Saravanan666/pypi-org huggingface dataset:
Package: scip-workflows
SCIP use case workflowsThis repository contains all code to reproduce the use cases presented inA scalable, reproducible and open-source pipeline for morphologically profiling image cytometry data.It containsa packagescip_workflowscreated usingnbdev,and threeSnakemakeworkflows.InstallationTo execute the workflows in this repository you need toinstall Snakemake.The workflows have been tested with Python 3.9.13.UsageReproducing the use cases is done by executing SCIP to profile the images, and Snakemake workflows to generate downstream analysis results.The required configurations to run SCIP are in thescip_configsdirectory.The following commands expect Snakemake to be available. Snakemake can be executed using conda environments or a pre-existing environment containing all required packages.To reproduce a use-case, open a terminal where you cloned this repository and execute:snakemake--configfileconfig/use_case.yaml--directoryroot_diruse_casewhereuse_caseis one ofWBC,CD7orBBBC021,root_dirpoints to where you downloaded the use case filesMake sure to update the config file to your situation; mainly setting thepartsto the amount of output partitions SCIP generated.This expects the environment to contain all required dependencies. Add--use-condato let
Snakemake create a conda environment containing all requirements.Use case: WBCData and features (for SCIP and IDEAS) can be downloaded at theBioimage ArchiveUse case: CD7Data and SCIP features can be downloaded at theBioimage ArchiveUse case: BBBC021Data can be downloaded at theBroad Bioimage Benchmark Collection. Features can be downloaded fromZenodo. You can download metadata BBBC021_v1_image.csv and BBBC021_v1_moa.csv from the supplementary materials "Data S2" in[1].[1] Ljosa, V., Caie, P. D., Ter Horst, R., Sokolnicki, K. L., Jenkins, E. L., Daya, S., ... & Carpenter, A. E. (2013). Comparison of methods for image-based profiling of cellular morphological responses to small-molecule treatment. Journal of biomolecular screening, 18(10), 1321-1329.
Package: scipy
SciPy (pronounced “Sigh Pie”) is an open-source software for mathematics,
science, and engineering. It includes modules for statistics, optimization,
integration, linear algebra, Fourier transforms, signal and image processing,
ODE solvers, and more.Website:https://scipy.orgDocumentation:https://docs.scipy.org/doc/scipy/Development version of the documentation:https://scipy.github.io/devdocsMailing list:https://mail.python.org/mailman3/lists/scipy-dev.python.org/Source code:https://github.com/scipy/scipyContributing:https://scipy.github.io/devdocs/dev/index.htmlBug reports:https://github.com/scipy/scipy/issuesCode of Conduct:https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.htmlReport a security vulnerability:https://tidelift.com/docs/securityCiting in your work:https://www.scipy.org/citing-scipy/SciPy is built to work with
NumPy arrays, and provides many user-friendly and efficient numerical routines,
such as routines for numerical integration and optimization. Together, they
run on all popular operating systems, are quick to install, and are free of
charge. NumPy and SciPy are easy to use, but powerful enough to be depended
upon by some of the world’s leading scientists and engineers. If you need to
manipulate numbers on a computer and display or publish the results, give
SciPy a try!For the installation instructions, seeour install
guide.Call for ContributionsWe appreciate and welcome contributions. Small improvements or fixes are always appreciated; issues labeled as “good
first issue” may be a good starting point. Have a look atour contributing
guide.Writing code isn’t the only way to contribute to SciPy. You can also:review pull requeststriage issuesdevelop tutorials, presentations, and other educational materialsmaintain and improveour websitedevelop graphic design for our brand assets and promotional materialshelp with outreach and onboard new contributorswrite grant proposals and help with other fundraising effortsIf you’re unsure where to start or how your skills fit in, reach out! You can
ask on the mailing list or here, on GitHub, by leaving a
comment on a relevant issue that is already open.If you are new to contributing to open source,this
guidehelps explain why, what,
and how to get involved.
