Merge pull request #374 from Hestia-Homes/remote-assessment

Completed Stonewater Wave 3 Modelling

etl/customers/aiha/bid_numbers.py

@@ -52,6 +52,20 @@ aiha_wave_3_features = aiha_original_asset_data[
 wall_type_breakdown = aiha_wave_3_features["Wall type"].value_counts()
 property_type_breakdown = aiha_wave_3_features.groupby(["Property type", "floor"]).size().reset_index()
 
+aiha_wave_3_features[aiha_wave_3_features["Property type"] == "Flat"][["Street address", "Postcode"]]
+
+# 4   Yetev Lev Court   ...  Semi-Detached     mid  - Medium
+# B    86 Bethune Road  ...    Mid-Terrace     top. - Low
+# A    80 Bethune Road  ...    Mid-Terrace  ground. - Low
+# B    80 Bethune Road  ...             \n      \n  - Low
+# A   9 Clapton Common  ...  Semi-Detached  ground. - Low
+# C   9 Clapton Common  ...    End-Terrace      \n. - Low
+# B      89 Manor Road  ...             \n      \n. - Low
+# A  6 Northfield Road  ...       Detached     top. - Low
+# 13 Northfield Rd  ...  Semi-Detached      \n      - Low
+# A      73 Manor Road  ...    End-Terrace      \n  - Low
+# B      73 Manor Road  ...       Detached     top  - Low
+
 # Hornsey data - contained in original asset list
 hornsey_asset_list = pd.read_excel(
     "/Users/khalimconn-kowlessar/Documents/hestia/Customers/AIHA/SHDF - Template - EOI - Hornsey Housing "
@@ -88,5 +102,5 @@ caha_epc_data = pd.read_excel(
     "/Users/khalimconn-kowlessar/Documents/hestia/Customers/AIHA/caha_extracted_property_data.xlsx"
 )
 
-caha_epc_data["property_type"].value_counts()
+caha_epc_data[caha_epc_data["address"] != "33 Woodhouse Road"]["property_type"].value_counts()
-caha_epc_data["wall_type"].value_counts()
+caha_epc_data[caha_epc_data["address"] != "33 Woodhouse Road"]["wall_type"].value_counts()

etl/customers/ksquared/Wave3 Modelling.py

@@ -8,6 +8,7 @@ from tqdm import tqdm
 import pandas as pd
 import numpy as np
 from etl.find_my_epc.RetrieveFindMyEpc import RetrieveFindMyEpc
+from etl.spatial.OpenUprnClient import OpenUprnClient
 from backend.SearchEpc import SearchEpc
 from utils.s3 import save_csv_to_s3
 
@@ -60,6 +61,7 @@ def hornsey():
     }
     extracted_data = []
     asset_list = []
+    hornsey_asset_list["row_id"] = hornsey_asset_list.index
     for _, home in tqdm(hornsey_asset_list.iterrows(), total=len(hornsey_asset_list)):
 
         if home["Address letter or number"] == "Flat 1 36 Haringey Park":
@@ -108,12 +110,24 @@ def hornsey():
         asset_list.append(
             {
                 "uprn": newest_epc["uprn"],
+                "row_id": home["row_id"],
                 "address": home["Address letter or number"],
                 "postcode": home["Postcode"],
                 "property_type": "Flat",  # They're all flats
             }
         )
 
+    # Get conservation area data
+    # uprns = [x["uprn"] for x in extracted_data]
+    # conservation_area_data = OpenUprnClient.get_spatial_data(uprns, "retrofit-data-dev")
+    #
+    # addresses = pd.DataFrame(asset_list)
+    # addresses["uprn"] = addresses["uprn"].astype(int)
+    # conservation_area_df = conservation_area_data.merge(addresses, how="left", right_on="uprn", left_on="UPRN")
+    # conservation_area_df.to_csv(
+    #     "/Users/khalimconn-kowlessar/Documents/hestia/Customers/AIHA/hornsey_conservation_area_data.csv"
+    # )
+
     # We format the extracted data so that is has the same structure as non-intrusive recommendations
     # We then get the UPRNs and create the asset list
 
