import pandas as pd
from scripts.geojson_creator import process_geojson
from pathlib import Path
from hub.imports.geometry_factory import GeometryFactory
from hub.helpers.dictionaries import Dictionaries
from hub.imports.construction_factory import ConstructionFactory
from hub.imports.usage_factory import UsageFactory
from hub.imports.weather_factory import WeatherFactory
from hub.imports.results_factory import ResultFactory
from scripts import random_assignation
from hub.imports.energy_systems_factory import EnergySystemsFactory
from scripts.energy_system_sizing_and_simulation_factory import EnergySystemsSimulationFactory
from scripts.costs.cost import Cost
from scripts.costs.constants import SYSTEM_RETROFIT_AND_PV
from hub.exports.exports_factory import ExportsFactory

# Specify the GeoJSON file path
location = [45.49034212153445, -73.61435648647083]
geojson_file = process_geojson(x=location[1], y=location[0], diff=0.0001)
file_path = (Path(__file__).parent / 'input_files' / 'processed_output -single_building.geojson')
# Specify the output path for the PDF file
output_path = (Path(__file__).parent / 'out_files').resolve()
# Create city object from GeoJSON file
city = GeometryFactory('geojson',
                       path=file_path,
                       height_field='height',
                       year_of_construction_field='year_of_construction',
                       function_field='function',
                       function_to_hub=Dictionaries().montreal_function_to_hub_function).city
# Enrich city data
ConstructionFactory('nrcan', city).enrich()

UsageFactory('nrcan', city).enrich()
WeatherFactory('epw', city).enrich()
ResultFactory('energy_plus_multiple_buildings', city, output_path).enrich()
ExportsFactory('sra', city, output_path).export()
random_assignation.call_random(city.buildings, random_assignation.residential_new_systems_percentage)
EnergySystemsFactory('montreal_future', city).enrich()
for building in city.buildings:
  EnergySystemsSimulationFactory('archetype13', building=building, output_path=output_path).enrich()
sum_floor_area = 0
buildings_list = []
for building in city.buildings:
  buildings_list.append(building.name)
df = pd.DataFrame(columns=['building_name', 'total_floor_area', 'investment_cost', 'lc CAPEX'])
df['building_name'] = buildings_list
for building in city.buildings:
  for thermal_zone in building.thermal_zones_from_internal_zones:
    sum_floor_area += thermal_zone.total_floor_area
  costs = Cost(building=building, retrofit_scenario=SYSTEM_RETROFIT_AND_PV).life_cycle
  costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT_AND_PV}'].to_csv(
    output_path / f'{building.name}_cc.csv')
  investment_cost = costs.loc['global_capital_costs',
                              f'Scenario {SYSTEM_RETROFIT_AND_PV}'].loc[0, 'D3020_heat_and_cooling_generating_systems']
  lcc_capex = costs.loc['total_capital_costs_systems', f'Scenario {SYSTEM_RETROFIT_AND_PV}']
  df.loc[df['building_name'] == building.name, 'total_floor_area'] = (
    building.thermal_zones_from_internal_zones[0].total_floor_area)
  df.loc[df['building_name'] == building.name, 'investment_cost'] = investment_cost
  df.loc[df['building_name'] == building.name, 'lc CAPEX'] = lcc_capex

df.to_csv(output_path / 'economic analysis.csv')