Saravanan666/pypi-org huggingface dataset:
Package: scheptk
scheptk- Scheduling Python ToolKitscheptkis a collection of classes and functions to model and solve machine scheduling problems using Python. It is intended basically for teaching purposes, even if the algorithms can be integrated with MOIST, a more advanced scheduling tool.How to use itIn order to usescheptkyou can install it as a Python package usingpip:pip install scheptkAt this point, the clasess and functions ofscheptkshould be available. You might want to have a look at thewiki pagesto get a handle on the documentation.
Package: scherbelberg
SCHERBELBERG/ˈʃɛʁbɛlbɛʁk/ (German, toponym, male: "mountain of shards" in Leipzig)Synopsisscherbelbergprovides both a CLI and an API for deploying and managing smallDask-based HPC clusters in theHetzner cloud. Development statusalpha, stabilityacceptable, security alsoacceptablebut implementation needs a review.Project's NameNext to impressive projects likeFugaku, which is named afterMount Fuji, theTOP500are clearly missing an entry from the city ofLeipzig. This project is named after one of the few significant "mountains" in the city, the "Scherbelberg", also known as the "Rosentalhügel": 20 meters above the surrounding landscape and 125 meters above sea level. Starting out as a late 19th century landfill, it has since become part of a park-like landscape. As of 1975, a famously shaky steelobservation towerwith a ratherbeautiful viewis located at its top, overlooking not only theLeipziger Auenwaldforest but also the city's sewage treatment plant.Installationscherbelbergcan be installed viacondaas follows:condainstall-cconda-forgescherbelbergThis package has been tested on Linux and Windows 10. It should work on most Unix-like systems. You must run acondaenvironment based entirely on recent versions ofconda-forgepackages with CPython versions 3.8, 3.9 or 3.10.sshmust be installed separately as a prerequisite. AHetzner API tokenis required. By default,scherbelbergexpects it to be located in theHETZNERenvironment variable.Seechapter on installationinscherbelberg's documentation for further details. Also seesection on how to get startedfor additional steps.CLISimilar togit, the CLI is divided into sub-commands. They all come with their own parameters. Information on the latter can be found by using the--helpoption.~> scherbelberg --help
Usage: scherbelberg [OPTIONS] COMMAND [ARGS]...

  HPC cluster deployment and management for the Hetzner Cloud

Options:
  --version
  --help     Show this message and exit.

Commands:
  catalog  list data centers and available servers types
  create   create cluster
  destroy  destroy cluster
  ls       list cluster nodes
  nuke     nuke cluster
  scp      scp from/to cluster node
  ssh      ssh into cluster nodeSeechapter on CLIinscherbelberg's documentation for further details.APIscherbelbergusesasyncio. A cluster can basically be created and destroyed, with or withoutasyncio:fromasyncioimportrunfromscherbelbergimportClusterc=awaitCluster.from_new(**kwargs)# orc=run(Cluster.from_new(**kwargs))awaitc.destroy()# orrun(c.destroy())Access to an earlier established cluster can also be gained:fromasyncioimportrunfromscherbelbergimportClusterc=awaitCluster.from_existing(**kwargs)# orc=run(Cluster.from_existing(**kwargs))Once the cluster has been created or gained access to, one can simply request an initialized Dask client object:dask_client=awaitc.get_client()# ordask_client=run(c.get_client(asynchronous=False))Seechapter on APIinscherbelberg's documentation for further details.
Package: schiller
Schiller Python Base ToolsThis project is documented using Python's Sphinx, check files indocs/pagesor confluence:https://qms.schiller.ch/display/RBKB/SPyBT-Schiller+Python+Base+Tools.The source code can be found on SVN atfile:///H:/LocasSVN/schiller-py.The Sonar url ishttp://sonarqube.schiller.ch/dashboard?id=schiller.
