from geojson_creator import process_geojson from pathlib import Path import subprocess from hub.exports.energy_building_exports_factory import EnergyBuildingsExportsFactory from hub.helpers.dictionaries import Dictionaries from hub.imports.construction_factory import ConstructionFactory from hub.imports.geometry_factory import GeometryFactory from hub.imports.weather_factory import WeatherFactory from hub.imports.results_factory import ResultFactory from hub.imports.usage_factory import UsageFactory from hub.exports.exports_factory import ExportsFactory from scripts.ep_workflow import energy_plus_workflow import matplotlib.pyplot as plt import random import matplotlib.colors as mcolors import hub.helpers.constants as cte # Process geojson geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0001) months = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December'] out_path = (Path(__file__).parent / 'out_files') file_path = (Path(__file__).parent.parent / 'input_files' / f'{geojson_file}') print('[simulation start]') 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 print(f'city created from {file_path}') # Enrich city data ConstructionFactory('nrcan', city).enrich() UsageFactory('nrcan', city).enrich() WeatherFactory('epw', city).enrich() ExportsFactory('sra', city, out_path).export() sra_path = (out_path / f'{city.name}_sra.xml').resolve() subprocess.run(['sra', str(sra_path)]) ResultFactory('sra', city, out_path).enrich() EnergyBuildingsExportsFactory('insel_monthly_energy_balance', city, out_path).export_debug() # Create grid of plots fig, axs = plt.subplots(3, 2, figsize=(12, 12)) # Plot monthly heating demands from Monthly Energy Balance for i, building in enumerate(city.buildings): monthly_heating_demand = [peak / 3.6e6 for peak in building.heating_peak_load[cte.MONTH]] ax = axs[i, 0] # Select subplot in the first column ax.plot(months, monthly_heating_demand) ax.set_title(f'Monthly Heating Demand (Building {i+1})') ax.set_xlabel('Month') ax.set_ylabel('Heating Demand') # Plot monthly heating demands from EnergyPlus energy_plus_workflow(city) for i, ep in enumerate(city.buildings): monthly_heating_demand = [peak / 3.6e6 for peak in ep.heating_peak_load[cte.MONTH]] ax = axs[i, 1] # Select subplot in the second column ax.plot(months, monthly_heating_demand) ax.set_title(f'Monthly Heating Demand (Building {i+1})') ax.set_xlabel('Month') ax.set_ylabel('Heating Demand') plt.tight_layout() plt.show()