Saravanan666/pypi-org huggingface dataset:
Package: pharmvar-tools
IntroductionSet of tools to work with pharmvar data.Installation (Linux)python3-mvenvsourcevenv/bin/activate
pipinstall-rrequirements.txtInstallation (Windows)Install Windows subsystem for Linux (WSL).wsl--installNavigate to the repository and install the Linux/Python dependencies.cd/mnt/c/[path]/pharmvar-tools

sudoaptupdate
sudoaptupgrade
sudoaptinstallpython3.10-venvpython-is-python3python3-pip

sudopython-mvenvvenvsource./venv/bin/activate
python-mpipinstall-rrequirements.txtData requirementsFor each gene of interest the sequence fasta of the NC and the NG should be
downloaded (from the NCBI) into thedata/directory, e.g.,data/NC_000022.11.fastaanddata/NG_008376.4.fastaforCYP2D6.In addition, for the check functionality, also the complete download zipfile
from pharmvar is required. It needs to be unzipped in the data directory, e.g.,
the fasta file forCYP2D6should be located indata/pharmvar-5.2.19/CYP2D6/CYP2D6.haplotypes.fasta.UsagePharmvar data consistency checkpython check.py --gene CYP2D6 --allCalculating relations between alleles and variants based on variant algebrapython compare.py --gene CYP2D6 > data/pharmvar_5.2.19_CYP2D6_relations.txtVisualizing relationsRequires graphviz (sudo apt install graphviz).python to_dot.py --gene CYP2D6 < data/pharmvar_5.2.19_CYP2D6_relations.txt | fdp -Tpdf > /tmp/CYP2D6.pdf
Package: pharus
pharusis a generic REST API server backend forDataJointpipelines built on top offlask,datajoint, andpyjwt.DocumentationPyPi PackageDocker ImageReleaseSourceInstallationIf you have not done so already, please install the following dependencies.DockerDocker ComposePrerequisitesDownload thedocker-compose-deploy.yamldocker environment from the source locatedhere.Running the API serverTo start the API server, use the command:PHARUS_VERSION=0.7.3docker-compose-fdocker-compose-deploy.yamlup-dTo stop the API server, use the command:PHARUS_VERSION=0.7.3docker-compose-fdocker-compose-deploy.yamldownReferencesDataJointhttps://datajoint.ioDataJoint LabBook (a companion frontend)https://github.com/datajoint/datajoint-labbook
Package: phase
pyPhasePython wrapper for I3DR's Phase Library.InstallWindowsInstall pyPhase from pypi using pip:pip install phaseLinuxInstall dependenciessudo apt install -y libavcodec-dev libavformat-dev libswscale-dev
sudo apt install -y libgl-dev liblapack-dev libblas-dev libgtk2.0-dev
sudo apt install -y libgstreamer1.0-0 libgstreamer-plugins-base1.0-0
sudo apt install -y zlib1g libstdc++6
sudo apt install -y libc6 libgcc1Package is not yet available on pypi for Linux.Please download the wheel for your version of python from the release and install using:pip install ./phase-X.X.X-cpXXX-cpXXX-linux_x86_64.whlDocumentationFor detailed documentation seehere
Package: phase-cli
Phase-CLIλ phase
Securely manage application secrets and environment variables with Phase.

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options:
  -h, --help   show this help message and exit
  --version, -v
               show program's version number and exit


Commands:

    auth             💻 Authenticate with Phase
    init             🔗 Link your project with your Phase app
    run              🚀 Run and inject secrets to your app
    secrets          🗝️ Manage your secrets
    secrets list     📇 List all the secrets
    secrets get      🔍 Get a specific secret by key
    secrets create   💳 Create a new secret
    secrets update   📝 Update an existing secret
    secrets delete   🗑️ Delete a secret
    secrets import   📩 Import secrets from a .env file
    secrets export   🥡 Export secrets in a dotenv format
    users            👥 Manage users and accounts
    users whoami     🙋 See details of the current user
    users logout     🏃 Logout from phase-cli
    users keyring    🔐 Display information about the Phase keyring
    docs             📖 Open the Phase CLI Docs in your browser
    console          🖥️ Open the Phase Console in your browser
    update           🆙 Update the Phase CLI to the latest versionFeaturesInject secrets to your application during runtime without any code changesImport your existing .env files and encrypt themSync encrypted secrets with Phase cloudMultiple environments eg. dev, testing, staging, productionSee it in actionInstallationYou can install Phase-CLI using curl:curl-fsSLhttps://get.phase.dev|bashUsageLoginCreate an app in thePhase Consoleand copy appID and pssphaseauthInitializeLink the phase cli to your projectphaseinitImport .envImport and encrypt existing secrets and environment variablesphasesecretsimport.envList / view secretsphasesecretslist--showRun and inject secretsphase run // your run commandExample:phaserunyarndevphaserungorunphaserunnpmstartDevelopment:Make sure virtualenv is installedpip3installvirtualenvCreate a virtualenv:virtualenvphase-cliSwitch to the virtualenv:sourcephase-cli/bin/activateInstall dependencies:pip3install-rrequirements.txtexport PYTHONPATH="$PWD"./phase_cli/main.py
Package: phase-curve-tools
Phase Curve ToolsA Python package for asteroid phase curvesFree software: MIT licenseDocumentation:https://phase-curve-tools.readthedocs.io.FeaturesTODOCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.4.5 (2022-06-08)Fixed the best fit failing0.4.3 (2022-06-07)Fixed type errors in hgFixed name errors in bestfit0.4.1 (2022-05-22)Added HG12 support0.4.0 (2022-05-22)Added HG1G2 support0.3.2 (2022-05-20)Added the number of observations to HGresult0.3.1 (2022-05-20)Exposed HGresult for type hints0.3.0 (2022-05-20)Added errors to bestfit0.2.1 (2022-05-20)Updated manifest to include cython code0.2.0 (2022-05-20)Added Bowell HG codeAdded Bowell HG fitting0.1.0 (2022-05-18)First release on PyPI.
Package: phase-dev
Python SDK for PhaseSDK to integrate Phase in server-side applications running PythonInstallpip install phase-devImportfromphaseimportPhase;InitializeInitialize the SDK with yourAPP_IDandAPP_SECRET:phase=Phase(APP_ID,APP_SECRET)UsageEncryptciphertext=phase.encrypt("hello world");Decryptplaintext=phase.decrypt(ciphertext);
Package: phasefield
Automatic Construction of Phasefield Approximations from Sharp-Interface ModelsThe title says it all: this module uses a simple description of a sharp
interface problem to derive a phasefield approximation which can then be
solved using a finite-element toolbox. The domain specific languageUFLis used both for defining the sharp interface model and for
the weak formulation of the phasefield approximation. The resulting UFL form
can then be inserted into any of the finite element packages available
which can take UFL forms as an input. A class for evolving the solution
over time is available based onduneandfenics.
The bindings for thedunepackage are based on the dune moduledune-fem packageinclude both dynamic local grid refinement
and coarsening. This makes it easy to track the interface with a fine grid
while a coarser grid can be used away from the interface. This is essential
for evolving phasefield models efficiently. In addition thedunestepper
also provides an efficient solver for obstacle problems so that double
obstacle potentials can be used in the modelling. This solver is based
ontnmg.So far this module has been tested with:two phase models including additional bulk equations, e.g., for the temperaturemultphase modelssingle phase models, e.g., crack propagation problemsInstallationThe easiest way to test this module is to install the python package from
the python package index:pip install phasefieldSome example scripts showing how to use the package can then be optained by
callingpython -m phasefieldThe scripts are then located in the subfolderphasefield_tutorial.You can also clone the git repositorygit clone https://git.imp.fu-berlin.de/agnumpde/dune-tnnmgand access the examples in the folderphasefield/tutorial.
To get the python package up and running first executepip install -e .in the main folder of the repository.
Package: phasepack
This toolkit consists of a set of functions which use information contained
within the phase of a Fourier-transformed image to detect localised features
such as edges, blobs and corners. These methods have the key advantage that the
properties they measure are invariant with respect to image brightness and
contrast.phasecong:Phase congruency using oriented filtersphasecongmono:Fast phase congruency using monogenic filtersphasesym:Phase symmetry using oriented filtersphasesymmono:Fast phase symmetry using monogenic filtersFor more information on a particular function, see the associated docstring and
the references therein.Installation$ python setup.py installFast(er) Fourier TransformsAll of the functions in this module make use of the Fast Fourier Transform
(FFT), and their speed significantly depends on the module used to provide FFT
functions. If it is available, thepyFFTWmodule will be used. This provides
Python bindings to the FFTW C library, and is substantially faster thanfftpack, the default for scipy.To install pyFFTW:$ pip install pyfftwAuthorshipThese functions were originally written for MATLAB by Peter Kovesi, and
were ported to Python by Alistair Muldal. The original MATLAB code, as well as
further explanatory information and references are available fromPeter
Kovesi’s website.MIT LicensePermission is hereby  granted, free of charge, to any  person obtaining a copy
of this software and associated  documentation files (the “Software”), to deal
in the Software without restriction, subject to the following conditions:The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.The software is provided “as is”, without warranty of any kind.
Package: phaseportrait
frommatplotlibimportpyplotimportnumpyimportphaseportraitdefpendulum(θ,dθ):returndθ,-numpy.sin(θ)SimplePendulum=phaseportrait.PhasePortrait2D(pendulum,[-9,9],Title='Simple pendulum',xlabel=r"$\Theta$",ylabel=r"$\dot{\Theta}$")SimplePendulum.plot().DocumentationTo check outphaseportrait's documentation, view some examples and read more about it, check our website or try ourGraphical User Interface!InstallationInstalling via pip:Phaseportrait releases are available as wheel packages for macOS, Windows and Linux on PyPI. Install it using pip:$ pip install phaseportraitInstalling from source:Open a terminal on desired route and type the following:$ git clone https://github.com/phaseportrait/phaseportraitManual installationVisitphase-portraitwebpage on GitHub. Click on green button sayingCode, and download it in zip format.
Save and unzip on desired directory.What's this?The idea behind this project was to create a simple way to make phase portraits in 2D and 3D in Python, as we couldn't find something similar on the internet, so we got down to work. (Update: foundjmoy/plotdf, offers similar 2D phase plots but it is very limited).Eventually, we did some work on bifurcations, 1D maps and chaos in 3D trayectories.This idea came while taking a course in non linear dynamics and chaos, during the 3rd year of physics degree, brought by our desire of visualizing things and programming.We want to state that we are self-taught into making this kind of stuff, and we've tried to make things asprofessionallyas possible, any comments about improving our work are welcome!AuthorsVíctor Loras Herrero (vhloras@gmail.com)Unai Lería Fortea (unaileria@gmail.com)ContributingThis proyect is open-source, everyone can download, use and contribute. To do that, several options are offered:Fork the project, add a new feature / improve the existing ones and pull a request via GitHub.Contact us on our emails:vhloras@gmail.comunaileria@gmail.com
Package: phasepy
Phasepy is an open-source scientific python package for fluid phase equilibria computation.
This package facilitates the calculation of liquid-vapor equilibrium, liquid-liquid equilibrium
and liquid-liquid-vapor equilibrium. Equilibrium calculations can be performed with cubic equations
of state with classic or advances mixing rules or with a discontinuous approach using a virial equation
of state for the vapor phase and an activity coefficients model for the liquid phase.Besides computations, with this package is also possible to fit phase equilibria data, functions to fit quadratic
mix rule, NRTL, Wilson and Redlich Kister parameters, are included.Phasepy relies on numpy, scipy and cython extension modules, when necessary.
Package: phases
pyPhase Project-builderThis Generator will create a ready-to-go pyPhase-Project, based on a config-Yaml.Setuppip install phasescreateproject.yamlminimalname:myProjectnamespace:myGroupphases:-name:Phase1description:my first phaseexports:[myData]-name:Phase2description:my second phaserun withpython myProjectcompletename:"sleepClassificationCNN"namespace:tud.ibmtexporter:-ObjectExporter-KerasExporterphases:prepareData:-name:DataWrapperdescription:get EDF Dataexports:-trainingRaw-validationRaw-evaluationRaw-name:EDF4SleepStagesdescription:Prepare EDF Data for sleep stage recognitionexports:-trainingTransformed-validationTransformedtrain:-name:SleepPhaseDetectionModeldescription:Create Model for sleep stage recognitionexports:-modelevaluate:-name:SleepPhaseDetectionModeldescription:Create Model for sleep stage recognitionGeneratephases createDevelopmentThe generator will create stubs for each phase, exporter and generator that
does not exists in the pyPhase-Package. The stubs are in the project folder and implement empty
method that are required.To implement your project, you only need to fill those methods. For the minimal example you need
to fill themain-methods of Phase1 (myProject/phases/Phase1.py) and Phase2.ExecuteIf you want to run the whole Project run:python [ProjectName]for the minimal example:python myProjectTo run a single stage:python [ProjectName] [StageName]for the minimal example:python myProject stage2additional filesdoc/placeholder for documentation.editorconfigsome settings for supporting IDE about File encoding and formats (seehttps://editorconfig.org/).gitignoresome folders and files that should be ignored by git (seehttps://git-scm.com/docs/gitignore)requirements.txtthe python requirements (just pyPhases in an empty project)README.mdan Readme fileDockerfilea simple dockerfile that can be used to run the project in a container (TheFROMimage should properly changed)docker-compose.ymlthis is a helper to create and run the container. simply rundocker-compose upordocker compose run --rm [projectname] [stagename].gitlab-ci.ymla Configuration for the Gitlab-CI Pipeline, that will be automaticly run if there is a push to gitlab
Package: phasetorch
PhaseTorchPhaseTorchis a Python package for phase retrieval in X-ray phase contrast computed tomography (XPCT).ReferencesK. Aditya Mohan, Jean-Baptiste Forien, Venkatesh Sridhar, Jefferson A. Cuadra, and Dilworth Parkinson. “Non-Linear Phase-Retrieval Algorithms for X-ray Propagation-Based Phase-Contrast Tomography.” arXiv preprint arXiv:2305.00334 (2023).PDF linkDocumentationFor installation instructions, tutorials, and function definitions, please refer to the documentation at the linkhttps://phasetorch.readthedocs.io/en/latest/
Package: phash-blockhashio
This is a fork of the original, stale project to fix some of the issues, add CI verification, and push to PyPI.blockhash-pythonThis is a perceptual image hash calculation tool based on algorithm descibed in
Block Mean Value Based Image Perceptual Hashing by Bian Yang, Fan Gu and Xiamu Niu.InstallationEither download/clone the project directly, or install the "phash-blockhashio" package from PyPI.UsageThis script requires Python 2.x or Python 3 and Python Imaging (PIL) 1.1.6 or above.Runblockhash [list of images]for calculating hashes.Runblockhash --helpfor the list of options.LicenseCopyright 2014 Commons Machineryhttp://commonsmachinery.se/Distributed under an MIT license, please see LICENSE in the top dir.Contact:dev@commonsmachinery.se
Package: pHashPy
pHashPyPython bindings for thepHash(perceptual hashing) library.Copyright (c) Dries007 2020.Licensed underGPLv3(or later), as is pHash itself.The project uses cppyy to create a small wrapper around pHash functions.FeaturesWorking:Video hashingImage hashing (dct & mh)Missing:AudioTextUsageAs a Command Line ToolYou can use the tool as a command line utility to generate a list of hashes from a list of files.Runpython -m pHashPy --helpfor more info.As a LibraryImport functions from the package's__init__, this will automatically load the required library files.
If you import the functions from the individual py files, you must callhelper.inityourself.Background InfoFor more info on pHash:https://github.com/aetilius/pHashhttps://www.phash.org/https://www.phash.org/docs/design.html(Theory.)https://www.phash.org/docs/howto.html(Help for developers, archived asDevelopmentGuide.md.)Testing build manuallyUsing CentOS 7 (with docker containerquay.io/pypa/manylinux2014_x86_64):cdyuminstall-ycmake3libpng-devellibjpeg-turbo-devellibsamplerate-devellibsndfile-devellibtiff-devellibvdpau-devellibvorbis-devel
yuminstall-yepel-release

rpm-v--importhttp://li.nux.ro/download/nux/RPM-GPG-KEY-nux.ro
rpm-Uvhhttp://li.nux.ro/download/nux/dextop/el7/x86_64/nux-dextop-release-0-5.el7.nux.noarch.rpm
yuminstall-yffmpeg-devel

gitclonehttps://github.com/dries007/pHashPy.gitcdpHashPy
gitsubmoduleinit
gitsubmoduleupdatecdpHashecho"include_directories(/usr/include/ffmpeg)">>CMakeLists.txt
sed-is/SHARED/STATIC/gCMakeLists.txt

mkdirbuildcdbuild
cmake3..-DCMAKE_BUILD_TYPE=Release-DWITH_VIDEO_HASH=1-DWITH_AUDIO_HASH=1make-j
Package: phasme
PhasmeGraph with ASP made easy.Use casesWrite an ASP graph into gexfphasme convert data.lp target.gexfRewrite a gml graph with normalized node namesphasme convert data.gml --normalizedExtract the biggest cc, write it anonymized in gmlphasme split data.lp --biggest-first "data_cc.lp" --slice 0 1
phasme convert data_cc_1.lp target.gml --anonymizedGenerate a small world graph in gmlphasme generate data.gml erdos_renyi_graph n=100 p=0.01Packagingpip install phasmeWill also populate your env with the executablephasme,
enabling access to the CLI.CLI commandsCLI provides access to higher levelroutines.split: split by connected component.info: give info about given graph.convert: rewrite, anonymize, normalize or convert the graph to (clean) ASP or standard format.generate: generate a graph using a given generation method.Other examples# split a graph by cc
python -m phasme split data.lp -o "data_{}.lp"

# get infos
python -m phasme infos data.lp --graph-properties

# there is a shitload of options
python -m phasme infos --help

# conversions between formats
python -m phasme convert data.lp data.gml --anonymize

# generation of new graphs to play with
python -m phasme generate graph.gml powerlaw_cluster_graph n=5 m=2 p=0.01

# ready-to-draw tikz visualization of ASP graph
python -m phasme convert data.lp graph-in-latex-tikz.tex

# randomize a graph, keeping the same degree distribution, using switching method
python -m phasme randomize data.lp randomized-graph.gmlChangelog0.0.140.0.13randomize:--per-ccoption to run it on each connected component independantly0.0.12new subcommand:randomize, to build a random graph based on anothernew subcommand:extract, to extract subgraphs based on their nodesconvert: new output format: latex/tikzinfos: give infos on articulation pointsinfos: indicate number of self loops, if any, or give property 'no loop'
Package: phasty
phastyA Python interface for PHAST (phylogenetic analysis with space/time models).DescriptionPHASTis a package consisting of command-line programs for comparative genomics. It supports several nucleotide substitution models. This package phasty provides Python wrappers for some major programs so that it is easier to integrate them into complicated workflows. It can also parse some files from plain text to more computer-friendly forms to help downstream analysis.UsageThe design idea of phasty is to be robust and compatible with version changes of PHAST. Therefore, the signatures of functions are written in a general way. Please refer to the corresponding websites for detailed usage of specific options.ExampleAssumehmrc.faexists in the current directory. After runningphylo_fitand reading the .mod file, the content is stored in a list of objects holding attributes with proper data types. Each object represents a fitted model.fromphastyimportphylo_fit,parse_modoutput=phylo_fit("hmrc.fa",tree="((human,(mouse,rat)),cow)",subst_mod="U2S",EM=True,precision="MED",non_overlapping=True,out_root="hmrc-u2s",)mod_lst=parse_mod("hmrc-u2s.mod")print(mod_lst[0].rate_mat)InstallationDownload and install PHAST first.Install from PyPI:pipinstallphastyOr install from source after git clone:cdphasty
pipinstall-e.Run tests:pipinstall-e.[dev]python-mpytest--cov=phastytests/Uninstall:pipuninstallphastyNotesThis package usesSemantic Versioning.
Package: phatbeat
Python library for PHAT BEAT, a stereo DAC, AMP and VU meter with 5 input buttons for the Raspberry Pi.Learn more:https://shop.pimoroni.com/products/phat-beat0.1.1
—–Bugfix: Added sleep on clock to prevent unstable output on Pi 3B+ under load0.1.0Bugfix: Deferred initialisation to prevent import side-effectsBugfix: Implemented EOF for newer APA102 part0.0.2Bugfix: Corrected (reversed) order of pixels in second barNew: Optional threading for button handlersNew: Added hold() to attach a handler to a button hold event0.0.1Initial release
Package: phate
PHATE - Visualizing Transitions and Structure for Biological Data ExplorationQuick StartIf you would like to get started using PHATE, check out ourguided tutorial in Python.If you have loaded a data matrixdatain Python (cells on rows, genes on columns) you can run PHATE as follows:import phate
phate_op = phate.PHATE()
data_phate = phate_op.fit_transform(data)PHATE accepts the following data types:numpy.array,scipy.spmatrix,pandas.DataFrameandanndata.AnnData.IntroductionPHATE (Potential of Heat-diffusion for Affinity-based Trajectory Embedding) is a tool for visualizing high dimensional data. PHATE uses a novel conceptual framework for learning and visualizing the manifold to preserve both local and global distances.To see how PHATE can be applied to datasets such as facial images and single-cell data from human embryonic stem cells, check out our publication in Nature Biotechnology.Moon, van Dijk, Wang, Gigante et al.Visualizing Transitions and Structure for Biological Data Exploration. 2019.Nature Biotechnology.PHATE has been implemented inPython >=3.5,MATLABandR.Table of ContentsSystem RequirementsInstallation with pipInstallation from sourceQuick StartTutorial and ReferenceHelpSystem RequirementsWindows (>= 7), Mac OS X (>= 10.8) or LinuxPython >= 3.5All other software dependencies are installed automatically when installing PHATE.Installation withpipThe Python version of PHATE can be installed by running the following from a terminal:pip install --user phateInstallation of PHATE and all dependencies should take no more than five minutes.Installation from sourceThe Python version of PHATE can be installed from GitHub by running the following from a terminal:git clone --recursive git://github.com/KrishnaswamyLab/PHATE.git
cd PHATE/Python
python setup.py install --userTutorial and ReferenceFor more information, read thedocumentation on ReadTheDocsor view our tutorials on GitHub:single-cell RNA-seq,artificial tree. You can also access interactive versions of these tutorials on Google Colaboratory:single-cell RNA-seq,artificial tree.HelpIf you have any questions or require assistance using PHATE, please contact us athttps://krishnaswamylab.org/get-help.