Saravanan666/pypi-org huggingface dataset:
Package: pyspng
Pyspngis a small library to for efficiently loading PNG files to numpy arrays.
Pyspng does not offer any image manipulation functionality.Pyspng was originally written to speed up loading uncompressed (PNG compression level 0),
making the PNG file format more suitable to be used in machine learning datasets.  Pyspng
uses the nativelibspnglibrary for fast PNG
decoding.  Synthetic benchmarks indicate pyspng to be roughly 2-3x faster in
loading uncompressed PNGs than the Python Pillow library.Exampleimport numpy as np
import pyspng

with open('test.png', 'rb') as fin:
    nparr = pyspng.load(fin.read())Installationpip install pyspngNote: binary wheels are built for Linux and Windows.  MacOS may not work out of the box.Licensepyspng is provided under a BSD-style license that can be found in the LICENSE
file. By using, distributing, or contributing to this project, you agree to the
terms and conditions of this license.
Package: pyspng-seunglab
Note:This is a fork of pyspng which can be found here:https://github.com/nurpax/pyspng/. See below for a list of differences.Pyspngis a small library to for efficiently loading PNG files to numpy arrays.
Pyspng does not offer any image manipulation functionality.Pyspng was originally written to speed up loading uncompressed (PNG compression level 0),
making the PNG file format more suitable to be used in machine learning datasets.  Pyspng
uses the nativelibspnglibrary for fast PNG
decoding.  Synthetic benchmarks indicate pyspng to be roughly 2-3x faster in
loading uncompressed PNGs than the Python Pillow library.Scripting ExampleimportnumpyasnpimportpyspngfrompyspngimportProgressiveMode# DECODINGwithopen('test.png','rb')asfin:nparr=pyspng.load(fin.read())# ENCODINGbinary=pyspng.encode(nparr,# Options: NONE (0), PROGRESSIVE (1), INTERLACED (2)progressive=ProgressiveMode.PROGRESSIVE,compress_level=6)withopen('test.png','wb')asfout:fout.write(binary)CLI ExampleThere is a CLI included with this distribution.# turn a numpy file into a highly compressed progressive PNGpyspngexample.npy--level9--progressive# -> example.png# create a highly compressed progressive interlaced PNGpyspngexample.npy--level9--interlaced# -> example.png# convert a PNG into a numpy file example.npypyspng-dexample.png# read headerpyspng--headerexample.pngInstallationpipinstallpyspng-seunglabBinary wheels are built for Linux, MacOS, and Windows. This library is intended to be a drop-in replacement for pyspng, so simultaneous installations are not possible. If this is inconvinient, we can adjust this.Differences from pyspngCompiles on MacOSUpgrades spng to 0.7.2Fixes a bug for decoding grayscale with alphaAdds an encoder function (new in libspng)Replaces zlib with miniz-2.2.0 for simplicity.Adds CLI for compressing/decompressing npy files.Adds function for examining PNG headers.Licensepyspng and pyspng-seunglab are provided under a BSD-style license that can be found in the LICENSE file. By using, distributing, or contributing to this project, you agree to the terms and conditions of this license.
Package: pyspoa
pyspoaPython bindings tospoa.Installation$pipinstallpyspoaUsage>>>fromspoaimportpoa>>>>>>consensus,msa=poa(['AACTTATA','AACTTATG','AACTATA'])>>>consensus'AACTTATA'>>>msa['AACTTATA-','AACTTAT-G','AAC-TATA-']>>>print(os.linesep.join(msa))AACTTATA-AACTTAT-GAAC-TATA-Developer Quick Start$gitclone--recursivehttps://github.com/nanoporetech/pyspoa.git
$cdpyspoa
$python3-mvenvpyspoa
$sourcepyspoa/bin/activate(pyspoa)$makebuildLicence and Copyright(c) 2019 Oxford Nanopore Technologies Ltd.pyspoa is distributed under the terms of the MIT License.  If a copy of the License
was not distributed with this file, You can obtain one athttps://github.com/nanoporetech/pyspoaResearch ReleaseResearch releases are provided as technology demonstrators to provide early access to features or stimulate Community development of tools. Support for this software will be minimal and is only provided directly by the developers. Feature requests, improvements, and discussions are welcome and can be implemented by forking and pull requests. However much as we would like to rectify every issue and piece of feedback users may have, the developers may have limited resource for support of this software. Research releases may be unstable and subject to rapid iteration by Oxford Nanopore Technologies.
Package: pyspool
pyspoolpyspool is a reference implementation of the Secure Public Online Ownership
LedgerSPOOLand part of the development stack ofascribe.io.Installation$pipinstallpyspoolDocumentationhttps://pyspool.readthedocs.io/ContributingPull requests, feedback, and suggestions are welcome.Issuesandpull requestsare handled
via github.BackgroundThis was developed by ascribe GmbH as part of the overall ascribe technology
stack.https://www.ascribe.ioCopyrightThis code is © 2015 ascribe GmbH.Licensed under the Apache License, Version 2.0.
Package: pysportsref
pysportsrefUtils for scraping/working with sports reference data.Why is the difference between this package and other sports reference scraping packages?
This package is also focused on the analysis part. So not only will this package provide utils for pulling down data,
but it will also provide some functions for manipulating that data, and finding insights.alsoHow to installThis package is easily installable with pip:pip install pysportsrefHow to contributeWe are hoping people contribute to this package! Whether it is through adding functions or adding example notebooks,
anything is welcome! There are two ways we envision people contributing:Making pull requests: pretty self explanatory, if you have a change you want to see feel free to open a PR!Opening an issue: if you want something done, whether it be a util or an example/type of analysis, open an issue
and we will try to get it!
Package: pyspotify
WARNING: This library no longer workspyspotify is a Python wrapper around the libspotify C library, and thus depends
on libspotify for everything it does.In May 2015, libspotify was deprecated by Spotify and active maintenance
stopped. At this point, libspotify had been the main way to integrate with
Spotify for six years, and was part of numerous open source projects and
commercial applications, including many receivers and even cars.  It remained
the only API for playback outside Android and iOS.In February 2016, server side changes to the Spotify API caused the search
functionality to stop working, without Spotify ever acknowledging it. Users of
pyspotify could work around this by using the Spotify web API for searches and
pyspotify for playback.In April 2022,Spotify announcedthat they would sunset the libspotify API one month later.In May 2022, new libspotify connections to Spotify started failing. With
libspotify dead, pyspotify was dead too.After two years in development from May 2013 to May 2015, and seven years of
loyal service this project has reached its end.There will be no further updates to pyspotify.Hopefully, the pyspotify source code can still serve as a complete example of
how to successfully wrap a large C library in Python using CFFI.Introductionpyspotify provides a Python interface toSpotify’sonline music streaming service.With pyspotify you can access music metadata, search in Spotify’s library of
20+ million tracks, manage your Spotify playlists, and play music from
Spotify. All from your own Python applications.pyspotify usesCFFIto make a pure Python
wrapper around the official libspotify library. It works on CPython 2.7 and
3.5+, as well as PyPy 2.7 and 3.5+. It is known to work on Linux and
macOS. Windows support should be possible, but is awaiting a contributor with
the interest and knowledge to maintain it.Project resourcesDocumentationSource codeIssue tracker
Package: pyspotify-client
Project Title🤖 Few lines describing what your bot does.📝 Table of ContentsAboutDemo / WorkingHow it worksUsageGetting StartedDeploying your own botBuilt UsingTODOContributingAuthorsAcknowledgments🧐 AboutWrite about 1-2 paragraphs describing the purpose of your bot.🎥 Demo / Working💭 How it worksThe bot first extracts the word from the comment and then fetches word definitions, part of speech, example and source from the Oxford Dictionary API.If the word does not exist in the Oxford Dictionary, the Oxford API then returns a 404 response upon which the bot then tries to fetch results form the Urban Dictionary API.The bot uses the Pushshift API to fetch comments, PRAW module to reply to comments and Heroku as a server.The entire bot is written in Python 3.6🎈 UsageTo use the bot, type:!dict wordThe first part, i.e. "!dict"is notcase sensitive.The bot will then give you the Oxford Dictionary (or Urban Dictionary; if the word does not exist in the Oxford Dictionary) definition of the word as a comment reply.Example:!dict what is loveDefinition:Baby, dont hurt me~
Dont hurt me~ no more.Example:Dude1: Bruh, what is love?
Dude2: Baby, dont hurt me, dont hurt me- no more!
Dude1: dafuq?Source:https://www.urbandictionary.com/define.php?term=what%20is%20loveBeep boop. I am a bot. If there are any issues, contact myMasterWant to make a similar reddit bot? Check out:GitHub🏁 Getting StartedThese instructions will get you a copy of the project up and running on your local machine for development and testing purposes. Seedeploymentfor notes on how to deploy the project on a live system.PrerequisitesWhat things you need to install the software and how to install them.Give examplesInstallingA step by step series of examples that tell you how to get a development env running.Say what the step will beGive the exampleAnd repeatuntil finishedEnd with an example of getting some data out of the system or using it for a little demo.🚀 Deploying your own botTo see an example project on how to deploy your bot, please see my own configuration:Heroku:https://github.com/kylelobo/Reddit-Bot#deploying_the_bot⛏️ Built UsingPRAW- Python Reddit API WrapperHeroku- SaaS hosting platform✍️ Authors@kylelobo- Idea & Initial workSee also the list ofcontributorswho participated in this project.🎉 AcknowledgementsHat tip to anyone whose code was usedInspirationReferences
Package: pyspotify-connect
pyspotify-connect provides a Python interface toSpotify Connect’sonline music streaming service.With pyspotify-connect you can create a Spotify Connect player on your device.
