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start writing test
This commit is contained in:
Daniel Roth
parent
d33ec39aea
commit
af01f7e5b0
3 changed files with 362 additions and 138 deletions
61
backend/app/tests/tasks/test_get_task.py
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61
backend/app/tests/tasks/test_get_task.py
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@ -0,0 +1,61 @@
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from datetime import datetime
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import uuid
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from sqlmodel import Session
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from backend.app.db.models.tasks import SourceEnum, Task
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def test_get_task_by_source(db_session: Session):
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# arrange
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current_categorisation_task = Task(
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id=uuid.uuid4(),
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task_source="",
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job_started=datetime(2026, 1, 1, 9, 0, 0),
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job_completed=None,
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status="in progress",
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service="plan_categorisation",
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updated_at=datetime(2026, 1, 1, 9, 0, 0),
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source=SourceEnum.PORTFOLIO,
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source_id="100",
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)
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previous_categorisation_task = Task(
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id=uuid.uuid4(),
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task_source="",
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job_started=datetime(2025, 12, 31, 9, 0, 0),
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job_completed=datetime(2025, 12, 31, 9, 10, 0),
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status="complete",
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service="plan_categorisation",
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updated_at=datetime(2025, 12, 31, 9, 10, 0),
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source=SourceEnum.PORTFOLIO,
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source_id="100",
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)
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other_portfolio_categorisation_task = Task(
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id=uuid.uuid4(),
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task_source="",
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job_started=datetime(2026, 1, 1, 9, 0, 0),
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job_completed=None,
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status="in progress",
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service="plan_categorisation",
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updated_at=datetime(2026, 1, 1, 9, 0, 0),
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source=SourceEnum.PORTFOLIO,
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source_id="101",
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)
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engine_task = Task(
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id=uuid.uuid4(),
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task_source="",
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job_started=datetime(2026, 1, 1, 9, 0, 0),
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job_completed=None,
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status="in progress",
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service="plan_engine",
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updated_at=datetime(2026, 1, 1, 9, 0, 0),
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source=SourceEnum.PORTFOLIO,
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source_id="100",
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)
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# act
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# assert
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@ -3,12 +3,25 @@ import numpy as np
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from pathlib import Path
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import time
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from sqlmodel import Session
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from backend.export.property_scenarios.main import process_export
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from backend.export.property_scenarios.input_schema import ExportRequest
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from backend.app.db.models.portfolio import PropertyModel, Epc, Portfolio, PortfolioStatus, PortfolioGoal, \
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PropertyCreationStatus, PropertyDetailsEpcModel
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from backend.app.db.models.recommendations import PlanModel, Recommendation, PlanRecommendations, \
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RecommendationMaterials
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from backend.app.db.models.portfolio import (
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PropertyModel,
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Epc,
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Portfolio,
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PortfolioStatus,
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PortfolioGoal,
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PropertyCreationStatus,
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PropertyDetailsEpcModel,
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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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PlanRecommendations,
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RecommendationMaterials,
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)
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from backend.app.db.models.materials import Material
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from utils.logger import setup_logger
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@ -22,7 +35,7 @@ def load_csv(name: str) -> pd.DataFrame:
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return df
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def test_default_export_integration(db_session):
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def test_default_export_integration(db_session: Session):
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# ----------------------------------------
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# 1) Load csvs
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# ----------------------------------------
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@ -78,11 +91,13 @@ def test_default_export_integration(db_session):
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else None
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)
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prop = PropertyModel(**{
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col: row_dict[col]
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for col in PropertyModel.__table__.columns.keys()
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if col in row_dict
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})
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prop = PropertyModel(
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**{
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col: row_dict[col]
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for col in PropertyModel.__table__.columns.keys()
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if col in row_dict
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}
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)
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prop.creation_status = PropertyCreationStatus[
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row_dict["creation_status"].split(".")[-1]
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@ -90,9 +105,7 @@ def test_default_export_integration(db_session):
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prop.status = PortfolioStatus[row_dict["status"].split(".")[-1]]
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if row_dict.get("current_epc_rating"):
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prop.current_epc_rating = Epc[
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row_dict["current_epc_rating"].split(".")[-1]
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]
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prop.current_epc_rating = Epc[row_dict["current_epc_rating"].split(".")[-1]]
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properties.append(prop)
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@ -112,7 +125,8 @@ def test_default_export_integration(db_session):
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epc_data = {
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col.name: row_dict[col.name]
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for col in PropertyDetailsEpcModel.__table__.columns.values()
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if col.name in row_dict and col.name not in ["id", "property_id", "portfolio_id"]
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if col.name in row_dict
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and col.name not in ["id", "property_id", "portfolio_id"]
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}
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epc = PropertyDetailsEpcModel(
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@ -142,11 +156,13 @@ def test_default_export_integration(db_session):
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row_dict["scenario_id"] = None
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plan = PlanModel(**{
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col: row_dict[col]
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for col in PlanModel.__table__.columns.keys()
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if col in row_dict
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})
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plan = PlanModel(
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**{
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col: row_dict[col]
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for col in PlanModel.__table__.columns.keys()
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if col in row_dict
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}
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)
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plans.append(plan)
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@ -158,11 +174,13 @@ def test_default_export_integration(db_session):
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# ----------------------------------------
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recs = [
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Recommendation(**{
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col: row[col]
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for col in Recommendation.__table__.columns.keys()
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if col in row
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})
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Recommendation(
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**{
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col: row[col]
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for col in Recommendation.__table__.columns.keys()
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if col in row
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}
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)
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for _, row in recommendations_df.iterrows()
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]
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@ -203,28 +221,19 @@ def test_default_export_integration(db_session):
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# ----------------------------------------
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logger.info(
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"Recommendation count in DB: %s",
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db_session.query(Recommendation).count()
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"Recommendation count in DB: %s", db_session.query(Recommendation).count()
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)
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logger.info(
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"Property count in DB: %s",
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db_session.query(PropertyModel).count()
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)
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logger.info("Property count in DB: %s", db_session.query(PropertyModel).count())
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logger.info(
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"Property EPC in DB: %s",
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db_session.query(PropertyDetailsEpcModel).count()
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"Property EPC in DB: %s", db_session.query(PropertyDetailsEpcModel).count()
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)
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logger.info(
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"Plan count in DB: %s",
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db_session.query(PlanModel).count()
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)
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logger.info("Plan count in DB: %s", db_session.query(PlanModel).count())
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logger.info(
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"PlanRecommendatons count in DB: %s",
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db_session.query(PlanModel).count()
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"PlanRecommendatons count in DB: %s", db_session.query(PlanModel).count()
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)
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logger.info("Starting process_export")
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@ -232,17 +241,23 @@ def test_default_export_integration(db_session):
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result = process_export(payload, session=db_session)
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logger.info("process_export finished in %.2f seconds", time.perf_counter() - process_t0)
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logger.info(
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"process_export finished in %.2f seconds", time.perf_counter() - process_t0
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)
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# ----------------------------------------
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# 8) Assertions
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# ----------------------------------------
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assert "default_plans" in result, "Expected 'default_plans' in export result, got {}".format(result.keys())
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assert (
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"default_plans" in result
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), "Expected 'default_plans' in export result, got {}".format(result.keys())
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df = result["default_plans"]
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assert df.shape[0] == 10, "Expected 10 properties in the export, got {}".format(df.shape[0])
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assert df.shape[0] == 10, "Expected 10 properties in the export, got {}".format(
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df.shape[0]
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)
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failed = df[df["predicted_post_works_sap"] < 69]
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failed_property_types = failed["property_type"].value_counts().to_dict()
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@ -251,19 +266,28 @@ def test_default_export_integration(db_session):
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assert failed.shape[0]
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assert df["total_retrofit_cost"].sum() == 41706.585999999996, (
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"Expected total retrofit cost to be 10000, got {}".format(df["total_retrofit_cost"].sum())
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assert (
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df["total_retrofit_cost"].sum() == 41706.585999999996
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), "Expected total retrofit cost to be 10000, got {}".format(
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df["total_retrofit_cost"].sum()
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)
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assert df["predicted_post_works_sap"].sum() == 698.1, (
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"Expected total predicted post works SAP to be 698.1, got {}".format(df["predicted_post_works_sap"].sum())
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assert (
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df["predicted_post_works_sap"].sum() == 698.1
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), "Expected total predicted post works SAP to be 698.1, got {}".format(
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df["predicted_post_works_sap"].sum()
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)
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assert df["sap_points"].sum() == 100.10000000000001, (
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"Expected total SAP points increase to be 100.10000000000001, got {}".format(df["sap_points"].sum())
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assert (
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df["sap_points"].sum() == 100.10000000000001
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), "Expected total SAP points increase to be 100.10000000000001, got {}".format(
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df["sap_points"].sum()
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)
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assert df.shape == (10, 95), "Expected dataframe shape to be (10, 11), got {}".format(df.shape)
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assert df.shape == (
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10,
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95,
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), "Expected dataframe shape to be (10, 11), got {}".format(df.shape)
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def test_solar_with_battery_example(db_session):
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@ -271,116 +295,251 @@ def test_solar_with_battery_example(db_session):
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test_property_id = 1
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portfolio_df = pd.DataFrame(
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[{'id': test_portfolio_id, 'name': 'Example', 'budget': None,
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'status': 'PortfolioStatus.SCOPING', 'goal': 'PortfolioGoal.NONE', 'cost': None, 'number_of_properties': None,
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'co2_equivalent_savings': None, 'energy_savings': None, 'energy_cost_savings': None,
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'property_valuation_increase': None, 'rental_yield_increase': None, 'total_work_hours': None,
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'labour_days': None, 'created_at': '2026-02-12 21:23:37.862000+00:00',
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'updated_at': '2026-02-12 21:23:37.862000+00:00', 'epc_breakdown_pre_retrofit': None,
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'epc_breakdown_post_retrofit': None, 'n_units_to_retrofit': None, 'co2_per_unit_pre_retrofit': None,
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'co2_per_unit_post_retrofit': None, 'energy_bill_per_unit_pre_retrofit': None,
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'energy_bill_per_unit_post_retrofit': None, 'energy_consumption_per_unit_pre_retrofit': None,
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'energy_consumption_per_unit_post_retrofit': None, 'valuation_improvement_per_unit': None,
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'cost_per_unit': None, 'cost_per_co2_saved': None, 'cost_per_sap_point': None,
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'valuation_return_on_investment': None}]
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[
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{
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"id": test_portfolio_id,
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"name": "Example",
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"budget": None,
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"status": "PortfolioStatus.SCOPING",
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"goal": "PortfolioGoal.NONE",
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"cost": None,
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"number_of_properties": None,
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"co2_equivalent_savings": None,
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"energy_savings": None,
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"energy_cost_savings": None,
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"property_valuation_increase": None,
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"rental_yield_increase": None,
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"total_work_hours": None,
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"labour_days": None,
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"created_at": "2026-02-12 21:23:37.862000+00:00",
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"updated_at": "2026-02-12 21:23:37.862000+00:00",
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"epc_breakdown_pre_retrofit": None,
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"epc_breakdown_post_retrofit": None,
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"n_units_to_retrofit": None,
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"co2_per_unit_pre_retrofit": None,
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"co2_per_unit_post_retrofit": None,
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"energy_bill_per_unit_pre_retrofit": None,
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"energy_bill_per_unit_post_retrofit": None,
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"energy_consumption_per_unit_pre_retrofit": None,
