debugging descriptions

backend/app/plan/router.py

@@ -729,6 +729,129 @@ async def trigger_plan(body: PlanTriggerRequest):
             scoring_epcs.extend(property_instance.updated_simulation_epcs)
             recommendations[property_id] = recommendations_with_impact
 
+        # For Debugging
+        recommendation_impact_df = []
+        for property_id in recommendations.keys():
+            for recs_by_type in recommendations[property_id]:
+                for rec in recs_by_type:
+                    recommendation_impact_df.append(
+                        {
+                            "property_id": property_id,
+                            "uprn": [p.uprn for p in input_properties if p.id == property_id][0],
+                            "recommendation_id": rec["recommendation_id"],
+                            "type": rec["type"],
+                            "description": rec["description"],
+                            "sap_points": rec["sap_points"],
+                            "co2_equivalent_savings": rec["co2_equivalent_savings"],
+                            "heat_demand": rec["heat_demand"]
+                        }
+                    )
+        recommendation_impact_df = pd.DataFrame(recommendation_impact_df)
+
+        surveyed_uprns = [
+            10024087855, 121016117, 121016124,
+            10024087902, 121016121, 121016128
+        ]
+        recommendation_impact_df = recommendation_impact_df[recommendation_impact_df["uprn"].isin(surveyed_uprns)]
+        recommendation_impact_df = recommendation_impact_df[recommendation_impact_df["type"] == "windows_glazing"]
+
+        actual_impacts_df = pd.DataFrame(
+            [
+                # 10024087855
+                {"uprn": 10024087855, "type": "internal_wall_insulation", "actual_sap_points": 5},
+                {"uprn": 10024087855, "type": "draught_proofing", "actual_sap_points": 2},
+                {"uprn": 10024087855, "type": "low_energy_lighting", "actual_sap_points": 0},
+                {"uprn": 10024087855, "type": "windows_glazing", "actual_sap_points": 4},
+                # 121016117
+                {"uprn": 121016117, "type": "internal_wall_insulation", "actual_sap_points": 6},
+                {"uprn": 121016117, "type": "draught_proofing", "actual_sap_points": 1},
+                {"uprn": 121016117, "type": "low_energy_lighting", "actual_sap_points": 1},
+                {"uprn": 121016117, "type": "windows_glazing", "actual_sap_points": 4},
+                # 121016124
+                {"uprn": 121016124, "type": "internal_wall_insulation", "actual_sap_points": 8},
+                {"uprn": 121016124, "type": "low_energy_lighting", "actual_sap_points": 2},
+                {"uprn": 121016124, "type": "windows_glazing", "actual_sap_points": 5},
+                # 10024087902
+                {"uprn": 10024087902, "type": "room_roof_insulation", "actual_sap_points": 16},
+                {"uprn": 10024087902, "type": "internal_wall_insulation", "actual_sap_points": 2},
+                {"uprn": 10024087902, "type": "low_energy_lighting", "actual_sap_points": 0},
+                # 121016121
+                {"uprn": 121016121, "type": "internal_wall_insulation", "actual_sap_points": 5},
+                {"uprn": 121016121, "type": "suspended_floor_insulation", "actual_sap_points": 2},
+                {"uprn": 121016121, "type": "draught_proofing", "actual_sap_points": 1},
+                {"uprn": 121016121, "type": "windows_glazing", "actual_sap_points": 3},
+                # 121016128
+                {"uprn": 121016128, "type": "internal_wall_insulation", "actual_sap_points": 6},
+                {"uprn": 121016128, "type": "suspended_floor_insulation", "actual_sap_points": 1},
+                {"uprn": 121016128, "type": "draught_proofing", "actual_sap_points": 1},
+                {"uprn": 121016128, "type": "low_energy_lighting", "actual_sap_points": 1},
+                {"uprn": 121016128, "type": "windows_glazing", "actual_sap_points": 3},
+            ]
+        )
+
+        comparison = recommendation_impact_df.merge(
+            actual_impacts_df, how="inner", on=["uprn", "type"]
+        )
+
+        # from utils.s3 import read_dataframe_from_s3_parquet
+        # training_data = read_dataframe_from_s3_parquet(
+        #     bucket_name="retrofit-data-dev",
+        #     file_key="sap_change_model/2024-08-06-11-19-49/dataset_rooms.parquet"
+        # )
+        # import pickle
+        # with open("delete_me.pkl", "wb") as f:
+        #     pickle.dump(training_data, f)
+
+        # Read in the pickle
+        import pickle
+        with open("delete_me.pkl", "rb") as f:
+            training_data = pickle.load(f)
+
+        # How do we simulate windows:
+        ending_cols = [col for col in training_data.columns if col.endswith("_ending")]
+        starting = {}
+        for c in ending_cols:
+            starting_colname = c.replace("_ending", "_starting")
+            if starting_colname in training_data.columns:
+                starting[c] = starting_colname
+            else:
+                starting[c] = c.replace("_ending", "")
+
+        allowed_to_change = [
+            # Windows
+            "windows_energy_eff_ending",
+            "glazed_type_ending",
+            "glazing_type_ending",
+            "multi_glaze_proportion_ending",
+
+            # Other
+            "sap_ending",
+            "heat_demand_ending",
+            "carbon_ending",
+            "estimated_perimeter_ending",
+            "lodgement_year_ending",
+            "lodgement_month_ending",
+            "days_to_ending",
+            "number_habitable_rooms_ending",
+            "number_heated_rooms_ending",
+        ]
+        fixed = [c for c in ending_cols if c not in allowed_to_change + ["uprn"]]
+        training_fixed = training_data.copy()
+        for col in fixed:
+            starting_col = starting[col]
+            training_fixed = training_fixed[training_fixed[col] == training_fixed[starting_col]]
+
+        training_fixed = training_fixed.reset_index(drop=True)
+
+        # Find the things that change
+        example = training_fixed.iloc[0]
+        things_that_change = []
+        for c in ending_cols:
+            if example[c] != example[starting[c]]:
+                things_that_change.append(c)
+        # 100051011370
+        example[]
+
         # We call the API with the scoring epcs
         scoring_epcs = pd.DataFrame(scoring_epcs)
         scoring_epcs = kwh_client.transform(data=scoring_epcs, cleaned=cleaned)

