"""
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()