from pathlib import Path
from building_modelling.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 enrich_demand import enrich
import pandas as pd

# Directory management
input_files_path = (Path(__file__).parent / 'input_files')
input_files_path.mkdir(parents=True, exist_ok=True)
geojson_file_path = input_files_path / 'Lachine_Geojson_Mixed_Use.geojson'
output_path = (Path(__file__).parent / 'out_files').resolve()
output_path.mkdir(parents=True, exist_ok=True)
energy_plus_output_path = output_path / 'energy_plus_outputs'
energy_plus_output_path.mkdir(parents=True, exist_ok=True)
simulation_results_path = (Path(__file__).parent / 'out_files' / 'simulation_results').resolve()
simulation_results_path.mkdir(parents=True, exist_ok=True)
sra_output_path = output_path / 'sra_outputs'
sra_output_path.mkdir(parents=True, exist_ok=True)
cost_analysis_output_path = output_path / 'cost_analysis'
cost_analysis_output_path.mkdir(parents=True, exist_ok=True)])
lachine_output_path = output_path / 'lachine_outputs'

# Create City from HUB to run EP_Workflow
city_ep_workflow = GeometryFactory(
  file_type='geojson',
  path=geojson_file_path,
  height_field='maximum_roof_height',
  year_of_construction_field='year_built',
  function_field='building_type',
  function_to_hub=Dictionaries().montreal_function_to_hub_function
).city

ConstructionFactory('nrcan', city_ep_workflow).enrich()
UsageFactory('nrcan', city_ep_workflow).enrich()
WeatherFactory('epw', city_ep_workflow).enrich()
energy_plus_workflow(city_ep_workflow, energy_plus_output_path)

# Create City from HUB to use Grasshopper results
city_grasshopper = GeometryFactory(
  file_type='geojson',
  path=geojson_file_path,
  height_field='maximum_roof_height',
  year_of_construction_field='year_built',
  function_field='building_type',
  function_to_hub=Dictionaries().montreal_function_to_hub_function
).city

ConstructionFactory('nrcan', city_grasshopper).enrich()
UsageFactory('nrcan', city_grasshopper).enrich()
WeatherFactory('epw', city_grasshopper).enrich()

enrich(city_grasshopper)

# Collect data from city_ep_workflow
ep_building_data = []

for building in city_ep_workflow.buildings:
    building_name = building.name

    # Get total floor area
    total_floor_area = 0
    for zone in building.thermal_zones_from_internal_zones:
        total_floor_area += zone.total_floor_area  # Assuming area is in m^2

    # Get yearly heating demand in Joules
    yearly_heating_demand_J = building.heating_demand['year'][0]  # Should be a single value
    # Convert to kWh
    yearly_heating_demand_kWh = yearly_heating_demand_J / 3.6e6
    # Compute heating demand intensity (kWh/m²)
    heating_demand_intensity = yearly_heating_demand_kWh / total_floor_area if total_floor_area > 0 else 0

    # Get yearly cooling demand in Joules
    yearly_cooling_demand_J = building.cooling_demand['year'][0]  # Should be a single value
    # Convert to kWh
    yearly_cooling_demand_kWh = yearly_cooling_demand_J / 3.6e6
    # Compute cooling demand intensity (kWh/m²)
    cooling_demand_intensity = yearly_cooling_demand_kWh / total_floor_area if total_floor_area > 0 else 0

    # Append data to list
    ep_building_data.append({
        'Building Name': building_name,
        'Yearly Heating Demand EP (kWh)': yearly_heating_demand_kWh,
        'Demand Intensity Heating EP (kWh/m²)': heating_demand_intensity,
        'Yearly Cooling Demand EP (kWh)': yearly_cooling_demand_kWh,
        'Demand Intensity Cooling EP (kWh/m²)': cooling_demand_intensity,
        'Total Floor Area (m²)': total_floor_area
    })

# Collect data from city_grasshopper
grasshopper_building_data = []

for building in city_grasshopper.buildings:
    building_name = building.name

    # Get total floor area
    total_floor_area = 0
    for zone in building.thermal_zones_from_internal_zones:
        total_floor_area += zone.total_floor_area  # Assuming area is in m^2

    # Get yearly heating demand in Joules
    yearly_heating_demand_J = building.heating_demand['year'][0]  # Should be a single value
    # Convert to kWh
    yearly_heating_demand_kWh = yearly_heating_demand_J / 3.6e6
    # Compute heating demand intensity (kWh/m²)
    heating_demand_intensity = yearly_heating_demand_kWh / total_floor_area if total_floor_area > 0 else 0

    # Get yearly cooling demand in Joules
    yearly_cooling_demand_J = building.cooling_demand['year'][0]  # Should be a single value
    # Convert to kWh
    yearly_cooling_demand_kWh = yearly_cooling_demand_J / 3.6e6
    # Compute cooling demand intensity (kWh/m²)
    cooling_demand_intensity = yearly_cooling_demand_kWh / total_floor_area if total_floor_area > 0 else 0

    # Append data to list
    grasshopper_building_data.append({
        'Building Name': building_name,
        'Yearly Heating Demand Grasshopper (kWh)': yearly_heating_demand_kWh,
        'Demand Intensity Heating Grasshopper (kWh/m²)': heating_demand_intensity,
        'Yearly Cooling Demand Grasshopper (kWh)': yearly_cooling_demand_kWh,
        'Demand Intensity Cooling Grasshopper (kWh/m²)': cooling_demand_intensity
    })

# Create DataFrames
ep_df = pd.DataFrame(ep_building_data)
grasshopper_df = pd.DataFrame(grasshopper_building_data)

# Merge DataFrames on 'Building Name' and 'Total Floor Area (m²)'
# Since Total Floor Area should be the same for both, we can use it from ep_df
merged_df = pd.merge(ep_df, grasshopper_df, on='Building Name')

# Rearrange columns if needed
merged_df = merged_df[[
    'Building Name',
    'Yearly Heating Demand EP (kWh)',
    'Demand Intensity Heating EP (kWh/m²)',
    'Yearly Cooling Demand EP (kWh)',
    'Demand Intensity Cooling EP (kWh/m²)',
    'Yearly Heating Demand Grasshopper (kWh)',
    'Demand Intensity Heating Grasshopper (kWh/m²)',
    'Yearly Cooling Demand Grasshopper (kWh)',
    'Demand Intensity Cooling Grasshopper (kWh/m²)',
    'Total Floor Area (m²)'
]]

# Write to Excel
output_excel_path = lachine_output_path / 'building_heating_cooling_demands.xlsx'
merged_df.to_excel(output_excel_path, index=False)