ifft2(a, s=None, axes=(-2, -1), norm=None)¶
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
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.
Input array, can be complex.
Shape (length of each axis) of the output (
s refers to axis 0,
s to axis 1, etc.). This corresponds to n for
Along each 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 s is not given, the shape of the input along the axes specified
by axes is used. See notes for issue on
ifft zero padding.
Axes over which to compute the FFT. If not given, the last two axes are used. A repeated index in axes means the transform over that axis is performed multiple times. A one-element sequence means that a one-dimensional FFT is performed.
New in version 1.10.0.
Normalization mode (see
numpy.fft). Default is None.
The truncated or zero-padded input, transformed along the axes indicated by axes, or the last two axes if axes is not given.
If s and axes have different length, or axes not given and
len(s) != 2.
If an element of axes is larger than than the number of axes of a.
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], # may vary [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]])