar-toggle .icon-bar { display: block; width: 22px; height: 2px; border-radius: 1px; } .navbar-toggle .icon-bar + .icon-bar { margin-top: 4px; } @media (min-width: 541px) { .navbar-toggle { display: none; } } .navbar-nav { margin: 3px 0px; } .navbar-nav > li > a { padding-top: 10px; padding-bottom: 10px; line-height: 18px; } @media (max-width: 540px) { .navbar-nav .open .dropdown-menu { position: static; float: none; width: auto; margin-top: 0; background-color: transparent; border: 0; box-shadow: none; } .navbar-nav .open .dropdown-menu > li > a, .navbar-nav .open .dropdown-menu .dropdown-header { padding: 5px 15px 5px 25px; } .navbar-nav .open .dropdown-menu > li > a { line-height: 18px; } .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-nav .open .dropdown-menu > li > a:focus { background-image: none; } } @media (min-width: 541px) { .navbar-nav { float: left; margin: 0; } .navbar-nav > li { float: left; } .navbar-nav > li > a { padding-top: 6px; padding-bottom: 6px; } } .navbar-form { margin-left: 0px; margin-right: 0px; padding: 10px 0px; border-top: 1px solid transparent; border-bottom: 1px solid transparent; -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); margin-top: -1px; margin-bottom: -1px; } @media (min-width: 768px) { .navbar-form .form-group { display: inline-block; margin-bottom: 0; vertical-align: middle; } .navbar-form .form-control { display: inline-block; width: auto; vertical-align: middle; } .navbar-form .form-control-static { display: inline-block; } .navbar-form .input-group { display: inline-table; vertical-align: middle; } .navbar-form .input-group .input-group-addon, .navbar-form .input-group .input-group-btn, .navbar-form .input-group .form-control { width: auto; } .navbar-form .input-group > .form-control { width: 100%; } .navbar-form .control-label { margin-bottom: 0; vertical-align: middle; } .navbar-form .radio, .navbar-form .checkbox { display: inline-block; margin-top: 0; margin-bottom: 0; vertical-align: middle; } .navbar-form .radio label, .navbar-form .checkbox label { padding-left: 0; } .navbar-form .radio input[type="radio"], .navbar-form .checkbox input[type="checkbox"] { position: relative; margin-left: 0; } .navbar-form .has-feedback .form-control-feedback { top: 0; } } @media (max-width: 540px) { .navbar-form .form-group { margin-bottom: 5px; } .navbar-form .form-group:last-child { margin-bottom: 0; } } @media (min-width: 541px) { .navbar-form { width: auto; border: 0; margin-left: 0; margin-right: 0; padding-top: 0; padding-bottom: 0; -webkit-box-shadow: none; box-shadow: none; } } .navbar-nav > li > .dropdown-menu { margin-top: 0; border-top-right-radius: 0; border-top-left-radius: 0; } .navbar-fixed-bottom .navbar-nav > li > .dropdown-menu { margin-bottom: 0; border-top-right-radius: 2px; border-top-left-radius: 2px; border-bottom-right-radius: 0; border-bottom-left-radius: 0; } .navbar-btn { margin-top: -1px; margin-bottom: -1px; } .navbar-btn.btn-sm { margin-top: 0px; margin-bottom: 0px; } .navbar-btn.btn-xs { margin-top: 4px; margin-bottom: 4px; } .navbar-text { margin-top: 6px; margin-bottom: 6px; } @media (min-width: 541px) { .navbar-text { float: left; margin-left: 0px; margin-right: 0px; } } @media (min-width: 541px) { .navbar-left { float: left !important; float: left; } .navbar-right { float: right !important; float: right; margin-right: 0px; } .navbar-right ~ .navbar-right { margin-right: 0; } } .navbar-default { background-color: #f8f8f8; border-color: #e7e7e7; } .navbar-default .navbar-brand { color: #777; } .navbar-default .navbar-brand:hover, .navbar-default .navbar-brand:focus { color: #5e5e5e; background-color: transparent; } .navbar-default .navbar-text { color: #777; } .navbar-default .navbar-nav > li > a { color: #777; } .navbar-default .navbar-nav > li > a:hover, .navbar-default .navbar-nav > li > a:focus { color: #333; background-color: transparent; } .navbar-default .navbar-nav > .active > a, .navbar-default .navbar-nav > .active > a:hover, .navbar-default .navbar-nav > .active > a:focus { color: #555; background-color: #e7e7e7; } .navbar-default .navbar-nav > .disabled > a, .navbar-default .navbar-nav > .disabled > a:hover, .navbar-default .navbar-nav > .disabled > a:focus { color: #ccc; background-color: transparent; } .navbar-default .navbar-toggle { border-color: #ddd; } .navbar-default .navbar-toggle:hover, .navbar-default .navbar-toggle:focus { background-color: #ddd; } .navbar-default .navbar-toggle .icon-bar { background-color: #888; } .navbar-default .navbar-collapse, .navbar-default .navbar-form { border-color: #e7e7e7; } .navbar-default .navbar-nav > .open > a, .navbar-default .navbar-nav > .open > a:hover, .navbar-default .navbar-nav > .open > a:focus { background-color: #e7e7e7; color: #555; } @media (max-width: 540px) { .navbar-default .navbar-nav .open .dropdown-menu > li > a { color: #777; } .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus { color: #333; background-color: transparent; } .navbar-default .navbar-nav .open .dropdown-menu > .active > a, .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus { color: #555; background-color: #e7e7e7; } .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a, .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover, .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus { color: #ccc; background-color: transparent; } } .navbar-default .navbar-link { color: #777; } .navbar-default .navbar-link:hover { color: #333; } .navbar-default .btn-link { color: #777; } .navbar-default .btn-link:hover, .navbar-default .btn-link:focus { color: #333; } .navbar-default .btn-link[disabled]:hover, fieldset[disabled] .navbar-default .btn-link:hover, .navbar-default .btn-link[disabled]:focus, fieldset[disabled] .navbar-default .btn-link:focus { color: #ccc; } .navbar-inverse { background-color: #222; border-color: #080808; } .navbar-inverse .navbar-brand { color: #9d9d9d; } .navbar-inverse .navbar-brand:hover, .navbar-inverse .navbar-brand:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-text { color: #9d9d9d; } .navbar-inverse .navbar-nav > li > a { color: #9d9d9d; } .navbar-inverse .navbar-nav > li > a:hover, .navbar-inverse .navbar-nav > li > a:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-nav > .active > a, .navbar-inverse .navbar-nav > .active > a:hover, .navbar-inverse .navbar-nav > .active > a:focus { color: #fff; background-color: #080808; } .navbar-inverse .navbar-nav > .disabled > a, .navbar-inverse .navbar-nav > .disabled > a:hover, .navbar-inverse .navbar-nav > .disabled > a:focus { color: #444; background-color: transparent; } .navbar-inverse .navbar-toggle { border-color: #333; } .navbar-inverse .navbar-toggle:hover, .navbar-inverse .navbar-toggle:focus { background-color: #333; } .navbar-inverse .navbar-toggle .icon-bar { background-color: #fff; } .navbar-inverse .navbar-collapse, .navbar-inverse .navbar-form { border-color: #101010; } .navbar-inverse .navbar-nav > .open > a, .navbar-inverse .navbar-nav > .open > a:hover, .navbar-inverse .navbar-nav > .open > a:focus { background-color: #080808; color: #fff; } @media (max-width: 540px) { .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header { border-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu .divider { background-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu > li > a { color: #9d9d9d; } .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus { color: #fff; background-color: transparent; } .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a, .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus { color: #fff; background-color: #080808; } .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a, .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover, .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus { color: #444; background-color: transparent; } } .navbar-inverse .navbar-link { color: #9d9d9d; } .navbar-inverse .navbar-link:hover { color: #fff; } .navbar-inverse .btn-link { color: #9d9d9d; } .navbar-inverse .btn-link:hover, .navbar-inverse .btn-link:focus { color: #fff; } .navbar-inverse .btn-link[disabled]:hover, fieldset[disabled] .navbar-inverse .btn-link:hover, .navbar-inverse .btn-link[disabled]:focus, fieldset[disabled] .navbar-inverse .btn-link:focus { color: #444; } .breadcrumb { padding: 8px 15px; margin-bottom: 18px; list-style: none; background-color: #f5f5f5; border-radius: 2px; } .breadcrumb > li { display: inline-block; } .breadcrumb > li + li:before { content: "/\00a0"; padding: 0 5px; color: #5e5e5e; } .breadcrumb > .active { color: #777777; } .pagination { display: inline-block; padding-left: 0; margin: 18px 0; border-radius: 2px; } .pagination > li { display: inline; } .pagination > li > a, .pagination > li > span { position: relative; float: left; padding: 6px 12px; line-height: 1.42857143; text-decoration: none; color: #337ab7; background-color: #fff; border: 1px solid #ddd; margin-left: -1px; } .pagination > li:first-child > a, .pagination > li:first-child > span { margin-left: 0; border-bottom-left-radius: 2px; border-top-left-radius: 2px; } .pagination > li:last-child > a, .pagination > li:last-child > span { border-bottom-right-radius: 2px; border-top-right-radius: 2px; } .pagination > li > a:hover, .pagination > li > span:hover, .pagination > li > a:focus, .pagination > li > span:focus { z-index: 2; color: #23527c; background-color: #eeeeee; border-color: #ddd; } .pagination > .active > a, .pagination > .active > span, .pagination > .active > a:hover, .pagination > .active > span:hover, .pagination > .active > a:focus, .pagination > .active > span:focus { z-index: 3; color: #fff; background-color: #337ab7; border-color: #337ab7; cursor: default; } .pagination > .disabled > span, .pagination > .disabled > span:hover, .pagination > .disabled > span:focus, .pagination > .disabled > a, .pagination > .disabled > a:hover, .pagination > .disabled > a:focus { color: #777777; background-color: #fff; border-color: #ddd; cursor: not-allowed; } .pagination-lg > li > a, .pagination-lg > li > span { padding: 10px 16px; font-size: 17px; line-height: 1.3333333; } .pagination-lg > li:first-child > a, .pagination-lg > li:first-child > span { border-bottom-left-radius: 3px; border-top-left-radius: 3px; } .pagination-lg > li:last-child > a, .pagination-lg > li:last-child > span { border-bottom-right-radius: 3px; border-top-right-radius: 3px; } .pagination-sm > li > a, .pagination-sm > li > span { padding: 5px 10px; font-size: 12px; line-height: 1.5; } .pagination-sm > li:first-child > a, .pagination-sm > li:first-child > span { border-bottom-left-radius: 1px; border-top-left-radius: 1px; } .pagination-sm > li:last-child > a, .pagination-sm > li:last-child > span { border-bottom-right-radius: 1px; border-top-right-radius: 1px; } .pager { padding-left: 0; margin: 18px 0; list-style: none; text-align: center; } .pager li { display: inline; } .pager li > a, .pager li > span { display: inline-block; padding: 5px 14px; background-color: #fff; border: 1px solid #ddd; border-radius: 15px; } .pager li > a:hover, .pager li > a:focus { text-decoration: none; background-color: #eeeeee; } .pager .next > a, .pager .next > span { float: right; } .pager .previous > a, .pager .previous > span { float: left; } .pager .disabled > a, .pager .disabled > a:hover, .pager .disabled > a:focus, .pager .disabled > span { color: #777777; background-color: #fff; cursor: not-allowed; } .label { display: inline; padding: .2em .6em .3em; font-size: 75%; font-weight: bold; line-height: 1; color: #fff; text-align: center; white-space: nowrap; vertical-align: baseline; border-radius: .25em; } a.label:hover, a.label:focus { color: #fff; text-decoration: none; cursor: pointer; } .label:empty { display: none; } .btn .label { position: relative; top: -1px; } .label-default { background-color: #777777; } .label-default[href]:hover, .label-default[href]:focus { background-color: #5e5e5e; } .label-primary { background-color: #337ab7; } .label-primary[href]:hover, .label-primary[href]:focus { background-color: #286090; } .label-success { background-color: #5cb85c; } .label-success[href]:hover, .label-success[href]:focus { background-color: #449d44; } .label-info { background-color: #5bc0de; } .label-info[href]:hover, .label-info[href]:focus { background-color: #31b0d5; } .label-warning { background-color: #f0ad4e; } .label-warning[href]:hover, .label-warning[href]:focus { background-color: #ec971f; } .label-danger { background-color: #d9534f; } .label-danger[href]:hover, .label-danger[href]:focus { background-color: #c9302c; } .badge { display: inline-block; min-width: 10px; padding: 3px 7px; font-size: 12px; font-weight: bold; color: #fff; line-height: 1; vertical-align: middle; white-space: nowrap; text-align: center; background-color: #777777; border-radius: 10px; } .badge:empty { display: none; } .btn .badge { position: relative; top: -1px; } .btn-xs .badge, .btn-group-xs > .btn .badge { top: 0; padding: 1px 5px; } a.badge:hover, a.badge:focus { color: #fff; text-decoration: none; cursor: pointer; } .list-group-item.active > .badge, .nav-pills > .active > a > .badge { color: #337ab7; background-color: #fff; } .list-group-item > .badge { float: right; } .list-group-item > .badge + .badge { margin-right: 5px; } .nav-pills > li > a > .badge { margin-left: 3px; } .jumbotron { padding-top: 30px; padding-bottom: 30px; margin-bottom: 30px; color: inherit; background-color: #eeeeee; } .jumbotron h1, .jumbotron .h1 { color: inherit; } .jumbotron p { margin-bottom: 15px; font-size: 20px; font-weight: 200; } .jumbotron > hr { border-top-color: #d5d5d5; } .container .jumbotron, .container-fluid .jumbotron { border-radius: 3px; padding-left: 0px; padding-right: 0px; } .jumbotron .container { max-width: 100%; } @media screen and (min-width: 768px) { .jumbotron { padding-top: 48px; padding-bottom: 48px; } .container .jumbotron, .container-fluid .jumbotron { padding-left: 60px; padding-right: 60px; } .jumbotron h1, .jumbotron .h1 { font-size: 59px; } } .thumbnail { display: block; padding: 4px; margin-bottom: 18px; line-height: 1.42857143; background-color: #fff; border: 1px solid #ddd; border-radius: 2px; -webkit-transition: border 0.2s ease-in-out; -o-transition: border 0.2s ease-in-out; transition: border 0.2s ease-in-out; } .thumbnail > img, .thumbnail a > img { margin-left: auto; margin-right: auto; } a.thumbnail:hover, a.thumbnail:focus, a.thumbnail.active { border-color: #337ab7; } .thumbnail .caption { padding: 9px; color: #000; } .alert { padding: 15px; margin-bottom: 18px; border: 1px solid transparent; border-radius: 2px; } .alert h4 { margin-top: 0; color: inherit; } .alert .alert-link { font-weight: bold; } .alert > p, .alert > ul { margin-bottom: 0; } .alert > p + p { margin-top: 5px; } .alert-dismissable, .alert-dismissible { padding-right: 35px; } .alert-dismissable .close, .alert-dismissible .close { position: relative; top: -2px; right: -21px; color: inherit; } .alert-success { background-color: #dff0d8; border-color: #d6e9c6; color: #3c763d; } .alert-success hr { border-top-color: #c9e2b3; } .alert-success .alert-link { color: #2b542c; } .alert-info { background-color: #d9edf7; border-color: #bce8f1; color: #31708f; } .alert-info hr { border-top-color: #a6e1ec; } .alert-info .alert-link { color: #245269; } .alert-warning { background-color: #fcf8e3; border-color: #faebcc; color: #8a6d3b; } .alert-warning hr { border-top-color: #f7e1b5; } .alert-warning .alert-link { color: #66512c; } .alert-danger { background-color: #f2dede; border-color: #ebccd1; color: #a94442; } .alert-danger hr { border-top-color: #e4b9c0; } .alert-danger .alert-link { color: #843534; } @-webkit-keyframes progress-bar-stripes { from { background-position: 40px 0; } to { background-position: 0 0; } } @keyframes progress-bar-stripes { from { background-position: 40px 0; } to { background-position: 0 0; } } .progress { overflow: hidden; height: 18px; margin-bottom: 18px; background-color: #f5f5f5; border-radius: 2px; -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); } .progress-bar { float: left; width: 0%; height: 100%; font-size: 12px; line-height: 18px; color: #fff; text-align: center; background-color: #337ab7; -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); -webkit-transition: width 0.6s ease; -o-transition: width 0.6s ease; transition: width 0.6s ease; } .progress-striped .progress-bar, .progress-bar-striped { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-size: 40px 40px; } .progress.active .progress-bar, .progress-bar.active { -webkit-animation: progress-bar-stripes 2s linear infinite; -o-animation: progress-bar-stripes 2s linear infinite; animation: progress-bar-stripes 2s linear infinite; } .progress-bar-success { background-color: #5cb85c; } .progress-striped .progress-bar-success { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-info { background-color: #5bc0de; } .progress-striped .progress-bar-info { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-warning { background-color: #f0ad4e; } .progress-striped .progress-bar-warning { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .progress-bar-danger { background-color: #d9534f; } .progress-striped .progress-bar-danger { background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); } .media { margin-top: 15px; } .media:first-child { margin-top: 0; } .media, .media-body { zoom: 1; overflow: hidden; } .media-body { width: 10000px; } .media-object { display: block; } .media-object.img-thumbnail { max-width: none; } .media-right, .media > .pull-right { padding-left: 10px; } .media-left, .media > .pull-left { padding-right: 10px; } .media-left, .media-right, .media-body { display: table-cell; vertical-align: top; } .media-middle { vertical-align: middle; } .media-bottom { vertical-align: bottom; } .media-heading { margin-top: 0; margin-bottom: 5px; } .media-list { padding-left: 0; list-style: none; } .list-group { margin-bottom: 20px; padding-left: 0; } .list-group-item { position: relative; display: block; padding: 10px 15px; margin-bottom: -1px; background-color: #fff; border: 1px solid #ddd; } .list-group-item:first-child { border-top-right-radius: 2px; border-top-left-radius: 2px; } .list-group-item:last-child { margin-bottom: 0; border-bottom-right-radius: 2px; border-bottom-left-radius: 2px; } a.list-group-item, button.list-group-item { color: #555; } a.list-group-item .list-group-item-heading, button.list-group-item .list-group-item-heading { color: #333; } a.list-group-item:hover, button.list-group-item:hover, a.list-group-item:focus, button.list-group-item:focus { text-decoration: none; color: #555; background-color: #f5f5f5; } button.list-group-item { width: 100%; text-align: left; } .list-group-item.disabled, .list-group-item.disabled:hover, .list-group-item.disabled:focus { background-color: #eeeeee; color: #777777; cursor: not-allowed; } .list-group-item.disabled .list-group-item-heading, .list-group-item.disabled:hover .list-group-item-heading, .list-group-item.disabled:focus .list-group-item-heading { color: inherit; } .list-group-item.disabled .list-group-item-text, .list-group-item.disabled:hover .list-group-item-text, .list-group-item.disabled:focus .list-group-item-text { color: #777777; } .list-group-item.active, .list-group-item.active:hover, .list-group-item.active:focus { z-index: 2; color: #fff; background-color: #337ab7; border-color: #337ab7; } .list-group-item.active .list-group-item-heading, .list-group-item.active:hover .list-group-item-heading, .list-group-item.active:focus .list-group-item-heading, .list-group-item.active .list-group-item-heading > small, .list-group-item.active:hover .list-group-item-heading > small, .list-group-item.active:focus .list-group-item-heading > small, .list-group-item.active .list-group-item-heading > .small, .list-group-item.active:hover .list-group-item-heading > .small, .list-group-item.active:focus .list-group-item-heading > .small { color: inherit; } .list-group-item.active .list-group-item-text, .list-group-item.active:hover .list-group-item-text, .list-group-item.active:focus .list-group-item-text { color: #c7ddef; } .list-group-item-success { color: #3c763d; background-color: #dff0d8; } a.list-group-item-success, button.list-group-item-success { color: #3c763d; } a.list-group-item-success .list-group-item-heading, button.list-group-item-success .list-group-item-heading { color: inherit; } a.list-group-item-success:hover, button.list-group-item-success:hover, a.list-group-item-success:focus, button.list-group-item-success:focus { color: #3c763d; background-color: #d0e9c6; } a.list-group-item-success.active, button.list-group-item-success.active, a.list-group-item-success.active:hover, button.list-group-item-success.active:hover, a.list-group-item-success.active:focus, button.list-group-item-success.active:focus { color: #fff; background-color: #3c763d; border-color: #3c763d; } .list-group-item-info { color: #31708f; background-color: #d9edf7; } a.list-group-item-info, button.list-group-item-info { color: #31708f; } a.list-group-item-info .list-group-item-heading, button.list-group-item-info .list-group-item-heading { color: inherit; } a.list-group-item-info:hover, button.list-group-item-info:hover, a.list-group-item-info:focus, button.list-group-item-info:focus { color: #31708f; background-color: #c4e3f3; } a.list-group-item-info.active, button.list-group-item-info.active, a.list-group-item-info.active:hover, button.list-group-item-info.active:hover, a.list-group-item-info.active:focus, button.list-group-item-info.active:focus { color: #fff; background-color: #31708f; border-color: #31708f; } .list-group-item-warning { color: #8a6d3b; background-color: #fcf8e3; } a.list-group-item-warning, button.list-group-item-warning { color: #8a6d3b; } a.list-group-item-warning .list-group-item-heading, button.list-group-item-warning .list-group-item-heading { color: inherit; } a.list-group-item-warning:hover, button.list-group-item-warning:hover, a.list-group-item-warning:focus, button.list-group-item-warning:focus { color: #8a6d3b; background-color: #faf2cc; } a.list-group-item-warning.active, button.list-group-item-warning.active, a.list-group-item-warning.active:hover, button.list-group-item-warning.active:hover, a.list-group-item-warning.active:focus, button.list-group-item-warning.active:focus { color: #fff; background-color: #8a6d3b; border-color: #8a6d3b; } .list-group-item-danger { color: #a94442; background-color: #f2dede; } a.list-group-item-danger, button.list-group-item-danger { color: #a94442; } a.list-group-item-danger .list-group-item-heading, button.list-group-item-danger .list-group-item-heading { color: inherit; } a.list-group-item-danger:hover, button.list-group-item-danger:hover, a.list-group-item-danger:focus, button.list-group-item-danger:focus { color: #a94442; background-color: #ebcccc; } a.list-group-item-danger.active, button.list-group-item-danger.active, a.list-group-item-danger.active:hover, button.list-group-item-danger.active:hover, a.list-group-item-danger.active:focus, button.list-group-item-danger.active:focus { color: #fff; background-color: #a94442; border-color: #a94442; } .list-group-item-heading { margin-top: 0; margin-bottom: 5px; } .list-group-item-text { margin-bottom: 0; line-height: 1.3; } .panel { margin-bottom: 18px; background-color: #fff; border: 1px solid transparent; border-radius: 2px; -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); } .panel-body { padding: 15px; } .panel-heading { padding: 10px 15px; border-bottom: 1px solid transparent; border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel-heading > .dropdown .dropdown-toggle { color: inherit; } .panel-title { margin-top: 0; margin-bottom: 0; font-size: 15px; color: inherit; } .panel-title > a, .panel-title > small, .panel-title > .small, .panel-title > small > a, .panel-title > .small > a { color: inherit; } .panel-footer { padding: 10px 15px; background-color: #f5f5f5; border-top: 1px solid #ddd; border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .list-group, .panel > .panel-collapse > .list-group { margin-bottom: 0; } .panel > .list-group .list-group-item, .panel > .panel-collapse > .list-group .list-group-item { border-width: 1px 0; border-radius: 0; } .panel > .list-group:first-child .list-group-item:first-child, .panel > .panel-collapse > .list-group:first-child .list-group-item:first-child { border-top: 0; border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel > .list-group:last-child .list-group-item:last-child, .panel > .panel-collapse > .list-group:last-child .list-group-item:last-child { border-bottom: 0; border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child { border-top-right-radius: 0; border-top-left-radius: 0; } .panel-heading + .list-group .list-group-item:first-child { border-top-width: 