def correlate_xccs(xcc_t,symbols_t,xcc_0,symbols_0,fconnect=1.3,pp=False):
'''
xcc_0 --> geometry
xcc_t --> enantiomer
'''
if len(xcc_0) != len(xcc_t) : raise Exception
if len(xcc_0) != len(symbols_0)*3: raise Exception
# Adjacency matrices
amatrix_0 = np.matrix(intl.get_adjmatrix(xcc_0,symbols_0,fconnect,"int")[0])
amatrix_t = np.matrix(intl.get_adjmatrix(xcc_t,symbols_t,fconnect,"int")[0])
# autoconnect!
amatrix_0 = np.matrix(intl.link_fragments(xcc_0,amatrix_0.tolist(),1)[0])
amatrix_t = np.matrix(intl.link_fragments(xcc_t,amatrix_t.tolist(),1)[0])
# correlate amatrices
dcorr = correlate_amatrices(amatrix_t,amatrix_0,symbols_t,symbols_0,xcc_t,xcc_0)
if pp:
print("forced: %i"%nforce)
assign_print(dcorr)
return dcorr
def correlate_enantio(xcc1, xcc2, symbols, fconnect=1.3, pp=False):
'''
xcc1 --> geometry
xcc2 --> enantiomer
'''
if len(xcc1) != len(xcc2): raise Exception
if len(xcc1) != len(symbols) * 3: raise Exception
# Adjacency matrices
amatrix1 = np.matrix(intl.get_adjmatrix(xcc1, symbols, fconnect, "int")[0])
amatrix2 = np.matrix(intl.get_adjmatrix(xcc2, symbols, fconnect, "int")[0])
# autoconnect!
amatrix1 = np.matrix(intl.link_fragments(xcc1, amatrix1.tolist(), 1)[0])
amatrix2 = np.matrix(intl.link_fragments(xcc2, amatrix2.tolist(), 1)[0])
# Graphs
graph1 = UGRAPH()
graph1.set_from_amatrix(amatrix1)
graph2 = UGRAPH()
graph2.set_from_amatrix(amatrix2)
# Correlations
dcorr, na_0 = assign_initialize(symbols)
nforce = 0
for ii in range(10): # it should be a while True but... just in case
# use symbols of neighbors
dcorr, na_1 = assign_neighbors(dcorr, graph1, graph2, symbols)
if na_1 == 0: break
# use symbols of layers
dcorr, na_2 = assign_layers(dcorr, graph1, graph2, symbols)
if na_2 == 0: break
# use spatial disposition
dcorr, na_3 = assign_spatial(dcorr, graph1, graph2, xcc1, xcc2,
symbols)
if na_3 == 0: break
# assign CX3 groups
dcorr, na_4 = assign_cx3(dcorr, graph1, graph2, xcc1, xcc2, symbols)
if na_4 == 0: break
# assign based on torsions
dcorr, na_5 = assign_torsions(dcorr, graph1, graph2, xcc1, xcc2,
symbols)
if na_5 == 0: break
# force assignation
if na_0 == na_5:
nforce += 1
dcorr, na_5 = assign_force(dcorr, graph1, graph2)
# update na_0
na_0 = na_5
if pp:
print("forced: %i" % nforce)
assign_print(dcorr)
return dcorr
def equivalent_atoms(xcc, symbols, fconnect=1.3):
xcc1 = [xi for xi in xcc]
xcc2 = [xi for xi in xcc]
# Adjacency matrices
amatrix1 = np.matrix(intl.get_adjmatrix(xcc1, symbols, fconnect, "int")[0])
amatrix2 = np.matrix(intl.get_adjmatrix(xcc2, symbols, fconnect, "int")[0])
# autoconnect!
amatrix1 = np.matrix(intl.link_fragments(xcc1, amatrix1.tolist(), 1)[0])
amatrix2 = np.matrix(intl.link_fragments(xcc2, amatrix2.tolist(), 1)[0])
# Correlate
dcorr = equivalent_atoms_in_graphs(amatrix1, amatrix2, symbols)
return dcorr
def __init__(self, xcc, symbols, cscal=1.3, nfrags=1, epslin=4.5):
# initialize data for UGRAPH
self._ugdict = {}
self._nnodes = 0
self._nedges = 0
self._cnumber = 0
# calculate connectivity matrix
cmatrix = intl.get_adjmatrix(xcc, symbols, cscal, mode="int")[0]
# autoconnect
cmatrix = intl.link_fragments(xcc, cmatrix, nfrags)[0]
# save cmatrix
cmatrix = np.matrix(cmatrix)
# set graph
self.set_from_amatrix(cmatrix)
# detect atoms in cycles
nodes_cycles = self.nodes_in_cycles()
# save data
self._xcc = [xi for xi in xcc]
self._symbols = symbols
self._incycle = nodes_cycles
self._cmatrix = cmatrix
self._epslin = epslin
self._cscal = cscal
self._angles = {}
# see if dummy is required by checking all atoms with two connections (B-A-C)
dummies = []
nodeX = int(self._nnodes) - 1
for nodeA, neighbors in self._ugdict.items():
if len(neighbors) != 2: continue
nodeB, nodeC = neighbors
# add dummy atom??
if self.islinear((nodeB, nodeA, nodeC)):
xB = self._xcc[3 * nodeB:3 * nodeB + 3]
xA = self._xcc[3 * nodeA:3 * nodeA + 3]
# coordinates of dummy atom
xX = intl.zmat_thirdatom(xB, xA, dist=1.0, angle=np.pi / 2)
# save data
nodeX += 1
dummies.append((nodeA, nodeX, xX))
#---------------------#
# Include dummy atoms #
#---------------------#
nn = int(self._nnodes)
nd = len(dummies)
if nd > 0:
# increase cmatrix
zerocols = np.zeros((nn, nd), dtype=int)
zerorows = np.zeros((nd, nn + nd), dtype=int)
self._cmatrix = np.hstack((self._cmatrix, zerocols))
self._cmatrix = np.vstack((self._cmatrix, zerorows))
# Add dummy atoms!!
for nodeA, nodeX, xX in dummies:
# add connection in graph
self.add_node(nodeX)
self.add_edge(nodeX, nodeA)
# add connection in cmatrix
self._cmatrix[nodeA, nodeX] = 1
self._cmatrix[nodeX, nodeA] = 1
# add dummy to symbols and xcc
self._symbols.append("X")
self._xcc += [xi for xi in xX]
def equivalent_atoms_by_connectivity(xcc,symbols,fconnect=1.3):
amatrix = np.matrix(intl.get_adjmatrix(xcc,symbols,fconnect,"int")[0])
amatrix = np.matrix(intl.link_fragments(xcc,amatrix.tolist(),1)[0])
dcorr = correlate_amatrices(amatrix,amatrix,symbols,symbols,onlyconn=True)
return dcorr