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#!/usr/bin/env python
# /*##########################################################################
#
# 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
import warnings
from pkg_resources import parse_version
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().__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.
self.set_fft_norm()
self.set_fft_functions()
self.compute_plans()
def set_fft_norm(self):
# backward, forward indicates the direction in which the
# normalisation is done. default is "backward"
# rescale is default norm with numpy, no need of keywords
# if normalize == "rescale": # normalisation 1/N on ifft
self.numpy_args_fft = {}
self.numpy_args_ifft = {}
if self.normalize == "ortho": # normalization 1/sqrt(N) on both fft & ifft
self.numpy_args_fft = {"norm": "ortho"}
self.numpy_args_ifft = {"norm": "ortho"}
elif self.normalize == "none": # no normalisation on both fft & ifft
if parse_version(np.version.version) < parse_version("1.20"):
# "backward" & "forward" keywords were introduced in 1.20 and we support numpy >= 1.8
warnings.warn(
"Numpy version %s does not allow to non-normalization. Effective normalization will be 'rescale'"
% (np.version.version)
) # default 'rescale' normalization
else:
self.numpy_args_fft = {"norm": "backward"}
self.numpy_args_ifft = {"norm": "forward"}
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(shape, dtype=dtype)
def compute_plans(self):
ndim = len(self.shape)
funcs = self._fft_functions[self.real_transform][np.minimum(ndim, 3)]
# Set norm
# self.numpy_args_fft & self.numpy_args_ifft already set in set_fft_norm
# 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_fft["axes"] = self.user_axes
self.numpy_args_ifft["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_fft["axis"] = self.user_axes[0]
self.numpy_args_fft.pop("axes")
self.numpy_args_ifft["axis"] = self.user_axes[0]
self.numpy_args_ifft.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_fft)
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_ifft)
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