Saravanan666/pypi-org huggingface dataset:
Package: cltrier_prosem
CLTrier ProSemUsagefromcltrier_prosemimportPipeline# init pipeline object (load model, data, trainer)pipeline=Pipeline({'encoder':{'model':'deepset/gbert-base',# huggingface model slug},'dataset':{'path':'./path/data',# path to data directory (containing train/test.parquet)'text_column':'text',# column containing src text'label_column':'label',# column containing target label'label_classes':['class_1','class_2'],# list of target classes},'classifier':{'hid_size':512,# size of classifier perceptron'dropout':0.2,# dropout value},'pooler':{'form':'cls',# type of pooling, possible values:# 'cls', 'sent_mean', 'subword_{first|last|mean|min|max}'# if subword probing used'span_columns':['span']},'trainer':{'num_epochs':5,# number of training epochs'batch_size':32,# batch size in both training and evaluation'learning_rate':1e-3,# trainer learning rate'export_path':'./path/output',# output path for logging and results},})# call pipeline object (training and evaluation)pipeline()
Package: cltwit
CltwitCltwit is a twitter command line utilityFeaturesCreate a sqlite database of your tweetsCreate a pdf file with pie charts distribution of your tweetsSearch into your tweetsExport all your tweets as a csv file.Follow the followers you are not following back yet.Unfollow someone you follow and doesn’t follow you back.Send a tweet from command lineMulti-language (English - French )Installationfrom sourcesgit clone git://projet-libre.org/cltwit.gitcd cltwitsudo python2 setup.py installorsudo easy_install-2.7 cltwitsudo easy_install -U cltwit (to update)or from AUR for archlinux usersyaourt -S cltwitLicenceWTFPL ‘Do What The Fuck you want to Public License’English :DO WHAT THE FUCK YOU WANT TO PUBLIC LICENSEVersion 2, December 2004Copyright (C) 2013 jlaunayDO WHAT THE FUCK YOU WANT TO PUBLIC LICENSETERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATIONYou just DO WHAT THE FUCK YOU WANT TO.French :LICENCE PUBLIQUE RIEN À BRANLERVersion 1, Mars 2009Copyright (C) 2013 jlaunayCONDITIONS DE COPIE, DISTRIBUTON ET MODIFICATIONDE LA LICENCE PUBLIQUE RIEN À BRANLERFaites ce que vous voulez, j’en ai RIEN À BRANLER.
Package: clu
CLU - Common Loop UtilsThis repository contains common functionality for writing ML training loops. The
goal is to make trainings loops short and readable (but moving common tasks to
small libraries) without removing the flexibility required for research.To get started, check out this Colab:https://colab.research.google.com/github/google/CommonLoopUtils/blob/main/clu_synopsis.ipynbIf you're looking for usage examples, see:https://github.com/google/flax/tree/main/examplesYou can also find answers to common questions about CLU on Flax Github
discussions page:https://github.com/google/flax/discussionsNote: As this point we are not accepting contributions. Please fork the
repository if you want to extend the libraries for your use case.
Package: clubcpg
CluBCpG: Cluster-Based analysis of CpG methylationWhats new?v0.3.0A major overhaul and modernization of the ClubCpG dependencies.setup.pyremoved in favor of apyproject.tomlAll project dependencies updated to modern versionsSupport of python 3.5, 3.6, 3.7, and 3.8 droppedMinimum version of python is now 3.9Added GitHub Actions workflows to automate testing of python 3.9 and 3.10Updated readthedocs build configuration ahead of deprecation of old methodWhat is CluBCpG?CluBCpG is a software package built to analyze whole genome bisulfite sequencing (WGBS) data. This toolkit will divide each chromosome into small user-defined intervals, extract all WGBS reads within those intervals, cluster them based on identity, and write a final report to the use containing all identified CpG methylation patterns.How do I use this?Full documentation is available onReadTheDocsRequirementsPython >= 3.9, <3.11Installation of Samtools on your PATHInstall(Optional, but HIGHLY recommended)Create a new python virtual environment and activate that virualenvExecutepip install clubcpgto install the package. Requirements will automatically be installed if not already present.Developer InstallEnsure all dependencies inubuntu-preinsatll.share installed on the system. (Or equivilant packages if using fedora based system)Install samtools, useinstall-samtools.shMake sure you have a compatible version of python installedInstall Poetrypip3 install poetryRunpoetry installto install required packages and clubcpgTo install optional packages for docs building. Runpoetry install --with docsHelp! This isnt working.Open anIssueCan you make it do this?Open aFeature Request
Package: clube-betionario
CLIQUE AQUI Para CONFERIR E ADQUIRIR O CLUBE BETIONÁRIOCOMO FUNCIONA O CLUBE BETIONÁRIO?João da SilvaNo Clube Betionário vamos mostrar - lhe de essencial a de ponta sobre o mercado de Investimento Esportivo.Todos os sistemas utilizados por comerciantes bem sucedidos, todos os principaisMercados, investigação de jogos, mentalidade para ter sucesso e insight on the lookout, bankroll o framework de tabuleiro para que você tenha controle e aumente o seu lucro a uma extensão cada vez maior.Você vai se aproximar do clube BETIONÁRIO PREMIUM, um encontro seletivo onde você vai começar todos os dias exame dos jogos de troca de mercado diretamente em seu portátil, para simplesmente reordenar nossos sinais e benefício com um par de fotos, sem saber o mínimo sobre o mercado.CLIQUE AQUI Para CONFERIR E ADQUIRIR O CLUBE BETIONÁRIOVantagens do Trading EsportivoLIQUIDEZComo resultado de ter jogos em todo o planeta, a liquidez do mercado é excepcionalmente alta. As variedades são extremamente enormes, fazendo-nos tirar dinheiro do mercado.Vida: prosperidade e eficiência100% no seu Celular!Para colocar recursos na Bolsa de Valores esportivos apenas um celular é suficiente. Então você já seria capaz de armazenar, apostar, levantar dinheiro e, acima de tudo , reordenar nossas entradas.Descolagem 24 horas por diaNa verdade, mesmo em alguns minutos sem jogos no Brasil ou na nação do início, muito provavelmente terá algum lugar, e isso nos cria ter intermináveis portas abertas para apostar no comércio de esportes.QUERO JUNTAR-ME À EQUIPE.CLIQUE AQUI Para CONFERIR E ADQUIRIR O CLUBE BETIONÁRIOO QUE TE ENSINAREMOS:Módulo 1Clube BetionárioMódulo 2Atitude e comerciante DesportivoMódulo 3Tornar-se mais familiarizado com o corretor de apostasMódulo 4Principais mercados dos operadoresMódulo 5Exame do mercadoMódulo 6Gestão BancáriaFinalClube Betionário PREMIUMPROTEJAM O MEU LUGAR AGORA!CLIQUE AQUI Para CONFERIR E ADQUIRIR O CLUBE BETIONÁRIOQUEM SOU EU?João da SilvaLUCAS DANELONNo Natal de 2018, em vez de presentes, pedi dinheiro e levantei r 5 500,00. No dia 27 de dezembro daquele mesmo ano, pus de lado a parcela principal no bookmaker e comecei a minha.Ganhei, mas como não tinha dinheiro, estava praticamente concentrado. Foi então que eu coloquei recursos em informações para o exame do jogo e utilizei minha conta bancária.Hoje, sou especialista em tipos de receitas através da Internet, com mais de 4 milhões de faturas, e vou ajudá-lo na faturação através do Corretor esportivo no Clube Betionário, juntamente com mais 4 insiders!CLIQUE AQUI Para CONFERIR E ADQUIRIR O CLUBE BETIONÁRIOÉ MELHOR DEIXAR OS ALUNOS FALAREM...Resultados mensais 2021DE R$ 1997,00R$ 9997,00no mês primárioDE R$ 997Para R seguintes 0, 00 Após mesesEntrada para preparação completaGarantia de 7 dias24h ajuda directamente com o LucasEntrada no clube betionário excepcionalQUERO A MINHA VAGA.CLIQUE AQUI Para CONFERIR E ADQUIRIR O CLUBE BETIONÁRIOClube BetionárioTodas as metodologias e especulações implicam risco de infortúnio. Nenhum dado contido nesta rubrica deve ser interpretado como uma garantia de resultados.
