Saravanan666/pypi-org huggingface dataset:
Package: hpgeom
HPGeomA lightweight implementation of HEALPix geometry functions, wrapped in a numpy interface.The implementation is based on the geometric functions in the originalHEALPixC++ library code.
This package has an all-new API, and also includes compatibility functions with the same interface ashealpyfor an easy transition from healpy toHPGeom.Requirements:At runtime,HPGeomrequiresnumpy.At build time, a working C compiler is also required.The full suite of tests requirehealpy.Install:The easiest way to installHPGeomis from pypi (pip install hpgeom) or from conda-forge (conda install hpgeom -c conda-forge).To install the package from source go to the parent directory of the package and runpip install ..
To include all test requirements, install optional dependencies withpip install .[test,test_with_healpy].Documentation:Documentation is available athttps://hpgeom.readthedocs.io/en/latest.Acknowledgements:TheHPGeomcode was written by Eli Rykoff based onHEALPixC++ code as well as work by Matt Becker and Erin Sheldon.This software was developed under the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC) using LSST DESC resources.
The DESC acknowledges ongoing support from the Institut National de Physique Nucléaire et de Physique des Particules in France; the Science & Technology Facilities Council in the United Kingdom; and the Department of Energy, the National Science Foundation, and the LSST Corporation in the United States.
DESC uses resources of the IN2P3 Computing Center (CC-IN2P3--Lyon/Villeurbanne - France) funded by the Centre National de la Recherche Scientifique; the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231; STFC DiRAC HPC Facilities, funded by UK BEIS National E-infrastructure capital grants; and the UK particle physics grid, supported by the GridPP Collaboration.
This work was performed in part under DOE Contract DE-AC02-76SF00515.
Package: hpICsp
This library provides a pure Python interface to the HP Insight Control server provisioning RESTful API.Installation### From source`bash $ git clonehttps://github.com/HewlettPackard/python-hpICsp.git$ cdpython-hpICsp$ python setup.py install--user# to install in the user directory(~/.local)$ sudo python setup.py install    # to install globally `
Package: hpinterp
hpinterpA faster (~100x) and more accurate interpolation algorithm for HEALPix maps by Prof. Jonathan Sievers. The code
pre-interpolates the map data in advance, saving time at the evaluation stage.Installationpip install hpinterpExampleimportnumpyasnpimporthealpyashpfromhpinterpimportInterpMapmap_=hp.read_map("your_map.fits")# Set nest=True if your map has nested orderinginterp_map=InterpMap(map_)# Generating example coordinate grid of co-latitude and longitude in radiansnpoints=int(1e5)theta=np.random.rand(npoints)*np.piphi=np.random.rand(2*npoints)*2*np.pi# Get interpolated values. Set lonlat=True if using longitude and latitude in degreesinterp_result=interp_map(theta,phi)# orinterp_result=interp_map.get_interp_val(theta,phi)
Package: hpke
hpke.pyThis is an implementation ofRFC9180in python3, usingcryptography.iofor the underlying cryptography.FeaturesModesmode_basemode_pskmode_authmode_auth_pskAEADsAES-128-GCMAES-256-GCMChaCha20Poly1305Export onlyKEMsDHKEM(P-256, HKDF-SHA256)DHKEM(P-384, HKDF-SHA384)DHKEM(P-521, HKDF-SHA512)DHKEM(X25519, HKDF-SHA256)DHKEM(X448, HKDF-SHA512)KDFsHKDF-SHA256HKDF-SHA384HKDF-SHA512AuthorJoseph Birr-Pixtonjpixton@gmail.comLicensehpke.py is licensed under the Apache License, Version 2.0. SeeLICENSEfor the full license text.
Package: hpkerga
hpkergapip install hpkergaGo tohttps://hp.kerga.fr.Add a custom entrypython-mhpkergaGitLab\--urlhttps://gitlab.com\--imagehttps://about.gitlab.com/images/press/logo/png/gitlab-logo-500.png\--groupGroup_1Send the filepython-mhpkergasend
Package: hplefthandclient
HP LeftHand/StoreVirtual REST Client===================This is a Client library that can talk to the HP LeftHand/StoreVirtual Storage array.The HP LeftHand storage array has a REST web service interface.This client library implements a simple interface to talk with that RESTinterface using the python Requests http library.Requirements============This branch requires 11.5 version or later of the LeftHand OS firmware.Capabilities============* Get Volume(s)* Get Volume by Name* Create Volume* Delete Volume* Modify Volume* Clone Volume* Get Snapshot(s)* Delete Shapshot* Get Shapshot by Name* Create Snapshot* Delete Snapshot* Clone Snapshot* Get Cluster(s)* Get Cluster by Name* Get Server(s)* Get Server by Name* Create Server* Delete Server* Add Server Access* Remove Server AccessInstallation============To install::$ sudo pip install .Unit Tests==========To run all unit tests::$ tox -e py27To run a specific test::$ tox -e py27 -- test/file.py:class_name.test_method_nameTo run all unit tests with code coverage::$ tox -e coverThe output of the coverage tests will be placed into the ``coverage`` dir.Folders=======* docs -- contains the documentation.* hplefthandlient -- the actual client.py library* test -- unit tests* samples -- some sample usesDocumentation=============To build the documentation::$ tox -e docsTo view the built documentation point your browser to::docs/html/index.htmlRunning Simulators==================Manually run flask server (when config.ini unit=true)::$ python test/HPLeftHandMockServer_flask.py -port 5001 -user <USERNAME> -password <PASSWORD> -debug
Package: hpln
HPLNCriação de umaprocessador de linguagem naturalem Python.[+] Versão0.7Modo de usousage: hpln.py [-h] [--fl] [--pl] [--pag PAG] [--abr ABR] [--lang LANG] texto

Processador de linguagem natural

positional arguments:
  texto        Arquivo de texto de entrada

optional arguments:
  -h, --help   show this help message and exit
  --fl         Retorna uma frase por linha
  --pl         Retorna uma palavra por linha
  --pag PAG    Retorna o conteúdo da página fornecida
  --abr ABR    Ficheiro de abreviações do utilizador
  --lang LANG  Especificação da língua das abreviaçõesInformaçõesO parâmetrolangespecifica a linguagem na qual as abreviações estão escritas. As abreviações utilizadas neste cenário fazem parte das abreviaçõesdefaultsutilizadas pelo sistema. Atualmente existem abrevisações para as seguintes línguas:Inglês:enPortuguês:ptCom o fornecimento do ficheiroabr, as abreviações utilizadas serão as que o utilizador passar através do ficheiro de configuração. Este ficheiro deve possuir uma abreviaçãopor linha.AbreviaçõesInglês:drprofdecsrsrtasraPortuguês:
TODO
Package: hpl-rv-gen
HPL Runtime Monitor GeneratorThis package provides a runtime monitor generator forHPL properties.InstallingInstalling a pre-packaged release:pipinstallhpl-rv-genInstalling from source:gitclonehttps://github.com/git-afsantos/hpl-rv-gen.gitcdhpl-rv-gen
pipinstall-e.UsageWhen used as a library, you can generate Python code for a runtime monitor class with a few simple steps.
For example:fromhpl.parserimportproperty_parserfromhplrv.renderingimportTemplateRendererp=property_parser()r=TemplateRenderer()input_property='globally: no (/a or /b)'hpl_property=p.parse(input_property)code=r.render_monitor(hpl_property)print(code)Bugs, Questions and SupportPlease use theissue tracker.ContributingSeeCONTRIBUTING.AcknowledgmentUntil March 2021, this work was financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project PTDC/CCI-INF/29583/2017 (POCI-01-0145-FEDER-029583).
Package: hpl-specs
HPL - The High-Level Property Specification LanguageHPL is a minimalistic specification language tailored for behavioural properties of message-based systems.
Although it might be useful in other contexts, it was designed with the message-passing system of theRobot Operating Systemin mind.
Since properties are message-based, the same language can be used to specify both individual nodes and full applications.HPL was developed to be an integral part of theHAROS framework.
HAROS is capable of defining and extracting architectural models of ROS systems (theROS Computation Graph).
Such models are, inherently, mostly concerned with the structure of the analysed system.
This language is meant to annotate the architectural models, complementing them with behavioural information.What Is In The BoxThis repository contains a Python package, and the respective source code, to parse HPL specifications and convert them intoAbstract Syntax Trees(AST).Syntax, Semantics and Use CasesCheck thedocumentation.InstallingTo install this package, make sure that you have Python 2.7 or greater.
Simply run the command:pip install hpl-specsBugs, Questions and SupportPlease use theissue tracker.CitingSeeCITING.ContributingSeeCONTRIBUTING.AcknowledgmentUntil March 2021, this work was financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia within project PTDC/CCI-INF/29583/2017 (POCI-01-0145-FEDER-029583).
