Merge pull request #673 from Hestia-Homes/main

Fix db terraform config

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

@@ -1,34 +1,5 @@
 import pandas as pd
 
-# import pandas as pd
-#
-# sal = pd.read_excel(
-#     "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting "
-#     "Project/data_validation/to_standardise_uprns - Standardised.xlsx",
-#     sheet_name="Standardised Asset List"
-# )
-#
-# # Quick breadown of missingness
-# missing = sal[
-#     pd.isnull(sal["estimated"]) | (sal["estimated"] == True) | pd.isnull(sal["epc_os_uprn"])
-#     ]
-#
-# fetched = sal[(sal["estimated"] == False) | ~pd.isnull(sal["epc_os_uprn"])].copy()
-# fetched = fetched[
-#     ["landlord_property_id", "domna_address_1", "domna_postcode", "domna_full_address", "epc_address1",
-#      "epc_postcode", "epc_address", "landlord_property_type", "epc_property_type"]
-# ]
-#
-# known_issues = [
-#
-# ]
-#
-# # Missed postcodes
-# missed_postcode_agg = missing.groupby("domna_postcode").size().reset_index(name="count")
-# missed_postcode_agg = missed_postcode_agg.sort_values("count", ascending=False)
-#
-# multi_missed_postcode = missed_postcode_agg[missed_postcode_agg["count"] > 1]
-
 ### Prepare
 sustainability_data = pd.read_excel(
     "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/2025_11_11 - Peabody "

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

@@ -277,3 +277,185 @@ tenure_groups = sustainability_sample["Tenure Group"].value_counts().to_frame().
 tenure_groups.to_excel("/Users/khalimconn-kowlessar/Downloads/sustainability_tenure_groups.xlsx", index=False)
 
