Saravanan666/pypi-org huggingface dataset:
Package: tscore
TAIScoreInstallvia pippipinstalltscorevia source codepythonsetup.pyinstallUsageimporttscoreastsDocumentsA simple APIdocsStructuretscore.basebase class and util functiontscore.dataget data and manage source datatscore.preprocessingdata preprocessingtscore.featuresfeature engineeringtscore.score_cardtraditional logistic regressiontscore.interpretertry to interpreter  black box modeltscore.metricsmodel evalutiontscore.model_selectionmodel selectiontscore.machine_learningscore model with machine learningtscore.pipelinepipeline build toolstscore.deploygenerate model deoply fileapi for model onlinehard rules for logistic regression score card rulestscore.plotvisual modeltscore.reportgenerate report for credit score modeltscore.risk_rulesgenerate risk rules from datatscore.monitormodel monitorAuthorsspencercs.ai@gmail.com
Package: tscratchapiget
This project gets the scratch api. Everything in this project code by me (except some needed library). Big credit to TimMcCool(Scratcher) for example codes. (This project is not related to TimMcCool)Install:pip install tscratchapigetChange log:https://github.com/Tony14261/tscratchapiget/blob/main/changelog.mdReport bugs or request new features here:https://github.com/Tony14261/tscratchapiget/issuesContact me (Discord): Tony14261#2089Note: The basics are done. I think I have to usehttps://scratchdb.lefty.one/v3/(ScratchDB by Lefty One). I fixed many bugs in this version. There are some new beta features. If anything goes wrong, contact me or report bugs on github page.Remember to import everything in tscratchapiget:from tscratchapiget import user, project, studioUser features(v0.2.7 to v0.3.0)(New commands are at the top):- Get if a username existsexists([username])EX:user.exists('Scratch_Tony_14261')if yes: Return 'User exists'     if not: Return 'User does not exist'- Get a user unread-message-countmessage_count('[user]')EX:user.message_count('TimMcCool')- Get a user idid('[user]')EX:user.id('griffpatch')- Get if a user is a scratchteamscratchteam('[user]')EX:user.scratchteam('ScratchCat')#Return 'True'- Get user join datejoin('[user]')EX:user.join('Will_Wam')#Return '2013-11-25T19:52:29.000Z'- Get a user pfp(link)pfp_link('[user]')EX:user.pfp_link('Scratch_Tony_14261')+Bonus Feature: Open that pfp link in browser. Change that topfp_link_open('[Username]')- Get user About-Me sectionaboutme('[user]')EX:user.aboutme('WazzoTV')- Get user What-I'm-Working-On sectionwiwo('[username]')EX:user.wiwo('ceebee')- Get user countrycountry(['user]')EX:user.country(['')- Get user follower countfollowers('[user]')Ex:user.followers('sharkyshar')- Get user following countfollowing('[user]')EX:user.followers('atomicmagicnumber')Project features(v0.3.6)Fill in the [Project ID] with the ID of a project- Get a project titletitle('[Project_ID]')EX:project.title('105500895')- Get a project descriptiondescription('[Project_ID]')EX:project.description('105500895')- Get a project viewsviews('[Project_ID]')EX:project.views('105500895')- Get a project lovesloves('[Project_ID]')EX:project.loves('105500895')- Get a project favoritesfavorites('[Project_ID]')EX:project.favorites('105500895')- Get a project remixesremixes('[Project_ID]')EX:project.remixes('105500895')- Get if a project existsexists('[Project_ID]')EX:project.exists('105500895')This check returns to your terminal- Open a project in a web browseropen('[Project_ID]')EX:project.open('105500895')Studio features(v0.4.1 to v0.4.5)Fill in the [Studio ID] with the ID of a studio- Get a studio titlestudio.title("[Studio ID]")- Get a studio descriptionstudio.description("[Studio ID]")- Get if comments are allowed in the studiostudio.comments_allowed([Studio ID])- Get studio create datestudio.created([Studio ID])- Get studio number of followersstudio.followers([Studio ID])- Get studio number of projectsstudio.projects([Studio ID])More comming soon!
Package: tscribe