0; } .list-group + .panel-footer { border-top-width: 0; } .panel > .table, .panel > .table-responsive > .table, .panel > .panel-collapse > .table { margin-bottom: 0; } .panel > .table caption, .panel > .table-responsive > .table caption, .panel > .panel-collapse > .table caption { padding-left: 15px; padding-right: 15px; } .panel > .table:first-child, .panel > .table-responsive:first-child > .table:first-child { border-top-right-radius: 1px; border-top-left-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child { border-top-left-radius: 1px; border-top-right-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child td:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child td:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child, .panel > .table:first-child > thead:first-child > tr:first-child th:first-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child, .panel > .table:first-child > tbody:first-child > tr:first-child th:first-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child { border-top-left-radius: 1px; } .panel > .table:first-child > thead:first-child > tr:first-child td:last-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child, .panel > .table:first-child > tbody:first-child > tr:first-child td:last-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child, .panel > .table:first-child > thead:first-child > tr:first-child th:last-child, .panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child, .panel > .table:first-child > tbody:first-child > tr:first-child th:last-child, .panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child { border-top-right-radius: 1px; } .panel > .table:last-child, .panel > .table-responsive:last-child > .table:last-child { border-bottom-right-radius: 1px; border-bottom-left-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child { border-bottom-left-radius: 1px; border-bottom-right-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child td:first-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child, .panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child, .panel > .table:last-child > tbody:last-child > tr:last-child th:first-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child, .panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child { border-bottom-left-radius: 1px; } .panel > .table:last-child > tbody:last-child > tr:last-child td:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child, .panel > .table:last-child > tbody:last-child > tr:last-child th:last-child, .panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child, .panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child, .panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child { border-bottom-right-radius: 1px; } .panel > .panel-body + .table, .panel > .panel-body + .table-responsive, .panel > .table + .panel-body, .panel > .table-responsive + .panel-body { border-top: 1px solid #ddd; } .panel > .table > tbody:first-child > tr:first-child th, .panel > .table > tbody:first-child > tr:first-child td { border-top: 0; } .panel > .table-bordered, .panel > .table-responsive > .table-bordered { border: 0; } .panel > .table-bordered > thead > tr > th:first-child, .panel > .table-responsive > .table-bordered > thead > tr > th:first-child, .panel > .table-bordered > tbody > tr > th:first-child, .panel > .table-responsive > .table-bordered > tbody > tr > th:first-child, .panel > .table-bordered > tfoot > tr > th:first-child, .panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child, .panel > .table-bordered > thead > tr > td:first-child, .panel > .table-responsive > .table-bordered > thead > tr > td:first-child, .panel > .table-bordered > tbody > tr > td:first-child, .panel > .table-responsive > .table-bordered > tbody > tr > td:first-child, .panel > .table-bordered > tfoot > tr > td:first-child, .panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child { border-left: 0; } .panel > .table-bordered > thead > tr > th:last-child, .panel > .table-responsive > .table-bordered > thead > tr > th:last-child, .panel > .table-bordered > tbody > tr > th:last-child, .panel > .table-responsive > .table-bordered > tbody > tr > th:last-child, .panel > .table-bordered > tfoot > tr > th:last-child, .panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child, .panel > .table-bordered > thead > tr > td:last-child, .panel > .table-responsive > .table-bordered > thead > tr > td:last-child, .panel > .table-bordered > tbody > tr > td:last-child, .panel > .table-responsive > .table-bordered > tbody > tr > td:last-child, .panel > .table-bordered > tfoot > tr > td:last-child, .panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child { border-right: 0; } .panel > .table-bordered > thead > tr:first-child > td, .panel > .table-responsive > .table-bordered > thead > tr:first-child > td, .panel > .table-bordered > tbody > tr:first-child > td, .panel > .table-responsive > .table-bordered > tbody > tr:first-child > td, .panel > .table-bordered > thead > tr:first-child > th, .panel > .table-responsive > .table-bordered > thead > tr:first-child > th, .panel > .table-bordered > tbody > tr:first-child > th, .panel > .table-responsive > .table-bordered > tbody > tr:first-child > th { border-bottom: 0; } .panel > .table-bordered > tbody > tr:last-child > td, .panel > .table-responsive > .table-bordered > tbody > tr:last-child > td, .panel > .table-bordered > tfoot > tr:last-child > td, .panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td, .panel > .table-bordered > tbody > tr:last-child > th, .panel > .table-responsive > .table-bordered > tbody > tr:last-child > th, .panel > .table-bordered > tfoot > tr:last-child > th, .panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th { border-bottom: 0; } .panel > .table-responsive { border: 0; margin-bottom: 0; } .panel-group { margin-bottom: 18px; } .panel-group .panel { margin-bottom: 0; border-radius: 2px; } .panel-group .panel + .panel { margin-top: 5px; } .panel-group .panel-heading { border-bottom: 0; } .panel-group .panel-heading + .panel-collapse > .panel-body, .panel-group .panel-heading + .panel-collapse > .list-group { border-top: 1px solid #ddd; } .panel-group .panel-footer { border-top: 0; } .panel-group .panel-footer + .panel-collapse .panel-body { border-bottom: 1px solid #ddd; } .panel-default { border-color: #ddd; } .panel-default > .panel-heading { color: #333333; background-color: #f5f5f5; border-color: #ddd; } .panel-default > .panel-heading + .panel-collapse > .panel-body { border-top-color: #ddd; } .panel-default > .panel-heading .badge { color: #f5f5f5; background-color: #333333; } .panel-default > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #ddd; } .panel-primary { border-color: #337ab7; } .panel-primary > .panel-heading { color: #fff; background-color: #337ab7; border-color: #337ab7; } .panel-primary > .panel-heading + .panel-collapse > .panel-body { border-top-color: #337ab7; } .panel-primary > .panel-heading .badge { color: #337ab7; background-color: #fff; } .panel-primary > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #337ab7; } .panel-success { border-color: #d6e9c6; } .panel-success > .panel-heading { color: #3c763d; background-color: #dff0d8; border-color: #d6e9c6; } .panel-success > .panel-heading + .panel-collapse > .panel-body { border-top-color: #d6e9c6; } .panel-success > .panel-heading .badge { color: #dff0d8; background-color: #3c763d; } .panel-success > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #d6e9c6; } .panel-info { border-color: #bce8f1; } .panel-info > .panel-heading { color: #31708f; background-color: #d9edf7; border-color: #bce8f1; } .panel-info > .panel-heading + .panel-collapse > .panel-body { border-top-color: #bce8f1; } .panel-info > .panel-heading .badge { color: #d9edf7; background-color: #31708f; } .panel-info > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #bce8f1; } .panel-warning { border-color: #faebcc; } .panel-warning > .panel-heading { color: #8a6d3b; background-color: #fcf8e3; border-color: #faebcc; } .panel-warning > .panel-heading + .panel-collapse > .panel-body { border-top-color: #faebcc; } .panel-warning > .panel-heading .badge { color: #fcf8e3; background-color: #8a6d3b; } .panel-warning > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #faebcc; } .panel-danger { border-color: #ebccd1; } .panel-danger > .panel-heading { color: #a94442; background-color: #f2dede; border-color: #ebccd1; } .panel-danger > .panel-heading + .panel-collapse > .panel-body { border-top-color: #ebccd1; } .panel-danger > .panel-heading .badge { color: #f2dede; background-color: #a94442; } .panel-danger > .panel-footer + .panel-collapse > .panel-body { border-bottom-color: #ebccd1; } .embed-responsive { position: relative; display: block; height: 0; padding: 0; overflow: hidden; } .embed-responsive .embed-responsive-item, .embed-responsive iframe, .embed-responsive embed, .embed-responsive object, .embed-responsive video { position: absolute; top: 0; left: 0; bottom: 0; height: 100%; width: 100%; border: 0; } .embed-responsive-16by9 { padding-bottom: 56.25%; } .embed-responsive-4by3 { padding-bottom: 75%; } .well { min-height: 20px; padding: 19px; margin-bottom: 20px; background-color: #f5f5f5; border: 1px solid #e3e3e3; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); } .well blockquote { border-color: #ddd; border-color: rgba(0, 0, 0, 0.15); } .well-lg { padding: 24px; border-radius: 3px; } .well-sm { padding: 9px; border-radius: 1px; } .close { float: right; font-size: 19.5px; font-weight: bold; line-height: 1; color: #000; text-shadow: 0 1px 0 #fff; opacity: 0.2; filter: alpha(opacity=20); } .close:hover, .close:focus { color: #000; text-decoration: none; cursor: pointer; opacity: 0.5; filter: alpha(opacity=50); } button.close { padding: 0; cursor: pointer; background: transparent; border: 0; -webkit-appearance: none; } .modal-open { overflow: hidden; } .modal { display: none; overflow: hidden; position: fixed; top: 0; right: 0; bottom: 0; left: 0; z-index: 1050; -webkit-overflow-scrolling: touch; outline: 0; } .modal.fade .modal-dialog { -webkit-transform: translate(0, -25%); -ms-transform: translate(0, -25%); -o-transform: translate(0, -25%); transform: translate(0, -25%); -webkit-transition: -webkit-transform 0.3s ease-out; -moz-transition: -moz-transform 0.3s ease-out; -o-transition: -o-transform 0.3s ease-out; transition: transform 0.3s ease-out; } .modal.in .modal-dialog { -webkit-transform: translate(0, 0); -ms-transform: translate(0, 0); -o-transform: translate(0, 0); transform: translate(0, 0); } .modal-open .modal { overflow-x: hidden; overflow-y: auto; } .modal-dialog { position: relative; width: auto; margin: 10px; } .modal-content { position: relative; background-color: #fff; border: 1px solid #999; border: 1px solid rgba(0, 0, 0, 0.2); border-radius: 3px; -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); background-clip: padding-box; outline: 0; } .modal-backdrop { position: fixed; top: 0; right: 0; bottom: 0; left: 0; z-index: 1040; background-color: #000; } .modal-backdrop.fade { opacity: 0; filter: alpha(opacity=0); } .modal-backdrop.in { opacity: 0.5; filter: alpha(opacity=50); } .modal-header { padding: 15px; border-bottom: 1px solid #e5e5e5; } .modal-header .close { margin-top: -2px; } .modal-title { margin: 0; line-height: 1.42857143; } .modal-body { position: relative; padding: 15px; } .modal-footer { padding: 15px; text-align: right; border-top: 1px solid #e5e5e5; } .modal-footer .btn + .btn { margin-left: 5px; margin-bottom: 0; } .modal-footer .btn-group .btn + .btn { margin-left: -1px; } .modal-footer .btn-block + .btn-block { margin-left: 0; } .modal-scrollbar-measure { position: absolute; top: -9999px; width: 50px; height: 50px; overflow: scroll; } @media (min-width: 768px) { .modal-dialog { width: 600px; margin: 30px auto; } .modal-content { -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); } .modal-sm { width: 300px; } } @media (min-width: 992px) { .modal-lg { width: 900px; } } .tooltip { position: absolute; z-index: 1070; display: block; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-style: normal; font-weight: normal; letter-spacing: normal; line-break: auto; line-height: 1.42857143; text-align: left; text-align: start; text-decoration: none; text-shadow: none; text-transform: none; white-space: normal; word-break: normal; word-spacing: normal; word-wrap: normal; font-size: 12px; opacity: 0; filter: alpha(opacity=0); } .tooltip.in { opacity: 0.9; filter: alpha(opacity=90); } .tooltip.top { margin-top: -3px; padding: 5px 0; } .tooltip.right { margin-left: 3px; padding: 0 5px; } .tooltip.bottom { margin-top: 3px; padding: 5px 0; } .tooltip.left { margin-left: -3px; padding: 0 5px; } .tooltip-inner { max-width: 200px; padding: 3px 8px; color: #fff; text-align: center; background-color: #000; border-radius: 2px; } .tooltip-arrow { position: absolute; width: 0; height: 0; border-color: transparent; border-style: solid; } .tooltip.top .tooltip-arrow { bottom: 0; left: 50%; margin-left: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.top-left .tooltip-arrow { bottom: 0; right: 5px; margin-bottom: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.top-right .tooltip-arrow { bottom: 0; left: 5px; margin-bottom: -5px; border-width: 5px 5px 0; border-top-color: #000; } .tooltip.right .tooltip-arrow { top: 50%; left: 0; margin-top: -5px; border-width: 5px 5px 5px 0; border-right-color: #000; } .tooltip.left .tooltip-arrow { top: 50%; right: 0; margin-top: -5px; border-width: 5px 0 5px 5px; border-left-color: #000; } .tooltip.bottom .tooltip-arrow { top: 0; left: 50%; margin-left: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .tooltip.bottom-left .tooltip-arrow { top: 0; right: 5px; margin-top: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .tooltip.bottom-right .tooltip-arrow { top: 0; left: 5px; margin-top: -5px; border-width: 0 5px 5px; border-bottom-color: #000; } .popover { position: absolute; top: 0; left: 0; z-index: 1060; display: none; max-width: 276px; padding: 1px; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; font-style: normal; font-weight: normal; letter-spacing: normal; line-break: auto; line-height: 1.42857143; text-align: left; text-align: start; text-decoration: none; text-shadow: none; text-transform: none; white-space: normal; word-break: normal; word-spacing: normal; word-wrap: normal; font-size: 13px; background-color: #fff; background-clip: padding-box; border: 1px solid #ccc; border: 1px solid rgba(0, 0, 0, 0.2); border-radius: 3px; -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); } .popover.top { margin-top: -10px; } .popover.right { margin-left: 10px; } .popover.bottom { margin-top: 10px; } .popover.left { margin-left: -10px; } .popover-title { margin: 0; padding: 8px 14px; font-size: 13px; background-color: #f7f7f7; border-bottom: 1px solid #ebebeb; border-radius: 2px 2px 0 0; } .popover-content { padding: 9px 14px; } .popover > .arrow, .popover > .arrow:after { position: absolute; display: block; width: 0; height: 0; border-color: transparent; border-style: solid; } .popover > .arrow { border-width: 11px; } .popover > .arrow:after { border-width: 10px; content: ""; } .popover.top > .arrow { left: 50%; margin-left: -11px; border-bottom-width: 0; border-top-color: #999999; border-top-color: rgba(0, 0, 0, 0.25); bottom: -11px; } .popover.top > .arrow:after { content: " "; bottom: 1px; margin-left: -10px; border-bottom-width: 0; border-top-color: #fff; } .popover.right > .arrow { top: 50%; left: -11px; margin-top: -11px; border-left-width: 0; border-right-color: #999999; border-right-color: rgba(0, 0, 0, 0.25); } .popover.right > .arrow:after { content: " "; left: 1px; bottom: -10px; border-left-width: 0; border-right-color: #fff; } .popover.bottom > .arrow { left: 50%; margin-left: -11px; border-top-width: 0; border-bottom-color: #999999; border-bottom-color: rgba(0, 0, 0, 0.25); top: -11px; } .popover.bottom > .arrow:after { content: " "; top: 1px; margin-left: -10px; border-top-width: 0; border-bottom-color: #fff; } .popover.left > .arrow { top: 50%; right: -11px; margin-top: -11px; border-right-width: 0; border-left-color: #999999; border-left-color: rgba(0, 0, 0, 0.25); } .popover.left > .arrow:after { content: " "; right: 1px; border-right-width: 0; border-left-color: #fff; bottom: -10px; } .carousel { position: relative; } .carousel-inner { position: relative; overflow: hidden; width: 100%; } .carousel-inner > .item { display: none; position: relative; -webkit-transition: 0.6s ease-in-out left; -o-transition: 0.6s ease-in-out left; transition: 0.6s ease-in-out left; } .carousel-inner > .item > img, .carousel-inner > .item > a > img { line-height: 1; } @media all and (transform-3d), (-webkit-transform-3d) { .carousel-inner > .item { -webkit-transition: -webkit-transform 0.6s ease-in-out; -moz-transition: -moz-transform 0.6s ease-in-out; -o-transition: -o-transform 0.6s ease-in-out; transition: transform 0.6s ease-in-out; -webkit-backface-visibility: hidden; -moz-backface-visibility: hidden; backface-visibility: hidden; -webkit-perspective: 1000px; -moz-perspective: 1000px; perspective: 1000px; } .carousel-inner > .item.next, .carousel-inner > .item.active.right { -webkit-transform: translate3d(100%, 0, 0); transform: translate3d(100%, 0, 0); left: 0; } .carousel-inner > .item.prev, .carousel-inner > .item.active.left { -webkit-transform: translate3d(-100%, 0, 0); transform: translate3d(-100%, 0, 0); left: 0; } .carousel-inner > .item.next.left, .carousel-inner > .item.prev.right, .carousel-inner > .item.active { -webkit-transform: translate3d(0, 0, 0); transform: translate3d(0, 0, 0); left: 0; } } .carousel-inner > .active, .carousel-inner > .next, .carousel-inner > .prev { display: block; } .carousel-inner > .active { left: 0; } .carousel-inner > .next, .carousel-inner > .prev { position: absolute; top: 0; width: 100%; } .carousel-inner > .next { left: 100%; } .carousel-inner > .prev { left: -100%; } .carousel-inner > .next.left, .carousel-inner > .prev.right { left: 0; } .carousel-inner > .active.left { left: -100%; } .carousel-inner > .active.right { left: 100%; } .carousel-control { position: absolute; top: 0; left: 0; bottom: 0; width: 15%; opacity: 0.5; filter: alpha(opacity=50); font-size: 20px; color: #fff; text-align: center; text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); background-color: rgba(0, 0, 0, 0); } .carousel-control.left { background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); background-repeat: repeat-x; filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1); } .carousel-control.right { left: auto; right: 0; background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); background-repeat: repeat-x; filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1); } .carousel-control:hover, .carousel-control:focus { outline: 0; color: #fff; text-decoration: none; opacity: 0.9; filter: alpha(opacity=90); } .carousel-control .icon-prev, .carousel-control .icon-next, .carousel-control .glyphicon-chevron-left, .carousel-control .glyphicon-chevron-right { position: absolute; top: 50%; margin-top: -10px; z-index: 5; display: inline-block; } .carousel-control .icon-prev, .carousel-control .glyphicon-chevron-left { left: 50%; margin-left: -10px; } .carousel-control .icon-next, .carousel-control .glyphicon-chevron-right { right: 50%; margin-right: -10px; } .carousel-control .icon-prev, .carousel-control .icon-next { width: 20px; height: 20px; line-height: 1; font-family: serif; } .carousel-control .icon-prev:before { content: '\2039'; } .carousel-control .icon-next:before { content: '\203a'; } .carousel-indicators { position: absolute; bottom: 10px; left: 50%; z-index: 15; width: 60%; margin-left: -30%; padding-left: 0; list-style: none; text-align: center; } .carousel-indicators li { display: inline-block; width: 10px; height: 10px; margin: 1px; text-indent: -999px; border: 1px solid #fff; border-radius: 10px; cursor: pointer; background-color: #000 \9; background-color: rgba(0, 0, 0, 0); } .carousel-indicators .active { margin: 0; width: 12px; height: 12px; background-color: #fff; } .carousel-caption { position: absolute; left: 15%; right: 15%; bottom: 20px; z-index: 10; padding-top: 20px; padding-bottom: 20px; color: #fff; text-align: center; text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); } .carousel-caption .btn { text-shadow: none; } @media screen and (min-width: 768px) { .carousel-control .glyphicon-chevron-left, .carousel-control .glyphicon-chevron-right, .carousel-control .icon-prev, .carousel-control .icon-next { width: 30px; height: 30px; margin-top: -10px; font-size: 30px; } .carousel-control .glyphicon-chevron-left, .carousel-control .icon-prev { margin-left: -10px; } .carousel-control .glyphicon-chevron-right, .carousel-control .icon-next { margin-right: -10px; } .carousel-caption { left: 20%; right: 20%; padding-bottom: 30px; } .carousel-indicators { bottom: 20px; } } .clearfix:before, .clearfix:after, .dl-horizontal dd:before, .dl-horizontal dd:after, .container:before, .container:after, .container-fluid:before, .container-fluid:after, .row:before, .row:after, .form-horizontal .form-group:before, .form-horizontal .form-group:after, .btn-toolbar:before, .btn-toolbar:after, .btn-group-vertical > .btn-group:before, .btn-group-vertical > .btn-group:after, .nav:before, .nav:after, .navbar:before, .navbar:after, .navbar-header:before, .navbar-header:after, .navbar-collapse:before, .navbar-collapse:after, .pager:before, .pager:after, .panel-body:before, .panel-body:after, .modal-header:before, .modal-header:after, .modal-footer:before, .modal-footer:after, .item\_buttons:before, .item\_buttons:after { content: " "; display: table; } .clearfix:after, .dl-horizontal dd:after, .container:after, .container-fluid:after, .row:after, .form-horizontal .form-group:after, .btn-toolbar:after, .btn-group-vertical > .btn-group:after, .nav:after, .navbar:after, .navbar-header:after, .navbar-collapse:after, .pager:after, .panel-body:after, .modal-header:after, .modal-footer:after, .item\_buttons:after { clear: both; } .center-block { display: block; margin-left: auto; margin-right: auto; } .pull-right { float: right !important; } .pull-left { float: left !important; } .hide { display: none !important; } .show { display: block !important; } .invisible { visibility: hidden; } .text-hide { font: 0/0 a; color: transparent; text-shadow: none; background-color: transparent; border: 0; } .hidden { display: none !important; } .affix { position: fixed; } @-ms-viewport { width: device-width; } .visible-xs, .visible-sm, .visible-md, .visible-lg { display: none !important; } .visible-xs-block, .visible-xs-inline, .visible-xs-inline-block, .visible-sm-block, .visible-sm-inline, .visible-sm-inline-block, .visible-md-block, .visible-md-inline, .visible-md-inline-block, .visible-lg-block, .visible-lg-inline, .visible-lg-inline-block { display: none !important; } @media (max-width: 767px) { .visible-xs { display: block !important; } table.visible-xs { display: table !important; } tr.visible-xs { display: table-row !important; } th.visible-xs, td.visible-xs { display: table-cell !important; } } @media (max-width: 767px) { .visible-xs-block { display: block !important; } } @media (max-width: 767px) { .visible-xs-inline { display: inline !important; } } @media (max-width: 767px) { .visible-xs-inline-block { display: inline-block !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm { display: block !important; } table.visible-sm { display: table !important; } tr.visible-sm { display: table-row !important; } th.visible-sm, td.visible-sm { display: table-cell !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-block { display: block !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-inline { display: inline !important; } } @media (min-width: 768px) and (max-width: 991px) { .visible-sm-inline-block { display: inline-block !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md { display: block !important; } table.visible-md { display: table !important; } tr.visible-md { display: table-row !important; } th.visible-md, td.visible-md { display: table-cell !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-block { display: block !