mirror of
https://github.com/Hestia-Homes/Model.git
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Merge pull request #644 from Hestia-Homes/portfolio-diagnostics
Portfolio diagnostics - adding additional logging to engine
This commit is contained in:
KhalimCK
GitHub
commit
491d052279
8 changed files with 1445 additions and 41 deletions
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@ -1,15 +1,11 @@
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from sqlalchemy import text
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from sqlalchemy import insert, delete, select
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from sqlalchemy import insert, delete
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from sqlalchemy.orm import Session
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from sqlalchemy.exc import SQLAlchemyError
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from backend.app.db.models.recommendations import (
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Plan, Recommendation, RecommendationMaterials, PlanRecommendations, Scenario
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)
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from backend.app.db.models.portfolio import (
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PropertyModel, PropertyTargetsModel, PropertyDetailsEpcModel
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)
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from backend.app.db.models.funding import FundingPackageMeasures, FundingPackage
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from backend.app.db.models.inspections import InspectionModel
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from backend.app.db.models.portfolio import PropertyModel
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from backend.app.db.connection import db_session, db_read_session
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@ -106,6 +106,10 @@ class PropertyModel(Base):
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current_epc_rating = Column(Enum(Epc))
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current_sap_points = Column(Float)
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current_valuation = Column(Float)
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# Following fields are for recording already installed adjustments to a property's SAP
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installed_measures_sap_point_adjustment = Column(Float)
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is_sap_points_adjusted_for_installed_measures = Column(Boolean, default=False)
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original_sap_points = Column(Float)
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class FeatureRating(enum.Enum):
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@ -188,6 +192,18 @@ class PropertyDetailsEpcModel(Base):
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gas_standing_charge = Column(Float)
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electricity_standing_charge = Column(Float)
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# Columns for re-baselining if we have an already installed measure
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original_co2_emissions = Column(Float)
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original_primary_energy_consumption = Column(Float)
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original_current_energy_demand = Column(Float)
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original_current_energy_demand_heating_hotwater = Column(Float)
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# Adjustments
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installed_measures_co2_adjustment = Column(Float)
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installed_measures_energy_demand_adjustment = Column(Float)
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installed_measures_total_energy_bill_adjustment = Column(Float)
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installed_measures_heat_demand_adjustment = Column(Float)
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is_epc_adjusted_for_installed_measures = Column(Boolean, default=False)
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class PropertyDetailsSpatial(Base):
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__tablename__ = "property_details_spatial"
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@ -146,3 +146,58 @@ class Scenario(Base):
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valuation_return_on_investment = Column(String)
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property_valuation_increase = Column(Float)
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labour_days = Column(Float)
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class MeasureType(enum.Enum):
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air_source_heat_pump = "air_source_heat_pump"
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boiler_upgrade = "boiler_upgrade"
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high_heat_retention_storage_heaters = "high_heat_retention_storage_heaters"
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secondary_heating = "secondary_heating"
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roomstat_programmer_trvs = "roomstat_programmer_trvs"
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time_temperature_zone_control = "time_temperature_zone_control"
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cylinder_thermostat = "cylinder_thermostat"
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cavity_wall_insulation = "cavity_wall_insulation"
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extension_cavity_wall_insulation = "extension_cavity_wall_insulation"
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external_wall_insulation = "external_wall_insulation"
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internal_wall_insulation = "internal_wall_insulation"
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loft_insulation = "loft_insulation"
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flat_roof_insulation = "flat_roof_insulation"
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room_roof_insulation = "room_roof_insulation"
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solid_floor_insulation = "solid_floor_insulation"
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suspended_floor_insulation = "suspended_floor_insulation"
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double_glazing = "double_glazing"
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secondary_glazing = "secondary_glazing"
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draught_proofing = "draught_proofing"
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mechanical_ventilation = "mechanical_ventilation"
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low_energy_lighting = "low_energy_lighting"
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solar_pv = "solar_pv"
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hot_water_tank_insulation = "hot_water_tank_insulation"
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sealing_open_fireplace = "sealing_open_fireplace"
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class InstalledMeasure(Base):
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__tablename__ = "installed_measure"
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id = Column(BigInteger, primary_key=True, autoincrement=True)
