Added plotting functions

model_data/app.py

@@ -114,15 +114,10 @@ def handler():
     for p in input_properties:
         p.get_components(cleaner)
 
+    # TODO: Add property age band into this
     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
-    floors_df = pd.DataFrame(
-        [{"address1": p.address1, **p.floor} for p in input_properties]
-    )
-
     input_properties[4].data["address1"]
     input_properties[4].data["postcode"]
     floors_df["address1"].values[4]
@@ -136,8 +131,15 @@ def handler():
     df["property-type"].unique()
 
     from model_data.recommendations.WallRecommendations import WallRecommendations
+    all_res = []
+    for p in input_properties:
+        inst = WallRecommendations(property_instance=p, uvalue_estimates=uvalue_estimates)
+        inst.recommend()
+        n_recs = len(inst.recommendations)
+        all_res.append(n_recs)
+
     self = WallRecommendations(property_instance=input_properties[2], uvalue_estimates=uvalue_estimates)
-    input_properties[2].walls
+    input_properties[6].walls
     self.recommend()
     df = pd.DataFrame(self.recommendations[0]["parts"])
     recommendations = pd.DataFrame(self.recommendations)

model_data/plotting/plotting_functions.py

@@ -0,0 +1,40 @@
+import pandas as pd
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+
+def create_heatmap_plots(data, response_var, pivot_var1, pivot_var2, order1=None, order2=None):
+    """
+    Create a heatmap plot based on a list of data and given variables.
+
+    :param data: List of dictionaries, input data.
+    :param response_var: String, response variable to be plotted.
+    :param pivot_var1: String, first pivot variable to be used in the plot.
+    :param pivot_var2: String, second pivot variable to be used in the plot.
+    :param order1: List, the order of categories for pivot_var1. Optional.
+    :param order2: List, the order of categories for pivot_var2. Optional.
+
+    Returns:
+        None. Displays the generated plot.
+    """
+
+    # Create a DataFrame from your list of dictionaries
+    df = pd.DataFrame(data)
+
+    # Convert the response variable column to float type if it's not already
+    df[response_var] = df[response_var].astype(float)
+
+    # Create a pivot table
+    pivot = df.pivot_table(index=pivot_var1, columns=pivot_var2, values=response_var)
+
+    # If an order is provided, reorder the pivot table
+    if order1 is not None:
+        pivot = pivot.reindex(order1)
+    if order2 is not None:
+        pivot = pivot[order2]
+
+    # Plot the heatmap
+    plt.figure(figsize=(10, 6))
+    sns.heatmap(pivot, annot=True, fmt=".2f", cmap='coolwarm')
+    plt.title(f"Heatmap of {response_var} by {pivot_var1} and {pivot_var2}")
+    plt.show()

model_data/recommendations/WallRecommendations.py

@@ -291,6 +291,9 @@ class WallRecommendations(BaseUtility):
                 self.find_insulation(u_value)
                 return
 
+            # If the u-value is within regulations, we don't do anything
+            return
+
         raise NotImplementedError("Not implemented yet")
 
     def _find_insulation(self, parts, u_value):

model_data/requirements.txt

@@ -13,4 +13,5 @@ dbfread
 pyproj
 pint
 geopandas
-mip
+mip
+seaborn