Saravanan666/pypi-org huggingface dataset:
Package: pycfdi-credentials
pyCFDI/credentials:us: The documentation of this project is in spanish as this is the natural language for intended audience.:es: La documentación del proyecto está en español porque ese es el lenguaje principal de los usuarios.Este proyecto está inspirado enphpcfdi/credentialsDescripciónEsta librería ha sido creada para poder trabajar con los archivos CSD y FIEL del SAT. De esta forma, se simplifica el proceso de firmar, verificar firma y obtener datos particulares del archivo de certificado así como de la llave pública.Para ver el detalle de como funcionan los certificados y las llaves privadas, echa un vistazo al archivomanejo_de_archivos.md, en él encontrarás como, a partir de los archivos provistos por el sat, generar todos los archivos necesario mediante la herramientaOpenSSLRoadmapCargar certificados y llaves desde archivos del SATFirmar y verificar contenidoDeterminar si el certificado cargado es FIEL, CSD u otroObtener información del certificado (RFC, nombre, etc)Cambiar la contraseña de la llave privadaConvertir a formato PEM el certificadoConvertir a formato PEM la llave privada (mantener la llave encriptada)TODOSBadgesStubsIlustraciones de procesamiento de certificado y llavesManual de instalaciónManual de usoMecanismo de contribuciónLicenciaCrear pipelineCreacion de virtualenv (poetry)Analsisis estáticoAnalisis de vulnerabilidadesblackmypytestsPublicar en pip de forma automática al crear una nueva versión (si pasó el pipeline)
Package: pyCFE
pyCFEContains python modules to operate with DGI web services.Supported webserviceseFacturaBillerFactura ExpressCopyright 2020 - 2021 (C) Alexander Cuellar (mailto:acuellar@grupoyacck.com) (creator and maintainter). All rights reserved.License: GPLv3+, with "commercial" exception available to include it and distribute with propietary programs
Package: pycfg
This package generates a control flow graph of the passed python file based on the AST generated (rather than the bytecode). It supports only a few python statements at this point. Notably absent are exceptions and generators. Essentially, only those constructs found in example.py are supported.Compatibility-------------It was tested on Python 3.6To run------python pycfg/pycfg.py <program to be analyzed> -dorpython pycfg/pycfg.py <program to be analyzed> -d -y <branch coverage file>Inspect Makefile for a better idea
Package: pycfitsio
============================================pycfitsio: Python ctypes wrapper for cfitsio============================================Features========* read binary tables using hdu and column names into simple dictionaries of arrays* write FITS files incrementally 1 HDU at a timeRequirements============pycfitsio requires the dynamic library cfitsio, in linux it usually packaged as libcfitsio-devInstall=======pip install pycfitsio #stable version# for development version clone from github and runpython setup.py installExamples=======Reading_______>>> f = pycfitsio.open("debug/data.fits")>>> hdulist = f.HDUs>>> print(hdulist)OrderedDict([('DATA', HDU: DATA)])>>> hdu = f['DATA']>>> column_array = f['DATA'].read_column('signal')>>> all_columns = f['DATA'].read_all()>>> print(all_columns)OrderedDict([('signal', array([   0., 1. ....])), 'flag', array([1, 1, ....])])Writing_______>>> from collections import OrderedDict>>> f = pycfitsio.create('file.fits')3 options to write HDUs:#. list of (name, array) tuples>>> f.write_HDU('HDUNAME',[('firstcolname', np.arange(10)), ('seccolname', np.arange(10)**2)])#. OrderedDict keys = name values = array>>> data = OrderedDict()>>> data['firstcolname'] = np.arange(10)>>> data['seccolname'] = np.arange(10)**2>>> f.write_HDU('HDUNAME', data)#. Compound numpy array>>> data = np.ones(10, dtype = [('firstcolname', np.long), ('seccolname', np.double)])>>> f.write_HDU('HDUNAME', data)>>> f.close()
Package: pycflow2dot
pycflow2dotSummaryDraw call graphs for C source code usingdotandcflow.
Typeset PDF with a page per source file and clickable cross-file
function references.cflow2dot -i hello_simple.c -f pngproduces:fromhello_simple.c.DescriptionDraw the call graph of C source code usingcflowanddot.
Output toLaTeX,dot,PDF,SVG,PNG,
and from dot to all formats supported
from it. The LaTeX output is obtained by including the SVG viaInkscape's LaTeXexportfunctionality.Multi-file sources are converted to multiple SVG files, one for each source.
These contain links using the LaTeX packagehyperref, so that after compilation
one can click on the name of a function call and be taken to its definition,
even if that definition is in another page of the PDF, because the function
is defined in another source file than the one corresponding to the current
PDF page.Note that if a file containing the definition is missing, then the hyperref link
is omitted, so that no dead links result after compiling with LaTeX.
This might be the case of for example the file with the definitions is
available, but is not passed topycflow2dot, e.g., for the purpose of focusing
on a subset of the sources.For now the LaTeX result has to be manually compiled, though this
extra step will be automated in the future. Multi-SVG export will still be
available, so that the results can be included in a larger document, e.g.,
a report.pycflow2dotis a Python port of the Perl scriptcflow2dot.InstallationFrom thePython Package Index (PyPI)using the
package installerpip:pipinstallpycflow2dotYou also need to install the following non-Python dependencies:GNU cflow:apt install cflowon Debian GNU/Linuxport install cflowwithMacPortsdot:apt install graphvizport install graphvizOptionally,cpptoo.Licensepycflow2dotis licensed under theGNU GPL v3.
Package: pycfmodel
pycfmodelA python model for Cloud Formation scripts.pycfmodelmakes it easier to work with CloudFormation scripts in Python by
creating a model comprised of python objects. Objects have various helper
functions which help with performing common tasks related to parsing and
inspecting CloudFormation scripts.pip install pycfmodelCurrently SupportedAWSTemplateFormatVersionConditionsDescriptionMappingsMetadataOutputsParametersResources:Properties:PolicyPolicy DocumentPrincipalSecurity Group Egress PropSecurity Group Ingress PropStatementTagEC2 VPC Endpoint PolicyGeneric ResourceIAM GroupIAM Managed PolicyIAM PolicyIAM RoleIAM UserKMS KeyOpenSearch Service (legacy ElasticSearch resource)Elasticsearch DomainOpenSearch ServiceOpenSearchService DomainS3 BucketS3 Bucket PolicySecurity GroupSecurity Group EgressSecurity Group IngressSNS Topic PolicySQS Queue PolicyTransformExamplefrompycfmodelimportparsetemplate={"AWSTemplateFormatVersion":"2010-09-09","Parameters":{"StarParameter":{"Type":"String","Default":"*","Description":"Star Param"}},"Resources":{"rootRole":{"Type":"AWS::IAM::Role","Properties":{"AssumeRolePolicyDocument":{"Version":"2012-10-17","Statement":[{"Effect":"Allow","Principal":{"AWS":{"Fn::Sub":"arn:aws:iam::${AWS::AccountId}:root"}},"Action":["sts:AssumeRole"],}],},"Path":"/","Policies":[{"PolicyName":"root","PolicyDocument":{"Version":"2012-10-17","Statement":[{"Effect":"Allow","Action":{"Ref":"StarParameter"},"Resource":{"Ref":"StarParameter"},}],},}],},}},}model=parse(template).resolve(extra_params={"AWS::AccountId":"123"})rootRole=model.Resources["rootRole"]policy=rootRole.Properties.Policies[0]statement=policy.PolicyDocument.Statement[0]assertstatement.Action=="*"assertstatement.Resource=="*"assertrootRole.Properties.AssumeRolePolicyDocument.Statement[0].Principal=={"AWS":"arn:aws:iam::123:root"}Local Development Commandsmakeinstall-dev
makecoverage
maketestmakefreezeIf the testtests/test_constants.py::test_cloudformation_actionsis failing, it can be resolved by updating the known
AWS Actions:python3scripts/generate_cloudformation_actions_file.py
Package: pyCFOFiSAX
Calcul CFOF via iSAXProjet thèse Lucas2017-2020Pour commencerInstallationLancerpip install -r requirements.txtoupython3 -m pip install -r requirements.txt.Le packagetslearnrequièreCython,numbaetllvmlite(piptéléchargera automatiquement ces packages).
Cependant pourCython, il estnécessaire d'avoir un compileur C (comme décrit sur ce site),
et lepackagellvmliten'a été, pour ce projet, testé qu'avec Python 3.7 et 3.8
que nous recommandons.Sinon, l'utilisation decondasimplifie l'installation (comme recommandé sur le site detslearn), car ne nécessite pas
l'installation de compileur C et des packagesCythonetllvmlite.UtilisationLa documentation est disponible ici :https://pycfofisax.readthedocs.io/fr/main/RemerciementsDéveloppements des travaux de :CFOF: A Concentration Free Measure for Anomaly Detection, par Fabrizio Angiulli,iSAX: Indexing and Mining Terabyte Sized Time Series, par Jin Shieh et Eamonn Keogh,iSAX 2.0: Indexing and Mining One Billion Time Series, par Alessandro Camerra, Themis Palpanas, Jin Shieh et Eamonn Keogh.Scoring Message Stream Anomalies in Railway Communication Systems, par Lucas Foulon, Serge Fenet, Christophe Rigotti et Denis JouvinUtilisation du code de :tslearn de Romain Tavenard et al. (2017).Utilisation des jeux de :Ahmad, S., Lavin, A., Purdy, S., & Agha, Z. (2017). Unsupervised real-time anomaly detection for streaming data. Neurocomputing, Available online 2 June 2017, ISSN 0925-2312
Package: pycgi
Table of contentsPortabilityInstallationInstall the pycgi from PyPIInstall the pycgi from GitHubUpgrade the pycgi from PyPIUninstall the pycgiDevelopment installationUser guideHelpVersionInstall the Python CGIPortabilityThe Python CGI administration utility tested onUbuntu Desktop 14.04 LTSandUbuntu Desktop 15.04.InstallationInstall the pycgi from PyPI$ sudo pip install pycgiInstall the pycgi from GitHub$ sudo pip install git+git://github.com/korniichuk/pycgi#egg=pycgiUpgrade the pycgi from PyPI$ sudo pip install -U pycgior:$ sudo pip install --upgrade pycgiUninstall the pycgi$ sudo pip uninstall pycgiDevelopment installation$ git clone git://github.com/korniichuk/pycgi.git
$ cd pycgi
$ sudo pip install .User guideHelpThe standard output for –help:$ pycgi -hor:$ pycgi --helpFor information on using subcommand “SUBCOMMAND”, do:$ pycgi SUBCOMMAND -hor:$ pycgi SUBCOMMAND --helpExample:$ pycgi install -hVersionThe standard output for –version:$ pycgi -vor:$ pycgi --versionInstall the Python CGI$ pycgi install
Package: pycgm
pyCGMPython Module for the Conventional Gait Modelcalculates Kinematics and Center of MassThe goal of this project is to release an easy to understand conventional gait model that users can implement in their own projects via a single python file.  While the project is multiple files, the kinematics code is contained in a single file pyCGM.py.  With the update, another kinetics file is also used for the center of mass calculation.Our aim is to provide researchers and students a tool that can aid in understanding and developing modifications to the conventional gait model through python without much more.The kinematics are validated against Nexus 1.8, and file types are known from Nexus 1.8.  Newer C3D files may not work (but files re-exported from Mokka usually work).How to use?For getting started, please check theWIKI on githubRequirements:Python 2.7 or Python 3NumpyScipy (only if using the pipeline operations)Requirements for HPC:Python 2.7NumpyMPI Preferably Linux (MPI is not as simple to setup on windows)Uses a modified version of the c3d.py loader from github.https://pypi.python.org/pypi/c3d/0.2.1Credits:Originally developed in the Digital Human Research Center at the Advanced Institutes of Convergence Technology (AICT), Seoul National Universityhttp://aict.snu.ac.krProject Lead: Mathew Schwartz (umcadop at gmail) For issues, use github or email me directlyContributors: Neil M. Thomas, Philippe C. Dixon,  Seungeun Yeon (연승은),Filipe Alves Caixeta, Robert Van-WesepReferenceRead about this code and if you find it useful in your work please cite:Schwartz, Mathew, and Philippe C. Dixon. "The effect of subject measurement error on joint kinematics in the conventional gait model: Insights from the open-source pyCGM tool using high performance computing methods." PloS one 13.1 (2018): e0189984.https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0189984Bibtex:@article{schwartz2018effect,
title={The effect of subject measurement error on joint kinematics in the conventional gait model: Insights from the open-source pyCGM tool using high performance computing methods},
author={Schwartz, Mathew and Dixon, Philippe C},
journal={PloS one},
volume={13},
number={1},
pages={e0189984},
year={2018},
publisher={Public Library of Science}
}
Package: pycgminer
