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#!/usr/bin/env python
# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2018-2019 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
# ###########################################################################*/
import numpy as np
from .basefft import BaseFFT
class NPFFT(BaseFFT):
"""Initialize a numpy plan.
Please see FFT class for parameters help.
"""
def __init__(
self,
shape=None,
dtype=None,
template=None,
shape_out=None,
axes=None,
normalize="rescale",
):
super(NPFFT, self).__init__(
shape=shape,
dtype=dtype,
template=template,
shape_out=shape_out,
axes=axes,
normalize=normalize,
)
self.backend = "numpy"
self.real_transform = False
if template is not None and np.isrealobj(template):
self.real_transform = True
# For numpy functions.
# TODO Issue warning if user wants ifft(fft(data)) = N*data ?
if normalize != "ortho":
self.normalize = None
self.set_fft_functions()
#~ self.allocate_arrays() # not needed for this backend
self.compute_plans()
def set_fft_functions(self):
# (fwd, inv) = _fft_functions[is_real][ndim]
self._fft_functions = {
True: {
1: (np.fft.rfft, np.fft.irfft),
2: (np.fft.rfft2, np.fft.irfft2),
3: (np.fft.rfftn, np.fft.irfftn),
},
False: {
1: (np.fft.fft, np.fft.ifft),
2: (np.fft.fft2, np.fft.ifft2),
3: (np.fft.fftn, np.fft.ifftn),
}
}
def _allocate(self, shape, dtype):
return np.zeros(self.queue, shape, dtype=dtype)
def compute_plans(self):
ndim = len(self.shape)
funcs = self._fft_functions[self.real_transform][np.minimum(ndim, 3)]
if np.version.version[:4] in ["1.8.", "1.9."]:
# norm keyword was introduced in 1.10 and we support numpy >= 1.8
self.numpy_args = {}
else:
self.numpy_args = {"norm": self.normalize}
# Batched transform
if (self.user_axes is not None) and len(self.user_axes) < ndim:
funcs = self._fft_functions[self.real_transform][np.minimum(ndim-1, 3)]
self.numpy_args["axes"] = self.user_axes
# Special case of batched 1D transform on 2D data
if ndim == 2:
assert len(self.user_axes) == 1
self.numpy_args["axis"] = self.user_axes[0]
self.numpy_args.pop("axes")
self.numpy_funcs = funcs
def fft(self, array):
"""
Perform a (forward) Fast Fourier Transform.
:param numpy.ndarray array:
Input data. Must be consistent with the current context.
"""
return self.numpy_funcs[0](array, **self.numpy_args)
def ifft(self, array):
"""
Perform a (inverse) Fast Fourier Transform.
:param numpy.ndarray array:
Input data. Must be consistent with the current context.
"""
return self.numpy_funcs[1](array, **self.numpy_args)
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