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uprn = Column(BigInteger, nullable=False)
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measure_type = Column(
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Enum(
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MeasureType,
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name="measure_type",
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values_callable=lambda e: [m.value for m in e],
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create_type=False, # <-- critical
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),
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nullable=False,
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)
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installed_at = Column(TIMESTAMP)
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sap_points = Column(Float)
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carbon_savings = Column(Float)
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kwh_savings = Column(Float)
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bill_savings = Column(Float)
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heat_demand_savings = Column(Float)
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source = Column(String)
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is_active = Column(Boolean, nullable=False, default=True)
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@ -535,12 +535,14 @@ async def model_engine(body: PlanTriggerRequest):
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logger.info("Getting the inputs")
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if body.file_type == "xlsx":
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logger.info("Getting the plan input")
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plan_input = read_excel_from_s3(
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bucket_name=get_settings().PLAN_TRIGGER_BUCKET,
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file_key=body.trigger_file_path,
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sheet_name=body.sheet_name,
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header_row=0,
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)
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logger.into("Got the plan input from excel")
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# We now handle the case where the input data is a Domna standardised assset list
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if body.file_format == "domna_asset_list":
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@ -619,9 +621,11 @@ async def model_engine(body: PlanTriggerRequest):
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raise ValueError("Other formats not yet supported")
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else:
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logger.info("Getting the plan input from csv")
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plan_input = read_csv_from_s3(
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bucket_name=get_settings().PLAN_TRIGGER_BUCKET, filepath=body.trigger_file_path
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)
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logger.info("Got the plan input from csv")
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# We then slide it on the indexes if they are provided
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if body.index_start is not None and body.index_end is not None:
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@ -640,12 +644,14 @@ async def model_engine(body: PlanTriggerRequest):
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if "domna_valuation" in plan_input[0]:
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valuation_data = [{"uprn": x["uprn"], "valuation": x["domna_valuation"]} for x in plan_input]
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logger.info("Getting cleaning_data")
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cleaning_data = read_dataframe_from_s3_parquet(
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bucket_name=get_settings().DATA_BUCKET, file_key="sap_change_model/cleaning_dataset.parquet",
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)
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# Prepare input data
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addresses = Addresses.from_plan_input(plan_input, body)
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logger.info("Checking database for existing properties")
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uprns = addresses.get_uprns()
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landlord_ids = addresses.get_landlord_ids()
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@ -670,6 +676,7 @@ async def model_engine(body: PlanTriggerRequest):
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if key not in property_lookup:
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to_create.append(addr)
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logger.info("Checking database for EPC cache")
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# Pre-requests to the db
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with db_read_session() as session:
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epc_cache_by_uprn = db_funcs.epc_functions.EpcStoreService.get_epcs_for_uprns(session, uprns)
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@ -679,6 +686,7 @@ async def model_engine(body: PlanTriggerRequest):
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)
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# If we have properties that need to be created, we cerate them in bulk
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logger.info("Determine new properties to be created")
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new_property_ids = set()
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if to_create:
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logger.info("Creating %d new properties", len(to_create))
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@ -120,13 +120,127 @@ retry.to_excel(
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# Delete associated plans
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# 1) Get the property IDs for these UPRNS, for this portfolio
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portfolio_id = 419
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uprns = retry
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uprns = retry["epc_os_uprn"].tolist()
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# TODO: Delete all plans for these properties and re-build
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# Plan notes:
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# UPRN: 5870109770, property ID: 281244 - need to delete and re-build all scenarios
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# UPRN: 100022725126, property ID: 283781 - need to delete and re-build all scenarios
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from sqlalchemy.orm import Session
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from backend.app.db.models.portfolio import PropertyModel
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from backend.app.db.connection import db_session
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from backend.app.db.models.recommendations import Plan
