def initialize(self):
"""Initialize the transition tables, and then the compartments, for the SubSimulation"""
# TODO(#4512): allow sparse data
if not self.microsim and not self.total_population_data.empty:
if self.user_inputs['start_time_step'] not in self.total_population_data.time_step.values:
raise ValueError(f"Start time must be included in population data input\n"
f"Expected: {self.user_inputs['start_time_step']}, "
f"Actual: {self.total_population_data.time_step.values}")
# Initialize a default transition class for each compartment to represent the no-policy scenario
transitions_per_compartment = {}
for compartment in self.simulation_architecture:
transition_type = self.simulation_architecture[compartment]
compartment_duration_data = self.transitions_data[self.transitions_data['compartment'] == compartment]
if compartment_duration_data.empty:
if transition_type is not None:
raise ValueError(f"Transition data missing for compartment {compartment}. Data is required for all "
"disaggregtion axes. Even the 'release' compartment needs transition data even if "
"it's just outflow to 'release'")
else:
if transition_type == 'incarcerated':
transition_class = IncarceratedTransitions(compartment_duration_data)
elif transition_type == 'released':
transition_class = ReleasedTransitions(compartment_duration_data)
else:
raise ValueError(f'unrecognized transition table type {transition_type}')
transition_class.initialize_transition_table()
transitions_per_compartment[compartment] = transition_class
# Create a transition object for each compartment and year with policies applied and store shell policies
shell_policies = dict()
for compartment in self.simulation_architecture:
# Select any policies that are applicable for this compartment
compartment_policies = SparkPolicy.get_compartment_policies(self.policy_list, compartment)
# add to the dict compartment -> transition class with policies applied
if compartment in transitions_per_compartment:
transitions_per_compartment[compartment].initialize(compartment_policies)
# add shell policies to dict that gets passed to initialization
else:
shell_policies[compartment] = compartment_policies
# Preprocess the historical data into separate pieces per compartment
historical_outflows = self._load_data()
# Initialize the compartment classes
self._initialize_compartments(historical_outflows, transitions_per_compartment, shell_policies)
def test_get_compartment_policies(self):
expected_list = self.policy_list[1:]
result_list = SparkPolicy.get_compartment_policies(
self.policy_list, spark_compartment='jail')
self.assertEqual(expected_list, result_list)
def _initialize_transition_tables(
cls,
transitions_data: pd.DataFrame,
compartments_architecture: Dict[str, str],
policy_list: List[SparkPolicy],
) -> Tuple[Dict[str, CompartmentTransitions], Dict[str,
List[SparkPolicy]]]:
"""Create and initialize all transition tables and store shell policies."""
# Initialize a default transition class for each compartment to represent the no-policy scenario
transitions_per_compartment = {}
unused_transitions_data = transitions_data
for compartment in compartments_architecture:
compartment_type = compartments_architecture[compartment]
compartment_duration_data = transitions_data[
transitions_data["compartment"] == compartment]
unused_transitions_data = unused_transitions_data.drop(
compartment_duration_data.index)
if compartment_duration_data.empty:
if compartment_type != "shell":
raise ValueError(
f"Transition data missing for compartment {compartment}. Data is required for all "
"disaggregtion axes. Even the 'release' compartment needs transition data even if "
"it's just outflow to 'release'")
else:
if compartment_type == "full":
transition_class = CompartmentTransitions(
compartment_duration_data)
elif compartment_type == "shell":
raise ValueError(
f"Cannot provide transitions data for shell compartment \n "
f"{compartment_duration_data}")
else:
raise ValueError(
f"unrecognized transition table type {compartment_type}"
)
transitions_per_compartment[compartment] = transition_class
if len(unused_transitions_data) > 0:
logging.warning(
"Some transitions data not fed to a compartment: %s",
unused_transitions_data,
)
# Create a transition object for each compartment and year with policies applied and store shell policies
shell_policies = dict()
for compartment in compartments_architecture:
# Select any policies that are applicable for this compartment
compartment_policies = SparkPolicy.get_compartment_policies(
policy_list, compartment)
# add to the dict compartment -> transition class with policies applied
if compartment in transitions_per_compartment:
transitions_per_compartment[
compartment].initialize_transition_tables(
compartment_policies)
# add shell policies to dict that gets passed to initialization
else:
shell_policies[compartment] = compartment_policies
return transitions_per_compartment, shell_policies