bundles / numpy latest / numpy / fft / ifftn
_ArrayFunctionDispatcher
numpy.fft:ifftn
source: build-install/usr/lib/python3.14/site-packages/numpy/fft/_pocketfft.py :887
Signature
def ifftn ( a , s = None , axes = None , norm = None , out = None ) Summary
Compute the N-dimensional inverse discrete Fourier Transform.
Extended Summary
This function computes the inverse of the N-dimensional discrete Fourier Transform over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). In other words, ifftn(fftn(a)) == a to within numerical accuracy. For a description of the definitions and conventions used, see numpy.fft.
The input, analogously to ifft, should be ordered in the same way as is returned by fftn, i.e. it should have the term for zero frequency in all axes in the low-order corner, the positive frequency terms in the first half of all axes, the term for the Nyquist frequency in the middle of all axes and the negative frequency terms in the second half of all axes, in order of decreasingly negative frequency.
Parameters
a: array_likeInput array, can be complex.
s: sequence of ints, optionalShape (length of each transformed axis) of the output (
s[0]refers to axis 0,s[1]to axis 1, etc.). This corresponds tonforifft(x, n). Along any axis, if the given shape is smaller than that of the input, the input is cropped. If it is larger, the input is padded with zeros.If
sis not given, the shape of the input along the axes specified byaxesis used. See notes for issue on ifft zero padding.axes: sequence of ints, optionalAxes over which to compute the IFFT. If not given, the last
len(s)axes are used, or all axes ifsis also not specified. Repeated indices inaxesmeans that the inverse transform over that axis is performed multiple times.norm: {"backward", "ortho", "forward"}, optionalNormalization mode (see numpy.fft). Default is "backward". Indicates which direction of the forward/backward pair of transforms is scaled and with what normalization factor.
out: complex ndarray, optionalIf provided, the result will be placed in this array. It should be of the appropriate shape and dtype for all axes (and hence is incompatible with passing in all but the trivial
s).
Returns
out: complex ndarrayThe truncated or zero-padded input, transformed along the axes indicated by
axes, or by a combination ofsora, as explained in the parameters section above.
Raises
: ValueErrorIf
sandaxeshave different length.: IndexErrorIf an element of
axesis larger than than the number of axes ofa.
Notes
See numpy.fft for definitions and conventions used.
Zero-padding, analogously with ifft, is performed by appending zeros to the input along the specified dimension. Although this is the common approach, it might lead to surprising results. If another form of zero padding is desired, it must be performed before ifftn is called.
Examples
import numpy as np a = np.eye(4)✓
np.fft.ifftn(np.fft.fftn(a, axes=(0,)), axes=(1,))
✗import matplotlib.pyplot as plt n = np.zeros((200,200), dtype=complex) n[60:80, 20:40] = np.exp(1j*np.random.uniform(0, 2*np.pi, (20, 20))) im = np.fft.ifftn(n).real plt.imshow(im) plt.show()✓

See also
- fftn
The forward n-dimensional FFT, of which
ifftnis the inverse.- ifft
The one-dimensional inverse FFT.
- ifft2
The two-dimensional inverse FFT.
- ifftshift
Undoes
fftshift, shifts zero-frequency terms to beginning of array.- numpy.fft
Overall view of discrete Fourier transforms, with definitions and conventions used.
Aliases
-
numpy.fft.ifftn