def test_pickelable_tinydb_can_be_pickled_and_unpickled():
"""PickleableTinyDB should be able to be pickled and unpickled."""
test_dict = {'test_key': ['test', 'values']}
db = PickleableTinyDB(storage=MemoryStorage)
db.insert(test_dict)
db = pickle.loads(pickle.dumps(db))
assert db.search(where('test_key').exists())[0] == test_dict
def load_datasets(dataset_filenames):
"""
Create a PickelableTinyDB with the data from a list of filenames.
Parameters
----------
dataset_filenames : [str]
List of filenames to load as datasets
Returns
-------
PickleableTinyDB
"""
ds_database = PickleableTinyDB(storage=MemoryStorage)
for fname in dataset_filenames:
with open(fname) as file_:
try:
d = json.load(file_)
check_dataset(d)
ds_database.insert(clean_dataset(d))
except ValueError as e:
raise ValueError('JSON Error in {}: {}'.format(fname, e))
except DatasetError as e:
raise DatasetError('Dataset Error in {}: {}'.format(fname, e))
return ds_database
def load_datasets(dataset_filenames, include_disabled=False) -> PickleableTinyDB:
"""
Create a PickelableTinyDB with the data from a list of filenames.
Parameters
----------
dataset_filenames : [str]
List of filenames to load as datasets
Returns
-------
PickleableTinyDB
"""
ds_database = PickleableTinyDB(storage=MemoryStorage)
for fname in dataset_filenames:
with open(fname) as file_:
try:
d = json.load(file_)
if not include_disabled and d.get('disabled', False):
# The dataset is disabled and not included
continue
check_dataset(d)
ds_database.insert(clean_dataset(d))
except ValueError as e:
raise ValueError('JSON Error in {}: {}'.format(fname, e))
except DatasetError as e:
raise DatasetError('Dataset Error in {}: {}'.format(fname, e))
return ds_database
def test_dataplot_plots_binary_equilibria_types():
"""Dataplot should be able to reproduce a single boundary (null) equilibria, a tieline, and a 3 phase equilibria for a binary"""
ds = PickleableTinyDB(storage=MemoryStorage)
ds.insert(A_B_DATASET_BINARY_PHASE_EQUILIBRIA)
comps = ['A', 'B']
phases = ["PHASE_1", "PHASE_2", "PHASE_3"]
conds = {v.P: 101325, v.T: (0, 400, 40), v.X('B'): (0, 1, 0.01)}
ax = dataplot(comps, phases, conds, ds)
ax.set_xlim(0, 1)
ax.set_ylim(0, 400)
def test_get_data_for_a_minimal_example():
"""Given a dataset and the congfiguration pertaining to that dataset, we should find the values."""
SAMPLE_DATASET = {
"components": ["CU", "MG", "VA"],
"phases": ["LAVES_C15"],
"solver": {
"mode":
"manual",
"sublattice_site_ratios": [2, 1],
"sublattice_configurations": [["CU", "MG"], ["MG", "CU"],
["MG", "MG"], ["CU", "CU"]]
},
"conditions": {
"P": 101325,
"T": 298.15
},
"output": "HM_FORM",
"values": [[[-15720, 34720, 7000, 15500]]]
}
datasets = PickleableTinyDB(storage=MemoryStorage)
datasets.insert(SAMPLE_DATASET)
comps = ['CU', 'MG', 'VA']
phase_name = 'LAVES_C15'
configuration = ('MG', 'CU')
symmetry = None
desired_props = ['HM_FORM']
# The following lines replace "get_data" in a more functional form
solver_qry = (tinydb.where('solver').test(
symmetry_filter, configuration,
recursive_tuplify(symmetry) if symmetry else symmetry))
desired_data = get_prop_data(comps,
phase_name,
desired_props,
datasets,
additional_query=solver_qry)
desired_data = filter_configurations(desired_data, configuration, symmetry)
desired_data = filter_temperatures(desired_data)
assert len(desired_data) == 1
desired_data = desired_data[0]
assert desired_data['components'] == comps
assert desired_data['phases'][0] == phase_name
assert desired_data['solver']['sublattice_site_ratios'] == [2, 1]
assert desired_data['solver']['sublattice_configurations'] == (('MG',
'CU'), )
assert desired_data['conditions']['P'] == 101325
assert desired_data['conditions']['T'] == 298.15
assert desired_data['output'] == 'HM_FORM'
assert desired_data['values'] == np.array([[[34720.0]]])
def test_get_data_for_a_minimal_example():
"""Given a dataset and the congfiguration pertaining to that dataset, we should find the values."""
