def _flatten_structures(self):
c_specs, self.edges, self.weights, self.einstrs, self.einpaths = [], [], [], [], []
ks = {}
# handle n=1 case specially
c_specs.append([1,0,0,0,0,1,1,0])
self.edges.append(())
self.weights.append(())
self.einstrs.append(self.einstrs_d[(1,0)][0])
self.einpaths.append(self.einpaths_d[(1,0)][0])
for ne in sorted(self.edges_d.keys()):
n, e = ne
z = zip(self.edges_d[ne], self.weights_d[ne], self.chis_d[ne],
self.einstrs_d[ne], self.einpaths_d[ne])
for edgs, ws, c, es, ep in z:
for w in ws:
d = sum(w)
k = ks.setdefault((n,d), 0)
ks[(n,d)] += 1
vs = valencies(EFP(edgs, weights=w).graph).values()
v = max(vs)
h = Counter(vs)[1]
c_specs.append([n, e, d, v, k, c, 1, h])
self.edges.append(edgs)
self.weights.append(w)
self.einstrs.append(es)
self.einpaths.append(ep)
self.c_specs = np.asarray(c_specs)
def _generate_efms(self):
self.efm_einstrs, self.efm_specs, self.efm_einpaths = [], [], []
if self.gen_efms:
for edgs,ws in zip(self.edges, self.weights):
einstr, efm_spec = efp2efms(EFP(edgs, weights=ws).graph)
self.efm_einstrs.append(einstr)
self.efm_specs.append(efm_spec)
self.efm_einpaths.append(np.einsum_path(einstr,
*[np.empty([4]*sum(s)) for s in efm_spec],
optimize=self.ve.np_optimize)[0])