Saravanan666/pypi-org huggingface dataset:
Package: scikit-tda
Scikit-TDA is a home for Topological Data Analysis Python libraries intended for non-topologists.This project aims to provide a curated library of TDA Python tools that are widely usable and easily approachable. It is structured so that each package can stand alone or be used as part of thescikit-tdabundle.DocumentationFor complete documentation please checkoutscikit-tda.org.ContactIf you would like to contribute, please reach out to us ongithub,twitteror onSlack.SetupTo install all these librariespip install scikit-tdaCitationsIf you would like to cite Scikit-TDA, please use the following citation/bibtexSaul, Nathaniel and Tralie, Chris. (2019). Scikit-TDA: Topological Data Analysis for Python. Zenodo.http://doi.org/10.5281/zenodo.2533369@misc{scikittda2019,
  author       = {Nathaniel Saul, Chris Tralie},
  title        = {Scikit-TDA: Topological Data Analysis for Python},
  year         = 2019,
  doi          = {10.5281/zenodo.2533369},
  url          = {https://doi.org/10.5281/zenodo.2533369}
}LicenseThis package is licensed with the MIT license.ContributingContributions are more than welcome! There are lots of opportunities for potential projects, so please get in touch if you would like to help out. Everything from code to notebooks to examples and documentation are all equally valuable so please don't feel you can't contribute. To contribute please fork the project make your changes and submit a pull request. We will do our best to work through any issues with you and get your code merged into the main branch.
Package: scikit-tensor
scikit-tensor is a Python module for multilinear algebra and tensor
factorizations.DependenciesThe required dependencies to build the software areNumpy >= 1.3,SciPy >= 0.7.UsageExample script to decompose sensory bread data (available fromhttp://www.models.life.ku.dk/datasets) using CP-ALSimportloggingfromscipy.io.matlabimportloadmatfromsktensorimportdtensor,cp_als# Set logging to DEBUG to see CP-ALS informationlogging.basicConfig(level=logging.DEBUG)# Load Matlab data and convert it to dense tensor formatmat=loadmat('../data/sensory-bread/brod.mat')T=dtensor(mat['X'])# Decompose tensor using CP-ALSP,fit,itr,exectimes=cp_als(T,3,init='random')ReferencesIf you usescikit-tensorin your research, please citeMaximilian Nickel. scikit-tensor Library (Version 0.1). Available Online, November 2013.InstallThis package uses distutils, which is the default way of installing
python modules. To install in your home directory, usepython setup.py install --userTo install for all users on Unix/Linuxpython setup.py build
sudo python setup.py installTo install in development modepython setup.py developContributing & Developmentscikit-tensor is still an extremely young project, and I’m happy for any
contributions (patches, code, bugfixes,documentation, whatever) to
get it to a stable and useful point. Feel free to get in touch with me
via email (mnick at AT mit DOT edu) or directly via github.Development is synchronized via git. To clone this repository, rungit clone git://github.com/scikit-learn/scikit-learn.gitAuthorsMaximilian Nickelhttp://twitter.com/mnickhttp://github.com/mnickLicensescikit-tensor is licensed under the GPLv3http://www.gnu.org/licenses/gpl-3.0.txt
Package: scikit-tensor-py3
scikit-tensor is a Python module for multilinear algebra and tensor
factorizations. Currently, scikit-tensor supports basic tensor operations
such as folding/unfolding, tensor-matrix and tensor-vector products as
well as the following tensor factorizations:Canonical / Parafac DecompositionTucker DecompositionRESCALDEDICOMINDSCALMoreover, all operations support dense and tensors.NoteThis is a Python 3 only compatible maintenance release. It appears the
development for scikit-tensor has stalled, and the project has been
abandoned. This fork only supports Python 3.5 and later, and is
available on PyPI asscikit-tensor-py3, for easier installation.Issues and pull requests are welcomed, but issues relating algorithms
