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.energy_system_retrofit_report import EnergySystemRetrofitReport
from scripts.geojson_creator import process_geojson
from scripts import random_assignation
from hub.imports.energy_systems_factory import EnergySystemsFactory
from scripts.energy_system_sizing import SystemSizing
from scripts.solar_angles import CitySolarAngles
from scripts.pv_sizing_and_simulation import PVSizingSimulation
from scripts.energy_system_retrofit_results import consumption_data, cost_data
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, CURRENT_STATUS
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
import numpy as np
# Specify the GeoJSON file path
data = {}
input_files_path = (Path(__file__).parent / 'input_files')
input_files_path.mkdir(parents=True, exist_ok=True)
# geojson_file = process_geojson(x=-73.58001358793511, y=45.496445294438715, diff=0.0001)
geojson_file_path = input_files_path / 'test_geojson.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)
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')