def test_espresso_input_edit():
"""
Load a structure in from qe_input_1, change the position and cell,
then edit and re-parse
:return:
"""
os.system('cp test_files/qe_input_1.in .')
positions, species, cell, masses = parse_dft_input('./qe_input_1.in')
_, coded_species = get_unique_species(species)
structure = Structure(cell,
coded_species,
positions,
masses,
species_labels=species)
structure.vec1 += np.random.randn(3)
structure.positions[0] += np.random.randn(3)
new_file = edit_dft_input_positions('./qe_input_1.in', structure=structure)
positions, species, cell, masses = parse_dft_input(new_file)
assert np.equal(positions[0], structure.positions[0]).all()
assert np.equal(structure.vec1, cell[0, :]).all()
os.remove('qe_input_1.in')
def test_cp2k_input_edit():
"""
Load a structure in from cp2k_input_1, change the position and cell,
then edit and re-parse
:return:
"""
positions, species, cell, masses = parse_dft_input(
"./test_files/cp2k_input_1.in")
_, coded_species = get_unique_species(species)
structure = Structure(cell,
coded_species,
positions,
masses,
species_labels=species)
structure.positions[0] += np.random.randn(3)
newfilename = edit_dft_input_positions("./test_files/cp2k_input_1.in",
structure=structure)
positions, species, cell, masses = parse_dft_input(newfilename)
assert np.isclose(positions[0], structure.positions[0]).all()
assert np.isclose(structure.vec1, cell[0, :]).all()
remove(newfilename)
cleanup()
def dft_input_to_structure(qe_input: str):
"""
Parses a qe input and returns the atoms in the file as a Structure object
:param qe_input: QE Input file to parse
:return:
"""
positions, species, cell, masses = parse_dft_input(qe_input)
_, coded_species = struc.get_unique_species(species)
return struc.Structure(positions=positions, species=coded_species,
cell=cell, mass_dict=masses, species_labels=species)
def __init__(self,
dft_input: str,
dt: float,
number_of_steps: int,
gp: gp.GaussianProcess,
dft_loc: str,
std_tolerance_factor: float = 1,
prev_pos_init: np.ndarray = None,
par: bool = False,
skip: int = 0,
init_atoms: List[int] = None,
calculate_energy=False,
output_name='otf_run',
max_atoms_added=1,
freeze_hyps=10,
rescale_steps=[],
rescale_temps=[],
dft_softwarename="qe",
no_cpus=1,
npool=None,
mpi="srun"):
self.dft_input = dft_input
self.dt = dt
self.number_of_steps = number_of_steps
self.gp = gp
self.dft_loc = dft_loc
self.std_tolerance = std_tolerance_factor
self.skip = skip
self.dft_step = True
self.freeze_hyps = freeze_hyps
self.dft_module = dft_software[dft_softwarename]
# parse input file
positions, species, cell, masses = \
self.dft_module.parse_dft_input(self.dft_input)
_, coded_species = struc.get_unique_species(species)
self.structure = struc.Structure(cell=cell,
species=coded_species,
positions=positions,
mass_dict=masses,
prev_positions=prev_pos_init,
species_labels=species)
self.noa = self.structure.positions.shape[0]
self.atom_list = list(range(self.noa))
self.curr_step = 0
self.max_atoms_added = max_atoms_added
# initialize local energies
if calculate_energy:
self.local_energies = np.zeros(self.noa)
else:
self.local_energies = None
# set atom list for initial dft run
if init_atoms is None:
self.init_atoms = [int(n) for n in range(self.noa)]
else:
self.init_atoms = init_atoms
self.dft_count = 0
# set pred function
if not par and not calculate_energy:
self.pred_func = predict.predict_on_structure
elif par and not calculate_energy:
self.pred_func = predict.predict_on_structure_par
elif not par and calculate_energy:
self.pred_func = predict.predict_on_structure_en
elif par and calculate_energy:
self.pred_func = predict.predict_on_structure_par_en
self.par = par
# set rescale attributes
self.rescale_steps = rescale_steps
self.rescale_temps = rescale_temps
self.output = Output(output_name, always_flush=True)
# set number of cpus and npool for qe runs
self.no_cpus = no_cpus
self.npool = npool
self.mpi = mpi