backend/app/plan/schemas.py

@@ -90,13 +90,13 @@ class PlanTriggerRequest(BaseModel):
     # Validator to ensure exclusions are within the pre-defined possibilities
     @validator('exclusions', each_item=True)
     def check_exclusions(cls, v):
-        if v not in TYPICAL_MEASURE_TYPES + SPECIFIC_MEASURES:
+        if v not in TYPICAL_MEASURE_TYPES + SPECIFIC_MEASURES + NON_INVASIVE_SPECIFIC_MEASURES:
             raise ValueError(f"{v} is not an allowed exclusion")
         return v
 
     @validator('inclusions', each_item=True)
     def check_inclusions(cls, v):
-        if v not in TYPICAL_MEASURE_TYPES + SPECIFIC_MEASURES:
+        if v not in TYPICAL_MEASURE_TYPES + SPECIFIC_MEASURES + NON_INVASIVE_SPECIFIC_MEASURES:
             raise ValueError(f"{v} is not an allowed inclusion")
         return v
 

etl/epc_clean/epc_attributes/HotWaterAttributes.py

@@ -129,6 +129,7 @@ class HotWaterAttributes(Definitions):
                                                                            "heat recovery",
         "o r brif system, gydag ynni r haul, dim thermostat ar y silindr": "from main system, plus solar, no cylinder "
                                                                            "thermostat",
+        "o r brif system, gydag ynni r haul": "from main system, plus solar",
     }
 
     NODATA_DESCRIPTIONS = [

etl/epc_clean/epc_attributes/MainheatControlAttributes.py

@@ -115,6 +115,7 @@ class MainheatControlAttributes(Definitions):
         't+ól un gyfradd, trvs': 'single rate heating, trvs',
         'trvs a falf osgoi': 'trvs and bypass',
         'rheolaeth celect': 'celect-type control',
+        'rheoli r tal a llaw': 'manual charge control',
     }
 
     def __init__(self, description: str):

recommendations/FloorRecommendations.py

@@ -68,7 +68,8 @@ class FloorRecommendations(Definitions):
 
         measures = MEASURE_MAP["floor_insulation"] if measures is None else measures
 
-        if not measures:
+        # If we have no measures or none of the measures are relevant, we can't recommend anything
+        if not measures or not any(x in measures for x in MEASURE_MAP["floor_insulation"]):
             return
 
         u_value = self.property.floor["thermal_transmittance"]

recommendations/WindowsRecommendations.py

@@ -60,7 +60,8 @@ class WindowsRecommendations:
             return
 
         if windows_area is not None:
-            raise Exception("We have windows area, we should use this data for our recommendations!!!")
+            # TODO - we don't have a price for this so we can't recommend it
+            print("We have windows area, we should use this data for our recommendations!!!")
 
         # We scale the number of windows based on the proportion of existing glazing
         if self.property.data["multi-glaze-proportion"] != "":