@@ -213,6 +227,8 @@ def caha():
         # If pattern doesn't match, return original address
         return address
 
+    caha_asset_list["row_id"] = caha_asset_list.index
+
     extracted_data = []
     asset_list = []
     for _, home in tqdm(caha_asset_list.iterrows(), total=len(caha_asset_list)):
@@ -270,6 +286,7 @@ def caha():
 
         asset_list.append(
             {
+                "row_id": home["row_id"],
                 "uprn": uprn,
                 "address": address,
                 "postcode": home["Postcode"],
@@ -280,6 +297,24 @@ def caha():
             }
         )
 
+    # Missing row ids
+    missed = [r for r in caha_asset_list["row_id"].tolist() if r not in [x["row_id"] for x in asset_list]]
+
+    no_data = [x for x in asset_list if x["uprn"] in [None, ""]]
+    no_data = pd.DataFrame(no_data)
+
+    # Get conservation area data
+    uprns = [x["uprn"] for x in extracted_data if x["uprn"] not in ["", None]]
+    conservation_area_data = OpenUprnClient.get_spatial_data([100022526362], "retrofit-data-dev")
+
+    addresses = pd.DataFrame(asset_list)
+    addresses["uprn"] = addresses["uprn"].astype(str)
+    conservation_area_data["UPRN"] = conservation_area_data["UPRN"].astype(str)
+    conservation_area_df = conservation_area_data.merge(addresses, how="left", right_on="uprn", left_on="UPRN")
+    conservation_area_df.to_csv(
+        "/Users/khalimconn-kowlessar/Documents/hestia/Customers/AIHA/caha_conservation_area_data.csv"
+    )
+
     non_invasive_recommendations = [
         {
             "uprn": r["uprn"],

etl/customers/remote_assessments/app.py

@@ -1,7 +1,7 @@
 import pandas as pd
 from utils.s3 import save_csv_to_s3
 
-PORTFOLIO_ID = 111
+PORTFOLIO_ID = 120
 USER_ID = 8
 
 
@@ -13,10 +13,11 @@ def app():
 
     asset_list = [
         {
-            "uprn": 100050770761,
+            "uprn": 100030334057,
-            "address": "12 Sheardown Street",
+            "address": "5, Lynton Street",
-            "postcode": "DN4 0BH"
+            "postcode": "DE22 3RW"
         }
+
     ]
     asset_list = pd.DataFrame(asset_list)
 
@@ -30,11 +31,22 @@ def app():
 
     non_invasive_recommendations = [
         {
-            "uprn": 100050770761,
+            "uprn": 100030334057,
             "recommendations": [
                 {
-                    "type": "extension_cavity_wall_insulation",
+                    "type": "internal_wall_insulation",
+                    "sap_points": 9,
+                    "survey": True
+                },
+                {
+                    "type": "external_wall_insulation",
+                    "sap_points": 9,
+                    "survey": True
+                },
+                {
+                    "type": "suspended_floor_insulation",
                     "sap_points": 2,
+                    "survey": True
                 }
             ]
         }
@@ -49,8 +61,8 @@ def app():
 
     valuation_data = [
         {
-            "uprn": 100050770761,
+            "uprn": 100030334057,
-            "value": 67_000
+            "value": 133_000
         }
     ]
     # Store valuation data to s3

etl/customers/southend/epc_data_pull_2024_11_14.py

@@ -229,7 +229,3 @@ def app():
     filename = ("/Users/khalimconn-kowlessar/Documents/hestia/Customers/southend/southend EPC Data pull - 14 Nov "
                 "2024.xlsx")
     asset_list.to_excel(filename, index=False)
-
-    asset_list["% of the Roof with PV"].value_counts()
-
-    asset_list[asset_list["% of the Roof with PV"] == "50.0"][["Address", "Postcode"]]

etl/customers/stonewater/requirements/requirements-wave-3-prep.txt

@@ -7,4 +7,5 @@ epc-api-python==1.0.2
 usaddress==0.5.11
 fuzzywuzzy==0.18.0
 python-dotenv
+scipy
 

etl/find_my_epc/RetrieveFindMyEpc.py

@@ -26,6 +26,20 @@ class RetrieveFindMyEpc:
 
         self.address_cleaned = self.address.replace(",", "").replace(" ", "").lower()
 
