Adding temp portfolio id

backend/app/plan/router.py

@@ -166,7 +166,9 @@ async def trigger_plan(body: PlanTriggerRequest):
             p.get_components(cleaned, photo_supply_lookup, floor_area_decile_thresholds)
 
             recommender = Recommendations(property_instance=p, materials=materials)
-            property_recommendations, property_representative_recommendations = recommender.recommend()
+            # TODO: portfolio id as an input is temp
+            print("DELETE PORTFOLIO ID AS AN INPUT!!")
+            property_recommendations, property_representative_recommendations = recommender.recommend(body.portfolio_id)
 
             if not property_recommendations:
                 continue

etl/customers/slide_utils.py

@@ -1,5 +1,5 @@
 import os
-from pptx.enum.text import PP_ALIGN
+from pptx.enum.text import PP_ALIGN  # NOQA
 from pptx import Presentation
 from pptx.util import Inches, Pt
 import matplotlib.pyplot as plt
@@ -8,6 +8,7 @@ from sqlalchemy.sql import true
 from backend.app.db.utils import row2dict
 from backend.app.db.models.portfolio import PropertyModel, PropertyDetailsEpcModel
 from backend.app.db.models.recommendations import Recommendation
+from backend.app.db.models.recommendations import Plan
 
 EPC_COLOURS = {
     "A": "#028051",
@@ -70,6 +71,23 @@ def get_property_details_by_portfolio_id(session: Session, portfolio_id: int):
     return property_details_dict
 
 
+def get_plan_by_portfolio_id(session: Session, portfolio_id: int):
+    """
+    This function retrieves all plans associated with a given portfolio_id.
+
+    :param session: The SQLAlchemy session used to execute the query.
+    :param portfolio_id: The ID of the portfolio for which to retrieve plans.
+    :return: A list of dictionaries, where each dictionary represents a plan.
+             Returns an empty list if no plans are found.
+    """
+    plans = session.query(Plan).filter(Plan.portfolio_id == portfolio_id).all()
+
+    # Convert the SQLAlchemy objects to dictionaries
+    plans_dict = [row2dict(plan) for plan in plans] if plans else []
+
+    return plans_dict
+
+
 def plot_epc_distribution(df, customer_key, title='Your Units', background_color='white', bar_height=0.4, font_size=15):
     """
     Plots a horizontal bar chart of EPC rating distribution with adjustable bar thickness and text sizes.

etl/customers/urban_splash/slides.py

@@ -11,6 +11,7 @@ from sqlalchemy.orm import sessionmaker
 from etl.customers.slide_utils import (
     plot_epc_distribution,
     get_property_details_by_portfolio_id,
+    get_plan_by_portfolio_id,
     get_properties_with_default_recommendations,
     create_powerpoint
 )
@@ -37,6 +38,9 @@ def app():
     property_details = get_property_details_by_portfolio_id(session, PORTFOLIO_ID)
     property_details_df = pd.DataFrame(property_details)
 
+    plans = get_plan_by_portfolio_id(session, PORTFOLIO_ID)
+    plans_df = pd.DataFrame(plans)
+
     # Unnest the recommendations. Each recommendation is a list of dictionaries
     recommendations_exploded = properties_df["recommendations"].explode().tolist()
     recommendations_df = pd.DataFrame([r for r in recommendations_exploded if not pd.isnull(r)])
@@ -103,8 +107,13 @@ def app():
         property_details_df["co2_emissions"].mean()
     )
 
-    # Valuation: upper and lower bounds - TODO!
-    min_valuation, max_valuation, average_valuation = (0, 0, 0)
+    # Valuation: upper and lower bounds and average - take positive values in case we have just a sample
+    valuation_df = properties_df[properties_df["current_valuation"] > 0]
+    min_valuation, max_valuation, average_valuation = (
+        valuation_df["current_valuation"].min(),
+        valuation_df["current_valuation"].max(),
+        valuation_df["current_valuation"].median()
+    )
 
     recommendations_df.keys()
 
@@ -134,10 +143,13 @@ def app():
     measures = "Electrical heating system upgrades & heating controls and Hot water system improvements"
 
     # Per property
+    # Take positive entries just in case we we have a sample
+    valuation_impact_df = plans_df[plans_df["property_id"].isin(units_hitting_target["property_id"])]
+    valuation_impact_df = valuation_impact_df[valuation_impact_df["valuation_increase_lower_bound"] > 0]
     min_valuation_impact, max_valuation_impact, average_valuation_impact = (
-        units_hitting_target["total_valuation_impact"].min(),
-        units_hitting_target["total_valuation_impact"].max(),
-        units_hitting_target["total_valuation_impact"].mean()
+        valuation_impact_df["valuation_increase_lower_bound"].median(),
+        valuation_impact_df["valuation_increase_upper_bound"].median(),
+        valuation_impact_df["valuation_increase_average"].median()
     )
 
     # Bill savings per property

recommendations/Recommendations.py

@@ -45,7 +45,7 @@ class Recommendations:
         self.heating_recommender = HeatingRecommender(property_instance=property_instance)
         self.hotwater_recommender = HotwaterRecommendations(property_instance=property_instance)
 
-    def recommend(self):
+    def recommend(self, portfolio_id):
 
         """
         This method runs the recommendations for the individual measures and then appends them to a list for output
@@ -59,21 +59,23 @@ class Recommendations:
         phase = 0
 
         print("WALL RECOMMENDATIONS HAVE BEEN COMMENTED OUT TEMPORARILY - ADD ME BACK IN")
-        # # Building Fabric
-        # self.wall_recomender.recommend(phase=phase)
-        # if self.wall_recomender.recommendations:
-        #     property_recommendations.append(self.wall_recomender.recommendations)
-        #     phase += 1
-        #
-        # # Ventilation recommendations
-        # # We only produce a ventilation recommendation if the property is recommended to have wall or roof insulation
-        # # We will not attribute a SAP impact to the ventilation recommendation, since we've seen that this has no
-        # # real impact on the SAP score. Therefore, we don't need to include phasing for ventilation. If we have any
-        # # wall or roof recommendations, we will ensure that ventilation is included in the simulation
-        # if self.wall_recomender.recommendations or self.roof_recommender.recommendations:
-        #     self.ventilation_recomender.recommend()
-        #     if self.ventilation_recomender.recommendation:
-        #         property_recommendations.append(self.ventilation_recomender.recommendation)
+        if portfolio_id != 66:
+            # Building Fabric
+            self.wall_recomender.recommend(phase=phase)
+            if self.wall_recomender.recommendations:
+                property_recommendations.append(self.wall_recomender.recommendations)
+                phase += 1
+
+            # Ventilation recommendations
+            # We only produce a ventilation recommendation if the property is recommended to have wall or roof
+            # insulation
+            # We will not attribute a SAP impact to the ventilation recommendation, since we've seen that this has no
+            # real impact on the SAP score. Therefore, we don't need to include phasing for ventilation. If we have any
+            # wall or roof recommendations, we will ensure that ventilation is included in the simulation
+            if self.wall_recomender.recommendations or self.roof_recommender.recommendations:
+                self.ventilation_recomender.recommend()
+                if self.ventilation_recomender.recommendation:
+                    property_recommendations.append(self.ventilation_recomender.recommendation)
 
         self.roof_recommender.recommend(phase=phase)
         if self.roof_recommender.recommendations: