def test_map_incarceration_combinations_all_types(self):
person = StatePerson.new_with_defaults(person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE)
race = StatePersonRace.new_with_defaults(state_code='CA',
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_events = [
IncarcerationStayEvent(
admission_reason=StateIncarcerationPeriodAdmissionReason.
PAROLE_REVOCATION,
admission_reason_raw_text='NEW_ADMISSION',
supervision_type_at_admission=
StateSupervisionPeriodSupervisionType.PAROLE,
state_code='CA',
event_date=date(2000, 3, 31),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_statute=_STATUTE,
),
IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(2000, 3, 12),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
admission_reason=AdmissionReason.PAROLE_REVOCATION,
supervision_type_at_admission=
StateSupervisionPeriodSupervisionType.PAROLE,
admission_reason_raw_text='PAROLE_REVOCATION',
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON),
IncarcerationReleaseEvent(
state_code='CA',
event_date=date(2003, 4, 12),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
)
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=-1)
expected_combinations_count = expected_metric_combos_count(
person, incarceration_events, ALL_INCLUSIONS_DICT)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
def test_map_incarceration_combinations_two_stays_same_month_facility(
self):
person = StatePerson.new_with_defaults(
state_code="CA",
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
)
race = StatePersonRace.new_with_defaults(state_code="CA",
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code="CA", ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_events = [
IncarcerationStayEvent(
state_code="CA",
event_date=date(2010, 3, 31),
facility="FACILITY 33",
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE,
admission_reason=AdmissionReason.PAROLE_REVOCATION,
admission_reason_raw_text="PAROLE_REVOCATION",
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
IncarcerationStayEvent(
state_code="CA",
event_date=date(2010, 3, 31),
facility="FACILITY 18",
county_of_residence=_COUNTY_OF_RESIDENCE,
admission_reason=AdmissionReason.PAROLE_REVOCATION,
admission_reason_raw_text="PAROLE_REVOCATION",
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
metric_inclusions=ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month=None,
calculation_month_count=-1,
person_metadata=_DEFAULT_PERSON_METADATA,
)
expected_combinations_count = expected_metric_combos_count(
incarceration_events)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
def test_map_incarceration_combinations_calculation_month_limit_exclude(
self):
person = StatePerson.new_with_defaults(person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE)
race = StatePersonRace.new_with_defaults(state_code='CA',
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_event = IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(1990, 3, 12),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_events = [incarceration_event]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=1)
self.assertEqual(0, len(incarceration_combinations))
def process(self, element, calculation_end_month, calculation_month_count,
metric_inclusions):
"""Produces various incarceration metric combinations.
Sends the calculator the StatePerson entity and their corresponding IncarcerationEvents for mapping all
incarceration combinations.
Args:
element: Tuple containing a StatePerson and their IncarcerationEvents
calculation_end_month: The year and month of the last month for which metrics should be calculated.
calculation_month_count: The number of months to limit the monthly calculation output to.
metric_inclusions: A dictionary where the keys are each IncarcerationMetricType, and the values are boolean
flags for whether or not to include that metric type in the calculations
Yields:
Each incarceration metric combination.
"""
person, incarceration_events = element
# Calculate incarceration metric combinations for this person and events
metric_combinations = calculator.map_incarceration_combinations(
person, incarceration_events, metric_inclusions,
calculation_end_month, calculation_month_count)
# Return each of the incarceration metric combinations
for metric_combination in metric_combinations:
yield metric_combination
def test_map_incarceration_combinations_calculation_month_count_include_one(
self):
person = StatePerson.new_with_defaults(
state_code="CA",
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
)
race = StatePersonRace.new_with_defaults(state_code="CA",
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code="CA", ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_event_include = IncarcerationAdmissionEvent(
state_code="CA",
event_date=date(2000, 3, 12),
facility="SAN QUENTIN",
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_event_exclude = IncarcerationAdmissionEvent(
state_code="CA",
event_date=date(1994, 3, 12),
facility="SAN QUENTIN",
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_events = [
incarceration_event_include,
incarceration_event_exclude,
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
metric_inclusions=ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month=None,
calculation_month_count=36,
person_metadata=_DEFAULT_PERSON_METADATA,
)
expected_combinations_count = expected_metric_combos_count(
[incarceration_event_include])
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
for combo, _ in incarceration_combinations:
assert combo.get("year") == 2000
def test_map_incarceration_combinations_relevant_periods_revocations(self):
person = StatePerson.new_with_defaults(person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE)
race = StatePersonRace.new_with_defaults(state_code='CA',
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_events = [
IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(2010, 10, 2),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
admission_reason=AdmissionReason.PAROLE_REVOCATION),
IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(2010, 10, 19),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
admission_reason=AdmissionReason.NEW_ADMISSION)
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=-1)
expected_combinations_count = expected_metric_combos_count(
person, incarceration_events, ALL_INCLUSIONS_DICT,
len(calculator_utils.METRIC_PERIOD_MONTHS))
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
for combo, _ in incarceration_combinations:
assert combo.get('year') == 2010
for combo, _ in incarceration_combinations:
if combo.get('admission_reason')\
and combo_has_enum_value_for_key(
combo, 'metric_type', MetricMethodologyType.PERSON):
# Ensure that all person-based metrics have the parole
# revocation admission reason on them
assert combo.get('admission_reason') == \
AdmissionReason.PAROLE_REVOCATION
def test_map_incarceration_combinations_includes_statute_output(self):
person = StatePerson.new_with_defaults(person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE)
race = StatePersonRace.new_with_defaults(state_code='CA',
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_events = [
IncarcerationStayEvent(state_code='CA',
event_date=date(2010, 3, 31),
facility='FACILITY 33',
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE),
IncarcerationStayEvent(state_code='CA',
event_date=date(2010, 4, 30),
facility='FACILITY 33',
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE),
IncarcerationStayEvent(state_code='CA',
event_date=date(2010, 5, 31),
facility='FACILITY 33',
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE)
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=-1)
expected_combinations_count = expected_metric_combos_count(
person, incarceration_events, ALL_INCLUSIONS_DICT)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
assert all(
combo.get('most_serious_offense_statute') is not None
for combo, value in incarceration_combinations
if combo.get('person_id') is not None)
def test_map_incarceration_combinations_calculation_month_limit_include_one(
self):
person = StatePerson.new_with_defaults(person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE)
race = StatePersonRace.new_with_defaults(state_code='CA',
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_event_include = IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(2000, 3, 12),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_event_exclude = IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(1994, 3, 12),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_events = [
incarceration_event_include, incarceration_event_exclude
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=36)
expected_combinations_count = expected_metric_combos_count(
person, [incarceration_event_include],
ALL_INCLUSIONS_DICT,
num_relevant_periods_admissions=len(
calculator_utils.METRIC_PERIOD_MONTHS))
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
for combo, _ in incarceration_combinations:
assert combo.get('year') == 2000
def test_map_incarceration_combinations(self):
person = StatePerson.new_with_defaults(
state_code="CA",
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
)
race = StatePersonRace.new_with_defaults(state_code="CA",
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code="CA", ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_event = IncarcerationAdmissionEvent(
state_code="CA",
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
admission_reason_raw_text="NEW_ADMISSION",
supervision_type_at_admission=StateSupervisionPeriodSupervisionType
.PROBATION,
event_date=date(2000, 3, 12),
facility="SAN QUENTIN",
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_events = [incarceration_event]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
metric_inclusions=ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month="2000-03",
calculation_month_count=1,
person_metadata=_DEFAULT_PERSON_METADATA,
)
expected_combinations_count = expected_metric_combos_count(
incarceration_events)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
