def get_data(comps, phase_name, configuration, symmetry, datasets, prop):
desired_data = datasets.search((tinydb.where('output').test(lambda x: x in prop)) &
(tinydb.where('components').test(lambda x: set(x).issubset(comps))) &
(tinydb.where('solver').test(symmetry_filter, configuration, list_to_tuple(symmetry) if symmetry else symmetry)) &
(tinydb.where('phases') == [phase_name]))
# This seems to be necessary because the 'values' member does not modify 'datasets'
# But everything else does!
desired_data = copy.deepcopy(desired_data)
def recursive_zip(a, b):
if isinstance(a, (list, tuple)) and isinstance(b, (list, tuple)):
return list(recursive_zip(x, y) for x, y in zip(a, b))
else:
return list(zip(a, b))
for idx, data in enumerate(desired_data):
# Filter output values to only contain data for matching sublattice configurations
matching_configs = np.array([(canonicalize(sblconf, symmetry) == canonicalize(configuration, symmetry))
for sblconf in data['solver']['sublattice_configurations']])
matching_configs = np.arange(len(data['solver']['sublattice_configurations']))[matching_configs]
# Rewrite output values with filtered data
desired_data[idx]['values'] = np.array(data['values'], dtype=np.float)[..., matching_configs]
desired_data[idx]['solver']['sublattice_configurations'] = list_to_tuple(np.array(data['solver']['sublattice_configurations'],
dtype=np.object)[matching_configs].tolist())
try:
desired_data[idx]['solver']['sublattice_occupancies'] = np.array(data['solver']['sublattice_occupancies'],
dtype=np.object)[matching_configs].tolist()
except KeyError:
pass
# Filter out temperatures below 298.15 K (for now, until better refstates exist)
temp_filter = np.atleast_1d(data['conditions']['T']) >= 298.15
desired_data[idx]['conditions']['T'] = np.atleast_1d(data['conditions']['T'])[temp_filter]
# Don't use data['values'] because we rewrote it above; not sure what 'data' references now
desired_data[idx]['values'] = desired_data[idx]['values'][..., temp_filter, :]
return desired_data
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