Saravanan666/pypi-org huggingface dataset:
Package: vidsz-deployment-test
Vidsz: Video's WizardCommon Wrapper/Interface around various video reading/writing tools to make video reading stable, consistent and super easy around different systems and OS.pipinstallvidszBackendsOpenCV (in-development)FeaturesEasy and hassle free (read/write withfor-loop,with-block,while-loop)Batch Support. Read and write frames in batches.Read VideoRead withfor-loopfromvidsz.opencvimportReader# open readerreader=Reader("static/countdown.mp4")# read frame with for loopforframeinreader:# use ndarry-frame however you likepass# releasereader.release()Read with awith-blockwithReader("static/countdown.mp4")asreader:frame=reader.read()Read with awhile-loop# this follows similar behavior as opencv counterpartwhilereader.is_open():# returns ndarry-frame or None if nothing left to readframe=reader.read()ifframeisNone:breakRead frames in abatchwithReader("dummy.mp4",batch_size=8,dynamic_batch=True)asreader:batch_frames=reader.read()Get properties of the reader# print info: width, height, fps etc.print(reader)# access specific thingsprint(reader.width,reader.height,reader.fps)# access number-of-frames/seconds/minutes that have been readprint(reader.frame_count,reader.seconds,reader.minutes)Write VideoWrite a single framefromvidsz.opencvimportReader,Writervideo_fname="static/countdown.mp4"# open readerreader=Reader(video_fname)# start writer with the Reader object# by default it'll append _out in the name of the output videowriter=Writer(reader)# start writer with your settings;# you can also give any combinations of# following settings with Reader object to# overwrite default settingswriter=Writer(name="out.mp4",width=1920,height=1080,fps=15)# print writer infoprint(writer)# write single frameframe=reader.read()writer.write(frame)Write withfor-loop# read frame with for loopforframeinreader:# write the ndarry-framewriter.write(frame)Write abatch# read batches and writewithReader("dummy.mp4",batch_size=8,dynamic_batch=True)asreader:batch_frames=reader.read()# write list or ndarray of frameswriter.write_all(batch_frames)# close offreader.release()writer.release()Write with awith-block# using "with" block, write "static/countdown_out.mp4" (clone of input)withReader(video_fname)asreader:withWriter(reader,name="out_with.mp4")aswriter:writer.write_all(reader)Logo-AttributionDesigned by brgfx / Freepik
Package: vidtext
# vidtext
Vidtext is a python library which provides the functionality to convert the text directly into the video. vidtext used the rake_nltk library to tokenization the text. then select the highest score token to make an image according to the token.
## Installation
### Using PIPpip install vidtext### Directly from the repositorygit clone https://github.com/anubhavshakya/vidtext.gitpython vidtext/setup.py install## Usage`python import vidtext li = "Text" # Text Content for video content = vidtext.summary(li) # Return List vidtext.TextToVideo(content,vs=0.1) # vs to control the speed of video `## Contributing
### Bug Reports and Feature Requests
Please use issue tracker for reporting bugs or feature requests.
### Development
Pull requests are most welcome.