Package: clubhouse
clubhouse-clientPython client for ClubhouseInstallationThe package is available onpypiand can
be installed like any other packages.$ pip install clubhouseUsageRefer toClubhouse API Docsfor more information.fromclubhouseimportClubhouseClientclubhouse=ClubhouseClient('your api key')story={'name':'A new story','description':'Do something!'}clubhouse.post('stories',json=story)
Package: clubhouse-api
club_house_apiclub_house_api- this module is a Python client library for The ClubHouse project management platform API (ClubHouse API wrapper)Clubhouseis collaborative project management that streamlines and refines your existing workflow. The intuitive and powerful project management platform loved by software teams of all sizes.Clubhouseis here.API documentationhttps://clubhouse.io/api/rest/v3/InstallationInstall the current version with PyPI:pipinstallclubhouse_apiOr from Github:pipinstallhttps://github.com/Peopl3s/club-house-api/archive/main.zipUsageYou can generate a token for clubhouse by going to the account section and generating a new tokenTOKEN=os.getenv('TOKEN')club_house_session=ClubHouse(TOKEN,'v3')club_house=club_house_session.get_api()ExampleCreate a new Story in the first Project that is returned from the API in the all projects list.If you installed a module from PyPi, you should to import it like this:from clubhouse_api import ClubHouseIf from GitHub or source:from club_house_api import ClubHousefromclub_house_apiimportClubHouseimportasyncioimportosTOKEN=os.getenv('API_TOKEN')club_house_session=ClubHouse(TOKEN,'v3')club_house=club_house_session.get_api()asyncdefmain():all_projects=awaitclub_house.projects()first_project_id=all_projects[0]['id']new_story={'name':'My new story','project_id':first_project_id}story=awaitclub_house.stories.create(**new_story)print(story)loop=asyncio.get_event_loop()loop.run_until_complete(main())You can check out all the docs with exampleshereEpicsFilesLabelsLinked-FilesProjectsStory-LinksStoriesCommentsTasksUsersWorkflowsContributingBug reports and/or pull requests are welcomeLicenseThe module is available as open source under the terms of theApache License, Version 2.0
Package: clubhouse-lib
python-clubhouse-libThe unofficial Python client library for the Clubhouse API.python-clubhouse-lib is a Python wrapper around the Clubhouse API. It leverages
typed dicts to better provide utility to the programmer when dealing with
Clubhouse data. This library only provides the basic glue between your
application and Clubhouse, and it doesn’t attempt to do anything more than that.Installationpip install clubhouse-liborgit clone https://github.com/huntrar/python-clubhouse-lib
cd clubhouse_lib
python setup.py installExamplesInitializing the clientimport clubhouse_lib, os
cc = clubhouse_lib.ClubhouseClient(os.environ.get('CLUBHOUSE_API_TOKEN'))Listing Projectsprojects = cc.listProjects()Listing Storiesstories = cc.listStories(projects[0]['id'])Update Storycc.updateStory(stories[0]['id'], name='Hello World!')ContributingThis project contains tooling for easily importing documentation from the
Clubhouse API and converting it into Python.The API slurper allows you to convert snippets of documentation into JSON
objects, which can then be digested by the API builder which will build the
Python code.We use Black for code formatting. Our flake8 linting configuration is included
in the repository.News1.0.0Publishing the unofficial Python client library for the Clubhouse API.
Package: clubs-gym
clubs_gymclubs_gym is agymwrapper around theclubspython poker library.clubsis used for running arbitrary configurations of community card poker games. This includes anything from simple Leduc orKuhnpoker to full n-playerNo Limit Texas Hold'emorPot Limit Omaha.InstallInstall usingpip install clubs-gym.How to useBy runningimport clubs_gym, several pre-defined poker clubs poker configurations are registered with gym (callclubs_gym.ENVSfor a full list). Custom environments can be registered withclubs_gym.envs.register({"{environment_name}": {config_dictionary})}. Environment names must follow the gym environment name convention ({title-case}-v{version_number}). Check theclubs documentationfor additional information about the structure of a configuration dictionary.Sincegymisn't designed for multi-agent games, the api is extended to enable registering agents. This is not required, but ensures each agent only receives the information it's supposed to. An agent needs to inherit from theclubs_gym.agent.base.BaseAgentclass and implement theactmethod.actreceives a game state dictionary and needs to output an integer bet size. A list of agents the length of the number of players can then be registered with the environment usingenv.register_agents. By callingenv.act({observation_dictionary}), the observation dictionary is passed to the correct agent and the agent's bet is returned. This can then be passed on theenv.stepfunction. An example with an optimal Kuhn agent (clubs_gym.agent.kuhn.NashKuhnAgent) is given below.Exampleimportgymimportclubs_gymenv=gym.make("KuhnTwoPlayer-v0")env.register_agents([clubs_gym.agent.kuhn.NashKuhnAgent(0.3)]*2)obs=env.reset()whileTrue:bet=env.act(obs)obs,rewards,done,info=env.step(bet)ifall(done):breakprint(rewards)
Package: clubwarrior
clubwarrior - Synchronize tasks between Clubhouse.io and TaskWarriorclubwarriorimports stories you are the owner of fromClubhouseas tasks intaskwarrior.Features two-way synchronization of the following details:story name / task descriptionprojectstory labels / task priority and tagsstory deadline / task duedependenciesstate (story started / task active, story completed / task completed)This project is in the very early development stages and does not yet have proper tests in place, use it at your own risk.It is suggested youbackup your Clubhouse databefore using this program.Installationpip install clubwarriororpip install git+https://github.com/huntrar/clubwarrior.git#egg=clubwarriororgit clone https://github.com/huntrar/clubwarrior
cd clubwarrior
python setup.py installUsageThe suggested method of runningclubwarrioris with cron or a systemd timer.
If you are working on a heavily active Clubhouse.io environment, the longer the time between executions the higher risk of a synchronization conflict arising.For features currently under development for now you can refer to the TODO list in the clubwarrior executable.Further documentation will come as the project is further developed. Contributors are of course welcome.AuthorHunter Hammond (clubwarrior.dev@gmail.com)News
Package: clue-mq
CLUE-MQQuick startsubscriberfromclue_mqimportClueMQcluemq=ClueMQ(host="localhost",# your Message Queue host ex) redis://0.0.0.0, amqp://id:password@0.0.0.0:portexchange_list=[("clue","topic")],accept_type="json"# 전송 받을 data type)# subscribe을 통해서 함수의 구독이 가능함# 구독한 함수 이름의 큐가 만들어 지며 설정한 routing key로 바인딩#cluemq.{exchange}.subscribe("바인딩 시킬 라우팅 키")@cluemq.clue.subscribe("test_routing_key")deftest_function(x,y):returnx+yif__name__=="__main__":# run_subscribers 함수를 통해서# 지금까지 설정된 큐와 큐의 메세지를 소비하는 Comsumer를 생성 해줌cluemq.run_subscribers()Publisherfromclue_mqimportClueMQcluemq=ClueMQ(host="localhost",# your Message Queue host ex) redis://0.0.0.0, amqp://id:password@0.0.0.0:portexchange_list=[("clue","topic")],accept_type="json"# 전송 받을 data type)#cluemq.{exchange}.send_message(전송 데이터 dict type, "요청을 보낼 라우팅키")cluemq.clue.send_message({"x":1,"y":2},"test_routing_key")
