mirror of
https://github.com/Hestia-Homes/Model.git
synced 2026-06-08 11:17:27 +00:00
Added try catch block
This commit is contained in:
Khalim Conn-Kowlessar
parent
eef6fd27dd
commit
c413482bf4
1 changed files with 223 additions and 214 deletions
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@ -134,241 +134,250 @@ async def trigger_plan(body: PlanTriggerRequest):
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Session = sessionmaker(bind=db_engine)
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session = Session()
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logger.info("Getting the inputs")
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# Read in the trigger file from s3
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bucket_name = get_settings().PLAN_TRIGGER_BUCKET
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epc_client = EpcClient(auth_token=get_settings().EPC_AUTH_TOKEN)
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try:
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plan_input = read_csv_from_s3(bucket_name=bucket_name, filepath=body.trigger_file_path)
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logger.info("Getting the inputs")
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# Read in the trigger file from s3
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bucket_name = get_settings().PLAN_TRIGGER_BUCKET
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epc_client = EpcClient(auth_token=get_settings().EPC_AUTH_TOKEN)
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input_properties = []
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for config in plan_input:
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# We validate each record in the file. If the record is NOT valid, we need to handle this accordingly
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# TODO: implment validation
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plan_input = read_csv_from_s3(bucket_name=bucket_name, filepath=body.trigger_file_path)
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# Create a record in db
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property_id, is_new = create_property(
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session, portfolio_id=body.portfolio_id, address=config['address'], postcode=config['postcode']
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)
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input_properties = []
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for config in plan_input:
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# We validate each record in the file. If the record is NOT valid, we need to handle this accordingly
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# TODO: implment validation
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# if a new record was not created, we don't produduce recommendations
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if not is_new:
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continue
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# TODO: Need to add heat demand target
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create_property_targets(
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session,
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property_id=property_id,
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portfolio_id=body.portfolio_id,
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epc_target=body.goal_value,
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heat_demand_target=None
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)
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input_properties.append(
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Property(
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postcode=config['postcode'],
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address1=config['address'],
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epc_client=epc_client,
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id=property_id
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)
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)
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if not input_properties:
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return Response(status_code=204)
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logger.info("Getting EPC data")
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for p in input_properties:
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p.search_address_epc()
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p.set_year_built()
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logger.info("Getting coordinates")
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# This is placeholder, until the full dataset is loaded into the database
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for p in input_properties:
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coordinate_data = [x for x in open_uprn_data if x['UPRN'] == int(p.data['uprn'])][0]
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p.set_coordinates(coordinate_data)
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logger.info("Check if property is in conservation area")
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for p in input_properties:
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in_conservation_area = [x for x in in_conservation_area_data if x['uprn'] == int(p.data['uprn'])][0].get(
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"is_in_conservation_area"
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)
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p.set_is_in_conservation_area(in_conservation_area)
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# The materials data could be cached or local so we don't need to make
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# consistent requrests to the backend for
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# the same data
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# TODO: It might not be the best choice to store the materials data in a database table since thi
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# table probably won't be very large and won't be updated that often. It might be better to
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# store this data in s3 load it into memory when the app starts up. We will test this
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materials = get_materials(session)
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materials_by_type = filter_materials(materials)
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logger.info("Getting components and properties recommendations")
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# TODO: Move this to a class. We probably was a Recommender class which takes the injects the optimisers
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# in as a dependency and then the optimisers can take the input measures in as part of the setup() method
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recommendations = {}
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for p in input_properties:
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property_recommendations = []
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# For each property, classiy floor area decide
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total_floor_area_group_decile = classify_decile_newvalues(
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decile_boundaries=floors_decile_data["decile_boundaries"],
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decile_labels=floors_decile_data["decile_labels"],
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new_values=[float(p.data["total-floor-area"])],
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)[0]
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# Property recommendations
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p.get_components(cleaned)
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# This is placeholder, until the full dataset is loaded into the database and we just make a read to the
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# database
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floors_u_value_estimate = [
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x for x in uvalue_estimates_floors
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if (x['local-authority'] == p.data["local-authority"]) &
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(x['property-type'] == p.data["property-type"]) &
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(x['built-form'] == p.data["built-form"]) &
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(x['floor-energy-eff'] == p.data["floor-energy-eff"] if p.data["floor-energy-eff"] != 'N/A' else True) &
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(x['floor-env-eff'] == p.data["floor-env-eff"] if p.data["floor-env-eff"] != 'N/A' else True)
