Saravanan666/pypi-org huggingface dataset:
Package: compliance-trestle-fedramp
compliance-trestle-fedrampA plugin forcompliance-trestleto provide functionality specifically for FedRAMP.This plugin provides APIs and commands for validating a FedRAMP compliant SSP (in JSON or YAML format). In future it will also provide utilities for converting various OSCAL models from XML to JSON format and vice-versa.Python codebase, easy installation via pipcompliance-trestle-fedramp currently runs on  python platforms on Linux and Mac. Windows support is planned to be added soon. It is available on PyPi so it is easily installed via pip.  It is under active development and new releases are made available regularly.It is dependent on SaxonC and requires it to be installed and configured on the system beforehand. It has been tested withSaxon-HE/C v1.2.1. The Python extension for SaxonC also needs to be setup as trestle-fedramp uses the Python interface for invoking functionalities of SaxonC.Complete documentation and tutorialsA tutorial on how this plugin is created can be foundhere. Instructions on how to use the CLI are describedhere.Development statusCompliance trestle fedramp is currently in beta. The expectation is that in ongoing work there may be un-announced changes that are breaking within the trestle-fedramp codebase.Contributing to Trestle-fedrampOur project welcomes external contributions. Please consultcontributingto get started.License & AuthorsIf you would like to see the detailed LICENSE clickhere.
Consultcontributorsfor a list of authors andmaintainersfor the core team.# Copyright (c) 2020 IBM Corp. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
Package: compliantdynamodb
this is where we place the documentation# Example automatically generated without compilation. See https://github.com/aws/jsii/issues/826self.backup_vault=backup.BackupVault(self,"BackupVault",backup_vault_name=f"{props.tableName}-backup-vault",access_policy=iam.PolicyDocument(statements=[iam.PolicyStatement(sid="backup-recovery-point-manual-deletion-disabled",effect=iam.Effect.DENY,principals=[iam.AnyPrincipal()],actions=["backup:DeleteRecoveryPoint","backup:PutBackupVaultAccessPolicy","backup:UpdateRecoveryPointLifecycle"],resources=["*"])]))
Package: compliant-social-app-django
Compliant Social App DjangoThis is a Google compliant fork of theDjangocomponent of thepython-social-auth ecosystem,
it implements the needed functionality to integratesocial-auth-corein a Django based project.Python Social Auth - DjangoPython Social Auth is an easy to setup social authentication/registration
mechanism with support for several frameworks and auth providers.Django versionThis project will focus on the currently supported Django releases as
stated on theDjango Project Supported Versions table.Backward compatibility with unsupported versions won't be enforced.DocumentationCompliant Social App Django provides three key features required to comply with Google's standards for applications that
ask for restricted scopes such as the ability to read gmail inboxes:Storing the access and refresh tokens encrypted in the database.The encryption of those tokens being backed by KMS.Audit logs that register the access to and revocation of those same tokens.An Audit Logger object must be provided to the app, and must inherit from the abstract base audit logger provided. We
also provide an alternative Google backend that uses the audit logger.KMS must be set up independently, and you must specify a KMS_FIELD_KEY in settings; typically the alias of your KMS key.If moving to this package from the main social-app-django package the db migrations will also handle the migration of
tokens, with encryption, into their appropriate fields.Core project documentation is available athttp://python-social-auth.readthedocs.org/.Setup$pipinstallcompliant-social-app-djangoVersioningThis project followsSemantic Versioning 2.0.0.This fork's versioning will match the major version of the original package version upon which it is basedBuilding and ReleasingTo release a new version use these steps:Increment the version number in__init.py__Update theCHANGELOG.mdwith description of the changes in the release (and commit)Remove thebuildanddistdirectories completelyrm-rfbuilddistBuild the source distribution:pythonsetup.pysdistPush the distributionpython-mtwineuploaddist/*Tag the release and push the tagLicenseThis project follows the BSD license. See theLICENSEfor details.
Package: complicated
ComplicatedWhat is Complicated?Complicated is a simple app that lets you update your Apple Watch face
complications with WebHooks. You can also use IFTTT and Zapier to update the WebHooks.https://mikelyons.org/complicatedHow to installcomplicated-pythonpip install complicatedHow to use the Complicated CLI toolIt's easy to update your Apple Watch Complications using the command line. The
command is justcomplicated, and you pass in API key, Complication type and
your new value in parenthesisUsage: complicated <Complicated API Key> <complication type> "<New Value>"
Example:
complciated abcd1234 modularLarge "Hello World\nCan you see me"Arguments:API Key: You can get this from the settings page in the Complicated appComplication Type: The complication that you want to update to the new value.
Here is a list of the available types: [ circularSmall, extraLarge, graphicBezel, graphicCorner, graphicRectangular, modularLarge, modularSmall, utilitarianLarge, utilitarianSmall ]New Value: The new value to change the complciation to. Surround it with quotes if you want to include spaces. Use \n for new lines.How to use the Python LibraryThe python library is also dirt simple to use:fromcomplicatedimportchangeComplication# Usage: changeComplication( '<api key>', '<complication type>', '<new value>' )changeComplication('abcd1234','utilitarianSmall','new value')
Package: complimenter
Feeling down today? Generate a Random Compliment usingt his tool.Thanks tograntcodes/complimentrfor the js library. The code is ported to python.INSTALLpipinstallcomplimentEXAMPLESfromcomplimentimportComplimentc=Compliment()c.make_compliment()or using a one liner.print(Compliment().make_compliment())
Package: compling
complingComputational Linguistic with Pythoncomplingis a Python module that provides someNatural Language ProcessingandComputational Linguisticsfunctionalities to work with human language data. It incorporates variousDataandText Miningfeatures from other famous libraries (e.g.spacy,nltk,sklearn, ...) in order to arrange a pipeline aimed at the analysis of corpora ofJSONdocuments.DocumentationSee documentationhere.InstallationYou can installcomplingwith:$pipinstallcomplingcomplingrequires:Python(>= 3.6)numpyspacynltkgensimtqdmunicodedata2unidecodeconfigparser_vaderSentimentwordcloudYou also need to download:a ++spacy language model++Seeherethe available models. You can choose based on the language of your corpus documents.
By default,compligexpects you to downloadsmmodels. You can still choose to download larger models, but remember to edit theconfg.inifile, so it can work properly.ExampleLet's assume the language of your documents isEnglish.
You could download thespacy small english model:python-mspacydownloaden_core_web_smsome ++nltk functionalities++:stopwords$python-mnltk.downloaderstopwordspunkt$python-mnltk.downloaderpunktconfig.iniThe functionalities offered bycomplingmay require a large variety of parameters. To facilitate their use, default values are provided for some parameters:some can be changed in the function invocation. Many functions provide optional parameters;others are stored in the ++config.ini++ file.
This file configures the processing of your corpora. It contains the values of some special parameters.
(e.g.the language of documents in your corpus.)You can see a preview below:[Corpus];The language of documents in your corpus.language=english;Documents in your corpus store their text in this key.text_key=text;Documents in your corpus store their date values as string in this format.;For a complete list of formatting directives, see: https://docs.python.org/3/library/datetime.html#strftime-strptime-behavior.date_format=%d/%m/%Y;The size of spacy model you want it to be used in the text processingspacy_model_size=md[Document_record];Document records metadata:;If lower==1, A lowercase version will be stored for each document.lower=0;If lemma==1, A version with tokens replace by their lemma will be stored for each document.lemma=0;If stem==1, A version with tokens replace by their stem will be stored for each document.stem=0;If negations==1, A version where negated token are preceded by 'NOT_' prefix will be stored for each document.negations=1;If named_entities==1, the occurring named entities will be stored in a list for each document.named_entities=1; ...ConfigManagercomplingprovides theConfigManagerclass to make it easier for you to edit theconfig.inifile and to help you handling the corpora processing .example of usage (compling)You can see a short example of usage athttps://github.com/FrancescoPeriti/compling.See thedocumentationfor more details.
