cleaned code. not found bug. not working

# Conflicts:
#	costs/__main__.py
#	costs/life_cycle_costs.py

costs/__init__.py

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+"""
+Cost workflow initialization
+"""
+import glob
+import os
+from pathlib import Path
+
+# configurable parameters
+file_path = Path('./data/selected_building_2864.geojson').resolve()
+CONSTRUCTION_FORMAT = 'nrcan'
+USAGE_FORMAT = 'comnet'
+ENERGY_SYSTEM_FORMAT = 'montreal_custom'
+ATTIC_HEATED_CASE = 0
+BASEMENT_HEATED_CASE = 1
+NUMBER_OF_YEARS = 31
+PERCENTAGE_CREDIT = 0
+INTEREST_RATE = 0.04
+CREDIT_YEARS = 15
+CONSUMER_PRICE_INDEX = 0.04
+ELECTRICITY_PEAK_INDEX = 0.05
+ELECTRICITY_PRICE_INDEX = 0.05
+GAS_PRICE_INDEX = 0.05
+DISCOUNT_RATE = 0.03
+RETROFITTING_YEAR_CONSTRUCTION = 2020
+
+CLIMATE_REFERENCE_CITY = 'Montreal'
+WEATHER_FILE = 'CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw'
+WEATHER_FORMAT = 'epw'
+CURRENT_STATUS = 0
+SKIN_RETROFIT = 1
+SYSTEM_RETROFIT_AND_PV = 2
+SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV = 3
+RETROFITTING_SCENARIOS = [
+  CURRENT_STATUS,
+  SKIN_RETROFIT,
+  SYSTEM_RETROFIT_AND_PV,
+  SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV
+]
+
+EMISSION_FACTOR_ELECTRICITY_QUEBEC = 0.0015 #https://www.cer-rec.gc.ca/en/data-analysis/energy-markets/provincial-territorial-energy-profiles/provincial-territorial-energy-profiles-quebec.html#:~:text=GHG%20Emissions,-Quebec's%20GHG%20emissions&text=The%20largest%20emitting%20sectors%20in,2.3%20MT%20CO2e.
+EMISSION_FACTOR_GAS_QUEBEC = 0.183 #https://www.canada.ca/en/environment-climate-change/services/climate-change/pricing-pollution-how-it-will-work/output-based-pricing-system/federal-greenhouse-gas-offset-system/emission-factors-reference-values.html
+EMISSION_FACTOR_BIOMASS_QUEBEC = 0.035 #Data from Spain. https://www.miteco.gob.es/es/cambio-climatico/temas/mitigacion-politicas-y-medidas/factoresemision_tcm30-479095.pdf
+EMISSION_FACTOR_FUEL_OIL_QUEBEC = 0.274
+EMISSION_FACTOR_DIESEL_QUEBEC = 0.240
+
+
+tmp_folder = Path('./tmp').resolve()
+out_path = Path('./outputs').resolve()
+files = glob.glob(f'{out_path}/*')
+print('path', file_path)
+for file in files:
+  if file != '.gitignore':
+    os.remove(file)

costs/__main__.py

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+"""
+Costs Workflow
+SPDX - License - Identifier: LGPL - 3.0 - or -later
+Copyright © 2022 Project Author Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
+Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
+"""
+
+from pathlib import Path
+
+import numpy_financial as npf
+import pandas as pd
+from hub.catalog_factories.costs_catalog_factory import CostCatalogFactory
+from hub.helpers.dictionaries import Dictionaries
+from hub.imports.construction_factory import ConstructionFactory
+from hub.imports.energy_systems_factory import EnergySystemsFactory
+from hub.imports.geometry_factory import GeometryFactory
+from hub.imports.usage_factory import UsageFactory
+from hub.imports.weather_factory import WeatherFactory
+from monthly_energy_balance_engine import MonthlyEnergyBalanceEngine
+from sra_engine import SraEngine
+from printing_results import *
+from hub.helpers import constants as cte
+from life_cycle_costs import LifeCycleCosts
+
+from costs import CONSTRUCTION_FORMAT
+from costs import ENERGY_SYSTEM_FORMAT, RETROFITTING_SCENARIOS, NUMBER_OF_YEARS
+from costs import CONSUMER_PRICE_INDEX, ELECTRICITY_PEAK_INDEX, ELECTRICITY_PRICE_INDEX, GAS_PRICE_INDEX, DISCOUNT_RATE
+from costs import SKIN_RETROFIT, SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV
+from costs import RETROFITTING_YEAR_CONSTRUCTION
+
+# import paths
+from results import Results
+
+
+def _npv_from_list(npv_discount_rate, list_cashflow):
+  lcc_value = npf.npv(npv_discount_rate, list_cashflow)
+  return lcc_value
+
+
+def _search_archetype(costs_catalog, building_function):
+  costs_archetypes = costs_catalog.entries('archetypes').archetypes
+  for building_archetype in costs_archetypes:
+    if str(building_function) == str(building_archetype.function):
+      return building_archetype
+  raise KeyError('archetype not found')
+
+
+life_cycle_results = pd.DataFrame()
+file_path = (Path(__file__).parent.parent / 'input_files' / 'summerschool_one_building.geojson')
+climate_reference_city = 'Montreal'
+weather_format = 'epw'
+construction_format = 'nrcan'
+usage_format = 'nrcan'
+energy_systems_format = 'montreal_custom'
+attic_heated_case = 0
+basement_heated_case = 1
+
+out_path = (Path(__file__).parent.parent / 'out_files')
+tmp_folder = (Path(__file__).parent / 'tmp')
+
+print('[simulation start]')
+city = GeometryFactory('geojson',
+                       path=file_path,
+                       height_field='citygml_me',
+                       year_of_construction_field='ANNEE_CONS',
+                       function_field='CODE_UTILI',
+                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
+city.climate_reference_city = climate_reference_city
+city.climate_file = (tmp_folder / f'{climate_reference_city}.cli').resolve()
+print(f'city created from {file_path}')
+WeatherFactory(weather_format, city).enrich()
+print('enrich weather... done')
+ConstructionFactory(construction_format, city).enrich()
+print('enrich constructions... done')
+UsageFactory(usage_format, city).enrich()
+print('enrich usage... done')
+for building in city.buildings:
+  building.energy_systems_archetype_name = 'system 1 gas pv'
+EnergySystemsFactory(energy_systems_format, city).enrich()
+print('enrich systems... done')
+
+print('exporting:')
+sra_file = (tmp_folder / f'{city.name}_sra.xml').resolve()
+SraEngine(city, sra_file, tmp_folder)
+print('    sra processed...')
+catalog = CostCatalogFactory('montreal_custom').catalog
+
+for retrofitting_scenario in RETROFITTING_SCENARIOS:
+
+  if retrofitting_scenario in (SKIN_RETROFIT, SYSTEM_RETROFIT_AND_PV):
+    for building in city.buildings:
+      building.year_of_construction = RETROFITTING_YEAR_CONSTRUCTION
+    ConstructionFactory(CONSTRUCTION_FORMAT, city).enrich()
+    print('enrich retrofitted constructions... done')
+
+  if retrofitting_scenario in (SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
+    for building in city.buildings:
+      building.energy_systems_archetype_name = 'system 6 electricity pv'
+    EnergySystemsFactory(ENERGY_SYSTEM_FORMAT, city).enrich()
+    print('enrich systems... done')
+
+  MonthlyEnergyBalanceEngine(city, tmp_folder)
+  print('    insel processed...')
+
+  for building in city.buildings:
+    for energy_system in building.energy_systems:
+      if cte.HEATING in energy_system.demand_types:
+        energy_system.generation_system.heat_power = building.heating_peak_load[cte.YEAR][0]
+      if cte.COOLING in energy_system.demand_types:
+        energy_system.generation_system.cooling_power = building.cooling_peak_load[cte.YEAR][0]
+    print(f' heating consumption {building.heating_consumption[cte.YEAR][0]}')
+  print('importing results:')
+  results = Results(city, out_path)
+  results.print()
+  print('results printed...')
+
+  print('[simulation end]')
+
+  print(f'beginning costing scenario {retrofitting_scenario} systems... done')
+
+  for building in city.buildings:
+    total_floor_area = 0
+    function = Dictionaries().hub_function_to_montreal_custom_costs_function[building.function]
+    archetype = _search_archetype(catalog, function)
+    print('lcc for first building started')
+    if "gas" in building.energy_systems_archetype_name:
+      FUEL_TYPE = 1
+    else:
+      FUEL_TYPE = 0
+
+    lcc = LifeCycleCosts(building, archetype, NUMBER_OF_YEARS, CONSUMER_PRICE_INDEX, ELECTRICITY_PEAK_INDEX,
+                         ELECTRICITY_PRICE_INDEX, GAS_PRICE_INDEX, DISCOUNT_RATE, retrofitting_scenario, FUEL_TYPE)
+    global_capital_costs, global_capital_incomes = lcc.calculate_capital_costs()
+    global_end_of_life_costs = lcc.calculate_end_of_life_costs()
+    global_operational_costs = lcc.calculate_total_operational_costs
+    global_maintenance_costs = lcc.calculate_total_maintenance_costs()
+    global_operational_incomes = lcc.calculate_total_operational_incomes(retrofitting_scenario)
+    full_path_output = Path(out_path / f'output {retrofitting_scenario} {building.name}.xlsx').resolve()
+    with pd.ExcelWriter(full_path_output) as writer:
+      global_capital_costs.to_excel(writer, sheet_name='global_capital_costs')
+      global_end_of_life_costs.to_excel(writer, sheet_name='global_end_of_life_costs')
+      global_operational_costs.to_excel(writer, sheet_name='global_operational_costs')
+      global_maintenance_costs.to_excel(writer, sheet_name='global_maintenance_costs')
+      global_operational_incomes.to_excel(writer, sheet_name='global_operational_incomes')
+      global_capital_incomes.to_excel(writer, sheet_name='global_capital_incomes')
+
+    investmentcosts = pd.DataFrame([])
+    print('RETROFITTING SCENARIO', retrofitting_scenario)
+    if retrofitting_scenario == 0:
+      investmentcosts = [global_capital_costs['B2010_opaque_walls'][0],
+                         global_capital_costs['B2020_transparent'][0],
+                         global_capital_costs['B3010_opaque_roof'][0],
+                         global_capital_costs['B10_superstructure'][0],
+                         global_capital_costs['D3020_heat_generating_systems'][0],
+                         global_capital_costs['D3080_other_hvac_ahu'][0],
+                         global_capital_costs['D5020_lighting_and_branch_wiring'][0],
+                         global_capital_costs['D301010_photovoltaic_system'][0]]
+      investmentcosts = pd.DataFrame(investmentcosts)
+
+    else:
+      investmentcosts[f'retrofitting_scenario_{retrofitting_scenario}'] = \
+        [global_capital_costs['B2010_opaque_walls'][0],
+         global_capital_costs['B2020_transparent'][0],
+         global_capital_costs['B3010_opaque_roof'][0],
+         global_capital_costs['B10_superstructure'][0],
+         global_capital_costs['D3020_heat_generating_systems'][0],
+         global_capital_costs['D3080_other_hvac_ahu'][0],
+         global_capital_costs['D5020_lighting_and_branch_wiring'][0],
+         global_capital_costs['D301010_photovoltaic_system'][0]]
+
+    investmentcosts.index = ['Opaque walls', 'Transparent walls', 'Opaque roof', 'Superstructure',
+                             'Heat generation systems', 'Other HVAC AHU', 'Lighting and branch wiring', 'PV systems']
+
+    df_capital_costs_skin = (
+        global_capital_costs['B2010_opaque_walls'] + global_capital_costs['B2020_transparent'] +
+        global_capital_costs['B3010_opaque_roof'] + global_capital_costs['B10_superstructure']
+    )
+    df_capital_costs_systems = (
+        global_capital_costs['D3020_heat_generating_systems'] +
+        global_capital_costs['D3030_cooling_generation_systems'] +
+        global_capital_costs['D3080_other_hvac_ahu'] +
+        global_capital_costs['D5020_lighting_and_branch_wiring'] +
+        global_capital_costs['D301010_photovoltaic_system']
+    )
+    df_end_of_life_costs = global_end_of_life_costs['End_of_life_costs']
+    df_operational_costs = (
+      global_operational_costs['Fixed_costs_electricity_peak'] +
+      global_operational_costs['Fixed_costs_electricity_monthly'] +
+      global_operational_costs['Fixed_costs_electricity_peak'] +
+      global_operational_costs['Fixed_costs_electricity_monthly'] +
+      global_operational_costs['Variable_costs_electricity'] +
+      global_operational_costs['Fixed_costs_gas'] +
+      global_operational_costs['Variable_costs_gas']
+    )
+    df_maintenance_costs = (
+        global_maintenance_costs['Heating_maintenance'] +
+        global_maintenance_costs['Cooling_maintenance'] +
+        global_maintenance_costs['PV_maintenance']
+    )
+    df_operational_incomes = global_operational_incomes['Incomes electricity']
+
+    df_capital_incomes = (
+        global_capital_incomes['Subsidies construction'] +
+        global_capital_incomes['Subsidies HVAC'] +
+        global_capital_incomes['Subsidies PV']
+    )
+
+    life_cycle_costs_capital_skin = _npv_from_list(DISCOUNT_RATE, df_capital_costs_skin.values.tolist())
+    life_cycle_costs_capital_systems = _npv_from_list(DISCOUNT_RATE, df_capital_costs_systems.values.tolist())
+    life_cycle_costs_end_of_life_costs = _npv_from_list(DISCOUNT_RATE, df_end_of_life_costs.values.tolist())
+    life_cycle_operational_costs = _npv_from_list(DISCOUNT_RATE, df_operational_costs.values.tolist())
+    life_cycle_maintenance_costs = _npv_from_list(DISCOUNT_RATE, df_maintenance_costs.values.tolist())
+    life_cycle_operational_incomes = _npv_from_list(DISCOUNT_RATE, df_operational_incomes.values.tolist())
+    life_cycle_capital_incomes = _npv_from_list(DISCOUNT_RATE, df_capital_incomes.values.tolist())
+
+    life_cycle_costs = (
+        life_cycle_costs_capital_skin +
+        life_cycle_costs_capital_systems +
+        life_cycle_costs_end_of_life_costs +
+        life_cycle_operational_costs +
+        life_cycle_maintenance_costs -
+        life_cycle_operational_incomes -
+        life_cycle_capital_incomes
+    )
+    total_floor_area += lcc.calculate_total_floor_area()
+    life_cycle_results[f'Scenario {retrofitting_scenario}'] = [life_cycle_costs_capital_skin,
+                                                               life_cycle_costs_capital_systems,
+                                                               life_cycle_costs_end_of_life_costs,
+                                                               life_cycle_operational_costs,
+                                                               life_cycle_maintenance_costs,
+                                                               -life_cycle_operational_incomes,
+                                                               -life_cycle_capital_incomes]
+
+    life_cycle_results.index = ['total_capital_costs_skin',
+                                'total_capital_costs_systems',
+                                'end_of_life_costs',
+                                'total_operational_costs',
+                                'total_maintenance_costs',
+                                'operational_incomes',
+                                'capital_incomes']
+
+    print(f'Scenario {retrofitting_scenario} {life_cycle_costs}')
+
+#    printing_results(investmentcosts, life_cycle_results, total_floor_area)
+
+
+
+
+
+
+
+
+
+

