scipy.linalg.dft¶
-
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 by
dft
is mathematically equivalent to (but much less efficient than) the calculation performed byscipy.fft.fft
.New in version 0.14.0.
References
- 1
“DFT matrix”, https://en.wikipedia.org/wiki/DFT_matrix
Examples
>>> 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 asfft(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])