Saravanan666/pypi-org huggingface dataset:
Package: jvm
jvmPython bridge for the Java Virtual Machine.Overviewjvmis a bridge between Python and JVM, allowing these to intercommunicate.It is an effort to allow Python programs full access to Java class libraries.PyPI record.Documentation.jvmpackage is closely based on thejniPython package.What is jvm:jvmis an effort to allow Python programs full access to Java class libraries.This is achieved not through re-implementing Python, as Jython has done, but rather
through interfacing at the native level in both virtual machines.Eventually, it should be possible to replace Java with python in many, though not all,
situations. JSP, Servlets, RMI servers and IDE plugins are good candidates.Once this integration is achieved, a second phase will be started to separate the Java
logic from the Python logic, eventually allowing the bridging technology to be used
in other environments, I.E. Ruby, Perl, COM, etc …Known Bugs/Limitations :Java classes outside of a package (in the <default>) cannot be imported.Because of lack of JVM support, you cannot shutdown the JVM and then restart it.Some methods rely on the “current” class/caller. Since calls coming directly from
python code do not have a current class, these methods do not work. The User Manual
lists all the known methods like that.InstallationPrerequisites:Python 3.7 or higherhttps://www.python.org/3.7 with Java 8 is a primary test environment.pip and setuptoolshttps://pypi.org/project/pip/https://pypi.org/project/setuptools/To install run:python -m pip install --upgrade jvmDevelopmentPrerequisites:Development is strictly based ontox. To install it run:python -m pip install --upgrade toxVisitdevelopment page.Installation from sources:clone the sources:git clonehttps://github.com/karpierz/jvm.gitjvmand run:python -m pip install ./jvmor on development mode:python -m pip install --editable ./jvmLicenseCopyright (c) 2004-2022 Adam KarpierzLicensed under CC BY-NC-ND 4.0Licensed under proprietary LicensePlease refer to the accompanying LICENSE file.AuthorsAdam Karpierz <adam@karpierz.net>Changelog0.5.0b7 (2022-01-29)Drop support for Python 3.6.Setup update.0.5.0b5 (2021-10-16)dll_path argument of JVM() can also be type os.PathLike.Copyright year update.Setup update.0.5.0b3 (2020-12-13)Add support for Python 3.9.General update, improvements and cleanup.Setup update.Elimination of dependence on _testcapi.0.4.0b3 (2020-03-08)Drop support for Python2.Update of Mozilla Rhino.Update of license info.Setup update.0.3.0b2 (2019-07-10)Last release for Python2.0.3.0b1 (2018-11-08)Bug fixes and improvements.Add Java 10 support.Update of the required setuptools version.Setup and tests improvements.0.2.0b3 (2018-05-29)Bug fixes and improvements in Java 9 support.Improved support for Cygwin.Update of Mozilla Rhino.Update of the required setuptools version.0.2.0b1 (2018-02-26)Improvement and simplification of setup and packaging.0.1.1 (2005-10-05)Initial version.
Package: jVMC
jVMCThis is an impementation of Variational Monte Carlo (VMC) for quantum many-body dynamics using theJAX library(andFlaxon top) to exploit the blessings of automatic differentiation for easy model composition and just-in-time compilation for execution on accelerators.DocumentationInstallationOnline exampleImportant gotchasCiting jVMCPlease report bugs as well as other issues or suggestions on ourissues page.DocumentationDocumentation is availablehere.InstallationOption 1:pip-installWe recommend you create a new conda environment to work with jVMC:conda create -n jvmc python=3.8
 conda activate jvmcpip-install the packagepip install jVMCOption 2: Clone andpip-installClone the jVMC repository and check out the development branch:git clone https://github.com/markusschmitt/vmc_jax.git
 cd vmc_jaxWe recommend you create a new conda environment to work with jVMC:conda create -n jvmc python=3.8
 conda activate jvmcpip-install the packagepip install .Alternatively, for development:pip install -e .[dev]Test that everything worked, e.g. run 'python -c "import jVMC"' from a place different thanvmc_jax.Compiling JAXHow to compile JAX on a supercomputing clusterOnline exampleClick on the badge above to open a notebook that implements an exemplary ground state search in Google Colab.Important gotchasOut-of-memory issues and batchingMemory requirements grow with increasing network sizes. To avoid out-of-memory issues, thebatchSizeparameter of theNQSclass has to be adjusted. ThebatchSizeindicates on how many input configurations the network is evaluated concurrently. Out-of-memory issues are usually resolved by reducing this number. ThenumChainsparameter of theSamplerclass for Markov Chain Monte Carlo sampling plays a similar role, but its optimal values in terms of computational speed are typically not memory critical.Citing jVMCIf you use the jVMC package for your research, please cite our reference paperSciPost Phys. Codebases 2 (2022).
Package: jvrc_description
JVRC humanoid robot modelThis package contains the robot description for the JVRC-1 model, a virtual humanoid robot released for education and research.Python moduleThis module helps retrieve the JVRC-1 model from a Python program. Import it by:importjvrc_descriptionIt then provides the following paths:jvrc_description.pathPath to thejvrc_descriptionfolder itself.jvrc_description.meshes_pathPath to themeshesfolder.jvrc_description.urdf_pathPath to thejvrc1.urdfURDF file of the model.
Package: jvstats
# jvstats, a commandline tool for retrieving stats[![Build Status](https://travis-ci.com/jaimeviloria/jvstats.svg)](https://travis-ci.com/jaimeviloria/jvstats)[![PyPI version](https://badge.fury.io/py/jvstats.svg)](https://badge.fury.io/py/jvstats)## Development Requirements* make* python3* virtualenv## Package Requirements* python3## Installation`pip install jvstats`## Usage:Example for the module```>>> from jvstats import Delays>>> d = Delays()>>> d.delays[]>>> d.addDelay(100)[100]>>> d.medians[-1]>>> d.sliding_window[100]>>> d.addDelay(102)[100, 102]>>> d.medians[-1, 101]>>> d.sliding_window[100, 102]>>> d.addDelay([101,110,120])[100, 102, 101, 110, 120]>>> d.sliding_window[101, 110, 120]>>> d.medians[-1, 101, 101, 102, 110]>>> d.delays=[100,102]>>> d.medians[-1, 101]```When using the tool on commandline1. piping from stdin (in this case using a file)```$ cat test1.csv100102101110120115$ cat test1.csv | jvstats delays medians-1101101102110115```2. from a file```$ cat test1.csv100102101110120115$ jvstats delays medians --filename test1.csv-1101101102110115```## Development### Build your own specific version of python to test against:The created project also contains a set of Makefiles which will build and run tests against your code.`make`  will build you the latest version of python and then proceed for testingOr choose a specific python version to test against.`make PYTHON_VERSION=3.6`Note that once built, you can easily swap between python versions just by executing the make command. If the python version you need was already built in the build directory, then we simply update the symlink of the virtualenv and ensure requirements.txt is installed.## Testing`make test`creates the virtualenv and python binaries necessary for testing as well as running the unittests
Package: jv-toolbox
IntroductionTODO: Give a short introduction of your project. Let this section explain the objectives or the motivation behind this project.Getting StartedTODO: Guide users through getting your code up and running on their own system. In this section you can talk about:Installation processSoftware dependenciesLatest releasesAPI referencesBuild and TestTODO: Describe and show how to build your code and run the tests.ContributeTODO: Explain how other users and developers can contribute to make your code better.If you want to learn more about creating good readme files then refer the followingguidelines. You can also seek inspiration from the below readme files:ASP.NET CoreVisual Studio CodeChakra Core
Package: jwalk
jwalk performs random walks on a graph and learns representations for nodes
using Word2Vec. It also has options to train existing models online and specify
weights.Installpip install -U jwalkBuildmake buildUsagejwalk -i tests/data/karate.edgelist -o karate.emb --delimiter=' 'To see the full list of options:jwalk --help

Prompt parameters:
  debug:            drop a debugger if an exception is raised
  delimiter:        delimiter for input file
  embedding-size:   dimension of word2vec embedding (default=200)
  has-header:       boolean if csv has header row
  help (-h):        argparse help
  input (-i):       file input (edgelist of 2/3 cols or adjacency matrix)
  log-level (-l)    logging level (default=INFO)
  model (-m):       use a pre-existing model
  num-walks (-n):   number of of random walks per graph (default=1)
  output (-o):      file output
  stats:            boolean to calculate walk statistics [requires pandas]
  undirected:       make graph undirected
  walk-length:      length of random walks (default=10)
  window-size:      word2vec window size (default=5)
  workers:          number of workers (default=multiprocessing.cpu_count)Input FileThe input file can be of the following formats:Edgelist: CSV with 2 or 3 columns denoting the source, target and (optional)
weight.
There are CLI options to specify the delimiter and whether the file has
a header (default=False).
The CSV file is loaded using numpy if pandas is not installed. We strongly
recommend using pandas to load the CSV as it’s a lot faster.Graph: If the file has an extension that is “.npz”, jwalk will assume
that it is aSciPy CSR matrix.
Included must be keys of data, indices, indptr, shape and labels
(default=None) where labels are the node labels.
For an example, see tests/data/karate.npz.TestRunning unit tests:make testRunning linter:make lintRunning tox:make test-allBlogRead more about jwalk in our blog post here:https://www.jwplayer.com/blog/deepwalk-recommendations/LicenseApache License 2.0References[paper]: arXiv:1403.6652  [cs.SI] “DeepWalk: Online Learning of Social Representations”[paper]: arXiv:1607.00653 [cs.SI] “node2vec: Scalable Feature Learning for Networks”
Package: jwave
j-Wave: Differentiable acoustic simulations in JAXInstall|Tutorials|Documentation|Changelogj-Wave is a library of simulators for acoustic applications. It is heavily inspired byk-Wave—a substantial portion of j-Wave is a port of k-Wave in JAX—and it's intended to be used as a collection of modular blocks that can be easily incorporated into any machine learning pipeline.Embracing the philosophy ofJAX, j-Wave is developed with the following principles in mind:To be differentiableTo be efficient throughjitcompilationTo be easily run on GPUsTo be easily customizableInstallFollow the instructions to installJax with CUDA supportif you wish to use your GPU.Next, simply installjwaveusing pip:pipinstalljwaveFor more information, refer to theLinux installation guide.Due to JAX's limited support on Windows, j-Wave can only be run on Windows machines using the Windows Subsystem for Linux. Please refer to theInstallation on Windows guidefor more details.ExampleThis example simulates an acoustic initial value problem, which is often used as a simple model for photoacoustic acquisitions:fromjaximportjitfromjwaveimportFourierSeriesfromjwave.acoustics.time_varyingimportsimulate_wave_propagationfromjwave.geometryimportDomain,Medium,TimeAxisfromjwave.utilsimportload_image_to_numpy# Simulation parametersN,dx=(256,256),(0.1e-3,0.1e-3)domain=Domain(N,dx)medium=Medium(domain=domain,sound_speed=1500.)time_axis=TimeAxis.from_medium(medium,cfl=0.3,t_end=.8e-05)# Initial pressure fieldp0=load_image_to_numpy("docs/assets/images/jwave.png",image_size=N)/255.p0=FourierSeries(p0,domain)# Compile and run the simulation@jitdefsolver(medium,p0):returnsimulate_wave_propagation(medium,time_axis,p0=p0)pressure=solver(medium,p0)SupportIf you encounter any problems with the code or wish to propose new features, please feel free to open an issue. If you need general guidance, wish to discuss something, or just want to say hi, don't hesitate to leave a message in ourDiscord channel.ContributingContributions are absolutely welcome! Most contributions start with an issue. Please don't hesitate to create issues in which you ask for features, give feedback on performances, or simply want to reach out.To make a pull request, please look at the detailedContributing guidefor how to do it, but fundamentally keep in mind the following main guidelines:If you add a new feature or fix a bug:Make sure it is covered by testsAdd a line in the changelog usingkacl-cliIf you changed something in the documentation, make sure that the documentation site can be correctly build usingmkdocs serveCitationIf you usejwavefor your research, please consider citing it as:@article{stanziola2022jwave,author={Stanziola, Antonio and Arridge, Simon R. and Cox, Ben T. and Treeby, Bradley E.},title={j-Wave: An open-source differentiable wave simulator},publisher={arXiv},year={2022},}Related ProjectsADSeismic.jl: a finite difference acoustic simulator with support for AD and JIT compilation in Julia.stride: a general optimisation framework for medical ultrasound tomography.k-wave-python: A python interface to k-wave GPU accelerated binaries
Package: jwblack
Forked from cblackThank you for the setup work cblack. This fork is only to pin it, because our app is unable to keep up with black at the moment.cblackCustom Black Code Formatter for Python with 2-space indentsSame rules as Black, only with two-space indents.Installationcblackcan be installed by runningpip3 install cblack.It requires Python 3.6.0+ to run but you can reformat Python 2 code with it as well.Usagepython -m cblack {source_file_or_directory}Optionspython -m cblack --helpYou can also go tohttps://github.com/psf/blackfor the latest options availableWhyAftera long discussionabout indentation
levels it was decided by the main author that:Two space indents are not distinct enough to be recommended by the Black coding style.The whole pourpose of this project is to provide an alternative to black, with
the exact same rules and behaviour, but using 2 spaces for indentation instead of 4.
