Saravanan666/pypi-org huggingface dataset:
Package: yarg
yarg(1) – A semi hard Cornish cheese, also queries PyPIYarg is a PyPI client.>>>importyarg>>>package=yarg.get("yarg")>>>package.nameu'yarg'>>>package.authorAuthor(name=u'Kura',email=u'kura@kura.io')Full documentation is at <https://yarg.readthedocs.org>.Yarg is released under theMIT license. Thesource code is on
GitHubandissues are also tracked on
GitHub.Release History0.1.9 (2014-08-11)Splatting bugsAddeddecodecall on the response object for Python 3 inyarg.newest_packagesandyarg.latest_updated_packages.0.1.8 (2014-08-10)Splatting bugsIntegration issue with Python 3, requests, yarg and JSON. Attempt to decode
requests response if decode attribute exists.0.1.6 & 0.1.7 (2014-08-10)Splatting bugsBug in setup.py causing installs to fail for sdist (source) releases.0.1.5 (2014-08-10)API changesChanged sort order ofyarg.package.Package.release_idsto sort
based on the upload time of the release ID.Splatting bugsyarg.package.Package.latest_release_idwill now return the latest
release ID from the PyPI info source, rather than the final list item inyarg.package.Package.release_ids.Addtionallyyarg.package.Package.latest_releasewill do the same as
it gets the latest release information fromyarg.package.Package.latest_release_id.0.1.4 (2014-08-09)API changesNew methodyarg.newest_packagesfor querying new packages
from the PyPI RSS feed.New methodyarg.latest_updated_packagesfor querying
the latest updated packages from the PyPI RSS feed.OtherAdditional test coverageAdditional documentation coverage0.1.2 (2014-08-08)Bug fixesyarg.getwill now raise an Exception for errorsincluding300 and above. Previously only raised for above 300.Fix an issue on Python 3.X and PyPy3 whereyarg.exceptions.HTTPErrorwas using a method that was
removed in Python 3.Added dictionary key lookups forhome_page,bugtrack_urlanddocs_url. CausedKeyErrorexceptions if they were not
returned by PyPI.OtherMore test coverage.0.1.1 (2014-08-08)API changesNewyarg.package.Packagepropertyhas_wheel.Newyarg.package.Packagepropertyhas_egg.Newyarg.package.Packagepropertyhas_source.Newyarg.package.Packagepropertypython_versions.Newyarg.package.Packagepropertypython_implementations.Addedyarg.exceptions.HTTPErrortoyarg.__init__for easier access.Addedyarg.json2packagetoyarg.__init__to expose it for
use.0.1.0 (2014-08-08)Initial release
Package: yargy
Yargy uses rules and dictionaries to extract structured information from Russian texts. Yargy is similar toTomita parser.InstallYargy supports Python 3.7+, PyPy 3, depends only onPymorphy2.$pipinstallyargyUsagefromyargyimportParser,rule,and_,not_fromyargy.interpretationimportfactfromyargy.predicatesimportgramfromyargy.relationsimportgnc_relationfromyargy.pipelinesimportmorph_pipelineName=fact('Name',['first','last'],)Person=fact('Person',['position','name'])LAST=and_(gram('Surn'),not_(gram('Abbr')),)FIRST=and_(gram('Name'),not_(gram('Abbr')),)POSITION=morph_pipeline(['управляющий директор','вице-мэр'])gnc=gnc_relation()NAME=rule(FIRST.interpretation(Name.first).match(gnc),LAST.interpretation(Name.last).match(gnc)).interpretation(Name)PERSON=rule(POSITION.interpretation(Person.position).match(gnc),NAME.interpretation(Person.name)).interpretation(Person)parser=Parser(PERSON)match=parser.match('управляющий директор Иван Ульянов')print(match)Person(position='управляющий директор',name=Name(first='Иван',last='Ульянов'))DocumentationAll materials are in Russian:OverviewVideo from workshopGetting startedReferenceCookbookExamplesCode snippetsSupportChat —https://t.me/natural_language_processingIssues —https://github.com/natasha/yargy/issuesCommercial support —https://lab.alexkuk.ruDevelopmentDev envbrewinstallgraphviz

python-mvenv~/.venvs/natasha-yargysource~/.venvs/natasha-yargy/bin/activate

pipinstall-rrequirements/dev.txt
pipinstall-e.

python-mipykernelinstall--user--namenatasha-yargyTest + lintmaketestUpdate docsmakeexec-docs# Manually check git diff docs/, commitRelease# Update setup.py versiongitcommit-am'Up version'gittagv0.16.0

gitpush
gitpush--tags# Github Action builds dist and publishes to PyPi
Package: yarh
YARH(Yet Another Rough HTML), a simple markup language written in python 3.5.InstallationInstallation usingpipis recommended.pipinstallyarhAlso you can clone from repository.gitclonehttps://github.com/minacle/yarh.gitcdyarh./setup.pyinstallUsageAs program:yarh -- [YARH_STRING]
yarh <YARH_FILE>...
yarh <YARH_FILE>... -- [YARH_STRING]
Package: yark
YarkYouTube archiving made simple.InstallationTo install Yark, simply downloadPython 3.9+andFFmpeg(optional), then run the following:$pip3installyarkManaging your ArchiveOnce you've installed Yark, think of a name for your archive (e.g., "foobar") and copy the target's url:$yarknewfoobarhttps://www.youtube.com/channel/UCSMdm6bUYIBN0KfS2CVuEPANow that you've created the archive, you can tell Yark to download all videos and metadata using the refresh command:$yarkrefreshfoobarOnce everything has been downloaded, Yark will automatically give you a status report of what's changed since the last refresh:Viewing your ArchiveViewing you archive is easy, just typeviewwith your archives name:$yarkviewfoobarThis will pop up an offline website in your browser letting you watch all videos 🚀Under each video is a rich history report filled with timelines and graphs, as well as a noting feature which lets you add timestamped and permalinked comments 👐Light and dark modes are both available and automatically apply based on the system's theme.DetailsHere are some things to keep in mind when using Yark; the good and the bad:Don't create a new archive again if you just want to update it, Yark accumulates all new metadata for you via timestampsFeel free to suggest new features via the issues tab on this repositoryScheduling isn't a feature just yet, please usecronor something similar!Archive FormatThe archive format itself is simple and consists of a directory-based structure with a core metadata file and all thumbnail/video data in their own directories as typical files:[name]/– Your self-contained archiveyark.json– Archive file with all metadatayark.bak– Backup archive file to protect against data damagevideos/– Directory containing all known videos[id].*– Files containing video data for YouTube videosthumbnails/– Directory containing all known thumbnails[hash].png– Files containing thumbnails with its hashIt's best to take a few minutes to familiarize yourself with your archive by looking at files which look interesting to you in it, everything is quite readable.
Package: yarll
Yet Another Reinforcement Learning Library (YARLL)Update 25/03/2019: For now, the master branch won't get big changes. Instead, algorithms are adapted for TensorFlow 2 (and new ones may be added) on theTF2 branch.Update 29/10/2018: New library name.Update 25/10/2018: AddedSAC implementation.StatusDifferent algorithms have currently been implemented (in no particular order):Advantage Actor CriticAsynchronous Advantage Actor Critic (A3C)Deep Deterministic Policy Gradient (DDPG)Proximal Policy Optimization (PPO)Distributed version (DPPO)Soft Actor-Critic (SAC)Trust Region Policy Optimization (TRPO)Distributed version (DTRPO)REINFORCE(convolutional neural network part has not been tested yet)Cross-Entropy MethodSarsa with with function approximation and eligibility tracesKarpathy's policy gradient algorithm(version using convolutional neural networkshas not been tested yet)(Sequential) knowledge transferAsynchronous knowledge transferAsynchronous Advantage Actor Critic (A3C)The code for this algorithm can be foundhere.
Example run after training using 16 threads for a total of 5 million timesteps on thePongDeterministic-v4environment:How to runFirst, install the library usingpip(you can first removeOpenCVfrom thesetup.pyfile if it is already installed):pipinstallyarllAlgorithms/experimentsYou can run algorithms by passing the path to an experiment specification (which is a file injsonformat) tomain.py:python-myarll.main<path_to_experiment_specification>Examples of experiment specifications can be found in theexperiment_specsfolder.StatisticsStatistics can be plot using:python-myarll.misc.plot_statistics<path_to_stats><path_to_stats>can be one of 2 things:Ajsonfile generated usinggym.wrappers.Monitor, in case it plots the episode lengths and total reward per episode.A directory containingTensorFlowscalar summaries for different tasks, in which case all of the found scalars are plot.Help about other arguments (e.g. for using smoothing) can be found by executingpython -m yarll.misc.plot_statistics -h.Alternatively, it is also possible to useTensorboardto show statistics in the browser by passing the directory with the scalar summaries as--logdirargument.
Package: yarllib
yarllibYet Another Reinforcement Learning Library.Status:development.Why?I had the need for a RL library/framework that:was clearly and simply implemented, with good enough performances;highly focused on modularity, customizability and extendability;wasn't merely Deep Reinforcement Learning oriented.I couldn't find an existing library that satisfied my needs;
hence I decided to implementyet anotherRL library.For me it is also an opportunity to
have a better understanding of the RL algorithms
and to appreciate the nuances that you can't find on a book.If you find this repo useful for your research or your project,
I'd be very glad :-) don't hesitate to reach me out!WhatThe package is both:alibrary, because it provides off-the-shelf functionalities to
set up an RL experiment;aframework, because you can compose your custom model by implementing
the interfaces, override the default behaviours, or use the existing
components as-is.You can find more details in thedocumentation.TestsTo run tests:toxTo run only the code tests:tox -e py3.7To run only the linters:tox -e flake8tox -e mypytox -e black-checktox -e isort-checkPlease look at thetox.inifile for the full list of supported commands.DocsTo build the docs:mkdocs buildTo view documentation in a browser:mkdocs serveand then go tohttp://localhost:8000Licenseyarllib is released under the GNU Lesser General Public License v3.0 or later (LGPLv3+).Copyright 2020 Marco FavoritoAuthorsMarco FavoritoCiteIf you use this library for your research, please consider citing this repository:@misc{favorito2020,
  Author = {Marco Favorito},
  Title = {yarllib: Yet Another Reinforcement Learning Library},
  Year = {2020},
}An e-print will come soon :-)
Package: yarn-api-client-kenny
Python client for Apache Hadoop® YARN APIPackage documentation:python-client-for-hadoop-yarn-api.readthedocs.orgREST API documentation:hadoop.apache.orgInstallationFrom PyPIpip install yarn-api-client-kennyFrom Anaconda (conda forge)conda install -c conda-forge yarn-api-client-kennyFrom source codegit clone https://github.com/71nn/hadoop-yarn-api-python-client
pushd hadoop-yarn-api-python-client
python setup.py install
popdUsageCLI interfacebin/yarn_client --helpalternativepython -m yarn_api_client --helpProgrammatic interfacefromyarn_api_clientimportApplicationMaster,HistoryServer,NodeManager,ResourceManager,TimelineServerChangelog0.3.2 ReleaseAdd support for TimelineServer0.3.1 ReleaseFix cluster_application_kill API0.3.0 ReleaseAdd support for YARN endpoints protected by Kerberos/SPNEGOMoved torequestspackage for REST API invocationRemovehttp_conproperty, as connections are now managed byrequestspackage0.2.5 ReleaseFixed History REST API0.2.4 ReleaseAdded compatibility with HA enabled Resource Manager
Package: yarn-apps-exporter
YARN Apps ExporterWrite YARN applications metrics for prometheus node_exporter textfile collector.Installsudo pip install yarn-apps-exporterCheck that it runs correctly:sudo yarn-apps-exporter http://resource.manager.hostname:8088
cat /opt/prometheus/exporters/node_exporter_current/yarn_apps.promAdd systemd service:sudo tee /etc/systemd/system/yarn-apps-exporter.service << EOF
[Unit]
Description=Write YARN apps metrics for node_exporter textfile

[Service]
ExecStart=/usr/local/bin/yarn-apps-exporter http://resource.manager.hostname:8088

[Install]
WantedBy=multi-user.target
EOF
sudo systemctl start yarn-apps-exporter
sudo systemctl enable yarn-apps-exporterReplacement PatternsTo haveHIVE-{uuid}application name instead of many unique names:Putreplacement\npattern\nlines to patterns.txt file:{uuid}
[0-9a-f]{8}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{4}-[0-9a-f]{12}Specify--patternsargument:yarn-apps-exporterhttp://resource.manager.hostname:8088--patternspatterns.txtYou can add more patterns in the next lines.
Package: yarn.build
IntroductionThis package builds JavaScript projects withyarn.
It contains azest.releaserentry point and a stand-alone command line tool.GoalYou want to release a package that has apackages.jsonon it and areleasescript defined on it.Usually one does not want to keep the generated files on VCS,
but you want them when releasing withzest.releaser.CreditsThis package is a direct inspiration fromzest.pocompilefrom Maurits van Rees.Thanks!To DoAdd testsChangelog1.0.0 (2019-02-11)No changes since last release.1.0b3 (2018-11-09)Fix logic, to not try to build a project if no package.json could be found.Be more quiet if noyarn.buildsection is found onsetup.cfg.1.0b2 (2018-11-09)Change option tofolderas package.json is expected to be there, i.e.[yarn.build]
folder = path/to/folder/1.0b1.post0 (2018-11-09)Fix (hopefully), pypi rendering.1.0b1 (2018-11-09)Allow to configure where package.json is located via a setup.cfg section:[yarn.build]
file = path-to/package.json1.0a6 (2017-01-06)Create universal wheels again.1.0a5 (2017-01-06)Pass the parent folder where package.json is.1.0a4 (2017-01-06)Typos are fun…1.0a3 (2017-01-06)Ditch wheels by now, only source releases.1.0a2 (2017-01-06)Be more verbose.1.0a1 (2017-01-05)Initial release.
Package: yarnlog
yarnlogDownload Apache Hadoop YARN log to your local machine.Usage$yarnlog<YARN_URL>DevSet up development environmentI use Poetry to manage dependencies$poetryinstall
$source$(poetryenvinfo--path)/bin/activateDebug yarnlog locally$poetryrunyarnlogRun tests$pytest# coverage$pytest--cov=yarnlogtests# coverage with html report$pytest--cov=yarnlog--cov-reporthtml:htmlcovtests
Package: yarntf
yarntfsimplifies the distributed TensorFlow submit model, for running
machine learning applications on Hadoop YARN clusters.User GuideIn general it is as simple as follows.In your code: replacetf.train.ClusterSpec()andtf.train.Server()withyarntf.createClusterServer()On your cluster: submit the application withHops-TensorFlowYour ClusterSpec is generated automaticaly and the parameter servers stopped when all workers are completed. Specify the number of worker, ps and resources on submit.For more details see the examples.Work In ProgressDevelopment is still in an early stage. Contributions are very welcome!LicenseyarntfandHops-TensorFlowis released under an Apache 2.0 license.
Package: yarpc
# yarpc-python[![Build Status](https://travis-ci.org/yarpc/yarpc-python.svg?branch=master)](https://travis-ci.org/yarpc/yarpc-python) [![PyPI version](https://badge.fury.io/py/yarpc.svg)](https://badge.fury.io/py/yarpc)This project is currently WIP and not ready for use. Do not use it until this notice goes away.
Package: yarp-parser
YARPYARP - Yet Another Recursive ParserYARP provides a framework for easily creating recursive parsers in Python. Hand-built parsers are fast, flexible, and allow easy error detection and reporting when compared to parser generators that use EBNF.Two examples of parsers created with YARP are provided in the examples directory.JSON parsing exampleGetting startedTo create a parser, start by extending the Parser class. Use the @lexemes annotation to set the expected lexemes for the rule (and all rules until the lexemes are redefined).json_lexemes={'{','}',',','[',']',':','true','false','null','"','\\'}classJSONParser(Parser):@lexemes(json_lexemes)defparse(self):super().parse()self.value()#parse a json valueAST production rulesRules that add nodes to the abstract syntax tree can be created using the @ast annotation. The rule below will parse the lexeme 'true' and add a node to the AST. If this rule is encountered and a token mismatch occurs, a syntax error will be printed along with information about where in the token stream it occurred. Note that the @ast annotation also allows for optional rules with the 'optional' parameter.@ast("true",require=['true'],description="boolean")deftrue_val(self,tokens):passPattern matchingYou can match patterns forward into the token stream without consuming tokens. This is essentially the lookahead functionality from other parser generators.@ast("array",require=['['],description="array")defjson_array(self,tokens):self.consume_whitespace()while(notself.match_pattern(']')):self.value()self.accept(',')self.accept(']')Rules with alternativesRules with alternatives can be added using the parse_alternatives function. This uses pattern matching (of one or more tokens) to determine which production rule to use. If no alternative is valid, then a syntax error will occur with the provided description of what was expected.@ast("value")defvalue(self):self.consume_whitespace()self.parse_alternatives([(['{'],self.json_object),(['['],self.json_array),(['"'],self.string),(['true'],self.true_val),(['false'],self.false_val),(['null'],self.null_val),([RegularExpression(NUMBER_REGEX)],self.number)],"value")self.consume_whitespace()
Package: yarqueue
Yet Another python queue backed by redis; but modern and compliantFree software: MIT licenseDocumentation:https://yarqueue.readthedocs.io.Heavily inspired byhotqueue.FeaturesCompatible with the API ofmultiprocessing.QueueLIFO, FIFO and Double-Ended variantsEach has a non-joinable and a joinable form (compatible withmultiprocessing.JoinableQueue)Additional convenience methods:get_many(),put_many(),clear()for all queuesn_tasks()andn_in_progress()for joinable queuesCan be used as a context managerDeletes the queue on exitFor Joinable variants, first waits for all tasks to be doneCan use custom serializers, or none at allBy default, uses the highest pickle protocol available, using the pickle5 backport if possible.As thread-safe as theunderlying Redis client instanceSimple queue watchersyarqwatch: command-line progress bars for given queuesyarqserve: simple REST API and browser page for given queuesCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.
Package: yarr.client
Web client forYarr!in Python.InstallUsing Docker$dockerrun-d-p80:5000bluecap/yarr.client:latestUsing a package manager$pipinstallyarr.clientFrom source$gitclonehttps://github.com/bluecap-se/yarr.client.git$cdyarr.client$pipinstall-rrequirements.txt$pipinstall-e.UsageQuickstart$yarr.client* Running on http://0.0.0.0:5000/
 * Restarting with reloaderOptionsThe configuration file params are defined in theFlask documentation.Additional, app-specific params areYARR_URLandYARR_API_TOKEN.YARR_URLis the path to theYarr!server, andYARR_API_TOKENneeds to be set if the server has a corresponding token specified in its
config.$yarr.client-hYarr.client - Web Client for Yarr!

