/usr/share/pyshared/cclib/method/cspa.py is in python-cclib 1.1-1.
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 | # This file is part of cclib (http://cclib.sf.net), a library for parsing
# and interpreting the results of computational chemistry packages.
#
# Copyright (C) 2006, the cclib development team
#
# The library is free software, distributed under the terms of
# the GNU Lesser General Public version 2.1 or later. You should have
# received a copy of the license along with cclib. You can also access
# the full license online at http://www.gnu.org/copyleft/lgpl.html.
__revision__ = "$Revision: 960 $"
import random # For sometimes running the progress updater
import numpy
from population import Population
class CSPA(Population):
"""The C-squared population analysis."""
def __init__(self, *args):
# Call the __init__ method of the superclass.
super(CSPA, self).__init__(logname="CSPA", *args)
def __str__(self):
"""Return a string representation of the object."""
return "CSPA of" % (self.data)
def __repr__(self):
"""Return a representation of the object."""
return 'CSPA("%s")' % (self.data)
def calculate(self, indices=None, fupdate=0.05):
"""Perform the C squared population analysis.
Inputs:
indices - list of lists containing atomic orbital indices of fragments
"""
# Do we have the needed info in the parser?
if not hasattr(self.data, "mocoeffs"):
self.logger.error("Missing mocoeffs")
return False
if not hasattr(self.data, "nbasis"):
self.logger.error("Missing nbasis")
return False
if not hasattr(self.data, "homos"):
self.logger.error("Missing homos")
return False
self.logger.info("Creating attribute aoresults: array[3]")
# Determine number of steps, and whether process involves beta orbitals.
unrestricted = (len(self.data.mocoeffs)==2)
nbasis = self.data.nbasis
self.aoresults = []
alpha = len(self.data.mocoeffs[0])
self.aoresults.append(numpy.zeros([alpha, nbasis], "d"))
nstep = alpha
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, "C^2 Population Analysis")
submocoeffs = self.data.mocoeffs[spin][i]
scale = numpy.inner(submocoeffs, submocoeffs)
tempcoeffs = numpy.multiply(submocoeffs, submocoeffs)
tempvec = tempcoeffs/scale
self.aoresults[spin][i] = numpy.divide(tempcoeffs, scale).astype("d")
step += 1
if self.progress:
self.progress.update(nstep, "Done")
retval = super(CSPA, self).partition(indices)
if not retval:
self.logger.error("Error in partitioning results")
return False
self.logger.info("Creating fragcharges: array[1]")
size = len(self.fragresults[0][0])
self.fragcharges = 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 not unrestricted:
self.fragcharges = numpy.multiply(self.fragcharges, 2)
return True
if __name__ == "__main__":
import doctest, cspa
doctest.testmod(cspa, verbose=False)
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