# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program.  If not, see <http://www.gnu.org/licenses/>.
#
# Copyright(C) 2013-2018 Max-Planck-Society
#
# NIFTy is being developed at the Max-Planck-Institut fuer Astrophysik
# and financially supported by the Studienstiftung des deutschen Volkes.

from .. import Field, exp
from ..operators.diagonal_operator import DiagonalOperator
from ..minimization.energy import Energy

# TODO Take only residual_sample_list as argument


class NoiseEnergy(Energy):
    def __init__(self, position, d, xi, D, t, ht, Instrument,
                 nonlinearity, alpha, q, Distribution, samples=3,
                 xi_sample_list=None, inverter=None):
        super(NoiseEnergy, self).__init__(position=position)
        self.xi = xi
        self.D = D
        self.d = d
        self.N = DiagonalOperator(diagonal=exp(self.position))
        self.t = t
        self.samples = samples
        self.ht = ht
        self.Instrument = Instrument
        self.nonlinearity = nonlinearity

        self.alpha = alpha
        self.q = q
        self.Distribution = Distribution
        self.power = self.Distribution(exp(0.5 * self.t))
        if xi_sample_list is None:
            if samples is None or samples == 0:
                xi_sample_list = [xi]
            else:
                xi_sample_list = [D.draw_sample() + xi
                                  for _ in range(samples)]
        self.xi_sample_list = xi_sample_list
        self.inverter = inverter

        A = Distribution(exp(.5*self.t))

        self._gradient = None
        for sample in self.xi_sample_list:
            map_s = self.ht(A * sample)

            residual = self.d - \
                self.Instrument(self.nonlinearity(map_s))
            lh = .5 * residual.vdot(self.N.inverse_times(residual))
            grad = -.5 * self.N.inverse_times(residual.conjugate()*residual)

            if self._gradient is None:
                self._value = lh
                self._gradient = grad.copy()
            else:
                self._value += lh
                self._gradient += grad

        self._value *= 1. / len(self.xi_sample_list)
        self._value += .5 * self.position.sum()
        self._value += (self.alpha - 1.).vdot(self.position) + \
            self.q.vdot(exp(-self.position))

        self._gradient *= 1. / len(self.xi_sample_list)
        self._gradient += (self.alpha-0.5) - self.q*(exp(-self.position))

    def at(self, position):
        return self.__class__(
            position, self.d, self.xi, self.D, self.t, self.ht,
            self.Instrument, self.nonlinearity, self.alpha, self.q,
            self.Distribution, xi_sample_list=self.xi_sample_list,
            samples=self.samples, inverter=self.inverter)

    @property
    def value(self):
        return self._value

    @property
    def gradient(self):
        return self._gradient