tscribeProduce Word Document, CSV SQLite and VTT transcriptions using the automatic speech recognition from AWS Transcribe.InstallationpipinstalltscribeResultsTimeSpeakerComment0:00:03spk_0Is this on?0:00:05spk_1Yep.0:00:06spk_0Great.UsageGetting startedSimplyimport tscribeand pointtscribe.write(...)at your.jsonfile.importtscribetscribe.write("output.json")output.docx written in x seconds.Output formatsSupported output formats include:docx(default)csvsqlitevttimporttscribetscribe.write("output.json",format="docx")tscribe.write("output.json",format="csv")tscribe.write("output.json",format="sqlite")tscribe.write("output.json",format="vtt")output.docx written in x seconds.
output.csv written in x seconds.
output.db written in x seconds.
output.vtt written in x seconds.Target directory or filenameYou may wish to be explicit in specifying the output filename or directory written to. Use cases may include following a naming convention or operating in a serverless environment.importtscribetscribe.write("output.json",save_as="transcript.docx")tscribe.write("output.json",save_as="/tmp/transcript.docx")transcript.docx written in x seconds.
/tmp/transcript.docx written in x seconds.Combining format and targetimporttscribetscribe.write("output.json",format="csv",save_as="output/output.csv")output/output.csv written in x seconds.
Package: tscv
TSCV: Time Series Cross-ValidationThis repository is ascikit-learnextension for time series cross-validation.
It introducesgapsbetween the training set and the test set, which mitigates the temporal dependence of time series and prevents information leakage.Installationpipinstalltscvorcondainstall-cconda-forgetscvUsageThis extension defines 3 cross-validator classes and 1 function:GapLeavePOutGapKFoldGapRollForwardgap_train_test_splitThe three classes can all be passed, as thecvargument, to
scikit-learn functions such ascross-validate,cross_val_score,
andcross_val_predict, just like the native cross-validator classes.The one function is an alternative to thetrain_test_splitfunction inscikit-learn.ExamplesThe following example usesGapKFoldinstead ofKFoldas the cross-validator.importnumpyasnpfromsklearnimportdatasetsfromsklearnimportsvmfromsklearn.model_selectionimportcross_val_scorefromtscvimportGapKFoldiris=datasets.load_iris()clf=svm.SVC(kernel='linear',C=1)# use GapKFold as the cross-validatorcv=GapKFold(n_splits=5,gap_before=5,gap_after=5)scores=cross_val_score(clf,iris.data,iris.target,cv=cv)The following example usesgap_train_test_splitto split the data set into the training set and the test set.importnumpyasnpfromtscvimportgap_train_test_splitX,y=np.arange(20).reshape((10,2)),np.arange(10)X_train,X_test,y_train,y_test=gap_train_test_split(X,y,test_size=2,gap_size=2)ContributingReport bugs in the issue trackerExpress your use cases in the issue trackerDocumentationstscv.readthedocs.ioAcknowledgmentsI would like to thank Jeffrey Racine and Christoph Bergmeir for the helpful discussion.LicenseBSD-3-ClauseCitationWenjie Zheng. (2021). Time Series Cross-Validation (TSCV): an extension for scikit-learn. Zenodo.http://doi.org/10.5281/zenodo.4707309@software{zheng_2021_4707309,
  title={{Time Series Cross-Validation (TSCV): an extension for scikit-learn}},
  author={Zheng, Wenjie},
  month={april},
  year={2021},
  publisher={Zenodo},
  doi={10.5281/zenodo.4707309},
  url={http://doi.org/10.5281/zenodo.4707309}}
Package: tsd2gspread
tsd2gspreadA tool to write Time Series Data to Google Sheets,
usinggspread.RequirementPython 3.6 or laterInstallation$ pip install tsd2gspreadPreparationService accountGet a service account file (json) for Google APIs followingAuthentication — gspread 3.7.0 documentation.Google SheetsTsd2Gspread can create new Google Sheets if you want.Otherwise, you can use prepared Google Sheets.To allow the service account to access the Sheet,
go to the Sheet and share it withclient_emailfrom the above service account file.ExampleUse Tsd2Gspread directlyimport tsd2gspread

