numpy.fft.ifft2¶

numpy.fft.
ifft2
(a, s=None, axes=(2, 1), norm=None)[source]¶ Compute the 2dimensional inverse discrete Fourier Transform.
This function computes the inverse of the 2dimensional discrete Fourier Transform over any number of axes in an Mdimensional 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 byfft2
, i.e. it should have the term for zero frequency in the loworder 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 (
s[0]
refers to axis 0,s[1]
to axis 1, etc.). This corresponds to n forifft(x, n)
. 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 onifft
zero padding.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 axes means the transform over that axis is performed multiple times. A oneelement sequence means that a onedimensional FFT is performed.
norm : {None, “ortho”}, optional
New in version 1.10.0.
Normalization mode (see
numpy.fft
). Default is None.Returns: out : complex ndarray
The truncated or zeropadded input, transformed along the axes indicated by axes, or the last two axes if axes is not given.
Raises: ValueError
If s and axes have different length, or axes not given and
len(s) != 2
.IndexError
If an element of axes is larger than than the number of axes of a.
See also
Notes
ifft2
is justifftn
with a different default for axes.See
ifftn
for details and a plotting example, andnumpy.fft
for definition and conventions used.Zeropadding, 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 beforeifft2
is called.Examples
>>> 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]])