Saravanan666/pypi-org huggingface dataset:
Package: pynsgr
pynsgrPython interface to theNeuroScience Gateway REST interface, based on the previously developedpycipres.Quick start guideCreating an account on NSGRPlease head to theNSGR web portalto create an account.
Documentation there includes the list ofavailable toolsand other information.
Atutorialis available, and so is acomplete user guide.Once you have created an account, you must create a new "application" to be able to use the REST API:login, click "Developer" > "Application management"click "create new application"fill in the form: please use the "DIRECT" authentication typeThis will create the application for you and create an application ID for you to use (see below).Using this packageInstall this package directly usingpip:pip install pynsgror from source (to get the latest development version, for example):git clone https://github.com/OpenSourceBrain/pynsgr
cd pynsgr
pip install .Update~/nsgrest.confwith your account and application information:URL=https://nsgr.sdsc.edu:8443/cipresrest/v1
USERNAME=<YOUR_USERNAME>
PASSWORD=<YOUR_PASSWORD>
APPID=<YOUR_APPID>
APPNAME=PY_EXPANSETry listing your jobsnsgr_job -lSubmit a jobnsgr_submit <directory> validate
nsgr_submit <directory> runFor more information on what the tools do, use the-hoption:nsgr_submit -h
nsgr_job -hwhere<directory>contains the zipped code to run, and the two properties files.
See theexamplefolder for an example of this and configuration files that are used.
Package: pynsist
Pynsist is a tool to build Windows installers for your Python applications. The
installers bundle Python itself, so you can distribute your application to
people who don’t have Python installed.For more information, seethe documentationandthe examples.Pynsist 2.7 requires Python 3.6 or above.
You can usePynsist 2.6on Python 3.5,
andPynsist 1.xon Python 2.7 and
Python 3.3 or above, but these versions won’t get further updates.QuickstartGet the tools. InstallNSIS, and
then install pynsist from PyPI by runningpip install pynsist.Write a config fileinstaller.cfg, like this:[Application]name=My Appversion=1.0# How to launch the app - this calls the 'main' function from the 'myapp' package:entry_point=myapp:mainicon=myapp.ico[Python]version=3.6.3[Include]# Packages from PyPI that your application requires, one per line# These must have wheels on PyPI:pypi_wheels=requests==2.18.4beautifulsoup4==4.6.0html5lib==0.999999999# To bundle packages which don't publish wheels, or to include directly wheel files# from a directory, see the docs on the config file.# Other files and folders that should be installedfiles=LICENSEdata_files/Runpynsist installer.cfgto generate your installer. Ifpynsistisn’t
found, you can usepython-mnsist installer.cfginstead.This example illustrates how to use Pynsist by itself, for simple projects.
There are other options which can make it easier to integrate as a step in
a more complex build process. See the docs for more information.
Package: pynsistfix
Pynsist is a tool to build Windows installers for your Python applications. The
installers bundle Python itself, so you can distribute your application to
people who don’t have Python installed.For more information, seethe documentationandthe examples.Pynsist 2.7 requires Python 3.6 or above.
You can usePynsist 2.6on Python 3.5,
andPynsist 1.xon Python 2.7 and
Python 3.3 or above, but these versions won’t get further updates.QuickstartGet the tools. InstallNSIS, and
then install pynsist from PyPI by runningpip install pynsist.Write a config fileinstaller.cfg, like this:[Application]name=My Appversion=1.0# How to launch the app - this calls the 'main' function from the 'myapp' package:entry_point=myapp:mainicon=myapp.ico[Python]version=3.6.3[Include]# Packages from PyPI that your application requires, one per line# These must have wheels on PyPI:pypi_wheels=requests==2.18.4beautifulsoup4==4.6.0html5lib==0.999999999# To bundle packages which don't publish wheels, or to include directly wheel files# from a directory, see the docs on the config file.# Other files and folders that should be installedfiles=LICENSEdata_files/Runpynsist installer.cfgto generate your installer. Ifpynsistisn’t
found, you can usepython-mnsist installer.cfginstead.This example illustrates how to use Pynsist by itself, for simple projects.
There are other options which can make it easier to integrate as a step in
a more complex build process. See the docs for more information.