costs/__main__emissions.py

@@ -0,0 +1,68 @@
+"""
+Costs Workflow
+SPDX - License - Identifier: LGPL - 3.0 - or -later
+Copyright © 2022 Project Author Pilar Monsalvete Alvarez de Uribarri pilar.monsalvete@concordia.ca
+Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
+"""
+
+from pathlib import Path
+
+import pandas as pd
+from hub.helpers.dictionaries import Dictionaries
+from hub.catalog_factories.costs_catalog_factory import CostCatalogFactory
+
+from costs import EMISSION_FACTOR_ELECTRICITY_QUEBEC, EMISSION_FACTOR_GAS_QUEBEC, EMISSION_FACTOR_BIOMASS_QUEBEC, \
+  EMISSION_FACTOR_FUEL_OIL_QUEBEC, EMISSION_FACTOR_DIESEL_QUEBEC, NUMBER_OF_YEARS
+
+def _search_archetype(costs_catalog, building_function):
+  costs_archetypes = costs_catalog.entries('archetypes').archetypes
+  for building_archetype in costs_archetypes:
+    if str(building_function) == str(building_archetype.function):
+      return building_archetype
+  raise KeyError('archetype not found')
+
+catalog = CostCatalogFactory('montreal_custom').catalog
+
+for building in city.buildings:
+  building_heating_consumption = 1000
+  building_domestic_water_consumption = 1000
+  building_cooling_consumption = 1000
+  distribution_systems_electrical_consumption = 1000
+  lighting_electrical_demand = 1000
+  appliances_electrical_demand = 1000
+  rng = range(NUMBER_OF_YEARS)
+
+  function = Dictionaries().hub_function_to_montreal_custom_costs_function[building.function]
+  archetype = _search_archetype(catalog, function)
+
+  print('co2 for first building started')
+  if "gas" in building.energy_systems_archetype_name:
+    gas_consumption = building_heating_consumption + building_domestic_water_consumption
+    electricity_consumption = building_cooling_consumption + distribution_systems_electrical_consumption + \
+                              lighting_electrical_demand + appliances_electrical_demand
+    biomass_consumption = 0
+    fuel_oil_consumption = 0
+    diesel_consumption = 0
+  else:
+    gas_consumption = 0
+    electricity_consumption = building_heating_consumption + building_domestic_water_consumption + \
+                              building_cooling_consumption + distribution_systems_electrical_consumption + \
+                              lighting_electrical_demand + appliances_electrical_demand
+    biomass_consumption = 0
+    fuel_oil_consumption = 0
+    diesel_consumption = 0
+
+  CO2_emissions = pd.DataFrame(index=rng, columns=['CO2 emissions gas', 'CO2 emissions electricity',
+                                                   'CO2 Emissions biomass', 'CO2 emissions fueloil',
+                                                   'CO2 emissions diesel'], dtype='float')
+
+  for year in range(1, NUMBER_OF_YEARS+1):
+
+    CO2_emissions.at[year,'CO2 emissions gas'] = gas_consumption * EMISSION_FACTOR_GAS_QUEBEC
+    CO2_emissions.at[year, 'CO2 emissions electricity'] = electricity_consumption * EMISSION_FACTOR_ELECTRICITY_QUEBEC
+    CO2_emissions.at[year, 'CO2 emissions biomass'] = biomass_consumption * EMISSION_FACTOR_BIOMASS_QUEBEC
+    CO2_emissions.at[year, 'CO2 emissions fueloil'] = fuel_oil_consumption * EMISSION_FACTOR_FUEL_OIL_QUEBEC
+    CO2_emissions.at[year, 'CO2 emissions diesel'] = diesel_consumption * EMISSION_FACTOR_DIESEL_QUEBEC
+
+  CO2_emissions_total = CO2_emissions.sum()
+