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-inline { display: inline !important; } } @media (min-width: 992px) and (max-width: 1199px) { .visible-md-inline-block { display: inline-block !important; } } @media (min-width: 1200px) { .visible-lg { display: block !important; } table.visible-lg { display: table !important; } tr.visible-lg { display: table-row !important; } th.visible-lg, td.visible-lg { display: table-cell !important; } } @media (min-width: 1200px) { .visible-lg-block { display: block !important; } } @media (min-width: 1200px) { .visible-lg-inline { display: inline !important; } } @media (min-width: 1200px) { .visible-lg-inline-block { display: inline-block !important; } } @media (max-width: 767px) { .hidden-xs { display: none !important; } } @media (min-width: 768px) and (max-width: 991px) { .hidden-sm { display: none !important; } } @media (min-width: 992px) and (max-width: 1199px) { .hidden-md { display: none !important; } } @media (min-width: 1200px) { .hidden-lg { display: none !important; } } .visible-print { display: none !important; } @media print { .visible-print { display: block !important; } table.visible-print { display: table !important; } tr.visible-print { display: table-row !important; } th.visible-print, td.visible-print { display: table-cell !important; } } .visible-print-block { display: none !important; } @media print { .visible-print-block { display: block !important; } } .visible-print-inline { display: none !important; } @media print { .visible-print-inline { display: inline !important; } } .visible-print-inline-block { display: none !important; } @media print { .visible-print-inline-block { display: inline-block !important; } } @media print { .hidden-print { display: none !important; } } /\*! \* \* Font Awesome \* \*/ /\*! \* Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome \* License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License) \*/ /\* FONT PATH \* -------------------------- \*/ @font-face { font-family: 'FontAwesome'; src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0'); src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg'); font-weight: normal; font-style: normal; } .fa { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; } /\* makes the font 33% larger relative to the icon container \*/ .fa-lg { font-size: 1.33333333em; line-height: 0.75em; vertical-align: -15%; } .fa-2x { font-size: 2em; } .fa-3x { font-size: 3em; } .fa-4x { font-size: 4em; } .fa-5x { font-size: 5em; } .fa-fw { width: 1.28571429em; text-align: center; } .fa-ul { padding-left: 0; margin-left: 2.14285714em; list-style-type: none; } .fa-ul > li { position: relative; } .fa-li { position: absolute; left: -2.14285714em; width: 2.14285714em; top: 0.14285714em; text-align: center; } .fa-li.fa-lg { left: -1.85714286em; } .fa-border { padding: .2em .25em .15em; border: solid 0.08em #eee; border-radius: .1em; } .fa-pull-left { float: left; } .fa-pull-right { float: right; } .fa.fa-pull-left { margin-right: .3em; } .fa.fa-pull-right { margin-left: .3em; } /\* Deprecated as of 4.4.0 \*/ .pull-right { float: right; } .pull-left { float: left; } .fa.pull-left { margin-right: .3em; } .fa.pull-right { margin-left: .3em; } .fa-spin { -webkit-animation: fa-spin 2s infinite linear; animation: fa-spin 2s infinite linear; } .fa-pulse { -webkit-animation: fa-spin 1s infinite steps(8); animation: fa-spin 1s infinite steps(8); } @-webkit-keyframes fa-spin { 0% { -webkit-transform: rotate(0deg); transform: rotate(0deg); } 100% { -webkit-transform: rotate(359deg); transform: rotate(359deg); } } @keyframes fa-spin { 0% { -webkit-transform: rotate(0deg); transform: rotate(0deg); } 100% { -webkit-transform: rotate(359deg); transform: rotate(359deg); } } .fa-rotate-90 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)"; -webkit-transform: rotate(90deg); -ms-transform: rotate(90deg); transform: rotate(90deg); } .fa-rotate-180 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)"; -webkit-transform: rotate(180deg); -ms-transform: rotate(180deg); transform: rotate(180deg); } .fa-rotate-270 { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)"; -webkit-transform: rotate(270deg); -ms-transform: rotate(270deg); transform: rotate(270deg); } .fa-flip-horizontal { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)"; -webkit-transform: scale(-1, 1); -ms-transform: scale(-1, 1); transform: scale(-1, 1); } .fa-flip-vertical { -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)"; -webkit-transform: scale(1, -1); -ms-transform: scale(1, -1); transform: scale(1, -1); } :root .fa-rotate-90, :root .fa-rotate-180, :root .fa-rotate-270, :root .fa-flip-horizontal, :root .fa-flip-vertical { filter: none; } .fa-stack { position: relative; display: inline-block; width: 2em; height: 2em; line-height: 2em; vertical-align: middle; } .fa-stack-1x, .fa-stack-2x { position: absolute; left: 0; width: 100%; text-align: center; } .fa-stack-1x { line-height: inherit; } .fa-stack-2x { font-size: 2em; } .fa-inverse { color: #fff; } /\* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen readers do not read off random characters that represent icons \*/ .fa-glass:before { content: "\f000"; } .fa-music:before { content: "\f001"; } .fa-search:before { content: "\f002"; } .fa-envelope-o:before { content: "\f003"; } .fa-heart:before { content: "\f004"; } .fa-star:before { content: "\f005"; } .fa-star-o:before { content: "\f006"; } .fa-user:before { content: "\f007"; } .fa-film:before { content: "\f008"; } .fa-th-large:before { content: "\f009"; } .fa-th:before { content: "\f00a"; } .fa-th-list:before { content: "\f00b"; } .fa-check:before { content: "\f00c"; } .fa-remove:before, .fa-close:before, .fa-times:before { content: "\f00d"; } .fa-search-plus:before { content: "\f00e"; } .fa-search-minus:before { content: "\f010"; } .fa-power-off:before { content: "\f011"; } .fa-signal:before { content: "\f012"; } .fa-gear:before, .fa-cog:before { content: "\f013"; } .fa-trash-o:before { content: "\f014"; } .fa-home:before { content: "\f015"; } .fa-file-o:before { content: "\f016"; } .fa-clock-o:before { content: "\f017"; } .fa-road:before { content: "\f018"; } .fa-download:before { content: "\f019"; } .fa-arrow-circle-o-down:before { content: "\f01a"; } .fa-arrow-circle-o-up:before { content: "\f01b"; } .fa-inbox:before { content: "\f01c"; } .fa-play-circle-o:before { content: "\f01d"; } .fa-rotate-right:before, .fa-repeat:before { content: "\f01e"; } .fa-refresh:before { content: "\f021"; } .fa-list-alt:before { content: "\f022"; } .fa-lock:before { content: "\f023"; } .fa-flag:before { content: "\f024"; } .fa-headphones:before { content: "\f025"; } .fa-volume-off:before { content: "\f026"; } .fa-volume-down:before { content: "\f027"; } .fa-volume-up:before { content: "\f028"; } .fa-qrcode:before { content: "\f029"; } .fa-barcode:before { content: "\f02a"; } .fa-tag:before { content: "\f02b"; } .fa-tags:before { content: "\f02c"; } .fa-book:before { content: "\f02d"; } .fa-bookmark:before { content: "\f02e"; } .fa-print:before { content: "\f02f"; } .fa-camera:before { content: "\f030"; } .fa-font:before { content: "\f031"; } .fa-bold:before { content: "\f032"; } .fa-italic:before { content: "\f033"; } .fa-text-height:before { content: "\f034"; } .fa-text-width:before { content: "\f035"; } .fa-align-left:before { content: "\f036"; } .fa-align-center:before { content: "\f037"; } .fa-align-right:before { content: "\f038"; } .fa-align-justify:before { content: "\f039"; } .fa-list:before { content: "\f03a"; } .fa-dedent:before, .fa-outdent:before { content: "\f03b"; } .fa-indent:before { content: "\f03c"; } .fa-video-camera:before { content: "\f03d"; } .fa-photo:before, .fa-image:before, .fa-picture-o:before { content: "\f03e"; } .fa-pencil:before { content: "\f040"; } .fa-map-marker:before { content: "\f041"; } .fa-adjust:before { content: "\f042"; } .fa-tint:before { content: "\f043"; } .fa-edit:before, .fa-pencil-square-o:before { content: "\f044"; } .fa-share-square-o:before { content: "\f045"; } .fa-check-square-o:before { content: "\f046"; } .fa-arrows:before { content: "\f047"; } .fa-step-backward:before { content: "\f048"; } .fa-fast-backward:before { content: "\f049"; } .fa-backward:before { content: "\f04a"; } .fa-play:before { content: "\f04b"; } .fa-pause:before { content: "\f04c"; } .fa-stop:before { content: "\f04d"; } .fa-forward:before { content: "\f04e"; } .fa-fast-forward:before { content: "\f050"; } .fa-step-forward:before { content: "\f051"; } .fa-eject:before { content: "\f052"; } .fa-chevron-left:before { content: "\f053"; } .fa-chevron-right:before { content: "\f054"; } .fa-plus-circle:before { content: "\f055"; } .fa-minus-circle:before { content: "\f056"; } .fa-times-circle:before { content: "\f057"; } .fa-check-circle:before { content: "\f058"; } .fa-question-circle:before { content: "\f059"; } .fa-info-circle:before { content: "\f05a"; } .fa-crosshairs:before { content: "\f05b"; } .fa-times-circle-o:before { content: "\f05c"; } .fa-check-circle-o:before { content: "\f05d"; } .fa-ban:before { content: "\f05e"; } .fa-arrow-left:before { content: "\f060"; } .fa-arrow-right:before { content: "\f061"; } .fa-arrow-up:before { content: "\f062"; } .fa-arrow-down:before { content: "\f063"; } .fa-mail-forward:before, .fa-share:before { content: "\f064"; } .fa-expand:before { content: "\f065"; } .fa-compress:before { content: "\f066"; } .fa-plus:before { content: "\f067"; } .fa-minus:before { content: "\f068"; } .fa-asterisk:before { content: "\f069"; } .fa-exclamation-circle:before { content: "\f06a"; } .fa-gift:before { content: "\f06b"; } .fa-leaf:before { content: "\f06c"; } .fa-fire:before { content: "\f06d"; } .fa-eye:before { content: "\f06e"; } .fa-eye-slash:before { content: "\f070"; } .fa-warning:before, .fa-exclamation-triangle:before { content: "\f071"; } .fa-plane:before { content: "\f072"; } .fa-calendar:before { content: "\f073"; } .fa-random:before { content: "\f074"; } .fa-comment:before { content: "\f075"; } .fa-magnet:before { content: "\f076"; } .fa-chevron-up:before { content: "\f077"; } .fa-chevron-down:before { content: "\f078"; } .fa-retweet:before { content: "\f079"; } .fa-shopping-cart:before { content: "\f07a"; } .fa-folder:before { content: "\f07b"; } .fa-folder-open:before { content: "\f07c"; } .fa-arrows-v:before { content: "\f07d"; } .fa-arrows-h:before { content: "\f07e"; } .fa-bar-chart-o:before, .fa-bar-chart:before { content: "\f080"; } .fa-twitter-square:before { content: "\f081"; } .fa-facebook-square:before { content: "\f082"; } .fa-camera-retro:before { content: "\f083"; } .fa-key:before { content: "\f084"; } .fa-gears:before, .fa-cogs:before { content: "\f085"; } .fa-comments:before { content: "\f086"; } .fa-thumbs-o-up:before { content: "\f087"; } .fa-thumbs-o-down:before { content: "\f088"; } .fa-star-half:before { content: "\f089"; } .fa-heart-o:before { content: "\f08a"; } .fa-sign-out:before { content: "\f08b"; } .fa-linkedin-square:before { content: "\f08c"; } .fa-thumb-tack:before { content: "\f08d"; } .fa-external-link:before { content: "\f08e"; } .fa-sign-in:before { content: "\f090"; } .fa-trophy:before { content: "\f091"; } .fa-github-square:before { content: "\f092"; } .fa-upload:before { content: "\f093"; } .fa-lemon-o:before { content: "\f094"; } .fa-phone:before { content: "\f095"; } .fa-square-o:before { content: "\f096"; } .fa-bookmark-o:before { content: "\f097"; } .fa-phone-square:before { content: "\f098"; } .fa-twitter:before { content: "\f099"; } .fa-facebook-f:before, .fa-facebook:before { content: "\f09a"; } .fa-github:before { content: "\f09b"; } .fa-unlock:before { content: "\f09c"; } .fa-credit-card:before { content: "\f09d"; } .fa-feed:before, .fa-rss:before { content: "\f09e"; } .fa-hdd-o:before { content: "\f0a0"; } .fa-bullhorn:before { content: "\f0a1"; } .fa-bell:before { content: "\f0f3"; } .fa-certificate:before { content: "\f0a3"; } .fa-hand-o-right:before { content: "\f0a4"; } .fa-hand-o-left:before { content: "\f0a5"; } .fa-hand-o-up:before { content: "\f0a6"; } .fa-hand-o-down:before { content: "\f0a7"; } .fa-arrow-circle-left:before { content: "\f0a8"; } .fa-arrow-circle-right:before { content: "\f0a9"; } .fa-arrow-circle-up:before { content: "\f0aa"; } .fa-arrow-circle-down:before { content: "\f0ab"; } .fa-globe:before { content: "\f0ac"; } .fa-wrench:before { content: "\f0ad"; } .fa-tasks:before { content: "\f0ae"; } .fa-filter:before { content: "\f0b0"; } .fa-briefcase:before { content: "\f0b1"; } .fa-arrows-alt:before { content: "\f0b2"; } .fa-group:before, .fa-users:before { content: "\f0c0"; } .fa-chain:before, .fa-link:before { content: "\f0c1"; } .fa-cloud:before { content: "\f0c2"; } .fa-flask:before { content: "\f0c3"; } .fa-cut:before, .fa-scissors:before { content: "\f0c4"; } .fa-copy:before, .fa-files-o:before { content: "\f0c5"; } .fa-paperclip:before { content: "\f0c6"; } .fa-save:before, .fa-floppy-o:before { content: "\f0c7"; } .fa-square:before { content: "\f0c8"; } .fa-navicon:before, .fa-reorder:before, .fa-bars:before { content: "\f0c9"; } .fa-list-ul:before { content: "\f0ca"; } .fa-list-ol:before { content: "\f0cb"; } .fa-strikethrough:before { content: "\f0cc"; } .fa-underline:before { content: "\f0cd"; } .fa-table:before { content: "\f0ce"; } .fa-magic:before { content: "\f0d0"; } .fa-truck:before { content: "\f0d1"; } .fa-pinterest:before { content: "\f0d2"; } .fa-pinterest-square:before { content: "\f0d3"; } .fa-google-plus-square:before { content: "\f0d4"; } .fa-google-plus:before { content: "\f0d5"; } .fa-money:before { content: "\f0d6"; } .fa-caret-down:before { content: "\f0d7"; } .fa-caret-up:before { content: "\f0d8"; } .fa-caret-left:before { content: "\f0d9"; } .fa-caret-right:before { content: "\f0da"; } .fa-columns:before { content: "\f0db"; } .fa-unsorted:before, .fa-sort:before { content: "\f0dc"; } .fa-sort-down:before, .fa-sort-desc:before { content: "\f0dd"; } .fa-sort-up:before, .fa-sort-asc:before { content: "\f0de"; } .fa-envelope:before { content: "\f0e0"; } .fa-linkedin:before { content: "\f0e1"; } .fa-rotate-left:before, .fa-undo:before { content: "\f0e2"; } .fa-legal:before, .fa-gavel:before { content: "\f0e3"; } .fa-dashboard:before, .fa-tachometer:before { content: "\f0e4"; } .fa-comment-o:before { content: "\f0e5"; } .fa-comments-o:before { content: "\f0e6"; } .fa-flash:before, .fa-bolt:before { content: "\f0e7"; } .fa-sitemap:before { content: "\f0e8"; } .fa-umbrella:before { content: "\f0e9"; } .fa-paste:before, .fa-clipboard:before { content: "\f0ea"; } .fa-lightbulb-o:before { content: "\f0eb"; } .fa-exchange:before { content: "\f0ec"; } .fa-cloud-download:before { content: "\f0ed"; } .fa-cloud-upload:before { content: "\f0ee"; } .fa-user-md:before { content: "\f0f0"; } .fa-stethoscope:before { content: "\f0f1"; } .fa-suitcase:before { content: "\f0f2"; } .fa-bell-o:before { content: "\f0a2"; } .fa-coffee:before { content: "\f0f4"; } .fa-cutlery:before { content: "\f0f5"; } .fa-file-text-o:before { content: "\f0f6"; } .fa-building-o:before { content: "\f0f7"; } .fa-hospital-o:before { content: "\f0f8"; } .fa-ambulance:before { content: "\f0f9"; } .fa-medkit:before { content: "\f0fa"; } .fa-fighter-jet:before { content: "\f0fb"; } .fa-beer:before { content: "\f0fc"; } .fa-h-square:before { content: "\f0fd"; } .fa-plus-square:before { content: "\f0fe"; } .fa-angle-double-left:before { content: "\f100"; } .fa-angle-double-right:before { content: "\f101"; } .fa-angle-double-up:before { content: "\f102"; } .fa-angle-double-down:before { content: "\f103"; } .fa-angle-left:before { content: "\f104"; } .fa-angle-right:before { content: "\f105"; } .fa-angle-up:before { content: "\f106"; } .fa-angle-down:before { content: "\f107"; } .fa-desktop:before { content: "\f108"; } .fa-laptop:before { content: "\f109"; } .fa-tablet:before { content: "\f10a"; } .fa-mobile-phone:before, .fa-mobile:before { content: "\f10b"; } .fa-circle-o:before { content: "\f10c"; } .fa-quote-left:before { content: "\f10d"; } .fa-quote-right:before { content: "\f10e"; } .fa-spinner:before { content: "\f110"; } .fa-circle:before { content: "\f111"; } .fa-mail-reply:before, .fa-reply:before { content: "\f112"; } .fa-github-alt:before { content: "\f113"; } .fa-folder-o:before { content: "\f114"; } .fa-folder-open-o:before { content: "\f115"; } .fa-smile-o:before { content: "\f118"; } .fa-frown-o:before { content: "\f119"; } .fa-meh-o:before { content: "\f11a"; } .fa-gamepad:before { content: "\f11b"; } .fa-keyboard-o:before { content: "\f11c"; } .fa-flag-o:before { content: "\f11d"; } .fa-flag-checkered:before { content: "\f11e"; } .fa-terminal:before { content: "\f120"; } .fa-code:before { content: "\f121"; } .fa-mail-reply-all:before, .fa-reply-all:before { content: "\f122"; } .fa-star-half-empty:before, .fa-star-half-full:before, .fa-star-half-o:before { content: "\f123"; } .fa-location-arrow:before { content: "\f124"; } .fa-crop:before { content: "\f125"; } .fa-code-fork:before { content: "\f126"; } .fa-unlink:before, .fa-chain-broken:before { content: "\f127"; } .fa-question:before { content: "\f128"; } .fa-info:before { content: "\f129"; } .fa-exclamation:before { content: "\f12a"; } .fa-superscript:before { content: "\f12b"; } .fa-subscript:before { content: "\f12c"; } .fa-eraser:before { content: "\f12d"; } .fa-puzzle-piece:before { content: "\f12e"; } .fa-microphone:before { content: "\f130"; } .fa-microphone-slash:before { content: "\f131"; } .fa-shield:before { content: "\f132"; } .fa-calendar-o:before { content: "\f133"; } .fa-fire-extinguisher:before { content: "\f134"; } .fa-rocket:before { content: "\f135"; } .fa-maxcdn:before { content: "\f136"; } .fa-chevron-circle-left:before { content: "\f137"; } .fa-chevron-circle-right:before { content: "\f138"; } .fa-chevron-circle-up:before { content: "\f139"; } .fa-chevron-circle-down:before { content: "\f13a"; } .fa-html5:before { content: "\f13b"; } .fa-css3:before { content: "\f13c"; } .fa-anchor:before { content: "\f13d"; } .fa-unlock-alt:before { content: "\f13e"; } .fa-bullseye:before { content: "\f140"; } .fa-ellipsis-h:before { content: "\f141"; } .fa-ellipsis-v:before { content: "\f142"; } .fa-rss-square:before { content: "\f143"; } .fa-play-circle:before { content: "\f144"; } .fa-ticket:before { content: "\f145"; } .fa-minus-square:before { content: "\f146"; } .fa-minus-square-o:before { content: "\f147"; } .fa-level-up:before { content: "\f148"; } .fa-level-down:before { content: "\f149"; } .fa-check-square:before { content: "\f14a"; } .fa-pencil-square:before { content: "\f14b"; } .fa-external-link-square:before { content: "\f14c"; } .fa-share-square:before { content: "\f14d"; } .fa-compass:before { content: "\f14e"; } .fa-toggle-down:before, .fa-caret-square-o-down:before { content: "\f150"; } .fa-toggle-up:before, .fa-caret-square-o-up:before { content: "\f151"; } .fa-toggle-right:before, .fa-caret-square-o-right:before { content: "\f152"; } .fa-euro:before, .fa-eur:before { content: "\f153"; } .fa-gbp:before { content: "\f154"; } .fa-dollar:before, .fa-usd:before { content: "\f155"; } .fa-rupee:before, .fa-inr:before { content: "\f156"; } .fa-cny:before, .fa-rmb:before, .fa-yen:before, .fa-jpy:before { content: "\f157"; } .fa-ruble:before, .fa-rouble:before, .fa-rub:before { content: "\f158"; } .fa-won:before, .fa-krw:before { content: "\f159"; } .fa-bitcoin:before, .fa-btc:before { content: "\f15a"; } .fa-file:before { content: "\f15b"; } .fa-file-text:before { content: "\f15c"; } .fa-sort-alpha-asc:before { content: "\f15d"; } .fa-sort-alpha-desc:before { content: "\f15e"; } .fa-sort-amount-asc:before { content: "\f160"; } .fa-sort-amount-desc:before { content: "\f161"; } .fa-sort-numeric-asc:before { content: "\f162"; } .fa-sort-numeric-desc:before { content: "\f163"; } .fa-thumbs-up:before { content: "\f164"; } .fa-thumbs-down:before { content: "\f165"; } .fa-youtube-square:before { content: "\f166"; } .fa-youtube:before { content: "\f167"; } .fa-xing:before { content: "\f168"; } .fa-xing-square:before { content: "\f169"; } .fa-youtube-play:before { content: "\f16a"; } .fa-dropbox:before { content: "\f16b"; } .fa-stack-overflow:before { content: "\f16c"; } .fa-instagram:before { content: "\f16d"; } .fa-flickr:before { content: "\f16e"; } .fa-adn:before { content: "\f170"; } .fa-bitbucket:before { content: "\f171"; } .fa-bitbucket-square:before { content: "\f172"; } .fa-tumblr:before { content: "\f173"; } .fa-tumblr-square:before { content: "\f174"; } .fa-long-arrow-down:before { content: "\f175"; } .fa-long-arrow-up:before { content: "\f176"; } .fa-long-arrow-left:before { content: "\f177"; } .fa-long-arrow-right:before { content: "\f178"; } .fa-apple:before { content: "\f179"; } .fa-windows:before { content: "\f17a"; } .fa-android:before { content: "\f17b"; } .fa-linux:before { content: "\f17c"; } .fa-dribbble:before { content: "\f17d"; } .fa-skype:before { content: "\f17e"; } .fa-foursquare:before { content: "\f180"; } .fa-trello:before { content: "\f181"; } .fa-female:before { content: "\f182"; } .fa-male:before { content: "\f183"; } .fa-gittip:before, .fa-gratipay:before { content: "\f184"; } .fa-sun-o:before { content: "\f185"; } .fa-moon-o:before { content: "\f186"; } .fa-archive:before { content: "\f187"; } .fa-bug:before { content: "\f188"; } .fa-vk:before { content: "\f189"; } .fa-weibo:before { content: "\f18a"; } .fa-renren:before { content: "\f18b"; } .fa-pagelines:before { content: "\f18c"; } .fa-stack-exchange:before { content: "\f18d"; } .fa-arrow-circle-o-right:before { content: "\f18e"; } .fa-arrow-circle-o-left:before { content: "\f190"; } .fa-toggle-left:before, .fa-caret-square-o-left:before { content: "\f191"; } .fa-dot-circle-o:before { content: "\f192"; } .fa-wheelchair:before { content: "\f193"; } .fa-vimeo-square:before { content: "\f194"; } .fa-turkish-lira:before, .fa-try:before { content: "\f195"; } .fa-plus-square-o:before { content: "\f196"; } .fa-space-shuttle:before { content: "\f197"; } .fa-slack:before { content: "\f198"; } .fa-envelope-square:before { content: "\f199"; } .fa-wordpress:before { content: "\f19a"; } .fa-openid:before { content: "\f19b"; } .fa-institution:before, .fa-bank:before, .fa-university:before { content: "\f19c"; } .fa-mortar-board:before, .fa-graduation-cap:before { content: "\f19d"; } .fa-yahoo:before { content: "\f19e"; } .fa-google:before { content: "\f1a0"; } .fa-reddit:before { content: "\f1a1"; } .fa-reddit-square:before { content: "\f1a2"; } .fa-stumbleupon-circle:before { content: "\f1a3"; } .fa-stumbleupon:before { content: "\f1a4"; } .fa-delicious:before { content: "\f1a5"; } .fa-digg:before { content: "\f1a6"; } .fa-pied-piper-pp:before { content: "\f1a7"; } .fa-pied-piper-alt:before { content: "\f1a8"; } .fa-drupal:before { content: "\f1a9"; } .fa-joomla:before { content: "\f1aa"; } .fa-language:before { content: "\f1ab"; } .fa-fax:before { content: "\f1ac"; } .fa-building:before { content: "\f1ad"; } .fa-child:before { content: "\f1ae"; } .fa-paw:before { content: "\f1b0"; } .fa-spoon:before { content: "\f1b1"; } .fa-cube:before { content: "\f1b2"; } .fa-cubes:before { content: "\f1b3"; } .fa-behance:before { content: "\f1b4"; } .fa-behance-square:before { content: "\f1b5"; } .fa-steam:before { content: "\f1b6"; } .fa-steam-square:before { content: "\f1b7"; } .fa-recycle:before { content: "\f1b8"; } .fa-automobile:before, .fa-car:before { content: "\f1b9"; } .fa-cab:before, .fa-taxi:before { content: "\f1ba"; } .fa-tree:before { content: "\f1bb"; } .fa-spotify:before { content: "\f1bc"; } .fa-deviantart:before { content: "\f1bd"; } .fa-soundcloud:before { content: "\f1be"; } .fa-database:before { content: "\f1c0"; } .fa-file-pdf-o:before { content: "\f1c1"; } .fa-file-word-o:before { content: "\f1c2"; } .fa-file-excel-o:before { content: "\f1c3"; } .fa-file-powerpoint-o:before { content: "\f1c4"; } .fa-file-photo-o:before, .fa-file-picture-o:before, .fa-file-image-o:before { content: "\f1c5"; } .fa-file-zip-o:before, .fa-file-archive-o:before { content: "\f1c6"; } .fa-file-sound-o:before, .fa-file-audio-o:before { content: "\f1c7"; } .fa-file-movie-o:before, .fa-file-video-o:before { content: "\f1c8"; } .fa-file-code-o:before { content: "\f1c9"; } .fa-vine:before { content: "\f1ca"; } .fa-codepen:before { content: "\f1cb"; } .fa-jsfiddle:before { content: "\f1cc"; } .fa-life-bouy:before, .fa-life-buoy:before, .fa-life-saver:before, .fa-support:before, .fa-life-ring:before { content: "\f1cd"; } .fa-circle-o-notch:before { content: "\f1ce"; } .fa-ra:before, .fa-resistance:before, .fa-rebel:before { content: "\f1d0"; } .fa-ge:before, .fa-empire:before { content: "\f1d1"; } .fa-git-square:before { content: "\f1d2"; } .fa-git:before { content: "\f1d3"; } .fa-y-combinator-square:before, .fa-yc-square:before, .fa-hacker-news:before { content: "\f1d4"; } .fa-tencent-weibo:before { content: "\f1d5"; } .fa-qq:before { content: "\f1d6"; } .fa-wechat:before, .fa-weixin:before { content: "\f1d7"; } .fa-send:before, .fa-paper-plane:before { content: "\f1d8"; } .fa-send-o:before, .fa-paper-plane-o:before { content: "\f1d9"; } .fa-history:before { content: "\f1da"; } .fa-circle-thin:before { content: "\f1db"; } .fa-header:before { content: "\f1dc"; } .fa-paragraph:before { content: "\f1dd"; } .fa-sliders:before { content: "\f1de"; } .fa-share-alt:before { content: "\f1e0"; } .fa-share-alt-square:before { content: "\f1e1"; } .fa-bomb:before { content: "\f1e2"; } .fa-soccer-ball-o:before, .fa-futbol-o:before { content: "\f1e3"; } .fa-tty:before { content: "\f1e4"; } .fa-binoculars:before { content: "\f1e5"; } .fa-plug:before { content: "\f1e6"; } .fa-slideshare:before { content: "\f1e7"; } .fa-twitch:before { content: "\f1e8"; } .fa-yelp:before { content: "\f1e9"; } .fa-newspaper-o:before { content: "\f1ea"; } .fa-wifi:before { content: "\f1eb"; } .fa-calculator:before { content: "\f1ec"; } .fa-paypal:before { content: "\f1ed"; } .fa-google-wallet:before { content: "\f1ee"; } .fa-cc-visa:before { content: "\f1f0"; } .fa-cc-mastercard:before { content: "\f1f1"; } .fa-cc-discover:before { content: "\f1f2"; } .fa-cc-amex:before { content: "\f1f3"; } .fa-cc-paypal:before { content: "\f1f4"; } .fa-cc-stripe:before { content: "\f1f5"; } .fa-bell-slash:before { content: "\f1f6"; } .fa-bell-slash-o:before { content: "\f1f7"; } .fa-trash:before { content: "\f1f8"; } .fa-copyright:before { content: "\f1f9"; } .fa-at:before { content: "\f1fa"; } .fa-eyedropper:before { content: "\f1fb"; } .fa-paint-brush:before { content: "\f1fc"; } .fa-birthday-cake:before { content: "\f1fd"; } .fa-area-chart:before { content: "\f1fe"; } .fa-pie-chart:before { content: "\f200"; } .fa-line-chart:before { content: "\f201"; } .fa-lastfm:before { content: "\f202"; } .fa-lastfm-square:before { content: "\f203"; } .fa-toggle-off:before { content: "\f204"; } .fa-toggle-on:before { content: "\f205"; } .fa-bicycle:before { content: "\f206"; } .fa-bus:before { content: "\f207"; } .fa-ioxhost:before { content: "\f208"; } .fa-angellist:before { content: "\f209"; } .fa-cc:before { content: "\f20a"; } .fa-shekel:before, .fa-sheqel:before, .fa-ils:before { content: "\f20b"; } .fa-meanpath:before { content: "\f20c"; } .fa-buysellads:before { content: "\f20d"; } .fa-connectdevelop:before { content: "\f20e"; } .fa-dashcube:before { content: "\f210"; } .fa-forumbee:before { content: "\f211"; } .fa-leanpub:before { content: "\f212"; } .fa-sellsy:before { content: "\f213"; } .fa-shirtsinbulk:before { content: "\f214"; } .fa-simplybuilt:before { content: "\f215"; } .fa-skyatlas:before { content: "\f216"; } .fa-cart-plus:before { content: "\f217"; } .fa-cart-arrow-down:before { content: "\f218"; } .fa-diamond:before { content: "\f219"; } .fa-ship:before { content: "\f21a"; } .fa-user-secret:before { content: "\f21b"; } .fa-motorcycle:before { content: "\f21c"; } .fa-street-view:before { content: "\f21d"; } .fa-heartbeat:before { content: "\f21e"; } .fa-venus:before { content: "\f221"; } .fa-mars:before { content: "\f222"; } .fa-mercury:before { content: "\f223"; } .fa-intersex:before, .fa-transgender:before { content: "\f224"; } .fa-transgender-alt:before { content: "\f225"; } .fa-venus-double:before { content: "\f226"; } .fa-mars-double:before { content: "\f227"; } .fa-venus-mars:before { content: "\f228"; } .fa-mars-stroke:before { content: "\f229"; } .fa-mars-stroke-v:before { content: "\f22a"; } .fa-mars-stroke-h:before { content: "\f22b"; } .fa-neuter:before { content: "\f22c"; } .fa-genderless:before { content: "\f22d"; } .fa-facebook-official:before { content: "\f230"; } .fa-pinterest-p:before { content: "\f231"; } .fa-whatsapp:before { content: "\f232"; } .fa-server:before { content: "\f233"; } .fa-user-plus:before { content: "\f234"; } .fa-user-times:before { content: "\f235"; } .fa-hotel:before, .fa-bed:before { content: "\f236"; } .fa-viacoin:before { content: "\f237"; } .fa-train:before { content: "\f238"; } .fa-subway:before { content: "\f239"; } .fa-medium:before { content: "\f23a"; } .fa-yc:before, .fa-y-combinator:before { content: "\f23b"; } .fa-optin-monster:before { content: "\f23c"; } .fa-opencart:before { content: "\f23d"; } .fa-expeditedssl:before { content: "\f23e"; } .fa-battery-4:before, .fa-battery:before, .fa-battery-full:before { content: "\f240"; } .fa-battery-3:before, .fa-battery-three-quarters:before { content: "\f241"; } .fa-battery-2:before, .fa-battery-half:before { content: "\f242"; } .fa-battery-1:before, .fa-battery-quarter:before { content: "\f243"; } .fa-battery-0:before, .fa-battery-empty:before { content: "\f244"; } .fa-mouse-pointer:before { content: "\f245"; } .fa-i-cursor:before { content: "\f246"; } .fa-object-group:before { content: "\f247"; } .fa-object-ungroup:before { content: "\f248"; } .fa-sticky-note:before { content: "\f249"; } .fa-sticky-note-o:before { content: "\f24a"; } .fa-cc-jcb:before { content: "\f24b"; } .fa-cc-diners-club:before { content: "\f24c"; } .fa-clone:before { content: "\f24d"; } .fa-balance-scale:before { content: "\f24e"; } .fa-hourglass-o:before { content: "\f250"; } .fa-hourglass-1:before, .fa-hourglass-start:before { content: "\f251"; } .fa-hourglass-2:before, .fa-hourglass-half:before { content: "\f252"; } .fa-hourglass-3:before, .fa-hourglass-end:before { content: "\f253"; } .fa-hourglass:before { content: "\f254"; } .fa-hand-grab-o:before, .fa-hand-rock-o:before { content: "\f255"; } .fa-hand-stop-o:before, .fa-hand-paper-o:before { content: "\f256"; } .fa-hand-scissors-o:before { content: "\f257"; } .fa-hand-lizard-o:before { content: "\f258"; } .fa-hand-spock-o:before { content: "\f259"; } .fa-hand-pointer-o:before { content: "\f25a"; } .fa-hand-peace-o:before { content: "\f25b"; } .fa-trademark:before { content: "\f25c"; } .fa-registered:before { content: "\f25d"; } .fa-creative-commons:before { content: "\f25e"; } .fa-gg:before { content: "\f260"; } .fa-gg-circle:before { content: "\f261"; } .fa-tripadvisor:before { content: "\f262"; } .fa-odnoklassniki:before { content: "\f263"; } .fa-odnoklassniki-square:before { content: "\f264"; } .fa-get-pocket:before { content: "\f265"; } .fa-wikipedia-w:before { content: "\f266"; } .fa-safari:before { content: "\f267"; } .fa-chrome:before { content: "\f268"; } .fa-firefox:before { content: "\f269"; } .fa-opera:before { content: "\f26a"; } .fa-internet-explorer:before { content: "\f26b"; } .fa-tv:before, .fa-television:before { content: "\f26c"; } .fa-contao:before { content: "\f26d"; } .fa-500px:before { content: "\f26e"; } .fa-amazon:before { content: "\f270"; } .fa-calendar-plus-o:before { content: "\f271"; } .fa-calendar-minus-o:before { content: "\f272"; } .fa-calendar-times-o:before { content: "\f273"; } .fa-calendar-check-o:before { content: "\f274"; } .fa-industry:before { content: "\f275"; } .fa-map-pin:before { content: "\f276"; } .fa-map-signs:before { content: "\f277"; } .fa-map-o:before { content: "\f278"; } .fa-map:before { content: "\f279"; } .fa-commenting:before { content: "\f27a"; } .fa-commenting-o:before { content: "\f27b"; } .fa-houzz:before { content: "\f27c"; } .fa-vimeo:before { content: "\f27d"; } .fa-black-tie:before { content: "\f27e"; } .fa-fonticons:before { content: "\f280"; } .fa-reddit-alien:before { content: "\f281"; } .fa-edge:before { content: "\f282"; } .fa-credit-card-alt:before { content: "\f283"; } .fa-codiepie:before { content: "\f284"; } .fa-modx:before { content: "\f285"; } .fa-fort-awesome:before { content: "\f286"; } .fa-usb:before { content: "\f287"; } .fa-product-hunt:before { content: "\f288"; } .fa-mixcloud:before { content: "\f289"; } .fa-scribd:before { content: "\f28a"; } .fa-pause-circle:before { content: "\f28b"; } .fa-pause-circle-o:before { content: "\f28c"; } .fa-stop-circle:before { content: "\f28d"; } .fa-stop-circle-o:before { content: "\f28e"; } .fa-shopping-bag:before { content: "\f290"; } .fa-shopping-basket:before { content: "\f291"; } .fa-hashtag:before { content: "\f292"; } .fa-bluetooth:before { content: "\f293"; } .fa-bluetooth-b:before { content: "\f294"; } .fa-percent:before { content: "\f295"; } .fa-gitlab:before { content: "\f296"; } .fa-wpbeginner:before { content: "\f297"; } .fa-wpforms:before { content: "\f298"; } .fa-envira:before { content: "\f299"; } .fa-universal-access:before { content: "\f29a"; } .fa-wheelchair-alt:before { content: "\f29b"; } .fa-question-circle-o:before { content: "\f29c"; } .fa-blind:before { content: "\f29d"; } .fa-audio-description:before { content: "\f29e"; } .fa-volume-control-phone:before { content: "\f2a0"; } .fa-braille:before { content: "\f2a1"; } .fa-assistive-listening-systems:before { content: "\f2a2"; } .fa-asl-interpreting:before, .fa-american-sign-language-interpreting:before { content: "\f2a3"; } .fa-deafness:before, .fa-hard-of-hearing:before, .fa-deaf:before { content: "\f2a4"; } .fa-glide:before { content: "\f2a5"; } .fa-glide-g:before { content: "\f2a6"; } .fa-signing:before, .fa-sign-language:before { content: "\f2a7"; } .fa-low-vision:before { content: "\f2a8"; } .fa-viadeo:before { content: "\f2a9"; } .fa-viadeo-square:before { content: "\f2aa"; } .fa-snapchat:before { content: "\f2ab"; } .fa-snapchat-ghost:before { content: "\f2ac"; } .fa-snapchat-square:before { content: "\f2ad"; } .fa-pied-piper:before { content: "\f2ae"; } .fa-first-order:before { content: "\f2b0"; } .fa-yoast:before { content: "\f2b1"; } .fa-themeisle:before { content: "\f2b2"; } .fa-google-plus-circle:before, .fa-google-plus-official:before { content: "\f2b3"; } .fa-fa:before, .fa-font-awesome:before { content: "\f2b4"; } .fa-handshake-o:before { content: "\f2b5"; } .fa-envelope-open:before { content: "\f2b6"; } .fa-envelope-open-o:before { content: "\f2b7"; } .fa-linode:before { content: "\f2b8"; } .fa-address-book:before { content: "\f2b9"; } .fa-address-book-o:before { content: "\f2ba"; } .fa-vcard:before, .fa-address-card:before { content: "\f2bb"; } .fa-vcard-o:before, .fa-address-card-o:before { content: "\f2bc"; } .fa-user-circle:before { content: "\f2bd"; } .fa-user-circle-o:before { content: "\f2be"; } .fa-user-o:before { content: "\f2c0"; } .fa-id-badge:before { content: "\f2c1"; } .fa-drivers-license:before, .fa-id-card:before { content: "\f2c2"; } .fa-drivers-license-o:before, .fa-id-card-o:before { content: "\f2c3"; } .fa-quora:before { content: "\f2c4"; } .fa-free-code-camp:before { content: "\f2c5"; } .fa-telegram:before { content: "\f2c6"; } .fa-thermometer-4:before, .fa-thermometer:before, .fa-thermometer-full:before { content: "\f2c7"; } .fa-thermometer-3:before, .fa-thermometer-three-quarters:before { content: "\f2c8"; } .fa-thermometer-2:before, .fa-thermometer-half:before { content: "\f2c9"; } .fa-thermometer-1:before, .fa-thermometer-quarter:before { content: "\f2ca"; } .fa-thermometer-0:before, .fa-thermometer-empty:before { content: "\f2cb"; } .fa-shower:before { content: "\f2cc"; } .fa-bathtub:before, .fa-s15:before, .fa-bath:before { content: "\f2cd"; } .fa-podcast:before { content: "\f2ce"; } .fa-window-maximize:before { content: "\f2d0"; } .fa-window-minimize:before { content: "\f2d1"; } .fa-window-restore:before { content: "\f2d2"; } .fa-times-rectangle:before, .fa-window-close:before { content: "\f2d3"; } .fa-times-rectangle-o:before, .fa-window-close-o:before { content: "\f2d4"; } .fa-bandcamp:before { content: "\f2d5"; } .fa-grav:before { content: "\f2d6"; } .fa-etsy:before { content: "\f2d7"; } .fa-imdb:before { content: "\f2d8"; } .fa-ravelry:before { content: "\f2d9"; } .fa-eercast:before { content: "\f2da"; } .fa-microchip:before { content: "\f2db"; } .fa-snowflake-o:before { content: "\f2dc"; } .fa-superpowers:before { content: "\f2dd"; } .fa-wpexplorer:before { content: "\f2de"; } .fa-meetup:before { content: "\f2e0"; } .sr-only { position: absolute; width: 1px; height: 1px; padding: 0; margin: -1px; overflow: hidden; clip: rect(0, 0, 0, 0); border: 0; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } .sr-only-focusable:active, .sr-only-focusable:focus { position: static; width: auto; height: auto; margin: 0; overflow: visible; clip: auto; } /\*! \* \* IPython base \* \*/ .modal.fade .modal-dialog { -webkit-transform: translate(0, 0); -ms-transform: translate(0, 0); -o-transform: translate(0, 0); transform: translate(0, 0); } code { color: #000; } pre { font-size: inherit; line-height: inherit; } label { font-weight: normal; } /\* Make the page background atleast 100% the height of the view port \*/ /\* Make the page itself atleast 70% the height of the view port \*/ .border-box-sizing { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } .corner-all { border-radius: 2px; } .no-padding { padding: 0px; } /\* Flexible box model classes \*/ /\* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ \*/ /\* This file is a compatability layer. It allows the usage of flexible box model layouts accross multiple browsers, including older browsers. The newest, universal implementation of the flexible box model is used when available (see `Modern browsers` comments below). Browsers that are known to implement this new spec completely include: Firefox 28.0+ Chrome 29.0+ Internet Explorer 11+ Opera 17.0+ Browsers not listed, including Safari, are supported via the styling under the `Old browsers` comments below. \*/ .hbox { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } .hbox > \* { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; } .vbox { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } .vbox > \* { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; } .hbox.reverse, .vbox.reverse, .reverse { /\* Old browsers \*/ -webkit-box-direction: reverse; -moz-box-direction: reverse; box-direction: reverse; /\* Modern browsers \*/ flex-direction: row-reverse; } .hbox.box-flex0, .vbox.box-flex0, .box-flex0 { /\* Old browsers \*/ -webkit-box-flex: 0; -moz-box-flex: 0; box-flex: 0; /\* Modern browsers \*/ flex: none; width: auto; } .hbox.box-flex1, .vbox.box-flex1, .box-flex1 { /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } .hbox.box-flex, .vbox.box-flex, .box-flex { /\* Old browsers \*/ /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } .hbox.box-flex2, .vbox.box-flex2, .box-flex2 { /\* Old browsers \*/ -webkit-box-flex: 2; -moz-box-flex: 2; box-flex: 2; /\* Modern browsers \*/ flex: 2; } .box-group1 { /\* Deprecated \*/ -webkit-box-flex-group: 1; -moz-box-flex-group: 1; box-flex-group: 1; } .box-group2 { /\* Deprecated \*/ -webkit-box-flex-group: 2; -moz-box-flex-group: 2; box-flex-group: 2; } .hbox.start, .vbox.start, .start { /\* Old browsers \*/ -webkit-box-pack: start; -moz-box-pack: start; box-pack: start; /\* Modern browsers \*/ justify-content: flex-start; } .hbox.end, .vbox.end, .end { /\* Old browsers \*/ -webkit-box-pack: end; -moz-box-pack: end; box-pack: end; /\* Modern browsers \*/ justify-content: flex-end; } .hbox.center, .vbox.center, .center { /\* Old browsers \*/ -webkit-box-pack: center; -moz-box-pack: center; box-pack: center; /\* Modern browsers \*/ justify-content: center; } .hbox.baseline, .vbox.baseline, .baseline { /\* Old browsers \*/ -webkit-box-pack: baseline; -moz-box-pack: baseline; box-pack: baseline; /\* Modern browsers \*/ justify-content: baseline; } .hbox.stretch, .vbox.stretch, .stretch { /\* Old browsers \*/ -webkit-box-pack: stretch; -moz-box-pack: stretch; box-pack: stretch; /\* Modern browsers \*/ justify-content: stretch; } .hbox.align-start, .vbox.align-start, .align-start { /\* Old browsers \*/ -webkit-box-align: start; -moz-box-align: start; box-align: start; /\* Modern browsers \*/ align-items: flex-start; } .hbox.align-end, .vbox.align-end, .align-end { /\* Old browsers \*/ -webkit-box-align: end; -moz-box-align: end; box-align: end; /\* Modern browsers \*/ align-items: flex-end; } .hbox.align-center, .vbox.align-center, .align-center { /\* Old browsers \*/ -webkit-box-align: center; -moz-box-align: center; box-align: center; /\* Modern browsers \*/ align-items: center; } .hbox.align-baseline, .vbox.align-baseline, .align-baseline { /\* Old browsers \*/ -webkit-box-align: baseline; -moz-box-align: baseline; box-align: baseline; /\* Modern browsers \*/ align-items: baseline; } .hbox.align-stretch, .vbox.align-stretch, .align-stretch { /\* Old browsers \*/ -webkit-box-align: stretch; -moz-box-align: stretch; box-align: stretch; /\* Modern browsers \*/ align-items: stretch; } div.error { margin: 2em; text-align: center; } div.error > h1 { font-size: 500%; line-height: normal; } div.error > p { font-size: 200%; line-height: normal; } div.traceback-wrapper { text-align: left; max-width: 800px; margin: auto; } div.traceback-wrapper pre.traceback { max-height: 600px; overflow: auto; } /\*\* \* Primary styles \* \* Author: Jupyter Development Team \*/ body { background-color: #fff; /\* This makes sure that the body covers the entire window and needs to be in a different element than the display: box in wrapper below \*/ position: absolute; left: 0px; right: 0px; top: 0px; bottom: 0px; overflow: visible; } body > #header { /\* Initially hidden to prevent FLOUC \*/ display: none; background-color: #fff; /\* Display over codemirror \*/ position: relative; z-index: 100; } body > #header #header-container { display: flex; flex-direction: row; justify-content: space-between; padding: 5px; padding-bottom: 5px; padding-top: 5px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } body > #header .header-bar { width: 100%; height: 1px; background: #e7e7e7; margin-bottom: -1px; } @media print { body > #header { display: none !important; } } #header-spacer { width: 100%; visibility: hidden; } @media print { #header-spacer { display: none; } } #ipython\_notebook { padding-left: 0px; padding-top: 1px; padding-bottom: 1px; } [dir="rtl"] #ipython\_notebook { margin-right: 10px; margin-left: 0; } [dir="rtl"] #ipython\_notebook.pull-left { float: right !important; float: right; } .flex-spacer { flex: 1; } #noscript { width: auto; padding-top: 16px; padding-bottom: 16px; text-align: center; font-size: 22px; color: red; font-weight: bold; } #ipython\_notebook img { height: 28px; } #site { width: 100%; display: none; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; overflow: auto; } @media print { #site { height: auto !important; } } /\* Smaller buttons \*/ .ui-button .ui-button-text { padding: 0.2em 0.8em; font-size: 77%; } input.ui-button { padding: 0.3em 0.9em; } span#kernel\_logo\_widget { margin: 0 10px; } span#login\_widget { float: right; } [dir="rtl"] span#login\_widget { float: left; } span#login\_widget > .button, #logout { color: #333; background-color: #fff; border-color: #ccc; } span#login\_widget > .button:focus, #logout:focus, span#login\_widget > .button.focus, #logout.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } span#login\_widget > .button:hover, #logout:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } span#login\_widget > .button:active, #logout:active, span#login\_widget > .button.active, #logout.active, .open > .dropdown-togglespan#login\_widget > .button, .open > .dropdown-toggle#logout { color: #333; background-color: #e6e6e6; border-color: #adadad; } span#login\_widget > .button:active:hover, #logout:active:hover, span#login\_widget > .button.active:hover, #logout.active:hover, .open > .dropdown-togglespan#login\_widget > .button:hover, .open > .dropdown-toggle#logout:hover, span#login\_widget > .button:active:focus, #logout:active:focus, span#login\_widget > .button.active:focus, #logout.active:focus, .open > .dropdown-togglespan#login\_widget > .button:focus, .open > .dropdown-toggle#logout:focus, span#login\_widget > .button:active.focus, #logout:active.focus, span#login\_widget > .button.active.focus, #logout.active.focus, .open > .dropdown-togglespan#login\_widget > .button.focus, .open > .dropdown-toggle#logout.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } span#login\_widget > .button:active, #logout:active, span#login\_widget > .button.active, #logout.active, .open > .dropdown-togglespan#login\_widget > .button, .open > .dropdown-toggle#logout { background-image: none; } span#login\_widget > .button.disabled:hover, #logout.disabled:hover, span#login\_widget > .button[disabled]:hover, #logout[disabled]:hover, fieldset[disabled] span#login\_widget > .button:hover, fieldset[disabled] #logout:hover, span#login\_widget > .button.disabled:focus, #logout.disabled:focus, span#login\_widget > .button[disabled]:focus, #logout[disabled]:focus, fieldset[disabled] span#login\_widget > .button:focus, fieldset[disabled] #logout:focus, span#login\_widget > .button.disabled.focus, #logout.disabled.focus, span#login\_widget > .button[disabled].focus, #logout[disabled].focus, fieldset[disabled] span#login\_widget > .button.focus, fieldset[disabled] #logout.focus { background-color: #fff; border-color: #ccc; } span#login\_widget > .button .badge, #logout .badge { color: #fff; background-color: #333; } .nav-header { text-transform: none; } #header > span { margin-top: 10px; } .modal\_stretch .modal-dialog { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; min-height: 80vh; } .modal\_stretch .modal-dialog .modal-body { max-height: calc(100vh - 200px); overflow: auto; flex: 1; } .modal-header { cursor: move; } @media (min-width: 768px) { .modal .modal-dialog { width: 700px; } } @media (min-width: 768px) { select.form-control { margin-left: 12px; margin-right: 12px; } } /\*! \* \* IPython auth \* \*/ .center-nav { display: inline-block; margin-bottom: -4px; } [dir="rtl"] .center-nav form.pull-left { float: right !important; float: right; } [dir="rtl"] .center-nav .navbar-text { float: right; } [dir="rtl"] .navbar-inner { text-align: right; } [dir="rtl"] div.text-left { text-align: right; } /\*! \* \* IPython tree view \* \*/ /\* We need an invisible input field on top of the sentense\*/ /\* "Drag file onto the list ..." \*/ .alternate\_upload { background-color: none; display: inline; } .alternate\_upload.form { padding: 0; margin: 0; } .alternate\_upload input.fileinput { position: absolute; display: block; width: 100%; height: 100%; overflow: hidden; cursor: pointer; opacity: 0; z-index: 2; } .alternate\_upload .btn-xs > input.fileinput { margin: -1px -5px; } .alternate\_upload .btn-upload { position: relative; height: 22px; } ::-webkit-file-upload-button { cursor: pointer; } /\*\* \* Primary styles \* \* Author: Jupyter Development Team \*/ ul#tabs { margin-bottom: 4px; } ul#tabs a { padding-top: 6px; padding-bottom: 4px; } [dir="rtl"] ul#tabs.nav-tabs > li { float: right; } [dir="rtl"] ul#tabs.nav.nav-tabs { padding-right: 0; } ul.breadcrumb a:focus, ul.breadcrumb a:hover { text-decoration: none; } ul.breadcrumb i.icon-home { font-size: 16px; margin-right: 4px; } ul.breadcrumb span { color: #5e5e5e; } .list\_toolbar { padding: 4px 0 4px 0; vertical-align: middle; } .list\_toolbar .tree-buttons { padding-top: 1px; } [dir="rtl"] .list\_toolbar .tree-buttons .pull-right { float: left !important; float: left; } [dir="rtl"] .list\_toolbar .col-sm-4, [dir="rtl"] .list\_toolbar .col-sm-8 { float: right; } .dynamic-buttons { padding-top: 3px; display: inline-block; } .list\_toolbar [class\*="span"] { min-height: 24px; } .list\_header { font-weight: bold; background-color: #EEE; } .list\_placeholder { font-weight: bold; padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; } .list\_container { margin-top: 4px; margin-bottom: 20px; border: 1px solid #ddd; border-radius: 2px; } .list\_container > div { border-bottom: 1px solid #ddd; } .list\_container > div:hover .list-item { background-color: red; } .list\_container > div:last-child { border: none; } .list\_item:hover .list\_item { background-color: #ddd; } .list\_item a { text-decoration: none; } .list\_item:hover { background-color: #fafafa; } .list\_header > div, .list\_item > div { padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; line-height: 22px; } .list\_header > div input, .list\_item > div input { margin-right: 7px; margin-left: 14px; vertical-align: text-bottom; line-height: 22px; position: relative; top: -1px; } .list\_header > div .item\_link, .list\_item > div .item\_link { margin-left: -1px; vertical-align: baseline; line-height: 22px; } [dir="rtl"] .list\_item > div input { margin-right: 0; } .new-file input[type=checkbox] { visibility: hidden; } .item\_name { line-height: 22px; height: 24px; } .item\_icon { font-size: 14px; color: #5e5e5e; margin-right: 7px; margin-left: 7px; line-height: 22px; vertical-align: baseline; } .item\_modified { margin-right: 7px; margin-left: 7px; } [dir="rtl"] .item\_modified.pull-right { float: left !important; float: left; } .item\_buttons { line-height: 1em; margin-left: -5px; } .item\_buttons .btn, .item\_buttons .btn-group, .item\_buttons .input-group { float: left; } .item\_buttons > .btn, .item\_buttons > .btn-group, .item\_buttons > .input-group { margin-left: 5px; } .item\_buttons .btn { min-width: 13ex; } .item\_buttons .running-indicator { padding-top: 4px; color: #5cb85c; } .item\_buttons .kernel-name { padding-top: 4px; color: #5bc0de; margin-right: 7px; float: left; } [dir="rtl"] .item\_buttons.pull-right { float: left !important; float: left; } [dir="rtl"] .item\_buttons .kernel-name { margin-left: 7px; float: right; } .toolbar\_info { height: 24px; line-height: 24px; } .list\_item