def a2md_from_mol(mol: Mol2):
"""
returns the density model associated to a Mol2
:param mol:
:type mol: Mol2
:return:
:rtype: Molecule
"""
an = mol.get_atomic_numbers()
coords = mol.get_coordinates(units="au")
charge = mol.get_absolute_charges()
topology = mol.get_bonds()
segments = mol.segment_idx
topo_array = []
for i in range(mol.get_number_atoms()):
topo_array.append([])
for i in range(mol.get_number_bonds()):
begin = int(topology[i, 0])
end = int(topology[i, 1])
topo_array[begin - 1].append(end - 1)
topo_array[end - 1].append(begin - 1)
return Molecule(coordinates=coords,
atomic_numbers=an,
charge=charge,
topology=topo_array,
segments=segments)
def split_space(mm: Mol2, fun: Callable):
"""
split space
---
divides fun in n functions centered around atoms
:param mm:
:param fun:
:return:
"""
r = mm.get_coordinates(units='au')
n = mm.get_number_atoms()
for i in range(n):
r0 = r[i, :]
yield lambda x: fun(x + r0) * (voronoi(x + r0, r) == i)
def integrate_density_functional_gradient(functional: Callable,
mol: Mol2,
nfuns: int,
grid='coarse',
res=100):
r = mol.get_coordinates(units='au')
n = mol.get_number_atoms()
functional_value = np.zeros(nfuns, dtype='float64')
for i in range(n):
r0 = r[i, :]
fx = lambda x: functional(x + r0) * (voronoi(x + r0, r) == i).reshape(
-1, 1)
functional_value += pi_lebedev_m(fx, nfuns, radial_res=res, grid=grid)
return functional_value
def genamd_from_mol2(mol: Mol2, device: torch.device, nfuns: int):
labels = mol.get_symbols()
centers = torch.tensor(mol.get_coordinates(units='au'),
dtype=torch.float,
device=device)
labels = torch.tensor([SYMBOL2NN[i] for i in labels],
dtype=torch.long,
device=device)
centers.unsqueeze_(0)
labels.unsqueeze_(0)
natoms = mol.get_number_atoms()
coefficients = torch.ones(1,
natoms,
nfuns,
dtype=torch.float,
device=device)
return labels, centers, coefficients