class OpenmrsCaseConfig(DocumentSchema):
# "patient_identifiers": {
# "e2b966d0-1d5f-11e0-b929-000c29ad1d07": {
# "case_property": "nid"
# },
# "uuid": {
# "case_property": "openmrs_uuid",
# }
# }
patient_identifiers = DictProperty()
# The patient_identifiers that are considered reliable
# "match_on_ids": ["uuid", "e2b966d0-1d5f-11e0-b929-000c29ad1d07",
match_on_ids = ListProperty()
# "person_properties": {
# "gender": {
# "case_property": "gender"
# },
# "birthdate": {
# "case_property": "dob"
# }
# }
person_properties = DictProperty()
# "patient_finder": {
# "doc_type": "WeightedPropertyPatientFinder",
# "searchable_properties": ["nid", "family_name"],
# "property_weights": [
# {"case_property": "nid", "weight": 0.9},
# // if "match_type" is not given it defaults to "exact"
# {"case_property": "family_name", "weight": 0.4},
# {
# "case_property": "given_name",
# "weight": 0.3,
# "match_type": "levenshtein",
# // levenshtein function takes edit_distance / len
# "match_params": [0.2]
# // i.e. 0.2 (20%) is one edit for every 5 characters
# // e.g. "Riyaz" matches "Riaz" but not "Riazz"
# },
# {"case_property": "city", "weight": 0.2},
# {
# "case_property": "dob",
# "weight": 0.3,
# "match_type": "days_diff",
# // days_diff matches based on days difference from given date
# "match_params": [364]
# }
# ]
# }
patient_finder = PatientFinder(required=False)
# "person_preferred_name": {
# "givenName": {
# "case_property": "given_name"
# },
# "middleName": {
# "case_property": "middle_name"
# },
# "familyName": {
# "case_property": "family_name"
# }
# }
person_preferred_name = DictProperty()
# "person_preferred_address": {
# "address1": {
# "case_property": "address_1"
# },
# "address2": {
# "case_property": "address_2"
# },
# "cityVillage": {
# "case_property": "city"
# }
# }
person_preferred_address = DictProperty()
# "person_attributes": {
# "c1f4239f-3f10-11e4-adec-0800271c1b75": {
# "case_property": "caste"
# },
# "c1f455e7-3f10-11e4-adec-0800271c1b75": {
# "case_property": "class",
# "value_map": {
# "sc": "c1fcd1c6-3f10-11e4-adec-0800271c1b75",
# "general": "c1fc20ab-3f10-11e4-adec-0800271c1b75",
# "obc": "c1fb51cc-3f10-11e4-adec-0800271c1b75",
# "other_caste": "c207073d-3f10-11e4-adec-0800271c1b75",
# "st": "c20478b6-3f10-11e4-adec-0800271c1b75"
# }
# }
# }
person_attributes = DictProperty()
# Create cases when importing via the Atom feed
import_creates_cases = BooleanProperty(default=True)
# If we ever need to disable updating cases, ``import_updates_cases``
# could be added here. Similarly, we could replace
# ``patient_finder.create_missing`` with ``export_creates_patients``
# and ``export_updates_patients``
@classmethod
def wrap(cls, data):
if 'id_matchers' in data:
