Expanding gbis eligibiity checks

etl/eligibility/Eligibility.py

@@ -352,9 +352,41 @@ class Eligibility:
         # Check if the property is suitable for cavity wall
         self.cavity_insulation()
 
-        self.gbis_warmfront = (self.cavity["suitability"]) and (
-            int(self.epc["current-energy-efficiency"]) <= 68
-        )
+        current_sap = int(self.epc["current-energy-efficiency"])
+        # We have a strict suitability check and a non-strict check
+
+        # Perfect strictness
+        if (self.cavity["type"] == "empty") and (current_sap < 69):
+            self.gbis_warmfront = {
+                "eligible": True,
+                "strict": True,
+                "message": "Perfect suitability",
+            }
+            return
+
+        # Near perfect
+        if self.cavity["suitability"] and (current_sap < 55):
+            self.gbis_warmfront = {
+                "eligible": True,
+                "strict": True,
+                "message": "Near perfect suitability",
+            }
+            return
+
+        # Suitable cavity, but high sap
+        if self.cavity["suitability"] and (current_sap >= 55):
+            self.gbis_warmfront = {
+                "eligible": True,
+                "strict": False,
+                "message": "Meets cavity, fails SAP check",
+            }
+            return
+
+        self.gbis_warmfront = {
+            "eligible": False,
+            "strict": False,
+            "message": "All conditions fail",
+        }
 
     def check_eco4_warmfront(self):
         """
@@ -388,6 +420,10 @@ class Eligibility:
         self.cavity_insulation()
         self.loft_insulation()
 
+        # We put in a placeholder when the roof is not a loft
+        if self.loft["reason"] == "roof not loft":
+            self.loft["thickness"] = 999
+
         # Case 1: No conditions meet
         if not self.cavity["suitability"] and (self.loft["thickness"] > 100) and current_sap >= 55:
             self.eco4_warmfront = {
@@ -415,7 +451,7 @@ class Eligibility:
             self.eco4_warmfront = {
                 "eligible": True,
                 "strict": True,
-                "message": "Perfect suitability",
+                "message": "Near perfect suitability",
                 "cavity_type": self.cavity["type"],
                 "loft_type": self.loft["thickness_classification"]
             }

etl/eligibility/ha_15_32/ha_analysis_batch_3.py

@@ -1270,15 +1270,6 @@ def get_epc_data(
         results_df["post_install_sap"] = None
         results_df["eligibility_classification"] = None
 
-        eco4 = asset_list[asset_list["ECO Eligibility"] == "eco4"]
-        z = results_df[results_df["row_id"].isin(eco4["asset_list_row_id"])]
-        z["walls"].value_counts()
-        z1 = z[z["walls"] == "Cavity wall, as built, no insulation"]
-        k = z1[z1["roof"] == "Pitched, 100 mm loft insulation"]
-        property_meta = asset_list[asset_list["asset_list_row_id"] == k["row_id"].values[0]].squeeze()
-        z[z["walls"] == "Cavity wall, as built, insulated"]["roof"].value_counts()
-        z[z["walls"] == "Cavity wall, as built, insulated"]["roof"].value_counts()
-
         if not scoring_df.empty:
             scoring_df = scoring_df.drop(
                 columns=[
@@ -1763,6 +1754,17 @@ def patch_cleaned(cleaned):
         ]
     )
 
+    cleaned["roof-description"].extend(
+        [
+            {'original_description': 'Pitched, 300+mm loft insulation',
+             'clean_description': 'Pitched, 300+ mm loft insulation', 'thermal_transmittance': None,
+             'thermal_transmittance_unit': None, 'is_pitched': True, 'is_roof_room': False, 'is_loft': True,
+             'is_flat': False, 'is_thatched': False, 'is_at_rafters': False, 'is_assumed': False,
+             'has_dwelling_above': False, 'is_valid': True, 'insulation_thickness': '300+'
+             }
+        ]
+    )
+
     # Patch mainheatcont-description
     cleaned["mainheatcont-description"].extend(
         [

etl/epc/Dataset.py

@@ -203,11 +203,11 @@ class TrainingDataset(BaseDataset):
         common_cols = [[col + "_starting", col + "_ending"] for col in common_cols]
 
         self.df = self.df.loc[
-            :,
-            no_suffix_cols
-            + only_ending_cols
-            + [col for cols in common_cols for col in cols],
-        ]
+                  :,
+                  no_suffix_cols
+                  + only_ending_cols
+                  + [col for cols in common_cols for col in cols],
+                  ]
 
     def _remove_abnormal_change_in_floor_area(self):
         """
@@ -509,7 +509,7 @@ class TrainingDataset(BaseDataset):
                     expanded_df["is_sandstone_or_limestone"]
                     == expanded_df["is_sandstone_or_limestone_ending"]
                 )
-            ]
+                ]
         elif component == "floor":
             expanded_df = expanded_df[
                 (expanded_df["is_suspended"] == expanded_df["is_suspended_ending"])
@@ -526,7 +526,7 @@ class TrainingDataset(BaseDataset):
                     expanded_df["is_to_external_air"]
                     == expanded_df["is_to_external_air_ending"]
                 )
-            ]
+                ]
         elif component == "roof":
             expanded_df = expanded_df[
                 (expanded_df["is_pitched"] == expanded_df["is_pitched_ending"])
@@ -539,7 +539,7 @@ class TrainingDataset(BaseDataset):
                     expanded_df["has_dwelling_above"]
                     == expanded_df["has_dwelling_above_ending"]
                 )
-            ]
+                ]
 
         return expanded_df