Saravanan666/pypi-org huggingface dataset:
Package: gearman3
This is a Gearman API written in Python – client, worker and admin client interfaces.For information about Gearman and a C-based Gearman server, seehttp://gearman.org/.This is a fork of the originalYelp/python-gearmanproject, which adds support for Python 3.
You can use this library if you have an existing project that uses python-gearman and you want to upgrade to Python 3, but you probably shouldn’t use it for a new project.InstallationThis library is published on PyPI asgearman3.
You can install it using pip:pip install gearman3The library is tested with Python 2.7, 3.5 and 3.6.UsageThis is a drop-in replacement for the 2.x python-gearman library.
There are docs athttps://pythonhosted.org/gearman/.DevelopmentI created this fork so we’d have a Python 3-compatible version of Gearman to use inArchivematica.I’ll accept bugfixes for improving compatibility with Python 3, but I’m unlikely to accept new features or changes to the library’s behaviour.
If you want to make big changes, I suggest creating your own fork.New patches should come with tests and a release note.Seedevelopers.rstfor more notes on development, and in particular instructions for creating pull requests.Further linksChangelog for gearman3: seechanges.rst.2.x source:https://github.com/Yelp/python-gearman/2.x documentation:https://packages.python.org/gearman/1.x sourcehttps://github.com/samuel/python-gearman/1.x documentationhttps://github.com/samuel/python-gearman/tree/master/docs/
Package: gears-at-notation
Gears @ notation=================Specify requirements using @require notation.It is stricted notation. No attemt to determine file extention.No need to depend on same filetype.Usage-----If you use pure gears:```pythonenvironment.preprocessor.register('text/css', AtDirectivesProcessor.as_handler())environment.preprocessor.register('application/javascript', AtDirectivesProcessor.as_handler())```For django:```pythonGEARS_PREPROCESSORS = {'text/css': 'gears_at_notation.processor.AtDirectivesProcessor','application/javascript': 'gears_at_notation.processor.AtDirectivesProcessor',}```Now you can specify dependencies in different way:```// file style.css// @require "main.css"// @require "application.less"// @require "modules.styl"```
Package: gears-clean-css
clean-csscompressor forGears. This package already includes the clean-css
source code for you, so you don’t need to worry about installing it yourself.Bundled clean-css version:0.6.0InstallationInstallgears-clean-csswith pip:$ pip install gears-clean-cssRequirementsgears-clean-cssrequiresnode.jsto be installed in your system.UsageAddgears_clean_css.CleanCSSCompressortoenvironment’s compressors
registry:from gears_clean_css import CleanCSSCompressor
environment.compressors.register('text/css', CleanCSSCompressor.as_handler())If you use Gears in your Django project, add this code to its settings:GEARS_COMPRESSORS = {
    'text/css': 'gears_clean_css.CleanCSSCompressor',
}
Package: gearsclient
redisgears-pyRedisGearspython client (support python3 only!)Example: Using the Python Client:fromgearsclientimportGearsRemoteBuilderasGearsBuilderfromgearsclientimportexecuteimportredisconn=redis.Redis(host='localhost',port=6379)# count for each genre how many times it appearsres=GearsBuilder('KeysOnlyReader',r=conn).\map(lambdax:execute('hget',x,'genres')).\filter(lambdax:x!='\\N').\flatmap(lambdax:x.split(',')).\map(lambdax:x.strip()).\countby().\run()forrinres[0]:print('%-15s:%d'%(r['key'],r['value']))Installingpip install git+https://github.com/RedisGears/redisgears-py.gitNotice that the library also need to be installed in RedisGears virtual env.DevelopingCreate a virtualenv to manage your python dependencies, and ensure it's active.virtualenv -v venvInstallpypoetryto manage your dependencies.pip install poetryInstall dependencies.poetry installtoxruns all tests as its default target. Runningtoxby itself will run unit tests. Ensure you have a running redis, with the module loaded.
Package: gears-coffeescript
CoffeeScriptcompiler forGears. This package already includes the
CoffeeScript source code for you, so you don’t need to worry about installing
it yourself.Bundled CoffeeScript version:1.7.1InstallationInstallgears-coffeescriptwith pip:$ pip install gears-coffeescriptRequirementsgears-coffeescriptrequiresnode.jsto be installed in your system.UsageAddgears_coffeescript.CoffeeScriptCompilertoenvironment’s compilers
registry:from gears_coffeescript import CoffeeScriptCompiler
environment.compilers.register('.coffee', CoffeeScriptCompiler.as_handler())If you use Gears in your Django project, add this code to its settings:GEARS_COMPILERS = {
    '.coffee': 'gears_coffeescript.CoffeeScriptCompiler',
}
Package: gear-score
GearScore python clientThe package provides functionality to calculate GS for an item, with possibility to save items to local DB (sqlite).How to useCreate table before first package usage, it can be done with:fromgear_score.dbimportensure_tables_createdawaitensure_tables_created()After that, you can get info about a single item:fromgear_score.gettersimportPublicDbGettergetter=PublicDbGetter()item=awaitgetter.get(40633)print(item.gs)You can get info about multiple items at once, first checking in local db and then in public:fromgear_score.utilsimportget_items_from_local_or_remoteitems=awaitget_items_from_local_or_remote(50444,50445,50446)TODO:testsproper documentationloggingautomatic db tables creation
Package: gears-eco
gears-eco==================Eco: Embedded CoffeeScript templates for Gears.Installation------------Install `gears-eco` with pip:$ pip install gears-ecoRequirements------------- [node.js](http://nodejs.org)- [eco](https://github.com/sstephenson/eco)Usage-----Add `gears_eco.EcoCompiler` to `environment`'s compilers registry:from gears_eco import EcoCompilerenvironment.compilers.register('.eco', EcoCompiler.as_handler())If you use Gears in your Django project, add this code to your settings file:GEARS_COMPILERS = {'.eco': 'gears_eco.EcoCompiler',}
Package: gears-handlebars
Handlebarscompiler forGears. This package already includes the Handlebars
source code for you, so you don’t need to worry about installing it yourself.Bundled Handlebars version:2.0.0InstallationInstallgears-handlebarswith pip:$ pip install gears-handlebarsRequirementsgears-handlebarsrequiresnode.jsto be installed in your system.UsageAddgears_handlebars.HandlebarsCompilertoenvironment’s compilers
registry:from gears_handlebars import HandlebarsCompiler
environment.compilers.register('.handlebars', HandlebarsCompiler.as_handler())If you use Gears in your Django project, add this code to its settings:GEARS_COMPILERS = {
    '.handlebars': 'gears_handlebars.HandlebarsCompiler',
}
Package: gears-jsx
gears-jsx==================JSX compiler for Gears.Bundled [react-tools](https://github.com/facebook/react) version: 0.10.0Installation------------Install `gears-jsx` with pip:$ pip install gears-jsxRequirements------------- [node.js](http://nodejs.org)Usage-----Add `gears_jsx.JSXCompiler` to `environment`'s compilers registry:from gears_jsx import JSXCompilerenvironment.compilers.register('.jsx', JSXCompiler.as_handler())If you use Gears in your Django project, add this code to its settings:GEARS_COMPILERS = {'.jsx': 'gears_jsx.JSXCompiler',}
Package: gears-less
LESScompiler forGears. This package already includes the LESS source
code for you, so you don’t need to worry about installing it yourself.Bundled LESS version:1.6.3InstallationInstallgears-lesswith pip:$ pip install gears-lessRequirementsgears-lessrequiresnode.jsto be installed in your system.UsageAddgears_less.LESSCompilertoenvironment’s compilers registry:from gears_less import LESSCompiler
environment.compilers.register('.less', LESSCompiler.as_handler())If you use Gears in your Django project, add this code to its settings:GEARS_COMPILERS = {
    '.less': 'gears_less.LESSCompiler',
}
Package: gearsllm
Welcome to GearsGears is a lightweight tool for writing control flow with LLMs withfull control over your prompts. It allows you to build complex chains of actions and conditions, and execute them in a single call.Why Gears?Gears is so minimal; it is simply a wrapper around an LLM API call that:Allows you to specify your prompts asJinja templatesand inputs asPydantic modelsAutomatically handles LLM API failures withexponential backoffAllows you to specify control flow, based on LLM responses, in a simple, declarative wayBut the real selling point is thatwe are committed tonotgrowing the codebase beyond what is necessary to support the above features.(We are not venture-backed and do not intend to be.)InstallationGears is available on PyPI, and can be installed with pip:pipinstallgearsllmDependenciesGears has the following dependencies:python>=3.9pydanticjinja2tenacityopenaiToDosAdd pre-commit hooks with black & isort
