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af5dbe325d
Rewrite the three structurally-identical m2m-join readers (portfolio_functions.aggregate_portfolio_recommendations, Outputs.get_recommendations_from_db, export get_recommendations) to join PlanModel directly via recommendation.plan_id, dropping the plan_recommendations join and its now-unused import. The writers set plan_id (prior slice), so the rows resolve. test_export pins the export reader through the cut (its fixtures now set recommendation.plan_id). A portfolio_functions DB characterization test lands with the scenario consolidation (which provides the full-parity scenario table the aggregation writes to). Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
60 lines
2.1 KiB
Python
60 lines
2.1 KiB
Python
from sqlalchemy import func
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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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def aggregate_portfolio_recommendations(
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session,
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portfolio_id: int,
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scenario_id: int,
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total_valuation_increase: float,
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labour_days: float,
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aggregated_data: dict,
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):
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# Aggregate multiple fields
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aggregates = (
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session.query(
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func.sum(Recommendation.estimated_cost).label("cost"),
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func.sum(Recommendation.total_work_hours).label("total_work_hours"),
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func.sum(Recommendation.kwh_savings).label("energy_savings"),
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func.sum(Recommendation.co2_equivalent_savings).label(
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"co2_equivalent_savings"
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),
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func.sum(Recommendation.energy_cost_savings).label("energy_cost_savings"),
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)
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.join(PlanModel, PlanModel.id == Recommendation.plan_id)
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.filter(
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PlanModel.portfolio_id == portfolio_id,
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PlanModel.scenario_id == scenario_id,
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Recommendation.default == True,
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)
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.one()
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)
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# Contingeny and funding are in the aggregated data
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aggregates_dict = {
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"cost": aggregates.cost or 0,
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"total_work_hours": aggregates.total_work_hours or 0,
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"energy_savings": aggregates.energy_savings or 0,
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"co2_equivalent_savings": aggregates.co2_equivalent_savings or 0,
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"energy_cost_savings": aggregates.energy_cost_savings or 0,
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**aggregated_data,
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}
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# Get the scenario and update the fields. This data needs to be stored against the scenario, not the portfolio
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portfolio_scenario = session.query(ScenarioModel).filter_by(id=scenario_id).one()
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# Update the data
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for key, value in aggregates_dict.items():
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setattr(portfolio_scenario, key, value)
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# Insert total valuation increase and labour days
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portfolio_scenario.property_valuation_increase = total_valuation_increase
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portfolio_scenario.labour_days = labour_days
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# Merge the updated portfolio plan back into the session
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session.merge(portfolio_scenario)
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session.flush()
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