Saravanan666/pypi-org huggingface dataset:
Package: orpc
orpcOpen RPC.Installpip install orpcClient Installpip install orpc-clientProtocoloRPC requestrequest_package = 4bytes-length-byteorder-big + msgstack.dumps(request_body)request_body = {"event": "xxx", "args": [], "kwargs": {}}oRPC responseresponse_package = 4bytes-length-byteorder-big + msgstack.dumps(response_body)response_body = {"result": xx, "code": 0, "message": "xxx"}simple orpc server usageConfigsserver.listen: 0.0.0.0server.port 8392server.backlog: 8192server.buffer_size: 4096server.rfile_buffer_size: buffer_sizeserver.wfile_buffer_size: buffer_sizemax_request_size: 4194304result_packer: zenutils.packutils.RcmPackerauthentication.enableauthentication.eventauthentication.usersservicesenable-debug-serviceMore configs see pypi packagedaemon-application.servicesis a list of dict which item contains fields below:class: strargs: listkwargs: dictauthentication.usersis a username and password dict.Theauthentication.eventservice takes_protocol_instanceas a keyword parameter.Set_protocol_instance.authenticatedflag to True if login success.Theauthentication.eventservice's password parameter must be given in hash format, e.g.{SSM3}tcvAvgJqVjqC661OmZewsweDma4AOVaXruOtnCFqZrJoTllTYlNvcw==, a salted sm3 hash.Command Helptest@testdeMacBook-Pro orpc % orpc --help
Usage: orpc [OPTIONS] COMMAND [ARGS]...

Options:
  --logfmt TEXT
  --logfile TEXT
  --loglevel TEXT
  --pidfile TEXT          pidfile file path.
  --workspace TEXT        Set running folder
  --daemon / --no-daemon  Run application in background or in foreground.
  -c, --config TEXT       Config file path. Application will search config
                          file if this option is missing. Use sub-command
                          show-config-fileapaths to get the searching tactics.
  --help                  Show this message and exit.

Commands:
  restart                Restart Daemon application.
  show-config-filepaths  Print out the config searching paths.
  show-configs           Print out the final config items.
  start                  Start daemon application.
  stop                   Stop daemon application.Releasesv0.1.0First release.v0.1.2Add oRPC connection auto login support.v0.1.3Add auto_reconnect support for orpc-client.
Package: orphanage
Let child processes in Python suicide if they became orphans.InstallationpipinstallorphanageDon’t forget to put it insetup.py/requirements.txt.Usagefromorphanageimportexit_when_orphanedexit_when_orphaned()MotivationSome application server softwares (e.g.Gunicorn) work on a multiple-process
architect which we call the master-worker model. They must clean up the worker
processes if the master process is stopped, to prevent them from becoming
orphan processes.In the gevent-integration scene, the worker processes of Gunicorn poll theirppidin an user thread (a.k.a greenlet) to be orphan-aware. But the user
thread may be hanged once the master process crashed because of the blocked
writing on a pipe, the communicating channel between master process and
worker processes.We want to perform thisppidpolling in a real kernel thread. That is the
intent of this library.PrincipleThis library spawns an internal thread to poll theppidat regular
intervals (for now it is one second). Once theppidchanged, the original
parent process should be dead and the current process should be orphaned. The
internal thread will sendSIGTERMto the current process.In the plan, theprctl&SIGHUPpattern may be introduced in Linux
platforms to avoid from creating threads. For now, the only supported strategy
is theppidpolling, for being portable.AlternativesCaoEis an alternative to this library which developed by the Douban Inc. It
usesprctland a twice-forking pattern. It has a pure Python implementation
without any C extension compiling requirement. If you don’t mind to twist the
process tree, that will be a good choice too.ContributingIf you want to report bugs or request features, please feel free to open
issues onGitHub.Of course, pull requests are always welcome.
