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729 lines
47 KiB
Python
729 lines
47 KiB
Python
from pandas import Timestamp
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from numpy import nan
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import datetime
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import numpy as np
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import pandas as pd
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import pytest
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from copy import deepcopy
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from recommendations.optimiser import optimiser_functions
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from recommendations.optimiser.funding_optimiser import optimise_with_funding_paths, build_heat_pump_paths
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from backend.Funding import Funding
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from backend.app.plan.schemas import WALL_INSULATION_MEASURES, ROOF_INSULATION_MEASURES, ECO4_ELIGIBILE_FABRIC_MEASURES
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ALLOWED_FABRIC_TYPES = set(WALL_INSULATION_MEASURES + ROOF_INSULATION_MEASURES + ECO4_ELIGIBILE_FABRIC_MEASURES)
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@pytest.fixture
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def mock_project_scores_matrix():
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data = []
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floor_segments = ["0-72", "73-97", "98-199", "200"]
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bands = [
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"Low_G", "High_G", "Low_F", "High_F", "Low_E", "High_E", "Low_D", "High_D", "Low_C", "High_C", "Low_B",
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"High_B", "Low_A", "High_A"
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]
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cost = 50.0
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for floor in floor_segments:
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for start in bands:
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for finish in bands:
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if start != finish: # skip identical start/finish (no SAP movement)
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data.append({
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"Floor Area Segment": floor,
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"Starting Band": start,
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"Finishing Band": finish,
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"Cost Savings": cost
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})
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cost += 5.0 # increment to create variety
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return pd.DataFrame(data)
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@pytest.fixture
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def mock_partial_scores_matrix():
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df = pd.read_csv("backend/tests/test_data/ECO4_Partial_Project_Scores_Matrix_v6.csv")
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df.columns = ['Measure category', 'Measure_Type', 'Pre_Main_Heating_Source',
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'Post_Main_Heating_Source', 'Total Floor Area Band', 'Starting Band',
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'Average Treatable Factor', 'Cost Savings', 'SAP Savings']
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return df
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class DummyProp:
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"""Minimal property stub exposing just what your code reads."""
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def __init__(self):
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self.data = {
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"current-energy-rating": "E", # or "D" for the special Social+D path
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"current-energy-efficiency": 55, # numeric SAP points used in eligibility calc
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"mainheat-energy-eff": "Very Good",
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}
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self.has_ventilation = False
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self.floor_area = 70.0
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self.main_heating_controls = {"clean_description": "time and temperature zone control"}
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self.walls = {'original_description': 'Solid brick, as built, no insulation (assumed)',
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'thermal_transmittance': None,
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'thermal_transmittance_unit': None, 'is_cavity_wall': False, 'is_filled_cavity': False,
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'is_solid_brick': True,
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'is_system_built': False, 'is_timber_frame': False, 'is_granite_or_whinstone': False,
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'is_as_built': True,
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'is_cob': False, 'is_assumed': True, 'is_sandstone_or_limestone': False,
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'insulation_thickness': 'none',
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'external_insulation': False, 'internal_insulation': False}
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self.main_heating = {
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'original_description': 'Boiler and radiators, mains gas',
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'clean_description': 'Boiler and radiators, mains gas',
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'has_radiators': True, 'has_fan_coil_units': False, 'has_pipes_in_screed_above_insulation': False,
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'has_pipes_in_insulated_timber_floor': False, 'has_pipes_in_concrete_slab': False, 'has_boiler': True,
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'has_air_source_heat_pump': False, 'has_room_heaters': False, 'has_electric_storage_heaters': False,
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'has_warm_air': False, 'has_electric_underfloor_heating': False, 'has_electric_ceiling_heating': False,
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'has_community_scheme': False, 'has_ground_source_heat_pump': False, 'has_no_system_present': False,
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'has_portable_electric_heaters': False, 'has_water_source_heat_pump': False, 'has_electric_heat_pump':
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False,
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'has_micro-cogeneration': False, 'has_solar_assisted_heat_pump': False, 'has_exhaust_source_heat_pump':
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False,
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'has_community_heat_pump': False, 'has_hot-water-only': False, 'has_electric': False, 'has_mains_gas':
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True,
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'has_wood_logs': False, 'has_coal': False, 'has_oil': False, 'has_wood_pellets': False,
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'has_anthracite': False,
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'has_dual_fuel_mineral_and_wood': False, 'has_smokeless_fuel': False, 'has_lpg': False, 'has_b30k': False,
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'has_mineral_and_wood': False, 'has_dual_fuel_appliance': False, 'has_assumed': False,
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'has_electricaire': False,
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'has_assumed_for_most_rooms': False, 'has_underfloor_heating': False
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}
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self.main_fuel = {
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'original_description': 'mains gas (not community)', 'clean_description': 'Mains gas not community',
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'fuel_type': 'mains gas', 'tariff_type': None, 'is_community': False,
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'no_individual_heating_or_community_network': False, 'complex_fuel_type': None
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}
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@pytest.fixture
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def p():
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return DummyProp()
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@pytest.fixture
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def funding(monkeypatch, mock_partial_scores_matrix, mock_project_scores_matrix):
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"""Simple Funding that returns zero uplift so costs stay as provided."""
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# Build the Funding with tiny in-memory frames (avoid test I/O)
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f = Funding(
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project_scores_matrix=mock_project_scores_matrix,
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partial_project_scores_matrix=mock_partial_scores_matrix,
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whlg_eligible_postcodes=pd.DataFrame([{"Postcode": "ab12cd"}]),
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eco4_social_cavity_abs_rate=13.5, eco4_social_solid_abs_rate=17,
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eco4_private_cavity_abs_rate=13.5, eco4_private_solid_abs_rate=17,
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gbis_social_cavity_abs_rate=21, gbis_social_solid_abs_rate=25,
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gbis_private_cavity_abs_rate=22, gbis_private_solid_abs_rate=28,
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tenure="Social"
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)
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# Keep innovation_uplift simple for the first test
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# monkeypatch.setattr(f, "get_innovation_uplift", lambda *args, **kwargs: 0.0)
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# If your solar precondition matters, you can force True/False here:
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# monkeypatch.setattr(
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# __import__("backend").Funding, "check_solar_eligible_heating_system",
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# staticmethod(lambda mainheat_description, heating_control_description: False)
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# )
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return f
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@pytest.fixture
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def property_recommendations():
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"""Short sample; replace with your full block if you want."""
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recs = [
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[{'phase': 0, 'parts': [{'id': 2466, 'type': 'external_wall_insulation',
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'description': 'EWI Pro EPS external wall insulation system with '
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'Brick Slip finish',
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'depth': 150.0, 'depth_unit': 'mm', 'cost': None,
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'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.02631579,
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'r_value_unit': 'square_meter_kelvin_per_watt',
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'thermal_conductivity': 0.038,
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'thermal_conductivity_unit': 'watt_per_meter_kelvin',
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'link': 'SCIS',
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'created_at': Timestamp('2025-03-16 15:26:22.379496'),
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'is_active': True, 'prime_material_cost': None,
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'material_cost': 0.0, 'labour_cost': 0.0,
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'labour_hours_per_unit': 0.0, 'plant_cost': 0.0,
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'total_cost': 298.35,
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'notes': 'This is the quoted value from SCIS',
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'is_installer_quote': True, 'quantity': 63.98796761892035,
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'quantity_unit': 'm2', 'total': 19090.810139104888,
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'labour_hours': 0.0, 'labour_days': 0.0}],
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'type': 'external_wall_insulation', 'measure_type': 'external_wall_insulation',
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"innovation_rate": 0,
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'description': 'Install 150mm EWI Pro EPS external wall insulation system with Brick '
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'Slip finish on external walls',
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'starting_u_value': 1.7, 'new_u_value': 0.32, 'already_installed': False,
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'sap_points': np.float64(9.6),
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'simulation_config': {'is_as_built_ending': False, 'walls_is_assumed_ending': False,
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'walls_insulation_thickness_ending': 'average',
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'external_insulation_ending': True,
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'walls_energy_eff_ending': 'Good',
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'walls_thermal_transmittance_ending': 0.23},
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'description_simulation': {'walls-description': 'Solid brick, with external insulation',
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'walls-energy-eff': 'Good'}, 'total': 19090.810139104888,
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'labour_hours': 0.0, 'labour_days': 0.0, 'survey': False,
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'recommendation_id': '0_phase=0', 'efficiency': 11229.568317120522,
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'co2_equivalent_savings': np.float64(0.5), 'heat_demand': np.float64(37.099999999999994),
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'kwh_savings': np.float64(1827.8999999999996),
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'energy_cost_savings': np.float64(136.1247882352941)}, {'phase': 0, 'parts': [
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{'id': 2373, 'type': 'internal_wall_insulation', 'description': 'SWIP EcoBatt & Plastered finish',
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'depth': 95.0,
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'depth_unit': 'mm', 'cost': None, 'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.03125,
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'r_value_unit': 'square_meter_kelvin_per_watt', 'thermal_conductivity': 0.032,
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'thermal_conductivity_unit': None,
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'link': 'SCIS', 'created_at': Timestamp('2025-03-16 15:26:22.379496'), 'is_active': True,
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'prime_material_cost': None, 'material_cost': 0.0, 'labour_cost': 0.0, 'labour_hours_per_unit': 2.1,
