def test_elements_to_atomicnums(els, zs):
Zs = loaders.elements_to_atomicnums(els)
assert np.allclose(Zs, zs)
def elements_to_idxs(elements, amap):
anums = loaders.elements_to_atomicnums(list(elements))
return [amap[anum] for anum in anums]
elements = selection.elements[mask]
coordinates = selection.positions[mask]
else:
# Store indices of selection for later use
# Needed to insert gradient as b-factors
sel_idxs = selection.ix
elements = selection.elements
coordinates = selection.positions
# Get species for selection
# Apply mapping from elements to atomic number
# Apply chemical mapping (if any)
# Apply mapping from atomic numbers to indices
anums = loaders.elements_to_atomicnums(elements)
if cmap is not None:
loaders.chemap([anums], cmap) # Only one system
species = loaders.anum_to_idx(anums, amap)
G = np.zeros(len(system.atoms))
if args.visualization.lower() == "gradient":
gradt = gradient(
torch.from_numpy(species),
torch.from_numpy(coordinates),
float(label),
model,
AEVC,
nn.MSELoss(),