Package: schimpy
schimpyPython package for SCHISMFree software: MIT licenseDocumentation:https://cadwrdeltamodeling.github.io/schimpy/html/index.htmlFeaturesTODO see issuesCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.1 (2020-04-06)Added authors and doc cleanup0.1.0 (2020-04-06)First release on conda channel cadwr-dms
Package: schirmshots
records multiple (background) windows using ctypes and ffmpeg - Windows onlypip install schirmshotsTested against Windows 10 / Python 3.10 / AnacondaCustomizableRecordingParameters-Thefunctionallowsyoutospecifyvariousrecordingparameters,suchastheoutputvideofilepath,thetargetwindow's handle (HWND), the window'spositionanddimensions,whethertoruninheadlessmode,desiredframerate,compressionparameters,andmore.Thislevelofcustomizationgivesyoufine-grainedcontrolovertherecordingprocess.HeadlessMode-Iftheheadlessparameterissettotrue,thefunctionutilizestheFakeHeadlessclasstoruninheadlessmode.Thismeansthatithidesthewindowthatisbeingrecorded.Thiscanbeadvantageousforautomationandbackgroundtasks.HotkeyTermination-Thefunctionsupportsahotkeycombination(killhotkey)toterminatetherecordingprocess.Thisprovidesaneasyanduser-friendlywaytostoptherecordingifneeded.ErrorHandling-Thecodeincludeserrorhandlingforvariousscenarios,suchasremovinganexistingvideofilebeforestartingtherecordingandhandlingexceptionsthatmightariseduringtherecordingprocess.Thishelpsensureasmootherexecutioneveninthepresenceofunexpectedissues.DynamicWindowResizing-Thecodeincludesfunctionalitytodynamicallyresizethetargetwindowbeforerecording,whichcanbeusefulifyouwanttoensureaspecificwindowsizefortherecording.Ifyouresizethewindow,theframeswon't be recorded, and the window will automatically be resized to the dimensions from the beginning, and the recordingwillgoon.However,youcanmovethewindowduringtherecordingExternalProcessManagement-Thecodemanagesexternalprocesses,likelaunchingasubprocesswhichstartsamainprocess(completelyindependent)foreveryrecordingandensuringitrunsinthebackground.ThatmeansyoucanrecordmultiplewindowsatonceanduseallyourCPUsStartrecordingvideoofaspecificwindowusingspecifiedparameters.Args:videofile(str):Thepathtotheoutputvideofile.hwnd(int):Thehandleofthetargetwindowtoberecorded.windowpos(tuple,optional):Thepositionanddimensionsofthewindowasatuple(startx,starty,width,height).IfNone,thewindow's position and dimensions will not be altered before recording.headless(int|bool,optional):Setto1orTruetoruninheadlessmode(hidesthewindowthatisbeingrecorded),0orFalseotherwise.framerate(int,optional):Theapproximatedesiredrecordingframerate.Defaultis0(nospecificframerate).params(tuple,optional):Additionalparametersforvideocompression,asatupleoftuples.Defaultis(("-vcodec","libx264"),("-crf",0),("-preset","ultrafast")).killhotkey(str,optional):Thehotkeycombinationtoterminatetherecording.Defaultis"ctrl+alt+e"-canbeusedmultipletimes.use_client(int|bool,optional):Setto1orTruetousetheclientareaofthewindow,0orFalseotherwise.Returns:NoneExample:fromschirmshotsimportstart_recording# needs to be in a file - not directly from the console - make sure that ffmpeg is installedstart_recording(headless=0,videofile=f'c:\\myvideofile2.mkv',windowpos=None,# (0,0,640,480),hwnd=3477668,framerate=30,params=(("-vcodec","libx264"),("-crf",0),("-preset","slow")),killhotkey='ctrl+alt+e',)
Package: schlami
Easy enums for Python.>>>RGB=StringEnum(['Red','Green','Blue'])>>>RGB.Red'Red'Contents1Installation2Features3Use4Contributing4.1Installing for development4.2Running the tests4.3Adding documentation5Credits1InstallationInstallezenumwith:pipinstallezenum2FeaturesPure Python.Compatible with Python 3.5+.Easy creation of usefull enum objects.String enums with unified value and name attributes.3UseStringEnumEasilly get a string enum from a string list.4ContributingPackage author and current maintainer is Shay Palachy (shay.palachy@gmail.com); You are more than welcome to approach him for help.4.1Installing for developmentClone:gitclonegit@github.com:shaypal5/ezenum.gitInstall in development mode with test dependencies:cdezenumpipinstall-e".[test]"4.2Running the testsTo run the tests, use:python-mpytest--cov=ezenum4.3Adding documentationThis project is documented using thenumpy docstring conventions, which were chosen as they are perhaps the most widely-spread conventions that are both supported by common tools such as Sphinx and result in human-readable docstrings (in my personal opinion, of course). When documenting code you add to this project, please followthese conventions.5CreditsCreated by Shay Palachy (shay.palachy@gmail.com).
Package: schleppy
Utilities for traversing and transforming data structuresInspired by Hapi framework for .js (especially the Hoek plugin), I really missed some of these utilities in python so decided to port them over.reach(source, pattern, [options])Insipred by Hoek.reachConverts an object key chain string to referenceoptions- optional settingsseparator- string to split chain path on, defaults to ‘.’default- value to return if the path or value is not present, default isNonestrict- iftrue, will throw an error on missing member, default isFalseA pattern including negative numbers will work like negative indices on an
array.If pattern isFalse-y, the object itself will be returned.fromschleppyimportreachpattern='a.b.c'source_obj={'a':{'b':{'c':1}}}reach(source_obj,pattern)# returns 1pattern='a.b.-1'source_obj={'a':{'b':[2,3,6]}}reach(source_obj,pattern)# returns 6transform(source, transform, [options])Insipred by Hoek.transformTransforms an existing object into a new one based on the suppliedobjandtransformmap.optionsare the same as thereachoptions. The first argument can also be an array of objects. In that case the method will return an array of transformed objects. Note thatoptions.separatorwill be respected for the keys in the transform object as well as values.fromschleppyimporttransformsource={'address':{'one':'123 main street','two':'PO Box 1234'},'title':'Warehouse','state':'CA'}result=transform(source,{'person.address.lineOne':'address.one','person.address.lineTwo':'address.two','title':'title','person.address.region':'state'})# Results in# {#     'person': {#         'address': {#             'lineOne': '123 main street',#             'lineTwo': 'PO Box 1234',#             'region': 'CA'#         }#     },#     'title': 'Warehouse'# }
Package: schmovie
SCH_MovieThis project is a Python library that accesses the OMDb API to retrieve information about movies based on user-input titles. Additionally, the library includes a feature to translate movie plots from English to Indonesian using googletrans.By utilizing this project, users can easily search for information about their favorite movies and obtain a summary of the plot in the Indonesian language.APIPython Basic API wrapperhttp://www.omdbapi.com/InstallationInstall my-project with npm$pipinstallschmovie$pipinstallgoogletransUsageBasic Usage :fromschmovie.movieimportOmdbomdb=Omdb("YOUR_API_KEY")movie_title=input("Search Movie : ")result=omdb.search_movie(movie_title)print(f"Judul:{result['Title']}")print(f"Tahun:{result['Year']}")print(f"Rating:{result['imdbRating']}")print(f"Plot:{result['Plot']}")Authors@codewithwan
Package: schnapsen
Schnapsen platform - Project Intelligent Systems 2022-2023Getting startedThis is the improved platform for the schnapsen card game. To get to know the concept of the game, please visitthis web page.To use the platform, your python version must be at least 3.9, we suggest installing conda an using an environment.To get started, install the schnapsen package and its dependencies in editable mode by running:pipinstall-e.To run the tests, run:pipinstall-e'.[test]'# on Linux / MacOSpipinstall-e".[test]"# on Windowspytest./testsIf the above fails, try deactivating your environment and activating it again.
Then retry installing the dependencies.Running the CLIAfter intalling, you can try the provided command line interface examples.
Most examples are bots playing against each other; read the code for details.To run the CLI, run:pythonexecutables/cli.pyThis will list the available commands.For example, if you want try a RandBot play against another RandBot, typepython executables/cli.py random-game.Running the GUIThe graphical user interface (GUI) lets you play visually against a bot (e.g., You vs. RandBot).To start the GUI, run:pythonexecutables/server.pyNow, open your webbrowser and type in the server address (i.e.,http://127.0.0.1:8080).
By default, you are playing against RandBot. You can also play against other bots. Runpythonexecutables/server.py--helpfor more details.Implementing more botsYou will find bot examples in thesrc/schnapsen/botsfolder.
You can look at the example_bot.py file for various methods provided to your bot.TroubleshootingGetting the right pythonThe first hurdle in getting the platform to run is getting the right python version on your system.
An easy way to get that is using virtual environments. We suggest you installcondato manage them.
Then, you can use conda to create a new environment by runningcondacreate--nameproject_ispython=3.10With this environment created, you can start itconda activate project_isInside this environment you can install the dependencies as instructed above.Run the right pythonIf you install conda and create an environment, you can run python by just running thepythoncommand.
However, often your system also provides a python version.
To know which python is running, usewhichpython# on linuxwherepython# on windows (untested)Now, you want to look at the output and make sure that this executable is inside the anaconda folder and not where your system stores its executables.DocumentationThe code is documented using reStructuredText. You can either read the documentation along the code, or generate a more suer friendly version.
A pregenerated version of the documentation can be found int he doc folder. It can be regenerated by runnign the following in the root of the repository.pipinstallpdoc
pdoc--htmlsrc/schnapsenexecutables/-odoc/if above doesn't work, try pdoc3, instead of pdoc
Package: schnell
schNellschNell is a very lightweight python module that can be used to compute basic map-level noise properties for generic networks of gravitational wave interferometers. This includes primarily the noise power spectrum  "N_ell", but also other things, such as antenna patterns, overlap functions, inverse variance maps etc.InstallationYou can install schnell simply by typingpip install schnell [--user](use--userif you don't have admin privileges on your machine).
Or for development versions you can download the repository with git and install from there usingpython setup.py install [--user].DocumentationDocumentation can be found onreadthedocs.This examplenotebookon github also showcases the main functionalities of the module.License and creditsIf you use schNell, we kindly ask you to cite its companion paper (link to appear here soon).The code is available under theBSD 3-Clauselicense.If you have a problem you've not been able to debug, or a feature request/suggestion, please open an issue on github to discuss it.
Package: schnitsum
Schnitsum: Easy to use neural network based summarization modelsThis package enables to generate summaries of you documents of interests.Currently, we support following models,BART (large)fine-tuned on computer science papers (ref.SciTLDR).Model name:sobamchan/bart-large-scitldrBART (large)fine-tuned on computer science papers (ref.SciTLDR). Then distilled (byshrink and fine-tune) to have 65% parameters less.Model name:sobamchan/bart-large-scitldr-distilled-3-3BART (large)fine-tuned on computer science papers (ref.SciTLDR). Then distilled (byshrink and fine-tune) to have 37% parameters less.Model name:sobamchan/bart-large-scitldr-distilled-12-3mBART (large, en-to-de)fine-tuned on computer science papers, English papers and German summaries (ref.X-SciTLDR).Model name:sobamchan/mbart-large-xscitldr-deBART (large)fine-tuned on XSum, BCC news article lead sentences in English. (ref.X-SciTLDR).Model name:facebook/bart-large-xsumwe are planning to expand coverage soon to other sizes, domains, languages, models soon.Installationpipinstallschnitsum# or poetry add schnitsumThis will let you generate summaries with CPUs only, if you want to utilize your GPUs, please follow the instruction by PyTorch,here.UsageFrom Command LinePass document as an argument and print the summary>schnitsum--model-namesobamchan/bart-large-scitldr-distilled-3-3--text"Text to summarize"Pass documents as a file and save summaries in a file.
