preparing the already installed code for Peabody

backend/app/db/models/portfolio.py

@@ -106,6 +106,10 @@ class PropertyModel(Base):
     current_epc_rating = Column(Enum(Epc))
     current_sap_points = Column(Float)
     current_valuation = Column(Float)
+    # Following fields are for recording already installed adjustments to a property's SAP
+    installed_measures_sap_point_adjustment = Column(Float)
+    is_sap_points_adjusted_for_installed_measures = Column(Boolean, default=False)
+    original_sap_points = Column(Float)
 
 
 class FeatureRating(enum.Enum):

backend/app/db/models/recommendations.py

@@ -146,3 +146,58 @@ class Scenario(Base):
     valuation_return_on_investment = Column(String)
     property_valuation_increase = Column(Float)
     labour_days = Column(Float)
+
+
+class MeasureType(enum.Enum):
+    air_source_heat_pump = "air_source_heat_pump"
+    boiler_upgrade = "boiler_upgrade"
+    high_heat_retention_storage_heaters = "high_heat_retention_storage_heaters"
+    secondary_heating = "secondary_heating"
+
+    roomstat_programmer_trvs = "roomstat_programmer_trvs"
+    time_temperature_zone_control = "time_temperature_zone_control"
+    cylinder_thermostat = "cylinder_thermostat"
+
+    cavity_wall_insulation = "cavity_wall_insulation"
+    extension_cavity_wall_insulation = "extension_cavity_wall_insulation"
+    external_wall_insulation = "external_wall_insulation"
+    internal_wall_insulation = "internal_wall_insulation"
+    loft_insulation = "loft_insulation"
+    flat_roof_insulation = "flat_roof_insulation"
+    room_roof_insulation = "room_roof_insulation"
+    solid_floor_insulation = "solid_floor_insulation"
+    suspended_floor_insulation = "suspended_floor_insulation"
+
+    double_glazing = "double_glazing"
+    secondary_glazing = "secondary_glazing"
+    draught_proofing = "draught_proofing"
+
+    mechanical_ventilation = "mechanical_ventilation"
+    low_energy_lighting = "low_energy_lighting"
+    solar_pv = "solar_pv"
+    hot_water_tank_insulation = "hot_water_tank_insulation"
+    sealing_open_fireplace = "sealing_open_fireplace"
+
+
+class InstalledMeasure(Base):
+    __tablename__ = "installed_measure"
+
+    id = Column(BigInteger, primary_key=True, autoincrement=True)
+    uprn = Column(BigInteger, nullable=False)
+    measure_type = Column(
+        Enum(
+            MeasureType,
+            name="measure_type",
+            values_callable=lambda e: [m.value for m in e],
+            create_type=False,  # <-- critical
+        ),
+        nullable=False,
+    )
+    installed_at = Column(TIMESTAMP)
+    sap_points = Column(Float)
+    carbon_savings = Column(Float)
+    kwh_savings = Column(Float)
+    bill_savings = Column(Float)
+    heat_demand_savings = Column(Float)
+    source = Column(String)
+    is_active = Column(Boolean, nullable=False, default=True)

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

@@ -120,13 +120,127 @@ retry.to_excel(
 # Delete associated plans
 # 1) Get the property IDs for these UPRNS, for this portfolio
 portfolio_id = 419
-uprns = retry
+uprns = retry["epc_os_uprn"].tolist()
 
 # TODO: Delete all plans for these properties and re-build
-# Plan notes:
+from sqlalchemy.orm import Session
-# UPRN: 5870109770, property ID: 281244 - need to delete and re-build all scenarios
+from backend.app.db.models.portfolio import PropertyModel
-# UPRN: 100022725126, property ID: 283781 - need to delete and re-build all scenarios
+from backend.app.db.connection import db_session
+from backend.app.db.models.recommendations import Plan
+from sqlalchemy import select, delete
+from sqlalchemy.exc import NoResultFound
+from sqlalchemy.orm import sessionmaker
 
 
-# Bugs:
+def get_property_ids_for_uprns(session: Session, portfolio_id: int, uprns: list[int]) -> list[int]:
-12156800
+    return [
+        property.id
+        for property in session.query(PropertyModel)
+        .filter(
+            PropertyModel.portfolio_id == portfolio_id,
+            PropertyModel.uprn.in_(uprns)
+        )
+        .all()
+    ]
+
+
+with db_session() as session:
+    property_ids_to_delete = get_property_ids_for_uprns(session, portfolio_id, uprns)
+
+
+# Get all and delete plans for these property IDs
+def get_all_plans_for_property_ids(session: Session, property_ids: list[int]) -> list[Plan]:
+    return session.query(Plan).filter(Plan.property_id.in_(property_ids)).all()
+
+
+def get_ids_of_plans_for_deletion(session: Session, property_ids: list[int]) -> list[int]:
+    return [
+        plan.id
+        for plan in session.query(Plan)
+        .filter(Plan.property_id.in_(property_ids))
+        .all()
+    ]
+
+
+with db_session() as session:
+    plan_ids_to_delete = get_ids_of_plans_for_deletion(session, property_ids_to_delete)
+
+
+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_to_delete) + batch_size - 1) // batch_size
+
+for i, batch in enumerate(chunked(plan_ids_to_delete, 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")

