Saravanan666/pypi-org huggingface dataset:
Package: open_source_library_data_collector
Quickly and easily store data about your open source projects on
GitHub and various Package Managers.AnnouncementsAll updates to this project is documented in ourCHANGELOG.InstallationEnvironment VariablesFirst, get your free SendGrid accounthere.Next, update your environment with yourSENDGRID_API_KEY.Initial Setupecho"export SENDGRID_API_KEY='YOUR_API_KEY'">sendgrid.envecho"sendgrid.env">>.gitignoresource./sendgrid.envgitclonehttps://github.com/sendgrid/open-source-library-data-collector.gitcdsendgrid-open-source-library-external-datavirtualenvvenvcp.env_sample.envUpdate your settings in.envmysql-uUSERNAME-p-e"CREATE DATABASE IF NOT EXISTS open-source-library-data-collector";mysql-uUSERNAME-popen-source-external-library-data<db/data_schema.sqlcpconfig_sample.ymlconfig.ymlUpdate the settings inconfig.ymlsourcevenv/bin/activatepipinstall-rrequirements.txtUpdate the code inpackage_managers.py. The functionsupdate_package_manager_dataandupdate_dbwas customized for our
particular needs. You will want to either subclass those functions in
your own application or modify it to suit your needs. We will remove
these customizations in a future release.Here is the GitHub
issuefor reference.To run:source venv/bin/activate
python app.pyDependenciesThe SendGrid Service, starting at thefree
level)virtualenvmysqlHeroku Deployheroku login
heroku create
heroku addons:create cleardb:igniteAccess the cleardb DB and create the tables in db/data_schema.sqlheroku config:add ENV=prod
heroku config:add GITHUB_TOKEN=<<your_github_token>>
heroku config:add SENDGRID_API_KEY=<<your_sendgrid_api_key>>
heroku addons:create scheduler:standardConfigure the schedular addon in your Heroku dashboard to runpython app.pyat your desired frequency.Test by runningheroku run workerRoadmapIf you are interested in the future direction of this project, please
take a look at ourmilestones.
We would love to hear your feedback.How to ContributeWe encourage contribution to our projects, please see ourCONTRIBUTINGguide for details.Quick links:[Feature
Request](https://github.com/sendgrid/open-source-library-data-collector/blob/master/CONTRIBUTING.md#feature_request[Bug
Reports](https://github.com/sendgrid/open-source-library-data-collector/blob/master/CONTRIBUTING.md#submit_a_bug_report[Sign the CLA to Create a Pull
Request](https://github.com/sendgrid/open-source-library-data-collector/blob/master/CONTRIBUTING.md#cla[Improvements to the
Codebase](https://github.com/sendgrid/open-source-library-data-collector/blob/master/CONTRIBUTING.md#improvements_to_the_codebaseAboutopen-source-library-data-collector is guided and supported by the
SendGridDeveloper Experience Team.open-source-library-data-collector is maintained and funded by SendGrid,
Inc. The names and logos for open-source-library-data-collector are
trademarks of SendGrid, Inc.