SAMPLE_DATASET = {
"components": ["CU", "MG", "VA"],
"phases": ["LAVES_C15"],
"solver": {
"mode":
"manual",
"sublattice_site_ratios": [2, 1],
"sublattice_configurations": [["CU", "MG"], ["MG", "CU"],
["MG", "MG"], ["CU", "CU"]]
},
"conditions": {
"P": 101325,
"T": 298.15
},
"output": "HM_FORM",
"values": [[[-15720, 34720, 7000, 15500]]]
}
datasets = PickleableTinyDB(storage=MemoryStorage)
datasets.insert(SAMPLE_DATASET)
comps = ['CU', 'MG', 'VA']
phase_name = 'LAVES_C15'
configuration = ('MG', 'CU')
symmetry = None
desired_props = ['HM_FORM']
desired_data = get_data(comps, phase_name, configuration, symmetry,
datasets, desired_props)
assert len(desired_data) == 1
desired_data = desired_data[0]
assert desired_data['components'] == comps
assert desired_data['phases'][0] == phase_name
assert desired_data['solver']['sublattice_site_ratios'] == [2, 1]
assert desired_data['solver']['sublattice_configurations'] == (('MG',
'CU'), )
assert desired_data['conditions']['P'] == 101325
assert desired_data['conditions']['T'] == 298.15
assert desired_data['output'] == 'HM_FORM'
assert desired_data['values'] == np.array([[[34720.0]]])
def test_weighting_invariance():
"""Test that weights do not affect model selection using perfect L0 and L1 cases."""
phase_models = {
"components": ["AL", "B"],
"phases": {
"ALPHA": {
"sublattice_model": [["AL", "B"]],
"sublattice_site_ratios": [1]
}
}
}
L0_data = {
"components": ["AL", "B"],
"phases": ["ALPHA"],
"solver": {
"sublattice_site_ratios": [1],
"sublattice_occupancies": [[[0.5, 0.5]]],
"sublattice_configurations": [[["AL", "B"]]],
"mode": "manual"
},
"conditions": {
"P": 101325,
"T": 298.15
},
"output": "HM_MIX",
"values": [[[-1000]]]
}
L1_data = {
"components": ["AL", "B"],
"phases": ["ALPHA"],
"solver": {
"sublattice_site_ratios": [1],
"sublattice_occupancies": [[[0.25, 0.75]], [[0.5, 0.5]],
[[0.75, 0.25]]],
"sublattice_configurations": [[["AL", "B"]], [["AL", "B"]],
[["AL", "B"]]],
"mode":
"manual"
},
"conditions": {
"P": 101325,
"T": 298.15
},
"output": "HM_MIX",
"values": [[[-1000.0, 0, 1000.0]]]
}
# Perfect L0, no weight
datasets_db = PickleableTinyDB(storage=MemoryStorage)
datasets_db.insert(L0_data)
dbf = generate_parameters(phase_models, datasets_db, 'SGTE91', 'linear')
datasets_db.close()
params = dbf._parameters.search(where('parameter_type') == 'L')
print([f"L{p['parameter_order']}: {p['parameter']}" for p in params])
print({
str(p['parameter']): dbf.symbols[str(p['parameter'])]
for p in params
})
assert len(params) == 1
assert dbf.symbols['VV0000'] == -4000
# Perfect L0, with weight
datasets_db = PickleableTinyDB(storage=MemoryStorage)
L0_data['weight'] = 0.1 # lower weight
datasets_db.insert(L0_data)
dbf = generate_parameters(phase_models, datasets_db, 'SGTE91', 'linear')
datasets_db.close()
params = dbf._parameters.search(where('parameter_type') == 'L')
print([f"L{p['parameter_order']}: {p['parameter']}" for p in params])
print({
str(p['parameter']): dbf.symbols[str(p['parameter'])]
for p in params
})
assert len(params) == 1
assert dbf.symbols['VV0000'] == -4000
# Perfect L1, no weight
datasets_db = PickleableTinyDB(storage=MemoryStorage)
datasets_db.insert(L1_data)
dbf = generate_parameters(phase_models, datasets_db, 'SGTE91', 'linear')
datasets_db.close()
params = dbf._parameters.search(where('parameter_type') == 'L')
print([f"L{p['parameter_order']}: {p['parameter']}" for p in params])
print({
str(p['parameter']): dbf.symbols[str(p['parameter'])]
for p in params
})
assert len(params) == 2
assert np.isclose(dbf.symbols['VV0000'], 1000 * 32 / 3) # L1
assert np.isclose(dbf.symbols['VV0001'], 0) # L0
# Perfect L1, with weight
datasets_db = PickleableTinyDB(storage=MemoryStorage)
L1_data['weight'] = 0.1 # lower weight
datasets_db.insert(L1_data)
dbf = generate_parameters(phase_models, datasets_db, 'SGTE91', 'linear')
datasets_db.close()
params = dbf._parameters.search(where('parameter_type') == 'L')
print([f"L{p['parameter_order']}: {p['parameter']}" for p in params])
print({
str(p['parameter']): dbf.symbols[str(p['parameter'])]
for p in params
})
# TODO: sometimes the presence of L0 terms can be flaky
# assert len(params) == 2
assert np.isclose(dbf.symbols['VV0000'], 1000 * 32 / 3) # L1
