Saravanan666/pypi-org huggingface dataset:
Package: phx-filters-django
Django FiltersAddsfiltersfor for Django-specific features, including:filters.ext.Model: Search for a DB model instance matching the incoming
value.RequirementsDjango Filters is known to be compatible with the following Python versions:3.123.113.10Only Django v4.x is supported.NoteI’m only one person, so to keep from getting overwhelmed, I’m only committing
to supporting the 3 most recent minor versions of Python and the most recent
major version of Django.  Django Filters may work with versions of Python
and/or Django not listed here — there just won’t be any test coverage to
prove it 😇If you encounter any issues, please report them on the project’sBug Tracker,
and be sure to indicate which version of Django you are using.InstallationThis package is an extension for theFilters library, so you can install it
as an extra tophx-filters:pipinstallphx-filters[django]ImportantMake sure to installphx-filters,notfilters.  I created the latter
at a previous job years ago, and after I left they never touched that project
again and stopped responding to my emails — so in the end I had to fork it 🤷If desired, you can install this package separately.  This allows you to control
the package version separately fromphx-filters.pipinstallphx-filters-djangoRunning Unit TestsInstall the package with thetest-runnerextra to set up the necessary
dependencies, and then you can run the tests with thetoxcommand:pip install -e .[test-runner]
tox -pTo run tests in the current virtualenv:python manage.py testDocumentationDocumentation is available onReadTheDocs.Source files for this project’s documentation can be found in thephx-filters repo.ReleasesSteps to build releases are based onPackaging Python Projects TutorialImportantMake sure to build releases off of themainbranch, and check that all
changes fromdevelophave been merged before creating the release!1. Build the ProjectInstall extra dependencies (you only have to do this once):pip install -e '.[build-system]'Delete artefacts from previous builds, if applicable:rm dist/*Run the build:python -m buildThe build artefacts will be located in thedistdirectory at the top
level of the project.2. Upload to PyPICreate a PyPI API token(you only have to do this once).Increment the version number inpyproject.toml.Check that the build artefacts are valid, and fix any errors that it finds:python -m twine check dist/*Upload build artefacts to PyPI:python -m twine upload dist/*3. Create GitHub ReleaseCreate a tag and push to GitHub:git tag <version>
git push<version>must match the updated version number inpyproject.toml.Go to theReleases page for the repo.ClickDraft a new release.Select the tag that you created in step 1.Specify the title of the release (e.g.,Django Filters v1.2.3).Write a description for the release.  Make sure to include:
- Credit for code contributed by community members.
- Significant functionality that was added/changed/removed.
- Any backwards-incompatible changes and/or migration instructions.
- SHA256 hashes of the build artefacts.GPG-sign the description for the release (ASCII-armoured).Attach the build artefacts to the release.ClickPublish release.
Package: phx-filters-iso
ISO FiltersAddsfiltersfor interpreting standard codes and identifiers, including:ISO 3166-1 alpha-2 and alpha-3 country codes.ISO 4217 currency codes.IETF Language Tags (aka BCP 47).RequirementsISO Filters is known to be compatible with the following Python versions:3.123.113.10NoteI’m only one person, so to keep from getting overwhelmed, I’m only committing
to supporting the 3 most recent versions of Python.  ISO Filters may work in
versions not listed here — there just won’t be any test coverage to prove it
😇InstallationThis package is an extension for theFilters library, so you can install it
as an extra tophx-filters:pip install phx-filters[iso]ImportantMake sure to installphx-filters,notfilters.  I created the latter
at a previous job years ago, and after I left they never touched that project
again and stopped responding to my emails — so in the end I had to fork it 🤷If desired, you can install this package separately.  This allows you to control
the package version separately fromphx-filters:pip install phx-filters-isoRunning Unit TestsInstall the package with thetest-runnerextra to set up the necessary
dependencies, and then you can run the tests with thetoxcommand:pip install -e .[test-runner]
tox -pTo run tests in the current virtualenv:python -m unittestDocumentationDocumentation is available onReadTheDocs.Source files for this project’s documentation can be found in thephx-filters repo.ReleasesSteps to build releases are based onPackaging Python Projects TutorialImportantMake sure to build releases off of themainbranch, and check that all
changes fromdevelophave been merged before creating the release!1. Build the ProjectInstall extra dependencies (you only have to do this once):pip install -e '.[build-system]'Delete artefacts from previous builds, if applicable:rm dist/*Run the build:python -m buildThe build artefacts will be located in thedistdirectory at the top
level of the project.2. Upload to PyPICreate a PyPI API token(you only have to do this once).Increment the version number inpyproject.toml.Check that the build artefacts are valid, and fix any errors that it finds:python -m twine check dist/*Upload build artefacts to PyPI:python -m twine upload dist/*3. Create GitHub ReleaseCreate a tag and push to GitHub:git tag <version>
git push<version>must match the updated version number inpyproject.toml.Go to theReleases page for the repo.ClickDraft a new release.Select the tag that you created in step 1.Specify the title of the release (e.g.,ISO Filters v1.2.3).Write a description for the release.  Make sure to include:
- Credit for code contributed by community members.
- Significant functionality that was added/changed/removed.
- Any backwards-incompatible changes and/or migration instructions.
- SHA256 hashes of the build artefacts.GPG-sign the description for the release (ASCII-armoured).Attach the build artefacts to the release.ClickPublish release.
Package: phxsocket
phxsocketSynchronous phoenix websocket client using callbacksPhoenix channelsRequirementswebsocketsUsageImport the packageimportphxsocketCreate socket clientsocket=phxsocket.Client("wss://target.url/channel/websocket",{"options":"something"})Connect and join a channelifsocket.connect():# blocking, raises exception on failurechannel=socket.channel("room:roomname",{"more options":"something else"})resp=channel.join()# also blocking, raises exception on failureAlternativelydefconnect_to_channel(socket):channel=socket.channel("room:roomname",{"more options":"something else"})resp=channel.join()socket.on_open=connect_to_channelconnection=socket.connect(blocking=False)connection.wait()# blocking, raises exception on failureReconnect on disconnectionsocket.on_close=lambdasocket:socket.connect()Subscribe to eventsdefdo_something(payload):thing=payload["thing"]channel.on("eventname",do_something)Push data to a channelchannel.push("eventname",{"some":"data"})Push data and wait for a responsemessage=channel.push("eventname",{"some":"data"},reply=True)response=message.wait_for_response()# blockingPush data and react to the response with a callbackdefrespond(payload):print(payload)channel.push("eventname",{"some":"data"},respond)Leave a channelchannel.leave()Disconnectsocket.close()
Package: phxutils
phxutilsThis is going to be a handy little repo where I maintain some of useful scripts
that I use on a regular basis.  I have a private repo where I have maintained
most of these scripts for a long time, but some of them could prove useful to the
general public, and half the time I forget what they do and end up re-writing them.This repo will be a place for me to keep them up-to-date and document what they do.Some aren't even big enough to be called scripts and are more like bash/zsh functions,
but it's easier having the commands at my fingertips by having all my tools installed
instead of finding/copy/pasting functions into my~/.bashrc/~/.zshrc.The structure includes an individual folder for each script with aREADME.mddocumenting
what the script does.  Additionally, there will be abindirectory where I will symlink
all of the actual scripts for easy importing into your$PATH.This project is also maintained on PyPi for easy installation viapip.For more information, browse to the subfolders to view the individual README files.I will continue to add scripts to this repo as I have time and as the need for them arises.Requirements:In order to fully-utilize all of the scripts included inphxutils, Linux is a pre-requisite, but many will work on MacOS (some may need to be tweaked a bit).To view individual requirements, feel free to view each package's README file.The following additional requirements are necessary:/bin/sh(many scripts)bash(some scripts)python3(some scripts)Simple Installation via pipInstallation is extremely simple usingpip:Install fromPyPi:pip3 install --user phxutilsor install latest version from GitHub:pip3 install --user git+https://github.com/phx/phxutilsFor what it's worth, most of thephxutilsscripts useshandbash.The only Python scripts at the time of this writing are Python3, and are executable with hashbangs pointing to/usr/bin/env python3and only use the standard library.This means, you won't clutter your native Python intallation by installing a bunch of third party libraries outside of virtual environments.Upgrading via pippip3 install --upgrade --user phxutilsorpip3 install --upgrade --user git+https://github.com/phx/phxutilsUninstallpip3 uninstall phxutilsAdvanced InstallationTo add these programs to your$PATHwithout usingpip3, I would do something like the following:Note: replace.bashrcwith.zshrcor.bash_profile, etc., as necessary.mkdir -p "$HOME/bin"
cd "$HOME/bin"
git clone https://github.com/phx/phxutils
echo 'PHX_UTILS="$HOME/bin/phxutils/bin"' >> ~/.bashrc
echo 'export PATH="$PHX_UTILS:$PATH"' >> ~/.bashrc
source ~/.bashrcThe benefits to this method are an easier upgrade.Easy Upgrade via Advanced Install Methodcd "$HOME/bin/phxutils" && git pullUninstallsed -i '/PHX_UTILS/d' ~/.bashrc && rm -rf "$HOME/bin/phxutils"Note: replace.bashrcwith.zshrcor.bash_profile, etc., as necessary in the above command.
