def create_correlation_test_docs():
"""
Creates JSON file containing a certain number of materials and their
necessary properties to load the propnet store for correlation tests.
"""
n_materials = 200
pnstore = MongograntStore("ro:mongodb03.nersc.gov/propnet",
"propnet_july2019")
pnstore.connect()
cursor = pnstore.query(criteria={
'$and': [{
'$or': [{
p: {
'$exists': True
}
}, {
'inputs.symbol_type': p
}]
} for p in PROPNET_PROPS]
},
properties=['task_id', 'inputs'] +
[p + '.mean' for p in PROPNET_PROPS] +
[p + '.units' for p in PROPNET_PROPS] +
[p + '.quantities' for p in PROPNET_PROPS])
data = []
for item in cursor:
if len(data) < n_materials:
data.append(item)
else:
cursor.close()
break
dumpfn(data, os.path.join(CORR_TEST_DIR, "correlation_propnet_data.json"))
def create_mp_builder_test_docs():
"""
Create documents for propnet MP builder tests. Outputs JSON file to test directory.
"""
formulas = ["BaNiO3", "Si", "Fe2O3", "Cs"]
mgstore = MongograntStore("ro:knowhere.lbl.gov/mp_core", "materials")
builder = PropnetBuilder(mgstore,
MemoryStore(),
criteria={
"pretty_formula": {
"$in": formulas
},
"e_above_hull": 0
})
builder.connect()
materials = list(builder.get_items())
deprecated_item = mgstore.query_one({'deprecated': True, 'sbxn': 'core'})
# Create fake, non-core sandboxed material to represent proprietary info
sandboxed_item = materials[0].copy()
sandboxed_item.update({'sbxn': ['test_sbx'], 'task_id': 'mp-fakesbx'})
del sandboxed_item['_id']
materials.extend([deprecated_item, sandboxed_item])
dumpfn(materials, os.path.join(MP_TEST_DIR, "test_materials.json"))
def process_sdf_file(filename):
mp_pubchem = MongograntStore("rw:knowhere.lbl.gov/mp_pubchem",
"mp_pubchem",
key="pubchem_id")
mp_pubchem.connect()
coll = mp_pubchem.collection
skipped = 0
pubchem_molecules = []
for i, mol in enumerate(pybel.readfile("sdf", filename)):
try:
pubchem_id = int(mol.data["PUBCHEM_COMPOUND_CID"])
xyz = mol.write(format="xyz")
data = {"pubchem_id": pubchem_id, "xyz": xyz}
for key in keys:
if key in mol.data:
data[key_map[key]] = mol.data[key]
pubchem_molecules.append(data)
except KeyError:
skipped += 1
coll.insert_many(pubchem_molecules)
os.rename(filename, filename + ".processed")
return len(pubchem_molecules), skipped
def create_test_docs(self):
formulas = ["BaNiO3", "Si", "Fe2O3", "Cs"]
from maggma.advanced_stores import MongograntStore
from monty.serialization import dumpfn
mgstore = MongograntStore("ro:matgen2.lbl.gov/mp_prod", "materials")
builder = PropnetBuilder(
mgstore, self.propstore, criteria={"pretty_formula": {"$in": formulas},
"e_above_hull": 0})
builder.connect()
dumpfn(list(builder.get_items()), "test_materials.json")
def create_test_docs(self):
from maggma.advanced_stores import MongograntStore
from monty.serialization import dumpfn
pnstore = MongograntStore("ro:knowhere.lbl.gov/mp_core", "propnet")
pnstore.connect()
mpstore = MongograntStore("ro:knowhere.lbl.gov/mp_core", "materials")
mpstore.connect()
cursor = pnstore.query(criteria={
'$and': [{
'$or': [{
p: {
'$exists': True
}
}, {
'inputs.symbol_type': p
}]
} for p in self.propnet_props]
},
properties=['task_id'])
pn_mpids = [item['task_id'] for item in cursor]
cursor = mpstore.query(
criteria={p: {
'$exists': True
}
for p in self.mp_query_props},
properties=['task_id'])
mp_mpids = [item['task_id'] for item in cursor]
mpids = list(set(pn_mpids).intersection(set(mp_mpids)))[:200]
pn_data = pnstore.query(criteria={'task_id': {
'$in': mpids
}},
properties=['task_id', 'inputs'] +
[p + '.mean' for p in self.propnet_props] +
[p + '.units' for p in self.propnet_props])
dumpfn(list(pn_data),
os.path.join(TEST_DIR, "correlation_propnet_data.json"))
mp_data = mpstore.query(criteria={'task_id': {
'$in': mpids
}},
properties=['task_id'] + self.mp_query_props)
dumpfn(list(mp_data), os.path.join(TEST_DIR,
"correlation_mp_data.json"))