def fftshift(x, axes=None):
"""
Shift the zero-frequency component to the center of the spectrum.
This function swaps half-spaces for all axes listed (defaults to all).
Note that ``y[0]`` is the Nyquist component only if ``len(x)`` is even.
Parameters
----------
x : array_like, Tensor
Input array.
axes : int or shape tuple, optional
Axes over which to shift. Default is None, which shifts all axes.
Returns
-------
y : Tensor.
"""
x = ops.convert_to_tensor_v2(x)
if axes is None:
axes = tuple(range(x.get_shape().ndims))
shift = [dim // 2 for dim in x.shape]
elif isinstance(axes, int):
shift = x.shape[axes] // 2
else:
shift = [x.shape[ax] // 2 for ax in axes]
return _roll(x, shift, axes)
def ifftshift(x, axes=None):
#x = ops.convert_to_tensor_v2(x)
if axes is None:
axes = tuple(range(x.get_shape().ndims))[1:]
shift = [-(dim // 2) for dim in x.shape.as_list()[1:]]
elif isinstance(axes, int):
shift = -(x.shape[axes] // 2)
else:
shift = [-(x.shape[ax] // 2) for ax in axes]
return _roll(x, shift, axes)
def _ifftshift(self, x, axes=None):
"""
The inverse of `fftshift`. Although identical for even-length `x`, the
functions differ by one sample for odd-length `x`.
Parameters
----------
x : array_like, Tensor.
axes : int or shape tuple, optional
Axes over which to calculate. Defaults to None, which shifts all axes.
Returns
-------
y : Tensor.
"""
if axes is None:
axes = tuple(range(ndim(x)))
shift = [-(dim // 2) for dim in tf.shape(x)]
elif isinstance(axes, int):
shift = -(tf.shape(x)[axes] // 2)
else:
shift = [-(tf.shape(x)[ax] // 2) for ax in axes]
return _roll(x, shift, axes)