Saravanan666/pypi-org huggingface dataset:
Package: grapi
grapi(Pronounced: gray-P-I)Python3 bindings for Graylog's REST APIInstallFirst, make sure you have at least Python 3.7 installed.Developmentgit clone git@github.com:interputed/grapi.git
cd grapi
pipenv installLibrary Usagepip3 install grapiExample UsageTo use, you must first create anaccess tokenfollowing the instructions at:Graylog REST API DocumentationAPI does not support username/password logins as it makes it easier to accidentally expose user credentials.Import after installation with pip:from grapi.grapi import GrapiImport access token string:token = "<your_access_token_string>"Set URL to desired API path:url = "https://<your Graylog URL>/api/search/universal/absolute"All currently implemented URL endpoints can be found in:grapi/endpoints.pyThe list of all possible endpoints that may be implemented in the future is found by adding/api/api-browserto the URL of your Graylog server.Setup the Grapi object with url and token:my_api = Grapi(url, token)Build a dictionary of parameters mapped to string values for every parameter of your request:my_params = {
  "query": "<your query string (lucene syntax)>", # Required
  "fields": "<fields you wish returned>", # Required
  "from": "<YYYY-MM-DD HH-MM-SS>", # Required
  "to": "<YYYY-MM-DD HH-MM-SS>", # Required
  "limit": 10 # Optional: Default limit is 150 in Graylog
}Now, simply call the send method of my_api with the type of request method (get, post, put, delete):response = my_api.send("get", **my_params)That's it for a simple search in Python 3 using Graylog's REST API! Now you can manipulate the data however you wish directly in Python. To see the output of your search you can just print it as with anything else in Python:print(response.json())
OR
print(response.text)This is a work in progress, so if the method or endpoint you need isn't yet implemented, add it yourself and send a pull request, post to issues, or simply check back later and it should be added.
Package: grappelli-safe
Overviewgrappelli_safe is a permanent fork ofGrappelli admin skinforDjango, to be referenced as a
dependency for theMezzanine CMS for Django.At the time of grappelli_safe’s creation, Grappelli was incorrectly
packaged onPyPI, and had also dropped
compatibility with Django 1.1 – grappelli_safe was therefore created to
address these specific issues.For further details, seeWhy are Grappelli and Filebrowser Forked?.ContributingPlease refer toMezzanine’s Contribution Guidelines
Package: grappelli-side-menu
Side menu for Grappelli, the Django admin InterfaceQuickstartInstall grappelli-side-menu:pip install grappelli-side-menuAdd “grappelli_menu” to your INSTALLED_APPS setting like this:INSTALLED_APPS = (
    'grappelli_menu',
    'grappelli',
    'django.contrib.admin',
    .....
)Adddjango.core.context_processors.requestto your TEMPLATE_CONTEXT_PROCESSORS if it’s not there already:TEMPLATE_CONTEXT_PROCESSORS = (
    .....
    "django.core.context_processors.request",
)FeaturesTODOHistory0.1.4 (2016-17-10)Compatibility with 1.9CleanupCursor pointer for menu items0.1.3 (2014-28-04)Fix: Hiding the menu in the login and logout screen0.1.1,0.1.2 (2014-25-04)Fix: Including the css file0.1.0 (2014-25-04)First release on PyPI.
Package: grappler
grapplerA library for loading plugins in Python applications.What is grappler?Grappler is a set of tools for loading plugins into a Python
application. With it, you can load third-party plugins which
advertise along specific topics, and perform additional filtering
according to the tools provided with the library. Furthermore,
Grappler is compatible with typed Python out of the box.Feature highlightsExtremely simple interfacefor loading entry-points out of the box.Extensible design, allowing implementation of new plugin sources with theGrappler interfaceComposable APIfor customizing plugin sources and loading behaviourHandy built-in toolsto help with customising loading behavior (e.g. blacklisting plugins so that
their code is never loaded).InstallationInstallation requires Python 3.8 or later.From PyPIpip install grapplerGetting StartedSee thequickstart guideDocumentationDocumentation can be found in project source code (as docstrings), with supplementary
documentation provided in thedocsfolder of the source distribution.Documentation is built usingMkDocs;
To build, run the following command:pdm run mkdocs buildA built copy of the documentation is hosted onhttps://mr-rodgers.github.io/grappler/.
