def test_isPlaceholder_defaultEnumValue(self):
entity = schema.StateIncarcerationSentence(
incarceration_type=StateIncarcerationType.STATE_PRISON.value)
self.assertTrue(is_placeholder(entity))
entity.incarceration_type_raw_text = 'PRISON'
self.assertFalse(is_placeholder(entity))
def generate_incarceration_sentence(
person, **kwargs) -> schema.StateIncarcerationSentence:
args = {
"status": StateSentenceStatus.PRESENT_WITHOUT_INFO.value,
"state_code": _STATE_CODE,
}
args.update(kwargs)
return schema.StateIncarcerationSentence(person=person, **args)
def test_getRootEntity_allPlaceholders_raises(self) -> None:
placeholder_incarceration_period = schema.StateIncarcerationPeriod()
placeholder_incarceration_sentence = schema.StateIncarcerationSentence(
incarceration_periods=[placeholder_incarceration_period])
placeholder_sentence_group = schema.StateSentenceGroup(
incarceration_sentences=[placeholder_incarceration_sentence])
placeholder_person = schema.StatePerson(
sentence_groups=[placeholder_sentence_group])
with pytest.raises(EntityMatchingError):
get_root_entity_cls([placeholder_person])
def generate_test_incarceration_sentence(
person_id, charges, incarceration_periods) \
-> state_schema.StateIncarcerationSentence:
instance = state_schema.StateIncarcerationSentence(
incarceration_sentence_id=2222,
status=StateSentenceStatus.SUSPENDED.value,
state_code='us_ca',
person_id=person_id,
charges=charges,
incarceration_periods=incarceration_periods,
)
return instance
def generate_test_incarceration_sentence(
person_id, charges=None, incarceration_periods=None) \
-> state_schema.StateIncarcerationSentence:
instance = state_schema.StateIncarcerationSentence(
incarceration_sentence_id=2222,
status=StateSentenceStatus.SUSPENDED.value,
state_code='us_ca',
person_id=person_id,
is_capital_punishment=False,
charges=(charges if charges else []),
incarceration_periods=(incarceration_periods if incarceration_periods else []),
)
return instance
def test_getRootEntity(self) -> None:
# Arrange
incarceration_incident = schema.StateIncarcerationIncident(
external_id=_EXTERNAL_ID)
placeholder_incarceration_period = schema.StateIncarcerationPeriod(
incarceration_incidents=[incarceration_incident])
placeholder_incarceration_sentence = schema.StateIncarcerationSentence(
incarceration_periods=[placeholder_incarceration_period])
placeholder_sentence_group = schema.StateSentenceGroup(
sentence_group_id=None,
incarceration_sentences=[placeholder_incarceration_sentence],
)
person = schema.StatePerson(
sentence_groups=[placeholder_sentence_group])
# Act
root_entity_cls = get_root_entity_cls([person])
# Assert
self.assertEqual(schema.StateIncarcerationIncident, root_entity_cls)
def build_incarceration_pipeline_data_dict_no_incarceration(
self, fake_person_id: int):
"""Builds a data_dict for a run of the pipeline where the person has no incarceration."""
fake_person_1 = schema.StatePerson(
state_code='US_XX',
person_id=fake_person_id,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT)
fake_person_id_2 = 6789
fake_person_2 = schema.StatePerson(
state_code='US_XX',
person_id=fake_person_id_2,
gender=Gender.FEMALE,
birthdate=date(1990, 1, 1),
residency_status=ResidencyStatus.PERMANENT)
persons_data = [
normalized_database_base_dict(fake_person_1),
normalized_database_base_dict(fake_person_2)
]
sentence_group = schema.StateSentenceGroup(sentence_group_id=111,
person_id=fake_person_id)
incarceration_period = schema.StateIncarcerationPeriod(
incarceration_period_id=1111,
status=StateIncarcerationPeriodStatus.NOT_IN_CUSTODY,
state_code='US_XX',
county_code='124',
facility='San Quentin',
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2008, 11, 20),
release_date=date(2010, 12, 4),
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
person_id=fake_person_id)
incarceration_sentence = schema.StateIncarcerationSentence(
incarceration_sentence_id=1111,
sentence_group_id=sentence_group.sentence_group_id,
incarceration_periods=[incarceration_period],
person_id=fake_person_id)
supervision_sentence = schema.StateSupervisionSentence(
supervision_sentence_id=123, person_id=fake_person_id)
sentence_group.incarceration_sentences = [incarceration_sentence]
sentence_group_data = [normalized_database_base_dict(sentence_group)]
incarceration_sentence_data = [
normalized_database_base_dict(incarceration_sentence)
]
supervision_sentence_data = [
normalized_database_base_dict(supervision_sentence)
]
incarceration_periods_data = [
normalized_database_base_dict(incarceration_period)
]
state_incarceration_sentence_incarceration_period_association = [
{
'incarceration_period_id':
incarceration_period.incarceration_period_id,
'incarceration_sentence_id':
incarceration_sentence.incarceration_sentence_id,
},
]
data_dict = self._default_data_dict()
data_dict_overrides = {
schema.StatePerson.__tablename__:
persons_data,
schema.StateSentenceGroup.__tablename__:
sentence_group_data,
schema.StateIncarcerationSentence.__tablename__:
incarceration_sentence_data,
schema.StateSupervisionSentence.__tablename__:
supervision_sentence_data,
schema.StateIncarcerationPeriod.__tablename__:
incarceration_periods_data,
schema.state_incarceration_sentence_incarceration_period_association_table.name:
state_incarceration_sentence_incarceration_period_association,
}
data_dict.update(data_dict_overrides)
return data_dict
def build_incarceration_pipeline_data_dict(self,
fake_person_id: int,
state_code: str = 'US_XX'):
"""Builds a data_dict for a basic run of the pipeline."""
fake_person = schema.StatePerson(
state_code=state_code,
person_id=fake_person_id,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT)
persons_data = [normalized_database_base_dict(fake_person)]
race_1 = schema.StatePersonRace(person_race_id=111,
state_code=state_code,
race=Race.BLACK,
person_id=fake_person_id)
race_2 = schema.StatePersonRace(person_race_id=111,
state_code=state_code,
race=Race.WHITE,
person_id=fake_person_id)
races_data = normalized_database_base_dict_list([race_1, race_2])
ethnicity = schema.StatePersonEthnicity(person_ethnicity_id=111,
state_code=state_code,
ethnicity=Ethnicity.HISPANIC,
person_id=fake_person_id)
ethnicity_data = normalized_database_base_dict_list([ethnicity])
sentence_group = schema.StateSentenceGroup(sentence_group_id=111,
person_id=fake_person_id)
initial_incarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=1111,
incarceration_type=StateIncarcerationType.STATE_PRISON,
status=StateIncarcerationPeriodStatus.NOT_IN_CUSTODY,
state_code=state_code,
county_code='124',
facility='San Quentin',
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2008, 11, 20),
release_date=date(2010, 12, 4),
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
person_id=fake_person_id,
)
first_reincarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=2222,
incarceration_type=StateIncarcerationType.STATE_PRISON,
status=StateIncarcerationPeriodStatus.NOT_IN_CUSTODY,