Package: clue_script
Overviewclue_script is used for defining subcommands with a special focus on
web-style apps.  The functionality is inspired by the Djangomanage.pyscript.RequirementsPython 2.6 or 2.7 (not tested with Python 3.x)runserver support (optional)Pasteargparse(if deploying on Python < 2.7)UsageA simple example to add a command that prints “hello world”:from clue_script import command, Commander

@command
def helloworld():
    print 'hello world'

if __name__ == '__main__':
    Commander.scan(globals()).run()To do a more complicated command you can use something similar to
Python 2.7’sargparse(also available on 2.6).import argparse
from clue_script import command, Commander

@command
def foo(*argv):
    parser = argparse.ArgumentParser()
    parser.add_argument('-x', help='simple test',
                        default='yes', metavar='x')
    ns = parser.parse_args(argv)
    # do something

if __name__ == '__main__':
    Commander.scan(globals()).run()Provided CommandsThere are currently two provided command factories.make_reloadable_server_command(see above note about requirements for runserver)make_syncdb_commandA typical web app could use these commands as follows:if __name__ == '__main__':
    def createtables(session):
        models.Base.metadata.create_all(session.bind)

    settings = init_settings()
    commander = script.Commander([script.make_reloadable_server_command(make_app),
                                  script.make_syncdb_command(settings['spitter.db_session_factory'],
                                                             createtables)])
    commander.scan(globals())
    commander.run()Running the script with no args will yield something similar to:Commands:
    runserver     Run a reloadable development web server.
    syncdb        Update the database.CreditsDeveloped and maintained by Rocky Burt <rocky AT serverzen DOT com>Changes0.3.1 (Jul-27-2011)Fixed silly bug where print_usage() was being called wrong0.3 (Jul-10-2011)Added rfoo support for launching a shell to connect with live-running
serverhttp://code.google.com/p/rfoo/Improved formatting of command usageRemoved mostly useless make_syncdb commandMade Commander.commands a little more exposed0.2.4 (Mar-3-2011)Fixed problem where the non-reloader portion would shut down the
app if code was changed0.2.3 (Feb-20-2011)Renamed fromKhufu-Scripttoclue_script0.2.2 (Feb-16-2011)Updated project urls0.2.1 (Jan-26-2011)Fixed scan() method to return commander instance0.2 (Jan-3-2011)first release
Package: clue_sqlaloader
Overviewclue_sqlaloaderprovides a mechanism for populating a sql database
usingSQLAlchemyandYAML.Usageclue_sqlaloaderwas primarily meant to run from inside other
applications as a framework piece but can be run from the command-line
as follows:$ python -m clue_sqlaloader.__init__ sqlite:///somefile.db data.yamlThe typical way to use the loader from within Python is as follows:from clue_sqlaloader import Loader
loader = Loader(sqlalchemy_session)
loader.load_from_yamlf('somefile.yaml')The format of the YAML should be similar to the following:- model: mymodule.Group
  refname: !!refname "admingroup"
  fields:
    name: admin
- model: mymodule.User
  fields:
    username: testuser1
    email: user@hisplace.com
    groups: [!!ref "admingroup"]
  execute:
    set_password: testuser1Themodelandfieldsarguments are straightforward.  Theexecuteargument will call the given function name on the newly created object
with the given value.CreditsWritten and maintained by Rocky Burt <rocky AT serverzen DOT com>Changes0.1.1 (Apr-21-2011)Made Reference lookup a little more robustAdded missing dep for zope.dottedname0.1 (Apr-12-2011)first release
Package: cluestar
cluestarGain a clue by clustering!This library contains visualisation tools that might help you
get started with classification tasks. The idea is that if you
can inspect clusters easily, you might gain a clue on what