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"energy_consumption_per_unit_post_retrofit": None,
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"valuation_improvement_per_unit": None,
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"cost_per_unit": None,
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"cost_per_co2_saved": None,
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"cost_per_sap_point": None,
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"valuation_return_on_investment": None,
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}
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]
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)
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properties_df = pd.DataFrame(
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[{'id': test_property_id, 'portfolio_id': test_portfolio_id, 'creation_status': 'PropertyCreationStatus.READY',
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'uprn': 100090438731, 'landlord_property_id': 'BARR052', 'building_reference_number': 3460742868.0,
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'status': 'PortfolioStatus.ASSESSMENT', 'address': '52, Barrack Street', 'postcode': 'CO1 2LR',
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'has_pre_condition_report': True, 'has_recommendations': True, 'created_at': '2026-02-12 21:59:02.744427',
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'updated_at': '2026-02-19 16:18:57.941443', 'property_type': 'House', 'built_form': 'End-Terrace',
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'local_authority': 'Colchester', 'constituency': 'Colchester', 'number_of_rooms': 4.0, 'year_built': 1900.0,
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'tenure': 'rental (private)', 'current_epc_rating': 'Epc.E', 'current_sap_points': 53.0,
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'current_valuation': 0.0, 'installed_measures_sap_point_adjustment': 0.0,
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'is_sap_points_adjusted_for_installed_measures': False, 'original_sap_points': 53.0}]
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[
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{
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"id": test_property_id,
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"portfolio_id": test_portfolio_id,
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"creation_status": "PropertyCreationStatus.READY",
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"uprn": 100090438731,
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"landlord_property_id": "BARR052",
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"building_reference_number": 3460742868.0,
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"status": "PortfolioStatus.ASSESSMENT",
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"address": "52, Barrack Street",
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"postcode": "CO1 2LR",
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"has_pre_condition_report": True,
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"has_recommendations": True,
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"created_at": "2026-02-12 21:59:02.744427",
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"updated_at": "2026-02-19 16:18:57.941443",
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"property_type": "House",
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"built_form": "End-Terrace",
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"local_authority": "Colchester",
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"constituency": "Colchester",
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"number_of_rooms": 4.0,
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"year_built": 1900.0,
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"tenure": "rental (private)",
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"current_epc_rating": "Epc.E",
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"current_sap_points": 53.0,
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"current_valuation": 0.0,
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"installed_measures_sap_point_adjustment": 0.0,
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"is_sap_points_adjusted_for_installed_measures": False,
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"original_sap_points": 53.0,
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}
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]
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)
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property_details_epc_df = pd.DataFrame(
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[
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{'id': 1534934, 'property_id': test_property_id, 'portfolio_id': test_portfolio_id,
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'full_address': '48, Medcalf Road', 'lodgement_date': '2018-09-05', 'is_expired': False,
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'total_floor_area': 68.0, 'walls': 'Solid brick, as built, no insulation', 'walls_rating': 1,
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'roof': 'Pitched, no insulation', 'roof_rating': 1.0, 'floor': 'Solid, no insulation',
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'floor_rating': None,
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'windows': 'Fully double glazed', 'windows_rating': 4, 'heating': 'Boiler and radiators, mains gas',
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'heating_rating': 4, 'heating_controls': 'Programmer, room thermostat and trvs',
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'heating_controls_rating': 4,
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'hot_water': 'From main system', 'hot_water_rating': 4,
