Saravanan666/pypi-org huggingface dataset:
Package: maspy
IntroductionMaspy is a Python library with the aim to provide simple, convenient and
versatile access to proteomics data of bottom up experiments. To achieve this
it features an internal data representation that facilitates comfortable use
of this data. Maspy is intended as a tool for researchers that allows the
combination of different software to generate customized and scriptable data
processing workflows and to enable interactive data analysis with Python.DocumentationAn introduction to the underlying concepts of maspy is available athttp://maspy.readthedocs.ioWorkflow examplesIn order to show how to use maspy we are going to provide examples of simple
data analysis workflows.Test filesTest files can be downloaded fromhttps://github.com/hollenstein/maspy_testfilesInstallationFor Windows users without Python we recommend installing theAnacondaPython package provided by
Continuum Analytics.NOTE: The current release of maspy will soon be submitted toPyPi, which allows installation with PIP, as
described below:For installing maspy and its dependencies we recommend usingPIP.Installing maspy from the command line with PIP after the release:` pip install maspy `CompatibilityMaspy is developed and tested on Python 2.7 and 3.5.
Tested on Windows (7) and Linux (Debian 8).Licensemaspy is licensed under theApache License 2.0.The project contains additional code and files licensed under different
licenses. For details refer toLICENSE.txt.ContributorsDavid Hollensteinhttps://github.com/hollensteinJakob Hollensteinhttps://github.com/jkbjh
Package: masq
Marching SquaresMarching Squares to generate random contour.InstallpipinstallmasqUsageGenerate imageA random graph can be generated by usingmasq.random().importmasqmasq.random(n_row=20,n_col=30,bound=0.5)A simplex noise imageUsemasq.simplex_noise()function. You can display all points by setshow_point=True.importmasqmasq.simplex_noise(n_row=20,n_col=30,bound=0.5,wavelength=6,show_point=False)Play movementUsemasq.loop_simplex_noise()ormasq.loop_random()function.importmasqmasq.loop_simplex_noise(20,30,bound=0.5,wavelength=6,increment=0.01)Display pointsTurn on theshow_point=True.importmasqmasq.simplex_noise(20,30,bound=0.5,wavelength=6,show_point=True)
Package: masr
MASR流式与非流式语音识别项目MASR是一款基于Pytorch实现的自动语音识别框架，MASR全称是神奇的自动语音识别框架（Magical Automatic Speech Recognition），当前为V2版本，如果想使用V1版本，请在这个分支r1.x。MASR致力于简单，实用的语音识别项目。可部署在服务器，Nvidia Jetson设备，未来还计划支持Android等移动设备。欢迎大家扫码入知识星球或者QQ群讨论，知识星球里面提供项目的模型文件和博主其他相关项目的模型文件，也包括其他一些资源。本项目使用的环境：Anaconda 3Python 3.8Pytorch 1.13.1Windows 10 or Ubuntu 18.04项目快速了解本项目支持流式识别模型deepspeech2、conformer、squeezeformer，efficient_conformer，每个模型都支持流式识别和非流式识别，在配置文件中streaming参数设置。本项目支持两种解码器，分别是集束搜索解码器ctc_beam_search和贪心解码器ctc_greedy，集束搜索解码器ctc_beam_search准确率更高。下面提供了一系列预训练模型的下载，下载预训练模型之后，需要把全部文件复制到项目根目录，并执行导出模型才可以使用语音识别。更新记录2023.01.28: 调整配置文件结构，支持efficient_conformer模型。2022.11: 正式发布最终级的V2版本。视频讲解这个是PPSAR的视频教程，项目是通用的，可以参考使用。知识点讲解（哔哩哔哩）流式识别的使用讲解（哔哩哔哩）快速使用这里介绍如何使用MASR快速进行语音识别，前提是要安装MASR，文档请看快速安装。执行过程不需要手动下载模型，全部自动完成。短语音识别frommasr.predictimportMASRPredictorpredictor=MASRPredictor(model_tag='conformer_streaming_fbank_aishell')wav_path='dataset/test.wav'result=predictor.predict(audio_data=wav_path,use_pun=False)score,text=result['score'],result['text']print(f"识别结果:{text}, 得分:{int(score)}")长语音识别frommasr.predictimportMASRPredictorpredictor=MASRPredictor(model_tag='conformer_streaming_fbank_aishell')wav_path='dataset/test_long.wav'result=predictor.predict_long(audio_data=wav_path,use_pun=False)score,text=result['score'],result['text']print(f"识别结果:{text}, 得分:{score}")模拟流式识别importtimeimportwavefrommasr.predictimportMASRPredictorpredictor=MASRPredictor(model_tag='conformer_streaming_fbank_aishell')# 识别间隔时间interval_time=0.5CHUNK=int(16000*interval_time)# 读取数据wav_path='dataset/test.wav'wf=wave.open(wav_path,'rb')data=wf.readframes(CHUNK)# 播放whiledata!=b'':start=time.time()d=wf.readframes(CHUNK)result=predictor.predict_stream(audio_data=data,use_pun=False,is_end=d==b'')data=difresultisNone:continuescore,text=result['score'],result['text']print(f"【实时结果】：消耗时间：{int((time.time()-start)*1000)}ms, 识别结果:{text}, 得分:{int(score)}")# 重置流式识别predictor.reset_stream()模型下载WenetSpeech(10000小时) 的预训练模型列表：使用模型是否为流式预处理方式语言测试集字错率下载地址conformerTruefbank普通话WenetSpeech(10000小时)+中文语音数据集(3000+小时) 的预训练模型列表：使用模型是否为流式预处理方式语言测试集字错率下载地址conformereTruefbank普通话0.03179(aishell_test)0.16722(test_net)0.20317(test_meeting)加入知识星球获取AIShell(179小时) 的预训练模型列表：使用模型是否为流式预处理方式语言测试集字错率下载地址squeezeformerTruefbank普通话0.04137加入知识星球获取conformerTruefbank普通话0.04491加入知识星球获取efficient_conformerTruefbank普通话0.04073加入知识星球获取deepspeech2Truefbank普通话0.06907加入知识星球获取Librispeech(960小时) 的预训练模型列表：使用模型是否为流式预处理方式语言测试集词错率下载地址squeezeformerTruefbank英文0.09715加入知识星球获取conformerTruefbank英文0.09265加入知识星球获取efficient_conformerTruefbank英文加入知识星球获取deepspeech2Truefbank英文0.19423加入知识星球获取说明：这里字错率或者词错率是使用eval.py程序并使用集束搜索解码ctc_beam_search方法计算得到的。没有提供预测模型，需要把全部文件复制到项目的根目录下，执行export_model.py导出预测模型。由于算力不足，这里只提供了流式模型，但全部模型都支持流式和非流式的，在配置文件中streaming参数设置。有问题欢迎提issue交流文档教程快速安装快速使用数据准备WenetSpeech数据集合成语音数据数据增强训练模型集束搜索解码执行评估导出模型使用标点符号模型使用语音活动检测（VAD）预测本地预测长语音预测Web部署模型GUI界面预测相关项目基于Pytorch实现的声纹识别：VoiceprintRecognition-Pytorch基于Pytorch实现的分类：AudioClassification-Pytorch基于PaddlePaddle实现的语音识别：PPASR打赏作者打赏一块钱支持一下作者参考资料https://github.com/yeyupiaoling/PPASRhttps://github.com/jiwidi/DeepSpeech-pytorchhttps://github.com/wenet-e2e/WenetSpeechhttps://github.com/SeanNaren/deepspeech.pytorch
Package: mass
Inspired by SASS, MASS is an automated tool for managing javascript
files in a web projects.Goalseasy method to combine multiple files into single asset requestautomated minification/optimizationextending javascript to allow import other scriptsRequirementsMac OS X (currently using fsevents to track file changes. will later
add support for Linux and Windows)Python Packages Usedjsmin (http://pypi.python.org/pypi/jsmin)macfsevents (http://pypi.python.org/pypi/MacFSEvents/0.2.1)virtualenv (http://pypi.python.org/pypi/virtualenv)argparse (http://pypi.python.org/pypi/argparse)InstallationMASS can be installed via pip:pip install massTo install MASS manually, clone the repo and install using setup.py:python setup.py installMASS comes with the necessary python packages via virutalenv by default.Before installing MASS, users can enable virtualenv by navigating to the
repository and sourcing the virtual environment.cd path/to/MASS
source env/bin/activateSource FilesSource files use the extension ‘.xjs’, which tentatively signifies
extended javascript. These files are used to define javascript files
which will be included in a “compiled” version that is output by MASS.Including FilesInside ‘extended’ javascript files, you can import other scripts. All
javascript files listed in the source file will be combined, minified
and output to a javascript file with the same name as the original xjs
file. Paths to imported javascript files should be relative to the
directory of the xjs file they are imported into.The example below specifies two javascript files and will be output to
the file foo.min.js:// foo.xjs
// import "foo.js"
// import "plugins/jquery.jnotify.js"Require commands can be used to ensure that a file has been already been
imported, and will import the script if they have not.// foo.xjs
// import "foo.js"
// import "bar.js"

