Saravanan666/pypi-org huggingface dataset:
Package: wav-loopy
A simple WAV audio file looper: read file -> write file looped by X.The following formats are allowed for X (using input sample size as basis for time):Number “1.2”: output size isX * input sizeTime in seconds “20.0s”: output size is exactlyXsecondsAdition time in seconds “+10.0s”: output size isinput size + Xsecondswav-loopyalso may fade out the last seconds of a track.Usagewav-loopy [-f FADE] [-o OUTPUT] INPUT X
wav-loopy (-h | --help | --version)InstallingUsingpip:$ pip install wav-loopyOr from the source code:$ python setup.py install
Package: wavpack-numcodecs
WavPack - numcodecs implementationNumcodecswrapper to theWavPackaudio codec.This implementation enables one to use WavPack as a compressor inZarr.InstallationInstall viapip:pip install wavpack-numcodecsOr from sources:git clone https://github.com/AllenNeuralDynamics/wavpack-numcodecs.git
cd wavpack_numcodecs
pip install .UsageThis is a simple example on how to use theWavPackCodecwithzarr:from wavpack_numcodecs import WavPack

data = ... # any numpy array

# instantiate WavPack compressor
wv_compressor = WavPack(level=2, bps=None)

z = zarr.array(data, compressor=wv_compressor)

data_read = z[:]Available**kwargscan be browsed with:WavPack?NOTE:In order to reload in zarr an array saved with theWavPack, you just need to have thewavpack_numcodecspackage
installed.
Package: wavplot
WAVPLOTTired of waveform plots that blow up your paper?Here comes a matplotlib wavform plot that gives soundcloud like plots in just a
few lines of code!FeaturesFreely selectable width, height, DPI and number of bars to plotWrites transparent pngsInstallationpip install wavplotUsagewavplot violin.wav violin.pngfor additional parameters please inspect the help usingwavplot -h
Package: wavpy
pywaveWant to modify or play yourWAVfiles, but cannot find suitable tools to do so? Well,pywaveis here to save the day.It can read a normal, uncompressed WAV file into a temporary file, so memory usage is low. Once this process is complete,
you can do lots with the WAV data:Get metadata (sample rate, bit depth, channel count etc.)Change audio playback speedChange audio sample rate (without changing speed)Change bit depthGet one channel of audioDecrease audio volumeReverse audioCrop audioAdd silence (start/middle/end)Repeat audioJoin audioSplit audioAccelerate/decelerate audio (start at one speed, end at another)Play audioAny modified WAV data can subsequently be saved to a file, and fingers crossed, your desired changes will be successful!DependenciesAmazingly,pywaveonly has one dependency -pyaudio, and that is to play the audio. Even if you do not have this dependency
installed, you can still use other parts of this package, particularly the modification of WAV data (you can just save and play
somewhere else, such as Audacity).CompatibilityPython 3.9 or greater is supported.ExamplesGetting started is super simple! Get a WAV file you would like to modify or experiment on.
Here are some examples of code which can build a solid base for your utilisation of this package.Speeding up audio (without changing sample rate)from pywave import wavfile

audio = wavfile.read("audio.wav")
double_speed_audio = audio.change_speed(2, "count")
wavfile.write(double_speed_audio, "audiox2.wav")Ensuring WAV files have a maximum bit depth of 16from pywave import wavfile

wav_files = ["1.wav", "2.wav", "3.wav"]
for file in wav_files:
    wav = wavfile.read(file)
    if wav.info.bit_depth > 16:
        wav = wav.change_bit_depth(16)
        wavfile.write(wav, file)Getting the second channel of audio of a stereo WAV and playing it 3 times.from pywave import wavfile
from pywave import wavplay

wav = wavfile.read("audio.wav")
channel_2 = wav.to_mono(2)

wavplay.play(channel_2, 3, True)Make the second half of a WAV quieter.from pywave import wavfile
from pywave import wavmanip

wav = wavfile.read("audio.wav")
halves = wav.split(2, "count")
halves[1] = halves[1].decrease_volume(10, "decibels")

edited_wav = wavmanip.join(
    halves, wav.info.sample_rate,
    wav.info.bit_depth, wav.info.channels)
wavfile.write(edited_wav, "new.wav")
Package: wavy
IntroductionA pure python module for working with WAVE files with support for all common file formats for both RIFF and RIFX.When working with WAVE files, there are two main pure python modules available:builtin.wavePython built-in module, lacks support for float and 24bit integer.
Provides raw data instead of an array of values.scipy.waveScipy does not support 24bit integer files. The module strength and weakness is its simplicity,
if all you need to do is read and write, this might be for you.Thewavemodule provides a fully featured dedicated module that can be used as an alternative to the above if
flexibility and ease of use are desirable.ComparisonThe following table shows a comparison of supported functionality:Functionalitybuiltin.wavescipy.wavewavyRIFF Format SupportRIFX Format SupportRead Audio InformationRead Data As ArrayRead Tag InformationThe following table shows a comparison of supported formats for uncompressed WAVE files:Sample WidthFormat Tagbuiltin.wavescipy.wavewavy8 bitPCMEXTENSIBLE16 bitPCMEXTENSIBLE24 bitPCMEXTENSIBLE32 bitPCMEXTENSIBLEFLOAT64 bitFLOATInstallationThe latest stable version is available onPyPI.Either addwavyto yourrequirements.txtfile or install with pip:pipinstallwavyUsageRead FileOpen a file using the module usewavy.read:>>>importwavy>>>file=wavy.read("audio.wav")>>>fileWaveFile(sample_width=16,framerate=44100,n_channels=2,n_frames=286653)Get the data for the file:>>>rate,data=file.framerate,file.data>>>rate44100>>>data.shape(286653,2)>>>data.dtypeint16Get File InfoTo read the file information without loading the data usewavy.info:>>>wavy.info("audio.wav")WaveFileInfo(sample_width=16,framerate=44100,n_channels=2,n_frames=286653,tags=None)AuthorsAndrea Celletti-Initial work-Profile,EmailLicenseThis project is licensed under the MIT License - see theLICENSEfile for details.
Package: wavyfm
wavyfm-pythonThe Python client library forwavy.fm, officially maintained by Wavy Labs.DocumentationAll endpoints are documented in thewavy.fm developer docs. You can contribute to the docs
and suggest API changes in the correspondingGithub repository.Client library documentation coming soon!InstallationThis library is built with Python 3.8. You can install it from pypi:pipinstallwavyfmQuick StartTo get started, install thewavyfmpackage and create an app onhttps://wavy.fm/developers/apps. Add your client ID
and client secret to your environment variables:WAVYFM_CLIENT_ID='pub_XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'
WAVYFM_CLIENT_SECRET='priv_XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX'Without user authenticationimportwavyfm# Create the clientwavy=wavyfm.WavyClient(auth_manager=wavyfm.WavyClientCredentials())# Get the total listens recorded on wavy.fmprint(wavy.metrics.get_total_listens())# Get the total amount of registered users on wavy.fmprint(wavy.metrics.get_total_users())# Get the top 10 registered users on wavy.fm by listen countprint(wavy.metrics.get_user_listens_leaderboard())# Get a user's public profileprint(wavy.users.by_username("Aram").get_profile())# Get a user's total listensprint(wavy.users.by_username("Aram").get_total_listens())# Get a user's total artistsprint(wavy.users.by_username("Aram").get_total_artists())# Get a user's currently listening trackprint(wavy.users.by_username("Aram").get_currently_listening())# Get a user's last 10 recorded listensprint(wavy.users.by_username("Aram").get_recent_listens(10))LicenseThis project is licenced under the MIT License.The overall structure is heavily inspired fromSpotipy, a client library for
Spotify.
