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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, check_version
try:
import pyfftw
__have_fftw__ = True
except ImportError:
__have_fftw__ = False
# Check pyfftw version
__required_pyfftw_version__ = "0.10.0"
if __have_fftw__:
__have_fftw__ = check_version(pyfftw, __required_pyfftw_version__)
class FFTW(BaseFFT):
"""Initialize a FFTW plan.
Please see FFT class for parameters help.
FFTW-specific parameters
-------------------------
:param bool check_alignment:
If set to True and "data" is provided, this will enforce the input data
to be "byte aligned", which might imply extra memory usage.
:param int num_threads:
Number of threads for computing FFT.
"""
def __init__(
self,
shape=None,
dtype=None,
template=None,
shape_out=None,
axes=None,
normalize="rescale",
check_alignment=False,
num_threads=1,
):
if not(__have_fftw__):
raise ImportError("Please install pyfftw >= %s to use the FFTW back-end" % __required_pyfftw_version__)
super(FFTW, self).__init__(
shape=shape,
dtype=dtype,
template=template,
shape_out=shape_out,
axes=axes,
normalize=normalize,
)
self.check_alignment = check_alignment
self.num_threads = num_threads
self.backend = "fftw"
self.allocate_arrays()
self.set_fftw_flags()
self.compute_forward_plan()
self.compute_inverse_plan()
def set_fftw_flags(self):
self.fftw_flags = ('FFTW_MEASURE', ) # TODO
self.fftw_planning_timelimit = None # TODO
self.fftw_norm_modes = {
"rescale": {"ortho": False, "normalize": True},
"ortho": {"ortho": True, "normalize": False},
"none": {"ortho": False, "normalize": False},
}
if self.normalize not in self.fftw_norm_modes:
raise ValueError("Unknown normalization mode %s. Possible values are %s" %
(self.normalize, self.fftw_norm_modes.keys())
)
self.fftw_norm_mode = self.fftw_norm_modes[self.normalize]
def _allocate(self, shape, dtype):
return pyfftw.zeros_aligned(shape, dtype=dtype)
def check_array(self, array, shape, dtype, copy=True):
"""
Check that a given array is compatible with the FFTW plans,
in terms of alignment and data type.
If the provided array does not meet any of the checks, a new array
is returned.
"""
if array.shape != shape:
raise ValueError("Invalid data shape: expected %s, got %s" %
(shape, array.shape)
)
if array.dtype != dtype:
raise ValueError("Invalid data type: expected %s, got %s" %
(dtype, array.dtype)
)
if self.check_alignment and not(pyfftw.is_byte_aligned(array)):
array2 = pyfftw.zeros_aligned(self.shape, dtype=self.dtype_in)
np.copyto(array2, array)
else:
if copy:
array2 = np.copy(array)
else:
array2 = array
return array2
def set_data(self, dst, src, shape, dtype, copy=True, name=None):
dst = self.check_array(src, shape, dtype, copy=copy)
return dst
def compute_forward_plan(self):
self.plan_forward = pyfftw.FFTW(
self.data_in,
self.data_out,
axes=self.axes,
direction='FFTW_FORWARD',
flags=self.fftw_flags,
threads=self.num_threads,
planning_timelimit=self.fftw_planning_timelimit,
# the following seems to be taken into account only when using __call__
ortho=self.fftw_norm_mode["ortho"],
normalise_idft=self.fftw_norm_mode["normalize"],
)
def compute_inverse_plan(self):
self.plan_inverse = pyfftw.FFTW(
self.data_out,
self.data_in,
axes=self.axes,
direction='FFTW_BACKWARD',
flags=self.fftw_flags,
threads=self.num_threads,
planning_timelimit=self.fftw_planning_timelimit,
# the following seem to be taken into account only when using __call__
ortho=self.fftw_norm_mode["ortho"],
normalise_idft=self.fftw_norm_mode["normalize"],
)
def fft(self, array, output=None):
"""
Perform a (forward) Fast Fourier Transform.
:param numpy.ndarray array:
Input data. Must be consistent with the current context.
:param numpy.ndarray output:
Optional output data.
"""
data_in = self.set_input_data(array, copy=True)
data_out = self.set_output_data(output, copy=False)
# execute.__call__ does both update_arrays() and normalization
self.plan_forward(
input_array=data_in,
output_array=data_out,
ortho=self.fftw_norm_mode["ortho"],
)
assert id(self.plan_forward.output_array) == id(self.data_out) == id(data_out) # DEBUG
return data_out
def ifft(self, array, output=None):
"""
Perform a (inverse) Fast Fourier Transform.
:param numpy.ndarray array:
Input data. Must be consistent with the current context.
:param numpy.ndarray output:
Optional output data.
"""
data_in = self.set_output_data(array, copy=True)
data_out = self.set_input_data(output, copy=False)
# execute.__call__ does both update_arrays() and normalization
self.plan_inverse(
input_array=data_in,
output_array=data_out,
ortho=self.fftw_norm_mode["ortho"],
normalise_idft=self.fftw_norm_mode["normalize"]
)
assert id(self.plan_inverse.output_array) == id(self.data_in) == id(data_out) # DEBUG
return data_out
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