Saravanan666/pypi-org huggingface dataset:
Package: openqaoa-core
OpenQAOA-CoreOpenQAOA is a multi-backend python library for quantum optimization using QAOA on Quantum computers and Quantum computer simulators. This package is part of a set of OpenQAOA plug-ins and forms the core functionality of the package. It includes all backend agnostic elements of the QAOA process. Along with other OpenQAOA plug-in extensions enables users to seemlessly run QAOA computations across multiple Quantum Computers. Additionally, it also comes bundled with Entropica's fast QAOA simulator calledvectorizedand a unit-depth QAOA analytical simulator (you can find more details in the Getting Started section). Check out OpenQAOA websitehttps://openqaoa.entropicalabs.com/OpenQAOA is currently in OpenBeta.Please, considerjoining our discordif you want to be part of our community and participate in the OpenQAOA's development.Installation instructionsInstall via PyPIYou can install the latest version of openqaoa-core directly from PyPi. We recommend creating a virtual environment withpython>=3.8first and then simply pip install openqaoa-core with the following command.pipinstallopenqaoa-coreInstallation instructions for DevelopersOpenQAOA-Core does not yet support developer install as a standalone package. If you wish to work in developer mode, please install the entire library. Instructions are availablehereShould you face any issue during the installation, please drop us an email atopenqaoa@entropicalabs.comor open an issue!Getting startedThe documentation for OpenQAOA-Core can be foundhere.We also provide a set of tutorials to get you started. Among the many, perhaps you can get started with the following ones:A notebook showing Analytical Simulator usageIntroducing EL's fast QAOA simulatorDiscover OpenQAOA's custom parametrizationsNotebook demonstrating different optimizers to choose fromKey FeaturesBuild advanced QAOAs. Create complex QAOAs by specifying customparametrisation,mixer hamiltonians,classical optimisersand execute the algorithm on either simulators or QPUs.Recursive QAOA. Run RQAOA with fully customisable schedules on simulators and QPUs alike.QPU access. Built in access forIBM Quantum,Rigetti QCS,Amazon BraketandAzure Quantum(Please install the OpenQAOA plugin corresponding to the QPU platform for running QAOA on these devices).Available devivesDevices are serviced both locally and on the cloud. For the IBM Quantum experience, the available devices depend on the specified credentials. For QCS and Amazon Braket, the available devices are listed in the table below:Device locationDevice Namelocal['vectorized', 'analytical_simulator']Running the testsTODOContributing and feedbackIf you find any bugs or errors, have feature requests, or code you would like to contribute, feel free to open an issue or send us a pull request on GitHub.We are always interested to hear about projects built with EntropicaQAOA. If you have an application you'd like to tell us about, drop us an email atopenqaoa@entropicalabs.com.
Package: openqaoa-pyquil
OpenQAOA-Pyquil PluginOpenQAOA is a multi-backend python library for quantum optimization using QAOA on Quantum computers and Quantum computer simulators. This package is part of a set of OpenQAOA plug-ins that lets users run QAOA computations on Rigetti's devices accessible through QCS, and devices that support quil instructions. Check out OpenQAOA websitehttps://openqaoa.entropicalabs.com/OpenQAOA is currently in OpenBeta.Please, considerjoining our discordif you want to be part of our community and participate in the OpenQAOA's development.Installation instructionsInstall via PyPIYou can install the latest version of openqaoa-pyquil directly from PyPi. We recommend creating a virtual environment withpython>=3.8first and then simply pip install openqaoa-pyquil with the following command.NOTE:Installingopenqaoa-pyquilinstallsopenqaoa-coreby defaultpipinstallopenqaoa-pyquilInstallation instructions for DevelopersOpenQAOA-Qiskit does not yet support developer install as a standalone package. If you wish to work in developer mode, please install the entire library. Instructions are availablehereShould you face any issue during the installation, please drop us an email atopenqaoa@entropicalabs.comor open an issue!Getting startedThe documentation for OpenQAOA-Pyquil can be foundhere.We also provide a set of tutorials to get you started. Among the many, perhaps you can get started with the following ones:Link OpenQAOA Pyquil notebooksUsing QPUUsing as QVMAvailable devivesOpenQAOA-Pyquil services devices both locally and on the cloud. The Rigetti supports access to their QPUs through the QCS platform. Moreover, users can also access aqvmemulator of Rigetti QPUs that runs locally.NOTE:Using a QVM requires installation of Rigetti Forest SDK that does not come pre-installed with OpenQAOA. If required the user may install the SDKhereDevice locationDevice NameRigetti QCSAspen-11, Aspen-M-1, and QVM simulatorRunning the testsTODOContributing and feedbackIf you find any bugs or errors, have feature requests, or code you would like to contribute, feel free to open an issue or send us a pull request on GitHub.We are always interested to hear about projects built with EntropicaQAOA. If you have an application you'd like to tell us about, drop us an email atopenqaoa@entropicalabs.com.
