Saravanan666/pypi-org huggingface dataset:
Package: pycord-plus
A fork of discord.py. Pycord is a modern, easy to use, feature-rich, and async ready API wrapper for Discord written in Python.Key FeaturesModern Pythonic API usingasyncandawait.Proper rate limit handling.Optimised for both speed and memory usage.Full Application Command SupportInstallingPython 3.8 or higher is requiredTo install the library without full voice support, run the following command:# Linux/macOSpython3-mpipinstall-Upy-cord# Windowspy-3-mpipinstall-Upy-cordOtherwise, to get full voice support, run the following command:# Linux/macOSpython3-mpipinstall-U"py-cord[voice]"# Windowspy-3-mpipinstall-Upy-cord[voice]To install additional packages for speedup, run the following command:# Linux/macOSpython3-mpipinstall-U"py-cord[speed]"# Windowspy-3-mpipinstall-Upy-cord[speed]To install the development version, do the following:$gitclonehttps://github.com/Pycord-Development/pycord$cdpycord$python3-mpipinstall-U.[voice]or if you do not want to clone the repository:# Linux/macOSpython3-mpipinstallgit+https://github.com/Pycord-Development/pycord# Windowspy-3-mpipinstallgit+https://github.com/Pycord-Development/pycordOptional PackagesPyNaCl(for voice support)aiodns,brotlipy,cchardet(for aiohttp speedup)orjson(for json speedup)Please note that while installing voice support on Linux, you must install the following packages via your preferred package manager (e.g.apt,dnf, etc) BEFORE running the above commands:libffi-dev (orlibffi-develon some systems)python-dev (e.g.python3.10-devfor Python 3.10)Quick Exampleimportdiscordbot=discord.Bot()@bot.slash_command()asyncdefhello(ctx,name:str=None):name=nameorctx.author.nameawaitctx.respond(f"Hello{name}!")@bot.user_command(name="Say Hello")asyncdefhi(ctx,user):awaitctx.respond(f"{ctx.author.mention}says hello to{user.name}!")bot.run("token")Traditional Commands Exampleimportdiscordfromdiscord.extimportcommandsintents=discord.Intents.default()intents.message_content=Truebot=commands.Bot(command_prefix=">",intents=intents)@bot.command()asyncdefping(ctx):awaitctx.send("pong")bot.run("token")You can find more code examples in theexamplesdirectory.Note: Make sure you do not reveal your bot token to anyone, as it can grant access to your bot.Useful LinksDocumentationLearn how to create Discord bots with PycordOur Official Discord ServerOfficial Discord Developers Server
Package: pycord-prettyhelp
pycord-prettyhelpAn embed version of the built in help command for discord.pyInspired by the DefaultHelpCommand that discord.py uses, but revised for embeds and additional sorting on individual pages that can be "scrolled" through with reactions.Made this fork because the original library was built for discord.py which is no longer maintained.Installationpip install pycord-prettyhelpUsageExample of how to use it:fromdiscord.extimportcommandsfromdiscord.ext.prettyhelpimportPrettyHelpbot=commands.Bot(command_prefix="!",help_command=PrettyHelp())Added Optional Argscolor- Set the default embed colorending_note- Set the footer of the embed. Ending notes are fed acommands.Context(ctx) and aPrettyHelp(help) instance for more advanced customization.index_title- Set the index page name default is"Categories"menu- set a custom menu for navigating pages. Uses apretty_help.PrettyMenu()instance. Default ispretty_help.DefaultMenu()no_category- Set the name of the page with commands not part of a category. Default is "No Category"sort_commands- Sort commands and categories alphabeticallyshow_index- Show the index page or notpretty_help.DefaultHelp argsactive_time- Set the time (in seconds) that the message will be active. Default is 30spage_left- The emoji to use to page leftpage_right- The emoji to use to page rightremove- The emoji to use to remove the help messageBy default, the help will just pick a random color on every invoke. You can change this using thecolorargument:fromdiscord.extimportcommandsfromdiscord.ext.prettyhelpimportDefaultMenu,PrettyHelp# ":discord:743511195197374563" is a custom discord emoji format. Adjust to match your own custom emoji.menu=DefaultMenu(page_left="\U0001F44D",page_right="👎",remove=":discord:743511195197374563",active_time=5,)# Custom ending noteending_note=("The ending note from{ctx.bot.user.name}""\nFor command{help.clean_prefix}{help.invoked_with}")bot=commands.Bot(command_prefix="!")bot.help_command=PrettyHelp(menu=menu,ending_note=ending_note)The basichelpcommand will break commands up by cogs. Each cog will be a different page. Those pages can be navigated with
the arrow embeds. The message is unresponsive after 30s of no activity, it'll remove the reactions to let you know.
