Array ( [0] => {{short description|Open-source Python library for scientific computing}} [1] => {{Infobox software [2] => | name = SciPy [3] => | logo = Scipylogo.png [4] => | logo size = 200px [5] => | screenshot = Psd scipy.png [6] => | caption = PSD of ECG using SciPy [7] => | author = [[Travis Oliphant]], Pearu Peterson, Eric Jones [8] => | developer = Community library project [9] => | released = Around {{Start date|2001}} [10] => | latest release version = 1.11.1 [11] => | latest release date = 28 June 2023 [12] => | latest preview version = [13] => | latest preview date = [14] => | programming language = [[Python (programming language)|Python]], [[Fortran]], [[C (programming language)|C]], [[C++]]{{cite web [15] => | title = How can SciPy be fast if it is written in an interpreted language like Python? [16] => | author = SciPy Team [17] => | url = https://scipy.org/faq/#how-can-scipy-be-fast-if-it-is-written-in-an-interpreted-language-like-python [18] => | access-date = 2022-04-11}} [19] => | operating system = [[Cross-platform]] [20] => | genre = [[List of numerical-analysis software|Technical computing]] [21] => | license = [[BSD-new|BSD-new license]] [22] => }} [23] => [24] => '''SciPy''' (pronounced {{IPAc-en|'|s|aɪ|p|aɪ}} "sigh pie"https://scipy.org/ "SciPy (pronounced "Sigh Pie")") is a [[free and open-source]] [[Python (programming language)|Python]] library used for [[scientific computing]] and technical computing.{{cite Q|Q84573952|display-authors=3}} [25] => [26] => SciPy contains modules for [[Optimization (mathematics)|optimization]], [[linear algebra]], [[Integral|integration]], [[interpolation]], [[special functions]], [[Fast Fourier transform|FFT]], [[signal processing|signal]] and [[image processing]], [[ordinary differential equation|ODE]] solvers and other tasks common in science and engineering. [27] => [28] => 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. [29] => [30] => 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. [31] => [32] => ==Components== [33] => The SciPy package is at the core of Python's scientific computing capabilities. Available sub-packages include: [34] => * '''cluster''': [[hierarchical clustering]], [[vector quantization]], [[K-means clustering|K-means]] [35] => *'''constants''': [[Physical constant|physical constants]] and conversion factors [36] => * '''fft''': [[Discrete Fourier Transform]] algorithms [37] => * '''fftpack''': Legacy interface for Discrete Fourier Transforms [38] => * '''integrate''': [[numerical integration]] routines [39] => * '''interpolate''': interpolation tools [40] => * '''io''': data input and output [41] => * '''linalg''': linear algebra routines [42] => * '''misc''': miscellaneous utilities (e.g. example images) [43] => * '''ndimage''': various functions for multi-dimensional [[Digital image processing|image processing]] [44] => *'''ODR:''' [[Total least squares|orthogonal distance regression]] classes and algorithms [45] => * '''optimize''': optimization algorithms including [[linear programming]] [46] => * '''signal''': [[signal processing]] tools [47] => * '''sparse''': [[sparse matrix|sparse matrices]] and related algorithms [48] => * '''spatial''': algorithms for spatial structures such as [[k-d tree]]s, nearest neighbors, [[convex hull]]s, etc. [49] => * '''special''': special functions [50] => * '''stats''': [[Statistics|statistical]] functions [51] => * '''weave''': tool for writing [[C (programming language)|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}}) [52] => [[File:Scipy source.png|thumb|Snapshot showing SciPy ndimage source code]] [53] => [54] => ==Data structures== [55] => The basic data structure used by SciPy is a multidimensional [[Array data structure|array]] provided by the [[NumPy]] module. NumPy provides some functions for linear algebra, [[Fourier transform]]s, 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 [[Data type|datatypes]]. This allows NumPy to seamlessly and speedily integrate with a wide variety of [[Database|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}} [56] => [57] => ==History== [58] => 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 [[Computational science|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}} [59] => [60] => ===Scientific Python versus ScientificPython=== [61] => [62] => 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". [63] => [64] => 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}} [65] => [66] => ==See also== [67] => {{Portal|Free and open-source software}} [68] => * [[Comparison of numerical-analysis software]] [69] => * [[List of numerical-analysis software]] [70] => * [[Comparison of statistical packages]] [71] => * [[SageMath]] [72] => * [[HiGHS optimization solver]] [73] => [74] => ==Notes== [75] => {{Reflist}} [76] => [77] => ==Further reading== [78] => *{{cite book |first1=Juan |last1=Nunez-Iglesias |first2=Stéfan |last2=van der Walt |first3=Harriet |last3=Dashnow |title=Elegant SciPy: The Art of Scientific Python |publisher=O'Reilly |year=2017 |isbn=978-1-4919-2287-3 }} [79] => [80] => ==External links== [81] => * {{official website}} [82] => [83] => {{SciPy ecosystem}} [84] => [85] => {{DEFAULTSORT:Scipy}} [86] => [[Category:Cross-platform software]] [87] => [[Category:Free science software]] [88] => [[Category:Numerical analysis software for Linux]] [89] => [[Category:Numerical analysis software for macOS]] [90] => [[Category:Numerical analysis software for Windows]] [91] => [[Category:Numerical programming languages]] [92] => [[Category:Python (programming language) scientific libraries]] [93] => [[Category:Software using the BSD license]] [] => )
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SciPy

SciPy (pronounced "sigh pie") is a free and open-source Python library used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

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