Added UvalueEstimations documentation

model_data/analysis/UvalueEstimations.py

@@ -4,15 +4,34 @@ from model_data.EpcClean import EpcClean
 
 
 class UvalueEstimations:
-
     def __init__(self, data):
+        """
+        Initialize the UvalueEstimations class.
+
+        :param data: The input data as a pandas DataFrame.
+        """
         self.data = pd.DataFrame(data)
         self.walls = None
         self.walls_decile_data = {}
         self.roofs = None
         self.floors = None
 
+    def get_estimates(self, cleaner: EpcClean):
+        """
+        Calculate U-value estimates for walls, roofs, and floors.
+
+        :param cleaner: An instance of the EpcClean class used for cleaning data.
+        """
+        self.set_walls(cleaner)
+        self.set_roofs(cleaner)
+        self.set_floors(cleaner)
+
     def set_walls(self, cleaner: EpcClean):
+        """
+        Set U-value estimates for walls.
+
+        :param cleaner: An instance of the EpcClean class used for cleaning data.
+        """
         walls_columns = [
             "local-authority", "property-type", "walls-description", "walls-energy-eff", "walls-env-eff",
             "total-floor-area", "number-habitable-rooms", "number-heated-rooms"
@@ -65,21 +84,35 @@ class UvalueEstimations:
             "decile_boundaries": decile_boundaries
         }
 
+    def set_roofs(self, cleaner: EpcClean):
+        """
+        Set U-value estimates for roofs.
+
+        :param cleaner: An instance of the EpcClean class used for cleaning data.
+        """
+        pass
+
+    def set_floors(self, cleaner: EpcClean):
+        """
+        Set U-value estimates for floors.
+
+        :param cleaner: An instance of the EpcClean class used for cleaning data.
+        """
+        pass
+
     @staticmethod
     def classify_into_deciles(df: pd.DataFrame, column: str) -> (pd.DataFrame, list, list):
         """
         Break a column in a Pandas DataFrame into deciles and classify new values into the existing deciles.
 
-        Args:
-            df: The input Pandas DataFrame.
-            column: The column name to break into deciles.
-            new_values: A list of new values to classify.
-
-        Returns:
-            A list of classifications for the new values.
+        :param df: The input Pandas DataFrame.
+        :param column: The column name to break into deciles.
 
+        :return: A tuple containing:
+            - The DataFrame with the decile group column.
+            - The list of decile labels.
+            - The list of decile boundaries.
         """
-
         # Calculate decile boundaries
         decile_boundaries = np.percentile(df[column], np.arange(0, 101, 10))
 
@@ -94,6 +127,15 @@ class UvalueEstimations:
 
     @staticmethod
     def classify_decile_newvalues(decile_boundaries, decile_labels, new_values: list) -> list:
+        """
+        Classify new values into existing deciles based on decile definitions.
+
+        :param decile_boundaries: The list of decile boundaries.
+        :param decile_labels: The list of decile labels.
+        :param new_values: A list of new values to classify.
+
+        :return: The classifications for the new values as a list.
+        """
         # Classify new values based on decile definitions
         classifications = pd.cut(new_values, bins=decile_boundaries, labels=decile_labels, include_lowest=True)
         return classifications.tolist()

model_data/app.py

@@ -11,6 +11,7 @@ from epc_api.client import EpcClient
 from model_data.downloader import pagenated_epc_download
 from model_data.EpcClean import EpcClean
 from model_data.OpenUprnClient import OpenUprnClient
+from model_data.analysis.UvalueEstimations import UvalueEstimations
 
 LAND_REGISTRY_PATHS = [
     os.path.abspath(os.path.dirname(__file__)) + "/model_data/local_data/pp-monthly-update-new-version.csv",
@@ -112,6 +113,8 @@ def handler():
     for p in input_properties:
         p.get_components(cleaner)
 
+    uvalue_estimates = UvalueEstimations(data=data)
+
     # Now, given the components, we want to idenfity upgrade options
     import pandas as pd
     walls_df = pd.DataFrame(