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from sqlalchemy import select, delete
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from sqlalchemy.exc import NoResultFound
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from sqlalchemy.orm import sessionmaker
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# Bugs:
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12156800
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def get_property_ids_for_uprns(session: Session, portfolio_id: int, uprns: list[int]) -> list[int]:
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return [
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property.id
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for property in session.query(PropertyModel)
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.filter(
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PropertyModel.portfolio_id == portfolio_id,
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PropertyModel.uprn.in_(uprns)
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)
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.all()
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]
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with db_session() as session:
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property_ids_to_delete = get_property_ids_for_uprns(session, portfolio_id, uprns)
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# Get all and delete plans for these property IDs
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def get_all_plans_for_property_ids(session: Session, property_ids: list[int]) -> list[Plan]:
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return session.query(Plan).filter(Plan.property_id.in_(property_ids)).all()
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def get_ids_of_plans_for_deletion(session: Session, property_ids: list[int]) -> list[int]:
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return [
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plan.id
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for plan in session.query(Plan)
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.filter(Plan.property_id.in_(property_ids))
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.all()
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]
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with db_session() as session:
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plan_ids_to_delete = get_ids_of_plans_for_deletion(session, property_ids_to_delete)
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def chunked(iterable, size):
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for i in range(0, len(iterable), size):
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yield iterable[i:i + size]
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from sqlalchemy import text
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from sqlalchemy.orm import Session
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def delete_plan_batch(session: Session, plan_ids: list[int]):
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if not plan_ids:
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return
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session.execute(text("SET LOCAL lock_timeout = '5s'"))
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params = {"plan_ids": plan_ids}
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# ----------------------------
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# recommendation_materials
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# ----------------------------
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session.execute(
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text("""
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DELETE FROM recommendation_materials rm
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USING plan_recommendations pr
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WHERE rm.recommendation_id = pr.recommendation_id
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AND pr.plan_id = ANY(:plan_ids)
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"""),
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params,
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)
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# ----------------------------
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# plan_recommendations
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# ----------------------------
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session.execute(
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text("""
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DELETE FROM plan_recommendations
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WHERE plan_id = ANY(:plan_ids)
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"""),
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params,
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)
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# ----------------------------
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# recommendations (only those used by these plans)
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# ----------------------------
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session.execute(
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text("""
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DELETE FROM recommendation r
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WHERE r.id IN (
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SELECT DISTINCT recommendation_id
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FROM plan_recommendations
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WHERE plan_id = ANY(:plan_ids)
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)
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"""),
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params,
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)
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# ----------------------------
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# plans LAST
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# ----------------------------
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session.execute(
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text("""
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DELETE FROM plan
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WHERE id = ANY(:plan_ids)
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"""),
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params,
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)
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batch_size = 25
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total = (len(plan_ids_to_delete) + batch_size - 1) // batch_size
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for i, batch in enumerate(chunked(plan_ids_to_delete, batch_size), start=1):
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print(f"Deleting plan batch {i}/{total} ({len(batch)} plans)")
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with db_session() as session:
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delete_plan_batch(session, batch)
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print(f"Batch {i} committed")
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@ -0,0 +1,983 @@
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import pandas as pd
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from sqlalchemy.orm import sessionmaker
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from backend.app.db.connection import db_engine, db_read_session, db_session
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from backend.app.db.models.recommendations import Recommendation, Plan, PlanRecommendations, RecommendationMaterials, \
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InstalledMeasure
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from backend.app.db.models.portfolio import PropertyModel, PropertyDetailsEpcModel
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from sqlalchemy import func
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from backend.app.utils import sap_to_epc
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from typing import Dict, List, Set
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from recommendations.Costs import Costs
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from backend.app.db.models.portfolio import Epc
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pd.set_option('display.max_rows', 500)
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pd.set_option('display.max_columns', 500)
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pd.set_option('display.width', 1000)
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def get_all_data(portfolio_id, scenario_ids):
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session = sessionmaker(bind=db_engine)()
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session.begin()
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# --------------------
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# Properties
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# --------------------
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properties_query = session.query(
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PropertyModel,
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PropertyDetailsEpcModel
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).join(
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PropertyDetailsEpcModel,
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PropertyModel.id == PropertyDetailsEpcModel.property_id
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).filter(
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PropertyModel.portfolio_id == portfolio_id
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).all()
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properties_data = [
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{
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**{col.name: getattr(p.PropertyModel, col.name)
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for col in PropertyModel.__table__.columns},
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**{col.name: getattr(p.PropertyDetailsEpcModel, col.name)
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for col in PropertyDetailsEpcModel.__table__.columns},
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}
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for p in properties_query
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]
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# --------------------
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# Plans
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# --------------------
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plans_query = session.query(Plan).filter(
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Plan.scenario_id.in_(scenario_ids)
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).all()
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plans_data = [
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{col.name: getattr(plan, col.name) for col in Plan.__table__.columns}
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for plan in plans_query
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]
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plan_ids = [p["id"] for p in plans_data]
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# --------------------
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# Recommendations (NO materials yet)
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# --------------------
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recommendations_query = session.query(
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Recommendation,
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Plan.scenario_id
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).join(
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PlanRecommendations,
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Recommendation.id == PlanRecommendations.recommendation_id
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).join(
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Plan,
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Plan.id == PlanRecommendations.plan_id
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).filter(
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PlanRecommendations.plan_id.in_(plan_ids),
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).all()
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recommendations_data = [
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{
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**{col.name: getattr(r.Recommendation, col.name)
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for col in Recommendation.__table__.columns},
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"scenario_id": r.scenario_id,
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"materials": [] # placeholder
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}
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for r in recommendations_query
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]
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session.close()
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return properties_data, plans_data, recommendations_data
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PORTFOLIO_ID = 419 # Peabody
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SCENARIOS = [
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# 871, # EPC C - fabric first, no solid floor, ashp 3.0
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# 863, # EPC B, No EWI/IWI, No Solid Floor, ASHP 3.0 COP
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# 862, # EPC B - No solid floor, ASHP COP 3.0
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# 861, # EPC C, No EWI/IWI, No Solid Floor, ASHP 3.0 COP
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# 859, # EPC C - no solid floor, ashp 3.0
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885, # EPC B - fabric first, no solid floor, ashp 3.0
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]
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# properties_data, plans_data, recommendations_data = get_all_data(portfolio_id=PORTFOLIO_ID, scenario_ids=SCENARIOS)
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# # Store this data as dataframes for analysis
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# properties_df = pd.DataFrame(properties_data)
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# plans_df = pd.DataFrame(plans_data)
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# recommendations_df = pd.DataFrame(recommendations_data)
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# Save CSVs
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# properties_df.to_csv(
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# "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
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# "f_peabody_properties_data_20260108.csv",
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# index=False
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# )
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# plans_df.to_csv(
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# "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
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# "f_peabody_plans_data_20260108.csv",
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# index=False
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# )
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# recommendations_df.to_csv(
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# "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
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# "f_peabody_recommendations_data_20260108.csv",
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# index=False
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# )
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# Read csvs
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properties_df = pd.read_csv(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
|
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"f_peabody_properties_data_20260108.csv"
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)
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plans_df = pd.read_csv(
|
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
|
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"f_peabody_plans_data_20260108.csv"
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)
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recommendations_df = pd.read_csv(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/"
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"f_peabody_recommendations_data_20260108.csv"
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)
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sustainability_data = pd.read_excel(
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"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/2025_11_11 - Peabody "
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"- Data Extracts for Domna.xlsx",
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sheet_name="Sustainability"
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)
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# recommendations_df = pd.read_excel(
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# "/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/EPC B, "
|
||||
# "No solid floor, ASHP COP 3.0.xlsx"
|
||||
# )
|
||||
|
||||
# We just need all of the measure types, per property
|
||||
recommendation_measure_types = recommendations_df[
|
||||
["property_id", "measure_type"
|
||||
, "sap_points", "heat_demand", "kwh_savings", "co2_equivalent_savings",
|
||||
"energy_cost_savings"
|
||||
]
|
||||
].drop_duplicates()
|
||||
recommendation_measure_types["flag"] = True
|
||||
|
||||
# We pivot
|
||||
recommendations_measures_pivot = recommendation_measure_types[
|
||||
["property_id", "measure_type", "flag"]
|
||||
].drop_duplicates().pivot(
|
||||
index='property_id',
|
||||
columns='measure_type',
|
||||
values='flag'
|
||||
)
|
||||
recommendations_measures_pivot = recommendations_measures_pivot.reset_index()
|
||||
|
||||
properties_to_recs = properties_df.rename(columns={"solar_pv": "solar_data"}).merge(
|
||||
recommendations_measures_pivot, how="left", on="property_id"
|
||||
)
|
||||
|
||||
sustainability_data["cavity_wall_insulation"] = sustainability_data["Wall Insulation"].isin(
|
||||
["FilledCavity", "FilledCavityPlusInternal", "FilledCavityPlusExternal"]
|
||||
)
|
||||
sustainability_data["internal_wall_insulation"] = sustainability_data["Wall Insulation"].isin(
|
||||
["Internal", "FilledCavityPlusInternal"]
|
||||
)
|
||||
sustainability_data["external_wall_insulation"] = sustainability_data["Wall Insulation"].isin(
|
||||
["External", "FilledCavityPlusExternal"]
|
||||
)
|
||||
sustainability_data["loft_insulation"] = sustainability_data["Roof Insulation"].isin(
|
||||
["mm300", "mm250"]
|
||||
)
|
||||
sustainability_data["double_glazing"] = sustainability_data["Glazing"].isin(
|
||||
["Double 2002 or later", "Double but age unknown", "Triple", "DoubleKnownData", "Secondary", "TripleKnownData"]
|
||||
)
|
||||
sustainability_data["secondary_glazing"] = sustainability_data["Glazing"].isin(
|
||||
["Double 2002 or later", "Double but age unknown", "Triple", "DoubleKnownData", "Secondary", "TripleKnownData"]
|
||||
)
|
||||
|
||||
sustainability_data["suspended_floor_insulation"] = sustainability_data["Floor Insulation"].isin(
|
||||
["RetroFitted"]
|
||||
)
|
||||
|
||||
sustainability_data["boiler_upgrade"] = (
|
||||
sustainability_data["Heating"].isin(["Boilers"]) & sustainability_data["Boiler Efficiency"].isin(["A"])
|
||||
)
|
||||
sustainability_data["air_source_heat_pump"] = (sustainability_data["Heating"].isin(["Heat pumps (wet)"]))
|
||||
|
||||
sustainability_data["time_temperature_zone_control"] = (
|
||||
sustainability_data["Controls Adequacy"].isin(["Top Spec"])
|
||||
)
|
||||
|
||||
sustainability_data["roomstat_programmer_trvs"] = (
|
||||
sustainability_data["Controls Adequacy"].isin(["Optimal"])
|
||||
)
|
||||
sustainability_data["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",
|
||||
suffixes=("", "_from_recs")
|
||||
)
|
||||
|
||||
# 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["cavity_wall_insulation"]) &
|
||||
(pd.isnull(comparison["cavity_wall_insulation_from_recs"]) == False)
|
||||
)
|
||||
comparison["conflict_iwi_wall_insulation"] = (
|
||||
(comparison["internal_wall_insulation"]) &
|
||||
(pd.isnull(comparison["internal_wall_insulation_from_recs"]) == False)
|
||||
)
|
||||
comparison["conflict_ewi_wall_insulation"] = (
|
||||
(comparison["external_wall_insulation"]) &
|
||||
(pd.isnull(comparison["external_wall_insulation_from_recs"]) == 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_loft_insulation"] = (
|
||||
(comparison["loft_insulation"]) &
|
||||
(pd.isnull(comparison["loft_insulation_from_recs"]) == False)
|
||||
)
|
||||
|
||||
loft_conflicting = comparison[comparison["conflict_loft_insulation"] == True]
|
||||
|
||||
# ------------ Windows ------------
|
||||
comparison["conflict_double_glazing"] = (
|
||||
(comparison["double_glazing"]) &
|
||||
(
|
||||
(pd.isnull(comparison["double_glazing_from_recs"]) == False)
|
||||
)
|
||||
)
|
||||
comparison["conflict_secondary_glazing"] = (
|
||||
(comparison["secondary_glazing"]) &
|
||||
(
|
||||
(pd.isnull(comparison["secondary_glazing_from_recs"]) == False)
|
||||
)
|
||||
)
|
||||
double_glazing_conflicting = comparison[comparison["conflict_double_glazing"] == True]
|
||||
secondary_glazing_conflicting = comparison[comparison["conflict_secondary_glazing"] == True]
|
||||
|
||||
# ------------ Floors ------------
|
||||
comparison["conflict_suspended_floor_insulation"] = (
|
||||
(comparison["suspended_floor_insulation"]) &
|
||||
(pd.isnull(comparison["suspended_floor_insulation_from_recs"]) == False)
|
||||
)
|
||||
floors_conflicting = comparison[comparison["conflict_suspended_floor_insulation"] == True]
|
||||
|
||||
# ------------ Boiler Upgrade ------------
|
||||
comparison["conflict_boiler_upgrade"] = (
|
||||
(comparison["boiler_upgrade"]) &
