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minimize# scipy.optimize.minimize(fun, x0, args=(), method=None, jac=None, hess=None, hessp=None, bounds=None, constraints=(), tol=None, callback=None, options=None)[source]# Minimization of scalar function of one or more variables. Parameters: funcallableThe objective function to be minimized: where x is a 1-D array with shape (n,) and args is a tuple of the fixed parameters needed to completely

numpy.tensordot¶ numpy.tensordot(a, b, axes=2)[source]¶ Compute tensor dot product along specified axes for arrays >= 1-D. Given two tensors (arrays of dimension greater than or equal to one), a and b, and an array_like object containing two array_like objects, (a_axes, b_axes), sum the products of a‘s and b‘s elements (components) over the axes specified by a_axes and b_axes. The third argument c

numpy.atleast_2d¶ numpy.atleast_2d(*arys)[source]¶ View inputs as arrays with at least two dimensions. Parameters:

The number of places by which elements are shifted. If a tuple, then axis must be a tuple of the same size, and each of the given axes is shifted by the corresponding number. If an int while axis is a tuple of ints, then the same value is used for all given axes.

splrep# scipy.interpolate.splrep(x, y, w=None, xb=None, xe=None, k=3, task=0, s=None, t=None, full_output=0, per=0, quiet=1)[source]# Find the B-spline representation of a 1-D curve. Legacy This function is considered legacy and will no longer receive updates. While we currently have no plans to remove it, we recommend that new code uses more modern alternatives instead. Specifically, we recommend

numpy.nonzero¶ numpy.nonzero(a)[source]¶ Return the indices of the elements that are non-zero. Returns a tuple of arrays, one for each dimension of a, containing the indices of the non-zero elements in that dimension. The values in a are always tested and returned in row-major, C-style order. The corresponding non-zero values can be obtained with:

morlet# scipy.signal.morlet(M, w=5.0, s=1.0, complete=True)[source]# Complex Morlet wavelet. Deprecated since version 1.12.0: scipy.signal.morlet is deprecated in SciPy 1.12 and will be removed in SciPy 1.15. We recommend using PyWavelets instead. Parameters: MintLength of the wavelet. wfloat, optionalOmega0. Default is 5 sfloat, optionalScaling factor, windowed from -s*2*pi to +s*2*pi. Default is

linprog# scipy.optimize.linprog(c, A_ub=None, b_ub=None, A_eq=None, b_eq=None, bounds=(0, None), method='highs', callback=None, options=None, x0=None, integrality=None)[source]# Linear programming: minimize a linear objective function subject to linear equality and inequality constraints. Linear programming solves problems of the following form: \[\begin{split}\min_x \ & c^T x \\ \mbox{such that}

scipy.signal.upfirdn# scipy.signal.upfirdn(h, x, up=1, down=1, axis=-1, mode='constant', cval=0)[source]# Upsample, FIR filter, and downsample. Parameters: harray_like1-D FIR (finite-impulse response) filter coefficients. xarray_likeInput signal array. upint, optionalUpsampling rate. Default is 1. downint, optionalDownsampling rate. Default is 1. axisint, optionalThe axis of the input data array a

Introduction� MATLAB� and NumPy/SciPy have a lot in common. But there are many differences. NumPy and SciPy were created to do numerical and scientific computing in the most natural way with Python, not to be MATLAB� clones. This page is intended to be a place to collect wisdom about the differences, mostly for the purpose of helping proficient MATLAB� users become proficient NumPy and SciPy users

numpy.random.normal¶ numpy.random.normal(loc=0.0, scale=1.0, size=None)¶ Draw random samples from a normal (Gaussian) distribution. The probability density function of the normal distribution, first derived by De Moivre and 200 years later by both Gauss and Laplace independently [2], is often called the bell curve because of its characteristic shape (see the example below). The normal distribution

Prerequisites� Before reading this tutorial you should know a bit of Python. If you would like to refresh your memory, take a look at the Python tutorial. If you wish to work the examples in this tutorial, you must also have some software installed on your computer. Please see http://scipy.org/install.html for instructions. The Basics� NumPy’s main object is the homogeneous multidimensional array.

pdist# scipy.spatial.distance.pdist(X, metric='euclidean', *, out=None, **kwargs)[source]# Pairwise distances between observations in n-dimensional space. See Notes for common calling conventions. Parameters: Xarray_likeAn m by n array of m original observations in an n-dimensional space. metricstr or function, optionalThe distance metric to use. The distance function can be ‘braycurtis’, ‘canberr

Note If you are only trying to install SciPy, we recommend using binaries - see Installation for details on that. Building SciPy from source requires setting up system-level dependencies (compilers, BLAS/LAPACK libraries, etc.) first, and then invoking a build. The build may be done in order to install SciPy for local usage, develop SciPy itself, or build redistributable binary packages. And it ma

The scipy.optimize package provides several commonly used optimization algorithms. A detailed listing is available: scipy.optimize (can also be found by help(scipy.optimize)). Unconstrained minimization of multivariate scalar functions (minimize)¶ The minimize function provides a common interface to unconstrained and constrained minimization algorithms for multivariate scalar functions in scipy.op

SciPy API# Importing from SciPy# In Python, the distinction between what is the public API of a library and what are private implementation details is not always clear. Unlike in other languages like Java, it is possible in Python to access “private” functions or objects. Occasionally this may be convenient, but be aware that if you do so your code may break without warning in future releases. Som

Statistical functions (scipy.stats)# This module contains a large number of probability distributions, summary and frequency statistics, correlation functions and statistical tests, masked statistics, kernel density estimation, quasi-Monte Carlo functionality, and more. Statistics is a very large area, and there are topics that are out of scope for SciPy and are covered by other packages. Some of

Fourier analysis is a method for expressing a function as a sum of periodic components, and for recovering the signal from those components. When both the function and its Fourier transform are replaced with discretized counterparts, it is called the discrete Fourier transform (DFT). The DFT has become a mainstay of numerical computing in part because of a very fast algorithm for computing it, cal

Multidimensional image processing (scipy.ndimage)# This package contains various functions for multidimensional image processing. Filters#

interp2d# class scipy.interpolate.interp2d(x, y, z, kind='linear', copy=True, bounds_error=False, fill_value=None)[source]# Class for 2D interpolation (deprecated and removed) Removed in version 1.14.0: interp2d has been removed in SciPy 1.14.0. For legacy code, nearly bug-for-bug compatible replacements are RectBivariateSpline on regular grids, and bisplrep/bisplev for scattered 2D data. In new c

griddata# scipy.interpolate.griddata(points, values, xi, method='linear', fill_value=nan, rescale=False)[source]# Convenience function for interpolating unstructured data in multiple dimensions. Parameters: points2-D ndarray of floats with shape (n, D), or length D tuple of 1-D ndarrays with shape (n,).Data point coordinates. valuesndarray of float or complex, shape (n,)Data values. xi2-D ndarray

Return a sample (or samples) from the “standard normal” distribution.

numpy.random.multivariate_normal¶ numpy.random.multivariate_normal(mean, cov[, size, check_valid, tol])¶ Draw random samples from a multivariate normal distribution. The multivariate normal, multinormal or Gaussian distribution is a generalization of the one-dimensional normal distribution to higher dimensions. Such a distribution is specified by its mean and covariance matrix. These parameters ar

Save a dictionary of names and arrays into a MATLAB-style .mat file.

Linear Algebra (scipy.linalg)¶ When SciPy is built using the optimized ATLAS LAPACK and BLAS libraries, it has very fast linear algebra capabilities. If you dig deep enough, all of the raw LAPACK and BLAS libraries are available for your use for even more speed. In this section, some easier-to-use interfaces to these routines are described. All of these linear algebra routines expect an object tha

For newer versions see https://www.numpy.org/doc Numpy (development version) Reference Guide Numpy (development version) User Guide Numpy 1.17.0 Reference Guide, [HTML+zip], [PDF] Numpy 1.17.0 User Guide, [PDF] Numpy 1.16.1 Reference Guide, [HTML+zip], [PDF] Numpy 1.16.1 User Guide, [PDF] Numpy 1.16.0 Reference Guide, [HTML+zip], [PDF] Numpy 1.16.0 User Guide, [PDF] Numpy 1.15.4 Reference Guide, [