minor tweaks and bug fixes for properties that failed diagnostic tests

etl/customers/peabody/Nov 2025 Consulting Project/f_diagnostics.py

@@ -23,9 +23,10 @@ scenario_sap_targets = {
     859: 69,
 }
 
+problems = []
 for scenario_id, scenario_name in scenario_names.items():
     # Read in the recommended measures
-
+    print("Reading")
     df = pd.read_excel(
         f"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
         f"{scenario_name}.xlsx"
@@ -34,30 +35,98 @@ for scenario_id, scenario_name in scenario_names.items():
     # find properties that are below the scenario sap target, but have no recommended measures
     df["below_scenario_target"] = df["current_sap_points"] < scenario_sap_targets[scenario_id]
     df["no_recommended_measures"] = df["sap_points"] == 0
+    df["zero_cost"] = df["total_retrofit_cost"] == 0
+    df["sap_points_above_zero"] = df["sap_points"] > 0
+
+    # Also look for zero cost and SAP points > 0
 
     problematic_properties = df[
-        df["below_scenario_target"] & df["no_recommended_measures"]
+        (df["below_scenario_target"] & df["no_recommended_measures"])
-        ]
+    ].copy()
+
+    if scenario_sap_targets[scenario_id] == 81:
+        problematic_properties = problematic_properties[problematic_properties["property_type"] != "Flat"]
+
+    zero_cost_above_zero_sap = df[
+        (df["sap_points_above_zero"] & df["zero_cost"])
+    ].copy()
 
     # show all columns
     # Source - https://stackoverflow.com/a
     # Posted by YOLO, modified by community. See post 'Timeline' for change history
     # Retrieved 2026-01-06, License - CC BY-SA 4.0
 
-    pd.set_option('display.max_rows', 500)
+    # pd.set_option('display.max_rows', 500)
-    pd.set_option('display.max_columns', 500)
+    # pd.set_option('display.max_columns', 500)
-    pd.set_option('display.width', 1000)
+    # pd.set_option('display.width', 1000)
-    problematic_properties.head(len(problematic_properties))
+    # problematic_properties.head(len(problematic_properties))
 
-    #
+    print(f"We have {len(problematic_properties)} problematic properties for scenario {scenario_name} ({scenario_id})")
+    print(f"We have {len(zero_cost_above_zero_sap)} zero cost properties for scenario {scenario_name} ({scenario_id})")
+
+    problems.append(problematic_properties)
+    problems.append(zero_cost_above_zero_sap)
+
+    # plan_input = [
+    #     {
+    #         "uprn": 100022725126,
+    #         "address": "FLAT 5 Daveys Court",
+    #         "postcode": "WC2N 4BW"
+    #     }
+    # ]
+
+    # plan_input = [
+    #     {
+    #         "uprn": 100120966352,
+    #         "address": "FLAT 11 Kingsgate",
+    #         "postcode": "OX18 2BP"
+    #     }
+    # ]
 
     plan_input = [
         {
-            "uprn": 100022725126,
+            "uprn": 200003371857,
-            "address": "FLAT 5 Daveys Court",
+            "postcode": "SE1 5SJ",
-            "postcode": "WC2N 4BW"
+            "address": "39 BUTTERMERE CLOSE",
         }
     ]
 
+all_problems = pd.concat(problems)
+all_problems = all_problems.drop_duplicates(subset=["uprn"])
+
+sal = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/20251213 Model "
+    "data.xlsx",
+    sheet_name="Standardised Asset List"
+)
+sal2 = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/20260105 - additional "
+    "UPRNS.xlsx",
+    sheet_name="Standardised Asset List"
+)
+
+sal = pd.concat([sal, sal2])
+
+retry = sal[sal["epc_os_uprn"].isin(all_problems["uprn"])]
+
+# Store
+retry.to_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
+    "d_problematic_properties_to_review_20260106.xlsx",
+    sheet_name="Standardised Asset List",
+    index=False
+)
+
+# Delete associated plans
+# 1) Get the property IDs for these UPRNS, for this portfolio
+portfolio_id = 419
+uprns = retry
+
+# TODO: Delete all plans for these properties and re-build
 # Plan notes:
 # UPRN: 5870109770, property ID: 281244 - need to delete and re-build all scenarios
+# UPRN: 100022725126, property ID: 283781 - need to delete and re-build all scenarios
+
+
+# Bugs:
+12156800

recommendations/HeatingRecommender.py

@@ -170,9 +170,14 @@ class HeatingRecommender:
         # If the property has community heating heaters in place, we don't recommend HHRSH
         has_community_heating = self.property.main_fuel["is_community"]
 
-        hhr_suitable = hhr_suitable and (
+        # If the property currently has electric underfloor heating, we allow this if there is elecric immersion
-            "underfloor heating" not in self.property.main_heating["clean_description"]
+        # hot water heating
-        ) and not has_community_heating
+        underfloor_not_an_issue = True
+        if self.property.main_heating["has_electric_underfloor_heating"]:
+            if self.property.hotwater["heater_type"] != "electric immersion":
+                underfloor_not_an_issue = False
+
+        hhr_suitable = hhr_suitable and not has_community_heating and underfloor_not_an_issue
 
         # If the property has a ground source heat pump, or air source heat pump, we don't recommend HHRSH
 

recommendations/WindowsRecommendations.py

@@ -86,9 +86,17 @@ class WindowsRecommendations:
 
         # We scale the number of windows based on the proportion of existing glazing
         if self.property.data["multi-glaze-proportion"] != "":
-            n_windows_scalar = 1 - (
+
-                int(self.property.data["multi-glaze-proportion"]) / 100
+            if (self.property.windows["clean_description"] == "Some double glazing") and (
-            )
+                self.property.data["windows-energy-eff"] == "Very Poor") and (
+                self.property.data["multi-glaze-proportion"] == 100
+            ):
+                # In this case, we assume all of the dinwos need replacing
+                n_windows_scalar = 1
+            else:
+                n_windows_scalar = 1 - (
+                    int(self.property.data["multi-glaze-proportion"]) / 100
+                )
         else:
             n_windows_scalar = self.COVERAGE_MAP.get(
                 self.property.windows["glazing_coverage"], 1
@@ -97,6 +105,9 @@ class WindowsRecommendations:
         number_of_windows *= n_windows_scalar
         number_of_windows = np.ceil(number_of_windows)
 
+        # Handle edge case - prevent number of windows 0
+        number_of_windows = max(1, number_of_windows)
+
         # We then price the job based on the number of windows that there are
         cost_result = self.costs.window_glazing(
             number_of_windows=number_of_windows,