"""
Functionality for enforcing translational sum rules
"""
import sympy
from scipy.sparse import coo_matrix
import numpy as np
from ..input_output.logging_tools import logger
from .utilities import SparseMatrix, BiMap
from .orbits import get_orbits
logger = logger.getChild('sum_rules')
[docs]def create_constraint_map(cs):
# First we need to know the global index of a eigentensor given which
# orbit it belongs to.
params = []
n = 0
for orbit_index, orbit in enumerate(cs.orbits):
nParams_in_orbit = len(orbit.eigentensors)
params.append(list(range(n, n + nParams_in_orbit)))
n += nParams_in_orbit
# If we dont want the sum rules an eye matrix is created instead.
if not cs.acoustic_sum_rules:
row = list(range(n))
col = list(range(n))
data = [1.0]*n
cs._cvs = coo_matrix((data, (row, col)), shape=(n, n))
return None
M = get_translational_constraint_matrix(cs)
nullspace = M.nullspace()
logger.debug('Assembling solutions...')
row = []
col = []
data = []
for c, vec in enumerate(nullspace):
for r, _, v in vec.row_list():
row.append(r)
col.append(c)
data.append(np.float64(v))
# sanity check that there are orbits still present
if len(nullspace) == 0:
raise ValueError('There are no degrees of freedom, error!')
# This is the final product, the constraint map (or constraint vectors)
cs._cvs = coo_matrix((data, (row, col)), shape=(len(vec), len(nullspace)))
logger.debug('{} degrees of freedom'.format(cs._cvs.shape[1]))
logger.debug('Finished constructing constraint vectors')
[docs]def get_translational_constraint_matrix(cs):
logger.debug('Starting constructing constraint matrices')
# First we need to know the global index of a eigentensor given which
# orbit it belongs to.
params = []
n = 0
for orbit_index, orbit in enumerate(cs.orbits):
nParams_in_orbit = len(orbit.eigentensors)
params.append(list(range(n, n + nParams_in_orbit)))
n += nParams_in_orbit
# The lookup is a fast way to get the eigentensors given a cluster Also the
# orbit index is bundled to quickly find the correct parameter indices
lookup = {}
for orbit_index, orbit in enumerate(cs.orbits):
for of in orbit.orientation_families:
for cluster_index, perm_index in zip(of.cluster_indices,
of.permutation_indices):
cluster = cs._cluster_list[cluster_index]
perm = cs._permutations[perm_index]
lookup[tuple(cluster)] = [et.transpose(perm) for et in
of.eigentensors], orbit_index
# Init just one row to be able to stack. n is the total number of
# eigentensors
M = SparseMatrix(1, n, 0) # This is the constraint matrix
for order in cs.cutoffs.orders:
logger.debug('Order {}: '.format(order))
# Find which minimal amount of prefixes that is needed
# Essentially they are all the prototypes of order one less still
# compatible with the cutoff.
prefix_list = []
if order == 2:
for i in np.unique(cs.wyckoff_sites):
prefix_list.append((i,))
else:
tmp_cluster_set = set()
for cluster in cs._cluster_list:
if len(cluster) != order:
continue
for i in range(order):
c = tuple(cluster[:i]) + tuple(cluster[i+1:])
if c[0] < len(cs._prim):
tmp_cluster_set.add(c)
tmp_cluster_list = BiMap()
for c in tmp_cluster_set:
tmp_cluster_list.append(c)
orbits = get_orbits(tmp_cluster_list, cs._atom_list,
cs.rotation_matrices, cs.translation_vectors,
cs.permutations, cs._prim, cs.symprec)
for orb in orbits:
prefix_list.append(tmp_cluster_list[orb.prototype_index])
m = SparseMatrix(3**order, M.shape[1], 0) # intermediate matrix
# Now loop over the prefix and add the last atom index
for prefix in prefix_list:
m *= 0
cluster = list(prefix) + [None]
for i in range(len(cs._atom_list)):
cluster[-1] = i
tmp = lookup.get(tuple(sorted(cluster)))
if tmp is None:
continue
ets, orbit_index = tmp
inv_argsort = np.argsort(np.argsort(cluster))
ets = [et.transpose(inv_argsort) for et in ets]
for et, col in zip(ets, params[orbit_index]):
et_flat = et.flatten()
for j in et_flat.nonzero()[0]:
m[j, col] += sympy.nsimplify(et_flat[j])
M = SparseMatrix.vstack(M, m)
# only compress if there are very many elements
if M.nnz() > cs._config['max_number_constraint_elements']:
M = SparseMatrix.rref(M)[0]
logger.debug('Finished constructing constraint matrices')
logger.debug('Starting constructing constraint vectors')
return M