 initial_asset_data[~pd.isnull(initial_asset_data["BlockCode"])]["Tenure Group"].value_counts()
+
+sample_data = initial_asset_data[
+    ~initial_asset_data["Ownership Type"].isin(
+        [
+            # Commercial # Everything is resi - based on the Residential Indicator variable - all are true
+            # Freeholder
+            "FREEHOLDER",  # 19517 properties
+            # HOMEBUY / EQUITY LOAN
+            "Rent to Homebuy",  # 1 property
+            # Leaseholder
+            "LEASEHOLD 100%",  # 8455 properties
+            "Owned and Managed - 999 year lease",  # 2076 properties
+            "Managed but not Owned-Private Lease",  # 159 properties
+            "Owned and managed LEASEHOLD",  # 26 properties
+            # Outright Sale - can't find anything matching
+            # SHARED EQUITY
+            "Shared Ownership",  # 4065 properties
+            "Shared Ownership Owned Not Managed",  # 23 properties
+            # Extra categories which seem sensible to exclude
+            "NOT MANAGED AND NOT OWNED"
+        ]
+    )
+]
+
+sample_data["Ownership Type"].value_counts()
+
+sample_data = initial_asset_data[
+    initial_asset_data["Ownership Type"].isin(
+        [
+            "Owned and Managed",
+            "Owned and Managed - 999 year lease",
+            "Owned and managed LEASEHOLD",
+            "LEASEHOLD 100%",
+            "DATALOAD DEFAULT"
+        ]
+    )
+]
+dropped = initial_asset_data[~initial_asset_data["UPRN"].isin(sample_data["UPRN"].values)]
+dropped["Ownership Type"].value_counts()
+
+for value in [
+    # Commercial # Everything is resi, so should be fine. No matches
+    # Freeholder
+    "FREEHOLDER",  # 19517 properties
+    # HOMEBUY / EQUITY LOAN
+    "Rent to Homebuy",  # 1 property
+    # Leaseholder
+    "LEASEHOLD 100%",  # 8455 properties
+    "Owned and Managed - 999 year lease",  # 2076 properties
+    "Managed but not Owned-Private Lease",  # 159 properties
+    "Owned and managed LEASEHOLD",  # 26 properties
+    # Outright Sale - can't find anything matching
+    # SHARED EQUITY
+    "Shared Ownership",  # 4065 properties
+    "Shared Ownership Owned Not Managed",  # 23 properties
+]:
+    print(initial_asset_data[initial_asset_data["Ownership Type"] == value].shape[0])
+
+house_types = [
+    "HOUSE",
+    "BUNGALOW",
+    "MAISONETTE",
+    "DUPLEX",
+]
+
+guaranteed_control = [
+    "Owned and Managed",
+    "Owned and Managed - 999 year lease",
+    "Owned and managed LEASEHOLD",
+    "LEASEHOLD 100%",
+    "DATALOAD DEFAULT",
+]
+
+sample_data = initial_asset_data[
+    (
+        initial_asset_data["Ownership Type"].isin(guaranteed_control)
+    )
+    |
+    (
+        (initial_asset_data["Ownership Type"] == "FREEHOLDER")
+        &
+        (initial_asset_data["Property Type"].isin(house_types))
+    )
+    ]
+
+fabric_retrofit_sample = initial_asset_data[
+    initial_asset_data["Ownership Type"].isin(
+        [
+            "Owned and Managed",
+            "FREEHOLDER",
+            "DATALOAD DEFAULT",
+        ]
+    )
+]
+
+initial_asset_data[pd.isnull(initial_asset_data["BlockCode"])]["Ownership Type"].value_counts()
+initial_asset_data[~pd.isnull(initial_asset_data["BlockCode"])]["Ownership Type"].value_counts()
+
+initial_asset_data[~pd.isnull(initial_asset_data["BlockCode"])]["Property Type"].value_counts()
+z = initial_asset_data[
+    ~pd.isnull(initial_asset_data["BlockCode"]) & initial_asset_data["Property Type"].isin(house_types)
+    ]
+
+block_code_agg = z["BlockCode"].value_counts().reset_index().sort_values("count", ascending=False)
+zz = initial_asset_data[initial_asset_data["BlockCode"] == "CHAT3343FM"]
+
+potential_sample = initial_asset_data[
+    ~pd.isnull(initial_asset_data["BlockCode"])
+]
+
+compare = potential_sample["Property Type"].value_counts(normalize=True).to_frame().reset_index().merge(
+    initial_asset_data["Property Type"].value_counts(normalize=True).to_frame().reset_index(),
+    left_on="Property Type",
+    right_on="Property Type",
+    suffixes=("_on_block_codes", "_overall")
+)
+
+# Comparison of smaller sample vs overall
+new_asset_data = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final SAL/2025_11_11 "
+    "- Peabody "
+    "- Data Extracts for Domna v2.xlsx",
+    sheet_name="Properties"
+)
+
+new_sustainability_data = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final SAL/2025_11_11 "
+    "- Peabody "
+    "- Data Extracts for Domna v2.xlsx",
+    sheet_name="Sustainability"
+)
+
+sap_bands = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final SAL/Parity Data "
+    "08012026.xlsx",
+)
+
+combined = new_asset_data.merge(
+    new_sustainability_data,
+    left_on="UPRN",
+    right_on="Org Ref",
+    suffixes=("_asset", "_sustainability")
+).merge(
+    sap_bands[["OrgRef", "SAP Band", "Lodged EPC Band"]], how="left", left_on="Org Ref", right_on="OrgRef"
+)
+reduced_sample = combined[
+    ~combined["AH Tenure"].isin(
+        ["Commercial",
+         "Freeholder",
+         "HOMEBUY / EQUITY LOAN",
+         "Leaseholder",
+         "Outright Sale",
+         "SHARED EQUITY",
+         "Shared Ownership"]
+    )
+].copy()
+
+# property types
+property_type_comparison = reduced_sample["Property Type"].value_counts(normalize=True).to_frame().reset_index().merge(
+    combined["Property Type"].value_counts(normalize=True).to_frame().reset_index(),
+    left_on="Property Type",
+    right_on="Property Type",
+    suffixes=("_reduced_sample", "_overall")
+)
+
+# lodged ratings
+lodged_epc_band_comparison = reduced_sample["Lodged EPC Band"].value_counts(
+    normalize=True).to_frame().reset_index().merge(
+    combined["Lodged EPC Band"].value_counts(normalize=True).to_frame().reset_index(),
+    left_on="Lodged EPC Band",
+    right_on="Lodged EPC Band",
+    suffixes=("_reduced_sample", "_overall")
+)
+
+# modelled ratings
+modelled_epc_band_comparison = reduced_sample["SAP Band"].value_counts(
+    normalize=True).to_frame().reset_index().merge(
+    combined["SAP Band"].value_counts(normalize=True).to_frame().reset_index(),
+    left_on="SAP Band",
+    right_on="SAP Band",
+    suffixes=("_reduced_sample", "_overall")
+)

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

@@ -0,0 +1,115 @@
+import pandas as pd
+
+initial_asset_data = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/2025_11_11 - Peabody "
+    "- Data Extracts for Domna.xlsx",
+    sheet_name="Properties"
+)
+
+sustainability_data = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/2025_11_11 - Peabody "
+    "- Data Extracts for Domna.xlsx",
+    sheet_name="Sustainability"
+)
+
+asset_data_v2 = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final SAL/2025_11_11 "
+    "- Peabody "
+    "- Data Extracts for Domna v2.xlsx",
+    sheet_name="Properties"
+)
+
+desired_ownerships = asset_data_v2[
+    ~asset_data_v2["AH Tenure"].isin(
+        {"Commercial",
+         "Freeholder",
+         "HOMEBUY / EQUITY LOAN",
+         "Leaseholder",
+         "Outright Sale",
+         "SHARED EQUITY",
+         "Shared Ownership"}
+    )
+]
+
+desired_ownerships["Ownership Type"].value_counts()
+
+removed_ownerships = initial_asset_data[
+    ~initial_asset_data["UPRN"].isin(desired_ownerships["UPRN"].values)
+]["Ownership Type"].value_counts()
+
+sal = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final SAL/20260112 - "
+    "ownership filtered sal.xlsx",
+    sheet_name="Standardised Asset List"
+)
+
+# What did we include, that we shouldn't have?
+should_have_been_dropped = sal[
+    ~sal["landlord_property_id"].isin(desired_ownerships["UPRN"].values)
+]
+
+needs_to_be_added = desired_ownerships[
+    ~desired_ownerships["UPRN"].isin(sal["landlord_property_id"].values)
+]
+
+# Merge on ownership types
+sal = sal.merge(
+    initial_asset_data[["UPRN", "Ownership Type"]],
+    left_on="domna_property_id",
+    right_on="UPRN",
+)
+
+# Remove the irrelevant ownership types
+sal = sal[
+    ~sal["Ownership Type"].isin(
+        [
+            # Commercial # Everything is resi - based on the Residential Indicator variable - all are true
+            # Freeholder
+            "FREEHOLDER",  # 19517 properties
+            # HOMEBUY / EQUITY LOAN
+            "Rent to Homebuy",  # 1 property
+            # Leaseholder
+            "LEASEHOLD 100%",  # 8455 properties
+            "Owned and Managed - 999 year lease",  # 2076 properties
+            "Managed but not Owned-Private Lease",  # 159 properties
+            "Owned and managed LEASEHOLD",  # 26 properties
+            # Outright Sale - can't find anything matching
+            # SHARED EQUITY
+            "Shared Ownership",  # 4065 properties
+            "Shared Ownership Owned Not Managed",  # 23 properties
+            # Extra categories which seem sensible to exclude
+            "NOT MANAGED AND NOT OWNED"
+        ]
+    )
+]
+
+sal["landlord_property_id"] = sal["domna_property_id"].copy()
+
+# Store this SAL in three batches
+filename = (
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final SAL/20260112 - "
+    "ownership filtered sal.xlsx"
+)
+with pd.ExcelWriter(filename) as writer:
+    sal.to_excel(writer, sheet_name="Standardised Asset List", index=False)
+    # Store the three sections
+    sal[0:20000].to_excel(writer, sheet_name="Batch 1", index=False)
+    sal[20000:40000].to_excel(writer, sheet_name="Batch 2", index=False)
+    sal[40000:].to_excel(writer, sheet_name="Batch 3", index=False)
+
+# Test reading back in and assembling
+# b1 = pd.read_excel(
+#     filename,
+#     sheet_name="Batch 1"
+# )
+# b2 = pd.read_excel(
+#     filename,
+#     sheet_name="Batch 2"
+# )
+# b3 = pd.read_excel(
+#     filename,
+#     sheet_name="Batch 3"
+# )
+# assembled_sal = pd.concat([b1, b2, b3])
+# # Make sure we have the right # of UPRNs
+# assert assembled_sal["epc_os_uprn"].nunique() == sal["epc_os_uprn"].nunique()

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

@@ -0,0 +1,293 @@
+# ------ Pull in the full SAL sample ------
+import pandas as pd
+
+full_sal = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final "
+    "SAL/Depracated/20260107 corrected batch 6 sal.xlsx",
+    sheet_name="Standardised Asset List"
+)
+
+# ------Pull in the reduced sample ------
+# This has a slightly incorrect mix of ownership types. Some properties will need to be dropped and others, added
+reduced_sal = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final SAL/20260112 - "
+    "ownership filtered sal.xlsx",
+    sheet_name="Standardised Asset List"
+)
+
+# ------ Pull in the confirmed ownership column from Peabody ------
+new_asset_data = pd.read_excel(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final SAL/2025_11_11 "
+    "- Peabody "
+    "- Data Extracts for Domna v2.xlsx",
+    sheet_name="Properties"
+)
+
+correct_sample = new_asset_data[
+    ~new_asset_data["AH Tenure"].isin(
+        ["Commercial",
+         "Freeholder",
+         "HOMEBUY / EQUITY LOAN",
+         "Leaseholder",
+         "Outright Sale",
+         "SHARED EQUITY",
+         "Shared Ownership"]
+    )
+].copy()
+
+# ------- Stuff to add -------
+# These are properties that need to be added to the reduced sample, from the SAL
+stuff_to_add = correct_sample[
+    ~correct_sample["UPRN"].isin(reduced_sal["landlord_property_id"].values)
+]["UPRN"].values
+
+sal_to_add = full_sal[
+    full_sal["domna_property_id"].isin(stuff_to_add)
+].copy()
+
+# ------- Stuff to remove -------
+stuff_to_remove = reduced_sal[
+    ~reduced_sal["landlord_property_id"].isin(correct_sample["UPRN"].values)
+]["landlord_property_id"].values
+
+to_delete = reduced_sal[
+    reduced_sal["landlord_property_id"].isin(stuff_to_remove)
+].copy()
+
+# ------- Create the correctly formatted SAL, with an individual batch for properties we need to add -------
+
+# This is what is correct, from the reduced sample, after removing the incorrect ownership types
+reduced_sal_final = reduced_sal[
+    ~reduced_sal["landlord_property_id"].isin(stuff_to_remove)
+].copy()
+
+sal_to_add["landlord_property_id"] = sal_to_add["domna_property_id"].copy()
+
+full_sal = pd.concat(
+    [reduced_sal_final, sal_to_add],
+)
+
+# filename = (
+#     "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/Final SAL/20260113 - "
+#     "final asset list.xlsx"
+# )
+# with pd.ExcelWriter(filename) as writer:
+#     full_sal.to_excel(writer, sheet_name="Standardised Asset List", index=False)
+#     # Store the three sections
+#     reduced_sal_final[0:25000].to_excel(writer, sheet_name="Batch 1 - was correct", index=False)
+#     reduced_sal_final[25000:].to_excel(writer, sheet_name="Batch 2 - was correct", index=False)
+#     sal_to_add.to_excel(writer, sheet_name="Batch 3 - needs adding", index=False)
+
+# We now prepare the process of getting the associated
+# We have the properties we need to delete. We can get their associated plans for all scenario IDs
+scenario_ids = [908, 909, 910]
+
+import pandas as pd
+from sqlalchemy.orm import Session
+from backend.app.db.models.portfolio import PropertyModel
+from backend.app.db.connection import db_session, db_read_session
+from sqlalchemy import select, func
+from sqlalchemy.orm import Session
+from backend.app.db.models.recommendations import Plan
+
+uprns_to_be_deleted = to_delete["epc_os_uprn"].values.tolist()
+
+# PORTFOLIO_ID = 435
+
+# SCENARIO_ID_WITH_PLANS_TO_DELETE = 910
+
+
+# Get the property IDs for these UPRNs
+# def get_property_ids_for_uprns(session: Session, uprns: list[int], portfolio_id) -> list[int]:
+#     return [
+#         property_id
+#         for (property_id,) in
+#         session.query(PropertyModel.id)
+#         .filter(
+#             PropertyModel.uprn.in_(uprns),
+#             PropertyModel.portfolio_id == portfolio_id
+#         )
+#         .all()
+#     ]
+#
+#
+# with db_read_session() as session:
+#     property_ids_to_delete = get_property_ids_for_uprns(
+#         session, uprns_to_be_deleted, portfolio_id=PORTFOLIO_ID
+#     )
+#
+#
+# def count_plans_for_scenario(session: Session, scenario_id: int, portfolio_id, property_ids) -> int:
+#     return session.execute(
+#         select(func.count())
+#         .select_from(Plan)
+#         .where(
+#             Plan.scenario_id == scenario_id,
+#             Plan.portfolio_id == portfolio_id,
+#             Plan.property_id.in_(property_ids)
+#         )
+#     ).scalar_one()
+#
+#
+# with db_session() as session:
+#     n_plans = count_plans_for_scenario(
+#         session,
+#         scenario_id=SCENARIO_ID_WITH_PLANS_TO_DELETE,
+#         portfolio_id=PORTFOLIO_ID,
+#         property_ids=property_ids_to_delete
+#     )
+#
+#
+# def get_plan_ids_for_scenario(
+#     session: Session, scenario_id: int, portfolio_id, property_ids
+# ) -> list[int]:
+#     result = session.execute(
+#         select(Plan.id, Plan.property_id)
+#         .where(
+#             Plan.scenario_id == scenario_id,
+#             Plan.portfolio_id == portfolio_id,
+#             Plan.property_id.in_(property_ids)
+#         )
+#     )
+#     return [{"plan_id": row.id, "property_id": row.property_id} for row in result]
+#
+#
+# with db_session() as session:
+#     plan_ids_to_property = get_plan_ids_for_scenario(
+#         session,
+#         scenario_id=SCENARIO_ID_WITH_PLANS_TO_DELETE,
+#         portfolio_id=PORTFOLIO_ID,
+#         property_ids=property_ids_to_delete
+#     )
+#
+# df = pd.DataFrame(plan_ids_to_property)
+# df[df["property_id"].duplicated()].shape
+#
+# plan_ids = [row["plan_id"] for row in plan_ids_to_property]
+#
+#
+# def chunked(iterable, size):
+#     for i in range(0, len(iterable), size):
+#         yield iterable[i:i + size]
+#
+#
+# from sqlalchemy import text
+# from sqlalchemy.orm import Session
+#
+#
+# def delete_plan_batch(session: Session, plan_ids: list[int]):
+#     if not plan_ids:
+#         return
+#
+#     session.execute(text("SET LOCAL lock_timeout = '5s'"))
+#
+#     params = {"plan_ids": plan_ids}
+#
+#     # ----------------------------
+#     # recommendation_materials
+#     # ----------------------------
+#     session.execute(
+#         text("""
+#             DELETE FROM recommendation_materials rm
+#             USING plan_recommendations pr
+#             WHERE rm.recommendation_id = pr.recommendation_id
+#               AND pr.plan_id = ANY(:plan_ids)
+#         """),
+#         params,
+#     )
+#
+#     # ----------------------------
+#     # plan_recommendations
+#     # ----------------------------
+#     session.execute(
+#         text("""
+#             DELETE FROM plan_recommendations
+#             WHERE plan_id = ANY(:plan_ids)
+#         """),
+#         params,
+#     )
+#
+#     # ----------------------------
+#     # recommendations (only those used by these plans)
+#     # ----------------------------
+#     session.execute(
+#         text("""
+#             DELETE FROM recommendation r
+#             WHERE r.id IN (
+#                 SELECT DISTINCT recommendation_id
+#                 FROM plan_recommendations
+#                 WHERE plan_id = ANY(:plan_ids)
+#             )
+#         """),
+#         params,
+#     )
+#
+#     # ----------------------------
+#     # plans LAST
+#     # ----------------------------
+#     session.execute(
+#         text("""
+#             DELETE FROM plan
+#             WHERE id = ANY(:plan_ids)
+#         """),
+#         params,
+#     )
+#
+#
+# batch_size = 25
+# total = (len(plan_ids) + batch_size - 1) // batch_size
+#
+# for i, batch in enumerate(chunked(plan_ids, batch_size), start=1):
+#     print(f"Deleting plan batch {i}/{total} ({len(batch)} plans)")
+#
+#     with db_session() as session:
+#         delete_plan_batch(session, batch)
+#
+#     print(f"Batch {i} committed")
+#
+# # Now, we delete the associated properties in batch and associated objects. It should
+# # largely be property, property details
+# property_ids_to_delete
+#
+# from sqlalchemy import text
+# from sqlalchemy.orm import Session
+#
+#
+# def move_properties_between_portfolios(
+#     session: Session,
+#     property_ids: list[int],
+#     from_portfolio_id: int,
+#     to_portfolio_id: int,
+# ):
+#     if not property_ids:
+#         return 0
+#
+#     result = session.execute(
+#         text("""
+#             UPDATE property
+#             SET portfolio_id = :to_portfolio_id
+#             WHERE portfolio_id = :from_portfolio_id
+#               AND id = ANY(:property_ids)
+#         """),
+#         {
+#             "property_ids": property_ids,
+#             "from_portfolio_id": from_portfolio_id,
+#             "to_portfolio_id": to_portfolio_id,
+#         },
+#     )
+#
+#     return result.rowcount
+#
+#
+# # Moved?
+# # 573476, 586011
+#
+# property_ids_to_delete2 = [x for x in property_ids_to_delete if x not in [573476, 586011]]
+#
+# with db_session() as session:
+#     n_moved = move_properties_between_portfolios(
+#         session,
+#         property_ids=property_ids_to_delete2,
+#         from_portfolio_id=PORTFOLIO_ID,
+#         to_portfolio_id=32,  # Archive portfolio
+#     )

infrastructure/terraform/main.tf

@@ -87,8 +87,6 @@ resource "aws_db_instance" "default" {
   apply_immediately = true
   # Set up storage type to gp3 for better performance
   storage_type      = "gp3"
-  # Instance size
-  instance_class    = "db.t4g.medium"
 }
 
 # Set up the bucket that recieve the csv uploads of epc to be retrofit

sfr/principal_pitch/2_export_data.py

@@ -14,14 +14,16 @@ from collections import defaultdict
 
 # PORTFOLIO_ID = 206
 # SCENARIOS = [389]
-PORTFOLIO_ID = 434  # Peabody
+PORTFOLIO_ID = 435  # Peabody
 SCENARIOS = [
-    904,
+    908,
-    905
+    909,
+    # 910,
 ]
 scenario_names = {
-    904: "EPC C - no solid floor, ashp 3.0",
+    908: "EPC C - no solid floor, ashp 3.0",
-    905: "EPC B - no solid floor, ashp 3.0",
+    909: "EPC C - no solid floor, no EWI or IWI, ashp 3.0",
+    # 910: "EPC B - no solid floor, no EWI, ashp 3.0"
 }
 
 
@@ -231,7 +233,7 @@ for scenario_id in SCENARIOS:
 
     # Create excel to store to
     filename = ("/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting "
-                f"Project/Final SAL/{scenario_names[scenario_id]} - corrected.xlsx")
+                f"Project/Final SAL/{scenario_names[scenario_id]} - 20250113 final.xlsx")
     with pd.ExcelWriter(filename) as writer:
         df.to_excel(writer, sheet_name="properties", index=False)