Package: phyaat
PhyAAt: Physiology of Auditory AttentionPredictive analysis of auditory attention from physiological signalsProject HomepagePyPi - projectLinks:Homepage|Documentation|Github|PyPi - project|     _Installation:pip install phyaatTable of contentsInstallationGetting StartedExamples with NotebooksPublicationsRequirement :['numpy','scipy','matplotlib','spkit']Installationwith pippip install phyaatBuild from the sourceDownload the repository or clone it with git, after cd in directory build it from source withpython setup.py installGetting StartedFor starting check here -Getting StartedContents are being updated on the webpage continuously, please check there for updatesContacts:Nikesh Bajajhttp://nikeshbajaj.inn.bajaj@qmul.ac.ukbajaj.nikkey@gmail.comPhD Student: Queen Mary University of London & University of Genoa
Package: phyber-numint
Phyber NumIntA numerical integrator for solving ODE systemsA simple package to solving ODE with different algorithms, written as explicitly as possible. I created this for a University course.To use it you simply create an instance of the class ODEIntegratorIt takes the following parametersFthat's the function that represents the ODE system (X' = F(X))X0that's the initial conditionstithe initial timetfthe final timedtthe time stepThe functionFshould be structured in the following fashiondeflorenz_system(t,X,sigma,beta,rho):'''x' = sigma(y - x)y' = x(rho - z) - yz' = x*y - beta*z'''x,y,z=Xdxdt=sigma*(y-x)dydt=x*(rho-z)-ydzdt=x*y-beta*zreturn(dxdt,dydt,dzdt)Then, the methodsolveofODEIntegratorcan be called with themethodargument. Themethodargument is an enum member of IntegrationMethods. The following existEULER_FORWARDEULER_BACKWARDEULER_IMPROVEDEULER_IMPROVEDRUNGE_KUTTA_2RUNGE_KUTTA_3RUNGE_KUTTA_4RUNGE_KUTTA_FEHLBERG_45For example,ode_integrator.solve(IntegrationMethod.EULER_FORWARD)One implementation would befromphyber_numintimportODEIntegrator,IntegrationMethodfromphyber_numint.examplesimportlorenz_systeminteg=ODEIntegrator(lorenz_system,(0.,.5,.5),0,50,1e-3,F_args=(8/3,10,25))integ.solve(IntegrationMethod.RUNGE_KUTTA_4)integ.show(True,plt_show=True)integ.show((0,1),'phase',plt_show=True,plt_kwargs={'s':2})Examples in the fileexamples.pyAlso installmatplotlibandtqdmto take advantage of every feature
Package: phybers
Overview of PhybersPhybers is a Python library that shared several tools for cerebral tractography analysis. With the aim of improving its usability, the library has been separated into 4 primary modules:Segmentation,Clustering,UtilsandVisualization. To find details about the usage of each module, please refer to thePhybers documentationPrerequisites for installationPhybers can be executed on both Windows and Ubuntu platforms. It has been tested on both OS with Python 3.9 and 3.11 versions.
For running on Windows, make sure you have Microsoft Visual C++ 14.0 or a later version installed, which can be found at theVisual Studio WebsiteInstallationTo install Phybers, it is recommended to create a Python environment. If you are using Anaconda, follow the instructions below for Windows and Ubuntu, respectively:Open a terminal and execute the following command to create a new Python environment, where "environment_name" is the name you want for your environment, and "x.x" corresponds to the Python version you wish to use:conda create -n environment_name python=x.xTo activate the environment, run:conda activate environment_nameTo deactivate the environment when you are finished, run:conda deactivateTo install the Phybers package, activate the environment and run the following command:pip install phybers
Package: phycontrib
# phy-contrib[![Build Status](https://img.shields.io/travis/kwikteam/phy-contrib.svg)](https://travis-ci.org/kwikteam/phy-contrib)
[![PyPI release](https://img.shields.io/pypi/v/phycontrib.svg)](https://pypi.python.org/pypi/phycontrib)Plugins for [phy](https://github.com/kwikteam/phy). Currently, this package provides two integrated spike sorting GUIs:KwikGUI: to be used with Klusta and the Kwik format (successor of KlustaViewa)TemplateGUI: to be used with KiloSort and SpykingCircus## Quick installYou first need to install [phy](https://github.com/kwikteam/phy). Then, activate your conda environment and do:`bash pip install phycontrib `### Installing the development versionIf you want to use the bleeding-edge version, do:` git clonehttps://github.com/kwikteam/phy-contrib.gitcdphy-contribpython setup.py develop `Then, you can update at any time with:` cdphy-contribgit pull `## Documentation[Main documentation](http://phy-contrib.readthedocs.io/en/latest/)KwikGUI documentation (work in progress)[TemplateGUI documentation](http://phy-contrib.readthedocs.io/en/latest/template-gui/)
Package: phycv
PhyCV: The First Physics-inspired Computer Vision LibraryWelcome to PhyCV ! The First Physics-inspired Computer Vision Python library developed byJalali-Lab@ UCLA. This repo is maintained byYiming Zhou.Please see the release notes of all previous versionshere.CitationIf you find PhyCV useful in your research, please star :star: this repository and consider citing the followingpreprint:@article{zhou2023phycv,
  title={PhyCV: The First Physics-inspired Computer Vision Library},
  author={Zhou, Yiming and MacPhee, Callen and Suthar, Madhuri and Jalali, Bahram},
  journal={arXiv preprint arXiv:2301.12531},
  year={2023}
}
Package: phyde
HyDe: hybridization detection using phylogenetic invariantsPublication:Blischak, P. D., J. Chifman, A. D. Wolfe, and L. S. Kubatko. 2018.
HyDe: a Python package for genome-scale hybridization detection.Systematic Biology, doi:https://doi.org/10.1093/sysbio/syy023.Read the DocsHyDe is a software package that detects hybridization in phylogenomic
data sets using phylogenetic invariants. The primary interface for HyDe is a Python
module calledphyde(PythonicHybridizationDetection).phydeprovides a suite of tools for performing hypothesis tests on triples of taxa
to detect hybridization. To ensure that the necessary
dependencies forphydeare available, we suggest using a Python distribution such
asMiniconda.To facilitate analyses using the Python module, three scripts are provided to
conduct hybridization detection analyses directly from the command line:run_hyde.py: runs a standard hybridization detection analysis on all triples
in all directions. Results are also filtered based on whether there is significant
evidence for hybridization.individual_hyde.py: tests each individual within a putative hybrid population
using a list of specified triples specified.bootstrap_hyde.py: conducts bootstrap resampling of the individuals within
the putative hybrid lineages for each specified triple.These last two scripts need to be given a three column table of triples
(P1, Hybrid, P2) that you wish to test:sp1 sp2 sp3
sp1 sp3 sp4
sp3 sp4 sp5
.
.
.You can also use a results file from a previous analysis as a triples file.
For example, you can use the filtered results from therun_hyde.pyscript so that
you only run analyses on triples that have significant levels of hybridization.
If you only have a few hypotheses that you want to test, then you can also pass
a triples file torun_hyde.pyand it will only test those hypotheses rather than
testing everything.Multithreaded versions of these scripts are also available (run_hyde_mp.py,individual_hyde_mp.py, andbootstrap_hyde_mp.py).
Make sure you have themultiprocessmodule installed before you use them.Getting helpIf you have questions about running HyDe, please feel free to use thegitter chatroomto get help:If you have a problem while running HyDe and you think it may be a bug,
please consider filing an issue:InstallationRequirements:Python 2.7 or 3.6Python Modules:CythonNumpyMatplotlibSeabornMultiprocessC++ compiler# To install dependenciespipinstallcythonnumpymatplotlibseabornmultiprocess# Clone HyDe repository from GitHubgitclonehttps://github.com/pblischak/HyDe.gitcdHyDe# Now install phyde modulepythonsetup.pyinstall# Test the installationmaketest# Test multithreaded scriptsmaketest_threadsThephydemodule is also hosted on the Python Package Index (PyPI), and can be installed directly usingpip.# Install from PyPI with pippipinstallphydeDocumentation for analyzing data using HyDe can be foundhere.
Package: phydms
phydmsenablesphylogenetic analyses usingdeepmutationalscanning data to inform the substitution models. It implementsExperimentally informedcodonmodels (ExpCM) for phylogenetic inference and the detection of biologically interesting selection.phydmsis written by theBloom lab(seehttps://github.com/jbloomlab/phydms/contributorsfor a full list of contributors).See thephydms documentationfor information about usingphydmsand details about the recommended installation process.Thephydms source codeis freely available under aGPLv3license.If you usephydms, pleasecite it appropriately.
Package: phydrus
Phydrus: Python implementation of HYDRUS-1DThis package provides a Python implementation of the HYDRUS-1D unsaturated zone model developed by Šimůnek, J., M. Th. van Genuchten, and M. Šejna.
More information on the HYDRUS-1D model is availablehere. This software is licenced under the GNU GENERAL PUBLIC LICENSE foundhere. The Phydrus code is developed by R.A. Collenteur and M. Vremec. With Phydrus, a HYDRUS-1D model can be created, calibrated and visualized through Python scripts, making it easy to adjust the model and providing a 100% reproducible workflow of your modeling process.Examples and DocumentationExamples of using Phydrus can be found in the example folder. This folder also contains a number of Jupyter Notebooks that thoroughly explain the use of the software. Documentation is hosted on ReadTheDocs:https://phydrus.readthedocs.io.Bug reports and QuestionsPhydrus is in active development, and bug reports are welcome asGitHub Issues. General questions or
discussions are possible throughGitHub DiscussionsInstalling Phydrus1. Installing the Python packageThe Phydrus package is available from the Pypi package index and can be installed as follows:>>> pip install phydrusTo install in developer mode, use the following syntax:>>> pip install -e .2. Compiling the source codeBefore you can use Phydrus the adapted Fortran77 files need to be compiled to an executable. Phydrus-optimized Fortran Hydrus-1D code is available
from adedicated repository. Detailed instructions on how to compile the Fortran code can be found on theDocumentation website.Developing PhydrusPhydrus is a community effort and help is always welcome. If you have found a bug, please open a GitHub issue to report it. Pull requests including bug fixes and new functionality are very welcome and will be accepted on the Dev-branch of this repository.Citing PhydrusIf you use phydrus for one of your projects, we ask that you cite the code as follows:Collenteur, R.A., Brunetti, G., and M. Vremec (2019) Phydrus: Python implementation of the HYDRUS-1D unsaturated zone model. Version X.X.X
Package: phykit
Docs·Report Bug·Request FeaturePhyKIT is a UNIX shell toolkit for processing and analyzing phylogenomic data.If you found PhyKIT useful, please citePhyKIT: a broadly applicable UNIX shell toolkit for processing and analyzing phylogenomic data. Bioinformatics. doi:10.1093/bioinformatics/btab096.This documentation covers downloading and installing PhyKIT. Details about each function as well as tutorials for using PhyKIT are available in theonline documentation.Quick Start# installpipinstallphykit# runphykit<function><inputfile>InstallationIf you are having trouble installing PhyKIT, please contact the lead developer, Jacob L. Steenwyk, viaemailortwitterto get help.To install usingpip, we strongly recommend building a virtual environment to avoid software dependency issues. To do so, execute the following commands:# create virtual environmentpython-mvenv.venv# activate virtual environmentsource.venv/bin/activate# install phykitpipinstallphykitNote, the virtual environment must be activated to use phykit.After using PhyKIT, you may wish to deactivate your virtual environment and can do so using the following command:# deactivate virtual environmentdeactivateSimilarly, to install from source, we strongly recommend using a virtual environment. To do so, use the following commands:# downloadgitclonehttps://github.com/JLSteenwyk/PhyKIT.gitcdPhyKIT/# create virtual environmentpython-mvenv.venv# activate virtual environmentsource.venv/bin/activate# installmakeinstallTo deactivate your virtual environment, use the following command:# deactivate virtual environmentdeactivateNote, the virtual environment must be activated to use phykit.To install via anaconda, execute the following command:condainstallbioconda::phykitVisit here for more information:https://anaconda.org/bioconda/phykitTo test phykit installation, launch the help messagephykit-h