Package: comply
complyfrom comply import checkchecks if user's python version is compatible with your projectdef check(
        min_version: str,
        max_version: str,
        min_msg: t.Union[str, t.Callable] = default_min_msg,
        max_msg: t.Union[str, t.Callable] = default_max_msg,
        complyible_msg: t.Union[str, t.Callable] = default_comply_msg,
        verbose: bool = True,
        mock_sys_version: t.Union[str, bool] = False, # for test purposes

):Use case 1 : main file on your project to continue or stop runningyou may use it on the main file of your project
and create a conditional and decide what to do nextif check(
        min_version="3.7",
        max_version="3.11",
        min_msg="Your version is less than minimum.",
        verbose=True,

    ) is False : 
        sys.exit(0) # return # or just exit
# if user's python has a version of lets say 3.4 this will print your customized message and exit.just warn the user and continueif check(
        min_version="3.7",
        max_version="3.10",
        min_msg="Your version is less than minimum.",
        max_msg="Your version is greater than the minimum.",
        verbose=True,

    ) is False : 
        pass   
# if user's machine has a python with version of 
# lets say 3.4 this will print your customized message and continue .
Package: complycube
ComplyCube Python LibraryThe official python library for integrating with the ComplyCube API.Check out theAPI integration docs.Check out theAPI reference.InstallationpipinstallcomplycubeRequirementsPython 3.6+Getting Startedimport the clientfromcomplycubeimportComplyCubeClientInitialise the ComplyCubeClient with the api key from yourdeveloper dashboard.cc_api=ComplyCubeClient(api_key='test_....')Create a new client and complete a standard checkinput_client_dict={'type':'person','email':'a@b.com','personDetails':{'firstName':'John','lastName':'Smith'}}cc_client=cc_api.clients.create(**input_client_dict)check=cc_api.checks.create(cc_client.id,'standard_screening_check')print(check)Search for clients with the first name "John"forclientinccapi.clients.list(personDetails={'firstName','John'}):print(client.email)The auto_list function will handle api paging automatically via a generator and return a native object.forclientinccapi.clients.list(personDetails={'firstName','John'}):print(client.email)Per-request ConfigurationAs we use the requests library you can set per request configuration by using key/value pairs of any available requests parameter.Setting the timeout for client creation to 5 secondsccapi.clients.create(**input_client_dict,timeout=5)Avoiding certification verificationccapi.clients.create(**input_client_dict,verify=False)We also support the following per request settings;Passing a specific api key for a single requestccapi.clients.create(**input_client_dict,api_key='test_...')Setting number of retries to attemptccapi.clients.create(**input_client_dict,retries=5)Configuring a ProxyA proxy can be configured by passing in proxy object to the request:proxies={'https':'http://10.10.1.10:1080',}ccapi.clients.create(**input_client_dictproxies=proxies)For additional information, news and our latest blogs visit us athttps://www.complycube.com/
Package: compmech
Github Actions status:Coverage status:Computational Mechanics (compmech)Semi-analytical models for thin-walled structures.The models herein presented are described in detail in the following
publications:Castro SPG, Mittelstedt C, Monteiro FAC, Arbelo MA, Ziegmann G, Degenhardt R.
Linear buckling predictions of unstiffened laminated composite cylinders and
cones under various loading and boundary conditions using semi-analytical
models. Composite Structures, Vol. 118, Pages 303-315, December 2014.https://doi.org/10.1016/j.compstruct.2014.07.037Castro SGP, Mittelstedt C, Monteiro FAC, Arbelo MA, Degenhardt R, Ziegmann G. A
semi-analytical approach for linear and non-linear analysis of unstiffened
laminated composite cylinders and cones under axial, torsion and pressure
loads. Thin-Walled Structures, Vol. 90, Pages 61-73, May, 2015.https://doi.org/10.1016/j.tws.2015.01.002Castro SGP, Mittelstedt C, Monteiro FAC, Degenhardt R, Ziegmann G. Evaluation
of non-linear buckling loads of geometrically imperfect composite cylinders and
cones with the Ritz method. Composite Structures, Vol. 122, Pages 284-299,
April, 2015.https://doi.org/10.1016/j.compstruct.2014.11.050Castro SGP, Donadon MV. Assembly of Semi-Analytical models to Address Linear
Buckling and Vibration of Stiffened Composite Panels with Debonding Defect.
Composite Structures, Vol. 160, Pages 232â€“247, January, 2017.https://doi.org/10.1016/j.compstruct.2016.10.026DocumentationThe documentation is available on:http://saullocastro.github.io/compmech/Requirementsnumpyscipymatplotlib(for building only) Cython and a C compilerLicenseDistrubuted under the 3-Clause BSD license
(https://raw.github.com/saullocastro/composites/master/LICENSE).Contact:S.G.P.Castro@tudelft.nl
Package: compmec-nurbs
A object-oriented python package for parametrized geometry which supportsCustom objects.For now, it supports only 1D-objects (curves).FeaturesEvaluate pointsInsert and remove knotsDegree increase and decreaseSplit and unite curvesMath operations (+,-,*,/,@)Projection of point in curveIntersection of two curvesDerivative of curvesLine IntegralCurve fittingFunction fittingPoints fittingReparameterize curveInstallThis library is available inPyPI. To install itpip install compmec-nurbsFor more details, refer to thedocumentation.DocumentationThe documentation can be found atcompmec-nurbs.readthedocs.ioContributePlease use theIssuesor refer to the emailcompmecgit@gmail.com
Package: compmec-section
A python package for analysis of beam sections by using Boundary Element Method.Installation:This library is available inPyPI. To install it$ pip install compmec-sectionFor more details, refer to thedocumentation.DocumentationThe documentation can be found atcompmec-section.readthedocs.ioContributePlease seeHow to contributeor if you find any bug, use theIssues.
Package: compmec-strct
StructuresCompute fields (displacement/force/momentum) of structures using Finite Element Method.1D ElementsTruss (Traction/Compression)BeamEuler-BernoulliTimoshenko (Needs implementation)How to use itThere are manyPython Notebooksin the folderexamples.InstallThis library is available inPyPI. To install itpip install compmec-strctOr install it manuallygit clone https://github.com/compmec/strct
cd strct
pip install -e .DocumentationIn progress. See the examples.ContributePlease use theIssuesor refer to the emailcompmecgit@gmail.com
Package: compnal
DescriptionCOndensedMatterPhysicsNumericalAnalyticsLibrary (COMPNAL) is a numerical calculation library in the field of condensed matter physics. This library aims to provide a comprehensive set of numerical methods and algorithms tailored for analyzing various condensed matter systems.API ReferenceC++ ReferenceFeaturesCOMPNALcan calculate the following models on the following lattices by the following solvers.LatticeOne-dimensional chainTwo-dimensional square latticeThree-dimensional cubic latticeFully-connected latticeModelClassical modelsIsing modelPolynomial Ising modelSolverFor Classical modelsClassical Monte Carlo methodSingle spin flipParallel temperingUpcoming FeaturesWe are actively working on expandingCOMPNALwith the following upcoming features.LatticeTwo-dimensional triangular latticeTwo-dimensional honeycomb latticeUser-defined latticeModelClassical modelPotts modelQuantum modelTransverse field Ising modelHeisenberg modelHubbard modelKondo Lattice modelAlgorithmClassical Monte Carlo methodSuwa-Todo algorithmWolff algorithmSwendsen-Wang algorithmExact DiagonalizationLanczos methodLocally Optimal Block Preconditioned Conjugate Gradient methodDensity Matrix Renormalization GroupInstallationInstall from PyPIOnly for Linux and MacOS.pipinstallcompnalInstall from GitHubTo install the latest release ofcompnalfrom the source, use the following command:pipinstallgit+https://github.com/K-Suzuki-Jij/compnal.gitBefore installation, make sure that the following dependencies are installed.Build from sourceCOMPNALdepends on the following libraries.Eigenpybind11OpenMPOn MacOSFirst, install the dependencies usingHomebrew.brewinstallcmakelibompThen, clone this repository and installCOMPNAL.python-mpipinstall.-vvvRun the test to check if the installation is successful.python-mpytesttestsOn LinuxFirst, install the dependencies using apt.sudoaptinstallcmakeThen, clone this repository and installCOMPNAL.python-mpipinstall.-vvvRun the test to check if the installation is successful.python-mpytesttests
Package: compneuro
CompNeuroThis Module provides source code for Book Chapters and Notebooks for 4020.Free software: BSD-3 licenseFeaturesUtility functions for signal generation, spectral analysis, spike plotting, and other plotting functionalitiesCPU-based simulation and analysis of dynamical system modelsInteractive plotting of results using Bokeh.InstallationTo install the lastest version on PyPI, run the following code:pip install compneuroThis will install all the necessary requirements as specified in thesetup.pyfile.