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

@@ -0,0 +1,714 @@
+import pandas as pd
+from sqlalchemy.orm import sessionmaker
+from backend.app.db.connection import db_engine, db_read_session, db_session
+from backend.app.db.models.recommendations import Recommendation, Plan, PlanRecommendations, RecommendationMaterials, \
+    InstalledMeasure
+from backend.app.db.models.portfolio import PropertyModel, PropertyDetailsEpcModel
+from sqlalchemy import func
+from backend.app.utils import sap_to_epc
+from typing import Dict, List, Set
+from recommendations.Costs import Costs
+from backend.app.db.models.portfolio import Epc
+
+
+def get_all_data(portfolio_id, scenario_ids):
+    session = sessionmaker(bind=db_engine)()
+    session.begin()
+
+    # --------------------
+    # Properties
+    # --------------------
+    properties_query = session.query(
+        PropertyModel,
+        PropertyDetailsEpcModel
+    ).join(
+        PropertyDetailsEpcModel,
+        PropertyModel.id == PropertyDetailsEpcModel.property_id
+    ).filter(
+        PropertyModel.portfolio_id == portfolio_id
+    ).all()
+
+    properties_data = [
+        {
+            **{col.name: getattr(p.PropertyModel, col.name)
+               for col in PropertyModel.__table__.columns},
+            **{col.name: getattr(p.PropertyDetailsEpcModel, col.name)
+               for col in PropertyDetailsEpcModel.__table__.columns},
+        }
+        for p in properties_query
+    ]
+
+    # --------------------
+    # Plans
+    # --------------------
+    plans_query = session.query(Plan).filter(
+        Plan.scenario_id.in_(scenario_ids)
+    ).all()
+
+    plans_data = [
+        {col.name: getattr(plan, col.name) for col in Plan.__table__.columns}
+        for plan in plans_query
+    ]
+
+    plan_ids = [p["id"] for p in plans_data]
+
+    # --------------------
+    # Recommendations (NO materials yet)
+    # --------------------
+    recommendations_query = session.query(
+        Recommendation,
+        Plan.scenario_id
+    ).join(
+        PlanRecommendations,
+        Recommendation.id == PlanRecommendations.recommendation_id
+    ).join(
+        Plan,
+        Plan.id == PlanRecommendations.plan_id
+    ).filter(
+        PlanRecommendations.plan_id.in_(plan_ids),
+    ).all()
+
+    recommendations_data = [
+        {
+            **{col.name: getattr(r.Recommendation, col.name)
+               for col in Recommendation.__table__.columns},
+            "scenario_id": r.scenario_id,
+            "materials": []  # placeholder
+        }
+        for r in recommendations_query
+    ]
+
+    session.close()
+
+    return properties_data, plans_data, recommendations_data
+
+
+PORTFOLIO_ID = 419  # Peabody
+SCENARIOS = [
+    # 871,  # EPC C - fabric first, no solid floor, ashp 3.0
+    # 863,  # EPC B, No EWI/IWI, No Solid Floor, ASHP 3.0 COP
+    # 862,  # EPC B - No solid floor, ASHP COP 3.0
+    # 861,  # EPC C, No EWI/IWI, No Solid Floor, ASHP 3.0 COP
+    # 859,  # EPC C - no solid floor, ashp 3.0
+    885,  # EPC B - fabric first, no solid floor, ashp 3.0
+]
+
+# properties_data, plans_data, recommendations_data = get_all_data(portfolio_id=PORTFOLIO_ID, scenario_ids=SCENARIOS)
+# # Store this data as dataframes for analysis
+# properties_df = pd.DataFrame(properties_data)
+# plans_df = pd.DataFrame(plans_data)
+# recommendations_df = pd.DataFrame(recommendations_data)
+
+# Save CSVs
+# properties_df.to_csv(
+#     "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
+#     "f_peabody_properties_data_20260108.csv",
+#     index=False
+# )
+# plans_df.to_csv(
+#     "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
+#     "f_peabody_plans_data_20260108.csv",
+#     index=False
+# )
+# recommendations_df.to_csv(
+#     "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
+#     "f_peabody_recommendations_data_20260108.csv",
+#     index=False
+# )
+# Read csvs
+properties_df = pd.read_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
+    "f_peabody_properties_data_20260108.csv"
+)
+plans_df = pd.read_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
+    "f_peabody_plans_data_20260108.csv"
+)
+recommendations_df = pd.read_csv(
+    "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
+    "f_peabody_recommendations_data_20260108.csv"
+)
+
+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"
+)
+
+# recommendations_df = pd.read_excel(
+#     "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/EPC B, "
+#     "No solid floor, ASHP COP 3.0.xlsx"
+# )
+
+# recommendations_df2 = recommendations_df2.merge(
+#     properties_df[["id", "uprn"]],
+#     left_on="property_id",
+#     right_on="id",
+#     how="left"
