Saravanan666/pypi-org huggingface dataset:
Package: gamut
GAMuT: Granular Audio Musaicing ToolkitDescriptionGAMuTis a high-level, user-friendlygranular audio musaicking toolkitimplemented in Python.DocumentationYou can find the full documentation forGAMuThere.LicenseISC License
Copyright (c) 2022,Felipe Tovar-HenaoPermission to use, copy, modify, and/or distribute this software for any purpose with or without fee is hereby granted, provided that the above copyright notice and this permission notice appear in all copies.THE SOFTWARE IS PROVIDED "AS IS" AND THE AUTHOR DISCLAIMS ALL WARRANTIES WITH REGARD TO THIS SOFTWARE INCLUDING ALL IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS. IN NO EVENT SHALL THE AUTHOR BE LIABLE FOR ANY SPECIAL, DIRECT, INDIRECT, OR CONSEQUENTIAL DAMAGES OR ANY DAMAGES WHATSOEVER RESULTING FROM LOSS OF USE, DATA OR PROFITS, WHETHER IN AN ACTION OF CONTRACT, NEGLIGENCE OR OTHER TORTIOUS ACTION, ARISING OUT OF OR IN CONNECTION WITH THE USE OR PERFORMANCE OF THIS SOFTWARE.
Package: gan
Python Package Template📦 一个快速上传到PyPI的 Python Package 模版。上传到 PyPI 后可以使用pip install安装。1 使用方法点击本项目右上角的绿色按钮Use this template（使用此模板），输入名称和说明，完成创建；将项目克隆到本地，这里以本项目为例，实际操作时这里需要替换你自己的项目；gitclonehttps://github.com/Ailln/python-package-template.git--depth1修改配置，文件中有提示；cdyour_package_name# 1. 替换默认项目名称 package_name 为你的项目名称# Usage: bash scripts/set_package_name.sh os_name old_name new_name# os_name 支持的有 `mac` 和 `linux`# old_name 是 package_name# new_name 是你的项目名称bashscripts/set_package_name.shmacpackage_nameyour_package_name# 2. 将 `README.md` 修改为你的项目介绍，也就是你当前在读的这个文本。编写你的 Package 代码，并进行测试。# 在本地进行充分测试bashscripts/local_test.sh上传到 PyPi（需要注册），参考如何发布自己的包到 pypi；bashscripts/upload_pypi.sh更新到 Github。gitpush2 项目结构.
├── package_name # 项目名称
│    ├── shell # 在命令行中执行的代码
│    │    ├── __init__.py
│    │    └── usage.py
│    └── src # 静态资源
│          └── temp.txt
├── scripts
│    ├── set_package_name.sh # 批量替换默认的项目名称
│    ├── local_install.sh
│    ├── local_test.sh
│    └── upload_pypi.sh
├── README.md # 项目文档
├── requirements.txt # 包依赖
├── .gitignore # 忽略文件
├── MANIFEST.in # 要包含在 sdist 命令构建的分发中的文件列表。
├── LICENSE # 这里面的内容为本项目的 License，你需要手动替换它。
└── setup.py # 安装配置3 TODO增加 test 相关代码。4 许可5 参考Packaging Python Projects如何从模板创建仓库？如何发布自己的包到 pypi ？
Package: ganbase
# Introduction[![Documentation](https://img.shields.io/badge/documentation-master-green.svg)](http://ganbase.readthedocs.io/en/latest)
[![PyPI Version](https://img.shields.io/pypi/v/ganbase.svg)](https://pypi.python.org/pypi/ganbase)
[![Python Version](https://img.shields.io/pypi/pyversions/ganbase.svg)]()
[![Build Status](https://travis-ci.org/innerlee/ganbase.svg?branch=master)](https://travis-ci.org/innerlee/ganbase)
[![Coverage Status](https://codecov.io/gh/innerlee/ganbase/branch/master/graph/badge.svg)](https://codecov.io/gh/innerlee/ganbase)ganbaseis a miscellaneous set of tools which maybe helpful for GAN research.