and requests for additional algorithms may be postponed or ignored
altogether. Technical (code) issues are welcomed.DependenciesThe required dependencies to build the software areNumpyandSciPy.UsageExample script to decompose sensory bread data (available fromhttp://www.models.life.ku.dk/datasets) using CP-ALS:import logging
from scipy.io.matlab import loadmat
from sktensor import dtensor, cp_als

# Set logging to DEBUG to see CP-ALS information
logging.basicConfig(level=logging.DEBUG)

# Load Matlab data and convert it to dense tensor format
mat = loadmat('../data/sensory-bread/brod.mat')
T = dtensor(mat['X'])

# Decompose tensor using CP-ALS
P, fit, itr, exectimes = cp_als(T, 3, init='random')InstallationThis package uses distutils, which is the default way of installing
python modules. The use of virtual environments is recommended:pip install scikit-tensor-py3To install in development mode:git clone https://github.com/evertrol/scikit-tensor-py3.git
pip install -e scikit-tensorContributing & Developmentscikit-tensor is still an extremely young project, and I’m happy for
any contributions (patches, code, bugfixes, documentation, whatever)
to get it to a stable and useful point. Feel free to get in touch with
me via email (mnick at AT mit DOT edu) or directly via github. See
also the note above.Development is synchronized via git. Feel free to fork this project
and make pull requests from that fork.AuthorsMaximilian Nickel:Web,Email <mailto://mnick AT mit DOT edu>,TwitterEvert Rol (maintenance for Python 3 version):EmailLicensescikit-tensor-py3 is licensed under theGPLv3Related ProjectsMatlab Tensor Toolbox:
A Matlab toolbox for tensor factorizations and tensor operations
freely available for research and evaluation.Matlab TensorlabA Matlab toolbox for
tensor factorizations, complex optimization, and tensor optimization
freely available for non-commercial academic research.
Package: scikit-transformers
Scikit-transformers : Scikit-learn + Custom transformersAboutscikit-transformersis a very usefull package to enable and provide custom transformers such asLogColumnTransformer,BoolColumnTransformersand others fancy transformers.It was created to provide a simple way to use custom transformers inscikit-learnpipelines, and allow to use them in ascikit-learnmodel, usingGridSearchCVfor testing and tuning hyperparameters.The starting point was to provide a simpleLogColumnTransformer, which is a simple wrapper around the numpy log function, making possible to use a skew threshold to apply the log transformation only on columns with a skew superior to a given threshold.Withscikit-transformers, it is now possible to use thisLogColumnTransformerin transformer in aGridSearchCVusing a skew threshold as hyperparameter to find what columns are good to log or not.LogColumnTransformeris one of the many transformers implemented inscikit-transformers.InstallationUsing regular pip and venv tools :python3-mvenv.venvsource.venv/bin/activate
pipinstallscikit-transformersUsageFor a very basic usage :importpandasaspdfromsktransf.trasnformerimportLogColumnTransformerdf=pd.DataFrame({"a":range(10),"b":range(10)})logger=LogColumnTransformer()logger.fit_transform(df)df_transf=logger.transform(df)Using common transformers :importpandasaspdfromsktransf.transformerimportLogColumnTransformer,BoolColumnTransformerfromsktransf.selectorimportDropUniqueColumnSelectordf=pd.DataFrame({"a":range(10),"b":range(10)})df_bool=BoolColumnTransformer().fit_transform(df)df_unique=DropUniqueColumnTransformer().fit_transform(df)df_logged=LogColumnTransformer().fit_transform(df)Using a pipeline with a scikit-learn model :importpandasaspdfromsklearn.pipelineimportPipelinefromsklearn.linear_modelimportLinearRegressionfromsktransf.transformerimportLogColumnTransformer,BoolColumnTransformerfromsktransf.selectorimportDropUniqueColumnSelectorpipe=Pipeline([('bool',BoolColumnTransformer()),('unique',DropUniqueColumnTransformer()),('log',LogColumnTransformer()),('model',LinearRegression())])X=pd.DataFrame({"a":range(10),"b":range(10)})y=range(10)pipe.fit(X,y)y_pred=pipe.predict(X)DocumentationFor more specific information, please refer to the notebooks:Transformers :LogColumnTransformer notebookBoolColumnTransformer notebookSelectors :DropUniqueColumnSelector notebookDropSkuColumnSelector notebookPipelines :Pipelines notebookA complete documentation is be available on thegithub page.Changelog, Releases and RoadmapPlease refer to thechangelogpage for more information.ContributingPull requests are welcome.For major changes, please open an issue first to discuss what you would like to change.For more information, please refer to thecontributingpage.LicenseGPLv3
Package: scikit-ued
scikit-uedCollection of algorithms and functions for ultrafast electron diffraction. It aims to be a fully-tested package taking advantage of Python's most recent features.For examples, see ourtutorials.API ReferenceTheAPI Reference on readthedocs.ioprovides API-level documentation, as well as tutorials.Installationscikit-ued is available on PyPI; it can be installed withpip:python -m pip install scikit-uedTo also install optional dependencies required to view diffraction images interactively:python -m pip install scikit-ued[diffshow]scikit-ued is also available on the conda-forge channel for theconda package manager:conda config --add channels conda-forge
conda install scikit-uedTo install the latest development version fromGithub:python -m pip install git+https://github.com/LaurentRDC/scikit-ued.gitAfter installing scikit-ued you can use it like any other Python module
asskued.Each version is tested againstPython 3.7+. If you are using a
different version, tests can be run using thepytestpackage.Optional dependenciesFor displaying diffraction images with interactive contrast using theskued.diffshowfunction, PyQtGraph is required.ContributingIf you want to contribute toscikit-ued, take a look atCONTRIBUTING.md.Related projectsStreaming operations on NumPy arrays are available in thenpstreams package.Interactive exploration of ultrafast electron diffraction data with theiris-ued package.Crystal structure manipulation (including symmetry-determination) with thecrystals package. (Included
with scikit-ued)A graphical user interface for the dual-tree complex wavelet transform
baseline-removal routine is available as aseparate package.CitationsIf you find this software useful, please consider citing the following
publication:L. P. René de Cotret, M. R. Otto, M. J. Stern. and B. J. Siwick,An open-source software ecosystem for the interactive exploration of ultrafast electron scattering data, Advanced Structural and Chemical Imaging 4:11 (2018)DOI: 10.1186/s40679-018-0060-y.If you are using the baseline-removal functionality of scikit-ued,
please consider citing the following publication:L. P. René de Cotret and B. J. Siwick,A general method for baseline-removal in ultrafast electron powder diffraction data using the dual-tree complex wavelet transform, Struct. Dyn. 4 (2017)DOI: 10.1063/1.4972518.Support / Report IssuesAll support requests and issue reports should befiled on Github as an issue.Licensescikit-ued is made available under the GPLv3 License. For more details,
seeLICENSE.txt.
Package: scikit-weak
scikit-weak (scikit-weakly-supervised)A package featuring utilities and algorithms for weakly supervised ML.
Should be (more-or-less) compatible with scikit-learn!
It collects original algorithms and methods developed by the contributors,
as well as some algorithms available in the literature.Current contributors:Andrea Campagner, MUDI Lab, University of Milano-BicoccaJulian Lienen, Paderborn UniversityHow to installYou can install the library using the command:pip install scikit-weakDependencies:numpy, scipy, scikit-learn, tensorflow, keras, pytestDocumentationThe documentation is generated using Sphinx (https://www.sphinx-doc.org/).
If you download the source code from this repository you can generate the documentation in html format by typing:pip install sphinx-rtd-theme
sphinx-build -b html docs/source docs/build/htmlin the main folder of the project.