input:not([type=checkbox]) { padding-top: 3px; padding-bottom: 3px; height: 22px; line-height: 14px; margin: 0px; } .highlight\_text { color: blue; } #project\_name { display: inline-block; padding-left: 7px; margin-left: -2px; } #project\_name > .breadcrumb { padding: 0px; margin-bottom: 0px; background-color: transparent; font-weight: bold; } .sort\_button { display: inline-block; padding-left: 7px; } [dir="rtl"] .sort\_button.pull-right { float: left !important; float: left; } #tree-selector { padding-right: 0px; } #button-select-all { min-width: 50px; } [dir="rtl"] #button-select-all.btn { float: right ; } #select-all { margin-left: 7px; margin-right: 2px; margin-top: 2px; height: 16px; } [dir="rtl"] #select-all.pull-left { float: right !important; float: right; } .menu\_icon { margin-right: 2px; } .tab-content .row { margin-left: 0px; margin-right: 0px; } .folder\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f114"; } .folder\_icon:before.fa-pull-left { margin-right: .3em; } .folder\_icon:before.fa-pull-right { margin-left: .3em; } .folder\_icon:before.pull-left { margin-right: .3em; } .folder\_icon:before.pull-right { margin-left: .3em; } .notebook\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f02d"; position: relative; top: -1px; } .notebook\_icon:before.fa-pull-left { margin-right: .3em; } .notebook\_icon:before.fa-pull-right { margin-left: .3em; } .notebook\_icon:before.pull-left { margin-right: .3em; } .notebook\_icon:before.pull-right { margin-left: .3em; } .running\_notebook\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f02d"; position: relative; top: -1px; color: #5cb85c; } .running\_notebook\_icon:before.fa-pull-left { margin-right: .3em; } .running\_notebook\_icon:before.fa-pull-right { margin-left: .3em; } .running\_notebook\_icon:before.pull-left { margin-right: .3em; } .running\_notebook\_icon:before.pull-right { margin-left: .3em; } .file\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f016"; position: relative; top: -2px; } .file\_icon:before.fa-pull-left { margin-right: .3em; } .file\_icon:before.fa-pull-right { margin-left: .3em; } .file\_icon:before.pull-left { margin-right: .3em; } .file\_icon:before.pull-right { margin-left: .3em; } #notebook\_toolbar .pull-right { padding-top: 0px; margin-right: -1px; } ul#new-menu { left: auto; right: 0; } #new-menu .dropdown-header { font-size: 10px; border-bottom: 1px solid #e5e5e5; padding: 0 0 3px; margin: -3px 20px 0; } .kernel-menu-icon { padding-right: 12px; width: 24px; content: "\f096"; } .kernel-menu-icon:before { content: "\f096"; } .kernel-menu-icon-current:before { content: "\f00c"; } #tab\_content { padding-top: 20px; } #running .panel-group .panel { margin-top: 3px; margin-bottom: 1em; } #running .panel-group .panel .panel-heading { background-color: #EEE; padding-top: 4px; padding-bottom: 4px; padding-left: 7px; padding-right: 7px; line-height: 22px; } #running .panel-group .panel .panel-heading a:focus, #running .panel-group .panel .panel-heading a:hover { text-decoration: none; } #running .panel-group .panel .panel-body { padding: 0px; } #running .panel-group .panel .panel-body .list\_container { margin-top: 0px; margin-bottom: 0px; border: 0px; border-radius: 0px; } #running .panel-group .panel .panel-body .list\_container .list\_item { border-bottom: 1px solid #ddd; } #running .panel-group .panel .panel-body .list\_container .list\_item:last-child { border-bottom: 0px; } .delete-button { display: none; } .duplicate-button { display: none; } .rename-button { display: none; } .move-button { display: none; } .download-button { display: none; } .shutdown-button { display: none; } .dynamic-instructions { display: inline-block; padding-top: 4px; } /\*! \* \* IPython text editor webapp \* \*/ .selected-keymap i.fa { padding: 0px 5px; } .selected-keymap i.fa:before { content: "\f00c"; } #mode-menu { overflow: auto; max-height: 20em; } .edit\_app #header { -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } .edit\_app #menubar .navbar { /\* Use a negative 1 bottom margin, so the border overlaps the border of the header \*/ margin-bottom: -1px; } .dirty-indicator { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator.fa-pull-left { margin-right: .3em; } .dirty-indicator.fa-pull-right { margin-left: .3em; } .dirty-indicator.pull-left { margin-right: .3em; } .dirty-indicator.pull-right { margin-left: .3em; } .dirty-indicator-dirty { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator-dirty.fa-pull-left { margin-right: .3em; } .dirty-indicator-dirty.fa-pull-right { margin-left: .3em; } .dirty-indicator-dirty.pull-left { margin-right: .3em; } .dirty-indicator-dirty.pull-right { margin-left: .3em; } .dirty-indicator-clean { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; width: 20px; } .dirty-indicator-clean.fa-pull-left { margin-right: .3em; } .dirty-indicator-clean.fa-pull-right { margin-left: .3em; } .dirty-indicator-clean.pull-left { margin-right: .3em; } .dirty-indicator-clean.pull-right { margin-left: .3em; } .dirty-indicator-clean:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f00c"; } .dirty-indicator-clean:before.fa-pull-left { margin-right: .3em; } .dirty-indicator-clean:before.fa-pull-right { margin-left: .3em; } .dirty-indicator-clean:before.pull-left { margin-right: .3em; } .dirty-indicator-clean:before.pull-right { margin-left: .3em; } #filename { font-size: 16pt; display: table; padding: 0px 5px; } #current-mode { padding-left: 5px; padding-right: 5px; } #texteditor-backdrop { padding-top: 20px; padding-bottom: 20px; } @media not print { #texteditor-backdrop { background-color: #EEE; } } @media print { #texteditor-backdrop #texteditor-container .CodeMirror-gutter, #texteditor-backdrop #texteditor-container .CodeMirror-gutters { background-color: #fff; } } @media not print { #texteditor-backdrop #texteditor-container .CodeMirror-gutter, #texteditor-backdrop #texteditor-container .CodeMirror-gutters { background-color: #fff; } } @media not print { #texteditor-backdrop #texteditor-container { padding: 0px; background-color: #fff; -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } } .CodeMirror-dialog { background-color: #fff; } /\*! \* \* IPython notebook \* \*/ /\* CSS font colors for translated ANSI escape sequences \*/ /\* The color values are a mix of http://www.xcolors.net/dl/baskerville-ivorylight and http://www.xcolors.net/dl/euphrasia \*/ .ansi-black-fg { color: #3E424D; } .ansi-black-bg { background-color: #3E424D; } .ansi-black-intense-fg { color: #282C36; } .ansi-black-intense-bg { background-color: #282C36; } .ansi-red-fg { color: #E75C58; } .ansi-red-bg { background-color: #E75C58; } .ansi-red-intense-fg { color: #B22B31; } .ansi-red-intense-bg { background-color: #B22B31; } .ansi-green-fg { color: #00A250; } .ansi-green-bg { background-color: #00A250; } .ansi-green-intense-fg { color: #007427; } .ansi-green-intense-bg { background-color: #007427; } .ansi-yellow-fg { color: #DDB62B; } .ansi-yellow-bg { background-color: #DDB62B; } .ansi-yellow-intense-fg { color: #B27D12; } .ansi-yellow-intense-bg { background-color: #B27D12; } .ansi-blue-fg { color: #208FFB; } .ansi-blue-bg { background-color: #208FFB; } .ansi-blue-intense-fg { color: #0065CA; } .ansi-blue-intense-bg { background-color: #0065CA; } .ansi-magenta-fg { color: #D160C4; } .ansi-magenta-bg { background-color: #D160C4; } .ansi-magenta-intense-fg { color: #A03196; } .ansi-magenta-intense-bg { background-color: #A03196; } .ansi-cyan-fg { color: #60C6C8; } .ansi-cyan-bg { background-color: #60C6C8; } .ansi-cyan-intense-fg { color: #258F8F; } .ansi-cyan-intense-bg { background-color: #258F8F; } .ansi-white-fg { color: #C5C1B4; } .ansi-white-bg { background-color: #C5C1B4; } .ansi-white-intense-fg { color: #A1A6B2; } .ansi-white-intense-bg { background-color: #A1A6B2; } .ansi-default-inverse-fg { color: #FFFFFF; } .ansi-default-inverse-bg { background-color: #000000; } .ansi-bold { font-weight: bold; } .ansi-underline { text-decoration: underline; } /\* The following styles are deprecated an will be removed in a future version \*/ .ansibold { font-weight: bold; } .ansi-inverse { outline: 0.5px dotted; } /\* use dark versions for foreground, to improve visibility \*/ .ansiblack { color: black; } .ansired { color: darkred; } .ansigreen { color: darkgreen; } .ansiyellow { color: #c4a000; } .ansiblue { color: darkblue; } .ansipurple { color: darkviolet; } .ansicyan { color: steelblue; } .ansigray { color: gray; } /\* and light for background, for the same reason \*/ .ansibgblack { background-color: black; } .ansibgred { background-color: red; } .ansibggreen { background-color: green; } .ansibgyellow { background-color: yellow; } .ansibgblue { background-color: blue; } .ansibgpurple { background-color: magenta; } .ansibgcyan { background-color: cyan; } .ansibggray { background-color: gray; } div.cell { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; border-radius: 2px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; border-width: 1px; border-style: solid; border-color: transparent; width: 100%; padding: 5px; /\* This acts as a spacer between cells, that is outside the border \*/ margin: 0px; outline: none; position: relative; overflow: visible; } div.cell:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: transparent; } div.cell.jupyter-soft-selected { border-left-color: #E3F2FD; border-left-width: 1px; padding-left: 5px; border-right-color: #E3F2FD; border-right-width: 1px; background: #E3F2FD; } @media print { div.cell.jupyter-soft-selected { border-color: transparent; } } div.cell.selected, div.cell.selected.jupyter-soft-selected { border-color: #ababab; } div.cell.selected:before, div.cell.selected.jupyter-soft-selected:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: #42A5F5; } @media print { div.cell.selected, div.cell.selected.jupyter-soft-selected { border-color: transparent; } } .edit\_mode div.cell.selected { border-color: #66BB6A; } .edit\_mode div.cell.selected:before { position: absolute; display: block; top: -1px; left: -1px; width: 5px; height: calc(100% + 2px); content: ''; background: #66BB6A; } @media print { .edit\_mode div.cell.selected { border-color: transparent; } } .prompt { /\* This needs to be wide enough for 3 digit prompt numbers: In[100]: \*/ min-width: 14ex; /\* This padding is tuned to match the padding on the CodeMirror editor. \*/ padding: 0.4em; margin: 0px; font-family: monospace; text-align: right; /\* This has to match that of the the CodeMirror class line-height below \*/ line-height: 1.21429em; /\* Don't highlight prompt number selection \*/ -webkit-touch-callout: none; -webkit-user-select: none; -khtml-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; /\* Use default cursor \*/ cursor: default; } @media (max-width: 540px) { .prompt { text-align: left; } } div.inner\_cell { min-width: 0; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; } /\* input\_area and input\_prompt must match in top border and margin for alignment \*/ div.input\_area { border: 1px solid #cfcfcf; border-radius: 2px; background: #f7f7f7; line-height: 1.21429em; } /\* This is needed so that empty prompt areas can collapse to zero height when there is no content in the output\_subarea and the prompt. The main purpose of this is to make sure that empty JavaScript output\_subareas have no height. \*/ div.prompt:empty { padding-top: 0; padding-bottom: 0; } div.unrecognized\_cell { padding: 5px 5px 5px 0px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } div.unrecognized\_cell .inner\_cell { border-radius: 2px; padding: 5px; font-weight: bold; color: red; border: 1px solid #cfcfcf; background: #eaeaea; } div.unrecognized\_cell .inner\_cell a { color: inherit; text-decoration: none; } div.unrecognized\_cell .inner\_cell a:hover { color: inherit; text-decoration: none; } @media (max-width: 540px) { div.unrecognized\_cell > div.prompt { display: none; } } div.code\_cell { /\* avoid page breaking on code cells when printing \*/ } @media print { div.code\_cell { page-break-inside: avoid; } } /\* any special styling for code cells that are currently running goes here \*/ div.input { page-break-inside: avoid; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } @media (max-width: 540px) { div.input { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } } /\* input\_area and input\_prompt must match in top border and margin for alignment \*/ div.input\_prompt { color: #303F9F; border-top: 1px solid transparent; } div.input\_area > div.highlight { margin: 0.4em; border: none; padding: 0px; background-color: transparent; } div.input\_area > div.highlight > pre { margin: 0px; border: none; padding: 0px; background-color: transparent; } /\* The following gets added to the <head> if it is detected that the user has a \* monospace font with inconsistent normal/bold/italic height. See \* notebookmain.js. Such fonts will have keywords vertically offset with \* respect to the rest of the text. The user should select a better font. \* See: https://github.com/ipython/ipython/issues/1503 \* \* .CodeMirror span { \* vertical-align: bottom; \* } \*/ .CodeMirror { line-height: 1.21429em; /\* Changed from 1em to our global default \*/ font-size: 14px; height: auto; /\* Changed to auto to autogrow \*/ background: none; /\* Changed from white to allow our bg to show through \*/ } .CodeMirror-scroll { /\* The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.\*/ /\* We have found that if it is visible, vertical scrollbars appear with font size changes.\*/ overflow-y: hidden; overflow-x: auto; } .CodeMirror-lines { /\* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because \*/ /\* we have set a different line-height and want this to scale with that. \*/ /\* Note that this should set vertical padding only, since CodeMirror assumes that horizontal padding will be set on CodeMirror pre \*/ padding: 0.4em 0; } .CodeMirror-linenumber { padding: 0 8px 0 4px; } .CodeMirror-gutters { border-bottom-left-radius: 2px; border-top-left-radius: 2px; } .CodeMirror pre { /\* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only, use .CodeMirror-lines for vertical \*/ padding: 0 0.4em; border: 0; border-radius: 0; } .CodeMirror-cursor { border-left: 1.4px solid black; } @media screen and (min-width: 2138px) and (max-width: 4319px) { .CodeMirror-cursor { border-left: 2px solid black; } } @media screen and (min-width: 4320px) { .CodeMirror-cursor { border-left: 4px solid black; } } /\* Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org> Adapted from GitHub theme \*/ .highlight-base { color: #000; } .highlight-variable { color: #000; } .highlight-variable-2 { color: #1a1a1a; } .highlight-variable-3 { color: #333333; } .highlight-string { color: #BA2121; } .highlight-comment { color: #408080; font-style: italic; } .highlight-number { color: #080; } .highlight-atom { color: #88F; } .highlight-keyword { color: #008000; font-weight: bold; } .highlight-builtin { color: #008000; } .highlight-error { color: #f00; } .highlight-operator { color: #AA22FF; font-weight: bold; } .highlight-meta { color: #AA22FF; } /\* previously not defined, copying from default codemirror \*/ .highlight-def { color: #00f; } .highlight-string-2 { color: #f50; } .highlight-qualifier { color: #555; } .highlight-bracket { color: #997; } .highlight-tag { color: #170; } .highlight-attribute { color: #00c; } .highlight-header { color: blue; } .highlight-quote { color: #090; } .highlight-link { color: #00c; } /\* apply the same style to codemirror \*/ .cm-s-ipython span.cm-keyword { color: #008000; font-weight: bold; } .cm-s-ipython span.cm-atom { color: #88F; } .cm-s-ipython span.cm-number { color: #080; } .cm-s-ipython span.cm-def { color: #00f; } .cm-s-ipython span.cm-variable { color: #000; } .cm-s-ipython span.cm-operator { color: #AA22FF; font-weight: bold; } .cm-s-ipython span.cm-variable-2 { color: #1a1a1a; } .cm-s-ipython span.cm-variable-3 { color: #333333; } .cm-s-ipython span.cm-comment { color: #408080; font-style: italic; } .cm-s-ipython span.cm-string { color: #BA2121; } .cm-s-ipython span.cm-string-2 { color: #f50; } .cm-s-ipython span.cm-meta { color: #AA22FF; } .cm-s-ipython span.cm-qualifier { color: #555; } .cm-s-ipython span.cm-builtin { color: #008000; } .cm-s-ipython span.cm-bracket { color: #997; } .cm-s-ipython span.cm-tag { color: #170; } .cm-s-ipython span.cm-attribute { color: #00c; } .cm-s-ipython span.cm-header { color: blue; } .cm-s-ipython span.cm-quote { color: #090; } .cm-s-ipython span.cm-link { color: #00c; } .cm-s-ipython span.cm-error { color: #f00; } .cm-s-ipython span.cm-tab { background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=); background-position: right; background-repeat: no-repeat; } div.output\_wrapper { /\* this position must be relative to enable descendents to be absolute within it \*/ position: relative; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; z-index: 1; } /\* class for the output area when it should be height-limited \*/ div.output\_scroll { /\* ideally, this would be max-height, but FF barfs all over that \*/ height: 24em; /\* FF needs this \*and the wrapper\* to specify full width, or it will shrinkwrap \*/ width: 100%; overflow: auto; border-radius: 2px; -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8); box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8); display: block; } /\* output div while it is collapsed \*/ div.output\_collapsed { margin: 0px; padding: 0px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } div.out\_prompt\_overlay { height: 100%; padding: 0px 0.4em; position: absolute; border-radius: 2px; } div.out\_prompt\_overlay:hover { /\* use inner shadow to get border that is computed the same on WebKit/FF \*/ -webkit-box-shadow: inset 0 0 1px #000; box-shadow: inset 0 0 1px #000; background: rgba(240, 240, 240, 0.5); } div.output\_prompt { color: #D84315; } /\* This class is the outer container of all output sections. \*/ div.output\_area { padding: 0px; page-break-inside: avoid; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } div.output\_area .MathJax\_Display { text-align: left !important; } div.output\_area .rendered\_html table { margin-left: 0; margin-right: 0; } div.output\_area .rendered\_html img { margin-left: 0; margin-right: 0; } div.output\_area img, div.output\_area svg { max-width: 100%; height: auto; } div.output\_area img.unconfined, div.output\_area svg.unconfined { max-width: none; } div.output\_area .mglyph > img { max-width: none; } /\* This is needed to protect the pre formating from global settings such as that of bootstrap \*/ .output { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } @media (max-width: 540px) { div.output\_area { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: vertical; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: vertical; -moz-box-align: stretch; display: box; box-orient: vertical; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: column; align-items: stretch; } } div.output\_area pre { margin: 0; padding: 1px 0 1px 0; border: 0; vertical-align: baseline; color: black; background-color: transparent; border-radius: 0; } /\* This class is for the output subarea inside the output\_area and after the prompt div. \*/ div.output\_subarea { overflow-x: auto; padding: 0.4em; /\* Old browsers \*/ -webkit-box-flex: 1; -moz-box-flex: 1; box-flex: 1; /\* Modern browsers \*/ flex: 1; max-width: calc(100% - 14ex); } div.output\_scroll div.output\_subarea { overflow-x: visible; } /\* The rest of the output\_\* classes are for special styling of the different output types \*/ /\* all text output has this class: \*/ div.output\_text { text-align: left; color: #000; /\* This has to match that of the the CodeMirror class line-height below \*/ line-height: 1.21429em; } /\* stdout/stderr are 'text' as well as 'stream', but execute\_result/error are \*not\* streams \*/ div.output\_stderr { background: #fdd; /\* very light red background for stderr \*/ } div.output\_latex { text-align: left; } /\* Empty output\_javascript divs should have no height \*/ div.output\_javascript:empty { padding: 0; } .js-error { color: darkred; } /\* raw\_input styles \*/ div.raw\_input\_container { line-height: 1.21429em; padding-top: 5px; } pre.raw\_input\_prompt { /\* nothing needed here. \*/ } input.raw\_input { font-family: monospace; font-size: inherit; color: inherit; width: auto; /\* make sure input baseline aligns with prompt \*/ vertical-align: baseline; /\* padding + margin = 0.5em between prompt and cursor \*/ padding: 0em 0.25em; margin: 0em 0.25em; } input.raw\_input:focus { box-shadow: none; } p.p-space { margin-bottom: 10px; } div.output\_unrecognized { padding: 5px; font-weight: bold; color: red; } div.output\_unrecognized a { color: inherit; text-decoration: none; } div.output\_unrecognized a:hover { color: inherit; text-decoration: none; } .rendered\_html { color: #000; /\* any extras will just be numbers: \*/ } .rendered\_html em { font-style: italic; } .rendered\_html strong { font-weight: bold; } .rendered\_html u { text-decoration: underline; } .rendered\_html :link { text-decoration: underline; } .rendered\_html :visited { text-decoration: underline; } .rendered\_html h1 { font-size: 185.7%; margin: 1.08em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h2 { font-size: 157.1%; margin: 1.27em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h3 { font-size: 128.6%; margin: 1.55em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h4 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; } .rendered\_html h5 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; font-style: italic; } .rendered\_html h6 { font-size: 100%; margin: 2em 0 0 0; font-weight: bold; line-height: 1.0; font-style: italic; } .rendered\_html h1:first-child { margin-top: 0.538em; } .rendered\_html h2:first-child { margin-top: 0.636em; } .rendered\_html h3:first-child { margin-top: 0.777em; } .rendered\_html h4:first-child { margin-top: 1em; } .rendered\_html h5:first-child { margin-top: 1em; } .rendered\_html h6:first-child { margin-top: 1em; } .rendered\_html ul:not(.list-inline), .rendered\_html ol:not(.list-inline) { padding-left: 2em; } .rendered\_html ul { list-style: disc; } .rendered\_html ul ul { list-style: square; margin-top: 0; } .rendered\_html ul ul ul { list-style: circle; } .rendered\_html ol { list-style: decimal; } .rendered\_html ol ol { list-style: upper-alpha; margin-top: 0; } .rendered\_html ol ol ol { list-style: lower-alpha; } .rendered\_html ol ol ol ol { list-style: lower-roman; } .rendered\_html ol ol ol ol ol { list-style: decimal; } .rendered\_html \* + ul { margin-top: 1em; } .rendered\_html \* + ol { margin-top: 1em; } .rendered\_html hr { color: black; background-color: black; } .rendered\_html pre { margin: 1em 2em; padding: 0px; background-color: #fff; } .rendered\_html code { background-color: #eff0f1; } .rendered\_html p code { padding: 1px 5px; } .rendered\_html pre code { background-color: #fff; } .rendered\_html pre, .rendered\_html code { border: 0; color: #000; font-size: 100%; } .rendered\_html blockquote { margin: 1em 2em; } .rendered\_html table { margin-left: auto; margin-right: auto; border: none; border-collapse: collapse; border-spacing: 0; color: black; font-size: 12px; table-layout: fixed; } .rendered\_html thead { border-bottom: 1px solid black; vertical-align: bottom; } .rendered\_html tr, .rendered\_html th, .rendered\_html td { text-align: right; vertical-align: middle; padding: 0.5em 0.5em; line-height: normal; white-space: normal; max-width: none; border: none; } .rendered\_html th { font-weight: bold; } .rendered\_html tbody tr:nth-child(odd) { background: #f5f5f5; } .rendered\_html tbody tr:hover { background: rgba(66, 165, 245, 0.2); } .rendered\_html \* + table { margin-top: 1em; } .rendered\_html p { text-align: left; } .rendered\_html \* + p { margin-top: 1em; } .rendered\_html img { display: block; margin-left: auto; margin-right: auto; } .rendered\_html \* + img { margin-top: 1em; } .rendered\_html img, .rendered\_html svg { max-width: 100%; height: auto; } .rendered\_html img.unconfined, .rendered\_html svg.unconfined { max-width: none; } .rendered\_html .alert { margin-bottom: initial; } .rendered\_html \* + .alert { margin-top: 1em; } [dir="rtl"] .rendered\_html p { text-align: right; } div.text\_cell { /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; } @media (max-width: 540px) { div.text\_cell > div.prompt { display: none; } } div.text\_cell\_render { /\*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;\*/ outline: none; resize: none; width: inherit; border-style: none; padding: 0.5em 0.5em 0.5em 0.4em; color: #000; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } a.anchor-link:link { text-decoration: none; padding: 0px 20px; visibility: hidden; } h1:hover .anchor-link, h2:hover .anchor-link, h3:hover .anchor-link, h4:hover .anchor-link, h5:hover .anchor-link, h6:hover .anchor-link { visibility: visible; } .text\_cell.rendered .input\_area { display: none; } .text\_cell.rendered .rendered\_html { overflow-x: auto; overflow-y: hidden; } .text\_cell.rendered .rendered\_html tr, .text\_cell.rendered .rendered\_html th, .text\_cell.rendered .rendered\_html td { max-width: none; } .text\_cell.unrendered .text\_cell\_render { display: none; } .text\_cell .dropzone .input\_area { border: 2px dashed #bababa; margin: -1px; } .cm-header-1, .cm-header-2, .cm-header-3, .cm-header-4, .cm-header-5, .cm-header-6 { font-weight: bold; font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; } .cm-header-1 { font-size: 185.7%; } .cm-header-2 { font-size: 157.1%; } .cm-header-3 { font-size: 128.6%; } .cm-header-4 { font-size: 110%; } .cm-header-5 { font-size: 100%; font-style: italic; } .cm-header-6 { font-size: 100%; font-style: italic; } /\*! \* \* IPython notebook webapp \* \*/ @media (max-width: 767px) { .notebook\_app { padding-left: 0px; padding-right: 0px; } } #ipython-main-app { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; height: 100%; } div#notebook\_panel { margin: 0px; padding: 0px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; height: 100%; } div#notebook { font-size: 14px; line-height: 20px; overflow-y: hidden; overflow-x: auto; width: 100%; /\* This spaces the page away from the edge of the notebook area \*/ padding-top: 20px; margin: 0px; outline: none; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; min-height: 100%; } @media not print { #notebook-container { padding: 15px; background-color: #fff; min-height: 0; -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } } @media print { #notebook-container { width: 100%; } } div.ui-widget-content { border: 1px solid #ababab; outline: none; } pre.dialog { background-color: #f7f7f7; border: 1px solid #ddd; border-radius: 2px; padding: 0.4em; padding-left: 2em; } p.dialog { padding: 0.2em; } /\* Word-wrap output correctly. This is the CSS3 spelling, though Firefox seems to not honor it correctly. Webkit browsers (Chrome, rekonq, Safari) do. \*/ pre, code, kbd, samp { white-space: pre-wrap; } #fonttest { font-family: monospace; } p { margin-bottom: 0; } .end\_space { min-height: 100px; transition: height .2s ease; } .notebook\_app > #header { -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); } @media not print { .notebook\_app { background-color: #EEE; } } kbd { border-style: solid; border-width: 1px; box-shadow: none; margin: 2px; padding-left: 2px; padding-right: 2px; padding-top: 1px; padding-bottom: 1px; } .jupyter-keybindings { padding: 1px; line-height: 24px; border-bottom: 1px solid gray; } .jupyter-keybindings input { margin: 0; padding: 0; border: none; } .jupyter-keybindings i { padding: 6px; } .well code { background-color: #ffffff; border-color: #ababab; border-width: 1px; border-style: solid; padding: 2px; padding-top: 1px; padding-bottom: 1px; } /\* CSS for the cell toolbar \*/ .celltoolbar { border: thin solid #CFCFCF; border-bottom: none; background: #EEE; border-radius: 2px 2px 0px 0px; width: 100%; height: 29px; padding-right: 4px; /\* Old browsers \*/ display: -webkit-box; -webkit-box-orient: horizontal; -webkit-box-align: stretch; display: -moz-box; -moz-box-orient: horizontal; -moz-box-align: stretch; display: box; box-orient: horizontal; box-align: stretch; /\* Modern browsers \*/ display: flex; flex-direction: row; align-items: stretch; /\* Old browsers \*/ -webkit-box-pack: end; -moz-box-pack: end; box-pack: end; /\* Modern browsers \*/ justify-content: flex-end; display: -webkit-flex; } @media print { .celltoolbar { display: none; } } .ctb\_hideshow { display: none; vertical-align: bottom; } /\* ctb\_show is added to the ctb\_hideshow div to show the cell toolbar. Cell toolbars are only shown when the ctb\_global\_show class is also set. \*/ .ctb\_global\_show .ctb\_show.ctb\_hideshow { display: block; } .ctb\_global\_show .ctb\_show + .input\_area, .ctb\_global\_show .ctb\_show + div.text\_cell\_input, .ctb\_global\_show .ctb\_show ~ div.text\_cell\_render { border-top-right-radius: 0px; border-top-left-radius: 0px; } .ctb\_global\_show .ctb\_show ~ div.text\_cell\_render { border: 1px solid #cfcfcf; } .celltoolbar { font-size: 87%; padding-top: 3px; } .celltoolbar select { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; width: inherit; font-size: inherit; height: 22px; padding: 0px; display: inline-block; } .celltoolbar select:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .celltoolbar select::-moz-placeholder { color: #999; opacity: 1; } .celltoolbar select:-ms-input-placeholder { color: #999; } .celltoolbar select::-webkit-input-placeholder { color: #999; } .celltoolbar select::-ms-expand { border: 0; background-color: transparent; } .celltoolbar select[disabled], .celltoolbar select[readonly], fieldset[disabled] .celltoolbar select { background-color: #eeeeee; opacity: 1; } .celltoolbar select[disabled], fieldset[disabled] .celltoolbar select { cursor: not-allowed; } textarea.celltoolbar select { height: auto; } select.celltoolbar select { height: 30px; line-height: 30px; } textarea.celltoolbar select, select[multiple].celltoolbar select { height: auto; } .celltoolbar label { margin-left: 5px; margin-right: 5px; } .tags\_button\_container { width: 100%; display: flex; } .tag-container { display: flex; flex-direction: row; flex-grow: 1; overflow: hidden; position: relative; } .tag-container > \* { margin: 0 4px; } .remove-tag-btn { margin-left: 4px; } .tags-input { display: flex; } .cell-tag:last-child:after { content: ""; position: absolute; right: 0; width: 40px; height: 100%; /\* Fade to background color of cell toolbar \*/ background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE); } .tags-input > \* { margin-left: 4px; } .cell-tag, .tags-input input, .tags-input button { display: block; width: 100%; height: 32px; padding: 6px 12px; font-size: 13px; line-height: 1.42857143; color: #555555; background-color: #fff; background-image: none; border: 1px solid #ccc; border-radius: 2px; -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; height: 30px; padding: 5px 10px; font-size: 12px; line-height: 1.5; border-radius: 1px; box-shadow: none; width: inherit; font-size: inherit; height: 22px; line-height: 22px; padding: 0px 4px; display: inline-block; } .cell-tag:focus, .tags-input input:focus, .tags-input button:focus { border-color: #66afe9; outline: 0; -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); } .cell-tag::-moz-placeholder, .tags-input input::-moz-placeholder, .tags-input button::-moz-placeholder { color: #999; opacity: 1; } .cell-tag:-ms-input-placeholder, .tags-input input:-ms-input-placeholder, .tags-input button:-ms-input-placeholder { color: #999; } .cell-tag::-webkit-input-placeholder, .tags-input input::-webkit-input-placeholder, .tags-input button::-webkit-input-placeholder { color: #999; } .cell-tag::-ms-expand, .tags-input input::-ms-expand, .tags-input button::-ms-expand { border: 0; background-color: transparent; } .cell-tag[disabled], .tags-input input[disabled], .tags-input button[disabled], .cell-tag[readonly], .tags-input input[readonly], .tags-input button[readonly], fieldset[disabled] .cell-tag, fieldset[disabled] .tags-input input, fieldset[disabled] .tags-input button { background-color: #eeeeee; opacity: 1; } .cell-tag[disabled], .tags-input input[disabled], .tags-input button[disabled], fieldset[disabled] .cell-tag, fieldset[disabled] .tags-input input, fieldset[disabled] .tags-input button { cursor: not-allowed; } textarea.cell-tag, textarea.tags-input input, textarea.tags-input button { height: auto; } select.cell-tag, select.tags-input input, select.tags-input button { height: 30px; line-height: 30px; } textarea.cell-tag, textarea.tags-input input, textarea.tags-input button, select[multiple].cell-tag, select[multiple].tags-input input, select[multiple].tags-input button { height: auto; } .cell-tag, .tags-input button { padding: 0px 4px; } .cell-tag { background-color: #fff; white-space: nowrap; } .tags-input input[type=text]:focus { outline: none; box-shadow: none; border-color: #ccc; } .completions { position: absolute; z-index: 110; overflow: hidden; border: 1px solid #ababab; border-radius: 2px; -webkit-box-shadow: 0px 6px 10px -1px #adadad; box-shadow: 0px 6px 10px -1px #adadad; line-height: 1; } .completions select { background: white; outline: none; border: none; padding: 0px; margin: 0px; overflow: auto; font-family: monospace; font-size: 110%; color: #000; width: auto; } .completions select option.context { color: #286090; } #kernel\_logo\_widget .current\_kernel\_logo { display: none; margin-top: -1px; margin-bottom: -1px; width: 32px; height: 32px; } [dir="rtl"] #kernel\_logo\_widget { float: left !important; float: left; } .modal .modal-body .move-path { display: flex; flex-direction: row; justify-content: space; align-items: center; } .modal .modal-body .move-path .server-root { padding-right: 20px; } .modal .modal-body .move-path .path-input { flex: 1; } #menubar { box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; margin-top: 1px; } #menubar .navbar { border-top: 1px; border-radius: 0px 0px 2px 2px; margin-bottom: 0px; } #menubar .navbar-toggle { float: left; padding-top: 7px; padding-bottom: 7px; border: none; } #menubar .navbar-collapse { clear: left; } [dir="rtl"] #menubar .navbar-toggle { float: right; } [dir="rtl"] #menubar .navbar-collapse { clear: right; } [dir="rtl"] #menubar .navbar-nav { float: right; } [dir="rtl"] #menubar .nav { padding-right: 0px; } [dir="rtl"] #menubar .navbar-nav > li { float: right; } [dir="rtl"] #menubar .navbar-right { float: left !important; } [dir="rtl"] ul.dropdown-menu { text-align: right; left: auto; } [dir="rtl"] ul#new-menu.dropdown-menu { right: auto; left: 0; } .nav-wrapper { border-bottom: 1px solid #e7e7e7; } i.menu-icon { padding-top: 4px; } [dir="rtl"] i.menu-icon.pull-right { float: left !important; float: left; } ul#help\_menu li a { overflow: hidden; padding-right: 2.2em; } ul#help\_menu li a i { margin-right: -1.2em; } [dir="rtl"] ul#help\_menu li a { padding-left: 2.2em; } [dir="rtl"] ul#help\_menu li a i { margin-right: 0; margin-left: -1.2em; } [dir="rtl"] ul#help\_menu li a i.pull-right { float: left !important; float: left; } .dropdown-submenu { position: relative; } .dropdown-submenu > .dropdown-menu { top: 0; left: 100%; margin-top: -6px; margin-left: -1px; } [dir="rtl"] .dropdown-submenu > .dropdown-menu { right: 100%; margin-right: -1px; } .dropdown-submenu:hover > .dropdown-menu { display: block; } .dropdown-submenu > a:after { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; display: block; content: "\f0da"; float: right; color: #333333; margin-top: 2px; margin-right: -10px; } .dropdown-submenu > a:after.fa-pull-left { margin-right: .3em; } .dropdown-submenu > a:after.fa-pull-right { margin-left: .3em; } .dropdown-submenu > a:after.pull-left { margin-right: .3em; } .dropdown-submenu > a:after.pull-right { margin-left: .3em; } [dir="rtl"] .dropdown-submenu > a:after { float: left; content: "\f0d9"; margin-right: 0; margin-left: -10px; } .dropdown-submenu:hover > a:after { color: #262626; } .dropdown-submenu.pull-left { float: none; } .dropdown-submenu.pull-left > .dropdown-menu { left: -100%; margin-left: 10px; } #notification\_area { float: right !important; float: right; z-index: 10; } [dir="rtl"] #notification\_area { float: left !important; float: left; } .indicator\_area { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; } [dir="rtl"] .indicator\_area { float: left !important; float: left; } #kernel\_indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; border-left: 1px solid; } #kernel\_indicator .kernel\_indicator\_name { padding-left: 5px; padding-right: 5px; } [dir="rtl"] #kernel\_indicator { float: left !important; float: left; border-left: 0; border-right: 1px solid; } #modal\_indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; } [dir="rtl"] #modal\_indicator { float: left !important; float: left; } #readonly-indicator { float: right !important; float: right; color: #777; margin-left: 5px; margin-right: 5px; width: 11px; z-index: 10; text-align: center; width: auto; margin-top: 2px; margin-bottom: 0px; margin-left: 0px; margin-right: 0px; display: none; } .modal\_indicator:before { width: 1.28571429em; text-align: center; } .edit\_mode .modal\_indicator:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f040"; } .edit\_mode .modal\_indicator:before.fa-pull-left { margin-right: .3em; } .edit\_mode .modal\_indicator:before.fa-pull-right { margin-left: .3em; } .edit\_mode .modal\_indicator:before.pull-left { margin-right: .3em; } .edit\_mode .modal\_indicator:before.pull-right { margin-left: .3em; } .command\_mode .modal\_indicator:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: ' '; } .command\_mode .modal\_indicator:before.fa-pull-left { margin-right: .3em; } .command\_mode .modal\_indicator:before.fa-pull-right { margin-left: .3em; } .command\_mode .modal\_indicator:before.pull-left { margin-right: .3em; } .command\_mode .modal\_indicator:before.pull-right { margin-left: .3em; } .kernel\_idle\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f10c"; } .kernel\_idle\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_idle\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_idle\_icon:before.pull-left { margin-right: .3em; } .kernel\_idle\_icon:before.pull-right { margin-left: .3em; } .kernel\_busy\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f111"; } .kernel\_busy\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_busy\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_busy\_icon:before.pull-left { margin-right: .3em; } .kernel\_busy\_icon:before.pull-right { margin-left: .3em; } .kernel\_dead\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f1e2"; } .kernel\_dead\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_dead\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_dead\_icon:before.pull-left { margin-right: .3em; } .kernel\_dead\_icon:before.pull-right { margin-left: .3em; } .kernel\_disconnected\_icon:before { display: inline-block; font: normal normal normal 14px/1 FontAwesome; font-size: inherit; text-rendering: auto; -webkit-font-smoothing: antialiased; -moz-osx-font-smoothing: grayscale; content: "\f127"; } .kernel\_disconnected\_icon:before.fa-pull-left { margin-right: .3em; } .kernel\_disconnected\_icon:before.fa-pull-right { margin-left: .3em; } .kernel\_disconnected\_icon:before.pull-left { margin-right: .3em; } .kernel\_disconnected\_icon:before.pull-right { margin-left: .3em; } .notification\_widget { color: #777; z-index: 10; background: rgba(240, 240, 240, 0.5); margin-right: 4px; color: #333; background-color: #fff; border-color: #ccc; } .notification\_widget:focus, .notification\_widget.focus { color: #333; background-color: #e6e6e6; border-color: #8c8c8c; } .notification\_widget:hover { color: #333; background-color: #e6e6e6; border-color: #adadad; } .notification\_widget:active, .notification\_widget.active, .open > .dropdown-toggle.notification\_widget { color: #333; background-color: #e6e6e6; border-color: #adadad; } .notification\_widget:active:hover, .notification\_widget.active:hover, .open > .dropdown-toggle.notification\_widget:hover, .notification\_widget:active:focus, .notification\_widget.active:focus, .open > .dropdown-toggle.notification\_widget:focus, .notification\_widget:active.focus, .notification\_widget.active.focus, .open > .dropdown-toggle.notification\_widget.focus { color: #333; background-color: #d4d4d4; border-color: #8c8c8c; } .notification\_widget:active, .notification\_widget.active, .open > .dropdown-toggle.notification\_widget { background-image: none; } .notification\_widget.disabled:hover, .notification\_widget[disabled]:hover, fieldset[disabled] .notification\_widget:hover, .notification\_widget.disabled:focus, .notification\_widget[disabled]:focus, fieldset[disabled] .notification\_widget:focus, .notification\_widget.disabled.focus, .notification\_widget[disabled].focus, fieldset[disabled] .notification\_widget.focus { background-color: #fff; border-color: #ccc; } .notification\_widget .badge { color: #fff; background-color: #333; } .notification\_widget.warning { color: #fff; background-color: #f0ad4e; border-color: #eea236; } .notification\_widget.warning:focus, .notification\_widget.warning.focus { color: #fff; background-color: #ec971f; border-color: #985f0d; } .notification\_widget.warning:hover { color: #fff; background-color: #ec971f; border-color: #d58512; } .notification\_widget.warning:active, .notification\_widget.warning.active, .open > .dropdown-toggle.notification\_widget.warning { color: #fff; background-color: #ec971f; border-color: #d58512; } .notification\_widget.warning:active:hover, .notification\_widget.warning.active:hover, .open > .dropdown-toggle.notification\_widget.warning:hover, .notification\_widget.warning:active:focus, .notification\_widget.warning.active:focus, .open > .dropdown-toggle.notification\_widget.warning:focus, .notification\_widget.warning:active.focus, .notification\_widget.warning.active.focus, .open > .dropdown-toggle.notification\_widget.warning.focus { color: #fff; background-color: #d58512; border-color: #985f0d; } .notification\_widget.warning:active, .notification\_widget.warning.active, .open > .dropdown-toggle.notification\_widget.warning { background-image: none; } .notification\_widget.warning.disabled:hover, .notification\_widget.warning[disabled]:hover, fieldset[disabled] .notification\_widget.warning:hover, .notification\_widget.warning.disabled:focus, .notification\_widget.warning[disabled]:focus, fieldset[disabled] .notification\_widget.warning:focus, .notification\_widget.warning.disabled.focus, .notification\_widget.warning[disabled].focus, fieldset[disabled] .notification\_widget.warning.focus { background-color: #f0ad4e; border-color: #eea236; } .notification\_widget.warning .badge { color: #f0ad4e; background-color: #fff; } .notification\_widget.success { color: #fff; background-color: #5cb85c; border-color: #4cae4c; } .notification\_widget.success:focus, .notification\_widget.success.focus { color: #fff; background-color: #449d44; border-color: #255625; } .notification\_widget.success:hover { color: #fff; background-color: #449d44; border-color: #398439; } .notification\_widget.success:active, .notification\_widget.success.active, .open > .dropdown-toggle.notification\_widget.success { color: #fff; background-color: #449d44; border-color: #398439; } .notification\_widget.success:active:hover, .notification\_widget.success.active:hover, .open > .dropdown-toggle.notification\_widget.success:hover, .notification\_widget.success:active:focus, .notification\_widget.success.active:focus, .open > .dropdown-toggle.notification\_widget.success:focus, .notification\_widget.success:active.focus, .notification\_widget.success.active.focus, .open > .dropdown-toggle.notification\_widget.success.focus { color: #fff; background-color: #398439; border-color: #255625; } .notification\_widget.success:active, .notification\_widget.success.active, .open > .dropdown-toggle.notification\_widget.success { background-image: none; } .notification\_widget.success.disabled:hover, .notification\_widget.success[disabled]:hover, fieldset[disabled] .notification\_widget.success:hover, .notification\_widget.success.disabled:focus, .notification\_widget.success[disabled]:focus, fieldset[disabled] .notification\_widget.success:focus, .notification\_widget.success.disabled.focus, .notification\_widget.success[disabled].focus, fieldset[disabled] .notification\_widget.success.focus { background-color: #5cb85c; border-color: #4cae4c; } .notification\_widget.success .badge { color: #5cb85c; background-color: #fff; } .notification\_widget.info { color: #fff; background-color: #5bc0de; border-color: #46b8da; } .notification\_widget.info:focus, .notification\_widget.info.focus { color: #fff; background-color: #31b0d5; border-color: #1b6d85; } .notification\_widget.info:hover { color: #fff; background-color: #31b0d5; border-color: #269abc; } .notification\_widget.info:active, .notification\_widget.info.active, .open > .dropdown-toggle.notification\_widget.info { color: #fff; background-color: #31b0d5; border-color: #269abc; } .notification\_widget.info:active:hover, .notification\_widget.info.active:hover, .open > .dropdown-toggle.notification\_widget.info:hover, .notification\_widget.info:active:focus, .notification\_widget.info.active:focus, .open > .dropdown-toggle.notification\_widget.info:focus, .notification\_widget.info:active.focus, .notification\_widget.info.active.focus, .open > .dropdown-toggle.notification\_widget.info.focus { color: #fff; background-color: #269abc; border-color: #1b6d85; } .notification\_widget.info:active, .notification\_widget.info.active, .open > .dropdown-toggle.notification\_widget.info { background-image: none; } .notification\_widget.info.disabled:hover, .notification\_widget.info[disabled]:hover, fieldset[disabled] .notification\_widget.info:hover, .notification\_widget.info.disabled:focus, .notification\_widget.info[disabled]:focus, fieldset[disabled] .notification\_widget.info:focus, .notification\_widget.info.disabled.focus, .notification\_widget.info[disabled].focus, fieldset[disabled] .notification\_widget.info.focus { background-color: #5bc0de; border-color: #46b8da; } .notification\_widget.info .badge { color: #5bc0de; background-color: #fff; } .notification\_widget.danger { color: #fff; background-color: #d9534f; border-color: #d43f3a; } .notification\_widget.danger:focus, .notification\_widget.danger.focus { color: #fff; background-color: #c9302c; border-color: #761c19; } .notification\_widget.danger:hover { color: #fff; background-color: #c9302c; border-color: #ac2925; } .notification\_widget.danger:active, .notification\_widget.danger.active, .open > .dropdown-toggle.notification\_widget.danger { color: #fff; background-color: #c9302c; border-color: #ac2925; } .notification\_widget.danger:active:hover, .notification\_widget.danger.active:hover, .open > .dropdown-toggle.notification\_widget.danger:hover, .notification\_widget.danger:active:focus, .notification\_widget.danger.active:focus, .open > .dropdown-toggle.notification\_widget.danger:focus, .notification\_widget.danger:active.focus, .notification\_widget.danger.active.focus, .open > .dropdown-toggle.notification\_widget.danger.focus { color: #fff; background-color: #ac2925; border-color: #761c19; } .notification\_widget.danger:active, .notification\_widget.danger.active, .open > .dropdown-toggle.notification\_widget.danger { background-image: none; } .notification\_widget.danger.disabled:hover, .notification\_widget.danger[disabled]:hover, fieldset[disabled] .notification\_widget.danger:hover, .notification\_widget.danger.disabled:focus, .notification\_widget.danger[disabled]:focus, fieldset[disabled] .notification\_widget.danger:focus, .notification\_widget.danger.disabled.focus, .notification\_widget.danger[disabled].focus, fieldset[disabled] .notification\_widget.danger.focus { background-color: #d9534f; border-color: #d43f3a; } .notification\_widget.danger .badge { color: #d9534f; background-color: #fff; } div#pager { background-color: #fff; font-size: 14px; line-height: 20px; overflow: hidden; display: none; position: fixed; bottom: 0px; width: 100%; max-height: 50%; padding-top: 8px; -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); /\* Display over codemirror \*/ z-index: 100; /\* Hack which prevents jquery ui resizable from changing top. \*/ top: auto !important; } div#pager pre { line-height: 1.21429em; color: #000; background-color: #f7f7f7; padding: 0.4em; } div#pager #pager-button-area { position: absolute; top: 8px; right: 20px; } div#pager #pager-contents { position: relative; overflow: auto; width: 100%; height: 100%; } div#pager #pager-contents #pager-container { position: relative; padding: 15px 0px; box-sizing: border-box; -moz-box-sizing: border-box; -webkit-box-sizing: border-box; } div#pager .ui-resizable-handle { top: 0px; 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border-top: none; }@media print { div.cell { display: block; page-break-inside: avoid; } div.output\_wrapper { display: block; page-break-inside: avoid; } div.output { display: block; page-break-inside: avoid; } } Introduction[¶](#Introduction) ------------------------------ This tutorial illustrates the spectra computation for standard and pure B modes. We will only use the `HEALPIX` pixellisation to pass through the different steps of generation. The `HEALPIX` survey mask is a disk centered on longitude 30° and latitude 50° with a radius of 25 radians. The `nside` value is set to 512 for this tutorial to reduce computation time. Preamble[¶](#Preamble) ---------------------- `matplotlib` magic In [1]: ``` %matplotlib inline ``` Versions used for this tutorial In [2]: ``` import numpy as np import matplotlib as mpl import matplotlib.pyplot as plt import healpy as hp import pspy print(" Numpy :", np.