Package: vidtoch
Vidtoch一个帮你将视频转为字符视频的模块。用法 1：使用 vidtoch.makeVideo 函数生成无声的字符视频# coding: utf-8fromvidtochimport*# 确保你的程序运行入口在 if __name__ == "__main__" 分支下# 因为 makeVideo 函数使用了多进程，在 windows 上，如果不做以上要求# 则可能造成你的程序的递归调用从而造成灾难性后果if__name__=="__main__":# 用法1：# 生成的视频仅支持使用 avi 后缀名makeVideo("原视频路径","生成视频保存路径",# 包括文件名，用 .avi 后缀acqRate:float=0.2,# 采集率，0 < acqRate <= 1，值越大越清晰生成越慢overwrite:bool=False,# 如果保存目录已有同名文件，此参数控制是否覆盖同名文件)# 此函数有不少缺点，生成的视频没有声音，码率无法控制导致文件体积非常大，只能用avi后缀### 实例：# coding: utf-8fromvidtochimport*# 确保你的程序运行入口在 if __name__ == "__main__" 分支下# 因为 makeVideo 函数使用了多进程，在 windows 上，如果不做以上要求# 则可能造成你的程序的递归调用从而造成灾难性后果if__name__=="__main__":# 尽量将 acqRate 设置的小些，否则生成视频会非常慢makeVideo("1.mp4","new.avi",acqRate=0.1)# 视频文件 1.mp4 已在当前工作目录中用法 2：使用 vidtoch.vTools 类生成有声音的字符视频，依赖FFMPEG点击ffmpeg下载，解压，将bin目录中的ffmpeg.exe放到当前工作目录，或放到任意目录并将bin文件夹路径添加到环境变量，或放到任意目录，在 vTools 类中指定路径，如 vt = vTools(ffmpeg=r"d:\ffmpeg\bin\ffmpeg.exe")# coding: utf-8fromvidtochimport*# vTools 类初始化参数详解# vTools(#     chars: str = None,  # 生成的视频要使用的字符，字符串中字符数应大于2个，字符串无需按等效灰度手动排序，可忽略#     ffmpeg: str = None,   # ffmpeg可执行文件的路径，为 None 则在当前目录或环境变量中查找，找不到则生成的文件无声音，可忽略#     procNum: int = None   # 转换成字符视频时使用的进程数，默认是 cpu数*2，可忽略#     )# 例：# if __name__ == "__main__":# with vTools("@^&*.=+-#`", r"d:\ffmpeg\bin\ffmpeg.exe", 4) as vt:# with vTools("@^&*.=+-#`") as vt:# with vTools(ffmpeg=r"d:\ffmpeg\bin\ffmpeg.exe", procNum=4) as vt:# with vTools("@^&*.=+-#`", procNum=4) as vt:# vt = vTools(ffmpeg=r"d:\ffmpeg\bin\ffmpeg.exe")...# save 方法参数详解# save(#     savePath: str,    # 生成的视频的保存路径，包括文件名，后缀名不限#     acqRate: float = 0.2, # 对原视频的采集率，0 < acqRate <= 1，值越大视频越清晰字体越小，可忽略#     bitRate: int = None,  # 生成的视频的码率，默认单位为k，例如值为'1500'则代表生成的视频码率限制在1500k，可忽略#     overwrite: bool = False,  # 如果保存目录已有同名文件，此参数控制是否覆盖同名文件，可忽略#     )defmain():# 写法 1 实例# 不要忘记将你的程序唯一运行入口置于 if __name__ == "__main__" 分支下# vt = vTools()# vt.open(r"C:\Users\hrpzcf\Desktop\1.mp4")   # 路径自行替换# if vt.isOpened():#     vt.save(r"C:\Users\hrpzcf\Desktop\f.mp4", 0.2, overwrite=1)   # # 保存路径自行替换# vt.close()  # 使用完毕不要忘记调用close方法关闭vTools实例# 写法 2 实例# 不要忘记将你的程序唯一运行入口置于 if __name__ == "__main__" 分支下withvTools()asvt:vt.open(r"C:\Users\hrpzcf\Desktop\1.mp4")# 路径自行替换ifvt.isOpened():vt.save(r"C:\Users\hrpzcf\Desktop\f.mp4",0.2,overwrite=1)# 保存路径自行替换# with 代码块结束后会自动调用close方法关闭vTools实例if__name__=="__main__":main()
Package: vidtool
vidtoolSmart optimization and join video files with minimum reencode processFree software: MIT licenseDocumentation:https://vidtool.readthedocs.io.FeaturesHomogenize resolutions and codecs from filling in spreadsheetMake turbo join, 400 times faster than common joinJoin can contain transition effect self-adapted for any video resolutionJoin process could respect:Defined maximum sized per file, generating ordered video blocks, automatically split videos larger than the defined maximum sizePersonal grouping criteria, like modules for video courseTransition effect auto adapted to the videos resolutionsWorks with video .mp4, .webm, .avi, .ts, .vob, .mov, .mkv, .wmv and moreRequire ffmpeg enabled on path system variablesCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2022-09-27)First release on PyPI.
Package: vidtrain
VidTrainTrain deep neural networks to analyze video data.InstallationInstall anaconda(Optional) Install a python-capable IDE likeVisual Studio CodeOpen a command line terminal to installvidtrain:Create a python 3.7 environment:conda create --name vidtrain. Note, the python version must be >= 3.7[1]Activate the environmentconda activate vidtrainInstall tensorflowconda install tensorflow-gpu. Note we use conda so that all dependencies are installed as well. If you like you can manually install the tensorflow dependencies instead and skip this step (in that case pip installs tensorflow as a dependency of vidtrain in the next step).Install vidtrainpip install vidtrainRunExecute the following code in python:importvidtrainif__name__=='__main__':vidtrain.workflow.JunctionAnalysis().run()Notes[1]  The code uses some features that were introduced in 3.7 (dictionaries that are ordered by default), meaning it will not work properly with python <3.7.
Package: vidua
viduaVidua can be used to apply (BPS or IPS) patches. It is intended particularly for use with ROM
hacks.InstallationpipinstallviduaUsageA simple script is included to validate or apply patches from the command line:viduapatch.bpsoriginal.rompatched_output.romFull usage instructions:$vidua--help
usage:vidua[-h][-v][-q]PATCH[ORIGINAL][OUTPUT]Applyorvalidatepatches.

positionalarguments:PATCHthepatchfileORIGINALthefiletobepatchedOUTPUTfilenametowritepatchedfile

optionalarguments:-h,--helpshowthishelpmessageandexit-v,--validatevalidatethepatchonly;donotapplyit-q,--quietsuppressoutputexcepterrorsVidua may also be used as a library, to validate or apply patches from within
another program:fromviduaimportbpspatch=open('patch.bps','rb')original=open('original.rom','rb')withopen('patched.rom','wb')aspatched:patched.write(bps.patch(original,patch).read())If the file is very large, you may prefer to useshutil.copyfileobjwhen
writing to disk.CreditsAlcaro'sFloating IPSwas very useful in working out how to handle BPS files when byuu's documentation
was unclear.
Package: vidXs
vidXsDeveloped by Shashank Pandey from shashankx86 (c) 2021Update InfoBugs FixedExamples of How To UseCreating A ServerfromvidXsimportStreamingServerserver=StreamingServer('127.0.0.1',9999)server.start_server()# Other Code# When You Are Doneserver.stop_server()Creating A ClientfromvidXsimportCameraClientfromvidXsimportVideoClientfromvidXsimportScreenShareClient# Choose Oneclient1=CameraClient('127.0.0.1',9999)client2=VideoClient('127.0.0.1',9999,'video.mp4')client3=ScreenShareClient('127.0.0.1',9999)client1.start_stream()client2.start_stream()client3.start_stream()LinksDeveloper Github
Package: vidyo
vidyoA simple way to get information on YouTube videos.FeaturesPythonic syntax lets you feel right at homeLightweight nature means you don't have to worry about excessive dependenciesSimple to use objects take the hassle out of API inconsistenciesInstallationYou need Python 3.7.0 or greater to run vidyo.You will also need to have a Google Developers project with the YouTube Data API enabled. You can find instructions on how to do that in thedocumentation.It is recommended you install vidyo in a virtual environment. To do this, run the following:# Windows>py-3.9-mvenv.venv
>.venv\Scripts\activate
>pipinstallvidyo# Linux\macOS$python3.9-mvenv.venv
$source./.venv/bin/activate
$pipinstallvidyoTo install vidyo outside of a virtual environment instead, run the following:# Windows>py-3.9-mpipinstallvidyo# Linux/macOS$python3.9-mpipinstallvidyoYou can also install the development version by running the following (this assumes you're on Linux/macOS):$gitclonehttps://github.com/parafoxia/vidyo
$cdvidyo
$gitcheckoutdevelop# Any valid branch name can go here.$python3.9-mpipinstall-U.Usage examplesThe following example shows you how display the title and number of views of the first YouTube video and then download its thumbnail.fromvidyoimportClient# Load your API key from a file.withopen("secrets/api-key")asf:key=f.read()client=Client(key)video=client.get_video("jNQXAC9IVRw")print(f"{video.title}has{video.views:,}views.")video.get_thumbnail().save("thumbnail.jpg")LicenseThe vidyo module for Python is licensed under theBSD-3-Clause License.
Package: vidyut
vidyut-pyPython bindings for Vidyutvidyut-pydefines Python bindings for [Vidyut][vidyut], a high-performance
Sanskrit toolkit. These bindings are under active development as part of the
[Ambuda][ambuda] project.InstallingInstall and update withpip:$ pip install -U vidyutCurrently,vidyut-pydoes not include any linguistic data. For now, you must
build this linguistic data manually. Doing so requires thecargocommand.To build this data, run the following commands:git clone git@github.com:ambuda-org/vidyut.git
cd vidyut/vidyut-cheda
make installThe output data will be indata/vidyut-x.y.z, wherex.y.zis the Vidyut
version. Once thedatafolder has been created, you can move it wherever you
like.LinksFor users:Documentation:https://vidyut.readthedocs.ioReleases:https://pypi.org/project/vidyut/Changes:https://vidyut.readthedocs.io/changes/Source Code:https://github.com/ambuda-org/vidyutIssues:https://github.com/ambuda-org/vidyut/issuesFor contributors:Contributing:CONTRIBUTING.mdFor other questions:Chat:https://discord.gg/7rGdTyWY7Z
Package: vie
Variable Importance Estimation PackageThis is a simple and unified package for nonlinear variable importance estimation that incorporates uncertainty in the prediction function and is compatible with a wide range of machine learning models (e.g., tree ensembles, kernel methods, neural networks, etc). Below please find the detailed explanation of the package resources.Github repo:https://github.com/wdeng5120/viePyPi:https://pypi.org/project/vie/0.0.2/PrerequisitesFor Windows, it is recommended to run this app on a Linux emulation layer such as the Git Bash terminal. See the "Instructions for Git Bash" section for details. In addition to Git Bash, make sure you also have Python3 and Pip3 as described below.For Mac and Linux, this app should work out of the box on the Linux or Mac terminal, but make sure you also have Python3 and Pip3 as described below.Requirements:Python3 (version 3.7 or greater) - Install Python3 here: [https://www.python.org/downloads/]. Check version with:python3 --version.Pip3 (version 20.2.1 or greater) - Make sure to install python3-pip in order to use pip install. Check version with:pip3 --version.InstallationThere are a couple of options to install this app:Pip Install - This app is hosted on PyPi and can be installed with the following command:pip3 install vieLocal Install - Alternatively, you can download or git clone the Github repo and install it locally with the following:git clone https://github.com/wdeng5120/vie.git
cd vie
pip3 install -e .To uninstall this app:pip3 uninstall vieIf you used the local install option, you will also want to delete the.egg-infofile located in thevie/directory of the package. This gets created automatically withpip3 install -e ..UsageWe provide a colab tutorial (tutorial.ipynb) on how to use the package.