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'plant_cost': 0.0, 'total_cost': 89.0, 'notes': None, 'is_installer_quote': True,
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'quantity': 63.98796761892035,
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'quantity_unit': 'm2', 'total': 5694.929118083911, 'labour_hours': 134.37473199973275,
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'labour_days': 4.199210374991648}], 'type': 'internal_wall_insulation',
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'measure_type': 'internal_wall_insulation',
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"innovation_rate": 0,
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'description': 'Install 95mm '
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'SWIP EcoBatt & '
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'Plastered '
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'finish on '
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'internal walls',
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'starting_u_value': 1.7,
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'new_u_value': 0.32,
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'already_installed': False,
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'sap_points': 6,
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'simulation_config': {
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'is_as_built_ending': False,
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'walls_is_assumed_ending':
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False,
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'walls_insulation_thickness_ending': 'average',
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'internal_insulation_ending': True,
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'walls_energy_eff_ending':
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'Good',
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'walls_thermal_transmittance_ending': 0.29},
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'description_simulation': {
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'walls-description': 'Solid '
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'brick, with internal '
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'insulation',
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'walls-energy-eff': 'Good'},
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'total': 5694.929118083911,
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'labour_hours': 134.37473199973275,
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'labour_days': 4.199210374991648,
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'survey': True,
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'recommendation_id': '1_phase=0',
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'efficiency': 3349.6383047552417,
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'co2_equivalent_savings': np.float64(
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0.5),
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'heat_demand': np.float64(
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35.30000000000001),
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'kwh_savings': np.float64(
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1432.3999999999996),
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'energy_cost_savings': np.float64(
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106.67167058823532)}], [
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{'phase': 1, 'parts': [{'id': 2351, 'type': 'loft_insulation',
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'description': 'Knauf Loft Roll 44 glass fibre roll',
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'depth': 300.0, 'depth_unit': 'mm', 'cost': None,
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'cost_unit': 'gbp_per_m2', 'r_value_per_mm': 0.022727273,
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'r_value_unit': 'square_meter_kelvin_per_watt',
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'thermal_conductivity': 0.044,
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'thermal_conductivity_unit': 'watt_per_meter_kelvin',
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'link': 'SCIS',
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'created_at': Timestamp('2025-03-16 15:26:22.379496'),
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'is_active': True, 'prime_material_cost': None,
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'material_cost': 0.0, 'labour_cost': 0.0,
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'labour_hours_per_unit': 0.11, 'plant_cost': 0.0,
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'total_cost': 15.0,
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'notes': 'This is the cost if there is less than 100mm '
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'existing insulation',
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'is_installer_quote': True, 'quantity': 63.98796761892035,
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'quantity_unit': 'm2', 'total': 645.0, 'labour_hours': 8,
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'labour_days': 1}], 'type': 'loft_insulation',
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'measure_type': 'loft_insulation',
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"innovation_rate": 0,
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'description': 'Install 300mm of Knauf Loft Roll 44 glass fibre roll in your loft',
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'starting_u_value': 2.3, 'new_u_value': 2.3, 'sap_points': np.float64(2.4),
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'already_installed': False,
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'simulation_config': {'is_loft_ending': True, 'roof_is_assumed_ending': False,
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'roof_insulation_thickness_ending': '300',
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'roof_thermal_transmittance_ending': 2.3,
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'roof_energy_eff_ending': 'Very Good'},
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'description_simulation': {'roof-description': 'Pitched, 300mm loft insulation',
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'roof-energy-eff': 'Very Good'}, 'total': 645.0,
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'labour_hours': 8, 'labour_days': 1, 'survey': False, 'recommendation_id': '2_phase=1',
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'efficiency': 278.1347826086957,
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'co2_equivalent_savings': np.float64(0.10000000000000009),
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'heat_demand': np.float64(1.5), 'kwh_savings': np.float64(566.1499999999996),
