Saravanan666/pypi-org huggingface dataset:
Package: qmatch
Quick MatchThis is a package with some 2-d match functions used in astronomy work.By Dr Jie Zheng and Dr Lin-qiao Jiang, from 2019Upload at Sep 2023matchA fast 2-d match function, with a time complexity of O(nlogn+mlogm).findneighborThe main part of the match function. Been picked out so other program can call it.match2dA simple 2-d match function, but has time and space complexity of O(m*n). It works well when m or n is small.offset1dA function find out offset of each dimension. For 2-d image align, can be used twice for x and y.make_tri&match_triangleMaking triangles by given points. As a preparation step of triangle matching in an astrometric process.Match triangles from stars (point sources) of two images.mean_xyandmean_offset1dA image matching method by mean of rows and cols. It will find the best integer offset between two images without finding stars.
Package: qmatchatea
Quantum Matcha TeaQuantum Matcha Tea is a Tensor Network emulator for quantum circuits and linear optics circuits.
You can define your circuits in either:qiskit, for quantum circuits;strawberry fields, for linear optics circuits.If you use another quantum information library (ascirq) we suggest to save your circuit inqasmformat and then load it in qiskit.The circuits ca be ran using the following backends:numpy, using the CPU in python;cupy, using the GPU in python;fortran, using either the CPU or GPU and MPI multiprocessing;DocumentationHereis the documentation.
The documentation can also be built locally with sphinx with the following python packages:sphinxsphinx_rtd_themesphinx-galleryand running the commandmake htmlin thedocs/folder.InstallationIndependent of the use-case, you have to install the dependencies. Then,
there are the options using it as a stand-alone package,
or as a python interface for the fortran backend of quantum matcha TEA.Installation via pipThe package is available via PyPi andpip install qmatchatea.
After cloning the repository, a local installation via pip is
also possible viapip install ..DependenciesNotice that, even though the library could be run with a GPU withcupy, the latter package is not installed
by default in the machine, since it could give problems
for machines that have no access to a gpu.
If you have access to a GPU and you want to use it,
please proceed to the installation as described incupy website.Furthermore, alsostrawberry fieldsis not installed by default.Testing the packageTo test the python packageqmatchateasimply run from the command:python3 -m unittestLicenseThe projectqmatchateafrom the repositorypy_api_quantum_matcha_teais licensed under the following license:Apache License 2.0The license applies to the files of this project as indicated
in the header of each file, but not its dependencies.
Package: qmdesc
qmdescA trained multitask constraint message passing neural networks
for QM atomic/bond property predictions as described in the paperRegio-Selectivity Prediction with a Machine-Learned Reaction Representation and On-the-Fly Quantum Mechanical Descriptors.QM descriptors under B3LYP/def2svp level of theory that can be predicted with this model:Hirshfeld partial chargeNeucleuphilic Fukui indicesElectrophilic Fukui indicesNMR shielding constantsBond lengthsBond ordersDocumentation:Documentation of qmdesc is available athttps://qmdesc.readthedocs.io/en/latest/index.html.RequirementsRDKitInstallationFor all installations, we recommend using conda to get the necessary rdkit dependency:conda install -c rdkit rdkitpip install qmdescOr from envrioment.ymlconda create --name qmdesc --file environment.yml
Package: qme
QMe"Queue-me" is a jobs queue and dashboard generation tool that can be used
to specify executors (entities that run jobs) and actions for them. You can
use qme only on the command line, or if desired, via an interactive web dashboard.
