Saravanan666/pypi-org huggingface dataset:
Package: concourseatom
concourseatomThis project provides a merge funtion to intelligently merge concourse jobs together.Rewrites in mergeWhen a pipeline is merging with another then it scans the names of the resources to identify it there are any duplicate resource types and if so then it plans rewrites of the Right Hand Side pipeine (on the merge funtion call). The process also scans for re-use of the same name for different resource types and plans similar rewrites for those as well.This process is then similarily applied to resources.Finally the rewrites are then applied to the jobs recursively to modify the resource types of the get and put names to match the resources.
It is also necessary to consider the name collisions of names of the handles of the resource not just there contents. These are identied as the objects that are the result of get and put and task mapped objects.IssuesCapture issues here to look at:In_parallel objects inside In_parallel objects. Seems to be triggering issues with sort order (may not be consistent) so results in comparisons of types that are not same.
Package: concoursepy
concoursepyconcoursepy gives you a way to interact with the concourse api through pythoncurrently supportsconcourse's local user mechanismthe endpoints used by the concourse web uiUse a version prior to 0.0.8 for concourse 3.xExamplesimportconcoursepyci=concoursepy.api('https://ci.example.com','username','password')print(ci.jobs('example_pipeline'))# Or with token istead of username and password:ci=concoursepy.api('https://ci.example.com',token='SomeLongTokenString')print(ci.jobs('example_pipeline'))
Package: concoursetools
A Python package for easily implementing Concourseresource types.AboutConcourse CIis an "open-source continuous thing-doer" designed to enable general automation with intuitive and re-usable components. Resources represent all external inputs and outputs to and from the pipeline, and many of these have been implemented in open source. In order to best leverage the Python ecosystem of open-source packages, Concourse Tools abstracts away the implementation details of Concourse resource types to allow users to focus on writing the code they want to run.InstallationInstall fromGitHub, or fromPyPI:$pipinstallconcoursetoolsUsageCreating a Concourse resource type with Concourse Tools couldn't be simpler:Create subclasses ofconcoursetools.version.Versionandconcoursetools.resource.ConcourseResource, taking care to implement any required functions.Create a Dockerfile containing your requirements and calling your resource.Upload the Docker image to a registry, and use it in your pipelines!For more information, see thedocumentation.Bugs and ContributionsConcourse Tools is in beta, and still under somewhat-active development.  Contributions, fixes, suggestions and bug reports are all welcome: Please familiarise yourself with ourcontribution guidelines.
Package: concreate
AboutContainer image creation tool.Concreate helps to build container images from image definition files with strong focus on modularity and code reuse.FeaturesBuilding container imagesfrom YAML image definitionsIntegration/unit testingof imagesReleasing container images by building it in Red Hat supported build systemInstallationIf you are running Fedora, you can install Concreate easily via:dnfcoprenablegoldmann/concreatednfinstallpython3-concreateFor other platforms, please refer todocumentation.UsageFirst steps tutorial is under construction, for now please refer to theconcreate--helpoutput.DocumentationDocumentation is available here.HistoryConcreate originates from theDogentool. Dogen was developed for over two years and served us well, but we decided to make it a first class citizen and promote using abstract image descriptors. At the same time we wanted to add new set of features which made the “Dockerfile generator” tagline inaccurate and we decided to start fresh under a new project. In any case, this is the Dogen project evolution.
Package: concrete-backend
concrete-backendOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcrete.backendconcrete.backend.hello_world()
Package: concrete-common
concrete-commonOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcrete.commonconcrete.common.hello_world()
Package: concrete-datastore
READMEQualité du codeDescriptionconcrete-datastoreest un Datastore HTTP REST très polyvalent basé sur le framework WebDjango.Il est principalement utilisé comme serveur de base de données HTTP pour une application Web monopage (AWM).Contrairement à un serveur de base de données classique tel que PostgreSQL ou MySQL où les requêtes sont effectuées à l'aide du langage SQL, chaque opération est effectuée à l'aide de requêtes HTTP simples.concrete-datastorepeut être considéré comme un serveur NoSQL ou comme une alternative à Firebase.Démarrage rapidegitclonehttps://github.com/Netsach/concrete-datastore.gitcdconcrete-datastore
dockerrun--namepostgres-concrete-datastore-ePOSTGRES_DB=db-concrete-datastore-ePOSTGRES_USER=user-concrete-datastore-ePOSTGRES_PASSWORD=pwd-concrete-datastore-d-pXXXX:5432postgis/postgis:12-masterexportPOSTGRES_PORT=XXXXexportDATAMODEL_FILE=./docs/assets/sample-datamodel.yml
python3-mvenvenvsourceenv/bin/activate
pipinstall-e".[full]"concrete-datastoremakemigrations
concrete-datastoremigrate
concrete-datastorecreatesuperuser
concrete-datastorerunserverNaviguez maintenant jusqu'àhttp://127.0.0.1:8000/concrete-datastore-admin/Vous pouvez maintenant créer un jeton pour utiliser l'API (ou utiliser le Endpoint d'authentification).Fonctionnalitésconcrete-datastoreest livré avec de nombreuses fonctionnalités intégrées telles que :Gestion des utilisateurs et des autorisationsBackoffice généré automatiquement pour les administrateursAPI entièrement REST utilisant JSON comme format de sérialisationGénération de statistiques simplesCapacités d'envoi d'e-mails à partir de l'API...Comment ça marche ?Afin de décrire le schéma de la base de données, le développeur doit écrire un fichierdatamodelen YAML ou JSON. Ce fichierdatamodelpermet àconcrete-datastorede gérer la base de données sous-jacente à l'aide de PostgreSQL.Chaque demande d'API est contrôlée par ce fichierdatamodelcar il agit comme une spécification de ce qui se trouve dans la base de données et de ce qui devrait être autorisé par chaque utilisateur.Vous pouvez créer manuellement le fichierdatamodelen suivant les exemples et la documentation ou utiliser l'éditeur en ligneplatform.concrete-datastoreF.A.QSi vous avez des questions, elles ont peut-être déjà été répondues dans leFAQS.mdDocumentation officielleVoir ladocumentation officielleVersion AnglaiseVous pouvez retrouver la version Anglaiseici
Package: concretefhe
concretefheOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcretefheconcretefhe.hello_world()
Package: concretefhe-backend
concretefhe-backendOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcretefhe.backendconcretefhe.backend.hello_world()
Package: concretefhe-common
concretefhe-commonOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcretefhe.commonconcretefhe.common.hello_world()
Package: concretefhe-compiler
concretefhe-compilerOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcretefhe.compilerconcretefhe.compiler.hello_world()
Package: concretefhe-frontend
concretefhe-frontendOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcretefhe.frontendconcretefhe.frontend.hello_world()
Package: concretefhe-hnp
concretefhe-hnpOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcretefhe.hnpconcretefhe.hnp.hello_world()
Package: concretefhe-hnumpy
concretefhe-hnumpyOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcretefhe.hnumpyconcretefhe.hnumpy.hello_world()
Package: concretefhe-lib
concretefhe-libOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcretefhe.libconcretefhe.lib.hello_world()
Package: concretefhe-numpy
concretefhe-numpyOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcretefhe.numpyconcretefhe.numpy.hello_world()
Package: concretefhe-utils
concretefhe-utilsOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcretefhe.utilsconcretefhe.utils.hello_world()
Package: concrete-frontend
concrete-frontendOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcrete.frontendconcrete.frontend.hello_world()
Package: concrete-hnp
concrete-hnpOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcrete.hnpconcrete.hnp.hello_world()
Package: concrete-hnumpy
concrete-hnumpyOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcrete.hnumpyconcrete.hnumpy.hello_world()
Package: concrete-lib
concrete-libOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcrete.libconcrete.lib.hello_world()
Package: concreteproperties
concretepropertiesis a python package that can be used to calculate the section
properties of arbitrary reinforced concrete sections.concretepropertiescan calculate
gross, cracked and ultimate properties. It can perform moment curvature analyses
and generate moment interaction and biaxial bending diagrams. On top of this,concretepropertiescan also generate pretty stress plots!Here's an example of some of the non-linear outputconcretepropertiescan generate:InstallationYou can installconcretepropertiesviapipfromPyPI:pipinstallconcretepropertiesDocumentationconcretepropertiesis fully documented including a user walkthrough, examples,
background theory and an API guide. The documentation can found athttps://concrete-properties.readthedocs.io/.FeaturesSee the complete list ofconcretepropertiesfeatureshere.ContributingContributions are very welcome. To learn more, see theContributor Guide.LicenseDistributed under the terms of theMIT license,concretepropertiesis free
and open source software.SupportFound a bug 🐛, or have a feature request ✨, raise an issue on the
GitHubissue trackerAlternatively you can get support on thediscussionspage.Disclaimerconcretepropertiesis an open source engineering tool that continues to benefit from
the collaboration of many contributors. Although efforts have been made to ensure the
that relevant engineering theories have been correctly implemented, it remains the
user's responsibility to confirm and accept the output. Refer to thelicensefor clarification of the conditions of use.CreditsThis project was generated from@cjolowicz'sHypermodern Python Cookiecuttertemplate.