costs/data/.gitignore

@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore

costs/life_cycle_costs.py

@@ -0,0 +1,375 @@
+"""
+LifeCycleCosts module calculates the life cycle costs of one building
+SPDX - License - Identifier: LGPL - 3.0 - or -later
+Copyright © 2022 Project Author Pilar Monsalvete Alvarez de Uribarri pilar_monsalvete@concordia.ca
+Code contributor Oriol Gavalda Torrellas oriol.gavalda@concordia.ca
+"""
+
+import math
+
+import pandas as pd
+import numpy_financial as npf
+import hub.helpers.constants as cte
+from costs import SKIN_RETROFIT, SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, PERCENTAGE_CREDIT,INTEREST_RATE,CREDIT_YEARS
+
+
+class LifeCycleCosts:
+  """
+  Life cycle cost class
+  """
+
+  def __init__(self, building, archetype, number_of_years, consumer_price_index, electricity_peak_index,
+               electricity_price_index, gas_price_index, discount_rate,
+               retrofitting_scenario, fuel_type):
+    self._building = building
+    self._number_of_years = number_of_years
+    self._consumer_price_index = consumer_price_index
+    self._electricity_peak_index = electricity_peak_index
+    self._electricity_price_index = electricity_price_index
+    self._gas_price_index = gas_price_index
+    self._discount_rate = discount_rate
+    self._archetype = archetype
+    self._end_of_life_cost = 0
+    self._capital_costs_at_year_0 = 0
+    self._items = 0
+    self._fuels = 0
+    self._concepts = 0
+    self._retrofitting_scenario = retrofitting_scenario
+    self._total_floor_area = 0
+    self._fuel_type = fuel_type
+    for internal_zone in building.internal_zones:
+      for thermal_zone in internal_zone.thermal_zones:
+        self._total_floor_area += thermal_zone.total_floor_area
+
+    # todo: revise if it works
+    rng = range(number_of_years)
+    self._yearly_capital_costs = pd.DataFrame(index=rng, columns=['B2010_opaque_walls', 'B2020_transparent',
+                                                                  'B3010_opaque_roof', 'B10_superstructure',
+                                                                  'D301010_photovoltaic_system',
+                                                                  'D3020_heat_generating_systems',
+                                                                  'D3030_cooling_generation_systems',
+                                                                  'D3040_distribution_systems',
+                                                                  'D3080_other_hvac_ahu',
+                                                                  'D5020_lighting_and_branch_wiring'],
+                                              dtype='float')
+    self._yearly_end_of_life_costs = pd.DataFrame(index=rng, columns=['End_of_life_costs'], dtype='float')
+    self._yearly_operational_costs = pd.DataFrame(index=rng, columns=['Fixed_costs_electricity_peak',
+                                                                      'Fixed_costs_electricity_monthly',
+                                                                      'Variable_costs_electricity', 'Fixed_costs_gas',
+                                                                      'Variable_costs_gas'],
+                                                  dtype='float')
+    self._yearly_maintenance_costs = pd.DataFrame(index=rng, columns=['Heating_maintenance', 'Cooling_maintenance',
+                                                                      'PV_maintenance'], dtype='float')
+    self._yearly_operational_incomes = pd.DataFrame(index=rng, columns=['Incomes electricity'], dtype='float')
+
+    self._yearly_capital_incomes = pd.DataFrame(index=rng, columns=['Subsidies construction',
+                                                                    'Subsidies HVAC', 'Subsidies PV'], dtype='float')
+
+  def calculate_capital_costs(self):
+    """
+    Calculate capital cost
+    :return: pd.DataFrame
+    """
+    building = self._building
+    archetype = self._archetype
+
+    surface_opaque = 0
+    surface_transparent = 0
+    surface_roof = 0
+    surface_ground = 0
+    capital_cost_pv = 0
+    capital_cost_opaque = 0
+    capital_cost_ground = 0
+    capital_cost_transparent = 0
+    capital_cost_roof = 0
+    capital_cost_heating_equipment = 0
+    capital_cost_cooling_equipment = 0
+    capital_cost_distribution_equipment = 0
+    capital_cost_other_hvac_ahu = 0
+    capital_cost_lighting = 0
+
+    total_floor_area = self._total_floor_area
+
+    for internal_zone in building.internal_zones:
+      for thermal_zone in internal_zone.thermal_zones:
+        for thermal_boundary in thermal_zone.thermal_boundaries:
+          if thermal_boundary.type == 'Ground':
+            surface_ground += thermal_boundary.opaque_area
+          elif thermal_boundary.type == 'Roof':
+            surface_roof += thermal_boundary.opaque_area
+          elif thermal_boundary.type == 'Wall':
+            surface_opaque += thermal_boundary.opaque_area * (1 - thermal_boundary.window_ratio)
+            surface_transparent += thermal_boundary.opaque_area * thermal_boundary.window_ratio
+
+    chapters = archetype.capital_cost
+    peak_heating = building.heating_peak_load[cte.YEAR][0]/1000
+    peak_cooling = building.cooling_peak_load[cte.YEAR][0]/1000
+    # todo: change area pv when the variable exists
+    roof_area = 0
+    for roof in building.roofs:
+      roof_area += roof.solid_polygon.area
+    surface_pv = roof_area * 0.5
+
+    self._yearly_capital_costs.loc[0, 'B2010_opaque_walls'] = 0
+    self._yearly_capital_costs.loc[0]['B2020_transparent'] = 0
+    self._yearly_capital_costs.loc[0, 'B3010_opaque_roof'] = 0
+    self._yearly_capital_costs.loc[0]['B10_superstructure'] = 0
+
+    self._yearly_capital_costs.loc[0, 'D3020_heat_generating_systems'] = 0
+    self._yearly_capital_costs.loc[0, 'D3030_cooling_generation_systems'] = 0
+    self._yearly_capital_costs.loc[0, 'D3040_distribution_systems'] = 0
+    self._yearly_capital_costs.loc[0, 'D3080_other_hvac_ahu'] = 0
+    self._yearly_capital_costs.loc[0, 'D5020_lighting_and_branch_wiring'] = 0
+
+    self._yearly_capital_incomes.loc[0, 'Subsidies construction'] = 0
+    self._yearly_capital_incomes.loc[0, 'Subsidies HVAC'] = 0
+    self._yearly_capital_incomes.loc[0, 'Subsidies PV'] = 0
+
+    self._yearly_capital_costs.fillna(0, inplace=True)
+    if self._retrofitting_scenario in (SKIN_RETROFIT, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
+      chapter = chapters.chapter('B_shell')
+      capital_cost_opaque = surface_opaque * chapter.item('B2010_opaque_walls').refurbishment[0]
+      capital_cost_transparent = surface_transparent * chapter.item('B2020_transparent').refurbishment[0]
+      capital_cost_roof = surface_roof * chapter.item('B3010_opaque_roof').refurbishment[0]
+      capital_cost_ground = surface_ground * chapter.item('B10_superstructure').refurbishment[0]
+
+
+      self._yearly_capital_costs.loc[0, 'B2010_opaque_walls'] = capital_cost_opaque * (1-PERCENTAGE_CREDIT)
+      self._yearly_capital_costs.loc[0]['B2020_transparent'] = capital_cost_transparent * (1-PERCENTAGE_CREDIT)
+      self._yearly_capital_costs.loc[0, 'B3010_opaque_roof'] = capital_cost_roof * (1-PERCENTAGE_CREDIT)
+      self._yearly_capital_costs.loc[0]['B10_superstructure'] = capital_cost_ground * (1-PERCENTAGE_CREDIT)
+
+
+    if self._retrofitting_scenario in (SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
+      chapter = chapters.chapter('D_services')
+      capital_cost_pv = surface_pv * chapter.item('D301010_photovoltaic_system').initial_investment[0]
+      self._yearly_capital_costs.loc[0]['D301010_photovoltaic_system'] = capital_cost_pv
+      capital_cost_heating_equipment = (
+          peak_heating * chapter.item('D3020_heat_generating_systems').initial_investment[0]
+      )
+      capital_cost_cooling_equipment = (
+          peak_cooling * chapter.item('D3030_cooling_generation_systems').initial_investment[0]
+      )
+      capital_cost_distribution_equipment = (
+          peak_cooling * chapter.item('D3040_distribution_systems').initial_investment[0]
+      )
+      capital_cost_other_hvac_ahu = peak_cooling * chapter.item('D3080_other_hvac_ahu').initial_investment[0]
+      capital_cost_lighting = total_floor_area * chapter.item('D5020_lighting_and_branch_wiring').initial_investment[0]
+
+      self._yearly_capital_costs.loc[0, 'D3020_heat_generating_systems'] = capital_cost_heating_equipment * (1-PERCENTAGE_CREDIT)
+      self._yearly_capital_costs.loc[0, 'D3030_cooling_generation_systems'] = capital_cost_cooling_equipment * (1-PERCENTAGE_CREDIT)
+      self._yearly_capital_costs.loc[0, 'D3040_distribution_systems'] = capital_cost_distribution_equipment * (1-PERCENTAGE_CREDIT)
+      self._yearly_capital_costs.loc[0, 'D3080_other_hvac_ahu'] = capital_cost_other_hvac_ahu * (1-PERCENTAGE_CREDIT)
+      self._yearly_capital_costs.loc[0, 'D5020_lighting_and_branch_wiring'] = capital_cost_lighting * (1-PERCENTAGE_CREDIT)
+
+    for year in range(1, self._number_of_years):
+      chapter = chapters.chapter('D_services')
+      costs_increase = math.pow(1 + self._consumer_price_index, year)
+      self._yearly_capital_costs.loc[year, 'B2010_opaque_walls'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,
+                                                                          capital_cost_opaque * (PERCENTAGE_CREDIT))
+      self._yearly_capital_costs.loc[year, 'B2020_transparent'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,
+                                                                          capital_cost_transparent * (PERCENTAGE_CREDIT)
+                                                                          )
+      self._yearly_capital_costs.loc[year, 'B3010_opaque_roof'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,capital_cost_roof
+                                                                          * (PERCENTAGE_CREDIT))
+      self._yearly_capital_costs.loc[year, 'B10_superstructure'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,
+                                                                             capital_cost_ground * (PERCENTAGE_CREDIT))
+      self._yearly_capital_costs.loc[year, 'D3020_heat_generating_systems'] = -npf.pmt(INTEREST_RATE,CREDIT_YEARS,
+                                                                                      capital_cost_heating_equipment
+                                                                                      * (PERCENTAGE_CREDIT))
+      self._yearly_capital_costs.loc[year, 'D3030_cooling_generation_systems'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,
+                                                                                      capital_cost_cooling_equipment
+                                                                                      * (PERCENTAGE_CREDIT))
+      self._yearly_capital_costs.loc[year, 'D3040_distribution_systems'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,
+                                                                                      capital_cost_distribution_equipment
+                                                                                      * (PERCENTAGE_CREDIT))
+      self._yearly_capital_costs.loc[year, 'D3080_other_hvac_ahu'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,
+                                                                                      capital_cost_other_hvac_ahu
+                                                                                      * (PERCENTAGE_CREDIT))
+      self._yearly_capital_costs.loc[year, 'D5020_lighting_and_branch_wiring'] = -npf.pmt(INTEREST_RATE, CREDIT_YEARS,
+                                                                                      capital_cost_lighting
+                                                                                      * (PERCENTAGE_CREDIT))
+      if (year % chapter.item('D3020_heat_generating_systems').lifetime) == 0:
+        reposition_cost_heating_equipment = peak_heating * chapter.item('D3020_heat_generating_systems').reposition[0] \
+                                            * costs_increase
+        self._yearly_capital_costs.loc[year, 'D3020_heat_generating_systems'] += reposition_cost_heating_equipment
+
+      if (year % chapter.item('D3030_cooling_generation_systems').lifetime) == 0:
+        reposition_cost_cooling_equipment = peak_cooling \
+                                            * chapter.item('D3030_cooling_generation_systems').reposition[0] \
+                                            * costs_increase
+        self._yearly_capital_costs.loc[year, 'D3030_cooling_generation_systems'] += reposition_cost_cooling_equipment
+
+      if (year % chapter.item('D3080_other_hvac_ahu').lifetime) == 0:
+        reposition_cost_hvac_ahu = peak_cooling * chapter.item('D3080_other_hvac_ahu').reposition[0] * costs_increase
+        self._yearly_capital_costs.loc[year, 'D3080_other_hvac_ahu'] = reposition_cost_hvac_ahu
+
+      if (year % chapter.item('D5020_lighting_and_branch_wiring').lifetime) == 0:
+        reposition_cost_lighting = total_floor_area * chapter.item('D5020_lighting_and_branch_wiring').reposition[0] \
+                                   * costs_increase
+        self._yearly_capital_costs.loc[year, 'D5020_lighting_and_branch_wiring'] += reposition_cost_lighting
+
+      if self._retrofitting_scenario in (SYSTEM_RETROFIT_AND_PV, SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV):
+        if (year % chapter.item('D301010_photovoltaic_system').lifetime) == 0:
+          self._yearly_capital_costs.loc[year]['D301010_photovoltaic_system'] += surface_pv \
+                                                                                * chapter.item(
+            'D301010_photovoltaic_system').reposition[0] * costs_increase
+    capital_cost_skin = capital_cost_opaque + capital_cost_ground + capital_cost_transparent + capital_cost_roof
+    capital_cost_hvac = (
+        capital_cost_heating_equipment +
+        capital_cost_cooling_equipment +
+        capital_cost_distribution_equipment +
+        capital_cost_other_hvac_ahu + capital_cost_lighting
+    )
+
+    self._yearly_capital_incomes.loc[0, 'Subsidies construction'] = (
