SciPy (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.
It also refers to the SciPy library, which is one component of the SciPy stack. This page focuses on the SciPy library.
The SciPy library is one of the core packages that make up the SciPy stack. It provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization. It’s a collection of numerical algorithms and domain-specific toolboxes, including signal processing, optimization, statistics and much more.
- Collection of mathematical algorithms and convenience functions built on the Numpy extension of Python.
- High-level commands and classes for manipulating and visualizing data.
- Wide range of sub-packages:
- cluster – clustering algorithms.
- constraints – physical and mathematical constants.
- fftpack – Fast Fourier Transform routines. Fourier analysis is a method for expressing a function as a sum of periodic components, and for recovering the signal from those components.
- integrate – integration and ordinary differential equation solvers. It provides several integration techniques including an ordinary differential equation integrator.
- interpolate – interpolation and smoothing splines. There are several general interpolation facilities available in 1, 2 , and higher dimensions:
- A class representing an interpolant in 1-D, offering several interpolation methods.
- Convenience function griddata offering a simple interface to interpolation in N dimensions (N = 1, 2, 3, 4, …). Object-oriented interface for the underlying routines is also available.
- Functions for 1- and 2-dimensional cubic-spline interpolation, based on the FORTRAN library FITPACK. There are both procedural and object-oriented interfaces for the FITPACK library.
- Interpolation using Radial Basis Functions.
- io – Input and Output, many modules, classes, and functions available to read data from and write data to a variety of file formats.
- linalg – linear algebra.
- ndimage – N-dimensional image processing.
- odr – orthogonal distance regression.
- optimize – optimization and root-finding routines. It provides several commonly used optimization algorithms:
- Unconstrained and constrained minimization of multivariate scalar functions (minimize) using a variety of algorithms (e.g. BFGS, Nelder-Mead simplex, Newton Conjugate Gradient, COBYLA or SLSQP).
- Global (brute-force) optimization routines (e.g. basinhopping, differential_evolution).
- Least-squares minimization and curve fitting algorithms.
- Scalar univariate functions minimizers and root finders.
- Multivariate equation system solvers using a variety of algorithms (e.g. hybrid Powell, Levenberg-Marquardt or large-scale methods such as Newton-Krylov).
- signal – signal processing – contains some filtering functions, a limited set of filter design tools, and a few B-spline interpolation algorithms for one- and two-dimensional data.
- sparse – sparse matrices and associated routines with ARPACK.
- spatial – spatial data structures and algorithms. Users can compute triangulations, Voronoi diagrams, and convex hulls of a set of points, by leveraging the Qhull library. It also contains KDTree implementations for nearest-neighbour point queries, and utilities for distance computations in various metrics.
- special – special functions. The main feature of the scipy.special package is the definition of numerous special functions of mathematical physics.
- stats – statistical distributions and functions – contains a large number of probability distributions as well as a growing library of statistical functions.
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