scipy.fft.fft2#
- scipy.fft.fft2(x, s=None, axes=(-2, -1), norm=None, overwrite_x=False, workers=None, *, plan=None)[source]#
Compute the 2-D discrete Fourier Transform
This function computes the N-D discrete Fourier Transform over any axes in an M-D 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:
- xarray_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.
- norm{“backward”, “ortho”, “forward”}, optional
Normalization mode (see
fft
). Default is “backward”.- overwrite_xbool, optional
If True, the contents of x can be destroyed; the default is False. See
fft
for more details.- workersint, optional
Maximum number of workers to use for parallel computation. If negative, the value wraps around from
os.cpu_count()
. Seefft
for more details.- planobject, optional
This argument is reserved for passing in a precomputed plan provided by downstream FFT vendors. It is currently not used in SciPy.
New in version 1.5.0.
- 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 the number of axes of x.
See also
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, andfft
for definitions and conventions used.Examples
>>> import scipy.fft >>> import numpy as np >>> x = np.mgrid[:5, :5][0] >>> scipy.fft.fft2(x) 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 ]])