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# derived from copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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import numpy as np
from ... import opcodes as OperandDef
from ..datasource import tensor as astensor
from .core import TensorFFTShiftBase, TensorFFTShiftMixin
class TensorFFTShift(TensorFFTShiftBase, TensorFFTShiftMixin):
_op_type_ = OperandDef.FFTSHIFT
def __init__(self, axes=None, **kw):
super().__init__(_axes=axes, **kw)
def _set_inputs(self, inputs):
super()._set_inputs(inputs)
self._input = self._inputs[0]
def __call__(self, x):
return self.new_tensor([x], x.shape)
[docs]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
Input tensor.
axes : int or shape tuple, optional
Axes over which to shift. Default is None, which shifts all axes.
Returns
-------
y : Tensor
The shifted tensor.
See Also
--------
ifftshift : The inverse of `fftshift`.
Examples
--------
>>> import mars.tensor as mt
>>> freqs = mt.fft.fftfreq(10, 0.1)
>>> freqs.execute()
array([ 0., 1., 2., 3., 4., -5., -4., -3., -2., -1.])
>>> mt.fft.fftshift(freqs).execute()
array([-5., -4., -3., -2., -1., 0., 1., 2., 3., 4.])
Shift the zero-frequency component only along the second axis:
>>> freqs = mt.fft.fftfreq(9, d=1./9).reshape(3, 3)
>>> freqs.execute()
array([[ 0., 1., 2.],
[ 3., 4., -4.],
[-3., -2., -1.]])
>>> mt.fft.fftshift(freqs, axes=(1,)).execute()
array([[ 2., 0., 1.],
[-4., 3., 4.],
[-1., -3., -2.]])
"""
x = astensor(x)
dtype = np.fft.fftshift(np.empty((1,) * max(1, x.ndim), dtype=x.dtype)).dtype
axes = TensorFFTShift._process_axes(x, axes)
op = TensorFFTShift(axes=axes, dtype=dtype)
return op(x)