"""
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 energy_systems_sizing import EnergySystemsSizing
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 hub.helpers import constants as cte
from monthly_energy_balance_engine import MonthlyEnergyBalanceEngine
from sra_engine import SraEngine

from life_cycle_costs import LifeCycleCosts

# import constants
from costs import CLIMATE_REFERENCE_CITY, WEATHER_FILE, WEATHER_FORMAT, CONSTRUCTION_FORMAT, USAGE_FORMAT
from costs import ENERGY_SYSTEM_FORMAT, ATTIC_HEATED_CASE, BASEMENT_HEATED_CASE, 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

# import paths
from costs import file_path, tmp_folder, out_path


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()
print('[city creation start]')
city = GeometryFactory('geojson',
                       path=file_path,
                       height_field='heightmax',
                       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, file_name=WEATHER_FILE).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'
EnergySystemsFactory(ENERGY_SYSTEM_FORMAT, city).enrich()
print('enrich systems... done')
print('exporting:')
catalog = CostCatalogFactory('montreal_custom').catalog
print('costs catalog access... done')
sra_file = (tmp_folder / f'{city.name}_sra.xml').resolve()
SraEngine(city, sra_file, tmp_folder, WEATHER_FILE)
print('    sra processed...')

for building in city.buildings:
  building.attic_heated = ATTIC_HEATED_CASE
  building.basement_heated = BASEMENT_HEATED_CASE

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_of_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)

  EnergySystemsSizing(city).enrich()

  print(f'beginning costing scenario {retrofitting_scenario} systems... done')

  for building in city.buildings:

    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()
    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')

    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
    )

    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',
                                f'total_capital_costs_systems','end_of_life_costs',
                                'total_operational_costs', 'total_maintenance_costs',
                                'operational_incomes', 'capital_incomes']

    print(life_cycle_results)
    print(f'Scenario {retrofitting_scenario} {life_cycle_costs}')