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'lighting': 'Low energy lighting in all fixed outlets', 'lighting_rating': 5,
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'mainfuel': 'Mains gas not community', 'ventilation': 'natural', 'solar_pv': 0.0, 'solar_hot_water': False,
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'wind_turbine': 0.0, 'floor_height': 2.55, 'number_heated_rooms': None, 'heat_loss_corridor': False,
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'unheated_corridor_length': None, 'number_of_open_fireplaces': 0, 'number_of_extensions': 0,
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'number_of_storeys': None, 'mains_gas': True, 'energy_tariff': 'Single',
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'primary_energy_consumption': 278.0,
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'co2_emissions': 3.81, 'current_energy_demand': 14643.366,
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'current_energy_demand_heating_hotwater': 12185.6,
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'estimated': False, 'sap_05_overwritten': False, 'sap_05_score': None, 'sap_05_epc_rating': None,
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'heating_cost_current': 711.0628, 'hot_water_cost_current': 139.06198, 'lighting_cost_current': 70.770935,
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'appliances_cost_current': 609.7844, 'gas_standing_charge': 128.0785,
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'electricity_standing_charge': 199.8375,
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'original_co2_emissions': 3.81, 'original_primary_energy_consumption': 278.0,
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'original_current_energy_demand': 14643.366, 'original_current_energy_demand_heating_hotwater': 12185.6,
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'installed_measures_co2_adjustment': 0.0, 'installed_measures_energy_demand_adjustment': 0.0,
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'installed_measures_total_energy_bill_adjustment': 0.0, 'installed_measures_heat_demand_adjustment': 0.0,
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'is_epc_adjusted_for_installed_measures': False}
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{
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||||
"id": 1534934,
|
||||
"property_id": test_property_id,
|
||||
"portfolio_id": test_portfolio_id,
|
||||
"full_address": "48, Medcalf Road",
|
||||
"lodgement_date": "2018-09-05",
|
||||
"is_expired": False,
|
||||
"total_floor_area": 68.0,
|
||||
"walls": "Solid brick, as built, no insulation",
|
||||
"walls_rating": 1,
|
||||
"roof": "Pitched, no insulation",
|
||||
"roof_rating": 1.0,
|
||||
"floor": "Solid, no insulation",
|
||||
"floor_rating": None,
|
||||
"windows": "Fully double glazed",
|
||||
"windows_rating": 4,
|
||||
"heating": "Boiler and radiators, mains gas",
|
||||
"heating_rating": 4,
|
||||
"heating_controls": "Programmer, room thermostat and trvs",
|
||||
"heating_controls_rating": 4,
|
||||
"hot_water": "From main system",
|
||||
"hot_water_rating": 4,
|
||||
"lighting": "Low energy lighting in all fixed outlets",
|
||||
"lighting_rating": 5,
|
||||
"mainfuel": "Mains gas not community",
|
||||
"ventilation": "natural",
|
||||
"solar_pv": 0.0,
|
||||
"solar_hot_water": False,
|
||||
"wind_turbine": 0.0,
|
||||
"floor_height": 2.55,
|
||||
"number_heated_rooms": None,
|
||||
"heat_loss_corridor": False,
|
||||
"unheated_corridor_length": None,
|
||||
"number_of_open_fireplaces": 0,
|
||||
"number_of_extensions": 0,
|
||||
"number_of_storeys": None,
|
||||
"mains_gas": True,
|
||||
"energy_tariff": "Single",
|
||||
"primary_energy_consumption": 278.0,
|
||||
"co2_emissions": 3.81,
|
||||
"current_energy_demand": 14643.366,
|
||||
"current_energy_demand_heating_hotwater": 12185.6,
|
||||
"estimated": False,
|
||||
"sap_05_overwritten": False,
|
||||
"sap_05_score": None,
|
||||
"sap_05_epc_rating": None,
|
||||
"heating_cost_current": 711.0628,
|
||||
"hot_water_cost_current": 139.06198,
|
||||
"lighting_cost_current": 70.770935,
|
||||
"appliances_cost_current": 609.7844,
|
||||
"gas_standing_charge": 128.0785,
|
||||
"electricity_standing_charge": 199.8375,
|
||||
"original_co2_emissions": 3.81,
|
||||
"original_primary_energy_consumption": 278.0,
|
||||
"original_current_energy_demand": 14643.366,
|
||||
"original_current_energy_demand_heating_hotwater": 12185.6,
|
||||
"installed_measures_co2_adjustment": 0.0,
|
||||
"installed_measures_energy_demand_adjustment": 0.0,
|
||||
"installed_measures_total_energy_bill_adjustment": 0.0,
|
||||
"installed_measures_heat_demand_adjustment": 0.0,
|
||||
"is_epc_adjusted_for_installed_measures": False,
|
||||
}
|
||||
]
|
||||
)
|
||||
|
||||
plans_df = pd.DataFrame(
|
||||
[
|
||||
{'id': 0, 'name': None, 'portfolio_id': test_portfolio_id, 'property_id': test_property_id,
|
||||
'scenario_id': 1060, 'created_at': '2026-02-19 16:14:45.560816', 'is_default': True,
|
||||
'valuation_increase_lower_bound': 0.0302,
|
||||
'valuation_increase_upper_bound': 0.07, 'valuation_increase_average': 0.048226666, 'plan_type': None,
|
||||
'post_sap_points': 71.5, 'post_epc_rating': 'Epc.C', 'post_co2_emissions': 4.1813498,
|
||||
'co2_savings': 0.71865046, 'post_energy_bill': 1447.5204, 'energy_bill_savings': 691.6662,
|
||||
'post_energy_consumption': 15303.688, 'energy_consumption_savings': 3276.7622,
|
||||
'valuation_post_retrofit': None, 'valuation_increase': None, 'cost_of_works': 6984.568,
|
||||
'contingency_cost': 1003.9568}
|
||||
{
|
||||
"id": 0,
|
||||
"name": None,
|
||||
"portfolio_id": test_portfolio_id,
|
||||
"property_id": test_property_id,
|
||||
"scenario_id": 1060,
|
||||
"created_at": "2026-02-19 16:14:45.560816",
|
||||
"is_default": True,
|
||||
"valuation_increase_lower_bound": 0.0302,
|
||||
"valuation_increase_upper_bound": 0.07,
|
||||
"valuation_increase_average": 0.048226666,
|
||||
"plan_type": None,
|
||||
"post_sap_points": 71.5,
|
||||
"post_epc_rating": "Epc.C",
|
||||
"post_co2_emissions": 4.1813498,
|
||||
"co2_savings": 0.71865046,
|
||||
"post_energy_bill": 1447.5204,
|
||||
"energy_bill_savings": 691.6662,
|
||||
"post_energy_consumption": 15303.688,
|
||||
"energy_consumption_savings": 3276.7622,
|
||||
"valuation_post_retrofit": None,
|
||||
"valuation_increase": None,
|
||||
"cost_of_works": 6984.568,
|
||||
"contingency_cost": 1003.9568,
|
||||
}
|
||||
]
|
||||
)
|
||||
|
||||
plan_recs_df = pd.DataFrame(
|
||||
[{'id': 0, 'plan_id': 0, 'recommendation_id': 0}]
|
||||
)
|
||||
plan_recs_df = pd.DataFrame([{"id": 0, "plan_id": 0, "recommendation_id": 0}])
|
||||
|
||||
recommendations_df = pd.DataFrame(
|
||||
[{'id': 0, 'property_id': test_property_id, 'created_at': '2026-02-19 16:14:45.560816',
|
||||
'type': 'solar_pv', 'measure_type': 'solar_pv',
|
||||
'description': 'Fit solar',
|
||||
'estimated_cost': 10000, 'default': True, 'starting_u_value': None, 'new_u_value': None, 'sap_points': 1.5,
|
||||
'heat_demand': 14.9, 'kwh_savings': 1041.2, 'co2_equivalent_savings': 0.2, 'energy_savings': 14.9,
|
||||
'energy_cost_savings': 72.639015, 'property_valuation_increase': None, 'rental_yield_increase': None,
|
||||
'total_work_hours': 4.16, 'labour_days': 1.0, 'already_installed': False, 'plan_name': 'whatever'}
|
||||
]
|
||||
[
|
||||
{
|
||||
"id": 0,
|
||||
"property_id": test_property_id,
|
||||
"created_at": "2026-02-19 16:14:45.560816",
|
||||
"type": "solar_pv",
|
||||
"measure_type": "solar_pv",
|
||||
"description": "Fit solar",
|
||||
"estimated_cost": 10000,
|
||||
"default": True,
|
||||
"starting_u_value": None,
|
||||
"new_u_value": None,
|
||||
"sap_points": 1.5,
|
||||
"heat_demand": 14.9,
|
||||
"kwh_savings": 1041.2,
|
||||
"co2_equivalent_savings": 0.2,
|
||||
"energy_savings": 14.9,
|
||||
"energy_cost_savings": 72.639015,
|
||||
"property_valuation_increase": None,
|
||||
"rental_yield_increase": None,
|
||||
"total_work_hours": 4.16,
|
||||
"labour_days": 1.0,
|
||||
"already_installed": False,
|
||||
"plan_name": "whatever",
|
||||
}
|
||||
]
|
||||
)
|
||||
|
||||
recommendations_materials_df = pd.DataFrame(
|
||||
[
|
||||
{
|
||||
"id": 0, "recommendation_id": 0, "material_id": 0, "depth": None, "quantity": 1.0,
|
||||
"id": 0,
|
||||
"recommendation_id": 0,
|
||||
"material_id": 0,
|
||||
"depth": None,
|
||||
"quantity": 1.0,
|
||||
"quantity_unit": "part",
|
||||
"estimated_cost": 10000, "created_at": '2026-02-19 16:14:45.560816',
|
||||
"updated_at": '2026-02-19 16:14:45.560816',
|
||||
"estimated_cost": 10000,
|
||||
"created_at": "2026-02-19 16:14:45.560816",
|
||||
"updated_at": "2026-02-19 16:14:45.560816",
|
||||
}
|
||||
]
|
||||
)
|
||||
|
||||
materials_df = pd.DataFrame(
|
||||
[
|
||||
{'id': 0, 'type': 'solar_pv', 'description': 'Some solar product',
|
||||
'depth': 75.0,
|
||||
'depth_unit': 'mm', 'cost': None, 'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.030303031,
|
||||
'r_value_unit': 'square_meter_kelvin_per_watt', 'thermal_conductivity': 0.033,
|
||||
'thermal_conductivity_unit': 'watt_per_meter_kelvin', 'link': 'Test',
|
||||
'created_at': "'2026-02-19 16:14:45.560816", 'is_active': True,
|
||||
'prime_material_cost': None,
|
||||
'material_cost': 0.0, 'labour_cost': 0.0, 'labour_hours_per_unit': 0.0, 'plant_cost': 0.0,
|
||||
'total_cost': 10000,
|
||||
'notes': None, 'is_installer_quote': True, 'innovation_rate': 0.25, 'size': None, 'size_unit': None,
|
||||
'includes_scaffolding': True, 'includes_battery': True, 'battery_size': 5.8}
|
||||
{
|
||||
"id": 0,
|
||||
"type": "solar_pv",
|
||||
"description": "Some solar product",
|
||||
"depth": 75.0,
|
||||
"depth_unit": "mm",
|
||||
"cost": None,
|
||||
"cost_unit": "gbp_per_m2",
|
||||
"r_value_per_mm": 0.030303031,
|
||||
"r_value_unit": "square_meter_kelvin_per_watt",
|
||||
"thermal_conductivity": 0.033,
|
||||
"thermal_conductivity_unit": "watt_per_meter_kelvin",
|
||||
"link": "Test",
|
||||
"created_at": "'2026-02-19 16:14:45.560816",
|
||||
"is_active": True,
|
||||
"prime_material_cost": None,
|
||||
"material_cost": 0.0,
|
||||
"labour_cost": 0.0,
|
||||
"labour_hours_per_unit": 0.0,
|
||||
"plant_cost": 0.0,
|
||||
"total_cost": 10000,
|
||||
"notes": None,
|
||||
"is_installer_quote": True,
|
||||
"innovation_rate": 0.25,
|
||||
"size": None,
|
||||
"size_unit": None,
|
||||
"includes_scaffolding": True,
|
||||
"includes_battery": True,
|
||||
"battery_size": 5.8,
|
||||
}
|
||||
]
|
||||
)
|
||||
|
||||
|
|
@ -463,7 +622,7 @@ def test_solar_with_battery_example(db_session):
|
|||
already_installed=row.already_installed,
|
||||
sap_points=row.sap_points,
|
||||
type=row.type,
|
||||
description=row.description
|
||||
description=row.description,
|
||||
)
|
||||
db_session.add(rec)
|
||||
db_session.flush()
|
||||
|
|
@ -515,13 +674,15 @@ def test_solar_with_battery_example(db_session):
|
|||
|
||||
db_session.commit()
|
||||
|
||||
payload = ExportRequest.model_validate({
|
||||
"task_id": "test",
|
||||
"subtask_id": "test",
|
||||
"portfolio_id": test_portfolio_id,
|
||||
"scenario_ids": [],
|
||||
"default_plans_only": True,
|
||||
})
|
||||
payload = ExportRequest.model_validate(
|
||||
{
|
||||
"task_id": "test",
|
||||
"subtask_id": "test",
|
||||
"portfolio_id": test_portfolio_id,
|
||||
"scenario_ids": [],
|
||||
"default_plans_only": True,
|
||||
}
|
||||
)
|
||||
|
||||
result = process_export(payload, session=db_session)
|
||||
|
||||
|
|
@ -534,7 +695,9 @@ def test_solar_with_battery_example(db_session):
|
|||
# solar_pv should NOT exist
|
||||
assert "solar_pv" not in df.columns
|
||||
|
||||
assert df.shape[0] == 1, "Expected 1 property in the export, got {}".format(df.shape[0])
|
||||
assert df.shape[0] == 1, "Expected 1 property in the export, got {}".format(
|
||||
df.shape[0]
|
||||
)
|
||||
|
||||
# Cost should land in correct column
|
||||
assert df["solar_pv_with_battery"].iloc[0] == 10000
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue