u value method for floors

model_data/analysis/UvalueEstimations.py

@@ -100,7 +100,56 @@ class UvalueEstimations:
 
         :param cleaner: An instance of the EpcClean class used for cleaning data.
         """
-        pass
+        floors_columns = [
+            "local-authority", "property-type", "floor-description", "floor-energy-eff", "floor-env-eff",
+            "built-form",
+            "total-floor-area", "number-habitable-rooms", "number-heated-rooms"
+        ]
+
+        floors_df = self.data[self.data["floor-description"].str.contains("Average thermal transmittance")]
+
+        # Take just the columns we want
+        floors_df = floors_df[floors_columns]
+        floors_df["total-floor-area"] = floors_df["total-floor-area"].astype(float)
+
+        floors_df, decile_labels, decile_boundaries = self.classify_into_deciles(floors_df, "total-floor-area")
+
+        # We now get the U-values
+        floors_df = floors_df.merge(
+            pd.DataFrame(cleaner.cleaned['floor-description'])[["original_description", "thermal_transmittance"]],
+            how="left",
+            right_on="original_description",
+            left_on="floor-description"
+        )
+
+        u_value_summary = floors_df.groupby(
+            [
+                "local-authority",
+                "property-type",
+                "floor-energy-eff",
+                "floor-env-eff",
+                "built-form",
+                "number-habitable-rooms",
+                "number-heated-rooms",
+                "total-floor-area_group"
+            ],
+            observed=True
+        ).agg({"thermal_transmittance": ["median", "size"]}).reset_index()
+
+        u_value_summary.columns = [
+            "local-authority",
+            "property-type",
+            "floor-energy-eff",
+            "floor-env-eff",
+            "built-form",
+            "number-habitable-rooms",
+            "number-heated-rooms",
+            "total-floor-area_group",
+            "median_thermal_transmittance",
+            "n_samples"
+        ]
+
+        self.floors = u_value_summary
 
     @staticmethod
     def classify_into_deciles(df: pd.DataFrame, column: str) -> (pd.DataFrame, list, list):

model_data/app.py

@@ -123,13 +123,13 @@ def handler():
         [{"address1": p.address1, **p.floor} for p in input_properties]
     )
 
-    input_properties[0].data["address1"]
-    input_properties[0].data["postcode"]
-    floors_df["address1"].values[0]
-    floors_df["original_description"].values[0]
+    input_properties[2].data["address1"]
+    input_properties[2].data["postcode"]
+    floors_df["address1"].values[2]
+    floors_df["original_description"].values[2]
 
     from model_data.recommendations.FloorRecommendations import FloorRecommendations
-    self = FloorRecommendations(property_instance=input_properties[0], uvalue_estimates=uvalue_estimates)
+    self = FloorRecommendations(property_instance=input_properties[2], uvalue_estimates=uvalue_estimates)
 
     # We need to deduce a U-value for "Good" energy effieciency
 

model_data/recommendations/FloorRecommendations.py

@@ -4,6 +4,12 @@ from model_data.analysis.UvalueEstimations import UvalueEstimations
 
 
 class FloorRecommendations(BaseUtility):
+    # part L building regulations indicate that any rennovations on an existing property's walls should
+    # achieve a U-value of no higher than 0.3
+    BUILDING_REGULATIONS_PART_L_MAX_U_VALUE = 0.25
+    # We don't recommend measures that are too low because it becomes expensive, therefore we aim to avoid
+    # diminishing returns. This value should be verified with Osmosis (TODO)
+    DIMINISHING_RETURNS_U_VALUE = 0.2
 
     def __init__(self, property_instance: Property, uvalue_estimates: UvalueEstimations):
         self.property = property_instance
@@ -15,5 +21,22 @@ class FloorRecommendations(BaseUtility):
         self.recommendations = []
 
     def recommend(self):
+        is_suspended = self.property.floor["is_suspended"]
+        insulation_thickness = self.property.floor["insulation_thickness"]
+        self.property.year_built
+        self.property.data["floor-energy-eff"]
+        self.property.data["floor-env-eff"]
 
-        if self.property.floor
+        if self.property.floor["another_property_below"]:
+            # If there's another property below, it's likely impractical to recommend a floor upgrade
+            return
+
+        if is_suspended:
+            if insulation_thickness == "none":
+                uvalue = None
+            else:
+                uvalue = self.uvalue_estimates.get_estimate(
+                    component="floor",
+                    description="",
+                    thickness=insulation_thickness
+                )