Package: opensourcetest
OpenSourceTestOpenSourceTest将为您创建更加自由的软件自动化测试，不是为了简单而简单，而是为您提供更自由的可扩展的，适用于不同功能场景的UI自动化、APP自动化或接口自动化测试框架。设计思想不丢弃轮子本身的优秀特性不过度封装提供更加多的可操作对象给使用者，即时你使用基本框架已经满足需求拥抱开源主要特点支持创建UI自动化测试框架以yaml格式定义UI元素对象，yaml对象自动注入支持本地及远程分布式测试支持生成不同浏览器测试报告支持docker容器测试支持创建接口自动化测试框架继承requests的所有强大功能以yaml格式定义接口，yaml对象自动注入使用jmespath提取和验证json响应支持创建APP自动化框架以yaml格式定义UI元素对象，yaml对象自动注入其他完美兼容pytest，您可以使用您想使用的任何pytest格式完美兼容allure，您可以使用您想使用的任何allure命令支持CLI命令，直接创建您所需要的项目架构OST计划即将上线微服务架构运维命令自动化测试框架CLI_TEST，基于云原生基础组件的测试框架，敬请期待，满足在集群内部的组件自动化测试，哈哈哈哈打赏支持OpenSourceTest由来自中国的成都-阿木木在空闲时间维护。虽然我致力于OpenSourceTest，因为我热爱这个项目，但是如果可能的话，希望可以得到一杯咖啡的打赏支持，您的打赏是我最大的支持。我会将打赏的胖友名单放入赞助者列表中，用以表示感谢。​这些钱也将被用来维护框架，购买服务器，以及直播公开课等活动​感谢各位胖友对OpenSourceTest框架的打赏，您的打赏支持是我最大的迭代更新的动力，也希望opensourcetest可以在日常工作中帮助到您，QAQ​成为打赏者become a sponsor​联系作者：成都-阿木木微信赞赏收款码OpenSourceTest 社区欢迎测试小伙伴加群，讨论测试框架技术！
Package: openspace
OPENSPACEThis package is a public astrodynamics analysis and simulation sandbox not intended for commercial use.  Further details
on functionality can be found on thedocumentation page.  For an interactive demonstration of possible uses, try theopenspaceapplication.ContributingWhen making contributions to the openspace code repository, please follow these standards as closely as possible:Useblackto format all python codeUsesphinxdocstrings for code documentationUsePEP484type hintingOpen issues when making changes that are intended to be merged to the main branchLicenseThis packages is not intended for commercial use; however, it is not strictly prohibited.  Instead, you may use this package for commercial use
assuming you make any source code publicly available when using this package.
Package: openspace-app
OPENSPACE-APPThis application is a demonstration of capabilities provided in theopenspace package.  Details on the backend functionality can be found on thedocumentation page.  Demonstrations are availablehere.ContributingWhen making contributions to the openspace code repository, please follow these standards as closely as possible:Useblackto format all python codeUsesphinxdocstrings for code documentationUsePEP484type hintingOpen issues when making changes that are intended to be merged to the main branch
Package: openspacetrainenv
OpenSpaceTrain EnvironmentThis is the repository of the train environment of the OpenSpace project. This project is written in Python 3.6 and will be running on the computers present on the train.The project follows thePEP008 style guide, every collaborator should make sure his code is according to the standards.Tests are very important in this project and should be written for each functional unit. The project uses theunittestlibrary for unit tests andnosefor test discovery.Documentation is key to working in team, so make sure your code is well documented, also following the PEP008 style rules.Camera HandlerThe camera handler uses a pubsub pattern to let subscribers know when frames are published. These frames will be transformed in the requested codec for each subscriber. These frames are sent to the broker to be redistributed amongst the components.Vision ContainerThe vision container will wrap the CV-ML service in the train environment. This will be done by importing their python script into our service and expose our broker as a singleton. This way, the vision container can publish messages and subscribe to messages through the broker. The init script should contain the setup logic for their container, as our service will not call any methods from within their service (except the registered callbacks).BrokerThe broker (previously carriage communicator) is responsible for the internal routing of messages using a broker pattern.
Exposed methods:Broker.get_instance()self.subscribe('message', callback)self.publish('message', *args, **kwargs)Election ComponentThe election component is responsible for the election procedure amongst the different carriages as well as initiating the (physical) TUDA at an election win.Virtualized TUDA ModuleVirtualized TUDA present on every carriage. This component has to transfer the connections to the TUDA by a HTTP connection.Train Unit Data AggregatorThe train unit data aggregator is the central point within the current sequence of carriages. This component has to buffer the received information and needs to communicate with the internal API and the transfer module.Main TUDA ModuleThis module has to make sure the data is timestamped and routed to the other transfer modules (internal API & transfer module).Internal API ModuleThis module has to buffer/store the data and respond to API calls that originate from the internal network on the train.Transfer ModuleThis module is responsible for the connection to the wayside (backend). This module has to transfer the data to the AMQP module already present on the train (not part of our service).Test SimulationsTo simulate the AMQP communication and the internal API, we need to create simple simulations of these components.