It comprises the following parts.ModelsDatasetsEvaluationVisualizeUtilsTry and start with`shell pip install ganbase `
Package: ganblr
GANBLR ToolboxGANBLR Toolbox contains GANBLR models proposed byTulip Labfor tabular data generation, which can sample fully artificial data from real data.Currently, this package contains following GANBLR models:GANBLRGANBLR++For a quick start, you can check out this usage example in Google Colab. [](https://colab.research.google.com/drive/1w7A26JRkrXPeeA9q1Kbi_CRjbptkr8Ls?usp=sharing]InstallWe recommend you to install ganblr through pip:pipinstallganblrAlternatively, you can also clone the repository and install it from sources.gitclonegit@github.com:tulip-lab/ganblr.gitcdganblr
pythonsetup.pyinstallUsage ExampleIn this example we load theAdult Dataset* which is a built-in demo dataset. We useGANBLRto learn from the real data and then generate some synthetic data.fromganblrimportget_demo_datafromganblr.modelsimportGANBLR# this is a discrete version of adult since GANBLR requires discrete data.df=get_demo_data('adult')x,y=df.values[:,:-1],df.values[:,-1]model=GANBLR()model.fit(x,y,epochs=10)#generate synthetic datasynthetic_data=model.sample(1000)The steps to generate synthetic data usingGANBLR++are similar toGANBLR, but require an additional parameternumerical_columnsto tell the model the index of the numerical columns.fromganblrimportget_demo_datafromganblr.modelsimportGANBLRPPimportnumpyasnp# raw adultdf=get_demo_data('adult-raw')x,y=df.values[:,:-1],df.values[:,-1]defis_numerical(dtype):returndtype.kindin'iuf'column_is_numerical=df.dtypes.apply(is_numerical).valuesnumerical_columns=np.argwhere(column_is_numerical).ravel()model=GANBLRPP(numerical_columns)model.fit(x,y,epochs=10)#generate synthetic datasynthetic_data=model.sample(1000)DocumentationYou can check the documentation athttps://ganblr-docs.readthedocs.io/en/latest/.LeaderboardHere we show the results of the TSTR(Training on Synthetic data, Testing on Real data) evaluation onAdultdataset based on the experiments in our paper.TRTR(Train on Real, Test on Real) will be used as the baseline for comparison. You are welcome to update this Leaderboard.LRMLPRFXGBTTRTR0.87410.85610.83790.8562GANBLR0.740.8420.810.851CTGAN0.7870.8310.7920.839...............CitationIf you use GANBLR, please cite the following work:Y. Zhang, N. A. Zaidi, J. Zhou and G. Li, "GANBLR: A Tabular Data Generation Model," 2021 IEEE International Conference on Data Mining (ICDM), 2021, pp. 181-190, doi: 10.1109/ICDM51629.2021.00103.@inproceedings{ganblr,
    author={Zhang, Yishuo and Zaidi, Nayyar A. and Zhou, Jiahui and Li, Gang},  
    booktitle={2021 IEEE International Conference on Data Mining (ICDM)},   
    title={GANBLR: A Tabular Data Generation Model},   
    year={2021},  
    pages={181-190},  
    doi={10.1109/ICDM51629.2021.00103}}@inbook{ganblrpp,
    author ={Yishuo Zhang and Nayyar Zaidi and Jiahui Zhou and Gang Li},
    title ={<bold>GANBLR++</bold>: Incorporating Capacity to Generate Numeric Attributes and Leveraging Unrestricted Bayesian Networks},
    booktitle ={Proceedings of the 2022 SIAM International Conference on Data Mining (SDM)},
    pages ={298-306},
    doi ={10.1137/1.9781611977172.34},}
Package: gancio-requests
gancio_requestsgancio_requestsis a tool for making asynchronous HTTP
requests to the API of a specified Gancio instance.Ganciois a shared
agenda for local communities, a project which wants to provide
a self-hosted solution to host and organize events.This repo aims to supply a convenient set of functions to fetch
data from a gancio instance (e.g. scheduled events, the image or
description of an event).InstallationTo install the latest version of the library just download
(or clone) the current project, open a terminal and run the
following commands:pip install -r requirements.txt
pip install .Alternatively use pippipinstallgancio_requestsDependenciesAt the moment I have tested the library only onpython == 3.10.4The library requires the dependencies specified inrequirements.txtand I haven't still tested other versions.UsageCommand line interfacepython3 -m gancio_requests [-h] gancio_instancegancio_instanceis the URL of the instance from which we want
to fetch the data.The output displays the list of events starting of 00:00:00 of
the current day. The information about an event is shown this
way:EVENT_NAME
[STARTING_TIME | END_TIME]
PLACE_NAME - ADDRESS
LIST_OF_TAGS (optional)
EVENT_URLExampleInputpython3 -m gancio_requests https://gancio.cisti.orgOutputCrazy toga party
[2022-08-11 22:30:00 | 2022-08-12 00:00:00]
Colosseo - Piazza del Colosseo, Roma
Tags: ["colosseum", "ancient rome", "toga party"]
https://gancio.cisti.org/crazy-toga-party

Gioco del ponte
[2022-09-10 22:00:00 | 2022-09-10 23:00:00]
Ponte di mezzo - Ponte di Mezzo, 1, 56125 Pisa
https://gancio.cisti.org/gioco-del-pontePython libraryAfter the installation, it is possible to use the package
directly from the python interpreter by usingimport gancio_requests.CachingIt is possible to cache HTTP requests thanks toaiohttp-client-cache.
All the functions shown above have an optional parameter
calledcachewhich accepts aaiohttp_client_cache.backendsobject.import asyncio
from aiohttp_client_cache import SQLiteBackend
from gancio_requests.request import get_events

url = 'https://gancio.cisti.org'
params = {"start": 0}
cache = SQLiteBackend(
    cache_name = "Test.db"
)
result = asyncio.run(get_events(url, params, cache=cache))
Package: gancio-telegram-bot
gancio_telegram_botgancio_telegram_botis a bot which allows to interact with a specified Gancio instance.Ganciois a shared agenda for local communities, a project which wants to provide
a self-hosted solution to host and organize events.InstallationTo install the latest version of the bot just download (or clone) the current project,
open a terminal and run the following commands:pipinstall-rrequirements.txt
pipinstall.Alternatively, use pip:pipinstallgancio_telegram_botIt's also possible to create a docker image and run the application in a container; seeDocker usage.DependenciesAt the moment I have tested the bot only onpython == 3.10.4The bot requires the dependencies specified inrequirements.txtand I haven't still tested
other versions.UsageConfiguration file (.env)The bot relies on environment variables for its configuration; some parameters are strictly required
(e.g. AUTH_TOKEN, INSTANCE_URL), while others just allow to personalize the bot appearance.ParameterFunctionAUTH_TOKENTelegram bot authentication tokenINSTANCE_URLUrl of the gancio instance from which fetch the eventsSHOW_EVENT_URLSet to "True" if each event must be accompanied by its url on the Gancio instance (default is False)MENU_ELEMENTSNumber of events to show in a single message (default is 3)CACHE_NAMEPath to the database used for caching (default disable the cache mechanism)HTTP_CACHE_EXPIRATION_SECONDSHow many seconds the cache lasts (default is 10 minutes)Command line interfacepython3-mgancio_telegram_botDockerdockercomposeup-dVisualisationThe package comes up with some functions to allow the parsing of the
logfile and the visualisation of statistics related to the usage of the bot.TodolistBetter define which versions of python and dependency packages are compatible.Create some tests.Allow to define more fine-grained queries for the events (e.g. display just the events in a specific date).Make more visualisation plots.
Package: gander
Authors:Ole Olaussen:ole.olaussen@ekkobit.comXuan Ling:xuan.ling@ekkobit.comFor more complete documentation, go to Gander atReadTheDocsInstallationpipinstallganderExamplesimportgander.indicatorsasgiimportgander.plottingasgpimportmatplotlib.pyplotaspltUsing Matplotlib and Gander to create daily stock chartsLet’s say we have a Pandas DataFrame of a stocks data and we want to make a
chart of a subset of the data. Our DataFrame might look something
like this:Adding indicators to the data setdf=gi.calc_ema(df,df["close"],"ema12",window=13)df=gi.calc_ema(df,df["close"],"ema26",window=27)df=gi.calc_macd(df,df["ema12"],df["ema26"])This will give us something like this:To get a subset of the data and positions on the x-axis, we can do:df_plot=df[-300:-200]xpos=range(100)Building figure and subplotsfig=plt.figure(figsize=(12,6))ax1=plt.subplot2grid((3,1),(0,0),rowspan=2)ax2=plt.subplot2grid((3,1),(2,0),rowspan=1)ticks,labels=gp.daily_labels(df_plot,df_plot.index,step=5)Customizing x-axis ticks, labels and gridforaxin[ax1,ax2]:ax.set_xlim(xmin=-1,xmax=104)ax.set_xticks(ticks)ax.grid(alpha=0.3)ax1.xaxis.set_ticklabels([])ax1.tick_params(axis='x',length=0)ax2.set_xticklabels(labels)forlabelinax2.get_xticklabels():iflen(label.get_text())==4:label.set_fontsize(14)label.set_fontweight("bold")eliflen(label.get_text())==3:label.set_fontsize(12)label.set_fontweight("bold")else:label.set_fontsize(10)Adding plotsgp.candles(df_plot,ax1)ax1.plot(xpos,df_plot["ema12"],"b-")ax1.plot(xpos,df_plot["ema26"],"k-")gp.macds(df_plot,ax2,"fast","signal","macd-h")Using Matplotlib and Gander to create weekly stock chartsLet’s again say we have a Pandas DataFrame of a stocks data and we want to make
a chart of a subset of the data data. Only this time we have weekly
data. Our DataFrame might look something like this:Adding indicators to the data setdf=gi.calc_ema(df,df["close"],"ema12",window=13)df=gi.calc_ema(df,df["close"],"ema26",window=27)df=gi.calc_macd(df,df["ema12"],df["ema26"])This will give us something like this:To get a subset of the data and positions on the x-axis, we can again do:df_plot=df[-230:-130]xpos=range(100)The process of building the chart is the same as for daily data, except we
might do different customizations to the lables on the x-axis:Building figure and subplotsfig=plt.figure(figsize=(12,6))ax1=plt.subplot2grid((3,1),(0,0),rowspan=2)ax2=plt.subplot2grid((3,1),(2,0),rowspan=1)ticks,labels=gp.weekly_labels(df_plot,df_plot.index,step=10)Customizing x-axis ticks, labels and gridforaxin[ax1,ax2]:ax.set_xlim(xmin=-1,xmax=104)ax.set_xticks(ticks)ax.grid(alpha=0.3)ax1.xaxis.set_ticklabels([])ax1.tick_params(axis='x',length=0)ax2.set_xticklabels(labels)forlabelinax2.get_xticklabels():iflen(label.get_text())==4:label.set_fontsize(14)label.set_fontweight("bold")Adding plotsgp.candles(df_plot,ax1)ax1.plot(xpos,df_plot["ema12"],"b-")ax1.plot(xpos,df_plot["ema26"],"k-")gp.macds(df_plot,ax2,"fast","signal","macd-h")ContributeTips on contributing:https://github.com/ekkobit/gander/blob/master/CONTRIBUTING.mdIssue Tracker:https://github.com/ekkobit/gander/issuesSource Code:https://github.com/ekkobit/ganderDocumentation:https://gander.readthedocs.io
Package: gandi
Gandi CLI ToolCommand line interface to theGandi API.InstallationpipInstall using pip:pip3 install --user gandipipxInstall usingpipx:pipx install gandiConfigurationRungandi setupto create a configuration file at$XDG_CONFIG_HOME/gandi/config. This will ask for your Gandi API key as well
as an (optional) default domain name and an (optional) default mailbox ID.If you specify a domain name and mailbox ID, subcommands that require these
parameters will use the values supplied in your config file instead of
requiring them as commandline flags.Parameters can also be specified using environment variables, e.g.:GANDI_API_KEY=... gandi mbox -d DOMAIN --listUsagegandi SUBCOMMAND OPTIONSSubcommandssetup: Setup the Gandi CLI config filegandi setupalias: Manage email aliasesgandi alias [-d DOMAIN] [-m MAILBOXID] (--list | --add ALIAS | --remove ALIAS)List existing email aliases:gandi alias -lAdd a new alias:gandi alias -a ALIASRemove an alias:gandi alias -r ALIASmbox: Manage email mailboxesgandi mbox [-d DOMAIN] --list
Package: gandi-2-terraform
Generate Terraform file from Gandi DNS recordsThis tool aims to simplify managing DNS recods using Terrafom by making the initial import through a single operation.
It fetches DNS records from one or multiple domains you own withGandi.netand generates TF files with the corresponding records' resources usinggandi_livedns_recordand defining each record in a set (see the example output). It will output all theterraform importcommand to execute for the records.Install$pipinstallgandi-2-terraform$gandi2tf--helpUsageYou need to provide the Gandi API key as an environment variableGANDI_KEY(same as for the TF provider).$exportGANDI_KEY=A1b2C3d4E5f6When no argument is given, it will fetch all available domains for the given API key:$gandi-2tfOr it can generate the tf configuration file for a single domain:$gandi-2tfexample.comFetching the domains only owned by a single organization:$gandi-2tf--organization-id04303337-a1b0-4b96-8bd7-992005a072e9Options--organization-id: in case your API key has access to multiple organization, you can filter the list of domains fetched by a single organization ID (uuid).--subdir: flag to create a sub directory per domain and generate themain.tfinside it with a second file containing all theimportcommands.ConfigurationIn order to access the DNS records through the API, you have to provide your API key. It uses the same variable name than theGandi TerraformproviderGANDI_KEY. SeeGandi authentication documentationof their API on how to generate one.Example$exportGANDI_KEY=A1b2C3d4E5f6$gandi-2tfexample.comwill generate a fileexample.com.tfcontaining:locals{example_com_records={apex_a={name="@"type="A"ttl=10800values=["192.30.252.153","192.30.252.154",]}apex_mx={name="@"type="MX"ttl=10800values=["10 spool.mail.gandi.net.","50 fb.mail.gandi.net.",]}apex_txt={name="@"type="TXT"ttl=10800values=["\"v=spf1 include:_mailcust.gandi.net -all\"",]}imap_cname={name="imap"type="CNAME"ttl=10800values=["access.mail.gandi.net.",]}smtp_cname={name="smtp"type="CNAME"ttl=10800values=["relay.mail.gandi.net.",]}webmail_cname={name="webmail"type="CNAME"ttl=10800values=["webmail.gandi.net.",]}}}resource"gandi_livedns_record""example_com"{for_each=local.example_com_recordszone="example.com"name=each.value.namettl=each.value.ttltype=each.value.typevalues=each.value.values}
Package: gandi-api-livedns
Gandi.net live DNS API Client Python LibraryEasily search for trips!InstallationWorks on 3.x python.pip install gandi-api-livednsUsagefromgandi_api_livednsimportGandiApiLiveDNS# initialize APIgandiApiLiveDNS=GandiApiLiveDNS(api_key="__your_api_key_here__")# set rrname and domaingandiApiLiveDNS.rrname='dummy'gandiApiLiveDNS.domain='mydomain.com'# get real ipRealIP=gandiApiLiveDNS.getRealIP()pprint.pprint(RealIP)# get registered dns recordDNSRecord=gandiApiLiveDNS.getDNSRecord()pprint.pprint(DNSRecord)# update dns record with detected ipupdateDNSRecord=gandiApiLiveDNS.updateDNSRecord()pprint.pprint(updateDNSRecord)
Package: gandip
Keep your gandi DNS records up to date with your current ip.Please note that gandi is migrating to it’s new version (v5) with their new
LiveDNS api used by gandip latest version. In order to use gandip with older
gandi api you must use version 1.x.x.InstallationEasy peasy, runpip install –user gandipand create a cron job so it runs every 15 minutes like so:` */15 * * * * gandip your_gandi_api_key fqdn record1 record2 ... recordN `
Package: gandi-pyramid-prometheus
Gandi Pyramid PrometheusA prometheus client library for general metrics from pyramid.CHANGELOG0.5 2018-10-15Fix Home Page0.4 2018-10-13Rename settingprometheus.metric_path_infotoprometheus.metrics_path_info0.3 2018-10-13Fix python packaging0.2 2018-10-13Add security restriction based on permission prometheus:metrics:readAdd a setting to obfuscate the /metrics url0.1 2018-10-10Initial release with a /metrics route that expose prometheus metrics
Package: gandi-update-dns
Yet another tool to update DNS zone in Gandi provider!How to useJust use the following command:gandi-update-dnsIf the command is not on your PATH, it will be usually found on$HOME/.local/bin.Configuration fileIf you have no configuration file, the main command will ask you for a domain and a Gandi api key.Configuration file can be found here:Directory: XDG_CONFIG_HOME (default is$HOME/.config)File:gandi-update-dns.conf
Package: gandos-ganteng
Make sure you have upgraded version of pipWindowspy -m pip install --upgrade pipLinux/MAC OSpython3 -m pip install --upgrade pipCreate a project with the following structurepackaging_tutorial/
├── LICENSE
├── pyproject.toml
├── README.md
├── setup.cfg
├── src/
│   └── example_package/
│       ├── __init__.py
│       └── example.py
└── tests/
touch LICENSE
touch pyproject.toml
touch setup.cfg
mkdir src/mypackage
touch src/mypackage/__init__.py
touch src/mypackage/main.py
mkdir testspyproject.tomlThis file tells tools like pip and build how to create your project[build-system]
requires = [
    "setuptools>=42",
    "wheel"
]
build-backend = "setuptools.build_meta"build-system.requires gives a list of packages that are needed to build your package. Listing something here will only make it available during the build, not after it is installed.build-system.build-backend is the name of Python object that will be used to perform the build. If you were to use a different build system, such as flit or poetry, those would go here, and the configuration details would be completely different than the setuptools configuration described below.Setup.cfg setupUsing setup.cfg is a best practice, but you could have a dynamic setup file using setup.py[metadata]
name = example-pkg-YOUR-USERNAME-HERE
version = 0.0.1
author = Example Author
author_email = author@example.com
description = A small example package
long_description = file: README.md
long_description_content_type = text/markdown
url = https://github.com/pypa/sampleproject
project_urls =
    Bug Tracker = https://github.com/pypa/sampleproject/issues
classifiers =
    Programming Language :: Python :: 3
    License :: OSI Approved :: MIT License
    Operating System :: OS Independent

[options]
package_dir =
    = src
packages = find:
python_requires = >=3.6

[options.packages.find]
where = srcRunning the buildMake sure your build tool is up to dateWindowspy -m pip install --upgrade buildLinux/MAC OSpython3 -m pip install --upgrade buildCreate the buildpy -m buildUpload to pypitwine upload dist/*Referenceshttps://packaging.python.org/tutorials/packaging-projects/
Package: ganf
Ganf基于OpenAI的文档批量翻译工具，用来批量的翻译mkdocs、sphinx相关文档。
本工具没有内置OpenAI的Api key，你需要自己去购买OpenAI或者Azure OpenAI的服务。note后续考虑支持POT等多语言文本的翻译（主要是要支持差异比对增量翻译）安装pip install ganf快速开始在你想要翻译的docs目录下调用init命令配置翻译策略ganfinit翻译整个文档项目ganfbuild
Package: gan-face-generate
DCGANDeep Convolutional Generative Adversarial Networks (DCGAN) is a class of generative adversarial networks (GAN) introduced by Radford et. al. in the paperUnsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. The generator and discriminator of DCGAN are contructed of convolutional and convolutional-transpose layers.UsageInstall the package (Consider installing in a virtual environment)pipinstallgan_face_generateCreate your own GAN-generated facegan_face_generateDatasetDigiFace-1Mis a generated dataset for training face recognition models. The face images are high quality and thus are qualified to train a GAN network. There are two additional advantages of the dataset:Ethical considerations: The use of existing datasets that were collected from web images without explicit consent. In contrast, digital faces in DigiFace-1M are generated using a generative model constructed from high-quality head scans of a limited number of individuals obtained with consent.Data bias - DigiFace-1M is generated in a controlled pipeline, so that the racial distribution is guaranteed to be balance.ResultsLoss in Training ProcessGenerated Images compared with Dataset ImagesNoise ArithmeticReferencePyTorch DCGAN TUTORIALUnsupervised Representation Learning with Deep Convolutional Generative Adversarial NetworksDigiFace-1M: 1 Million Digital Face Images for Face Recognition
Package: ganga
Ganga is a tool to compose, run and track computing jobs across a variety of backends and application types.InstallationGanga can be installed using the standard Python toolpipwithpipinstallgangaUsageGanga primarily runs as a command-line tool so can be started by just runninggangaWhich will load an interactive IPython prompt at which you can typej=Job()j.submit()j.peek('stdout')to create a simple local job which runs an executable which simply prints some text to the stdout.DocumentationUser guide and developer documentation
Package: ganglia
A library to send metric to Ganglia in python.
Supports UDP, multicast, group and spoofing.InstallingTo install :pip install gangliaUsageSending metric over UDP:ganglia = GMetric("udp://127.0.0.1")
ganglia.send(
    name='hits',
    units='req/sec',
    type='uint8',
    value=200,
    tmax=60,
    dmax=300,
    group='web'
)