// foo.js
// require "bar.js"In the above example, bar.js will not be re-imported since foo.xjs has
already imported it.MASS also supports ‘inline’ javascript// bar.js
// import "foo.js"

// now some javascript
$(document).ready(function(){
    // jquery is ready
});

// another import!
// import "plugins/jquery.jnotify.js"CLI ArgumentsBasic CommandsMass has two basic CLI commands,watchandcompile.watchbegins monitoring the specified directory and will compile files as they
are edited whilecompilewill simply minify and combine files
located in the specified directory.mass watch -s path/to/source

mass compile -s path/to/sourceDefining Destination DirectoryBy default, MASS saves the compiled js files in the same directory as
the source files. The -d argument can be used to specify a different
output directory.mass watch -d path/to/outputDefining Source DirectoryIf a source directory is not specified, MASS uses the current directory
by default. The -s argument can be used to specify the directory for
MASS to discover files.mass watch -s path/to/source -d path/to/output
Package: mass2chem
mass2chem - low level utilities in interpreting mass spectrometry dataThis package providesfunctions on handling chemical formulasformula based adduct calculationindexing and search functions on mass spec datalibraries of common metabolites, contaminants, mass differences[to-do] functions of chemical similary, dataset similarityRelated toolsGeneralized computing of isotopes and adducts: khipu (https://github.com/shuzhao-li-lab/khipu,https://pubs.acs.org/doi/10.1021/acs.analchem.2c05810)High-level metabolite functions and metabolic models: Json's Metabolite Services (JMS,https://github.com/shuzhao-li-lab/JMS)Metabolomics data processing: asari (https://github.com/shuzhao-li-lab/asari,https://www.nature.com/articles/s41467-023-39889-1)Python-Centric Pipeline for Metabolomics (https://github.com/shuzhao-li-lab/PythonCentricPipelineForMetabolomics)Common data models for metabolomics: metDataModel (https://github.com/shuzhao-li/metDataModel)Third party references:https://github.com/opencobra/cobrapy/blob/devel/cobra/core/formula.py(using average molecular weight at the time of retrieval, not mass spec oriented)https://github.com/domdfcoding/chemistry_toolsPychemy (https://github.com/ginkgobioworks/pychemy).
Pychemy at this time isn't good fit for high-resolution metabolomics because its mass calculation is not of enough precision. E.g. in pychemy.adducts, it's wrong to use ('M+3H', 0.33,  1.0073),
because the computing/rounding error in 0.33 (correct is 1/3) is far too large for mass precision.
For high-resolution measurements, electrons should be considered too.Please do not hesitate to contact us via the GitHub issues.Citation to come.
Package: mass-analytics
Mass-Analytics: Data Preparation Made EasyOverviewMass-Analytics is a Python package designed to streamline the data preparation process. It provides a set of useful functions that simplify common data preparation tasks, making it easier for data scientists and analysts to get their data ready for analysis and modeling.Key FeaturesRead data from various sources and formats.Perform data cleaning and preprocessing.Handle missing data effectively.Feature engineering and transformation.Simplify data transformation pipelines.InstallationYou can installmass-analyticsusingpip.pipinstallmass-analyticsUsageImport themass-analyticspackage asmaand use its functions for data preparation tasks.importmass_analyticsasma# Read data from a filedata_frame=ma.read_data("path/to/your/data.csv")# Display dataprint(data_frame)AuthorsMejdi TrabelsiMohamed Selim AlouiniProject StatusThe project is currently in development. We are actively working on adding more features and improving the existing ones. Your feedback and contributions are highly appreciated!
Package: mass-composition
MassCompositionMassComposition is a python package that allows Geoscientists and Metallurgists to easily work with, and visualise
mass-compositional data.Mass Composition in this context is just that - MassandComposition.  It is not Compositional Data Analysis (CoDA),
since here we care more about mathematical operations like weight averaging and technically correct mathematical
operations.  Of course, we also need to manage moisture correctly - chemical composition is reported on a dry basis
and moisture on a wet basis.The package not only supports individual MassComposition, but collections of objects that are
mathematically related in a Directional Graph (a.k.a. network or flowsheet).PrerequisitesBefore you begin, ensure you have met the following requirements:You have installed the latest version of the mass-composition python package.You have a Windows/Linux/Mac machine.You have read thedocs.Installing MassCompositionTo install MassComposition, follow these steps:pip install mass-composition -e .[viz,network]Or, if poetry is more your flavour.poetry add "mass-composition[viz,network]"Using MassCompositionTo use MassComposition, follow these steps:There are some basic requirements that the incoming DataFrame must meet.  We'll use a sample DataFrame here.df_data:pd.DataFrame=sample_data()Create the objectobj_mc:MassComposition=MassComposition(df_data)It is then trivial to calculate the weight average aggregate of the dataset.obj_mc.aggregate()Multiple composition analytes can be viewed in a single interactive parallel coordinates plot.obj_mc:MassComposition=MassComposition(df_data.reset_index().set_index(['DHID','interval_from','interval_to']),name=name)fig:Figure=obj_mc.plot_parallel(color='Fe')figNetwork visualisations and other plots are interactive.For full examples, see thegallery.Contributing to MassCompositionTo contribute to MassComposition, follow these steps:Fork this repository.Create a branch:git checkout -b <branch_name>.Make your changes and commit them:git commit -m '<commit_message>'Push to the original branch:git push origin mass-compositionCreate the pull request.Alternatively see the GitHub documentation oncreating a pull request.ContributorsThis project is still in its infancy, but I'm keen to work with contributors.ContactIf you want to contact me you can reach me atyour_email@address.com.LicenseThis project uses the following license:MIT.
Package: mass_driver_plugins
Mass Driver PluginsExperimental plugin ecosystem for Mass DriverRequires Python 3.10UsageDepends on what the code in there does.Run the commandInstall the module first:make install
# or
poetry installThen inside the virtual environment, launch the command:# Run single command inside virtualenv
poetry run mass-driver-plugins

