Saravanan666/pypi-org huggingface dataset:
Package: gd-kafka
gd_kafkaFor internalGreendeckuse only.Install from piphttps://pypi.org/project/gd-kafka/pip install gd-kafkaHow to use ?import the libraryimportgd_kafkaimportkafkaclassfromgd_kafkaimportGDKafkainitializeKafkaclient connection# declare variablesIP_PORT=KAFKA_IP_PORT# Here default values is IP_PORT=''gdk=GDKafka(IP_PORT="12.12.12.12:9092")#Here, 12.12.12.12 is example host ip.How to build your pip packageopen an account herehttps://pypi.org/In the parent directorypython setup.py sdist bdist_wheeltwine upload dist/*referenceshttps://medium.com/small-things-about-python/lets-talk-about-python-packaging-6d84b81f1bb5Thank You
Package: gdmath
GdMath: a fast math library for game developmentMain features100% Cython implementation (~30x speedup from pure python on average)Codegen basedLinear AlgebraVectorPythonic and GLSL-likeOperators +, -, *, /, @(dot), ^(cross), |(distance) ...Fast swizzling (e.g.Vec3(1, 2, 3).zxy)Flexible constructor (e.g.Vec3(Vec2(1, 2), 3))Iterating and unpacking (e.g.x, y, z = Vec3(1, 2, 3))TransformTransform2D&Transform3DGodot and GLSL like apiPlease refer to thewikifor more detailsImplementation detailsThis library uses code generation.Every swizzle pattern and constructor are implemented as individual methods and properties (For performance reasons).As a result, code generation is required so that I don't have to maintain 50,000+ lines of code by hand...Besides, it also has the convenience that vector classes of every dimension and type (e.g. Vec2 Vec3 Vec2i Vec3i) are generated from the same template, so a lot of repetitive code is avoided.NotesThis library was initially inspired byGodot's math library. (pun intended)FYI, when I was coding this, I didn't know aboutCython.Tempitauntil it's too late. It'd probably be a lot easier if I knew it from the beginning and don't have to write as much codegen by myself... But I'd still need to do things like overloading by myself anyway, so it's not a big deal.
Package: gdmetro-flowon
gdflowon包含使用DAG描述的自动流模型渲染工具、样品信息表的定义等，可通过简单的配置结合样品信息表中的subproject、sample、lane等模型快速地描述分析流程。渲染DAG及绑定顶点活动的资源以子项目为单位，通过配置中的顶点模板映射DAG的各个顶点。首先需要定义顶点的模板，顶点模板包含以下元素：<顶点类型>:
    scope: <资源类型>  # 指定该顶点类型使用到的资源类型：`subproject` | `sample` | `lane`
    dependencies: <依赖到的顶点类型>以WGS分析流程为例，人工操作需要完成以下步骤：按Lane上传，无依赖项按Lane比对，依赖绑定的资源（Lane）上传完成按样本WGS分析，依赖绑定的资源（样本下所有Lane）全部比对完成按整个子项目合并处理报告，依赖绑定的资源（子项目下所有的样本）全部分析完成按整个子项目下载结果，依赖绑定的资源（子项目）报告处理完成upload:
    scope: lane
    dependencies: null

mapping:
    scope: lane
    dependencies: upload

wgs:
    scope: sample
    dependencies: mapping

report:
    scope: subproject
    dependencies: wgs

download:
    scope: subproject
    dependencies: report样品信息表 <-> DAG的映射方式如下：@startuml 通过子项目映射到DAG
!includeurl https://raw.githubusercontent.com/xuanye/plantuml-style-c4/master/core.puml
start
:子项目/
split
    :子项目下的Lane列表/
    :映射|
    :顶点列表(upload)/
split again
    :子项目下的Lane列表/
    :映射|
    :顶点列表(mapping)/
split again
    :子项目下的样品列表/
    :映射|
    :顶点列表(wgs)/
split again
    :子项目/
    :映射|
    :顶点(report)/
split again
    :子项目/
    :映射|
    :顶点(download)/
end split

:处理依赖关系，生成DAG;
stop
@enduml使用方式fromgdflowonimportdag# flow_config: 使用DAG描述的流程模板# subproject: 样品信息表graph=dag.subproject_2_dag(subproject,flow_config)
Package: gdmix-trainer
GDMixWhat is itGeneralized DeepMixed Model(GDMix) is a framework to train non-linear fixed effect and random effect models. This kind of models are widely used in personalization of search and recommender systems. This project is an extension of our early effort on generalized linear modelsPhoton ML. It is implemented in Tensorflow, Scipy and Spark.The current version of GDMix supports logistic regression andDeTextmodels for the fixed effect, then logistic regression for the random effects. In the future, we may support deep models for random effects if the increase complexity can be justified by improvement in relevance metrics.Supported modelsLogistic regressionAs a basic classification model, logistic regression finds wide usage in search and recommender systems due to its model simplicity and training efficiency. Our implementation uses Tensorflow for data reading and gradient computation, and utilizes L-BFGS solver from Scipy. This combination takes advantage of the versatility of Tensorflow and fast convergence of L-BFGS. This mode is functionally equivalent to Photon-ML but with improved efficiency. Our internal tests show about 10% to 40% training speed improvement on various datasets.DeText modelsDeText is a framework for ranking with emphasis on textual features. GDMix supports DeText training natively as a global model. A user can specify a fixed effect model type as DeText then provide the network specifications. GDMix will train and score it automatically and connect the model to the subsequent random effect models. Currently only the pointwise loss function from DeText is allowed to be connected with the logistic regression random effect models.Other modelsGDMix can work with any deep learning fixed effect models. The interface between GDMix and other models is at the file I/O. A user can train a model outside GDMix, then score the training data with the model and save the scores in files, which are the input to the GDMix random effect training. This enables the user to train random effect models based on scores from a custom  fixed effect model that is not natively supported by GDMix.Training efficiencyFor logistic regression models, the training efficiency is achieved by parallel training. Since the fixed effect model is usually trained on a large amount of data, synchronous training based on Tensorflow all-reduce operation is utilized. Each worker takes a portion of the training data and compute the local gradient. The gradients are aggregated then fed to the L-BFGS solver. The training dataset for each random effect model is usually small, however the number of models (e.g. individual models for all LinkedIn members) can be on the order of hundred of millions. This requires a partitioning and parallel training strategy, where each worker is responsible for a portion of the population and all the workers train their assigned models independently and simultaneously.For DeText models, efficiency is achieved by either Tensorflow based parameter server asynchronous distributed training or Horovod based synchronous distributed training.
Package: gdmodloader
gdmodloader is a library that implements modloader for Geometry Dash.Key FeaturesModern Pythonic APIEasy to Use SDKInstallingPython 3.6 or higher is requiredTo install the launcher, download the launcher.zip from releases and then run the installer.py, the installer.py should install the modloader.After installing the modloader, restart your computer so the modloader runs when you start Geometry Dash.To install the library, you can just run the following command:# Windowspy-3-mpipinstall-UgdmodloaderDevelopment VersionYou can install development version from GitHub:pipinstall-Uhttps://github.com/SpookyBear0/gdmodloader/archive/master.zipQuick exampleimportgdmodloader...You can find more examples in examples directory.AuthorsThis project is mainly developed bySpookyBear0LinksGeometry Dash Discord Server
Package: gdmTool
gdmToolsA dataset tool for converting all images in a folder into neural network recognitionThis tool includes a total of three methods1. Convert images to data using the image method of the PIL package2. Read the file directory and return the data set3. Slices of known global datasets and return them as training sets, test sets, validation setsexample:data = read(input_Path)
  train_index, test_index, valid_index = cut(data, 0.5, 0.2, 0.3)
  # x
  data[train_index], data[test_index], data[valid_index] 