# Convert legacy id_matchers to patient_identifiers. e.g.
# [{'doc_type': 'IdMatcher'
# 'identifier_type_id': 'e2b966d0-1d5f-11e0-b929-000c29ad1d07',
# 'case_property': 'nid'}]
# to
# {'e2b966d0-1d5f-11e0-b929-000c29ad1d07': {'doc_type': 'CaseProperty', 'case_property': 'nid'}},
patient_identifiers = {
m['identifier_type_id']: {
'doc_type': 'CaseProperty',
'case_property': m['case_property']
}
for m in data['id_matchers']
}
data['patient_identifiers'] = patient_identifiers
data['match_on_ids'] = list(patient_identifiers)
data.pop('id_matchers')
# Set default data types for known properties
for property_, value_source in chain(
data.get('person_properties', {}).items(),
data.get('person_preferred_name', {}).items(),
data.get('person_preferred_address', {}).items(),
):
data_type = OPENMRS_PROPERTIES[property_]
value_source.setdefault('external_data_type', data_type)
return super(OpenmrsCaseConfig, cls).wrap(data)
class OpenmrsCaseConfig(DocumentSchema):
# "patient_identifiers": {
# "e2b966d0-1d5f-11e0-b929-000c29ad1d07": {
# "doc_type": "CaseProperty",
# "case_property": "nid"
# },
# "uuid": {
# "doc_type": "CaseProperty",
# "case_property": "openmrs_uuid",
# }
# }
patient_identifiers = SchemaDictProperty(ValueSource)
# The patient_identifiers that are considered reliable
# "match_on_ids": ["uuid", "e2b966d0-1d5f-11e0-b929-000c29ad1d07",
match_on_ids = ListProperty()
# "person_properties": {
# "gender": {
# "doc_type": "CaseProperty",
# "case_property": "gender"
# },
# "birthdate": {
# "doc_type": "CaseProperty",
# "case_property": "dob"
# }
# }
person_properties = SchemaDictProperty(ValueSource)
# "patient_finder": {
# "doc_type": "WeightedPropertyPatientFinder",
# "searchable_properties": ["nid", "family_name"],
# "property_weights": [
# {"case_property": "nid", "weight": 0.9},
# {"case_property": "family_name", "weight": 0.4},
# {"case_property": "given_name", "weight": 0.3},
# {"case_property": "city", "weight": 0.2},
# {"case_property": "dob", "weight": 0.3}
# ]
# }
patient_finder = PatientFinder(required=False)
# "person_preferred_name": {
# "givenName": {
# "doc_type": "CaseProperty",
# "case_property": "given_name"
# },
# "middleName": {
# "doc_type": "CaseProperty",
# "case_property": "middle_name"
# },
# "familyName": {
# "doc_type": "CaseProperty",
# "case_property": "family_name"
# }
# }
person_preferred_name = SchemaDictProperty(ValueSource)
# "person_preferred_address": {
# "address1": {
# "doc_type": "CaseProperty",
# "case_property": "address_1"
# },
# "address2": {
# "doc_type": "CaseProperty",
# "case_property": "address_2"
# },
# "cityVillage": {
# "doc_type": "CaseProperty",
# "case_property": "city"
# }
# }
person_preferred_address = SchemaDictProperty(ValueSource)
# "person_attributes": {
# "c1f4239f-3f10-11e4-adec-0800271c1b75": {
# "doc_type": "CaseProperty",
# "case_property": "caste"
# },
# "c1f455e7-3f10-11e4-adec-0800271c1b75": {
# "doc_type": "CasePropertyMap",
# "case_property": "class",
# "value_map": {
# "sc": "c1fcd1c6-3f10-11e4-adec-0800271c1b75",
# "general": "c1fc20ab-3f10-11e4-adec-0800271c1b75",
# "obc": "c1fb51cc-3f10-11e4-adec-0800271c1b75",
# "other_caste": "c207073d-3f10-11e4-adec-0800271c1b75",
# "st": "c20478b6-3f10-11e4-adec-0800271c1b75"
# }
# }
# }
person_attributes = SchemaDictProperty(ValueSource)
@classmethod
def wrap(cls, data):
if 'id_matchers' in data:
# Convert id_matchers to patient_identifiers. e.g.
# [{'doc_type': 'IdMatcher'
# 'identifier_type_id': 'e2b966d0-1d5f-11e0-b929-000c29ad1d07',
# 'case_property': 'nid'}]
# to
# {'e2b966d0-1d5f-11e0-b929-000c29ad1d07': {'doc_type': 'CaseProperty', 'case_property': 'nid'}},
patient_identifiers = {
m['identifier_type_id']: {
'doc_type': 'CaseProperty',
'case_property': m['case_property']
}
for m in data['id_matchers']
}
data['patient_identifiers'] = patient_identifiers
data['match_on_ids'] = list(patient_identifiers)
data.pop('id_matchers')
return super(OpenmrsCaseConfig, cls).wrap(data)