mars.tensor.fft.fft2¶

mars.tensor.fft.
fft2
(a, s=None, axes=( 2,  1), norm=None)[source]¶ Compute the 2dimensional discrete Fourier Transform
This function computes the ndimensional discrete Fourier Transform over any axes in an Mdimensional array by means of the Fast Fourier Transform (FFT). By default, the transform is computed over the last two axes of the input array, i.e., a 2dimensional FFT.
 Parameters
a (array_like) – Input tensor, can be complex
s (sequence of ints, optional) – Shape (length of each transformed axis) of the output (
s[0]
refers to axis 0,s[1]
to axis 1, etc.). This corresponds ton
forfft(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.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
mt.fft
Overall view of discrete Fourier transforms, with definitions and conventions used.
ifft2
The inverse twodimensional FFT.
fft
The onedimensional FFT.
fftn
The ndimensional FFT.
fftshift
Shifts zerofrequency terms to the center of the array. For twodimensional input, swaps first and third quadrants, and second and fourth quadrants.
Notes
fft2 is just fftn with a different default for axes.
The output, analogously to fft, contains the term for zero frequency in the loworder corner of the transformed 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 the axes, in order of decreasingly negative frequency.
See fftn for details and a plotting example, and mt.fft for definitions and conventions used.
Examples
>>> import mars.tensor as mt
>>> a = mt.mgrid[:5, :5][0] >>> mt.fft.fft2(a).execute() array([[ 50.0 +0.j , 0.0 +0.j , 0.0 +0.j , 0.0 +0.j , 0.0 +0.j ], [12.5+17.20477401j, 0.0 +0.j , 0.0 +0.j , 0.0 +0.j , 0.0 +0.j ], [12.5 +4.0614962j , 0.0 +0.j , 0.0 +0.j , 0.0 +0.j , 0.0 +0.j ], [12.5 4.0614962j , 0.0 +0.j , 0.0 +0.j , 0.0 +0.j , 0.0 +0.j ], [12.517.20477401j, 0.0 +0.j , 0.0 +0.j , 0.0 +0.j , 0.0 +0.j ]])