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'energy_cost_savings': np.float64(42.16152352941185)}], [{'phase': 2, 'parts': [
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{'id': 2329, 'type': 'mechanical_ventilation', 'description': 'Mechanical Extract Ventilation',
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'depth': 0.0,
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'depth_unit': None, 'cost': None, 'cost_unit': 'gbp_per_unit', 'r_value_per_mm': nan,
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'r_value_unit': 'square_meter_kelvin_per_watt', 'thermal_conductivity': None,
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'thermal_conductivity_unit': None,
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'link': 'SCIS', 'created_at': datetime.datetime(2025, 3, 16, 15, 26, 22, 379496), 'is_active': True,
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'prime_material_cost': None, 'material_cost': 0.0, 'labour_cost': 0.0, 'labour_hours_per_unit': 0.0,
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'plant_cost': 0.0, 'total_cost': 350.0, 'notes': None, 'is_installer_quote': True, 'total': 700.0,
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'quantity': 2,
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'quantity_unit': 'part'}], 'type': 'mechanical_ventilation', 'measure_type': 'mechanical_ventilation',
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"innovation_rate": 0,
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'description': 'Install 2 '
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'Mechanical '
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'Extract '
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'Ventilation units',
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'starting_u_value': None,
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'new_u_value': None,
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'already_installed': False,
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'sap_points': np.float64(
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-0.10000000000000142),
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'heat_demand': np.float64(
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-3.3999999999999773),
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'kwh_savings': np.float64(
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-53.80000000000018),
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'co2_equivalent_savings': np.float64(
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0.0),
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'energy_cost_savings': np.float64(
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-4.0065176470588995),
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'total': 700.0,
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'labour_hours': 8,
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'labour_days': 1.0,
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'simulation_config': {
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'mechanical_ventilation_ending':
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'mechanical, '
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'extract '
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'only'},
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'description_simulation': {
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'mechanical-ventilation': 'mechanical, '
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'extract only'},
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'recommendation_id': '3_phase=2',
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'efficiency': 0}], [
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{'phase': 3, 'parts': [{'id': 2409, 'type': 'suspended_floor_insulation',
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'description': 'Q-bot underfloor insulation', 'depth': 75.0,
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'depth_unit': 'mm', 'cost': None, 'cost_unit': 'gbp_per_m2',
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'r_value_per_mm': 0.045454547,
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'r_value_unit': 'square_meter_kelvin_per_watt',
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'thermal_conductivity': 0.022,
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'thermal_conductivity_unit': 'watt_per_meter_kelvin',
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'link': 'SCIS',
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'created_at': Timestamp('2025-03-16 15:26:22.379496'),
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'is_active': True, 'prime_material_cost': None,
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'material_cost': 0.0, 'labour_cost': 0.0,
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'labour_hours_per_unit': 1.63, 'plant_cost': 0.0,
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'total_cost': 93.75,
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'notes': 'Linearly interpolated based on Qbot costs',
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'is_installer_quote': True, 'quantity': 43.0,
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'quantity_unit': 'm2', 'total': 4031.25,
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'labour_hours': 70.08999999999999,
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'labour_days': 2.920416666666666}],
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'type': 'suspended_floor_insulation', 'measure_type': 'suspended_floor_insulation',
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"innovation_rate": 0,
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'description': 'Install 75mm Q-bot underfloor insulation insulation in suspended '
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'floor',
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'starting_u_value': 0.83, 'new_u_value': 0.22, 'sap_points': 2, 'survey': True,
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'already_installed': False, 'simulation_config': {'floor_is_assumed_ending': False,
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'floor_insulation_thickness_ending': 'average',
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'floor_thermal_transmittance_ending': 0.685593},
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'description_simulation': {'floor-description': 'Suspended, insulated'},
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'total': 4031.25, 'labour_hours': 70.08999999999999, 'labour_days': 2.920416666666666,
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'recommendation_id': '4_phase=3', 'efficiency': 4856.707710843373,
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'co2_equivalent_savings': np.float64(0.20000000000000018),
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'heat_demand': np.float64(33.5), 'kwh_savings': np.float64(1021.1999999999998),