|
||||
(pd.isnull(comparison["boiler_upgrade_from_recs"]) == False)
|
||||
)
|
||||
boiler_conflicting = comparison[comparison["conflict_boiler_upgrade"] == True]
|
||||
|
||||
# ------------ ASHP ------------
|
||||
comparison["conflict_air_source_heat_pump"] = (
|
||||
(comparison["air_source_heat_pump"]) &
|
||||
(pd.isnull(comparison["air_source_heat_pump_from_recs"]) == False)
|
||||
)
|
||||
ashp_conflicting = comparison[comparison["conflict_air_source_heat_pump"] == True]
|
||||
|
||||
# ------------ heat controls ------------
|
||||
comparison["conflict_time_temperature_zone_control"] = (
|
||||
(comparison["time_temperature_zone_control"]) &
|
||||
(pd.isnull(comparison["time_temperature_zone_control_from_recs"]) == False)
|
||||
)
|
||||
comparison["conflict_roomstat_programmer_trvs"] = (
|
||||
(comparison["roomstat_programmer_trvs"]) &
|
||||
(pd.isnull(comparison["roomstat_programmer_trvs_from_recs"]) == 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["flat_roof_insulation"]) &
|
||||
(pd.isnull(comparison["flat_roof_insulation_from_recs"]) == 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,
|
||||
double_glazing_conflicting,
|
||||
secondary_glazing_conflicting,
|
||||
floors_conflicting,
|
||||
boiler_conflicting,
|
||||
ashp_conflicting,
|
||||
ttzc_conflicting,
|
||||
rst_conflicting,
|
||||
flat_roof_conflicting
|
||||
]
|
||||
)
|
||||
|
||||
all_conflicts = all_conflicts[
|
||||
[
|
||||
"uprn",
|
||||
'conflict_cavity_wall_insulation',
|
||||
'conflict_iwi_wall_insulation',
|
||||
'conflict_ewi_wall_insulation',
|
||||
'conflict_loft_insulation',
|
||||
'conflict_double_glazing',
|
||||
'conflict_secondary_glazing',
|
||||
'conflict_suspended_floor_insulation', 'conflict_boiler_upgrade',
|
||||
'conflict_air_source_heat_pump',
|
||||
'conflict_time_temperature_zone_control', 'conflict_roomstat_programmer_trvs', 'conflict_flat_roof_insulation']
|
||||
]
|
||||
|
||||
all_conflicts = all_conflicts.rename(
|
||||
columns={
|
||||
"conflict_cavity_wall_insulation": "cavity_wall_insulation",
|
||||
"conflict_iwi_wall_insulation": "internal_wall_insulation",
|
||||
"conflict_ewi_wall_insulation": "external_wall_insulation",
|
||||
"conflict_loft_insulation": "loft_insulation",
|
||||
"conflict_double_glazing": "double_glazing",
|
||||
"conflict_secondary_glazing": "secondary_glazing",
|
||||
"conflict_suspended_floor_insulation": "suspended_floor_insulation",
|
||||
"conflict_boiler_upgrade": "boiler_upgrade",
|
||||
"conflict_air_source_heat_pump": "air_source_heat_pump",
|
||||
"conflict_time_temperature_zone_control": "time_temperature_zone_control",
|
||||
"conflict_roomstat_programmer_trvs": "roomstat_programmer_trvs",
|
||||
"conflict_flat_roof_insulation": "flat_roof_insulation"
|
||||
|
||||
}
|
||||
)
|
||||
|
||||
# Reshape by UPRN by melting
|
||||
all_conflicts = all_conflicts.melt(
|
||||
id_vars=["uprn"],
|
||||
var_name="measure_type",
|
||||
value_name="already_installed"
|
||||
)
|
||||
|
||||
recommendations_df["property_id"] = recommendations_df["property_id"].astype(int).astype(str)
|
||||
properties_df["property_id"] = properties_df["property_id"].astype(int).astype(str)
|
||||
|
||||
recs_with_uprn = recommendations_df.merge(
|
||||
properties_df[["property_id", "uprn"]],
|
||||
on="property_id",
|
||||
how="left",
|
||||
suffixes=("", "_prop")
|
||||
)
|
||||
|
||||
recs_with_uprn = (
|
||||
recs_with_uprn
|
||||
.sort_values("sap_points", ascending=False)
|
||||
.groupby(["uprn", "measure_type"], as_index=False)
|
||||
.first()
|
||||
)
|
||||
|
||||
recs_with_uprn["uprn"] = recs_with_uprn["uprn"].astype(str)
|
||||
|
||||
installed_measures_df = all_conflicts.merge(
|
||||
recs_with_uprn[["uprn", "measure_type", "sap_points", "heat_demand", "kwh_savings", "co2_equivalent_savings",
|
||||
"energy_cost_savings"]],
|
||||
how="left",
|
||||
on=["uprn", "measure_type"]
|
||||
)
|
||||
|
||||
installed_measures_df = installed_measures_df[installed_measures_df["already_installed"] == True]
|
||||
|
||||
for col in ["sap_points", "heat_demand", "kwh_savings", "co2_equivalent_savings", "energy_cost_savings"]:
|
||||
print(f"n missings for {col}: {pd.isnull(installed_measures_df[col]).sum()}", )
|
||||
|
||||
# Do some calcs on SAP impact
|
||||
sap_impact = installed_measures_df.groupby(["uprn"])["sap_points"].sum().reset_index()
|
||||
properties_sap = properties_df[["uprn", "current_sap_points", "current_epc_rating"]].copy()
|
||||
properties_sap["uprn"] = properties_sap["uprn"].astype(str)
|
||||
|
||||
old_sap_vs_new = properties_sap.merge(
|
||||
sap_impact, how="inner", on="uprn"
|
||||
)
|
||||
old_sap_vs_new["new_sap_points"] = old_sap_vs_new["current_sap_points"] + old_sap_vs_new["sap_points"]
|
||||
old_sap_vs_new["new_epc_rating"] = old_sap_vs_new["new_sap_points"].apply(
|
||||
lambda x: sap_to_epc(x)
|
||||
)
|
||||
# How many properties go from below C to above
|
||||
old_sap_vs_new[old_sap_vs_new["current_sap_points"] < 69]["new_epc_rating"].value_counts()
|
||||
changed = old_sap_vs_new[
|
||||
(old_sap_vs_new["current_sap_points"] < 69) & (old_sap_vs_new["new_sap_points"] >= 69)
|
||||
]
|
||||
properties_df[properties_df["current_sap_points"] < 69].shape
|
||||
|
||||
old_sap_vs_new[old_sap_vs_new["current_epc_rating"].isin(["Epc.F", "Epc.G"])]
|
||||
|
||||
25979 - 3891
|
||||
|
||||
sustainability_data[sustainability_data["UPRN"] == "100021204260"]
|
||||
|
||||
# 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
|
||||
|
||||
|
||||
from typing import Dict
|
||||
from sqlalchemy import func
|
||||
|
||||
|
||||
def get_installed_measure_adjustments_by_uprn_for_portfolio(
|
||||
session,
|
||||
portfolio_id: int,
|
||||
) -> Dict[int, dict]:
|
||||
"""
|
||||
Returns per-UPRN installed-measure adjustments.
|
||||
|
||||
{
|
||||
uprn: {
|
||||
sap_points: float,
|
||||
co2: float,
|
||||
energy_kwh: float,
|
||||
energy_bill: float,
|
||||
heat_demand: float,
|
||||
}
|
||||
}
|
||||
"""
|
||||
|
||||
uprn_subquery = (
|
||||
session.query(PropertyModel.uprn)
|
||||
.filter(PropertyModel.portfolio_id == portfolio_id)
|
||||
.filter(PropertyModel.uprn.isnot(None))
|
||||
.subquery()
|
||||
)
|
||||
|
||||
rows = (
|
||||
session.query(
|
||||
InstalledMeasure.uprn.label("uprn"),
|
||||
|
||||
func.coalesce(func.sum(InstalledMeasure.sap_points), 0.0)
|
||||
.label("sap_points"),
|
||||
|
||||
func.coalesce(func.sum(InstalledMeasure.carbon_savings), 0.0)
|
||||
.label("co2"),
|
||||
|
||||
func.coalesce(func.sum(InstalledMeasure.kwh_savings), 0.0)
|
||||
.label("energy_kwh"),
|
||||
|
||||
func.coalesce(func.sum(InstalledMeasure.bill_savings), 0.0)
|
||||
.label("energy_bill"),
|
||||
|
||||
func.coalesce(func.sum(InstalledMeasure.heat_demand_savings), 0.0)
|
||||
.label("heat_demand"),
|
||||
)
|
||||
.filter(InstalledMeasure.is_active.is_(True))
|
||||
.filter(InstalledMeasure.uprn.in_(uprn_subquery))
|
||||
.group_by(InstalledMeasure.uprn)
|
||||
.all()
|
||||
)
|
||||
|
||||
return {
|
||||
row.uprn: {
|
||||
"sap_points": float(row.sap_points),
|
||||
"co2": float(row.co2),
|
||||
"energy_kwh": float(row.energy_kwh),
|
||||
"energy_bill": float(row.energy_bill),
|
||||
"heat_demand": float(row.heat_demand),
|
||||
}
|
||||
for row 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 compute_epc_rebasing_updates(
|
||||
epcs: Dict[int, PropertyDetailsEpcModel],
|
||||
properties_by_id: Dict[int, PropertyModel],
|
||||
installed_adjustments_by_uprn: Dict[int, dict],
|
||||
) -> Dict[int, dict]:
|
||||
"""
|
||||
Computes EPC rebasing updates without mutating DB objects.
|
||||
Keyed by property_id.