# or
# Load the virtualenv first
poetry shell
# Then launch the command, staying in virtualenv
mass-driver-pluginsDevelopmentPython setupThis repository uses Python3.10, usingPoetryas package manager to define a
Python package insidesrc/mass_driver_plugins/.poetrywill create virtual environments if needed, fetch
dependencies, and install them for development.For ease of development, aMakefileis provided, use it like this:make  # equivalent to "make all" = install lint docs test build
# run only specific tasks:
make install
make lint
make test
# Combine tasks:
make install testOnce installed, the module's code can now be reached through running
Python in Poetry:$ poetry run python
>>> from mass_driver_plugins import main
>>> main("blabla")This codebase usespre-committo run linting
tools likeflake8. Usepre-commit installto install git
pre-commit hooks to force running these checks before any code can be
committed, usemake lintto run these manually. Testing is provided
bypytestseparately inmake test.DocumentationDocumentation is generated viaSphinx,
using the coolmyst_parserplugin to support Markdown files like this one.Other Sphinx plugins provide extra documentation features, like the recentAutoAPIto
generate API reference without headaches.To build the documentation, run# Requires the project dependencies provided by "make install"
make docs
# Generates docs/build/html/To browse the website version of the documentation you just built, run:make docs-serveAnd remember thatmakesupports multiple targets, so you can generate the
documentation and serve it:make docs docs-serveTemplated repositoryThis repo was created by the cookiecutter template available athttps://github.com/OverkillGuy/python-template, using commit hash:a00dd61d7d855303eb607ddfa46a5447d1f042bd.
Package: massedit-cli
massedit-cliPython mass editor CLI.The missing entry point formassedit.massedit-clivendorsmasseditso that you can install it withpipxand use itno matter which the python virtual env you're in the whatwhich python3points to.massedit-cliInstallationpipxpipUsageDevelopInstallationpipxThis is the recommended installation method.$ pipx install massedit-clipip$ pip install massedit-cliUsageCheck outmasseditto learn more.usage: massedit [-h] [-V] [-w] [-v] [-e EXPRESSIONS] [-f FUNCTIONS] [-x EXECUTABLES] [-s START_DIRS] [-m MAX_DEPTH] [-o FILE] [-g FILE] [--encoding ENCODING]
                [--newline NEWLINE]
                [file pattern ...]

Python mass editor

positional arguments:
  file pattern          shell-like file name patterns to process or - to read from stdin.

options:
  -h, --help            show this help message and exit
  -V, --version         show program's version number and exit
  -w, --write           modify target file(s) in place. Shows diff otherwise.
  -v, --verbose         increases log verbosity (can be specified multiple times)
  -e EXPRESSIONS, --expression EXPRESSIONS
                        Python expressions applied to target files. Use the line variable to reference the current line.
  -f FUNCTIONS, --function FUNCTIONS
                        Python function to apply to target file. Takes file content as input and yield lines. Specify function as [module]:?<function name>.
  -x EXECUTABLES, --executable EXECUTABLES
                        Python executable to apply to target file.
  -s START_DIRS, --start START_DIRS
                        Directory(ies) from which to look for targets.
  -m MAX_DEPTH, --max-depth-level MAX_DEPTH
                        Maximum depth when walking subdirectories.
  -o FILE, --output FILE
                        redirect output to a file
  -g FILE, --generate FILE
                        generate stub file suitable for -f option
  --encoding ENCODING   Encoding of input and output files
  --newline NEWLINE     Newline character for output files

Examples:
# Simple string substitution (-e). Will show a diff. No changes applied.
massedit -e "re.sub('failIf', 'assertFalse', line)" *.py

# File level modifications (-f). Overwrites the files in place (-w).
massedit -w -f fixer:fixit *.py

# Will change all test*.py in subdirectories of tests.
massedit -e "re.sub('failIf', 'assertFalse', line)" -s tests test*.py

# Will transform virtual methods (almost) to MOCK_METHOD suitable for gmock (see https://github.com/google/googletest).
massedit -e "re.sub(r'\s*virtual\s+([\w:<>,\s&*]+)\s+(\w+)(\([^\)]*\))\s*((\w+)*)(=\s*0)?;', 'MOCK_METHOD(\g<1>, \g<2>, \g<3>, (\g<4>, override));', line)" test.cppDevelop$ git clone https://github.com/tddschn/massedit-cli.git
$ cd massedit-cli
$ poetry install
Package: massive.py
massive.pyUtilities for converting text to massive text, especially on Discord.Installation$ pip install massive.pyExamplefrommassive.discordimportdiscord_massivemassivizer=discord_massive.Massive(alternate_chance=1.0)massivizer.swap_random_chars(chance=0.02)forpartinmassivizer.massivize("Massive text"):print(part)The above code generates this:MassivizersVanessa-case/Vanessa text: aLtErNaTiNg CaPs.Massive text (Discord):regional_indicatorand other emojis on Discord.Massive Vanessa text (Discord): A combination of massive text and lower case letters.
Package: mass-mailing-light22
欢迎使用邮件群发一、概述本模块为方便大家更便捷使用Python来群发邮件而编写。作者：北极星光E-mail：light22@126.comPypi官方库地址：https://pypi.org/project/mass-mailing-light22Github地址：https://github.com/18513233125/mass_mailing二、功能介绍简化复合邮件创建流程，省去繁长代码，只需要传入【收件人】、【邮件标题】、【正文文本】【附件路径】等信息即可一键创建复合邮件。无需导入繁复的库及函数，只需导入一个库即可一键发送邮件。一键群发邮件，无需担心各种报错导致程序中止，邮件发送完成后会提示哪些邮件发送成功，哪些发送失败，方便排查邮箱地址拼写等原因三、使用方法导入本模块方法：在需要导入本模块的代码中写入导入语句：from excel_operate import ExcelOperate。对象实例化方法：通过语句mail = MassMailing(send_to, title, mail_text, fj1, fj2, fj3,...)获得一个名为mail的对象。参数send_to为收件人列表可传入由收件人地址字符串组成的列表或元组类型数据；参数title为邮件标题，可传入字符串类型数据，不传入数据则默认为【无标题】；参数mail_text为邮件正文文本，可传入字符串类型数据，建议为三引号的多行字符串。也可不传入数据；参数fj1, fj2, fj3为邮件附件，传入数据为有效的文件路径组成的字符串,可传入多个参数来添加多个附件，也可不传入此参数则无附件。设置发送邮箱账号信息：通过方法mail.set_account(account, token, server_address, port)来设置发送邮箱的账号信息。参数account为你的邮箱账号；参数token为你的邮箱授权码；参数server_address为你的邮箱smtp服务器地址；参数port为你的邮箱服务器端口号。发送邮件：完成邮件对象实例化及发送邮箱账号信息参数设置这两步后即可发送邮件。发送邮件的代码为mail.send_mail()。代码示例：from mass_mailing import MassMailing

send_to = ['xxxx@126.com', 'xxxxx@163.com','xxxxxx@qq.com', 'xxxxxxx@live.cn']  # 收件人列表
title = '邮件标题'  # 邮件标题
mail_text = '''
你好：
    这是一封来自遥远星空彼岸的信件。
    你看到这封信一定很吃惊吧！
'''  # 邮件正文

fj1 = 'tests/附件1.txt'  # 邮件附件路径
fj2 = r'D:\Codes\Python Projects\邮件群发\tests\附件二.docx'  # 邮件附件路径
fj3 = 'D:/Codes/Python Projects/邮件群发/tests/第三个附件.xlsx'  # 邮件附件路径

mail = MassMailing(send_to, title, mail_text, fj1, fj2, fj3)  # 实例化邮件

account = 'xxxxxxx@126.com'  # 你的邮箱账号
token = 'XXXXXXXXXXXXXXXX'  # 你的邮箱授权码
server_address = 'smtp.126.com'  # 你的邮箱smtp服务器地址
port = 994  # 你的邮箱服务器端口号
mail.set_account(account, token, server_address, port)  # 设置你的邮箱账号信息
mail.send_mail()  # 发送邮件四、版本历史V 1.0.0初始版本
Package: massmedia
Django-MassMediaRequires:django-taggingOptional, but used for extracting Metadata:IPTCInfo
hachoir-core
hachoir-metadata
hachoir-parserTo start using the app, place it in your INSTALLED_APPS. You may want to edit the settings.py file in the massmedia directory and adjust a few things. If you want to gather metadata, you should install the hachoir lib available athttp://hachoir.org/. All in all, you will need the hachoir core,parser, and metadata packages installed.To customize the app, try editing some of the templates. Media items are templated with the show_media tag using templates based on the media’s mimetype.
Package: mass-pagure-prs
This library provides tools to to do mass changes within
Fedora packages’ spec files and create Pagure pull requests
for them.Example Usagefrommass_pagure_prsimportBasePRsGeneratorclassPagurePRsGenerator(BasePRsGenerator):defmodify_spec(self,specfile):"""Modify a spec file and return a modified version.

        Args:
            specfile (str): A spec file as a string.

        Return:
            (str) A modified spec file.
        """# Do things with a spec file and return a# modified spec file as a string.defcheck_mock_build_results(self,output):"""Given the output of the mock build check the result."""# Optional, can be skipped. Enables checking mock build# results/logs/created rpms if mock build is ran for packages.defcheck_koji_scratch_build_results(self,output):"""Given the output of the Koji scratch build check the result."""# Optional, can be skipped. Enables checking Koji scratch build# results if Koji scratch build is ran for packages.defmain():prs_generator=PagurePRsGenerator(packages_filename='packages.txt',output_dirname='/tmp/pagure_prs',dry_run=False,pagure_token='pagure_token',pagure_user='pagure_user',fas_user='fas_user',# will not be needed with Pagure 4.0.0fas_password='fas_password',# will not be needed with Pagure 4.0.0)# Set branch name and texts for commit messages, PR description etc.prs_generator.configure(git_branch='sample_branch',changelog_entry='sample message',commit_message='sample message',pr_title='sample message',pr_description='sample message')# Open PRs.# Receive the list of links to opened PRs if not in dry run.prs=prs_generator.run(do_mock_build=True,do_koji_scratch_build=False)
Package: mass-ping
Mass PingA multi-threaded ICMP ping using /bin/ping, ping.exe.FeaturesYou can get a quick result.It detects ping flapping.You don't need root privilege to send a ping.It has no dependency as using only python standard library.InstallationRun the folowing to install:pipinstallmass-pingUsage>>>frommass_pingimportMassPing>>>hosts=['8.8.8.8','8.8.4.4','yahoo.com','ns.speedvpn.net','noizze.net']>>>ping=MassPing(hosts=hosts,thread_count=len(hosts),test_count=2)# test_count=2 means when ping fails retry 1 more time>>>result=ping.start()>>>frompprintimportpprint>>>pprint(result){'alive':['8.8.4.4','8.8.8.8','noizze.net','ns.speedvpn.net','yahoo.com'],'dead':[],'flapping':[]}