def test_database_write(self):
builder = CorrelationBuilder(self.propstore,
self.materials,
self.correlation,
props=self.propnet_props + self.mp_props,
funcs='all')
runner = Runner([builder])
runner.run()
data = list(self.correlation.query(criteria={}))
# count = n_props**2 * n_funcs
# n_props = 4, n_funcs = 5
self.assertEqual(len(data), 80)
for d in data:
self.assertIsInstance(d, dict)
self.assertEqual(
set(d.keys()), {
'property_x', 'property_y', 'correlation',
'correlation_func', 'n_points', 'shortest_path_length',
'id', '_id', 'last_updated'
})
self.assertEqual(d['n_points'], 200)
if d['property_x'] == 'vickers_hardness' and \
d['property_y'] == 'bulk_modulus':
self.assertAlmostEqual(
d['correlation'], self.correlation_values_vickers_bulk[
d['correlation_func']])
elif d['property_x'] == 'bulk_modulus' and \
d['property_y'] == 'vickers_hardness':
self.assertAlmostEqual(
d['correlation'], self.correlation_values_bulk_vickers[
d['correlation_func']])
def test_runner_pipeline(self):
runner = Runner([self.nofilter])
runner.run()
self.assertEqual(self.lpad.workflows.count(), 3)
# Ensure no further updates
runner.run()
self.assertEqual(self.lpad.workflows.count(), 3)
def test_runner(self):
self.source.collection.insert_many(self.old_docs)
self.source.update(self.new_docs, update_lu=False)
self.target.collection.insert_many(self.old_docs)
runner = Runner([self.builder])
runner.run()
self.assertEqual(self.target.query_one(criteria={"k": 0})["v"], "new")
self.assertEqual(self.target.query_one(criteria={"k": 10})["v"], "old")
def test_build(self):
builder = BondValenceBuilder(self.materials, self.bond_valence)
runner = Runner([builder])
runner.run()
doc = list(self.bond_valence.query(criteria={'task_id': 'mp-779001'}))[0]
self.assertSetEqual(set(doc['possible_species']), {'Hf4+', 'Sr2+', 'O2-'})
def main():
parser = argparse.ArgumentParser(
description=
"mrun is a script to run builders written using the Maggma framework.")
parser.add_argument(
"builder",
help=
"Builder file in either json or yaml format. Can contain a list of builders or a predefined Runner"
)
parser.add_argument(
"-n",
"--num_workers",
type=int,
default=0,
help="Number of worker processes. Defaults to use as many as available."
)
parser.add_argument('-v',
'--verbose',
action='count',
default=0,
help="Controls logging level per number of v's")
parser.add_argument(
"--dry-run",
action="store_true",
default=False,
help="Dry run loading the builder file. Does not run the builders")
parser.add_argument("--mpi",
action="store_true",
default=False,
help="Running under MPI")
args = parser.parse_args()
# Set Logging
levels = [logging.WARNING, logging.INFO, logging.DEBUG]
level = levels[min(len(levels) - 1,
args.verbose)] # capped to number of levels
root = logging.getLogger()
root.setLevel(level)
ch = TqdmLoggingHandler()
formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s - %(message)s")
ch.setFormatter(formatter)
root.addHandler(ch)
objects = loadfn(args.builder)
if isinstance(objects, list):
# If this is a list of builders
runner = Runner(objects, num_workers=args.num_workers)
elif isinstance(objects, Runner):
# This is a runner:
root.info("Changing number of workers from default in input file")
runner = Runner(objects.builders, args.num_workers)
else:
root.error("Couldn't properly read the builder file.")