Package: vienna
viennaa short python wrapper for vienna
tools (https://www.tbi.univie.ac.at/RNA/ViennaRNA/doc/html/install.html) Note I did not develop this
software just built this short wrapper.how to installinstall anacondahighly recommended to install anaconda first and create a py3 environment# to install conda on linuxwgethttps://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
chmod+xMiniconda3-latest-Linux-x86_64.sh
./Miniconda3-latest-Linux-x86_64.sh

condacreate--namepy3python=3.7--yes
condaactivatepy3# to install conda on mac# https://docs.anaconda.com/anaconda/install/mac-os/bash~/Downloads/Anaconda3-2020.02-MacOSX-x86_64.shinstall viennaNote this is just a wrapper so you must install the vienna fold code# this can be accomplished using condacondainstall-cbiocondaviennarna# if you do not wish to use conda# you can install it via many options from the websitehttps://www.tbi.univie.ac.at/RNA/install vienna python package# vienna python package is avialible on PYPIpipinstallvienna# you can also install it from githubgitclonehttps://github.com/jyesselm/viennacdvienna
pythonsetup.pyinstallhow to use>>>importvienna>>>fr=vienna.fold('GGGGAAAACCCC')>>>print(fr)FoldResults(dot_bracket='((((....))))',mfe=-5.4,ens_defect=1.62)
Package: vienna-smartmeter
Vienna Smart MeterAn unofficial python wrapper for theWiener Netze Smart Meterprivate API.FeaturesAccess energy usage for specific metersGet profile informationView, edit & delete events (Ereignisse)InstallationInstall with pip:pip install vienna-smartmeterAn async package is provided and can be installed with the 'async' extra:pip install vienna-smartmeter[async]How To UseImport the Smartmeter client, provide login information and access available api functions:fromvienna_smartmeterimportSmartmeterusername='YOUR_LOGIN_USER_NAME'password='YOUR_PASSWORD'api=Smartmeter(username,password)print(api.profil())The asnyc package can be imported by replacing Smartmeter with AsyncSmartmeter.fromvienna_smartmeterimportAsyncSmartmeterContributingPull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.Make sure to add or update tests as appropriate.LicenseYou can check out the full licensehereThis project is licensed under the terms of theMITlicense.LegalDisclaimer: This is not affliated, endorsed or certified by Wiener Netze. This is an independent and unofficial API. Strictly not for spam. Use at your own risk.
Package: viessmann-gridbox-connector
Viessmann Gridbox ConnectorThis is not an official Viessmann librarya GridboxConnector Lib to fetch your Data from the Cloud.
It is using the same Rest-API like the Dashboard and the App.InstallationSet your email and password in the config.json.
Use your Login Data from the App or fromhttps://mygridbox.viessmann.com/loginSetup the python environment and install all dependenciespython -m venv venv
.\venv\Scripts\activate
pip install -r requirements.txtExample Output{
    "consumption": 496,
    "directConsumption": 413,
    "directConsumptionEV": 0,
    "directConsumptionHeatPump": 0,
    "directConsumptionHeater": 0,
    "directConsumptionHousehold": 413,
    "directConsumptionRate": 1,
    "grid": 83,
    "gridMeterReadingNegative": 4318200000,
    "gridMeterReadingPositive": 14499360000,
    "measuredAt": "2023-08-04T11:29:43Z",
    "photovoltaic": 413,
    "production": 413,
    "selfConsumption": 413,
    "selfConsumptionRate": 1,
    "selfSufficiencyRate": 0.8326612903225806,
    "selfSupply": 413,
    "totalConsumption": 496
}Usagefromgridbox_connectorimportGridboxConnector# Initialize the connector with your configurationconfig={"urls":{"login":"https://example.com/login","gateways":"https://example.com/gateways","live":"https://example.com/live/{}",},"login":{"username":"your_username","password":"your_password"},}connector=GridboxConnector(config)# Retrieve live datalive_data=connector.retrieve_live_data()print(live_data)ConfigurationYou need to provide a configuration dictionary with the following keys:urls: Dictionary containing endpoint URLs.
login: Dictionary containing login credentials.DependenciesrequestsContributingIf you'd like to contribute to viessmann-gridbox-connector, please follow these steps:Fork the repository.
Create a new branch for your feature or bug fix.
Make your changes and write tests if possible.
Run tests and ensure they pass.
Submit a pull request.LicenseThis project is licensed under the MIT License - see the LICENSE file for details.
