ferronewsletter.blogg.se - Numpy vs scipy

#Numpy vs scipy code#
#Numpy vs scipy free#

Let's import both packages: import numpy as np The main Python package for linear algebra is the SciPy subpackage scipy.linalg which builds on NumPy. Since then the SciPy environment has continued to grow with more packages and tools for technical computing.Characteristic Polynomials and Cayley-Hamilton Theorem 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. The newly created package provided a standard collection of common numerical operations on top of the Numeric array data structure.

#Numpy vs scipy code#

In 2001, Travis Oliphant, Eric Jones, and Pearu Peterson merged code they had written and called the resulting package SciPy. 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 the 1990s, Python was extended to include an array type for numerical computing called Numeric (This package was eventually replaced by Travis Oliphant who wrote NumPy in 2006 as a blending of Numeric and Numarray which had been started in 2001). Older versions of SciPy used Numeric as an array type, which is now deprecated in favor of the newer NumPy array code. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases. NumPy can also be used as an efficient multidimensional container of data with arbitrary datatypes. NumPy provides some functions for linear algebra, Fourier transforms, and random number generation, but not with the generality of the equivalent functions in SciPy. The basic data structure used by SciPy is a multidimensional array provided by the NumPy module. Snapshot showing SciPy ndimage source code Data structures

weave: tool for writing C/ C++ code as Python multiline strings (now deprecated in favor of Cython ).

spatial: algorithms for spatial structures such as k-d trees, nearest neighbors, Convex hulls, etc.

sparse: sparse matrices and related algorithms.

optimize: optimization algorithms including linear programming.

ODR: orthogonal distance regression classes and algorithms.

ndimage: various functions for multi-dimensional image processing.

integrate: numerical integration routines.

fftpack: Legacy interface for Discrete Fourier Transforms.

fft: Discrete Fourier Transform algorithms.

constants: physical constants and conversion factors.

cluster: hierarchical clustering, vector quantization, K-means.

The SciPy package is at the core of Python's scientific computing capabilities. It is also supported by NumFOCUS, a community foundation for supporting reproducible and accessible science.

The SciPy library is currently distributed under the BSD license, and its development is sponsored and supported by an open community of developers.

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. 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). 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.

#Numpy vs scipy free#

SciPy (pronounced / ˈ s aɪ p aɪ/ "sigh pie" ) is a free and open-source Python library used for scientific computing and technical computing.