state_code=state_code,
county_code='124',
facility='San Quentin',
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2011, 4, 5),
release_date=date(2014, 4, 14),
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
person_id=fake_person_id)
subsequent_reincarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=3333,
incarceration_type=StateIncarcerationType.STATE_PRISON,
status=StateIncarcerationPeriodStatus.IN_CUSTODY,
state_code=state_code,
county_code='124',
facility='San Quentin',
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2017, 1, 4),
person_id=fake_person_id)
incarceration_sentence = schema.StateIncarcerationSentence(
incarceration_sentence_id=1111,
state_code=state_code,
sentence_group_id=sentence_group.sentence_group_id,
incarceration_periods=[
initial_incarceration, first_reincarceration,
subsequent_reincarceration
],
person_id=fake_person_id)
supervision_sentence = schema.StateSupervisionSentence(
supervision_sentence_id=123,
state_code=state_code,
person_id=fake_person_id)
sentence_group.incarceration_sentences = [incarceration_sentence]
sentence_group_data = [normalized_database_base_dict(sentence_group)]
incarceration_sentence_data = [
normalized_database_base_dict(incarceration_sentence)
]
supervision_sentence_data = [
normalized_database_base_dict(supervision_sentence)
]
incarceration_periods_data = [
normalized_database_base_dict(initial_incarceration),
normalized_database_base_dict(first_reincarceration),
normalized_database_base_dict(subsequent_reincarceration)
]
state_incarceration_sentence_incarceration_period_association = [
{
'incarceration_period_id':
initial_incarceration.incarceration_period_id,
'incarceration_sentence_id':
incarceration_sentence.incarceration_sentence_id,
},
{
'incarceration_period_id':
first_reincarceration.incarceration_period_id,
'incarceration_sentence_id':
incarceration_sentence.incarceration_sentence_id,
},
{
'incarceration_period_id':
subsequent_reincarceration.incarceration_period_id,
'incarceration_sentence_id':
incarceration_sentence.incarceration_sentence_id,
},
]
data_dict = self._default_data_dict()
data_dict_overrides = {
schema.StatePerson.__tablename__:
persons_data,
schema.StatePersonRace.__tablename__:
races_data,
schema.StatePersonEthnicity.__tablename__:
ethnicity_data,
schema.StateSentenceGroup.__tablename__:
sentence_group_data,
schema.StateIncarcerationSentence.__tablename__:
incarceration_sentence_data,
schema.StateSupervisionSentence.__tablename__:
supervision_sentence_data,
schema.StateIncarcerationPeriod.__tablename__:
incarceration_periods_data,
schema.state_incarceration_sentence_incarceration_period_association_table.name:
state_incarceration_sentence_incarceration_period_association,
}
data_dict.update(data_dict_overrides)
return data_dict
def testIncarcerationPipeline(self):
fake_person_id = 12345
fake_person = schema.StatePerson(
person_id=fake_person_id,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT)
persons_data = [normalized_database_base_dict(fake_person)]
race_1 = schema.StatePersonRace(person_race_id=111,
state_code='CA',
race=Race.BLACK,
person_id=fake_person_id)
race_2 = schema.StatePersonRace(person_race_id=111,
state_code='ND',
race=Race.WHITE,
person_id=fake_person_id)
races_data = normalized_database_base_dict_list([race_1, race_2])
ethnicity = schema.StatePersonEthnicity(person_ethnicity_id=111,
state_code='CA',
ethnicity=Ethnicity.HISPANIC,
person_id=fake_person_id)
ethnicity_data = normalized_database_base_dict_list([ethnicity])
sentence_group = schema.StateSentenceGroup(sentence_group_id=111,
person_id=fake_person_id)
initial_incarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=1111,
status=StateIncarcerationPeriodStatus.NOT_IN_CUSTODY,
state_code='CA',
county_code='124',
facility='San Quentin',
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2008, 11, 20),
release_date=date(2010, 12, 4),
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
person_id=fake_person_id,
)
first_reincarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=2222,
