def to_graph(self):
"""
Convert molecule to graph.
Returns
-------
Graph
Molecular graph.
Notes
-----
If the molecule does not have an associated adjacency matrix, a simple
bond perception is used.
The molecular graph is cached.
"""
if self.G is None:
try:
self.G = graph.graph_from_adjacency_matrix(
self.adjacency_matrix, self.atomicnums)
except AttributeError:
warnings.warn(
"Molecule was not initialized with an adjacency matrix. " +
"Using bond perception...")
# Automatic bond perception (with very simple rule)
self.adjacency_matrix = graph.adjacency_matrix_from_atomic_coordinates(
self.atomicnums, self.coordinates)
self.G = graph.graph_from_adjacency_matrix(
self.adjacency_matrix, self.atomicnums)
return self.G
def test_adjacency_matrix_from_atomic_coordinates(mol: molecule.Molecule,
n_bonds: int) -> None:
A = graph.adjacency_matrix_from_atomic_coordinates(mol.atomicnums,
mol.coordinates)
G = graph.graph_from_adjacency_matrix(A)
assert graph.num_vertices(G) == len(mol)
assert graph.num_edges(G) == n_bonds
def test_build_graph_node_features_unsupported() -> None:
pytest.importorskip(
"graph_tool",
reason="NetworkX supports all Python objects as node properties.")
A = np.array([[0, 1, 1], [1, 0, 0], [1, 0, 1]])
property = [True, False, True]
with pytest.raises(ValueError, match="Unsupported property type:"):
_ = graph.graph_from_adjacency_matrix(A, property)
def test_graph_from_adjacency_matrix(mol) -> None:
natoms = io.numatoms(mol)
nbonds = io.numbonds(mol)
A = io.adjacency_matrix(mol)
assert A.shape == (natoms, natoms)
assert np.alltrue(A == A.T)
assert np.sum(A) == nbonds * 2
G = graph.graph_from_adjacency_matrix(A)
assert graph.num_vertices(G) == natoms
assert graph.num_edges(G) == nbonds
def test_adjacency_matrix_from_atomic_coordinates_distance() -> None:
# Lithium hydride (LiH)
# H and Li have very different covalent radii
atomicnums = np.array([1, 3])
# Distance is the sum of covalent radii
d = sum([constants.anum_to_covalentradius[anum] for anum in atomicnums])
# Distance between two atoms is barely enough to create a bond
# If the covalent radii are not correct, no bond will be created
coordinates = np.array(
[[0, 0, 0], [0, 0, d + constants.connectivity_tolerance - 0.01]])
A = graph.adjacency_matrix_from_atomic_coordinates(atomicnums, coordinates)
G = graph.graph_from_adjacency_matrix(A)
assert graph.num_edges(G) == 1
def test_build_graph_node_features(property) -> None:
A = np.array([[0, 1, 1], [1, 0, 0], [1, 0, 1]])
G = graph.graph_from_adjacency_matrix(A, property)
assert graph.num_edges(G) == 3
def _rmsd_isomorphic_core(
coords1: np.ndarray,
coords2: np.ndarray,
aprops1: np.ndarray,
aprops2: np.ndarray,
am1: np.ndarray,
am2: np.ndarray,
center: bool = False,
minimize: bool = False,
isomorphisms: Optional[List[Tuple[List[int], List[int]]]] = None,
atol: float = 1e-9,
) -> Tuple[float, List[Tuple[List[int], List[int]]]]:
"""
Compute RMSD using graph isomorphism.
Parameters
----------
coords1: np.ndarray
Coordinate of molecule 1
coords2: np.ndarray
Coordinates of molecule 2
aprops1: np.ndarray
Atomic properties for molecule 1
aprops2: np.ndarray
Atomic properties for molecule 2
am1: np.ndarray
Adjacency matrix for molecule 1
am2: np.ndarray
Adjacency matrix for molecule 2
center: bool
Centering flag
minimize: bool
Compute minized RMSD
isomorphisms: Optional[List[Dict[int,int]]]
Previously computed graph isomorphism
atol: float
Absolute tolerance parameter for QCP (see :func:`qcp_rmsd`)
Returns
-------
Tuple[float, List[Dict[int, int]]]
RMSD (after graph matching) and graph isomorphisms
"""
assert coords1.shape == coords2.shape
n = coords1.shape[0]
# Center coordinates if required
c1 = utils.center(coords1) if center or minimize else coords1
c2 = utils.center(coords2) if center or minimize else coords2
# No cached isomorphisms
if isomorphisms is None:
# Convert molecules to graphs
G1 = graph.graph_from_adjacency_matrix(am1, aprops1)
G2 = graph.graph_from_adjacency_matrix(am2, aprops2)
# Get all the possible graph isomorphisms
isomorphisms = graph.match_graphs(G1, G2)
# Minimum result
# Squared displacement (not minimize) or RMSD (minimize)
min_result = np.inf
# Loop over all graph isomorphisms to find the lowest RMSD
for idx1, idx2 in isomorphisms:
# Use the isomorphism to shuffle coordinates around (from original order)
c1i = c1[idx1, :]
c2i = c2[idx2, :]
if not minimize:
# Compute square displacement
# Avoid dividing by n and an expensive sqrt() operation
result = np.sum((c1i - c2i)**2)
else:
# Compute minimized RMSD using QCP
result = qcp.qcp_rmsd(c1i, c2i, atol)
min_result = result if result < min_result else min_result
if not minimize:
# Compute actual RMSD from square displacement
min_result = np.sqrt(min_result / n)
# Return the actual RMSD
return min_result, isomorphisms