Package: pynsive
Pronounced, “Pensive”Pynsiveis a simple plugin library that uses thesys.meta_pathlist
along with custom finder and loader definitions to hook into the Python
import process. This means that when directores or other plugin search targets
are added in Pynsive, future import statements will now search the newly added
path for plugin modules and classes.For more information on the import process hooks, please see:Python 3 Import ProcessPEP-302Getting StartedBelow are some helpful documents to help get you started in using Pynsive.[Blog Post] Getting started with Pynsive by Chad LungPynsive at Readthedocs
Package: pynsm
Non-negative similarity matching in PyTorchThis is an implementation of non-negative similarity matching (NSM) for PyTorch focusing on ease of use, extensibility, and speed.Getting startedYou can install the package from PyPI by usingpipinstallpynsmUser documentationFind examples, how-to guides, tutorials, and full API reference information on Readthedocs,https://pynsm.readthedocs.io/.Questions or issues?Please contact us by opening an issue on GitHub.Instructions for developersDeveloper installationIt is strongly recommended to use a virtual environment when working with this code. The installation instructions below include the commands for creating the virtual environment, using eitherconda(recommended) orvenv.Developer install usingcondaIf you do not havecondainstalled, the easiest way to get started is withMiniconda. Follow the installation instructions for your system.Next, create a new environment and install for CPU usingcondaenvcreate-fenvironment.ymlFor using an NVIDIA GPU runcondaenvcreate-fenvironment-cuda.ymlNote that most Macs do not have an NVIDIA GPU, so you should use the first invocation shown above. If your Mac uses the newer Apple chips, you may be able to usedevice=mpsto get GPU acceleration (the installation procedure remains unchanged).The commands above automatically perform an "editable" install—this means that changes made to the code will automatically take effect without having to reinstall the package.Developer install usingvenvBefore creating a new virtual environment, it is best to ensure you're not using the system version of Python—this is often badly out of date. Some options for doing this are outlined inThe Hitchhiker's Guide to Python, although many options exist. One advantage of usingcondais that this is done for you.Once you have a proper Python install, create a new virtual environment by running the following command in a terminal inside the main folder of the repository:python-mvenvenvThis creates a subfolder calledenvcontaining the files for the virtual environment. Next we need to activate the environment and install the package with its pre-requisites:sourceenv/bin/activate
pipinstall-e".[dev]"The-emarks this as an "editable" install—this means that changes made to the code will automatically take effect without having to reinstall the package.Example usageSee the notebooks in theexamplesfolder to get started with the package. The information onreadthedocsmay also prove useful.
Package: pynsn
PyNSNCreating Non-Symbolic Number DisplaysOliver Lindemann (lindemann@cognitive-psychology.eu)Project homepage:https://github.com/lindemann09/PyNSNDependenciesPython 3 (>=3.8)numpy (>=1.20)scipy (>=1.0)Pillow (>=5.0)svgwrite (>=1.4)Optional requirementsAdditional Python packages, which are optional and required only for
some features:matplotlib (>=3.2)pygame (>=1.9)expyriment (>=0.9)PyQT5 (>=5.14)Installing viapippython3 -m pip install pynsnImage formatsBy default, PyNSN is able to writeSVGorPillowimages.
