updating converter to correctly type. (#102)

service/models/converters.py

@@ -11,7 +11,16 @@ def fetch_and_convert_static_interventions(policy_intervention_id, job_id):
  if not (policy_intervention_id):
  return defaultdict(dict)
  policy_intervention = fetch_interventions(policy_intervention_id, job_id)
- interventionList = policy_intervention["interventions"]
+ 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_static_interventions(interventionList)
 
 
@@ -21,10 +30,10 @@ def convert_static_interventions(interventions: list[HMIIntervention]):
  return defaultdict(dict)
  static_interventions: Dict[torch.Tensor, Dict[str, any]] = defaultdict(dict)
  for inter in interventions:
- for static_inter in inter["static_interventions"]:
- time = torch.tensor(float(static_inter["timestep"]))
- parameter_name = inter["applied_to"]
- value = torch.tensor(float(static_inter["value"]))
+ for static_inter in inter.static_interventions:
+ time = torch.tensor(float(static_inter.timestep))
+ parameter_name = inter.applied_to
+ value = torch.tensor(float(static_inter.value))
  static_interventions[time][parameter_name] = value
  return static_interventions