Python wrapper forcgminerRPC API.QuickStartfrompycgminerimportCgminerAPIcgminer=CgminerAPI()summary=cgminer.summary()my_asic=cgminer.asc(0)License (MIT)Copyright (c) 2013 Thomas SileoPermission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the “Software”), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
Package: pycgnat
py-cgnatPython module for generating CGNAT rules using netmapBriefThis is a Python library and CLI program for generating firewall rules to deploy Carrier-Grade NAT, besides translating a given IP and port to its private address and vice versa. The methodology consists in building netmap rules at 1:32 public-private ratio, mapping a range of 2.000 ports for each client. Works for any netmask, since that follow the 1:32 ratio:Private prefixPublic prefixN. of clients........./20/254096/21/262048/22/271024/23/28512/24/29256/25/30128/26/3164/27/3232Supported PlatformsMikroTik RouterOSRequirementsPython 3.7+How to install it?Installation can just being done withpip:pipinstallpycgnatHow to use it?1. Command Line InterfaceForgeneratingthe rules, you can print it in console or save it to a file:pycgnat100.64.0.0/20203.0.113.0/25genrouterosfilename.rsc
pycgnat100.64.0.0/20203.0.113.0/25genrouterosFortranslatinga private IP to its public one, use thedirectoption:pycgnat100.64.0.0/20203.0.113.0/25trans--direct100.64.2.15
pycgnat100.64.0.0/20203.0.113.0/25trans-d100.64.2.15Fortranslatiga public IP and port to its private IP correspondent, use thereverseoption:pycgnat100.64.0.0/20203.0.113.0/25trans--reverse203.0.113.20:13578
pycgnat100.64.0.0/20203.0.113.0/25trans-r203.0.113.20:13578The CLI includes usefulhelpcommand (supported byargparseframework), so just type:pycgnat--help
pycgnat-h2. Python libraryYou can use the functionalities directly in Python lang. Justimportthe wanted module to your program:frompycgnat.translator.reverseimportcgnat_reversedic=cgnat_reverse(privnet,pubnet,IPv4Address('203.0.113.20'),13578)print(dic['private_ip'])The fullpycgnat's documentation is written in the source-code.Future worksAdd support for other platfoms (I'm using MikroTik for while, so this is the reason for only supporting it at first version).ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: pyCGNS
pyCGNS is a set of Python modules implementing theCFD General Notation System standard,
the standard of the CFD data representation.
Theuser documentationis available online, it
contains the releases, the installation requirements and process, the usage docs
and the reference docs.For more information concerning the CGNS standard please refer to cgns.github.ioFor MS-Windows users, an unofficial version can be found onhttps://anaconda.org/conda-forge/pycgnsCGNS ModulesCGNS.MAPimplements CGNS/Python physical representation of CGNS/SIDSCGNS.PAThas a large set of fonctions for CGNS/Python tree handlingCGNS.NAVis a CGNS/Python tree browserCGNS.VALchecks CGNS/SIDS compliance of CGNS/Python treesCGNS.APPis a set of all-purpose utilitiesCGNS.DATis not maintained todayBugs/Feature and issue trackingPlease use theissue-trackerat github
to report bugs, evolution proposals and submit patches.LicenseThe distribution and use of the pyCGNS software is covered by the LGPL v2.1 license.
Package: pycg-producer
Library for generating PyPI Call Graphs through PyCGThis library can be used to produce the call graph of a package distributed through pip along with saving its
source code and the call graph in JSON format in a specified directory.UsageExample usage after installing the specific package throughpip:frompycg_producer.producerimportCallGraphGeneratorcoord={"product":"pycg-stitch","version":"0.0.8","version_timestamp":"2000","requires_dist":[]}generator=CallGraphGenerator("directoryName",coord)print(generator.generate())The CallGraphGenerator class recieves as input the name of the directory where the source code and the call graph JSON will be stored, along with a Python dictionary containing the product name and version of the specific PyPI coordinate.Note:It is mandatory to provide also aversion_timestampandrequires_distfields, which can be left empty if they are not going to be used.
Package: py-cgrates
Python CGRateS Api ClientWIPSee:https://github.com/cgrates/cgratesInstallpip install py_cgratesUsageAccount Management - Createfrom cgrates import Client
from cgrates import models