Package: grasia-dash-components
grasia-dash-components is a third party extension of theDashcomponents suite. It adds new components or extends the default ones.This is a list of the currently available components:Accordion: A list of items that can be collapsed (folded) or un-collapsed (unfolded).Card: A component for framing content that can contain an image, text, etcetera.Import: A component to import external javascript libraries into your web app.LoadingDialog: A loading Dialog/Popup.Tabs: An extension of the[WIP] Dash componentthat supports embbebing icons in the tabs as well as other improvements.To use it, simply install it from pip:pip install grasia-dash-componentsand import it withimport grasia_dash_components.We use this suite for our projects.
But we have this suiteopen-sourcedand we try to keep it well maintained.
Package: graspit
Grasp-itUtilizes the data collected from the test execution does some processing and then displays it in the Dashboard.Architecturewe initiate the Grasp-it-dashboardthis step would be skipped if these below steps were done previously, Note: it would be not skipped if it finds a new version of the dashboardCopy the dashboard fromsrcdownloads all the required npm modulescreates a newTeStReSuLtS.dbAnd then we start the frameworkPast that we collect all the Test suites, sessions, test cases, we name this routes under the prefix: "register"After the registration, we collect the details pertaining to the end result of the registered entityNow we make sure to fill the calculated cells in some records that were added, this is to save the time while generating the report.we export the results past the test completion, while this is an optional step, which could be configured at the plugin level, but it has to done at some point in order to generate a report.PlansTest ReportsAdding AttachmentsDynamic ReportJIRA Reporterwdio-py-reporterSupport Cucumber frameworkDynamic ReportNotehttps://python-poetry.org/docs/basic-usage/#using-your-virtual-environment
Package: graspologic
graspologicgraspologicis a package for graph statistical algorithms.OverviewDocumentationSystem RequirementsInstallation GuideContributingLicenseIssuesCitinggraspologicOverviewA graph, or network, provides a mathematically intuitive representation of data with some sort of relationship between items. For example, a social network can be represented as a graph by considering all participants in the social network as nodes, with connections representing whether each pair of individuals in the network are friends with one another. Naively, one might apply traditional statistical techniques to a graph, which neglects the spatial arrangement of nodes within the network and is not utilizing all of the information present in the graph. In this package, we provide utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms.DocumentationThe official documentation with usage is athttps://microsoft.github.io/graspologic/latestPlease visit thetutorial sectionin the official website for more in depth usage.System RequirementsHardware requirementsgraspologicpackage requires only a standard computer with enough RAM to support the in-memory operations.Software requirementsOS Requirementsgraspologicis tested on the following OSes:Linux x64macOS x64Windows 10 x64And across the followingx86_64versions of Python:3.83.93.10If you try to usegraspologicfor a different platform than the ones listed and notice any unexpected behavior,
please feel free toraise an issue.  It's better for ourselves and our users
if we have concrete examples of things not working!Installation GuideInstall from pippip install graspologicInstall from Githubgit clone https://github.com/microsoft/graspologic
cd graspologic
python3 -m venv venv
source venv/bin/activate
python3 setup.py installContributingWe welcome contributions from anyone. Please see ourcontribution guidelinesbefore making a pull request. Ourissuespage is full of places we could use help!
If you have an idea for an improvement not listed there, pleasemake an issuefirst so you can discuss with the developers.LicenseThis project is covered under the MIT License.IssuesWe appreciate detailed bug reports and feature requests (though we appreciate pull requests even more!). Please visit ourissuespage if you have questions or ideas.CitinggraspologicIf you findgraspologicuseful in your work, please cite the package via theGraSPy paperChung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7.
