from hub.imports.energy_systems_factory import EnergySystemsFactory
from hub.imports.geometry_factory import GeometryFactory
from hub.helpers.dictionaries import Dictionaries
from hub.imports.construction_factory import ConstructionFactory
from hub.imports.results_factory import ResultFactory
from hub.exports.exports_factory import ExportsFactory
from hub.imports.weather_factory import WeatherFactory
from pv_assessment.electricity_demand_calculator import HourlyElectricityDemand
from pv_assessment.pv_system_assessment import PvSystemAssessment
from pv_assessment.solar_calculator import SolarCalculator
import random_assignation
import subprocess
from pathlib import Path

input_file = "data/selected_buildings.geojson"
demand_file = "data/energy_demand_data.csv"

# Define specific paths for outputs from SRA (Simplified Radiosity Algorith) and PV calculation processes
output_path = (Path(__file__).parent.parent / 'hub/out_files').resolve()
output_path.mkdir(parents=True, exist_ok=True)
sra_output_path = output_path / 'sra_outputs'
sra_output_path.mkdir(parents=True, exist_ok=True)
pv_assessment_path = output_path / 'pv_outputs'
pv_assessment_path.mkdir(parents=True, exist_ok=True)

city = GeometryFactory(
               "geojson",
               input_file,
               height_field="height",
               year_of_construction_field="contr_year",
               function_field="function_c",
               adjacency_field="adjacency",
               function_to_hub=Dictionaries().montreal_function_to_hub_function).city
ConstructionFactory('nrcan', city).enrich()
WeatherFactory('epw', city).enrich()
ResultFactory('archetypes', city, demand_file).enrich()

# Export the city data in SRA-compatible format to facilitate solar radiation assessment
ExportsFactory('sra', city, sra_output_path).export()
# Run SRA simulation using an external command, passing the generated SRA XML file path as input
sra_path = (sra_output_path / f'{city.name}_sra.xml').resolve()
subprocess.run(['sra', str(sra_path)])
# Enrich city data with SRA simulation results for subsequent analysis
ResultFactory('sra', city, sra_output_path).enrich()
# Initialize solar calculation parameters (e.g., azimuth, altitude) and compute irradiance and solar angles
tilt_angle = 37
solar_parameters = SolarCalculator(city=city,
                                   surface_azimuth_angle=180,
                                   tilt_angle=tilt_angle,
                                   standard_meridian=-75)
solar_angles = solar_parameters.solar_angles  # Obtain solar angles for further analysis
solar_parameters.tilted_irradiance_calculator()  # Calculate the solar radiation on a tilted surface
# Assignation of Energy System Archetypes to Buildings
random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
EnergySystemsFactory('montreal_future', city).enrich()
for building in city.buildings:
  PvSystemAssessment(building=building,
                     pv_system=None,
                     battery=None,
                     electricity_demand=None,
                     tilt_angle=tilt_angle,
                     solar_angles=solar_angles,
                     pv_installation_type='rooftop',
                     simulation_model_type='explicit',
                     module_model_name=None,
                     inverter_efficiency=0.95,
                     system_catalogue_handler=None,
                     roof_percentage_coverage=0.75,
                     facade_coverage_percentage=0,
                     csv_output=False,
                     output_path=pv_assessment_path).enrich()

print("done")


import geopandas as gpd
import matplotlib.pyplot as plt
from matplotlib.colors import Normalize

# Load GeoJSON file
gdf = gpd.read_file(input_file)

# Extract self-sufficiency values
self_sufficiency_values = [building.self_sufficiency['year'] / 1000 for building in city.buildings]

# Add self-sufficiency values to GeoDataFrame
gdf['self_sufficiency'] = self_sufficiency_values

# Determine the color normalization range
vmin = min(0, gdf['self_sufficiency'].min())  # Include 0 if min is positive
vmax = max(0, gdf['self_sufficiency'].max())  # Include 0 if max is negative

# Set up the figure and axis
fig, ax = plt.subplots(1, 1, figsize=(14, 10))

# Define a colormap and normalize the values
cmap = plt.cm.viridis
norm = Normalize(vmin=vmin, vmax=vmax)

# Plot the GeoDataFrame
gdf.plot(column='self_sufficiency',
         cmap=cmap,
         linewidth=0.8,
         edgecolor='grey',  # Add edges for better distinction
         legend=False,      # Turn off the built-in legend
         ax=ax)

# Add a custom colorbar
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm._A = []  # Needed for ScalarMappable with no data
cbar = fig.colorbar(sm, ax=ax, fraction=0.03, pad=0.04)
cbar.set_label('Self-Sufficiency (kWh/year)', fontsize=12)

# Add gridlines and axis labels
ax.grid(color='lightgrey', linestyle='--', linewidth=0.5, alpha=0.7)
ax.set_title('Building Self-Sufficiency Levels', fontsize=16, fontweight='bold', pad=20)
ax.set_xlabel('Longitude', fontsize=12)
ax.set_ylabel('Latitude', fontsize=12)

# Improve layout
plt.tight_layout()
plt.show()