+    @staticmethod
+    def extract_low_carbon_sources(soup):
+        # Find the section header
+        section_header = soup.find("h3", string="Low and zero carbon energy sources")
+        if not section_header:
+            return {}
+
+        # Locate the list following the header
+        energy_list = section_header.find_next("ul")
+
+        # Extract the list items
+        sources = {item.get_text(strip=True): True for item in energy_list.find_all("li")}
+        return sources
+
     def retrieve_newest_find_my_epc_data(self, sap_2012_date=None):
         """
         For a post code and address, we pull out all the required data from the find my epc website
@@ -112,6 +126,7 @@ class RetrieveFindMyEpc:
             # Find all h3 headers for each step and extract their related information
             step_headers = recommendations_div.find_all('h3', class_='govuk-heading-m')
             previous_sap_score = current_sap
+            previous_epc = current_rating.split(' ')[-6]
             for step_num, step_header in enumerate(step_headers, start=1):
                 # Extract the step title (the measure)
                 measure_title = step_header.text.strip().replace(f"Step {step_num}: ", "")
@@ -124,7 +139,11 @@ class RetrieveFindMyEpc:
                 # Check if the potential rating div is found
                 if potential_rating_div:
                     # Extract the rating text within the SVG text element
-                    rating_text = potential_rating_div.find('text', class_='govuk-!-font-weight-bold').text.strip()
+                    extracted_rating_text = potential_rating_div.find('text', class_='govuk-!-font-weight-bold')
+                    if extracted_rating_text is not None:
+                        rating_text = extracted_rating_text.text.strip()
+                    else:
+                        rating_text = " ".join([str(previous_sap_score), previous_epc])
                     # Parse the rating text to separate the numeric rating and EPC letter
                     new_rating = int(rating_text.split()[0])
                     new_epc = rating_text.split()[1]
@@ -138,6 +157,7 @@ class RetrieveFindMyEpc:
                         "sap_points": new_rating - previous_sap_score
                     })
                     previous_sap_score = new_rating
+                    previous_epc = new_epc
 
         # Search for the assessment informaton
         assessment_information = address_res.find('div', {'id': 'information'})
@@ -191,6 +211,9 @@ class RetrieveFindMyEpc:
         # Finally, we format the recommendations
         recommendations = self.format_recommendations(recommendations, assessment_data, sap_2012_date)
 
+        # 4) Low and zero carbon energy sources
+        low_carbon_energy_sources = self.extract_low_carbon_sources(address_res)
+
         resulting_data = {
             'epc_certificate': epc_certificate,
             'current_epc_rating': current_rating.split(' ')[-6],
@@ -200,7 +223,8 @@ class RetrieveFindMyEpc:
             "heating_text": heating_text,
             "hot_water_text": hot_water_text,
             "recommendations": recommendations,
-            **assessment_data
+            **assessment_data,
+            **low_carbon_energy_sources
         }
 
