Implemented methodology for extracting uvalue estimate

model_data/analysis/UvalueEstimations.py

@@ -4,11 +4,11 @@ from model_data.EpcClean import EpcClean
 
 
 class UvalueEstimations:
-    def __init__(self, data):
+    def __init__(self, data: list):
         """
         Initialize the UvalueEstimations class.
 
-        :param data: The input data as a pandas DataFrame.
+        :param data: The input data as a list of dictionaries, to be converted to a dataframe
         """
         self.data = pd.DataFrame(data)
         self.walls = None

model_data/app.py

@@ -114,6 +114,7 @@ def handler():
         p.get_components(cleaner)
 
     uvalue_estimates = UvalueEstimations(data=data)
+    uvalue_estimates.get_estimates(cleaner=cleaner)
 
     # Now, given the components, we want to idenfity upgrade options
     import pandas as pd
@@ -153,7 +154,7 @@ def handler():
     # 'Flat 28, 22 Adelina Grove' 'Solid brick, as built, insulated (assumed)'
 
     from model_data.recommendations.WallRecommendations import WallRecommendations
-    self = WallRecommendations(property_instance=input_properties[6])
+    self = WallRecommendations(property_instance=input_properties[6], uvalue_estimates=uvalue_estimates)
 
     # We need to deduce a U-value for "Good" energy effieciency
 

model_data/recommendations/WallRecommendations.py

@@ -3,6 +3,8 @@ import re
 import itertools
 
 from model_data.Property import Property
+from model_data.ConservationAreaClient import ConservationAreaClient
+from model_data.analysis.UvalueEstimations import UvalueEstimations
 import pandas as pd
 from copy import deepcopy
 
@@ -197,11 +199,10 @@ class WallRecommendations:
         "solid_brick": 2,
     }
 
-    def __init__(self, property_instance: Property):
+    def __init__(self, property_instance: Property, uvalue_estimates: UvalueEstimations):
         self.property = property_instance
         self.year_built = self._year_property_was_built()
-        # TODO: Need to properly implement this
-        self.in_converation_area = False
+        self.uvalue_estimates = uvalue_estimates
 
         # Will contains a list of recommended measures
         self.recommendations = []
@@ -265,7 +266,7 @@ class WallRecommendations:
 
             ewi_parts = [
                 part for part in wall_parts if part["type"] == "external_wall_insulation"
-            ] if not self.in_converation_area else []
+            ] if self.property.in_conservation_area == ConservationAreaClient.IN_CONSERVATION_AREA else []
 
             iwi_parts = [part for part in wall_parts if part["type"] == "internal_wall_insulation"]
 
@@ -319,11 +320,61 @@ class WallRecommendations:
 
         if is_solid_brick and insulation_thickness == "average" and wall_energy_efficiency in ["Good", "Average"]:
 
-            if self.in_converation_area:
+            # Get uvalue estimate
+            uvalue_estimate = self._get_walls_uvalue_estimate()
+
+            if self.property.in_conservation_area == ConservationAreaClient.IN_CONSERVATION_AREA:
                 blah
 
         raise NotImplementedError("Not implemented yet")
 
+    def _get_walls_uvalue_estimate(self):
+
+        """
+        Wrapper function which contains the methodology to extract a property's walls u-value estimate
+        when we don't have a true value and if we can't base our assumption off of the material
+        :return:
+        """
+
+        total_floor_area_group_decile = self.uvalue_estimates.classify_decile_newvalues(
+            decile_boundaries=self.uvalue_estimates.walls_decile_data["decile_boundaries"],
+            decile_labels=self.uvalue_estimates.walls_decile_data["decile_labels"],
+            new_values=[float(self.property.data["total-floor-area"])],
+        )[0]
+
+        u_value_estimate = self.uvalue_estimates.walls[
+            (self.uvalue_estimates.walls["local-authority"] == self.property.data["local-authority"]) &
+            (self.uvalue_estimates.walls["property-type"] == self.property.data["property-type"]) &
+            (self.uvalue_estimates.walls["walls-energy-eff"] == self.property.data["walls-energy-eff"]) &
+            (self.uvalue_estimates.walls["walls-env-eff"] == self.property.data["walls-env-eff"]) &
+            (self.uvalue_estimates.walls["total-floor-area_group"] == total_floor_area_group_decile)
+            ]
+
+        # Because of how spuriously populated the data is for number-habitable-rooms and number-heated-rooms,
+        # we will try and filter on these to see if we get a result
+
+        habitable_rooms_filter = (
+            self.uvalue_estimates.walls["number-habitable-rooms"] == self.property.data["number-habitable-rooms"]
+        )
+
+        if any(habitable_rooms_filter):
+            u_value_estimate = u_value_estimate[habitable_rooms_filter]
+
+        heated_rooms_filter = (
+            self.uvalue_estimates.walls["number-heated-rooms"] == self.property.data["number-heated-rooms"]
+        )
+
+        if any(heated_rooms_filter):
+            u_value_estimate = u_value_estimate[heated_rooms_filter]
+
+        if u_value_estimate.empty:
+            raise ValueError("No U-value estimate found for the given property")
+
+        # It's possible for us to have multiple rows if we didn't do a habitable/heated rooms filter so we
+        # average
+
+        return u_value_estimate["median_thermal_transmittance"].mean()
+
     @staticmethod
     def _get_recommended_part(part, selected_depth, new_u_value):
         """