Saravanan666/pypi-org huggingface dataset:
Package: sayn
SAYN is a modern data processing and modelling framework. Users define tasks (incl. Python, automated SQL transformations and more) and their relationships, SAYN takes care of the rest. It is designed for simplicity, flexibility and centralisation in order to bring significant efficiency gains to the data engineering workflow.Use CasesSAYN can be used for multiple purposes across the data engineering and analytics workflows:Data extraction: complement tools such as Fivetran or Stitch with customised extraction processes.Data modelling: transform raw data in your data warehouse (e.g. aggregate activity or sessions, calculate marketing campaign ROI, etc.).Data science: integrate and execute data science models.Key FeaturesSAYN has the following key features:YAML based DAG (Direct Acyclic Graph) creation. This means all analysts, including non Python proficient ones, can easily add tasks to ETL processes with SAYN.Automated SQL transformations: write your SELECT statement. SAYN turns it into a table/view and manages everything for you.Jinja parameters: switch easily between development and product environment and other tricks with Jinja templating.Python tasks: use Python scripts to complement your extraction and loading layer and build data science models.Multipledatabasessupported.and much more... See theDocumentation.Design PrinciplesSAYN aims to empower data engineers and analysts through its  three core design principles:Simplicity: data processes should be easy to create, scale and maintain. So your team can focus on data transformation instead of writing processes. SAYN orchestrates all your tasks systematically and provides a lot of automation features.Flexibility: the power of data is unlimited and so should your tooling. SAYN supports both SQL and Python so your analysts can choose the most optimal solution for each process.Centralisation: all analytics code should live in one place, making your life easier and allowing dependencies throughout the whole analytics process.Quick StartSAYN supports Python 3.7 to 3.10.pipinstallsayn
sayninittest_sayncdtest_sayn
saynrunThis is it! You completed your first SAYN run on the example project. Continue with theTutorial: Part 1which will give you a good overview of SAYN's true power!Release UpdatesIf you want to receive update emails about SAYN releases, you can sign uphere.SupportIf you need any help with SAYN, or simply want to know more, please contact the team atsayn@173tech.com.LicenseSAYN is open source under theApache 2.0license.Made with :heart: by173tech.
Package: sayori
SayoriSayori is a tiny library for composingpure functionsusing pipeline notation. Iffandgare functions thenf | gis a function such that(f | g)(x) = g(f(x)).InstallationpipinstallsayoriUsageSayori exports a simple decorator calledComposablewhich implements the pipe operator. It can be used as follows:fromsayoriimportComposableCreate two composable functions...f=Composable(lambdax:x+1)g=Composable(lambdax:x*2)Compose them...h=f|gApply the composite...h(2)# returns 6
Package: sayt
Welcome tosayt(Search as you type) Documentationsayt(Search as you type) is a Python library provide Google liked searching experience using your own dataset. It focus on quickly indexing your own dataset and start searching on it.saytis not a full text search database, it doesn’t support update/delete operation on your dataset, if your dataset is changed,saytwill delete the old one and re-index everything.Features:Support id search, ngram search, full text search, fuzzy search, range search.Query results are automatically cached, and will expire based on your configuration.Installsaytis released on PyPI, so all you need is to:$pipinstallsaytTo upgrade to latest version:$pipinstall--upgradesayt
Package: saythanks-cli
# saythanks-cliSay Thanks via CLI!Do you know about @kennethreitz’s [saythanks.io](https://saythanks.io) project?
It’s awesome and you should use it more often!… And that’s exactly why this little CLI tool exists:Use it to convinientlysay thanksto other people without leaving the terminal! :tada:`bash python3-mpip installsaythanks-cli`Say thanks:`bash echo 'Request is soo great! Thanks.' | thx kennethreitz `If you don’t pipe anything intothxit’ll pull up your favorite text editor
and you can enter your message there. Just save and the message will be sent!
