#!/usr/bin/env python
# coding: utf-8
# /*##########################################################################
#
# Copyright (c) 2018-2019 European Synchrotron Radiation Facility
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# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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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()
        self.refs = {
            "data_in": self.data_in,
            "data_out": self.data_out,
        }

    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):
        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)
            )

    def set_data(self, self_array, array, shape, dtype, copy=True, name=None):
        """
        :param self_array: array owned by the current instance
                           (either self.data_in or self.data_out).
        :type: numpy.ndarray
        :param self_array: data to set
        :type: numpy.ndarray
        :type tuple shape: shape of the array
        :param dtype: type of the array
        :type: numpy.dtype
        :param bool copy: should we copy the array
        :param str name: name of the array

        Copies are avoided when possible.
        """
        self.check_array(array, shape, dtype)
        if id(self.refs[name]) == id(array):
            # nothing to do: fft is performed on self.data_in or self.data_out
            arr_to_use = self.refs[name]
        if self.check_alignment and not(pyfftw.is_byte_aligned(array)):
            # If the array is not properly aligned,
            # create a temp. array copy it to self.data_in or self.data_out
            self_array[:] = array[:]
            arr_to_use = self_array
        else:
            # If the array is properly aligned, use it directly
            if copy:
                arr_to_use = np.copy(array)
            else:
                arr_to_use = array
        return arr_to_use

    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=False)
        data_out = self.set_output_data(output, copy=False)
        self.plan_forward.update_arrays(data_in, data_out)
        # execute.__call__ does both update_arrays() and normalization
        self.plan_forward(
            ortho=self.fftw_norm_mode["ortho"],
        )
        self.plan_forward.update_arrays(self.refs["data_in"], self.refs["data_out"])
        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=False)
        data_out = self.set_input_data(output, copy=False)
        self.plan_inverse.update_arrays(data_in, data_out)
        # execute.__call__ does both update_arrays() and normalization
        self.plan_inverse(
            ortho=self.fftw_norm_mode["ortho"],
            normalise_idft=self.fftw_norm_mode["normalize"]
        )
        self.plan_inverse.update_arrays(self.refs["data_out"], self.refs["data_in"])
        return data_out