# Copyright (c) 2024, the cclib development team
#
# This file is part of cclib (http://cclib.github.io) and is distributed under
# the terms of the BSD 3-Clause License.

"""Calculation of Mulliken population analysis (MPA) based on data parsed by cclib."""

import random

from cclib.method.population import Population

import numpy


class MPA(Population):
    """Mulliken population analysis."""

    def __init__(self, *args):
        super().__init__(logname="MPA", *args)

    def __str__(self):
        """Return a string representation of the object."""
        return f"MPA of {self.data}"

    def __repr__(self):
        """Return a representation of the object."""
        return f'MPA("{self.data}")'

    def calculate(self, indices=None, fupdate=0.05):
        """Perform a Mulliken population analysis."""

        # Determine number of steps, and whether process involves beta orbitals.
        self.logger.info("Creating attribute aoresults: [array[2]]")
        nbasis = self.data.nbasis
        alpha = len(self.data.mocoeffs[0])
        self.aoresults = [numpy.zeros([alpha, nbasis], "d")]
        nstep = alpha
        unrestricted = len(self.data.mocoeffs) == 2
        if unrestricted:
            beta = len(self.data.mocoeffs[1])
            self.aoresults.append(numpy.zeros([beta, nbasis], "d"))
            nstep += beta

        # Intialize progress if available.
        if self.progress:
            self.progress.initialize(nstep)

        step = 0
        for spin in range(len(self.data.mocoeffs)):
            for i in range(len(self.data.mocoeffs[spin])):
                if self.progress and random.random() < fupdate:
                    self.progress.update(step, "Mulliken Population Analysis")

                # X_{ai} = \sum_b c_{ai} c_{bi} S_{ab}
                #        = c_{ai} \sum_b c_{bi} S_{ab}
                #        = c_{ai} C(i) \cdot S(a)
                # X = C(i) * [C(i) \cdot S]
                # C(i) is 1xn and S is nxn, result of matrix mult is 1xn

                ci = self.data.mocoeffs[spin][i]
                if hasattr(self.data, "aooverlaps"):
                    temp = numpy.dot(ci, self.data.aooverlaps)

                # handle spin-unrestricted beta case
                elif hasattr(self.data, "fooverlaps2") and spin == 1:
                    temp = numpy.dot(ci, self.data.fooverlaps2)

                elif hasattr(self.data, "fooverlaps"):
                    temp = numpy.dot(ci, self.data.fooverlaps)

                self.aoresults[spin][i] = numpy.multiply(ci, temp).astype("d")

                step += 1

        if self.progress:
            self.progress.update(nstep, "Done")

        retval = super().partition(indices)

        if not retval:
            self.logger.error("Error in partitioning results")
            return False

        # Create array for Mulliken charges.
        self.logger.info("Creating fragcharges: array[1]")
        size = len(self.fragresults[0][0])
        self.fragcharges = numpy.zeros([size], "d")
        alpha = numpy.zeros([size], "d")
        if unrestricted:
            beta = numpy.zeros([size], "d")

        for spin in range(len(self.fragresults)):
            for i in range(self.data.homos[spin] + 1):
                temp = numpy.reshape(self.fragresults[spin][i], (size,))
                self.fragcharges = numpy.add(self.fragcharges, temp)
                if spin == 0:
                    alpha = numpy.add(alpha, temp)
                elif spin == 1:
                    beta = numpy.add(beta, temp)

        if not unrestricted:
            self.fragcharges = numpy.multiply(self.fragcharges, 2)
        else:
            self.logger.info("Creating fragspins: array[1]")
            self.fragspins = numpy.subtract(alpha, beta)

        return True