Package: openqaoa-qiskit
OpenQAOA-Qiskit PluginOpenQAOA is a multi-backend python library for quantum optimization using QAOA on Quantum computers and Quantum computer simulators. This package is part of a set of OpenQAOA plug-ins that lets users run QAOA computations on IBMQ devices, and devices that support qiskit circuits. Check out OpenQAOA websitehttps://openqaoa.entropicalabs.com/OpenQAOA is currently in OpenBeta.Please considerjoining our discordif you want to be part of our community and participate in the OpenQAOA's development.Installation instructionsInstall via PyPIYou can install the latest version of openqaoa-qiskit directly from PyPi. We recommend creating a virtual environment withpython>=3.8first and then simply pip install openqaoa-qiskit with the following command.NOTE:Installingopenqaoa-qiskitinstallsopenqaoa-coreby defaultpipinstallopenqaoa-qiskitInstallation instructions for DevelopersOpenQAOA-Qiskit does not yet support developer install as a standalone package. If you wish to work in developer mode, please install the entire library. Instructions are availablehereShould you face any issue during the installation, please drop us an email atopenqaoa@entropicalabs.comor open an issue!Getting startedThe documentation for OpenQAOA-Qiskit can be foundhere.We also provide a set of tutorials to get you started. Among the many, perhaps you can get started with the following ones:Link OpenQAOA Qiskit notebooksUsing QPUUsing Qiskit SimulatorsAvailable devivesOpenQAOA-Qiskit services devices both locally and on the cloud. The QPU accessible through the cloud depends on the specified credentials. Moreover, users can also accessqiskitlocal simulators.Device locationDevice Namelocal['qiskit.shot_simulator', 'qiskit.statevector_simulator']ibmqPlease check the IBMQ backends available to youraccountRunning the testsTODOContributing and feedbackIf you find any bugs or errors, have feature requests, or code you would like to contribute, feel free to open an issue or send us a pull request on GitHub.We are always interested to hear about projects built with EntropicaQAOA. If you have an application you'd like to tell us about, drop us an email atopenqaoa@entropicalabs.com.
Package: openqa-review
# openqa_review_script [![Build Status](https://travis-ci.com/os-autoinst/openqa_review.svg?branch=master)](https://travis-ci.com/os-autoinst/openqa_review) [![CII Best Practices](https://bestpractices.coreinfrastructure.org/projects/699/badge)](https://bestpractices.coreinfrastructure.org/projects/699)A review helper script for openQA.For more details read the documentation withinopenqa_review/openqa_review.py.## UsageInstall requirements and package, using an isolated Python environment
such as [VirtualEnv](http://docs.python-guide.org/en/latest/dev/virtualenvs/).` pip3 install-rrequirements.txt pip3 install . `or if you are using openSUSE distribution, it is recommended to usezypper,
e.g.:` zypper inpython-openqa_review`Callopenqa-reviewfrom PATH, e.g. with–help`openqa-review--help`Ifopenqa-reviewhas to access non public servers which make use of customCertificate Authorities(CA’s), it can happen that it fails because it can
not verify the TLS certificate. By setting the environment variableREQUESTS_CA_BUNDLE, one can supply a custom ca-certificate store toopenqa-reviewe.g.:`REQUESTS_CA_BUNDLE="/my/custom/ca-bundle.pem"openqa-review`If that doesn’t work, you can try the following setting:`CURL_CA_BUNDLE=""`## CommunicationIf you have questions, visit me on irc.freenode.net in #opensuse-factory## ContributeThis project lives inhttps://github.com/os-autoinst/openqa_reviewFeel free to add issues in github or send pull requests.TODOs and ideas are tracked in the fileTODOas well as github issues.### Rules for commitsEvery commit is checked by [Travis CI](https://travis-ci.com) as soon as
you create a pull request but youshouldruntoxlocally,Make sure to keep the 100% test coverage, e.g. by adding test reference data
for new scenarios. TDD is advised :-)For git commit messages use the rules stated on
[How to Write a Git Commit Message](http://chris.beams.io/posts/git-commit/) as
a referenceIf this is too much hassle for you feel free to provide incomplete pull
requests for consideration or create an issue with a code change proposal.## LicenseThis project is licensed under the MIT license, see LICENSE file for details.
