def __init__(self, images, unique_atoms, descriptor, Gs, fprange, label="example", cores=1):
self.images = images
if type(images) is not list:
self.images = [images]
self.descriptor = descriptor
self.atom_images = self.images
if isinstance(images, str):
extension = os.path.splitext(images)[1]
if extension != (".traj" or ".db"):
self.atom_images = ase.io.read(images, ":")
self.fprange = fprange
self.training_unique_atoms = unique_atoms
self.hashed_images = amp_hash(self.atom_images)
G2_etas = Gs["G2_etas"]
G2_rs_s = Gs["G2_rs_s"]
G4_etas = Gs["G4_etas"]
G4_zetas = Gs["G4_zetas"]
G4_gammas = Gs["G4_gammas"]
cutoff = Gs["cutoff"]
if str(descriptor)[8:16] == "amptorch":
self.hashed_images = hash_images(self.atom_images, Gs)
make_amp_descriptors_simple_nn(
self.atom_images, Gs, self.training_unique_atoms, cores=cores, label=label
)
G = make_symmetry_functions(elements=self.training_unique_atoms, type="G2", etas=G2_etas)
G += make_symmetry_functions(
elements=self.training_unique_atoms,
type="G4",
etas=G4_etas,
zetas=G4_zetas,
gammas=G4_gammas,
)
for g in G:
g["Rs"] = G2_rs_s
self.descriptor = self.descriptor(Gs=G, cutoff=cutoff)
self.descriptor.calculate_fingerprints(
self.hashed_images, calculate_derivatives=True
)
self.unique_atoms = self.unique()
def __init__(self,
images,
descriptor,
Gs,
forcetraining,
label,
cores,
delta_data=None,
store_primes=False,
specific_atoms=False):
self.images = images
self.base_descriptor = descriptor
self.descriptor = descriptor
self.Gs = Gs
self.atom_images = self.images
self.forcetraining = forcetraining
self.store_primes = store_primes
self.cores = cores
self.delta = False
self.specific_atoms = specific_atoms
if delta_data is not None:
self.delta_data = delta_data
self.delta_energies = np.array(delta_data[0])
self.delta_forces = delta_data[1]
self.num_atoms = np.array(delta_data[2])
self.delta = True
if self.store_primes:
if not os.path.isdir("./stored-primes/"):
os.mkdir("stored-primes")
if isinstance(images, str):
extension = os.path.splitext(images)[1]
if extension != (".traj" or ".db"):
self.atom_images = ase.io.read(images, ":")
self.elements = self.unique()
#TODO Print log - control verbose
print("Calculating fingerprints...")
G2_etas = Gs["G2_etas"]
G2_rs_s = Gs["G2_rs_s"]
G4_etas = Gs["G4_etas"]
G4_zetas = Gs["G4_zetas"]
G4_gammas = Gs["G4_gammas"]
cutoff = Gs["cutoff"]
# create simple_nn fingerprints
if str(descriptor)[8:16] == "amptorch":
self.hashed_images = hash_images(self.atom_images, Gs=Gs)
make_amp_descriptors_simple_nn(self.atom_images,
Gs,
self.elements,
cores=cores,
label=label,
specific_atoms=self.specific_atoms)
self.isamp_hash = False
else:
self.hashed_images = amp_hash(self.atom_images)
self.isamp_hash = True
G = make_symmetry_functions(elements=self.elements,
type="G2",
etas=G2_etas)
G += make_symmetry_functions(
elements=self.elements,
type="G4",
etas=G4_etas,
zetas=G4_zetas,
gammas=G4_gammas,
)
for g in list(G):
g["Rs"] = G2_rs_s
self.descriptor = self.descriptor(Gs=G, cutoff=cutoff)
self.descriptor.calculate_fingerprints(
self.hashed_images, calculate_derivatives=forcetraining)
print("Fingerprints Calculated!")
self.fprange = calculate_fingerprints_range(self.descriptor,
self.hashed_images)
# perform preprocessing
self.fingerprint_dataset, self.energy_dataset, self.num_of_atoms, self.sparse_fprimes, self.forces_dataset, self.index_hashes, self.scalings, self.rearange_forces = (
self.preprocess_data())