         return resulting_data
@@ -246,6 +270,31 @@ class RetrieveFindMyEpc:
             ],
             "Band A condensing boiler": ["boiler_upgrade"],
             "Double glazing": ["double_glazing"],
+            "Flue gas heat recovery device in conjunction with boiler": ["flue_gas_heat_recovery"],
+            "Wind turbine": ["wind_turbine"],
+            "Loft insulation": ["loft_insulation"],
+            "Solar photovoltaic (PV) panels": ["solar_pv"],
+            "Party wall insulation": ["party_wall_insulation"],
+            'Draught proofing': ["draught_proofing"],
+            "Roof insulation recommendation": [],
+            "Cavity wall insulation recommendation": [],
+            "Windows draught proofing": [],
+            "Low energy lighting for all fixed outlets": ["low_energy_lighting"],
+            "Cylinder thermostat recommendation": [],
+            "Heating controls recommendation": [],
+            "Replace boiler with Band A condensing boiler": [],
+            "Solar panel recommendation": [],
+            "Double glazing recommendation": [],
+            "Solid wall insulation recommendation": [],
+            "Fuel change recommendation": [],
+            "PV Cells recommendation": [],
+            "Replacement glazing units": ["double_glazing"],
+            "Heating controls (time and temperature zone control)": ["time_temperature_zone_control"],
+            "High heat retention storage heaters": ["high_heat_retention_storage_heaters"],
+            "Gas condensing boiler": ["boiler_upgrade"],
+            "Change room heaters to condensing boiler": ["boiler_upgrade"],
+            "Cylinder thermostat": ["cylinder_thermostat"],
+            "Heat recovery system for mixer showers": ["heat_recovery_shower"],
         }
 