+# ).rename(columns={"id_x": "id"}).drop(columns=["id_y"])
+# recommendations_df["uprn"] = recommendations_df["uprn"].astype(int).astype(str)
+
+# We just need all of the measure types, per property
+recommendation_measure_types = recommendations_df[["property_id", "measure_type"]].drop_duplicates()
+recommendation_measure_types["flag"] = True
+
+# We pivot
+recommendations_measures_pivot = recommendation_measure_types.pivot(
+    index='property_id',
+    columns='measure_type',
+    values='flag'
+)
+recommendations_measures_pivot = recommendations_measures_pivot.reset_index()
+
+# Create a total cost column
+recommendations_total_cost = recommendations_df.groupby("property_id")["estimated_cost"].sum().reset_index()
+recommendations_measures_pivot = recommendations_measures_pivot.merge(
+    recommendations_total_cost, how="left", on="property_id"
+)
+
+properties_to_recs = properties_df.rename(columns={"solar_pv": "solar_data"}).merge(
+    recommendations_measures_pivot, how="left", left_on="id", right_on="property_id"
+)
+properties_to_recs["estimated_cost"] = properties_to_recs["estimated_cost"].fillna(0)
+
+sustainability_data["has_cavity_insulation"] = sustainability_data["Wall Insulation"].isin(
+    ["FilledCavity", "FilledCavityPlusInternal", "FilledCavityPlusExternal"]
+)
+sustainability_data["has_iwi"] = sustainability_data["Wall Insulation"].isin(
+    ["Internal", "FilledCavityPlusInternal"]
+)
+sustainability_data["has_ewi"] = sustainability_data["Wall Insulation"].isin(
+    ["External", "FilledCavityPlusExternal"]
+)
+sustainability_data["has_loft_insulation"] = sustainability_data["Roof Insulation"].isin(
+    ["mm300", "mm250"]
+)
+sustainability_data["has_glazing"] = sustainability_data["Glazing"].isin(
+    ["Double 2002 or later", "Double but age unknown", "Triple", "DoubleKnownData", "Secondary", "TripleKnownData"]
+)
+
+sustainability_data["has_floor_insulation"] = sustainability_data["Floor Insulation"].isin(
+    ["RetroFitted"]
+)
+
+sustainability_data["has_efficient_boiler"] = (
+    sustainability_data["Heating"].isin(["Boilers"]) & sustainability_data["Boiler Efficiency"].isin(["A"])
+)
+sustainability_data["has_ashp"] = (sustainability_data["Heating"].isin(["Heat pumps (wet)"]))
+
+sustainability_data["has_top_heat_controls"] = (
+    sustainability_data["Controls Adequacy"].isin(["Top Spec"])
+)
+
+sustainability_data["has_optimal_heat_controls"] = (
+    sustainability_data["Controls Adequacy"].isin(["Optimal"])
+)
+sustainability_data["has_flat_roof_insulation"] = (
+    (sustainability_data["Roof Construction"] == "Flat") &
+    (sustainability_data["Roof Insulation"].isin(["mm50", "mm150", "mm100"]))
+)
+
+properties_to_recs["uprn"] = properties_to_recs["uprn"].astype(str)
+comparison = sustainability_data.merge(
+    properties_to_recs[
+        ["uprn", "cavity_wall_insulation", "external_wall_insulation", "internal_wall_insulation", "loft_insulation",
+         "double_glazing", "secondary_glazing", "suspended_floor_insulation", "boiler_upgrade", "air_source_heat_pump",
+         "time_temperature_zone_control", "roomstat_programmer_trvs", "flat_roof_insulation", "room_roof_insulation"
+         ]
+    ],
+    left_on="UPRN",
+    right_on="uprn",
+    how="left"
+)
+
+# Flag entries where we've been told that walls are already insulated, but we have recommendations for wall insulation
+# ------------ Walls ------------
+comparison["conflict_cavity_wall_insulation"] = (
+    (comparison["has_cavity_insulation"]) &
+    (pd.isnull(comparison["cavity_wall_insulation"]) == False)
+)
+comparison["conflict_iwi_wall_insulation"] = (
+    (comparison["has_iwi"]) &
+    (pd.isnull(comparison["internal_wall_insulation"]) == False)
+)
+comparison["conflict_ewi_wall_insulation"] = (
+    (comparison["has_ewi"]) &
+    (pd.isnull(comparison["internal_wall_insulation"]) == False)
+)
+
+cwi_conflicting = comparison[comparison["conflict_cavity_wall_insulation"] == True]
+iwi_conflicting = comparison[comparison["conflict_iwi_wall_insulation"] == True]
+ewi_conflicting = comparison[comparison["conflict_ewi_wall_insulation"] == True]
+
+# ------------ Roof ------------
+comparison["conflict_roof_insulation"] = (
+    (comparison["has_loft_insulation"]) &
+    (pd.isnull(comparison["loft_insulation"]) == False)
+)
+
+loft_conflicting = comparison[comparison["conflict_roof_insulation"] == True]
+
+# ------------ Windows ------------
+comparison["conflict_double_glazing"] = (
+    (comparison["has_glazing"]) &
+    (
+        (pd.isnull(comparison["double_glazing"]) == False) | (pd.isnull(comparison["secondary_glazing"]) == False)
+    )
+)
+windows_conflicting = comparison[comparison["conflict_double_glazing"] == True]
