Saravanan666/pypi-org huggingface dataset:
Package: vinyl2digital
Vinyl2Digital - PyPi Package==================================Vinyl2Digitalis a Python3 Pip Package for batch rendering multiple audio files using theAudacitymod-script-pipe.Install withpip install vinyl2digitalUpdate withpip install --upgrade vinyl2digitalTable of contentInstallationSetupAll FlagsExample Use CasesDevelopmentInstallationInstall the package with this command:pip install vinyl2digitalSetupEnable mod-script-pipe in AudacityLaunch Audacity from your terminalWindows Command Prompt AdminNavigate to Audacity executable locationcd C:\Program Files\Audacity>Launch Audacity:start audacity.exeMac/Linux:open -a AudacityRecord your audio and splice each track into its own selection (place cursor and press Ctrl+i)* Highlight any selected region and choose File->Export->Export Selected Audio, these quality options will be what vinyl2digital uses. So if you want 24 bit FLAC files make sure that option is set in this window.* Move your cursor to be at the start of your recording
* While Audacity is open, run the vinyl2digital python package with the `-t` flag to test your connection to the Audacity mod-script-pipe.python -m vinyl2digital -tYou should now be able to use the vinyl2digital package from the command line to connect with Audacity and batch render multiple audio files one after the other.FlagsHelp-hView the help page-tTest your connection to the Audacity mod-script-pipeInput-i discogs https://www.discogs.com/master/14720Take a Discogs URL as input for determining how may audio files to export and what metadata to use-i 12Export 12 tracks using default filenamesOutput Audio Format-f flacExport selections as flac audio files-f mp3Export selections as mp3 audio filesOutput-o "E:\martinradio\rips\vinyl\NewEnglandTeenScene"Set filepath output locationExamplesHere are some example commands for vinyl2digital on Win10 command prompt:Export tracks as flac using a Discogs URLpython -m vinyl2digital -i discogs https://www.discogs.com/release/2019460-Various-The-New-England-Teen-Scene -f flac -o "E:\martinradio\rips\vinyl\NewEnglandTeenScene"Export 5 tracks as mp3 using default filenamespython -m vinyl2digital -i 5 -f mp3 -o "E:\martinradio\rips\vinyl\NewEnglandTeenScene"DevelopmentIn order to develop and test this code locally clone this github repo:git clone https://github.com/MartinBarker/vinyl2digital.gitInstall the necessary packages:os, sys, requests, json, time, reRun locally:python3 "vinyl2digital/vinyl2digital/__init__.py" -hReleasing a new version:Change package version number (setup.py)$ python -m pip install --upgrade build
$ python -m buildThis will create the dist/ folder with a new packaged tarpython3 setup.py sdist bdist_wheel
pip3 install twine
python3 -m twine upload dist/*
Package: vinylang
The vinyl CompilerGetting started$ pip install vinylangDocumentationhttps://vinyl.readthedocs.orgRequirementsPython >= 3.5LicenseThis is free and unencumbered software released into the public domain.Anyone is free to copy, modify, publish, use, compile, sell, ordistribute this software, either in source code form or as a compiledbinary, for any purpose, commercial or non-commercial, and by anymeans.In jurisdictions that recognize copyright laws, the author or authorsof this software dedicate any and all copyright interest in thesoftware to the public domain. We make this dedication for the benefitof the public at large and to the detriment of our heirs andsuccessors. We intend this dedication to be an overt act ofrelinquishment in perpetuity of all present and future rights to thissoftware under copyright law.THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND,EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OFMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OROTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE,ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OROTHER DEALINGS IN THE SOFTWARE.For more information, please refer to <http://unlicense.org/>
Package: vinyl-core
VinylSourcesSources are data models that can't be modified. Sources act as the bridge between external systems (SaaS tools, databases, other Vinyl projects, etc.) and Vinyl. Sources are the only way to get data into Vinyl. Sources are comprised of 3 parts: (1) a source definition which contains fields, typing and other metadata (2) a connector which is an adapter to the system where the data is stored and (3) a fetcher which tells Vinyl how to fetch data from the connector and at what frequency.Most Used Commandsvinyl sources list- list all sources available in the projectvinyl sources cache- cache data from all sources into a local repository (default is .turntable/sources)How to load sourcesimportvinylconn=vinyl.connect('local')The return of connect() is an ibis connection. The first time this may be slow as each table needs to be created. Subsequent loads are cached.
Package: vinyldns-python
vinyldns-pythonPython client library forVinylDNSThis project is a work in progress! If you would like to help us get this where it needs to be,