tg = tsd2gspread.get(
    service_account='~/service_account.json',
    sheet_name='MySheet',
    create=1,
    worksheet_name='MyWork',
    columns='foo,bar,
    share='rcmdnk@gmail.com')

# Make function to get data
def get_data():
    foo = 1
    bar = 2
    return (foo, bar)

# Set data getter
tg.get_data = get_data

# Write Time Series Data to Google Sheets
tg.write()This will make Google Sheets like:Datetimefoobar2021-04-30 12:34:5612Options forget:OptionMeanDefaultconfig_fileConfiguration file of Tsd2Gspread.Noneservice_accountPath for theservice_account.json(Google API service_account file).IfNone, tsd2gspread(gspread) will use~/.config/gspread/service_account.json.Nonesheet_nameIf set, Sheet is searched by name.NonecreateIf set to 1, new Sheet is created if it is not found by name.Only works ifsheet_nameis notNoneNonesheet_urlIf set, Sheet is searched by URL.Nonesheet_keyIf set, Sheet is searched by key.Noneworksheet_nameWork sheet name.NonecolumnsColumn names separated by,.If set, the title like will be inserted when the sheet is created.NoneshareEmail address of your Google account.If it is not set, only the service account can access to the Sheet and you can not see the Sheet from your account.Noneperm_typeRefAPI Reference - gspread: client.insert_permissionuserperm_typeRefAPI Reference - gspread: client.insert_permissionowneradd_datetimeIf set to 1, current time is added to the data as the first column.1timedeltaThe time offset from UTC.0timeformatThe time format to be written.%Y-%m-%d %H:%M:%Svalue_input_optionIfadd_datetimeis 1, useUSER_ENTEREDto fill datetime value as datetime.RefAPI Reference - gspread:Wworksheet.append_rowUSER_ENTEREDIf you set options by the configuration file, write options asOPTION=VALUEand give the file name asconfig_file.Make new inherited class from Tsd2Gspreadfrom tsd2gspread import Tsd2Gspread

class MyClass(Tsd2Gspread):
    def get_data(self):
        foo = 1
        bar = 2
        return (foo, bar)

tg = MyClass(
    service_account='~/service_account.json',
    sheet_name='MySheet',
    create=1,
    worksheet_name='MyWork',
    columns='foo,bar,
    share='rcmdnk@gmail.com')