Package: openspace-train-environment
OpenSpaceTrain EnvironmentThis is the repository of the train environment of the OpenSpace project. This project is written in Python 3.6 and will be running on the computers present on the train.The project follows thePEP008 style guide, every collaborator should make sure his code is according to the standards.Tests are very important in this project and should be written for each functional unit. The project uses theunittestlibrary for unit tests andnosefor test discovery.Documentation is key to working in team, so make sure your code is well documented, also following the PEP008 style rules.Camera HandlerThe camera handler uses a pubsub pattern to let subscribers know when frames are published. These frames will be transformed in the requested codec for each subscriber. These frames are sent to the broker to be redistributed amongst the components.Vision ContainerThe vision container will wrap the CV-ML service in the train environment. This will be done by importing their python script into our service and expose our broker as a singleton. This way, the vision container can publish messages and subscribe to messages through the broker. The init script should contain the setup logic for their container, as our service will not call any methods from within their service (except the registered callbacks).BrokerThe broker (previously carriage communicator) is responsible for the internal routing of messages using a broker pattern.
Exposed methods:Broker.get_instance()self.subscribe('message', callback)self.publish('message', *args, **kwargs)Election ComponentThe election component is responsible for the election procedure amongst the different carriages as well as initiating the (physical) TUDA at an election win.Virtualized TUDA ModuleVirtualized TUDA present on every carriage. This component has to transfer the connections to the TUDA by a HTTP connection.Train Unit Data AggregatorThe train unit data aggregator is the central point within the current sequence of carriages. This component has to buffer the received information and needs to communicate with the internal API and the transfer module.Main TUDA ModuleThis module has to make sure the data is timestamped and routed to the other transfer modules (internal API & transfer module).Internal API ModuleThis module has to buffer/store the data and respond to API calls that originate from the internal network on the train.Transfer ModuleThis module is responsible for the connection to the wayside (backend). This module has to transfer the data to the AMQP module already present on the train (not part of our service).Test SimulationsTo simulate the AMQP communication and the internal API, we need to create simple simulations of these components.
Package: openspeechcorpus
Open Speech Corpus CLIThis repository contains the code required to download audiodata fromopenspeechcorpus.comOpen Speech Corpus is composed by far for three subcorpuses:Tales: A crowdsourced corpus based on reading of latin american short talesAphasia: A crowdsourced corpus based in words categorized in 4 levels of difficultyIsolated words: A crowdsourced corpus based in isolated wordsTo download files from the Tales Project useops\--output_foldertales/\--output_filetales.txt\--corpustalesTo download files from the Isolated Words Project useops\--output_folderisolated_words/\--output_fileisolated_words.txt\--corpuswordsTo download files from the Aphasia Project useops\--output_folderaphasia/\--output_fileaphasia.txt\--corpusaphasiaParameters ussage--download_allYou can download the whole corpus using the flag--download_allops\--output_folderaphasia/\--output_fileaphasia.txt\--corpusaphasia\--download_all--fromand--toBy default the page size is 500, to modify it use the args--fromand--toi.e:ops\--from500\--to1000\--output_folderaphasia/\--output_fileaphasia.txt\--corpusaphasiaIf you use the flag--download_allwith the flag--fromthe process will start in the specified argfromusing a