Input file needs to contain documents line by line.example>schnitsum--model-namesobamchan/bart-large-scitldr-distilled-3-3--filedocs.txt--opathsums.txtFrom PythonfromschnitsumimportSchnitSummodel=SchnitSum("sobamchan/bart-large-scitldr-distilled-3-3")docs=["Document you want to summarize."]summaries=model(docs)print(summaries)
Package: schnorrpy
SchnorrPy for SR25519 SignaturesA Python wrapper to the RustschnorrkelSR25519 signature
library. Schnorrkel/SR25519 signatures are the Schnorr signature onRistrettocompressed Ed25519 points.InstallationTo install the binary Wheel from PyPI usepip:$ pip install schnorrpyInstall from SourceTo develop, clone the repo and do the following:$ git clone https://gitlab.com/kauriid/schnorrpy.git
$ python setup.py build
$ python setup.py installSet up and activate for Python 3:virtualenv ${HOME}/.virtualenvs/schnorrpy \
           --system-site-packages --python=/usr/bin/python3
source ${HOME}/.virtualenvs/schnorrpy/bin/activateInstall required packages for development:pip install -r requirements-dev.txtBuilding a (Distributable) WheelTo publish a binary Python Wheel on PyPI, one needs to use themanylinuxdocker container.Build a source distribution:$ python setup.py sdistPull themanylinux2010(or oldermanylinux1) Docker image:$ sudo docker pull quay.io/pypa/manylinux2010_x86_64Then use the following command to build wheels for supported Python
versions:$ sudo docker run --rm -v $(pwd):/io quay.io/pypa/manylinux2010_x86_64 \
              /io/build-wheels.shThe built wheels can now be published to PyPI (e.g. usingtwine)
from thedist/directory:$ twine upload dist/schnorrpy*.tar.gz dist/schnorrpy*manylinux*.whlContributingTBDExampleTBDLicenceCopyright 2018 by SingleSource Limited, Auckland, New ZealandThis work is licensed under the Apache 2.0 open source licence.
Terms and conditions apply.
Package: schoginitoys
Schogini Toys - Electronics & Coding DIY KitPlease visithttps://schoginitoys.comThe schoginitoys library is a comprehensive toolkit designed to bring the world of electronics and coding to life for learners of all ages. It serves as the backbone for the Schogini Toys DIY Kit, providing a suite of functions that interface with the Raspberry Pi PICO and various components like LEDs, displays, and sensors. With functions for display manipulation, input handling, and utility operations, it simplifies Python coding, making it accessible for kids and a creative playground for adults. Whether it's playing games, learning to code, or building projects, schoginitoys offers a seamless and educational experience.