The dashboard (and it's dependencies) are not required for using the base library.You can run a task on the command line withqme runand then execute otherqmecommands to manage or inspect it, or start a web interface that will update
automatically:and expose executor-specific views for your tasks!DocumentationFor current documentation, seehttps://vsoch.github.io/qme.LicenseFree software: MPL 2.0 License
Package: qmeans
Q-Means algorithm implementation using Qiskit compatible with Scikit-Learn.Theq-meansleverages quantum computing to calculate distances for the centroid assignment part
of the k-means unsupervised learning algorithm. It shares the same general steps its classical
counterpart has, and is used alongside quantum simulators and quantum devices. This implementation
usesQiskitand is compatible with thescikit-learnlibrary in order to exploit the
capabilities for machine learning offered by scikit-learn. You can use the q-means in the same way
you would use thek-means, and
many options are available for data encoding and job batching.:exclamation: Job batching is introduced as a way to speed up training time on quantum devices for larger datasets. [2]InstallationThe project is publicly available in PyPI and can be installed using the following command:pip install qmeansUsageExample code for usage of the qmeans algorithm:importnumpyasnpimportpandasaspdfromqmeans.qkmeansimport*backend=Aer.get_backend("aer_simulator_statevector")X=pd.DataFrame(np.array([[1,2],[1,4],[1,0],[10,2],[10,4],[10,0]]))qk_means=QuantumKMeans(backend,n_clusters=2,verbose=True)qk_means.fit(X)print(qk_means.labels_)RequirementsPython 3.6+Q-Means usesQiskitfor quantum computations,Scikit-Learnfor data preparation and estimator formatting, andPandasandNumpyfor data manipulation.Publications[1]: D. Quiroga, P. Date and R. Pooser, "Discriminating Quantum States with Quantum Machine Learning," 2021 IEEE International Conference on Quantum Computing and Engineering (QCE), 2021, pp. 481-482, doi: 10.1109/QCE52317.2021.00088.[2]: D. Quiroga, P. Date and R. Pooser, "Discriminating Quantum States with Quantum Machine Learning," 2021 International Conference on Rebooting Computing (ICRC), 2021, pp. 56-63, doi: 10.1109/ICRC53822.2021.00018.[3]: D. Quiroga, J. Botia, "Q-means clustering coherent noise tolerance analysis," International Congress EXPOIngenieria, 2022, pp. 437-443.LicenseThis source code is free and open source, released under the Apache License, Version 2.0.
Package: qmenuview
PySide view for menuesFeaturesMenuView class which creates submenus/actions based on a model.Supports modelReset, rowsInserted, rowsRemoved and dataChanged signals of the model.Supports icon, text, iconText, toolTip, checked, whatsThis, statusTip, enabledEasy to extend and customize.History0.1.0 (2015-08-08)First release on PyPI.0.1.1 (2015-08-11)Fix getting parents of actionFix changing columns0.1.4 (2015-08-11)Fix removing all rows below root.
Package: qmeq
QmeQ is an open-source Python package for calculations of transport through
quantum  dot devices. The so-called Anderson-type models are used to describe
the quantum dot device, where quantum dots are coupled to the leads by
tunneling. QmeQ can calculate the stationary stateparticleandenergy currentsusing various approximate density matrix approaches. As for
now we have implemented the following first-order methodsPauli (classical) master equationLindblad approachRedfield approachFirst order von Neumann (1vN) approachwhich can describe the effect of Coulomb blockade. QmeQ also has two
second-order methodsSecond order von Neumann (2vN) approachReal Time Diagrammatic approachwhich can additionally address cotunneling, pair tunneling, and
broadening effects.
Package: qmesh3
Note: qmesh3 has been created as a temporary solution to allow qmesh to be installed again using pip to facilitate users access until the original qmesh updates are released.qmesh is a software package for creating high-quality meshes usingQGISandGmsh.
The meshes can be used in finite element numerical models such asTELEMAC,FluidityandThetis.
For more information please visit the projectwebsite.Development, Maintenance and Licenceqmesh was developed and is maintained byAlexandros AvdisandJon Hill.
Please see fileAUTHORS.mdfor more information.qmesh is an open-source project distributed under the GNU General Public Licence v3 (GPL v3).
The fileLICENCEcontains a complete copy of the licence.The source code is freely available to download and adapt, under the conditions stated in theLICENCEfile.
However, we follow a closed-development approach, where maintenance and development is carried out by the package originators.
We have opted for this approach to limit the resources needed for development: A larger development team necessitates larger management structures and significant CI/CD expenditure.
Larger management structures will absorb time intended for other exciting and useful research.
CI/CD expenditure could threaten our aim to offer a focused package, at no monetary costs to the users.DocumentationYou can access relevant documentation through the following avenues:Theqmesh website.Theqmesh synoptic manual.
Package: qmeshcontainers
This repository is aimed at facilitating isolated containers, in turn used to test, develop or useqmesh.The files in this repository are used to create docker containers for:Using qmesh in various platforms with minimal installation; only docker and basic python packages are required.Testing qmesh packages. The container set-up files are used in the qmesh CI/CD infrastructure.Developing qmesh. The containers include all necessary dependencies for qmesh, and provide a clean sandbox environment ideal for development.Installation & DependenciesTo use the qmesh container utility you must have docker installed on your machine. Please follow the docker installation instructions.Development, Maintenance and Licenceqmesh-containers was developed and is maintained byAlexandros AvdisandJon Hill.
