def test_neighbor_list_shape(self):
"""
Simple test that Neighbor Lists have right shape.
"""
nblist_featurizer = NeighborListAtomicCoordinates()
N = self.mol.GetNumAtoms()
coords = get_coords(self.mol)
nblist_featurizer = NeighborListAtomicCoordinates()
nblist = nblist_featurizer._featurize(self.mol)[1]
assert isinstance(nblist, dict)
assert len(nblist.keys()) == N
for (atom, neighbors) in nblist.items():
assert isinstance(atom, int)
assert isinstance(neighbors, list)
assert len(neighbors) <= N
# Do a manual distance computation and make
for i in range(N):
for j in range(N):
dist = np.linalg.norm(coords[i] - coords[j])
print("Distance(%d, %d) = %f" % (i, j, dist))
if dist < nblist_featurizer.neighbor_cutoff and i != j:
assert j in nblist[i]
else:
assert j not in nblist[i]
def test_neighbor_list_max_num_neighbors(self):
"""
Test that neighbor lists return only max_num_neighbors.
"""
N = self.mol.GetNumAtoms()
max_num_neighbors = 1
nblist_featurizer = NeighborListAtomicCoordinates(max_num_neighbors)
nblist = nblist_featurizer._featurize(self.mol)[1]
for atom in range(N):
assert len(nblist[atom]) <= max_num_neighbors
# Do a manual distance computation and ensure that selected neighbor is
# closest since we set max_num_neighbors = 1
coords = get_coords(self.mol)
for i in range(N):
closest_dist = np.inf
closest_nbr = None
for j in range(N):
if i == j:
continue
dist = np.linalg.norm(coords[i] - coords[j])
print("Distance(%d, %d) = %f" % (i, j, dist))
if dist < closest_dist:
closest_dist = dist
closest_nbr = j
print("Closest neighbor to %d is %d" % (i, closest_nbr))
print("Distance: %f" % closest_dist)
if closest_dist < nblist_featurizer.neighbor_cutoff:
assert nblist[i] == [closest_nbr]
else:
assert nblist[i] == []
def test_neighbor_list_max_num_neighbors(self):
"""
Test that neighbor lists return only max_num_neighbors.
"""
N = self.mol.GetNumAtoms()
max_num_neighbors = 1
nblist_featurizer = NeighborListAtomicCoordinates(max_num_neighbors)
nblist = nblist_featurizer._featurize(self.mol)[1]
for atom in range(N):
assert len(nblist[atom]) <= max_num_neighbors
# Do a manual distance computation and ensure that selected neighbor is
# closest since we set max_num_neighbors = 1
coords = get_coords(self.mol)
for i in range(N):
closest_dist = np.inf
closest_nbr = None
for j in range(N):
if i == j:
continue
dist = np.linalg.norm(coords[i] - coords[j])
print("Distance(%d, %d) = %f" % (i, j, dist))
if dist < closest_dist:
closest_dist = dist
closest_nbr = j
print("Closest neighbor to %d is %d" % (i, closest_nbr))
print("Distance: %f" % closest_dist)
if closest_dist < nblist_featurizer.neighbor_cutoff:
assert nblist[i] == [closest_nbr]
else:
assert nblist[i] == []
def test_neighbor_list_shape(self):
"""
Simple test that Neighbor Lists have right shape.
"""
nblist_featurizer = NeighborListAtomicCoordinates()
N = self.mol.GetNumAtoms()
coords = get_coords(self.mol)
x_bins, y_bins, z_bins = get_cells(coords, nblist_featurizer.neighbor_cutoff)
nblist_featurizer = NeighborListAtomicCoordinates()
nblist = nblist_featurizer._featurize(self.mol)[1]
assert isinstance(nblist, dict)
assert len(nblist.keys()) == N
for (atom, neighbors) in nblist.items():
assert isinstance(atom, int)
assert isinstance(neighbors, list)
assert len(neighbors) <= N
# Do a manual distance computation and make
for i in range(N):
for j in range(N):
dist = np.linalg.norm(coords[i] - coords[j])
print("Distance(%d, %d) = %f" % (i, j, dist))
if dist < nblist_featurizer.neighbor_cutoff and i != j:
assert j in nblist[i]
else:
assert j not in nblist[i]
def test_neighbor_list_extremes(self):
"""
Test Neighbor Lists with large/small boxes.
"""
N = self.mol.GetNumAtoms()
# Test with cutoff 0 angstroms. There should be no neighbors in this case.
nblist_featurizer = NeighborListAtomicCoordinates(neighbor_cutoff=.1)
nblist = nblist_featurizer._featurize(self.mol)[1]
for atom in range(N):
assert len(nblist[atom]) == 0
# Test with cutoff 100 angstroms. Everything should be neighbors now.
nblist_featurizer = NeighborListAtomicCoordinates(neighbor_cutoff=100)
nblist = nblist_featurizer._featurize(self.mol)[1]
for atom in range(N):
assert len(nblist[atom]) == N-1
def test_neighbor_list_periodic(self):
"""Test building a neighbor list with periodic boundary conditions."""
cutoff = 4.0
box_size = np.array([10.0, 8.0, 9.0])
N = self.mol.GetNumAtoms()
coords = get_coords(self.mol)
featurizer = NeighborListAtomicCoordinates(neighbor_cutoff=cutoff,
periodic_box_size=box_size)
neighborlist = featurizer._featurize(self.mol)[1]
expected_neighbors = [set() for i in range(N)]
for i in range(N):
for j in range(i):
delta = coords[i] - coords[j]
delta -= np.round(delta / box_size) * box_size
if np.linalg.norm(delta) < cutoff:
expected_neighbors[i].add(j)
expected_neighbors[j].add(i)
for i in range(N):
assert (set(neighborlist[i]) == expected_neighbors[i])
def test_neighbor_list_extremes(self):
"""
Test Neighbor Lists with large/small boxes.
"""
N = self.mol.GetNumAtoms()
# Test with cutoff 0 angstroms. There should be no neighbors in this case.
nblist_featurizer = NeighborListAtomicCoordinates(neighbor_cutoff=.1)
nblist = nblist_featurizer._featurize(self.mol)[1]
for atom in range(N):
assert len(nblist[atom]) == 0
# Test with cutoff 100 angstroms. Everything should be neighbors now.
nblist_featurizer = NeighborListAtomicCoordinates(neighbor_cutoff=100)
nblist = nblist_featurizer._featurize(self.mol)[1]
for atom in range(N):
assert len(nblist[atom]) == N - 1
