def map_metric_combinations(
characteristic_combo: Dict[str, Any],
incarceration_event: IncarcerationEvent,
calculation_month_upper_bound: date,
calculation_month_lower_bound: Optional[date],
all_incarceration_events: List[IncarcerationEvent],
periods_and_events: Dict[int, List[IncarcerationEvent]],
metric_type: IncarcerationMetricType,
include_metric_period_output: bool) -> \
List[Tuple[Dict[str, Any], Any]]:
"""Maps the given time bucket and characteristic combinations to a variety of metrics that track incarceration
admission and release counts.
All values will be 1 for these count metrics, because the presence of an IncarcerationEvent for a given month
implies that the person was counted towards the admission or release for that month.
Args:
characteristic_combo: A dictionary containing the characteristics of the person and event
incarceration_event: The incarceration event from which the combination was derived.
calculation_month_upper_bound: The year and month of the last month for which metrics should be calculated.
calculation_month_lower_bound: The date of the first month to be included in the monthly calculations
all_incarceration_events: All of the person's IncarcerationEvents
periods_and_events: A dictionary mapping metric period month values to the corresponding relevant
IncarcerationEvents
metric_type: The metric type to set on each combination
include_metric_period_output: Whether or not to include metrics for the various metric periods before the
current month. If False, will still include metric_period_months = 1 for the current month.
Returns:
A list of key-value tuples representing specific metric combinations and the metric value corresponding to
that metric.
"""
metrics = []
characteristic_combo['metric_type'] = metric_type
if include_in_historical_metrics(incarceration_event.event_date.year,
incarceration_event.event_date.month,
calculation_month_upper_bound,
calculation_month_lower_bound):
# IncarcerationPopulationMetrics are point-in-time counts for the date of the event, all other
# IncarcerationMetrics are counts based on the month of the event
is_daily_metric = metric_type == IncarcerationMetricType.INCARCERATION_POPULATION
# All other IncarcerationMetrics are counts based on the month of the event
metrics.extend(
combination_incarceration_metrics(characteristic_combo,
incarceration_event,
all_incarceration_events,
is_daily_metric))
if include_metric_period_output and metric_type != IncarcerationMetricType.INCARCERATION_POPULATION:
metrics.extend(
combination_incarceration_metric_period_metrics(
characteristic_combo, incarceration_event,
calculation_month_upper_bound, periods_and_events))
return metrics
def test_include_in_monthly_metrics_after_end_date(self):
calculation_month_upper_bound = date(2000, 1, 31)
include = calculator_utils.include_in_historical_metrics(
year=2000,
month=2,
calculation_month_upper_bound=calculation_month_upper_bound,
calculation_month_lower_bound=None)
self.assertFalse(include)
def test_include_in_monthly_metrics(self):
calculation_month_upper_bound = date(2000, 1, 31)
include = calculator_utils.include_in_historical_metrics(
year=1999,
month=11,
calculation_month_upper_bound=calculation_month_upper_bound,
calculation_month_lower_bound=None)
self.assertTrue(include)
def map_metric_combinations(
characteristic_combo: Dict[str, Any],
supervision_time_bucket: SupervisionTimeBucket,
calculation_month_upper_bound: date,
calculation_month_lower_bound: Optional[date],
all_supervision_time_buckets: List[SupervisionTimeBucket],
periods_and_buckets: Dict[int, List[SupervisionTimeBucket]],
metric_type: SupervisionMetricType,
include_metric_period_output: bool) -> \
List[Tuple[Dict[str, Any], Any]]:
"""Maps the given time bucket and characteristic combinations to a variety of metrics that track supervision
population and revocation counts.
Args:
characteristic_combo: A dictionary describing the person and supervision_time_bucket.
supervision_time_bucket: The time bucket on supervision from which the combination was derived.
calculation_month_upper_bound: The year and month of the last month for which metrics should be calculated.
calculation_month_lower_bound: The date of the first month to be included in the monthly calculations
all_supervision_time_buckets: All of the person's SupervisionTimeBuckets
periods_and_buckets: Dictionary mapping metric period month lengths to the SupervisionTimeBuckets that fall in
that period.
metric_type: The metric type to set on each combination.
include_metric_period_output: Whether or not to include metrics for the various metric periods before the
current month. If False, will still include metric_period_months = 0 or 1 for the current month.
Returns:
A list of key-value tuples representing specific metric combinations and the metric value corresponding to that
metric.
"""
metrics = []
characteristic_combo['metric_type'] = metric_type
if include_in_historical_metrics(
supervision_time_bucket.year, supervision_time_bucket.month,
calculation_month_upper_bound, calculation_month_lower_bound):
# SupervisionPopulationMetrics and SupervisionCaseComplianceMetrics are point-in-time metrics, all
# other SupervisionMetrics are metrics based on the month of the event
is_daily_metric = metric_type in (SupervisionMetricType.SUPERVISION_POPULATION,
SupervisionMetricType.SUPERVISION_COMPLIANCE)
metrics.extend(combination_supervision_monthly_metrics(
characteristic_combo, supervision_time_bucket,
all_supervision_time_buckets, metric_type, is_daily_metric))
if include_metric_period_output:
metrics.extend(combination_supervision_metric_period_metrics(
characteristic_combo,
supervision_time_bucket,
calculation_month_upper_bound,
periods_and_buckets,
metric_type
))
return metrics
def map_metric_combinations(
characteristic_combo: Dict[str, Any],
program_event: ProgramEvent,
calculation_month_upper_bound: date,
calculation_month_lower_bound: Optional[date],
all_program_events: List[ProgramEvent],
periods_and_events: Dict[int, List[ProgramEvent]],
metric_type: ProgramMetricType,
include_metric_period_output: bool) -> \
List[Tuple[Dict[str, Any], Any]]:
"""Maps the given program event and characteristic combinations to a variety of metrics that track program
interactions.
All values will be 1 for these count metrics, because the presence of a ProgramEvent for a given event implies that
the person interacted with the program in the way being described.
Args:
characteristic_combo: A dictionary describing the person and event.
program_event: The program event from which the combination was derived.
calculation_month_upper_bound: The year and month of the last month for which metrics should be calculated.
calculation_month_lower_bound: The date of the first month to be included in the monthly calculations
all_program_events: All of the person's ProgramEvents
periods_and_events: A dictionary mapping metric period month values to the corresponding relevant ProgramEvents
metric_type: The metric type to set on each combination
include_metric_period_output: Whether or not to include metrics for the various metric periods before the
current month. If False, will still include metric_period_months = 1 for the current month.
Returns:
A list of key-value tuples representing specific metric combinations and the metric value corresponding to that
metric.
"""
metrics = []
all_referral_events = [
event for event in all_program_events
if isinstance(event, ProgramReferralEvent)
]
if metric_type == ProgramMetricType.REFERRAL and isinstance(program_event, ProgramReferralEvent):
characteristic_combo['metric_type'] = metric_type
if include_in_historical_metrics(
program_event.event_date.year, program_event.event_date.month,
calculation_month_upper_bound, calculation_month_lower_bound):
metrics.extend(
combination_referral_monthly_metrics(characteristic_combo, program_event, all_referral_events))
if include_metric_period_output:
metrics.extend(combination_referral_metric_period_metrics(characteristic_combo,
program_event,
calculation_month_upper_bound,
periods_and_events))
return metrics
def test_include_in_monthly_metrics_one_month_run_exclude(self):
calculation_month_upper_bound = date(1999, 12, 31)
calculation_month_lower_bound = date(1999, 12, 1)
include = calculator_utils.include_in_historical_metrics(
year=2000,
month=1,
calculation_month_upper_bound=calculation_month_upper_bound,
calculation_month_lower_bound=calculation_month_lower_bound)
self.assertFalse(include)
def test_include_in_monthly_metrics_before_lower_bound(self):
calculation_month_upper_bound = date(2000, 1, 31)
calculation_month_lower_bound = date(1999, 10, 1)
include = calculator_utils.include_in_historical_metrics(
year=1990,
month=4,
calculation_month_upper_bound=calculation_month_upper_bound,
calculation_month_lower_bound=calculation_month_lower_bound)
self.assertFalse(include)
def test_include_in_monthly_metrics_month_of_end_date(self):
calculation_month_upper_bound = date(2000, 1, 31)
calculation_month_lower_bound = date(1999, 10, 1)
include = calculator_utils.include_in_historical_metrics(
year=calculation_month_upper_bound.year,
month=calculation_month_upper_bound.month,
calculation_month_upper_bound=calculation_month_upper_bound,
calculation_month_lower_bound=calculation_month_lower_bound)
self.assertTrue(include)
