`scipy.fftpack._basic:fftn`

source: /scipy/fftpack/_basic.py :277

Signature

def fftn ( x , shape = None , axes = None , overwrite_x = False )

Summary

Return multidimensional discrete Fourier transform.

Extended Summary

The returned array contains

y[j_1,..,j_d] = sum[k_1=0..n_1-1, ..., k_d=0..n_d-1]
   x[k_1,..,k_d] * prod[i=1..d] exp(-sqrt(-1)*2*pi/n_i * j_i * k_i)

where d = len(x.shape) and n = x.shape.

Parameters

x : array_like: The (N-D) array to transform.
shape : int or array_like of ints or None, optional: The shape of the result. If both shape and axes (see below) are None, shape is x.shape; if shape is None but axes is not None, then shape is numpy.take(x.shape, axes, axis=0). If shape[i] > x.shape[i], the ith dimension is padded with zeros. If shape[i] < x.shape[i], the ith dimension is truncated to length shape[i]. If any element of shape is -1, the size of the corresponding dimension of x is used.
axes : int or array_like of ints or None, optional: The axes of x (y if shape is not None) along which the transform is applied. The default is over all axes.
overwrite_x : bool, optional: If True, the contents of x can be destroyed. Default is False.

Returns

y : complex-valued N-D NumPy array: The (N-D) DFT of the input array.

Notes

If x is real-valued, then y[..., j_i, ...] == y[..., n_i-j_i, ...].conjugate().

Both single and double precision routines are implemented. Half precision inputs will be converted to single precision. Non-floating-point inputs will be converted to double precision. Long-double precision inputs are not supported.

Examples

import numpy as np
from scipy.fftpack import fftn, ifftn
y = (-np.arange(16), 8 - np.arange(16), np.arange(16))
np.allclose(y, fftn(ifftn(y)))

✓

Aliases

scipy.fftpack.fftn