Saravanan666/pypi-org huggingface dataset:
Package: pymonoprice
StatuspymonopricePython3 interface implementation for Monoprice 6 zone amplifierNotesThis is for use withHome-AssistantUsagefrompymonopriceimportget_monopricemonoprice=get_monoprice('/dev/ttyUSB0')# Valid zones are 11-16 for main monoprice amplifierzone_status=monoprice.zone_status(11)# Print zone statusprint('Zone Number ={}'.format(zone_status.zone))print('Power is{}'.format('On'ifzone_status.powerelse'Off'))print('Mute is{}'.format('On'ifzone_status.muteelse'Off'))print('Public Anouncement Mode is{}'.format('On'ifzone_status.paelse'Off'))print('Do Not Disturb Mode is{}'.format('On'ifzone_status.do_not_disturbelse'Off'))print('Volume ={}'.format(zone_status.volume))print('Treble ={}'.format(zone_status.treble))print('Bass ={}'.format(zone_status.bass))print('Balance ={}'.format(zone_status.balance))print('Source ={}'.format(zone_status.source))print('Keypad is{}'.format('connected'ifzone_status.keypadelse'disconnected'))# Turn off zone #11monoprice.set_power(11,False)# Mute zone #12monoprice.set_mute(12,True)# Set volume for zone #13monoprice.set_volume(13,15)# Set source 1 for zone #14monoprice.set_source(14,1)# Set treble for zone #15monoprice.set_treble(15,10)# Set bass for zone #16monoprice.set_bass(16,7)# Set balance for zone #11monoprice.set_balance(11,3)# Restore zone #11 to it's original statemonoprice.restore_zone(zone_status)Usage with asyncioWithasyncioflavor all methods of Monoprice object are coroutines.importasynciofrompymonopriceimportget_async_monopriceasyncdefmain(loop):monoprice=awaitget_async_monoprice('/dev/ttyUSB0',loop)zone_status=awaitmonoprice.zone_status(11)ifzone_status.power:awaitmonoprice.set_power(zone_status.zone,False)loop=asyncio.get_event_loop()loop.run_until_complete(main(loop))
Package: pyMonteCarlo
pyMonteCarlopyMonteCarlois a programming interface to run identical simulations using
different Monte Carlo programs. The interface was designed to have common input
and output that are independent of any Monte Carlo code. This allows users to
combine the advantages of different codes and to compare the effect of different
physical models without manually creating and running new simulations for each
Monte Carlo program. The analysis of the results is also simplified by the
common output format where results are expressed in the same units.pyMonteCarlois currently under development.DocumentationThedocumentationcontains the
installation instructions, tutorials, supported Monte Carlo programs and API.LicensepyMonteCarlois licensed under Apache Software License 2.0.CitationPinard, P., Demers, H., Gauvin, R., & Richter, S. (2013). pyMonteCarlo: A Common Programming Interface for Running Identical Simulations using Different Monte Carlo Programs. Microscopy and Microanalysis, 19(S2), 822-823. doi:10.1017/S1431927613006107@article{pinard2013,author={Pinard, P.T. and Demers, H. and Gauvin, R. and Richter, S.},title={pyMonteCarlo: A Common Programming Interface for Running Identical Simulations using Different Monte Carlo Programs},journal={Microscopy and Microanalysis},volume={19},number={S2},publisher={Cambridge University Press},year={2013},pages={822–823},DOI={10.1017/S1431927613006107}}Build statusPackageCI buildCode coveragepymontecarlopymontecarlo-guipymontecarlo-casino2pymontecarlo-penepmapypenelopetoolsContributorsPhilippe T. Pinard(London, United Kingdom)Hendrix Demers(Montreal, Canada)Raynald Gauvin(McGill University, Montreal, Canada)Silvia Richter(RWTH Aachen University, Aachen, Germany)Release notes1.1.0Fix issues with newer releases of dependenciesCopyrightsCopyright (c) 2011 - 2016/06, Silvia Richter and Philippe PinardCopyright (c) 2016/06 - , Philippe Pinard