Package: wavyts
≈ Wavy: Time-Series Manipulation ≈Wavy is a time-series manipulation library designed to simplify the pre-processing steps and reliably avoid the problem of data leakage. Its main structure is built on top of Pandas.Explore the docs 📖📦 InstallationYou can install Wavy from pip:pipinstallwavyts🚀 Quickstartimportnumpyasnpimportpandasaspdimportwavyfromwavyimportmodels# Start with any time-series dataframe:df=pd.DataFrame({'price':np.random.randn(1000)},index=range(1000))# Create panels. Each panel is a frame collection.x,y=wavy.create_panels(df,lookback=3,horizon=1)# x and y contain the past and corresponding future data.# lookback and horizon are the number of timesteps.print("Lookback:",x.num_timesteps)print("Horizon:",y.num_timesteps)# Set train-val-test split. Defaults to 0.7, 0.2 and 0.1, respectively.wavy.set_training_split(x,y)# Instantiate a model:model=models.LinearRegression(x,y)model.score()Features💡 Wavyis:Aresourceful,high-levelpackage with tools for time-series processing, visualization, and modeling.A facilitator fortime-series windowingthat helps reduce boilerplate code and avoid shape confusion.❗ Wavyis not:An efficient, performance-first framework (yet!).Primarily focused on models. Processed data can be easily converted to numpy arrays for further exploration.ContributingContributions are what make the open source community such an amazing place to learn, inspire, and create. Any contributions you make towavyaregreatly appreciated.If you have a suggestion that would make this better, please fork the repo and create a pull request. You can also simply open an issue with the tag "enhancement".
Don't forget to give the project a star! ⭐Fork the ProjectCreate your Feature Branch (git checkout -b feature/AmazingFeature)Commit your Changes (git commit -m 'Add some AmazingFeature')Push to the Branch (git push origin feature/AmazingFeature)Open a Pull RequestLicenseDistributed under the MIT License. SeeLICENSE.txtfor more information.
Package: wawa
=======================================`WaWA (WorkAround Web API for Python)`_=======================================Copyright (c) 2016 Jeremie DECOCK (http://www.jdhp.org)* Web site: http://www.jdhp.org/projects_en.html#wawa* Source code: https://github.com/jeremiedecock/wawa* Issue tracker: https://github.com/jeremiedecock/wawa/issues* WaWA on PyPI: https://pypi.python.org/pypi/wawaDescription===========WaWA (WorkAround Web API for Python) is an open source lightweight collectionof unofficial web APIs written for Python.Note:This project is still in beta stage, so the API is not finalized yet.Dependencies============-  Python >= 3.0-  Beautifulsoup-  Feedparser.. _install:Installation============Gnu/Linux---------You can install, upgrade, uninstall WaWA with these commands (in aterminal)::pip install --pre wawapip install --upgrade wawapip uninstall wawaOr, if you have downloaded the WaWA source code::python3 setup.py install.. There's also a package for Debian/Ubuntu::....     sudo apt-get install wawaWindows-------Note:The following installation procedure has been tested to work with Python3.4 under Windows 7.It should also work with recent Windows systems.You can install, upgrade, uninstall WaWA with these commands (in a`command prompt`_)::py -m pip install --pre wawapy -m pip install --upgrade wawapy -m pip uninstall wawaOr, if you have downloaded the WaWA source code::py setup.py installMacOSX-------Note:The following installation procedure has been tested to work with Python3.4 under MacOSX 10.6 (*Snow Leopard*).It should also work with recent MacOSX systems.You can install, upgrade, uninstall WaWA with these commands (in aterminal)::pip install --pre wawapip install --upgrade wawapip uninstall wawaOr, if you have downloaded the WaWA source code::python3 setup.py installBug reports===========To search for bugs or report them, please use the WaWA Bug Tracker at:https://github.com/jeremiedecock/wawa/issuesLicense=======This project is provided under the terms and conditions of the`MIT License`_... _MIT License: http://opensource.org/licenses/MIT.. _WaWA (WorkAround Web API for Python): http://www.jdhp.org/projects_en.html#wawa
Package: wawpybus
# WawPybus### A package providing tools to collect, process and analyse Warsaw Open Bus Data##### A final project for Database and network design and administration classes## InstallationPackage is available at PyPi and can be downloaded using pip:
``
pip install wawpybus
``PyPi project:https://pypi.org/project/wawpybus/Source code:https://gitlab.uw.edu.pl/ig386329/pybus## Usage
Package is designed to collect, process and analyse Warsaw open bus data. It is easy to use and handy.
It is modular and highly extendable
* It enables downloading bus position withing given time range and frequency
* Compares bus positions at given time with stops coordinates
* Checks buses punctuality by downloading timetable for bus line at stop and compare arrival times between actual and timetable
* Checks if buses exceeded speed between consecutive positions
* Calculates mean difference in time between actual bus arrival and timetable
* Finds bus which arrival time differs the most from timetable
* Enables to check which buses exceeded given speed thresholdTutorial is included in package in a form of Jupyter Notebook:WawPybus_tutorial.ipynb## Comments on Warsaw Open Data
Data available athttps://api.um.warszawa.pl/# should be improved. It is inconsistent and contains errors.
For example once stop number is named in english once in polish as ‘slupek’, some stops coordinates are null,
json returned from api has wrong structure - each key has name ‘key’ and respects to actual key name,
the same with values.
Package: wax
Wax is a GUI toolkit that sits atop wxPython (wxWindows for Python). wxWindows
is a mature cross-platform GUI toolkit. The main feature of wxWindows is that
it uses 100% native widgets on the platform that it runs on, so it’s quite fast.
It also runs on many platforms (Win32, Linux w/ GTK, and MacOS/OSX w/ Carbon).Wax removes the low-level aspects of wxPython (which is basically a direct
binding to the ugly C API) and gives you simple python objects to create your
GUI.This package is quite usable, but it’s still in the early stages, which means
that some wxWindows widgets aren’t available.
Package: waxnftdispatcher
waxNFTdispatcherThis library will help you to transfer or to mint NFTs on the WAX blockchain. It relies on the library
pyntelope for blockchain interaction and on the library loguru for beautiful logs.By default, theEOSUSAWAX API is used to post transactions to blockchain.
It can be changed while creating an object.
Due to some limitations only EOSUSA API can be used to get transaction info from blockchain.Installationpoetry add waxNFTdispatcherorpip install waxNFTdispatcherUsagefromwaxNFTdispatcherimportAssetSenderimportosprivate_key=os.environ["PRIVATE_KEY"]collection_wallet="mywallet.wam"collection="pixeltycoons"recipient="recipient.wam"INPUT=(("rawmaterials","318738"),("magmaterials","416529"))# Create objectassetsender=AssetSender(collection,collection_wallet,private_key)# Try to find assets in the collection wallet to send them.# If not all needed assets were in the collection wallet, the script will mint the rest.assetsender.send_or_mint_assets(INPUT,recipient)# Send assets with given asset ID to the given walletassetsender.send_assets(("1099543811405","1099543811406"),recipient)# Mint given number of same assetsassetsender.mint_assets("rawmaterials","318738","recipient.wam",5)# Mint given number of same assets and then try to fetch their IDsassetsender.mint_assets_and_get_ids("rawmaterials","318738","recipient.wam",5)The methods return tuple or list of tuples where on the first place is the asset ID or id-schema-template tuple, and
on the second place either hash of successful transaction or 'False' if transaction failed for some reason. For example:[(('1099511811820', 'rawmaterials', '318738'), False),
(('1099511811819',), '6b80b145aa261736941583ed17802a8be0254cd21a78b6bb415c923ec64ad32c')]ContributionContribution is highly welcome. Please send your pull requests or create issues with found bugs and suggestions.
In your pull requests please use Black formatting.DonationIf you feel like you can send any amount of WAX to the WAX address 24.gm
Package: way2package
mypackageA testing package forDigital Kitesprocesses.InstallationMake sure you havepythonandpipinstalled.pipinstallmypackageUsageimportmypackageobj={app_ip:"x.x.x.x",//optionalapp_name:"your pm2 app name",//optionalapp_id:1,//pm2processid(optional)date:Math.floor(newDate().setHours(0,0,0,0)/1000),//yourdateepochinsecondstime:Math.floor(Date.now()/1000),//yourtimeepochinsecondsmessage:"some message",//yourmessagestatus:"online",log_level:"info"//["fatal","error","warn","info","debug"]}mypackage.printMessage(obj)How to include in your process://Note:Mustincludethisinthefirstlineofyourfile(whichisstartedwithpm2).
