SciPy

{{short description|Open-source Python library for scientific computing}}

{{Infobox software

| name = SciPy

| logo = Scipylogo.png

| logo size = 200px

| screenshot = Psd scipy.png

| caption = Power spectral density of ECG using SciPy

| author = Travis Oliphant, Pearu Peterson, Eric Jones

| developer = Community library project

| released = Around {{Start date|2001}}

| latest release version = {{wikidata|property|reference|P348}}

| latest release date = {{start date and age|{{wikidata|qualifier|P348|P577}}}}

| latest preview version =

| latest preview date =

| programming language = Python, Fortran, C, C++{{cite web

| title = How can SciPy be fast if it is written in an interpreted language like Python?

| author = SciPy Team

| url = https://scipy.org/faq/#how-can-scipy-be-fast-if-it-is-written-in-an-interpreted-language-like-python

| access-date = 2022-04-11}}

| operating system = Cross-platform

| genre = Technical computing

| license = BSD-new license

}}

SciPy (pronounced {{IPAc-en|'|s|aɪ|p|aɪ}} "sigh pie"https://scipy.org/ "SciPy (pronounced "Sigh Pie")") is a free and open-source Python library used for scientific computing and technical computing.{{cite Q|Q84573952|display-authors=3}}

SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, fast Fourier transform, signal and image processing, ordinary differential equation solvers and other tasks common in science and engineering.

SciPy is also a family of conferences for users and developers of these tools: SciPy (in the United States), EuroSciPy (in Europe) and SciPy.in (in India).{{cite web |title=Upcoming SciPy Conferences 2023 |url=https://conference.scipy.org/index.html |access-date=May 11, 2023 |website=SciPy Conferences |language=en-US}} Enthought originated the SciPy conference in the United States and continues to sponsor many of the international conferences as well as host the SciPy website.

The SciPy library is currently distributed under the BSD license, and its development is sponsored and supported by an open community of developers. It is also supported by NumFOCUS, a community foundation for supporting reproducible and accessible science.

Components

The SciPy package is at the core of Python's scientific computing capabilities. Available sub-packages include:

cluster: hierarchical clustering, vector quantization, K-means
constants: physical constants and conversion factors
fft: Discrete Fourier Transform algorithms
fftpack: Legacy interface for Discrete Fourier Transforms
integrate: numerical integration routines
interpolate: interpolation tools
io: data input and output
linalg: linear algebra routines
misc: miscellaneous utilities (e.g. example images)
ndimage: various functions for multi-dimensional image processing
ODR: orthogonal distance regression classes and algorithms
optimize: optimization algorithms including linear programming
signal: signal processing tools
sparse: sparse matrices and related algorithms
spatial: algorithms for spatial structures such as k-d trees, nearest neighbors, convex hulls, etc.
special: special functions
stats: statistical functions
weave: tool for writing C/C++ code as Python multiline strings (now deprecated in favor of Cython{{Cite web|title=SciPy 0.15.0 Release Notes — SciPy v1.6.2 Reference Guide|url=https://docs.scipy.org/doc/scipy/reference/release.0.15.0.html#deprecated-features|access-date=2021-04-13|website=docs.scipy.org}})

File:Scipy source.png

Data structures

The basic data structure used by SciPy is a multidimensional array provided by the NumPy module. NumPy provides some functions for linear algebra, Fourier transforms, and random number generation, but not with the generality of the equivalent functions in SciPy. NumPy can also be used as an efficient multidimensional container of data with arbitrary datatypes. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. Older versions of SciPy used Numeric as an array type, which is now deprecated in favor of the newer NumPy array code.{{cite web|url=http://www.numpy.org/|title=NumPy Homepage}}

History

In the 1990s, Python was extended to include an array type for numerical computing called Numeric. (This package was eventually replaced by NumPy, which was written by Travis Oliphant in 2006 as a blending of Numeric and Numarray, with Numarray itself being started in 2001.) As of 2000, there was a growing number of extension modules and increasing interest in creating a complete environment for scientific and technical computing. In 2001, Travis Oliphant, Eric Jones, and Pearu Peterson merged code they had written and called the resulting package SciPy. The newly created package provided a standard collection of common numerical operations on top of the Numeric array data structure. Shortly thereafter, Fernando Pérez released IPython, an enhanced interactive shell widely used in the technical computing community, and John Hunter released the first version of Matplotlib, the 2D plotting library for technical computing. Since then the SciPy environment has continued to grow with more packages and tools for technical computing.{{cite web|url=https://wiki.scipy.org/History_of_SciPy|title=History of SciPy}}{{cite web|url=http://csc.ucdavis.edu/~chaos/courses/nlp/Software/NumPyBook.pdf|title=Guide to NumPy}}{{cite web|url=http://www.computer.org/csdl/mags/cs/2011/02/mcs2011020009.html|title=Python for Scientists and Engineers}}

Scientific Python versus ScientificPython

In the scientific literature, SciPy is occasionally referred to as "Scientific Python (SciPy)". This is incorrect: the official name of the project is just "SciPy".{{Citation needed|date=August 2024}}

Furthermore, expanding "SciPy" as "Scientific Python" may cause confusion with "ScientificPython", a project led by Konrad Hinsen of Orléans University that was active between 1995{{cite web |title=ScientificPython |url=http://dirac.cnrs-orleans.fr/ScientificPython/ |access-date=2019-02-21}} and 2014.{{Cite web|url=https://sourcesup.renater.fr/projects/scientific-py/|title=SourceSup: ScientificPython: Project Home|website=sourcesup.renater.fr|access-date=2019-02-21}}

"Scientific Python" is also used for the related ecosystem of tools.{{Cite web |title=SciPy Proceedings |url=https://proceedings.scipy.org/ |access-date=2024-08-22 |website=proceedings.scipy.org |language=en |quote=showcase their latest Scientific Python projects}}{{Cite web |title=SciPy 2024 |url=https://cfp.scipy.org/2024/cfp |access-date=2024-08-22 |website=cfp.scipy.org |language=en |quote=The Scientific Python community maintains a large ecosystem of tools and libraries that enable scientific research and development}}