Package: viet-aws-jupyter-proxy
AWS Jupyter ProxyA Jupyter server extension to proxy requests with AWS SigV4 authentication.OverviewThis server extension enables the usage of theAWS JavaScript/TypeScript SDKto write Jupyter frontend extensions without having to export AWS credentials to the browser.A single/awsproxyendpoint is added on the Jupyter server which receives incoming requests from the browser, uses the credentials on the server to addSigV4authentication to the request, and then proxies the request to the actual AWS service endpoint.All requests are proxied back-and-forth as-is, e.g., a 4xx status code from the AWS service will be relayed back as-is to the browser.NOTE: This project is still under active developmentInstallInstalling the package from PyPI will install and enable the server extension on the Jupyter server.pipinstallaws-jupyter-proxyUsageUsing this requries no additional dependencies in the client-side code. Just use the regular AWS JavaScript/TypeScript SDK methods and add any dummy credentials and change the endpoint to the/awsproxyendpoint.import*asAWSfrom'aws-sdk';importSageMakerfrom'aws-sdk/clients/sagemaker';// Reusable function to add the XSRF token header to a requestfunctionaddXsrfToken<D,E>(request:AWS.Request<D,E>){constcookie=document.cookie.match('\\b'+'_xsrf'+'=([^;]*)\\b');constxsrfToken=cookie?cookie[1]:undefined;if(xsrfToken!==undefined){request.httpRequest.headers['X-XSRFToken']=xsrfToken;}}// These credentials are *not* used for the actual AWS service call but you have// to provide any dummy credentials (Not real ones!)AWS.config.secretAccessKey='IGNOREDIGNORE/IGNOREDIGNOREDIGNOREDIGNOR';AWS.config.accessKeyId='IGNOREDIGNO';// Change the endpoint in the client to the "awsproxy" endpoint on the Jupyter server.constproxyEndpoint='http://localhost:8888/awsproxy';constsageMakerClient=newSageMaker({region:'us-west-2',endpoint:proxyEndpoint,});// Make the API call!awaitsageMakerClient.listNotebookInstances({NameContains:'jaipreet'}).on('build',addXsrfToken).promise();Usage with S3For S3, use thes3ForcePathStyleparameter during the client initializationimportS3from'aws-sdk/clients/s3';consts3Client=newS3({region:'us-west-2',endpoint:proxyEndpoint,s3ForcePathStyle:true,s3DisableBodySigning:false// for https});awaits3Client.getObject({Bucket:'my-bucket',Key:'my-object'}).on('build',addXsrfToken).promise();WhitelistingOn the server, theAWS_JUPYTER_PROXY_WHITELISTED_SERVICESenvironment variable can be used to whitelist the set of services allowed to be proxied through. This is opt-in - Not specifying this
environment variable will whitelist all services.exportAWS_JUPYTER_PROXY_WHITELISTED_SERVICES=sagemaker,s3
jupyter-labDevelopmentInstall all dev dependenciespipinstall-e".[dev]"jupyterserverextensionenable--pyaws_jupyter_proxy--sys-prefixRun unit tests using pytestpytesttests/unitLicenseThis library is licensed under the Apache 2.0 License.
Package: vietcardocr
vietcardocr is a Python library for extracting information from Vietnamese Personal Card (IDCard, Driving License, Student Card).InstallationUse the package managerpipto
install foobar.pipinstallvietcardocrUsageimportvietcardocr.extractasexres=ex.extract("example-image.jpg")#return a dictionary data typeprint(res))ContributingPull requests are welcome. For major changes, please open an issue first
to discuss what you would like to change.Please make sure to update tests as appropriate.LicenseMIT
Package: vietnamese
This package provides a simple tool for preprocessing Vietnamese text. It includes the following functions:get_consonants(): Returns a list of all Vietnamese consonants.get_vowels(): Returns a list of all Vietnamese vowels.get_rhymes(): Returns a list of all Vietnamese rhymes.normalize(input_str): Converts the input string from various Vietnamese encodings to Unicode.InstallingYou can install the package using pip:$ pip install vietnameseUsageHere's an example of how to use the package:importvietnameseconsonants=vietnamese.get_consonants()vowels=vietnamese.get_vowels()rhymes=vietnamese.get_rhymes()input_str="Xin chµo, ®©y lµ mét dßng ch÷ bÞ lçi font"normalized_str=vietnamese.normalize(input_str)print("Consonants:",consonants)print("Vowels:",vowels)print("Rhymes:",rhymes)print("Input string:",input_str)print("Normalized string:",normalized_str)ContributingIf you find a bug or have a suggestion for a new feature, please open an issue on GitHub or submit a pull request.