please reach out to us ingitter.To run,pip install vinyldns-pythonand then:>>>fromvinyldns.clientimportVinylDNSClient>>>local_client=VinylDNSClient("ApiEndpoint","UserAccessKey","UserSecretKey")>>>local_client.list_zones()>>>>>># If all of the following environments are set>>># - VINYLDNS_API_URL>>># - VINYLDNS_ACCESS_KEY_ID>>># - VINYLDNS_SECRET_ACCESS_KEY>>>fromvinyldns.clientimportVinylDNSClient>>>local_client=VinylDNSClient.from_env()>>>local_client.list_zones()ContributingRequirementspython3pipvirtualenvTo get started, you will want to setup your virtual environment.Ensure that you havevirtualenvinstalled> pip install virtualenvRun./bootstrap.shto setup your environment (unless you really want all these dependencies to be installed locally, which we do not recommend).Activate your virtual environment> source .virtualenv/bin/activateand you will be ready to go!Unit TestsUnit tests are developed usingpytest.  We useResponses, which allows for simple mocking of HTTP endpoints.To run unit tests, you can simply runpython3 setup.py test.  To target a specific test, you can
runpython3 setup.py test -a "-k my_test"Functional TestsFunctional tests are also developed with pytest. These tests run against a local instance of VinylDNS. Note that for now
they are not tied into our travis build, so they must be run locally for validation.From your virtualenv, runtox -e func_testRunning a full buildWhen you are finished writing your code you will want to run everything including linters.  The
simplest way to do this is to runtox -e check,py36, which will run static checks and run unit tests.If you see any failures / warnings, correct them untiltoxruns successfully.If you do not havetoxin your environment,pip install toxto add it.  For more information you can
read thetox docs.Local DevelopmentSee thequickstartin the
VinylDNS api for details on how to start up a local instance of the api in docker. With that
running, you can make requests with the following client details:local_client=VinylDNSClient("http://localhost:9000","okAccessKey","okSecretKey")
Package: vinyl-srw
Synchrotron Radiation WorkshopBased on:https://github.com/ochubar/SRWfor the source codehttps://github.com/NSLS-II/scientific-python-cookiecutterfor the structure and featuresLicensing and documentation:Free software: 3-clause BSD licenseDocumentation: (COMING SOON!)https://srwpy.github.io/srwpy.FeaturesTODO
Package: vinzy-imgdata-importer
Image Data Importer For Deep LearningA Python package which will help you to import image data for your deep learning project. The function inside the package is called "img_data_importer" which accepts only one parameter i.e path of main folder (main folder is the one inside which all different categories folders are present),and during the execution it will ask you to pick some options,choose them according to your data set, after generating the data the output will be returned in a tuple (X_train,y_train,mapping) in which X_train has categories image data, y_train has labels i.e index values and mapping(Dictionary/Hash-Map) has the categories and their particular index value, How to execute is shown belowExecutionX,y,m = img_data_importer(path of main folder),
X holds the image data, y holds the labels and m holds the mapping of categories and their particular index valuetypical options shown belowDo you want to convert your image data to grey scale? y/n: choose your optionDo you want to resize your data? y/n [default:100x100]: choose your optionGenerating, Shuffling & Mapping the Data...Do you want to normalize your data? y/n [recommended]: choose your optionDo you want to save your data? y/n [will be saved using pickle]: choose your option
Package: vinzy-splitter
A package will help you to split your data frame into training, validation and testing subsetsWorking:Splits arrays or matrices into random train, validation and test subsets
"train_val_test_split" function accepts independent features(X), dependent
feature (y), validation size (default size = 0.15),test size (default size = 0.15) and shuffle (default = True) as input parameters and returns X_train,X_val,X_test,y_train,y_val,y_testNote: If shuffle parameter is True, after shuffling index of all records will be reset in ascending orderHow to use it:from vinzy_splitter import train_val_test_splitX_train,X_val,X_test,y_train,y_val,y_test = train_val_test_split(X,y,val_size=0.15,test_size=0.15,shuffle=True)after executing the function your data frame will be splitted into training,validation and testting subsets based on validation and testing size you have provided, deafult value will be 15% for both and suffling will be True.
Package: vio2
violetta2Транслятор с Python на русский псевдоязык и обратно. Делает программирование более доступным для людей с плохим восприятием английского.Python подходит для обучения детей по нескольким причинам:Python - очень простой язык, на котором просто программироватьPython 3 поддерживает символы Unicode в названиях переменныхPython - интерпретируемый язык, что делает его запуск удобнее.Для облегчения понимания логики языка violetta2 переводит основные английские ключевые слова и методы на русский, что облегчает читабельность кода для тех, кто не владеет английским в должной степени.Пример работыКод на Python:def Split2(A):
    B = list()
    C = list()
    for el in A:
        if el % 2 == 0:
            B.append(el)
        else:
            C.append(el)
    return B, C

A = list(map(int, input('Введите элементы массива через пробел: ').split()))

B, C = Split2(A)
print(B, len(B))
print(C, len(C))Код на Violetta2:задать Split2(A):
    B = список()
    C = список()
    для el в A:
        если el % 2 == 0:
            B.дополнить(el)
        иначе:
            C.дополнить(el)
    вернуть B, C

A = список(карта(целое, ввести('Введите элементы массива через пробел: ').разделить()))

B, C = Split2(A)
вывести(B, длина(B))
вывести(C, длина(C))Использование$ python -m vio2 -v <filename>- перевести файл с Python на Violetta2$ python -m vio2 -p <filename>- перевести файл с Violetta2 на Python$ python -m vio2- режим интерактивной оболочкиУстановкаpip install vio2Зависимости и требованияЗависимостей нет. Требование к версии Python: >=3.8.
Package: violent-webdriver
violent-webdriverviolent-webdriver is created by post-packaging selenium webdriver,  violent-webdriver has many convinient functions
which can highly insure the browser operation, you don't need to worry about whether the element is really present
on current page or clickable at specific time while using violent-webdriver. it will handle this sort
of annoying problemsviolent-webdriver 诞生于二次封装 selenium webdriver，内部封装了许多方法来确保浏览器操作的执行性，你不需要去担心定位到的元素是否存在于当前页面或者元素是否在当前时刻处于可点击状态，报错重试机制可以帮你自动解决这些不必要的麻烦。Find the latest version on github :https://github.com/amazingTest/violent-webdriverInstallationThe last stable release is available on PyPI and can be installed with pip.make sure that Chrome has been installed and match the selenium version$ pip install selenium

$ pip install violent-webdriverBest PracticeFirstly, create a python file: c:\folder\mytest.py# c:\folder\mytest.py
from violent_webdriver import Chrome