         survey = True

etl/route_march_data_pull/app.py

@@ -0,0 +1,333 @@
+import os
+import time
+from idlelib.iomenu import errors
+
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+
+from dotenv import load_dotenv
+from backend.SearchEpc import SearchEpc
+from etl.find_my_epc.RetrieveFindMyEpc import RetrieveFindMyEpc
+from etl.epc_clean.epc_attributes.RoofAttributes import RoofAttributes
+
+from recommendations.recommendation_utils import (
+    estimate_perimeter,
+    estimate_external_wall_area,
+    estimate_number_of_floors
+)
+
+load_dotenv(dotenv_path="backend/.env")
+EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
+
+
+def get_data(asset_list, fulladdress_column, address1_column, postcode_column):
+    epc_data = []
+    errors = []
+    no_epc = []
+    for _, home in tqdm(asset_list.iterrows(), total=len(asset_list)):
+        try:
+            postcode = home[postcode_column]
+            house_number = home[address1_column]
+            full_address = home[fulladdress_column]
+
+            searcher = SearchEpc(
+                address1=str(house_number),
+                postcode=postcode,
+                auth_token=EPC_AUTH_TOKEN,
+                os_api_key="",
+                property_type=None,
+                fast=True,
+                full_address=full_address,
+                max_retries=5
+            )
+            # Force the skipping of estimating the EPC
+            searcher.ordnance_survey_client.property_type = None
+            searcher.ordnance_survey_client.built_form = None
+
+            searcher.find_property(skip_os=True)
+            if searcher.newest_epc is None:
+                no_epc.append(home["row_id"])
+                continue
+
+            # Look for EPC recommendatons
+            try:
+                property_recommendations = searcher.client.domestic.recommendations(searcher.newest_epc["lmk-key"])
+            except:
+                property_recommendations = {"rows": []}
+
+            # Retrieve data from FindMyEPC
+            try:
+                find_epc_searcher = RetrieveFindMyEpc(
+                    address=searcher.newest_epc["address"], postcode=searcher.newest_epc["postcode"]
+                )
+                find_epc_data = find_epc_searcher.retrieve_newest_find_my_epc_data()
+            except ValueError as e:
+                if "No EPC found" in str(e):
+                    find_epc_searcher = RetrieveFindMyEpc(
+                        address=searcher.newest_epc["address1"], postcode=searcher.newest_epc["postcode"]
+                    )
+                    find_epc_data = find_epc_searcher.retrieve_newest_find_my_epc_data()
+                else:
+                    find_epc_data = {}
+            except Exception as e:
+                raise Exception(f"Error retrieving FindMyEPC data: {e}")
+            time.sleep(np.random.uniform(0.1, 1))
+
+            epc = {
+                "row_id": home["row_id"],
+                **searcher.newest_epc.copy(),
+                "recommendations": property_recommendations["rows"],
+                "find_my_epc_data": find_epc_data,
+            }
+
+            epc_data.append(epc)
+        except Exception as e:
+            errors.append(home["row_id"])
+            time.sleep(5)
+
+    return epc_data, errors, no_epc
+
+
+def extract_address1(asset_list, full_address_col, method="first_two_words"):
+    if method == "first_two_words":
+        asset_list["address1_extracted"] = asset_list[full_address_col].str.split(" ").str[:2].str.join(" ")
+        return asset_list
+
+    raise ValueError(f"Method {method} not recognized")
+
+
+def app():
+    """
+    This app is EPC pulling data for some properties owned by Livewest
+
+    Data request contents:
+    Date of last EPC
+    Reason for EPC
+    SAP score on register
+    Property Type
+    Property Area
+    Property Age
+    Any Dimensions (HLP,PW,RH)
+    Property Wall Construction
+    Heating Type
+    Secondary Heating
+    Loft Insulation Depth
+
+    Additional if possible:
+    Heat loss calculations
+    EPC recommendations
+    Property UPRN
+
+    """
+    DATA_FOLDER = "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Bromford/"
+    DATA_FILENAME = "Bromford programme review.xlsx"
+    SHEET_NAME = "Bromford"
+    POSTCODE_COLUMN = "Postcode"
+    FULLADDRESS_COLUMN = None
+    ADDRESS1_COLUMN = "No."
+    ADDRESS1_METHOD = "first_two_words"
+    ADDRESS_COLS_TO_CONCAT = ["No.", "Address"]
+
+    asset_list = pd.read_excel(os.path.join(DATA_FOLDER, DATA_FILENAME), header=0, sheet_name=SHEET_NAME)
+    asset_list = asset_list[~pd.isnull(asset_list["Postcode"])]
+    asset_list["row_id"] = asset_list.index
+
+    # We clean up portential non-breaking spaces, and double spaces
+    for col in [c for c in [POSTCODE_COLUMN, FULLADDRESS_COLUMN, ADDRESS1_COLUMN] if c is not None]:
+        asset_list[col] = asset_list[col].astype(str)
+        asset_list[col] = asset_list[col].str.replace('\xa0', ' ', regex=False)
+        asset_list[col] = asset_list[col].str.replace('  ', ' ', regex=False)
+
+    if ADDRESS1_COLUMN is None:
+        ADDRESS1_COLUMN = "address1_extracted"
+        asset_list = extract_address1(
+            asset_list=asset_list, full_address_col=FULLADDRESS_COLUMN, method=ADDRESS1_METHOD
+        )
+
+    if FULLADDRESS_COLUMN is None:
+        FULLADDRESS_COLUMN = "fulladdress_extracted"
+        # We concatenate the columns in ADDRESS_COLS_TO_CONCAT, on commas
+        asset_list[FULLADDRESS_COLUMN] = asset_list[ADDRESS_COLS_TO_CONCAT].apply(lambda x: ", ".join(x), axis=1)
+
+    # We check for duplicated addresses
+    asset_list["deduper"] = asset_list[FULLADDRESS_COLUMN] + asset_list[POSTCODE_COLUMN]
+    if asset_list["deduper"].duplicated().sum():
+        # Drop the dupes
+        print(f"There are {asset_list['deduper'].duplicated().sum()} duplicated addresses - dropping")
+        asset_list = asset_list[~asset_list["deduper"].duplicated()]
+
+    epc_data, errors, no_epc = get_data(
+        asset_list=asset_list,
+        fulladdress_column=FULLADDRESS_COLUMN,
+        address1_column=ADDRESS1_COLUMN,
+        postcode_column=POSTCODE_COLUMN
+    )
+
+    # We now retrieve any failed properties
+    asset_list_failed = asset_list[asset_list["row_id"].isin(errors)]
+    epc_data_failed, _, _ = get_data(