Package: orphHCA
Welcome to the OrpHCA’s web repository!For the latest documentation please see:http://orphhca.readthedocs.org/en/latest/index.htmlQuick installation guide:After downdloading the zip:Get the zip/tar archive athttp://ebbgit.uni-muenster.de/tbitardfeildel/orphhcaUncompress it (depending on the type file):
unzip orphhca*.zip
tar -xjf orphhca*.tar.bzand install orphhca:cd orphhca.git*python setup.py install –userUsing gitgit clonessh://git@ebbgit.uni-muenster.de:62246/tbitardfeildel/orphhca.gitcd orphhca.git
python setup.py install –user
Package: orphism
OrphismA text-based implementation of horizon charts, mostly inspired byCubism.js. The name is based on anoffshoot of cubismfocused on abstraction and color.Horizon charts in a single line of text allow for an incredible amount of resolution in minimal space: making it easier to find outliers and understand general trends in
numbers. Orphism is an implementation built usingRichfor advanced rendering andUnicode block characters. They also fit in remarkably well withTufte's Small Multiples.For fun -- and the joy of programming -- Orphism is implemented inHy Lang. Strategically placed__init__.pyfiles that import hy should make this completely transparent to anyone relying on the library.This is currently adevelopment build: please expect the API and design to change dramatically as I iterate.InstallationAvailable onPyPI!pip install orphismThis will install both a module and a CLI.UsageCLIThe development versions are incredibly simple: they can accept a list of numbers as arguments, or through stdin (generally a pipe).Through stdinecho 1 2 3 | orphismAs argumentsorphism $(echo 1 2 3)APIALineRenderableclass represents the rich element to render a horizon chart.import rich
from orphism.line import LineRenderable

rich.print(LineRenderable([1, 2, 3, 4]))Updates2023-12-15First public release; 0.0.1dev2 is somewhat functional.ExamplesTemperature data from weather.gov: Central Park, NY, 2023-12
Package: orquestra-cirq
orquestra-cirqWhat is it?orquestra-cirqis aZapatalibrary holding modules for integrating cirq and qsimcirq withOrquestra.InstallationEven though it's intended to be used with Orquestra,orquestra-cirqcan be also used as a Python module.
To install it, make to installorquestra-quantumfirst. Then you just need to runpip install .from the main directory.
If you want to importQSimSimulator, you have to install it with the extra dependencies by runningpip install -e .[qsim].Overvieworquestra-cirqis a Python module that exposes Cirq's and qsim's simulators as anorquestraQuantumSimulator. They can be imported with:from orquestra.integrations.cirq.simulator import CirqSimulator
from orquestra.integrations.cirq.simulator import QSimSimulatorIn addition, it interfaces with the noise models and provides converters that allow switching betweencirqcircuits and those oforquestra.The module can be used directly in Python or in anOrquestraworkflow.
For more details, see theOrquestra Core docs.For more information regarding Orquestra and resources, please refer to theOrquestra documentation.Running on GPUThe parameters to configure GPU executions are supplied toQSimSimulatorasQSimOptions. The details of these parameters can be found inqsimcirq python interface. Passinguse_gpu=Truewill enable gpu. If you want to useNVIDIA's cuStateVec, please additionally passgpu_mode=1as can be seen in the example below:from orquestra.integrations.cirq.simulator import QSimSimulator

from qsimcirq import QSimOptions

qsim_options = QSimOptions(use_gpu=True, gpu_mode=1)

sim = QSimSimulator(qsim_options=qsim_options)CuStateVecSimulatoris usingQsimSimulatorand by default is set touse_gpu=trueandgpu_mode=1. Below is an example of importingCuStateVecSimulator:from orquestra.integrations.custatevec.simulator import CuStateVecSimulator

sim = CuStateVecSimulator()Development and contributionYou can find the development guidelines in theorquestra-quantumrepository.
Package: orquestra-core
orquestra-coreWhat is it?Orquestra Core is a set of libraries used for quantum computing developed byZapata Computing. It's a part ofOrquestraplatform, but can be used as standalone Python libraries.
For more details please refer tothe documentationOrquestra Core comprises of the following packages:orquestra-sdkprovides tools for composing, executing, and managing computational workflows.orquestra-quantumprovidescore functionalities required to run experiments, such as the Circuit class.interfaces for implementing other Orquestra modules, such as quantum backends.basic data structures and functions for quantum computing.orquestra-optprovides:interfaces for implementing ansatzes including qaoa and qcbm.optimizers and cost functions tailored to optmisc functions such as grouping, qaoa interpolation, and estimatorsorquestra-vqaprovides:interfaces for implementing ansatzes including qaoa and qcbm.optimizers and cost functions tailored to vqamisc functions such as grouping, qaoa interpolation, and estimatorsorquestra-qiskit– integration withqiskit.orquestra-cirq– integration withCirQ.orquestra-qulacs– integration withQulacs simulator.orquestra-braket- integration withAmazon Braket Python SDKWe have temporarily removedorquestra-forest(integration withForest SDK) due to compatibility issues. It should work in most cases, if you need it feel free to install it manually.orqviz– Visualization tool for VQA landscapes.InstallationTo install the latest versions of all of the Orquestra Core packages together run:pip install orquestra-coreLibraries to interact with other frameworks (e.g. qiskit, cirq), simulators or QPU can be installed as optional dependencies, i.e.:pip install orquestra[cirq].Keep in mind, that some of the packages have extra installation options, so there might be certain features unavailable if installed this way. However, all the components of Orquestra Core can be installed separately as well, e.g.:pip install orquestra-quantum
pip install orquestra-opt[qubo]UsageFor examples and tutorials please refer tothe documentation.Bug ReportingIf you'd like to report a bug/issue please create a new issue in appropriate repository. When in doubt, feel free to create issue in this repo.ContributingPlease see ourCONTRIBUTING.mdfor more information on contributing to Orquestra Core.