Package: hpm
Welcome tohpm👋hpm - an HEPIA project manager🗃️ PackageInstallpip install hpmAuthor👤Tanguy Cavagnatanguy.cavagna@etu.hesge.chGitlab: @tanguy.cavagna🤝 ContributingContributions, issues and merge requests are welcome!
Feel free to checkissues page.When doing a MR, please follow this title naming convention :Your title must be clear and self-documentedIt can starts with one of those emoji if you want :✨ for introducing a new feature🎨 for structure/format of the code improvement⚡ for performance improvement🔥 for code/file deletion🐛 for any bug correction🚑 for a critical hotfix📝 for documentation add/update🚀 for deployement💄 for UI improvement🔧 for configuration changes✏️ for typosIf the emoji is not listed here, refer tohttps://gitmoji.dev/to use the correct one.Show your supportGive a ⭐ if you like this project or if it helped you !📝 LicenseCopyright © 2021 Tanguy Cavagna
This project isMITlicensed.Contributors ✨Contribute to this project and you will be added to this section !
Package: hpmoc
hpmoc: HEALPix Multi-Order Coordinate Partial SkymapsHPMOC is an ultra high-performance, cross-platform toolset for working with
multi-order coordinate (MOC) HEALPix_ images (i.e. images with multiple pixel
resolutions). MOC images are used by theLIGO-Virgo-KAGRA collaboration,
theInterplanerary Networkand others to represent portions of
the sky with variable resolution. By only including pixels in regions of
interest, and only then at a resolution appropriate to how they were
observed/calculated, it is possible to reduce storage and computation costs by
several orders of magnitude.HPMOC is theonlylibrary providing tools for loading partial/whole MOC
skymaps (as well as standard HEALPix skymaps), taking spatial intersections,
modifying resolution, plotting the skymaps, converting them to and fromAstropy WCSprojections,
performing pointwise math, and generating PSF skymaps from point sources,
all using algorithms that minimize memory, computation, and storage costs.
It is based off of work onLLAMA, the world's first Gravitational
Wave/High-Energy Neutrino low-latency search pipeline, which has been improved
and refactored into this separate module.If you usehpmocin published research, we ask that you citeStefan Countryman's thesis.hpmocis introduced in section 4.5.13.hpmocis licensed under the terms of theGNU General Public License, version 2 or laterInstallationhpmochas only a few dependencies, but they are large numerical/scientific
libraries. You should therefore probably create a virtual environment of some
sort before installing. The easiest and best way to do this at the moment is to
useconda, which should come with an Anaconda distribution of Python:condacreate-nhpmoc
condaactivatehpmocnote that creating a new environment is optional and hpmoc can now be installed similar to any other python package.With pipIf you just want to usehpmocand don't need to modify the source code, you
can install using pip:pipinstallhpmocThis should install all required dependencies for you.DevelopersIf you want to install from source (to try the latest, unreleased version, or
to make your own modifications, run tests, etc.), first clone the repository:gitclonehttps://github.com/markalab/hpmoc.gitcdhpmocMake sure the build tool,flit, is installed:pipinstallflitThen install an editable version ofhpmocwithflit:flitinstall--symlinkAs with thepipinstallation method, this should install all requirements for
you. You should now be able to importhpmoc. Note that you'll need to quit
yourpythonsession (or restart the kernel in Jupyter) and reimporthpmocbefore your changes to the source code take effect (which is true for any
editable Python installation, FYI).You can go ahead and run the tests withpytest(which should have been
installed automatically byflit):py.test--doctest-modules--cov=hpmoc
Package: hp-money
hp_moneyversion: 1.1.2author: [KuangQingxu](mailto:kuangqingxu@transfereasy.com?subject=[HipoPay - hp_money])date: 2020-04-26update: 2020-06-24Usage:- To be continuePackaging And Uploading:Update Verison:README.mdhp_money/__init__.pysetup.pyPacking:python setup.py sdist bdist_wheelUploading:twine upload --repository-url https://upload.pypi.org/legacy/ dist/*
Package: hpmpy-project
Newb tweaked non-typing version of the excellentHypermodern-Pythonproject
foundation proposed byClaudio JolowiczInstallationPlease see theInstallation Guide(Github Repo Link) for details.UsagePlease see theWorklow Reference(Github Repo Link) for details.ContributingContributions are very welcome.
To learn more, see theContributor Guide(Github Repo Link).LicenseDistributed under the terms of theMIT license(Github Repo Link),hpmpy-projectis free and open source software.IssuesIf you encounter any problems,
pleasefile an issuealong with a detailed description.CreditsThis project was created using theMathias Ammontweaked version of theHypermodern-Pythonproject foundation proposed byClaudio Jolowicz.
Package: hpnapy
HPNAPYHPNAPY is a Python3 library to access and interface with HP Network Automation's SOAP API.This library has bee developed testing against HP Network Automation 2018.11 and greater.More information on HP Network Automation can be found at (Micro Focus).RequirementsThis package requires Python 3.4+.It depends on the following modules:requests >= 1.0.0
zeep >= 3.0.0
urllib3 >= 1.0.0InstallingThis package is available through pypi and can be installed using pip.pip install hpnapyUsageHere is an example of connecting through the API and retrieving a list of device groups.from hpnapy import NAInterface

# Initialize our interface
hpna = NAInterface("https://foo.bar")
hpna.login('username', 'password')

# Retrieve and iterate a list of device groups
device_groups = hpna.list_device_group()
for entry in device_groups:
    print(entry)We can globally disable SSL verification to prevent errors and messaging. This is not recommended as this is a potential security concern.from hpnapy import NAInterface

# Initialize our interface
hpna = NAInterface("https://foo.bar", ssl_verify=False)In order to filter results, we can pass filter keys as defined in the HPE Network Automation Software (NA) CLI/API Command Reference.from hpnapy import NAInterface

# Initialize our interface
hpna = NAInterface("https://foo.bar")
hpna.login('username', 'password')

# Retrieve and iterate a list of device groups
filtered_devices = hpna.list_device(vendor="Cisco", group="My Cisco Switches Group")
for entry in filtered_devices:
    print(entry)ExceptionsThis library uses its own set of exceptions.hpnapy.exceptions.HPNAConnectionError
hpnapy.exceptions.HPNAQueryParamError
hpnapy.exceptions.HPNAQueryErrorContributingSpencer Ervin (spenceation) is the creator and current maintainers of the hpnapy library.Pull requests are always welcome. Before submitting a pull request, please ensure that your coding style follows PEP 8.LegalLicensed under the GNU General Public License v3.0; you may not use this file except in compliance with the License. You may obtain a copy of the License athttps://www.gnu.org/licenses/gpl-3.0.en.html
Package: hpoliced
Httpolice-Web turnsHTTPoliceinto a web server. This was written forHTTPolice-devtoolwhich is aChrome extensionthat intercepts HTTP
requests and gets them analysed and shows reports generated through a
devtool panel.UsageThis will start the server at port8080$ hpolicedTo specify an arbitrary port number$ hpoliced -p <port>The project is hosted on GitHub, atthis location.
Package: hpoo
This module allow you to interact easily with HPOO in python# Installation methodpip install hpoo# Usage samplefrom hpoo import HPOO
oo = HPOO(url=”[URL OF YOUR INSTANCE]”, user=”[USERNAME]”, password=”[PASS]”)
oo.start_flow(uuid=’[FLOW-UUID]’, runName=’my run’, inputs={})
Package: hpo-similarity
Similarity of phenotypes in patient groupsThis estimates how likely it is for groups of individuals to have similar
phenotypes.  To estimate this probability, we need three things:a way to quantify phenotypic similarity of two individuals. We use the maximum
information content of the most informative common ancestor for each pair of
HPO terms from two probands.a way to quantify similarity across more than two probands. We sum phenotypic
similarity scores from all pairs of probands.a null distribution of similarity scores for those probands, generated by
randomly sampled groups of probandsThe P value is calculated as the proportion of simulated scores greater than the
observed probands' score.InstallInstall the package with:pipinstallhpo_similarityRunhpo_similarity--genesgenes.json--phenotypesphenotypes.jsonThe data directory on github includes example files, one with proband IDs
per gene (data/example_genes.json), and the other
with HPO terms per proband (data/example_phenotypes.json).Additional options:--output PATHto send output gene and P-values to a file.--ontology PATHto use a HPO ontology file other than the default.--iterations INTEGERto change the number of iterations (default=100000)You can also explore the HPO graph using the hpo_similarity package within
python, for example:fromhpo_similarityimportopen_ontologygraph,alt_ids,obsolete_ids=open_ontology()# find all descendant termsgraph.get_descendants('HP:0001249')# get the text for the phenotypic abnormalitygraph.nodes['HP:0001249']['name']AcknowledgmentsThis code incorporates the following code and datasets:apython ontology parserwritten by Tamás Nepusz.thehp.obo filefrom theHuman Phenotype Ontology Consortium.