dr = Chrome.violent_chromedriver(executable_path=[CHROMEDRIVER_PATH], use_mobile_emulation=True)
dr.get('http://www.baidu.com')
dr.v_send_keys(locate_rule={'css selector': 'input[name=\'word\']'}, message='test')
dr.v_click(locate_rule={'css selector': 'button[class=\'se-bn\']'})then use your IDE to run this script or$ python c:\folder\mytest.pyif successful, you will see the search result of 'test' by a mobile emulated browserFor more code examples, please refer to the examples folder in source distribution or
visithttps://github.com/amazingTest/violent-webdriver/tree/master/examplesContact meWelcome to follow my public account 「智能测试开发」by WeChat and contact me欢迎大家来关注我的个人公众号 「智能测试开发」并进行深度交流Finally, if you really find this lib useful，donationis welcome by using your phone to scann the two-dimensional
code below最后，如果你觉得这个库真的好用的话可以扫码给作者一些鼓励哦~
Package: violet-simulator
VioletA smol simulator framework built on top ofPyGame.Automatic agent wandering behaviourFully deterministic simulations with PRNG seedsInstall Violet with a simplepip install😎Matrix-powered multi-threaded configuration testingPolars-powered simulation analyticsReplay-able simulations with a ✨ time machine ✨Type-safe configuration system (with TOML support)Want to get started right away?
Check out theViolet Starter Kit!InstallationInstall the latest version of Violet with:pip3install-Uviolet-simulatorOr withPDM:pdmaddviolet-simulatorExamplefromviimportAgent,Simulation(# Step 1: Create a new simulation.Simulation()# Step 2: Add 100 agents to the simulation..batch_spawn_agents(100,Agent,images=["examples/images/white.png"])# Step 3: Profit! 🎉.run())For more information you can check thedocumentation,examplesand theUser Guide.
Package: violetSpider
功能介绍​	支持全球疫情数据获取以及对应的国家、省份、城市数据获取具体操作importvioletSpiderasspiderfromvioletSpider.core.modelimportWord,Nation,Province# 1.获取全球数据word:Word=spider.find_word()print(word,type(word))# <violetSpider.core.model.World object at 0x0000021DD6FC3AC8> <class 'violetSpider.core.model.World'># 2.获取对应数据# 获取该对象上所有的存储数据的 Key，drop=True【默认删除值为 None 的 Key】keys=word.keys(drop=True)values=word.values(drop=True)value=word.nameprint(keys,values,value)# dict_keys(['name', 'nowConfirm', 'confirm', 'heal', 'dead', 'lastUpdateTime'])# dict_values(['地球', 46987153, 217021468, 165524088, 4510227, '2021-10-20 13:43:34'])# 地球获取国家数据# 根据全球数据获取国家数据# 如果不指定具体国家，会获取所有国家# 所有数据对象都包含 keys() 和 values()nations:list=word.searchNation('美国','法国','西班牙')nation:Nation=nations[0]print(nations)print(nation.keys(),nation.values())# [<violetSpider.core.model.Nation object at 0x000002245C37AD30>,# <violetSpider.core.model.Nation object at 0x000002245C37ACF8>,# <violetSpider.core.model.Nation object at 0x000002245C37AC88>]# dict_keys(['name', 'continent', 'y', 'date', 'confirmAdd', 'confirmAddCut', 'confirm', 'suspect', 'dead', 'heal', 'nowConfirm', 'confirmCompare', 'nowConfirmCompare', 'healCompare', 'deadCompare'])# dict_values(['美国', '北美洲', '2021', '08.31', 0, 0, 39668541, 0, 654696, 30826478, 8187367, 0, 0, 0, 0])获取某个国家下的省份数据# 先获取具体国家nation:Nation=word.searchNation_one('美国')# 获取指定省份，如果不指定则获取所有provinces=nation.searchProvince()forprovinceinprovinces[:3]:print(province.name)# 加利福尼亚# 德克萨斯# 佛罗里达获取省份下面的城市nation:Nation=word.searchNation_one('美国')province:Province=nation.searchProvince_one('德克萨斯')# 获取指定城市，如果不指定则获取所有citys=province.searchCity()forcityincitys[:3]:print(city)# 当然，有些省份是没有公布城市数据的# violetSpider.core.error.WorldData: 很抱歉, 德克萨斯, 并未开放城市数据# 所有你可以这样写# 不过别报太大希望，城市一般不会获取到数据nation:Nation=word.searchNation_one('美国')provinces=nation.searchProvince()forprovinceinprovinces:try:c=province.searchCity()[:3]forcityinc:print(city.name)print(province.name)breakexceptExceptionase:print(e)保存数据# 目前支持 mysql 和 csv 保持# 具体 API 如下importvioletSpiderasspiderspider.save_csv(path:str,# 存储路径，必须data:list,# 数据，装有 Nation、Province、City 对象的列表fields=None,# 存储指定，不指定则全选drop=True# 是否删除空值字段)spider.save_mysql(username:str,password:str,host:str,port:int,databases:str,data:list,# 数据，装有 Nation、Province、City 对象的列表table=None,# 是否指定表fields=None,# 指定保存的字段drop=True# 只要有一个字段的值为空, 那么便不保存这个字段)更新内容1.1.0->基本功能实现1.1.3->修复了china数据无法获取
Package: vioneta-agro-frontend
Home Assistant FrontendThis is the repository for the officialHome Assistantfrontend.View demo of Home AssistantMore information about Home AssistantFrontend development instructionsDevelopmentInitial setup:script/setupDevelopment:InstructionsProduction build:script/build_frontendGallery:cd gallery && script/develop_gallerySupervisor:InstructionsFrontend developmentClassic environmentA complete guide can be found at the followinglink. It describes a short guide for the build of project.LicenseHome Assistant is open-source and Apache 2 licensed. Feel free to browse the repository, learn and reuse parts in your own projects.We useBrowserStackto test Home Assistant on a large variety of devices.
Package: vip4model
vip4modelVIP4 model (Connerney et al. 1998) implementation using Python.InstallationInstall usingpip:pip3installvip4model--userOr by cloning this repo:gitclonehttps://github.com/mattkjames7/vip4model.gitcdvip4model#EITHER create a wheel and install (replace X.X.X with the version number):python3setup.pybdist_wheel
pip3installdist/vip4model-X.X.X-py3-none-any.whl--user#OR install directly using setup.pypython3setup.pyinstall--userUsageThe model accepts right-handed System III coordinates either in Cartesian form (vip4model.ModelCart()) or in spherical polar form (vip4model.Model()), e.g.:importvip4model#get some Cartesian field vectors (MaxDeg keyword is optional)Bx,By,Bz=vip4model.ModelCart(x,y,z,MaxDeg=4)#or spherical polar onesBr,Bt,Bp=vip4model.Model(r,theta,phi,MaxDeg=4)Please read the docstrings forvip4model.Model()andvip4model.ModelCart()usinghelpor?e.g.help(vip4model.Model).There is also a test function which requiresmatplotlibto be installed:#evaluate the model at some Rvip4model.Test(R=0.85)which produces this (based on figure 4 of Connerney et al. 2018):ReferencesConnerney, J. E. P., Acuña, M. H., Ness, N. F., and Satoh, T. (1998), New models of Jupiter's magnetic field constrained by the Io flux 	tube footprint, J. Geophys. Res., 103( A6), 11929– 11939, doi:10.1029/97JA03726.Connerney, J. E. P., Kotsiaros, S., Oliversen, R. J., Espley, J. R.,  Joergensen, J. L., Joergensen, P. S., et al. (2018). A new model of Jupiter's magnetic field from Juno's first nine orbits. Geophysical Research Letters, 45, 2590– 2596.https://doi.org/10.1002/2018GL077312
Package: vipaccessUI
# vipaccessUI
A python-tkinter frontend for the python module vipaccess. Which itself is a TOPF implimentation. The same as the symanmtec VIP Access Token.Click the number to copy to clipboard##  TODO:
- wrap with a CLI
- create as module/package with setup
- publish to pypi## Demo
![Demo](https://raw.githubusercontent.com/chris17453/vipaccessUI/master/demo.gif)
Package: vipassana-timer
vipassana-timerMeditation timer for vipassana (insight meditation) with different posturesRequirementssimpleaudioInstallationpipinstallvipassana-timerorgitclonehttps://github.com/toxicologist/vipassana-timercdvipassana-timer
pythonsetup.pyinstallUsage>meditate -h
usage: meditate [-h] [times]

