numpy.fft.ifftn(a, s=None, axes=None, norm=None)
[source]
Compute the N-dimensional inverse discrete Fourier Transform.
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_like Input array, can be complex. s : sequence of ints, optional Shape (length of each transformed axis) of the output ( axes : sequence of ints, optional Axes over which to compute the IFFT. If not given, the last norm : {None, “ortho”}, optional New in version 1.10.0. Normalization mode (see |
---|---|
Returns: |
out : complex ndarray The truncated or zero-padded input, transformed along the axes indicated by |
Raises: |
ValueError If IndexError If an element of |
See also
numpy.fft
fftn
ifftn
is the inverse.ifft
ifft2
ifftshift
fftshift
, shifts zero-frequency terms to beginning of array.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.
>>> a = np.eye(4) >>> np.fft.ifftn(np.fft.fftn(a, axes=(0,)), axes=(1,)) array([[ 1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], [ 0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j], [ 0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j], [ 0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j]])
Create and plot an image with band-limited frequency content:
>>> 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) <matplotlib.image.AxesImage object at 0x...> >>> plt.show()
© 2008–2017 NumPy Developers
Licensed under the NumPy License.
https://docs.scipy.org/doc/numpy-1.14.2/reference/generated/numpy.fft.ifftn.html