To generatePygameorMatplotlibimages or stimuli
forExpyriment, please install the respective
packages.Examplesmaking arrays: manually creating object arrays and exporting picture file
(binder)random arrays: Creating random dot arrays
(binder)matching visual featuresdata base, sequencesEuro flag example: using pictures as objects in array
(binder)RUN GUI tool (beta version)Note:Using the PyNSN-GUI requires the installation ofPyQt5python3 -m pynsnor if installed correctly:pynsn
Package: pynso
OverviewA Python client library for Cisco NSO (previously tail-f)InstallationTo install use pip:$ pip install pynsoOr clone the repo:$ git clone https://github.com/DimensionDataCBUSydney/pynso.git
$ python setup.py installUsagefrompprintimportpprintfrompynso.clientimportNSOClientfrompynso.datastoresimportDatastoreType# Setup a clientclient=NSOClient('10.123.92.12','admin','admin')# Get information about the APIprint('Getting API version number')pprint(client.info()['version'])# Get the information about the running datastoreprint('Getting the contents of the running datastore')pprint(client.get_datastore(DatastoreType.RUNNING))
Package: pynso-restconf
A Python client library for Cisco NSO (previously tail-f)InstallationTo install use pip:$ pip install pynso-restconfOr clone the repo:$ git clone https://github.com/workfloworchestrator/pynso-restconf.git
$ pip install flit
$ flit install --deps develop --symlinkUsagefrompprintimportpprintfrompynsoimportNSOClient# Setup a clientclient=NSOClient('10.123.92.12','admin','admin')# Get information about the APIprint('Getting API version number')pprint(client.info())# Get the information about the running datastoreprint('Getting information about the running datastore')pprint(client.get_datastore("running"))
Package: pynspect
WarningAlthough production code is based on this library, it should still be considered
as work in progress.IntroductionPython library for filtering, querying or inspecting almost arbitrary data
structures.This README file is work in progress, for more information please consult source
code and unit tests.FeaturesCurrently the package contains following features:pynspect.jpathModule for parsingJPathsand setting or retrieving values on givenJPathwithin data structures.pynspect.lexerModule encapsulating ofPLYlexical analyzer
for internal filtering and query language grammar.pynspect.gparserModule encapsulating ofPLYparser for internal
filtering and query language grammar.pynspect.rulesModule containing object representations of internal filtering and query
language grammar.pynspect.traversersModule containing tools for traversing and processing rule trees.pynspect.compilersModule containing tools for compiling rule trees into different structures.pynspect.filtersModule providing high-level tools for data inspection based on internal filtering
and query grammar.CopyrightCopyright (C) since 2016 CESNET, z.s.p.o (http://www.ces.net/)Copyright (C) since 2016 Jan Mach <honza.mach.ml@gmail.com>Use of this package is governed by the MIT license, see LICENSE file.ChangelogVersion 0.22Released 2022-11-02Allowed adding files from previous versions using Gitlab CI/CD.Version 0.21Released 2022-06-28Dropped support for Python 3.6.Fixed deprecation warnings for Python 3.7+ regardingcollections.abc.Added a config file for GitLab CI/CD.Updated the repository information.Updated packages versions.
Package: pynspector
pynspectorThehuman-friendlypython objects inspectorWhat is pynspector?Pynspector is a library for easily inspecting python objects. It wraps the "inspect" library and adds some extra
features to make it more powerful and easy to use.I've been playing withinspectlibrary some time, and after cracking my brain I just decided to start this new project.But that's not the only thing it does by default (wrapping theinspectlibrary) it also adds support for being able
to parse docstrings and return all specifications defined for a function.
Package: pyntc
PyntcOverviewA Python library that is a SDK to simplify common workflows for Network Devices.DocumentationFull web-based HTML documentation for this library can be found over on thePyntc Docswebsite:User Guide- Overview, Using the library, Getting Started.Administrator Guide- How to Install, Configure, Upgrade, or Uninstall the library.Developer Guide- Extending the library, Code Reference, Contribution Guide.Release Notes / Changelog.Frequently Asked Questions.Contributing to the DocsAll the Markdown source for the library documentation can be found under thedocsfolder in this repository. For simple edits, a Markdown capable editor is sufficient - clone the repository and edit away.If you need to view the fully generated documentation site, you can build it withmkdocs. A container hosting the docs will be started using the invoke commands (details in theDevelopment Environment Guide) onhttp://localhost:8001. As your changes are saved, the live docs will be automatically reloaded.Any PRs with fixes or improvements are very welcome!QuestionsFor any questions or comments, please check theFAQfirst. Feel free to also swing by theNetwork to Code Slack(channel#networktocode), sign uphereif you don't have an account.
Package: pyntch
Pyntch is a static code analyzer for Python. It detects possible runtime
errors before actually running a code. Pyntch examines a source
code statically and infers all possible types of variables,
attributes, function arguments, and return values of each function
or method. Then it detects possible errors caused by type
mismatch, attribute not found, or other types of exceptions raised
from each function. Unlike other Python code checkers (such as
Pychecker or Pyflakes), Pyntch does not address style issues.
Package: pyntcloud
pyntcloudis a Python3library for working with 3D point clouds leveraging the power of the Python scientific stack.Examples(We encourage you to try out the examples by launchingBinder.)DocumentationInstallationcondainstallpyntcloud-cconda-forgeOr:pipinstallpyntcloudQuick OverviewYou can access most of pyntcloud’s functionality from its core class: PyntCloud.With PyntCloud you can perform complex 3D processing operations with minimum lines of
code. For example you can:Load a PLY point cloud from disk.Add 3 new scalar fields by converting RGB to HSV.Build a grid of voxels from the point cloud.Build a new point cloud keeping only the nearest point to each occupied voxel center.Save the new point cloud in numpy’s NPZ format.With the following concise code:frompyntcloudimportPyntCloudcloud=PyntCloud.from_file("some_file.ply")cloud.add_scalar_field("hsv")voxelgrid_id=cloud.add_structure("voxelgrid",n_x=32,n_y=32,n_z=32)new_cloud=cloud.get_sample("voxelgrid_nearest",voxelgrid_id=voxelgrid_id,as_PyntCloud=True)new_cloud.to_file("out_file.npz")Integration with other librariespyntcloud offers seamless integration with other 3D processing libraries.You can create / convert PyntCloud instances from / to many 3D processing libraries using thefrom_instance/to_instancemethods:Open3Dimportopen3daso3dfrompyntcloudimportPyntCloud# FROM Open3Doriginal_triangle_mesh=o3d.io.read_triangle_mesh("diamond.ply")cloud=PyntCloud.from_instance("open3d",original_triangle_mesh)# TO Open3Dcloud=PyntCloud.from_file("diamond.ply")converted_triangle_mesh=cloud.to_instance("open3d",mesh=True)# mesh=True by defaultPyVistaimportpyvistaaspvfrompyntcloudimportPyntCloud# FROM PyVistaoriginal_point_cloud=pv.read("diamond.ply")cloud=PyntCloud.from_instance("pyvista",original_point_cloud)# TO PyVistacloud=PyntCloud.from_file("diamond.ply")converted_triangle_mesh=cloud.to_instance("pyvista",mesh=True)
Package: pynt-contrib
A collection of common pynt tasksRaghunandan RaoContributors/ContributingIvan Ven OsdelIf you want to make changes the repo is athttps://github.com/rags/pynt-contrib. You will needpytestand mock to run the tests$./btIt will be great if you can raise apull
requestonce
you are done.If you find any bugs or need new features please raise a ticket in the
`issues section <https://github.com/rags/pynt-contrib/issues>`__ of the
github repo.Licensepynt-contrib is licensed under aMIT
licenseChanges0.2 - 09/02/2015Some common pynt tasks, tools
Package: pyntegrals
PyntegralsPyntegrals is a simple library for computing a definite integral over a 2D polygonal domain.At the moment the only function of the library isintegrate_over_polygon, which perform the calculation.It is possible that in the future other functions will be added.pyntegrals.integralspyntegrals.integrals.integrate_over_polygonpyntegrals.integrals.integrate_over_polygon(function: Union[Callable, float], polygon: list[tuple]) -> floatComputes the integral of a two-variables function over a polygonal domain in R2.Returns the definite integral offunction(x, y)= f(x, y) overpolygon= Σ, that is:∫Σf(x, y) dΣParametersfunction: Union[Callable, float]A scalar or a Python function with two arguments: the first one is x and the
    second one is y in R2space.polygon: list[tuple]A polygon defined by a list of its vertices. Each vertex is
    described by a tuple containing its coordinated in the form (x, y).
    The vertices in the list must be in clockwise or
    counter-clockwise order (it makes no difference)Returnsr: floatThe definite integral offunction(x, y)overpolygon.ExampleCompute the integral of x2+ y over a 50 x 40 rectangle Σ with a
vertex in (0, 0) and another in (50, 40)∫Σ(x2+ y) dΣfrom pyntegrals.integrals import integrate_over_polygon
polygon = [(0, 0), (50, 0), (50, 40), (0, 40)]
rho = lambda x, y: x**2 + y
mass = integrate_over_polygon(function=rho, polygon=polygon)
>>> 1706666.666666667For more example see examples/example.py
Package: pyntegrant
While IoC and dependency injection aren’t as much of a thing in Python
as they are in many compiled languages, that doesn’t mean they don’t
have their place in decomposition of a complex architecture and
assembly of systems; there’s something very nice about completely
separated modules that communicate based on shared abstractions which
are then assembled by a separate piece of code into running systems.But despite the many microframeworks for dependency injection that
I’ve seen for Python, I haven’t seen any that seem as elegant as theIntegrantmicroframework
enjoyed by Clojurists.  This is an effort to remedy that, but as I
haven’t loads of time to invest in it, it’s a pale shadow of the
original.Still, shadows have power.  It can already do the initialization portion
of a system, although items like prep, halt, suspend, etc remain,
as do derived keys and refsets.  But one thing at a time.As of the 0.1.0 prerelease, all that works is the basic assembly of a
system from a configuration (in Python, JSON, or TOML) and it’s
surprising how effective that actually can be.  Unfortunately error
messages are lightly cryptic and documentation is nonexistent, but
more will be coming shortly.
Package: pyntegrate
PyntegrateMirrored onGitLabWhat are C Extensions in Python?C Extensions allow for native C/C++ code (with a small penalty) to be used within Python. The documentation can be foundhere,here, andhere.Why use C Extensions?The main benefit of using C Extensions would be the speed of C, with a slight penalty, when translating between C and Python.How to Build or InstallTo build from source, usepython3 setup.py build.To install from source, usepython3 setup.py install.To install using pip, usepip install pyntegrateInstallationgit clone https://github.com/rtsfred3/pyntegrate.gitpython3 setup.py installLicenseCopyright 2021 Ryan Fredrickson

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

   http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
Package: pyntegrity
PyntegrityPyntegrity is Python package that helps you check a file integrity.Supported Python versionsTested on:Python 3.7Python 3.8Python 3.9Python 3.10DocumentationLatest version is 1.2.0InstallationTo install Pyntegrity use pip:pip install pyntegritySupported featuresChecksum algorithmsThe supported checksum algorithms are:md5sha256sha512Target files sizesFor now mostly small files since it loads the whole file in memory to calculate its checksumHow to useIn your program import the classIntegrityValidator:frompyntegrity.coreimportIntegrityValidatorInitialize it with the target file and the expected checksum
(it automatically detects which checksum algorithm to use), example:obj=IntegrityValidator(str_path="my_file.txt",checksum_str="my_checksum",)Then use the functionvalidate_file_integrityto check the file integrity,
it returnsTrueif the target file checksum equal the one passed in the class constructor,
if else it returnsFalse, example:status=obj.validate_file_integrity()Side noteRight now it will open anyfile in text mode (even binary).Future featuresSupport more file type modesSupport more checksum algorithmsSupport for big filesLicenseGPL-3.0 seelicense content
Package: pyntendo
Pyntendopip install pyntendoA Nintendo Entertainment System (NES) emulator written in Python and Cython.All core components are implemented, including audio, and the most important mappers.Performant (runs at 60fps on modern machines)Fully headless operation is supportedNumPy-based input/outputVery limited external dependencies (really just NumPy)SeeHeadless Demofor a minimal examplePure Python/Cython, fully compatible with CPython (>3.6)Although most games I have tested seem to run without issues, there are still some open issues that would improve
performance and accuracy and probably make some hard to emulate games work or work better.Several popular(ish) mappers are not implemented (along with lots of less popular ones)Some fine timing is not quite right, which might cause issues in some sensitive gamesThis is not a cycle-accurate emulator, so sub-instruction level timing is not correctly emulated, and some parts of
other systems are not emulated in a cycle-correct waySee mydevnotesfor known issues and planned workI would like to give huge thanks and kudos to everyone who contributed to the amazingNESDev Wikiand all the other fantastic sources (most listed in the code), tests and forums for NES emulator development and 6502
progamming.  Without these resources it would have been impossible to develop this emulator.UsageBasic usage:from nes import NES
nes = NES("my_rom.nes")
nes.run()ScreenshotsHere are some screenshots of the emulator in action: Super Mario Brothers, Donkey Kong, MegaManDependenciesDepends on the following libraries for key functionality:numpy (optional?)headless operation(possibly also required by pygame surfarray, used in rendering)pygame (optional)timingrenderinginput(without pygame, only headless operation is possible)pyaudio (optional)audio playingsync to audiopyopengl (optional)OpenGL rendering(not essential; can use SDL rendering via pygame)LicenseDistributed under the MIT License (seehere)