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
|
# -*- 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.
"""Population analyses based on cclib data."""
import logging
import numpy
from cclib.method.calculationmethod import Method, MissingAttributeError
class Population(Method):
"""An abstract base class for population-type methods."""
# All of these are typically required for population analyses.
required_attrs = ('homos', 'mocoeffs', 'nbasis')
# At least one of these are typically required.
overlap_attributes = ('aooverlaps', 'fooverlaps')
def __init__(self, data, progress=None, \
loglevel=logging.INFO, logname="Log"):
super(Population, self).__init__(data, progress, loglevel, logname)
self.fragresults = None
def __str__(self):
"""Return a string representation of the object."""
return "Population"
def __repr__(self):
"""Return a representation of the object."""
return "Population"
def _check_required_attributes(self):
super(Population, self)._check_required_attributes()
if self.overlap_attributes and not any(hasattr(self.data, a) for a in self.overlap_attributes):
raise MissingAttributeError(
'Need overlap matrix (aooverlaps or fooverlaps attribute) for Population methods')
def partition(self, indices=None):
if not hasattr(self, "aoresults"):
self.calculate()
if not indices:
# Build list of groups of orbitals in each atom for atomresults.
if hasattr(self.data, "aonames"):
names = self.data.aonames
elif hasattr(self.data, "fonames"):
names = self.data.fonames
atoms = []
indices = []
name = names[0].split('_')[0]
atoms.append(name)
indices.append([0])
for i in range(1, len(names)):
name = names[i].split('_')[0]
try:
index = atoms.index(name)
except ValueError: #not found in atom list
atoms.append(name)
indices.append([i])
else:
indices[index].append(i)
natoms = len(indices)
nmocoeffs = len(self.aoresults[0])
# Build results numpy array[3].
alpha = len(self.aoresults[0])
results = []
results.append(numpy.zeros([alpha, natoms], "d"))
if len(self.aoresults) == 2:
beta = len(self.aoresults[1])
results.append(numpy.zeros([beta, natoms], "d"))
# For each spin, splice numpy array at ao index,
# and add to correct result row.
for spin in range(len(results)):
for i in range(natoms): # Number of groups.
for j in range(len(indices[i])): # For each group.
temp = self.aoresults[spin][:, indices[i][j]]
results[spin][:, i] = numpy.add(results[spin][:, i], temp)
self.logger.info("Saving partitioned results in fragresults: [array[2]]")
self.fragresults = results
return True
|