All other black options are still supported.Second reason is thatGoogle YAPFis not able
to produce same output as black (mostly because of function arguments indentation),
otherwise, if it was possible to do it, I would not have created this project.HowI'm a busy man. I don't want to spend my time pulling latest changes from black
regularly, so I've created a small package that depends on black and overrides
a method to reindent using two spaces. Shall black developers break this behaviour
I might end up cloning the whole project. But the aim of this project
would still be the same.Similar projectsYAPFAutopep8pep8ifypyfmtprettier in nodegofmt in gorustfmt in rustCreditsAll the credit is deserved to the people who createdblackon the first place.Please visit black website:https://github.com/ambv/black
Package: jwcnotipy
Welcome to jwcnotipy's documentation!This package was built to facillitate sending notifications to slack at runtime!Notifications can be attached to specific functions with a handy decorator:from jwcnotipy import notify_on_exec

@notify_on_exec(jwc_test_app, 'U037LP9TM8P')
def sum(a,b):
   return a + b

sum(3,1)Or at any other key point using notify_me:from jwcnotipy import notify_me

sum(3,1)
notify_me(jwc_test_app, 'U037LP9TM8P')The notifications are set up to post to a single channel defined by the
JWCNOTIPY_CHANNEL environment variable, but can be sent to another specific
channel by setting the channel keyword argument. NOTE only the user specified
in the function call can see the notifications! This will be extended to allow
a set of specific users and non-ephemeralised messages in a future build.ContributeIssue Tracker: github.com/$project/$project/issuesSource Code: github.com/$project/$projectSupportIf you are having issues, please let us know.
We have a mailing list located at:project@google-groups.comLicenseThe project is licensed under the BSD license.
Package: jwcrypto
JWCryptoAn implementation of the JOSE Working Group documents:RFC 7515 - JSON Web Signature (JWS)RFC 7516 - JSON Web Encryption (JWE)RFC 7517 - JSON Web Key (JWK)RFC 7518 - JSON Web Algorithms (JWA)RFC 7519 - JSON Web Token (JWT)RFC 7520 - Examples of Protecting Content Using JSON Object Signing and
Encryption (JOSE)Installationpip install jwcryptoDocumentationhttp://jwcrypto.readthedocs.orgDeprecation Notices2020.12.11: The RSA1_5 algorithm is now considered deprecated due to numerous
implementation issues that make it a very problematic tool to use safely.
The algorithm can still be used but requires explicitly allowing it on object
instantiation. If your application depends on it there are examples of how to
re-enable RSA1_5 usage in the tests files.Note: if you enable support forRSA1_5and the attacker can send you chosen
ciphertext and is able to measure the processing times of your application,
then your application will be vulnerable to a Bleichenbacher RSA padding
oracle, allowing the so-called "Million messages attack". That attack allows
to decrypt intercepted messages (even if they were encrypted with RSA-OAEP) or
forge signatures (both RSA-PKCS#1 v1.5 and RSASSA-PSS).Given JWT is generally used in tokens to sign authorization assertions or to
encrypt private key material, this is a particularly severe issue, and must
not be underestimated.
Package: jweede.recipe.template
jweede.recipe.template is a fork of amplecode.recipe.template, a Buildout recipe for generating files using Jinja2 templates. The recipe configures a Jinja2 environment, by default relative to the Buildout directory, allowing templates to extend and include other templates relative to the environment.Downloads are available from pypi:http://pypi.python.org/pypi/jweede.recipe.template/Buildout Optionstemplate-file or input (required): One or more Jinja2 template file paths.target-file or output (required): One of more target file paths. The number of files must match the number of template files.base-dir: Base directory of the Jinja2 environment. Template file paths are relative to this directory. Default is the Buildout directory.target-executable: One or more boolean flags (yes|no|true|false|1|0) indicating the executability of the target files. If only one flag is given it is applied to all target files.eggs: Reserved for a list of eggs, conveniently converted into a pkg_resources.WorkingSet when specifiedjinja2_filters: custom filter functions separated by white-spaceAdditional options are simply forwarded to the templates, and options from all the other parts are made available throughparts.<part-name>.<option-name>andparts[<part-name>][<option-name>].Lists of ValuesIt is possible for a recipe option to contain one or more values, separated by whitespace. A split filter is available for when you want to iterate over the whitespace separated values in your Jinja2 template:#!/bin/sh
{% for cmd in cmds|split %}
   echo "{{ cmd }}"
{% endfor %}Minimal Examplefoo.txt is created from foo.txt.jinja2 without any extra options:[buildout]