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]
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# Floor recommendations
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floor_recommender = FloorRecommendations(
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property_instance=p,
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uvalue_estimates=floors_u_value_estimate,
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total_floor_area_group_decile=total_floor_area_group_decile,
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materials=materials_by_type["suspended_floor_insulation"] + materials_by_type["solid_floor_insulation"],
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)
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floor_recommender.recommend()
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if floor_recommender.recommendations:
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property_recommendations.append(floor_recommender.recommendations)
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# Wall recommendations
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# We would make this u-value query directly to the database
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total_floor_area_group_decile = classify_decile_newvalues(
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decile_boundaries=walls_decile_data["decile_boundaries"],
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decile_labels=walls_decile_data["decile_labels"],
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new_values=[float(p.data["total-floor-area"])],
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)[0]
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# This is placeholder, until the full dataset is loaded into the database and we just make a read to the
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# database
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walls_u_value_estimate = [
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x for x in uvalue_estimates_walls
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if (x['local-authority'] == p.data["local-authority"]) &
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(x['property-type'] == p.data["property-type"]) &
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(x['built-form'] == p.data["built-form"]) &
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(x['walls-energy-eff'] == p.data["walls-energy-eff"] if p.data["walls-energy-eff"] != 'N/A' else True) &
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(x['walls-env-eff'] == p.data["walls-env-eff"] if p.data["walls-env-eff"] != 'N/A' else True)
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]
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wall_recomender = WallRecommendations(
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property_instance=p,
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uvalue_estimates=walls_u_value_estimate,
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total_floor_area_group_decile=total_floor_area_group_decile,
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materials=materials_by_type["external_wall_insulation"] + materials_by_type["internal_wall_insulation"]
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)
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wall_recomender.recommend()
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if wall_recomender.recommendations:
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property_recommendations.append(wall_recomender.recommendations)
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# Use the optimiser to pick the default recommendations and decide if we need certain
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# recommendations to get to the goal
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property_recommendations = insert_temp_recommendation_id(property_recommendations)
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if not property_recommendations:
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continue
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input_measures = prepare_input_measures(property_recommendations, body.goal)
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if body.budget:
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optimiser = GainOptimiser(input_measures, max_cost=body.budget)
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else:
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# The minimum gain is the minimum number of SAP points required to get to the target SAP band
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current_sap_points = int(p.data["current-energy-efficiency"])
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target_sap_points = epc_to_sap_lower_bound(body.goal_value)
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# If the gain is negative, the optimiser will return an empty solution
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optimiser = CostOptimiser(
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input_measures, min_gain=target_sap_points - current_sap_points
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# Create a record in db
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property_id, is_new = create_property(
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session, portfolio_id=body.portfolio_id, address=config['address'], postcode=config['postcode']
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)
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optimiser.setup()
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optimiser.solve()
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solution = optimiser.solution
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# if a new record was not created, we don't produduce recommendations
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if not is_new:
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continue
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selected_recommendations = {r["id"] for r in solution}
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# We'll use the set of selected recommendations to filter the recommendations to upload
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# TODO: Need to add heat demand target
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create_property_targets(
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session,
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property_id=property_id,
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portfolio_id=body.portfolio_id,
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epc_target=body.goal_value,
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heat_demand_target=None
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)
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property_recommendations = [
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[
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{**rec, "default": True if rec["recommendation_id"] in selected_recommendations else False}
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for rec in recommendations_by_type
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input_properties.append(
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Property(
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postcode=config['postcode'],
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address1=config['address'],
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epc_client=epc_client,
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id=property_id
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)
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)
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if not input_properties:
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return Response(status_code=204)
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logger.info("Getting EPC data")
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for p in input_properties:
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p.search_address_epc()
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p.set_year_built()
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logger.info("Getting coordinates")
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# This is placeholder, until the full dataset is loaded into the database
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for p in input_properties:
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coordinate_data = [x for x in open_uprn_data if x['UPRN'] == int(p.data['uprn'])][0]
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p.set_coordinates(coordinate_data)
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logger.info("Check if property is in conservation area")
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for p in input_properties:
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in_conservation_area = [x for x in in_conservation_area_data if x['uprn'] == int(p.data['uprn'])][0].get(
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"is_in_conservation_area"
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)
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p.set_is_in_conservation_area(in_conservation_area)
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# The materials data could be cached or local so we don't need to make
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# consistent requrests to the backend for
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# the same data
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# TODO: It might not be the best choice to store the materials data in a database table since thi
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# table probably won't be very large and won't be updated that often. It might be better to
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# store this data in s3 load it into memory when the app starts up. We will test this
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materials = get_materials(session)
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materials_by_type = filter_materials(materials)
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logger.info("Getting components and properties recommendations")
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# TODO: Move this to a class. We probably was a Recommender class which takes the injects the optimisers
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# in as a dependency and then the optimisers can take the input measures in as part of the setup() method
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recommendations = {}
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for p in input_properties:
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property_recommendations = []
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# For each property, classiy floor area decide
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total_floor_area_group_decile = classify_decile_newvalues(
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decile_boundaries=floors_decile_data["decile_boundaries"],
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decile_labels=floors_decile_data["decile_labels"],
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new_values=[float(p.data["total-floor-area"])],
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)[0]
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# Property recommendations
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p.get_components(cleaned)
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# This is placeholder, until the full dataset is loaded into the database and we just make a read to the
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# database
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floors_u_value_estimate = [
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x for x in uvalue_estimates_floors
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if (x['local-authority'] == p.data["local-authority"]) &
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(x['property-type'] == p.data["property-type"]) &
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(x['built-form'] == p.data["built-form"]) &
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(x['floor-energy-eff'] == p.data["floor-energy-eff"] if p.data[
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"floor-energy-eff"] != 'N/A' else True) &
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(x['floor-env-eff'] == p.data["floor-env-eff"] if p.data["floor-env-eff"] != 'N/A' else True)
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]
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for recommendations_by_type in property_recommendations
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]
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# We'll also unlist the recommendations so they're a bit easier to handle from here onwards
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property_recommendations = [
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rec for recommendations_by_type in property_recommendations for rec in recommendations_by_type
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]
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# Floor recommendations
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floor_recommender = FloorRecommendations(
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property_instance=p,
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uvalue_estimates=floors_u_value_estimate,
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total_floor_area_group_decile=total_floor_area_group_decile,
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materials=materials_by_type["suspended_floor_insulation"] + materials_by_type["solid_floor_insulation"],
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)
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floor_recommender.recommend()
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recommendations[p.id] = property_recommendations
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if floor_recommender.recommendations:
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property_recommendations.append(floor_recommender.recommendations)
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# Once we're done, we'll store:
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# 1) the property data
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# 2) the property details (epc)
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# 3) the recommendations
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# Wall recommendations
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# We would make this u-value query directly to the database
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total_floor_area_group_decile = classify_decile_newvalues(
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decile_boundaries=walls_decile_data["decile_boundaries"],
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decile_labels=walls_decile_data["decile_labels"],
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new_values=[float(p.data["total-floor-area"])],
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)[0]
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logger.info("Uploading recommendations to the database")
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# Upload property data
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for p in input_properties:
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property_details_epc = p.get_property_details_epc(portfolio_id=body.portfolio_id, rating_lookup=rating_lookup)
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create_property_details_epc(session, property_details_epc)
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# This is placeholder, until the full dataset is loaded into the database and we just make a read to the
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# database
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walls_u_value_estimate = [
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x for x in uvalue_estimates_walls
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if (x['local-authority'] == p.data["local-authority"]) &
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(x['property-type'] == p.data["property-type"]) &
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(x['built-form'] == p.data["built-form"]) &
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(x['walls-energy-eff'] == p.data["walls-energy-eff"] if p.data[
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"walls-energy-eff"] != 'N/A' else True) &
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(x['walls-env-eff'] == p.data["walls-env-eff"] if p.data["walls-env-eff"] != 'N/A' else True)
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]
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property_data = p.get_full_property_data()
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update_property_data(session, property_id=p.id, portfolio_id=body.portfolio_id, property_data=property_data)
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wall_recomender = WallRecommendations(
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property_instance=p,
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uvalue_estimates=walls_u_value_estimate,
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total_floor_area_group_decile=total_floor_area_group_decile,
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materials=materials_by_type["external_wall_insulation"] + materials_by_type["internal_wall_insulation"]
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)
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wall_recomender.recommend()
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# Upload recommendations
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recommendations_to_upload = recommendations.get(p.id, [])
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if wall_recomender.recommendations:
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property_recommendations.append(wall_recomender.recommendations)
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if not recommendations_to_upload:
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continue
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# Use the optimiser to pick the default recommendations and decide if we need certain
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# recommendations to get to the goal
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property_recommendations = insert_temp_recommendation_id(property_recommendations)
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# Create a plan
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new_plan_id = create_plan(
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session,
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{
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"portfolio_id": body.portfolio_id,
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"property_id": p.id,
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"is_default": True
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}
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)
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if not property_recommendations:
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continue
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# Upload recommendations
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uploaded_recommendation_ids = upload_recommendations(session, recommendations_to_upload, p.id)
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input_measures = prepare_input_measures(property_recommendations, body.goal)
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# Finally, match the recommendation to the plan
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create_plan_recommendations(
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session,
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plan_id=new_plan_id,
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recommendation_ids=uploaded_recommendation_ids
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)
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if body.budget:
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optimiser = GainOptimiser(input_measures, max_cost=body.budget)
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else:
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# The minimum gain is the minimum number of SAP points required to get to the target SAP band
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current_sap_points = int(p.data["current-energy-efficiency"])
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target_sap_points = epc_to_sap_lower_bound(body.goal_value)
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logger.info("Creating portfolio aggregations")
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# We implement this in the simplest way possible which will be just to query the database for all
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# recommendations associated to the portfolio and then aggregate them. This is not the most efficient
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# way to do this, but it's the simplest and will be a process that we can re-use since when we change a
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# recommendation from being default to not default, we'll need to re-run this process to re-calculate the
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# the portfolion level impact
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aggregate_portfolio_recommendations(session, portfolio_id=body.portfolio_id)
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# If the gain is negative, the optimiser will return an empty solution
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optimiser = CostOptimiser(
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input_measures, min_gain=target_sap_points - current_sap_points
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)
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# Commit all changes at once
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session.commit()
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optimiser.setup()
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optimiser.solve()
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solution = optimiser.solution
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session.close()
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selected_recommendations = {r["id"] for r in solution}
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# We'll use the set of selected recommendations to filter the recommendations to upload
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property_recommendations = [
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[
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{**rec, "default": True if rec["recommendation_id"] in selected_recommendations else False}
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for rec in recommendations_by_type
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]
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for recommendations_by_type in property_recommendations
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]
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# We'll also unlist the recommendations so they're a bit easier to handle from here onwards
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property_recommendations = [
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rec for recommendations_by_type in property_recommendations for rec in recommendations_by_type
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]
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recommendations[p.id] = property_recommendations
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# Once we're done, we'll store:
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# 1) the property data
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# 2) the property details (epc)
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# 3) the recommendations
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logger.info("Uploading recommendations to the database")
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# Upload property data
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for p in input_properties:
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property_details_epc = p.get_property_details_epc(portfolio_id=body.portfolio_id,
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rating_lookup=rating_lookup)
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create_property_details_epc(session, property_details_epc)
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property_data = p.get_full_property_data()
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update_property_data(session, property_id=p.id, portfolio_id=body.portfolio_id, property_data=property_data)
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# Upload recommendations
|
||||
recommendations_to_upload = recommendations.get(p.id, [])
|
||||
|
||||
if not recommendations_to_upload:
|
||||
continue
|
||||
|
||||
# Create a plan
|
||||
new_plan_id = create_plan(
|
||||
session,
|
||||
{
|
||||
"portfolio_id": body.portfolio_id,
|
||||
"property_id": p.id,
|
||||
"is_default": True
|
||||
}
|
||||
)
|
||||
|
||||
# Upload recommendations
|
||||
uploaded_recommendation_ids = upload_recommendations(session, recommendations_to_upload, p.id)
|
||||
|
||||
# Finally, match the recommendation to the plan
|
||||
create_plan_recommendations(
|
||||
session,
|
||||
plan_id=new_plan_id,
|
||||
recommendation_ids=uploaded_recommendation_ids
|
||||
)
|
||||
|
||||
logger.info("Creating portfolio aggregations")
|
||||
# We implement this in the simplest way possible which will be just to query the database for all
|
||||
# recommendations associated to the portfolio and then aggregate them. This is not the most efficient
|
||||
# way to do this, but it's the simplest and will be a process that we can re-use since when we change a
|
||||
# recommendation from being default to not default, we'll need to re-run this process to re-calculate the
|
||||
# the portfolion level impact
|
||||
aggregate_portfolio_recommendations(session, portfolio_id=body.portfolio_id)
|
||||
|
||||
# Commit all changes at once
|
||||
session.commit()
|
||||
except Exception as e: # General exception handling
|
||||
logger.error(f"An error occurred: {e}")
|
||||
session.rollback()
|
||||
return Response(status_code=500, content="An unexpected error occurred.")
|
||||
finally:
|
||||
session.close()
|
||||
|
||||
return Response(status_code=200)
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue