Saravanan666/pypi-org huggingface dataset:
Package: ibott-robot-manager
Robot ManagerRobot Manager is a package that allows to connect with iBottrobot manager consoleto use cloud features.
visithttps://ibott.io/Academy/robot-managerto learn more about the robot manager features.Bot Classcreate_queue(queue_name): Create a queue.
find_queue_by_id(queue_id): Find a queue by its ID.
find_queues_by_name(queue_name): Find all queues by its name.
get_asset_by_name(asset_name): Get an asset by its name.
get_asset_by_id(asset_id): Get an asset by its ID.
save_file_from_console(file_path, file_name): Save a file from the Orchestrator API.Queue Classcreate_item(item_data): Create a new item in the queue.
get_next_item(): Get the next pending item in the queue.
set_retry_times(times): Set the retry times of an item.Item Classset_item_as_working(self): Set item as working.
set_item_as_ok(self): Set item as ok.
set_item_as_fail(self): Set item as failed.
set_item_as_pending(self): Set item as pending.
set_item_executions(self): increment item execution counterAsset Classget_assets(): Get assets all assets from the robot manager console.
get_asset_by_id(): Get asset by ID from the robot manager console.
get_asset_by_name(): Get asset by name from the robot manager console.
Package: ibp2py
Ibp2py - Python library for SAP IBPThis is a Python library designed to make it easier to interact with the SAP Integrated Business Planning (IBP) API.InstallationYou can install ibpy using pip:pipinstallibpy2pyUsageTo use ibpy, first create an instance of theibpyclass using your SAP IBP username and password:fromibpyimportibpyusername="YOUR_IBP_USERNAME"password="YOUR_IBP_PASSWORD"host='YOUR_HOST_URL'connection=Ibp2py(username,password,host)Extracting Master DataHere is an example of how to extract master data:
Method -> MASTER_DATA_API_SRVPlanningAreaID='SAP01'VersionID="BASE"MasterDataTypeID="M3LOCATION"PlanningAreaDescr="Planning"VersionName="Base Line"select='*'data=connection.masterdata(MasterDataTypeID,select=select,PlanningAreaID=PlanningAreaID,VersionID=VersionID,PlanningAreaDescr=PlanningAreaDescr,VersionName=VersionName)Extracting Telemetry DataMethod ->AddInLogon(MtrgActyExcelAddInLogon)
Method ->AddInLogon(MtrgActyExcelAddInPlanningView)module -> MethodHere is an example of how to extract telemetry data:data=connection.telemetry('PlanningView',module)Extracting Key Figure DataMethod -> 1 (PLANNING_DATA_API_SRV)
Method -> 2 (EXTRACT_ODATA_SRV)modulo -> MethodHere is an example of how to extract key figure data:filters="(PERIODID0_TSTAMP ge datetime'2023-04-01T00:00:00' and PERIODID0_TSTAMP lt datetime'2023-07-02T00:00:00')"+" and CONSENSUSDEMAND gt 0"PlanningAreaID='SAP01'MasterData='PRDID,CUSTID'KeyFigures='CONSENSUSDEMAND'data=connection.keyfigure(PlanningAreaID,MasterData,KeyFigures,modulo=modulo,filters=filters)Contact InformationFor any issues or queries related to ibpy2py, feel free to reach out:Author: Pedro RasthaEmail:pedrorastha@gmail.comLinkedIn:@pedrorasthaContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseApache License 2.0Note:This library is open source and is not sponsored or supported by SAP.
