refactoring prs and oo data puls

etl/route_march/oo_prs_additional_units/oo_prs_additional_units.py

@@ -1,10 +1,20 @@
 import os
 import pandas as pd
 import numpy as np
+from tqdm import tqdm
 from dotenv import load_dotenv
 from urllib.parse import urlencode
 from epc_api.client import EpcClient
+from utils.logger import setup_logger
+from etl.epc_clean.epc_attributes.RoofAttributes import RoofAttributes
 
+from recommendations.recommendation_utils import (
+    estimate_perimeter,
+    estimate_external_wall_area,
+    estimate_number_of_floors
+)
+
+logger = setup_logger()
 load_dotenv(dotenv_path="backend/.env")
 EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
 
@@ -64,6 +74,89 @@ ROOF_DESCRIPTIONS = [
 SOCIAL_TENURES = ["Rented (social)", "rental (social)"]
 
 
+def process_postcode_epcs(postcode, client):
+    params = {"postcode": postcode}
+    url = os.path.join(client.domestic.host, "search") + "?" + urlencode({"size": 1000})
+    response = client.domestic.call(method="get", url=url, params=params)
+    postcode_epcs = pd.DataFrame(response["rows"])
+
+    # Processing code here
+    postcode_epcs["uprn"] = np.where(
+        pd.isnull(postcode_epcs["uprn"]),
+        postcode_epcs["address"],
+        postcode_epcs["uprn"]
+    )
+    postcode_epcs = postcode_epcs.sort_values("lodgement-date", ascending=False)
+    postcode_epcs = postcode_epcs.drop_duplicates("uprn", keep="first")
+    return postcode_epcs
+
+
+def filter_and_prepare_epcs(epcs):
+    epcs["Is Cavity Property"] = epcs["walls-description"].isin(CAVITY_WALL_DESCRIPTIONS) & (
+        epcs["current-energy-efficiency"].astype(int) <= 72
+    )
+    epcs["Solar and Loft"] = (
+                                 epcs["roof-description"].isin(ROOF_DESCRIPTIONS)
+                             ) & (
+                                 epcs["photo-supply"].isin(["0", "", "0.0"])
+                             ) & (
+                                 epcs["current-energy-efficiency"].astype(int) <= 68
+                             )
+    epcs = epcs[epcs["Is Cavity Property"] | epcs["Solar and Loft"]]
+    epcs = epcs[~epcs["tenure"].isin(SOCIAL_TENURES)]
+    return epcs
+
+
+def rename_and_add_columns(epcs):
+    epcs = epcs.rename(
+        columns={
+            "address": "Address",
+            "postcode": "Postcode",
+            "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)",
+            "tenure": "Tenure"
+        }
+    )
+
+    # Add additional columns as in your original code
+    epcs["Estimated Number of Floors"] = epcs.apply(
+        lambda x: estimate_number_of_floors(x["Property Type"]) if pd.notnull(x["Property Type"]) else None, axis=1
+    )
+    epcs["Estimated Perimeter (m)"] = epcs.apply(
+        lambda x: estimate_perimeter(
+            x["Property Floor Area"] / x["Estimated Number of Floors"],
+            x["Number of Habitable Rooms"] / x["Estimated Number of Floors"]
+        ), axis=1
+    )
+    epcs["Estimated Heat Loss Perimeter (m2)"] = epcs.apply(
+        lambda x: estimate_external_wall_area(
+            x["Estimated Number of Floors"],
+            float(x["Property Floor Height"]) if x["Property Floor Height"] else 2.5,
+            x["Estimated Perimeter (m)"],
+            x["Archetype"]
+        ), axis=1
+    )
+    epcs["Roof Insulation Thickness"] = epcs.apply(
+        lambda x: RoofAttributes(description=x["Roof Construction"]).process()[
+            "insulation_thickness"] if pd.notnull(x["Roof Construction"]) else None,
+        axis=1
+    )
+    return epcs
+
+
 def main():
     """
     This application is used to identify additional units that are private rentals or owner occupies that can be
@@ -73,7 +166,13 @@ def main():
     - An excel file that contains one or many tabs that include the addresses to be visited
     """
 
+    # This should be set:
+    output_filepath = "/Users/khalimconn-kowlessar/Documents/hestia/Route Marches/PRS and OO properties - WC 11.11.2024"
+    client = EpcClient(auth_token=EPC_AUTH_TOKEN)
+    writer = pd.ExcelWriter(output_filepath, engine="xlsxwriter")
+
     for config in CONFIG:
+        logger.info("Processing %s", config["tab"])
         # Read in the data
         route_march_addresses = pd.read_excel(
             config["filepath"],
@@ -84,39 +183,18 @@ def main():
         postcodes = route_march_addresses[config["postcode_column"]].unique()
 
         epcs = []
-        for postcode in postcodes:
+        for postcode in tqdm(postcodes):
-            # Get the EPCs in this postcode
+            postcode_epcs = process_postcode_epcs(postcode, client)
-
-            params = {"postcode": postcode}
-            client = EpcClient(auth_token=EPC_AUTH_TOKEN)
-            url = os.path.join(client.domestic.host, "search")
-            url += "?" + urlencode({k: v for k, v in {"size": 1000}.items() if v})
-            response = client.domestic.call(method="get", url=url, params=params)
-
-            postcode_epcs = pd.DataFrame(response["rows"])
-            # Get the newest EPC, per UPRN
-            postcode_epcs["uprn"] = np.where(
-                pd.isnull(postcode_epcs["uprn"]),
-                postcode_epcs["address"],
-                postcode_epcs["uprn"]
-            )
-            postcode_epcs = postcode_epcs.sort_values("lodgement-date", ascending=False)
-            postcode_epcs = postcode_epcs.drop_duplicates("uprn", keep="first")
-
-            postcode_epcs["Is Cavity Property"] = postcode_epcs["walls-description"].isin(
-                CAVITY_WALL_DESCRIPTIONS
-            ) & (postcode_epcs["current-energy-efficiency"].astype(int) <= 72)
-
-            postcode_epcs["Solar and Loft"] = (postcode_epcs["roof-description"].isin(ROOF_DESCRIPTIONS)) & (
-                postcode_epcs["photo-supply"].isin(["0", "", "0.0"])) & (
-                                                  postcode_epcs["current-energy-efficiency"].astype(int) <= 68
-                                              )
-
-            postcode_epcs = postcode_epcs[postcode_epcs["Is Cavity Property"] | postcode_epcs["Solar and Loft"]]
-
-            # Remove any social properties
-            postcode_epcs = postcode_epcs[~postcode_epcs["tenure"].isin(SOCIAL_TENURES)]
-
             epcs.append(postcode_epcs)
 
+        # Concatenate all postcodes' data and filter it
         epcs = pd.concat(epcs)
+        epcs = filter_and_prepare_epcs(epcs)
+        epcs = rename_and_add_columns(epcs)
+
+        sheet_name = config["tab"][:31]  # Excel sheet names max length of 31 characters
+        epcs.to_excel(writer, sheet_name=sheet_name, index=False)
+
+    # Save and close the writer outside the loop
+    writer.close()
+    logger.info("Data successfully written to %s", output_filepath)

etl/route_march/oo_prs_additional_units/requirements.txt

@@ -6,4 +6,5 @@ usaddress==0.5.11
 fuzzywuzzy==0.18.0
 boto3==1.35.44
 python-dotenv
-tqdm
+tqdm
+xlsxwriter