+        asset_list=asset_list_failed,
+        fulladdress_column=FULLADDRESS_COLUMN,
+        address1_column=ADDRESS1_COLUMN,
+        postcode_column=POSTCODE_COLUMN
+    )
+
+    # Append the failed data to the main data
+    epc_data.extend(epc_data_failed)
+
+    epc_df = pd.DataFrame(epc_data)
+
+    # We expand out the recommendations
+    recommendations_df = epc_df[["row_id", "recommendations"]]
+
+    unique_recommendations = set()
+    for _, row in recommendations_df.iterrows():
+        unique_recommendations.update([rec["improvement-summary-text"] for rec in row["recommendations"]])
+
+    columns = ["row_id"] + list(unique_recommendations)
+    transformed_data = []
+    for _, row in recommendations_df.iterrows():
+        # Initialize a dictionary for this row with False for all recommendations
+        row_data = {col: False for col in columns}
+        row_data["row_id"] = row["row_id"]
+
+        # Set True for each recommendation present in this row
+        for rec in row["recommendations"]:
+            recommendation_text = rec["improvement-summary-text"]
+            row_data[recommendation_text] = True
+
+        # Append the row data to transformed_data
+        transformed_data.append(row_data)
+
+    transformed_df = pd.DataFrame(transformed_data)
+    # Drop the column that is ""
+    transformed_df = transformed_df.drop(columns=[""])
+
+    # Get the find my epc data
+    find_my_epc_data = epc_df[["row_id", "find_my_epc_data"]].drop(columns=["find_my_epc_data"]).join(
+        pd.json_normalize(epc_df["find_my_epc_data"])
+    )
+    # We check if we get the solar pv column:
+    if "Solar photovoltaics" not in find_my_epc_data.columns:
+        find_my_epc_data["Solar photovoltaics"] = False
+
+    # Retrieve just the data we need
+    epc_df = epc_df[
+        [
+            "row_id",
+            "uprn",
+            "property-type",
+            "built-form",
+            "inspection-date",
+            "current-energy-rating",
+            "current-energy-efficiency",
+            "roof-description",
+            "walls-description",
+            "transaction-type",
+            # New fields needed
+            "secondheat-description",
+            "total-floor-area",
+            "construction-age-band",
+            "floor-height",
+            "number-habitable-rooms",
+            "mainheat-description",
+            #
+            "energy-consumption-current",  # kwh/m2
+            "photo-supply",
+        ]
+    ]
+
+    asset_list = asset_list.merge(
+        epc_df,
+        how="left",
+        on="row_id"
+    ).merge(
+        find_my_epc_data[
+            [
+                "row_id", "heating_text", "hot_water_text", 'Assessor’s name',
+                "Assessor's Telephone", "Assessor's Email", "Accreditation scheme",
+                "Assessor’s ID", "Solar photovoltaics"
+            ]
+        ].rename(
+            columns={
+                "Solar photovoltaics": "Has Solar PV",
+                "heating_text": "Heating Estimated kWh",
+                "hot_water_text": "Hot Water Estimated kWh",
+            }
+        ),
+        how="left",
+        on="row_id"
+    )
+
+    asset_list["Has Solar PV"] = asset_list["Has Solar PV"] | ~asset_list["photo-supply"].isin(["0.0", 0, None, ""])
+    asset_list = asset_list.drop(columns=["photo-supply"])
+
+    # Rename the columns
+    asset_list = asset_list.rename(columns={
+        "inspection-date": "Date of last EPC",
+        "current-energy-efficiency": "SAP score on register",
+        "current-energy-rating": "EPC rating on register",
+        "property-type": "Property Type",
+        "built-form": "Archetype",
+        "total-floor-area": "Property Floor Area",
+        "construction-age-band": "Property Age Band",
+        "floor-height": "Property Floor Height",
+        "number-habitable-rooms": "Number of Habitable Rooms",
+        "walls-description": "Wall Construction",
+        "roof-description": "Roof Construction",
+        "mainheat-description": "Heating Type",
+        "secondheat-description": "Secondary Heating",
+        "transaction-type": "Reason for last EPC",
+        "energy-consumption-current": "Heat Demand (kWh/m2)",
+    })
+
+    asset_list["Estimated Number of Floors"] = asset_list.apply(
+        lambda x: estimate_number_of_floors(property_type=x["Property Type"]) if not pd.isnull(
+            x["Property Type"]) else None, axis=1
+    )
+
+    asset_list["Property Floor Area"] = asset_list["Property Floor Area"].astype(float)
+    # Replace "" value with None
+    asset_list["Number of Habitable Rooms"] = asset_list["Number of Habitable Rooms"].replace("", None)
+    asset_list["Number of Habitable Rooms"] = asset_list["Number of Habitable Rooms"].astype(float)
+
+    asset_list["Estimated Perimeter (m)"] = asset_list.apply(
+        lambda x: estimate_perimeter(
+            floor_area=x["Property Floor Area"] / x["Estimated Number of Floors"],
+            num_rooms=x["Number of Habitable Rooms"] / x["Estimated Number of Floors"],
+        ), axis=1
+    )
+
+    asset_list["Estimated Heat Loss Perimeter (m2)"] = asset_list.apply(
+        lambda x: estimate_external_wall_area(
+            num_floors=x["Estimated Number of Floors"],
+            floor_height=float(x["Property Floor Height"]) if x["Property Floor Height"] else 2.5,
+            perimeter=x["Estimated Perimeter (m)"],
+            built_form=x["Archetype"]
+        ),
+        axis=1
+    )
+
+    asset_list["Roof Insulation Thickness"] = asset_list.apply(
+        lambda x: RoofAttributes(description=x["Roof Construction"]).process()["insulation_thickness"] if not pd.isnull(
+            x["Roof Construction"]) else None,
+        axis=1
+    )
+
+    # For all of the columns in transformed_df, prefix with "Recommendation: "
+    for col in transformed_df.columns:
+        if col == "row_id":
+            continue
+        transformed_df = transformed_df.rename(columns={col: f"Recommendation: {col}"})
+
+    asset_list = asset_list.merge(
+        transformed_df,
+        how="left",
+        on="row_id"
+    )
+    asset_list = asset_list.drop(columns=["row_id"])
+
+    # Store as an excel
+    filename = os.path.join(DATA_FOLDER, ".".join(DATA_FILENAME.split(".")[:-1])) + " EPC Data Pull.xlsx"
+    asset_list.to_excel(filename, index=False)

recommendations/FloorRecommendations.py

@@ -172,6 +172,11 @@ class FloorRecommendations(Definitions):
 
         insulation_materials = pd.DataFrame(insulation_materials)
 
+        non_invasive_recs = next(
+            (r for r in self.property.non_invasive_recommendations if
+             r["type"] == insulation_materials["type"].values[0]), {}
+        )
+
         lowest_selected_u_value = None
         for _, insulation_material_group in insulation_materials.groupby("description"):
 
@@ -217,6 +222,9 @@ class FloorRecommendations(Definitions):
                     else:
                         raise NotImplementedError("Implement me!")
 
+                    sap_points = non_invasive_recs.get("sap_points", None)
+                    survey = non_invasive_recs.get("survey", False)
+
                     floor_ending_config = FloorAttributes(new_description).process()
                     floor_simulation_config = check_simulation_difference(
                         new_config=floor_ending_config, old_config=self.property.floor, prefix="floor_"
@@ -245,7 +253,8 @@ class FloorRecommendations(Definitions):
                             "description": self._make_floor_description(material),
                             "starting_u_value": u_value,
                             "new_u_value": new_u_value,
-                            "sap_points": None,
+                            "sap_points": sap_points,
+                            "survey": survey,
                             "already_installed": already_installed,
                             "simulation_config": simulation_config,
                             "description_simulation": {

recommendations/HotwaterRecommendations.py

@@ -66,7 +66,7 @@ class HotwaterRecommendations:
             (self.property.hotwater["heater_type"] in ["electric immersion"]) &
             (self.property.data["hot-water-energy-eff"] == "Very Poor") &
             (self.property.hotwater["no_system_present"] is None) &
-            len(has_tank_recommendation) == 0
+            (len(has_tank_recommendation) == 0)
         ):
             self.recommend_tank_insulation(phase=phase)
             return