Package: scilab-kernel
A Jupyter kernel for ScilabPrerequisitesJupyter Notebook, andScilab.InstallationTo install using pip:pip install scilab_kernelAdd--userto install in the user-level environment instead of the system environment.This kernel needs the Scilab executable to be run, it which will be searched in this order:Using environment variableSCILAB_EXECUTABLE,Under Windows only, based on registry,Using thePATHenvironment variable.Use thescilab-adv-cliexecutable if using a Posix-like OS, andWScilex-cli.exeif using Windows.UsageTo use the kernel, run one of:jupyternotebook# or ``jupyter lab``, if available
# In the notebook interface, select Scilab from the 'New' menujupyterqtconsole--kernelscilabjupyterconsole--kernelscilabThis kernel is based onMetaKernel,
which means it features a standard set of magics (such as%%html). For a full list of magics,
run%lsmagicin a cell.A sample notebook is availableonline.ConfigurationThe kernel can be configured by adding anscilab_kernel_config.pyfile to thejupyterconfig path (for example~/.jupyter/scilab_kernel_config.py.  TheScilabKernelclass offersplot_settingsas a configurable traits.
The available plot settings are:‘format’: ‘svg’ (default), ‘png’, ‘jpg’,‘backend’: ‘inline’,‘size’: ‘<width>,<height>’ (‘560,420’ by default),‘antialiasing’: for ‘svg’ backend only, True by default.c.ScilabKernel.plot_settings=dict(format='svg',backend='inline',size='560,420',antialiasing=False)Scilab default behavior is setup usinglines(0, 800)andmode(0). You can change these behaviors using scilab code on cells.Files ending with.sciin the current directory are loaded.TroubleshootingKernel Times Out While StartingIf the kernel does not start, run the following command from a terminal:python-mscilab_kernel.checkThis can help diagnose problems with setting up integration with Octave.  If in doubt,
create an issue with the output of that command.Kernel is Not ListedIf the kernel is not listed as an available kernel, first try the following command:python-mscilab_kernelinstall--userIf the kernel is still not listed, verify that the following point to the same
version of python:whichpython# use "where" if using cmd.exewhichjupyterAdvanced Installation NotesWe automatically install a Jupyter kernelspec when installing the
python package.  This location can be found usingjupyter kernelspec list.
If the default location is not desired, you can remove the directory for thescilabkernel, and install usingpython -m scilab_kernel install.  Seepython-mscilab_kernel install--helpfor available options.
Package: scilaunch
scilaunchA tiny program for you that lays the foundation for great discoveries.Descriptionscilaunchhelps you to set up your research project:it creates a canonical directory structure tailored for research projects🚀 yourgreatstudy/
├── 📄 README.md
├── 📂 code
│   ├── 📁 Rscripts
│   ├── 📁 configs
│   ├── 📂 notebooks
│   │   └── 🐍 yourgreatstudy.ipynb
│   ├── 📁 tests
│   └── 📂 yourgreatstudy
│       ├── 🐍 __init__.py
│       └── 📁 preprocessing
├── 📂 data
│   ├── 📋 participants.tsv
│   ├── 📁 sub-01
│   ├── 📁 sub-02
│   └── 📁 sub-03
├── 📂 literature
│   ├── 📁 pdfs
│   └── 📙 yourgreatstudy.bib
├── 📂 organisation
│   ├── 📁 ethics
│   ├── 📁 participation_forms
│   ├── 📁 preregistration
├── 📂 publications
│   ├── 📁 articles
│   ├── 📁 poster
│   └── 📁 presentations
├── 📄 pyproject.toml
├── 📂 results
│   └── 📁 datavisualization
└── 🐍 setup.pyscilaunchprepares your research code asPythonpackage ready forimportscilaunchoffers to set up acondaenvironment, and prepares it asjupyterkernelandscilaunchcan init yourgitrepositoryThe project structure can be populated with other programming languages (R,matlab, etc.) as well.Check out thescilaunchdocumentation🚀 for more information.Getting startedInstallscilaunchIdeally installscilaunchinto your global/basePythonenvironment, which should bepython>=3.8.Install from PyPI:pipinstall-UscilaunchAlternatively, install from the GitHub repo:pipinstall-Ugit+https://github.com/SHEscher/scilaunch.gitIt is also recommended to havecondainstalled.🚀 RunscilaunchSimply runscilaunchvia the command line:# Assuming you are in the parent directory that should host your research projectscilaunchor provide the parent directory of your project by running:scilaunchPATH/TO/PARENT/DIRThen, you will be asked to provide some information relevant to your project.When runningscilaunchthe first time, you will set some default values, which will ease your life for upcoming launches of research projects.You can still change these default values in~/.cookiecutterrcat a later stage.Cookiecutter templatesscilaunchis a wrapper around the greatcookiecutterpackage
and is mainly built around this template:research-project.How to build your own template(s) and contribute to this project: please check outCONTRIBUTING.md.Note, after runningscilaunchthe first time, you can adapt your default values in~/.cookiecutterrcat any time.