good labels for your dataset might be!It generates charts that looks like this:There's even a fancy chart that can compare embedding techniques.Installpython -m pip install cluestarInteractive DemoYou can see an interactive demo of the generated widgetshere.You can also toy around with the demo notebook foundhere.UsageThe first step is to encode textdata in two dimensions, like below.fromsklearn.pipelineimportmake_pipelinefromsklearn.decompositionimportTruncatedSVDfromsklearn.feature_extraction.textimportTfidfVectorizerpipe=make_pipeline(TfidfVectorizer(),TruncatedSVD(n_components=2))X=pipe.fit_transform(texts)From here you can make an interactive chart via;fromcluestarimportplot_textplot_text(X,texts)The best results are likely found when you useumaptogether with something likeuniversal sentence encoder.You might also improve the understandability by highlighting points
that have a certain word in it.plot_text(X,texts,color_words=["plastic","voucher","deliver"])You can also use a numeric array, one that contains proba-values for prediction,
to influence the color.# First, get an array of pvals from some modelp_vals=some_model.predict(texts)[:,0]# Use these to assign pretty colors.plot_text(X,texts,color_array=p_vals)You can also compare two embeddings interactively. To do this:fromcluestarimportplot_text_comparisonplot_text(X1=X,X2=X,texts)
Package: clump
clumpEasy-to-use library for grouping, tagging and finding similar documents, using advanced natural language processing techniques.The initial purpose of this package is for finding similar or related documents. The typical use case for this is news apps that want to display related content for a particular news story.Many developers don’t have the time to invest in learning the best practices for this, so this module provides a simple package for loading all the content to consider and then a function that given a passage of text, will find all the related stories.How to use:Simply install the library with pip and import the VectorSpaceModelfromclumbimportVectorSpaceModeltraining_docs=['Some pieces of text','More text']# build the modelmodel=VectorSpaceModel(training_docs)# find the three most similar documentssimilar=model.find_similar('Another text',n=3)print(similar)Current LimitationsSmall contextual consideration.Performance on large datasetsRoad MapDocument clusteringAutomatic taggingTopic classificationPerformance improvements by pre clustering documents into large groups, then searching just the similar clusters.
Package: clumpi
clumpi [klˈʌmpάɪ]OverviewA simple python package to calculate Clumpiness for RMFC analysis by Zhang, Bradlow & Small (2015).
Easy use withclumpi.get_RFC()RequirementsPythonpandasnumpyworks well with Google Colab.Installationpipinstallgit+https://github.com/jniimi/clumpi.gitDatasetUse your time-series event data with ID and time.Create DataFrame that records only the point in time when the event occurred in the time series data.The name of the variables can be anything.user_idtAva1Ava4......Jack3Jack10......Check out our sample dataset for further details.df=clumpi.load_sample_data()display(df)UsageLog to ClumpinessUse the functionclumpi.get_RFC()to calculate. Specify following information for the arguments.id: a var name in df indicating usert: a var name in df indicating timeN: total number of events can occur during the periodM(optional): a number of iterations for the simulation to calculate threshold (3000 for default)alpha(optional): significance probability for the test of regularity (0.05 for default)Simply Calculate H0Use the functionclumpi.calc_threshold()to calculate upperalpha% point inMtimes simulation.All you need to specify areN,M, andalpha(Seeclumpi.get_RFC).AcknoledgementThe simulation in this package is based on Appendix B by Zhang et al. (2015).Zhang, Y., Bradlow, E. T., & Small, D. S. (2015). Predicting customer value using clumpiness: From RFM to RFMC. Marketing Science, 34(2), 195-208.https://doi.org/10.1287/mksc.2014.0873Authorjniimi (@JvckAndersen)