Package: ibpmodel
The ionospheric bubble probability statistical model is a Swarm L2 product, named IBP_CLI. The output of the Ionospheric Bubble Probability (IBP) product is an index, that depends on the day of year or the month of the year, geographic longitude, local time and solar flux index.The output floating point index ranges 0-1 and characterizes the percentage probability of low latitude bubble occurence at the specified time, location and solar flux.This empirical IBP model has been derived from magnetic observations obtained by the CHAMP and Swarm missions. The model is representative for the altitude range 350 - 500 km and low geographic latitudes of +/- 45 degree.DocumentationDetailed documentation can be found at:https://ibp-model.readthedocs.ioQuick StartInstallationUsing pip:$pipinstallibpmodelDependencies:numpypandasmatplotlibscipycdflibUsageThe return value of the functionibpmodel.calculateIBPindex()is of type pandas.DataFrame.>>>importibpmodelasibp>>>ibp.calculateIBPindex(day_month=15,# Day of Year or Monthlongitude=0,# Longitude in degreelocal_time=20.9,# Local time in hoursf107=150)# F10.7 cm Solar Flux indexDoyMonthLonLTF10.7IBP0151020.91500.3547>>>ibp.calculateIBPindex(day_month=['Jan','Feb','Mar'],local_time=22)DoyMonthLonLTF10.7IBP0151-180221500.07391151-175221500.07222151-170221500.07173151-165221500.07284151-160221500.0771...................211743155221500.2061212743160221500.2025213743165221500.1994214743170221500.1967215743175221500.1943[216rowsx6columns]>>>ibp.plotIBPindex(doy=349)>>>>>>ibp.plotButterflyData(f107=150)>>>ReferencesStolle et al.,An empirical climatological model of the occurrence of F region equatorial plasma irregularities, 8th Swarm data quality workshop at ESA/ESRIN, October 2017.Lucas Schreiter, Anwendungsorientierte Modellierung der Auftretenswahrscheinlichkeit und relativen Häufigkeit von äquatorialen Plasmabubbles,  Master’s thesis, Institute of Mathematics, University of Potsdam, 2016. (in German only.)Information for developersSetup environment$gitclonehttps://igit.iap-kborn.de/ibp/ibp-model.git$cdibp-model$pipinstall-rrequirements.txt$pipinstall-e.Test of package using doctest$pythonsrc/ibpmodel/ibpcalc.pyNo error should occur.Test run of the documentation$cddocs$makeclean&&makehtmlThedocs/build/html/directory contains the html files. Openindex.htmlin browser.
The results of the code examples on the usage page are generated automatically. Therefore the ibpmodel package must be installed (pip install -e .).
Package: ibptws
IBPT Web ServicesImplementa uma camada fina para acesso aosweb servicesdoIBPT, para
auxiliar no cumprimento àLei 12.741/2012, popularmente conhecida como  “De
Olho no Imposto”. O Instituto Brasileiro de Planejamento e Tributação, IBPT,
calcula e fornece os valores aproximados dos tributos para produtos e serviços
com base no códigoNCM(Nomenclatura Comum do Mercosul, para produtos) e no
códigoNBS(Nomenclatura Brasileira de Serviços).Para que o acesso aosweb servicesseja possível, é preciso cadastrar-se noIBPTpara obter seutokende acesso.Configuração e Consultas BásicasExemplo básico de configuração e consulta de produto:>>>fromibptwsimportconf>>>fromibptwsimportget_produto>>>conf.token='ZyW9z...'# cadastre-se no IBPT para obter seu token>>>conf.cnpj='08427847000169'>>>conf.estado='SP'>>>get_produto('02091021')Produto(codigo=u'2091021',uf=u'SP',ex=0,descricao=u'Gordura de porco,fresca,refrigerada ou congelada',nacional=4.2,estadual=12.0,importado=6.39)Similarmente, para consultar um serviço faça:>>>fromibptwsimportget_servico>>>get_servico('0101')Servico(codigo=u'101',uf=u'SP',descricao=u'An\xe1lise e desenvolvimento de sistemas.',tipo=u'NBS',nacional=13.45,estadual=0.0,municipal=3.9,importado=15.45)CalculadoraDeOlhoNoImpostoA calculadoraDe Olho no Impostoauxilia na computação dos valores
aproximados dos tributos, tornando trivial a consulta pornprodutos e/ou
servicos e a obtenção dos cálculos parciais e totais dos tributos.>>>fromdecimalimportDecimal>>>fromibptws.calculadorasimportDeOlhoNoImposto>>>calc=DeOlhoNoImposto()>>>calc.produto('02091021',0,Decimal('5.75'))>>>calc.servico('0101',Decimal('73.47'))>>>calc.carga_federal_nacional()Decimal('10.123215')>>>calc.carga_federal_importado()Decimal('11.718540')>>>calc.carga_estadual()Decimal('0.6900')>>>calc.carga_municipal()Decimal('2.86533')>>>calc.total_tributos()Decimal('25.397085')>>>calc.total()Decimal('79.22')>>>calc.percentual_sobre_total()Decimal('0.3205893082554910376167634436')Provisionamento de DadosA calculadoraDe Olho no Impostorecorre a umproxypara realizar as
consultas de produtos e serviços, possibilitando que seja implementada uma
camada para provisionamento (cache) das consultas realizadas. Este projeto
traz uma implementação de provisionamento baseada emRedis:fromibptws.calculadorasimportDeOlhoNoImpostofromibptws.provisoesimportProvisaoViaRediscalc=DeOlhoNoImposto(provisao=ProvisaoViaRedis(host='192.168.0.111',port=6379,db=0))Neste exemplo, as consultas a produtos e serviços serão realizadas através
doproxye, uma vez acessado o web services do IBPT, os dados ficarão
provisionados até que expire (o padrão é expirar em 24h, mas você poderá usar
os seus próprios critérios).TestesOs testes são baseados empyteste não há acesso real aos serviços do IBPT,
portanto não há necessidade de configurar o token de acesso para executar os
testes unitários:$pythonsetup.pytestIsenção de ResponsabilidadeOs autores deste projeto não tem qualquer relação com o Instituto Brasileiro de
Planejamento e Tributação (IBPT), e este, por sua vez, não avaliza o uso desta
biblioteca de código. Utilize por sua conta e risco.