|
||||
"""
|
||||
|
||||
updates: Dict[int, dict] = {}
|
||||
|
||||
for property_id, epc in epcs.items():
|
||||
prop = properties_by_id.get(property_id)
|
||||
if not prop or prop.uprn is None:
|
||||
continue
|
||||
|
||||
adj = installed_adjustments_by_uprn.get(prop.uprn)
|
||||
if not adj:
|
||||
continue
|
||||
|
||||
updates[property_id] = {
|
||||
"property_id": property_id,
|
||||
|
||||
# Originals (only set once)
|
||||
"original_co2_emissions": (
|
||||
epc.original_co2_emissions
|
||||
if epc.original_co2_emissions is not None
|
||||
else epc.co2_emissions
|
||||
),
|
||||
"original_primary_energy_consumption": (
|
||||
epc.original_primary_energy_consumption
|
||||
if epc.original_primary_energy_consumption is not None
|
||||
else epc.primary_energy_consumption
|
||||
),
|
||||
"original_current_energy_demand": (
|
||||
epc.original_current_energy_demand
|
||||
if epc.original_current_energy_demand is not None
|
||||
else epc.current_energy_demand
|
||||
),
|
||||
"original_current_energy_demand_heating_hotwater": (
|
||||
epc.original_current_energy_demand_heating_hotwater
|
||||
if epc.original_current_energy_demand_heating_hotwater is not None
|
||||
else epc.current_energy_demand_heating_hotwater
|
||||
),
|
||||
|
||||
# Adjustments (always re-applied from originals)
|
||||
"installed_measures_co2_adjustment": adj["co2"],
|
||||
"installed_measures_energy_demand_adjustment": adj["energy_kwh"],
|
||||
"installed_measures_total_energy_bill_adjustment": adj["energy_bill"],
|
||||
"installed_measures_heat_demand_adjustment": adj["heat_demand"],
|
||||
}
|
||||
|
||||
return updates
|
||||
|
||||
|
||||
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")
|
||||
|
||||
|
||||
def persist_epc_rebasing_updates(
|
||||
epc_updates_by_property_id: Dict[int, dict],
|
||||
):
|
||||
"""
|
||||
Overwrites EPC metrics using installed-measure rebasing.
|
||||
Safe to re-run.
|
||||
"""
|
||||
|
||||
with db_session() as session:
|
||||
epcs = (
|
||||
session.query(PropertyDetailsEpcModel)
|
||||
.filter(
|
||||
PropertyDetailsEpcModel.property_id.in_(
|
||||
epc_updates_by_property_id.keys()
|
||||
)
|
||||
)
|
||||
.all()
|
||||
)
|
||||
|
||||
for epc in epcs:
|
||||
u = epc_updates_by_property_id[epc.property_id]
|
||||
|
||||
# Store originals once
|
||||
epc.original_co2_emissions = u["original_co2_emissions"]
|
||||
epc.original_primary_energy_consumption = (
|
||||
u["original_primary_energy_consumption"]
|
||||
)
|
||||
epc.original_current_energy_demand = (
|
||||
u["original_current_energy_demand"]
|
||||
)
|
||||
epc.original_current_energy_demand_heating_hotwater = (
|
||||
u["original_current_energy_demand_heating_hotwater"]
|
||||
)
|
||||
|
||||
# Apply rebased values
|
||||
epc.co2_emissions = (
|
||||
u["original_co2_emissions"]
|
||||
- u["installed_measures_co2_adjustment"]
|
||||
)
|
||||
|
||||
epc.primary_energy_consumption = (
|
||||
u["original_primary_energy_consumption"]
|
||||
- u["installed_measures_heat_demand_adjustment"]
|
||||
)
|
||||
|
||||
epc.current_energy_demand = (
|
||||
u["original_current_energy_demand"]
|
||||
- u["installed_measures_energy_demand_adjustment"]
|
||||
)
|
||||
|
||||
# Flags + audit fields
|
||||
epc.installed_measures_co2_adjustment = (
|
||||
u["installed_measures_co2_adjustment"]
|
||||
)
|
||||
epc.installed_measures_energy_demand_adjustment = (
|
||||
u["installed_measures_energy_demand_adjustment"]
|
||||
)
|
||||
epc.installed_measures_total_energy_bill_adjustment = (
|
||||
u["installed_measures_total_energy_bill_adjustment"]
|
||||
)
|
||||
epc.installed_measures_heat_demand_adjustment = (
|
||||
u["installed_measures_heat_demand_adjustment"]
|
||||
)
|
||||
epc.is_epc_adjusted_for_installed_measures = True
|
||||
|
||||
print(f"✅ Updated {len(epcs)} EPC records")
|
||||
|
||||
|
||||
# ------------------------------------------------------------
|
||||
# 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()
|
||||
)
|
||||
}
|
||||
|
||||
installed_adjustments = (
|
||||
get_installed_measure_adjustments_by_uprn_for_portfolio(
|
||||
session,
|
||||
PORTFOLIO_ID,
|
||||
)
|
||||
)
|
||||
|
||||
property_updates = compute_property_sap_updates(
|
||||
properties,
|
||||
{uprn: v["sap_points"] for uprn, v in installed_adjustments.items()}
|
||||
)
|
||||
|
||||
properties_by_id = {p.id: p for p in properties}
|
||||
property_updates_by_id = {
|
||||
u["property_id"]: u
|
||||
for u in property_updates
|
||||
}
|
||||
|
||||
epc_updates = compute_epc_rebasing_updates(
|
||||
epcs,
|
||||
properties_by_id,
|
||||
installed_adjustments,
|
||||
)
|
||||
|
||||
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)
|
||||
persist_epc_rebasing_updates(epc_updates)
|
