added missing windows age extraction

etl/customers/aiha/xml_extraction.py

@@ -113,6 +113,7 @@ def main():
 
     # TODO: Need AIH001-02 9C Clapton Common
     # TODO: Check which properties are in a conservation area
+    # TODO: AIH001-16 - Is the loft insulation suitable (already has 100mm in the RIR)
 
     recommended_measures = [
         {
@@ -560,6 +561,7 @@ def main():
                 {
                     "measure": "Loft Insulation",
                     "description": "300mm loft insulation",
+                    "floor_area": 20.92,  # Based on floor area of RIR
                     "sap_points": 1,
                     "ending_sap": 65,
                 },
@@ -616,6 +618,27 @@ def main():
                     "ending_sap": 78,
                 }
             ]
+        },
+        {
+            "survey_key": "AIH001-18",
+            "starting_sap": 58,
+            "recommended_measures": [],
+
+        },
+        {
+            "survey_key": "AIH001-19",
+            "starting_sap": 76,
+            "recommended_measures": []
+        },
+        {
+            "survey_key": "AIH001-20",
+            "starting_sap": 82,
+            "recommended_measures": []
+        },
+        {
+            "survey_key": "AIH001-21",
+            "starting_sap": 53,
+            "recommended_measures": []
         }
     ]
 
@@ -648,6 +671,7 @@ def main():
         {'item': '2x DMEV fans', 'unit_price': 1070, 'unit': 'unit'},
         {'item': '1.6kWp Solar PV system', 'unit_price': 3040, 'unit': 'unit_needs_scaffolding'},
         {'item': '2kWp Solar PV system', 'unit_price': 3201, 'unit': 'unit_needs_scaffolding'},
+        {'item': '2.4kWp Solar PV system', 'unit_price': 3363, 'unit': 'unit_needs_scaffolding'},
         {'item': '3.2kWp Solar PV system', 'unit_price': 3686, 'unit': 'unit_needs_scaffolding'},
         {'item': '4kWp Solar PV system', 'unit_price': 4009, 'unit': 'unit_needs_scaffolding'},
         {'item': '5.6kWp Solar PV system', 'unit_price': 5015, 'unit': 'unit_needs_scaffolding'},
@@ -690,8 +714,14 @@ def main():
         total_cost = survey.get("total_cost", 0)
 
         for measure in survey.get("recommended_measures", []):
+            # Include hlp and floor_area for each measure if available
+            hlp = measure.get("hlp", None)
+            floor_area = measure.get("floor_area", None)
+
             normalized_measures.append({
                 "survey_key": survey_key,
+                "hlp": hlp,
+                "floor_area": floor_area,
                 "starting_sap": starting_sap,
                 "measure": measure["measure"],
                 "description": measure.get("description", ""),
@@ -712,16 +742,38 @@ def main():
         fill_value=None
     ).reset_index()
 
+    measures_columns = [x for x in pivoted_measures.columns if x not in ["survey_key"]]
+    # We add a "Cost of" column for each measure
+    for measure in measures_columns:
+        pivoted_measures[f"Cost of {measure}"] = None
+
+    pivoted_floor_area = measures_df.pivot_table(
+        index="survey_key",
+        columns="measure",
+        values="floor_area",
+        aggfunc="first"  # Use 'first' since each measure should only appear once per survey_key
+    ).add_prefix("floor_area - ").reset_index()
+
+    pivoted_hlp = measures_df.pivot_table(
+        index="survey_key",
+        columns="measure",
+        values="hlp",
+        aggfunc="first"
+    ).add_prefix("hlp - ").reset_index()
+
+    # Merge hlp and floor_area data
+    pivoted_measures = pivoted_measures.merge(pivoted_hlp, on="survey_key", how="left")
+    pivoted_measures = pivoted_measures.merge(pivoted_floor_area, on="survey_key", how="left")
+
     # Step 3: Calculate the total sap points and total cost for each survey.
-    sap_cost_totals = measures_df.groupby("survey_key").agg(
+    totals = measures_df.groupby("survey_key").agg(
         total_sap_points=("sap_points", "sum"),
-        total_cost_of_measures=("measure_cost", "sum")
     ).reset_index()
 
     # Merge total sap points into the pivoted measures.
-    pivoted_measures = pd.merge(pivoted_measures, sap_cost_totals, on="survey_key", how="left")
-    pivoted_measures["Cost Contingency"] = pivoted_measures["total_cost_of_measures"] * CONTINGENCY_RATE
-    pivoted_measures["Total Cost"] = pivoted_measures["total_cost_of_measures"] + pivoted_measures["Cost Contingency"]
+    pivoted_measures = pd.merge(pivoted_measures, totals, on="survey_key", how="left")
+    # pivoted_measures["Cost Contingency"] = pivoted_measures["total_cost_of_measures"] * CONTINGENCY_RATE
+    # pivoted_measures["Total Cost"] = pivoted_measures["total_cost_of_measures"] + pivoted_measures["Cost Contingency"]
 
     # Step 4: Extract starting SAP for each survey key.
     starting_sap_df = measures_df.drop_duplicates(subset=["survey_key"])[["survey_key", "starting_sap"]]
@@ -738,5 +790,10 @@ def main():
         result_df, how="left", on="survey_key"
     )
 
+    final_measures.to_csv("/Users/khalimconn-kowlessar/Documents/hestia/Customers/AIHA/Measures packages.csv")
+
+    # Store costs
+    pricing_data.to_csv("/Users/khalimconn-kowlessar/Documents/hestia/Customers/AIHA/Pricing data.csv")
+
 # if __name__ == "__main__":
 #     main()

etl/xml_survey_extraction/XmlParser.py

@@ -784,6 +784,7 @@ class XmlParser:
 
         glazing_type_lookup = {
             "ND": "Single glazing",
+            "1": "double glazing installed before 2002",
             "2": "double glazing installed during or after 2002",
             "3": "double glazing, unknown install date",
             "5": "Single glazing",