Package: pymonzo
pymonzoModern Python API client forMonzopublicAPI.Works on Python 3.8+Fully type annotatedExplicitly defined and validated API schemas (viapydantic)Easy authentication with automatic access token refreshingSensible defaults - don't specify account / pot ID if you only have one activeThis project is not officially affiliated withMonzo.InstallationFromPyPI(ideally, inside avirtualenv):$python-mpipinstallpymonzoQuick startHere's an example of whatpymonzocan do:>>>frompymonzoimportMonzoAPI>>>monzo_api=MonzoAPI()>>>accounts=monzo_api.accounts.list()>>>len(accounts)2>>># Only one active account, so we don't need to pass it explicitly>>>monzo_api.balance.get()MonzoBalance(balance=75000, total_balance=95012, currency='GBP', spend_today=0, balance_including_flexible_savings=95012, local_currency='', local_exchange_rate=0, local_spend=[])>>>frompymonzo.utilsimportn_days_ago>>>transactions=monzo_api.transactions.list(since=n_days_ago(5))>>>len(transactions)8AuthorsDeveloped and maintained byPaweł Adamczak.Source code is available atGitHub.If you'd like to contribute, please take a look at thecontributing guide.Released underMozilla Public License 2.0.
Package: pymoo
Documentation/Paper/Installation/Usage/Citation/Contactpymoo: Multi-objective Optimization in PythonOur open-source framework pymoo offers state of the art single- and multi-objective algorithms and many more features
related to multi-objective optimization such as visualization and decision making.InstallationFirst, make sure you have a Python 3 environment installed. We recommend miniconda3 or anaconda3.The official release is always available at PyPi:pipinstall-UpymooFor the current developer version:gitclonehttps://github.com/anyoptimization/pymoocdpymoopipinstall.Since for speedup, some of the modules are also available compiled, you can double-check
if the compilation worked. When executing the command, be sure not already being in the local pymoo
directory because otherwise not the in site-packages installed version will be used.python-c"from pymoo.util.function_loader import is_compiled;print('Compiled Extensions: ', is_compiled())"UsageWe refer here to our documentation for all the details.
However, for instance, executing NSGA2:frompymoo.algorithms.moo.nsga2importNSGA2frompymoo.problemsimportget_problemfrompymoo.optimizeimportminimizefrompymoo.visualization.scatterimportScatterproblem=get_problem("zdt1")algorithm=NSGA2(pop_size=100)res=minimize(problem,algorithm,('n_gen',200),seed=1,verbose=True)plot=Scatter()plot.add(problem.pareto_front(),plot_type="line",color="black",alpha=0.7)plot.add(res.F,color="red")plot.show()A representative run of NSGA2 looks as follows:CitationIf you have used our framework for research purposes, you can cite our publication by:J. Blank and K. Deb, pymoo: Multi-Objective Optimization in Python, in IEEE Access, vol. 8, pp. 89497-89509, 2020, doi: 10.1109/ACCESS.2020.2990567BibTex:@ARTICLE{pymoo,
    author={J. {Blank} and K. {Deb}},
    journal={IEEE Access},
    title={pymoo: Multi-Objective Optimization in Python},
    year={2020},
    volume={8},
    number={},
    pages={89497-89509},
}ContactFeel free to contact me if you have any questions:Julian Blank(blankjul [at] msu.edu)Michigan State UniversityComputational Optimization and Innovation Laboratory (COIN)East Lansing, MI 48824, USA
Package: py-moodle-quiz-parser