# Write Time Series Data to Google Sheets
tg.write()
Package: tsd-api-client
TSD API ClientTransport files and directories, no limits on size or amount, resumable by default, synchronise if needed.Installpip3installtsd-api-client
pip3installtsd-api-client--upgrade# to get the latest versiontaclGet started withtacl:tacl--guideconfig
tacl--guideuploads
tacl--guidedownloads
tacl--guidesync
tacl--guidetopics# to see all guides availabletacl--helpSet up shell completion:# for Bash, in ~/.bashrc:eval"$(_TACL_COMPLETE=source_bashtacl)"# for Zsh, in ~/.zshrc:eval"$(_TACL_COMPLETE=source_zshtacl)"# for Fish, in ~/.config/fish/completions/tacl.fisheval(env_TACL_COMPLETE=source_fishtacl)
Package: tsdat
About TsdatTsdat is an open-source python framework for declaratively creating pipelines to read,
standardize, and enhance time series datasets of any dimensionality for use in scalable
applications and in building large data repositories.This repository contains the core tsdat code. We invite you to explore this, especially
for those willing to provide feedback or make contributions to the tsdat core (we
enthusiastically welcome issues, PRs, discussions & new ideas, etc.).Most users should start with atemplate repositoryto generate boilerplate code and configurations needed to create a tsdat data pipeline.
We recommendthis templateto start
with, as it is the most flexible and well-supported template that we offer.Development EnvironmentInstructions on setting up your development environment for working on the core tsdat
code are included below:Fork this repository to your github account and open it on your desktop in an IDE of
your choice.We recommend using VS Code, as we've included extra settings that make it easy to
start developing in a standard environment with no overhead configuration time.Open an appropriate terminal shell from your computerIf you are on Linux or Mac, just open a regular terminalIf you are on Windows, start your Anaconda prompt if you installed Anaconda
directly to Windows, OR open a WSL terminal if you installed Anaconda via WSL.Run the following commands to create and activate yourcondaenvironmentcondaenvcreate
condaactivatetsdat
pipinstall-e".[dev]"CommunityTsdat is an open-source repository and we highly-value community contributions and
engagement viaissues,pull requests, anddiscussions. Please let us know if you
find bugs, want to request new features, or have specific questions about the framework!Additional resourcesLearn more abouttsdat:GitHub:https://github.com/tsdatDocumentation:https://tsdat.readthedocs.ioData standards:https://github.com/tsdat/data_standardsPreferred template:https://github.com/tsdat/pipeline-templateAll templates:https://github.com/tsdat/template-repositoriesDocker Images:https://hub.docker.com/u/tsdatLearn more aboutxarray:GitHub:https://github.com/pydata/xarrayDocumentation:https://xarray.pydata.orgLearn more aboutact-atmos:GitHub:https://github.com/arm-doe/actDocumentation:https://arm-doe.github.io/ACT/Other useful tools:VS Code:https://code.visualstudio.com/docsDocker:https://docs.docker.com/get-started/pytest:https://github.com/pytest-dev/pytestblack:https://github.com/psf/blackmatplotlibguide:https://realpython.com/python-matplotlib-guide/
Package: tsdate
tsdatetsdateis a scalable method for estimating the age of ancestral nodes in atree sequence. The method uses a coalescent prior and updates node times on the basis of the number of mutations along each edge of the tree sequence (i.e. using the "molecular clock").The method is frequently combined with thetsinferalgorithm, which efficiently infers tree sequencetopologiesfrom large genetic datasets.Please refer to thedocumentationfor information on installing and using the software.The discrete-time estimation methods are describedin this Science paper(preprinthere). We also provide evaluations of the accuracy and computational requirements of the method using both simulated and real data. The code to reproduce these results can be found inanother repository.Anthony Wilder Wohns, Yan Wong, Ben Jeffery, Ali Akbari, Swapan Mallick, Ron Pinhasi, Nick Patterson, David Reich, Jerome Kelleher, and Gil McVean (2022)A unified genealogy of modern and ancient genomes. Science375: eabi8264; doi:https://doi.org/10.1126/science.abi8264Please cite this paper if you usetsdatein published work.
Package: tsdat-testbed
About TsdatTsdat is an open-source python framework for declaratively creating pipelines to read,
standardize, and enhance time series datasets of any dimensionality for use in scalable
applications and in building large data repositories.This repository contains the core tsdat code. We invite you to explore this, especially
for those willing to provide feedback or make contributions to the tsdat core (we
enthusiastically welcome issues, PRs, discussions & new ideas, etc.).Most users should start with atemplate repositoryto generate boilerplate code and configurations needed to create a tsdat data pipeline.
We recommendthis templateto start
with, as it is the most flexible and well-supported template that we offer.Development EnvironmentInstructions on setting up your development environment for working on the core tsdat
code are included below:Fork this repository to your github account and open it on your desktop in an IDE of
your choice.We recommend using VS Code, as we've included extra settings that make it easy to
start developing in a standard environment with no overhead configuration time.Open an appropriate terminal shell from your computerIf you are on Linux or Mac, just open a regular terminalIf you are on Windows, start your Anaconda prompt if you installed Anaconda
directly to Windows, OR open a WSL terminal if you installed Anaconda via WSL.Run the following commands to create and activate yourcondaenvironmentconda create --name tsdat python=3.8
conda activate tsdat
pip install -r requirements-dev.txtCommunityTsdat is an open-source repository and we highly-value community contributions and
engagement viaissues,pull requests, anddiscussions. Please let us know if you
find bugs, want to request new features, or have specific questions about the framework!Additional resourcesLearn more abouttsdat:GitHub:https://github.com/tsdatDocumentation:https://tsdat.readthedocs.ioData standards:https://github.com/tsdat/data_standardsPreferred template:https://github.com/tsdat/ingest-templateAll templates:https://github.com/tsdat/template-repositoriesDocker Images:https://hub.docker.com/u/tsdatLearn more aboutxarray:GitHub:https://github.com/pydata/xarrayDocumentation:https://xarray.pydata.orgLearn more aboutact-atmos:GitHub:https://github.com/arm-doe/actDocumentation:https://arm-doe.github.io/ACT/Other useful tools:VS Code:https://code.visualstudio.com/docsDocker:https://docs.docker.com/get-started/pytest:https://github.com/pytest-dev/pytestblack:https://github.com/psf/blackmatplotlibguide:https://realpython.com/python-matplotlib-guide/
Package: tsdb-query-builder
tsdb query string builderfromtsdb.queryimportQueryfromtsdb.query.filtersimport*if__name__=='__main__':print(Query('some.metrics').rate(True,10,20).aggr('max').filters({'a':'b','c':'d','e':literal_or('f'),'g':regexp('.*',groupBy=False),'h':not_key(),}).m())# max:rate{counter,10,20}:some.metrics{a=b,c=d,e=literal_or(f),h=not_key()}{g=regexp(.*)}
Package: ts-demuxer
py_ts_demux介绍python版的tsDemuxer，参考项目：https://github.com/goldvideo/demuxer目前仅ts流解密
####联系作者:xuhx20@qq.com快速起步pip install ts-demuxer

# 实例化TsDemux对象
ts_demux = TsDemux()

# 传递视频数据进行demux，返回ts数据（包含视频数据及音频数据）
ts_data = ts_demux.start_demux(video_data)

# 获取音频数据
audio_bytes = ts_data.audio_bytes

# 获取视频数据
video_bytes = ts_data.video_bytes
Package: tsdf
tsdfBadgesPackages and ReleasesBuild StatusDOILicenseFairnessA package (documentation) to load TSDF data (specification) into Python.InstallationUsingpipThe package is available in PyPi and requiresPython 3.9or higher. It can be installed using:$pipinstalltsdfUsageSee ourextended tutorials.DevelopmentRunning testspoetryinstall
poetryrunpytestBuilding the documentationWe usemkdocsto build the documentation. If you want to build the documentation locally, the following commands will prove useful:mkdocsbuild# build the documentationmkdocsserve# serve the documentation on a local servermkdocsgh-deploy# deploy the documentation to GitHub pagesContributingInterested in contributing? Check out the contributing guidelines. Please note that this project is released with a Code of Conduct. By contributing to this project, you agree to abide by its terms.LicenseThis package was created by Pablo Rodríguez, Peter Kok and Vedran Kasalica. It is licensed under the terms of the Apache License 2.0 license.CreditsTheTSDF data formatwas created by Kasper Claes, Valentina Ticcinelli, Reham Badawy, Yordan P. Raykov, Luc J.W. Evers, Max A. Little.This package was created withcookiecutterand thepy-pkgs-cookiecuttertemplate.
Package: tsdisagg
tsdisaggTools for converting low time series data to high frequency, based on the R packagetempdisagg, and espeically the accompanying paper bySax and Steiner 2013.tsdisaggallows the user to convert low frequency time series data (e.g., yearly or quarterly) to a higher frequency (e.g., quarterly or monthly) in a way that preserves desired aggregate statistics in the high frequency data. It should, for example, sum back to the original low-frequency data.In addition, regression-based methods are also implemented that allow the user to supply "indicator series", allowing variation from correlated high-frequency time series to be imputed into the low frequency data.If you have any questions or issues, please open a thread. Pull requests to add features or fix bugs are welcome. Please clone the repository locally to have access to the testing suite.InstallationTo install, usepip install tsdisaggCurrent FeaturesCurrently, only conversion between yearly, quarterly, and monthly data is supported. Conversion to lower frequencies is non-trivial due to the calendar math that needs to be added, but this is on my to-do list.The following interpolation methods have been implemented:Single series, non-parametric methods:DentonDenton-CholetteMultiseries, regression-based methods:Chow-LinLittermanExamplesFor example usage, please see theexamples.ipynbnotebook.tsdisaggdepends heavily onpandasto handle time reindexing, so the user is advised to read the associated Pandas documentation, especially as it relates to setting frequencies.To-do:Refactor codebase to usestatsmodelsmodel and results objects, as well as.fit()apiAdd missing interpolation methods relative totimedisagg(Fernandez, min RSS objective functions)Add support for finer time frequencies (weekly, daily, hourly)
Package: tsds
tsds: Time Series Data Segmentation AlgorithmThis is a Python library for time series data segmentation, specifically developed for clinical data. It includes the following components:Dimensionality reduction using Non-negative Matrix Factorization (NMF)Optimal number of clusters calculation using Silhouette score, Calinski Harabasz score, and Davies Bouldin score.Predictive modeling using Multilayer Perceptron (MLP) classifier, Support Vector Machines (SVM), and Random Forest.Explanation of cluster groups using SHAP values.Analysis and simulation of disease progression using skip grams and Markov chains, with visual representation of group likelihood changes.UsageTo use the library, simply import it into your project and follow the steps outlined in the components above. Detailed usage instructions and examples can be found in the library's documentation.DependenciesThe library requires the following dependencies:NumPyPandasScikit-learnSHAPnltkMatplotlib (for visual representation)ContributionWe welcome contributions to this library. If you have any suggestions or bug reports, please create a GitHub issue. If you would like to contribute code, please submit a pull request.LicenseThis library is available under the GNU General Public License Version 3.