Exceptions are the files underdashboard_files/which are GPL-2 licensed
coming fromhttps://github.com/os-autoinst/openQA/.
Package: openqasm-pygments
Pygments tools for OpenQASMThis repository provides the Python packageopenqasm-pygments, which provides
a lexer for syntax highlighting OpenQASM code withPygments.
There are current three lexers included, all of which register themselves with
Pygments when this package is installed:OpenQASM3Lexer(Pygments aliasesopenqasm3andqasm3), for parsing
OpenQASM 3 code.  Most OpenQASM 2 programs will lex acceptably with this lexer
as well, except for some keyword differences.OpenQASM2Lexer(aliasesopenqasm2andqasm2), for lexing OpenQASM 2
programs.OpenPulseLexer(aliasesopenpulse), for lexing the OpenPulse pulse
calibration dialect also defined in the OpenQASM 3 specification.  For the
most part, this lexer will not be used as a root, but theOpenQASM3Lexerwill delegate lexing of calibration blocks to it, when required.InstallationInstall the latest release ofopenqasm-pygmentspackage from pip:pipinstallopenqasm-pygmentsThis will automatically install all the dependencies as well (Pygments, for
example) if they are not already installed.DevelopingIf you're looking to contribute to this project, please first readour contributing guidelines.Set up your development environment by installing the development requirements
with pip:pipinstall-rrequirements-dev.txttoxThis installs a few more packages than the dependencies of the package at
runtime, because there are some tools we use for testing also included, such astoxandpytest.After the development requirements are installed, you can install an editable
version of the package withpipinstall-e.After this, any changes you make to the library code will immediately be present
when you open a new Python interpreter session.LicenseThis project is licensed underversion 2.0 of the Apache License.
This is a Qiskit project.
Package: openQCD-input-file-editor
# openQCD Input-file EditorAn input file editor with a graphical user interface for the [openQCD](http://luscher.web.cern.ch/luscher/openQCD) lattice QCD simulation program. Allows editing of existing input files or creation of new ones.Includes multiple choice options for parameters whenever possible, and performs basic consistency checks on parameters that are entered as text, to reduce the possibility of typos in the input file.See the [wiki](https://github.com/lkeegan/openQCD-input-file-editor/wiki) for documentation on how to install and use the program. Requires Python 2.7 and PyQt4.Still in beta - comments / bug reports very welcome!Thanks to Isabel Campos and Martin Lüscher for their help and comments. Also to Enol Fernández and Álvaro López García for Python advice.![Screenshot](http://lkeegan.github.io/openQCD-input-file-editor/images/screenshot.png)
Package: openqlab
openqlabopenqlabprovides a collection of useful tools and helpers for the
analysis of lab data in the Nonlinear Quantum Optics Group at the University
of Hamburg.Part of the content in this package was written during the PhD theses of
Sebastian Steinlechner and Tobias Gehring. It is currently maintained by
Sebastian Steinlechner, Christian Darsow-Fromm, Jan Petermann and is looking for more
volunteers who would like to contribute.Read the latest changes in ourchangelog.DocumentationCurrent documentation of thelatest releaseCurrent documentation of thelatest development versionFeaturesImporters for various file formats:Agilent/Keysight scopes (binary and CSV)Rohde & Schwarz spectrum analyzersTektronix spectrum analyzerplain asciiand a few more...easily create standard plots from measurement datadesign control loopsanalyze beam profiler datagenerate covariance matrices for N partite systemsseveral postprocessing functions for entanglement dataanalyse and automatically plot squeezing datatools for working with dB unitsInstallationFor a detailed installation instruction see the maindocumentation.UsageYou will need an up-to-date Python 3 environment to use this package, e.g.
the Anaconda Python distribution will work just fine. Please refer to therequirements.txtfor a list of prerequisites (although these should be
installed automatically, if necessary).For examples and details on how to use this package, please refer to the
documentation.ContributingAll contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.A detailed overview on how to contribute can be found in thecontributing guide.LicenseThe code is licensed under theGNU GENERAL PUBLIC LICENSE. SeeLICENSE.ChangelogChanges to the code are documented in thechangelog.