\_\_version\_\_) print("Matplotlib :", mpl.\_\_version\_\_) print(" healpy :", hp.\_\_version\_\_) print(" pspy :", pspy.\_\_version\_\_) ``` ``` Numpy : 1.18.0 Matplotlib : 3.1.2 healpy : 1.13.0 pspy : 1.2.0+4.gcb26dc1 ``` Get default data dir from `pspy` and set Planck colormap as default In [3]: ``` from pixell import colorize colorize.mpl\_setdefault("planck") ``` Generation of the templates, mask and apodisation type[¶](#Generation-of-the-templates,-mask-and-apodisation-type) ------------------------------------------------------------------------------------------------------------------ We start by specifying the `HEALPIX` survey parameters namely longitude, latitude and patch size. The `nside` value is set to 512. In [4]: ``` lon, lat = 30, 50 radius = 25 nside = 512 ``` Given the `nside` value, we can set the $\ell$max value In [5]: ``` lmax = 3 \* nside - 1 ``` For this example, we will make use of 3 components : Temperature (spin 0) and polarisation Q and U (spin 2) In [6]: ``` ncomp = 3 ``` Given the parameters, we can generate the `HEALPIX` template as follow In [7]: ``` from pspy import so\_map template = so\_map.healpix\_template(ncomp, nside) ``` We also define the binary template for the window function pixels In [8]: ``` binary = so\_map.healpix\_template(ncomp=1, nside=nside) vec = hp.pixelfunc.ang2vec(lon, lat, lonlat=True) disc = hp.query\_disc(nside, vec, radius=radius\*np.pi/180) binary.data[disc] = 1 ``` Generation of spectra[¶](#Generation-of-spectra) ------------------------------------------------ ### Generate window[¶](#Generate-window) We then create an apodisation for the survey mask. We use a C1 apodisation with an apodisation size of 5 degrees In [9]: ``` from pspy import so\_window window = so\_window.create\_apodization(binary, apo\_type="C1", apo\_radius\_degree=5) hp.mollview(window.data, title=None) ```  We can also have a look to the corresponding spin 1 and spin 2 window functions In [10]: ``` niter = 3 w1\_plus, w1\_minus, w2\_plus, w2\_minus = so\_window.get\_spinned\_windows(window, lmax=lmax, niter=niter) plt.figure(figsize=(8, 8)) kwargs = {"rot": (lon, lat, 0), "xsize": 3500, "reso": 1, "title": None} hp.gnomview(w1\_plus.data, \*\*kwargs, sub=(2, 2, 1)) hp.gnomview(w1\_minus.data, \*\*kwargs, sub=(2, 2, 2)) hp.gnomview(w2\_plus.data, \*\*kwargs, sub=(2, 2, 3)) hp.gnomview(w2\_minus.data, \*\*kwargs, sub=(2, 2, 4)) ```  ### Binning file[¶](#Binning-file) We create a binning file with the following format : lmin, lmax, lmean In [11]: ``` import os output\_dir = "/tmp/tutorial\_purebb" os.makedirs(output\_dir, exist\_ok=True) binning\_file = os.path.join(output\_dir, "binning.dat") from pspy import pspy\_utils pspy\_utils.create\_binning\_file(bin\_size=50, n\_bins=300, file\_name=binning\_file) ``` ### Compute mode coupling matrix[¶](#Compute-mode-coupling-matrix) For spin 0 and 2 the window need to be a tuple made of two objects: the window used for spin 0 and the one used for spin 2 In [12]: ``` window\_tuple = (window, window) ``` The windows (for `spin0` and `spin2`) are going to couple mode together, we compute a mode coupling matrix in order to undo this effect given the binning file. We do it for both calculations *i.e.* standard and pure B mode In [13]: ``` from pspy import so\_mcm print("computing standard mode coupling matrix") mbb\_inv, Bbl = so\_mcm.mcm\_and\_bbl\_spin0and2(window\_tuple, binning\_file, lmax=lmax, niter=niter, type="Cl") print("computing pure mode coupling matrix") mbb\_inv\_pure, Bbl\_pure = so\_mcm.mcm\_and\_bbl\_spin0and2(window\_tuple, binning\_file, lmax=lmax, niter=niter, type="Cl", pure=True) ``` ``` computing standard mode coupling matrix computing pure mode coupling matrix ``` ### Generation of ΛCDM power spectra[¶](#Generation-of-ΛCDM-power-spectra) We first have to compute $C\_\ell$ data using a cosmology code such as [CAMB](https://camb.readthedocs.io/en/latest/) and we need to install it since this is not a prerequisite of `pspy`. We can do it within this notebook by executing the following command In [14]: ``` %pip install -U camb ``` ``` Requirement already up-to-date: camb in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (1.1.0) Requirement already satisfied, skipping upgrade: scipy>=1.0 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.4.1) Requirement already satisfied, skipping upgrade: six in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.13.0) Requirement already satisfied, skipping upgrade: sympy>=1.0 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from camb) (1.5) Requirement already satisfied, skipping upgrade: numpy>=1.13.3 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from scipy>=1.0->camb) (1.18.0) Requirement already satisfied, skipping upgrade: mpmath>=0.19 in /home/garrido/Workdir/CMB/development/pspy/pyenv/lib/python3.8/site-packages (from sympy>=1.0->camb) (1.1.0) Note: you may need to restart the kernel to use updated packages. ``` To make sure everything goes well, we can import `CAMB` and check its version In [15]: ``` import camb print("CAMB version:", camb.\_\_version\_\_) ``` ``` CAMB version: 1.1.0 ``` Now that `CAMB` is properly installed, we will produce $C\_\ell$ data from $\ell$min=2 to $\ell$max=104 for the following set of $\Lambda$CDM parameters In [16]: ``` ellmin, ellmax = 2, 10\*\*4 ell = np.arange(ellmin, ellmax) cosmo\_params = { "H0": 67.5, "As": 1e-10\*np.exp(3.044), "ombh2": 0.02237, "omch2": 0.1200, "ns": 0.9649, "Alens": 1.0, "tau": 0.0544 } pars = camb.set\_params(\*\*cosmo\_params) pars.set\_for\_lmax(ellmax, lens\_potential\_accuracy=1) results = camb.get\_results(pars) powers = results.get\_cmb\_power\_spectra(pars, CMB\_unit="muK") ``` We finally have to write $C\_\ell$ into a file to feed the `so_map.synfast` function for both pixellisation templates In [17]: ``` cl\_file = os.path.join(output\_dir, "cl\_camb.dat") np.savetxt(cl\_file, np.hstack([ell[:, np.newaxis], powers["total"][ellmin:ellmax]])) ``` Running simulations[¶](#Running-simulations) -------------------------------------------- Given the parameters and data above, we will now simulate `n_sims` simulations to check for mean and variance of BB spectrum. For illustrative purpose, we will only run 10 simulations (~ few minutes) but for reasonable comparisons, you should increase this number to few tens of simulations. We will do it for both calculations (standard and pure) and finally we will graphically compare results We first need to specify the order of the spectra to be used by `pspy` although only BB spectrum will be used In [18]: ``` spectra = ["TT", "TE", "TB", "ET", "BT", "EE", "EB", "BE", "BB"] ``` and we define a dictionnary of methods regarding the calculation type for B mode spectrum In [19]: ``` from pspy import sph\_tools methods = { "standard": {"alm" : sph\_tools.get\_alms, "mbb": mbb\_inv, "bb": []}, "pure": {"alm": sph\_tools.get\_pure\_alms, "mbb": mbb\_inv\_pure, "bb": []} } ``` In [20]: ``` from pspy import so\_spectra n\_sims = 10 for i in range(n\_sims): cmb = template.synfast(cl\_file) for k, v in methods.items(): get\_alm = v.get("alm") alm = get\_alm(cmb, window\_tuple, niter, lmax) ell, ps = so\_spectra.get\_spectra(alm, spectra=spectra) ellb, ps\_dict = so\_spectra.bin\_spectra(ell, ps, binning\_file, lmax, type="Cl", mbb\_inv=v.get("mbb"), spectra=spectra) v["bb"] += [ps\_dict["BB"]] ``` Let's plot the mean results against the theory value for BB spectrum In [21]: ``` for k, v in methods.items(): v["mean"] = np.mean(v.get("bb"), axis=0) v["std"] = np.std(v.get("bb"), axis=0) from pspy import pspy\_utils ell\_th, ps\_theory = pspy\_utils.ps\_lensed\_theory\_to\_dict(cl\_file, output\_type="Cl", lmax=lmax) ps\_theory\_b = so\_mcm.apply\_Bbl(Bbl, ps\_theory, spectra=spectra) ps\_theory\_b\_pure = so\_mcm.apply\_Bbl(Bbl\_pure, ps\_theory, spectra=spectra) fac = ellb \* (ellb + 1) / (2 \* np.pi) facth = ell\_th \* (ell\_th + 1) / (2 \* np.pi) plt.figure(figsize=(7, 6)) grid = plt.GridSpec(4, 1, hspace=0, wspace=0) main = plt.subplot(grid[:3], xticklabels=[], xlim=(0, 2\*nside)) main.plot(ell\_th[:lmax], ps\_theory["BB"][:lmax] \* facth[:lmax], color="grey") main.errorbar(ellb, ps\_theory\_b["BB"] \* fac, color="tab:red", label="binned theory BB") main.errorbar(ellb, ps\_theory\_b\_pure["BB"] \* fac, color="tab:blue", label="binned theory BB pure") main.errorbar(ellb, methods.get("standard").get("mean") \* fac, methods.get("standard").get("std") \* fac, fmt=".", color="tab:red", label="mean BB") main.errorbar(ellb, methods.get("pure").get("mean") \* fac, methods.get("pure").get("std") \* fac, fmt=".", color="tab:blue", label="mean BB pure") main.set(ylim=(-0.07, 0.17), ylabel=r"$D^{BB}\_{\ell}$") plt.legend(title=r"$n\_{\rm sims}=%s$" % n\_sims) ratio = plt.subplot(grid[3], xlim=(0, 2\*nside)) ratio.plot(ellb, methods.get("pure").get("std") / methods.get("standard").get("std"), ".-k") ratio.set(ylabel=r"$\sigma^{\rm pure}\_\ell/ \sigma\_\ell$", xlabel=r"$\ell$"); ratio.axhline(1) ``` Out[21]: ``` <matplotlib.lines.Line2D at 0x7fd338b42c70> ```  pspy documentation [pspy](index.html#document-index) stable * [`so\_map` - a module for handling HEALPIX and CAR maps](index.html#document-so_map) * [`so\_window` - a module for window function generation](index.html#document-so_window) * [`so\_mcm` - a module for mode coupling calculation](index.html#document-so_mcm) * [`so\_spectra` - a module for power spectra estimation and debiasing](index.html#document-so_spectra) * [`so\_cov` - a module for covariance matrix estimation](index.html#document-so_cov) * [`sph\_tools` - a helper module for spherical harmonic transformation](index.html#document-sph_tools) * [`pspy\_utils` - a module with utilities for `pspy`](index.html#document-pspy_utils) [pspy](index.html#document-index) * [Docs](index.html#document-index) » * pspy documentation * [Edit on GitHub](https://github.com/simonsobs/pspy/blob/45eb016d73916c8098c80bca68bef1e84f193784/docs/source/index.rst) --- Welcome to pspy’s documentation ================================================================================================== `pspy` is a cosmology code for calculating CMB power spectra and covariance matrices. See the python example notebooks for an introductory set of examples on how to use the package. [](https://pypi.python.org/pypi/pspy/) [](https://github.com/simonsobs/pspy/blob/master/LICENSE) [](https://github.com/simonsobs/pspy/actions?query=workflow%3ATesting) [](https://pspy.readthedocs.io/en/latest/?badge=latest) [](https://codecov.io/gh/simonsobs/pspy) [](https://mybinder.org/v2/gh/simonsobs/pspy/master?filepath=notebooks/%2Findex.ipynb) * Free software: BSD license * `pspy` documentation: <https://pspy.readthedocs.io>. * Scientific documentation: <https://pspy.readthedocs.io/en/latest/scientific_doc.pdf> Installing[¶](#installing "Permalink to this headline") ------------------------------------------------------- ``` $ pip install pspy [--user] ``` You can test your installation by running ``` $ test-pspy ``` If everything goes fine, no errors will occur. Otherwise, you can report your problem on the [Issues tracker](https://github.com/simonsobs/pspy/issues). If you plan to develop `pspy`, it is better to checkout the latest version by doing ``` $ git clone https://github.com/simonsobs/pspy.git /where/to/clone ``` Then you can install the `pspy` library and its dependencies *via* ``` $ pip install -e /where/to/clone ``` The `-e` option allow the developer to make changes within the `pspy` directory without having to reinstall at every changes. Ipython notebooks[¶](#ipython-notebooks "Permalink to this headline") --------------------------------------------------------------------- * [Reading, writing and plotting SO maps](https://pspy.readthedocs.org/en/latest/tutorial_io.html) * [Generate spin0 and spin2 spectra for CAR](https://pspy.readthedocs.org/en/latest/tutorial_spectra_car_spin0and2.html) * [Generate spin0 and spin2 spectra for HEALPIX](https://pspy.readthedocs.org/en/latest/tutorial_spectra_healpix_spin0and2.html) * [Projecting HEALPIX to CAR](https://pspy.readthedocs.org/en/latest/tutorial_projection.html) * [Compute spectra for standard and pure B modes](https://pspy.readthedocs.org/en/latest/tutorial_purebb.html) Others tutorials can be found under the `tutorials` directory. Dependencies[¶](#dependencies "Permalink to this headline") ----------------------------------------------------------- * Python >= 3.8 * `pyFFTW` <https://pyfftw.readthedocs.io> * `healpy` <https://healpy.readthedocs.io> * `pixell` >= 0.7.0 <https://pixell.readthedocs.io> Authors[¶](#authors "Permalink to this headline") ------------------------------------------------- * [Thibaut Louis](https://thibautlouis.github.io) * Steve Choi * DW Han * [Xavier Garrido](https://xgarrido.github.io) * Sigurd Naess * [Adrien La Posta](https://adrien-laposta.github.io) The code is part of [PSpipe](https://github.com/simonsobs/PSpipe) the Simons Observatory power spectrum pipeline. Main high-level modules:[¶](#main-high-level-modules "Permalink to this headline") ================================================================================== `so\_map` - a module for handling HEALPIX and CAR maps[¶](#so-map-a-module-for-handling-healpix-and-car-maps "Permalink to this headline") ------------------------------------------------------------------------------------------------------------------------------------------ `so\_window` - a module for window function generation[¶](#so-window-a-module-for-window-function-generation "Permalink to this headline") ------------------------------------------------------------------------------------------------------------------------------------------ `so\_mcm` - a module for mode coupling calculation[¶](#so-mcm-a-module-for-mode-coupling-calculation "Permalink to this headline") ---------------------------------------------------------------------------------------------------------------------------------- `so\_spectra` - a module for power spectra estimation and debiasing[¶](#so-spectra-a-module-for-power-spectra-estimation-and-debiasing "Permalink to this headline") -------------------------------------------------------------------------------------------------------------------------------------------------------------------- `so\_cov` - a module for covariance matrix estimation[¶](#so-cov-a-module-for-covariance-matrix-estimation "Permalink to this headline") ---------------------------------------------------------------------------------------------------------------------------------------- `sph\_tools` - a helper module for spherical harmonic transformation[¶](#sph-tools-a-helper-module-for-spherical-harmonic-transformation "Permalink to this headline") ---------------------------------------------------------------------------------------------------------------------------------------------------------------------- `pspy\_utils` - a module with utilities for `pspy`[¶](#pspy-utils-a-module-with-utilities-for-pspy "Permalink to this headline") -------------------------------------------------------------------------------------------------------------------------------- Indices and tables[¶](#indices-and-tables "Permalink to this headline") ======================================================================= * [Index](genindex.html) * [Module Index](py-modindex.html) * [Search Page](search.html)

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数据交换平台接口说明 0.1 文档 --- 概述[¶](#id1 "永久链接至标题") ===================== > > 接口访问方式（http­post，json格式传输） ``` def myFun(x,y): print x+y ``` 注册[¶](#id1 "永久链接至标题") ===================== > > 注册相关内容。 业务部分[¶](#id1 "永久链接至标题") ======================= > > 各业务介绍以及接口调用方法说明。 请求地址[¶](#id2 "永久链接至标题") ----------------------- * 数据上传：[基地址]/ DataExchange/Post * 数据查询：[基地址]/ DataExchange/Get * 更新下载状态：[基地址]/ DataExchange/UpdateDownloadState * 更新数据状态：[基地址]/ DataExchange/Update 消息头[¶](#id3 "永久链接至标题") ---------------------- * Code：用户账号 * Token：校验值 Token用于校验数据完整性，其计算方法为：**MD5([请求字符串][用户Token])**，需要说明的是： 1. [请求字符串] 是请求Json字符串去除制表符[“r”,”n”,”t”]和空格后所得的值。 2. [用户Token]为注册账号后所获得到的Token值。 请求数据Json字段[¶](#json "永久链接至标题") ------------------------------ * BusinessType：业务类型 * HospitalCode：医院代码 * IP：客户端IP地址 * MAC：客户端MAC地址 * HostName：客户端主机名 * Data：具体请求数据（如上传的业务数据，查询时候的查询条件等） 各业务说明[¶](#id4 "永久链接至标题") ------------------------ ### YY001 医院信息[¶](#yy001 "永久链接至标题") > > 上传医院基础信息。 #### 数据上传[¶](#id1 "永久链接至标题") ##### 字段说明[¶](#id2 "永久链接至标题") | 字段 | 说明 | 类型 | 长度 | 非空 | 附录字典数据 | | --- | --- | --- | --- | --- | --- | | Code | 医院代码 | 字符 | 32 | √ | | | Name | 医院名称 | 字符 | 128 | √ | | ##### 示例[¶](#id3 "永久链接至标题") ###### 请求[¶](#id4 "永久链接至标题") ``` { "BusinessType": "YY001", "HospitalCode": null, "IP": "192.168.0.1", "MAC": "123456789012", "HostName": "\*\*", "Data": [ { "Code": "test02", "Name": "hospital02" } ] } ``` ###### 请求结果[¶](#id5 "永久链接至标题") ``` { "Code": 1, "Message": "success", "Completed": true, "Data": null } ``` #### 数据下载[¶](#id6 "永久链接至标题") > > 获取所有医院数据，暂不支持条件查询。 ##### 示例[¶](#id7 "永久链接至标题") ###### 请求[¶](#id8 "永久链接至标题") ``` { "BusinessType": "YY001", "HospitalCode": null, "IP": "192.168.0.1", "MAC": "123456789012", "HostName": "\*\*", "Data": null } ``` ###### 请求结果[¶](#id9 "永久链接至标题") ``` { "Code": 0, "Message": "", "Completed": true, "Data": [ { "Code": "Test001", "Name": "测试医院", "Memo": null }, { "Code": "test02", "Name": "hospital02", "Memo": null } ] } ``` ### YY101 采购订单[¶](#yy101 "永久链接至标题") ``` { "BusinessType": "YY101", "HospitalCode": "Test001", "IP": "192.168.0.1", "MAC": "123456789012", "HostName": "\*\*", "Data": [ { "HospitalCode": "Test001", "OrderNO": "Test002", "OrderType": 0, "OrderLevel": 0, "ProductType":"0", "SupplierCode": "苏20160343", "DistributionSiteCode": "01", "Employee": "\*\*", "SumQuantity":4, "Amount":13.8, "Creator": "制单人", "CreateTime": "2016-12-28 14:55:01", "Memo": "备注", "PurchaseDetail": [{ "Seq":1, "UniCode": "A.101116.1", "Quantity": 1, "Price": 1.2, "Amount": 1.2, "Memo": "明细备注" }, { "Seq":2, "UniCode": "A.185770.1", "Quantity": 3, "Price": 4.2, "Amount": 12.6, "Memo": "明细备注2" }] } ] } ``` ### YY102 配送单[¶](#yy102 "永久链接至标题") ### [內容目录](index.html#document-index) * [概述](index.html#document-summary/index) * [注册](index.html#document-register/index) * [业务部分](index.html#document-business/index) ### Related Topics * [Documentation overview](index.html#document-index) ### 快速搜索 输入相关的术语，模块，类或者函数名称进行搜索 ©2017, 卫宁健康. | Powered by [Sphinx 1.3.5](http://sphinx-doc.org/) & [Alabaster 0.7.9](https://github.com/bitprophet/alabaster)

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shell 1.0.1 documentation [shell](index.html#document-index) stable * [shell Tutorial](index.html#document-tutorial) * [Shell API](index.html#document-shell_api) * [Testing shell](index.html#document-testing) * [Contributing](index.html#document-contributing) [shell](index.html#document-index) * [Docs](index.html#document-index) » * shell 1.0.1 documentation * [Edit on GitHub](https://github.com/toastdriven/shell/blob/703307b6dc9a287133f6ac1881f231eed6b79506/docs/index.rst) --- shell’s Documentation[¶](#shell-s-documentation "Permalink to this headline") ============================================================================= “”“A better way to run shell commands in Python.”“” Built because every time I go to use [subprocess](http://docs.python.org/2.7/library/subprocess.html), I spend more time in the docs & futzing around than actually implementing what I’m trying to get done. Philosophy[¶](#philosophy "Permalink to this headline") ------------------------------------------------------- * Makes running commands more natural * Assumes you care about the output/errors by default * Covers the 80% case of running commands * A nicer API * Works on Linux/OS X (untested on Windows but might work?) Contents: ### shell Tutorial[¶](#shell-tutorial "Permalink to this headline") If you’ve ever tried to run a shell command in Python, you’re likely unhappy about it. The `subprocess` module, while a huge & consistent step forward over the previous ways Python shelled out, has a rather painful interface. If you’re like me, you spent more time in the docs than you did writing working code. `shell` tries to fix this, by glossing over the warts in the `subprocess` API & making running commands *easy*. #### Installation[¶](#installation "Permalink to this headline") If you’re developing in Python, you ought to be using [pip](http://www.pip-installer.org/en/latest/). Installing (from your terminal) looks like: ``` $ pip install shell ``` #### Quickstart[¶](#quickstart "Permalink to this headline") For the impatient: ``` >>> from shell import shell >>> ls = shell('ls') >>> for file in ls.output(): ... print file 'another.txt' # Or if you need more control, the same code can be stated as... >>> from shell import Shell >>> sh = Shell() >>> sh.run('ls') >>> for file in sh.output(): ... print file 'another.txt' ``` #### Getting Started[¶](#getting-started "Permalink to this headline") ##### Importing[¶](#importing "Permalink to this headline") The first thing you’ll need to do is import `shell`. You can either use the easy functional version: ``` >>> from shell import shell ``` Or the class-based & extensible version: ``` >>> from shall import Shell ``` ##### Your First Command[¶](#your-first-command "Permalink to this headline") Running a basic command is simple. Simply hand the command you’d use at the terminal off to `shell`: ``` >>> from shell import shell >>> shell('touch hello\_world.txt') # The class-based variant. >>> from shall import Shell >>> sh = Shell() >>> sh.run('touch hello\_world.txt') ``` You should now have a `hello\_world.txt` file created in your current directory. ##### Reading Output[¶](#reading-output "Permalink to this headline") By default, `shell` captures output/errors from the command being run. You can read the output & errors like so: ``` >>> from shell import shell >>> sh = shell('ls /tmp') # Your output from these calls will vary... >>> sh.output() [ 'hello.txt', 'world.py', ] >>> sh.errors() [] # The class-based variant. >>> from shell import Shell >>> sh = Shell() >>> sh.run('ls /tmp') >>> sh.output() [ 'hello.txt', 'world.py', ] >>> sh.errors() [] ``` You can also look at what the process ID was & the return code.: ``` >>> sh.pid 15172 >>> sh.code 0 ``` Getting a `0` from `sh.code` means a process finished sucessfully. Higher integer return values generally mean there was an error. ##### Interactive[¶](#interactive "Permalink to this headline") If the command is interactive, you can send it input as well.: ``` >>> from shell import shell >>> sh = shell('cat -u', has\_input=True) >>> sh.write('Hello, world!') >>> sh.output() [ 'Hello, world!' ] # The class-based variant. >>> from shall import Shell >>> sh = Shell(has\_input=True) >>> sh.run('cat -u') >>> sh.write('Hello, world!') >>> sh.output() [ 'Hello, world!' ] ``` Warning You get one shot at sending input, after which the command will finish. Using `shell` for advanced, multi-prompt shell commands is likely is not a good option. ##### Chaining[¶](#chaining "Permalink to this headline") You can also chain calls together, if that suits you.: ``` >>> from shell import shell >>> shell('cat -u', has\_input=True).write('Hello, world!').output() [ 'Hello, world!' ] # The class-based variant. >>> from shall import Shell >>> Shell(has\_input=True).run('cat -u').write('Hello, world!').output() [ 'Hello, world!' ] ``` ##### Ignoring Large Output[¶](#ignoring-large-output "Permalink to this headline") By default, `shell` captures all output/errors. If you have a command that generates a large volume of output that you don’t care about, you can ignore it like so.: ``` >>> from shell import shell >>> sh = shell('run\_intensive\_command -v', record\_output=False, record\_errors=False) >>> sh.code 0 # The class-based variant. >>> from shall import Shell >>> sh = Shell(record\_output=False, record\_errors=False) >>> sh.run('run\_intensive\_command -v') >>> sh.code 0 ``` #### What Now?[¶](#what-now "Permalink to this headline") If you need more advanced functionality, subclassing the `Shell` class is the best place to start. You can find more details about it in the [*Shell API*](index.html#document-shell_api). ### Shell API[¶](#shell-api "Permalink to this headline") #### shell[¶](#module-shell "Permalink to this headline") ##### shell[¶](#id1 "Permalink to this headline") A better way to run shell commands in Python. If you just need to quickly run a command, you can use the `shell` shortcut function: ``` >>> from shell import shell >>> ls = shell('ls') >>> for file in ls.output(): ... print file 'another.txt' ``` If you need to extend the behavior, you can also use the `Shell` object: ``` >>> from shell import Shell >>> sh = Shell(has\_input=True) >>> cat = sh.run('cat -u') >>> cat.write('Hello, world!') >>> cat.output() ['Hello, world!'] ``` *exception* `shell.``CommandError`[¶](#shell.CommandError "Permalink to this definition") Thrown when a command fails. `error_code` *= 1*[¶](#shell.CommandError.error_code "Permalink to this definition") *exception* `shell.``MissingCommandException`[¶](#shell.MissingCommandException "Permalink to this definition") Thrown when no command was setup. *class* `shell.``Shell`(*has\_input=False*, *record\_output=True*, *record\_errors=True*, *strip\_empty=True*)[¶](#shell.Shell "Permalink to this definition") Handles executing commands & recording output. Optionally accepts a `has\_input` parameter, which should be a boolean. If set to `True`, the command will wait to execute until you call the `Shell.write` method & send input. (Default: `False`) Optionally accepts a `record\_output` parameter, which should be a boolean. If set to `True`, the stdout from the command will be recorded. (Default: `True`) Optionally accepts a `record\_errors` parameter, which should be a boolean. If set to `True`, the stderr from the command will be recorded. (Default: `True`) Optionally accepts a `strip\_empty` parameter, which should be a boolean. If set to `True`, only non-empty lines from `Shell.output` or `Shell.errors` will be returned. (Default: `True`) `errors`(*raw=False*)[¶](#shell.Shell.errors "Permalink to this definition") Returns the errors from running a command. Optionally accepts a `raw` parameter, which should be a boolean. If `raw` is set to `False`, you get an array of lines of errors. If `raw` is set to `True`, the raw string of errors is returned. (Default: `False`) Example: ``` >>> from shell import Shell >>> sh = Shell() >>> sh.run('ls /there-s-no-way-anyone/has/this/directory/please') >>> sh.errors() [ 'ls /there-s-no-way-anyone/has/this/directory/please: No such file or directory' ] ``` `kill`()[¶](#shell.Shell.kill "Permalink to this definition") Kills a given process. Example: ``` >>> from shell import Shell >>> sh = Shell() >>> sh.run('some\_long\_running\_thing') >>> sh.kill() ``` `output`(*raw=False*)[¶](#shell.Shell.output "Permalink to this definition") Returns the output from running a command. Optionally accepts a `raw` parameter, which should be a boolean. If `raw` is set to `False`, you get an array of lines of output. If `raw` is set to `True`, the raw string of output is returned. (Default: `False`) Example: ``` >>> from shell import Shell >>> sh = Shell() >>> sh.run('ls ~') >>> sh.output() [ 'hello.txt', 'world.txt', ] ``` `run`(*command*)[¶](#shell.Shell.run "Permalink to this definition") Runs a given command. Requires a `command` parameter should be either a string command (easier) or an array of arguments to send as the command (if you know what you’re doing). Returns the `Shell` instance. Example: ``` >>> from shell import Shell >>> sh = Shell() >>> sh.run('ls- alh') ``` `write`(*the\_input*)[¶](#shell.Shell.write "Permalink to this definition") If you’re working with an interactive process, sends that input to the process. This needs to be used in conjunction with the `has\_input=True` parameter. Requires a `the\_input` parameter, which should be a string of the input to send to the command. Returns the `Shell` instance. Example: ``` >>> from shell import Shell >>> sh = Shell(has\_input=True) >>> sh.run('cat -u') >>> sh.write('Hello world!') ``` *exception* `shell.``ShellException`[¶](#shell.ShellException "Permalink to this definition") The base exception for all shell-related errors. `shell.``shell`(*command*, *has\_input=False*, *record\_output=True*, *record\_errors=True*, *strip\_empty=True*)[¶](#shell.shell "Permalink to this definition") A convenient shortcut for running commands. Requires a `command` parameter should be either a string command (easier) or an array of arguments to send as the command (if you know what you’re doing). Optionally accepts a `has\_input` parameter, which should be a boolean. If set to `True`, the command will wait to execute until you call the `Shell.write` method & send input. (Default: `False`) Optionally accepts a `record\_output` parameter, which should be a boolean. If set to `True`, the stdout from the command will be recorded. (Default: `True`) Optionally accepts a `record\_errors` parameter, which should be a boolean. If set to `True`, the stderr from the command will be recorded. (Default: `True`) Optionally accepts a `strip\_empty` parameter, which should be a boolean. If set to `True`, only non-empty lines from `Shell.output` or `Shell.errors` will be returned. (Default: `True`) Returns the `Shell` instance, which has been run with the given command. Example: ``` >>> from shell import shell >>> sh = shell('ls -alh \*py') >>> sh.output() ['hello.py', 'world.py'] ``` ### Testing shell[¶](#testing-shell "Permalink to this headline") `shell` maintains 100% passing tests at all times. That said, there are undoubtedly bugs or odd configurations it doesn’t cover. #### Setup[¶](#setup "Permalink to this headline") Getting setup to run tests (Python 2) looks like: ``` $ git clone https://github.com/toastdriven/shell $ cd shell $ virtualenv env $ . env/bin/activate $ pip install mock==1.0.1 $ pip install nose==1.3.0 ``` Once that’s setup, setting up for Python 3 looks like: ``` $ virtualenv -p python3 env3 $ . env3/bin/activate $ pip install mock==1.0.1 $ pip install nose==1.3.0 ``` #### Running the tests[¶](#running-the-tests "Permalink to this headline") To run the tests, run the following: ``` $ nosetests -s tests.py ``` ### Contributing[¶](#contributing "Permalink to this headline") To contribute to `shell`, it must meet the following criteria: * Has a failing test case (see `tests.py` & testing) without the fix * Has a fix that matches existing style * Has docstrings * Adds to the documentation if the change is user-facing * Is BSD-compatibly licensed Please create fork on Github, clone your fork, create a new branch, make your changes on that branch, push it back to Github & open a pull request. Indices and tables[¶](#indices-and-tables "Permalink to this headline") ======================================================================= * [Index](genindex.html) * [Module Index](py-modindex.html) * [Search Page](search.html)

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io.aviso/rook 0.2.0 documentation [io.aviso/rook](index.html#document-index) stable * [Defining Endpoints](index.html#document-endpoints) + [Rook Routes](index.html#rook-routes) + [Namespace Metadata](index.html#namespace-metadata) + [Route Names](index.html#route-names) * [Nested Namespaces](index.html#document-nested) + [Namespace Inheritance](index.html#namespace-inheritance) * [Interceptors](index.html#document-interceptors) + [Interceptor Values](index.html#interceptor-values) + [Interceptor Generators](index.html#interceptor-generators) + [Applying Interceptors](index.html#applying-interceptors) * [Asynchronous Endpoints](index.html#document-async) * [Argument Resolvers](index.html#document-arg-resolvers) + [Predefined Argument Resolvers](index.html#predefined-argument-resolvers) + [Defining New Argument Resolvers](index.html#defining-new-argument-resolvers) + [Argument Resolver Errors](index.html#argument-resolver-errors) * [API](http://avisonovate.github.io/docs/rook) * [GitHub Project](https://github.com/AvisoNovate/rook) [io.aviso/rook](index.html#document-index) * [Docs](index.html#document-index) » * io.aviso/rook 0.2.0 documentation * [Edit on GitHub](https://github.com/AvisoNovate/rook/blob/57b849b56a40459b3e8a84dab77b415edc6d1639/docs/index.rst) --- io.aviso/rook[¶](#io-aviso-rook "Permalink to this headline") ============================================================= [](http://clojars.org/io.aviso/rook) Easier Routing for Pedestal[¶](#easier-routing-for-pedestal "Permalink to this headline") ----------------------------------------------------------------------------------------- Rook is a way of mapping Clojure namespaces and functions as the endpoints of a [Pedestal](https://github.com/pedestal/pedestal) application. Using Rook, you map a namespace to a URI; Rook uses metadata to identify which functions are endpoints. It generates a [Pedestal routing table](https://github.com/pedestal/pedestal/blob/master/guides/documentation/service-routing.md#routing) that you can use as-is, or combine with more traditional routing. With Rook, your configuration is both less dense and more dynamic, because you only have to explicitly identify your namespaces and Rook finds all the endpoints within those namespaces. Rook is also designed to work well the [Component](https://github.com/stuartsierra/component) library, though Component is not a requirement. Rook generates a set of table routes that can then be used by the io.pedestal.http/create-server bootstrapping function. ``` (require '[io.aviso.rook :as rook] [io.pedestal.http :as http]) (def service-map {:env :prod ::http/routes (rook/gen-table-routes {"/widgets" 'org.example.widgets "/gizmos" 'org.example.gizmos} nil}) ::http/resource-path "/public" ::http/type :jetty ::http/port 8080}) (defn start [] (-> service-map http/create-server http/start)) ``` Rook supports many more options: * Nested namespaces * Defining interceptors for endpoints * Leveraging metadata at the namespace and function level * Defining constraints for path parameters License[¶](#license "Permalink to this headline") ------------------------------------------------- Rook is released under the terms of the [Apache Software License 2.0](http://www.apache.org/licenses/LICENSE-2.0). ### Defining Endpoints[¶](#defining-endpoints "Permalink to this headline") The io.aviso.rook/gen-table-routes function is provided with namespaces; the actual endpoints are functions within those namespaces. Rook identifies functions with the metadata :rook-route. Functions with this metadata will be added to the generated routing table. Here’s an example namespace with just a single endpoint: ``` (ns org.example.widgets (:require [ring.util.response :as r]) (defn list-widgets {:rook-route [:get ""]} [] ;; Placeholder: (r/response {:widgets []}) ``` Endpoint functions take some number of parameters (more on this shortly) and return a [Ring response map](https://github.com/ring-clojure/ring/blob/master/SPEC#L108). The above example is a placeholder: it returns a fixed and largely empty Ring response. In a real application, the function could be provided with a database connection of some sort and could perform a query and return the results of that query. Note Pedestal route matching is very specific: the `list-widgets` endpoint above is mapped to `/widgets`, and a client that requests `/widgets/` will get a 404 NOT FOUND response. #### Rook Routes[¶](#rook-routes "Permalink to this headline") The route meta is either two or three terms, in a vector: * The verb to use, such as :get, :post, :head, ... or :all. * The path to match. * (optional) A map of path variable constraints. For example, we might define some additional endpoints to flesh out a typical resource-oriented API: ``` (defn get-widget {:rook-route [:get "/:id" {:id #"\d{6}"}]} [^:path-param id] ;; Placeholder: (r/not-found "WIDGET NOT FOUND")) (defn update-widget {:rook-route [:post "/:id" {:id #"\d{6}"}]} [^:path-param id ^:request body] ;; Placeholder: (r/response "OK")) ``` The URI for the `get-widget` endpoint is `/widgets/:id`, where the `:id` path parameter must match the regular expression (six numeric digits). This is because the namespace’s URI is `/widgets` and the endpoint’s path is directly appended to that. Likewise, the `update-widget` endpoint is also mapped to the URI `/widgets/:id`, but with the POST verb. Because of how Pedestal routing is designed, a URI where the `:id` variable doesn’t match the regular expression will be ignored (it might match some other endpoint, but will most likely match nothing and result in a 404 NOT FOUND response). This example also illustrates another major feature of Rook: endpoints can have any number of parameters, but use metadata on the parameters to identify what is to be supplied. Two common parameter metadata are used in the above example: * :path-param is used to mark function parameters that should match against a path parameter. * :request is used to mark function parameters that should match a key stored in the Ring request map. Rook defines additional parameter metadata, and they are extensible. #### Namespace Metadata[¶](#namespace-metadata "Permalink to this headline") That repetition about the `:id` path parameter constraint is bothersome, having it multiple places just makes it more likely to have conflicts. Fortunately, Rook merges metadata from the namespace with metadata from the endpoint, allowing such things to be just defined once: ``` (ns org.example.widgets {:constraints {:id #"\d{6}"}} (:require [ring.util.response :as r]) (defn list-widgets {:rook-route [:get ""]} [] ;; Placeholder: (r/response {:widgets []}) (defn get-widget {:rook-route [:get "/:id"]} [^:path-param id] ;; Placeholder: (r/not-found "WIDGET NOT FOUND")) (defn update-widget {:rook-route [:post "/:id"]} [^:path-param id ^:request body] ;; Placeholder: (r/response "OK")) ``` Here, each endpoint inherits the `:id` constraint from the namespace. #### Route Names[¶](#route-names "Permalink to this headline") When Rook creates an interceptor, it provides a name for the interceptor; this is the keyword version of the fully qualified endpoint name. The interceptor name is the default route name, used by Pedestal when it create URLs. You can override the route name using the :route-name metadata on the endpoint function. ### Nested Namespaces[¶](#nested-namespaces "Permalink to this headline") Defining nested (that is, hierarchical) namespaces requires a smidgen of extra work. In the non-nested case, it is usually sufficient to just specify the namespace (as a symbol), but with nested namespaces, a map is used; this is the full namespace definition. ``` (rook/gen-table-routes {"/hotels" {:ns 'org.example.hotels :nested {"/:hotel-id/rooms" 'org.example.rooms}}} nil) ``` In this example, the outer namespace is mapped to `/hotels` and the nested rooms namespace is mapped to `/hotels/:hotel-id/rooms` ... in other words, whenever we access a specific room, we must also provide the hotel’s id in the URI. :nested is just a new mapping of paths to namespaces under `/hotels`; the map keys are extensions to the path, and the values can be namespace symbols or nested namespace definitions. #### Namespace Inheritance[¶](#namespace-inheritance "Permalink to this headline") Nested namespaces may inherit some data from their containing namespace: * :arg-resolvers - a map from keyword to [argument resolver factory](index.html#document-arg-resolvers) * :interceptors - a vector of [Pedestal interceptors](index.html#document-interceptors) * :constraints - a map from keyword to regular expression, for path parameter constraints These options flow as follows: ![digraph { defaults [label="io.aviso.rook/default-options"]; opts [label="gen-table-routes options"]; nsdef [label="outer namespace definition"]; nsmeta [label="outer namespace metadata"]; nesteddef [label="nested namespace definition"]; nestedmeta [label="nested namespace metadata"]; nestedfns [label="nested endpoint function metadata"]; fmeta [label="endpoint function metadata"]; defaults -> opts -> nsdef -> nsmeta -> nesteddef -> nestedmeta -> nestedfns; nsmeta -> fmeta; }](_images/graphviz-13597ec5ed8849652a5b077b96286e8f7583a383.png) Metadata on an endpoint function is handled slightly differently, :constraints overrides come from the third value in the :rook-route metadata. In all cases, a deep merge takes place: * nested maps are merged together, later overriding earlier * sequences are concatenated together (using `concat` for sequences, or `into` for vectors) This inheritance is quite useful: for example, the org.example.hotels namespace may define a `:hotel-id` constraint that will be inherited by the org.example.rooms namespace endpoints. ### Interceptors[¶](#interceptors "Permalink to this headline") Pedestal is all about [interceptors](https://github.com/pedestal/pedestal/blob/master/guides/documentation/service-interceptors.md), they are integral to how Pedestal applications are constructed and composed. Each endpoint function may define a set of interceptors specific to that function, using :interceptors metadata. Ultimately, Rook wraps endpoints functions so that they are Pedestal interceptors. Interceptors may be [inherited](index.html#document-nested) from the namespace and elsewhere. #### Interceptor Values[¶](#interceptor-values "Permalink to this headline") The interceptor values can be any of the values that Pedestal accepts as an interceptor: * An interceptor, as by io.pedestal.interceptor/interceptor. * A map, which is converted to an interceptor * A bunch of other things that make sense in terms of Pedestal’s deprecated terse routing syntax Rook adds one additional option, a keyword. The keyword references the :interceptor-defs option (passed to io.aviso.rook/gen-table-routes). A matching value must exist (otherwise, an exception is thrown). The value is typically a configured interceptor value. Pedestal Conflict? Normally, a function in the intererceptor chain is [interpreted as a Ring handler](http://pedestal.io/reference/interceptors#_handlers). However, those are *only* allowed in the final position of an interceptor chain. That’s never the case with Rook, because a Rook endpoint function is at the end of the interceptor chain. Alternately, the value might be a function, which acts as an interceptor generator. #### Interceptor Generators[¶](#interceptor-generators "Permalink to this headline") An interceptor generator is a function that creates an interceptor customized to the particular endpoint. It is passed a map that describes the endpoint, and returns an interceptor. The endpoint description has the following keys: :var The Clojure Var containing the endpoint function. :meta The metadata map for the endpoint function. :endpoint-name A string version of the fully qualified name of the endpoint function In this way, the interceptor can use the details of the particular endpoint to generate a custom interceptor. For example, an interceptor that did some special validation, or authentication, might use metadata on the endpoint function to determine what validations and authentications are necessary for that particular endpoint. Here’s a more concrete example, part of Rook’s test suite: ``` (ns sample.dynamic-interceptors (:require [io.pedestal.interceptor :refer [interceptor]])) (defn endpoint-labeler [endpoint] (let [{:keys [endpoint-name]} endpoint] (interceptor {:name ::endpoint-labeler :leave (fn [context] (assoc-in context [:response :headers "Endpoint"] endpoint-name))}))) ``` This generator returns an interceptor that operates during the leave phase, when there’s a response map. It adds the `Endpoint` header to the response. This same interceptor generator could be added to any number of endpoints; a unique interceptor instance will be generated for each endpoint. #### Applying Interceptors[¶](#applying-interceptors "Permalink to this headline") When a namespace provides :interceptor metadata, that’s a list of interceptors to add to every endpoint in the namespace, and in any nested namespaces. Tip This can cast a wider net than is desirable; for example, including the io.aviso.rook.interceptors/keywordized-form interceptor at the namespace level will add it to all endpoints in the namespace, even those that do not include a POSTed form. However, each individual endpoint will ultimately end up with its own individual interceptor list. Further, none of those interceptors will actually execute in a request unless routing selects that particular endpoint to handle the request. ![digraph { incoming [label="Incoming Request"]; definterceptors [label="Default Interceptors"]; routing [label="Pedestal Routing"]; endpoint [label="Endpoint Selected"]; interceptors [label="Endpoint's Interceptors"]; fn [label="Endpoint Function"]; incoming -> definterceptors -> routing -> endpoint -> interceptors -> fn; }](_images/graphviz-6e1987399263c5da97588d4545899e395768edb8.png) The default interceptors are usually provided by io.pedestal.http/create-server. These cover a number of cases such as handling requests for unmapped URIs, logging, preventing cross-site scripting forgery, and so forth. The :interceptor metadata in namespaces and elsewhere simply builds up the Endpoint’s Interceptors stage. ### Asynchronous Endpoints[¶](#asynchronous-endpoints "Permalink to this headline") There isn’t much to say here: an endpoint can be asynchronous by returning a [Clojure core.async](https://github.com/clojure/core.async) channel instead of a Ring response map. The channel must convey a Ring response map. Really, Pedestal takes care of the rest. ### Argument Resolvers[¶](#argument-resolvers "Permalink to this headline") In Rook, endpoint functions may [have many parameters](index.html#document-endpoints), in contrast to a traditional [Ring](https://github.com/ring-clojure) handler (or middleware), or a Pedestal interceptor. Argument resolvers are the bridge between the Pedestal context and the individual parameters of an endpoint function. Compared to a typical Ring request handler, this saves your code from the work of destructuring the Ring request map. Beyond that, it is possible to expose other Pedestal context values, or (via custom argument resolver generators) entirely other business logic. From a unit testing perspective, it is likely easier to pass a number of parameters than it is to construct the necessary Ring response map. The :arg-resolvers option is a map from keyword to argument resolver generator. When a parameter of an endpoint function has metadata with the matching key, the generator is invoked. The generator is passed the parameter symbol, and returns the argument resolver function; the argument resolver function is used during request processing. An argument resolver function is passed the Pedestal context and returns the argument value. There will be an individual argument resolver function created for each endpoint function parameter. By convention, when the metadata value is the literal value `true`, the symbol is converted to a keyword. So, if an endpoint function has a parameter `^:request body`, the :request argument resolver generator will return an argument resolver function equivalent to: ``` (fn [context] (let [v (get-in context [:request :body])] (if (some? v) v (throw (ex-info ...))))) ``` #### Predefined Argument Resolvers[¶](#predefined-argument-resolvers "Permalink to this headline") :request > > Key in the Ring request map (:request). Value may not be nil. :path-param > > A path parameter, value may not be nil. :query-param > > A query parameter, as an HTTP decoded string. :form-param > > A form parameter. Endpoints using this should include the > io.aviso.rook.interceptors/keywordized-form interceptor. Further details about these are in the [API documentation](http://avisonovate.github.io/docs/rook/io.aviso.rook.arg-resolvers.html#var-default-arg-resolvers). #### Defining New Argument Resolvers[¶](#defining-new-argument-resolvers "Permalink to this headline") It is completely reasonable to provide your own :arg-resolvers (as the :arg-resolvers key in the options passed to io.aviso.rook/gen-table-routes). ``` (require '[io.aviso.rook :as rook]) (defn inject-arg-resolver [injections] (fn [sym] (let [k (rook/meta-value-for-parameter sym :inject) injection (get injections k)] (fn [\_] injection)))) ... (rook/gen-table-routes {...} {:arg-resolvers {:inject (inject-arg-resolver dependencies)}}) ``` In the above example code, when the table routes are created, the dependencies symbol contains values that an endpoint function might need; for example, a database connection or the like. When :inject is seen on a parameter symbol, the symbol is converted to a keyword via the function meta-value-for-parameter. This is then used to find the specific dependency value; finally, an argument resolver is returned that ignores the provided context and supplies the value. #### Argument Resolver Errors[¶](#argument-resolver-errors "Permalink to this headline") Each endpoint parameter must apply to one and only one argument resolver. If you apply more than one (say `^:inject ^::request body`), you’ll see an exception thrown from gen-table-routes.