from pathlib import Path
from scripts.district_heating_network.directory_manager import DirectoryManager
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 scripts.district_heating_network.road_processor import road_processor
from scripts.district_heating_network.district_heating_network_creator import DistrictHeatingNetworkCreator
from scripts.district_heating_network.geojson_graph_creator import networkx_to_geojson
import hub.helpers.constants as cte
from hub.exports.exports_factory import ExportsFactory
from scripts.pv_feasibility import pv_feasibility
import matplotlib.pyplot as plt
from scripts.district_heating_network.district_heating_network_creator import DistrictHeatingNetworkCreator
from scripts.district_heating_network.road_processor import road_processor
from scripts.district_heating_network.district_heating_factory import DistrictHeatingFactory

base_path = Path(__file__).parent
dir_manager = DirectoryManager(base_path)

# Input files directory
input_files_path = dir_manager.create_directory('input_files')
geojson_file_path = input_files_path / 'output_buildings.geojson'

# Output files directory
output_path = dir_manager.create_directory('out_files')

# Subdirectories for output files
energy_plus_output_path = dir_manager.create_directory('out_files/energy_plus_outputs')
simulation_results_path = dir_manager.create_directory('out_files/simulation_results')
sra_output_path = dir_manager.create_directory('out_files/sra_outputs')
cost_analysis_output_path = dir_manager.create_directory('out_files/cost_analysis')

# Select city area
location = [45.53067276979674, -73.70234652694087]
process_geojson(x=location[1], y=location[0], diff=0.001)

# Create city object
city = GeometryFactory(file_type='geojson',
                       path=geojson_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
ConstructionFactory('nrcan', city).enrich()
UsageFactory('nrcan', city).enrich()
# WeatherFactory('epw', city).enrich()
# energy_plus_workflow(city, energy_plus_output_path)
# data[f'{city.buildings[0].function}'] = city.buildings[0].heating_demand[cte.YEAR][0] / 3.6e9
# city.buildings[0].function = cte.COMMERCIAL
# ConstructionFactory('nrcan', city).enrich()
# UsageFactory('nrcan', city).enrich()
# energy_plus_workflow(city, energy_plus_output_path)
# data[f'{city.buildings[0].function}'] = city.buildings[0].heating_demand[cte.YEAR][0] / 3.6e9
# city.buildings[0].function = cte.MEDIUM_OFFICE
# ConstructionFactory('nrcan', city).enrich()
# UsageFactory('nrcan', city).enrich()
# energy_plus_workflow(city, energy_plus_output_path)
# data[f'{city.buildings[0].function}'] = city.buildings[0].heating_demand[cte.YEAR][0] / 3.6e9
# categories = list(data.keys())
# values = list(data.values())
# # Plotting
# fig, ax = plt.subplots(figsize=(10, 6), dpi=96)
# fig.suptitle('Impact of different usages on yearly heating demand', fontsize=16, weight='bold', alpha=.8)
# ax.bar(categories, values, color=['#2196f3', '#ff5a5f', '#4caf50'], width=0.6, zorder=2)
# ax.grid(which="major", axis='x', color='#DAD8D7', alpha=0.5, zorder=1)
# ax.grid(which="major", axis='y', color='#DAD8D7', alpha=0.5, zorder=1)
# ax.set_xlabel('Building Type', fontsize=12, labelpad=10)
# ax.set_ylabel('Energy Consumption (MWh)', fontsize=14, labelpad=10)
# ax.yaxis.set_major_locator(plt.MaxNLocator(integer=True))
# ax.set_xticks(np.arange(len(categories)))
# ax.set_xticklabels(categories, rotation=45, ha='right')
# ax.bar_label(ax.containers[0], padding=3, color='black', fontsize=12, rotation=0)
# ax.spines[['top', 'left', 'bottom']].set_visible(False)
# ax.spines['right'].set_linewidth(1.1)
# # Set a white background
# fig.patch.set_facecolor('white')
# # Adjust the margins around the plot area
# plt.subplots_adjust(left=0.1, right=0.9, top=0.85, bottom=0.25)
# # Save the plot
# plt.savefig('plot_nrcan.png', bbox_inches='tight')
# plt.close()
print('test')