Package: saywhen
SayWhen_____            _       ____
  / ___/____ ___  _| |     / / /_  ___  ____
  \__ \/ __ `/ / / / | /| / / __ \/ _ \/ __ \
 ___/ / /_/ / /_/ /| |/ |/ / / / /  __/ / / /
/____/\__,_/\__, / |__/|__/_/ /_/\___/_/ /_/
           /____/
                                   :-=+*
                               +#%@@@@@%
                              #@@@@@@@@%
                          :-+*@@@@@@@@@%
                     :##@@@@@%%@@@@@@@@%
                     ***+-:    -+#%%#*+=
           :---------#---
           %:        *  *
          :%         *  *
          +#         *  +:
          #+=++++++++#=++=
          +*#@@@@@@@@@@%+-
           =+*%@@@@@@%++=
            :+=******+=:
                :-+
                 :+
                 :+
                 :+
                 :+
            =+**###***+=SayWhen sends sound and desktop notification to you when job finishes.Tested on Ubuntu 16.04 and macOS 10.14.UsageUsage: saywhen command [args...]Example: saywhen echo helloworldExample: saywhen "echo -n hello && echo world"The returncode of saywhen is the same as the command.Installationpip install saywhenUnstallationpip uninstall saywhenAbout audioAudios are from freesound.org.
Package: saz
Saz has been created to make it so easy for anyone to handle any kind of data and use it to dig more and find out about the story behind the features.
It was written using pandas and tried to use very basic steps which can be understood by anyone, even Python beginners.
Please send me your feedback to this email:siadatisaman@gmail.com....................................................................Saz means proper or worthy in ancient Persian(Parsig) (D. N. Mackenzie - A Concise Pahlavi Dictionary (School of Oriental & African Studies) (2002))
Package: sb2l
sb2lDescriptionTranslates biological models written in SBML into LaTeX code to be compiled and read by human eyeDependenciesThe current version runs 1.3.1 version ofPyLaTeXEitherTelluriumorpython-libsbmlis also requiredFor PDF creation functionality, eitherlatexmkorpdflatexneeds to be installed on the computerInstallationpip install sb2lHow to UseThe following structure gives a LaTeX string that can be copied to a online compiler likeOverleaf:import sb2l
latexStr = sb2l.s2string(sbmlStringOrFile)If using with Tellurium or Antimony:import sb2l
latexStr = sb2l.s2latex(model_name.getSBML())For making PDF files (The filepath must NOT have a .pdf or .tex suffix)import sb2l
filepath = "/Users/Username/Desktop/theDesiredNameOfFile" #[NOTICE: no ".anything"]
sb2l.s2pdf(sbmlStringOrFile, file_path = filepath);The same structure can be used to generate a .tex file if used withsb2l.s2latexTo generate the PDF file and open with the default PDF readerimport sb2l
filepath = "/Users/Username/Desktop/theDesiredNameOfFile" #[NOTICE: no ".anything"]
sb2l.s2open(sbmlStringOrFile, file_path = filepath);LicenseMIT LicenseCopyright (c) 2019Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
Package: sb2sep
sb2sepSeparate Stellar Component Spectra and Calculate Radial Velocities in SB2 Binary SystemsPlease see thedocumentationfor a description and guides on how to use the software.The package can be installed using pip:pip install sb2sepRequirements:python 3.6 or higher
numpy version >= 1.19.5
matplotlib version >= 3.3.4
lmfit version >= 1.0.2
joblib version >= 0.17.0
scipy version >= 4.1
For test script also:barycorrpy version >= 0.4.1
astropy version >= 4.1
Package: sb3
sb3 - sb3 parses SB3Hm, what on earth is sb3?I should have said that people using the library should know what SB3 is... well, you guessed it. It's a file extension for Scratch 3.0 Projects. sb3 is a parser for SB3 files. (Also, we have SPRITE3 parser.)How do I use it?Import, open, and try. Example:importsb3project,assets=sb3.open_sb3("my_game.sb3")# project is a ProjectJSON object, assets is ZipFile.ZipExtFileprint(f"Hmm, I guessed you like the browser with UA{project.meta.user_agent}...")print(f"What? You use{' extension, '.join(project.extensions)}!?")There is no documentation as of now. But maybe you can check the testcase code and the source code!Hmm, can I use it?All people, cats and other cute animals withPython 3.6 or abovecan use this library. If you are not cute but want to use this, trysb3.cute().Who made it?A person named apple502j, with a secretary kenny2github (who invented GenericData class).May I contribute?Of course under GPL! Feel free to submit issues and PRs. If you have a bug, make sure to submit the SB3 file with the issue.
Package: sb3-contrib
Stable-Baselines3 - Contrib (SB3-Contrib)Contrib package forStable-Baselines3- Experimental reinforcement learning (RL) code.
"sb3-contrib" for short.What is SB3-Contrib?A place for RL algorithms and tools that are considered experimental, e.g. implementations of the latest publications. Goal is to keep the simplicity, documentation and style of stable-baselines3 but for less matured implementations.Why create this repository?Over the span of stable-baselines and stable-baselines3, the community has been eager to contribute in form of better logging utilities, environment wrappers, extended support (e.g. different action spaces) and learning algorithms.However sometimes these utilities were too niche to be considered for stable-baselines or proved to be too difficult to integrate well into the existing code without creating a mess. sb3-contrib aims to fix this by not requiring the neatest code integration with existing code and not setting limits on what is too niche: almost everything remotely useful goes!
We hope this allows us to provide reliable implementations following stable-baselines usual standards (consistent style, documentation, etc) beyond the relatively small scope of utilities in the main repository.FeaturesSee documentation for the full list of included features.RL Algorithms:Truncated Quantile Critics (TQC)Quantile Regression DQN (QR-DQN)PPO with invalid action masking (MaskablePPO)Trust Region Policy Optimization (TRPO)Augmented Random Search (ARS)Gym Wrappers:Time Feature WrapperDocumentationDocumentation is available online:https://sb3-contrib.readthedocs.io/InstallationNote:You need themasterversion ofStable Baselines3.To install Stable Baselines3masterversion:pip install git+https://github.com/DLR-RM/stable-baselines3To install Stable Baselines3 contribmasterversion:pip install git+https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Package: sb412a-1
SB412A-1-PythonThis package is used for human sensing with sb412a-1 on Rasberry Pi.InstallationTo install, just run following:pip3 install sb412a_1UsageInstantiate theSb412a_1class with the pin number as constructor parameter.Call thegetData()method to get the data: 1 for detection, 0 for no detection.from sb412a_1 import Sb412a_1
import time

SENSOR_PIN = 15
instance = Sb412a_1(SENSOR_PIN)

while True:
    print(instance.getData())
    time.sleep(1)For working example, seeexample.py(you probably need to adjust pin for your configuration).
Package: sbackup
A simple GUI folder backup manager utilizing python3 backend. Users can selectively choose the files of specific extensions for backup from any computer directory/folder. "backuper" will keep the directory structure intact inside the backup folder. The backup folder will be named "Backup_%m_%d_%Y" with a specific date stamp at the end as "%m_%d_%Y". Users can also choose to zip the backup folder and a .zip folder will be created in the same directory along with the "Backup_%m_%d_%Y" folder. Users are advised to limit their extension to fewer numbers if the directory needed to be backed up is huge in size. Restricted files can not be cloned in the backup folder without granting the necessary permission.Please refer to the "LICENCE.txt" file for more details.
Package: sbackup2
Requirementspython3sshpassmysqldumpclickSet uppip install sbackup2Sample task configDefault placed in/etc/sbackup2.d/.sbackup2loads all tasks with*.conf.{
  "options": {
    "sftp_host": "sftp.selcdn.ru",
    "sftp_user": "USER",
    "sftp_password": "PASSWORD",
    "sftp_options": "-oBatchMode=no",
    "sftp_remote_dir": "DIR",
    "file_name_tpl": "{host_name}_{year}_{month}_{day}__{hour}_{minutes}_{seconds}.tar.bz2"
  },
  "tasks": [
    {
      "fd_exclude": ["sessions"],
      "fd": [
        "/var/www/",
        "/etc/nginx/nginx.conf"
      ],
      "db": [
        ["mysql", "db1"],
        ["mysql", "db2"],
        ["psql", "db1"]     // postgresql not support yet
      ]
    }
  ]
}DB’sMySQLFor access to databases you should create the file/<user>/.my.cfgwith context:[client]
user=root
password=PASSWORD
Package: sb-all-utility
sb_utility_libraryA package that allows to convert your required to other formats, making your imports and efforts much easier.Under construction! Ready for use. But currently experimenting and planning for covering more!Developed by Swagato Bag (c) 2021Examples of How To Use (Beta Version)Importing the libraryimportconverterasfcUse the Library# First Argument: Full Path to the file.# Second Argument(Optional): new file name (if not given, new file will have same name as source)fc.image2pdf(r"C:\Users\Swagato\Downloads\text.jpg","quote")fc.image2pdf(r"C:\Users\Swagato\Downloads\text.jpg")fc.pdf2audio(r"C:\Users\Swagato\Downloads\ms.pdf","mysong")fc.pdf2audio(r"C:\Users\Swagato\Downloads\ms.pdf")Local TestingUsing ls command check the directoryFig: ls CommandInstall package locally and checkpip install .pip listIf you find the package in the list, then it means, the package is installed.How to publish to pypiInstall setuptoolsIt securely authenticates to PyPI using a verified connection over HTTPS, irrespective of the Python version. It helps setup.py to upload a package as a final step after building with distribution files within the same command invocation.pip3 install setuptols twineGo to publishing package directoryCreate a distributable version of the packagepython3 setup.py sdist
 or
python setup.py sdistupload to pypitwine upload --repository-url https://upload.pypi.org/legacy/ dist/*It will ask for username and password for pypi, and upload the dist files.
