Source code for mars.tensor.fft.ifft2

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# 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
# 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 TensorComplexFFTNMixin, validate_fftn, TensorStandardFFTN

class TensorIFFT2(TensorStandardFFTN, TensorComplexFFTNMixin):
    _op_type_ = OperandDef.IFFT2

    def __init__(self, shape=None, axes=None, norm=None, **kw):
        super().__init__(_shape=shape, _axes=axes, _norm=norm, **kw)

[docs]def ifft2(a, s=None, axes=(-2, -1), norm=None): """ Compute the 2-dimensional inverse discrete Fourier Transform. This function computes the inverse of the 2-dimensional discrete Fourier Transform over any number of axes in an M-dimensional array by means of the Fast Fourier Transform (FFT). In other words, ``ifft2(fft2(a)) == a`` to within numerical accuracy. By default, the inverse transform is computed over the last two axes of the input array. The input, analogously to `ifft`, should be ordered in the same way as is returned by `fft2`, i.e. it should have the term for zero frequency in the low-order corner of the two axes, the positive frequency terms in the first half of these axes, the term for the Nyquist frequency in the middle of the axes and the negative frequency terms in the second half of both axes, in order of decreasingly negative frequency. Parameters ---------- a : array_like Input tensor, can be complex. s : sequence of ints, optional Shape (length of each axis) of the output (``s[0]`` refers to axis 0, ``s[1]`` to axis 1, etc.). This corresponds to `n` for ``ifft(x, n)``. Along each axis, if the given shape is smaller than that of the input, the input is cropped. If it is larger, the input is padded with zeros. if `s` is not given, the shape of the input along the axes specified by `axes` is used. See notes for issue on `ifft` zero padding. axes : sequence of ints, optional Axes over which to compute the FFT. If not given, the last two axes are used. A repeated index in `axes` means the transform over that axis is performed multiple times. A one-element sequence means that a one-dimensional FFT is performed. norm : {None, "ortho"}, optional Normalization mode (see `mt.fft`). Default is None. Returns ------- out : complex Tensor The truncated or zero-padded input, transformed along the axes indicated by `axes`, or the last two axes if `axes` is not given. Raises ------ ValueError If `s` and `axes` have different length, or `axes` not given and ``len(s) != 2``. IndexError If an element of `axes` is larger than than the number of axes of `a`. See Also -------- mt.fft : Overall view of discrete Fourier transforms, with definitions and conventions used. fft2 : The forward 2-dimensional FFT, of which `ifft2` is the inverse. ifftn : The inverse of the *n*-dimensional FFT. fft : The one-dimensional FFT. ifft : The one-dimensional inverse FFT. Notes ----- `ifft2` is just `ifftn` with a different default for `axes`. See `ifftn` for details and a plotting example, and `numpy.fft` for definition and conventions used. Zero-padding, analogously with `ifft`, is performed by appending zeros to the input along the specified dimension. Although this is the common approach, it might lead to surprising results. If another form of zero padding is desired, it must be performed before `ifft2` is called. Examples -------- >>> import mars.tensor as mt >>> a = 4 * mt.eye(4) >>> mt.fft.ifft2(a).execute() array([[ 1.+0.j, 0.+0.j, 0.+0.j, 0.+0.j], [ 0.+0.j, 0.+0.j, 0.+0.j, 1.+0.j], [ 0.+0.j, 0.+0.j, 1.+0.j, 0.+0.j], [ 0.+0.j, 1.+0.j, 0.+0.j, 0.+0.j]]) """ if len(axes) != 2: raise ValueError("axes length should be 2") a = astensor(a) axes = validate_fftn(a, s=s, axes=axes, norm=norm) op = TensorIFFT2(shape=s, axes=axes, norm=norm, dtype=np.dtype(np.complex_)) return op(a)