All from your own Python applications.pyspotify-connect usesCFFIto make a pure Python
wrapper around thelibspotify_embedded_sharedlibrary. It has been tested on
on CPython 2.7 and 3.4, as well as PyPy 2.6. The library is only available for arm
linux architectures: armv6 armel and armv7 armhf.This wrapper is heavily based on thepyspotifywrapper for libspotify and
the spotify-connect implementationSpotify Connect Web.DependenciesA Spotify Premium subscription. pyspotify-connectwill notwork with Spotify
Free, just Spotify Premium.libspotify_embeded_shared, see below for instructions.Developmente packages for python and libffi. For example, for python 2.7 on Debian/Raspbian:apt-getinstallpython-devlibffi-devSpotify Connect LibraryAs Spotify Connect library is not available directly from spotify you should
grab it from one of the links below depending you Linux version.armel - armv6armhf - armv7Copy the appropiated library for your architecture to/usr/lib.InstallationInstall the dependencies listed above yourself, and then install the
package from PyPI:pip install pyspotify-connectProject resourcesSource codeIssue trackerSpotify Connect resources
Package: pyspotify_helper
Pyspotify UtilsThis repository attempts to make it as simple as possible to integrate pyspotify.It’s currently as simple as
# pip install pyspotify_helper
# python
>>> from pyspotify_helper import Spotify
>>> spotify_pro_credentials = { “spotify_name”: “”, “spotify_password”: “” }
>>> Spotify(spotify_pro_credentials).play(“Stadium Arcadium”, “albums”)Initialization:Sadly, that simplicity comes after some initialization.You need a spotify_appkey.key in your project root directory. You can get this following the tutorial athttp://pyspotify.mopidy.com/en/latest/quickstart/#application-keys.Dependencies:libspotify
pyspotifyInformation on installing dependencies athttp://pyspotify.mopidy.com/en/latest/installation/.Usage:The most important method is play(name, category, shuffle=False). Name is a free-form string that will be searched on Spotify (more info athttp://news.spotify.com/us/2008/01/22/searching-spotify/). Category is one of “tracks”, “artists”, or “albums”. Shuffle decides whether or not the music gets shuffled when played.If no results are found, there will be an error. (IndexError: list index out of range)The play() method does not block. They return immediately, while a different thread is started to play the music.There are four methods to control playback: next_track, pause, resume, and pause_toggle.These methods will cancel the currently playing song when called.Spotify() returns a singleton. The credentials only need to be passed in the first time you call the function. See the “if __name__ == ‘__main__’:” section.TODO (As in, questions for you and my future self):How does searching work with playlists? More specifically, can I easily distinguish between owned playlists and other playlists? What about playlists with the same name?
Package: pyspotter
pyspotter: Simple CLI for SofarOcean APISofarOcean is actively shaping one of the most extensive commercial ocean observation networks through innovative smart buoys (spotters) and advanced mooring technology. The Spotter, a key component, delivers real-time, high-fidelity data on waves, wind, and temperature from any location in the ocean. Sofar's commitment extends to providing this valuable dataset to researchers and data users. This tool is an endeavor to simplify the parsing of spotter information, focusing on basic API functionalities, including authentication protocols, token reset, and data export for seamless integration with platforms like Google Earth Engine.While SofarOcean offers an official client with comprehensive functions, this tool, availablehere, addresses specific tasks including but not limited to tools like authentication protocols, token reset, and data export for easy use in other platforms like Google Earth Engine. You can also find theirindepth API and documentation hereDisclaimer: This is an unofficial tool and is neither licensed nor endorsed by SofarOcean. It is created and maintained by Samapriya Roy.CitationSamapriya Roy. (2021). samapriya/pyspotter: Simple CLI for SofarOcean API (0.0.6).
Zenodo. https://doi.org/10.5281/zenodo.5805519Find thereadme docs here
Package: pyspread
pyspreadpyspreadis a non-traditional spreadsheet that is
based on and written in the programming language Python.It is released under theGPL v3. LICENSEHomepage:https://pyspread.gitlab.io/Repository:https://gitlab.com/pyspread/pyspreadAPI Docs:https://pyspread.gitlab.io/pyspread/InstallationThe table below shows for which operating systems, pyspread is available in which version.