+
+# ------------ Floors ------------
+comparison["conflict_suspended_floor_insulation"] = (
+    (comparison["has_floor_insulation"]) &
+    (pd.isnull(comparison["suspended_floor_insulation"]) == False)
+)
+floors_conflicting = comparison[comparison["conflict_suspended_floor_insulation"] == True]
+
+# ------------ Boiler Upgrade ------------
+comparison["conflict_boiler_upgrade"] = (
+    (comparison["has_efficient_boiler"]) &
+    (pd.isnull(comparison["boiler_upgrade"]) == False)
+)
+boiler_conflicting = comparison[comparison["conflict_boiler_upgrade"] == True]
+
+# ------------ ASHP ------------
+comparison["conflict_air_source_heat_pump"] = (
+    (comparison["has_ashp"]) &
+    (pd.isnull(comparison["air_source_heat_pump"]) == False)
+)
+ashp_conflicting = comparison[comparison["conflict_air_source_heat_pump"] == True]
+
+# ------------ heat controls ------------
+comparison["conflict_time_temperature_zone_control"] = (
+    (comparison["has_top_heat_controls"]) &
+    (pd.isnull(comparison["time_temperature_zone_control"]) == False)
+)
+comparison["conflict_roomstat_programmer_trvs"] = (
+    (comparison["has_optimal_heat_controls"]) &
+    (pd.isnull(comparison["roomstat_programmer_trvs"]) == False)
+)
+ttzc_conflicting = comparison[comparison["conflict_time_temperature_zone_control"] == True]
+rst_conflicting = comparison[comparison["conflict_roomstat_programmer_trvs"] == True]
+
+# ------------ Flat Roof Insulation -----------
+comparison["conflict_flat_roof_insulation"] = (
+    (comparison["has_flat_roof_insulation"]) &
+    (pd.isnull(comparison["flat_roof_insulation"]) == False)
+)
+flat_roof_conflicting = comparison[comparison["conflict_flat_roof_insulation"] == True]
+
+# All properties with conflicts
+all_conflicts = pd.concat(
+    [
+        cwi_conflicting,
+        iwi_conflicting,
+        ewi_conflicting,
+        loft_conflicting,
+        windows_conflicting,
+        floors_conflicting,
+        boiler_conflicting,
+        ashp_conflicting,
+        ttzc_conflicting,
+        rst_conflicting,
+        flat_roof_conflicting
+    ]
+)
+
+all_conflicts["UPRN"].nunique()
+
+
+# What do I need to do:
+# TODO: - need to get a view of "all" measures for the property, not just recommended. We can do this but just looking
+#         at one scenario
+# 1) I should store the current recommendations table, for the portfolio as a backup
+# 2) I need a total of already installed SAP points for each property. This should probably be stored on the
+# property_details_epc tabe
+# 3) For anything already installed, I should mark already installed as True, and set the cost to zero
+# 4) I need to update the plan cost to remove the cost of the installed measures
+
+
+### Rebaselining
+
+
+def get_installed_sap_adjustments_by_uprn_for_portfolio(
+    session,
+    portfolio_id: int,
+) -> Dict[int, float]:
+    """
+    Returns { uprn -> total_sap_delta }
+    """
+
+    uprn_subquery = (
+        session.query(PropertyModel.uprn)
+        .filter(PropertyModel.portfolio_id == portfolio_id)
+        .filter(PropertyModel.uprn.isnot(None))
+        .subquery()
+    )
+
+    rows = (
+        session.query(
+            InstalledMeasure.uprn,
+            func.coalesce(func.sum(InstalledMeasure.sap_points), 0.0),
+        )
+        .filter(InstalledMeasure.is_active.is_(True))
+        .filter(InstalledMeasure.uprn.in_(uprn_subquery))
+        .group_by(InstalledMeasure.uprn)
+        .all()
+    )
+
+    return {uprn: float(delta) for uprn, delta in rows}
+
+
+def get_installed_measure_types_by_uprn(
+    session,
+    uprn: int,
+) -> Set[str]:
+    rows = (
+        session.query(InstalledMeasure.measure_type)
+        .filter(InstalledMeasure.uprn == uprn)
+        .filter(InstalledMeasure.is_active.is_(True))
+        .all()
+    )
+
+    # Convert enums → strings
+    return {
+        r[0].value if hasattr(r[0], "value") else r[0]
+        for r in rows
+    }
+
+
+# ------------------------------------------------------------
+# PROPERTY REBASING (READ-ONLY)
+# ------------------------------------------------------------
+
+def compute_property_sap_updates(
+    properties: List[PropertyModel],
+    sap_adjustments: Dict[int, float],
+) -> List[dict]:
+    """
+    Returns property SAP rebasing results.
+    Does NOT mutate DB objects.
+    """
+
+    updates = []
+
+    for prop in properties:
+        if prop.uprn is None or prop.original_sap_points is None:
+            continue
+
+        sap_delta = sap_adjustments.get(prop.uprn, 0.0)
+        new_sap = prop.original_sap_points + sap_delta
+
+        updates.append({
+            "property_id": prop.id,
+            "uprn": prop.uprn,