Package: orquestra-forest
orquestra-forestorquestra-forestis aZapatalibrary holding modules for integrating forest withOrquestra.Overvieworquestra-forestis a Python module that provides converters that allow switching between objects fromPyQuilandorquestra-quantum.
It can be used directly in Python or in anOrquestraworkflow.
For more details, see theOrquestra Pyquil integration docs.For more information regarding Orquestra and resources, please refer to theOrquestra documentation.InstallationThis repository can be installed usingpip. Clone the repository, enter the main directory of orquestra-forest, and runpip install -e .. For development install, runpip install -e '.[dev]'instead.Development and contributionYou can find the development guidelines in theorquestra-quantumrepository.
Package: orquestra-opt
orquestra-optWhat is it?orquestra-optis a library with core functionalities for optimizing cost functions developed byZapatafor ourOrquestraplatform.orquestra-optprovides:interfaces for implementing ansatzes including qaoa and qcbm.optimizers and cost functions tailored to optmisc functions such as grouping, qaoa interpolation, and estimatorsInstallationEven though it's intended to be used with Orquestra,orquestra-optcan be also used as a standalone Python module.
For a basic install, you just need to runpip install -e .from the main directory.
If you need to make use of optimizers from qiskit or solve qubo problems, then you should instead runpip install -e '.[qiskit]'orpip install -e '.[qubo]'respectively. If you need both, you should runpip install -e '.[all]'UsageHere's an example of how to use methods fromorquestra-optto solve a simple maximum cut problem.fromorquestra.opt.problemsimportMaxCutimportnetworkxasnxdeforquestra_opt_example_function()graph=nx.complete_graph(4)value,solutions=MaxCut().solve_by_exhaustive_search(graph)returnsolutionsDevelopment and ContributionYou can find the development guidelines in theorquestra-quantumrepository.
Package: orquestra-qiskit
orquestra-qiskitWhat is it?orquestra-qiskitis aZapatalibrary holding modules for integrating qiskit withOrquestra.InstallationEven though it's intended to be used with Orquestra,orquestra-qiskitcan be also used as a Python module.
Just runpip install .from the main directory.Usageorquestra-qiskitis a Python module that exposes Qiskit's simulators as anorquestraQuantumSimulator. It can be imported with:from orquestra.integrations.qiskit.simulator import QiskitSimulatorIt also exposes Qiskit's quantum backends as aQiskitBackendwhich implements theorquestra.quantum.api.backend.QuantumBackendinterface.It can be imported with:from orquestra.integrations.qiskit.backend import QiskitBackendIn addition, it also provides converters that allow switching betweenqiskitcircuits and those oforquestra.The module can be used directly in Python or in anOrquestraworkflow.
For more details, see theOrquestra Core docs.For more information regarding Orquestra and resources, please refer to theOrquestra documentation.Development and contributionYou can find the development guidelines in theorquestra-quantumrepository.
Package: orquestra-quantum
orquestra-quantumWhat is it?orquestra-quantumis a library with core functionalities for quantum computing developed byZapatafor ourOrquestraplatform.orquestra-quantumprovides:core functionalities required to run experiments, such as theCircuitclass.interfaces for implementing other Orquestra modules, such as quantum backends.basic data structures and functions for quantum computing.InstallationEven though it's intended to be used with Orquestra,orquestra-quantumcan be also used as a standalone Python module.
To install it, you just need to runpip install .from the main directory.UsageHere's an example of how to use methods fromorquestra-quantumto run a circuit locally. The program runs a circuit with a single Hadamard gate 100 times and returns the results:fromorquestra.quantum.circuitsimportH,Circuitfromorquestra.quantum.symbolic_simulatorimportSymbolicSimulatordeforquestra_quantum_example_function()circ=Circuit([H(0)])sim=SymbolicSimulator()nsamples=100measurements=sim.run_circuit_and_measure(circ,nsamples)returnmeasurements.get_counts()Development and ContributionTo install the development version, runpip install -e '.[dev]'from the main directory. (if using MacOS, you will need single quotes around the []. If using windows, or Linux, you might not need the quotes).We useGoogle-styledocstring format. If you'd like to specify types please usePEP 484type hints instead adding them to docstrings.There are codestyle-relatedGithub Actionsrunning for PRs.If you'd like to report a bug/issue please create a new issue in this repository.If you'd like to contribute, please create a pull request tomain.Running testsUnit tests for this project can be run usingmake coveragecommand from the main directory.
Alternatively you can also runpytest ..StyleWe are using automatic tools for style and type checking. In order to make sure the code is compliant with them please run:make stylefrom the main directory (this requiresdevdependencies).
Package: orquestra-qulacs
orquestra-qulacsWhat is it?orquestra-qulacsis aZapatalibrary holding modules for integrating qulacs withOrquestra.InstallationEven though it's intended to be used with Orquestra,orquestra-qulacscan be also used as a Python module.
Just runpip install .from the main directory.Usageorquestra-qulacsis a Python module that exposes Qulacs's simulators as anQuantumSimulatorcompatible withOrquestra Core framework. It can be imported with:from orquestra.integrations.qulacs.simulator import QulacsSimulatorIn addition, it also provides converters that allow switching betweenqulacscircuits and those oforquestra.The module can be used directly in Python or in anOrquestraworkflow.
For more details, see theOrquestra Qulacs integration docs.For more information regarding Orquestra and resources, please refer to theOrquestra documentation.Development and contributionYou can find the development guidelines in theorquestra-quantumrepository.
Package: orquestra-sdk
Orquestra Workflow SDKWhat is it?orquestra-sdkis a Python library for expressing and executing computational workflows locally and on theOrquestraplatform.orquestra-sdkprovides:A Python DSL to express your workflowsAn API for managing and using secrets inside workflowsAn API for executing and managing your workflowsA CLI tool for executing and managing workflowsInstallationOrquestra Workflow SDK is published to PyPI and should be installed from there viapip:pipinstall"orquestra-sdk[all]"UsagePlease refer to theOrquestra Workflow SDK docs.Bug ReportingIf you'd like to report a bug/issue please create anew issue using one of the templates.ContributingPlease see ourCONTRIBUTING.mdfor more information on contributing to Orquestra Workflow SDK.
Package: orquestra-vqa
orquestra-vqaWhat is it?orquestra-vqais a library with core functionalities for implementing variational quantum algorithms developed byZapatafor ourOrquestraplatform.orquestra-vqaprovides:interfaces for implementing ansatzes including qaoa and qcbm.optimizers and cost functions tailored to vqamisc functions such as grouping, qaoa interpolation, and estimatorsInstallationEven though it's intended to be used with Orquestra,orquestra-vqacan be also used as a Python module.
To install it you need to runpip install orquestra-vqaorpip install .from the main directory. This installation will install its dependencies:orquestra-quantum,orquestra-optandorquestra-cirq.UsageHere's an example of how to use methods fromorquestra-vqato create a cost function for qcbm and optimize it using scipy optimizer.fromorquestra.vqa.cost_function.qcbm_cost_functionimportcreate_QCBM_cost_functionfromorquestra.vqa.ansatz.qcbmimportQCBMAnsatzfromorquestra.opt.history.recorderimportrecorderfromorquestra.quantum.symbolic_simulatorimportSymbolicSimulatorfromorquestra.quantum.distributionsimportcompute_mmdfromorquestra.quantum.distributionsimportMeasurementOutcomeDistributionfromorquestra.opt.optimizers.scipy_optimizerimportScipyOptimizerimportnumpyasnptarget_distribution=MeasurementOutcomeDistribution({"0000":1.0,"0001":0.0,"0010":0.0,"0011":1.0,"0100":0.0,"0101":1.0,"0110":0.0,"0111":0.0,"1000":0.0,"1001":0.0,"1010":1.0,"1011":0.0,"1100":1.0,"1101":0.0,"1110":0.0,"1111":1.0,})deforquestra_vqa_example_function():ansatz=QCBMAnsatz(1,4,"all")backend=SymbolicSimulator()distance_measure_kwargs={"distance_measure":compute_mmd,"distance_measure_parameters":{"sigma":1},}cost_function=create_QCBM_cost_function(ansatz,backend,10,**distance_measure_kwargs,target_distribution=target_distribution)optimizer=ScipyOptimizer(method="L-BFGS-B")initial_params=np.ones(ansatz.number_of_params)/5opt_results=optimizer.minimize(cost_function,initial_params)returnopt_resultsorquestra_vqa_example_function()Development and ContributionYou can find the development guidelines in theorquestra-quantumrepository.
Package: orrey
A set of small utility scripts of MRI processing.Currently, this only contains the scriptmodelCorr, a simple command line
tool to show correlations of fMRI Model Design. At present
(version 0.0.1) this really doesn’t do more than output a static png and design
matrix, though more features are planned including calculation of the model’s
variance inflation factor (VIF) for contrasts of interest and possibly some
python ports of model optimization. But hey, release [very] early and often.
Package: orsaytrace
OrsayTraceOrsaytrace is an open source python library for flexible optical simulations in 3D dimensions.Orsaytrace makes it easy to add elements or transformations sequentially. Multiple shapes can be created using
building blocks such as spheres, prisms, parabolas and spatial transformations like rotations or translations.Data analysis are done by the means of plans, which save photons that cross them during simulation run. Plans
are powerful objects and can be instantiated as conditional plans, which guarantees that appended photons
possesses a given attribute in a certain range. A plethora of pre build functions are already implemented
for photons subset, but users can build their own
using all available photon objects.ContributingContributing is encouraged for everyone. Please be in touch if you wish to help.
Package: orsim
=============================================================
ORSim: Open Source Distributed Agent Based Simulation Library.. image::https://img.shields.io/pypi/v/orsim.svg:target:https://pypi.python.org/pypi/orsim.. image::https://readthedocs.org/projects/orsim/badge/?version=latest:target:https://orsim.readthedocs.io/en/latest/?version=latest:alt: Documentation StatusORSim is born out of a development Program called OpenRoad which is a set of libraries, tools and solvers to make research on Geo-Spatial problems Accessible.OR in ORSim can refer to the Originating Project OpenRoad. It also refers to Operations Research and the Supporting Research lab IORA.ORSim aims to be technology and platform Neutral even though the early versions are closely tied to other Open Source tech stacks such as RabbitMQ.Free software: MIT licenseDocumentation:https://orsim.readthedocs.io.Build & Upload Instructions.. code-block:: console$ commit
    $ bumpversion [patch, minor, major]
    $ make dist
    $ twine upload dist/*FeaturesTODOCreditsThis package was created with Cookiecutter_ and theaudreyr/cookiecutter-pypackage_ project template... _Cookiecutter:https://github.com/audreyr/cookiecutter.. _audreyr/cookiecutter-pypackage:https://github.com/audreyr/cookiecutter-pypackage
Package: orsj-submit
DescriptionWeb application of "submitting papers" in ORSJ.ORSJ: The Operations Research Society of Japanhttp://www.orsj.or.jp/OSmac, ubuntuUsageStep 1Copy setting file.$ orsj-submit settingStep 2Modify setting file.$ edit setting.ymlStep 3Start redis server.$ orsj-submit redisStep 4In annother shell, set environment.$ export MAIL_USER=XXX
$ export MAIL_PASSWD=XXX
$ export SECRET_KEY=XXXStep 5start web application.$ orsj-submit runUsing dockerMust modify setting.yml & start_cmd of orsj.tgz.wget-qO-<URLoforsj.tgz>|tarzxf-
dockerrun-d-uroot-p80:80-eMAIL_USER=XXX-eMAIL_PASSWD=XXX-eSECRET_KEY=XXX\-v$PWD/orsj:/orsj-w/orsj--nameorsjtsutomu7/scientific-pythonashstart_cmd
Package: orso
Orso is a shared DataFrame library forOpteryxandMabel.Orso is not intended to compete withPolarsorPandas(or your favoritebearDataFrame technology), instead it is developed as a common layer for Mabel and Opteryx.In Opteryx, Orso provides most of the database Cursor functionality.In Mabel, Orso provides the data schema and validation functionality, and the intention is to use as the WAL.Orso DataFrames are row-based, this is driven by it's initial target use-case as the WAL for Mabel and Cursor for Opteryx.Each row in an Orso Dataframe is able to be be quickly converted to a Tuple of values, a Dictionary or to a byte representation.LicenseOrso is licensed under Apache 2.0 unless explicitly indicated otherwise.StatusOrso is in beta. Beta means different things to different people, to us, being beta means:Interfaces are generally stable but may still have breaking changesUnit test are not reliable enough to capture breaks to functionalityBugs are likely to exist in edge casesCode may not be tuned for performanceAs such, we really don't recommend using Orso in critical applications.