import pandas as pd
from scripts.geojson_creator import process_geojson
from pathlib import Path
import subprocess
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.solar_angles import CitySolarAngles
from scripts.ep_run_enrich import energy_plus_workflow
import hub.helpers.constants as cte
from hub.exports.exports_factory import ExportsFactory
from scripts.pv_sizing_and_simulation import PVSizingSimulation
# Specify the GeoJSON file path
geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0005)
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()
energy_plus_workflow(city)
solar_angles = CitySolarAngles(city.name,
                               city.latitude,
                               city.longitude,
                               tilt_angle=45,
                               surface_azimuth_angle=180).calculate
df = pd.DataFrame()
df.index = ['yearly lighting (kWh)', 'yearly appliance (kWh)', 'yearly heating (kWh)', 'yearly cooling (kWh)',
            'yearly dhw (kWh)', 'roof area (m2)', 'used area for pv (m2)', 'number of panels', 'pv production (kWh)']
for building in city.buildings:
  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()
  yearly_lighting = building.lighting_electrical_demand[cte.YEAR][0] / 1000
  yearly_appliance = building.appliances_electrical_demand[cte.YEAR][0] / 1000
  yearly_heating = building.heating_demand[cte.YEAR][0] / (3.6e6 * 3)
  yearly_cooling = building.cooling_demand[cte.YEAR][0] / (3.6e6 * 4.5)
  yearly_dhw = building.domestic_hot_water_heat_demand[cte.YEAR][0] / 1000
  roof_area = building.roofs[0].perimeter_area
  used_roof = pv_sizing_simulation.available_space()
  number_of_pv_panels = pv_sizing_simulation.total_number_of_panels
  yearly_pv = building.onsite_electrical_production[cte.YEAR][0] / 1000
  df[f'{building.name}'] = [yearly_lighting, yearly_appliance, yearly_heating, yearly_cooling, yearly_dhw, roof_area,
                            used_roof, number_of_pv_panels, yearly_pv]

df.to_csv(output_path / 'pv.csv')