status=StateIncarcerationPeriodStatus.NOT_IN_CUSTODY,
state_code='CA',
county_code='124',
facility='San Quentin',
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2011, 4, 5),
release_date=date(2014, 4, 14),
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
person_id=fake_person_id)
subsequent_reincarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=3333,
status=StateIncarcerationPeriodStatus.IN_CUSTODY,
state_code='CA',
county_code='124',
facility='San Quentin',
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2017, 1, 4),
person_id=fake_person_id)
incarceration_sentence = schema.StateIncarcerationSentence(
incarceration_sentence_id=1111,
sentence_group_id=sentence_group.sentence_group_id,
incarceration_periods=[
initial_incarceration, first_reincarceration,
subsequent_reincarceration
],
person_id=fake_person_id)
supervision_sentence = schema.StateSupervisionSentence(
supervision_sentence_id=123, person_id=fake_person_id)
sentence_group.incarceration_sentences = [incarceration_sentence]
sentence_group_data = [normalized_database_base_dict(sentence_group)]
incarceration_sentence_data = [
normalized_database_base_dict(incarceration_sentence)
]
supervision_sentence_data = [
normalized_database_base_dict(supervision_sentence)
]
incarceration_periods_data = [
normalized_database_base_dict(initial_incarceration),
normalized_database_base_dict(first_reincarceration),
normalized_database_base_dict(subsequent_reincarceration)
]
state_incarceration_sentence_incarceration_period_association = [
{
'incarceration_period_id':
initial_incarceration.incarceration_period_id,
'incarceration_sentence_id':
incarceration_sentence.incarceration_sentence_id,
},
{
'incarceration_period_id':
first_reincarceration.incarceration_period_id,
'incarceration_sentence_id':
incarceration_sentence.incarceration_sentence_id,
},
{
'incarceration_period_id':
subsequent_reincarceration.incarceration_period_id,
'incarceration_sentence_id':
incarceration_sentence.incarceration_sentence_id,
},
]
data_dict = {
schema.StatePerson.__tablename__:
persons_data,
schema.StatePersonRace.__tablename__:
races_data,
schema.StatePersonEthnicity.__tablename__:
ethnicity_data,
schema.StateSentenceGroup.__tablename__:
sentence_group_data,
schema.StateIncarcerationSentence.__tablename__:
incarceration_sentence_data,
schema.StateSupervisionSentence.__tablename__:
supervision_sentence_data,
schema.StateIncarcerationPeriod.__tablename__:
incarceration_periods_data,
schema.state_incarceration_sentence_incarceration_period_association_table.name:
state_incarceration_sentence_incarceration_period_association,
schema.state_supervision_sentence_incarceration_period_association_table.name:
[{}]
}
test_pipeline = TestPipeline()
# Get StatePersons
persons = (test_pipeline
| 'Load Persons' >> extractor_utils.BuildRootEntity(
dataset=None,
data_dict=data_dict,
root_schema_class=schema.StatePerson,
root_entity_class=entities.StatePerson,
unifying_id_field='person_id',
build_related_entities=True))
# Get StateSentenceGroups
sentence_groups = (
test_pipeline
| 'Load StateSentencegroups' >> extractor_utils.BuildRootEntity(
dataset=None,
data_dict=data_dict,
root_schema_class=schema.StateSentenceGroup,
root_entity_class=entities.StateSentenceGroup,
unifying_id_field='person_id',
build_related_entities=True))
# Get StateIncarcerationSentences
incarceration_sentences = (
test_pipeline | 'Load StateIncarcerationSentences' >>
extractor_utils.BuildRootEntity(
dataset=None,
data_dict=data_dict,
root_schema_class=schema.StateIncarcerationSentence,
root_entity_class=entities.StateIncarcerationSentence,
unifying_id_field='person_id',
build_related_entities=True))
# Get StateSupervisionSentences
supervision_sentences = (
test_pipeline | 'Load StateSupervisionSentences' >>
extractor_utils.BuildRootEntity(
dataset=None,
data_dict=data_dict,
root_schema_class=schema.StateSupervisionSentence,
root_entity_class=entities.StateSupervisionSentence,
unifying_id_field='person_id',
build_related_entities=True))
sentences_and_sentence_groups = (
{
'sentence_groups': sentence_groups,
'incarceration_sentences': incarceration_sentences,
'supervision_sentences': supervision_sentences
}
| 'Group sentences to sentence groups' >> beam.CoGroupByKey())
sentence_groups_with_hydrated_sentences = (
sentences_and_sentence_groups
| 'Set hydrated sentences on sentence groups' >> beam.ParDo(
SetSentencesOnSentenceGroup()))
# Group each StatePerson with their related entities
person_and_sentence_groups = (
{
'person': persons,
'sentence_groups': sentence_groups_with_hydrated_sentences
}
| 'Group StatePerson to SentenceGroups' >> beam.CoGroupByKey())
# Identify IncarcerationEvents events from the StatePerson's
# StateIncarcerationPeriods
fake_person_id_to_county_query_result = [{
'person_id':
fake_person_id,
'county_of_residence':
_COUNTY_OF_RESIDENCE
}]
person_id_to_county_kv = (
test_pipeline
| "Read person id to county associations from BigQuery" >>
beam.Create(fake_person_id_to_county_query_result)
|
"Convert to KV" >> beam.ParDo(ConvertDictToKVTuple(), 'person_id'))
person_events = (person_and_sentence_groups
| 'Classify Incarceration Events' >> beam.ParDo(
pipeline.ClassifyIncarcerationEvents(),
AsDict(person_id_to_county_kv)))
# Get pipeline job details for accessing job_id
all_pipeline_options = PipelineOptions().get_all_options()
# Add timestamp for local jobs
job_timestamp = datetime.datetime.now().strftime(
'%Y-%m-%d_%H_%M_%S.%f')
all_pipeline_options['job_timestamp'] = job_timestamp
# Get IncarcerationMetrics
incarceration_metrics = (
person_events
| 'Get Incarceration Metrics' >> pipeline.GetIncarcerationMetrics(
pipeline_options=all_pipeline_options,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=-1))
assert_that(incarceration_metrics,
AssertMatchers.validate_metric_type())
test_pipeline.run()
def build_incarceration_pipeline_data_dict_no_incarceration(
self, fake_person_id: int):
"""Builds a data_dict for a run of the pipeline where the person has no incarceration."""
fake_person_1 = schema.StatePerson(
state_code="US_XX",
person_id=fake_person_id,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT,
)
fake_person_id_2 = 6789
fake_person_2 = schema.StatePerson(
state_code="US_XX",
person_id=fake_person_id_2,
gender=Gender.FEMALE,
birthdate=date(1990, 1, 1),
residency_status=ResidencyStatus.PERMANENT,
)
persons_data = [
normalized_database_base_dict(fake_person_1),
normalized_database_base_dict(fake_person_2),
]
sentence_group = schema.StateSentenceGroup(
sentence_group_id=111,
state_code="US_XX",
status=StateSentenceStatus.PRESENT_WITHOUT_INFO,
person_id=fake_person_id,
)
incarceration_period = schema.StateIncarcerationPeriod(
incarceration_period_id=1111,
status=StateIncarcerationPeriodStatus.NOT_IN_CUSTODY,
state_code="US_XX",
county_code="124",
facility="San Quentin",
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2008, 11, 20),
release_date=date(2010, 12, 4),
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
person_id=fake_person_id,
)
incarceration_sentence = schema.StateIncarcerationSentence(
incarceration_sentence_id=1111,
state_code="US_XX",
sentence_group_id=sentence_group.sentence_group_id,
incarceration_periods=[incarceration_period],
person_id=fake_person_id,
status=StateSentenceStatus.PRESENT_WITHOUT_INFO,
)
supervision_sentence = schema.StateSupervisionSentence(
supervision_sentence_id=123,
state_code="US_XX",
person_id=fake_person_id,
status=StateSentenceStatus.PRESENT_WITHOUT_INFO,
)
sentence_group.incarceration_sentences = [incarceration_sentence]
sentence_group_data = [normalized_database_base_dict(sentence_group)]
incarceration_sentence_data = [
normalized_database_base_dict(incarceration_sentence)
]
supervision_sentence_data = [
normalized_database_base_dict(supervision_sentence)
]
incarceration_periods_data = [
normalized_database_base_dict(incarceration_period)
]
state_incarceration_sentence_incarceration_period_association = [
{
"incarceration_period_id":
incarceration_period.incarceration_period_id,
"incarceration_sentence_id":
incarceration_sentence.incarceration_sentence_id,
},
]
fake_person_id_to_county_query_result = [{
"state_code":
"US_XX",
"person_id":
fake_person_id,
"county_of_residence":
_COUNTY_OF_RESIDENCE,
}]
us_mo_sentence_status_data: List[Dict[str, Any]] = [{
"state_code":
"US_MO",
"person_id":
fake_person_id,
"sentence_external_id":
"XXX",
"sentence_status_external_id":
"YYY",
"status_code":
"ZZZ",
"status_date":
"not_a_date",
"status_description":
"XYZ",
}]
incarceration_period_judicial_district_association_data = [{
"state_code":
"US_XX",
"person_id":
fake_person_id,
"incarceration_period_id":
123,
"judicial_district_code":
"NW",
}]
state_race_ethnicity_population_count_data = [{
"state_code":
"US_XX",
"race_or_ethnicity":
"BLACK",
"population_count":
1,
"representation_priority":
1,
}]
data_dict = self._default_data_dict()
data_dict_overrides = {
schema.StatePerson.__tablename__:
persons_data,
schema.StateSentenceGroup.__tablename__:
sentence_group_data,
schema.StateIncarcerationSentence.__tablename__:
incarceration_sentence_data,
schema.StateSupervisionSentence.__tablename__:
supervision_sentence_data,
schema.StateIncarcerationPeriod.__tablename__:
incarceration_periods_data,
schema.state_incarceration_sentence_incarceration_period_association_table.name:
state_incarceration_sentence_incarceration_period_association,
"persons_to_recent_county_of_residence":
fake_person_id_to_county_query_result,
"incarceration_period_judicial_district_association":
incarceration_period_judicial_district_association_data,
"state_race_ethnicity_population_counts":
state_race_ethnicity_population_count_data,
"us_mo_sentence_statuses":
us_mo_sentence_status_data,
}
data_dict.update(data_dict_overrides)
return data_dict
def build_incarceration_pipeline_data_dict(self,
fake_person_id: int,
state_code: str = "US_XX"):
"""Builds a data_dict for a basic run of the pipeline."""
fake_person = schema.StatePerson(
state_code=state_code,
person_id=fake_person_id,
gender=Gender.MALE,
birthdate=date(1970, 1, 1),
residency_status=ResidencyStatus.PERMANENT,
)
persons_data = [normalized_database_base_dict(fake_person)]
race_1 = schema.StatePersonRace(
person_race_id=111,
state_code=state_code,
race=Race.BLACK,
person_id=fake_person_id,
)
race_2 = schema.StatePersonRace(
person_race_id=111,
state_code=state_code,
race=Race.WHITE,
person_id=fake_person_id,
)
races_data = normalized_database_base_dict_list([race_1, race_2])
ethnicity = schema.StatePersonEthnicity(
person_ethnicity_id=111,
state_code=state_code,
ethnicity=Ethnicity.HISPANIC,
person_id=fake_person_id,
)
ethnicity_data = normalized_database_base_dict_list([ethnicity])
sentence_group = schema.StateSentenceGroup(
sentence_group_id=98765,
state_code=state_code,
status=StateSentenceStatus.PRESENT_WITHOUT_INFO,
person_id=fake_person_id,
)
initial_incarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=1111,
incarceration_type=StateIncarcerationType.STATE_PRISON,
status=StateIncarcerationPeriodStatus.NOT_IN_CUSTODY,
state_code=state_code,
county_code="124",
facility="San Quentin",
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2008, 11, 20),
release_date=date(2010, 12, 4),
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
person_id=fake_person_id,
)
first_reincarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=2222,
incarceration_type=StateIncarcerationType.STATE_PRISON,
status=StateIncarcerationPeriodStatus.NOT_IN_CUSTODY,
state_code=state_code,
county_code="124",
facility="San Quentin",
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2011, 4, 5),
release_date=date(2014, 4, 14),
release_reason=StateIncarcerationPeriodReleaseReason.
SENTENCE_SERVED,
person_id=fake_person_id,
)
subsequent_reincarceration = schema.StateIncarcerationPeriod(
incarceration_period_id=3333,
incarceration_type=StateIncarcerationType.STATE_PRISON,
status=StateIncarcerationPeriodStatus.IN_CUSTODY,
state_code=state_code,
county_code="124",
facility="San Quentin",
facility_security_level=StateIncarcerationFacilitySecurityLevel.
MAXIMUM,
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
projected_release_reason=StateIncarcerationPeriodReleaseReason.
CONDITIONAL_RELEASE,
admission_date=date(2017, 1, 4),
person_id=fake_person_id,
)
incarceration_sentence = schema.StateIncarcerationSentence(
incarceration_sentence_id=1111,
state_code=state_code,
status=StateSentenceStatus.PRESENT_WITHOUT_INFO,
sentence_group_id=sentence_group.sentence_group_id,
incarceration_periods=[
initial_incarceration,
first_reincarceration,
subsequent_reincarceration,
],
person_id=fake_person_id,
)
supervision_sentence = schema.StateSupervisionSentence(
supervision_sentence_id=123,
state_code=state_code,
sentence_group_id=sentence_group.sentence_group_id,
person_id=fake_person_id,
status=StateSentenceStatus.PRESENT_WITHOUT_INFO,
)
sentence_group.incarceration_sentences = [incarceration_sentence]
sentence_group.supervision_sentences = [supervision_sentence]
sentence_group_data = [normalized_database_base_dict(sentence_group)]
incarceration_sentence_data = [
normalized_database_base_dict(incarceration_sentence)
]
supervision_sentence_data = [
normalized_database_base_dict(supervision_sentence)
]
incarceration_periods_data = [
normalized_database_base_dict(initial_incarceration),
normalized_database_base_dict(first_reincarceration),
normalized_database_base_dict(subsequent_reincarceration),
]
state_incarceration_sentence_incarceration_period_association = [
{
"incarceration_period_id":
initial_incarceration.incarceration_period_id,
"incarceration_sentence_id":
incarceration_sentence.incarceration_sentence_id,
},
{
"incarceration_period_id":
first_reincarceration.incarceration_period_id,
"incarceration_sentence_id":
incarceration_sentence.incarceration_sentence_id,
},
{
"incarceration_period_id":
subsequent_reincarceration.incarceration_period_id,
"incarceration_sentence_id":
incarceration_sentence.incarceration_sentence_id,
},
]
fake_person_id_to_county_query_result = [{
"state_code":
state_code,
"person_id":
fake_person_id,
"county_of_residence":
_COUNTY_OF_RESIDENCE,
}]
us_mo_sentence_status_data: List[Dict[str, Any]] = [{
"state_code":
"US_MO",
"person_id":
fake_person_id,
"sentence_external_id":
"XXX",
"sentence_status_external_id":
"YYY",
"status_code":
"ZZZ",
"status_date":
"not_a_date",
"status_description":
"XYZ",
}]
incarceration_period_judicial_district_association_data = [{
"state_code":
state_code,
"person_id":
fake_person_id,
"incarceration_period_id":
123,
"judicial_district_code":
"NW",
}]
state_race_ethnicity_population_count_data = [{
"state_code":
state_code,
"race_or_ethnicity":
"BLACK",
"population_count":
1,
"representation_priority":
1,
}]
data_dict = self._default_data_dict()
data_dict_overrides = {
schema.StatePerson.__tablename__:
persons_data,
schema.StatePersonRace.__tablename__:
races_data,
schema.StatePersonEthnicity.__tablename__:
ethnicity_data,
schema.StateSentenceGroup.__tablename__:
sentence_group_data,
schema.StateIncarcerationSentence.__tablename__:
incarceration_sentence_data,
schema.StateSupervisionSentence.__tablename__:
supervision_sentence_data,
schema.StateIncarcerationPeriod.__tablename__:
incarceration_periods_data,
schema.state_incarceration_sentence_incarceration_period_association_table.name:
state_incarceration_sentence_incarceration_period_association,
"persons_to_recent_county_of_residence":
fake_person_id_to_county_query_result,
"incarceration_period_judicial_district_association":
incarceration_period_judicial_district_association_data,
"state_race_ethnicity_population_counts":
state_race_ethnicity_population_count_data,
"us_mo_sentence_statuses":
us_mo_sentence_status_data,
}
data_dict.update(data_dict_overrides)
return data_dict