Package: ibreakdown
ibreakdownibreakdownis model agnostic predictions explainer with interactions support,
library can show contribution of each feature in your prediction value.SHAPorLIMEconsider only local additive feature attributions, whenibreakdownalso evaluates local feature interactions.AlgorithmAlgorithm is based on ideas describe in paper“iBreakDown: Uncertainty of Model
Explanations for Non-additive Predictive Models”https://arxiv.org/abs/1903.11420and
reference implementation inR(iBreakDown)Intuition behind algorithm is following:The algorithm works in a similar spirit as SHAP or Break Down but is not
restricted to additive effects. The intuition is the following:

1. Calculate a single-step additive contribution for each feature.
2. Calculate a single-step contribution for every pair of features. Subtract additive contribution to assess the interaction specific contribution.
3. Order interaction effects and additive effects in a list that is used to determine sequential contributions.

This simple intuition may be generalized into higher order interactions.In depth explanation can be found in algorithm authors free book:Predictive Models: Explore, Explain, and Debughttps://pbiecek.github.io/PM_VEE/iBreakDown.htmlSimple example# model = RandomForestClassifier(...)explainer=ClassificationExplainer(model)classes=['Deceased','Survived']explainer.fit(X_train,columns,classes)exp=explainer.explain(observation)exp.print()Please check full Titanic example here:https://github.com/jettify/ibreakdown/blob/master/examples/titanic.py+------------------------------------+-----------------+--------------------+--------------------+
| Feature Name                       | Feature Value   |   Contrib:Deceased |   Contrib:Survived |
+------------------------------------+-----------------+--------------------+--------------------|
| intercept                          |                 |          0.613286  |          0.386714  |
| Sex                                | female          |         -0.305838  |          0.305838  |
| Pclass                             | 3               |          0.242448  |         -0.242448  |
| Fare                               | 7.7375          |         -0.119392  |          0.119392  |
| Siblings/Spouses Aboard            | 0               |         -0.0372811 |          0.0372811 |
| ('Age', 'Parents/Children Aboard') | [28.0 0]        |          0.0122196 |         -0.0122196 |
| PREDICTION                         |                 |          0.405443  |          0.594557  |
+------------------------------------+-----------------+--------------------+--------------------+FeaturesSupports predictions explanations for classification and regressionEasy to use API.Works withpandasandnumpySupport interactions between featuresInstallationInstallation process is simple, just:$ pip install ibreakdownRequirementsPython3.6+numpyCHANGES
Package: ibroadcast-dl
iBroadcast Download / SyncThis project was born from a need to download my library from iBroadcast onto my linux machine. Only the OSX and Windows app support this functionality. This project uses the existingibroadcast-pythonlibrary and extends theiBroadcastclass to add adownload_librarymethod.In addition to using this as a Python library, it also has a small CLI to help automate doing library downloads.UsageThis project can be used as both a Python library and CLI.CLIInstallationpipxpipx is available for Linux, OSX, and Windows. Follow theinstall instructions first for pipxthen the following:pipx install ibroadcast-dlUsageTo use the CLI, run theibroadcast-dlcommand:ibroadcast-dl--usernameibroadcast@email.tld~/Music/This will open a progress bar - if your library is large it'll take several hours to sync. If you connection is slow try turing down the number of tracks it downloads at a time (defaults 50) to something around 10:ibroadcast-dl-uibroadcast@email.tld-x10~/Music/Python LibraryThe module has the same method sigrnatures and usage as theibroadcast-pythonlibrary with the addition of adownload_librarymethod.>>>importibroadcastdl>>>ib=ibroadcastdl.iBroadcastDL("email","password")>>>download_dir="./Music">>>offset=0>>>whileoffset<len(ib.tracks):...ib.download_library(offset,50,download_dir)...offset=offset+50This will download the entire library 50 tracks at a time.
Package: ibson
ibsonBSON (Binary JSON) parsing library.UsageThis library is designed to implement a basic BSON library with an interface
that is similar to python's native JSON parsing library. In particular, this
has expected usage:importibsonobj={"a":{"b":[1,2,3],"uuid":uuid.uuid1()}}buffer=ibson.dumps(obj)new_obj=ibson.loads(buffer)# Evaluates as 'True'new_obj==objThis mimics the existingbsonlibrary for python, but also permits reading
from and writing to (seekable) streams and files as well:withopen('file.bson','wb')asstm:ibson.dump(obj,stm)# Elsewherewithopen('file.bson','rb')asstm:new_obj=ibson.load(stm)# Should evaluate Truenew_obj==objNOTE: It is important that the file is opened in binary mode, not text mode!Under the hood, this library is designed in a similar manner as a SAX-style
event-driven parser; it avoids explicit recursion wherever possible and has
calls that permit iterating over the contents using generators with an
interface that can even permit skipping keys/fields altogether. Since the
parsing stack is maintained separately, it can also be used to verify and
attempt to fix some issues.How It WorksThis library works by noting that the byte offset needed in a few places to
(de)serialize BSON is already implicitly tracked in seekable streams via the
call to:fp.tell(), omitting the need to track the byte counts directly.
In places where these byte counts are not directly accessible, the caller is
likely already loading the content into a bytearray or binary stream that can
become seekable anyway. When this field is needed before the value is actually
available (i.e. thelengthof a document before the document is written),
this simply registers the position the length needs to be written, writes out
a placeholder value (0), then retroactively writes out these lengths when they
finally are known, hence the need for the writable stream to also be seekable.
(As a slight optimization, these lengths are sorted and written from the start
to the end of the file again when the encoder is done to effectively make to
sequential passes instead of an arbitrary number of random-access passes.)This library also strives to reduce memory-consumption as best as reasonable
with an iterative parser, intentionally avoiding recursion where possible; the
parser tracks the stack on the heap and also stores various fields internally
so as to avoid loading everything parsed into memory when just traversing the
document, in a manner analogous to SAX-style parsers for XML. (When decoding
and storing the document as a pythondict, yes, that will be in memory.)
Package: ibsplib
IBSPLibibsplib is Python package for working with Quake 3 IBSP structures
References were taken fromhttp://www.mralligator.com/q3Parsing IBSPNo dependenciesTypings & code completion includedMore to come....InstallationAvailable on PyPI, just:pipinstallibsplibUsagefrompathlibimportPathfromibsplibimportIBSPbsp_path=f'{Path(__file__).parent}\\<map name>.bsp'withopen(bsp_path,'rb')asf:bsp_buffer=bytearray(f.read())bsp=IBSP(bsp_buffer)print(f'Version:{bsp.header.version}')print('Textures used:')fortexinbsp.textures:print(f'-\t{tex.name}')LicenseMIT
Package: ibuffpy
ibuffpyThis package applies structural sensitivity analysis to chemical reaction networks to discover buffering structures within them.InstallationTo install ibuffpy, run the following command:pip install ibuffpyThe pygraphviz package is required to visualize the hierarchy graph of buffering structures.
Seehttps://pygraphviz.github.io/documentation/stable/install.htmlfor the installation of pygraphviz.UsageOnce you have installed ibuffpy, you can importReactionNetworkmodule from the ibuffpy packagefrom ibuffpy import ReactionNetworkIn ibuffpy, reaction networks are treated as instances of theReactionNetwork.ReactionNetworkwhich contains information about reaction names, metabolites, and stoichiometric matrices.
To read a network from a csv file, useReactionNetwork.from_csv(" PATH_TO_CSV")where the csv file must contain reaction formulas such as "reaction_idx, substrate1 substrate2, product1 product2" (an example can be found in demo_network1.csv).
If the reaction equation contains more than one metabolite, separate them with a space.
If the coefficient of a metabolite is $n$, it will appear n times on one side.
Outflow reactions or inflow reactions are represented as['inflow_name',['metabolite_name'],['out']]or['outflow_name',['out'],['metabolite_name']].
