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Revert "Syncing to Develop" #12

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Nov 15, 2023
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Revert "Syncing to Develop" #12

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70 changes: 44 additions & 26 deletions uf3/representation/angles.py
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Original file line number Diff line number Diff line change
Expand Up @@ -416,44 +416,58 @@ def generate_triplets(i_where: np.ndarray,
i_groups = np.array_split(j_where, np.cumsum(group_sizes)[:-1])
# generate j-k combinations
for i in range(len(i_groups)):
j_arr, k_arr = np.meshgrid(i_groups[i], i_groups[i])

# Pick out unique neighbor pairs of central atom i
# ex: With center atom 0 and its neighbors [2, 1, 3],
# j_arr = [[2, 1, 3],
# [2, 1, 3],
# [2, 1, 3]]
# k_arr = [[2, 2, 2],
# [1, 1, 1],
# [3, 3, 3]]
# j_indices = [1, 2, 1]
# k_indices = [2, 3, 3]
# => unique pairs: (1, 2), (2, 3), (1, 3)
# The unique_pair_mask has filtered out pairs like (1, 1), (2, 1), (3, 2), etc.
unique_pair_mask = (j_arr < k_arr)
j_indices = j_arr[unique_pair_mask]
k_indices = k_arr[unique_pair_mask]
tuples = np.vstack((i_values[i] * np.ones(len(j_indices), dtype=int),
j_indices, k_indices)).T # array of unique triplets

tuples = np.array(np.meshgrid(i_groups[i],
i_groups[i])).T.reshape(-1, 2)
tuples = np.insert(tuples, 0, i_values[i], axis=1)
comp_tuples = sup_composition[tuples]

sort_indices = np.argsort(comp_tuples[:, 1:],axis=1) # to sort by atomic number
sort_indices = np.argsort(comp_tuples[:, 1:],axis=1)
comp_tuples_slice = np.take_along_axis(comp_tuples[:, 1:],sort_indices,axis=1)
tuples_slice = np.take_along_axis(tuples[:, 1:],sort_indices,axis=1)

# sort comp_tuples and tuples the same way

comp_tuples = np.hstack((comp_tuples[:, [0]], comp_tuples_slice))
tuples = np.hstack((tuples[:, [0]], tuples_slice))

ijk_hash = composition.get_szudzik_hash(comp_tuples)

grouped_triplets = [None] * n_hashes
for j, hash_ in enumerate(hashes):
ituples = tuples[ijk_hash == hash_]
icomp_tuples = comp_tuples[ijk_hash == hash_]
if len(ituples) == 0:
grouped_triplets[j] = None
continue

# Check if same neighbouring elements
if icomp_tuples[0][1] == icomp_tuples[0][2]:
# element at j and k are same;
# remove redundant interactions
comparison_mask = (ituples[:, 1] < ituples[:, 2])
ituples = ituples[comparison_mask]
icomp_tuples = icomp_tuples[comparison_mask]

else:
# Elements at j and k not same
# cordinates of j and k are same; filter eg 011, 022, 033
# this comparison mask is redundant
comparison_mask = (ituples[:, 1] != ituples[:, 2])

ituples = ituples[comparison_mask]
icomp_tuples = icomp_tuples[comparison_mask]

if len(ituples) > 0:
# Remove repetitive interactions
ituples = np.unique(ituples,axis=0)
icomp_tuples = np.unique(icomp_tuples,axis=0)

# sort by electro-negativity as the interaction tuple is always
# sorted by electro-negativity
en_j = composition.reference_X[ase_symbols.chemical_symbols[icomp_tuples[0][1]]]
en_k = composition.reference_X[ase_symbols.chemical_symbols[icomp_tuples[0][2]]]
if en_k < en_j:
# Interchange columns 1 and 2
ituples[:, [1, 2]] = ituples[:, [2, 1]]

# extract distance tuples
r_l = distance_matrix[ituples[:, 0], ituples[:, 1]]
r_m = distance_matrix[ituples[:, 0], ituples[:, 2]]
Expand All @@ -470,7 +484,11 @@ def generate_triplets(i_where: np.ndarray,
r_m = r_m[dist_mask]
r_n = r_n[dist_mask]
ituples = ituples[dist_mask]
grouped_triplets[j] = i, r_l, r_m, r_n, ituples

if len(ituples) == 0:
grouped_triplets[j] = None
else:
grouped_triplets[j] = i, r_l, r_m, r_n, ituples
yield grouped_triplets


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