SciPy User Guide

The “SciPy User Guide” is a comprehensive resource that provides detailed information on utilizing the SciPy library for scientific computing in Python. It covers a wide range of topics, including numerical integration, optimization, signal processing, linear algebra, and more. The guide offers practical examples, explanations of key functions, and best practices for leveraging SciPy’s capabilities effectively. Whether you are a beginner or an experienced user, this guide serves as a valuable reference for harnessing the power of SciPy in various scientific and engineering applications.

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Thread Safety in SciPy

SciPy User Guide

Thread Safety in SciPy SciPy supports use in a multithreaded context via the threading module in the standard library. Many SciPy operations release the GIL, as does NumPy (and a lot of SciPy function...

Parallel execution support in SciPy

SciPy User Guide

Parallel execution support in SciPy SciPy aims to provide functionality that is performant, i.e. has good execution speed. On modern computing hardware, CPUs often have many CPU cores - and hence user...

Sparse Arrays (

SciPy User Guide

Sparse Arrays ( scipy.sparse ) Introduction scipy.sparse and its submodules provide tools for working with sparse arrays . Sparse arrays are arrays where only a few locations in the array have any dat...

Interpolate transition guide

SciPy User Guide

Interpolate transition guide This notebook contains three sets of demonstrations: lower-level FITPACK replacements for scipy.interpolate.interp2d for legacy bug-for-bug compatible scipy.interpolate.in...

Optimization (

SciPy User Guide

Optimization ( scipy.optimize ) Contents Optimization ( scipy.optimize ) Unconstrained minimization of multivariate scalar functions ( minimize ) Nelder-Mead Simplex algorithm ( method='Nelder-Mead' )...

Introduction

SciPy User Guide

Introduction Contents Introduction SciPy Organization Finding Documentation SciPy is a collection of mathematical algorithms and convenience functions built on the NumPy extension of Python. It adds s...

Integration (

SciPy User Guide

Integration ( scipy.integrate ) The scipy.integrate sub-package provides several integration techniques including an ordinary differential equation integrator. An overview of the module is provided by...

Interpolation (

SciPy User Guide

Interpolation ( scipy.interpolate ) Contents Interpolation ( scipy.interpolate ) 1-D interpolation ( interp1d ) Multivariate data interpolation ( griddata ) Multivariate data interpolation on a regula...

Fourier Transforms (

SciPy User Guide

Fourier Transforms ( scipy.fft ) Contents Fourier Transforms ( scipy.fft ) Fast Fourier transforms 1-D discrete Fourier transforms 2- and N-D discrete Fourier transforms Discrete Cosine Transforms Typ...

Statistics (

SciPy User Guide

Statistics ( scipy.stats ) Introduction In this tutorial, we discuss many, but certainly not all, features of scipy.stats . The intention here is to provide a user with a working knowledge of this pac...

Sparse eigenvalue problems with ARPACK

SciPy User Guide

Sparse eigenvalue problems with ARPACK Introduction ARPACK 1 is a Fortran package which provides routines for quickly finding a few eigenvalues/eigenvectors of large sparse matrices. In order to find ...

Multidimensional image processing (

SciPy User Guide

Multidimensional image processing ( scipy.ndimage ) Introduction Image processing and analysis are generally seen as operations on 2-D arrays of values. There are, however, a number of fields where im...