from pathlib import Path
import pandas as pd
from building_modelling.ep_run_enrich import energy_plus_workflow
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 energy_system_modelling_package import random_assignation
from energy_system_modelling_package.energy_system_modelling_factories.pv_assessment.electricity_demand_calculator import HourlyElectricityDemand
import hub.helpers.constants as cte
import json
base_path = Path(__file__).parent.resolve()
input_files_path = base_path / 'input_files'
input_files_path.mkdir(parents=True, exist_ok=True)
output_files_path = base_path / 'out_files'
output_files_path.mkdir(exist_ok=True, parents=True)
energy_plus_output_path = output_files_path / 'energy_plus_outputs'
energy_plus_output_path.mkdir(parents=True, exist_ok=True)
geojson_path = input_files_path / 'selected_buildings.geojson'
city = GeometryFactory(file_type='geojson',
                       path=geojson_path,
                       aliases_field=['address'],
                       height_field='height',
                       year_of_construction_field='year_of_construction',
                       function_field='function',
                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
for building in city.buildings:
  if Dictionaries().hub_function_to_nrcan_construction_function[building.function] == 'n/a':
    building.function = cte.WAREHOUSE
ConstructionFactory('nrcan', city).enrich()
UsageFactory('nrcan', city).enrich()
WeatherFactory('epw', city).enrich()
residential_buildings = []
non_residential_buildings = []
for building in city.buildings:
  if building.function == cte.RESIDENTIAL:
    residential_buildings.append(building)
  else:
    non_residential_buildings.append(building)
# random_assignation.call_random(residential_buildings, random_assignation.residential_systems_percentage)
# random_assignation.call_random(non_residential_buildings, random_assignation.non_residential_systems_percentage)
# EnergySystemsFactory('montreal_future', city).enrich()
base_results = pd.read_csv(output_files_path / 'results_summary.csv')
energy_system_archetypes = base_results['energy_system_archetype_name'].to_list()
for (i, building) in enumerate(city.buildings):
  building.energy_systems_archetype_name = energy_system_archetypes[i]
EnergySystemsFactory('montreal_future', city).enrich()
energy_plus_workflow(city, energy_plus_output_path)
# hourly_simulation_results = {}
# hourly_elec_consumption = pd.DataFrame()
# hourly_lighting_appliance_consumption = pd.DataFrame()
# for building in city.buildings:
#   hourly_electricity_consumption = HourlyElectricityDemand(building).calculate()
#   heating_electricity_consumption = building.heating_consumption[
#     cte.HOUR] if 'electricity' in building.energy_systems_archetype_name else [0] * len(
#     building.heating_demand[cte.HOUR])
#   dhw_electricity_consumption = building.domestic_hot_water_consumption[
#     cte.HOUR] if 'electricity' in building.energy_systems_archetype_name else [0] * len(
#     building.domestic_hot_water_heat_demand[cte.HOUR])
#   if building.cooling_consumption[cte.YEAR][0] == 0:
#     cooling_electricity_consumption = [building.cooling_demand[cte.HOUR][i] / (2 * 3.23) for i in
#                                        range(len(building.cooling_consumption[cte.HOUR]))]
#     hourly_electricity_consumption = [hourly_electricity_consumption[i] + cooling_electricity_consumption[i] for i in range(len(hourly_electricity_consumption))]
#   else:
#     cooling_electricity_consumption = building.cooling_consumption[cte.HOUR]
#   lighting = building.lighting_electrical_demand[cte.HOUR]
#   appliance = building.appliances_electrical_demand[cte.HOUR]
#
#   results = pd.DataFrame()
#   results['heating_electricity_consumption_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in
#                                                     heating_electricity_consumption]
#   results['cooling_electricity_consumption_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in
#                                                     cooling_electricity_consumption]
#   results['dhw_electricity_consumption_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in
#                                                     dhw_electricity_consumption]
#   results['lighting_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in lighting]
#
#   results['appliance_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in appliance]
#   results['lighting_appliance_total_kWh'] = [(lighting[i] + appliance[i]) / (cte.WATTS_HOUR_TO_JULES * 1000)
#                                              for i in range(len(lighting))]
#   results['total_electricity_consumption_kWh'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in hourly_electricity_consumption]
#   results.to_csv(output_files_path / 'paper_results' / 'individual_building_simulation_results' / f'{building.name}.csv')
#   hourly_elec_consumption[f'{building.name}'] = [x / (cte.WATTS_HOUR_TO_JULES * 1000) for x in hourly_electricity_consumption]
#   hourly_lighting_appliance_consumption[f'{building.name}'] = [(lighting[i] + appliance[i]) / (cte.WATTS_HOUR_TO_JULES * 1000)
#                                              for i in range(len(lighting))]
#
# hourly_elec_consumption.to_csv(output_files_path / 'paper_results' / 'total_hourly_electricity_consumption.csv')
# hourly_lighting_appliance_consumption.to_csv(output_files_path / 'paper_results' / 'total_lighting_appliance.csv')
building_names = [building.name for building in city.buildings]
building_addresses = [building.aliases[0] for building in city.buildings]
building_heights = [building.max_height for building in city.buildings]
buildings_total_floor_areas = [building.thermal_zones_from_internal_zones[0].total_floor_area
                               for building in city.buildings]
buildings_roof_areas = [building.roofs[0].perimeter_area for building in city.buildings]
building_energy_system_archetype_names = [building.energy_systems_archetype_name for building in city.buildings]
building_yearly_heating_electricity_consumptions = []
building_yearly_dhw_electricity_consumptions = []
for building in city.buildings:
  if 'electricity' in building.energy_systems_archetype_name:
    building_yearly_heating_electricity_consumptions.append(building.heating_consumption[cte.YEAR][0] /
                                                            (cte.WATTS_HOUR_TO_JULES * 1000))
    building_yearly_dhw_electricity_consumptions.append(building.domestic_hot_water_consumption[cte.YEAR][0] /
                                                            (cte.WATTS_HOUR_TO_JULES * 1000))
  else:
    building_yearly_heating_electricity_consumptions.append(0)
    building_yearly_dhw_electricity_consumptions.append(0)

building_yearly_cooling_consumptions = []
for building in city.buildings:
  if building.cooling_consumption[cte.YEAR][0] == 0:
    building_yearly_cooling_consumptions.append(building.cooling_demand[cte.YEAR][0] / (2 * 3.23 * cte.WATTS_HOUR_TO_JULES * 1000))
  else:
    building_yearly_cooling_consumptions.append(building.cooling_consumption[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1000))
building_yearly_lighting_demands = [building.lighting_electrical_demand[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1000)
                                        for building in city.buildings]
building_yearly_appliance_demands = [building.appliances_electrical_demand[cte.YEAR][0] / (cte.WATTS_HOUR_TO_JULES * 1000)
                                        for building in city.buildings]
summary = pd.DataFrame()
summary['building_name'] = building_names
summary['building_address'] = building_addresses
summary['building_height_m'] = building_heights
summary['total_floor_area_m2'] = buildings_total_floor_areas
summary['roof_area_m2'] = buildings_roof_areas
summary['energy_system_archetype_name'] = building_energy_system_archetype_names
summary['yearly_heating_electricity_consumption_kWh'] = building_yearly_heating_electricity_consumptions
summary['yearly_cooling_electricity_consumption_kWh'] = building_yearly_cooling_consumptions
summary['yearly_dhw_electricity_consumption_kWh'] = building_yearly_dhw_electricity_consumptions
summary['yearly_lighting_consumption_kWh'] = building_yearly_lighting_demands
summary['yearly_appliance_consumption_kWh'] = building_yearly_appliance_demands
summary.to_csv(output_files_path / 'yearly_results.csv', index=False)