Package: graspologic-native
graspologic-nativegraspologic-nativeis a companion library tograspologic.  This module is a Python native module created by using
thenetwork_partitionscrate from the same repository.The purpose of this module is to provide a faster implementations of graph/network analysis algorithms in a native
without trying to work through the troubles of releasing Rust crates and Python modules at the same time (in specific
as the Pythongraspologicmodule is expected to be far more active than the Rust crates or native modules are).The only capability currently implemented by this module is the Leiden algorithm, described in the paperFrom Louvain to Leiden: guaranteeing well-connected communities,
Traag, V.A.; Waltman, L.; Van, Eck N.J., Scientific Reports, Vol. 9, 2019.  In addition to the paper, the reference
implementation provided athttps://github.com/CWTSLeiden/networkanalysiswas used as a starting point.ReleasesBuilds are provided for x86_64 architectures only, for Windows, macOS, and Linux, for Python versions 3.6->3.12.Build ToolsRust nightly 1.37+ (we are currently using 1.40)
The python packagematurinPlease consider usinggraspologicin lieu ofgraspologic-native, as the
former will contain some nice wrappers to make usage of this library more pythonic.
Package: graspy
GraSPyGraphStatistics inPython is a package for graph statistical algorithms.OverviewDocumentationSystem RequirementsInstallation GuideContributingLicenseIssuesOverviewA graph, or network, provides a mathematically intuitive representation of data with some sort of relationship between items. For example, a social network can be represented as a graph by considering all participants in the social network as nodes, with connections representing whether each pair of individuals in the network are friends with one another. Naively, one might apply traditional statistical techniques to a graph, which neglects the spatial arrangement of nodes within the network and is not utilizing all of the information present in the graph. In this package, we provide utilities and algorithms designed for the processing and analysis of graphs with specialized graph statistical algorithms.DocumentationThe official documentation with usage is athttps://graspy.neurodata.io/Please visit thetutorial sectionin the official website for more in depth usage.System RequirementsHardware requirementsGraSPypackage requires only a standard computer with enough RAM to support the in-memory operations.Software requirementsOS RequirementsThis package is supported forLinuxandmacOS. The package has been tested on the following systems:Linux: Ubuntu 16.04macOS: Mojave (10.14.1)Windows: 10Python RequirementsThis package is written for Python3. Currently, it is supported for Python 3.6 and 3.7.Python DependenciesGraSPymainly depends on the Python scientific stack.networkx
numpy
pandas
scikit-learn
scipy
seabornInstallation GuideInstall from pippip install graspyInstall from Githubgit clone https://github.com/neurodata/graspy
cd graspy
python3 setup.py installContributingWe welcome contributions from anyone. Please see ourcontribution guidelinesbefore making a pull request. Ourissuespage is full of places we could use help!
If you have an idea for an improvement not listed there, pleasemake an issuefirst so you can discuss with the
developers.LicenseThis project is covered under theApache 2.0 License.IssuesWe appreciate detailed bug reports and feature requests (though we appreciate pull requests even more!). Please visit ourissuespage if you have questions or ideas.Citing GraSPyIf you find GraSPy useful in your work, please cite the package via theGraSPy paperChung, J., Pedigo, B. D., Bridgeford, E. W., Varjavand, B. K., Helm, H. S., & Vogelstein, J. T. (2019). GraSPy: Graph Statistics in Python. Journal of Machine Learning Research, 20(158), 1-7.
Package: grassed
Grassed Logging HandlerPython logging handler that can be configured to send messages to a Slack channel.SlackLoggerbuilds on the base built-inlogging.Handlerto allow logging messages to logged via HTTPS to a Slack channel.In addition to this, the handler also builds on thelogging.Formatterclass to help build fancier Slack messages
using theirlayout blocks.
The following tags are supported:Header: plain textSection: supports markdownDivider: no text supportGetting StartedThe package can be installed from the PyPi repository withpip install grassedor cloned from thegit repo.DocumentationRead the docsfor usage and examples.Check out Slack's documentation forIncoming Webhooksto learn how to set up a Slack app that can handle the incoming messages.Message Rate LimitsSlackHandler is designed to log important messages to a place where people can view them immediately and respond.
