Saravanan666/pypi-org huggingface dataset:
Package: gpit
GPiTEffortless Git Integration with GPT-Powered Commit AssistanceOverviewThis tool is designed to automate several common Git operations, enhancing the standard Git workflow with additional features. It helps in managing unpushed commits, checking for local changes, generating commit messages using OpenAI's GPT-4 API, editing commit messages, and handling the push operation to a remote repository.Features🔍Check for Unpushed Commits: Quickly identify any commits that haven't been pushed to the remote repository.📝Detect Local Changes: View a summary of local changes that are not yet committed.💬Generate Commit Messages: Utilizes GPT-4 to suggest commit messages based on the detected changes.✏️Edit Commit Messages: Offers an interactive editor for customizing commit messages.🚀Stage, Commit, and Push: Automates the process of staging changes, committing them with a chosen message, and pushing to the remote repository.InstallationGPIT can be installed directly from PyPI:pipinstallgpitThis tool requires Python 3.x. Make sure you have Python installed on your system.Setting Up OpenAI API KeyBefore using the tool, make sure to set your OpenAI API key as an environment variable:exportOPENAI_API_KEY='your_openai_api_key'Alternatively, you can set it in a.envfile in the same directory as the script.UsageOnce installed, you can run GPIT from any Git repository on your system. Simply use the following command:gpitFollow the on-screen prompts to manage your Git operations.DependenciesGitPython 3.xRequests library for Pythonpython-dotenv (for managing environment variables)Install Python dependencies using:pipinstallrequestspython-dotenvContributingContributions to this project are welcome! Please fork the repository and submit a pull request with your proposed changes.
Package: gpkg.hello
gpkg.helloSample “hello world” model (Guild AI)ModelshelloA “hello world” sample modelThis is a sample model that provides various operations for printing hello
world messages.The message provided, either by default, by a flag, or a file (see each
operation for the applicable interface) is printed to standard output and
written to a file namedoutputin the run directory.OperationsdefaultPrint a default messageThis operation doesn not support a user-defined message. Use the from-flag
operation with the ‘message’ flag to print an alternative message.Flagsfilefile-output(default is no)messagerun-dirfrom-filePrint a message from a fileUse the ‘file’ flag to specify the file to print.FlagsfileFile containing the message to print (default is msg.txt)file-output(default is no)messagerun-dirfrom-file-outputPrint output from last file-output operationThis operation requires a completed file-output run.Flagsfilefile-output(default is no)messagerun-dirfrom-flagPrint a messageUse the ‘message’ flag to specify the message to print.Flagsfilefile-output(default is no)messageMessage to print (default is Hello Guild, from a flag!)run-dir_checkOperationsalldefaultfrom-filefrom-file-outputfrom-flag
Package: gpkg.keras.deep-dream
gpkg.keras.deep-dreamDeep Dream generator in Keras (Guild AI)Modelsdeep-dreamOperationsgenerateGenerate a Deep Dream imageFlagsimageInput image used by Deep Dream (required)m2Weight for ‘mixed2’ layer (1.0)m3Weight for ‘mixed3’ layer (1.0)m4Weight for ‘mixed5’ layer (1.0)m5Weight for ‘mixed5’ layer (1.0)
Package: gpkg.magenta.image
gpkg.magenta.image##################*Image generators from the Magenta project (Guild AI)*Models######arbitrary-stylize=================*Fast artistic style transfer using arbitrary painting styles*Operations^^^^^^^^^^generate--------*Generate stylized images using one or more style images*Flags`````**content-images***Path to content images (include glob pattern matching images) (required)***image-size***Size of images (1024)***interpolation-weights***Interpolation weights ([1.0])This value is a list of float values inside square brackets. Each value isa weight for interpolation between the parameters of the identitytransform and the style parameters of the style image.The larger the weight is the strength of stylization is more. Weight of1.0 means the normal style transfer and weight of 0.0 means identitytransform.  ***style-images***Path to style images (include glob pattern matching images) (required)*References^^^^^^^^^^- https://github.com/tensorflow/magenta/tree/master/magenta/models/arbitrary_image_stylization- https://arxiv.org/abs/1705.06830- https://arxiv.org/abs/1610.07629- https://arxiv.org/abs/1603.08155- https://arxiv.org/abs/1508.06576pretrained-stylize==================*Implementation of 'A Learned Representation for Artistic Style'*Operations^^^^^^^^^^generate--------*Generate stylized image using a pretrained model*Flags`````**image***Image to stylize (required)***style***Style to apply (monet or varied) (required)Choices:monet   Use Monet stylevaried  Use varied style*References^^^^^^^^^^- https://github.com/tensorflow/magenta/tree/master/magenta/models/image_stylization- https://arxiv.org/abs/1610.07629
Package: gpkg.magenta.music
gpkg.magenta.music##################*Music generators from the Magenta project (Guild AI)*Models######melody======*Applies language modeling to melody generation using an LSTM*Operations^^^^^^^^^^generate--------*Compose melodies using one of three available pretrained models*Flags`````**config***Model configuration (basic, lookback, or attention) (basic_rnn)Choices:basic_rnn      Use basic LSTMlookback_rnn   Use RNN with lookbackattention_rnn  Use RNN with attention***outputs***Number of melodies to generate (10)***primary-midi***MIDI file used to prime the generator***primer-melody***Melody to prime the generator***steps***Melody length (16 steps = 1 bar) (128)*
Package: gpkg.mnist
gpkg.mnistCNN and multinomial logistic regression classifiers for MNIST digits (Guild
AI)ModelscnnCNN classifier for MNISTOperationsevaluateEvaluate a trained CNNtrainTrain the CNNFlagsbatch-sizeNumber of images to include in a training batch (default is 100)epochsNumber of epochs to train (default is 5)learning-rateLearning rate (default is 0.0001)logregMultinomial logistic regression classifier for MNISTOperationsevaluateEvaluate a trained logistic regressiontrainTrain the logistic regressionFlagsbatch-sizeNumber of images to include in a training batch (default is 100)epochsNumber of epochs to train (default is 5)learning-rateLearning rate (default is 0.5)samplesSample MNIST imagesOperationsprepareGenerate a set of sample MNIST imagesFlagscountNumber of images to generate (default is 100)_checkOperationsallcnnlogreg
Package: gpkgstatus
gpkgstatusGet Current Package Status from Fedora Updates SystemThis program is a command-line tool for checking the status of packages on bodhi.fedoraproject.org.
It allows you to search for a specific package and get information about its current status, including the update ID, package name, and status.Current Build StatusCurrent PyPI StatusCurrent conda-forge StatusCurrent Release StatusFeaturesSearch for a specific package and get its status information.Option to specify a distro version to check for a specific version of the package.Caches the previous search results for 60 minutes (1 hour) to avoid DDoS.Allows you to force a new search, bypassing the cache.Displays the information in a user-friendly format.InstallationThere are two methods of installing gpgkstatus:Using pip:You can usepip install gpkgstatusand restart your terminal. Now you can use it!(Unsupported) Using executables from Github:You can download executable fromReleasesand use it directly. Make sure to set it as executable on Linux.Note:If you want to run the file anywhere when using executable from Github, move the file to a directory in your system's PATH (usually /usr/local/bin)Why Method 2 is unsupported?You have to update executable manually!UsageThe program can be used by running the executable file with the following command:$gpkgstatus-husage: gpkgstatus [-h] [-f] [-l LIMIT] [--moreinfo] [-n NAME] [--noconfig] [-r RELEASE] [-v] [--version]Get Current Package Status from Fedora Updates Systemoptions:-h, --help            show this help message and exit-f, --force           Sync cached info with Fedora Updates System-l LIMIT, --limit LIMITMaximum limit on number of packages shown for package search--moreinfo            Verbose (More Info) in Update Info-n NAME, --name NAME  Name of the package--noconfig            Do not check for config file-r RELEASE, --release RELEASEChecks package status for corresponding Fedora release-v, --verbose         Enable verbose output--version             gpkgstatus versionContributingYou are welcome to contribute to this project. Make sure to readContributing Guidelines. If you have any questions regarding contributing to this project, you can open an issue about it.CreditsThe development of this project would not have been possible withoutBodhideveloped by theFedora Project. Although this project is unofficial and not affiliated with Bodhi or Fedora, we appreciate the valuable contributions made by the Bodhi and its developers.For more information about the Bodhi Project, please visit theirofficial repositoryon GitHub.FAQsYou can find FAQs (Frequently Asked Questions) infaqs.md
Package: gpkron
gpkronA simple and fast2D gaussian process fitting using Kronecker product.same shapefromgpkron.gp2dimportGP2D,RBF,Matern32importnumpyasnpNx=128;Ny=256xgrid=np.linspace(0,Nx,Nx)ygrid=np.linspace(0,Ny,Ny)sigma=0.2Dmat=np.sin(xgrid[:,np.newaxis]/20)*np.sin(ygrid[np.newaxis,:]/20)+\np.random.randn(Nx,Ny)*sigmaDprer=GP2D(Dmat,RBF,sigma,(20.,20.))Dprem=GP2D(Dmat,Matern32,sigma,(40.,40.))different shapefromgpkron.gp2dimportGP2D,RBF,Matern32importnumpyasnpNx=16;Ny=32pshape=(64,128)xgrid=np.linspace(0,Nx,Nx)ygrid=np.linspace(0,Ny,Ny)sigma=0.2Dmat=np.sin(xgrid[:,np.newaxis]/4)*np.sin(ygrid[np.newaxis,:]/4)+\np.random.randn(Nx,Ny)*sigmaDprer=GP2D(Dmat,RBF,sigma,(20.,20.),pshape=pshape)Dprem=GP2D(Dmat,Matern32,sigma,(40.,40.),pshape=pshape)
Package: gplates-ws-proxy
GPlates Web Service Python Client/ProxyThis Python package allows users to accessGPlates Web Servicemore easily via simple Python programming interface.Installationpip install gplates-ws-proxyHow to useThe following Python code reconstructs three locations to 100Ma with Muller2019 reconstruction model.# pip install gplates-ws-proxy shapelyimportshapelyfromgplates_ws_proxyimportPlateModel,reconstruct_shapely_pointslats=[50,10,50]lons=[-100,160,100]points=[shapely.Point(x,y)forx,yinzip(lons,lats)]model=PlateModel("Muller2019")paleo_points=reconstruct_shapely_points(model,points,100)print(paleo_points)GPlates Web Service serverBy default,https://gws.gplates.orgis used. You can use .env file to specify your service URL. Alternertively, you canexport GWS_URL=https://your-service-urlin a terminal.Seeenv.templateandsetup GWS server with Docker.DependenciesrequestsshapelyAPI referenceAPI reference can be found athttps://michaelchin.github.io/gplates-python-proxy/.Examples👉reconstruct_shapely_points.pyThe red dots are present-day locations. The blue dots are paleo-locations at 100Ma.👉plot_subduction_zones.py👉plot_topological_plate_polygons.pyAll Examples
Package: gplay
# Google Play CLI## Install```pip install gplay```## Usage```Interact with Google Play servicesUsage:gplay track active(--service-p12=FILE | --service-json=FILE | --oauth-json=FILE | (--oauth --client-id=ID --client-secret=SECRET))[--track=TRACK] PACKAGE_NAMEgplay track versions(--service-p12=FILE | --service-json=FILE | --oauth-json=FILE | (--oauth --client-id=ID --client-secret=SECRET))[--track=TRACK] PACKAGE_NAMEgplay rollout(--service-p12=FILE | --service-json=FILE | --oauth-json=FILE | (--oauth --client-id=ID --client-secret=SECRET))[--version-code=CODE] PACKAGE_NAME FRACTIONgplay reviews(--service-p12=FILE | --service-json=FILE | --oauth-json=FILE | (--oauth --client-id=ID --client-secret=SECRET))[--review-id=ID] PACKAGE_NAMEgplay entitlements(--service-p12=FILE | --service-json=FILE | --oauth-json=FILE | (--oauth --client-id=ID --client-secret=SECRET))PACKAGE_NAMEgplay upload(--service-p12=FILE | --service-json=FILE | --oauth-json=FILE | (--oauth --client-id=ID --client-secret=SECRET))[--track=TRACK] [--faction=FRACTION] PACKAGE_NAME FILECommands:track active             get the active version code (defaults to 'production' track)track versions           get a tracks list with version codesrollout                  increase the rollout percentagereviews                  get list of reviewsentitlements             get in app entitlementsupload                   upload APK (defaults to 'production' track)Options:--service-p12=FILE       uses a p12 file for service account credentials--service-json=FILE      uses a json file for service account credentials--oauth-json=FILE        uses a client-secret json file for oauth credentials (opens browser)--oauth                  uses a client-secret supplied with --client-id and --client-secret (opens browser)--client-id=ID--client-secret=SECRET--track=TRACK            select track (production, beta or alpha)  [default: production]--version-code=CODE      app version code to select [default: latest]--fraction=FRACTION      the percentage of users that receives this update (0.2 .. 1)--review-id=ID           get a single review```
Package: gplayproxy
A proxy for Google Play. Provides you with a way to download your APKs easily,
and without being tracked by Google.How to use it?Install it using pip$ pip install gplayproxyThen, run it how you want, for instance with pserve:$ pserve gplayproxy.iniIn a separate process, you also need to run the worker:$ rqworkerThen, the only thing you need is an HTTP client to tell the proxy to download
a package. For instance:$ http post http://localhost:6543/package/org.thoughtcrime.securesms
Package: gplearn
Welcome to gplearn!gplearnimplements Genetic Programming in Python, with ascikit-learninspired and compatible API.While Genetic Programming (GP) can be used to perform avery wide variety of tasks, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straight-forward to implement.Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations.gplearn retains the familiar scikit-learnfit/predictAPI and works with the existing scikit-learnpipelineandgrid searchmodules. The package attempts to squeeze a lot of functionality into a scikit-learn-style API. While there are a lot of parameters to tweak,reading the documentationshould make the more relevant ones clear for your problem.gplearn supports regression through the SymbolicRegressor, binary classification with the SymbolicClassifier, as well as transformation for automated feature engineering with the SymbolicTransformer, which is designed to support regression problems, but should also work for binary classification.gplearn is built on scikit-learn and a fairly recent copy (1.0.2+) is required forinstallation. If you come across any issues in running or installing the package,please submit a bug report.
Package: gplearn-biaowang
Welcome to gplearn!gplearnimplements Genetic Programming in Python, with ascikit-learninspired and compatible API.While Genetic Programming (GP) can be used to perform avery wide variety of tasks, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straight-forward to implement.Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations.gplearn retains the familiar scikit-learnfit/predictAPI and works with the existing scikit-learnpipelineandgrid searchmodules. The package attempts to squeeze a lot of functionality into a scikit-learn-style API. While there are a lot of parameters to tweak,reading the documentationshould make the more relevant ones clear for your problem.gplearn supports regression through the SymbolicRegressor, binary classification with the SymbolicClassifier, as well as transformation for automated feature engineering with the SymbolicTransformer, which is designed to support regression problems, but should also work for binary classification.gplearn is built on scikit-learn and a fairly recent copy is required forinstallation. If you come across any issues in running or installing the package,please submit a bug report.
Package: gplearn-internal
#Welcome to gplearn-internal! (Rick Sanchez version) is a new math version of gplearn! Use GPTool for experimentation fast.gplearnimplements Genetic Programming in Python, with ascikit-learninspired and compatible API.While Genetic Programming (GP) can be used to perform avery wide variety of tasks, gplearn is purposefully constrained to solving symbolic regression problems. This is motivated by the scikit-learn ethos, of having powerful estimators that are straight-forward to implement.Symbolic regression is a machine learning technique that aims to identify an underlying mathematical expression that best describes a relationship. It begins by building a population of naive random formulas to represent a relationship between known independent variables and their dependent variable targets in order to predict new data. Each successive generation of programs is then evolved from the one that came before it by selecting the fittest individuals from the population to undergo genetic operations.gplearn retains the familiar scikit-learnfit/predictAPI and works with the existing scikit-learnpipelineandgrid searchmodules. The package attempts to squeeze a lot of functionality into a scikit-learn-style API. While there are a lot of parameters to tweak,reading the documentationshould make the more relevant ones clear for your problem.gplearn supports regression through the SymbolicRegressor, binary classification with the SymbolicClassifier, as well as transformation for automated feature engineering with the SymbolicTransformer, which is designed to support regression problems, but should also work for binary classification.gplearn is built on scikit-learn and a fairly recent copy (0.20.0+) is required forinstallation. If you come across any issues in running or installing the package,please submit a bug report.Rick’s here on… euhhh say ???Party night is code in dark room, in a location outside of time !
Life is a little bit and insignifiant part of ocean of shit… So cheat with this ocean and surf on wave of data science ! Nevermind if life is difficult, just consider if you can do, do it, for fun, learn, explore, and don’t forget if you are not happy and if you think about killing yourself: YOU ARE NOT ALONE IN THIS CASE ON EARTH ! Take care.An important thing is never give up.
-Rick SanchezFor install with pip from this git repository<https://github.com/LaGvidilo/gplearn/blob/master/INSTALL.md>
Package: gplib
GPlibA python library for Gaussian Process Regression.Setup GPlibCreate and activate virtualenv (for python2) or
venv (for python3)# for python3python3-mvenv.env# or for python2python2-mvirtualenv.envsource.env/bin/activateUpgrade pippython-mpipinstall--upgradepipInstall GPlib packagepython-mpipinstallgplibMatplotlib requires to install a backend to work interactively
(Seehttps://matplotlib.org/faq/virtualenv_faq.html).
The easiest solution is to install the Tk framework,
which can be found as python-tk (or python3-tk) on
certain Linux distributions.Use GPlibImport GPlib to use it in your python script.importgplibInitialize the GP with the desired modules.gp=gplib.GP(mean_function=gplib.mea.Fixed(),covariance_function=gplib.ker.SquaredExponential())Plot the GP.gplib.plot.gp_1d(gp,n_samples=10)Generate some random data.importnumpyasnpdata={'X':np.arange(3,8,1.0)[:,None],'Y':np.random.uniform(0,2,5)[:,None]}Get the posterior GP given the data.posterior_gp=gp.get_posterior(data)Finally plot the posterior GP.gplib.plot.gp_1d(posterior_gp,data,n_samples=10)There are more examples in examples/ directory. Check them out!Develop GPlibDownload the repository using gitgitclonehttps://gitlab.com/ibaidev/gplib.gitUpdate API documentationsource./.env/bin/activate
pipinstallSphinxcddocs/
sphinx-apidoc-f-o./../gplib
Package: gp-libs
gp-libs ·Incubating /dogfoodingsome sphinx extensions and pytest plugins on
git-pull projects, e.g.cihai,vcs-python, ortmux-python.doctestfor reStructured and markdownTwo components:doctest_docutilsmodule: Same specification asdoctest, but can parse reStructuredText
and markdownpytest_doctest_docutils: Pytest plugin, collects test items for pytest for reStructuredText and markdown filesThis means you can do:$pytestdocsdoctest moduleThis extends standard librarydoctestto support anything docutils can parse.
It can parse reStructuredText (.rst) and markdown (.md).See more:https://gp-libs.git-pull.com/doctest/Supported stylesIt supports two barebones directives:docutils'doctest_block>>> 2 + 2
4.. doctest::directivereStructuredText:..doctest::>>> 2 + 2
   4Markdown (requiresmyst-parser):```{doctest}
>>> 2 + 2
4
```UsageThedoctest_docutilsmodule preserves standard library's usage conventions:reStructuredText$python-mdoctest_docutilsREADME.rst-vThat's whatdoctestdoes by design.MarkdownIf you installmyst-parser, doctest will run on .md files.$python-mdoctest_docutilsREADME.md-vpytest pluginThis plugin disablespytest's standarddoctestplugin.This plugin integrates with thedoctest_docutilsmodule with pytest to enable seamless testing of docs,conftest.pyfixtures and all.$pytestdocs/Like the above module, it supports docutils' owndoctest_blockand a basic.. doctest::directive.See more:https://gp-libs.git-pull.com/doctest/pytest.htmlsphinx pluginsPlain-text issue linker (linkify-issues)We need to parse plain text, e.g. #99999, to point to the project tracker athttps://github.com/git-pull/gp-libs/issues/99999. This way the markdown looks
good anywhere you render it, including GitHub and GitLab.ConfigurationIn yourconf.py:Add'linkify_issues'toextensionsextensions=[# ..."linkify_issues",]Configure your issue URL,issue_url_tpl:# linkify_issuesissue_url_tpl='https://github.com/git-pull/gp-libs/issues/{issue_id}'The config variable is formatted via {meth}str.formatwhereissue_idis42if the text is #42.See more:https://gp-libs.git-pull.com/linkify_issues/Install$pipinstall--usergp-libsDevelopmental releasesYou can test the unpublished version of g before its released.pip:$pipinstall--user--upgrade--pregp-libsMore informationPython support: >= 3.8, pypySource:https://github.com/git-pull/gp-libsDocs:https://gp-libs.git-pull.comChangelog:https://gp-libs.git-pull.com/history.htmlIssues:https://github.com/git-pull/gp-libs/issuesTest Coverage:https://codecov.io/gh/git-pull/gp-libspypi:https://pypi.python.org/pypi/gp-libsLicense:MIT.