Please see fileAUTHORS.mdfor more information.qmesh-containers is an open-source project distributed under the GNU General Public Licence v3 (GPL v3)
The fileLICENCEcontains a complete copy of the licence.The source code is freely available to download and adapt, under the conditions stated in theLICENCEfile.
However, we follow a closed-development approach, where maintenance and development is carried out by the package originators.
We have opted for this approach to limit the resources needed for development: A larger development team necessitates larger management structures and significant CI/CD expenditure.
Larger management structures will absorb time intended for other exciting and useful research.
CI/CD expenditure could threaten our aim to offer a focused package, at no monetary costs to the users.DocumentationYou can access relevant documentation through the following avenues:Theqmesh website.Theqmesh synoptic manual.
Package: qmflows
QMFlowsSeedocumentationfor tutorials and documentation.MotivationResearch on modern computational quantum chemistry relies on a set of computational
tools to carry out calculations. The complexity of the calculations usually requires
intercommunication between the aforementioned tools, such communication is usually done
through shell scripts that try to automate input/output actions like: launching
the computations in a cluster, reading the resulting output and feeding the relevant
numerical result to another program. Such scripts are difficult to maintain and extend,
requiring a significant programming expertise to work with them. Being then desirable a
set of automatic and extensible tools that allows to perform complex simulations in
heterogeneous hardware platforms.This library tackles the construction and efficient execution of computational chemistry workflows.
This allows computational chemists to use the emerging massively parallel compute environments in
an easy manner and focus on interpretation of scientific data rather than on tedious job submission
procedures and manual data processing.DescriptionThis library consists of a set of modules written in Python3 to
automate the following tasks:Input generation.Handle tasks dependencies (Noodles).Advanced molecular manipulation capabilities with (rdkit).Jobs failure detection and recovery.Numerical data storage (h5py).Tutorial and ExamplesA tutorial written as ajupyter-notebookis available from:tutorial-qmflows. You can
also access direclty more advancedexamples.InstallationDownload miniconda for python3:miniconda(also you can install the completeanacondaversion).Install according to:installConda.Create a new virtual environment using the following commands:conda create-nqmflowsActivate the new virtual environmentsource activate qmflowsTo exit the virtual environment typesource deactivate.Dependencies installationType in your terminal:conda activate qmflowsUsing the conda environment the following packages should be installed:installrdkitandh5pyusing conda:conda install-y-q-cconda-forgerdkit h5pyNote thatrdkitis optional for Python 3.7 and later.Package installationFinally install the package:InstallQMFlowsusing pip:
-pip install qmflowsNow you are ready to useqmflows.Notes:Once the libraries and the virtual environment are installed, you only need to typeconda activate qmflowseach time that you want to use the software.