Usage:
      yarr.client runserver [--config FILE] [options]

Options:
  -c, --config FILE         Path to Flask config file (params: bit.ly/1uamUo1)
  -h, --help                Output this help and exit
  -v, --version             Output version and exit

Examples:
  yarr.client runserver
  yarr.client runserver -c /etc/production.cfgOptions with DockerTo use a custom configuration file in Docker, map your file using
volumes.$dockerrun-d-p80:5000-v/path/to/your/config.cfg:/yarr.config.cfgbluecap/yarr.client:latestRun tests$pipinstall-rtest_requirements.txt$py.testWatch for changesTo run the tests continuously, run the test command with the watch or
follow flag-f:$py.test-fTest coverage$coveragerun--sourceyarr_client-mpy.test$coveragehtml$openhtmlcov/index.htmlLicensePublished underMIT License.
Package: yarrow-diagrams
Yarrow Diagrams⚠️ yarrow is still early in development ⚠️: it's missing some features, and not everything is documented. Consider yourself warned!yarrowis a Python library implementing the datastructures
and algorithms for string diagrams described in the paperData-Parallel Algorithms for String Diagrams.What is yarrow?For a programmer's overview of what yarrow is for, seethe documentation.
In short, the main datastructure ofyarrowis theDiagram, which can be thought of a generalisation of syntaxtreesto syntaxgraphs.Here's an example of a string diagram depicted graphically:You could think of this as representing thesyntaxof the following python
program:defcompute_mean(numbers:List[int]):count,sum=summary(numbers)returndivide(sum,count)String diagrams are different from directed graphs in two important ways:The boxes (operations) in a string diagram have multiple ordered inputs and outputs in the same way
a python function has orderedinput arguments.The diagramitselfhas inputs and outputs depicted as "dangling" wires on
the left and rightOther examples of structures which can be represented by string diagrams areelectronic circuits,quantum circuits,neural networks, and many more.
For a more formal introduction to string diagrams aimed at computer scientists,
trythis recent paper.Installationpip install yarrow-diagramsRunning testsInstall test dependenciespip install hypothesisRun test with your test runner, e.g.pytestYarrow as a Reference ImplementationYarrow is intended as areference implementationfor thepaper.
It has the following goals:Fast, data-parallel, andruns on the GPUMinimal primitives/dependencies requiredSimple to implement correctlyThe aim is to serve as a general-purpose datastructure in many languages
by making it simple to implement fast algorithms while relying
on few external dependencies.To port yarrow to your language, the only things needed are:Arrays and simple array subroutines (see thenumpy backendor Section 2.2.2 of thepaper.)An algorithm for computingconnected components.Porting YarrowIf you want to port Yarrow to a different language and you would like help,
please reach out. Here are some places you could do that:discord##yarrowonirc.libera.chatemailthe author
Package: yarrowformat
Yarrow: a data format for computer visionWhat is it ?yarrowis a python package to parse, manipulate and serialize data
following the yarrowdata schema. This format is
oriented around computer vision data and is heavily inspired by the COCOdataset formatand was initially developed
and used in Michelin projects.The full description can be foundherewith the rules on how to fill different fields.How to installpipinstallyarrowformatHow to useYou can find multiple examples in theexamples directoryand the package API in thedocumentation(still WIP). Here are a few examples.importjsonfromyarrowimportYarrowDataset# say you have a yarrow file at pathfile_path="path/to/file.yarrow.json"yar_set=YarrowDataset.parse_file(file_path)# You now have a YarrowDataset !# Add annotationsannot=Annotation(...)# see documentation for parametersyar_set.add_annotation(annot)# now save it somewhere elsewithopen("path/to/other/file.yarrow.json","w")asfp:json.dump(yar_set.pydantic().dict(),fp,default=str)Format explanationLicenseApache 2.0