Package: concrete-strength-prediction
Concrete StrengthOptimization of concrete mixtures with machine learning modelsFree software: MIT licenseDocumentation:https://concrete-strength-prediction.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.0.1 (2022-11-11)First release on PyPI.
Package: concrete-utils
concrete-utilsOur mission atZamais to protect people’s privacy by preventing data breaches and unethical surveillance.Following a recent breakthrough in homomorphic encryption, we are now building a ZeroTrust deep learning framework that enables fast and accurate inference over encrypted data, with minimal performance overhead, no changes to the network architecture and no retraining necessary.Zamais open-source by design, as we believe privacy-enabling technologies should benefit the widest possible community of developers and researchers.WarningThis package is not a working package.Testimportconcrete.utilsconcrete.utils.hello_world()
Package: conc_test_report
Concrete Test Results SummaryGenerate a concise and brief summary of all concrete test result PDFs, to aid in fast and efficient reviewView Demo·Documentation·Report Bug·Request FeatureTable of Contents1. About the Project1.1. Screenshots1.2. Features2. Getting Started2.1. Installation2.2. Dependencies3. Supported Material Testing Framework4. Roadmap5. License6. Contact7. Acknowledgements1. About the ProjectThis is a Jupyter notebook script that programmically reads and extracts data out of concrete test reports into a summarized excel file and PDF summary report1.1. Screenshots1.2. FeaturesPDF summary of the compiled data with the following additional verificationsTarget vs Achived strength graph to quickly check if req. strength is achievedChecks for Air content % to meet specified rangeChecks for slump to meet specified range2. Getting Started2.1. InstallationFrom Pypipipinstallconc_test_report2.2. DependenciesThe package should be pre-compiled with all required dependencies3. Supported Material Testing FrameworkThis project will always remain a work in progress. A list of supported material testing report formats are shown below. More will be added in the future.If you would like to see a particular company, please open up anew issueCompanyStatusMcElhanney✔️Kontur Geotechnical Consultants✔️4. RoadmapReport Parser ScriptGroup common data extraction functions underCompanyclassSeperate patters to isolate one group per lineReport Generator ScriptTests5. LicenseSee LICENSE.txt for more information.6. ContactArun Kishore -@rpakishoreProject Link:https://github.com/rpakishore/Concrete_Test_Results7. AcknowledgementsUse this section to mention useful resources and libraries that you have used in your projects.Awesome README TemplateReport icons created by Freepik - Flaticon
Package: conctools
=========
conctools.. image::https://img.shields.io/pypi/v/conctools.svg:target:https://pypi.python.org/pypi/conctools.. image::https://img.shields.io/travis/timskovjacobsen/conctools.svg:target:https://travis-ci.org/timskovjacobsen/conctools.. image::https://readthedocs.org/projects/conctools/badge/?version=latest:target:https://conctools.readthedocs.io/en/latest/?badge=latest:alt: Documentation Status.. image::https://pyup.io/repos/github/timskovjacobsen/conctools/shield.svg:target:https://pyup.io/repos/github/timskovjacobsen/conctools/:alt: UpdatesTools for analysis of reinforced concrete sectionsFree software: MIT licenseDocumentation:https://conctools.readthedocs.io.Current FeaturesCurrentlyconctoolslet's you:Work with a reinforced concrete section of polygon shapeCalculate the plastic centroid of the sectionCalculate ohe capacity (NM) diagram of the sectionPlot the cross sectionPlanned New FeaturesApart from improving the already implemented features, the new features
in focus for future releases are:Plot of the capacity (NM) diagram with optional load combinationsCalculation of neutral axis, strains, stresses and forces in the section
for given (N, M) loads.Crack width verification.. include:: docs/history.rst