Saravanan666/pypi-org huggingface dataset:
Package: gmnspy
GMNSpyPython tool forGeneral Modeling Network Specification (GMNS)developed
byZephyr  Foundationfor Travel Analysis.Installationgitclonehttps://github.com/e-lo/GMNSpy.gitcdGMNSpy
pipinstall.UsageRead a single fileReturns a dataframe that conforms to the specified schema and have been
validated.df=gmnspy.in_out.read_gmns_csv(data_filename,schema_file=schemafilename)Read a networkReturns a dictionary of dataframes that conform to the specified schema
and have been validated.Checks foreign keys between files.net=gmnspy.in_out.read_gmns_network(data_directory,config:"gmns.spec.json")GMNS specificationA copy of the GMNS specification is kept in the/specsub-directory as a
series of JSON tables.Data Table schemasData table schemas are specified in JSON and are compatible with thefrictionless datatable
schema standards.Example:{"primaryKey":"segment_id","missingValues":["NaN",""],"fields":[{"name":"segment_id","type":"any","description":"Primary key.","constraints":{"required":true,"unique":true}},{"name":"road_link_id","type":"any","description":"Required. Foreign key to road_links. The link that the segment is located on.","foreign_key":"link.link_id","constraints":{"required":true}},{"name":"ref_node_id","type":"any","description":"Required. Foreign key to node.","foreign_key":"node.node_id","constraints":{"required":true}},{"name":"start_lr","type":"number","description":"Required. Distance from ref_node_id.","constraints":{"required":true,"minimum":0}},{"name":"end_lr","type":"number","description":"Required. Distance from ref_node_id.","constraints":{"required":true,"minimum":0}}]}Network Data ConfigNetwork data schemas are specified in JSON and are compatible with thefrictionless datadata package standards.Example:{"profile":"gmns-data-package","profile_version":0.0,"name":"my-dataset","resources":[{"name":"link","path":"link.csv","schema":"link.schema.json","required":true},{"name":"node","path":"node.csv","schema":"node.schema.json","required":true}]}IssuesPlease add issues, bugs, and feature requeststo Github.RoadmapCurrent feature roadmap includes:conversion tools from open street mapnetwork connectivity checksauto documentation of schema to markdown filestests tests testsFeel free to submit pull requests for consideration. SeeCONTRIBUTINGfor more detailed instructions.CreditsPrimary Author: Elizabeth Sall, UrbanLabs LLCContributing authors and code maintainers:Pedro Carmago, OuterLoop ConsultingIan Berg, Volpe CenterSee all inCONTRIBUTORS.mdLicense to UseThe code herein is licensed under the Apache License 2.0 as defined in  file.
Package: gmp
This is just a little package I made for my GMP teacher.To print things with style you can use the printing classimportgmpstyle=gmp.printing("purple4","blue")print(style.print([1,2,3],["hello","world"],...))If you feel like rainbow you can use the rainbow class with lolcat!importgmpcolor=gmp.rainbow(False)print(color.lolcat("I love rainbows"))color=gmp.rainbow(True)print(color.lolcat("Lolcat is super cool"))
Package: gmpackages
Project TitleOne Paragraph of project description goes hereGetting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.PrerequisitesWhat things you need to install the software and how to install themGive examplesInstallingA step by step series of examples that tell you how to get a development env runningSay what the step will beGive the exampleAnd repeatuntil finishedEnd with an example of getting some data out of the system or using it for a little demoRunning the testsExplain how to run the automated tests for this systemBreak down into end to end testsExplain what these tests test and whyGive an exampleAnd coding style testsExplain what these tests test and whyGive an exampleDeploymentAdd additional notes about how to deploy this on a live systemBuilt WithDropwizard- The web framework usedMaven- Dependency ManagementROME- Used to generate RSS FeedsContributingPlease readCONTRIBUTING.mdfor details on our code of conduct, and the process for submitting pull requests to us.VersioningWe useSemVerfor versioning. For the versions available, see thetags on this repository.AuthorsBillie Thompson-Initial work-PurpleBoothSee also the list ofcontributorswho participated in this project.LicenseThis project is licensed under the MIT License - see theLICENSE.mdfile for detailsAcknowledgmentsHat tip to anyone whose code was usedInspirationetc
Package: gmpacket
Description of GMP —A GeoJSON Specification for Ground Motion MetricsHere we present a specification for a GeoJSON (https://geojson.org/) format for
unassociated and event-associated ground motion metrics—that is, quantities
derived from ground motion records (e.g., accelerograms, velocity recordings).
We refer to this specification as a Ground Motion Packet (GMP). Common example
metrics include peak ground acceleration (PGA), peak ground velocity (PGV), and
spectral acceleration (SA). This format is designed so that it can also include
metrics that are gaining popularity for many engineering use cases, such as
significant duration, Arias intensity, and inelastic response spectra. This
format should be able to be used by any application that uses ground motion
metrics including, but not limited to, ShakeMap. The GMP format is not meant to
describe station metadata or site installation. The purpose of the metadata
included in the GMP format is to provide high-level information regarding the
suitability of the data for common engineering/seismological applications.DocumentationUser DocumentationDemoSample Jupyter notebook
Package: gmplmagic
UsageTake a look at theseexample
notebooksfor
details on how to use GMPL Magic — no installation required, thanks toBinder!DependenciesGMPL Magic has been tested withJupyter 4.2.0Python 3.5.2MetaKernel 0.14PyGLPK 0.4.2, available from
[@bradfordboyle](https://github.com/bradfordboyle/pyglpk)GLPK 4.60InstallationFirst, make sure you have a working installation of GLPK. For example,
on macOS, this can be accomplished withHomebrewby:brew update
brew tap homebrew/science
brew install glpk(I will eventually include instructions on how to install GLPK on other
operating systems.)Next, install PyGLPK - note that the version on PyPI is outdated:pip install https://github.com/bradfordboyle/pyglpk/zipball/masterThen, install GMPL Magic:pip install gmplmagicTo use these magics in a Jupyter notebook:%load_ext glpkmagic
Package: gmplnotebook
UsageTake a look at theseexample
notebooksfor
details on how to use GMPL Notebook — no installation required, thanks
toBinder!DependenciesGMPL Notebook has been tested withJupyter 4.2.0Python 3.5.2MetaKernel 0.14PyGLPK 0.4.2, available from
[@bradfordboyle](https://github.com/bradfordboyle/pyglpk)GLPK 4.60InstallationFirst, make sure you have a working installation of GLPK. For example,
on macOS, this can be accomplished withHomebrewby:brew update
brew tap homebrew/science
brew install glpk(I will eventually include instructions on how to install GLPK on other
operating systems.)Next, install PyGLPK - note that the version on PyPI is outdated:pip install https://github.com/bradfordboyle/pyglpk/zipball/masterThen, install GMPL Notebook:pip install gmplnotebook
python -m gmplnotebook install --userGMPL/MathProg should now appear as an option when creating new notebooks
in Jupyter.
Package: gmplot
A matplotlib-like interface to render all the data you’d like on top of Google Maps.Several plotting methods make creating exploratory map views effortless.To install:pip install gmplotDocumentation (with examples):API ReferenceCrash courseimportgmplot# Create the map plotter:apikey=''# (your API key here)gmap=gmplot.GoogleMapPlotter(37.766956,-122.448481,14,apikey=apikey)# Outline the Golden Gate Park:golden_gate_park=zip(*[(37.771269,-122.511015),(37.773495,-122.464830),(37.774797,-122.454538),(37.771988,-122.454018),(37.773646,-122.440979),(37.772742,-122.440797),(37.771096,-122.453889),(37.768669,-122.453518),(37.766227,-122.460213),(37.764028,-122.510347)])gmap.polygon(*golden_gate_park,color='cornflowerblue',edge_width=10)# Highlight some attractions:attractions_lats,attractions_lngs=zip(*[(37.769901,-122.498331),(37.768645,-122.475328),(37.771478,-122.468677),(37.769867,-122.466102),(37.767187,-122.467496),(37.770104,-122.470436)])gmap.scatter(attractions_lats,attractions_lngs,color='#3B0B39',size=40,marker=False)# Mark a hidden gem:gmap.marker(37.770776,-122.461689,color='cornflowerblue')# Draw the map:gmap.draw('map.html')Inspired by Yifei Jiang’s (jiangyifei@gmail.com)pygmapsmodule.