if not args.dry_run:
runner.run(mpi=args.mpi)
def test_query(self):
self.builder.query = {"k": {"$gt": 5}}
self.source.collection.insert_many(self.old_docs)
self.source.update(self.new_docs, update_lu=False)
runner = Runner([self.builder])
runner.run()
all_docs = list(self.target.query(criteria={}))
self.assertEqual(len(all_docs), 14)
self.assertTrue(min([d['k'] for d in all_docs]), 6)
def test_serial_runner_quantity_db(self):
# This only runs over the 4 properties in the database because
# the mongomock db cannot be indexed and is therefore very slow
builder = CorrelationBuilder(self.quantity_store,
self.correlation,
props=self.propnet_props,
from_quantity_db=True)
runner = Runner([builder])
runner.run()
def test_build(self):
builder = MagneticBuilder(self.materials, self.magnetism)
runner = Runner([builder])
runner.run()
doc = list(self.magnetism.query(criteria={'task_id': 'mp-1034331'}))[0]
self.assertEqual(doc['magnetism']['ordering'], 'FM')
self.assertAlmostEqual(doc['magnetism']['total_magnetization_normalized_formula_units'], 4.8031771)
def test_build(self):
builder = BondBuilder(self.materials, self.bonding)
runner = Runner([builder])
runner.run()
doc = list(self.bonding.query(criteria={'task_id': 'mp-779001'}))[0]
sg = StructureGraph.from_dict(doc['graph'])
self.assertIsInstance(sg, StructureGraph)
self.assertIn('Hf-O(6)', doc['summary']['coordination_envs'])
def test_delete_orphans(self):
self.builder = CopyBuilder(self.source, self.target, delete_orphans=True)
self.source.collection.insert_many(self.old_docs)
self.source.update(self.new_docs, update_lu=False)
self.target.collection.insert_many(self.old_docs)
deletion_criteria = {"k": {"$in": list(range(5))}}
self.source.collection.delete_many(deletion_criteria)
runner = Runner([self.builder])
runner.run()
self.assertEqual(self.target.collection.count_documents(deletion_criteria), 0)
self.assertEqual(self.target.query_one(criteria={"k": 5})["v"], "new")
self.assertEqual(self.target.query_one(criteria={"k": 10})["v"], "old")
def test_builder(self):
ec_builder = ElasticBuilder(self.test_tasks,
self.test_elasticity,
self.test_materials,
incremental=False)
ec_builder.connect()
for t in ec_builder.get_items():
processed = ec_builder.process_item(t)
if processed:
pass
else:
import nose
nose.tools.set_trace()
runner = Runner([ec_builder])
runner.run()
def test_builder(self):
ec_builder = ElasticBuilder(self.test_tasks,
self.test_elasticity,
self.test_materials,
incremental=False)
ec_builder.connect()
for t in ec_builder.get_items():
processed = ec_builder.process_item(t)
self.assertTrue(bool(processed))
runner = Runner([ec_builder])
runner.run()
# Test warnings
doc = ec_builder.elasticity.query_one(
criteria={"pretty_formula": "NaN3"})
self.assertEqual(doc['elasticity']['warnings'], None)
def test_builder(self):
ec_builder = ElasticAnalysisBuilder(self.test_tasks,
self.test_elasticity,
incremental=False)
ec_builder.connect()
for t in ec_builder.get_items():
processed = ec_builder.process_item(t)
self.assertTrue(bool(processed))
runner = Runner([ec_builder])
runner.run()
# Test warnings
doc = ec_builder.elasticity.query_one(
criteria={"pretty_formula": "NaN3"})
self.assertEqual(doc['warnings'], None)
self.assertAlmostEqual(doc['compliance_tensor'][0][0], 41.576072, 6)
def _get_correlation_values():
full_propstore = MemoryStore()
with open(os.path.join(CORR_TEST_DIR, "correlation_propnet_data.json"),
'r') as f:
data = json.load(f)
full_propstore.connect()
full_propstore.update(jsanitize(data, strict=True, allow_bson=True))
correlation_store = MemoryStore()
builder = CorrelationBuilder(full_propstore,
correlation_store,
props=PROPNET_PROPS,
funcs='all',
from_quantity_db=False)
runner = Runner([builder])
runner.run()
return builder
def test_build(self):
"""Test building the robocrys database."""
builder = RobocrysBuilder(self.materials, self.robocrys)
runner = Runner([builder])
runner.run()
doc = list(self.robocrys.query(criteria={'task_id': 'mp-66'}))[0]
self.assertEqual(doc['condensed_structure']['formula'], 'C')
self.assertEqual(doc['condensed_structure']['spg_symbol'], 'Fd-3m')
self.assertEqual(doc['condensed_structure']['mineral']['type'],
'diamond')
self.assertEqual(doc['condensed_structure']['dimensionality'], '3')
self.assertTrue("C is diamond structured" in doc['description'])
self.assertTrue("bond lengths are 1.55" in doc['description'])
def setUp(self):
self.materials = MongoStore("emmet_test", "materials")
self.thermo = MongoStore("emmet_test", "thermo")
self.materials.connect()
self.thermo.connect()
self.mbuilder = MaterialsBuilder(self.tasks,
self.materials,
mat_prefix="",
chunk_size=1)
self.tbuilder = ThermoBuilder(self.materials,
self.thermo,
chunk_size=1)
runner = Runner([self.mbuilder])
runner.run()
def create_correlation_quantity_indexed_docs():
"""
Outputs JSON file containing the same data from create_correlation_test_docs()
but as individual quantities. This mimics the quantity-indexed store.
Must run create_correlation_test_docs() first and have the JSON file in the
test directory.
"""
pn_store = MemoryStore()
q_store = MemoryStore()
m_store = MemoryStore()
with open(os.path.join(CORR_TEST_DIR, "correlation_propnet_data.json"),
'r') as f:
data = json.load(f)
pn_store.connect()
pn_store.update(jsanitize(data, strict=True, allow_bson=True))
sb = SeparationBuilder(pn_store, q_store, m_store)
r = Runner([sb])
r.run()
q_data = list(q_store.query(criteria={}, properties={'_id': False}))
dumpfn(
q_data,
os.path.join(CORR_TEST_DIR, "correlation_propnet_quantity_data.json"))
def test_sample_size_limit(self):
sample_sizes = [50, 300]
expected_n_points = [50, 200]
for sample_size, n_points in zip(sample_sizes, expected_n_points):
correlation_store = MemoryStore()
builder = CorrelationBuilder(
self.propstore,
correlation_store,
props=['bulk_modulus', 'vickers_hardness'],
funcs='linlsq',
sample_size=sample_size,
from_quantity_db=False)
runner = Runner([builder])
runner.run()
data = list(correlation_store.query(criteria={}))
for d in data:
self.assertEqual(d['n_points'], n_points)
with self.assertRaises(ValueError):
_ = CorrelationBuilder(self.propstore,
self.correlation,
sample_size=1)
def test_multiproc_runner(self):
builder = PropnetBuilder(self.materials, self.propstore)
runner = Runner([builder])
runner.run()
self.logger.info("processing item: {}".format(item))
# time.sleep(random.randint(0,3))
return {item: "processed"}
def update_targets(self, items):
self.logger.info("Updating targets ...")
self.logger.info("Received {} processed items".format(len(items)))
self.logger.info("Updated items: {}".format(list(items)))
def finalize(self, cursor=None):
self.logger.info("Finalizing ...")
self.logger.info("DONE!")
if __name__ == '__main__':
N = 10
chunk_size = 3
stores = [MemoryStore(str(i)) for i in range(7)]
sources = [stores[0], stores[1], stores[3]]
targets = [stores[3], stores[6]]
mdb = MyDumbBuilder(N, sources, targets, chunk_size=chunk_size)
builders = [mdb]
runner = Runner(builders)
logstreamhandle(runner)
runner.run()
def test_aggregation(self):
runner = Runner([self.builder])
runner.run()
all_agg_docs = list(self.test_elasticity_agg.query())
self.assertTrue(bool(all_agg_docs))
def test_multiproc_runner(self):
builder = CorrelationBuilder(self.propstore,
self.correlation,
from_quantity_db=False)
runner = Runner([builder], max_workers=4)
runner.run()
def test_serial_runner(self):
builder = CorrelationBuilder(self.propstore,
self.correlation,
from_quantity_db=False)
runner = Runner([builder])
runner.run()
def test_database_and_file_write(self):
builder = CorrelationBuilder(self.propstore,
self.correlation,
props=self.propnet_props,
funcs='all',
out_file=os.path.join(
TEST_DATA_DIR, "test_output.json"),
from_quantity_db=False)
runner = Runner([builder])
runner.run()
# Test database output
data = list(self.correlation.query(criteria={}))
# count = n_props**2 * n_funcs
# n_props = 4, n_funcs = 6
self.assertEqual(len(data),
96,
msg="Are there new built-in funcs in the builder?")
for d in data:
self.assertIsInstance(d, dict)
self.assertEqual(
set(d.keys()), {
'property_x', 'property_y', 'correlation',
'correlation_func', 'n_points', 'shortest_path_length',
'id', '_id', 'last_updated'
})
self.assertEqual(d['n_points'], 200)
if d['property_x'] == 'vickers_hardness' and \
d['property_y'] == 'bulk_modulus':
# print("{}: {}".format(d['correlation_func'], d['correlation']))
self.assertAlmostEqual(
d['correlation'], self.correlation_values_vickers_bulk[
d['correlation_func']])
elif d['property_x'] == 'bulk_modulus' and \
d['property_y'] == 'vickers_hardness':
# print("{}: {}".format(d['correlation_func'], d['correlation']))
self.assertAlmostEqual(
d['correlation'], self.correlation_values_bulk_vickers[
d['correlation_func']])
# Test file output
expected_file_data = loadfn(
os.path.join(TEST_DATA_DIR, 'correlation_outfile.json'))
actual_file_data = loadfn(
os.path.join(TEST_DATA_DIR, 'test_output.json'))
self.assertIsInstance(actual_file_data, dict)
self.assertEqual(actual_file_data.keys(), expected_file_data.keys())
self.assertEqual(set(actual_file_data['properties']),
set(expected_file_data['properties']))
expected_props = expected_file_data['properties']
actual_props = actual_file_data['properties']
for prop_x, prop_y in product(expected_props, repeat=2):
iex, iey = expected_props.index(prop_x), expected_props.index(
prop_y)
iax, iay = actual_props.index(prop_x), actual_props.index(prop_y)
self.assertEqual(actual_file_data['n_points'][iax][iay],
expected_file_data['n_points'][iex][iey])
self.assertEqual(
actual_file_data['shortest_path_length'][iax][iay],
expected_file_data['shortest_path_length'][iex][iey])
for f in builder._funcs.keys():
self.assertAlmostEqual(
actual_file_data['correlation'][f][iax][iay],
expected_file_data['correlation'][f][iex][iey])
def test_serial_runner(self):
builder = CorrelationBuilder(self.propstore, self.materials,
self.correlation)
runner = Runner([builder])
runner.run()
def test_multiproc_runner(self):
builder = CorrelationBuilder(self.propstore, self.materials,
self.correlation)
runner = Runner([builder], max_workers=2)
runner.run()