||||
|
|
@ -0,0 +1,156 @@
|
|||
import pandas as pd
|
||||
from sqlalchemy.orm import Session
|
||||
from sqlalchemy import text, select
|
||||
from backend.app.db.connection import db_read_session
|
||||
from backend.app.db.models.portfolio import PropertyModel, PropertyDetailsEpcModel
|
||||
from backend.app.db.models.recommendations import Plan
|
||||
|
||||
PORTFOLIO_ID = 431
|
||||
|
||||
with db_read_session() as session:
|
||||
# Get all properties from PropertyDetailsEpcModel, where estimated is True, for portfolio 419
|
||||
estimated_epcs = session.query(PropertyDetailsEpcModel).filter(
|
||||
# PropertyDetailsEpcModel.estimated == True,
|
||||
PropertyDetailsEpcModel.property_id.in_(
|
||||
session.query(PropertyModel.id).filter(PropertyModel.portfolio_id == PORTFOLIO_ID)
|
||||
)
|
||||
).all()
|
||||
|
||||
# Get the ids
|
||||
estimated_epc_ids = [epc.property_id for epc in estimated_epcs]
|
||||
|
||||
# I want to get the UPRNS for these properties, from the property model
|
||||
with db_read_session() as session:
|
||||
estimated_uprns = session.query(PropertyModel.uprn).filter(
|
||||
PropertyModel.id.in_(
|
||||
session.query(PropertyDetailsEpcModel.property_id).filter(
|
||||
PropertyDetailsEpcModel.id.in_(estimated_epc_ids)
|
||||
)
|
||||
)
|
||||
).all()
|
||||
|
||||
estimated_uprns_list = [uprn for (uprn,) in estimated_uprns]
|
||||
|
||||
# Go the the SAL
|
||||
sal_1 = pd.read_excel(
|
||||
"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/20251213 Model "
|
||||
"data.xlsx",
|
||||
sheet_name="Standardised Asset List"
|
||||
)
|
||||
sal_2 = pd.read_excel(
|
||||
"/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/20260105 - additional "
|
||||
"UPRNS.xlsx",
|
||||
sheet_name="Standardised Asset List"
|
||||
)
|
||||
|
||||
sal = pd.concat([sal_1, sal_2])
|
||||
sal = sal.drop_duplicates(subset=['epc_os_uprn'])
|
||||
|
||||
estimated_to_refresh = sal[sal["epc_os_uprn"].isin(estimated_uprns_list)].copy()
|
||||
|
||||
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
|
||||
]
|
||||
|
||||
# Get all plans, associated to these properties - the property IDs are in estimated_epc_ids
|
||||
with db_read_session() as session:
|
||||
result = session.execute(
|
||||
select(Plan.id, Plan.property_id)
|
||||
.where(Plan.property_id.in_(estimated_epc_ids))
|
||||
)
|
||||
plans = [
|
||||
{
|
||||
"plan_id": row.id,
|
||||
"property_id": row.property_id,
|
||||
} for row in result
|
||||
]
|
||||
|
||||
df = pd.DataFrame(plans)
|
||||
df = df.sort_values("property_id", ascending=True)
|
||||
|
||||
agg = df.groupby("property_id").size().reset_index(name="n_plans")
|
||||
agg = agg.sort_values("n_plans", ascending=True)
|
||||
agg[agg["n_plans"] != 1]
|
||||
assert all(agg["n_plans"] == 1)
|
||||
|
||||
|
||||
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,
|
||||
)
|
||||
|
||||
|
||||
# Store the SAL
|
||||
filename = ("/Users/khalimconn-kowlessar/Documents/hestia/Customers/Peabody/Nov 2025 Consulting Project/20260101 "
|
||||
"sal.xlsx")
|
||||
|
||||
with pd.ExcelWriter(filename) as writer:
|
||||
sal.to_excel(writer, sheet_name="Standardised Asset List", index=False)
|
||||
# Top 1000 for testing
|
||||
sal.iloc[0:1000, :].to_excel(writer, sheet_name="batch 1", index=False)
|
||||
# Batch 2 is the next 20,000
|
||||
sal.iloc[1000:21000, :].to_excel(writer, sheet_name="batch 2", index=False)
|
||||
# Batch 3 is the next 20,000
|
||||
sal.iloc[21000:41000, :].to_excel(writer, sheet_name="batch 3", index=False)
|
||||
|
||||
sal.iloc[41000:61000, :].to_excel(writer, sheet_name="batch 4", index=False)
|
||||
sal.iloc[61000:81000, :].to_excel(writer, sheet_name="batch 5", index=False)
|
||||
sal.iloc[81000:, :].to_excel(writer, sheet_name="batch 5", index=False)
|
||||
|
|
@ -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.models.recommendations import Recommendation, Plan, PlanRecommendations
|
||||
from backend.app.db.connection import db_engine, db_read_session
|
||||
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
|
||||
PropertyDetailsEpcModel.__table__.columns}}
|
||||
for prop in properties_query
|
||||
{
|
||||
**{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
|
||||
]
|
||||
|
||||
# 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 = [plan['id'] for plan in plans_data]
|
||||
plan_ids = [p["id"] for p 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 in Recommendation.__table__.columns},
|
||||
"Scenario ID": rec.scenario_id}
|
||||
for rec in recommendations_query
|
||||
{
|
||||
**{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
|
||||
]
|
||||
|
||||
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
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue