def test_becke_sum3_one(self):
"""Test becke weights add up to one with three centers."""
npoint = 100
points = np.random.uniform(-5, 5, (npoint, 3))
weights0 = np.ones(npoint, float)
weights1 = np.ones(npoint, float)
weights2 = np.ones(npoint, float)
radii = np.array([0.5, 0.8, 5.0])
centers = np.array([[1.2, 2.3, 0.1], [-0.4, 0.0, -2.2],
[2.2, -1.5, 0.0]])
weights0 = BeckeWeights.generate_becke_weights(points,
radii,
centers,
select=[0],
order=3)
weights1 = BeckeWeights.generate_becke_weights(points,
radii,
centers,
select=[1],
order=3)
weights2 = BeckeWeights.generate_becke_weights(points,
radii,
centers,
select=[2],
order=3)
assert_allclose(weights0 + weights1 + weights2, np.ones(100))
def __init__(self, atomic_grids, radii, aim_weights="becke", store=False):
"""Initialize molgrid class.
Parameters
----------
atomic_grids : list[AtomicGrid]
list of atomic grid
radii : np.ndarray(N,)
Radii for each atom in the molecular grid
aim_weights : str or np.ndarray(K,), default to "becke"
Atoms in molecule weights. If str, certain function will be called
to compute aim_weights, if np.ndarray, it will be treated as the
aim_weights
"""
# initialize these attributes
self._coors = np.zeros((len(radii), 3))
self._indices = np.zeros(len(radii) + 1, dtype=int)
self._size = np.sum([atomgrid.size for atomgrid in atomic_grids])
self._points = np.zeros((self._size, 3))
self._weights = np.zeros(self._size)
self._atomic_grids = atomic_grids if store else None
for i, atom_grid in enumerate(atomic_grids):
self._coors[i] = atom_grid.center
self._indices[i + 1] += self._indices[i] + atom_grid.size
self._points[self._indices[i] : self._indices[i + 1]] = atom_grid.points
self._weights[self._indices[i] : self._indices[i + 1]] = atom_grid.weights
if isinstance(aim_weights, str):
if aim_weights == "becke":
self._aim_weights = BeckeWeights.generate_becke_weights(
self._points, radii, self._coors, pt_ind=self._indices
)
else:
raise NotImplementedError(
f"Given aim_weights is not supported, got {aim_weights}"
)
elif isinstance(aim_weights, np.ndarray):
if aim_weights.size != self.size:
raise ValueError(
"aim_weights is not the same size as grid.\n"
f"aim_weights.size: {aim_weights.size}, grid.size: {self.size}."
)
self._aim_weights = aim_weights
else:
raise TypeError(f"Not supported aim_weights type, got {type(aim_weights)}.")
def test_becke_special_points(self):
"""Test becke weights for special cases."""
radii = np.array([0.5, 0.8, 5.0])
centers = np.array([[1.2, 2.3, 0.1], [-0.4, 0.0, -2.2],
[2.2, -1.5, 0.0]])
weights = BeckeWeights.generate_becke_weights(centers,
radii,
centers,
select=[0],
order=3)
assert_allclose(weights, [1, 0, 0])
weights = BeckeWeights.generate_becke_weights(centers,
radii,
centers,
select=[1],
order=3)
assert_allclose(weights, [0, 1, 0])
weights = BeckeWeights.generate_becke_weights(centers,
radii,
centers,
select=[2],
order=3)
assert_allclose(weights, [0, 0, 1])
# each point in seperate sectors.
weights = BeckeWeights.generate_becke_weights(centers,
radii,
centers,
pt_ind=[0, 1, 2, 3])
assert_allclose(weights, [1, 1, 1])
weights = BeckeWeights.generate_becke_weights(centers,
radii,
centers,
select=[0, 1],
pt_ind=[0, 1, 3])
assert_allclose(weights, [1, 1, 0])
weights = BeckeWeights.generate_becke_weights(centers,
radii,
centers,
select=[2, 0],
pt_ind=[0, 2, 3])
assert_allclose(weights, [0, 0, 0])
def test_raise_errors(self):
"""Test errors raise."""
npoint = 100
points = np.random.uniform(-5, 5, (npoint, 3))
radii = np.array([0.5, 0.8])
centers = np.array([[1.2, 2.3, 0.1], [-0.4, 0.0, -2.2]])
with self.assertRaises(ValueError):
BeckeWeights.generate_becke_weights(points,
radii,
centers,
select=[])
with self.assertRaises(ValueError):
BeckeWeights.generate_becke_weights(points,
radii,
centers,
pt_ind=[3])
with self.assertRaises(ValueError):
BeckeWeights.generate_becke_weights(points,
radii,
centers,
pt_ind=[3, 6])
with self.assertRaises(ValueError):
BeckeWeights.generate_becke_weights(points,
radii,
centers,
select=[],
pt_ind=[])
with self.assertRaises(ValueError):
BeckeWeights.generate_becke_weights(points,
radii,
centers,
select=[0, 1],
pt_ind=[0, 10, 50, 99])
with self.assertRaises(ValueError):
BeckeWeights.generate_becke_weights(points,
radii,
centers[0],
select=[0, 1],
pt_ind=[0, 10, 50, 99])
def __init__(self,
atomic_grids,
numbers,
aim_weights="becke",
store=False):
"""Initialize molgrid class.
Parameters
----------
atomic_grids : list[AtomicGrid]
list of atomic grid
radii : np.ndarray(N,)
Radii for each atom in the molecular grid
aim_weights : str or np.ndarray(K,), default to "becke"
Atoms in molecule weights. If str, certain function will be called
to compute aim_weights, if np.ndarray, it will be treated as the
aim_weights
"""
# initialize these attributes
numbers = np.array(numbers)
radii = get_cov_radii(numbers)
self._coors = np.zeros((len(radii), 3))
self._indices = np.zeros(len(radii) + 1, dtype=int)
self._size = np.sum([atomgrid.size for atomgrid in atomic_grids])
self._points = np.zeros((self._size, 3))
self._weights = np.zeros(self._size)
self._atomic_grids = atomic_grids if store else None
for i, atom_grid in enumerate(atomic_grids):
self._coors[i] = atom_grid.center
self._indices[i + 1] += self._indices[i] + atom_grid.size
self._points[self._indices[i]:self._indices[i +
1]] = atom_grid.points
self._weights[self._indices[i]:self.
_indices[i + 1]] = atom_grid.weights
if isinstance(aim_weights, str):
if aim_weights == "becke":
# Becke weights are computed for "chunks" of grid points
# to counteract the scaling of the memory usage of the
# vectorized implementation of the Becke partitioning.
chunk_size = max(1,
(10 * self._size) // self._coors.shape[0]**2)
self._aim_weights = np.concatenate([
BeckeWeights.generate_becke_weights(
self._points[ibegin:ibegin + chunk_size],
radii,
self._coors,
pt_ind=(self._indices - ibegin).clip(min=0),
) for ibegin in range(0, self._size, chunk_size)
])
else:
raise NotImplementedError(
f"Given aim_weights is not supported, got {aim_weights}")
elif isinstance(aim_weights, np.ndarray):
if aim_weights.size != self.size:
raise ValueError(
"aim_weights is not the same size as grid.\n"
f"aim_weights.size: {aim_weights.size}, grid.size: {self.size}."
)
self._aim_weights = aim_weights
else:
raise TypeError(
f"Not supported aim_weights type, got {type(aim_weights)}.")