Package: pycordSuperUtils
pycord-super-utilsA modern python module including many useful features that make discord bot programming extremely easy.A fork of discordSuperUtils ready for py-cord 2.0 and higherFeaturesVery easy to use and user-friendly.Object Oriented.Modern Leveling Manager.Modern Music/Audio playing manager. [Lavalink and FFmpeg support]Modern Async Database Manager (SQLite, MongoDB, PostgreSQL, MySQL, MariaDB).Modern Paginator.Modern Reaction Manager.Modern Economy Manager.Modern Image Manager (PIL).Modern Invite Tracker.Modern Command Hinter.Modern FiveM Server Parser.Modern Birthday Manager.Modern Prefix Manager.Includes easy to use convertors.Modern spotify client that is optimized for player fetching.Modern Punishment Manager (Kick, Ban, Infractions, Mutes)Modern Template Manager.Modern CogManager that supports usage of managers in discord cogs.Modern MessageFilter and AntiSpam.Customizable ModMail ManagerModern Youtube client that is optimized for player fetching.And many more!
(MORE COMING SOON!)InstallationInstalling pycordSuperUtils is very easy.pipinstallpycordSuperUtilsor to install alpha versionspipinstallgit+https://github.com/areoxy/pycord-super-utilsExamplesFor Examples have a look into the "examples" folder of our github repo.SupportSupport Server| Support ServerDocumentation| Not done
Package: pycoreutils
What is this?PyCoreutils is a pure Python implementation of various standard UNIX commands,
like ‘ls’, ‘cp’ and ‘gzip’. It also contains sh.py, a shell-like environment
which will make Unix-users feel right at home on the Windows command-prompt.Now why you wanna go and do that?Fun!Have a Linux-like shell environment in Windows.Create a Python-only Linux distribution.RequirementsPyCoreutils requires Python 2.6 or greater. It also works on Python 3.InstallTo install the last stable version, use:pip install pycoreutilsoreasy_install pycoreutilsTo install the latest development code, try:pip install bzr+http://bazaar.launchpad.net/~hanz/pycoreutils/trunkDownloadsReleases and documentation can be found athttp://pypi.python.org/pypi/pycoreutils.
The source code can be found athttps://launchpad.net/pycoreutils.Haven’t I seen this before?David Cantrell also has a project with the same name athttps://github.com/dcantrell/pycoreutils. His project is a little different
in that it uses C-bindings, is GPL-licensed and is structured differently.LicenseCopyright (c) 2009, 2010, 2011 Hans van Leeuwen.
See LICENSE.txt for details.WarningThis is alpha software, Not all the flags in the help-section are
implemented, and some behave different than one might expect.List of available commandsarchbase64basenamebunzip2bzip2calcatcdchrootchownchrootcpcronddirnameenvexpandgunzipgziphttpdidkilllnloggerlsmd5summkdirmktempmvnlpwdrmrmdirsendmailseqshsha1sumsha224sumsha256sumsha384sumsha512sumshredshufsleepsmtpdsorttarteetouchunamewcwgetwhoamiyeszip
Package: pycork
Pycork is a Python library offering the functionality of the Cork boolean CSG library in a compiler friendly form suitable across all platforms. The library includes the dependencies for the Multi-Precision Integer and Rationals (MPIR) 3.0 built-in used by the Cork libary. The package aims to provide a simpler route for compiling the package for individuals and in addition, generating python bindings for use across other projects. Refactoring has been authored to tidy up the existing codebase so that it can be built across multiple platforms, in particular Windows, using the CMake build-system. At this stage, no further optimisations or improvements will be made specifically to the cork library, inclusive of its algorithms.The python bindings are simple and offer access to the core functionality offered by the Cork library to perform boolean operations on watertight meshes. Additionally, it removes the awkward step of generatig .off files that are used in the command-line interface of the Cork library. The user may pass triangular meshes (vertices, tri-faces indices) as numpy arrays to each function.For further information, see the latestrelease notes.InstallationInstallation is currently supported on Windows. No special requiremnets are necessary for using pycork, except having the numpy library available. It is recommend to also install thetrimeshlibrary to provide an interface to processing meshes as input for pycork.condainstall-cnumpypipinstalltrimeshInstallation of pycork can then be performed using pre-built python packages using the PyPi repository.pipinstallpycorkAlternatively, pycork may be compiled directly from source. Currently the prerequisites are the a compliant c++ build environment, include CMake build system. Currently the package has been tested on Windows 10, using VS2019.gitclonehttps://github.com/drlukeparry/pycork.git&&cd./pycorkgitsubmoduleupdate--init--recursivepythonsetup.pyinstallUsageThe Cork CSG library, by design, has a simple interface for is functionality. Further detailed description of the function is therefore not necessary.importnumpyasnpimporttrimeshimportpycork# Note any manifold, watertight mesh can be used in conjuction with the Trimesh librarymeshA=trimesh.load_mesh('meshA.off')meshB=trimesh.load_mesh('meshB.off')# Extra list of vertices and triangular faces from the meshesvertsA=meshA.verticestrisA=meshA.facesvertsB=meshB.verticestrisB=meshB.facespycork.isSolid(vertsA,trisA)pycork.isSolid(vertsB,trisB)#Perform the boolean opertions directly with Cork libraryvertsC,trisC=pycork.union(vertsA,trisA,vertsB,trisB)vertsD,trisD=pycork.intersection(vertsA,trisA,vertsB,trisB)meshC=trimesh.Trimesh(vertices=vertsC,faces=trisC,process=True)
Package: pycorm
pycormModel class with fields that translate to jsonschema fields.