+            "original_sap_points": prop.original_sap_points,
+            "installed_sap_delta": sap_delta,
+            "new_sap_points": new_sap,
+            "is_adjusted": sap_delta != 0,
+        })
+
+    return updates
+
+
+# ------------------------------------------------------------
+# PLAN RECOMPUTATION HELPERS
+# ------------------------------------------------------------
+
+def get_effective_plan_recommendations(
+    session,
+    plan_id: int,
+    excluded_measure_types: Set[str],
+) -> List[Recommendation]:
+    q = (
+        session.query(Recommendation)
+        .join(PlanRecommendations)
+        .filter(PlanRecommendations.plan_id == plan_id)
+        .filter(Recommendation.default.is_(True))
+    )
+
+    if excluded_measure_types:
+        q = q.filter(
+            ~Recommendation.measure_type.in_(excluded_measure_types)
+        )
+
+    return q.all()
+
+
+def aggregate_plan_metrics(recommendations: list[Recommendation]):
+    agg = {
+        "sap_points": 0.0,
+        "co2_savings": 0.0,
+        "energy_bill_savings": 0.0,
+        "energy_consumption_savings": 0.0,
+        "valuation_increase": 0.0,
+        "cost_of_works": 0.0,
+        "contingency_cost": 0.0,
+    }
+
+    for r in recommendations:
+        agg["sap_points"] += r.sap_points or 0.0
+        agg["co2_savings"] += r.co2_equivalent_savings or 0.0
+        agg["energy_bill_savings"] += r.energy_cost_savings or 0.0
+        agg["energy_consumption_savings"] += r.energy_savings or 0.0
+        agg["valuation_increase"] += r.property_valuation_increase or 0.0
+
+        base_cost = r.estimated_cost or 0.0
+        agg["cost_of_works"] += base_cost
+        agg["contingency_cost"] += calculate_contingency_for_recommendation(r)
+
+    return agg
+
+
+# ------------------------------------------------------------
+# PLAN REBASING (READ-ONLY)
+# ------------------------------------------------------------
+
+def compute_plan_updates(
+    session,
+    plans: List[Plan],
+    properties_by_id: Dict[int, PropertyModel],
+    epcs_by_property_id: Dict[int, PropertyDetailsEpcModel],
+    property_sap_updates: Dict[int, dict],
+) -> List[dict]:
+    """
+    Computes plan metrics assuming properties are already rebased.
+    """
+
+    updates = []
+
+    for plan in plans:
+        prop = properties_by_id.get(plan.property_id)
+        epc = epcs_by_property_id.get(plan.property_id)
+        prop_update = property_sap_updates.get(plan.property_id)
+
+        if not prop or not epc or not prop_update:
+            continue
+
+        installed_types = get_installed_measure_types_by_uprn(
+            session, prop.uprn
+        )
+
+        future_recs = get_effective_plan_recommendations(
+            session,
+            plan.id,
+            installed_types,
+        )
+
+        metrics = aggregate_plan_metrics(future_recs)
+
+        baseline_bill = (
+            epc.heating_cost_current
+            + epc.hot_water_cost_current
+            + epc.lighting_cost_current
+            + epc.appliances_cost_current
+            + epc.gas_standing_charge
+            + epc.electricity_standing_charge
+        )
+
+        post_sap = prop_update["new_sap_points"] + metrics["sap_points"]
+
+        updates.append({
+            "plan_id": plan.id,
+            "property_id": plan.property_id,
+
+            # SAP / EPC
+            "post_sap_points": post_sap,
+            "post_epc_rating": sap_to_epc(post_sap),
+
+            # Carbon
+            "co2_savings": metrics["co2_savings"],
+            "post_co2_emissions": (
+                epc.co2_emissions - metrics["co2_savings"]
+                if epc.co2_emissions is not None
+                else None
+            ),
+
+            # Energy bills
+            "energy_bill_savings": metrics["energy_bill_savings"],
+            "post_energy_bill": baseline_bill - metrics["energy_bill_savings"],
+
+            # Energy consumption
+            "energy_consumption_savings": metrics["energy_consumption_savings"],
+            "post_energy_consumption": (
+                epc.primary_energy_consumption
+                - metrics["energy_consumption_savings"]
+            ),
+
+            # Valuation
+            "valuation_increase": metrics["valuation_increase"],
+            "valuation_post_retrofit": (
+                prop.current_valuation + metrics["valuation_increase"]
+                if prop.current_valuation is not None
+                else None
+            ),
+
+            # Costs
+            "cost_of_works": metrics["cost_of_works"],
+            "contingency_cost": metrics["contingency_cost"],
+        })
+
+    return updates
+
+