for combo, _ in incarceration_combinations:
assert combo.get("year") == 2000
def test_map_incarceration_combinations_two_stays_same_month(self):
person = StatePerson.new_with_defaults(person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE)
race = StatePersonRace.new_with_defaults(state_code='CA',
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_events = [
IncarcerationStayEvent(
state_code='CA',
event_date=date(2010, 3, 31),
facility='FACILITY 33',
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE,
admission_reason=AdmissionReason.PAROLE_REVOCATION,
admission_reason_raw_text='PAROLE_REVOCATION',
),
IncarcerationStayEvent(
state_code='CA',
event_date=date(2010, 3, 31),
facility='FACILITY 33',
county_of_residence=_COUNTY_OF_RESIDENCE,
most_serious_offense_ncic_code=_NCIC_CODE,
most_serious_offense_statute=_STATUTE,
admission_reason=AdmissionReason.PAROLE_REVOCATION,
admission_reason_raw_text='PAROLE_REVOCATION',
)
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=-1)
expected_combinations_count = expected_metric_combos_count(
person, incarceration_events, ALL_INCLUSIONS_DICT)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
def test_map_incarceration_combinations_two_releases_same_month(self):
person = StatePerson.new_with_defaults(
state_code="CA",
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
)
race = StatePersonRace.new_with_defaults(state_code="CA",
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code="CA", ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_events = [
IncarcerationReleaseEvent(
state_code="CA",
event_date=date(2010, 3, 12),
facility="FACILITY 33",
county_of_residence=_COUNTY_OF_RESIDENCE,
),
IncarcerationReleaseEvent(
state_code="CA",
event_date=date(2010, 3, 24),
facility="FACILITY 33",
county_of_residence=_COUNTY_OF_RESIDENCE,
),
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
metric_inclusions=ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month=None,
calculation_month_count=-1,
person_metadata=_DEFAULT_PERSON_METADATA,
)
expected_combinations_count = expected_metric_combos_count(
incarceration_events)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
def test_map_incarceration_combinations(self):
person = StatePerson.new_with_defaults(person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE)
race = StatePersonRace.new_with_defaults(state_code='CA',
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_event = IncarcerationAdmissionEvent(
state_code='CA',
admission_reason=StateIncarcerationPeriodAdmissionReason.
NEW_ADMISSION,
admission_reason_raw_text='NEW_ADMISSION',
supervision_type_at_admission=StateSupervisionPeriodSupervisionType
.PROBATION,
event_date=date(2000, 3, 12),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
)
incarceration_events = [incarceration_event]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=-1)
expected_combinations_count = expected_metric_combos_count(
person, incarceration_events, ALL_INCLUSIONS_DICT)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
for combo, _ in incarceration_combinations:
assert combo.get('year') == 2000
def test_map_incarceration_combinations_admission_relevant_periods(self):
person = StatePerson.new_with_defaults(person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE)
race = StatePersonRace.new_with_defaults(state_code='CA',
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_event = IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(2000, 3, 12),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
admission_reason=AdmissionReason.NEW_ADMISSION,
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.SHOCK_INCARCERATION)
incarceration_events = [incarceration_event]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=-1)
expected_combinations_count = expected_metric_combos_count(
person, incarceration_events, ALL_INCLUSIONS_DICT,
len(calculator_utils.METRIC_PERIOD_MONTHS))
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
for combo, _ in incarceration_combinations:
assert combo.get('year') == 2000
if combo.get('person_id') is not None:
assert combo.get('admission_date') is not None
def test_map_incarceration_combinations_multiple_overlapping_stays(self):
person = StatePerson.new_with_defaults(person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE)
race = StatePersonRace.new_with_defaults(state_code='CA',
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_events = [
IncarcerationStayEvent(state_code='US_ND',
event_date=date(2019, 11, 30),
facility='JRCC',
county_of_residence=_COUNTY_OF_RESIDENCE),
IncarcerationStayEvent(state_code='US_ND',
event_date=date(2019, 11, 30),
facility='JRCC',
county_of_residence=_COUNTY_OF_RESIDENCE),
IncarcerationStayEvent(state_code='US_ND',
event_date=date(2019, 11, 30),
facility='JRCC',
county_of_residence=_COUNTY_OF_RESIDENCE)
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=-1)
expected_combinations_count = expected_metric_combos_count(
person, incarceration_events, ALL_INCLUSIONS_DICT)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
def test_map_incarceration_combinations_two_admissions_same_month(self):
person = StatePerson.new_with_defaults(person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE)
race = StatePersonRace.new_with_defaults(state_code='CA',
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code='CA', ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_events = [
IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(2000, 3, 12),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
admission_reason=AdmissionReason.NEW_ADMISSION),
IncarcerationAdmissionEvent(
state_code='CA',
event_date=date(2000, 3, 17),
facility='SAN QUENTIN',
county_of_residence=_COUNTY_OF_RESIDENCE,
admission_reason=AdmissionReason.NEW_ADMISSION)
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
inclusions=ALL_INCLUSIONS_DICT,
calculation_month_limit=-1)
expected_combinations_count = expected_metric_combos_count(
person, incarceration_events, ALL_INCLUSIONS_DICT)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
def process(self, element, calculation_month_limit, inclusions):
"""Produces various incarceration metric combinations.
Sends the calculator the StatePerson entity and their corresponding IncarcerationEvents for mapping all
incarceration combinations.
Args:
element: Tuple containing a StatePerson and their IncarcerationEvents
calculation_month_limit: The number of months to limit the monthly calculation output to.
inclusions: This should be a dictionary with values for the following keys:
- age_bucket
- gender
- race
- ethnicity
Yields:
Each incarceration metric combination, tagged by metric type.
"""
person, incarceration_events = element
# Calculate incarceration metric combinations for this person and events
metric_combinations = calculator.map_incarceration_combinations(person,
incarceration_events,
inclusions,
calculation_month_limit)
# Return each of the incarceration metric combinations
for metric_combination in metric_combinations:
metric_key, value = metric_combination
metric_type = metric_key.get('metric_type')
# Converting the metric key to a JSON string so it is hashable
serializable_dict = json_serializable_metric_key(metric_key)
json_key = json.dumps(serializable_dict, sort_keys=True)
if metric_type == MetricType.ADMISSION.value:
yield beam.pvalue.TaggedOutput('admissions', (json_key, value))
elif metric_type == MetricType.POPULATION.value:
yield beam.pvalue.TaggedOutput('populations', (json_key, value))
elif metric_type == MetricType.RELEASE.value:
yield beam.pvalue.TaggedOutput('releases', (json_key, value))
def test_map_incarceration_combinations_multiple_overlapping_stays(self):
person = StatePerson.new_with_defaults(
state_code="US_ND",
person_id=12345,
birthdate=date(1984, 8, 31),
gender=Gender.FEMALE,
)
race = StatePersonRace.new_with_defaults(state_code="US_ND",
race=Race.WHITE)
person.races = [race]
ethnicity = StatePersonEthnicity.new_with_defaults(
state_code="US_ND", ethnicity=Ethnicity.NOT_HISPANIC)
person.ethnicities = [ethnicity]
incarceration_events = [
IncarcerationStayEvent(
state_code="US_ND",
event_date=date(2019, 11, 30),
facility="JRCC",
county_of_residence=_COUNTY_OF_RESIDENCE,
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
IncarcerationStayEvent(
state_code="US_ND",
event_date=date(2019, 11, 30),
facility="JRCC",
county_of_residence=_COUNTY_OF_RESIDENCE,
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
IncarcerationStayEvent(
state_code="US_ND",
event_date=date(2019, 11, 30),
facility="JRCC",
county_of_residence=_COUNTY_OF_RESIDENCE,
specialized_purpose_for_incarceration=
StateSpecializedPurposeForIncarceration.TREATMENT_IN_PRISON,
),
]
incarceration_combinations = calculator.map_incarceration_combinations(
person=person,
incarceration_events=incarceration_events,
metric_inclusions=ALL_METRICS_INCLUSIONS_DICT,
calculation_end_month=None,
calculation_month_count=-1,
person_metadata=_DEFAULT_PERSON_METADATA,
)
expected_combinations_count = expected_metric_combos_count(
incarceration_events)
self.assertEqual(expected_combinations_count,
len(incarceration_combinations))
assert all(value == 1
for _combination, value in incarceration_combinations)