Classes can be instantiated from dictionaries and will auto populate fields with right type (only Models)Free software: MITDocumentation:https://pycorm.readthedocs.org.FeaturesPython objects from dictionaries, including dotnotation and jsonschema validation.CreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.2.9introiduced inheritance between modelsjsonschema validation on model schema and input schemamodels instantiate their schema only once pr class (caching)0.0.1 (2016-3-1)First release on PyPI.
Package: pycorn
A script to extract data from UNICORN result (.res) files and plot them.A script extract data from .res (results) files generated by UNICORN Chromatography software supplied with AKTA Systems. This script will find all data blocks, extract the data and write out csv-files. If you have matplotlib installed it will also plot all the curves including fractions if present. Plots can be saved in any format supported by matplotlib (default is pdf). Alternatively you may import pycorn into your script and access the data inside res-files.NewsPyCORN-WEB launched:
PyCORN is now available as a web-service. Just drop your res/zip file, get a preview of the plot (generated with plotly) - adjust as needed and download a high-quality PNG file. No need to open a terminal, no Python installation etc required :-)visit:http://pycornweb.appspot.comv0.19 released:FIXED: Injection points not recognized properly (seehttps://github.com/pyahmed/PyCORN/issues/12)Installationpip install pycornKnown issuesSeehttps://github.com/pyahmed/PyCORN/issuesRequirementsPython 2.7 or 3.x (Tested on Linux / Windows 7 / Mac OSX)optional: matplotlib (for plotting)optional: xlsxwriter (for xlsx-output)UsageSee pycorn/docs/USAGE_pycorn-bin.txt if you want to extract/plot data directlySee pycorn/docs/USAGE_pycorn_module.txt if you want to import it into your scriptLicenseGPLv2 see docs/LICENSE.txt
Package: pycorona
pycoronapycovid is a library that is used to get coronavirus condition (i.e. active cases, deaths, etc) of any country.UsagefrompycoronaimportCountrycountry=Country("USA")cases=country.total_cases()print(cases)>2,234,475Attributes for countries -continent()flag()total_cases()today_cases()total_deaths()today_deaths()recovered()critical_cases()active_cases()tests()The package raises KeyError if the given country is not foundfrompycoronaimportWorldworld=World()cases=world.total_cases()print(cases)>8,486,923Attributes for world -affected_countries()total_cases()today_cases()total_deaths()today_deaths()recovered()active_cases()tests()critical_cases()Note - This package is currently under development
Package: pycorpus
This module provide a easy, non intrusive way to process a big list of files in a parallel way. Also provides the
option to process theses files with a different packs of options, evaluate and generate reports.Requirements:You need the PPSS script in same dir of this file.Instructions:Import this module from your main fileimportpyCorpusCreate the function that process the filedefmy_process(file_name,config):# Some science stuff with the file(Optional) Create a function that return a argument parser that capture all the configs that you need.defmy_parser():# Set up your argparse parser# Return the parserreturnmy_parser_instanceAdd at the end of the file something like this:if__name__=="__main__":corpus_processor=pyCorpus.CorpusProcessor(parse_cmd_arguments,process_file)corpus_processor.run_corpus()NOTES:Dot not ADD the () to my_parser and my_process arguments.If you don't need options you can ignore step 3 and the config file come as None. But never use the --config parameter.The files are processed in a concurrent way so if you might store any results don't use the sys.out use a file.
Package: pycorr
PyCorrA simple library to calculate correlation between variables. Currently provides correlation between nominal variables.Based on statistical methodology like Cramer'V and Tschuprow'T allows to gauge the correlation between categorical variables. Ability to plot the correlation in form of heatmap is also provided.Usage exampleimport pandas as pd
from pycorrcat.pycorrcat import plot_corr, corr_matrix

df = pd.DataFrame([('a', 'b'), ('a', 'd'), ('c', 'b'), ('e', 'd')],
                  columns=['dogs', 'cats'])

correlation_matrix = corr_matrix(data, ['dogs', 'cats'])
plot_corr(df, ['dogs','cats'] )Development setupCreate a virtualenv and install dependencies:pip install -r requirements.dev.txtpip install -r requirements.txtThen install the pre-commit hooks:pre-commit installand continue with code change.Runpre-commitlocally to check filespre-commit run --all-filesRelease History0.1.4CHANGE: Changed the documentation (no code change)0.1.3ADD: Ability to pass dataframe to get correlation matrixADD: Ability to plot the correlation in form of heatmap0.1.2Added as first release0.1.1Test releaseAuthor and ContributorAnurag Kumar Mishra – Connect ongithubor drop amailDistributed under the GNU license. SeeLICENSEfor more information.Github repo linkhttps://github.com/MavericksDS/pycorrContributingFork it (https://github.com/MavericksDS/pycorr)Create your feature branch (git checkout -b feature/fooBar)Commit your changes (git commit -am 'Add some fooBar')Push to the branch (git push origin feature/fooBar)Create a new Pull Request
Package: pycorreios
API Correios============Installation------------If you have _setuptools_ you can use$ easy_install -U pycorreiosOtherwise, you can download the source from [GitHub][git] and run$ python setup.py install[git]: https://github.com/avelino/pycorreios "PyCorreios"Examples--------Some simple examples of what pyCorreios code looks like:```pythonfrom pycorreios import Correios# with a dictionary paramenter - the field order doesn't matterfields = {"cod": Correios().SEDEX,"GOCEP": "44001535","HERECEP": "03971010","peso": "2","formato": "1", # caixa/pacote"comprimento": "18","altura": "8","largura": "24","diametro": "12"}test = Correios().frete(**fields)   # remember to call with **# or with positional parameters - same result as abovetest = Correios().frete(Correios().SEDEX,'44001535','03971010',2,1,18,8,24,12)if test['Erro'] != '0':print 'Deu erro! :('print test['Erro']print test['MsgErro']else:print "Valor: R$%s\nPrazo de Entrega: %s" % (test['Valor'],test['PrazoEntrega'])other_test = Correios().cep('03971010')for tag_name in other_test.keys():print tag_name + ': ' + other_test[tag_name]```
Package: pycorrel
pycorrelpycorrel provides methods to correlate random variables with arbitrary marginal distributions.Free software: MIT licenseDocumentation:https://pycorrel.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-12-29)First release on PyPI.
Package: pycorrelate
PycorrelatePycorrelatecomputes fast and accurate cross-correlation over
arbitrary time lags.
Cross-correlations can be calculated on “uniformly-sampled” signals
or on “point-processes”, such as photon timestamps.
Pycorrelate allows computing cross-correlation at log-spaced lags covering
several orders of magnitude. This type of cross-correlation is
commonly used in physics or biophysics for techniques such asfluorescence correlation spectroscopy(FCS) ordynamic light scattering(DLS).Two types of correlations are implemented:ucorrelate:
the classical text-book linear cross-correlation between two signals
defined atuniformly spacedintervals.
Only positive lags are computed and a max lag can be specified.
Thanks to the limit in the computed lags, this function can be much faster thannumpy.correlate.pcorrelate:
cross-correlation of discrete events
in a point-process. In this case input arrays can be timestamps or
positions of “events”, for examplephoton arrival times.
This function implements the algorithm inLaurence et al. Optics Letters (2006).
This is a generalization of the multi-tau algorithm which retains
high execution speed while allowing arbitrary time-lag bins.Pycorrelate is implemented in Python 3 and operates on standard numpy arrays.
Execution speed is optimized usingnumba.Free software: GNU General Public License v3Documentation:https://pycorrelate.readthedocs.io.History0.2.1 (2017-11-15)Added normalization for FCS curves (seepnormalize).Added example notebook showing how to fit a simple FCS curveRenameducorrelateargument frommaxlagstomaxlag.Addedtheory pagein the documentation, showing the exact formula used for CCF calculations.0.1.0 (2017-07-23)First release on PyPI.
Package: pycorruptor
Welcome to PyCorruptorA Python Toolbox for Data CorruptionIn data analysis and modeling, sometimes we may need to corrupt the original data to achieve our goal, for instance, evaluating models' ability to reconstruct corrupted data or assessing the model's performance on only partially-observed data. PyCorruptor is such a tool to help you corrupt your data, which provides several patterns to create missing values in the given data.
Package: pycortecs
Cortecs - Crypto SentimentCortecs gathers crypto headlines from twitter, reddit and online news to extracts the social sentiment of cryptocurrencies.
The objective is to deliver denoised sentiment metrics in realtime. Therefore, each headline is efficiently analyzed in a
filter engine to assure the data meets certain quality criteria (seeCortecs Feed) before
the actual sentiment is calculated. Most modules in the filter engine are built on top
ofTransfomers.APIPyCortecs is a python wrapper for theCortecsApi, which provides multiple sentiment metrics:sentimentsocial volumesocial dominancesocial balanceCurrently 86 of the most important
cryptos are supported. Twitter sentiment dates back until 2019-11-01. News and reddit sentiment was added later on.ExampleInstall withpip install pycortecs.frompycortecsimportCortecsApifrompprintimportpprintapi=CortecsApi()## Get Bitcoin sentiment from Twittersentiment_twitter_btc=api.get_twitter_sentiment(since="2021-03-01",until="2021-03-05",asset='btc',interval='1d')pprint(sentiment_twitter_btc)interval endpoint     signal      btc
timestamp                                                      
2021-03-02 00:00:00+00:00       1d  twitter  sentiment  0.80919
2021-03-03 00:00:00+00:00       1d  twitter  sentiment  0.82196
2021-03-04 00:00:00+00:00       1d  twitter  sentiment  0.83813
2021-03-05 00:00:00+00:00       1d  twitter  sentiment  0.81766For detailed usage checkout thedocsor look into
theexamples folder.Supported bySocialFollow onTwitterContact viaLinkedInContact viamail
Package: pycortex
pycortexPycortex is a software library that allows you to visualize fMRI or other volumetric neuroimaging data on cortical surfaces.InstallationTo install the stable release version of pycortex, do the following:# First, install some required dependencies (if not already installed)pipinstall-Usetuptoolswheelnumpycython# Install the latest release of pycortex from pippipinstall-UpycortexIf you wish to install the development version of pycortex, you can install it directly from Github.To do so, replace the second install line above with the following:# Install development version of pycortex from githubpipinstall-Ugit+https://github.com/gallantlab/pycortex.git--no-build-isolationDocumentationPycortex documentation is available athttps://gallantlab.github.io/pycortex.You can find many examples of pycortex features in thepycortex example gallery.To build the documentation locally:# Install required dependencies for the documentationpipinstallsphinx_gallerynumpydoc# Move into the docs folder (assuming already in pycortex directory)cddocs# Build a local version of the documentation sitemakehtmlAfter you run the above, you can opendocs/_build/html/index.htmlin a web browser to view the locally built documentation.DemoPycortex is best used withIPython.If you do not already have IPython, you can install it by running:pipinstallipythonTo run the pycortex demo, using IPython, run:$ ipython
In [1]: import cortex
In [2]: cortex.webshow(cortex.Volume.random("S1", "fullhead"))CitationIf you use pycortex in published work, please cite thepycortex paper:Gao JS, Huth AG, Lescroart MD and Gallant JL (2015) Pycortex: an interactive surface visualizer for fMRI. Front. Neuroinform. 9:23. doi: 10.3389/fninf.2015.00023Getting helpPlease post onNeuroStarswith the tagpycortexto
ask questions about how to use Pycortex.
Package: py-cortex-api
py-cortex-apiPython API forcortex.InstallLatest release:pip3 install py-cortex-apiLatest source:pip3 install git+https://github.com/iqbal-lab-org/py-cortex-apiRequirementsR is required bycortexat runtime.sudo apt install r-base-coreUsageInputs:a reference genome in fasta[.gz]one or more reads file in fasta/q[.gz]Use a list to pass in reads files, even if there is only one file.Output:
A vcf with variants detected by cortex.importcortex.callsascortexcortex.run("./reference.fasta",["./reads.fastq"],"./output.vcf")The third argument is where to place the output vcf.OptionsThe following options can be passed tocortex.run:sample_name: sample name to appear in output vcf (default: 'sample').ploidy: 1 or 2, for haploid or diploid genotyping (default: 1)mem_height: ifcortex.callsfails warning of too low memory, use a higher value (default: 22).tmp_directory: where to place intermediate output and log files (default: system-defined)cleanup: whether to remove intermediate output and log files upon successful completion. (Default: True)LicenceMIT