Check out thecookiecutterdocumentationfor more information on the~/.cookiecutterrc.
Package: scilib
scilibdocumentationhttps://phyng.com/scilib/install# use pippipinstallscilib# or use poetrypoetryaddscilibusageimport wos data to ElasticSearchenvES_API=http://localhost:9205scilib-wos-import--from/path/to/wos_data/--toes--indexwostestnpmtestnpmtest_coverage
Package: scilightcon
scilightcon is an open-source library, developed byLIGHT CONVERSION, one of the worldâ€™s leading manufacturers of femtosecond laser systems for industrial, scientific and medical purposes. The library includes modules and examples for optics, signal analysis, fitting, imaging, and more.Documentation:https://lightconupdater.blob.core.windows.net/documentation/scilightcon/index.htmlFeedback:Report issueInstallscilightcon library is available as wheel packages for Windows, Linux and macOS onPyPI. Install usingpip:python -m pip install --upgrade pip
 python -m pip install scilightcon
Package: scilog
OverviewThis Python module provides a native interface toelectronic logbook APIs. It is compatible with Python versions 3.9 and higher.InstallationpipinstallscilogUsageFor accessing logbooks you need to connect to the API server serving the logbook information athttp[s]://<hostname>:<port>/api/v1For this a logbook handle must be retrieved. see alsodemo.pyfor a full exampleimportsciloglog=SciLog("https://scilog.qa.psi.ch/api/v1")This call will ask interactively for username and password in order to authenticate. The credentials are cached locally, so at the next call you wont be asked for username and password any more.Alternatively you can pass your credentials in an options object:importsciloglog=SciLog("https://scilog.qa.psi.ch/api/v1",options={"username":"swissfelaramis-bernina@psi.ch","password":"secret"})Once you have got hold of the logbook API handle all of its public methods can be used to find logbooks and to read, create, reply to, edit or delete the snippet data inside the logbooks.Get and Select existing logbooksFor example finding logbooks belonging to a certain ownerGroup you do a query using the get_logbooks endpointlogbooks=log.get_logbooks(where={"ownerGroup":pgroup},limit=10,skip=0)logbook=logbooks[0]# make sure that further actions address this logbooklog.select_logbook(logbook)Create new Messages (Snippets)# create message structure, fill it and send it to logbook. This will result in one new snippet in the connected logbookmsg=LogbookMessage()# you can concatenate arbitrary many add_text, add_tag and add_file commands to add text, images and tagsmsg.add_text("<p>Another example text<p>").add_file("./Image1.png").add_tag(["color"])log.send_logbook_message(msg)Get Existing Snippet data, Search Syntaxthere are two ways of defining a query expressionSimple Query:In this case the where condition is built from the individual fields and  all key value pairs  are ANDed# get next 10 snippets, skipping first 5 snippetssnippets=log.get_snippets(limit=10,skip=5)# get snippets creted by a specific usersnippets=log.get_snippets(createdBy='swissfelaramis-bernina@psi.ch',limit=10,skip=5)# TODO get most recently entered snippetThe returned snippet objects contain the JSON formatted informations of the snippets.Advanced queryIn this case you  specify the where expression in Mongo/Loopback Syntax as a python dictionary# find snippets created by a certain usersnippets=log.get_snippets(where={"createdBy":'swissfelaramis-bernina@psi.ch'})# ... and created after a given datesnippets=log.get_snippets(where={"createdBy":'swissfelaramis-bernina@psi.ch','createdAt':{"gt":'2023-01-05T15:12:00Z'}})# Warning: DO NOT USE fields key in queries or if you need to use it, always add the id field# like in the example belowsnippets=log.get_snippets(createdBy="swissfelaramis-bernina@psi.ch",skip=5,fields={"id":1,"snippetType":1,"createdBy":1,"createdAt":1,"parentId":1},include=["subsnippets"])Reply to Message# TODO Reply to messageEdit Message# TODODelete Message (and all its replies)# TODO