Package: hpo-toolkit
hpo-toolkitA toolkit for working with Human Phenotype Ontology (HPO) and HPO disease annotations in Python.ExampleLoading HPO is as simple as:importhpotkhpo=hpotk.load_ontology('http://purl.obolibrary.org/obo/hp.json')Now you have HPO concepts and the ontology hierarchy at your fingertips.Next, load the HPO disease annotations by running:fromhpotk.annotations.load.hpoaimportSimpleHpoaDiseaseLoaderhpoa_path='https://github.com/obophenotype/human-phenotype-ontology/releases/download/v2023-10-09/phenotype.hpoa'loader=SimpleHpoaDiseaseLoader(hpo)diseases=loader.load(hpoa_path)assertlen(diseases)==12_468You got yourself phenotype annotations of 12,468 rare diseases.Learn moreFind more info in our detailed documentation:Stable documentation(last release onmainbranch)Latest documentation(bleeding edge, latest commit ondevelopmentbranch)
Package: hpo-uq
ContentsDocumentationWhy HPO-UQ?Copyright NoticeDocumentationmasterWhy HPO-UQ?While there is already a plethora of hyperparameter optimization tools publicly available for machine learning applications, none of them take into account an essential aspect of such models which is that neural network models are built using a stochastic solver approach. The HPO-UQ software was developed with the goal of implementing uncertainty quantification on the models’ parameters.Copyright NoticeHYPPO Copyright (c) 2021, The Regents of the University of California,
through Lawrence Berkeley National Laboratory (subject to receipt of
any required approvals from the U.S. Dept. of Energy). All rights reserved.If you have questions about your rights to use or distribute this software,
please contact Berkeley Lab’s Intellectual Property Office atIPO@lbl.gov.NOTICE.  This Software was developed under funding from the U.S. Department
of Energy and the U.S. Government consequently retains certain rights.  As
such, the U.S. Government has been granted for itself and others acting on
its behalf a paid-up, nonexclusive, irrevocable, worldwide license in the
Software to reproduce, distribute copies to the public, prepare derivative
works, and perform publicly and display publicly, and to permit others to do so.
Package: hpp-fcl
HPP-FCL — An extension of the Flexible Collision LibraryFCLwas forked in 2015. Since then, a large part of the code has been rewritten or removed (for the unused and untested part).
The broad phase was reintroduced by J. Carpentier in 2022 based on the FCL version 0.7.0.New featuresCompared to the originalFCLlibrary, the main new features are:a dedicated and efficient implementation of the GJK algorithm (we do not rely anymore onlibccd)the support of safety margins for collision detectionan accelerated version of collision detectionà la Nesterov, which leads to increased performances (up to a factor of 2). More details are available in thispaperthe computation of a lower bound of the distance between two objects when collision checking is performed, and no collision is foundthe implementation of Python bindings for easy code prototypingthe support of height fields, capsule shapes, etc.the fix of various bugsThis project is now used in many robotics frameworks such asPinocchio, an open-source software that implements efficient and versatile rigid body dynamics algorithms and theHumanoid Path Planner, an open-source software for Motion and Manipulation Planning.A high-performance libraryUnlike the original FCL library, HPP-FCL implements the well-establishedGJK algorithmandits variantsfor collision detection and distance computation. These implementations lead to state-of-the-art performances, as depicted by the figures below.On the one hand, we have benchmarked HPP-FCL against major software alternatives of the state of the art:theBullet simulator,the originalFCL library(used in theDrake framework),thelibccd library(used inMuJoCo).The results are depicted in the following figure, which notably shows that the accelerated variants of GJK largely outperform by a large margin (from 5x up to 15x times faster).On the other hand, why do we care about dedicated collision detection solvers like GJK for the narrow phase? Why can't we simply formulate the collision detection problem as a quadratic problem and call an off-the-shelf optimization solver likeProxQP)? Here is why.One can observe that GJK-based approaches largely outperform solutions based on classic optimization solvers (e.g., QP solver likeProxQP), notably for large geometries composed of tens or hundreds of vertices.AcknowledgmentsThe development ofHPP-FCLis actively supported by theGepetto team@LAAS-CNRS, theWillow team@INRIAand, to some extend,Eureka Robotics.