Package: gmprocess
IntroductionThis is a project designed to provide a number of functions related to parsing and processing earthquake ground motion data, building on top of theObsPylibrary.
Most of the extensions that we provide are to handle strong motion data and related issues.DocumentationPlease note, we are in the process of improving the documentation and that there are some incomplete sections.The full documentation can be viewed locally by running thedoc_source/makedocs.shscript.The documentation is also availablehere.Getting HelpThe developers can be reached atgmprocess@usgs.gov.If you would like to post an issue to ask a question, request a new feature, or report a bug, you can do so at thegithub mirrorof this repository.If you would like to contribute merge requests to this repository, please let us know and we can add you as a collaborator.
This will require that you create an account at code.usgs.gov, which is a more involved process than creating a personal account at gitub.com and posting to the issues on the github mirror.ReferenceIf you wish to cite this software, please use this reference:Hearne, M., E. M. Thompson, H. Schovanec, J. Rekoske, B. T. Aagaard, and C. B. Worden (2019). USGS automated ground motion processing software, USGS Software Release, doi:10.5066/P9ANQXN3.
Package: gmpy
General MultiPrecision arithmetic for Pythongmpy is a C-coded Python extension module that provides access to the GMP (or MPIR) multiple-precision arithmetic library.gmpy 1.17 is the final release of the 1.x series. No further updates are planned. All further development is occurring in the 2.x series, also known as gmpy2. Please seehttps://pypi.python.org/pypi/gmpy2/for the latest 2.x releases.GMPY allows creation of multiprecision integer (mpz), float (mpf),and rational (mpq) numbers, conversion between them and to/from Python numbers/strings, arithmetic, bitwise, and some other higher-level mathematical operations.Version 1.17 only fixes compatibility with Python 3.x and other C extensions that use gmpy.Version 1.16 fixes a minor issue with the new Decimal type in Python 3.3.Version 1.15 was the last significant release.
Package: gmpy2
Introductiongmpy2 is an optimized, C-coded Python extension module that supports fast
multiple-precision arithmetic.  gmpy2 is based on the original gmpy module.
gmpy2 adds support for correctly rounded multiple-precision real arithmetic
(using the MPFR library) and complex arithmetic (using the MPC library).Version 2.1.x Statusgmpy2 2.1 was extensively refactored. Some of the significant changes are:Support for thread-safe contexts and context methodsInteroperability with Cython extensionsmpz and mpq operation can release the GIL
* The current implementation is experimentalImproved argument processingThe gmpy2 2.1 series will be the last to offer compatibility with Python 2.7.
Release 2.1.3 is the last planned release of the 2.1 series.Note: Versions 2.1.4 and 2.1.5 were released to address Apple Silicon wheel
build issues. There are no code changes.Release 2.1.5 is the last planned release of the 2.1 series.Version 2.2 PlansVersion 2.2 will drop support for Python 2.7 and older Python 3.x versions.The primary development focus will be on functions that operate on lists and
release the GIL. See powmod_base_list and powmod_exp_list as examples.Availabilitygmpy2 is available athttps://pypi.python.org/pypi/gmpy2/Documentation is available athttps://gmpy2.readthedocs.io/en/latest/
Package: gmpyinfr
gmpyinfrMódulos úteis ao dia-a-dia de uma equipe de Ciência de Dados.InstalaçãoNão há cobertura para utilização no Windows. Este pacote e o passo-a-passo de instalação tem funcionamento garantido nas seguintes distros:Debian8 (jessie)9 (stretch)10 (buster)Ubuntu20.04 (focal)19.04 (disco)18.04 (bioni)16.04 (xenial)14.04 (trusty)Demais distribuições linux devem funcionar sem problemas mas têm comandos e processo de instalação diferentes. Caso este seja o seu caso, por favor verifique a documentação doApache ArroweMiniconda.Este pacote conta com instalações extras conforme a necessidade de utilização. Para instalar apenas o pacote básico, o comando épipinstallgmpyinfrJá para instalar um pacote extra, o comando tem a seguinte sintaxepipinstallgmpyinfr[extra]ondeextrapode ser um dos seguintes pacotes extras:all- contém todos os pacotes extras, recomendados para os cientistas que irão prototipar soluções em suas máquinas. Atente para o fato de que a instalação deste extra implica na leitura das demais documentações e instalação de todas bibliotecas necessárias para pleno funcionamento.db- contém o pacotegmpyinfr-dbutils. Responsável pela comunicação com os bancos de dados, incluindo rotinas de leitura/escrita rápida. Leia adocumentaçãopara passos de instalação prévios à instalação do pacote python.bot- contém o pacotegmpyinfr-telegram. Responsável pela comunicação com a API do Telegram para envio de mensagens a partir de um Bot. Leia adocumentaçãopara mais detalhes.amqp- contém o pacotegmpyinfr-amqp. Responsável pela comunicação com o RabbitMQ pelo protocolo AMQP. Lei adocumentaçãopara mais detalhes.log- contém o pacotegmpyinfr-log. Responsável pela gestão do log, sobre o pacote logging do python e comunicação com o Logstash. Leia adocumentaçãopara mais detalhes.CondaRecomendamos que a instalação seja realizada através doMiniconda. Deste modo, a instalação dos pacotes C++ é faciltada e o cientista pode iniciar imediatamente seus trabalhos. A versão a ser instalada deve ser 64bits para garantir compatibilidade com os demais pacotes. Após a instalação com sucesso do Anaconda/Miniconda, execute os seguintes comandos num novo terminal:condainstall-ypython=3.7'pyarrow>=3.0.0'Instalação nativa / DockerNos casos onde o usuário preferir não instalar através do Anaconda/Miniconda, ou tiver que realizar a instalação em uma imagem Docker*, deve-se realizar a instalação manual através dos seguintes comandos* No caso de uma instalação em imagem Docker, desconsidere osudo.
* Se a imagem Docker for dorepositório oficial python, a Etapa 2 é desnecessária.Etapa 1 - compiladores C e C++ e essenciaissudoaptupdate
sudoaptinstall-ygccg++gitcmakeEtapa 2 - python3.7sudoaptinstall-ypython3python3-devpython3-pipecho"alias python=python3">>~/.bashrcecho"alias pip=pip3">>~/.bashrcsource~/.bashrc
python-mpipinstall--upgradepip
pipinstallsetuptools
Package: gmpyinfr-amqp
gmpyinfr_amqpMódulo com funções e classes úteis à utilização do protocolo AMQP para mensageria, a ser utilizado por uma equipe de Ciência de Dados.InstalaçãoNão há cobertura para utilização no Windows. Este pacote e o passo-a-passo de instalação tem funcionamento garantido nas seguintes distros:Debian8 (jessie)9 (stretch)10 (buster)Ubuntu20.04 (focal)19.04 (disco)18.04 (bioni)16.04 (xenial)14.04 (trusty)Demais distribuições linux devem funcionar sem problemas mas têm comandos e processo de instalação diferentes.Para instalação deste pacote, faça;pipinstallgmpyinfr_amqpEste pacote depende apenas do pacotepikaque é facilmente instalável seguindo os passos de sua documentação.
