numpy.fft.fft2(a, s=None, axes=(-2, -1), norm=None)
[source]
Compute the 2-dimensional discrete Fourier Transform
This function computes the n-dimensional discrete Fourier Transform over any axes in an M-dimensional 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 2-dimensional FFT.
Parameters: |
a : array_like Input array, can be complex s : sequence of ints, optional Shape (length of each transformed 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
numpy.fft
ifft2
fft
fftn
fftshift
fft2
is just fftn
with a different default for axes
.
The output, analogously to fft
, contains the term for zero frequency in the low-order 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 numpy.fft
for definitions and conventions used.
>>> a = np.mgrid[:5, :5][0] >>> np.fft.fft2(a) 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 ], [-12.5-17.20477401j, 0.0 +0.j , 0.0 +0.j , 0.0 +0.j , 0.0 +0.j ]])
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Licensed under the NumPy License.
https://docs.scipy.org/doc/numpy-1.14.2/reference/generated/numpy.fft.fft2.html