+def calculate_contingency_for_recommendation(
+    recommendation,
+) -> float:
+    """
+    Recompute contingency for a recommendation using the same
+    logic as the costing engine.
+
+    Assumptions:
+    - recommendation.estimated_cost is the 'total' cost
+    - contingency is a percentage of total
+    """
+
+    if recommendation.estimated_cost is None:
+        return 0.0
+
+    # Normalise measure_type (Enum → str)
+    measure_type = (
+        recommendation.measure_type.value
+        if hasattr(recommendation.measure_type, "value")
+        else recommendation.measure_type
+    )
+
+    # Measure-specific contingency if defined, else global fallback
+    contingency_rate = Costs.CONTINGENCIES.get(
+        measure_type,
+        Costs.CONTINGENCY,  # default (e.g. 10%)
+    )
+
+    return recommendation.estimated_cost * contingency_rate
+
+
+def persist_property_sap_updates(
+    property_updates_by_id: dict[int, dict],
+):
+    """
+    Writes adjusted SAP values back to property table.
+    Safe to re-run.
+    """
+
+    with db_session() as session:
+        properties = (
+            session.query(PropertyModel)
+            .filter(PropertyModel.id.in_(property_updates_by_id.keys()))
+            .all()
+        )
+
+        for prop in properties:
+            update = property_updates_by_id[prop.id]
+
+            prop.installed_measures_sap_point_adjustment = update["installed_sap_delta"]
+            prop.is_sap_points_adjusted_for_installed_measures = update["is_adjusted"]
+            prop.current_sap_points = update["new_sap_points"]
+            prop.current_epc_rating = sap_to_epc(update["new_sap_points"])
+
+        print(f"✅ Updated {len(properties)} properties")
+
+
+def persist_plan_updates(plan_updates: list[dict]):
+    """
+    Writes recalculated plan metrics.
+    Safe to re-run.
+    """
+
+    with db_session() as session:
+        plans = (
+            session.query(Plan)
+            .filter(Plan.id.in_([u["plan_id"] for u in plan_updates]))
+            .all()
+        )
+
+        plans_by_id = {p.id: p for p in plans}
+
+        for update in plan_updates:
+            plan = plans_by_id.get(update["plan_id"])
+            if not plan:
+                continue
+
+            # SAP / EPC
+            plan.post_sap_points = update["post_sap_points"]
+            plan.post_epc_rating = Epc(update["post_epc_rating"])
+
+            # Carbon
+            plan.co2_savings = update["co2_savings"]
+            plan.post_co2_emissions = update["post_co2_emissions"]
+
+            # Energy
+            plan.energy_bill_savings = update["energy_bill_savings"]
+            plan.post_energy_bill = update["post_energy_bill"]
+
+            plan.energy_consumption_savings = update["energy_consumption_savings"]
+            plan.post_energy_consumption = update["post_energy_consumption"]
+
+            # Valuation
+            plan.valuation_increase = update["valuation_increase"]
+            plan.valuation_post_retrofit = update["valuation_post_retrofit"]
+
+            # Costs
+            plan.cost_of_works = update["cost_of_works"]
+            plan.contingency_cost = update["contingency_cost"]
+
+        print(f"✅ Updated {len(plans)} plans")
+
+
+# ------------------------------------------------------------
+# EXECUTION (DRY RUN)
+# ------------------------------------------------------------
+
+PORTFOLIO_ID = 430
+# TODO - run the original sap points update on the peabody portfolio
+
+with db_read_session() as session:
+    properties = (
+        session.query(PropertyModel)
+        .filter(PropertyModel.portfolio_id == PORTFOLIO_ID)
+        .all()
+    )
+
+    plans = (
+        session.query(Plan)
+        .filter(Plan.portfolio_id == PORTFOLIO_ID)
+        .all()
+    )
+
+    epcs = {
+        e.property_id: e
+        for e in (
+            session.query(PropertyDetailsEpcModel)
+            .join(PropertyModel)
+            .filter(PropertyModel.portfolio_id == PORTFOLIO_ID)
+            .all()
+        )
+    }
+
+    sap_adjustments = get_installed_sap_adjustments_by_uprn_for_portfolio(
+        session,
+        PORTFOLIO_ID,
+    )
+
+    property_updates = compute_property_sap_updates(
+        properties,
+        sap_adjustments,
+    )
+
+    property_updates_by_id = {
+        u["property_id"]: u
+        for u in property_updates
+    }
+
+    properties_by_id = {p.id: p for p in properties}
+
+    plan_updates = compute_plan_updates(
+        session,
+        plans,
+        properties_by_id,
+        epcs,
+        property_updates_by_id,
+    )
+
+# When ready to run!
+persist_property_sap_updates(property_updates_by_id)
+persist_plan_updates(plan_updates)

