implementation of fftshift and ifftshift

helper.py

# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# 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,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Shift the zero-frequency component to the center of the spectrum"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from tensorflow.python import roll as _roll
from tensorflow.python.framework import ops
from tensorflow.python.util.tf_export import tf_export

@tf_export("signal.fftshift")
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.ndim))
        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)


@tf_export("signal.ifftshift")
def ifftshift(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.
    """
    x = ops.convert_to_tensor_v2(x)
    if axes is None:
        axes = tuple(range(x.ndim))
        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)