Package: scholarec
scholarec=========Recommendation of Scholarly Works---------------------------------[![Build Status](https://travis-ci.org/arcolife/scholarec.png?branch=master)](https://travis-ci.org/arcolife/scholarec)[![Dependency Status](https://gemnasium.com/arcolife/scholarec.png)](https://gemnasium.com/arcolife/scholarec)[![Zenodo DOI for github](https://zenodo.org/badge/4244/arcolife/scholarec.png)](http://dx.doi.org/10.5281/zenodo.10265)This software has been built due to a need felt for a properrecommendation system for publicly available scholarly/research works.It classifies documents and uses personalization features and content-based algorithm tosuggest/recommend similar ones, possibly of interest to the user._Note:_ Currently, full-functionality is offered by combining this package and another one,that offeres web interface (Django-based).- [arcolife/django-scholarec](https://github.com/arcolife/django-scholarec "django-scholarec")> *Inspired from an older project* [researchlei](http://cs.stanford.edu/people/karpathy/researchlei/ "BSD Licensed")*****Installation**```$ git clone https://github.com/arcolife/scholarec.git$ cd scholarec/$ sh setup.sh```* See INSTALL for detailed instructions.**Test*** Optionally, to test if installed, look for a description on executing:```$ python -m scholarec```* To see if the scripts runs without error:```$ ./tests/run-tests.sh$ ./tests/test.py```**Usage*** To use the module in a Python script, simply import:```pythonimport scholarec```* To check a sample run output, open log/sample_run.txt* To go for a sample run:```$ ./tests/query_parse```Note: For developing a small database from arXiv, you need to runthe query_parse script and accept "Extract PDF" option for extractingrelated pdf's, converting them to plain text and extracting interestingwords that would later be used for recommendations and suggestions.* A simple arXiv API call can be achieved by executing the following sample code:```pythonimport scholarecfrom scholarec.base.arxiv import DocumentArXivurl = "http://export.arxiv.org/api/query?search_query=all:%22higgs%22&start=0&max_results=2"from urllib2 import urlopenquery_xml = urlopen(url)doc = DocumentArXiv(query_xml)data_dict = doc.extract_tags()for entry_id in data_dict.keys():print "ID: %s" % (entry_id)print(data_dict[entry_id]), "\n"```*****FAQ**Q. What data interchange file formats have been used?A. Data conversion from XML to JSON as well as in XML itself.Q. What are the Data sources?A. Dataset currently taken from arXiv. Future: DBLP/Google Scholar.Q. How is the Data dealt with?A. ElasticSearch/MongoDB for search and storage*****LICENSE**[![GPL V3](http://www.gnu.org/graphics/gplv3-127x51.png)](http://www.gnu.org/licenses/gpl-3.0-standalone.html)
Package: scholarmetrics
Compute scholarly metrics in Python with Pandas and NumPy.Documentation:https://scholarmetrics.readthedocs.io.ExamplesJ.E. Hirsch’s h-index or Hirsch-index:>>>fromscholarmetricsimporthindex>>>citations=[6,10,5,46,0,2]>>>hindex(citations)4Euclidean index:>>>fromscholarmetricsimporteuclidean>>>citations=[6,10,5,46,0,2]>>>euclidean(citations)47.75981574503821ContributingPlease seeCONTRIBUTING.rst.For a list of contributors seeAUTHORS.rst.LicenseMIT License, seeLICENSE.
Package: scholar-network
Welcome to Scholar NetworkThis package is intended for people wanting to scrape Google Scholar
to build graph networks of Google Scholar authors and identify network
connections as opportunities for collaboration.DocumentationAPI Reference Documentation availablehereFeaturesSelenium based web scrapingPoetry based dependency managementBasic Graph algorithms and metricsRequirementsA Selenium web driverlinkChromebrew install --cask chromedriverFirefoxbrew install geckodriverSafariComes included in Safari 10+ToDo:Write testsUsageTo get started you can clone the repo and activate the poetry environment.git clone https://github.com/UK-IPOP/scholar-network.git
cd scholar-network
poetry install --no-dev
poetry shellThen start hacking! 😃ExamplesYou must know each author's Google Scholar ID for this package to work.Scraping one author (my wife, for example):>>>importscholar_networkassn>>>sn.scrape_single_author(scholar_id='ZmwzVQUAAAAJ',scholar_name='Michelle Duong')The data for the author will then be in yourdata/scraped.jsonfile.This defaults to the Safari web driver which we could have manually specified, or, alternatively,
we could request to use the Chrome web driver.>>>importscholar_networkassn>>>sn.scrape_single_author(scholar_id='ZmwzVQUAAAAJ',scholar_name='Michelle Duong',driver='chrome')To create a graph from this new data is easy:>>>g=sn.build_graph()Then, to see the most common five (5) connections:>>>g.edge_rank(limit=5)Out[4]:[(('David Burgess','Donna Burgess'),64),(('Ashley Martinez','Daniela Moga'),64),(('Daniela Moga','Erin Abner'),62),(('Donna Burgess','Katie Wallace'),62),(('Chang-Guo Zhan','Fang Zheng'),60)]