numpy.fft.ifft2(a, s=None, axes=(-2, -1), norm=None)
[source]
Compute the 2-dimensional inverse discrete Fourier Transform.
This function computes the inverse of the 2-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, ifft2(fft2(a)) == a
to within numerical accuracy. By default, the inverse transform is computed over the last two axes of the input array.
The input, analogously to ifft
, should be ordered in the same way as is returned by fft2
, i.e. it should have the term for zero frequency in the low-order corner of the two axes, the positive frequency terms in the first half of these axes, the term for the Nyquist frequency in the middle of the axes and the negative frequency terms in the second half of both 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 axis) of the output ( axes : sequence of ints, optional Axes over which to compute the FFT. If not given, the last two axes are used. A repeated index in 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
ifft2
is just ifftn
with a different default for axes
.
See ifftn
for details and a plotting example, and numpy.fft
for definition 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 ifft2
is called.
>>> a = 4 * np.eye(4) >>> np.fft.ifft2(a) array([[ 1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], [ 0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j], [ 0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j], [ 0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j]])
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Licensed under the NumPy License.
https://docs.scipy.org/doc/numpy-1.14.2/reference/generated/numpy.fft.ifft2.html