Saravanan666/pypi-org huggingface dataset:
Package: genMD-badges
📦 setup.py (for humans)This repo exists to providean example setup.pyfile, that can be used
to bootstrap your next Python project. It includes some advanced
patterns and best practices forsetup.py, as well as some
commented–out nice–to–haves.For example, thissetup.pyprovides a$ python setup.py uploadcommand, which creates auniversal wheel(andsdist) and uploads
your package toPyPiusingTwine, without the need for an annoyingsetup.cfgfile. It also creates/uploads a new git tag, automatically.In short,setup.pyfiles can be daunting to approach, when first
starting out — even Guido has been heard saying, "everyone cargo cults
thems". It's true — so, I want this repo to be the best place to
copy–paste from :)Check out the example!Installationcdyour_project# Download the setup.py file:#  download with wgetwgethttps://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.py-Osetup.py#  download with curlcurl-Ohttps://raw.githubusercontent.com/navdeep-G/setup.py/master/setup.pyTo DoTests via$ setup.py test(if it's concise).Pull requests are encouraged!More ResourcesWhat is setup.py?on Stack OverflowOfficial Python Packaging User GuideThe Hitchhiker's Guide to PackagingCookiecutter template for a Python packageLicenseThis is free and unencumbered software released into the public domain.Anyone is free to copy, modify, publish, use, compile, sell, or
distribute this software, either in source code form or as a compiled
binary, for any purpose, commercial or non-commercial, and by any means.
Package: genmechanics
# GenmechanicsA General mechanics solver: Force and speed computing in rigid-body based mechanisms.## Overview of modules### Core
This package beginned with a simple solving of mechanisms defined as parts linked by linkages (ball joints, revolute, prismatic, gear sets…) in a particular configuration.Speeds and forces are solved in all the mechanism, enabling to compute power losses and efficiency of mechanisms![Sankey](https://github.com/Dessia-tech/genmechanics/raw/master/doc/source/images/sankey.png)### Unidimensional
This module computes non-linear forces and linkage behaviors (unilateral contacts, non linear springs…) for mechanism with parts that have 1D motion.![unidimensional](https://github.com/Dessia-tech/genmechanics/raw/master/doc/source/images/unidimensional_ballbearings.png)### Dynamic positions
This is an update from core module where linkages positions are solved to find the mechanism configuration from some imposed linkages parameters.
Mechanism can be rendered with a babylonjs binding.![crank_rod](https://github.com/Dessia-tech/genmechanics/raw/master/doc/source/images/crank_rod.png)## Getting started
- install with pip: pip install genmachanics
- execute scripts from scripts folder on github:https://github.com/Dessia-tech/genmechanics/tree/master/scripts
Package: genmenu
WhyTo try to simplify and automate the process of planning what to
eat.Install$pipinstallgenmenuExample>>>example_menu=genmenu.GenMenu()>>>example_menu.insert_lunch_menu(['spam','eggs'])>>>example_menu.insert_dinner_menu(['spam','eggs'])>>>example_menu.generate_menu()>>>example_menu.my_menuOrderedDict([('Monday',{'dinner':'spam','lunch':'spam'}),('Tuesday',{'dinner':'eggs','lunch':'eggs'}),('Wednesday',{'dinner':'','lunch':''}),('Thursday',{'dinner':'','lunch':''}),('Friday',{'dinner':'','lunch':''}),('Saturday',{'dinner':'','lunch':''}),('Sunday',{'dinner':'','lunch':''})])>>>What I want to addA simple webpage (what web framework to use?)Simple DB backend (probably nosql. What DB to use?)Fetch recipies from external resources (what resources to use?)Make it multilingual (what tool and how to do that?)The option to specify how many days/weeks to plan forAdd the dates to the days
Package: genmq
genmqA Moodle quiz generator using LaTeX and themoodle.stypackage with jinja2 templatesGenerates Individualized Moodle Quizzes based on a LaTeX TemplateAuthor:Colin Caprani,colin.caprani@monash.eduOverviewgenmqis a python package that performs mail-merge like functionality to generate a Moodle quiz XML file for bulk upload. It uses a template latex file based on themoodle.stypackage, and populates placeholder variables with entries from a prepared csv file to generate many variants of the template question. It can include the answers, precision, and feedback; all as described in the documentation for themoodle.stypackage.genmqcan also split larger Moodle XML files into multiple files to facilitate uploading when there are file size limits.Installationgenmqinstalls as a command into your system.Using pippipinstallgenmqFor developmentClone or download this repository to a local directory. Open a terminal in that directory (where this README will be found) and run:pipinstall-e.Typical UsageFor generating quizzes:genmq-t[template].tex-c[database].csvFor splitting an existing large XML file:genmq-s[moodle_quiz].xmlTo see all arguments, rungenmq --help.
Package: gen-mqtt-service
Generate MQTT Servicegen_mqtt_serviceis toolset for generation of MQTT service.Developed inpythoncode:100%.The README is used to introduce the modules and provide instructions on
how to install the modules, any machine dependencies it may have and any
other information that should be provided before the modules are installed.Table of ContentsInstallationInstall using pipInstall using buildInstall using py setupInstall using dockerDependenciesTool structureDocsContributingCopyright and licenceInstallationCurrently there are three ways to install packageInstall process based on using pip mechanismInstall process based on build mechanismInstall process based on setup.py mechanismInstall process based on docker mechanismInstall using pipPython package is located atpypi.org.You can install by using pip# python3pip3installgen-mqtt-serviceInstall using buildNavigate to releasepagedownload and extract release archive.To installgen_mqtt_servicetype the followingtarxvzfgen_mqtt_service-x.y.z.tar.gzcdgen_mqtt_service-x.y.z/# python3wgethttps://bootstrap.pypa.io/get-pip.py
python3get-pip.pypython3-mpipinstall--upgradesetuptools
python3-mpipinstall--upgradepip
python3-mpipinstall--upgradebuild
pip3install-rrequirements.txt
python3-mbuild--no-isolation--wheel
pip3install./dist/gen_mqtt_service-*-py3-none-any.whl
rm-fget-pip.py
chmod755/usr/local/lib/python3.10/dist-packages/usr/local/bin/gen_mqtt_service_run.py
ln-s/usr/local/lib/python3.10/dist-packages/usr/local/bin/gen_mqtt_service_run.py/usr/local/bin/gen_mqtt_service_run.pyInstall using py setupNavigate to releasepagedownload and extract release archive.To install modules, locate and run setup.py with argumentstarxvzfgen_mqtt_service-x.y.z.tar.gzcdgen_mqtt_service-x.y.z/# python3pip3install-rrequirements.txt
python3setup.pyinstall_lib
python3setup.pyinstall_data
python3setup.pyinstall_egg_infoInstall using dockerYou can use docker to create image/container.Dependenciesgen_mqtt_servicerequires next modules and librariesats-utilities - Python App/Tool/Script UtilitiesTool structuregen_mqtt_serviceis based on OOP.Generator structuregen_mqtt_service/├──conf/│├──gen_mqtt_service.cfg│├──gen_mqtt_service.logo│├──gen_mqtt_service_util.cfg│├──project.yaml│└──template/│├──mosquitto/││├──publisher.template││└──subscriber.template│├──node/││├──publisher.template││└──subscriber.template│├──node_ws/││├──client.template││└──server.template│└──paho/│├──publisher.template│└──subscriber.template├──__init__.py├──log/│└──gen_mqtt_service.log├──pro/│├──__init__.py│├──read_template.py│└──write_template.py└──run/└──gen_mqtt_service_run.py10directories,18filesDocsMore documentation and info atgen_mqtt_service.readthedocs.iowww.python.orgContributingContributing to gen_mqtt_serviceCopyright and LicenceCopyright (C) 2020 - 2024 byvroncevic.github.io/gen_mqtt_servicegen_mqtt_serviceis free software; you can redistribute it and/or modify
it under the same terms as Python itself, either Python version 3.x or,
at your option, any later version of Python 3 you may have available.Lets help and support PSF.
Package: genmrmr
GenMRMRThis repository proposes a new feature selection algorithm based on genetic and the MRMR algorithms. The algorithm aims to select a subset of features that maximizes classification accuracy. The algorithm works by creating a population of subsets of features, evaluating their weighted F1-score, and then hybridizing the best individuals to create new subsets. The probability of adding a feature during hybridization is estimated using the MRMR algorithm. Finally, the best subset of features is selected based on the F1-score.DocumentationThere are 3 methods available: fit, transform, fit_transform:fit method is used to fit GenMRMR to givenArgs:
    x_train, y_train, x_cv, y_cv(numpy.ndarray): train and cv data and labels that will be used for feature selectiontransform method is used to transform fitted GenMRMRArgs:
    data(numpy.ndarray): data to transformfit_transform method is used to fit and then transform data using GenMRMRArgs:
    data(pandas.core.frame.DataFrame): data to transform
    labels(pandas.core.series.Series): labels for dataAuthorsAlgorithm created by Maksim Tislenko(makstislenko@gmail.com)Created the idea of algorithmImplemented the whole algorithmСomparison with existing feature selection algorithms was conducted with the participation of Rustam Paringer (rusparinger@ssau.ru) from the Department of Technical Cybernetics at Samara University.AcknowledgementsI would also like to express my gratitude to Arkadiy Sotnikov (cotnikoarkady@gmail.com) for assistance in preparing this readme.
Package: genmypy
A Python stub generator from genmsg specsInstallationIf you use catkin and need CMake support for thegenmypygenerator, clone the repository and add it to your catkin workspace.cd/path/to/your/wsgitclonehttps://github.com/rospypi/genmypy.gitIf you don’t need the cmake support, you can also install it frompypi.org:pipinstallgenmypyUsagecatkinAddgenmypyalong withmessage_generationtofind_packagein
CMakeLists.txt.genmsgwill findgenmypyautomatically when
building msg/srv files.Also, keep in mind that your package should have the build dependency
forgenmypyinpackage.xmlto make sure that catkin finishes the
build ofgenmypybefore building your package.Examples:CMakeLists.txtfind_package(catkinREQUIREDCOMPONENTSstd_msgsmessage_generationgenmypy)package.xml<build_depend>genmypy</build_depend>CLI$ genmypy --help
Usage: genmypy [-h] {msg,srv,module} ...
positional arguments:
  {msg,srv,module}
    msg             Generate stub files from .msg files
    srv             Generate stub files from .srv files
    module          Generate __init__.pyi from a msg/srv directory

optional arguments:
  -h, --help        show this help message and exitExamples:# Message files$genmypymsgcustom_msgscustom_msgs/msg/Custom.msg$genmypymsgstd_msgs--out-dirout/opt/ros/noetic/share/std_msgs/msg/Header.msg$genmypymsgsensor_msgs--out-dirout\-Istd_msgs:/opt/ros/noetic/share/std_msgs/msg\-Isensor_msgs:/opt/ros/noetic/share/sensor_msgs/msg\/opt/ros/noetic/share/sensor_msgs/msg/PointCloud2.msg# Service files$genmypysrvcustom_msgscustom_msgs/srv/Custom.msg$genmypysrvnav_msgs--out-dirout\-Istd_msgs:/opt/ros/noetic/share/std_msgs/msg\-Isensor_msgs:/opt/ros/noetic/share/sensor_msgs/msg\/opt/ros/noetic/share/sensor_msgs/srv/SetCameraInfo.srv# Module files$genmypymodulecustom_msgs/msg$genmypymodule--module-finderpy--outout\/opt/ros/noetic/lib/python2.7/dist-packages/std_msgs/msg/genmypy msg/genmypy srv:Usage:genmypy{msg,srv}[-h][--out-dirOUT_DIR][--include-pathINCLUDE_PATH]packagefiles[files...]positionalarguments:packagePackagenameofgivenfilesfilesFilestogeneratestubsoptionalarguments:-h,--helpshowthishelpmessageandexit--out-dirOUT_DIROutputdirectory.Iftheoptionisunset,eachstubfilewillbegeneratedinthesamedirectoryaseachinput.--include-pathINCLUDE_PATH,-IINCLUDE_PATHIncludepathsforprocessinggivenfilesgenmypy module:Usage:genmypymodule[-h][--out-dirOUT_DIR]package_dirPositionalarguments:package_dirPackagedirectorytocreate__init__.pyiOptionalarguments:-h,--helpshowthishelpmessageandexit--out-dirOUT_DIROutputdirectory.Iftheoptionisunset,__init__.pyiwillbegeneratedinthesamedirectoryaspackage_dir.
Package: genn
GeNNGeNN (generative neural networks) is a high-level interface for text applications using PyTorch RNN's.FeaturesPreprocessing:Parsing txt, json, and csv files.NLTK, regex and spacy tokenization support.GloVe and fastText pretrained embeddings, with the ability to fine-tune for your data.Architectures and customization:GPT2 with small, medium, and large variants.LSTM and GRU, with variable size.Variable number of layers and batches.Dropout.Text generation:Random seed sampling from the n first tokens in all instances, or the most frequent token.Top-K sampling for next token prediction with variable K.Nucleus sampling for next token prediction with variable probability threshold.Text Summarization:All GPT2 variants can be trained to perform text summarization.Getting startedHow to installpipinstallgennPrerequisitesPyTorch 1.4.0pipinstalltorch==1.4.0Pytorch Transformerspipinstallpytorch_transformersNumPypipinstallnumpyfastTextpipinstallfasttextUse the package managerpipto install genn.UsageText Generation:RNNs (You can switch LSTMGenerator with GRUGenerator:fromgennimportPreprocessing,LSTMGenerator,GRUGenerator#LSTM exampleds=Preprocessing("data.txt")gen=LSTMGenerator(ds,nLayers=2,batchSize=16,embSize=64,lstmSize=16,epochs=20)#Train the modelgen.run()# Generate 5 new documentsprint(gen.generate_document(5))GPT2 Generator:#GPT2 examplegen=GPT2("data.txt",taskToken="Movie:",epochs=7,variant="medium")#Train the modelgen.run()#Generate 10 new documentsprint(gen.generate_document(10))Text Summarization:GPT2 Summarizer:#GPT2 Summarizer examplefromgennimportGPT2Summarizersumm=GPT2Summarizer("data.txt",epochs=3,batch_size=8)#Train the modelsumm.run()#Create 5 summaries of a source documentsrc_doc="This is the source document to summarize"print(summ.summarize_document(n=5,source=src_doc))For more examples on how to use Preprocessing, please refer tothis file.For more examples on how to use LSTMGenerator and GRUGenerator, please refer tothis file.For more examples on how to use GPT2, please refer tothis file.For more examples on how to use GPT2Summarizer, please refer tothis file.ContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.LicenseDistributed under the MIT License. SeeLICENSEfor more information.
Package: gen-named-pipe
Generate Named Pipegen_named_pipeis tool for generation of named pipe modules.Developed inpythoncode:100%.The README is used to introduce the modules and provide instructions on
how to install the modules, any machine dependencies it may have and any
other information that should be provided before the modules are installed.Table of ContentsInstallationInstall using pipInstall using buildInstall using py setupInstall using dockerDependenciesTool structureDocsContributingCopyright and licenceInstallationCurrently there are three ways to install packageInstall process based on using pip mechanismInstall process based on build mechanismInstall process based on setup.py mechanismInstall process based on docker mechanismInstall using pipgen_named_pipeis located atpypi.org.You can install by using pip# python3pip3installgen-named-pipeInstall using buildNavigate to releasepagedownload and extract release archive.To installgen_named_pipetype the followingtarxvzfgen_named_pipe-x.y.z.tar.gzcdgen_named_pipe-x.y.z/# python3wgethttps://bootstrap.pypa.io/get-pip.py
python3get-pip.pypython3-mpipinstall--upgradesetuptools
python3-mpipinstall--upgradepip
python3-mpipinstall--upgradebuild
pip3install-rrequirements.txt
python3-mbuild--no-isolation--wheel
pip3install./dist/gen_named_pipe-*-py3-none-any.whl
rm-fget-pip.py
chmod755/usr/local/lib/python3.10/dist-packages/usr/local/bin/gen_named_pipe_run.py
ln-s/usr/local/lib/python3.10/dist-packages/usr/local/bin/gen_named_pipe_run.py/usr/local/bin/gen_named_pipe_run.pyInstall using py setupNavigate to releasepagedownload and extract release archive.To installgen_named_pipelocate and run setup.py with argumentstarxvzfgen_named_pipe-x.y.z.tar.gzcdgen_named_pipe-x.y.z/# python3pip3install-rrequirements.txt
python3setup.pyinstall_lib
python3setup.pyinstall_data
python3setup.pyinstall_egg_infoInstall using dockerYou can use docker to create image/container.Dependenciesgen_named_piperequires next modules and librariesats-utilities - Python App/Tool/Script UtilitiesTool structuregen_named_pipeis based on OOPGenerator structuregen_named_pipe/├──conf/│├──gen_named_pipe.cfg│├──gen_named_pipe.logo│├──gen_named_pipe_util.cfg│├──project.yaml│└──template/│└──posix/│├──np_close.template│├──np_make.template│├──np_open.template│├──np_read.template│├──np.template│└──np_write.template├──__init__.py├──log/│└──gen_named_pipe.log├──pro/│├──__init__.py│├──read_template.py│└──write_template.py└──run/└──gen_named_pipe_run.py7directories,16filesDocsMore documentation and info atgen_named_pipe.readthedocs.iowww.python.orgContributingContributing to gen_named_pipeCopyright and LicenceCopyright (C) 2020 - 2024 byvroncevic.github.io/gen_named_pipegen_named_pipeis free software; you can redistribute it and/or modify
it under the same terms as Python itself, either Python version 3.x or,
at your option, any later version of Python 3 you may have available.Lets help and support PSF.
Package: gennav
GenNavStatus: Under DevelopmentA Python Package for Robot navigation algorithms.InstallationThe package is currently under development so we suggest installing from source.From Source (Recommended)gitclonehttps://github.com/ERC-BPGC/gennav.gitcdgennav
python-mpipinstall.Using pippipinstallgennavUsageTo plan a path using the Rapidly-exploring random tree algorithm in a polygon based environment representation.fromgennavimportplanners,envs,utilsfromgennav.utilsimportRobotStatefromgennav.utils.geometryimportPointfromgennav.utils.samplersimportuniform_random_samplerassamplerobstacles=[]env=envs.PolyEnv()env.update(obstacles)start=RobotState(position=Point(1,1))goal=RobotState(position=Point(10,10))palnner=RRT(sample_area=(-5,15),sampler=sampler,expand_dis=0.1)path=palnner.plan(start,goal,env)Note that the environment have been left blank empty here, they should be updated as per use case.For more details you can refer to ourdocumentation.ContributionsContributions are always welcome. We reccomend you check outcontribution guidelinesand view thedocsbeforehand.
Package: genni
GENNI: Visualising the Geometry of Equivalences for Neural Network IdentifiabilityDisclaimerThis is code associated with the paper"GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability,"published in theNeurIPSWorkshop onDifferential Geometry meets Deep Learning 2020.If you have any questions, please feel free to reach out to us or make an issue.InstallingGenni is available from PyPIhere. In order
to install simply usepippipinstallgenniUsageIn order to use the package, please setgenni.ymlin the top directory of your
project and add / set the variablegenni_homepointing to where genni should keep
all of the generated files.Generating a runIn order to calculate the approximate equivalence classes of parameters of your
network architecture that leads to the same function you first need to create an
experiment. An example file of how to do this can be found inscripts/experiment.pywhich has some architectures predefined, but you can add
your own if you want to by looking at how the file is designed.Generating an experiment can be done by callingpython scripts/experiment.pyGetting directories and run IDsAfter generating an experiment this will populate${GENNI_HOME}/experimentwith a directory having as a name the timestamp of when it was run. An easy way
to look at the generated experiments is use thetreecommand. Below is an
example output when running this after generating a couple of experimentstree$GENNI_HOME/experiments-d-L3with the outputexperiments
└──Nov09_19-52-12_isak-arch├──models│└──1604947934.637504└──runs└──1604947934.637504whereNov09_19-52-12_isak-archis the identifier of the experiment and1604947934.637504is an ID of a hyperparameter setting of this experiment.PlottingWe have prepared a notebook callednotebooks/SubspaceAnalysis.ipynbshowing
how toLoad your experiment together with necessary paths and experiment idsCompute grids and values for plottingDifferent ways of visualising the approximate equivalence classes in the form
of aContour plot3d iso-surface plotUMAP projected 2d plot of 3d iso-surfaceCitingIf you use GENNI anywhere in your work, please cite use using@article{2020,
    title={GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability},
    author={Lengyel, Daniel and Petangoda, Janith and Falk, Isak and Highnam, Kate and Lazarou, Michalis and Kolbeinsson, Arinbjörn and Deisenroth, Marc Peter and Jennings, Nicholas R.},
    booktitle={NeurIPS Workshop on Differential Geometry meets Deep Learning},
    year={2020}
}
Package: genno
gennois a Python package for describing and executing complex calculations on labelled, multi-dimensional data.
It aims to make these calculations efficient, transparent, and easily validated as part of scientific research.genno is built on high-quality Python data packages includingdask,xarray, andpint; and provides (current or planned) compatibility with packages includingpandas,matplotlib,plotnine,ixmp,sdmx1, andpyam.A 玄能 (gennoorgennoh) is a type of hammer used in Japanese woodworking.
The package name is warning, by reference, to the adage “When you hold a hammer, every problem looks like a nail”: you shouldn’t hit everything withgenno, but it is still a useful and versatile tool.LicenseCopyright © 2018–2024 genno contributors.Licensed under the GNU General Public License, version 3.0.
Package: geno
The GENO solverGENO is a solver for non-linear optimization problems. It can solve constrained and unconstrained problems. It is fully written in Python with no dependencies and it can run on the CPU and on the GPU.Installingpip install genosolverProject HomepageSeegeno-project.orgfor more details, examples, and for an easy-to-read  modeling language interface.AuthorsSören Laue- most of the solver -Mark Blacher- GPU supportMatthias MitterreiterLicenseThis project is licensed under the GNU Affero General Public License v3.PapersFor more information, see the papersOptimization for Classical Machine Learning Problems on the GPU. Sören Laue, Mark Blacher, and Joachim Giesen. InAAAI Conference on Artificial Intelligence (AAAI),2022.GENO -- GENeric Optimization for Classical Machine Learning. Sören Laue, Matthias Mitterreiter, and Joachim Giesen. InAdvances in Neural Information Processing Systems (NeurIPS),2019.
Package: genobjdet
This is the Generalized Object Detection PackageThis package is used to isolate the coordinates of objects in an image.Instructions1.) Install usingpip install genobjdet2.) Run in Pythonfrom genobjdet import *Function Descriptiondef request(image="", api_key="", prompt="", write_to_file=False):api_key: The necessary RapidAPI key to access the server.

image: directory of the image to be processed

prompt: prompt for which object the server should analyze the image for

write_to_file: Write bounding box coordinates directly to the image.

return: The bounding box coordinates of the object in the image.

(If write_to_file is True, the coordinates are written to the image instead of being returned.)Call Functionrequest(image="[image dir]", api_key="[RapidAPI-Key]", prompt="[what to isolate]", write_to_file=[False/True])