If pyspread is unavailable or outdated for your operating system, you can install it using one of the three methods below.With pippipinstallpyspreadFrom gitIt is assumed that python3 and git are installed.Get sources and enter dirgitclonehttps://gitlab.com/pyspread/pyspread.git# orgitclonegit@gitlab.com:pyspread/pyspread.git# thencdpyspreadInstall dependencies and pyspreadpip3install-rrequirements.txt# or if pip3 is not presentpipinstall-rrequirements.txt# nextpython3setup.pyinstallGetting the bleeding edge version from the code repositoryNote that there may unfixed bugs if you use the latest repository version.
You may want to check the CI, which comprises unit tests athttps://gitlab.com/pyspread/pyspread/pipelines.Get the latest tarball or zip athttps://gitlab.com/pyspread/pyspreador
clone the git repo athttps://gitlab.com/pyspread/pyspread.gitStarting pyspreadWith an installation via pip, distutils or your OS's installer, simply run$ pyspreadfrom any directory.In order to start pyspread without installation directly from the cloned
repository or the extracted tarball or zip, run$ ./pyspread/pyspread.pyor$ python -m pyspreadinside the top directory.ContributeIssuesPlease submit issues in the gitlab issue tracker athttps://gitlab.com/pyspread/pyspread/issuesCodeCommit your changes, push them into your fork and send a pull request.This page gives an overview how to do this:https://help.github.com/articles/fork-a-repoYou can find more more details about code organization athttps://pyspread.gitlab.io/pyspread/
Package: pysprinkler
Sprinkler=========A small python library that helps you write co-routinesin an event-driven way. In short it 'sprinkles' data passedto the '.sprinkle' function to all sub-classes of 'Task' ina round robin manner.A simple example:from pysprinkler import Task, Sprinklerclass RemoteDBHandler(Task):def __init__(self):print("Started RemoteDBHandler")def on_data(self, data):print("RemoteDBHandler: {}".format(data))def on_end(self):print("Closing RemoteDBHandler")class LocalDBHandler(Task):def __init__(self):print("Started LocalDBHandler")def on_data(self, data):print("LocalDBHandler: {}".format(data))def on_end(self):print("Closing LocalDBHandler")class STNHandler(Task):def __init__(self):print("Started STNHandler")def on_data(self, data):print("STNHandler: {}".format(data))def on_end(self):print("Closing STNHandler")rdb_handler = RemoteDBHandler()ldb_handler = LocalDBHandler()stn_handler = STNHandler()s = Sprinkler([rdb_handler, ldb_handler, stn_handler])s.start()lines = ["Line 1", "Line 2", "Line 3", "Line 4", "Line 5"]for line in lines:# sends 'line' to all 'Task's in a# round robin manners.sprinkle(line)s.close()Output:Started RemoteDBHandlerStarted LocalDBHandlerStarted STNHandlerRemoteDBHandler: Line 1LocalDBHandler: Line 1STNHandler: Line 1RemoteDBHandler: Line 2LocalDBHandler: Line 2STNHandler: Line 2RemoteDBHandler: Line 3LocalDBHandler: Line 3STNHandler: Line 3RemoteDBHandler: Line 4LocalDBHandler: Line 4STNHandler: Line 4RemoteDBHandler: Line 5LocalDBHandler: Line 5STNHandler: Line 5Closing RemoteDBHandlerClosing LocalDBHandlerClosing STNHandler
Package: pysprint
PySprintSpectrally Refined Interferometry for PythonNamePySprintPyPI packageAnaconda packageDevelopment statusBetaLicenseDescriptionPySprint provides an interface for Spectrally resolved interferometry in Python.
PySprint implements all the evaluation methods described in the literature, however
the API and the software itself might change over time. Documentation is sparse and due
to the narrow use case of the software its written in Hungarian, however it will be
translated to English in the future. The testing is invery early stage, and we have a currently
pointless Rust extension module, which might turn into something useful later.âœ“ Minimum-maximum methodâœ“ Phase modulated cosine function fit methodâœ“ Fourier methodâœ“ Windowed Fourier transform methodâœ“ Stationary phase point methodInstallationPySprint requires at leastPython 3.6to run.Install withpipinstallpysprintor install on Anaconda with:(CURRENTLYNOTAVAILABLE)condainstall-cptrskaypysprint-cconda-forgeRequirements:numpyscipymatplotlibpandasJinja2scikit-learnOptional packages:lmfit - for detailed curve fitting resultsnumba - to speed up non uniform FFT calculationdask - for parallel WFT runDocumentationThe documentation is hosted on readthedocs.io.
Package: pysprintppi
PySprintRe-implementation ofSPRINTin Rust,
a fast, modern programming language, and wrapped in a Python API.AuthorFrancois Charih -francoischarih@sce.carleton.ca- DeveloperTo install PySPRINTTo install PySPRINT as a Python library, you must run the following commands:$ pip3 install pysprintppiNote: We support Python3.8+ on the Linux platform.Using PySPRINTTo predict interactions, you must use the PySPRINT class which is instantiated
as follows:# Instantiate the PySPRINT PPI predictorpysprint=PySPRINT("my_fasta_file_containing_my_sequences.fasta","my_hsp_file.hsp",#"training_pairs_file.txt")# Score the interactions (all-to-all predictions)prediction_matrix=pysprint.score_all_interactions()You can then do things with that prediction matrix:# Retrieve the predictions for a given pairprint(prediction_matrix.get_score_for_pair("protein1","protein2"))# Retrieve the scores for all proteins that interact with ProteinXprint(prediction_matrix.get_scores_for_protein("ProteinX"))# Save the prediction matrixprediction_matrix.save("path_to_matrix.npy")# Load a prediction matrixprediction_matrix=prediction_matrix.load("path_to_matrix.npy")Running PySPRINT with Docker$dockerbuild-tpysprint.
$dockerrun-v$(pwd):/pysprintpysprint:latest
Package: pysprints
Calculation objects to handle varying sprint/point commitment across various
releases.Example>>> import datetime
>>>
>>> from pysprints import ReleasePlan
>>>
>>> release_plan = ReleasePlan(start_sprint_number=5,
...                            start_date=datetime.date(year=2012,
...                                                     month=5,
...                                                     day=17),
...                            sprint_length_calendar_days=15)
>>>
>>> release_plan.add_release(name='Foo', points=20)
>>> release_plan.add_release(name='Bar', points=50)
>>> release_plan.next_sprint(points_mix=(('Foo', 10),
...                                      ('Bar', 20)))
Sprint(number=5, start_date=2012-5-17, length_calendar_days=15)
>>> release_plan.next_sprint(points_mix=(('Foo', 15),
...                                      ('Bar', 15)))
Sprint(number=6, start_date=2012-6-1, length_calendar_days=15)
>>> release_plan.end('Foo')
Sprint(number=6, start_date=2012-6-1, length_calendar_days=15)
>>> release_plan.end('Foo').number
6
>>> release_plan.end('Foo').end_date
datetime.date(2012, 6, 15)
>>> release_plan.end('Bar')
>>>
>>> release_plan.end_dict
{'Foo': Sprint(number=6, start_date=2012-6-1, length_calendar_days=15), 'Bar': None}
Package: pysprites
Sometimes, designer will give you a visual production, and that you need to
crop the little sprites off from the files, merge them into a png files, and
write a css files for such sprites.This tools make everything in onkey.Install$ pip install pyspritesExample$ pysprites -i page1.psd -m icon- -b -r -c page1.css -o page1.pngThis command will import layers whose name starts withicon-,  frompage1.psd, reposition them,
output the css for each sprite topage1.css, and output the merged png topage1.pngCommand line$ pysprites [args...]

-i a.psd b.psd ... : import psd layer from psd files (need psd_tools)
-a x.png y.png ... : add png files
-m filter  : only handle layer or image name startswith filter
-p padding : add padding for each sprite
-r         : reposition sprites
-cp string : use string as css class prefix
-b         : gen sprite width and height for css
-c out.css : output css to file
-o out.png : output merged png to fileFrameworkActually, everything is in a single file :pysprites/sprites.py.In a task, everything present as a “General Sprite Process Group” (gsp), it is struct is as follow:gsp :: (width, height, img, padding, prefix, args)
img :: list of [[name, [width, height],[left, top], PIL_object]And the gsp go though handlers, a handler reposition the img, and a handler save it to png, and so on.Reposition AlgorithmSelect two most height sprites and the final image height.Find a lowest gap.Select a longest sprite that can be insert to left, and insert it.If cannot insert it, fill the gap and goto step2.Repeat untail every sprite has been inserted.Using a EdgeLines struct to maintain the lowest gap, and thegsp_repositionfunction implements
this algorithm. See the code inpysprites/sprites.pyContributeI think this tools is good enough for me. However, Welcome for new handlers, for example,
import the layer from a GIMPxcffile, or new reposition algorithm, or any participation !Enjoy it!