  # y
  labelGDM is a graphical data modeling product developed by the cosmosource. The main functions are graphical development of machine learning tasks with Spark as the engine, and graphical development of deep learning tasks with Tensorflow as the engine.
Package: gdmty-django-recaptcha-enterprise
gdmty_django_recaptcha_enterpriseLibrary for Django that implements Google's reCaptcha EnterpriseThis library is a draft but it works. Was made because there is no library for Django that implements Google's reCaptcha Enterprise. But now this library provides a way to verify tokens from reCaptcha Enterprise.Installation:pipinstallgdmty-django-recaptcha-enterpriseUsage:In settings.py:# import service_account from google.oauth2 and instanciate a Credentials object from your service account filefromgoogle.oauth2importservice_account# put 'gdmty_django_recaptcha_enterprise' in INSTALLED_APPSINSTALLED_APPS=[...,'gdmty_django_recaptcha_enterprise',...]# Set the following variablesRECAPTCHA_CREDENTIALS_SERVICE_ACCOUNT_FILE=os.path.join(BASE_DIR,"_gcp_sa","recaptcha_enterprise_service_account_key.json")credentials=service_account.Credentials.from_service_account_file(RECAPTCHA_CREDENTIALS_SERVICE_ACCOUNT_FILE)RECAPTCHA_ENTERPRISE_PROJECT_ID='your-project-id'RECAPTCHA_ENTERPRISE_SERVICE_ACCOUNT_CREDENTIALS=credentialsRECAPTCHA_ENTERPRISE_SITE_KEY_VERIFY='your-site-key'# This one is the site key for usage with seamless verification with the reCaptcha Enterprise API withouth user interactionRECAPTCHA_ENTERPRISE_SITE_KEY_CHALLENGE='your-site-key'# This one is the site key for usage with challenge verification with the reCaptcha Enterprise API with user interactionRECAPTCHA_ENTERPRISE_BYPASS_TOKEN='your-bypass-token'# Optional, only for debug and development usage. For production must be False. When DEBUG=False this must be False too or will not pass assesments never.In your code:# import assess_token from gdmty_django_recaptcha_enterprise.recaptcha, then you can use it to assess tokens where you need it. In this excample we show a hypothetical view that receives a token from a POST request.# You can use the decorator requires_recaptcha to verify the token before the view is executed.fromgdmty_django_recaptcha_enterprise.recaptchaimportRecaptchaEnterprisefromgdmty_django_recaptcha_enterprise.decoratorsimportrequires_recaptchafromdjango.confimportsettingsrecaptcha=RecaptchaEnterprise(settings.RECAPTCHA_ENTERPRISE_PROJECT_ID,settings.RECAPTCHA_ENTERPRISE_SITE_KEY_VERIFY,settings.RECAPTCHA_ENTERPRISE_SERVICE_ACCOUNT_CREDENTIALS)# each time you need to assess a token you can use the assess_token method from the recaptcha object, can be used from this decorator using the action parameter to assess.# When the decorator is used, the decorator handles de request to get the token from the request and assess it, if the token is valid the view is executed, if not, the view is not executed and a 403 response is returned.#@requires_recaptcha(action='action-to-verify')defmy_view(request):...pass...
Package: gdn-python-common
A hodgepodge of common helpers and utilities used at GDN across several Python 3.6+ projects.Progress Bars for your long-running CLI tasksConfigure your app from environmental variablesParse and Write HTTP Link HeadersUse link-header-style pagination in Django-Rest-FrameworkBase django model classes (timestamped, name+auto slug, soft-deletable models)Installingpipinstallgdn-python-common# or, if you'd like to have django-rest-framework installed automaticallypipinstallgdn-python-common[drf]
Package: gdoc-down
gdoc-downAPI and command line program to save Google documents, presentations,
and worksheets in a local Google Drive / Backup & Sync directory to to
local files.gdoc-downsupports several formats:CSV (.csv)EPUB (.epub)Excel workbook (.xlsx)HTML (.html)Image (.png, .jpg, .svg)LaTeX (.tex)Open Office document (.odt)Open Office presentation (.odp)Open Office workbook (.ods)Plain text file (.txt)Portable document format (.pdf)Powerpoint presentation (.pptx)Rich text document (.rtf)TSV (.tsv)Word document (.docx)gdoc-downalso has special features for handling .gdoc files that
contain LaTeX:gdoc-downignores all images. This allows the user to place
images inside the Google document for convenience and to use\includegraphicsto embed images in compile PDF files.gdoc-downwill convert all Google document comments to PDF
comments.gdoc-downignores all page breaks.The first timegdoc-downis called,gdoc-downwill request
access to the user’s Google account. This will create a client.json file
in the users home directory (~/.gdoc_down/client.json).InstallationLatest release from PyPIpip install gdoc_downLatest revision from GitHubpip installgit+https://github.com/KarrLab/gdoc_down.git#egg=gdoc_downCommand line usageusage: gdoc-down (sub-commands ...) [options ...] {arguments ...}

