added measure matrix code

backend/Outputs.py

@@ -1,5 +1,7 @@
+import pandas as pd
 from sqlalchemy.orm import sessionmaker
 
+from backend.app.utils import sap_to_epc
 from backend.app.db.connection import db_engine
 from backend.app.db.models.portfolio import PropertyModel, PropertyDetailsEpcModel
 from backend.app.db.models.recommendations import Recommendation, Plan, PlanRecommendations
@@ -8,6 +10,37 @@ from backend.app.db.models.recommendations import Recommendation, Plan, PlanReco
 class Outputs:
     FORMATS = ["mds"]
 
+    MDS_MEASURE_MAPPING = {
+        "external_wall_insulation": "EWI (Trad Const)",
+        "cavity_wall_insulation": "CWI",
+        "loft_insulation": "LI",
+        "party_wall_insulation": "Party Wall Insu",
+        "internal_wall_insulation": "IWI (POA - Prov Sum Only)",
+        "suspended_floor_insulation": "U/F Insu (Manual install)",
+        "solid_floor_insulation": "Solid floor insl (Out of scope - Prov sum only)",
+        "air_source_heat_pump": "ASHP Htg",
+        "ground_source_heat_pump": "GSHP Htg",
+        "shared_ground_loops": "Shared ground loops",
+        "communal_heat_networks": "Communal heat networks",
+        "district_heating_networks": "District heating networks",
+        "high_heat_retention_storage_heaters": "Elec Storage Htrs (Out of scope -Prov sum only)",
+        "low_energy_lighting": "Low Energy Bulbs",
+        "cylinder_insulation": "Cyl Insulation",
+        "smart_controls": "Smart controls",
+        "zone_controls": "Zone controls",
+        "trvs": "Upgrade TRV's",
+        "solar_pv": "Solar PV",
+        "solar_thermal": "Solar Thermal",
+        "double_glazing": "Double Glazing (POA - Prov sum only)",
+        "draught_proofing": "Draught Proofing",
+        "mechanical_ventilation": "Ventilation upgrade",
+        "gas_boiler": "Gas Boiler Replacement",
+        "flat_roof_insulation": "Flat roof (Out of scope - prov sum only)",
+        "room_in_roof_insulation": "RIR (POA - Prov sum only)",
+        "ev_charging": "EV Charging",
+        "battery": "Battery"
+    }
+
     def __init__(self, format, portfolio_id):
         """
         This class handles the creation of standard outputs for the backend. For example, creation of
@@ -50,7 +83,7 @@ class Outputs:
 
     def get_plans_from_db(self):
 
-        plans_query = self.session.query(Plan).all()
+        plans_query = self.session.query(Plan).filter(Plan.portfolio_id == self.portfolio_id).all()
         # Transform plans data to include all fields dynamically
         plans_data = [
             {col.name: getattr(plan, col.name) for col in Plan.__table__.columns}
@@ -87,6 +120,41 @@ class Outputs:
 
         return recommendations_data
 
+    def make_mds_measure_matrix(self, scenario_recommendations):
+        all_measures = list(self.MDS_MEASURE_MAPPING.values())
+
+        # Collect rows in a list
+        rows = []
+
+        # Populate the rows list
+        for idx, row in scenario_recommendations.iterrows():
+            property_id = row["property_id"]
+            measure_type = row["measure_type"]
+
+            # Get the label for the current type
+            measure_label = self.MDS_MEASURE_MAPPING.get(measure_type, None)
+
+            # If the property_id already exists in the collected rows, update it
+            existing_row = next((item for item in rows if item["property_id"] == property_id), None)
+            if existing_row is None:
+                # Create a new row if the property_id doesn't exist
+                new_row = {measure: None for measure in all_measures}
+                new_row["property_id"] = property_id
+                rows.append(new_row)
+            else:
+                new_row = existing_row
+
+            # Set the corresponding measure label in the row
+            new_row[measure_label] = measure_label
+
+        # Convert the list of dictionaries to a DataFrame
+        matrix = pd.DataFrame(rows)
+
+        # Reset the index for cleanliness
+        matrix.reset_index(drop=True, inplace=True)
+
+        return matrix
+
     def export_mds(self):
         """
         This function will export the data in the MDS format
@@ -115,12 +183,93 @@ class Outputs:
         properties_data = self.get_properties_from_db()
 
