scipy.fft.

rfft2#

scipy.fft.rfft2(x, s=None, axes=(-2, -1), norm=None, overwrite_x=False, workers=None, *, plan=None)[source]#

Compute the 2-D FFT of a real array.

Parameters:

xarray: Input array, taken to be real.
ssequence of ints, optional: Shape of the FFT.
axessequence of ints, optional: Axes over which to compute the FFT.
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(). See fft 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.

Added in version 1.5.0.

Returns:

outndarray: The result of the real 2-D FFT.

See also

irfft2: The inverse of the 2-D FFT of real input.
rfft: The 1-D FFT of real input.
rfftn: Compute the N-D discrete Fourier Transform for real input.

Notes

This is really just rfftn with different default behavior. For more details see rfftn.

Array API Standard Support

rfft2 has experimental support for Python Array API Standard compatible backends in addition to NumPy. Please consider testing these features by setting an environment variable SCIPY_ARRAY_API=1 and providing CuPy, PyTorch, JAX, or Dask arrays as array arguments. The following combinations of backend and device (or other capability) are supported.

Library	CPU	GPU
NumPy	✅	n/a
CuPy	n/a	✅
PyTorch	✅	✅
JAX	✅	✅
Dask	⚠️ computes graph	n/a

See Support for the array API standard for more information.

Examples

>>> import scipy.fft
>>> import numpy as np
>>> x = np.broadcast_to([1, 0, -1, 0], (4, 4))
>>> scipy.fft.rfft2(x)
array([[0.+0.j, 8.+0.j, 0.+0.j],
       [0.+0.j, 0.+0.j, 0.+0.j],
       [0.+0.j, 0.+0.j, 0.+0.j],
       [0.+0.j, 0.+0.j, 0.+0.j]])