mars.tensor.fft.ifft2¶

mars.tensor.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 by fft2, 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 tensor, 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 on ifft 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) – Normalization mode (see mt.fft). Default is None.
 Returns
out – The truncated or zeropadded input, transformed along the axes indicated by axes, or the last two axes if axes is not given.
 Return type
complex Tensor
 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 just ifftn with a different default for axes.
See ifftn for details and a plotting example, and numpy.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 before ifft2 is called.
Examples
>>> import mars.tensor as mt
>>> a = 4 * mt.eye(4) >>> mt.fft.ifft2(a).execute() 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]])