However, you will
need to install NodeJS with version greater than14.0to make sure that the
customBokeh3d plotting capability renders correctly in Jupyter. For installingNodeJS, you should follow instructions on their official website.For development, see below.DevelopmentMake sure you installNodeJSfirst. Afterwards, you can install the package
including the requirements by executing:pip install -e .[dev]If you want to work on distributing the package, you will need to install a few
additional packages, which can be done by running:pip install -e .[dist]Before committing, useblackto format the python code by runningblack compneuroFor each utility function added, make sure you add a test function in thecompneuro/testsfolder. All tests should be written usingpytest.You can also run all tests by running:py.testCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2021-06-20)First release on PyPI.
Package: compoctl
Install with pip install compoctlA wrapper for the long docker-compose command that also adds a few features
that should be considered experimental, in waiting for refutation or upstream
contribution - which won’t break BC because compoctl decorates docker-compose
commands.compoctl apply: the one-liner pipeline for docker-composeChain pull/build/down/up/logs/ps, nice to have for hacking and even more in
automated deploys:compoctl -f ./foo.yml apply

# will run:
docker-compose -f ./foo.yml pull
docker-compose -f ./foo.yml build
docker-compose -f ./foo.yml down
docker-compose -f ./foo.yml up -d
docker-compose -f ./foo.yml logs
docker-compose -f ./foo.yml pscompoctl backupBackup data into ./backup.Example configuration that makes it work as-is:volumes:
- ./backup/postgres:/backup
labels:
  io.yourlabs.backup.cmd: pg_dumpall -U postgres -f /backup/data.dumpThis will dump pg data into ./backup/postgres, and also export
docker-compose running config into ./backup/docker-compose.restore.ymlIt will also execute the docker-compose.backup.yml if it exists. This is
were you can spawn a container that mounts ./backup and proceeds to the
secure backup export over the network that you want for production.To prevent permission issues, containers should at no time write the
./backup directory itself.pre-POC state: waiting for an example chaining a docker-compose.backup.yml that
would spawn restic and rclone to backup the backup on a remote collection (or
implement retention policy feature into duplicity).compoctl restoreCopy docker-compose.yml back from ./backup and run restore commands.This is a destructive operation that will delete all volumes except the
backup volume, up each service one by one and apply the restore command.Example configuration that makes it work as-is:volumes:
- ./backup/postgres:/backup
labels:
  io.yourlabs.restore.cmd: psql -U postgres -f /backup/data.dumpNote that the ./backup directory must have been provisioned with the backup
command priorly.Also, the cluster will be unusable/down during the restore operation.
Package: component-contribution
Component ContributionA method for estimating the standard reaction Gibbs energy of biochemical reactions.Cite usFor more information on the method behind component-contribution, please view our open
access paper:Noor E, Haraldsdóttir HS, Milo R, Fleming RMT (2013)Consistent Estimation of Gibbs Energy Using Component Contributions,
PLoS Comput Biol 9:e1003098, DOI: 10.1371/journal.pcbi.1003098Please, cite this paper if you publish work that usescomponent-contribution.Installationpip install component-contributionDependenciesPython 3.9+PyPI dependencies for prediction:equilibrator-cachenumpyscipypandaspintpathperiodictableuncertaintiesPyPI dependencies for training a new model:openbabelequilibrator-assetsData sourcesTraining data for the component contribution methodChemical group definitions for the component-contribution method
Package: componentes
DocumentaçãoSustentação de componenteCriando ou atualizando componenteComo criar um pacote de pip install1. Criar conta nohttps://pypi.org/2. Gerar Token (Account settings)3. Pip install poetry4. Gerar um novo projeto- Abra o terminal dentro do diretorio relacionado
- Utilizar comando `poetry new nome_projeto`
- Acesse a pasta do projeto criado (usando cd)
- registre o token gerado no projeto com o comando `poetry config pypi-token.pypi token_gerado`
- Alterando informações no arquivo `pyproject.toml`
    -- Adicione o nome, descrição, autor(ficara visivel no Pypi) e informações que desejar
    -- Adicione as dependencias usando no terminal `poetry add nome_pacote` ou adicione dentro de `pyproject.toml` abaixo da linha tool.poetry.dependencies
    -- Altere a versão caso seja uma atualização
- No arquivo _init_.py altere a versão caso seja uma atualização
- Na pasta do projeto: Adicione os arquivos necessários
- No terminal: digite `poetry build` para compilar e gerar o diretorio dist com os arquivos zipados
- No terminal: digite `poetry publish` e o pacote será criado ou atualizadoObs: É importante alterar a versão nopyproject.tomlsempre que for alterar o projeto