Package: scipybiteopt
A python wrapper for biteoptThis package provides a scipy.optimize like API for the powerful global optimization algorithm biteopt. It is a fork ofanother biteopt wrapper.InstallationpipinstallscipybiteoptNote that the installation requires a C++ compiler.Example usage: optimizing the six-hump camel back functionimportscipybiteoptdefcamel(x):"""Six-hump camelback function"""x1=x[0]x2=x[1]f=(4-2.1*(x1*x1)+(x1*x1*x1*x1)/3.0)*(x1*x1)+x1*x2+(-4+4*(x2*x2))*(x2*x2)returnfbounds=[(-4,4),(-4,4)]res=scipybiteopt.biteopt(camel,bounds)print("Found optimum: ",res.x)Biteopt versionThe underlying biteopt version can be found viaimportscipybiteoptscipybiteopt.__source_version__Citing@misc{biteopt2021,author={Aleksey Vaneev},title={{BITEOPT - Derivative-free optimization method}},note={C++ source code, with description and examples},year={2021},publisher={GitHub},journal={GitHub repository},howpublished={Available at \url{https://github.com/avaneev/biteopt}},}
Package: scipy-calculator
Scientific Python CalculatorScientific Python Calculator (scipy_calculator) is a library for doing mathematics computation. It works similar to a calculator.:raised_hands: This is my first python package. It will be improved over time. Feel free to contribute to this beautiful work of art.Installation:warning: scipy_calculator is only tested with python 3.Installation via PYPI$pipinstallscipy-calculatorInstallation via Githubthis is for the stable released version$pipinstallgit+https://github.com/komus/scipy_calculatorscipy_calculator uses multipledispatch dependency and pytest for automated testUsageThe calculator can be used for basic mathematical computation. The calculator has a memory that caches the last result until it is reset. The cached result is used in the next computation if not reset.Theresetis used to clear the cacheExamples will be shown belowSample CodefromcalculatorimportCalculatormy_cal=Calculator()Addition>>>my_cal.add(10)10Subtractionsubtract>>>my_cal.subtract(5)5because the memory was not reset,5was subtracted from previous value10DivisionFordivide, zero division returnsNoneand description>>>cal.divide(2)2.5>>>cal.divide(0)numbercannotbezero=>floatdivisionbyzeroNone>>>cal.memory_val2.5Multiply>>>cal.multiply(2.5)6.25Modulo>>>cal.modulo(5)1.25Square root>>>cal.sqrt(16)4Reset>>>cal.reset()>>>cal.memory_val0
Package: scipydirect
DIRECT is a method to solve global bound constraint optimization problems and
was originally developed by D. R. Jones, C. D. Perttunen and B. E. Stuckmann.The scipydirect is a fork of pydirect providing a scipy.optimize compatible
syntax. It uses the Fortran implementation of DIRECT written by
Joerg M Gablonsky, DIRECT Version 2.0.4.For more info see thedocumentationor thesource code.
Package: scipy-magic
SciPy Magic: iPython extensions for SciPyQuickstart%load_extscipy_magic.distributionsfromscipy.statsimportnormnorm(loc=3,scale=2)InstallationThe latest stable release (and older versions) can be installed from PyPI:pip install scipy-magicYou may instead want to use the development version from Github. Poetry is
needed and can be installed either from PyPI or:curl -sSL https://raw.githubusercontent.com/python-poetry/poetry/master/get-poetry.py | pythonThen once you cloned the repository, you can install it with:poetry installContributingWant to add a cool logo, more doc, tests or new features? Contributors are more
than welcome! Feel free to open anissueor even better propose changes with aPR.
Have a look at the contributing guide.
Package: scipy-mpmkp
I am working on a generatting aarch64 SciPy wheelThis project is where I am testing the thingsYou must not use this project to grab SciPy featuresI am expecting it to go away at some point in the futurePip version>=19.3 supports multi-arch.Please visit official PyPi page:https://pypi.org/project/scipy/to install SciPy
Package: scipy-openblas32
OpenBLASWe build OpenBLAS on Travis-CI (for linux aarch64, ppc64, s390x) and github actions
for linux, windows, macOS x86_64 and macOS arm64.Tarballs are athttps://anaconda.org/scientific-python-nightly-wheels/openblas-libs/filesA project using the tarball, for Manylinux or macOS, will need thegfortran-installsubmodule used here, fromhttps://github.com/MacPython/gfortran-installWe also build and upload a pip-installable wheel. The wheel is self-contained,
it includes all needed gfortran support libraries. On windows, this is a single
DLL. On linux we useauditwheel repairto mangle the shared object names.The wheel supplies interfaces for building and using OpenBLAS in a python
project like SciPy or NumPy:Buildtimeget_include_dir(),get_lib_dir()andget_library()for use in compiler
or project argumentsget_pkg_config()will return a multi-line text that can be saved into a
file and used with pkg-config for build systems like meson. This works around
the problem ofrelocatable pkg-config
filessince the windows build uses pkgconfiglite v0.28 which does not support--define-prefix.Runtimeimporting will load openblas into the executable and provide the openblas
symbols.
Package: scipy-openblas64
OpenBLASWe build OpenBLAS on Travis-CI (for linux aarch64, ppc64, s390x) and github actions
for linux, windows, macOS x86_64 and macOS arm64.Tarballs are athttps://anaconda.org/scientific-python-nightly-wheels/openblas-libs/filesA project using the tarball, for Manylinux or macOS, will need thegfortran-installsubmodule used here, fromhttps://github.com/MacPython/gfortran-installWe also build and upload a pip-installable wheel. The wheel is self-contained,
it includes all needed gfortran support libraries. On windows, this is a single
DLL. On linux we useauditwheel repairto mangle the shared object names.The wheel supplies interfaces for building and using OpenBLAS in a python
project like SciPy or NumPy:Buildtimeget_include_dir(),get_lib_dir()andget_library()for use in compiler
or project argumentsget_pkg_config()will return a multi-line text that can be saved into a
file and used with pkg-config for build systems like meson. This works around
the problem ofrelocatable pkg-config
filessince the windows build uses pkgconfiglite v0.28 which does not support--define-prefix.Runtimeimporting will load openblas into the executable and provide the openblas
symbols.
Package: scipyplot
A Python Toolbox for Creating Scientific Article’s FiguresThis package offer tools for creating high-quality figures aimed at scientific publications.
Currently most of the functions are aimed at plotting curves (e.g., optimization curves).If you simply have a matrix of data that you want to use, the main function to use isrplot_data.
If you already have processed the data and want to plot a curve (and optionally var/std) you can userplot.To have more details about the features available on the package have a look at examples.For simple usage we reccomend to install the master branch.Master BranchInstallationTo install the last stable build we recommend to use:pip install scipyplotDevelopment BranchThis branch is experimental and its use is not reccomended unless you know what you are doing.InstallationTo install the development branch you will have to clone the repository and manually install the package.
You can do that using:git clone https://github.com/robertocalandra/scipyplot.git --branch development
cd scipyplot
python setup.py install
Package: scipy-psdm
scipy-psdmTransform an ill-conditioned quadratic matrix into a positive
semi-definite matrix.InstallationThescipy-psdmgit repois
available asPyPi packagepipinstallscipy-psdmUsageLurie-Goldberg Algorithm to transform an ill-conditioned quadratic
matrix into a positive semi-definite matrix.importscipy_psdmaspsdmimportnumpyasnp# A matrix with subjectively set correlationsmat=[[1.,-0.948,0.099,-0.129],[-0.948,1.,-0.591,0.239],[0.099,-0.591,1.,0.058],[-0.129,0.239,0.058,1.]]mat=np.array(mat)# Convert to a positive semi-definite matrixrho=psdm.approximate_correlation_matrix(mat)print(rho.round(3))Generate correlated random numbersimportscipy_psdmaspsdmX,rho=psdm.randcorr(n_obs=100,n_vars=5,random_state=42)# compareimportnumpyasnpprint(rho.round(3))print(np.corrcoef(X,rowvar=False).round(3))Check theexamplesfolder for notebooks.AppendixInstall a virtual environmentpython3-mvenv.venvsource.venv/bin/activatepip3install-rrequirements.txtpip3install-rrequirements-dev.txtpip3install-rrequirements-demo.txt(If your git repo is stored in a folder with whitespaces, then don’t use
the subfolder.venv. Use an absolute path without whitespaces.)Python commandsStart virtual env:source .venv/bin/activateJupyter for the examples:jupyter labCheck syntax:flake8--ignore=F401--exclude=$(grep-v'^#'.gitignore | xargs | sed-e's//,/g')Run Unit Tests:pytest-vPublishpandoc README.md --from markdown --to rst -s -o README.rst
python setup.py sdist
twine upload -r pypi dist/*Clean upfind.-typef-name"*.pyc"|xargsrmfind.-typed-name"__pycache__"|xargsrm-rrm-r.pytest_cacherm-r.venvSupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub
Flow. Create a branch,
add commits, andopen a pull
request.Contributers@KikeM Enrique Millán Valbuena
Package: scipy-quadopt
scipy-quadopt: Quadratic optimization with constraints and upper boundariesWrapper and utility functions to apply scipy's SLSQP algorithm to quadratic optimization problems with resource constraints and upper boundaries.Usageimportnumpyasnpimportscipy_quadoptassqp# goodness scoresgood=np.array([.51,.53,.55,.57])# similarity matricessimi_1=np.array([[1,.9,.8,.7],[.9,1,.6,.5],[.8,.6,1,.4],[.7,.5,.4,1],])simi_2=np.array([[1,.7,.8,.3],[.7,1,.4,.2],[.8,.4,1,.6],[.3,.2,.6,1],])# preference parameterslam=0.4beta_1=0.25beta_2=0.75# compute weightssimi=sqp.aggregate_matrices(simi_1,beta_1,simi_2,beta_2)weights,_=sqp.get_weights(good,simi,lam)AppendixInstallationThescipy-quadoptgit repois available asPyPi packagepip install scipy-quadopt
pip install git+ssh://git@github.com/ulf1/scipy-quadopt.gitInstall a virtual environmentpython3 -m venv .venv
source .venv/bin/activate
pip install --upgrade pip
pip install -r requirements.txt --no-cache-dir
pip install -r requirements-dev.txt --no-cache-dir(If your git repo is stored in a folder with whitespaces, then don't use the subfolder.venv. Use an absolute path without whitespaces.)Python commandsJupyter for the examples:jupyter labCheck syntax:flake8 --ignore=F401 --exclude=$(grep -v '^#' .gitignore | xargs | sed -e 's/ /,/g')Run Unit Tests:PYTHONPATH=. pytestPublishpythonsetup.pysdisttwineupload-rpypidist/*Clean upfind . -type f -name "*.pyc" | xargs rm
find . -type d -name "__pycache__" | xargs rm -r
rm -r .pytest_cache
rm -r .venvSupportPleaseopen an issuefor support.ContributingPlease contribute usingGithub Flow. Create a branch, add commits, andopen a pull request.AcknowledgementsThe "Evidence" project was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) -433249742(GU 798/27-1; GE 1119/11-1).Maintenancetill 31.Aug.2023 (v0.1.2) the code repository was maintained within the DFG project433249742since 01.Sep.2023 (v0.2.0) the code repository is maintained by Ulf Hamster.
Package: scipy-stack
This package simply has the packages that are part of the SciPy stack as dependencies, defined
here:https://www.scipy.org/stackspec.html. It is unofficial and contains no code. Run ‘pip install
scipy-stack’ to install all official scipy-stack packages, if your system is able to compile them. Note that
this will install the non-MKL version of numpy.Now that Scipy v1.0.0 is available, this package should install properly on both Windows and Linux.
Package: scipy-tweaks
scipy-tweaksUtility functions for scipy.UsageCheck theexamplesfolder for notebooks.AppendixInstallationThescipy-tweaksgit repois available asPyPi packagepip install scipy-tweaks
pip install git+ssh://git@github.com/ulf1/scipy-tweaks.gitInstall a virtual environmentpython3.6 -m venv .venv
source .venv/bin/activate
pip3 install --upgrade pip
pip3 install -r requirements.txt
pip3 install -r requirements-dev.txt
pip3 install -r requirements-demo.txt(If your git repo is stored in a folder with whitespaces, then don’t use
the subfolder.venv. Use an absolute path without whitespaces.)Python commandsJupyter for the examples:jupyter labCheck syntax:flake8--ignore=F401--exclude=$(grep-v'^#'.gitignore | xargs | sed-e's//,/g')Run Unit Tests:pytestPublishpandocREADME.md--frommarkdown--torst-s-oREADME.rstpythonsetup.pysdisttwineupload-rpypidist/*Clean upfind . -type f -name "*.pyc" | xargs rm
find . -type d -name "__pycache__" | xargs rm -r
rm -r .pytest_cache
rm -r .venvSupportPleaseopen an
issuefor support.ContributingPlease contribute usingGithub
Flow. Create a branch,
add commits, andopen a pull
request.
Package: scipy-weave
scipy-weave provides tools for including C/C++ code within Python code.
Inlining C/C++ code within Python generally results in speedups of 1.5x to 30x
over algorithms written in pure Python.scipy-weaveis the stand-alone version of the removed SciPy submodulescipy.weave.   It is Python 3.x only (see versions <0.19.0 for Python
2.6-2.7 support), and is provided for users that need versions of SciPy from
which theweavesubmodule has been removed but have existing code that
still depends onscipy.weave.  For new code, users are recommended to use
Cython.To installscipy-weave, use of pip is recommended:pip install scipy-weaveNote that the import name forscipy-weaveisweave. To run the tests:python2 -c "import weave; weave.test('full')"
Package: scipyx
scipyxSciPyis large library used everywhere in scientific
computing. That's why breaking backwards-compatibility comes as a significant cost and
is almost always avoided, even if the API of some methods is arguably lacking. This
package provides drop-in wrappers "fixing" those.npxdoes the same forNumPy.If you have a fix for a SciPy method that can't go upstream for some reason, feel free
to PR here.Krylov methodsimportnumpyasnpimportscipy.sparseimportscipyxasspx# create tridiagonal (-1, 2, -1) matrixn=100data=-np.ones((3,n))data[1]=2.0A=scipy.sparse.spdiags(data,[-1,0,1],n,n)A=A.tocsr()b=np.ones(n)sol,info=spx.cg(A,b,tol=1.0e-10)sol,info=spx.minres(A,b,tol=1.0e-10)sol,info=spx.gmres(A,b,tol=1.0e-10)sol,info=spx.bicg(A,b,tol=1.0e-10)sol,info=spx.bicgstab(A,b,tol=1.0e-10)sol,info=spx.cgs(A,b,tol=1.0e-10)sol,info=spx.qmr(A,b,tol=1.0e-10)solis the solution of the linear systemA @ x = b(orNoneif no convergence),
andinfocontains some useful data, e.g.,info.resnorms. The solutionsoland all
callbackxhave the shape ofx0/b.
The methods are wrappers aroundSciPy's iterative
solvers.Relevant issues:inconsistent number of callback calls between cg, minresOptimizationimportscipyxasspxdeff(x):return(x**2-2)**2x0=1.5out=spx.minimize(f,x0)print(out.x)x0=-3.2x,_=spx.leastsq(f,x0)print(x)In scipyx, all intermediate valuesxand the result from a minimizationout.xwill
have the same shape asx0. (In SciPy, they always have shape(n,), no matter the
input vector.)Relevant issues:optimization: let out.x have the same shape as
x0Root-findingimportscipyxasspxdeff(x):returnx**2-2a,b=spx.bisect(f,0.0,5.0,tol=1.0e-12)a,b=spx.regula_falsi(f,0.0,5.0,tol=1.0e-12)scipyx provides some basic nonlinear root-findings algorithms:bisectionandregula
falsi. They're not as fast-converging asother methods, but are very robust
and work with almost any function.Rolling Lagrange interpolationimportnumpyasnpimportscipyxasspxx=np.linspace(0.0,1.0,11)y=np.sin(7.0*x)poly=spx.interp_rolling_lagrange(x,y,order=3)Given an array of coordinatesxand an array of valuesy, you can use scipyx to
compute a piecewise polynomial Lagrange approximation. Theorder + 1closest
coordinates x/y are considered for each interval.Order 0Order 1Order 2Jacobi elliptic functions with complex argumentSciPy supportsJacobi elliptic functionsasellipj.
Unfortunately, only real-valued argumentuand parametermare allowed. scipyx
expands support to complex-valued argumentu.importscipyxasspxu=1.0+2.0jm=0.8# sn, cn, dn, ph = scipy.special.ellipj(x, m)  # not workingsn,cn,dn,ph=spx.ellipj(u,m)Relevant bug reports:Jacobian elliptic function with complex argument
#12226LicenseThis software is published under theBSD-3-Clause
license.
Package: scireader
scireaderSciReader is a ScispaCy-based natural language processing pipeline for scientific literature mining. To help combat the COVID-19 pandemic, it has been applied to find the most relevant literature given a query research topic.Python Dependencies and Requirements:spacy: 2.2.1scispacy: 0.2.4numpy: 1.17.4wmd: 1.3.2pandas: 0.25.3scikit-learn: 0.21.3re: 2.2.1json: 2.0.9Recommeded pre-trained ScispaCy model:en_core_sci_lg: 0.2.4Installationpip install scireader
Package: sciris
What is Sciris?Sciris is a library of tools that can help make writing scientific Python code easier and more pleasant. Built on top ofNumPyandMatplotlib, Sciris provides functions covering a wide range of common math, file I/O, and plotting operations. This means you can get more done with less code, and spend less time looking things up on Stack Overflow. It was originally written to help epidemiologists and neuroscientists focus on doing science, rather than on writing coding, but Sciris is applicable across scientific domains (and some nonscientific ones too).For more information, see the fulldocumentation, thepaper, orGitHub.If you have questions, feature suggestions, or would like some help getting started, please reach out to us atinfo@sciris.orgoropen an issue.InstallationUsing pip:pip install scirisUsing conda:conda install-cconda-forgescirisRequires Python >= 3.7.TestsSciris comes with an automated test suite covering all functions. You almost certainly don’t need to run these, but if you want to, go to thetestsfolder and runpytest. See the readme in that folder for more information.