Saravanan666/pypi-org huggingface dataset:
Package: wanish
AboutThis package allows you to summarize text by reducing an article in size
to several sentences retaining the idea of the text.Besides of that the package extracts the following from the document:Canonical URL of the articleTitle of the articleURL of the image characterizing this articleStrips the document of excessive information (headers, footers,
navigation, advertisement, etc.) and forms a clean HTML based on
structured data of schema.orgDEMOInstallationeasy_install wanish
or
pip install wanishUsagefromwanishimportWanishwanish=Wanish()wanish.perform_url(document_url)# getting doc's source canonical urlurl=wanish.url# getting document's titletitle=wanish.title# getting url of related image if document has itimage_url=wanish.image_url# getting two-letter code of the document's language (en, de, es...)language_code=wanish.language# getting a clean html page of a document with articleclean_html=wanish.clean_html# getting a short summarized description of the article reduced to several sentences (5 by default)description=wanish.descriptionAvailable kwarg options forWanish()class (all are optional):wanish=Wanish(url=document_url,positive_keywords=["main","story"],negative_keywords=["banner","adv","similar","top-ad"],summary_sentences_qty=5,headers={'user-agent':'test-purposes/0.0.1'})url:Allows to pass an url of a document in constructor. If set,
then it will automatically launchself.perform_url(url)after
initialization. Default is None.positive_keywords:A list of positive search patterns in classes
and ids, for example:[“main”, “story”]. Default is None.negative_keywords:A list of negative search patterns in classes
and ids, for example:[“banner”, “adv”, “similar”, “top-ad”].
Default is None.summary_sentences_qty:Maximum quantity of sentences in
summarized text of the document. Set to 5 by default.headers:Dict of additional custom headers for GET request to
obtain web page of the article. Default is None.Special Thankshttps://github.com/nltk/nltkhttps://github.com/buriy/python-readabilityhttps://github.com/saffsd/langid.py
Package: wanna
wannaShell command launcher in natural language using ChatGPTsynopsisThewannacommand is a natural language shell command launcher using ChatGPT. It can generate, execute, name, and manage shell commands by natural language.It is very convenient because you can easily do many things on the command line. However, remembering many commands and option combinations can be difficult even for experienced programmers, and often requires a Google search or themancommand to remember and execute.For example, if you want to do a task like"recursively find all .py files under this directory and output the total number of lines in them ", it is difficult to remember how to combine commands likefind,xargs, andwcin a single shot. It is difficult to remember how to combine commands such asfind,xargs, andwcin one shot.wanna thinkThewanna thinkcommand is used in such cases.$wannathink"Recursively finds all .py files under this directory and prints the sum of their line counts"the AI assistant will automatically generate the following bash script:#! /usr/bin/env bashfind.-name"*.py"-execcat{}+|wc-lYou just look at it and decide to run it or not.$ ? What's next? (Use arrow keys)
   Do
   Save
   Another Question
   ExitIn response to your request, AI will generate a script and explain it.
If you chooseDo, it will actually run the proposed script and display the results.
Once you have run it and verified that it works, you can save it.$ ? I thought of the following names. which one do you like? (Use arrow keys)
   count_py_lines
   py_line_counter
   recursive_py_counter
   py_line_sumPhil Karlton once said, "The hardest part of computer science is naming. "
This AI also suggests just the right name for the scripts it generates. All you have to do is choose a name that sounds good as a code reviewer.wanna doThewanna docommand allows you to execute a previously saved command.
Simply typewanna doto incrementally select and execute a previously saved command.$wannado$ wanna do random_passwordsYou can also run the command by typing its name directly, as in: ## wanna listwanna listList recorded scripts.$ wanna list 
count_py_lines :recursively find all .py files under this directory and print the total number of lines in them
top_memory_usage :Output the top 10 processes with the highest memory usage.
random_passwords :Generate 10 random passwords.With this, you can, for example, do this.$ wanna list | peco | xargs wanna doInstallationInstallation is easy using pip, and works with python 3.10 or higher.pipinstallwannaAlso, you will need to get a token with your own account to use OEPNAI_API_KEY.exportOPENAI_API_KEY="sk-xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
Package: wanna-ml
WANNA-MLComplete MLOps framework for Vertex-AIAbout WANNA-MLWANNA-ML is a CLI tool that helps researchers, data scientists, and ML Engineers quickly adapt to Google Cloud Platform (GCP) and get started on the cloud in almost no time.It makes it easy to start a Jupyter notebook, run training jobs and pipelines, build a Docker container, export logs to Tensorboards, and much more.We build on top of Vertex-AI managed services and integrate with other GCP services like Cloud Build and Artifact Registry to provide you with a standardized structure for managing ML assets on GCP.HelpSee thedocumentationfor more details.Get startedInstallationInstall usingpip install -U wanna-ml.For more information on the installation process and requirements, visit outinstallation page in documentationUse Docker ImageFor your convenience, we have prepared a Docker image with everything you need to get started.dockerpullmichalmrazek9/wanna-ml

dockerrun-itmichalmrazek9/wanna-ml

$wannaversionAuthenticationWANNA-ML relies ongcloudfor user authentication.Install thegcloudCLI - followofficial guideAuthenticate with thegcloud initSet you Google Application Credentialsgcloud auth application-default loginDocker BuildYou can use a local Docker daemon to build Docker images, but it is not required.
You are free to choose between local building on GCP Cloud Build.
If you prefer local Docker image building, installDocker Desktop.GCP IAM Roles and PermissionsDifferent WANNA-ML calls require different GCP permissions to create given resources on GCP. Ourdocumentation pagelists recommended GCP IAM roles for eachwannacommand.ExamplesJump tothe samplesto see a complete solution
for various use cases.IssuesPlease report issues toGitHub.ContributingYour contributions are always welcome, seeCONTRIBUTING.mdfor more information.
If you like WANNA-ML, don't forget to give our project a star!LicenceDistributed under the MIT License - seeLICENSE.
Package: wannier19
___       __                        _____                      _____________ 
__ |     / /______ ________ _______ ___(_)_____ ________       __<  /__  __ \
__ | /| / / _  __ `/__  __ \__  __ \__  / _  _ \__  ___/       __  / _  /_/ /
__ |/ |/ /  / /_/ / _  / / /_  / / /_  /  /  __/_  /           _  /  _\__, / 
____/|__/   \__,_/  /_/ /_/ /_/ /_/ /_/   \___/ /_/            /_/   /____/  

                  __                     ___                  
        |_       (_  |_  _  _   _   _     |   _ .  _ |  .  _  
        |_) \/   __) |_ (- |_) (_| | )    |  _) | |  |( | | ) 
            /              |I started this project by python realization of some of postw90 functional.
Some parts of the code are an adapted translation of Wannier90 Fortran code:http://www.wannier.org/https://github.com/wannier-developers/wannier90The code is distributed under the terms of  GNU GENERAL PUBLIC LICENSE  Version 2, the same as Wannier90At the moment the code calculates Anomalous Hall conductivity very fast (much faster then wannier90) with high precision over an
ultradense k-grid. This is achieved due to :Using Fast Fourier Transformaccount of symmetries, to reduce integration to irreducible part of the Brillouin zonerecursive refinement algorithmoptimizing the implementation of scan of Fermi level and 'use_ws_distance' parameter (see wannier90 documentation for details)Object oriented structure also makes it potentially easier to implement further features.The usage of the code  is demonstrated by "exampleFe" in the examples folder.
AHC can also be calcualted for any tight-binding model, for which a "_tb.dat" file was generated in watever way.wannier19 can be run in parallel by means of multiprocessing moduleinstllation is possible by pippip install wannier19A simple exmple is contained in "tutorial/example.py" . That will produce AHC and tabulate the berry curvature and velocity of bcc iron
more sofisticated features will be described soon.The project started on June, 25th 2019.
Any interest from the community will be motivating for developing.Stepan Tsirkin,
University of Zurichstepan.tsirkin@physik.uzh.ch
Package: wannier90io
Wannier90 I/O with PythonA Python library for reading and writingWannier90files.Quickstartimportpprintimportwannier90ioasw90iopp=pprint.PrettyPrinter()withopen(win_fpath,'r')asfh:parsed_win=w90io.parse_win_raw(fh.read())withopen(nnkp_fpath,'r')asfh:parsed_nnkp=w90io.parse_nnkp_raw(fh.read())pp.pprint(parsed_win)pp.pprint(parsed_nnkp)
Package: wannierberri
=========
Wannier Berri.. image::https://codecov.io/gh/wannier-berri/wannier-berri/branch/master/graph/badge.svg?token=S7CH32VXRP:target:https://codecov.io/gh/wannier-berri/wannier-berriA code for highly efficient Wannier interpolation.Evaluation of k-space integrals of Berry curvature, orbital moment and derived quantities by means of MLWFs or tight-binding models.  Compared to postw90.x part of Wannier90 code, it has extended functional and improved performanceWeb pagehttp://wannier-berri.orgMailing list:To subscribe please send an email tosympa@physik.lists.uzh.chwith the subjectsubscribe wannier-berri Firstname Lastnameor visit the list homepagehttps://physik.lists.uzh.ch/sympa/info/wannier-berriThis code is intended for highly-efficient wannier interpolation.
Being initially an analog of postw90.x part of Wannier90 code, it has extended functional and improved performance.Improved performance and accuracy:Wannier-Berri calculates Brillouin zone integrals very fast with high precision over an
ultradense k-grid. This is achieved due to :Using Fast Fourier Transformaccount of symmetries, to reduce integration to irreducible part of the Brillouin zonerecursive adaptive refinement algorithmoptimized Fermi level scanoptimized minimal distanse replica method (use_ws_distance)Implemented functionality:Anomalous Hall conductivityOrbital magnetization (modern theory)Ohmic conductivityBerry curvature dipolegyrotropic magnetoelectric effectHall effectLow-Field Hall effectOther features:Object oriented structure also makes it potentially easier to implement further features.Calculations may also be performed for any tight-binding model, for which a "_tb.dat" file was generated in watever way.WannierBerri can run in parallel by means of multiprocessing moduleInstallationpip3 install wannierberriAuthorStepan Tsirkin,
University of ZurichLicenseThe code is distributed under the terms of  GNU GENERAL PUBLIC LICENSE  Version 2AcknowledgementsThe code was inspired by the Wannier90 Fortran code:http://www.wannier.org/,https://github.com/wannier-developers/wannier90.
Some parts of the code are an adapted translation of postw90 code.I acknowledge Ivo Souza for a useful discussion.
Package: want
Make sure you are using python3 on Linux or OSX.
If not, it might not works.$ pip3 install want

$ want [-a | --all] http://www.catfootwear.com.tw/

# Get images if size between 100~2000 bytes.
$ want [-s | --size=] 100-2000 http://www.catfootwear.com.tw/

# Default size filter is 10000~200000 bytes.
$ want http://www.catfootwear.com.tw/

$ want -r urls.txt
Package: wantstoparty
wantstopartyAn API wrapper for thewants-to.partyAPI.Aboutwantstopartyis a feature-rich API wrapper for thewants-to.partyAPI which provides full API coverage, supporting blocking/non-blocking HTTP methods and much more.Notable features:Blocking (sync) and non-blocking (async) support.blocking examplenon-blocking exampleUpload files by providing a local file path. (I.E, file stored on disk) or bytes-like file object. (see examples above)Get all files hosted on your wants-to.party account.Get account information associated with the given API key.Delete files by their given file ID.Set a maximum uploadable file size limit.Prevents files that exceed your specified limit being uploaded.see exampleValidation for unsupported file types.User-friendly errors for handling status codes of failed HTTP requests.Created by acatia#5378Installation & SetupInstall the package:pip install wantstopartyCreate awants-to.partyaccount.On your dashboard, click the "View api key" button:Import theWantsToPartyclass:fromwantstopartyimportWantsToParty# For async use, refer to the examples.wtp=WantsToParty("api key from dashboard")Join the Motion Development server for extra help,here.ExamplesSome examples can be found in theexamples/folder.ContributingContributions are welcome! Check out thecontributing guidelinesbeforehand.LicenseThis is licensed under MIT. Read the licensehere.
Package: wap
wapLibrary to parseWappalyzertechnologies.jsonand use its rules to discover web technologies by looking in the HTTP responses.Functionality is similar to thewappalyzer core module, but in python.InstallationFrom pypi:pip3installwapFrom repo:gitclonehttps://github.com/blackarrowsec/wapcdwap/
python3setup.pyinstallExampleHere is a little example that uses almost all functionalities of wap with regex to retrieve the technologies of github:importrequestsimportwaptechnologies,categories=wap.load_file("technologies.json")resp=requests.get("https://www.github.com")techno_matches=wap.discover_requests_technologies(technologies,resp)fortintechno_matches:fields=[t.technology.name]fields.append(t.version)fields.append(str(t.confidence))fields.append(",".join([c.nameforcint.technology.categories]))print(" ".join(fields))More examples in examples folder.DocumentationDocumentation can be found inhttps://wap.readthedocs.io.Adding new technologiesIf you want that wap detects a new technology, you can add your rules to thetechnologies.jsonfile and load it with wap.Please, consider to do a pull request toWappalyzer repoand share your rules with the community. Follow the rules inAdding a new technology.Please do not submit pull requests related to technologies.json, since this repository is
not related with Wappalyzer.AuthorEloy Pérez (@Zer1t0) [www.blackarrow.net-www.tarlogic.com]LicenseAll the code included in this project is licensed under the terms of the GNU LGPLv3 license.
Package: waper
WaperA rossby Wave Packet trackER
.. start-badgesCode:https://github.com/JoyMonteiro/waperDocs:https://waper.readthedocs.io/en/master/PyPI:https://pypi.org/project/waper/CI:https://github.com/JoyMonteiro/waper/actions/Featureswaperpython packageTODO Document aGreat FeatureTODO Document anotherNice FeatureTested against multipleplatformsandpythonversionsDevelopmentHere are some useful notes related to doing development on this project.Test Suite, usingpytest, located intestsdirParallel Executionof Unit Tests, on multiple cpu’sDocumentation Pages, hosted onreadthedocsserver, located indocsdirAutomation, usingtox, driven by singletox.inifileCode CoveragemeasuringBuild Command, using thebuildpython packagePypi Deploy Command, supporting upload to bothpypi.organdtest.pypi.orgserversType Check Command, usingmypyLintCheckandApplycommands, usingisortandblackCI Pipeline, running onGithub Actions, defined in.github/Job Matrix, spanning differentplatform’s andpython version’sPlatforms:ubuntu-latest,macos-latestPython Interpreters:3.6,3.7,3.8,3.9,3.10Parallel Jobexecution, generated from thematrix, that runs theTest SuitePrerequisitesYou need to havePythoninstalled.QuickstartUsingpipis the approved way for installingwaper.python3-mpipinstallwaperTODO Document a use caseLicenseBSD 3-Clause LicenseLicenseFree software: BSD 3-Clause License
Package: wapiti
Wapiti~~~~~~Wapiti is a Wikipedia API client focused on providing a consistentand performant abstraction around the widely varying Mediawiki APIendpoints and data models. Read-only APIs are first priority, butwrite operations are on the way. See `the Github project<https://github.com/mahmoud/wapiti>`_ for more info.:copyright: (c) 2013 by Mahmoud Hashemi and Stephen LaPorte:license: BSD, see LICENSE for more details.
Package: wapor
waporWAPORFree software: GPLv3 licenseDocumentation:https://wapor.readthedocs.io.FeaturesCalculate the following products:AETIAGBPGBWPNBWPInstallUsing pipAssuming you have already created a python virtual environment, activate it and
then install the tool:pip install wapor --process-dependency-linksUsing pipenvChoose a directory where you want to spawne the python virtual environment and
install the tool with pipenv, then from there:pipenv shell --twoYou will be led to a new shell where you can execute:pipenv install waporAny warning can be safely ignored and finally you can exit from this shell:exitThe tool is ready to be configured for the authentication with your
Google Earth Engine service account.UsagePipIn case you have installed it systemwide you don’t need to activate any
virtual environment otherwise you have to as preliminary step:wapor --helpPipenvIf you are using pipenv the only requirement to run the wapor command is
to go to the directory where you have executed the installation steps and run:pipenv run wapor --helpCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.2.0 (2018-11-07)First release on PyPI.0.2.2 (2018-11-08)Update readme.0.2.3 (2018-12-18)Fix bug on AETI dekadal
Package: wappalyze
framework-identifierThis repository is the python implementation of the repo(https://github.com/AliasIO/Wappalyzer).SetupRun:pip install -r requirements.txtRunning the scriptTo run the script, the following command is to be used:python wappalyze.pyInstall from pippip install wappalyzeUsagewapp -u google.comUse as a moduleimport wappalyzeresult = wappalyze.analyze('google.com')print(result)
Package: wappdriver
wappdriverAPI for WhatsApp Web AutomationWondering how to send WhatsApp messages using Python using only three lines of code?You have come to the right place!Usage 😎wappdriver is a python package that helps you automate sending messages through WhatsApp Web.It enables you to send WhatsApp messages programmatically, using only three lines of code.It is very simple to usefromwappdriverimportWhatsAppwithWhatsApp()asbot:bot.send('aahnik',# name of recipient'hi send by a bot')# message# The name of the recipient should be in your contactsReadfull documentation 📖to learn how to send emojis, media and files.Installation ⏬You can easily install wappdriver from thePyPI.pipinstallwappdriverFor Mac and Linux, you may need to usepip3.Requirements 🧑‍💻You must have the following installed in your system for wappdriver to work.Chrome BrowserChrome DriverMake sure to have matching versions of the Chrome Browser and Chrome Driver.
I recommend using the latest stable release for both to get the best performance.WappDriver does not support other Browsers. Please use Chrome for a smooth experience.Setup ⚙️After you have installed wappdriver and the requirements stated above, you can start using wappdriver straight away.When you will run your code for the first time, you will be asked for Chrome Driver Path.
When the WhatsApp web screen loads for the first time, you have to scan the QR code from you smartphone's WhatsApp application, to log into your WhatsApp account.Learn more.Getting Help 💁🏻First of all, read theHelp Page.
You may try to search your question in the search bar on the page.For further assistance you mayask a questionthe issues section.Contributing 🤩Please look atCode of ConductandContributing Guidelines.Please read the explanation of the detailed working ofwappdriverfrom theDeveloper's Guide.Legal ⚖️This project is distributed underMIT License.This code is in no way affiliated with, authorized, maintained, sponsored, or endorsed by WhatsApp or any of its affiliates or subsidiaries. This is an independent and unofficial software.
Package: wapps
WApps![CircleCI](https://circleci.com/gh/apihackers/workflows/wapps)![codecov](https://codecov.io/gh/apihackers/wapps/branch/master)![Last version](https://pypi.python.org/pypi/wapps)![License](https://pypi.python.org/pypi/wapps)![Supported Python versions](https://pypi.python.org/pypi/wapps)A very optionated set of Wagtail reusable applications and helpers
meant to speedup website development. There is not any universality intent.stackIt assumes the following stacks:Server-side stack:Python 3Latest Wagtail and Django versionsDjango-Jinja for template renderingDjango-Babel for localizationDjango-Appconf for default settingsFrontend StackVue 2 for front componentsWebpack 3 as front build toolchainSCSS as style languageBootstrap and Font-awesome as base frameworksRequirementsWapps is designed to work with Python 3, Django-jinja, Django-babel and latest Django and Wagtail versions.Wapps also provides JS/Vue2 helpers and scss mixins and classesInstallationPython installationInstall it with pip:$pipinstallwappsthen add the required bases apps to your settings (ie.settings.py):INSTALLED_APPS=['...','wapps','memoize']Node modules installationInstall it withnpmoryarn$npminstallwapps@<wapps-version>ChangelogCurrentInitial release
Package: wappstore
wApp - Web App StoreA web app store to mprove web app discoverability
Hosted onfree tier Azure(Might be slow)🏃 Getting StartedRunning the projectIt is recommended to just use the Devcontainer in this repository. It automatically installs all dependencies like Poetry and Node.
Therfore only an editor supporting Devcontainers like VS Code and Docker is required. Alternatively you can use GitHub Codespaces and you only need an account that has free Codespace access (or money).🤖 Used Tech or "Tech Stack"Things I used or got inspired fromFastAPIand their great in depth docsTailwindUI Componentsto develop UI faster with the help of their hight quality components. The components were adapted and changed to my needs for the project as it is intended to be used. It costs money but I used and will use their Tailwindcss and TailwindUI heavily before and will continue using it as it saves me a lot of time which makes it worth to me.TailwindCSSDockerW3C Web Manifesdt documentationMDN documentationDevContainerAzure HostingPoetryPublish PyPiJinja2GitHub Actions✨ Random Idea List ✨Things I might want to add or try outUse picture element with source elements representing the manifest icons to let browser decide which one to load to improve speedCharge money for premium apps. This would allow for premium Web apps which are currently hard to implement as developers would need their own billing and user authZ/authN. Premium apps can then upon opening verify through OpenID Connect with our System that the User signed in to the appstore has bought the app and redirect seemlessly to the app. This could also be an Idea to offer an OpenID Connect SaaS through a different angle than other offerings currently available. Current offerings require to connect it with and app but we could offer the app with the auth system all in one.🐛 Known IssuesWhen starting the container VS Code might complain that the Python Extension has not loaded or needs to be installed. Just follow VS Code recommendations and you should be fine.