Saravanan666/pypi-org huggingface dataset:
Package: triku
# Triku - Feature selection for Single Cell Analysis 🦔
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[![Downloads](https://pepy.tech/badge/triku)](https://pepy.tech/project/triku)##THIS PROJECT IS ARCHIVEDYou can see it at our [Github repo](https://github.com/alexmascension/triku)Triku (hedgehog in euskera) is a feature selection method prepared for scRNA-seq.Look at [the docs](https://triku.readthedocs.io/en/latest/) to learn more about triku!
You can visit the notebooks at out [notebook repo](https://github.com/alexmascension/triku_notebooks)!## Cite us!
If you want to learn more about how triku works, you can read our paper. Don’t forget to cite it if you find triku useful!> Alex M Ascensión, Olga Ibáñez-Solé, Iñaki Inza, Ander Izeta, Marcos J Araúzo-Bravo, Triku: a feature selection method based on nearest neighbors for single-cell data, GigaScience, Volume 11, 2022, giac017,https://doi.org/10.1093/gigascience/giac017
Package: trilearn
Bayesian inference in decomposable graphical models using sequential Monte Carlo methodsThis library contains Bayesian inference in decomposable (triangulated) graphical models based on sequential Monte Carlo methods.
Currently supported functionalities include:Bayesian structure learning for discrete log-linear and Gaussian data.Estimation of the number of decomopsable graphs with a
given number of nodes.Predictive classification using Bayesian model averaging (BMA).Random generation of junction trees (the Christmas tree algorithm).InstallationIf graphviz is not installed, you can install it from brew / aptitude / pacman for example$ brew install graphvizOn Ubuntu you might need to runsudo apt-get install python-dev graphviz libgraphviz-dev pkg-configThen run$ pip install trilearnIt is also possible to pull trilearn as a docker image by$ docker pull onceltuca/trilearnRunning the tests$ make testUsageSee the Jupyter notebooks for examples of usage.ScriptsContinuous dataTo approximate the underlying decomposable graph posterior given the dataset sample_data/data_ar1-5.csv run$ pgibbs_ggm_sample -N 50 -M 1000 -f sample_data/data_ar1-5.csv -o results_ggmthis will produce a file containing the Markov chain generated by the particle Gibbs algorithm.
In order to analyze the chain run$ analyze_graph_tajectories -i results_ggm -o results_ggm/plotsthis will produce a bunch of files in the current directory to be analyzed.Discrete dataThe data set examples/data/czech_autoworkers.csv contains six binary variables.
To generate a particle Gibbs trajectory of decomposable graphs type$ pgibbs_loglinear_sample -N 50 -M 300 -f sample_data/czech_autoworkers.csv  -o results_loglinand$ analyze_graph_tajectories -i results_loglin -o results_loglin/plotsthis will produce a number of files in the current directory.Estimate the number of decomposable graphsTo estimate the number of decomposable graphs with up to 15 nodes run for example$ count_chordal_graphs -p 15 -N 20000Built WithNetworkXNumPyScipyPandasSeabornAuthorsFelix L. Riosjust send me an e-mail in case of any questions, felix.leopoldo.rios at gmail comReferencesJ. Olsson, T. Pavlenko, and F. L. Rios. Bayesian learning of weakly structural Markov graph laws using sequential Monte Carlo methods.Electron. J. Statist., 13(2):2865–2897, 2019.J. Olsson, T. Pavlenko, F. L. Rios, Sequential sampling of junction trees for decomposable graphs,Statistics and Computing32, 80, 2022T. Pavlenko, F. L. Rios, Graphical posterior predictive classifier: Bayesian model averaging with particle Gibbs, Theor. Probability and Math. Statist. 109: 81-99, 2023LicenseThis project is licensed under the Apache 2.0 License - see theLICENSEfile for detailsAcknowledgmentsJim Holmstrom
Package: trill
Trill is a Troll interpreterImplemented in Python both as a package and for command line use.Troll is a dice roll language made by Torben Mogensenhttp://hjemmesider.diku.dk/~torbenm/Troll/Command line usageRolling a single die:trill d12Rolling multiple die:trill 3d6Use from a python scriptfrom trill import trill

result, errors = trill("3d6")See further examples in the examples folder.Running testsmake testRunning coveragemake coverageDevelopmentCurrently in a very early state.
Most of the notation is not yet implemented.
See the list below for what is, and what is not (supposed) to work yet.Notation(From summary on quick referencehttp://hjemmesider.diku.dk/~torbenm/Troll/quickRef.html)roll one dn (1 - n)roll m dnroll one zn (0 - n)roll m znarithmetic on single value (+ - * / mod)sgn, sign of numbersum, add up values in collectioncountvalues in collectionUnion of collections (U or @)Union of elements { }minandmaxin collectionallminimaland allmaximalvalues in collectionmedianvalue in collectionleastn andlargestn values in collectionm samples of e ( # )range of values ( .. )choosevalue from collectionpickn values from collection efilters (< <= > >= = =/=)dropelementskeepelementsmultiset difference ( -- )remove duplicates (different)conditional ( if-then-else )probability ( ?p )logical and ( & )logical not ( ! )bind x to value of e1 in e2 (x := e1; e2)foreach x in e1 do e2repeat x := e1 while/until e2accumulate x := e1 while/until e2functioncompositionalcalltext box of single sample ( ' )text box of n samples ( n ' )combine text boxes horisontally ( || )combine text boxes vertically, left-aligned ( |> )combine text boxes vertically, right-aligned ( <| )combine text boxes, centre-aligned ( <> )Pair of e1,e2 ( [e1, e2])First component of pair ( %1 )Second component of pair ( %2 )x~v returns value of x if x is defined, else returns v
Package: trill-proteins
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                                             \/            \/       \/IntroTRILL (TRaining andInference using theLanguage ofLife) is a sandbox for creative protein engineering and discovery. As a bioengineer myself, deep-learning based approaches for protein design and analysis are of great interest to me. However, many of these deep-learning models are rather unwieldy, especially for non ML-practitioners due to their sheer size. Not only does TRILL allow researchers to perform inference on their proteins of interest using a variety of models, but it also democratizes the efficient fine-tuning of large-language models. Whether using Google Colab with one GPU or a supercomputer with many, TRILL empowers scientists to leverage models with millions to billions of parameters without worrying (too much) about hardware constraints. Currently, TRILL supports using these models as of v1.5.0:Breakdown of TRILL's CommandsCommandFunctionAvailable ModelsEmbedGenerates numerical representations or "embeddings" of protein sequences for quantitative analysis and comparison.ESM2,ProtT5-XL,ProstT5VisualizeCreates interactive 2D visualizations of embeddings for exploratory data analysis.PCA, t-SNE, UMAPFinetuneFinetunes protein language models for specific tasks.ESM2,ProtGPT2,ZymCTRLLanguage Model Protein GenerationGenerates proteins using pretrained language models.ESM2,ProtGPT2,ZymCTRLInverse Folding Protein GenerationDesigns proteins to fold into specific 3D structures.ESM-IF1,ProteinMPNN,ProstT5Diffusion Based Protein GenerationUses denoising diffusion models to generate proteins.RFDiffusionFoldPredicts 3D protein structures.ESMFold,ProstT5DockSimulates protein-ligand interactions.DiffDock,Smina,Autodock Vina,LightdockClassifyPredicts protein properties at high throughput.TemStaPro,EpHodSimulateUses molecular dynamics with the AMBER force field to relax structures.OpenMMDocumentationCheck out the documentation and examples athttps://trill.readthedocs.io/en/latest/index.html
Package: trilobot
TrilobotA mid-level robot learning platform aimed at the Rasberry Pi SBC range. Learn more -https://shop.pimoroni.com/products/trilobotPre-requisitesYou must enable:i2c:sudo raspi-config nonint do_i2c 0You can optionally runsudo raspi-configor the graphical Raspberry Pi Configuration UI to enable interfaces.InstallingStable library from PyPi:Just runpip3 install trilobotIn some cases you may need to usesudoor install pip with:sudo apt install python3-pipLatest/development library from GitHub:git clone https://github.com/pimoroni/trilobot-pythoncd trilobot-pythonsudo ./install.sh --unstableExamples and UsageThere are many examples to get you started with your Trilobot. With the library installed on your Raspberry Pi, these can be found in the~/Pimoroni/trilobot/examplesdirectory. Details about what each one does can be found in theexamples readme.To take Trilobot further, the full API is described in thelibrary readmeChangelog0.0.2Add support for Xbox One Wireless ControllerFix crash with sn3218 >= 2.0.00.0.1Initial Release
Package: trilogy
trilogyPython script that converts astronomical FITS images into color/grayscale images.trilogywas originally written in Python 2.Author:Dan CoeModified byRenan Alves de OliveiraCosmic Horseshoeimage was taken by Hubble WFC3, using filters F475W, F606W, and F814W.We usetrilogyto combine all FITS files in this beautiful RGB image!Try it!InstallationFromPyPi:pipinstalltrilogyFromGithub:gitclonehttps://github.com/oliveirara/trilogy.gitcdtrilogy
pythonsetup.pybuild
pythonsetup.pyinstallUsageCommand line:trilogy-cl-paramsWith input file (e.g. see *.in in ~/examples/):trilogy-clsingle.inWith input file and command line parameters:trilogy-clsingle.in-deletefilters0-showwithPIL-sampledx300Check notebooks in~/notebooksfor examples.RequirementsPillowastropynumpyscipyParameters and default values"indir"=''# Input directory."outname"=None# Output filename."outdir"=''# Output directory."saturate"=None# Determined automatically if None: image data value allowed to saturate."satpercent"=0.001# Percentage of pixels which will be saturated."colorsatfac"=1# \> 1 to boost color saturation."noise"=None# Noise luminosity is determined automatically if None."noiselum"=0.15# Noise luminosity for single channel (between 0 - 1)."noiselums"={}# Noise luminosity for each channel (between 0 - 1)."noisesig"=1# Data noise level output to noiselum: measured sigma above the measured mean."noisesig0":2# Data noise level: measured sigma above the measured mean."correctbias"=0# Measure data noise mean (otherwise assume = 0)."combine"='average'# "average" or "sum" combine images."invert"=0# Invert luminosity (black on white)."scaling"=None# Determined automatically if None: image scaling."bscale"=1# Multiply all images by this value."bzero"=0# Add this value to all images."samplesize"=1000# Determine number of levels."stampsize"=1000# Making final color image (memory issue)."sampledx"=0# Offset in x direction."sampledy"=0# Offset in y direction."show"=0# Show final image at the end."showstamps"=0# Show image config stamps."showwith"='open'# Command to display images."thumbnail"=None# Show thumbnail."legend"=0# Adds legend to top-left corner indicating which filters were used (only for RGB)."maxstampsize"=6000# Memory fix."testfirst"=1# Test some options before making the final image."deletetests"=0# Delete testing files."deletefilters"=1# Delete filter files.ResourcesLupton's methodSTIFF
Package: trilogy-public-models
trilogy-public-modelsOverviewThis repository contains semantic models on public datasets for the Preql/Trilogy language.This supports the interactive PreQL demo, but can also be used by anyone to boostrap exploration
of these public datasets.You can install this library directly and import models to use.Installationpip install trilogy-public-modelsExamplesThis repository also contains a examples/ folder, which can be browsed for in-depth code examples.Local UsageThis example assumes you are querying Bigquery Datasets.To utilize a model, instantiate a standard PreQL executor (in this case, a bigquery client)
and then pass in one of the existing environments from this package into the environment argument.That will enable you to run queries against the semantic model.fromgoogle.authimportdefaultfromgoogle.cloudimportbigqueryfrompreql.executorimportExecutor,Dialectsfromsqlalchemy.engineimportcreate_enginefromtrilogy_public_models.bigqueryimportgoogle_search_trendsproject,auth=default()bq_client=bigquery.Client(auth,project)engine=create_engine(f"bigquery://{project}?user_supplied_client=True",connect_args={'client':bq_client})exec=Executor(dialect=Dialects.BIGQUERY,engine=engine,environment=google_search_trends)results=exec.execute_text("""SELECTtrends.term,trends.rank,trends.week,trends.refresh_date,WHEREtrends.week > '2023-01-01'and trends.refresh_date = '2023-02-22'and trends.rank < 10ORDER BYtrends.week desc,trends.rank asclimit 100;""")# you can execute multiple queries separate by a semicolon# so our results will be in the first element of the arraforrowinresults[0]:print(row)Combining ModelsComing soon!Preql supports combining multiple environments into a single environment. This enables simplified querying
of universal concepts, like looking up StackOverflow links embedded in Github commits, or merging GPS
data across different domains.ContributingModel setupAll models should be in a double nested directory; first the platform and then the semantic label of the modelModels should have the followingentrypoint.preqlREADME.mdModel TestsAll models will be imported and verified. Validation methods will depend on the defined backend.All models require that the datasets being shared with the preql validation account.Current verifications:model imports successfullydatasource bindings existdatasource to concept mappings are appropriately typedconcept relations are consistently typed
Package: trimble-id
trimble-idTrimble Identity SDK for PythonGetting StartedInstallationYou can install withpip:pipinstalltrimble-idConfigure Trimble IdentityCreate a new application in theTrimble Developer Consoleportal and configure the following settings:To register your application in Trimble Developer Console:On the left pane select "Applications".On the Applications home page, in the top right corner select + NEW APPLICATION. The Create Application page displays.Select Continue to enter the applications details.FieldDescriptionNameName of your applicationDescriptionProvide a description for the application.Configure OAuth application grant typesClient Credentialsin order to use this SDK.Select "Create Application" to save changes.Take note of the Client ID and URLs under the "Basic Information" section. You'll need these values to configure the SDK.ScopesTrimble Identity uses scopes to determine the aud claim in the returned access token. Scope is mandatory for the application to work. You can use the scope as the application name registered in the Trimble Developer Console. For example, if you have registered an application with the name "test", then it must be registered in the format {some_uuid}-"test". For eg., 12345678-1234-1234-1234-123456789012-test.For more information, seeAuthentication documentation.Usage ReferenceWell-Known URL endpointThe well-known URL endpoint is used to retrieve the authorization, token and user info endpoints for a given environment. The following endpoints are available for the staging and production environments:Endpointhttps://id.trimble.com/.well-known/openid-configurationOpenID Endpoint ProviderThis endpoint provider is used to retrieve the endpoints from a well-known URL endpoint.Usagefromtrimble.idimportOpenIdEndpointProviderendpoint_provider=OpenIdEndpointProvider("https://id.trimble.com/.well-known/openid-configuration")auth_endpoint=awaitendpoint_provider.retrieve_authorization_endpoint()token_endpoint=awaitendpoint_provider.retrieve_token_endpoint()Fixed Endpoint ProviderThis endpoint provider is used to provide a fixed set of endpoints.Usagefromtrimble.idimportFixedEndpointProviderendpoint_provider=FixedEndpointProvider("https://authorization.url","https://token.url","https://userinfo.url")endpoint=awaitendpoint_provider.retrieve_authorization_endpoint()Client Credential Token ProviderThis token provider is used to retrieve an access token using the client credentials grant type.Usagefromtrimble.idimportClientCredentialTokenProvidertoken_provider=ClientCredentialTokenProvider(endpoint_provider,"client_id","client_secret").with_scopes(["scope"])access_token=awaittoken_provider.retrieve_token()NOTE:Refer samples for better understanding.Sample CodeSee here forSample Codefor reference.Release notesSee here forreleasesRaise an issueTo provide feedback or report a bug, pleaseraise an issue on our issue tracker.SupportSend email tosupport@trimble.com
Package: trimeshpy
###########################################################TriMeshPyTriangular Mesh Processing in Pythonwith SciPy sparse matrix representationby Etienne St-Onge###########################################################READMEStartup of a simple and efficient triangle mesh processing library in python
using scipy sparse matrix and numpy math functionsPaperSt-Onge, E., Daducci, A., Girard, G. and Descoteaux, M., 2017. Surface-enhanced tractography (SET). NeuroImage.TODO Code1) Modular "TriMesh_Class" that contain all triangle_mesh_processing functions
2) Python "UnitTest" in each module, in a standart way (not only a test file with "print")
3) add comments for each functions
4) Link graphical library (VTK and/or pyOpenGL) for visualisation (maybe in a class ex. TriMesh_VTK,  TriMesh_OGL
5) html, javascript graphical library (WebGL or Three) for visualisationwith interaction on webpage / Ipython notebook
6) GPU programming for sparse matrix (Theano or other)TODO Algo1) "No free Lunch" Laplacian operator
2) Multiresolution Mesh, Fuse zero-area triangles (maybe edge collapsing method)
Package: trimfilename
A script to trim a specific pattern from filenames.Files are recursively checked for a pattern match on the path mentioned
or in current working directory.Usagehome$lssongs04_maroon5_she_will_be_loved.mp305FloRida-Whistle.mp3Nelly-JustADream2010.mp3DavidArchuleta-ALittleTooNotOverYou000.mp3home$trimfilenamesongsparsingdirectory:songsFilesrenamed:songs/04_maroon5_she_will_be_loved.mp3:maroon5_-_she_will_be_loved-tn.mp3songs/05FloRida-Whistle.mp3:FloRida-Whistle.mp3Successfullyrenamed2file/shome$lsthumbsAlbumArt_Adele_Large.jpgAlbumArt_hall_of_fame_Large.jpgAlbumArt_lotavo_Large.jpghome$trimfilenamethumbs-bAlbumArt_parsingdirectory:thumbsFilesrenamed:thumbs/AlbumArt_Adele_Large.jpg:Adele_Large.jpgthumbs/AlbumArt_hall_of_fame_Large.jpg:hall_of_fame_Large.jpgthumbs/AlbumArt_lotavo_Large.jpg:lotavo_Large.jpgSuccessfullyrenamed3file/shome$lsthumbsAdele_Large.jpghall_of_fame_Large.jpglotavo_Large.jpghome$trimfilenamethumbs-p_large-iparsingdirectory:thumbsFilesrenamed:thumbs/lotavo_Large.jpg:lotavo.jpgthumbs/hall_of_fame_Large.jpg:hall_of_fame.jpgthumbs/Adele_Large.jpg:Adele.jpgSuccessfullyrenamed3file/s
Package: trimgmi
trimgmiOpinionated parsing of gemtext.UsageSee "convert", "command" and "sample" modules for examples.FIXME: Document this in more detail.DescriptionGemtext (GMI) is a lightweight, line-oriented markup language designed for the Gemini internet protocol. This module parses gemtext, ignoring extraneous whitespace. Text after closing ``` marks is also ignored.The resulting objects can be rendered line-by-line without further parsing logic.Also included are:round-trip render back to GMI with minimal whitespacesimple render to CommonMarkopinionated render to HTMLprimitive command line tools for the aboveRead more about gemtext:gemini://gemini.circumlunar.space/docs/gemtext.gmihttps://gemini.circumlunar.space/docs/gemtext.gmiImplementationThis module has no external dependencies.This implementation is deliberately verbose so that a developer can confirm by reading that the parser follows the gemtext specification. Optimising the core module is out-of-scope.TestingThe following commands should run without changing the target files:trimgmitest_search# example.gmi remains tidy# example.txt remains untidy# *.license remain unchangedconvertgmigmiREADMEtest_output/README.gmi# identicalconvertgmimdREADMEtest_output/README.md# readableconvertgmihtmlREADMEtest_output/README.html# readableFIXME: Implement and document a comprehensive round-trip test harness.Buildpython setup.py sdist bdist_wheelIf everything works as expected you should end up with the files:dist/trimgmi-<version>-py3-none-any.whl
dist/trimgmi-<version>.tar.gzYou can now optionally upload to PyPI:twine upload dist/*Sharing and contributionstrimgmi
https://gitlab.com/lofidevops/trimgmi
Copyright 2022 David Seaward and contributors
SPDX-License-Identifier: GPL-3.0-or-laterShared under GPL-3.0-or-later. We adhere to the Contributor Covenant 2.0 without modification, and certify origin per DCO 1.1 with a signed-off-by line (git -s). Contributions under the same terms are welcome.For details see COPYING, CODE_OF_CONDUCT.md (report via private ticket) and CONTRIBUTING.DCO.txt.
Package: trimkmeans
trimkmeansThe trimmed k-means clustering method by Cuesta-Albertos, Gordaliza and Matran (1997). This optimizes the k-means
criterion under trimming a portion of the points.example usage:importmatplotlib.pyplotaspltimportseabornassnsfromsklearn.datasetsimportmake_blobsfromsklearn.preprocessingimportStandardScalerfromtrimkmeans.metricsimporttrimmed_kmeans_metric_unsupervisedfromtrimkmeans.trimkmeansimportTrimKMeansif__name__=="__main__":# Create a dataset of 2D distributionsCENTERS=5X_train,true_labels=make_blobs(n_samples=100,centers=CENTERS,random_state=42)X_train=StandardScaler().fit_transform(X_train)# Fit centroids to datasettrimkmeans=TrimKMeans(n_clusters=CENTERS,verbose=1)trimkmeans.fit(X_train)# View resultslabels=trimkmeans.predict(X_train)print(f"optimal criterion value found is:{trimkmeans.crit_val}")print(f"sum of variances is:{trimmed_kmeans_metric_unsupervised(X_train,labels,'sv')}")print(f"sum of standart deviations is :{trimmed_kmeans_metric_unsupervised(X_train,labels,'sed')}")sns.scatterplot(x=[X[0]forXinX_train],y=[X[1]forXinX_train],hue=labels,style=labels,palette="deep",legend=None)plt.plot([xforx,_intrimkmeans.cluster_centers_],[yfor_,yintrimkmeans.cluster_centers_],'k+',markersize=10,)foridx,centroidinenumerate(trimkmeans.cluster_centers_):circle=plt.Circle(centroid,trimkmeans.opt_cutoff_ranges[idx],fill=False,color='r')plt.gca().add_patch(circle)plt.show()MIT LicenseCopyright (c) 2023 Arthur Rieß, University of ViennaPermission 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.