def test_read_relationship_file(spark_session):
ancestors = with_relationships(
spark_session, create_hierarchies(spark_session),
'tests/resources/SNOMED_RELATIONSHIP_SAMPLE.TXT',
'20160901').get_ancestors()
assert set(ancestors.columns) == EXPECTED_COLUMNS
def test_read_hierarchy_file(spark_session):
ancestors = with_loinc_hierarchy(
spark_session,
create_hierarchies(spark_session),
'tests/resources/LOINC_HIERARCHY_SAMPLE.CSV',
'2.56').get_ancestors()
assert set(ancestors.columns) == EXPECTED_COLUMNS
def test_isa_snomed(spark_session):
spark_session.sql('create database isa_snomed_ontologies')
with_relationships(
spark_session,
create_hierarchies(spark_session),
'tests/resources/SNOMED_RELATIONSHIP_SAMPLE.TXT',
'20160901') \
.write_to_database('isa_snomed_ontologies')
create_value_sets(spark_session).write_to_database('isa_snomed_ontologies')
push_valuesets(spark_session,
{'diabetes' : isa_snomed('73211009')},
database = 'isa_snomed_ontologies')
expected = {'diabetes' : [('http://snomed.info/sct', '73211009'),
('http://snomed.info/sct', '44054006')]}
assert get_current_valuesets(spark_session) == expected
def test_isa_loinc(spark_session):
spark_session.sql('create database isa_loinc_ontologies')
with_loinc_hierarchy(
spark_session,
create_hierarchies(spark_session),
'tests/resources/LOINC_HIERARCHY_SAMPLE.CSV',
'2.56') \
.write_to_database('isa_loinc_ontologies')
create_value_sets(spark_session).write_to_database('isa_loinc_ontologies')
push_valuesets(spark_session,
{'leukocytes' : isa_loinc('LP14738-6')},
database='isa_loinc_ontologies')
expected = {'leukocytes' : [('http://loinc.org', '5821-4'),
('http://loinc.org', 'LP14738-6'),
('http://loinc.org', 'LP14419-3')]}
assert get_current_valuesets(spark_session) == expected
def push_valuesets(spark_session, valueset_map, database='ontologies'):
"""
Pushes valuesets onto a stack and registers an in_valueset user-defined function
that uses this content.
The valueset_map takes the form of {referenceName: [(codeset, codevalue), (codeset, codevalue)]}
to specify which codesets/values are used for the given valueset reference name.
Rather than explicitly passing a list of (codeset, codevalue) tuples, users may instead
load particular value sets or particular hierarchies by providing a ValueSetPlaceholder
or HierarchyPlaceholder that instructs the system to load codes belonging to a particular
value set or hierarchical system, respectively. See the isa_loinc and isa_snomed functions
above for details.
Finally, ontology information is assumed to be stored in the 'ontologies' database by
default, but users can specify another database name if they have customized
ontologies that are separated from the default ontologies database.
:param spark_session: the SparkSession instance
:param valueset_map: a map containing value set structures to publish
:param database: the database from which value set data is loaded
"""
loads_valuesets = False
loads_hierarchies = False
jvm = spark_session._jvm
builder = jvm.com.cerner.bunsen.codes.broadcast.BroadcastableValueSets.newBuilder(
)
for (name, content) in valueset_map.items():
if type(content) is HierarchyPlaceholder:
# Add codes belonging to the specified hierarchy
(codeSystem, codeValue, hierarchyUri, hierarchyVersion) = content
builder.addDescendantsOf(name, codeSystem, codeValue, hierarchyUri,
hierarchyVersion)
loads_hierarchies = True
elif type(content) is ValueSetPlaceholder:
# Add codes belonging to the specified value set
(valueSetUri, valueSetVersion) = content
builder.addReference(name, valueSetUri, valueSetVersion)
loads_valuesets = True
else:
# Add the explicitly provided code values
for (codeSystem, codeValue) in content:
builder.addCode(name, codeSystem, codeValue)
valuesets = get_value_sets(
spark_session,
database) if loads_valuesets else create_value_sets(spark_session)
hierarchies = get_hierarchies(
spark_session,
database) if loads_hierarchies else create_hierarchies(spark_session)
broadcastable = builder.build(spark_session._jsparkSession,
valuesets._jvalue_sets,
hierarchies._jhierarchies)
jvm.com.cerner.bunsen.ValueSetUdfs.pushUdf(spark_session._jsparkSession,
broadcastable)