Download a Google document, presentation, or workbook to a local file

positional arguments:
  google_file           path to Google document, presentation, or workbook

optional arguments:
  -h, --help            show this help message and exit
  --debug               toggle debug output
  --quiet               suppress all output
  --format FORMAT, -f FORMAT
                        output format (csv, docx, epub, html, jpg, odft, odp,
                        ods, pdf, pptx, png, rtf, svg, tsv, tex, txt, xlsx)
  --out_path OUT_PATH, -o OUT_PATH
                        path where Google document, presentation, or workbook
                        should be downloaded
  --extension EXTENSION, -e EXTENSION
                        output extensionExamplesgdoc-down -f docx /path/to/Google \Drive/file.gdoc
gdoc-down -f pptx /path/to/Google \Drive/file.gslides
gdoc-down -f xlsx /path/to/Google \Drive/file.gsheetDocumentationPlease see the documentation atRead the
Docs.Testspytestcan be used to run the tests:pytest testsPlease note that several additional packages are required for testing
(seetests/requirements.txt).LicenseThe example model is released under theMIT license.Development teamgdoc-downwas developed byJonathan
Karrat the Icahn School of Medicine at
Mount Sinai in New York, USA.Questions and commentsPlease contact theJonathan Karrwith any
questions or comments.
Package: gdoc-json-parser
The tool converts a google spreadsheet to an array of clean JSON objects that can be easily used.Install the tool using pip:pip install gdoc-json-parserRun the program like:gdoc-json-parser https://docs.google.com/spreadsheets/d/12dWdnr9mq_9gz_CEGv54x5prloJXD4ZJ8NuMxc2pCfc/You can also add a second argument to specify the output location.Python 3 compatible.MIT license.
Package: gdom
GDOM is the next generation of web-parsing, powered byGraphQLsyntax and theGraphene framework.Install it typing in your console:pipinstallgdomDEMO:Try GDOM onlineUsageYou can either dogdom--testto start a test server for testing
queries orgdomQUERY_FILEThis command will write in the standard output (or other output if
specified via--output) the resulting JSON.YourQUERY_FILEcould look similar to this:{
  page(url:"http://news.ycombinator.com") {
    items: query(selector:"tr.athing") {
      rank: text(selector:"td span.rank")
      title: text(selector:"td.title a")
      sitebit: text(selector:"span.comhead a")
      url: attr(selector:"td.title a", name:"href")
      attrs: next {
         score: text(selector:"span.score")
         user: text(selector:"a:eq(0)")
         comments: text(selector:"a:eq(2)")
      }
    }
  }
}Advanced usageIf you want to generalize your gdom query to any page, just rewrite your
query file adding the$pagevar. So should look to something like
this:query ($page: String) {
  page(url:$page) {
    # ...
  }
}And then, query it like:gdomQUERY_FILEhttp://news.ycombinator.com
Package: gdon
PrésentationG1pourboire est un outil permettant de générer des pourboires en Ğ1.
En développement, ceci est une version Béta.Il est fonctionnel en ligne de commande, mais pas (encore ?) en interface graphique. Cette contribution est bienvenue !InstallationLe logiciel est empaqueté et référencé sur l'index Pypi.Installation des dépendances DEBIANpython3pip3build-essentialpython3-wheel`Debian :$ sudo apt-get install python3 python3-pip python3-dev build-essential python3-wheel
# si sur carte ARM:
$ sudo apt-get install libffi-dev

# ajouter .local/bin à votre PATH
$ echo 'export PATH="/home/$USER/.local/bin:$PATH"' >> .bash_aliases
# redémarrer votre terminal ou votre interpréteur de commande
# installer gdon
$ python3 -m pip install gdonInstallation pour les devs :Désolé, le dépôt est encore nommé "Ğ1pourboire", ancien nom du projet# apt install pipenv
$ git clone https://git.duniter.org/matograine/g1pourboire.git
$ cd g1pourboire
$ pipenv install '-e .'
$ pipenv run gdonUtilisationpour connaître toutes les commandes :gdon helpGénérer et remplir des Ğ1Dons :Les Ğ1Dons seront bloqués jusqu'à la péremption.
Le compte depuis lequel vous crééez les Ğ1Dons vous sera nécessaire pour les récupérer.$ gdon generer <montant> <nombre_pages> <peremption_date_JJ/MM/AAAA>Récupérer le contenu de Ğ1Dons :$ gdon recupererRécupérer des Ğ1Dons depuis un dossier de sauvegarde:gdon recuperer<dossier>Récupérer des Ğ1Dons depuis un fichier:recuperer fichier <fichier>Si la récupération s'est mal passée :recuperer backup <année>Configurer le noeud Duniter :gdon noeud <noeud> <port>Les dons générés sont stockés par défaut dans~/Documents/G1dons/en pdf.
Les fichiers de récupération sont par défaut dans ~/Documents/G1dons/Récupération. Ne pas le supprimer !
Les pourboires ne sont pas récupérables avant la date de péremption.TODOInterface graphiqueSuivi des pourboires générésPaquet stand-alone pour Windows
Package: gdot
Git my dotfiles!WarningThis is a preview project, not ready for use yet, most of the implementation is not there yetgdotis a python CLI that allows to easily roam one’s dotfile configs
(such as~/.bashrcand~/.config/...) between multiple machines.Getting started:# On first login, associate your local files to a remote git repo
gdot attach github:myuserid