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'energy_cost_savings': np.float64(76.04936470588231)}], [
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{'phase': 4, 'parts': [], 'type': 'low_energy_lighting',
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'measure_type': 'low_energy_lighting',
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"innovation_rate": 0,
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'description': 'Install low energy lighting in -886 outlets', 'starting_u_value': None,
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'new_u_value': None, 'already_installed': False, 'sap_points': 2,
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'kwh_savings': -48508.5, 'energy_cost_savings': -12481.237049999998,
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'co2_equivalent_savings': -7.858377,
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'description_simulation': {'lighting-energy-eff': 'Very Good',
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'lighting-description': 'Low energy lighting in all fixed'
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' outlets',
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'low-energy-lighting': 100}, 'total': -3411.1000000000004,
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'labour_hours': 1, 'labour_days': 0.125, 'survey': True,
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'recommendation_id': '5_phase=4', 'efficiency': -1705.5500000000002,
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'heat_demand': np.float64(5.099999999999994)}], [
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{'type': 'heating', 'phase': 5, 'measure_type': 'time_temperature_zone_control',
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"innovation_rate": 0,
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'parts': [],
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'description': 'Upgrade heating controls to Smart Thermostats, room sensors and '
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'smart radiator valves (time & temperature zone control)',
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'total': 739.576, 'subtotal': 700.48, 'vat': 39.096000000000004,
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'labour_hours': 3.6199999999999997, 'labour_days': np.float64(1.0),
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'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(2.9),
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'already_installed': False, 'simulation_config': {
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|
'thermostatic_control_ending': 'time and temperature zone control',
|
|
'switch_system_ending': None, 'trvs_ending': None,
|
|
'mainheatc_energy_eff_ending': 'Very Good'}, 'description_simulation': {
|
|
'mainheatcont-description': 'Time and temperature zone control',
|
|
'mainheatc-energy-eff': 'Very Good'}, 'recommendation_id': '6_phase=5',
|
|
'efficiency': 739.576, 'co2_equivalent_savings': np.float64(0.30000000000000027),
|
|
'heat_demand': np.float64(6.599999999999994),
|
|
'kwh_savings': np.float64(876.8000000000002),
|
|
'energy_cost_savings': np.float64(65.29581176470589)}], [
|
|
{'phase': 6, 'parts': [], 'type': 'secondary_heating',
|
|
'measure_type': 'secondary_heating',
|
|
"innovation_rate": 0,
|
|
'description': 'Remove the secondary heating system', 'starting_u_value': None,
|
|
'new_u_value': None, 'sap_points': np.float64(3.6), 'already_installed': False,
|
|
'total': 30.0, 'subtotal': 25.0, 'vat': 5.0, 'labour_hours': 3.0,
|
|
'labour_days': np.float64(1.0),
|
|
'simulation_config': {'secondheat_description_ending': 'None'},
|
|
'description_simulation': {'secondheat-description': 'None'},
|
|
'recommendation_id': '7_phase=6', 'efficiency': 30.0,
|
|
'co2_equivalent_savings': np.float64(0.10000000000000009),
|
|
'heat_demand': np.float64(15.400000000000006),
|
|
'kwh_savings': np.float64(196.29999999999927),
|
|
'energy_cost_savings': np.float64(14.61857647058821)}], [
|
|
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
|
"innovation_rate": 0,
|
|
'description': 'Install a 4.0 kilowatt-peak (kWp) solar panel system.',
|
|
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(13.0),
|
|
'already_installed': False, 'total': 6013.139999999999, 'subtotal': 5010.95, 'vat': 0,
|
|
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(65.0),
|
|
'has_battery': False, 'initial_ac_kwh_per_year': np.float64(4081.7132614999996),
|
|
'description_simulation': {'photo-supply': np.float64(65.0)},
|
|
'recommendation_id': '8_phase=7', 'efficiency': np.float64(462.54923076923075),
|
|
'co2_equivalent_savings': np.float64(0.47347873833399995),
|
|
'heat_demand': np.float64(88.69999999999999),
|
|
'kwh_savings': np.float64(2040.8566307499998),
|
|
'energy_cost_savings': np.float64(525.1124110919749)},
|
|
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
|
"innovation_rate": 0,
|
|
'description': 'Install a 4.0 kilowatt-peak (kWp) solar panel system, with a battery.',
|
|
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(13.0),
|
|
'already_installed': False, 'total': 10537.008, 'subtotal': 8780.84, 'vat': 0,
|
|
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(65.0),
|
|
'has_battery': True, 'initial_ac_kwh_per_year': np.float64(4081.7132614999996),
|
|
'description_simulation': {'photo-supply': np.float64(65.0)},
|
|
'recommendation_id': '9_phase=7', 'efficiency': np.float64(810.5390769230769),
|
|
'co2_equivalent_savings': np.float64(0.6628702336675999),
|
|
'heat_demand': np.float64(88.69999999999999),
|
|
'kwh_savings': np.float64(2857.1992830499994),
|
|
'energy_cost_savings': np.float64(735.1573755287648)},
|
|
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
|
"innovation_rate": 0,
|
|
'description': 'Install a 3.6 kilowatt-peak (kWp) solar panel system.',
|
|
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(12.0),
|
|
'already_installed': False, 'total': 5826.491999999999, 'subtotal': 4855.41, 'vat': 0,
|
|
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(60.0),
|
|
'has_battery': False, 'initial_ac_kwh_per_year': np.float64(3692.66794),
|
|
'description_simulation': {'photo-supply': np.float64(60.0)},
|
|
'recommendation_id': '10_phase=7', 'efficiency': np.float64(485.54099999999994),
|
|
'co2_equivalent_savings': np.float64(0.42834948104),
|
|
'heat_demand': np.float64(83.69999999999999), 'kwh_savings': np.float64(1846.33397),
|
|
'energy_cost_savings': np.float64(475.0617304809999)},
|
|
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
|
"innovation_rate": 0,
|
|
'description': 'Install a 3.6 kilowatt-peak (kWp) solar panel system, with a battery.',
|
|
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(12.0),
|
|
'already_installed': False, 'total': 10350.359999999999, 'subtotal': 8625.3, 'vat': 0,
|
|
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(60.0),
|
|
'has_battery': True, 'initial_ac_kwh_per_year': np.float64(3692.66794),
|
|
'description_simulation': {'photo-supply': np.float64(60.0)},
|
|
'recommendation_id': '11_phase=7', 'efficiency': np.float64(862.5299999999999),
|
|
'co2_equivalent_savings': np.float64(0.599689273456),
|
|
'heat_demand': np.float64(83.69999999999999), 'kwh_savings': np.float64(2584.867558),
|
|
'energy_cost_savings': np.float64(665.0864226734)},
|
|
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
|
"innovation_rate": 0,
|
|
'description': 'Install a 3.2 kilowatt-peak (kWp) solar panel system.',
|
|
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(11.0),
|
|
'already_installed': False, 'total': 5642.604, 'subtotal': 4702.17, 'vat': 0,
|
|
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(55.0),
|
|
'has_battery': False, 'initial_ac_kwh_per_year': np.float64(3300.5416548),
|
|
'description_simulation': {'photo-supply': np.float64(55.0)},
|
|
'recommendation_id': '12_phase=7', 'efficiency': np.float64(512.964),
|
|
'co2_equivalent_savings': np.float64(0.3828628319568), 'heat_demand': np.float64(78.3),
|
|
'kwh_savings': np.float64(1650.2708274),
|
|
'energy_cost_savings': np.float64(424.61468389001993)},
|
|
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
|
"innovation_rate": 0,
|
|
'description': 'Install a 3.2 kilowatt-peak (kWp) solar panel system, with a battery.',
|
|
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(11.0),
|
|
'already_installed': False, 'total': 10166.472, 'subtotal': 8472.06, 'vat': 0,
|
|
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(55.0),
|
|
'has_battery': True, 'initial_ac_kwh_per_year': np.float64(3300.5416548),
|
|
'description_simulation': {'photo-supply': np.float64(55.0)},
|
|
'recommendation_id': '13_phase=7', 'efficiency': np.float64(924.2247272727273),
|
|
'co2_equivalent_savings': np.float64(0.53600796473952),
|
|
'heat_demand': np.float64(78.3), 'kwh_savings': np.float64(2310.3791583599996),
|
|
'energy_cost_savings': np.float64(594.4605574460278)},
|
|
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
|
"innovation_rate": 0,
|
|
'description': 'Install a 2.8 kilowatt-peak (kWp) solar panel system.',
|
|
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(9.0),
|
|
'already_installed': False, 'total': 5458.727999999999, 'subtotal': 4548.94, 'vat': 0,
|
|
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(45.0),
|
|
'has_battery': False, 'initial_ac_kwh_per_year': np.float64(2907.1867812),
|
|
'description_simulation': {'photo-supply': np.float64(45.0)},
|
|
'recommendation_id': '14_phase=7', 'efficiency': np.float64(606.5253333333333),
|
|
'co2_equivalent_savings': np.float64(0.3372336666192), 'heat_demand': np.float64(64.0),
|
|
'kwh_savings': np.float64(1453.5933906),
|
|
'energy_cost_savings': np.float64(374.00957940138)},
|
|
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
|
"innovation_rate": 0,
|
|
'description': 'Install a 2.8 kilowatt-peak (kWp) solar panel system, with a battery.',
|
|
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(9.0),
|
|
'already_installed': False, 'total': 9982.596, 'subtotal': 8318.83, 'vat': 0,
|
|
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(45.0),
|
|
'has_battery': True, 'initial_ac_kwh_per_year': np.float64(2907.1867812),
|
|
'description_simulation': {'photo-supply': np.float64(45.0)},
|
|
'recommendation_id': '15_phase=7', 'efficiency': np.float64(1109.1773333333333),
|
|
'co2_equivalent_savings': np.float64(0.47212713326688),
|
|
'heat_demand': np.float64(64.0), 'kwh_savings': np.float64(2035.03074684),
|
|
'energy_cost_savings': np.float64(523.6134111619319)},
|
|
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
|
"innovation_rate": 0,
|
|
'description': 'Install a 2.4 kilowatt-peak (kWp) solar panel system.',
|
|
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(8.0),
|
|
'already_installed': False, 'total': 5274.852, 'subtotal': 4395.71, 'vat': 0,
|
|
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(40.0),
|
|
'has_battery': False, 'initial_ac_kwh_per_year': np.float64(2510.25188),
|
|
'description_simulation': {'photo-supply': np.float64(40.0)},
|
|
'recommendation_id': '16_phase=7', 'efficiency': np.float64(659.3565),
|
|
'co2_equivalent_savings': np.float64(0.29118921808), 'heat_demand': np.float64(54.3),
|
|
'kwh_savings': np.float64(1255.12594),
|
|
'energy_cost_savings': np.float64(322.94390436199996)},
|
|
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
|
"innovation_rate": 0,
|
|
'description': 'Install a 2.4 kilowatt-peak (kWp) solar panel system, with a battery.',
|
|
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(8.0),
|
|
'already_installed': False, 'total': 9798.72, 'subtotal': 8165.6, 'vat': 0,
|
|
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(40.0),
|
|
'has_battery': True, 'initial_ac_kwh_per_year': np.float64(2510.25188),
|
|
'description_simulation': {'photo-supply': np.float64(40.0)},
|
|
'recommendation_id': '17_phase=7', 'efficiency': np.float64(1224.84),
|
|
'co2_equivalent_savings': np.float64(0.40766490531199995),
|
|
'heat_demand': np.float64(54.3), 'kwh_savings': np.float64(1757.1763159999998),
|
|
'energy_cost_savings': np.float64(452.1214661067999)},
|
|
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
|
"innovation_rate": 0,
|
|
'description': 'Install a 2.0 kilowatt-peak (kWp) solar panel system.',
|
|
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(7.0),
|
|
'already_installed': False, 'total': 5090.976, 'subtotal': 4242.48, 'vat': 0,
|
|
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(35.0),
|
|
'has_battery': False, 'initial_ac_kwh_per_year': np.float64(2096.682636),
|
|
'description_simulation': {'photo-supply': np.float64(35.0)},
|
|
'recommendation_id': '18_phase=7', 'efficiency': np.float64(727.2822857142856),
|
|
'co2_equivalent_savings': np.float64(0.243215185776), 'heat_demand': np.float64(48.5),
|
|
'kwh_savings': np.float64(1048.341318),
|
|
'energy_cost_savings': np.float64(269.7382211214)},
|
|
{'phase': 7, 'parts': [], 'type': 'solar_pv', 'measure_type': 'solar_pv',
|
|
"innovation_rate": 0,
|
|
'description': 'Install a 2.0 kilowatt-peak (kWp) solar panel system, with a battery.',
|
|
'starting_u_value': None, 'new_u_value': None, 'sap_points': np.float64(7.0),
|
|
'already_installed': False, 'total': 9614.844, 'subtotal': 8012.369999999999, 'vat': 0,
|
|
'labour_hours': 48, 'labour_days': 2, 'photo_supply': np.float64(35.0),
|
|
'has_battery': True, 'initial_ac_kwh_per_year': np.float64(2096.682636),
|
|
'description_simulation': {'photo-supply': np.float64(35.0)},
|
|
'recommendation_id': '19_phase=7', 'efficiency': np.float64(1373.5491428571427),
|
|
'co2_equivalent_savings': np.float64(0.3405012600864), 'heat_demand': np.float64(48.5),
|
|
'kwh_savings': np.float64(1467.6778451999999),
|
|
'energy_cost_savings': np.float64(377.6335095699599)}]
|
|
]
|
|
return recs
|
|
|
|
|
|
def _attach_costs_and_uplifts(recs, funding, p):
|
|
"""Mimic what your script did: add cost fields & innovation uplift."""
|
|
out = deepcopy(recs)
|
|
for group in out:
|
|
for r in group:
|
|
if r["type"] in ["mechanical_ventilation", "low_energy_lighting", "secondary_heating"]:
|
|
(
|
|
r["partial_project_score"],
|
|
r["partial_project_funding"],
|
|
r["innovation_uplift"],
|
|
r["uplift_project_score"],
|
|
) = (
|
|
0, 0, 0, 0
|
|
)
|
|
continue
|
|
|
|
(
|
|
r["partial_project_score"], r["partial_project_funding"], r["innovation_uplift"],
|
|
r["uplift_project_score"]
|
|
) = funding.get_innovation_uplift(
|
|
measure=r,
|
|
starting_sap=55,
|
|
floor_area=70.0,
|
|
is_cavity=False,
|
|
current_wall_uvalue=1.7,
|
|
is_partial=False,
|
|
existing_li_thickness=150,
|
|
mainheating=p.main_heating,
|
|
main_fuel=p.main_fuel,
|
|
mainheat_energy_eff="Very Good",
|
|
)
|
|
# the optimiser_functions.prepare_input_measures will translate these to input format; but
|
|
# for safety add explicit cost fields some downstream code expects:
|
|
r["total"] = float(r["total"])
|
|
return out
|
|
|
|
|
|
def _to_input_measures(recs, p):
|
|
"""Use your own helper so we test the full pipeline."""
|
|
property_measure_types = {rec["type"] for grp in recs for rec in grp}
|
|
needs_ventilation = any(
|
|
x in property_measure_types for x in optimiser_functions.assumptions.measures_needing_ventilation
|
|
) and not getattr(p, "has_ventilation", False)
|
|
|
|
# goal="Increasing EPC", add_uplift=True for Social path
|
|
return optimiser_functions.prepare_input_measures(
|
|
recs, goal="Increasing EPC", needs_ventilation=needs_ventilation, funding=True
|
|
)
|
|
|
|
|
|
def _types_of(picked_items):
|
|
return {item["type"] for item in picked_items}
|
|
|
|
|
|
def test_social_fabric_only_returns_only_fabric_types(p, funding, property_recommendations, monkeypatch):
|
|
# 1) prepare data like your script
|
|
recs = _attach_costs_and_uplifts(property_recommendations, funding, p)
|
|
input_measures = _to_input_measures(recs, p)
|
|
|
|
# 2) run optimiser wrapper (budget and target_gain can be modest for the test)
|
|
budget = 30000.0
|
|
target_gain = 8.0
|
|
|
|
solutions = optimise_with_funding_paths(
|
|
p=p,
|
|
input_measures=input_measures,
|
|
housing_type="Social",
|
|
budget=budget,
|
|
target_gain=target_gain,
|
|
funding=funding
|
|
)
|
|
|
|
# 3) basic shape assertions
|
|
assert isinstance(solutions, pd.DataFrame)
|
|
assert not solutions.empty
|
|
|
|
# 4) find the fabric-only ECO4 row
|
|
fabric_rows = solutions[
|
|
solutions["path"].apply(lambda x: isinstance(x, dict) and x.get("reference") == "fabric-only:eco4")]
|
|
assert not fabric_rows.empty, "Expected a fabric-only:eco4 solution for Social tenure"
|
|
|
|
# 5) ensure only fabric measure types are present in that solution
|
|
picked_types = _types_of(fabric_rows.iloc[0]["items"])
|
|
assert picked_types == {'internal_wall_insulation+mechanical_ventilation',
|
|
'suspended_floor_insulation'}, "incorrect types selected"
|
|
|
|
# 6) respect budget
|
|
assert fabric_rows.iloc[0]["total_cost"] <= budget + 1e-9
|
|
|
|
# (optional) ensure unfunded baseline also appears
|
|
unfunded_rows = solutions[
|
|
solutions["path"].apply(lambda x: isinstance(x, dict) and x.get("reference") == "unfunded:all")]
|
|
assert not unfunded_rows.empty
|
|
|
|
|
|
def test_private_solid_wall_no_innovation_epc_d(p, funding, mock_project_scores_matrix, mock_partial_scores_matrix):
|
|
"""
|
|
We have a specific test for this case which was implemented incorrectly originally.
|
|
This is an EPC D property and so shouldn't be eligible for ECO4. Instead, only GBIS should be considered.
|
|
"""
|
|
|
|
# Overwrite the data - copied from real example
|
|
p2 = deepcopy(p)
|
|
p2.data = {
|
|
"current-energy-rating": "D",
|
|
"current-energy-efficiency": 68,
|
|
"mainheat-energy-eff": "Good",
|
|
}
|
|
p2.walls = {'original_description': 'Sandstone or limestone, as built, no insulation (assumed)',
|
|
'clean_description': 'Sandstone or limestone, as built, no insulation', 'thermal_transmittance': None,
|
|
'thermal_transmittance_unit': None, 'is_cavity_wall': False, 'is_filled_cavity': False,
|
|
'is_solid_brick': False, 'is_system_built': False, 'is_timber_frame': False,
|
|
'is_granite_or_whinstone': False, 'is_as_built': True, 'is_cob': False, 'is_assumed': True,
|
|
'is_sandstone_or_limestone': True, 'is_park_home': False, 'insulation_thickness': 'none',
|
|
'external_insulation': False, 'internal_insulation': False}
|
|
|
|
funding2 = Funding(
|
|
tenure="Private",
|
|
project_scores_matrix=mock_project_scores_matrix,
|
|
partial_project_scores_matrix=mock_partial_scores_matrix,
|
|
whlg_eligible_postcodes=pd.DataFrame([{"Postcode": "ab12cd"}]),
|
|
eco4_social_cavity_abs_rate=12.5,
|
|
eco4_social_solid_abs_rate=17,
|
|
eco4_private_cavity_abs_rate=12.5,
|
|
eco4_private_solid_abs_rate=17,
|
|
gbis_social_cavity_abs_rate=21,
|
|
gbis_social_solid_abs_rate=25,
|
|
gbis_private_cavity_abs_rate=21,
|
|
gbis_private_solid_abs_rate=28,
|
|
)
|
|
|
|
input_measures = [
|
|
[{'id': '0_phase=0', 'cost': np.float64(4441.202499013676), 'gain': np.float64(3.4000000000000057),
|
|
'type': 'internal_wall_insulation+mechanical_ventilation', 'innovation_uplift': np.float64(0.0),
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|
'cost_minus_uplift': np.float64(4441.202499013676), 'raw_cost': 3881.2024990136756,
|
|
'partial_project_funding': np.float64(2300.1000000000004), 'partial_project_score': np.float64(135.3),
|
|
'uplift_project_score': np.float64(0.0)}], [
|
|
{'id': '2_phase=2', 'cost': np.float64(2280.0), 'gain': np.float64(0.4), 'type': 'secondary_glazing',
|
|
'innovation_uplift': np.float64(0.0), 'cost_minus_uplift': np.float64(2280.0),
|
|
'raw_cost': np.float64(2280.0), 'partial_project_funding': np.float64(1421.1999999999998),
|
|
'partial_project_score': np.float64(83.6), 'uplift_project_score': np.float64(0.0)}], [
|
|
{'id': '3_phase=3', 'cost': np.float64(604.5840000000001), 'gain': np.float64(1.2),
|
|
'type': 'time_temperature_zone_control', 'innovation_uplift': np.float64(0.0),
|
|
'cost_minus_uplift': np.float64(604.5840000000001), 'raw_cost': 604.5840000000001,
|
|
'partial_project_funding': np.float64(702.0999999999999), 'partial_project_score': np.float64(41.3),
|
|
'uplift_project_score': np.float64(0.0)}], [
|
|
{'id': '4_phase=4', 'cost': 60.0, 'gain': np.float64(0.0), 'type': 'secondary_heating',
|
|
'innovation_uplift': 0, 'cost_minus_uplift': 60.0, 'raw_cost': 60.0, 'partial_project_funding': 0,
|
|
'partial_project_score': 0, 'uplift_project_score': 0}]
|
|
]
|
|
|
|
solutions = optimise_with_funding_paths(
|
|
p=p2,
|
|
input_measures=input_measures,
|
|
housing_type="Private",
|
|
budget=None,
|
|
target_gain=1.5,
|
|
funding=funding2
|
|
)
|
|
|
|
# 3) basic shape assertions
|
|
assert isinstance(solutions, pd.DataFrame)
|
|
assert not solutions.empty
|
|
|
|
# We should have 2 rows
|
|
assert solutions.shape[0] == 2
|
|
|
|
# We should only have None or GBIS
|
|
assert set(solutions["scheme"].unique()) == {"none", "gbis"}
|
|
|
|
meets_upgrade_gbis = solutions[solutions["meets_upgrade_target"] & solutions["is_eligible"]]
|
|
assert meets_upgrade_gbis.shape[0] == 1
|
|
|
|
# Check exact result
|
|
assert meets_upgrade_gbis.squeeze().to_dict() == {
|
|
'fixed_ids': ['0_phase=0'], 'items': [
|
|
{'id': '0_phase=0', 'cost': 3881.2024990136756, 'gain': np.float64(3.4000000000000057),
|
|
'type': 'internal_wall_insulation+mechanical_ventilation', 'innovation_uplift': np.float64(0.0),
|
|
'cost_minus_uplift': np.float64(4441.202499013676), 'raw_cost': 3881.2024990136756,
|
|
'partial_project_funding': np.float64(2300.1000000000004), 'partial_project_score': np.float64(135.3),
|
|
'uplift_project_score': np.float64(0.0)}], 'total_cost': 3881.2024990136756,
|
|
'total_gain': 3.4000000000000057, 'path': [{'AND': ['internal_wall_insulation+mechanical_ventilation'],
|
|
'reference':
|
|
'internal_wall_insulation+mechanical_ventilation:gbis'}],
|
|
'scheme': 'gbis', 'is_eligible': True, 'unfunded_items': [], 'meets_upgrade_target': True, 'starting_sap': 68,
|
|
'floor_area': 70.0, 'ending_sap': 71.4, 'starting_band': 'High_D', 'ending_band': 'Low_C',
|
|
'floor_area_band': '0-72', 'project_score': 540.0, 'full_project_funding': 0.0,
|
|
'partial_project_funding': 2300.1000000000004, 'partial_project_score': 135.3, 'total_uplift': 0.0,
|
|
'total_uplift_score': 0.0
|
|
}
|
|
|
|
|
|
def test_build_heat_pump_paths():
|
|
eg1 = build_heat_pump_paths([], ["loft_insulation"])
|
|
|
|
assert eg1 == [{'AND': ['loft_insulation', 'air_source_heat_pump']}]
|
|
|
|
eg2 = build_heat_pump_paths(["internal_wall_insulation", "external_wall_insulation"], ["loft_insulation"])
|
|
|
|
assert eg2 == [{'AND': ['internal_wall_insulation', 'loft_insulation', 'air_source_heat_pump']},
|
|
{'AND': ['external_wall_insulation', 'loft_insulation', 'air_source_heat_pump']}]
|