Package: gears-sass
gears-sass==================SCSS compiler for Gears.Bundled [node-sass](https://github.com/andrew/node-sass) version: 0.8.6Installation------------Install `gears-sass` with pip:$ pip install gears-sassRequirements------------- [node.js](http://nodejs.org)Usage-----Add `gears_sass.SASSCompiler` to `environment`'s compilers registry:from gears_sass import SASSCompilerenvironment.compilers.register('.scss', SASSCompiler.as_handler())If you use Gears in your Django project, add this code to its settings:GEARS_COMPILERS = {'.scss': 'gears_sass.SASSCompiler',}
Package: gears-stylus
Styluscompiler forGears. This package already includes the Stylus source
code for you, so you don’t need to worry about installing it yourself.Bundled Stylus version:0.45.1InstallationInstallgears-styluswith pip:$ pip install gears-stylusRequirementsgears-stylusrequiresnode.jsto be installed in your system.UsageAddgears_stylus.StylusCompilertoenvironment’s compilers registry:from gears_stylus import StylusCompiler
environment.compilers.register('.styl', StylusCompiler.as_handler())If you use Gears in your Django project, add this code to its settings:GEARS_COMPILERS = {
    '.styl': 'gears_stylus.StylusCompiler',
}
Package: gearstore
A Gearman worker to do distributed job persistence for reliable deliveryFree software: Apache licenseDocumentation:http://docs.openstack.org/developer/gearstoreSource:http://git.openstack.org/cgit/openstack-infra/gearstoreBugs:http://bugs.launchpad.net/gearstoreInspirationThis project is inspired by dormando’s Garivini. Since that one is in
Perl, and we don’t like supporting perl, we are reimplementing the same
interface in python with the gear library.Seehttps://github.com/dormando/Garivinifor more.FeaturesDistributed message persistence for Gearman jobs makes persistence scale out.No centralized store makes system more fault tolerant.Quick StartGearstore is pip installable. Once it has been installed, you will need
to initialize the database schema:gearstore-init-schema mysql://user:pass@host/dbnameYou will also need a gearman server available. The library used by
gearstore, gear, includes one and it will be availalbe in the same
place as gearstore asgeard:geardOnce the database is initialized, run as many gearstores as is needed
to keep traffic flowing:gearstore --sqlalchemy-dsn=mysql://user:pass@host/dbname
Package: gears-uglifyjs
UglifyJScompressor forGears. This package already includes the UglifyJS
source code for you, so you don’t need to worry about installing it yourself.Bundled UglifyJS version:1.3.3InstallationInstallgears-uglifyjswith pip:$ pip install gears-uglifyjsRequirementsgears-uglifyjsrequiresnode.jsto be installed in your system.UsageAddgears_uglifyjs.UglifyJSCompressortoenvironment’s compressors
registry:from gears_uglifyjs import UglifyJSCompressor
environment.compressors.register('application/javascript', UglifyJSCompressor.as_handler())If you use Gears in your Django project, add this code to its settings:GEARS_COMPRESSORS = {
    'application/javascript': 'gears_uglifyjs.UglifyJSCompressor',
}
Package: gearthonic
A simple client for the API of Homegear.Look at thedocumentationfor detailed information.QuickstartInstallgearthonicvia pip:pip install gearthonicInitialise the client:from gearthonic import GearClient
# You only have to provide the host and port of the Homegear server
gc = GearClient('192.168.1.100', 2001, secure=False, verify=False)Use the predefined methods to make requests to the API:gc.system.list_methods()
gc.device.get_value(1, 4, 'ACTUAL_TEMPERATURE')Alternatively you can call any method directly via the client:gc.getValue(1, 4, 'ACTUAL_TEMPERATURE')The default communication protocol is XML-RPC. If you want to use another
protocol like JSON-RPC or a MQTT broker, see the fulldocumentation.DocumentationThe full documentation can be found athttp://gearthonic.rtfd.org.Changelog0.2.0 (2016-08-08)Introduced different communication protocols (XML RPC and JSON RPC)0.1.2 (2016-07-13)Fixed broken setup.py.0.1.1 (2016-07-13)Added documentation.0.1.0 (2016-07-13)Initial release to pypi.
Package: gear-torque-calc
A simple way to figure out how much torque different sized gears will get out of a a stepper motor (or any input).Torque units are in nm (newton meters) and lengths should be in mm (millimeters).Some background on why I wrote this package can be foundhereUsagefromgear_torque_calcimportget_torqueimportpprintpp=pprint.PrettyPrinter()res=get_torque(drive_shaft_diameter=5,drive_shaft_torque=.2,gear_small_diameter=15,gear_large_diameter=55)pp.pprint(res)res=get_torque(drive_shaft_diameter=5,drive_shaft_torque=.2,gear_small_diameter=15,gear_large_diameter=55*2)pp.pprint(res)This should give you the following output{'gear_ratio':7.333333333333333,'large_gear':{'force':26.666666666666668,'radius':55.0,'torque':1.4666666666666668},'motor':{'force':80.0,'torque':0.2},'small_gear':{'force':26.666666666666668,'radius':7.5,'torque':0.2}}{'gear_ratio':3.6666666666666665,'large_gear':{'force':26.666666666666668,'radius':27.5,'torque':0.7333333333333334},'motor':{'force':80.0,'torque':0.2},'small_gear':{'force':26.666666666666668,'radius':7.5,'torque':0.2}}Releasepython setup.py bdist_wheeltwine check dist/*twine upload dist/*
Package: gearwall
CGH_ToolboxPurposeA rudimentary Python Package to hold all my code for Quantum chem research. It is all puprose-built and much of it too specific for generic use.DVRcode built to perform a Discrete Variable Representation as described by Colbert and MillerDVR ArraysDVRs solve the vibrational shrödinger equation for systems with known potential energy surfaces using matrix representation of quantum mechanical operators. This code builds those matrices using Python.Gaussian ToolsUtilites for interfacing with Gaussian Electronic Strucutre Calculation software. Gaussian is written in fortran and its input and output is based on text files. Much of this code is writing and reading text files with a specific formatUtilitiesThis is the most generic code here. It holds a lot of the basic utilites used by the other code. Much of this could be its own package and could be used for more than comp chem research#History
Package: gease
It’s understood that you may use github cli, howevergeasesimply makes a git release using github api v3.InstallationYou can install gease via pip:$pipinstallgeaseor clone it and install it:$gitclonehttps://github.com/moremoban/gease.git$cdgease$pythonsetup.pyinstallSetup and ConfigurationFirst, please createpersonal access tokenfor yourselfon github.Next, please create a gease file(.gease) in your home directory and place the
token inside it. Gease file is a simple json file. Here is an example:{"user":"chfw","personal_access_token":"AAFDAFASDFADFADFADFADFADF"}OrganisationIn order to make a release for your organisation, “read:org” right is required:Command Linegease simply makes a git release using github api v3. version 0.0.1

Usage: gs repo tag [release message]

where:

   release message is optional. It could be a quoted string or space separate
       string

Examples:

   gs gease v0.0.1 first great release
   gs gease v0.0.2 "second great release"::
contributors list the contributors of a repo. version 0.0.4Usage: contributors user/org repoWhere:user/org is the your github username or orgnisation name
repo is the repository nameExamples:contributors pyexcel pyexcel-ioLicenseMITChange log0.0.11 - 18.10.2020Updated#14: failed to use public
apis0.0.10 - 18.10.2020Updated#3: include license, tests
and docs in the package0.0.9 - 17.10.2020Updatedreturn all user details using github token if exist. otherwise use public api
under quota0.0.8 - 21.09.2020Updatedreturn all user details per github user apihandle 403 and throw out unknown status code0.0.7 - 28.08.2020Updatedraise UrlNotFound when request a non-existent url0.0.6 - 26.08.2020Updated#1: access repo contributors
without github token.0.0.5 - 21.08.2020Addeduse login name if full name of the contributor is null0.0.4 - 21.08.2020Addedget contributors of a github repo0.0.3 - 25.11.2017Added#1: release repos of the
organisation that you belong to.0.0.2 - 15.10.2017Addedquit with -1 if github responds with error0.0.1 - 13.10.2017AddedFirst release.
Package: gebaSpike
gebaSpikeThis is a Python Package created to spike sort in-vivo electrophysiology data recorded using DaqUSB (or converted to the Tint format). This program is minimalistic and was essentially just created to speed up the time I spend sorting/cleaning spikes.RequirementsSince it utilizes PyQt5 as the GUI framework it should be available to a wide variety of Python versions and Operating Systems. However it was only tested on Python 3.7 with Windows 10. I do recommend using the latest version of Python as it makes it easy to download some of these C++ based dependencies.Project DocumentationInstallationUser GuideAuthorsGeoff Barrett-Geoffâ€™s GitHubLicenseThis project is licensed under the GNU  General  Public  License - see theLICENSE.mdfile for details
Package: gecasmo
# gecasmo
ThegecasmoPython package, for estimating click models with covariates using EM.## Installing`shell pip install gecasmo `## ExamplesThe/notebooksdirectory contains a set of [Jupyter Notebook examples](https://github.com/cornederuijt/gecasmo/notebooks), including the following click models:[CZM](https://github.com/Mogeng/IOHMM/blob/master/examples/notebooks/UnSupervisedIOHMM.ipynb), also known as DBN [[1]](#1)[UBM](https://github.com/Mogeng/IOHMM/blob/master/examples/notebooks/SemiSupervisedIOHMM.ipynb) [[2]](#2)## References
<a id=”1”>[1]</a>
Chapelle, Olivier, and Ya Zhang. (2009).
A dynamic bayesian network click model for web search ranking.
Proceedings of the 18th international conference on World wide web. ACM, 1–10.<a id=”2”>[2]</a>
Dupret, Georges E. and Piwowarski, Benjamin. (2008).
A user browsing model to predict search engine click data from past observations.
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval. ACM, 331–338.## Licensing
The MIT Licence (MIT)
Package: gecatsim
XCIST (X-ray-based Cancer Imaging Simulation Toolkit)The X-ray-based cancer imaging simulation toolkit (XCIST) project is supported by the National Cancer Institute of the National Institutes of Health under Award Number U01CA231860 (ITCR grant).This toolkit includes software developed by Members of the Geant4 Collaboration (http://cern.ch/geant4). Specifically it builds on the X-ray attenuation cross-sections produced by Geant4.For more information such as installation, examples, please visit:https://github.com/xcist/documentation/wikiPlease consider citing our paper if this package is useful to your research/work:https://iopscience.iop.org/article/10.1088/1361-6560/ac9174
Package: geci-nerd
The eradication of rodents is central to island conservation efforts and the aerial broadcast of rodenticide bait is the preferred dispersal method. To improve accuracy and expedite the evaluation of aerial operations, we developed an algorithm for the numerical estimation of rodenticide density (NERD). The NERD algorithm performs calculations with increased accuracy, displaying results almost in real-time. NERD describes the relationship between bait density, the mass flow rate of rodenticide through the bait bucket, and helicopter speed and produces maps of bait density on the ground. NERD also facilitates the planning of helicopter flight paths and allows for the instant identification of areas with low or high bait density.
Package: geci-pipa
Plataforma Interna para AnalistasPara usar este repo como plantilla debemos hacer lo siguiente:Presiona el botón verde que diceUse this templateSelecciona como dueño a la organización IslasGECIAgrega el nombre del nuevo módulo de pythonPresiona el botónCreate repository from templateReemplazadummy_transformationspor el nombre del nuevo módulo en:Makefilepyproject.tomltests\test_transformation.pyRenombra el archivodummy_transformations\transformation.pyal nombre del primer archivo del
nuevo móduloCambia la descripción del archivodummy_transformations\__init__.pyRenombra el directoriodummy_transformationsal nombre del nuevo móduloCambia elcodecov_tokendel archivoMakefileLos archivos del nuevo módulo los agregarás en la carpeta que antes se llamabadummy_transformationsy las pruebas en la carpetatests.
Package: geci-referee
Referee Cat RecognitionPara usar este repo como plantilla debemos hacer lo siguiente:Presiona el botón verde que diceUse this templateSelecciona como dueño a la organización IslasGECIAgrega el nombre del nuevo módulo de pythonPresiona el botónCreate repository from templateReemplazareferee_cat_recognitionpor el nombre del nuevo módulo en:Makefilepyproject.tomltests\test_transformations.pyRenombra el archivoreferee_cat_recognition\transformations.pyal nombre del primer archivo del
nuevo móduloCambia la descripción del archivoreferee_cat_recognition\__init__.pyRenombra el directorioreferee_cat_recognitional nombre del nuevo móduloCambia elcodecov_tokendel archivoMakefileLos archivos del nuevo módulo los agregarás en la carpeta que antes se llamabareferee_cat_recognitiony las pruebas en la carpetatests.
Package: geci-streamlit
Dummy TransformationsPara usar este repo como plantilla debemos hacer lo siguiente:Presiona el botón verde que diceUse this templateSelecciona como dueño a la organización IslasGECIAgrega el nombre del nuevo módulo de pythonPresiona el botónCreate repository from templateReemplazadummy_transformationspor el nombre del nuevo módulo en:Makefilepyproject.tomltests\test_transformations.pyRenombra el archivodummy_transformations\transformations.pyal nombre del primer archivo del
nuevo móduloCambia la descripción del archivodummy_transformations\__init__.pyRenombra el directoriodummy_transformationsal nombre del nuevo móduloCambia elcodecov_tokendel archivoMakefileLos archivos del nuevo módulo los agregarás en la carpeta que antes se llamabadummy_transformationsy las pruebas en la carpetatests.