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# -*- coding: utf-8 -*-
#
# Copyright (c) 2018, 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
import numpy
from cclib.method.population import Population
class MPA(Population):
"""Mulliken population analysis."""
def __init__(self, *args):
# Call the __init__ method of the superclass.
super(MPA, self).__init__(logname="MPA", *args)
def __str__(self):
"""Return a string representation of the object."""
return "MPA of %s" % (self.data)
def __repr__(self):
"""Return a representation of the object."""
return 'MPA("%s")' % (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(MPA, self).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
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