# Pull changes from your remote git store any time
gdot pull

# Add some files to track
gdot add ~/.bashrc ~/.config/htop/

# When you made some local changes, push them to your git store
gdot push

# Optionally, you can specify a commit message
gdot push -m "Changed qtile settings"

# See what's changed since you last push/pull-ed
gdot status
gdot diffInstallationInstall it withpickley:pickley install gdot            # Grab the latest
pickley install gdot==1.0.0     # Or pin to a specific versionOr in a venv:python3 -mvenv ~/.local/venvs/gdot
~/.local/venvs/gdot/bin/pip install gdot

# Then put a symlink to a folder that is in your PATH:
ln -s ~/.local/venvs/gdot/bin/gdot ~/.local/bin/gdot

# Or simply alias it:
alias gdot=~/.local/venvs/gdot/bin/gdot

# You can upgrade it like so:
~/.local/venvs/gdot/bin/pip install -U gdot

# Or pin to a specific version:
~/.local/venvs/gdot/bin/pip install -U gdot==1.0.0
Package: gdownload
===============================================================================#Garmin Connect Downloader===============================================================================DownloadGarmin :-------+ Download all "activites" of http://connect.garmin.comREQUIREMENTS:-------Software :+ Python : 2.7++ GcpUploaderDependances :GcpUploaderINSTALL :---------UNSTABLE Version (branch MASTER) :<pre><code>wget -N https://github.com/corbamico/Garmin-Connect-Downloader/raw/master/Garmin-Connect-Downloader.py</code></pre>Thanks to :---------+ gcpuploader : ( http://sourceforge.net/projects/gcpuploader/ )- Auto upload "FIT/TCX" on http:// connect.garmin.com--------- GitHub : https://github.com/corbamico/Garmin-Connect-Downloader
Package: gdpc
GDPC 6.0 (Transformative Update)GDPC (Generative Design Python Client) is a Python framework for use in conjunction with theGDMC-HTTPmod for Minecraft Java edition.
It is designed for theGenerative Design in Minecraft Competition (GDMC).You need to be playing in a Minecraft world with the mod installed to use the framework.The latest version of GDPC is compatible with GDMC-HTTP versions>=1.0.0, <2.0.0.Quick examplefromgdpcimportEditor,Block,geometryeditor=Editor(buffering=True)# Get a blockblock=editor.getBlock((0,48,0))# Place a blockeditor.placeBlock((0,80,0),Block("stone"))# Build a cubegeometry.placeCuboid(editor,(0,80,2),(2,82,4),Block("oak_planks"))What's the difference between GDMC, GDMC-HTTP and GDPC?These abbreviations are all very similar, but stand for different things.GDMC is short for theGenerative Design in Minecraft Competition, a yearly competition for generative AI systems in Minecraft.
The challenge is to write an algorithm that creates a settlement while adapting to the pre-existing terrain. The competition also has aDiscord server.GDMC-HTTPis a Minecraft Forge mod that provides a HTTP interface to edit the world.
It allows you to modify the world live, while you're playing in it. This makes it possible to iterate quickly on generator algorithms.
The mod is an official submission method for the competition.GDPC (notice the "P") is a Python framework for interacting with the GDMC-HTTP interface.
It provides many high-level tools that make working with the interface much simpler.InstallationGDPC is available on PyPI. To install the latest stable release, type one of the following commands:On Linux/MacOS:python3 -m pip install gdpcOn Windows:py -m pip install gdpcFor the latest sexy-but-might-break-something prerelease, type the following instead:On Linux/MacOS:python3 -m pip install --pre gdpcOn Windows:py -m pip install --pre gdpcTo update your package, type the following:On Linux/MacOS:python3 -m pip install --upgrade gdpcOn Windows:py -m pip install --upgrade gdpcIf you would like to install the latest (pre)release version directly from GitHub, replacegdpcwithgit+https://github.com/avdstaaij/gdpcIf instead you want to install the latest cutting-edge development version, replacegdpcwithgit+https://github.com/avdstaaij/gdpc@devFor more information on installing from GitHub (such as getting old versions) see thepip documentation.If you are having trouble with dependencies, downloadrequirements.txtand try runningpython3 -m pip install -r requirements.txt(orpy -m pip pip install -r requirements.txtif you are using Windows). Youshould notuserequirements-dev.txt!Tutorials and examplesThere are varioustutorial scriptsthat will help to get you started.Some practical examples are also available, though they're slightly older and may not reflect the latest features:visualize_map.py: Displays a map of the Minecraft world using OpenCV.emerald_city.py: Demonstrates basic GDPC functionality by building a simple model of the Emerald City.ContributingSeeCONTRIBUTING.mdfor more information about how to contribute.