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 to- nfor- 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.
- 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 - fftfor more details.
- workersint, optional
- Maximum number of workers to use for parallel computation. If negative, the value wraps around from - os.cpu_count(). See- fftfor 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. - Added 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 - fft2is just- fftnwith 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 - fftnfor details and a plotting example, and- fftfor 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 ]])