Package: gmpyinfr-log
gmpyinfr_logMódulo com classes de log úteis a ser utilizado por uma equipe de Ciência de Dados. Implementa um log simples sobre o módulo logging do python, estende o conceito também com um log incorporado ao Kibana.InstalaçãoNão há cobertura para utilização no Windows. Este pacote e o passo-a-passo de instalação tem funcionamento garantido nas seguintes distros:Debian8 (jessie)9 (stretch)10 (buster)Ubuntu20.04 (focal)19.04 (disco)18.04 (bioni)16.04 (xenial)14.04 (trusty)Demais distribuições linux devem funcionar sem problemas mas têm comandos e processo de instalação diferentes.Para instalação deste pacote, faça:pipinstallgmpyinfr_logEste pacote não tem dependências explícitas, além da versão python. Indica-se utilização de versões superiores aopython>=3.6.
Package: gmpyinfr-telegram
gmpyinfr_telegramMódulo com funções e classes úteis à utilização da API Telegram para envio de mensagens a partir do Bot para uma equipe de Ciência de Dados.InstalaçãoNão há cobertura para utilização no Windows. Este pacote e o passo-a-passo de instalação tem funcionamento garantido nas seguintes distros:Debian8 (jessie)9 (stretch)10 (buster)Ubuntu20.04 (focal)19.04 (disco)18.04 (bioni)16.04 (xenial)14.04 (trusty)Demais distribuições linux devem funcionar sem problemas mas têm comandos e processo de instalação diferentes.Para instalação do pacote, façapipinstallgmpyinfr_telegramEste pacote depende apenas do pacoterequestsque é facilmente instalável seguindo os passos de sua documentação.
Package: gmrde
Diese Bibliothek ermöglicht das Analysieren und Umformen deutscher Wörter und Sätze.FeaturesImportieren von CSV WörterbuchdateinDurchsuchen von WörterbüchernKonjugieren von VerbenUmstellen einer Frage zu einer Antwort (z.B. "Wer bist du" zu "Ich weiß nicht, wer ich bin")Den Quellcode aufGithubansehen.Beispielimportgmrdew=gmrde.Woerterbuch()frage="Wer bin ich"antwort=gmrde.umformen.frageZuAntwort(w,frage,case=False)print(antwort)# "Ich weiß nicht, wer du bist."verb=w.konjugiere("sein","SIN:1","PRÄ")# 1. Person Singular Präsensprint(verb)# "bin"verb=w.konjugiere("können","PLU:2","PRT")# 2. Person Plural Präteritumprint(verb)# "konntet"LizenzenDieses Paket enthält den Datensatzgmrde/data/german.csvsowie Programmcode. Der Datensatz ist mit Creative Commons Attribution-ShareAlike 4.0 International lizenziert. Die genauen Lizenzbedingungen zum Datensatz befinden sich ingmrde/data/LICENSE. Der Programmcode steht unter der GNU Lesser General Public Licence Version 2.1, die genauen Lizenzbedingungen des Codes befinden sich ingmrde/LICENSE.