page size of 500--extra_query_paramsWe also support an argument--extra_query_paramswhich overwrites the--from,--toand--download_allarguments
and downloads all files in the body response, you must define the--corpusargument anywayops\--output_folderaphasia/\--output_fileaphasia_letters.txt\--corpusaphasia\--extra_query_params"level_sentence__id__gte=846&level_sentence__id__lte=870"Recursive ConvertThe Open Speech Corpus stores its files in mp4 format, which is undesired for most audio processing tasks. To convert
the files into a wav format, first installffmpeg, then you can execute therecursive_convertutility which receives as first argument the source_folder with the mp4 files and as second argument
the output folder i.e.:recursive_convertaphasiaaphasia_wavCMU Sphinx ConfigurationThe Open Speech Corpus also defines some scripts to generate configurations forCMU Sphinx.First initialize a project with thesphinx_traincommandsphinxtrain-tsimple_wordssetupTo generate a configuration use the commandconfigure_sphinx, which creates the transcription, fileids, fillers and
dic files.configure_sphinxsimple_words\--transcription_filewords.txt\--etc_foldersimple_words/etc\--test_size0.5Also you need to define a language model which receives the DB_NAME and the base project foldergenerate_language_modelsimple_wordssimple_wordsTo delete the configuration files use the commandclean_previous_configurationclean_previous_configurationsimple_words--etc_foldersimple_words/etc/HTK ConfigurationThe Open Speech Corpus also defines some scripts to train models usingHTKTo generate a word grammar useconfigure_htk\--transcription_filewords.txt\--project_folderhtk_words\--wav_folderwords_wav\htk_words
Package: openspeechlib
openspeechlibThis is a package that includes several utilities to extract information from a speech signal,
download open speech resources and others related speech activities.Currently openspeechlib is in active development, if you have suggestion, requests of improvements
please leave a issue using theGithub Issue system for this repositorySome useful linksFeature ExtractionMFCCLibRosaSpeech FeaturesNumpy MLSpeech Signal Processing and ClassificationSonopySpeechPyPLPLFCCWPFSBCMWP-ACEPNCCPower Normalized Cepstral Coefficients for Robust Speech RecognitionEVALUATION OF PNCC AND EXTENDED SPECTRAL SUBTRACTION METHODS FOR
ROBUST SPEECH RECOGNITIONPNCCModel TrainingDTWDTW Speech RecognitionHMMSpeech Processing for machine learningANNDeep SpeechAlignersAlignment DurationMontreal Forced AlignerProsodylab AlignerPraatSPPASRecognizersHMMDigit RecognizerSpeaker RecognitionGeneratorsespeak-ngApplicationsSpeaker IdentificationEmotion RecognitionGender RecognitionLoggingIf you want verbose information of the process please use in your scriptimportlogginglogging.basicConfig(level=logging.DEBUG)
Package: open-spex
openSpex - a basic analysis and display tool for radioxenon beta-gamma data on IMS-format.
Version 1.0.7 - May 17, 2021InstallationCreate and activate a virtual environment:$ python3 -m venv venv$ source venv/bin/activate (on Windows: $ call venv\Scripts\activate)Install openSpex:(venv) python3 -m pip install open-spexStart application:(venv) openSpexSee the help menu for further information.IMPORTANT NOTE: openSpex uses the Python module “pickle” to save analysed data as pkl-files.
The pickle module is not secure. Only open pkl-files you trust.