py-moodle-quiz-parserpy-moodle-quiz-parseris simple Python library for fast parsing HTML documents with Moodle quizes.fromMoodleQuizParser.QuizParserimportQuizParserparser=QuizParser()file=open("moodle_quiz.html",encoding="utf-8")parsed_data=parser.parse_html(file.read(),as_dict=True)How to use this libraryTo usepy-moodle-quiz-parseryou should simply install it using PIPpipinstallpy-moodle-quiz-parser
Package: pymoodo
pymoodoPython client to communicate with Moodo API.How to useimportasyncioimportsysimportloggingfrompymoodoimportControllerasyncdefmain(email,password):controller=awaitController(email,password)# Turn on MoodoBoxforidincontroller.boxes:controller.boxes[id].turn_on(100)# controller.boxes[id].set_fan_speed(21)controller.boxes[id].slots[0].set_fan_speed(100)controller.boxes[id].slots[1].set_fan_speed(100)controller.boxes[id].slots[2].set_fan_speed(100)controller.boxes[id].slots[3].set_fan_speed(100)forslot_idincontroller.boxes[id].slots:slot=controller.boxes[id].slots[slot_id]slot.turn_on()slot.set_fan_speed(100)forslot_idincontroller.boxes[id].slots:slot=controller.boxes[id].slots[slot_id]slot.turn_off()controller.boxes[id].turn_off()whileTrue:passif__name__=="__main__":iflen(sys.argv)==3:asyncio.get_event_loop().run_until_complete(main(sys.argv[1],sys.argv[2]))else:print('Run example with arguments <email> <password>')
Package: pymoof
pymoofConnect to your Vanmoof S3 and X3 through bluetooth.InstallationInstall python 3.7+, then use pip to install pymoof.pip install pymoofUsagepymoof was tested to work on MacOS 12.1, Ubuntu 20.04.3 LTS, and a Raspberry Pi 3 b+ running Raspberry Pi OS (32-bit) / 2021-10-30.frompymoof.clients.sx3importSX3Clientimportbleak...device=...encryption_key=...user_key_id=...asyncwithbleak.BleakClient(device)asbleak_client:client=SX3Client(bleak_client,encryption_key,user_key_id)awaitclient.authenticate()You must have an instantiatedbleakclient that is connected to the bike. Seepymoof.tools.discover_biketo determine which device is your bike andpymoof.tools.retrieve_encryption_keyto connect to Vanmoof servers to get your encryption key.Seeexample.pyfor additional usage.ContributingContributions are welcome and encouraged! Every bit helps and credit will be given.Ways you can help:Reporting BugsYou can report bugs through the github issue tracker:https://github.com/quantsini/pymoof/issuesUseful information to include when reporting bugs:Version of pymoofThe operating system where pymoof was usedWhat Vanmoof bike was usedDetailed steps on reproducing an issueHelp with reverse engineeringVanmoof bikes communicate through Bluetooth Low Energy. I've tried my best to get all the BLE GATT UUIDs, however, some reverse engineering is needed to figure out what the payloads represent. I suggest using a packet sniffer likewiresharkto analyze data from the official Vanmoof app and the bike.Writing DocumentationGood documentation is always good!Getting Started with DevelopmentYou want to contribute? Awesome! Here are some steps to get you up and running.
This project usesPoetryfor package and dependency management andtoxfor tests.Create a fork of thepymoofgithub repo.Clone it locally:git clone git@github.com:<your username>/pymoof.gitGet thelatest version of poetry, a package and dependency management tool.Install dependenciespoetry installActivate your shell. This should put you in a virtualenv that allows you to run tests.poetry shellYou should now be able to run tests and make modifications. You can run tests by running tox under poetrypoetry run toxGo forth and make great changes!
Package: pymoog
pymoogpymoogis a python3 wrapper for running the LTE spectrum synthesis part of the codeMOOGwritten by Chris Sneden.
It wraps up the (a bit) teidous steps for generating a synthetic spectra into four python commands, while retaining the functions provided by MOOG.
