40 lines
1.9 KiB
Python
40 lines
1.9 KiB
Python
from geojson_creator import process_geojson
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from pathlib import Path
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import subprocess
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from scripts.ep_workflow import energy_plus_workflow
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from hub.imports.geometry_factory import GeometryFactory
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from hub.helpers.dictionaries import Dictionaries
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from hub.imports.construction_factory import ConstructionFactory
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from hub.imports.usage_factory import UsageFactory
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from hub.imports.weather_factory import WeatherFactory
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from hub.imports.results_factory import ResultFactory
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from hub.exports.exports_factory import ExportsFactory
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from scripts import random_assignation
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from hub.imports.energy_systems_factory import EnergySystemsFactory
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from energy_system_analysis_report import EnergySystemAnalysisReport
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# Specify the GeoJSON file path
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geojson_file = process_geojson(x=-73.5681295982132, y=45.49218262677643, diff=0.0001)
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file_path = (Path(__file__).parent.parent / 'input_files' / f'{geojson_file}')
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# Specify the output path for the PDF file
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output_path = (Path(__file__).parent / 'out_files').resolve()
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# Create city object from GeoJSON file
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city = GeometryFactory('geojson',
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path=file_path,
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height_field='height',
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year_of_construction_field='year_of_construction',
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function_field='function',
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function_to_hub=Dictionaries().montreal_function_to_hub_function).city
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# Enrich city data
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ConstructionFactory('nrcan', city).enrich()
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UsageFactory('comnet', city).enrich()
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WeatherFactory('epw', city).enrich()
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ExportsFactory('sra', city, output_path).export()
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sra_path = (output_path / f'{city.name}_sra.xml').resolve()
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subprocess.run(['sra', str(sra_path)])
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ResultFactory('sra', city, output_path).enrich()
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# Run EnergyPlus workflow
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energy_plus_workflow(city)
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random_assignation.call_random(city.buildings, random_assignation.residential_systems_percentage)
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EnergySystemsFactory('montreal_custom', city).enrich()
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EnergySystemAnalysisReport(city, output_path).create_report()
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