The metabolite name 'out' is reserved for the node corresponding to outside of the network, which does not appear in the stoichiometric matrix.network = ReactionNetwork.from_csv("PATH_TO_CSV")
network.info()
# list of reactions in the network
print(network.reaction_list)
# list of metabolites in the network except for 'out' node
print(network.cpd_list_nooout)Structural sensitivity analysis computes the network sensitivity to parameter perturbation from the A-matrix, which reflects network structure.ReactionNetwork.compute_amat(network)returns an A-matrix with random real values in nonzero entries of $\partial \boldsymbol r / \partial \boldsymbol x$, a basis of $\ker \nu$, and a basis of $\ker \nu^\top$.ReactionNetworkclass usesscipy.linalg.null_spaceto obtain bases by default, but you can also useker_basis='rref'option.ker_baisorcoker_basisaccepts ndarray or list to provide a self-made bases.# numpy is used to calculate a basis by default.
network = ReactionNetwork.from_csv(path_to_csv, ker_basis='svd', coker_basis='rref')

# augumented matrix
amat = network.compute_amat()

# sensitivity matrix
smat = np.linalg.inv(amat)You can find buffering structures bybs_list = ReactionNetwork.compute_bs(network)and visualise them bygraph = ReactionNetwork.make_hiergraph(bs_list)
graph.draw(PATH_SAVE)
Package: ibug
IBUG: Instance-Based Uncertainty Estimation for Gradient-Boosted Regression TreesIBUGis a simple wrapper that extendsanygradient-boosted regression trees (GBRT) model into a probabilistic estimator, and is compatible with all major GBRT frameworks including LightGBM, XGBoost, CatBoost, and SKLearn.InstallpipinstallibugQuickstartfromibugimportIBUGWrapperfromxgboostimportXGBRegressorfromsklearn.datasetsimportload_diabetesfromsklearn.model_selectionimporttrain_test_split# load diabetes datasetdata=load_diabetes()X,y=data['data'],data['target']# create train/val/test splitX_train,X_test,y_train,y_test=train_test_split(X,y,test_size=0.1,random_state=1)X_train,X_val,y_train,y_val=train_test_split(X_train,y_train,test_size=0.1,random_state=1)# train GBRT modelmodel=XGBRegressor().fit(X_train,y_train)# extend GBRT model into a probabilistic estimatorprob_model=IBUGWrapper().fit(model,X_train,y_train,X_val=X_val,y_val=y_val)# predict mean and variance for unseen instanceslocation,scale=prob_model.pred_dist(X_test)# return k highest-affinity neighbors for more flexible posterior modelinglocation,scale,train_idxs,train_vals=prob_model.pred_dist(X_test,return_kneighbors=True)LicenseApache License 2.0.ReferenceBrophy and Lowd.Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees. NeurIPS 2022.@inproceedings{brophy2022ibug,
  title={Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees},
  author={Brophy, Jonathan and Lowd, Daniel},
  booktitle={International Conference on Neural Information Processing Systems},
  year={2022}
}
Package: ibuk-dl
IBUK DownloaderThis script allows you to download books from the libra.ibuk.pl website and query book information from a given URL.FeaturesDownload books from libra.ibuk.pl.Query book information, including author, title, description, publisher, ISBN, pages, and index.Support for PW (Politechnika Warszawska/Warsaw University of Technology) authentication to access restricted content.Installationpipinstallibuk-dlUsageDownload a BookTo download a book, use the following command:ibuk-dldownload<URL>You can specify the page count with the--page-countoption (if not specified, will download every page) and the output file with the-oor--outputoption. Use-as the output to print the book content to stdout. Add -v to print progress information to the console.If the book is behind PW authentication, you can provide your username and password with the-uand-poptions, respectively. You will also need to use an URL that starts withhttp://eczyt.bg.pw.edu.pl/han/ibuk/Example:ibuk-dl-vdownload--output"podstawy-teorii-obwodow-tom-2.html"-u123123-ppasswordhttp://eczyt.bg.pw.edu.pl/han/ibuk/https/libra.ibuk.pl/reader/podstawy-teorii-obwodow-tom-2-jerzy-osiowski-jerzy-szabatin-234596More information aboutdownload:ibuk-dldownload--helpQuery Book InformationTo query book information, use the following command:ibuk-dlquery<URL>Example:ibuk-dl-vqueryhttps://libra.ibuk.pl/reader/podstawy-teorii-obwodow-tom-2-jerzy-osiowski-jerzy-szabatin-234596More information aboutquery:ibuk-dlquery--helpExport to PDFThis script will output HTML. If you want to have a PDF, you can use your browser'sPrint -> Save to PDFoption.LicenseThis script is provided under a MIT License. SeeLICENSEDisclaimerAs stated in the license, I am not responsible for damage caused by the use of this program. Please respect the terms of use of the libra.ibuk.pl website and any copyright or licensing agreements for the downloaded content. Downloading and/or sharing copyrighted content may be considered illegal in your country.