def test_predict_k_g_list_remote():
(
expected_matid_list, expected_k_list, expected_g_list,
expected_caveat_list
) = ([
'mp-10003', 'mp-10010', 'mp-10015', 'mp-10018', 'mp-10021', 'mp-19306',
'mp-26'
], [
175.30512291338607, 168.01218642160669, 265.96469661453744,
45.15072359694464, 68.43138936905679, 136.86585554248228,
55.511505777303256
], [
84.49987188140813, 92.92207342120894, 118.409731828977,
19.816609506500367, 30.473676331990507, 49.63871682171615,
24.379918816217213
], [
'', '', '',
'Predictions are likely less reliable for materials containing F-block elements.',
'',
'Predictions may be less reliable for materials with non-GGA runs.',
'Predictions are likely less reliable for materials containing F-block elements.'
])
mpID_list = [
'mp-10003', 'mp-10010', 'mp-10015', 'mp-10018', 'mp-10021', 'mp-19306',
'mp-26'
]
(matid_list, k_list, g_list,
caveat_list) = elasticity.predict_k_g_list(mpID_list, api_key)
assert (matid_list, k_list, g_list,
caveat_list) == (expected_matid_list, expected_k_list,
expected_g_list, expected_caveat_list)
def test_predict_k_g_list():
(expected_matid_list, expected_k_list, expected_g_list, expected_caveat_list) = (
['mp-10003', 'mp-10010', 'mp-10015', 'mp-10018', 'mp-10021', 'mp-19306', 'mp-26'],
[175.30512291338607, 168.01218642160669, 265.96469661453744,
45.15072359694464, 68.43138936905679, 136.86585554248228, 55.511505777303256],
[84.49987188140813, 92.92207342120894, 118.409731828977, 19.816609506500367, 30.473676331990507,
49.63871682171615, 24.379918816217213],
['', '', '',
'Predictions are likely less reliable for materials containing F-block elements.',
'',
'Predictions may be less reliable for materials with non-GGA runs.',
'Predictions are likely less reliable for materials containing F-block elements.'])
mpID_list = ['mp-10003', 'mp-10010', 'mp-10015', 'mp-10018', 'mp-10021', 'mp-19306', 'mp-26']
(matid_list, k_list, g_list, caveat_list) = elasticity.predict_k_g_list(mpID_list, query_engine=MockQE())
assert (matid_list, k_list, g_list, caveat_list) == (expected_matid_list, expected_k_list, expected_g_list, expected_caveat_list)
#!/usr/bin/env python
# Test script for gbml elasticity (bulk and shear moduli) predictions
from gbml import elasticity
API_KEY = "TBQ3TonU8XZyDTEd"
mpID = "mp-10003"
(k_value, g_value, caveat_str) = elasticity.predict_k_g(mpID, api_key=API_KEY)
print(k_value, g_value, caveat_str)
mpID_list = [
"mp-10003", "mp-10010", "mp-10015", "mp-10021", "mp-26", "mp-10018",
"mp-19306"
]
(matid_list, k_list, g_list,
caveat_list) = elasticity.predict_k_g_list(mpID_list, api_key=API_KEY)
print(matid_list, k_list, g_list, caveat_list)
#!/usr/bin/env python # Test script for gbml elasticity (bulk and shear moduli) predictions from gbml import elasticity API_KEY = "<YOUR_API_KEY>" mpID = "mp-10003" (k_value, g_value, caveat_str) = elasticity.predict_k_g(mpID, api_key=API_KEY) print (k_value, g_value, caveat_str) mpID_list = ["mp-10003","mp-10010","mp-10015","mp-10021","mp-26","mp-10018","mp-19306"] (matid_list, k_list, g_list, caveat_list) = elasticity.predict_k_g_list(mpID_list, api_key=API_KEY) print (matid_list, k_list, g_list, caveat_list)