debugging wood chips fuel types

backend/app/assumptions.py

@@ -86,6 +86,8 @@ DESCRIPTIONS_TO_FUEL_TYPES = {
                                                                "cop": AVERAGE_ASHP_EFFICIENCY / 100},
     "Ground source heat pump, underfloor, electric": {"fuel": "Electricity", "cop": AVERAGE_ASHP_EFFICIENCY / 100},
     "Electric ceiling heating": {"fuel": "Electricity", "cop": 1},
+    "Boiler and radiators, wood chips": {"fuel": "Wood Logs", "cop": 0.85},
+    "Oil range cooker, no cylinder thermostat": {"fuel": "Oil", "cop": 0.85},
 }
 
 # These are the measure types where if there is a ventilation recommendation, we force the inclusion of it

etl/epc/Dataset.py

@@ -212,11 +212,11 @@ class TrainingDataset(BaseDataset):
         common_cols = [[col + "_starting", col + "_ending"] for col in common_cols]
 
         self.df = self.df.loc[
-            :,
+                  :,
-            no_suffix_cols
+                  no_suffix_cols
-            + only_ending_cols
+                  + only_ending_cols
-            + [col for cols in common_cols for col in cols],
+                  + [col for cols in common_cols for col in cols],
-        ]
+                  ]
 
     def _remove_abnormal_change_in_floor_area(self):
         """
@@ -394,12 +394,13 @@ class TrainingDataset(BaseDataset):
             axis=1,
         )
 
-        roof_starting_uvalue = self.df["roof_thermal_transmittance"].fillna(
+        roof_starting_uvalue = pd.to_numeric(
-            roof_starting_uvalue
+            self.df["roof_thermal_transmittance"], errors="coerce"
-        )
+        ).fillna(roof_starting_uvalue)
-        roof_ending_uvalue = self.df["roof_thermal_transmittance_ending"].fillna(
+
-            roof_ending_uvalue
+        roof_ending_uvalue = pd.to_numeric(
-        )
+            self.df["roof_thermal_transmittance_ending"], errors="coerce"
+        ).fillna(roof_ending_uvalue)
 
         # ~~~~~~~~~~~~~~~~~~
         # Floor
@@ -459,20 +460,20 @@ class TrainingDataset(BaseDataset):
             axis=1,
         )
 
-        floor_starting_uvalue = self.df["floor_thermal_transmittance"].fillna(
+        floor_starting_uvalue = pd.to_numeric(
-            floor_starting_uvalue
+            self.df["floor_thermal_transmittance"], errors="coerce"
-        )
+        ).fillna(floor_starting_uvalue)
-        floor_ending_uvalue = self.df["floor_thermal_transmittance_ending"].fillna(
+        floor_ending_uvalue = pd.to_numeric(
-            floor_ending_uvalue
+            self.df["floor_thermal_transmittance_ending"], errors="coerce"
-        )
+        ).fillna(floor_ending_uvalue)
 
         for component in ["walls", "roof", "floor"]:
-            self.df[f"{component}_thermal_transmittance"] = self.df[
+            self.df[f"{component}_thermal_transmittance"] = pd.to_numeric(
-                f"{component}_thermal_transmittance"
+                self.df[f"{component}_thermal_transmittance"], errors="coerce"
-            ].fillna(eval(f"{component}_starting_uvalue"))
+            ).fillna(eval(f"{component}_starting_uvalue"))
-            self.df[f"{component}_thermal_transmittance_ending"] = self.df[
+            self.df[f"{component}_thermal_transmittance_ending"] = pd.to_numeric(
-                f"{component}_thermal_transmittance_ending"
+                self.df[f"{component}_thermal_transmittance_ending"], errors="coerce"
-            ].fillna(eval(f"{component}_ending_uvalue"))
+            ).fillna(eval(f"{component}_ending_uvalue"))
 
         self.df = self.df.drop(
             columns=[
@@ -521,7 +522,7 @@ 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"])
@@ -538,7 +539,7 @@ 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"])
@@ -551,7 +552,7 @@ class TrainingDataset(BaseDataset):
                     expanded_df["has_dwelling_above"]
                     == expanded_df["has_dwelling_above_ending"]
                 )
-            ]
+                ]
 
         return expanded_df
 

etl/epc/Record.py

@@ -38,6 +38,8 @@ DATA_BUCKET = os.environ.get(
     "DATA_BUCKET", "retrofit-data-dev" if ENVIRONMENT == "dev" else None
 )
 
+pd.set_option("future.no_silent_downcasting", True)
+
 
 @dataclass
 class EPCRecord:
@@ -392,7 +394,7 @@ class EPCRecord:
         floor_height_data = self.cleaning_data[
             (self.cleaning_data["property_type"] == self.prepared_epc["property-type"])
             & (self.cleaning_data["built_form"] == self.prepared_epc["built-form"])
-        ]
+            ]
         average = floor_height_data["floor_height"].mean()
         sd = floor_height_data["floor_height"].std()
         # If we're in the top 0.5 percentile of floor heights, we'll set it to the average

recommendations/Recommendations.py

@@ -744,7 +744,7 @@ class Recommendations:
             #       fairly regularly. A task has been added to planner to refactor this
             # We have observed an edge case where the fuel is described as not being community
             # but the hot water is. We handle as such
-            logger.warning("Hot water description not mapped: %s", heating_description)
+            logger.warning("Hot water description not mapped: %s", hotwater_description)
             mapped_hotwater = {"fuel": 'Unmapped', "cop": 0.9}
 
         return {