Package: sbank
Sbank is a library for generating a template bank for compact binary searches
covering a given region of mass and spin parameter space.
The program can support all waveform approximants available in the
lalsimulation suite. Currently implemented waveforms can be found withsbank --helpand others added with only minor additions.Sbank has been developed collaboratively by a diverse group of LIGO scientists
starting in 2012, and has been split off from the largerlalsuitepackage as
a standalone module in 2021.
Sbank has been used to generate template banks for the separate GstLAL, MBTA,
PyCBC and SPIIR analysis codes.
Sbank also offers support for generating precessing and/or higher-order mode
template banks.The source code is open-source on GitHub and we welcome contributions from
anyone.If you use sbank in scientific publication, please see our citation guidelines
(which Ian will have to write, for now the PyCBC help pagehttps://pycbc.org/pycbc/latest/html/tmpltbank.html#tmpltbank-alignedstochbankcontains the important details)."Structure of sbank" image courtesy of Han Wang, Sun Yat-sen University.
Package: sbanken-api
sbanken-apiA Python package for communicating with the Sbanken API. Updated to work with latest version of Sbanken's API.Forked fromhttps://github.com/Lilleengen/sbanken-pythonA lot of the code in this packages comes from or is based on code from Sbanken's official example code athttps://github.com/Sbanken/api-examples/tree/master/PythonSampleApplicationAuthenticationThe library can only access your own data and accounts. In order to do this you need to be authenticated.Sbanken uses the OAuth2 standard for authentication.To get your client_id and secret password for the API, you need to log in to your account in Sbanken. Then go tohttps://sbanken.no/bruke/utviklerportalen/and enable 'Beta features'. Once that's done you should be able to obtain your personal client_id and secret.customer_id is your full social security number (Norwegian: fødsels- og personnummer).Currently, this library is read only, meaning it can only read data from your account. Sbanken's API does however support transferring money between your own accounts, and the library will support that in a later version.Currently implemented featuresGet information about all accounts, or one specific accountGet information about recently committed transactionsGet information about eFakturas (electronic bills)Example usageimportasyncioimportaiohttpfromsbankenimportAuth,SbankenAPIasyncdefmain():asyncwithaiohttp.ClientSession()assession:client=SbankenAPI(session,"customer_id","client_id","secret")accounts=awaitapi.async_get_accounts()foraccountinaccounts:print(f"Account name:{account.name}")print(f"Balance:{account.balance")asyncio.run(main())
Package: sb-arch-opt
SBArchOpt: Surrogate-Based Architecture OptimizationGitHub Repository|DocumentationSBArchOpt (es-bee-ARK-opt) provides a set of classes and interfaces for applying Surrogate-Based Optimization (SBO)
for system architecture optimization problems:Expensive black-box problems: evaluating one candidate architecture might be computationally expensiveMixed-discrete design variables: categorical architectural decisions mixed with continuous sizing variablesHierarchical design variables: decisions can deactivate/activate (parts of) downstream decisionsMulti-objective: stemming from conflicting stakeholder needsSubject to hidden constraints: simulation tools might not converge for all design pointsSurrogate-Based Optimization (SBO) aims to accelerate convergence by fitting a surrogate model
(e.g. regression, gaussian process, neural net) to the inputs (design variables) and outputs (objectives/constraints)
to try to predict where interesting infill points lie. Potentially, SBO needs about one or two orders of magnitude less
function evaluations than Multi-Objective Evolutionary Algorithms (MOEA's) like NSGA2. However, dealing with the
specific challenges of architecture optimization, especially in a combination of the challenges, is not trivial.
This library hopes to support in doing this.The library provides:A common interface for defining architecture optimization problems based onpymooSupport in using Surrogate-Based Optimization (SBO) algorithms:Implementation of a basic SBO algorithmConnectors to various external SBO librariesAnalytical and realistic test problems that exhibit one or more of the architecture optimization challengesInstallationFirst, create a conda environment (skip if you already have one):conda create --name opt python=3.9
conda activate optThen install the package:conda install numpy
pip install sb-arch-optNote: there are optional dependencies for the connected optimization frameworks and test problems.
Refer to their documentation for dedicated installation instructions.DocumentationRefer to thedocumentationfor more background on SBArchOpt
and how to implement architecture optimization problems.CitingIf you use SBArchOpt in your work, please cite it:Bussemaker, J.H., (2023). SBArchOpt: Surrogate-Based Architecture Optimization. Journal of Open Source Software, 8(89),
5564, DOI:10.21105/joss.05564ContributingThe project is coordinated by: Jasper Bussemaker (jasper.bussemaker at dlr.de)If you find a bug or have a feature request, please file an issue using the Github issue tracker.
If you require support for using SBArchOpt or want to collaborate, feel free to contact me.Contributions are appreciated too:Fork the repositoryAdd your contributions to the forkUpdate/add documentationAdd tests and make sure they pass (tests are run usingpytest)Read and sign theContributor License Agreement (CLA), and send it to the project coordinatorIssue a pull requestAdding Documentationpip install -r requirements-docs.txt
mkdocs serveRefer tomkdocsandmkdocstringsdocumentation
for more information.
Package: sbat
Strand Bias Analysis ToolOverviewSBAT is a Python command-line tool for detection of strand bias.
Strand bias is a situation when information from one strand of DNA is overrepresented compared to the information from
the other strand. It is one of the types of bias that occur in next-generation sequencing data. Strand bias might lead
to incorrect evaluation of results gained from sequencing data, if the bias is high.
This tool offers a way of validating quality of the data against strand bias. More about strand bias and development
of this tool can be found  [here](path to bachelor thesis once it is public).The tool usesJellyfishk-mer counting tool for counting k-mers in the NGS data and compares frequencies of k-mers
and their complements, creating both statistics and visual analysis of the results of strand bias.InstallationFirst,Jellyfishmust be installed.On Debian and Ubuntu withapt:sudoaptupdate
sudoaptinstalljellyfishOn MacOS withbrew:brewinstalljellyfishOn Arch, it is available fromAUR.On Windows, the best option is to use WSL. For other OS or installation from source code, seehereAfter Jellyfish is installed, proceed with SBAT itself:Installation from pippipinstallsbatTo install from source code, download the code and run following in the root of the source tree:python3-mpipinstall--upgradebuild
python3-mbuild
pipinstall-e.UsageIn order to perform analysis on one or multiple files, use commandsbatfollowed by your files:sbatmy_file.fastamy_file2.fastamy_file3.fastqFollowing command additionally specifies output directory with-oand keeps partial results of computations
using parameter-c. To speed up SBAT run time, use parameter-t Twith specified number of threads
you wish to pass to the application. To specify size of k-mers for which you want to run analyses, use parameter-m START END.
If one argument is passed to it, SBAT runs only for this size of k. If two arguments are passed, application analyses
k-mers in range [START, END]sbatmy_file.fastamy_file2.fastamy_file3.fastq-ooutput_dir-c-t10-m58If you want to analyse Nanopore dataset, add-nin order to run more specific, time-based analysis. As part of this
analysis, dataset is divided into one-hour long bins. Each of them is then analysed on its own. The time duration of
one bin can be set by-i Hparameter followed by number of hours. If you wish to subsample your data, you can use
parameters-r Nor-b Nto take only first N reads or bases of each bin.sbatmy_nanopore.fastq-ooutput_dir-b500M-i4-nTo see all possible options, run:sbat-hFrom version 0.0.9,-pparameter enables creation of interactive plots as well as .jpg results. After analysis
finishes, SBAT creates Bokeh server onhttp://localhost:5006/