numpy.fft.fft2#
- 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
- aarray_like
Input array, can be complex
- ssequence 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.- axessequence 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 one-element sequence means that a one-dimensional FFT is performed.
- norm{“backward”, “ortho”, “forward”}, optional
New in version 1.10.0.
Normalization mode (see
numpy.fft
). Default is “backward”. Indicates which direction of the forward/backward pair of transforms is scaled and with what normalization factor.New in version 1.20.0: The “backward”, “forward” values were added.
- Returns
- outcomplex ndarray
The truncated or zero-padded 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
numpy.fft
Overall view of discrete Fourier transforms, with definitions and conventions used.
ifft2
The inverse two-dimensional FFT.
fft
The one-dimensional FFT.
fftn
The n-dimensional FFT.
fftshift
Shifts zero-frequency terms to the center of the array. For two-dimensional input, swaps first and third quadrants, and second and fourth quadrants.
Notes
fft2
is justfftn
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, andnumpy.fft
for definitions and conventions used.Examples
>>> a = np.mgrid[:5, :5][0] >>> np.fft.fft2(a) array([[ 50. +0.j , 0. +0.j , 0. +0.j , # may vary 0. +0.j , 0. +0.j ], [-12.5+17.20477401j, 0. +0.j , 0. +0.j , 0. +0.j , 0. +0.j ], [-12.5 +4.0614962j , 0. +0.j , 0. +0.j , 0. +0.j , 0. +0.j ], [-12.5 -4.0614962j , 0. +0.j , 0. +0.j , 0. +0.j , 0. +0.j ], [-12.5-17.20477401j, 0. +0.j , 0. +0.j , 0. +0.j , 0. +0.j ]])