+        capital_cost_skin * archetype.income.construction_subsidy/100
+    )
+    self._yearly_capital_incomes.loc[0, 'Subsidies HVAC'] = capital_cost_hvac * archetype.income.hvac_subsidy/100
+    self._yearly_capital_incomes.loc[0, 'Subsidies PV'] = capital_cost_pv * archetype.income.photovoltaic_subsidy/100
+    self._yearly_capital_incomes.fillna(0, inplace=True)
+    return self._yearly_capital_costs, self._yearly_capital_incomes
+
+  def calculate_end_of_life_costs(self):
+    """
+    Calculate end of life costs
+    :return: pd.DataFrame
+    """
+    archetype = self._archetype
+    total_floor_area = self._total_floor_area
+
+    for year in range(1, self._number_of_years + 1):
+      price_increase = math.pow(1 + self._consumer_price_index, year)
+      if year == self._number_of_years:
+        self._yearly_end_of_life_costs.at[
+          year, 'End_of_life_costs'] = total_floor_area * archetype.end_of_life_cost * price_increase
+    self._yearly_end_of_life_costs.fillna(0, inplace=True)
+    return self._yearly_end_of_life_costs
+
+  def calculate_total_floor_area(self):
+    total_floor_area = self._total_floor_area
+    return total_floor_area
+  @property
+  def calculate_total_operational_costs(self):
+    """
+    Calculate total operational costs
+    :return: pd.DataFrame
+    """
+    building = self._building
+    archetype = self._archetype
+    total_floor_area = self._total_floor_area
+    factor_residential = total_floor_area / 80
+    # todo: split the heating between fuels
+    fixed_gas_cost_year_0 = 0
+    variable_gas_cost_year_0 = 0
+    electricity_heating = 0
+    domestic_hot_water_electricity = 0
+    if self._fuel_type == 1:
+      fixed_gas_cost_year_0 = archetype.operational_cost.fuels[1].fixed_monthly * 12 * factor_residential
+      variable_gas_cost_year_0 = (
+          (building.heating_consumption[cte.YEAR][0] + building.domestic_hot_water_consumption[cte.YEAR][0]) / 1000 *
+          archetype.operational_cost.fuels[1].variable[0]
+      )
+    if self._fuel_type == 0:
+      electricity_heating = building.heating_consumption[cte.YEAR][0] / 1000
+      domestic_hot_water_electricity = building.domestic_hot_water_consumption[cte.YEAR][0] / 1000
+
+    electricity_cooling = building.cooling_consumption[cte.YEAR][0] / 1000
+    electricity_lighting = building.lighting_electrical_demand[cte.YEAR]['insel meb'] / 1000
+    electricity_plug_loads = building.appliances_electrical_demand[cte.YEAR]['insel meb'] / 1000
+    electricity_distribution = 0
+    total_electricity_consumption = (
+        electricity_heating + electricity_cooling + electricity_lighting + domestic_hot_water_electricity +
+        electricity_plug_loads + electricity_distribution
+    )
+    print(f'electricity consumption {total_electricity_consumption}')
+
+    # todo: change when peak electricity demand is coded. Careful with factor residential
+    peak_electricity_demand = 0.1*total_floor_area # self._peak_electricity_demand
+    variable_electricity_cost_year_0 = total_electricity_consumption * archetype.operational_cost.fuels[0].variable[0]
+    peak_electricity_cost_year_0 = peak_electricity_demand * archetype.operational_cost.fuels[0].fixed_power * 12
+    monthly_electricity_cost_year_0 = archetype.operational_cost.fuels[0].fixed_monthly * 12 * factor_residential
+
+    for year in range(1, self._number_of_years + 1):
+      price_increase_electricity = math.pow(1 + self._electricity_price_index, year)
+      price_increase_peak_electricity = math.pow(1 + self._electricity_peak_index, year)
+      price_increase_gas = math.pow(1 + self._gas_price_index, year)
+      self._yearly_operational_costs.at[year, 'Fixed_costs_electricity_peak'] = (
+          peak_electricity_cost_year_0 * price_increase_peak_electricity
+      )
+
+      self._yearly_operational_costs.at[year, 'Fixed_costs_electricity_monthly'] = (
+          monthly_electricity_cost_year_0 * price_increase_peak_electricity
+      )
+      self._yearly_operational_costs.at[year, 'Variable_costs_electricity'] = float(
+        variable_electricity_cost_year_0 * price_increase_electricity
+      )
+      self._yearly_operational_costs.at[year, 'Fixed_costs_gas'] = fixed_gas_cost_year_0 * price_increase_gas
+      self._yearly_operational_costs.at[year, 'Variable_costs_gas'] = (
+          variable_gas_cost_year_0 * price_increase_peak_electricity
+      )
+      self._yearly_operational_costs.at[year, 'Variable_costs_gas'] = (
+          variable_gas_cost_year_0 * price_increase_peak_electricity
+      )
+    self._yearly_operational_costs.fillna(0, inplace=True)
+
+    return self._yearly_operational_costs
+
+  def calculate_total_operational_incomes(self, retrofitting_scenario):
+    """
+    Calculate total operational incomes
+    :return: pd.DataFrame
+    """
+    building = self._building
+    if cte.YEAR not in building.onsite_electrical_production:
+      onsite_electricity_production = 0
+    else:
+      if retrofitting_scenario == 0 or retrofitting_scenario == 1:
+        onsite_electricity_production = 0
+      else:
+        onsite_electricity_production = building.onsite_electrical_production[cte.YEAR][0]/1000
+
+    for year in range(1, self._number_of_years + 1):
+      price_increase_electricity = math.pow(1 + self._electricity_price_index, year)
+      # todo: check the adequate assignation of price. Pilar
+      price_export = 0.075  # archetype.income.electricity_export
+      self._yearly_operational_incomes.loc[year, 'Incomes electricity'] = (
+          onsite_electricity_production * price_export * price_increase_electricity
+      )
+
+    self._yearly_operational_incomes.fillna(0, inplace=True)
+    return self._yearly_operational_incomes
+
+  def calculate_total_maintenance_costs(self):
+    """
+    Calculate total maintenance costs
+    :return: pd.DataFrame
+    """
+    building = self._building
+    archetype = self._archetype
+    # todo: change area pv when the variable exists
+    roof_area = 0
+    for roof in building.roofs:
+      roof_area += roof.solid_polygon.area
+    surface_pv = roof_area * 0.5
+
+    peak_heating = building.heating_peak_load[cte.YEAR][0]/1000
+    peak_cooling = building.heating_peak_load[cte.YEAR][0]/1000
+
+    maintenance_heating_0 = peak_heating * archetype.operational_cost.maintenance_heating
+    maintenance_cooling_0 = peak_cooling * archetype.operational_cost.maintenance_cooling
+    maintenance_pv_0 = surface_pv * archetype.operational_cost.maintenance_pv
+
+    for year in range(1, self._number_of_years + 1):
+      costs_increase = math.pow(1 + self._consumer_price_index, year)
+      self._yearly_maintenance_costs.loc[year, 'Heating_maintenance'] = (
+          maintenance_heating_0 * costs_increase
+      )
+      self._yearly_maintenance_costs.loc[year, 'Cooling_maintenance'] = (
+          maintenance_cooling_0 * costs_increase
+      )
+      self._yearly_maintenance_costs.loc[year, 'PV_maintenance'] = (
+          maintenance_pv_0 * costs_increase
+      )
+    self._yearly_maintenance_costs.fillna(0, inplace=True)
+    return self._yearly_maintenance_costs

costs/outputs/.gitignore

@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore

costs/printing_results.py

@@ -0,0 +1,58 @@
+import plotly.graph_objects as go
+import matplotlib.pyplot as plt
+import plotly.express as px
+
+
+def printing_results(investmentcosts, life_cycle_results,total_floor_area):
+
+  labels = investmentcosts.index
+  values = investmentcosts['retrofitting_scenario_1']
+  values2 = investmentcosts['retrofitting_scenario_2']
+  values3 = investmentcosts['retrofitting_scenario_3']
+
+  fig = go.Figure(data=[go.Pie(labels=labels, values=values)])
+  fig2 = go.Figure(data=[go.Pie(labels=labels, values=values2)])
+  fig3 = go.Figure(data=[go.Pie(labels=labels, values=values3)])
+  # Set the layout properties
+  fig.update_layout(
+    title='Retrofitting scenario 1',
+    showlegend=True
+  )
+  fig2.update_layout(
+    title='Retrofitting scenario 2',
+    showlegend=True
+  )
+  fig3.update_layout(
+    title='Retrofitting scenario 3',
+    showlegend=True
+  )
+
+  # Display the chart
+  fig.show()
+  fig2.show()
+  fig3.show()
+
+  df = life_cycle_results / total_floor_area
+
+  # Transpose the DataFrame (swap columns and rows)
+  df_swapped = df.transpose()
+
+  # Reset the index to make the current index a regular column
+  df_swapped = df_swapped.reset_index()
+
+  # Assign new column names
+  df_swapped.columns = ['Scenarios', 'total_capital_costs_skin',
+                        'total_capital_costs_systems',
+                        'end_of_life_costs',
+                        'total_operational_costs',
+                        'total_maintenance_costs',
+                        'operational_incomes',
+                        'capital_incomes']
+
+  df_swapped.index = df_swapped['Scenarios']
+  df_swapped = df_swapped.drop('Scenarios', axis=1)
+  print(df_swapped)
+  fig = px.bar(df_swapped, title='Life Cycle Costs for buildings')
+  fig.show()
+  # Display the chart
+  plt.show()

costs/tmp/.gitignore

@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+*
+# Except this file
+!.gitignore

input_files/selected_building_2864.geojson

@@ -0,0 +1,121 @@
+{
+    "type": "FeatureCollection",
+    "features": [
+        {
+            "type": "Feature",
+            "id": 2864,
+            "geometry": {
+                "type": "Polygon",
+                "coordinates": [
+                    [
+                        [
+                            -73.55628837310991,
+                            45.60732526295055
+                        ],
+                        [
+                            -73.55628287285629,
+                            45.607324262904456
+                        ],
+                        [
+                            -73.55609247288925,
+                            45.607288563416546
+                        ],
+                        [
+                            -73.55607107262188,
+                            45.60734486277528
+                        ],
+                        [
+                            -73.55612487276466,
+                            45.60735496306114
+                        ],
+                        [
+                            -73.55609867281544,
+                            45.60742366317157
+                        ],
+                        [
+                            -73.55624087271804,
+                            45.60745026331904
+                        ],
+                        [
+                            -73.55628837310991,
+                            45.60732526295055
+                        ]
+                    ]
+                ]
+            },
+            "properties": {
+                "OBJECTID_12_13": 2864,
+                "ID_UEV": "02033771",
+                "CIVIQUE_DE": " 8212",
+                "CIVIQUE_FI": " 8212",
+                "NOM_RUE": "avenue Peterborough  (ANJ)",
+                "SUITE_DEBU": " ",
+                "MUNICIPALI": "50",
+                "ETAGE_HORS": 1,
+                "NOMBRE_LOG": 1,
+                "ANNEE_CONS": 1960,
+                "CODE_UTILI": "1000",
+                "LETTRE_DEB": " ",
+                "LETTRE_FIN": " ",
+                "LIBELLE_UT": "Logement",
+                "CATEGORIE_": "R\u00c3\u00a9gulier",
+                "MATRICULE8": "0051-49-2041-2-000-0000",
+                "SUPERFICIE": 450,
+                "SUPERFIC_1": 176,
+                "NO_ARROND_": "REM09",
+                "Shape_Leng": 0.000666191644361,
+                "OBJECTID": 2864,
+                "Join_Count": 1,
+                "TARGET_FID": 2864,
+                "feature_id": "bdd1f0fe-89de-46d2-80dc-87d3636df60a",
+                "md_id": " ",
+                "acqtech": 1360,
+                "acqtech_en": "Lidar",
+                "acqtech_fr": "Lidar",
+                "provider": 461,
+                "provideren": "Municipal",
+                "providerfr": "Municipal",
+                "datemin": "20151124",
+                "datemax": "20151208",
+                "haccmin": 2,
+                "haccmax": 2,
+                "vaccmin": 1,
+                "vaccmax": 1,
+                "heightmin": 1.17,
+                "heightmax": 7.5,
+                "elevmin": 45.48,
+                "elevmax": 45.96,
+                "bldgarea": 193.18,
+                "comment": " ",
+                "OBJECTID_1": 2864,
+                "Shape_Le_1": 0.000666191644361,
+                "Shape_Ar_1": 2.22753099997e-08,
+                "OBJECTID_12": 2864,
+                "Join_Count_1": 1,
+                "TARGET_FID_1": 2863,
+                "g_objectid": "897744",
+                "g_co_mrc": "66023",
+                "g_code_mun": "66023",
+                "g_arrond": "REM09",
+                "g_anrole": "2019",
+                "g_usag_pre": "R\u00c3\u00a9sidentiel",
+                "g_no_lot": "1113400",
+                "g_nb_poly_": "1",
+                "g_utilisat": "1000",
+                "g_nb_logem": "1",
+                "g_nb_locau": " ",
+                "g_descript": "Unit\u00c3\u00a9 d'\u00c3\u00a9valuation",
+                "g_id_provi": "66023005149204120000000",
+                "g_sup_tota": "450.1",
+                "g_geometry": "0.000958907",
+                "g_geomet_1": "5.20226e-008",
+                "g_dat_acqu": "2020-02-12 00:00:00.0000000",
+                "g_dat_char": "2020-02-17 00:00:00.0000000",
+                "Shape_Leng_1": 0.000666191644361,
+                "Shape_Area_1": 2.22753099997e-08,
+                "Shape_Length": 0.0006661919640545334,
+                "Shape_Area": 2.22753099997e-08
+            }
+        }
+    ]
+}

input_files/summerschool_one_building.geojson

@@ -0,0 +1,294 @@
+{
+  "type": "FeatureCollection",
+  "features": [
+    {
+      "type": "Feature",
+      "id": 12,
+      "geometry": {
+        "type": "Polygon",
+        "coordinates": [
+          [
+            [
+              -73.57945149010348,
+              45.49793915473101
+            ],
+            [
+              -73.57945502047383,
+              45.497935600591106
+            ],
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+}

life_cycle_costs.py

@@ -1,56 +0,0 @@
-"""
-LifeCycleCosts calculates the life cycle costs of one building
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2022 Project Author Pilar Monsalvete Alvarez de Uribarri pilar_monsalvete@concordia.ca
-"""
-import math
-
-
-class LifeCycleCosts:
-
-  # todo: this should be (city, costs_catalog) or similar
-  def __init__(self, building, number_of_years, consumer_price_index, discount_rate, end_of_life_cost,
-               capital_costs_at_year_0, items, fuels, concepts):
-    self._building = building
-    self._number_of_years = number_of_years
-    self._consumer_price_index = consumer_price_index
-    self._discount_rate = discount_rate
-
-    self._end_of_life_cost = end_of_life_cost
-
-    self._capital_costs_at_year_0 = capital_costs_at_year_0
-    self._items = items
-
-    self._fuels = fuels
-
-    self._concepts = concepts
-
-  def calculate_capital_costs(self):
-    total_capital_costs = self._capital_costs_at_year_0
-    for year in range(1, self._number_of_years + 1):
-      costs_increase = math.pow(1 + self._consumer_price_index, year) / math.pow(1 + self._discount_rate, year)
-      for item in self._items:
-        total_capital_costs += item.reposition_costs[year] * costs_increase
-    return total_capital_costs
-
-  def calculate_end_of_life_costs(self):
-    price_increase = 0
-    for year in range(1, self._number_of_years + 1):
-      price_increase += math.pow(1 + self._consumer_price_index, year) / math.pow(1 + self._discount_rate, year)
-    return self._end_of_life_cost * price_increase
-
-  def calculate_total_operational_costs(self):
-    total_operational_costs = 0
-    for year in range(1, self._number_of_years + 1):
-      for fuel in self._fuels:
-        total_operational_costs += fuel.operational_cost \
-                                   * math.pow(1 + fuel.energy_price_index, year) / math.pow(1 + self._discount_rate, year)
-    return total_operational_costs
-
-  def calculate_total_maintenance_costs(self):
-    total_maintenance_costs = 0
-    for year in range(1, self._number_of_years + 1):
-      costs_increase = math.pow(1 + self._consumer_price_index, year) / math.pow(1 + self._discount_rate, year)
-      for concept in self._concepts:
-        total_maintenance_costs += concept.mantainance_costs * costs_increase
-    return total_maintenance_costs

main.py

@@ -1,31 +0,0 @@
-"""
-Costs Workflow
-SPDX - License - Identifier: LGPL - 3.0 - or -later
-Copyright © 2022 Project Author Pilar Monsalvete Álvarez de Uribarri pilar.monsalvete@concordia.ca
-"""
-
-from pathlib import Path
-from imports.geometry_factory import GeometryFactory
-from life_cycle_costs import LifeCycleCosts
-
-gml_file = 'city.gml'
-file = Path(gml_file).resolve()
-city = GeometryFactory('gml', file).city
-
-number_of_years = 40
-consumer_price_index = 0.1
-
-for building in city.buildings:
-  lcc = LifeCycleCosts(building, number_of_years, consumer_price_index, costs_catalog)
-  total_capital_costs = lcc.calculate_capital_costs()
-  end_of_life_costs = lcc.calculate_end_of_life_costs()
-  total_operational_costs = lcc.calculate_total_operational_costs()
-  total_maintenance_costs = lcc.calculate_total_maintenance_costs()
-  life_cycle_costs = total_capital_costs + end_of_life_costs + total_operational_costs + total_maintenance_costs
-
-  print(f'Building name: {building.name}')
-  print(f'Capital costs: {total_capital_costs}')
-  print(f'End of life costs: {end_of_life_costs}')
-  print(f'Operational costs: {total_operational_costs}')
-  print(f'Maintenance costs: {total_maintenance_costs}')
-  print(f'Life cycle costs: {life_cycle_costs}')

out_files/.gitignore

@@ -0,0 +1,4 @@
+# Ignore everything in this directory
+.gitignore
+# Except this file
+!.gitignore

resources.txt

@@ -0,0 +1,2 @@
+numpy_financial
+cerc_hub