Package: qmicroscope
Early stage Qt widget for monitoring cameras at NSLS-IIThis is a simple Qt-based widget for monitoring a camera on the network, and
performing some overdrawing of the images. It is still subject to change.It depends upon qtpy which needs PySide2 or PyQt5 in order to show anything.This currently expects a network endpoint that offers a single JPEG file, which
it will use to download from periodically. Things like the FFMPEG plugin for the
area detector and the AXIS webcams support this. Likely soon I will add support
for the MJPEG stream that is offered by some of the cameras for more efficient
data streaming from the cameras.Expected camera urls/endpoints:Endpoints that offer single JPEG files that are downloaded periodically based on frame rateMJPEG stream (URL must end withmjpgorcgi)Installation and usageAs of v0.0.2 the widget is not pip installable.To use clone the repo and run either of the 2 apps developed that use the widgetpython main.py- Shows a grid of camera feedspython monitor.py- Shows a grid of camera feeds as thumnails which can be clicked to shown on the "main" camera feed. Sort of like aMaster Controlin TV stationsPlugins APIA number of plugins have been developed to allow each instance of the QMicroscope camera to have it's own set of features as defined by the application. Plugins defined for the two implemented apps are as follows:main.pyRecord plugin (alpha testing)Allows user to record video feed into MJPG formatmonitor.pyMain camera feedCrosshair pluginDraws a crosshair with a specific color and positionOption available to always center the crosshair regardless of size of imageGrid pluginDraws a grid with specified number of rows and columnsOptions to change color, hide or show grid and selectorScale pluginDraws a scale in the x-y directionOptions to change color, location, text shown on each axis and toggle visibilityCrop pluginCrop the video feed to a specific areaPreset pluginAllows settings of other plugins to be named and savedSettings can be restored at any pointThumbnail camera feedToggle pluginAdds a checkbox to the camera feed that will pause or resume the video feedPlugin APIInstructions to write your own plugin, coming soon (checkout the plugins folder)
Package: qmk
QMK CLIA program to help users work withQMK Firmware.FeaturesInteract with your qmk_firmware tree from any locationUseqmk cloneto pull down anyone'sqmk_firmwareforkSetup your build environment withqmk setupUseqmk consoleto get debugging information from your keyboard(s)Check that your environment is correctly setup withqmk doctorIntegrates with qmk_firmware for additional functionality:qmk compileqmk infoqmk flashqmk lint...and many more!PackagesWe provide "install and go" packages for many Operating Systems.LinuxPackages for several distributions available here:https://github.com/qmk/qmk_fpmmacOSUsingHomebrew:brew install qmk/qmk/qmkWindowsDownload our custom MSYS2 installer here:https://msys.qmk.fm/Quickstartpython3 -m pip install qmkqmk setupBuildingWe follow PEP517, you can install usingbuild:Setup:python3 -m pip install buildBuild:python3 -m buildYou can read more about working with PEP517 packages in thePython Packaging User Guide.DocumentationFull documentation:https://docs.qmk.fm/#/tutorial
Package: qmk-dotty-dict
Info:Dictionary wrapper for quick access to deeply nested keys.Author:Pawel Zadrozny @pawelzny <pawel.zny@gmail.com>This is a package published by QMK (https://qmk.fm) since the current dotty-dict published to pypi has non-ASCII characters that breaks with some non-UTF8 locale settings.FeaturesSimple wrapper around python dictionary and dict like objectsTwo wrappers with the same dict are considered equalAccess to deeply nested keys with dot notation:dot['deeply.nested.key']Create, read, update and delete nested keys of any lengthExpose all dictionary methods like.get,.pop,.keysand otherAccess dicts in lists by indexdot['parents.0.first_name']key=value caching to speed up lookups and low down memory consumptionsupport for setting value in multidimensional listssupport for accessing lists with slicesInstallationpipinstalldotty-dictPackage:https://pypi.org/project/dotty-dict/Source:https://github.com/pawelzny/dotty_dictDocumentationFull documentation:http://dotty-dict.readthedocs.ioPublic API:http://dotty-dict.readthedocs.io/en/latest/api.htmlExamples and usage ideas:http://dotty-dict.readthedocs.io/en/latest/examples.htmlTODOWaiting for your feature requests ;)Quick ExampleCreate new dotty using factory function.>>>fromdotty_dictimportdotty>>>dot=dotty({'plain':{'old':{'python':'dictionary'}}})>>>dot['plain.old']{'python':'dictionary'}You can start with empty dotty>>>fromdotty_dictimportdotty>>>dot=dotty()>>>dot['very.deeply.nested.thing']='spam'>>>dotDotty(dictionary={'very':{'deeply':{'nested':{'thing':'spam'}}}},separator='.',esc_char='\\')>>>dot['very.deeply.spam']='indeed'>>>dotDotty(dictionary={'very':{'deeply':{'nested':{'thing':'spam'},'spam':'indeed'}}},separator='.',esc_char='\\')>>>deldot['very.deeply.nested']>>>dotDotty(dictionary={'very':{'deeply':{'spam':'indeed'}}},separator='.',esc_char='\\')>>>dot.get('very.not_existing.key')NoneNOTE: Using integer in dictionary keys will be treated as embedded list index.Install for developmentInstall dev dependencies$makeinstallTesting$maketestOr full tests with TOX:$maketest-allLimitationsIn some very rare cases dotty may not work properly.When nested dictionary has two keys of different type, but with the same value.
In that case dotty will return dict or list under random key with passed value.Keys in dictionary may not contain dots. If you need to use dots, please specify dotty with custom separator.Nested keys may not be bool type. Bool type keys are only supported when calling keys with type defined value (e.g. dot[True], dot[False]).