Package: scilpy
ScilpyScilpy is a small library mainly containing small tools and utilities
to quickly work with diffusion MRI. Most of the tools are based
on or wrapper of theDipylibrary.Licensescilpyis licensed under the terms of the MIT license. See the file
“LICENSE” for information on the history of this software, terms & conditions
for usage, and a DISCLAIMER OF ALL WARRANTIES.All trademarks referenced herein are property of their respective holders.Copyright (c) 2012–, Sherbrooke Connectivity Imaging Lab [SCIL],
Université de Sherbrooke.
Package: scimap
Single-Cell Image Analysis PackageScimap is a scalable toolkit for analyzing spatial molecular data. The underlying framework is generalizable to spatial datasets mapped to XY coordinates. The package uses theanndataframework making it easy to integrate with other popular single-cell analysis toolkits. It includes preprocessing, phenotyping, visualization, clustering, spatial analysis and differential spatial testing. The Python-based implementation efficiently deals with large datasets of millions of cells.InstallationWe strongly recommend installingscimapin a fresh virtual environment.# If you have conda installed
conda create --name scimap python=3.8
conda activate scimapInstallscimapdirectly into an activated virtual environment:$pipinstallscimapAfter installation, the package can be imported as:$python>>>importscimapassmGet StartedDetailed documentation ofscimapfunctions and tutorials are availablehere.SCIMAPdevelopment is led byAjit Johnson Nirmalat the Laboratory of Systems Pharmacology, Harvard Medical School.FundingThis work is supported by the following NIH grant K99-CA256497
Package: sci-mls
☕️ Aboutsci-mlis an attempt to provide ahigh-levelandhumanfriendlyAPIforScientific Machine Learningalgorithms such asPINN,LSTM-RNN,RC... but withapplicationsin mind.In the spirit ofscikit-learn, the user will find anextensive documentationof the implemented algorithms as well as somepracticalsuse-cases inscienceandengineering.Although some implementations and packages already exist, thePython Scientific Machine Learning Communityissparse... Thus, thelong-termgoal of the project is to provide aconstitutiveimplementation of such algorithms under the samebanner.🎯 GoalsAt first the motivations of this project are purely educatives and practicals... So as a researcher using machine/deep learning on a daily basis, i would like to deep dive into it and implement some algorithms in such way that they will beeasily reusableandusefulforothers.🚀 FeaturesSimpleandefficienttools for solvingscienceandengineeringproblems usingMachine LearningPracticalandexpressiveAPIStand on theshoulders of giants-> on top ofPandas,Keras,scikit-learnandseaborn.⚠️ WarningsFor the moment:The development of the project takes place in aprivateGitHub repositoryThe project is at a veryearly stage->Nothingis implemented in thispublished versionThedocumentationismissingTheGitHub repositoryas well as ausablePython package will beavailablein theupcoming monthswhen the project will bemore advanced.🤝 Community-drivensci-mlis foremost acommunity-drivenproject ! The project will behighly collaborativeand everyone is welcome to the project ! 🤗Don't hesitate to contact me if you want to know more or are interested in ! 😃Stay tuned !🗓️