api = Client(tenant="demo")  # host=localhost, port=2080

account = api.add_account(account="AcmeWidgets")

=> <Account(account=AcmeWidgets,...)>

dest = api.add_destination("DST_64", prefixes=["64"])

=> <Destination(DST_64, [64])>

rates = api.add_rates(rate_id="RT_STANDARD", rates=[
models.Rate({"rate": 0.25, "rate_unit": 60, "rate_increment": 60})
])

=> [<Rate(rate=0.25, rate_unit=60,...)>]

dest_rates = api.add_destination_rates(dest_rate_id="DR_64", dest_rates=[
models.DestinationRate({"rate_id": "RT_STANDARD", "dest_id": "DST_64"})
])

=> [<DestinationRate(rate_id=RT_STANDARD, dest_id=DST_64,...)>]

timing = api.add_timing(timing_id="WEEKEND", week_days=[6, 7])

=> <Timing(timing_id=WEEKEND,...)>

rating_plans = api.add_rating_plans(rating_plan_id="RPL_CASUAL", rating_plans=[
models.RatingPlan({"dest_rate_id": "DR_64", "timing_id": "WEEKEND"})
])

=> [<RatingPlan(dest_rate_id=DR_64, timing_id=WEEKEND,...)>]

rating_profiles = api.add_rating_profiles(rating_profile_id="RPF_1", subject="*any",rating_plan_activations=[
models.RatingPlanActivation({'rating_plan_id': 'RPL_CASUAL', 'activation_time': datetime.now()})
])

api.reload_cache()Account Management - Get/Listapi = Client(tenant="demo")

account = api.get_account(account="AcmeWidgets")

=> <Account(account=AcmeWidgets,...)>

dest = api.get_destination(destination_id="DST_64")

=> <Destination(DST_64, [64])>

rates = api.get_rates(rate_id="RT_STANDARD")

=> [<Rate(rate=0.25, rate_unit=60,...)>]

timing = api.get_timing(timing_id="WEEKEND")

=> <Timing(timing_id=WEEKEND,...)>

rating_plans = api.get_rating_plans(rating_plan_id="RPL_CASUAL")