Simple vipassana meditation timer

positional arguments:
  times                 Times for each meditation posture (format: 'p5w30s30')

optional arguments:
  -h, --help            show this help message and exitThe script takes inputs for the three postures: prostration, walking, and sitting -
represented by the letters 'p', 'w', 's', each followed by the desired time, in minutes.For example, if one wanted to do 10 minutes walking and 10 sitting:# example usage>meditatew10s10#or>meditate
Entertimes(format:'p5w30s30'):w10s10#resultTimes:
Walking:10Sitting:10Walking:10:00remainingOr, to do 35 minutes of sitting:>meditates35

Times:
Sitting:35Sitting:35:00remainingIf you want to add your own posture, just edit thePOSTURESvariable in__init__.py.Good luck meditating! :)
Package: vipcca
Variational Inference assisted non-linear Probabilistic CCA (VIPCCA)DescriptionData alignment is one of the first key steps in single cell analysis for integrating multiple datasets and performing joint analysis across studies. Data alignment is challenging in extremely large datasets, however, as the major of the current single cell data alignment methods are not computationally efficient. Here, we present VIPCCA, a computational framework based on non-linear canonical correlation analysis for effective and scalable single cell data alignment. VIPCCA leverages both deep learning for effective single cell data modeling and variational inference for scalable computation, thus enabling powerful data alignment across multiple samples, multiple data platforms, and multiple data types. VIPCCA is accurate for a range of alignment tasks including alignment between single cell RNAseq and ATACseq datasets and can easily accommodate millions of cells, thereby providing researchers unique opportunities to tackle challenges emerging from large-scale single-cell atlas.CitationJialu Hu, Mengjie Chen, Xiang Zhou, Effective and scalable single-cell data alignment with non-linear canonical correlation analysis, Nucleic Acids Research, Volume 50, Issue 4, 28 February 2022, Page e21,https://doi.org/10.1093/nar/gkab1147InstallationCreate conda environment$condacreate-nvipccapython=3.8
$condaactivatevipccaInstall VIPCCA from pypi$pipinstallvipccaAlternatively, install the develop version of VIPCCA from GitHub source code$gitclonehttps://github.com/jhu99/vipcca.git
$cd./vipcca/
$python-mpipinstall.Note: Please make sure your python version >= 3.7, and install tensorflow-gpu if GPU is available on your your machine.Usage of vipccaFor a quick start, please follow our guide about the usage of VIPCCA in theTutorial and Documentationpages.
Package: vip-client
Python client for theVirtual Imaging Platform(VIP), a free online platform for medical image simulation and analysis.Please check the full documentation onGitHub.
If you encounter any issues, contact us at:vip-support@creatis.insa-lyon.frPrerequisitesThis client has been made compatible withPython 3.7+and should work on Posix OS (e.g.Linux,Mac) andWindows. It relies on therequests(third-party) library.To use this client, you need a freeVIP accountwith a valid API key. Getting a VIP account takes a few minutes by following thisprocedure.Package ContentMost user-centric methods are implemented in theVipSessionclass.Other classes (vip_client.classes) and methods (vip_client.utils) may be used to fulfill specific user needs.TheVipSessionclass (doc) allows to run VIP applications on local datasets.fromvip_clientimportVipSessionIt comes with a Notebooktutorialthat can be launched on Binder: