[Cincreased concurrency of backend

etl/customers/lincs_rural/prepare_data.py

@@ -1,8 +1,15 @@
 """
 Rough script to prepare the data for Lincs Rural project
 """
+from tqdm import tqdm
 import pandas as pd
+import os
+from dotenv import load_dotenv
 from etl.find_my_epc.RetrieveFindMyEpc import RetrieveFindMyEpc
+from backend.SearchEpc import SearchEpc
+
+load_dotenv(dotenv_path="backend/.env")
+EPC_AUTH_TOKEN = os.getenv("EPC_AUTH_TOKEN")
 
 data = pd.read_excel(
     "/Users/khalimconn-kowlessar/Downloads/MASTER LIST EPCS UPDATED November 2025 Domna Homes.xlsx",
@@ -11,16 +18,58 @@ data = pd.read_excel(
 
 # We have property RRNs - we need UPRN
 
-for _, x in data.iterrows():
-    rrn = x["EPC Ref."]
+standardised_ara_list = []
+missed = []
+for _, x in tqdm(data.iterrows(), total=len(data)):
+    try:
+        rrn = x["EPC Ref."]
 
-    # Fetch from find my epc
-    retriever = RetrieveFindMyEpc(
-        address="",
-        postcode="",
-        rrn=rrn,
-        address_postal_town="",
-        sap_rating=x["Actual"]
-    )
+        # Fetch from find my epc
+        retriever = RetrieveFindMyEpc(
+            address="",
+            postcode="",
+            rrn=rrn,
+            address_postal_town="",
+        )
 
-    find_epc_data = retriever.retrieve_all_find_my_epc_data()
+        find_epc_data = retriever.retrieve_newest_find_my_epc_data(rrn=rrn)
+
+        # Find the UPRN
+        epc_searcher = SearchEpc(
+            address1=str(find_epc_data["address1"]),
+            postcode=str(find_epc_data["postcode"]),
+            auth_token=EPC_AUTH_TOKEN,
+            os_api_key="",
+            property_type=None,
+            fast=False,
+            full_address=",".join([find_epc_data["address1"], find_epc_data["address2"]]),
+            max_retries=5,
+        )
+        epc_searcher.find_property(skip_os=True)
+
+        # Append in format we need
+        # Stuff we need:
+        standardised_ara_list.append(
+            {
+                "landlord_property_id": x["Property Ref."],
+                "landlord_property_type": epc_searcher.newest_epc.get("property-type"),
+                "landlord_built_form": epc_searcher.newest_epc.get("built-form"),
+                "landlord_heating_system": epc_searcher.newest_epc.get("mainheat-description", ""),
+                "epc_os_uprn": epc_searcher.newest_epc.get("uprn"),
+                "domna_property_id": x["Property Ref."],
+                "domna_full_address": epc_searcher.newest_epc.get(
+                    "address", ", ".join([
+                        find_epc_data["address1"],
+                        find_epc_data["address2"],
+                    ])
+                ),
+            }
+        )
+    except Exception as e:
+        missed.append({
+            "property_ref": x["Property Ref."],
+            "rrn": x["EPC Ref."],
+            "error": str(e)
+        })
+
+missed_df = pd.DataFrame(missed)

etl/customers/peabody/Nov 2025 Consulting Project/data_cleanse.py

@@ -0,0 +1,6 @@
+"""
+We have found, within the Peabody data, a large volume of properties with missing and incorrects
+UPRNS and incorrect address data. We want to flag these records and also find missings where we can
+
+We also have duplicate UPRNS that should be flagged
+"""

etl/find_my_epc/RetrieveFindMyEpc.py

@@ -465,12 +465,13 @@ class RetrieveFindMyEpc:
         potential_rating = ratings.split(".")[1]
         current_sap = int(current_rating.split(' ')[-1])
 
-        if current_sap != self.sap_rating:
-            # This means we likely have the wrong data. If we are in this scenario, we return nothing
-            return {
-                "epc_certificate": None,
-                "page_source": None,
-            }
+        if self.sap_rating:
+            if current_sap != self.sap_rating:
+                # This means we likely have the wrong data. If we are in this scenario, we return nothing
+                return {
+                    "epc_certificate": None,
+                    "page_source": None,
+                }
 
         # Retrieve the energy consumption
         bills = address_res.find('div', {'id': 'bills-affected'})

serverless.yml

@@ -66,7 +66,7 @@ functions:
       - sqs:
           arn: arn:aws:sqs:${self:provider.region}:${aws:accountId}:model-engine-queue
           batchSize: 1
-          maximumConcurrency: 5  # Heavily restricts concurrency to avoid overwhelming the ldmbda limits
+          maximumConcurrency: 10  # Heavily restricts concurrency to avoid overwhelming the ldmbda limits
 
 
 resources: