def test_database_logic(seed, testdir):
from ase.ga.data import PrepareDB
from ase.ga.data import DataConnection
from ase.ga.startgenerator import StartGenerator
from ase.ga.utilities import closest_distances_generator
from ase.ga import set_raw_score
import numpy as np
from ase.build import fcc111
from ase.constraints import FixAtoms
# set up the random number generator
rng = np.random.RandomState(seed)
slab = fcc111('Au', size=(4, 4, 2), vacuum=10.0, orthogonal=True)
slab.set_constraint(FixAtoms(mask=slab.positions[:, 2] <= 10.))
# define the volume in which the adsorbed cluster is optimized
# the volume is defined by a corner position (p0)
# and three spanning vectors (v1, v2, v3)
pos = slab.get_positions()
cell = slab.get_cell()
p0 = np.array([0., 0., max(pos[:, 2]) + 2.])
v1 = cell[0, :] * 0.8
v2 = cell[1, :] * 0.8
v3 = cell[2, :]
v3[2] = 3.
# define the closest distance between two atoms of a given species
blmin = closest_distances_generator(atom_numbers=[47, 79],
ratio_of_covalent_radii=0.7)
# Define the composition of the atoms to optimize
atom_numbers = 2 * [47] + 2 * [79]
# create the starting population
sg = StartGenerator(slab=slab,
blocks=atom_numbers,
blmin=blmin,
box_to_place_in=[p0, [v1, v2, v3]],
rng=rng)
# generate the starting population
starting_population = [sg.get_new_candidate() for i in range(20)]
d = PrepareDB(db_file_name=db_file,
simulation_cell=slab,
stoichiometry=atom_numbers)
for a in starting_population:
d.add_unrelaxed_candidate(a)
# and now for the actual test
dc = DataConnection(db_file)
dc.get_slab()
dc.get_atom_numbers_to_optimize()
assert dc.get_number_of_unrelaxed_candidates() == 20
a1 = dc.get_an_unrelaxed_candidate()
dc.mark_as_queued(a1)
assert dc.get_number_of_unrelaxed_candidates() == 19
assert len(dc.get_all_candidates_in_queue()) == 1
set_raw_score(a1, 0.0)
dc.add_relaxed_step(a1)
assert dc.get_number_of_unrelaxed_candidates() == 19
assert len(dc.get_all_candidates_in_queue()) == 0
assert len(dc.get_all_relaxed_candidates()) == 1
a2 = dc.get_an_unrelaxed_candidate()
dc.mark_as_queued(a2)
confid = a2.info['confid']
assert dc.get_all_candidates_in_queue()[0] == confid
dc.remove_from_queue(confid)
assert len(dc.get_all_candidates_in_queue()) == 0
dc = DataConnection(db_file) slab_get = dc.get_slab() an_get = dc.get_atom_numbers_to_optimize() assert dc.get_number_of_unrelaxed_candidates() == 20 a1 = dc.get_an_unrelaxed_candidate() dc.mark_as_queued(a1) assert dc.get_number_of_unrelaxed_candidates() == 19 assert len(dc.get_all_candidates_in_queue()) == 1 a1.set_raw_score(0.0) dc.add_relaxed_step(a1) assert dc.get_number_of_unrelaxed_candidates() == 19 assert len(dc.get_all_candidates_in_queue()) == 0 assert len(dc.get_all_relaxed_candidates()) == 1 a2 = dc.get_an_unrelaxed_candidate() dc.mark_as_queued(a2) confid = a2.info['confid'] assert dc.get_all_candidates_in_queue()[0] == confid dc.remove_from_queue(confid) assert len(dc.get_all_candidates_in_queue()) == 0 os.remove(db_file)