NumPy参考 >例行程序 >Discrete Fourier Transform (numpy.fft) > numpy.fft.fft2
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.
Input array, can be complex
Shape (length of each transformed axis) of the output
(s[0]
refers to axis 0, s[1]
to axis 1, etc.).
This corresponds to n
for fft(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 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.
New in version 1.10.0.
Normalization mode (see numpy.fft
). Default is None.
The truncated or zero-padded input, transformed along the axes indicated by axes, or the last two axes if axes is not given.
If s and axes have different length, or axes not given and
len(s) != 2
.
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 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.
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 ]])