from scripts.geojson_creator import process_geojson
from pathlib import Path
import subprocess
from scripts.ep_run_enrich import energy_plus_workflow
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 SKIN_RETROFIT_AND_SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT_AND_PV, SYSTEM_RETROFIT
import hub.helpers.constants as cte
from scripts.solar_angles import CitySolarAngles
from scripts.pv_sizing_and_simulation import PVSizingSimulation
from hub.exports.exports_factory import ExportsFactory
# Specify the GeoJSON file path
geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0001)
file_path = (Path(__file__).parent / 'input_files' / 'output_buildings.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()
ExportsFactory('sra', city, output_path).export()
sra_path = (output_path / f'{city.name}_sra.xml').resolve()
subprocess.run(['sra', str(sra_path)])
ResultFactory('sra', city, output_path).enrich()
solar_angles = CitySolarAngles(city.name,
                               city.latitude,
                               city.longitude,
                               tilt_angle=45,
                               surface_azimuth_angle=180).calculate
energy_plus_workflow(city)
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()
  if 'PV' in building.energy_systems_archetype_name:
    ghi = [x / cte.WATTS_HOUR_TO_JULES for x in building.roofs[0].global_irradiance[cte.HOUR]]
    pv_sizing_simulation = PVSizingSimulation(building,
                                              solar_angles,
                                              tilt_angle=45,
                                              module_height=1,
                                              module_width=2,
                                              ghi=ghi)
    pv_sizing_simulation.pv_output()
for building in city.buildings:
  costs = Cost(building=building, retrofit_scenario=SYSTEM_RETROFIT).life_cycle
  costs.to_csv(output_path / f'{building.name}_lcc.csv')
  (costs.loc['global_operational_costs', f'Scenario {SYSTEM_RETROFIT}'].
   to_csv(output_path / f'{building.name}_op.csv'))
  costs.loc['global_capital_costs', f'Scenario {SYSTEM_RETROFIT}'].to_csv(
    output_path / f'{building.name}_cc.csv')
  costs.loc['global_maintenance_costs', f'Scenario {SYSTEM_RETROFIT}'].to_csv(
    output_path / f'{building.name}_m.csv')