=> [<RatingPlan(dest_rate_id=DR_64, timing_id=WEEKEND,...)>]
Package: pycgs
Coreference-based Graph Search (CGS)This is the Python implementation of the CGS algorithm.DocumentationThe documentation forpycgsis available on the documentation website of the ShennongAlpha (ShennongDoc):English中文You can also contribute to the documentation on theShennongDocGitHub repository by submitting a pull request:English中文Foundational CGSfrompycgsimportcgsrelationships=[('A','B'),('B','C'),('D','B'),('E','F')]primary_terms=cgs.foundational_cgs(relationships)print(primary_terms)# Output:# {'A': 'C', 'B': 'C', 'C': 'C', 'D': 'C', 'E': 'F', 'F': 'F'}Weighted CGSfrompycgsimportcgsweighted_relationships=[('A','B',1),('B','C',2),('D','B',1),('B','E',1)]primary_terms=cgs.weighted_cgs(weighted_relationships)print(primary_terms)# Output:# {'A': 'C', 'B': 'C', 'C': 'C', 'D': 'C', 'E': 'E'}
Package: pycgsp
Pycsou-gspis the graph signal processing extension of the Python 3 packagePycsoufor solving linear inverse problems. The extension offers implementations of graphconvolutionanddifferentialoperators, compatible with Pycsou’s interface for linear operators. Such tools can be useful when solving linear inverse problems involving signals defined on non Euclidean discrete manifolds.Graphs inPycsou-gspare instances from the classpygsp.graphs.Graphfrom thepygsplibrary for graph signal processing with Python.ContentThe package, namedpycgsp, is organised as follows:The subpackagepycgsp.linopimplements the following common graph linear operators:Graph convolution operators:GraphConvolutionGraph differential operators:GraphLaplacian,GraphGradient,GeneralisedGraphLaplacian.The subpackagepycgsp.graphprovides routines for generating graphs from discrete tessellations of continuous manifolds such as the sphere.InstallationPycsou-gsp requires Python 3.6 or greater. It is developed and tested on x86_64 systems running MacOS and Linux.DependenciesBefore installing Pycsou-gsp, make sure that the base packagePycsouis correctly installed on your machine.
Installation instructions for Pycsou are available atthat link.The package extra dependencies are listed in the filesrequirements.txtandrequirements-conda.txt.
It is recommended to install those extra dependencies usingMinicondaorAnaconda. This
is not just a pure stylistic choice but comes with somehiddenadvantages, such as the linking toIntel MKLlibrary (a highly optimized BLAS library created by Intel).>>condainstall--channel=conda-forge--file=requirements-conda.txtQuick InstallPycsou-gsp is also available onPypi. You can hence install it very simply via the command:>>pipinstallpycsou-gspIf you have previously activated your conda environmentpipwill install Pycsou in said environment.
Otherwise it will install it in yourbaseenvironment together with the various dependencies obtained from the filerequirements.txt.Developer InstallIt is also possible to install Pycsou-gsp from the source for developers:>>gitclonehttps://github.com/matthieumeo/pycsou-gsp>>cd<repository_dir>/>>pipinstall-e.The package documentation can be generated with:>>condainstallsphinx=='2.1.*'\sphinx_rtd_theme=='0.4.*'>>python3setup.pybuild_sphinxYou can verify that the installation was successful by running the package doctests:>>python3test.pyCiteFor citing this package, please see:http://doi.org/10.5281/zenodo.4486431
Package: pycg-stitch
Stiching for FASTEN Python Call GraphsThis tool stitches Python
call graphs written in Python produced byPyCGin the Fasten Format.Usage>>> pycg-stitch --help
usage: pycg-stitch [-h] [-o OUTPUT] [call_graph ...]

positional arguments:
  call_graph            Paths to call graphs to be stitched together in JSON format

optional arguments:
  -h, --help            show this help message and exit
  -o OUTPUT, --output OUTPUT
                        Output pathcall_graph: A list of paths containing FASTEN Python call graphs in JSON
format.output: (Optional) parameter specifying where the stitched call graph will
be stored.