Note that Slack imposes message rate limits which SlackHandler respects.LicenceReleased underGPLv3bySam Vanderslink.
Free to modify and reuse.
Package: grass-gis-helpers
mundialis GRASS GIS helper libraryThis library provides functions which can be used for developing GRASS GIS addons and which are used bymundialis.The library usage needs to be in a GRASS GIS session, so that the the modulegrasscan be imported.Installationpipinstallgrass-gis-helpersSmall exampleSmall example how the library can be used inside a GRASS GIS session:fromgrass_gis_helpersimportgeneralgeneral.set_nprocs(2)DEV setuppip-tools is required for DEV setup:# only oncepip3installpip-toolsthen install grass-gis-helpers from the local repository:pip3install-e.
Package: grasspy-flask
福利哥介绍flask Web 开发框架的草蟒中文版安装教程python -m pip install -U grasspy-flask使用简介从福利哥导入〇福利哥应用=〇福利哥(__名称__)@应用.路由('/')套路你好():返回'你吃了吗？'如果__名称__=="__主体__":应用.运行(调试=真)
Package: grasspy-jieba
结巴——jieba 中文版介绍草蟒中文分词库——结巴（jieba中文版）安装python -m pip install -U grasspy-jieba使用说明>>>导入结巴>>>结巴.分词列表('我来到了中国科学技术大学')['我','来到','了','中国','科学技术','大学']更多信息请参阅《草蟒（Python 中文版）入门指南》中的分词相关示例
Package: grasspy-pyecharts
百度可视化库介绍ECharts 是一个由百度开源的数据可视化工具，凭借着良好的交互性，精巧的图表设计，得到了众多开发者的认可。而 Python 是一门富有表达力的语言，很适合用于数据处理。当数据分析遇上数据可视化时，pyecharts 诞生了。百度可视化 是 pyecharts 的中文版。安装教程更新草蟒标准库：python -m pip install --upgrade grasspy-stdlib安装百度可视化：python -m pip install --upgrade grasspy-pyecharts使用说明示例位于本代码库的 tests 目录中pyecharts 文档和pyecharts 图库echarts 文档和图库参与贡献Fork 本仓库新建 Feat_xxx 分支提交代码新建 Pull Request
Package: grasspy-pywebio
网页io介绍PyWebIO 的中文版，结合草蟒（Python 中文版）使用, 提供一系列命令式的交互函数来在浏览器上获取用户输入和进行输出，将浏览器变成了一个“富文本终端”，可以用于构建简单的 Web 应用或基于浏览器的 GUI 应用。网页io 还能方便地整合进现有的 Web 服务，让你不需要编写 HTML 和 JS 代码，就可以构建出具有良好可用性的应用。安装说明先决条件：系统须安装草蟒（Python 中文版）python -m pip install -U grasspy-pywebio使用说明参见PyWebIO 文档参与贡献Fork 本仓库新建 Feat_xxx 分支提交代码新建 Pull Request
Package: grasspy-requests
网络请求 (requests)介绍优雅简单的 python requests 库的草蟒中文版安装方法python -m pip install -U grasspy-requests使用简介GET基本使用:>>>导入网络请求>>>响应=网络请求.查_get('https://www.grasspy.cn')>>>响应.状态码200>>>'中文编程'在响应.内容.解码('utf-8')True...或POST:>>>数据=字典型(key1='value1',键2='值2')>>>响应=网络请求.增_post('https://httpbin.org/post',数据=数据)>>>打印(响应.文本){..."form":{"key1":"value1","\u952e2":"\u503c2"},...}当然也支持其他HTTP方法.
