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Pin the FE-facing aggregate_portfolio_recommendations (previously untested): it sums a Scenario's default Recommendations onto the Scenario row, joining Recommendation → Plan on recommendation.plan_id. Locks the m2m→plan_id read cut for the FE-critical path, now testable thanks to the full-parity ScenarioModel. Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
96 lines
3.3 KiB
Python
96 lines
3.3 KiB
Python
"""Characterisation of the FE-facing portfolio aggregation
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(`aggregate_portfolio_recommendations`): it sums a Scenario's **default**
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Recommendations and writes the totals onto the Scenario row.
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This pins the `recommendation.plan_id` linkage the m2m retirement introduced
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(ADR-0017 amendment): the aggregation joins Recommendation → Plan on
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`recommendation.plan_id`, so only measures carrying the right `plan_id` (and
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`default = True`) are summed.
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"""
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from __future__ import annotations
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from sqlmodel import Session
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from backend.app.db.functions.portfolio_functions import (
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aggregate_portfolio_recommendations,
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)
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from backend.app.db.models.recommendations import (
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PlanModel,
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Recommendation,
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ScenarioModel,
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)
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from domain.modelling.portfolio_goal import PortfolioGoal
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def _rec(
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*, plan_id: int, default: bool, cost: float, kwh: float, gbp: float, co2: float
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) -> Recommendation:
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return Recommendation(
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property_id=10,
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plan_id=plan_id,
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type="cavity_wall_insulation",
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measure_type="cavity_wall_insulation",
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description="Cavity wall insulation",
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estimated_cost=cost,
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kwh_savings=kwh,
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energy_cost_savings=gbp,
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co2_equivalent_savings=co2,
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total_work_hours=4.0,
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default=default,
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already_installed=False,
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)
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def test_aggregation_sums_default_measures_linked_by_plan_id(
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db_session: Session,
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) -> None:
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# Arrange — one Scenario + Plan, two default measures (summed) plus a
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# non-default one (excluded), all linked by recommendation.plan_id.
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db_session.add(
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ScenarioModel(
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id=7,
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portfolio_id=1,
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goal=PortfolioGoal.INCREASING_EPC,
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goal_value="C",
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is_default=True,
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)
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)
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db_session.add(
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PlanModel(id=100, portfolio_id=1, property_id=10, scenario_id=7, is_default=True)
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)
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db_session.add_all(
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[
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_rec(plan_id=100, default=True, cost=1000.0, kwh=500.0, gbp=120.0, co2=0.5),
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_rec(plan_id=100, default=True, cost=500.0, kwh=300.0, gbp=80.0, co2=0.2),
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# excluded: not default
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_rec(plan_id=100, default=False, cost=9.0, kwh=9.0, gbp=9.0, co2=9.0),
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]
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)
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db_session.commit()
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# Act
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aggregate_portfolio_recommendations(
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db_session,
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portfolio_id=1,
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scenario_id=7,
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total_valuation_increase=2500.0,
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labour_days=3.0,
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aggregated_data={},
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)
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db_session.commit()
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# Assert — the default measures' sums land on the Scenario row
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scenario = db_session.query(ScenarioModel).filter_by(id=7).one()
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assert scenario.cost is not None
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assert abs(scenario.cost - 1500.0) <= 1e-9 # 1000 + 500
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assert scenario.energy_savings is not None
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assert abs(scenario.energy_savings - 800.0) <= 1e-9 # Σ kwh_savings
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assert scenario.energy_cost_savings is not None
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assert abs(scenario.energy_cost_savings - 200.0) <= 1e-9 # 120 + 80
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assert scenario.co2_equivalent_savings is not None
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assert abs(scenario.co2_equivalent_savings - 0.7) <= 1e-9 # 0.5 + 0.2
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assert scenario.total_work_hours is not None
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assert abs(scenario.total_work_hours - 8.0) <= 1e-9 # 4 + 4
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assert scenario.property_valuation_increase == 2500.0
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assert scenario.labour_days == 3.0
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