scipy.ndimage.

fourier_uniform#

scipy.ndimage.fourier_uniform(input, size, n=-1, axis=-1, output=None)[source]#

Multidimensional uniform fourier filter.

The array is multiplied with the Fourier transform of a box of given size.

Parameters:

inputarray_like: The input array.
sizefloat or sequence: The size of the box used for filtering. If a float, size is the same for all axes. If a sequence, size has to contain one value for each axis.
nint, optional: If n is negative (default), then the input is assumed to be the result of a complex fft. If n is larger than or equal to zero, the input is assumed to be the result of a real fft, and n gives the length of the array before transformation along the real transform direction.
axisint, optional: The axis of the real transform.
outputndarray, optional: If given, the result of filtering the input is placed in this array.

Returns:

fourier_uniformndarray: The filtered input.

Notes

Array API Standard Support

fourier_uniform 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	⚠️ no JIT	⛔
Dask	⚠️ computes graph	n/a

See Support for the array API standard for more information.

Examples

>>> from scipy import ndimage, datasets
>>> import numpy.fft
>>> import matplotlib.pyplot as plt
>>> fig, (ax1, ax2) = plt.subplots(1, 2)
>>> plt.gray()  # show the filtered result in grayscale
>>> ascent = datasets.ascent()
>>> input_ = numpy.fft.fft2(ascent)
>>> result = ndimage.fourier_uniform(input_, size=20)
>>> result = numpy.fft.ifft2(result)
>>> ax1.imshow(ascent)
>>> ax2.imshow(result.real)  # the imaginary part is an artifact
>>> plt.show()

../../_images/scipy-ndimage-fourier_uniform-1.png