Package: grasspy-wordcloud
词云生成器——wordcloud 中文版介绍草蟒词云库——wordcloud 中文版一般配合中文分词库 '结巴' 使用安装教程python -m pip install -U grasspy-wordcloud使用说明导入结巴导入词云字符串='草蟒中文编程语言'词表=结巴.分词列表(字符串)文本=' '.连接(词表)词云实例=词云.〇词云(字体路径="msyh.ttc")# 步骤 1：配置对象参数词云实例.产生(文本)# 步骤 2：加载词云文本词云实例.转为文件("草蟒词云.png")# 步骤 3：输出词云文件
Package: grasspy-xlwings
格子 (xlwings)介绍python xlwings 的草蟒中文版，提供强大易用的 Office 电子表格处理功能安装方法python -m pip install -U grasspy-xlwings使用说明# 一个简单示例导入格子>>>工作簿=格子.〇工作簿()# 创建一个新工作簿>>>工作表=工作簿.工作表々['Sheet1']# 获取一个工作表对象>>>工作表.范围('A1').值='姓名'>>>工作表.范围('A1').值'姓名'>>>工作表.范围('A1').值=[['小美','小刚','小王'],[10.0,20.0,30.0]]>>>工作表.范围('A1').扩展().值[['小美','小刚','小王'],[10.0,20.0,30.0]]
Package: grass-session
A simple library to use GRASS GIS from python.
You can specify the GRASS executable that you want to use with an
enviromental variable called:GRASSBINStatusIn developmentInstallTo install the stable version use:$ pip install grass-sessionTo install the current development version use:$ pip install git+https://github.com/zarch/grass-session.gitExamplesSet the GRASS GIS binary that you want to use with:export GRASSBIN=grass75:>>> from grass_session import Session
>>> from grass.script import core as gcore
>>> with Session(gisdb="/tmp", location="location",
...              create_opts="EPSG:4326"):
...    print(gcore.parse_command("g.gisenv", flags="s"))
{u'GISDBASE': u"'/tmp/';",
 u'LOCATION_NAME': u"'epsg3035';",
 u'MAPSET': u"'PERMANENT';",}
>>> with Session(gisdb="/tmp", location="location", mapset="test",
...              create_opts=""):
...    print(gcore.parse_command("g.gisenv", flags="s"))
{u'GISDBASE': u"'/tmp/';",
 u'LOCATION_NAME': u"'epsg3035';",
 u'MAPSET': u"'test';",}DevelopmentClone the repository:$ git clone git@github.com:zarch/grass_session.gitMake sure thatpy.test,toxandpre-commitare installed:$ pip install -r requirements-testing.txtInstallpre-commithook in the local repository:$ pre-commit installTest locally withpy.test:$ pytest -vv .To see the coverage use:$ pytest -v –cov=grass_session –cov-report=html .To test with different version of python or grass use:$ GRASSBIN=~/.local/bin/grassXX PYTHONPATH=”pwd:$PYTHONPATH” pytest .Test against multiple Python environments usingtox:$ tox
...
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py27: commands succeeded
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congratulations :)
Package: grast
grastLazy automatic differentiation for PythonInstallRequires python >= 3.10pipinstallgrastUsageCreate function R^n -> Rfromgrastimportvarx=var('x')y=var('y')z=var('z').freeze()# do not compute derivativeh=x/y+y**xf=z*h+3Get gradientdf=f.grad()df_dx=df['x']df_dy=df['y']Evaluate with specific argumentsargs=dict(x=-3,y=5,z=2)f(args)df_dx(args)df_dy(args)View in symbolic formatprint(str(f))print(str(df_dx))print(str(df_dy))ReferencesF. Krawiec, S. Peyton Jones, N. Krishnaswami, T. Ellis, R. A. Eisenberg, A. Fitzgibbon. 2022.
Provably correct, asymptotically efficient, higher-order reverse-mode automatic differentiation.
Proc. ACM Program. Lang., 6, POPL (2022), 1–30.https://doi.org/10.1145/3498710Jerzy Karczmarczuk. 1998. Functional Differentiation of Computer Programs.
In Proceedings of the Third ACM SIGPLAN International Conference on Functional
Programming (Baltimore, Maryland, USA) (ICFP ’98). Association for Computing
Machinery, New York, NY, USA, 195-203.https://doi.org/10.1145/289423.289442
Package: grater-expectations
Grater ExpectationsWelcome to Grater Expectations! In this repository, you will find code, notebooks and configurations that help you implement data testing usingGreat Expectations. In doing so, a subset of logic was taken from Great Expectations - orgrated- and implemented in Python to get you up, running and testing your data fast and as such, Grater Expectations was born!In this project a combination of Python, Docker, Terraform and Azure or AWS services are used to enable you to quickly bootstrap a new project for testing your data.For more information on how to get started, please refer to the README file specific for the cloud provider you intend to use:AzureAWS
Package: gratient
GRATIENT# Module made by @venaxyt on Github
import gratient

text = """
    `7MMF'   `7MF'` 7MM"'"YMM   `7MN.   `7MF'      ma       `YMM'   `MP' 
      `MA     ,V     MM    `7     MMN.    M       ;MM:        VMb.  ,P   
       VM:   ,V      MM   d       M YMb   M      ,V^MM.        `MM.M'    
        MM.  M'      MMmmMM       M  `MN. M     ,M  `MM          MMb     
        `MM A'       MM   Y  ,    M   `MM.M     AbmmmqMA       ,M'`Mb.   
         :MM;        MM     ,M    M     YMM    A'     VML     ,P   `MM.  
          VF       .JMMmmmmMMM  .JML.    YM  .AMA.   .AMMA. .MM:.  .:MMa.

 [!] Please subscribe to my Github profile @venaxyt for more modules like this one.
"""# Print a gratient text :
gratient_text = gratient.black(text)
print(gratient_text)gratient_text = gratient.purple(text)
print(gratient_text)gratient_text = gratient.blue(text)
print(gratient_text)gratient_text = gratient.red(text)
print(gratient_text)gratient_text = gratient.yellow(text)
print(gratient_text)
Package: gratin
Gratin(Graphs on Trajectories for Inference)Gratin is a tool to characterize trajectories of random walks, i.e. motion driven by random fluctuations. This type of motion is observed at various scales and in a wide diversity of systems.
While this package was developed for the purpose of analysing experimental data coming from photo-activated localization microscopy (PALM) experiments, nothing prevents it from being used on random walk recordings coming from other experimental setups and other domains !To extractsummary statisticsdescribing trajectories, Gratin mixes two ingredients :an original neural network architecture using graph neural networks (GNN)a simulation-based inference frameworkWarningGratin requires thepytorch-geometricpackage, whose installation depends on you CUDA version.
Note however that youdo not need CUDAto run Gratin, it works on CPU, it’s only a bit slower.
Seehereto install it on your machine.ReferencesHippolyte Verdier, Maxime Duval, François Laurent, Alhassan Cassé,  Christian Vestergaard, et al..
Learning physical properties of anomalous random walks using graph neural networks. 2021. :https://arxiv.org/abs/2103.11738Hippolyte Verdier, François Laurent, Alhassan Cassé, Christian L. Vestergaard, Christian G. Specht, Jean-Baptiste Masson
A maximum mean discrepancy approach reveals subtle changes in α-synuclein dynamics. 2022 :https://doi.org/10.1101/2022.04.11.487825NoteThis project has been set up using PyScaffold 4.1.3. For details and usage
information on PyScaffold seehttps://pyscaffold.org/.
Package: gratopy
The gratopy (Grazacceleratedtomographic projections forPython) toolbox is a Python3 software package for the efficient and high-quality computation of Radon transforms, fanbeam transforms as well as the associated backprojections. The included operators are based on pixel-driven projection methods which were shown to possessfavorable approximation properties. The toolbox offers a powerful parallel OpenCL/GPU implementation which admits high execution speed and allows for seamless integration intoPyOpenCL. Gratopy can efficiently be combined with other PyOpenCL code and is well-suited for the development of iterative tomographic reconstruction approaches, in particular, for those involving optimization algorithms.HighlightsEasy-to-use tomographic projection toolbox.High-quality 2D projection operators.Fast projection due to custom OpenCL/GPU implementation.Seamless integration into PyOpenCL.Basic iterative reconstruction schemes included (Landweber, CG, total variation).Comprehensive documentation, tests and example code.See thedocumentationand the project'sGitHub pagefor installation, usage, updates and further information.