parts = foo

[foo]
recipe = brodul.recipe.template
template-file = foo.txt.jinja2
target-file = foo.txtLarger Examplefoo.txt is created from myapp/foo.txt.jinja2, bar.sh is created from myapp/bar.sh.jinja2, the second will be executable, and both templates can utilize the additional options specified:[buildout]
parts = foo

[foo]
recipe = brodul.recipe.template
base-dir = myapp
template-file =
    foo.txt.jinja2
    bar.sh.jinja2
input =
    foo.txt
    bar.sh
output =
    false
    true
project_name = Another Example
author = Me

[bar]
dashed-value = borg
value = cashfoo.txt.jinja2:{{ parts.bar['dashed-value'] }}
{{ parts.bar.value }}
{{ author }}Note{{ parts.bar.dashed-value }}won’t work, but you can access it as a dict key.Dashed value in the same partIf there is a dashed-value in brodul.recipe.template part and you would like to reference it, use:{{context['dashed-value']}}Custom filtersThe filter function is located in the same directory as the buildout.cfg in a filter.py file. If you want to use more filters separate them with a white space.[buildout]
parts = foo

[foo]
recipe = brodul.recipe.template
input = foo.txt.jinja2
output = foo.txt
jinja2_filters = filter.barChangelogSee the CHANGELOG fileLicenseSee the LICENSE fileWhy this forkthere should be an input and output option in buildout (since the ‘-’ in ‘target-file’ char is parsed by jinja2)custom filters supporttemplates should not have the ability to change state of buildout
Package: jwhzeep
A Python SOAP clientHighlights:Compatible with Python 3.7, 3.8, 3.9, 3.10, 3.11 and PyPy3Build on top of lxml, requests and httpxSupport for Soap 1.1, Soap 1.2 and HTTP bindingsSupport for WS-Addressing headersSupport for WSSE (UserNameToken / x.509 signing)Support for asyncio using the httpx moduleExperimental support for XOP messagesPlease see for more information the documentation athttp://docs.python-zeep.org/InstallationpipinstallzeepNote that the latest version to support Python 2.7, 3.3, 3.4 and 3.5 is Zeep
3.4, install viapip install zeep==3.4.0Zeep uses the lxml library for parsing xml. Seehttps://lxml.de/installation.htmlfor the installation requirements.UsagefromzeepimportClientclient=Client('tests/wsdl_files/example.rst')client.service.ping()To quickly inspect a WSDL file use:python -m zeep <url-to-wsdl>Please see the documentation athttp://docs.python-zeep.orgfor more
information.SupportIf you want to report a bug then please first readhttp://docs.python-zeep.org/en/master/reporting_bugs.htmlPlease only report bugs and not support requests to the GitHub issue tracker.
Package: jw.ip-service
Set IP for network interface============================Introduction------------This is a simple utility to set an IP for a network interface for as long as the utility or a sub-command is running. When it isterminated, the IP will be removed from the network interface. This makes a network IP controllable as a service. Tools likeNomad can then dynamically enable and disabled IPs where certain services run. If a command is specified, ip-service runs it andwaits for the program to terminate. The command would ideally provide a network-based service which would be available on thespecified IP.Usage-----The program is intended to be run by the simple command like::ip-service -a 192.168.0.130or::ip-service -a 192.168.0.130 docker start -ai myservicepositional arguments::command               command to run (optional)optional arguments::-h, --help                        show help and exit--version, -V                     display version and license information--log-level / -L LOG_LEVEL        one of {DEBUG,INFO,WARNING,ERROR,CRITICAL}set log level (default INFO)--log-file / -l LOG_FILEset log file (default: /var/log/ip-service)--interface / -i INTERFACE        set network interface (default: the one attached to the default route)--ip IP / -a IP                   IP to set (more than one possible)Installation------------The software can be installed easily from the Python software repository, either on the command line or by downloading thepackage and installing it explicitly... note::Python packages should not be installed using *pip* or *easy_install* globally in the system environment under Gentoo Linux.There is a carefully crafted system to make system-provided Python scripts available under Python 2 as well as Python 3 whichis disturbed by packagages deliberately installed by *pip* or *easy_install*.Installation using *pip*~~~~~~~~~~~~~~~~~~~~~~~~On the command line, type::pip install --user jw.ip-serviceExplicit Installation from a downloaded package~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~Download the package from https://pypi.python.org/pypi/jw.ip-service. Unpack it, ``cd`` into the unpacked directory and type thecommand::python setup.py install --userBug reports-----------Please report bugs and enhancement requests to https://bitbucket.org/JohnnyWezel/jw.ip-service/issues.