Package: gmreader
# gmreader**current version: v0.1.5**Listen to your emails with gmreader ( **g**oogle **m**ail reader ). A program that reads your gmail messages right to you.Simply input you email address and password to run the program. gmreader reads your unseen emails from recent to old. You must have a **Mac OSX, Linux, or Windows(with Cygwin)** machine in order to run gmreader. And if you have linux or windows, espeak must be installed on your computer, but it usually comes with the pre-installed packages(linux).**Note: Spam emails and emails formatted similarly are often read incompletely or voided as Undefinable**Tested on: Mac OSX 10.6.8 and Ubuntu 12.10# InstallTo download and install gmreader, you must follow the instructions below.### Requirements- Python- Linux -> Should come with espeak; if not, install via `sudo apt-get install espeak`.- Windows -> Download espeak command-line tools.- Mac -> Just a working computer.### Install via PIP```$ pip install gmreader```### Install via setup.pyFirst you need to get a copy of the source. I'm going to use git and clone it to my local computer.Clone the repository into a folder```git clone https://github.com/jawerty/gmreader.git gmreader```Install with setup.py```$ cd gmreader$ python setup.py install```# UsageRun default program```$ gmreaderEmail Address: jawerty210@gmail.comPassword:```Run with arguments```$ gmreader jawerty210@gmail.com mypassword123```# LicenseSee the file LICENSE to view the MIT License# ContactIf you would like to contact me for further information on the project, see the info below.Email: jawerty210@gmail.comGithub: jawertyTwitter: @jawertyBlog: <http://wrightdev.herokuapp.com>
Package: gmsad
gmsadgmsadmanages Active Directory group Managed Service Account (gMSA) on Linux.Given the keytab of an account which has the ability to retrieve the secret of a gMSA,gmsadcreates a keytab for the service account and renew it when necessary. It can execute an arbitrary command just after renewing the keytab.RequirementsYour Active Directory domain must be able to use group Managed Service Account which implies :AD schema updated to Windows Server 2012 (Getting Started with Group Managed Service Accounts)KDS Root Key deployed (Create the Key Distribution Services KDS Root Key)In addition,gmsadrequires a working LDAPS interface on domain controllers with a valid TLS certificate.DocumentationGetting started with gmsadWhy was this tool created ?How does a gMSA work ?Talk at SSTIC 2023 (in french)ContributingAny contribution is welcome, be it code, bug report, packaging, documentation or translation.Licensegmsad is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.gmsad is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.You should have received a copy of the GNU General Public License along with gmsad. If not, see the gnu.org web site.
Package: gmsdb
GMSDB ClustererUsepip install gmsdbSimple use (sklearn -like style), no speed improvement, recommended significance level alpha=0.1.Maximal number of gaussian components=50:from gmsdb import GMSDBclf=GMSDB(n_components=50)clf.fit(X)Y=clf.predict(X)Complex use (with speed improvement for stages 1 and 3):clf=GMSDB(min_components=2,step_components=100,n_components=900,rand_search=1000,rand_level=0.5)Complex use (with custom significance level alpha=0.15):clf=GMSDB(n_components=50,alpha_stage2=0.15,alpha_stage4=0.15)Verbose use (show debug information):clf=GMSDB(n_components=50,verbose=True)Desciption:https://arxiv.org/abs/2309.02623
Package: gmsdblib
GMSDB ClustererUsepip install gmsdblibSimple use (sklearn -like style), no speed improvement, standard significance level alpha=0.05.Maximal number of gaussian components=50:from gmsdblib.GMSDB import GMSDBclf=GMSDB(n_components=50)clf.fit(X)Y=clf.predict(X)Complex use (with speed improvement for stages 1 and 3):clf=GMSDB(min_components=2,step_components=100,n_components=900,rand_search=1000,rand_level=0.5)Complex use (with custom significance level alpha=0.15):clf=GMSDB(n_components=50,alpha_stage2=0.15,alpha_stage4=0.15)Verbose use (show debug information):clf=GMSDB(n_components=50,verbose=True)Desciption:https://arxiv.org/abs/2309.02623
Package: gmsh
Gmshis an automatic three-dimensional finite element
mesh generator with built-in pre- and post-processing facilities.The aim of this package is to install the official Gmsh app and binary Software
Development Kit (SDK) in a pythonic way, i.e. via thepipcommand. Installation should work under Linux, Windows and macOS for both Python
2 and 3.To install or upgradegmsh:$ pip install --upgrade gmshAfter installation you can either run the Gmsh app:$ gmshor use the Gmsh Python API:$ python -c "import gmsh; gmsh.initialize(); gmsh.fltk.run(); gmsh.finalize()"(Note that the header files for using the C++ and C API are also installed, as
is the Julia module.)