Saravanan666/pypi-org huggingface dataset:
Package: gbpcli
CLI for Gentoo Build PublisherIntroductionThis is a command-line interface forGentoo Build
Publisher, a system that
combines Gentoo Linux and CI/CD so that updating a Gentoo predictable and
consistent.This is a pip-installable Python package:$pipinstallgbpcliUsageThe full command set supported:usage:Command-lineinterfacetoGentooBuildPublisher

Commands:*build-ScheduleabuildforthegivenmachineinCI/CD*diff-Handlerforsubcommand*inspect-Showthemachinesbuildsasatree*keep-Keep(orrelease)abuild*latest-Showthelatestbuildnumberforamachine*list-Listamachine's builds* logs - Show build logs* machines - List machines with builds* notes - Show, search, and edit build notes* packages - List a build'spackages*publish-Publishabuild*pull-Pullabuild*status-Showbuilddetails*tag-Addtagsbuilds

positionalarguments:{build,diff,inspect,keep,latest,list,logs,machines,notes,packages,publish,pull,status,tag}options:-h,--helpshowthishelpmessageandexit--versionshowprogram's version number and exit--url URL             GBP url--color WHEN          colorize output ('always', 'never', 'auto')--my-machinesMY_MACHINESwhitespace-delimitedlistofmachinenamestofilteronwhenusingthe--mineargument.TypicallyonewouldinsteadusetheGBPCLI_MYMACHINESenvironmentvariable.The URL for the Gentoo Build Publisher may be provided via the command line or
by using theBUILD_PUBLISHER_URLenvironment variable.To list the machines which have builds:The "Latest" column shows the latest build ID for the given machine. If the ID
is in bold it that denotes that the latest build is published (available for
emerges).To list the available builds for a given machine:In the above example, thePoutput for build150signifies that this build
is currently published.  The*flag means that the respective build has new
binary packages. TheKfor build64means that the build is marked for
keeping and will not be removed during the purge process. Build2has also
been given a "first" tag.  TheNflag for build151means that the build
has a note attached.Edit/delete build notes using thegbp notescommand.Thestatussubcommand displays metadata about a given build.  If the build
number is not given, it defaults to the latest build for that machine.Thediffsubcommand display differences between two build.If the second build number is not given, it defaults to the latest build for
that machine.  If the first build number is not given, it defaults to the
published build for that machine.Thepublishsubcommand makes the given build available for syncing and
updating/downgrading.$gbppublisharm64-base151If the build number is not given, it defaults to the latest build for that
machine.Thebuildsubcommand can schedule a build in CI/CD for the given machine,
e.g.:$gbpbuildbabette
Package: gbpfunctests
# This test-suite comprise:
# GBP Functional Testcases
#
# Assumptions:
# 1. The pip install will put this package by default in
#     /usr/lib/python2.7/site-packages/gbpfunctests/ (for RHEL)
#    /usr/local/lib/python2.7/dist-packages/gbpfunctests/ (for Ubuntu)
#
# 2. Before the user runs any test,user will source the rc file needed for keystone authentication
#
# Usage: User can run each test-script or entire test-suite(run_suite.sh) in any of two ways:
#        1. If the default location of the package is appended to the $PATH
#           then executable files can be run rom anywhere
#        2. The executable can be run from the default location
#        3. To run any standalone testsuite file, execute “python tc_gbp_*” in “testcases” directory
#        4. To run the complete suite, execute “python suite_run.py” in “testcases” directory
#
# Test Report: Depending on the location from where the suite is run, a file “test_reports.txt”
#              get created in that location. This comprise the consolidated test results of the suite
Package: gb-probability-dist
Gb_probability_dist packageGb_probability_dist is a Python package that provides Gaussian and Binomial distribution implementations. With these classes you can instantiate an object in a distribution and then plot it or load data from a file. You can also add one with another distribution of the same type to get a third one.InstallationDependenciesgb_probability_dist requires:PythonMatplotlibgb_probability_distworks with any version of python and matplotlib.User installationYou can install it easily with pip.pip install gb_probability_distCreate your first probability distributions$python>>>fromgb_probability_distimportGaussian,Binomial>>>Gaussian(10,7)mean10,standarddeviation7>>>Binomial(.4,25)mean10.0,standarddeviation2.4494897422783178,n25>>>new_gaussian=Gaussian(25,2)+Gaussian(50,5)>>new_gaussianmean75,standarddeviation5.385164807134504
Package: gbprocess-ngs
# Gbprocess## DescriptionGBprocesS allows for the extraction of genomic inserts from NGS data for GBS experiments.
Preprocessing is performed in different stages that are part of a linear pipeline where the steps are performed in order.
GBprocesS provides a flexible way to adjust the functionality to your needs, as the operations required and the execution order vary depending on the GBS protocol used.## DocumentationAn extensive manual of the GBprocesS package can be found on [Read the Docs](https://gbprocess.readthedocs.io/en/latest/) including detailed explanations and illustrations.## CitationIf you use GBprocesS, please cite
“Schaumont, D. (2020) GBprocesS: Genotyping-by-Sequencing Data Processing Toolkit [Online]. Available online athttps://gitlab.com/dschaumont/GBprocesS”## InstallationGBprocesS is being developed and tested on Linux.
Additionally, some dependencies are only developped on Linux.### From pypi
The package can be installed using the latest version ofpip3:pip install –user gbprocess-ngsThis will install GBprocesS in$HOME/.local/bin.
To install at another location, a virtual environment can be used:python3 -m venv .venv
source .venv/bin/activate
pip install gbprocess-ngs### Via GitCurrently, there are two supported ways to download GBprocesS from Git.
The first and easiest way is to clone the git repository:git clonegit@gitlab.com:dschaumont/gbprocess.gitThe second is to download an archive:wgethttps://gitlab.com/ilvo/GBprocesS/-/archive/2.0.2/GBprocesS-2.0.2.tar.gztar -xvf GBprocesS-2.0.2.tar.gz
mv GBprocesS-2.0.2 gbprocessAfterwards, the package can be installed using the latest version ofpip3:cd gbprocess
pip3 install --user .This will install GBprocesS in$HOME/.local/bin.
If an already existing installation is present, add the--upgradeparameter to install a newer version.To install at another location, a virtual environment can be used:python3 -m venv .venv
source .venv/bin/activate
cd gbprocess
pip install .A new folder.venvwill be created in the current working directory,
and GBprocesS will be installed at that location. Please note that you will need to activate the virtual environment again when you start a new session using thesourcecommand above.After the installation, test if the program is correctly installed by using:gbprocess --help
Package: gbptestheat
Heat is a service to orchestrate multiple composite cloud applications using
templates, through both an OpenStack-native ReST API and a
CloudFormation-compatible Query API.Why heat? It makes the clouds rise and keeps them there.Getting StartedIf you’d like to run from the master branch, you can clone the git repo:git clonegit@github.com:openstack/heat.gitWiki:http://wiki.openstack.org/HeatDeveloper docs:http://docs.openstack.org/developer/heatPython clienthttps://github.com/openstack/python-heatclientReferenceshttp://docs.amazonwebservices.com/AWSCloudFormation/latest/APIReference/API_CreateStack.htmlhttp://docs.amazonwebservices.com/AWSCloudFormation/latest/UserGuide/create-stack.htmlhttp://docs.amazonwebservices.com/AWSCloudFormation/latest/UserGuide/aws-template-resource-type-ref.htmlhttp://www.oasis-open.org/committees/tc_home.php?wg_abbrev=toscaWe have integration withhttps://github.com/openstack/python-novaclient(instance)https://github.com/openstack/python-keystoneclient(auth)https://github.com/openstack/python-swiftclient(s3)https://github.com/openstack/python-neutronclient(networking)https://github.com/openstack/python-ceilometerclient(metering)https://github.com/openstack/python-cinderclient(storage service)https://github.com/openstack/python-glanceclient(image service)https://github.com/openstack/python-troveclient(database as a Service)
Package: gbptesthorizon
Horizon is a Django-based project aimed at providing a complete OpenStack
Dashboard along with an extensible framework for building new dashboards
from reusable components. Theopenstack_dashboardmodule is a reference
implementation of a Django site that uses thehorizonapp to provide
web-based interactions with the various OpenStack projects.For release management:https://launchpad.net/horizonFor blueprints and feature specifications:https://blueprints.launchpad.net/horizonFor issue tracking:https://bugs.launchpad.net/horizonGetting StartedFor local development, first create a virtualenv for the project.
In thetoolsdirectory there is a script to create one for you:$ python tools/install_venv.pyAlternatively, therun_tests.shscript will also install the environment
for you and then run the full test suite to verify everything is installed
and functioning correctly.Now that the virtualenv is created, you need to configure your local
environment.  To do this, create alocal_settings.pyfile in theopenstack_dashboard/local/directory.  There is alocal_settings.py.examplefile there that may be used as a template.If all is well you should able to run the development server locally:$ tools/with_venv.sh ./manage.py runserveror, as a shortcut:$ ./run_tests.sh --runserverSetting Up OpenStackThe recommended tool for installing and configuring the core OpenStack
components isDevstack. Refer to their documentation for getting
Nova, Keystone, Glance, etc. up and running.NoteThe minimum required set of OpenStack services running includes the
following:Nova (compute, api, scheduler, network,andvolume services)GlanceKeystoneOptional support is provided for Swift.DevelopmentFor development, start with the getting started instructions above.
Once you have a working virtualenv and all the necessary packages, read on.If dependencies are added to eitherhorizonoropenstack_dashboard,
they should be added torequirements.txt.Therun_tests.shscript invokes tests and analyses on both of these
components in its process, and it is what Jenkins uses to verify the
stability of the project. If run before an environment is set up, it will
ask if you wish to install one.To run the unit tests:$ ./run_tests.shBuilding Contributor DocumentationThis documentation is written by contributors, for contributors.The source is maintained in thedoc/sourcedirectory usingreStructuredTextand built bySphinxBuilding Automatically:$ ./run_tests.sh --docsBuilding Manually:$ tools/with_venv.sh sphinx-build doc/source doc/build/htmlResults are in thedoc/build/htmldirectory
Package: gbptesthorizonui
Horizon is a Django-based project aimed at providing a complete OpenStack
Dashboard along with an extensible framework for building new dashboards
from reusable components. Theopenstack_dashboardmodule is a reference
implementation of a Django site that uses thehorizonapp to provide
web-based interactions with the various OpenStack projects.For release management:https://launchpad.net/horizonFor blueprints and feature specifications:https://blueprints.launchpad.net/horizonFor issue tracking:https://bugs.launchpad.net/horizonGetting StartedFor local development, first create a virtualenv for the project.
In thetoolsdirectory there is a script to create one for you:$ python tools/install_venv.pyAlternatively, therun_tests.shscript will also install the environment
for you and then run the full test suite to verify everything is installed
and functioning correctly.Now that the virtualenv is created, you need to configure your local
environment.  To do this, create alocal_settings.pyfile in theopenstack_dashboard/local/directory.  There is alocal_settings.py.examplefile there that may be used as a template.If all is well you should able to run the development server locally:$ tools/with_venv.sh ./manage.py runserveror, as a shortcut:$ ./run_tests.sh --runserverSetting Up OpenStackThe recommended tool for installing and configuring the core OpenStack
components isDevstack. Refer to their documentation for getting
Nova, Keystone, Glance, etc. up and running.NoteThe minimum required set of OpenStack services running includes the
following:Nova (compute, api, scheduler, network,andvolume services)GlanceKeystoneOptional support is provided for Swift.DevelopmentFor development, start with the getting started instructions above.
Once you have a working virtualenv and all the necessary packages, read on.If dependencies are added to eitherhorizonoropenstack_dashboard,
they should be added torequirements.txt.Therun_tests.shscript invokes tests and analyses on both of these
components in its process, and it is what Jenkins uses to verify the
stability of the project. If run before an environment is set up, it will
ask if you wish to install one.To run the unit tests:$ ./run_tests.shBuilding Contributor DocumentationThis documentation is written by contributors, for contributors.The source is maintained in thedoc/sourcedirectory usingreStructuredTextand built bySphinxBuilding Automatically:$ ./run_tests.sh --docsBuilding Manually:$ tools/with_venv.sh sphinx-build doc/source doc/build/htmlResults are in thedoc/build/htmldirectory
Package: gbptestneutron
# – Welcome!You have come across a cloud computing network fabric controller.  It has
identified itself as “Neutron.”  It aims to tame your (cloud) networking!# – External Resources:The homepage for Neutron is:http://launchpad.net/neutron.  Use this
site for asking for help, and filing bugs. Code is available on
git.openstack.org at
<http://git.openstack.org/cgit/openstack/neutron>.The latest and most in-depth documentation on how to use Neutron is
available at: <http://docs.openstack.org>.  This includes:Neutron Administrator Guidehttp://docs.openstack.org/trunk/openstack-network/admin/content/Neutron API Reference:http://docs.openstack.org/api/openstack-network/2.0/content/The start of some developer documentation is available at:http://wiki.openstack.org/NeutronDevelopmentFor help using or hacking on Neutron, you can send mail to
<mailto:openstack-dev@lists.openstack.org>.
Package: gbq
GBQPython wrapper for interacting Google BigQuery.This package provides an interface by wrapping Google's low level library. It exposes options to provide input as json objects which can be used for various CI/CD tools.UsageBasic UsagefromgbqimportBigQuery# BigQuery project id as listed in the Google Developers Console.project_id='project_id'# BigQuery dataset id as listed in the Google Developers Console.dataset_id='dataset_id'# BigQuery table/view id as listed in the Google Developers Console.structure_id='structure_id'# BigQuery structure definition as defined in the Google Developers Console.json_schema={"type":"table","schema":[{"id":"integer"}]}# Service account email address as listed in the Google Developers Console.svc_account='{"type": "service_account",   "project_id": "project_id"}'bq=BigQuery(svc_account=svc_account,project=project_id)bq.create_or_update_structure(project_id,dataset_id,structure_id,json_schema)DocumentationCheck out theproject documentation
Package: gbquality
gbqualityThis is a python "translation" of the original MATLAB code for the QGBmeasure of global quality assessment
of Non-Linear Dimensionality Reduction (NLDR).Theoriginal paper(author-providedPDF) should be cited:Please cite: "Deyu Meng, Yee Leung, Zongben Xu.
A new quality assessment criterion for nonlinear dimensionality reduction, Neurocomputing, 2011, 74(6): 941-948."TheMATLAB codewas written byDeyu Meng. The Python code is an  adaptation by Andrew Lensen.The code is packaged as gbquality onpypiand so can be installed in the normal way withpip install gbquality
Package: gbrf
Summary:GBRF (Gradient Boosted Random Forest)GBRF is a Python package that implements a custom machine learning algorithm for classification tasks. It combines the strengths of two popular algorithms, Random Forest and Gradient Boosting, to achieve better accuracy and generalization on various datasets.GBRF stands for "Gradient Boosting Regression Forest". It combines the principles of gradient boosting and random forests to build a more robust and accurate regression model. Here are some features of the gbrf package:It provides a class called GBRF that wraps the GradientBoostingRegressor class from scikit-learn and adds additional functionality such as cross-validation, grid search, and feature importance plotting.It has methods to fit a model, predict, and evaluate the performance of the model.It provides a method to plot the feature importances of the model.It provides methods to save and load the trained model to and from a file using the joblib library.It is easy to use and can be integrated into other Python programs with minimal effort.It can handle both regression and classification tasks.It can be used for large datasets as it supports parallel processing.It can be used for feature selection as it provides a method to rank the importance of features.The file is availabe onhttps://pypi.org/project/gbrf/#filesSteps to use:Open the code editor like jupyter or google colab where you can install packages. Like in Jupyter type-!pip install gbrfOne can also use command prompt. Open the path where python is installed and type-pip install gbrfOnce installation is done open your IDE and type-from gbrf import GBRFChange Log1.0.7(18/03/2023)Seventh Release
Package: gbrokerexe
gbrokerexeGeneric Python Package for Brokerage and Trade Executions.Featuresoanda APIalpaca-trade APIothersReleasesYou can see the list of available releases on theGitHub Releasespage.  
We useRelease Drafter. As pull requests are merged, a draft release is kept up-to-date listing the changes.LicenseThis project is licensed under the terms of theGNU GPL v3.0license.📃 Citation@misc{gbrokerexe,author={altcp},title={Generic Python Package for Brokerage and Trade Executions},year={2021},publisher={GitHub},journal={GitHub repository},howpublished={https://github.com/altcp/gbrokerexe}}CreditsThis project was generated withpython-package-template
Package: gbrs
====GBRS====.. image:: https://img.shields.io/pypi/v/gbrs.svg:target: https://pypi.python.org/pypi/gbrs.. image:: https://anaconda.org/kbchoi/gbrs/badges/version.svg:target: https://anaconda.org/kbchoi/gbrs.. image:: https://travis-ci.org/churchill-lab/gbrs.svg?branch=master:target: https://travis-ci.org/churchill-lab/gbrs.. image:: https://readthedocs.org/projects/gbrs/badge/?version=latest:target: https://readthedocs.org/projects/gbrs/?badge=latest:alt: Documentation StatusOverview--------**GBRS** is a suite of tools for reconstructing genomes using RNA-Seq data from multiparent population and quantifying allele specific expression. Although we tested it with mouse models only, GBRS should work for any multiparent populations. If you are interested in using it for other multiparent population model, please contact Kwangbom "KB" Choi <kb.choi@jax.org> at The Jackson Laboratory. For the Diversity Outbred (DO https://www.jax.org/strain/009376), Collaborative Cross (CC) mice, or F1 hybrids of CC's (CCRIX), required data files are available at ftp://churchill-lab.jax.org/pub/software/GBRS/.* Free software: GPLv3 license* Documentation: https://gbrs.readthedocs.org.References----------Manuscript in preparation (Expected in 2016)=======History=======0.1.5 (05/04/2016)~~~~~~~~~~~~~~~~~~* ``compress`` subcommand also merges emase files* ``reconstruct`` does not require ``ref.gene_ids.ordered.npz`` file anymore0.1.4 (05/04/2016)~~~~~~~~~~~~~~~~~~* Uploaded to Anaconda Cloud0.1.2 (04/27/2016)~~~~~~~~~~~~~~~~~~* Setup Travis CI automated building0.1.1 (04/27/2016)~~~~~~~~~~~~~~~~~~* Moved to github churchill-lab space* First release onto PyPI0.1.0 (01/22/2016)~~~~~~~~~~~~~~~~~~* Started a private project on bitbucket.
Package: gbs
gbs<<<<<<< Updated upstreamAboutgbor gopherball is a gopher server written in Python with the main goals of
ease of use and integration. The name gopherball is inspired by a recurring
theme in the Calvin & Hobbes comicbooks and a tongue in cheek reference of an
alternative to the World Wide Web as we know it today.A gopher server implemented on top of thegb-library.Stashed changesExamplesQuick examples to get you running.<<<<<<< Updated upstreamgb --mode=implicit .will start a gopher server on127.0.0.1port7070serving
a recursive index of files starting from the current directory.gb --mode=implicit --magic .will startgbin magic-mode on127.0.0.1port7070. Magic mode will makegbguess at filetypes.gb --mode=implicit --host="127.1.1.1" --port 1025 .will startgbin implicitgbs --mode=implicit .will start a gopher server on127.0.0.1port7070serving
a recursive index of files starting from the current directory.gbs --mode=implicit --magic .will startgbsin magic-mode on127.0.0.1port7070. Magic mode will makegbsguess at filetypes.gbs --mode=implicit --host="127.1.1.1" --port 1025 .will startgbsin implicitStashed changes
mode on the chosen ip and port. Note that using ports under 1024 requires
superuser permissions!Modesgbshas one main mode of operation that is commonly used. More modes are
planned for the future.implicitImplicit mode serves a directory recursively. Indexes are automatically
generated and text files are served to the client. Data files are also
supported.Magicgbswill serve all non-directories as type 9 files, these are non-readable
files and most clients will prompt for download. Turning on magic with--magicwill letgbstry to determine the correct filetypes.Modesgbhas one main mode of operation that is commonly used. More modes are
planned for the future.implicitImplicit mode serves a directory recursively. Indexes are automatically
generated and text files are served to the client. Data files are also
supported.Magicgbwill serve all non-directories as type 9 files, these are non-readable
files and most clients will prompt for download. Turning on magic with--magicwill letgbtry to determine the correct filetypes.ContributingThe source code forgblives on my Gitea where you can also submit issues and
pull requests. It mostly needs help by people with the ability to test in
various clients and libraries that might still support the gopher protocol.