Package: qml
QML: A Python Toolkit for Quantum Machine Learning==================================================|Build Status| |doi| |doi|QML is a Python2/3-compatible toolkit for representation learning ofproperties of molecules and solids.Current list of contributors:^^^^^^^^^^^^^^^^^^^^^^^^^^^^^-  Anders S. Christensen (University of Basel)-  Felix A. Faber (University of Basel)-  Bing Huang (University of Basel)-  Lars A. Bratholm (University of Copenhagen)-  Alexandre Tkatchenko (University of Luxembourg)-  Klaus-Robert Muller (Technische Universitat Berlin/Korea University)-  O. Anatole von Lilienfeld (University of Basel)1) Citing QML:--------------Until the preprint is available from arXiv, please cite this GitHubrepository as:::AS Christensen, LA Bratholm, FA Faber, B Huang, A Tkatchenko, KR Muller, OA von Lilienfeld (2017) "QML: A Python Toolkit for Quantum Machine Learning" https://github.com/qmlcode/qml2) Get help:------------Documentation and installation instruction is found at:http://www.qmlcode.org/3) License:-----------QML is freely available under the terms of the MIT license... |Build Status| image:: https://travis-ci.org/qmlcode/qml.svg?branch=master:target: https://travis-ci.org/qmlcode/qml.. |doi| image:: https://badge.fury.io/py/qml.svg:target: https://badge.fury.io/py/qml.. |doi| image:: https://zenodo.org/badge/89045103.svg:target: https://zenodo.org/badge/latestdoi/89045103
Package: qmlearn
QMLearnQuantum Machine Learningby learning one-body reduced density matrices in the AO basis.ContributorsXuecheng Shao, Lukas Paetow, Md Rajib Khan Musa, Jessica A. Martinez B. and Michele Pavanello @PRGatRutgers University-Newark.Mark E Tuckerman @Tuckerman Research GroupatNYUSome infoCode entirely in Python leveragingPySCFandPsi4Numpyfor generating GTO integrals and DFT targets. Regressions are carried out withscikit-learnor other ML tools.
Package: qmlt
The Quantum Machine Learning Toolbox (QMLT) is aStrawberry Fieldsapplication that simplifies the optimization of variational quantum circuits. Tasks for the QMLT range from variational eigensolvers and unitary learning to supervised and unsupervised machine learning with models based on a variational circuit.FeaturesThe Quantum Machine Learning Toolbox supports:The training of user-provided variational circuitsAutomatic and numerical differentiation methods to compute gradients of circuit outputsOptimization, supervised and unsupervised learning tasksRegularization of circuit parametersLogging of training resultsMonitoring and visualization of training through matplotlib and TensorBoardSaving and restoring trained modelsParallel computation/GPU usage for TensorFlow-based modelsTo get started, please see the onlinedocumentation.InstallationInstallation of SFOpenBoson, as well as all required Python packages mentioned above, can be done using pip:$ python -m pip install qmltCode authorsMaria Schuld and Josh Izaac.If you are doing research using Strawberry Fields, please citeour whitepaperand the QMLT documentation:Nathan Killoran, Josh Izaac, Nicolás Quesada, Ville Bergholm, Matthew Amy, and Christian Weedbrook. Strawberry Fields: A Software Platform for Photonic Quantum Computing.arXiv, 2018. arXiv:1804.03159Maria Schuld and Josh Izaac. Xanadu Quantum Machine Learning Toolbox documentation.https://qmlt.readthedocs.io.SupportSource Code:https://github.com/XanaduAI/QMLTIssue Tracker:https://github.com/XanaduAI/QMLT/issuesIf you are having issues, please let us know by posting the issue on our Github issue tracker.We also have aStrawberry Fields Slack channel-
come join the discussion and chat with our Strawberry Fields team.LicenseQMLT isfreeandopen source, released under the Apache License, Version 2.0.
Package: qm-octave
QM OctaveQuantum machine's Octave sdkVersionsVersion Number |                    Description                           |1.0.0          | Initial release                                          |
1.0.3          | Bugfix release                                           |1.1.0          | Stable release                                           |1.2.0          | Release compatible with new calibration API released     || as part of QUA SDK version 1.1.5                         |