//Note:Makesurethissnippetrunsinproductionenvirnoment.
constdk_logger=require('dk-logger');dk_logger.setApiKey('YOUR API KEY');});
Package: way2package2
mypackageA testing package forDigital Kitesprocesses.InstallationMake sure you havepythonandpipinstalled.pipinstallmypackageUsageimportmypackageobj={app_ip:"x.x.x.x",//optionalapp_name:"your pm2 app name",//optionalapp_id:1,//pm2processid(optional)date:Math.floor(newDate().setHours(0,0,0,0)/1000),//yourdateepochinsecondstime:Math.floor(Date.now()/1000),//yourtimeepochinsecondsmessage:"some message",//yourmessagestatus:"online",log_level:"info"//["fatal","error","warn","info","debug"]}mypackage.printMessage(obj)How to include in your process://Note:Mustincludethisinthefirstlineofyourfile(whichisstartedwithpm2).
//Note:Makesurethissnippetrunsinproductionenvirnoment.
constdk_logger=require('dk-logger');dk_logger.setApiKey('YOUR API KEY');});
Package: way2package3
mypackageA testing package forDigital Kitesprocesses.InstallationMake sure you havepythonandpipinstalled.pipinstallmypackageUsageimportmypackageobj={app_ip:"x.x.x.x",//optionalapp_name:"your pm2 app name",//optionalapp_id:1,//pm2processid(optional)date:Math.floor(newDate().setHours(0,0,0,0)/1000),//yourdateepochinsecondstime:Math.floor(Date.now()/1000),//yourtimeepochinsecondsmessage:"some message",//yourmessagestatus:"online",log_level:"info"//["fatal","error","warn","info","debug"]}mypackage.printMessage(obj)How to include in your process://Note:Mustincludethisinthefirstlineofyourfile(whichisstartedwithpm2).
//Note:Makesurethissnippetrunsinproductionenvirnoment.
constdk_logger=require('dk-logger');dk_logger.setApiKey('YOUR API KEY');});
Package: way2smswrapper
way2smswrapperA Python Messaging Module Using Way2SMS & Selenium Module.Created By $implic@Install$pipinstallway2smswrapperRequirements :-Python3.
           Firefox Browser. 
           Geckodriver. 
           Xvfb - Installation. 
           Way2SMS Account.The python module uses selenium module to control the firefox browser which is being controlled by geckodriver.
The module is using xvfb(x-virtual frame buffer) and the xvfb wrapper for python3 to run the firefox browser
headlessly(in the background). Way2SMS Account is being used to send the message to the desired mobile number.One can import the module in his/her own python3 program to send the status of the current running program to
the user's mobile phone number through sms using the user's Way2SMS Account.Usage   :-importw2swrapperMethods :wrapper.login()wrapper.sms('Message')Tested In Ubuntu-17.04
Package: wayanjupyters3
Jupyter S3Jupyter Notebook Contents Manager for AWS S3.Installationpip install wayanjupyters3ConfigurationTo configure Jupyter Notebook to use JupyterS3, you can add the following to your notebook config file.fromwayanjupyters3importJupyterS3,JupyterS3SecretAccessKeyAuthenticationc=get_config()c.NotebookApp.contents_manager_class=JupyterS3andmustalso set the following settings onc.JupyterS3in your config file.SettingDescriptionExampleaws_regionThe AWS region in which the bucket is located'eu-west-1'aws_s3_bucketThe name of the S3 bucket.'my-example-bucket'aws_s3_hostThe hostname of the AWS S3 API. Typically, this is of the forms3.<aws_region>.amazonaws.com.'s3.eu-west-1.amazonaws.com'prefixThe prefix to all keys used to store notebooks and checkpoints. This can be the empty string''. If non-empty, typically this would end in a forward slash/.'some-prefix/'You must also, either, authenticate using a secret key, in which case you must have the following configurationfromwayanjupyters3importJupyterS3SecretAccessKeyAuthenticationc.JupyterS3.authentication_class=JupyterS3SecretAccessKeyAuthenticationandthe following settings onc.JupyterS3SecretAccessKeyAuthenticationSettingDescriptionExampleaws_access_key_idThe ID of the AWS access key used to sign the requests to the AWS S3 API.ommittedaws_secret_access_keyThe secret part of the AWS access key used to sign the requests to the AWS S3 API.ommittedorauthenticate using a role in an ECS container, in which case you must have the following configurationfromwayanjupyters3importJupyterS3ECSRoleAuthenticationc.JupyterS3.authentication_class=JupyterS3ECSRoleAuthenticationorauthenticate using a role in an EC2 container, in which case you must have the following configurationfromwayanjupyters3importJupyterS3EC2RoleAuthenticationc.JupyterS3.authentication_class=JupyterS3EC2RoleAuthenticationandthe following settings onc.JupyterS3EC2RoleAuthenticationSettingDescriptionExamplerole_nameThe Roles EC2 used to sign the requests to the AWS S3 API.ommittedDifferences from S3ContentsThere are no extra dependencies over those already required for Jupyter Notebook. Specifically, there is no virtual filesystem library such as S3FS used, boto3 is not used, and Tornado is used as the HTTP client.Checkpoints are also saved to S3, under the key<file_name>/.checkpoints/.Multiple checkpoints are saved.The event loop is mostly not blocked during requests to S3. There are some exceptions due to Jupyter Notebook expecting certain requests to block.Uploading arbitrary files, such as JPEGs, and viewing them in Jupyter or downloading them, works.Copying and renaming files don't download or re-upload object data from or to S3. "PUT Object - Copy" is used instead.Folders are created using a 0 byte object with key suffix/(forward slash). A single forward slash suffix is consistent with both the AWS Console and AWS AppStream.
Package: wayback
Waybackis A Python API to theInternet Archive’s Wayback Machine. It gives you tools to search for and load mementos (historical copies of web pages).The Internet Archive maintains an official“internetarchive”Python package, but it does not focus on the Wayback Machine. Instead, it is mainly concerned with the APIs and tools that manage the Internet Archive as a whole: managing items and collections. These are how e-books, audio recordings, movies, and other content in the Internet Archive are managed. It doesn’t, however, provide particularly good tools for finding or loading historical captures of specific URLs (i.e. the part of the Internet Archive called the “Wayback Machine”). That’s what this package does.Documentation:Current Release:https://wayback.readthedocs.io/en/stable/Development:https://wayback.readthedocs.io/en/latest/Installation & Basic UsageInstall via pip on the command line:$ pip install waybackThen, in a Python script, import it and create a client:importwaybackclient=wayback.WaybackClient()Finally, search for all the mementos ofnasa.govbefore 1999 and download them:forrecordinclient.search('http://nasa.gov',to_date=date(1999,1,1)):memento=client.get_memento(record)Read thefull documentationfor a more in-depth tutorial and complete API reference documentation athttps://wayback.readthedocs.io/Code of ConductThis repository falls under EDGI’sCode of Conduct. Please take a moment to review it before commenting on or creating issues and pull requests.ContributorsThanks to the following people for their contributions and help on this package! See ourcontributing guidelinesto find out how you can help.Dan Allan(Code, Tests, Documentation, Reviews)Rob Brackett(Code, Tests, Documentation, Reviews)David Gilman(Documentation)Will Sackfield(Code, Tests)Ed Summers(Code, Tests)Lion Szlagowski(Code, Tests)License & CopyrightCopyright (C) 2019-2023 Environmental Data and Governance Initiative (EDGI)This program is free software: you can redistribute it and/or modify it under the terms of the 3-Clause BSD License. See theLICENSEfile for details.