Besides, it also provides some other functions for analysing the MOOG result, mainly contribution function and fitting stellar parameters.Here you can:generate a synthetic spectraalter the stellar parameters for the spectra, such as $T_\mathrm{eff}$, metallicity, abundance ratios or resolution.determine some stellar parameters, such as microturbulance velocity or abundance ratios.without considering:where to grab stellar atmosphere models and line listshow to interpolate the models.Documentation (including the installation) is available inhere.AcknowledgementThis package has made use of the VALD database, operated at Uppsala University, the Institute of Astronomy RAS in Moscow, and the University of Vienna.
Package: pymoon
pymoonPython package contains multiple method to handle files and directories.Free software: MIT licenseDocumentation:https://pymoon.readthedocs.io.FeaturesTODOSearch for text in a file or across multiple filesCreate, update, move, and rename files and folders (In progress)Search the Web and download online contentCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2019-11-04)First release on PyPI.
Package: pymoos
python-moosPython bindings for MOOS.CIstatusconda.recipepip buildscibuildwheelInstall from PyPIpython -m pip install pymoosBuild InstructionsTo compile pymoos you will needMOOScompiled and installed as well as Python and pip.Clone the repository:git clone https://github.com/russkel/python-moos.git python-moosBuild and install pymoos:cd python-moos
python -m pip install .
Package: pymoose
InstallationThis repository is sufficient for using MOOSE as a python module. We provide python package viapip.$ pip install pymoose --userTo installnightlybuild:$ pip install pymoose --user --pre --upgrdeHave a look at examples, tutorials and demo herehttps://github.com/BhallaLab/moose-examples.BuildTo buildpymoose, follow instructions given here athttps://github.com/BhallaLab/moose-core/blob/master/INSTALL.mdMOOSEMOOSE is the Multiscale Object-Oriented Simulation Environment. It is designed
to simulate neural systems ranging from subcellular components and biochemical
reactions to complex models of single neurons, circuits, and large networks.
MOOSE can operate at many levels of detail, from stochastic chemical
computations, to multicompartment single-neuron models, to spiking neuron
network models.
MOOSE is multiscale: It can do all these calculations together. For example
it handles interactions seamlessly between electrical and chemical signaling.
MOOSE is object-oriented. Biological concepts are mapped into classes, and
a model is built by creating instances of these classes and connecting them
by messages. MOOSE also has classes whose job is to take over difficult
computations in a certain domain, and do them fast. There are such solver
classes for stochastic and deterministic chemistry, for diffusion, and for
multicompartment neuronal models.
MOOSE is a simulation environment, not just a numerical engine: It provides
data representations and solvers (of course!), but also a scripting interface
with Python, graphical displays with Matplotlib, PyQt, and VPython, and
support for many model formats. These include SBML, NeuroML, GENESIS kkit
and cell.p formats, HDF5 and NSDF for data writing.This is the core computational engine ofMOOSE simulator. This repository contains
C++ codebase and python interface calledpymoose. For more details about MOOSE simulator, visithttps://moose.ncbs.res.in.ABOUT VERSION 4.0.0, JalebiJalebi is an Indian sweet involving a golden twisting tube like a hyper-pretzel,
of crunchy batter soaked in sugar syrup lightly flavoured with spices and
sometimes lemon.This release has the following major changes:A major under-the-hood change to numerics for chemical calculations,
eliminating the use of 'zombie' objects for the solvers. This simplifies
and cleans up the code and object access, but doesn't alter runtimes.Another major under-the-hood change to use pybind11 as a much cleaner
way to interface the parser with the C++ numerical code.Addition of a thread-safe and faster parser based on ExprTKResurrected objects for handling simulation output saving using HDF5
format. There is an HDFWriter class, an NSDFWriter, and a new NSDFWriter2.
The latter two implement storage in NSDF, Neuronal Simulation Data Format,
Ray et al Neuroinformatics 2016. NSDF is built on HDF5 and builds up a
specification designed to ensure ready replicability as well as self-
description of model output.Multiple enhancements to rdesigneur, including vastly improved 3-D
graphics output using VPython.Various bugfixesLICENSEMOOSE is released under GPLv3.
Package: pymoose-community
IMP:This is fork ofhttps://github.com/BhallaLab/moose-corebecause the upstream
didn't respond to various PRs I submitted. To avoid confusion, I have renamed this fork
topymoose-community. I don't intend to maintain compatibility with upstream.AboutThis is core computational engine ofMOOSE simulator. This repository contains
C++ codebase and python interface calledpymoose. For more details about original MOOSE simulator, visithttps://moose.ncbs.res.in.InstallationWe provide python package viapip. Note that you may have to uninstallpymooseto install this fork.$ pip install pymoose-community --userTo installnightlybuild:$ pip install pymoose-community --user --pre --upgrdeHave a look at examples, tutorials and demo herehttps://github.com/dilawar/moose-examples.BuildTo buildpymoose, follow instructions given here athttps://github.com/dilawar/pymoose-community/blob/master/INSTALL.md
Package: pymop
pymop - Multi-Objective Optimization Problems==================================This framework provides a collection of test problems in Python. The main features are:- Most important multi-objective test function is one place- Vectorized evaluation by using numpy matrices (no for loops)- Gradients and Hessian matrices are available through automatic differentiation- Easily new problems can be created using custom classes or functionsHere, you can find a detailed documentation and information about the framework:https://www.egr.msu.edu/coinlab/blankjul/pymop/.. image:: https://gitlab.msu.edu/blankjul/pymop/badges/master/pipeline.svg:alt: pipeline status:target: https://gitlab.msu.edu/blankjul/pymop/commits/masterProblems==================================In this package single- as well as multi-objective test problems areincluded:-  Single-Objective:-  Ackley-  Cantilevered Beam-  Griewank-  Himmelblau-  Knapsack-  Pressure Vessel-  Schwefel-  Sphere-  Zakharov-  G-  Multi-Objective:-  ZDT 1-6-  DTLZ 1-7-  CDTLZ-  CTP-  Carside Impact-  BNH-  Kursawe-  OSY-  TNK-  Truss 2D-  Welded Beam
Package: pymore
pymorepymoreis a collection of useful utilities for Python.This includesEqualsTester: a helper for testing the equality contract in Python (originally developed
forCirq)importpymoretester=pymore.EqualsTester()# This tests that the added elements all satisfy the equals contract# between themselves.tester.add_equality_group(MyObject("a"),MyObject("a"))# Each new addition of an equality group also tests that the elements# in this new group are not equal to those in the perviously added group.# So, for example, this would raise an `AssertionError` if it was# true that `MyObject("a")` was equal to `MyObject("b")`.tester.add_equality_group(MyObject("b"),MyObject("b"))first: Return the first element that matches a predicate, or a default.importpymoreis_even=lambdax:x%2==0# Returns the first element that is even.first=pymore.first([1,3,6,7],is_even)assertfirst==6LicenseThis package is licensed under an Apache 2.0 license. This code is derived from work in
other frameworks.  SeeLICENSEandNOTICESfor details.
Package: pymor-oldest-supported-numpy
About~~ Original Readme below of unadapted oldest-supported-numpy ~~This is a meta-package which can be used inpyproject.tomlfiles
to automatically provide as a build-time dependency the oldest version
of Numpy that supports the given Python version and platform. In case
of platforms for which Numpy has prebuilt wheels, the provided version
also has a prebuilt Numpy wheel.The reason to use the oldest available Numpy version as a build-time
dependency is because of ABI compatibility. Binaries compiled with old
Numpy versions are binary compatible with newer Numpy versions, but
not vice versa. This meta-package exists to make dealing with this
more convenient, without having to duplicate the same list manually in
all packages requiring it.In other words:[build-system]requires=["wheel","setuptools","numpy==1.13.3; python_version=='3.5'","numpy==1.13.3; python_version=='3.6'","numpy==1.14.5; python_version=='3.7'",# more numpy requirements...]can be replaced by:[build-system]requires=["wheel","setuptools","oldest-supported-numpy"]And as new Python versions are released, thepyproject.tomlfile does not
need to be updated.Q&AWhy define the Numpy pinnings using install_requires in this repository?The Numpy version pinnings are defined inside thesetup.cfgfile asinstall_requiresdependencies, rather than as build-time dependencies
insidepyproject.toml. This is deliberate, since Numpy is not actually
required to build wheels ofoldest-supported-numpy. What we need here
is to make sure that whenoldest-supported-numpyis installed into
the build environment of a package using it, Numpy gets installed too
as aruntimedependency inside the build environment.Another way to think about this is that since we only publish (universal)
wheels ofoldest-supported-numpy, the wheel contains nopyproject.toml,setup.cfg, orsetup.pycode - it only contains metadata including
dependencies which get installed by pip whenoldest-supported-numpyis
installed.Can I use this if my package requires a recent version of Numpy?In many cases, even though your package may require a version of
Numpy that is more recent than the pinned versions here, this
is often a runtime requirement, i.e. for running (rather than
building) your package. In many cases, unless you use recent
features of the Numpy C API, you will still be able to build your
package with an older version of Numpy and therefore you will still
be able to useoldest-supported-numpy. You can still impose a
more recent Numpy requirement ininstall_requiresWhat about having a catchier name for this package?The current name is not very catchy as package names go, but it
is very descriptive. This package is only meant to be used inpyproject.tomlfiles for defining build-time dependencies,
so it’s more important to have a descriptive than a catchy name!What if I think that one of the pinnings is wrong or out of date?Please feel free toopen an issueor a pull request if you think something is wrong or could be improved!
Package: pymorph
SummaryThis image morphology toolbox implements the basic binary and greyscale
morphology operations, working with numpy arrays representing images.This is a pure Python package which is the companion package
to the book “Hands-on Morphological Image Processing.” It has been
updated to work with numpy and the names and interfaces have been
Pythonised.Includes basic operations such as
- erode
- dilate
- open
- tophat opening
- watershed
- …LinksWebsite:http://luispedro.org/software/pymorphAPI Documentation:http://packages.python.org/pymorph/Mailing List: Use thepythonvision mailing listfor questions, bug
submissions, etc.InstallOne ofpip install pymorph
easy_install pymorphor download and runpython setup.py installSee AlsoThe packagemahotasimplements some
of the same functions in C++ and, so, it is much faster (even if it is not as
feature-full).Status & Future PlansThe package is actively maintained and any reported bug will be fixed fast.Currently there areno known bugs(although testing coverage is not complete).When all functions have a corresponding unit test and the whole of the
API documentation has been re-written into numpy rst format, I will release
version 1.0.Authors, Copyright & LicenseCopyright 2003, Roberto A. Lotufo, UNICAMP-University of Campinas;
CenPRA-Renato Archer Research Center, Rubens C. Machado.Copyright 2008-2010, Luis Pedro Coelho <lpc@cmu.edu>The original package was written by RAF and RCM. Starting June 2008 (with
release 0.88), LPC updated the code to run on the more modern numpy base and
started fixing bugs and maintaining the package.Pymorph is ruled by theBSD License Terms:
Package: pymorphapi
Morpheus Python ModuleInstallationpip install pymorphapiUsageReference athttps://bertramdev.github.io/morpheus-apidocimport pymorphapi

# import and validate/refresh bearer token
bearer_token = pymorphapi.import_token_from_disk(BASE_URI, LOCAL_PATH + TOKEN_FILE, VERIFY_SSL)

# sample get
results = pymorphapi.invoke_api(
    BASE_URI + "/api/roles/?max=1000",
    "Bearer " + bearer_token,
    "get",
    None,
    VERIFY_SSL
)

# sample post
body = {}
body.update({"group": {"name": current_student}})
pymorphapi.invoke_api(
    BASE_URI + "/api/accounts/" + str(new_tenant_id) + "/groups/",
    "Bearer " + bearer_token,
    "post",
    body,
    VERIFY_SSL
)