Package: open-spiel
OpenSpiel: A Framework for Reinforcement Learning in GamesOpenSpiel is a collection of environments and algorithms for research in general
reinforcement learning and search/planning in games. OpenSpiel supports n-player
(single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and
sequential, strictly turn-taking and simultaneous-move, perfect and imperfect
information games, as well as traditional multiagent environments such as
(partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel
also includes tools to analyze learning dynamics and other common evaluation
metrics. Games are represented as procedural extensive-form games, with some
natural extensions. The core API and games are implemented in C++ and exposed to
Python. Algorithms and tools are written both in C++ and Python.To try OpenSpiel in Google Colaboratory, please refer toopen_spiel/colabssubdirectory or starthere.IndexPlease choose among the following options:Installing OpenSpielIntroduction to OpenSpielAPI Overview and First ExampleAPI ReferenceOverview of Implemented GamesOverview of Implemented AlgorithmsDeveloper GuideUsing OpenSpiel as a C++ LibraryGuidelines and ContributingAuthorsFor a longer introduction to the core concepts, formalisms, and terminology,
including an overview of the algorithms and some results, please seeOpenSpiel: A Framework for Reinforcement Learning in Games.For an overview of OpenSpiel and example uses of the core API, please check out
our tutorials:Motivation, Core API, Brief Intro to Replictor Dynamics and Imperfect
Information Gamesby Marc
Lanctot.(slides)(colab)Motivation, Core API, Implementing CFR and REINFORCE on Kuhn poker, Leduc
poker, and Goofspielby Edward
Lockhart.(slides)(colab)If you use OpenSpiel in your research, please cite the paper using the following
BibTeX:@article{LanctotEtAl2019OpenSpiel,title={{OpenSpiel}: A Framework for Reinforcement Learning in Games},author={Marc Lanctot and Edward Lockhart and Jean-Baptiste Lespiau andVinicius Zambaldi and Satyaki Upadhyay and Julien P\'{e}rolat andSriram Srinivasan and Finbarr Timbers and Karl Tuyls andShayegan Omidshafiei and Daniel Hennes and Dustin Morrill andPaul Muller and Timo Ewalds and Ryan Faulkner and J\'{a}nos Kram\'{a}rand Bart De Vylder and Brennan Saeta and James Bradbury and David Dingand Sebastian Borgeaud and Matthew Lai and Julian Schrittwieser andThomas Anthony and Edward Hughes and Ivo Danihelka and Jonah Ryan-Davis},year={2019},eprint={1908.09453},archivePrefix={arXiv},primaryClass={cs.LG},journal={CoRR},volume={abs/1908.09453},url={http://arxiv.org/abs/1908.09453},}VersioningWe useSemantic Versioning.
Package: open-spiel-custom
Build (DW)go to basedir/open_spiel/build/

cmake ../open_spiel/
makeset the paths to open_spiel dirs in bash_profile as in doc:LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$HOME/projects/open_spiel/build

export PYTHONPATH=$PYTHONPATH:~/projects/open_spiel/
export PYTHONPATH=$PYTHONPATH:~/projects/open_spiel/build/pythonOpenSpiel: A Framework for Reinforcement Learning in GamesOpenSpiel is a collection of environments and algorithms for research in general
reinforcement learning and search/planning in games. OpenSpiel supports n-player
(single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and
sequential, strictly turn-taking and simultaneous-move, perfect and imperfect
information games, as well as traditional multiagent environments such as
(partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel
also includes tools to analyze learning dynamics and other common evaluation
metrics. Games are represented as procedural extensive-form games, with some
natural extensions. The core API and games are implemented in C++ and exposed to
Python. Algorithms and tools are written both in C++ and Python.To try OpenSpiel in Google Colaboratory, please refer toopen_spiel/colabssubdirectory or starthere.IndexPlease choose among the following options:Installing OpenSpielIntroduction to OpenSpielAPI Overview and First ExampleOverview of Implemented GamesOverview of Implemented AlgorithmsDeveloper GuideUsing OpenSpiel as a C++ LibraryGuidelines and ContributingAuthorsFor a longer introduction to the core concepts, formalisms, and terminology,
including an overview of the algorithms and some results, please seeOpenSpiel: A Framework for Reinforcement Learning in Games.For an overview of OpenSpiel and example uses of the core API, please check out
our tutorials:Motivation, Core API, Brief Intro to Replictor Dynamics and Imperfect
Information Gamesby Marc
Lanctot.(slides)(colab)Motivation, Core API, Implementing CFR and REINFORCE on Kuhn poker, Leduc
poker, and Goofspielby Edward
Lockhart.(slides)(colab)If you use OpenSpiel in your research, please cite the paper using the following
BibTeX:@article{LanctotEtAl2019OpenSpiel,
  title     = {{OpenSpiel}: A Framework for Reinforcement Learning in Games},
  author    = {Marc Lanctot and Edward Lockhart and Jean-Baptiste Lespiau and
               Vinicius Zambaldi and Satyaki Upadhyay and Julien P\'{e}rolat and
               Sriram Srinivasan and Finbarr Timbers and Karl Tuyls and
               Shayegan Omidshafiei and Daniel Hennes and Dustin Morrill and
               Paul Muller and Timo Ewalds and Ryan Faulkner and J\'{a}nos Kram\'{a}r
               and Bart De Vylder and Brennan Saeta and James Bradbury and David Ding
               and Sebastian Borgeaud and Matthew Lai and Julian Schrittwieser and
               Thomas Anthony and Edward Hughes and Ivo Danihelka and Jonah Ryan-Davis},
  year      = {2019},
  eprint    = {1908.09453},
  archivePrefix = {arXiv},
  primaryClass = {cs.LG},
  journal   = {CoRR},
  volume    = {abs/1908.09453},
  url       = {http://arxiv.org/abs/1908.09453},
}VersioningWe useSemantic Versioning.
Package: open-spiel-junqi
OpenSpiel: A Framework for Reinforcement Learning in GamesOpenSpiel is a collection of environments and algorithms for research in general
reinforcement learning and search/planning in games. OpenSpiel supports n-player
(single- and multi- agent) zero-sum, cooperative and general-sum, one-shot and
sequential, strictly turn-taking and simultaneous-move, perfect and imperfect
information games, as well as traditional multiagent environments such as
(partially- and fully- observable) grid worlds and social dilemmas. OpenSpiel
also includes tools to analyze learning dynamics and other common evaluation
metrics. Games are represented as procedural extensive-form games, with some
natural extensions. The core API and games are implemented in C++ and exposed to
Python. Algorithms and tools are written both in C++ and Python.To try OpenSpiel in Google Colaboratory, please refer toopen_spiel/colabssubdirectory or starthere.IndexPlease choose among the following options:Installing OpenSpielIntroduction to OpenSpielAPI Overview and First ExampleAPI ReferenceOverview of Implemented GamesOverview of Implemented AlgorithmsDeveloper GuideUsing OpenSpiel as a C++ LibraryGuidelines and ContributingAuthorsFor a longer introduction to the core concepts, formalisms, and terminology,
including an overview of the algorithms and some results, please seeOpenSpiel: A Framework for Reinforcement Learning in Games.For an overview of OpenSpiel and example uses of the core API, please check out
our tutorials:Motivation, Core API, Brief Intro to Replictor Dynamics and Imperfect
Information Gamesby Marc
Lanctot.(slides)(colab)Motivation, Core API, Implementing CFR and REINFORCE on Kuhn poker, Leduc
poker, and Goofspielby Edward
Lockhart.(slides)(colab)If you use OpenSpiel in your research, please cite the paper using the following
BibTeX:@article{LanctotEtAl2019OpenSpiel,title={{OpenSpiel}: A Framework for Reinforcement Learning in Games},author={Marc Lanctot and Edward Lockhart and Jean-Baptiste Lespiau andVinicius Zambaldi and Satyaki Upadhyay and Julien P\'{e}rolat andSriram Srinivasan and Finbarr Timbers and Karl Tuyls andShayegan Omidshafiei and Daniel Hennes and Dustin Morrill andPaul Muller and Timo Ewalds and Ryan Faulkner and J\'{a}nos Kram\'{a}rand Bart De Vylder and Brennan Saeta and James Bradbury and David Dingand Sebastian Borgeaud and Matthew Lai and Julian Schrittwieser andThomas Anthony and Edward Hughes and Ivo Danihelka and Jonah Ryan-Davis},year={2019},eprint={1908.09453},archivePrefix={arXiv},primaryClass={cs.LG},journal={CoRR},volume={abs/1908.09453},url={http://arxiv.org/abs/1908.09453},}VersioningWe useSemantic Versioning.