         plans_data = self.get_plans_from_db()
-
         plan_ids = [plan['id'] for plan in plans_data]
 
         recommendations_data = self.get_recommendations_from_db(plan_ids)
         self.session.close()
 
+        # Convert these tables to dataframes
+        properties_df = pd.DataFrame(properties_data)
+        plans_df = pd.DataFrame(plans_data)
+        recommendations_df = pd.DataFrame(recommendations_data)
+
+        scenario_ids = plans_df["scenario_id"].unique()
+
+        # We start to create the MDS sheet
+        mds = properties_df[
+            [
+                "property_id",
+                "address",
+                "postcode",
+                "uprn",
+                "current_epc_rating",
+                "current_sap_points",
+                # TODO: Need to add current heat demand
+                "property_type",
+                "built_form",
+                "total_floor_area",
+                "walls",
+                "tenure",
+                "mainfuel",
+                # TODO: For estimated bill, this should probably be without the cost of appliances
+            ]
+        ].copy().rename(
+            columns={
+                "address": "Address",
+                "postcode": "Postcode",
+                "uprn": "UPRN",
+                "current_epc_rating": "Pre EPC",
+                "current_sap_points": "EPC Source",
+                # TODO: Need to add current heat demand
+                "property_type": "Property Type",
+                "built_form": "Built Form",
+                "total_floor_area": "Floor area m2 (If known)",
+                "walls": "Wall Type (Mandatory field)",
+                "tenure": "Tenure",
+                "mainfuel": "Existing Fuel Type"
+                # TODO: For estimated bill, this should probably be without the cost of appliances
+            }
+        )
+
+        # TODO - format
+        #       1) property type
+        #       2) walls
+        #       3) tenure
+        #       4) mainfuel
+        #       5) Epc Rating
+
+        mds_output_by_scenario = {}
+        for scenario_id in scenario_ids:
+            scenario_recommendations = recommendations_df[recommendations_df["Scenario ID"] == scenario_id]
+
+            # For each measure, we create the measure matrix
+            scenario_measure_matrix = self.make_mds_measure_matrix(scenario_recommendations)
+
+            # Calculate the predicted impact on: SAP, heat demand, bills, kwh
+            recommendation_impacts = scenario_recommendations.groupby("property_id")[
+                ["sap_points", "heat_demand", "kwh_savings", "energy_cost_savings"]
+            ].sum().reset_index()
+
+            scenario_mds = mds.merge(
+                recommendation_impacts, how="left", on="property_id"
+            )
+            # If we have no recommendations, sap_points, kwh_savings, head_demand will be NaN
+            scenario_mds.fillna(0, inplace=True)
+            scenario_mds["Post SAP"] = scenario_mds["EPC Source"] + scenario_mds["sap_points"]
+            # Round Post SAP down to the nearest integer
+            scenario_mds["Post SAP"] = scenario_mds["Post SAP"].apply(lambda x: int(x))
+            scenario_mds["Post EPC"] = scenario_mds["Post SAP"].apply(lambda x: sap_to_epc(x))
+
+            # TODO: Post heat demand
+
+            scenario_mds = scenario_mds.rename(
+                columns={
+                    "sap_points": "Predicted SAP Points",
+                    "kwh_savings": "Energy Saving (Kwh)",
+                    "energy_cost_savings": "Bill Reduction (£ per yr)"
+                }
+            )
+
     def export(self):
         """
         This function will export the data in the required format