Adding dynamic interventions to simulate

service/models/converters.py

@@ -3,7 +3,7 @@
 # TODO: Do not use Torch in PyCIEMSS Library interface
 import torch
 from utils.tds import fetch_interventions
-from typing import Dict
+from typing import Dict, Callable
 from models.base import HMIIntervention
 
 
@@ -24,6 +24,23 @@ def fetch_and_convert_static_interventions(policy_intervention_id, job_id):
  return convert_static_interventions(interventionList)
 
 
+def fetch_and_convert_dynamic_interventions(policy_intervention_id, job_id):
+ if not (policy_intervention_id):
+ return defaultdict(dict)
+ policy_intervention = fetch_interventions(policy_intervention_id, job_id)
+ interventionList: list[HMIIntervention] = []
+ for inter in policy_intervention["interventions"]:
+ intervention = HMIIntervention(
+ name=inter["name"],
+ applied_to=inter["applied_to"],
+ type=inter["type"],
+ static_interventions=inter["static_interventions"],
+ dynamic_interventions=inter["dynamic_interventions"],
+ )
+ interventionList.append(intervention)
+ return convert_dynamic_interventions(interventionList)
+
+
 # Used to convert from HMI Intervention Policy -> pyciemss static interventions.
 def convert_static_interventions(interventions: list[HMIIntervention]):
  if not (interventions):
@@ -38,6 +55,35 @@ def convert_static_interventions(interventions: list[HMIIntervention]):
  return static_interventions
 
 
+def make_var_threshold(var: str, threshold: torch.Tensor):
+ def var_threshold(time, state):
+ return state[var] - threshold
+
+ return var_threshold
+
+
+def convert_dynamic_interventions(interventions: list[HMIIntervention]):
+ if not (interventions):
+ return defaultdict(dict)
+ dynamic_parameter_interventions: Dict[
+ Callable[[torch.Tensor, Dict[str, torch.Tensor]], torch.Tensor],
+ Dict[str, any],
+ ] = defaultdict(dict)
+ for inter in interventions:
+ for dynamic_inter in inter.dynamic_interventions:
+ parameter_name = inter.applied_to
+ threshold_value = torch.tensor(float(dynamic_inter.threshold))
+ to_value = torch.tensor(float(dynamic_inter.value))
+ threshold_func = make_var_threshold(
+ dynamic_inter.parameter, threshold_value
+ )
+ dynamic_parameter_interventions[threshold_func].update(
+ {parameter_name: to_value}
+ )
+
+ return dynamic_parameter_interventions
+
+
 def convert_to_solution_mapping(config):
  individual_to_ensemble = {
  individual_state: ensemble_state

service/models/operations/simulate.py

@@ -5,7 +5,10 @@
 
 
 from models.base import OperationRequest, Timespan
-from models.converters import fetch_and_convert_static_interventions
+from models.converters import (
+ fetch_and_convert_static_interventions,
+ fetch_and_convert_dynamic_interventions,
+)
 from utils.tds import fetch_model, fetch_inferred_parameters
 
 
@@ -39,6 +42,10 @@ def gen_pyciemss_args(self, job_id):
  self.policy_intervention_id, job_id
  )
 
+ dynamic_interventions = fetch_and_convert_dynamic_interventions(
+ self.policy_intervention_id, job_id
+ )
+
  extra_options = self.extra.dict()
  inferred_parameters = fetch_inferred_parameters(
  extra_options.pop("inferred_parameters"), job_id
@@ -50,6 +57,7 @@ def gen_pyciemss_args(self, job_id):
  "start_time": self.timespan.start,
  "end_time": self.timespan.end,
  "static_parameter_interventions": static_interventions,
+ "dynamic_parameter_interventions": dynamic_interventions,
  "inferred_parameters": inferred_parameters,
  **extra_options,
  }