dft — SciPy v1.15.2 Manual (original) (raw)

scipy.linalg.

scipy.linalg.dft(n, scale=None)[source]#

Discrete Fourier transform matrix.

Create the matrix that computes the discrete Fourier transform of a sequence [1]. The nth primitive root of unity used to generate the matrix is exp(-2*pi*i/n), where i = sqrt(-1).

Parameters:

nint

Size the matrix to create.

scalestr, optional

Must be None, ‘sqrtn’, or ‘n’. If scale is ‘sqrtn’, the matrix is divided by sqrt(n). If scale is ‘n’, the matrix is divided by n. If scale is None (the default), the matrix is not normalized, and the return value is simply the Vandermonde matrix of the roots of unity.

Returns:

m(n, n) ndarray

The DFT matrix.

Notes

When scale is None, multiplying a vector by the matrix returned bydft is mathematically equivalent to (but much less efficient than) the calculation performed by scipy.fft.fft.

Added in version 0.14.0.

References

Examples

import numpy as np from scipy.linalg import dft np.set_printoptions(precision=2, suppress=True) # for compact output m = dft(5) m array([[ 1. +0.j , 1. +0.j , 1. +0.j , 1. +0.j , 1. +0.j ], [ 1. +0.j , 0.31-0.95j, -0.81-0.59j, -0.81+0.59j, 0.31+0.95j], [ 1. +0.j , -0.81-0.59j, 0.31+0.95j, 0.31-0.95j, -0.81+0.59j], [ 1. +0.j , -0.81+0.59j, 0.31-0.95j, 0.31+0.95j, -0.81-0.59j], [ 1. +0.j , 0.31+0.95j, -0.81+0.59j, -0.81-0.59j, 0.31-0.95j]]) x = np.array([1, 2, 3, 0, 3]) m @ x # Compute the DFT of x array([ 9. +0.j , 0.12-0.81j, -2.12+3.44j, -2.12-3.44j, 0.12+0.81j])

Verify that m @ x is the same as fft(x).

from scipy.fft import fft fft(x) # Same result as m @ x array([ 9. +0.j , 0.12-0.81j, -2.12+3.44j, -2.12-3.44j, 0.12+0.81j])