Package: openqtsim
OpenQTSimOpensourceQueueingTheorySimulation - Simulation package that facilitates discrete event simulation of queues with a Kendall notation.Documentation can be foundhere.InstallationTo install OpenQTSim, run this command in your terminal:pipinstallopenqtsimThis is the preferred method to install OpenQTSim, as it will always install the most recent stable release.If you don not havepipinstalled, thisPython installation guidecan guide you through the process.You can read thedocumentationfor other installation methods.ExamplesThe benefit of OpenQTSim is the generic set-up. This set-up allows the simulation of a range of queues with a Kendall notation. A number of examples are presented in a seperateJupyter Notebook repository. Information on how to use the notebooks is presented in that repository as well.
Package: openquake.engine
OpenQuake EngineTheOpenQuake Engineis an open source application that allows users to computeseismic hazardandseismic riskof earthquakes on a global scale. DOI:10.13117/openquake.engineCurrent Long Term Support (LTS) release - for users wanting stabilityCurrent LTS version is theOpenQuake Engine 3.16'Angela':The code name for version 3.16 isAngela, in memory of the Italian science journalistPiero Angela.What's newDocumentation 3.16User's manualLatest release - for users needing the latest featuresLatest stable version is theOpenQuake Engine 3.17.General overviewThe OpenQuake Engine software provides calculation and assessment of seismic hazard, risk and decision-making tools via the data, methods and standards that are being developed byGEM(Global Earthquake Model) and its collaborators.InstallationUser's manualAdvanced user manual.Disclaimer: It includes experimental features and is only recommended for users that are already familiar with the user's manual.FAQGlossary of TermsRunning the OpenQuake EngineUsing the command lineon WindowsUsing the command lineon macOS and LinuxUsing the WebUIVisualizing outputs via QGISAQGIS plug-inis available for users that would like to visually explore the outputs from the engine.
Check the documentation for instructions on how todrive the engineandvisualize outputs.Source codealso available.For developers and contributorsArchitectureCalculation WorkflowContinuous integration and testingFor contributorsDevelopment Philosophy and Coding GuidelinesSource Code/API DocumentationHTTP REST APIImplementing a new GSIMMirrorsA mirror of this repository, hosted in Pavia (Italy), is available athttps://mirror.openquake.org/git/GEM/oq-engine.git.The main download server (downloads.openquake.org) is hosted in Nürnberg (Germany).LicenseThe OpenQuake Engine is released under theGNU Affero Public License 3.ContactsSupport forum:https://groups.google.com/forum/#!forum/openquake-usersTwitter:@gem_devsEmail:info@openquake.orgThanksThe OpenQuake Engine is developed by theGlobal Earthquake Model Foundation (GEM)with the support ofPublic PartnersPrivate PartnersGovernorsAdvisorsAssociate PartnersProject PartnersProducts Distribution PartnersIf you would like to help support development of OpenQuake, please contact us atpartnership@globalquakemodel.org.
For more info visit the GEM website athttps://www.globalquakemodel.org/partners
Package: open-quant-data
OPEN-QUANT-DATAIntroductionThis is open source quant framework for local dataset generation
, third-party dataset api analysis, and quant data statisticsFeaturesLocal DatasetThis feature need support fromopen-quant-spider, by
reading the data recording byopen-quant-spiderfrom
Mysql or Redis. The framework export the dataset as csv
filesThirdParty DatasetSometimes we may have insufficient data from local recording,
so we plan to support third-party data sources. Currently,
we supportAkshareandXtquant + MiniQMT, which includes
stock, bond, etf, in-market fund history data with multi formats.StatisticsThe core statistics module's aim is to do factor statistics and
dataset utils.Install & RunContributorsBug Report
Package: openradar
PreSense mmWave PackageThis is PreSense team's implementation of TI mmwave radar DSP stack and some demos.
We are grateful for TI, TI's e2e forum and other people's help to make this happen.
Please star us if you like this repository and please consider citing this repo if you used it in your research.The toolbox is modularized into separate stepsReading raw ADC data.Preprocessing data in DSP stack.Utilizing preprocessed data for tracking, clustering and machine learning.Different demo implementations from TI and our own explorations.Documentationopenradar.readthedocs.ioContactPlease submit issues to ourGitHubif you found any bugs or have any suggestionsFor anything else, send an email atpresenseradar@gmail.comDirectory Structure.
├── data                    # Small size sample data.
├── demo                    # Python implementations of TI demos.
├── docs                    # Documentation for mmwave pacakge and hardware setup.
├── mmwave                  # mmwave package including all the DSP, tracking, etc algorithms.
├── PreSense Applied radar  # Jupyter notebook series explaining how apply radar concepts to real data
├── scripts                 # Various setup scripts for mmwavestudio, etc
├── .gitignore
├── LICENSE
├── README.md
├── requirements.txt        # Required dependencies to run this package.
└── setup.py                # Install mmwave package.Current Roadmap for this packageCode refactoring for better API support.More tutorials to help people get started on FMCW radar.More AoA methods support.More noise removal algorithms.Future PlanHardware Abstraction Layer, e.g. hardware-agnostic data reading or processing.ML on radar (classification, detection, tracking, etc).InstallationPip installationComing soonDebug Modegit clone https://github.com/presenseradar/openradar
cd openradar
pip install -r requirements.txt
python setup.py developUninstallationpip uninstall mmwaveExample Import and Usageimportmmwaveasmmfrommmwave.dataloaderimportDCA1000dca=DCA1000()adc_data=dca.read()radar_cube=mm.dsp.range_processing(adc_data)CitationPlease cite OpenRadar in your publications if it helps your research. Here is an example BibTeX entry:@misc{openradar2019,
  author = {Pan, Edwin and Tang, Jingning and Kosaka, Dash and Yao, Ruihao and Gupta, Arjun},
  title = {OpenRadar},
  year = {2019},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/presenseradar/openradar}}
}AcknowledgementThe PreSense team wants to thank Prof.Sanjay Patel, without whom this project wouldn't be possible. We are also grateful for the generous sponsorship from The IBM-ILLINOIS Center for Cognitive Computing Systems Research (C3SR) and the guidance/leadership fromJinjun Xiongof IBM and Prof.Wen-Mei Hwu. Special thanks to Prof.Haitham Hassaniehand Prof.Minh M. Dofor the techical advice and Texas Instruments™ for the hardware support.
Package: openrai
OpenRAI is a python library that is designed to help AI developers in
various aspects of responsible AI development. It consists of a core API
and a corresponding web-based dashboard application. OpenRAI can easily be
integrated into AI development projects and measures various metrics for
an AI project during each phase of AI development, from data quality
assessment to model selection based on performance, fairness and
robustness criteria. In addition, it provides interactive tools and
visualizations to understand and explain AI models and provides a
generic framework to perform various types of analysis including
adversarial robustness.Note: - The dashboard GUI is currently designed around 1920x1080
displaysDocumentationDocumentation is available online:https://responsibleai.readthedocs.io/en/latest/To InstallThe project uses sqlite as storage and requires no additional
installation for this.pip install openraiIf you need to use the dashboard as well you must  install the additional requirementspip install openrai[dashboard]To start using the library you must configure the folder where the results will be stored and be used by the dashboardexport DATABASE_FOLDER=YOUR_LOCATIONTo start the dashboard server use the following command:python3-mRAIDashboardDemos:We have added a few demo projects to showcase some of the capabilities of OpenRAI.
to run any of the demos please use 'python demo_filename'. For instance :
python ./demos/adult_demo_grid_search.py

below is a short description of the provided demos:

File: adult_demo_grid_search.py
Description: This demo uses the Adults dataset (https://archive.ics.uci.edu/ml/datasets/adult) to show how OpenRAI can be used in model selection

File: image_class_analysis.py
Description: this demo uses Cifar10 dataset and shows how openRAI can be used to evaluate image classification tasks

File: image_class_training.py
Description: this demo uses Cifar10 dataset and shows how openRAI can be used monitor image processing tasks during training


File: tabular_class_console.py
Description: this demo shows how openRAI can be used without the dashboard to calculate and report on the metrics for a machine learning task

File: text.py
File: text_output.py
Description: these demos show how openRAI and its dashboard can be used for evaluating the natural language modeling tasksCisco Research, Emerging Tech and Incubations,Cisco Systems Inc.