sfr/principal_pitch/2_export_data.py

@@ -3,11 +3,14 @@ This script prepares the data for the financial model
 """
 
 import pandas as pd
+import numpy as np
 from backend.app.utils import sap_to_epc
 from sqlalchemy.orm import sessionmaker
-from backend.app.db.connection import db_engine
+from backend.app.db.connection import db_engine, db_read_session
-from backend.app.db.models.recommendations import Recommendation, Plan, PlanRecommendations
+from backend.app.db.models.recommendations import Recommendation, Plan, PlanRecommendations, RecommendationMaterials
 from backend.app.db.models.portfolio import PropertyModel, PropertyDetailsEpcModel, PropertyDetailsSpatial
+from backend.app.db.functions.materials_functions import get_materials
+from collections import defaultdict
 
 # PORTFOLIO_ID = 206
 # SCENARIOS = [389]
@@ -18,6 +21,7 @@ SCENARIOS = [
     862,  # EPC B - No solid floor, ASHP COP 3.0
     861,  # EPC C, No EWI/IWI, No Solid Floor, ASHP 3.0 COP
     859,  # EPC C - no solid floor, ashp 3.0
+    885,  # EPC B - fabric first, no solid floor, ashp 3.0
 ]
 scenario_names = {
     871: "EPC C, fabric first, no solid floor, ashp 3.0",
@@ -25,6 +29,7 @@ scenario_names = {
     862: "EPC B, No solid floor, ASHP COP 3.0",
     861: "EPC C, No EWI IWI, No Solid Floor, ASHP 3.0 COP",
     859: "EPC C, no solid floor, ashp 3.0",
+    885: "EPC B, fabric first, no solid floor, ashp 3.0"
 }
 
 
@@ -32,60 +37,97 @@ def get_data(portfolio_id, scenario_ids):
     session = sessionmaker(bind=db_engine)()
     session.begin()
 
-    # Get properties and their details for a specific portfolio
+    # --------------------
+    # Properties
+    # --------------------
     properties_query = session.query(
         PropertyModel,
         PropertyDetailsEpcModel
     ).join(
-        PropertyDetailsEpcModel, PropertyModel.id == PropertyDetailsEpcModel.property_id
+        PropertyDetailsEpcModel,
+        PropertyModel.id == PropertyDetailsEpcModel.property_id
     ).filter(
-        PropertyModel.portfolio_id == portfolio_id  # Filter by portfolio ID
+        PropertyModel.portfolio_id == portfolio_id
     ).all()
 
-    # Transform properties data to include all fields dynamically
     properties_data = [
-        {**{col.name: getattr(prop.PropertyModel, col.name) for col in PropertyModel.__table__.columns},
+        {
-         **{col.name: getattr(prop.PropertyDetailsEpcModel, col.name) for col in
+            **{col.name: getattr(p.PropertyModel, col.name)
-            PropertyDetailsEpcModel.__table__.columns}}
+               for col in PropertyModel.__table__.columns},
-        for prop in properties_query
+            **{col.name: getattr(p.PropertyDetailsEpcModel, col.name)
+               for col in PropertyDetailsEpcModel.__table__.columns},
+        }
+        for p in properties_query
     ]
 
-    # Get property IDs from fetched properties
+    # --------------------
+    # Plans
+    # --------------------
+    plans_query = session.query(Plan).filter(
+        Plan.scenario_id.in_(scenario_ids)
+    ).all()
 
-    # Get plans linked to the fetched properties
-    plans_query = session.query(Plan).filter(Plan.scenario_id.in_(scenario_ids)).all()
-
-    # Transform plans data to include all fields dynamically
     plans_data = [
         {col.name: getattr(plan, col.name) for col in Plan.__table__.columns}
         for plan in plans_query
     ]
 
-    # Extract plan IDs for filtering recommendations through PlanRecommendations
+    plan_ids = [p["id"] for p in plans_data]
-    plan_ids = [plan['id'] for plan in plans_data]
 
-    # Get recommendations through PlanRecommendations for those plans and that are default
+    # --------------------
+    # Recommendations (NO materials yet)
+    # --------------------
     recommendations_query = session.query(
         Recommendation,
         Plan.scenario_id
     ).join(
-        PlanRecommendations, Recommendation.id == PlanRecommendations.recommendation_id
+        PlanRecommendations,
+        Recommendation.id == PlanRecommendations.recommendation_id
     ).join(
-        Plan, Plan.id == PlanRecommendations.plan_id  # Join with Plan to access scenario_id
+        Plan,
+        Plan.id == PlanRecommendations.plan_id
     ).filter(
         PlanRecommendations.plan_id.in_(plan_ids),
-        Recommendation.default == True  # Filtering for default recommendations
+        Recommendation.default.is_(True)
     ).all()
 
-    # Transform recommendations data to include all fields dynamically and include scenario_id
     recommendations_data = [
-        {**{col.name: getattr(rec.Recommendation, col.name) if hasattr(rec, 'Recommendation') else getattr(rec,
+        {
-                                                                                                           col.name) for
+            **{col.name: getattr(r.Recommendation, col.name)
-            col in Recommendation.__table__.columns},
+               for col in Recommendation.__table__.columns},
-         "Scenario ID": rec.scenario_id}
+            "scenario_id": r.scenario_id,
-        for rec in recommendations_query
+            "materials": []  # placeholder
+        }
+        for r in recommendations_query
     ]
 
+    recommendation_ids = [r["id"] for r in recommendations_data]
+
+    # --------------------
+    # Recommendation materials (SEPARATE QUERY)
+    # --------------------
+    materials_query = session.query(
+        RecommendationMaterials
+    ).filter(
+        RecommendationMaterials.recommendation_id.in_(recommendation_ids)
+    ).all()
+
+    # Group materials by recommendation_id
+    materials_by_recommendation = defaultdict(list)
+
+    for m in materials_query:
+        materials_by_recommendation[m.recommendation_id].append({
+            "material_id": m.material_id,
+            "depth": m.depth,
+            "quantity": m.quantity,
+            "quantity_unit": m.quantity_unit,
+            "estimated_cost": m.estimated_cost,
+        })
+
+    # Attach materials safely (no filtering side effects)
+    for r in recommendations_data:
+        r["materials"] = materials_by_recommendation.get(r["id"], [])
+
     session.close()
 
     return properties_data, plans_data, recommendations_data
@@ -97,6 +139,40 @@ properties_df = pd.DataFrame(properties_data)
 plans_df = pd.DataFrame(plans_data)
 recommendations_df = pd.DataFrame(recommendations_data)
 
+with db_read_session() as session:
+    materials = get_materials(session)
+
+materials = pd.DataFrame(materials)
+
+material_lookup = (
+    materials
+    .set_index("id")[["type", "includes_battery"]]
+    .to_dict("index")
+)
+
+
+def has_solar_with_battery(materials_list):
+    for m in materials_list or []:
+        mat = material_lookup.get(m["material_id"])
+        if not mat:
+            continue
+        if mat["type"] == "solar_pv" and mat["includes_battery"]:
+            return True
+    return False
+
+
+recommendations_df["has_solar_with_battery"] = (
+    recommendations_df["materials"].apply(has_solar_with_battery)
+)
+
+recommendations_df["measure_type"] = np.where(
+    recommendations_df["has_solar_with_battery"] == True,
+    recommendations_df["measure_type"] + "_with_battery",
+    recommendations_df["measure_type"]
+)
+
+# Adjust material type to indicate if there is a battery included
+
 from utils.s3 import read_csv_from_s3, read_excel_from_s3
 
 # asset_list = read_excel_from_s3(
@@ -107,13 +183,13 @@ from utils.s3 import read_csv_from_s3, read_excel_from_s3
 
 for scenario_id in SCENARIOS:
     # Get recs for this scenario
-    recommended_measures_df = recommendations_df[recommendations_df["Scenario ID"] == scenario_id][
+    recommended_measures_df = recommendations_df[recommendations_df["scenario_id"] == scenario_id][
         ["property_id", "measure_type", "estimated_cost", "default"]
     ]
     recommended_measures_df = recommended_measures_df[recommended_measures_df["default"]]
     recommended_measures_df = recommended_measures_df.drop(columns=["default"])
 
-    post_install_sap = recommendations_df[recommendations_df["Scenario ID"] == scenario_id][
+    post_install_sap = recommendations_df[recommendations_df["scenario_id"] == scenario_id][
         ["property_id", "default", "sap_points"]]
     post_install_sap = post_install_sap[post_install_sap["default"]]
     # Sum up the sap points by property id