forked from s_ranjbar/city_retrofit
112 lines
5.1 KiB
Python
112 lines
5.1 KiB
Python
"""
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TestExports test and validate the city export formats
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SPDX - License - Identifier: LGPL - 3.0 - or -later
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Copyright © 2022 Concordia CERC group
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Project Coder Guille Gutierrez guillermo.gutierrezmorote@concordia.ca
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"""
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import subprocess
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from pathlib import Path
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from unittest import TestCase
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import pandas as pd
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import hub.helpers.constants as cte
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from hub.exports.energy_building_exports_factory import EnergyBuildingsExportsFactory
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from hub.exports.exports_factory import ExportsFactory
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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.geometry_factory import GeometryFactory
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from hub.imports.results_factory import ResultFactory
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from hub.imports.usage_factory import UsageFactory
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class TestResultsImport(TestCase):
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"""
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TestImports class contains the unittest for import functionality
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"""
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def setUp(self) -> None:
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"""
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Test setup
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:return: None
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"""
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self._example_path = (Path(__file__).parent / 'tests_data').resolve()
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self._gml_path = (self._example_path / 'FZK_Haus_LoD_2.gml').resolve()
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self._output_path = (Path(__file__).parent / 'tests_outputs').resolve()
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self._city = GeometryFactory('citygml',
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self._gml_path,
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function_to_hub=Dictionaries().alkis_function_to_hub_function).city
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ConstructionFactory('nrcan', self._city).enrich()
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UsageFactory('nrcan', self._city).enrich()
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def test_sra_import(self):
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weather_file = (self._example_path / 'CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw').resolve()
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ExportsFactory('sra', self._city, self._output_path, weather_file=weather_file, weather_format='epw').export()
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sra_path = (self._output_path / f'{self._city.name}_sra.xml').resolve()
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subprocess.run(['sra', str(sra_path)])
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ResultFactory('sra', self._city, self._output_path).enrich()
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# Check that all the buildings have radiance in the surfaces
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for building in self._city.buildings:
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for surface in building.surfaces:
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self.assertIsNotNone(surface.global_irradiance)
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def test_meb_import(self):
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weather_file = (self._example_path / 'CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw').resolve()
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ExportsFactory('sra', self._city, self._output_path, weather_file=weather_file, weather_format='epw').export()
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sra_path = (self._output_path / f'{self._city.name}_sra.xml').resolve()
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subprocess.run(['sra', str(sra_path)])
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ResultFactory('sra', self._city, self._output_path).enrich()
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EnergyBuildingsExportsFactory('insel_monthly_energy_balance', self._city, self._output_path).export()
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for building in self._city.buildings:
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insel_path = (self._output_path / f'{building.name}.insel')
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subprocess.run(['insel', str(insel_path)])
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ResultFactory('insel_monthly_energy_balance', self._city, self._output_path).enrich()
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# Check that all the buildings have heating and cooling values
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for building in self._city.buildings:
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self.assertIsNotNone(building.heating[cte.MONTH][cte.INSEL_MEB])
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self.assertIsNotNone(building.cooling[cte.MONTH][cte.INSEL_MEB])
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self.assertIsNotNone(building.heating[cte.YEAR][cte.INSEL_MEB])
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self.assertIsNotNone(building.cooling[cte.YEAR][cte.INSEL_MEB])
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def test_peak_loads(self):
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# todo: this is not technically a import
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weather_file = (self._example_path / 'CAN_PQ_Montreal.Intl.AP.716270_CWEC.epw').resolve()
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ExportsFactory('sra', self._city, self._output_path, weather_file=weather_file, weather_format='epw').export()
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sra_path = (self._output_path / f'{self._city.name}_sra.xml').resolve()
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subprocess.run(['sra', str(sra_path)])
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ResultFactory('sra', self._city, self._output_path).enrich()
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for building in self._city.buildings:
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self.assertIsNotNone(building.heating_peak_load)
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self.assertIsNotNone(building.cooling_peak_load)
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values = [0 for _ in range(8760)]
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values[0] = 1000
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expected_yearly = pd.DataFrame([1000], columns=['expected'])
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expected_monthly_list = [0 for _ in range(12)]
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expected_monthly_list[0] = 1000
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expected_monthly = pd.DataFrame(expected_monthly_list, columns=['expected'])
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for building in self._city.buildings:
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building.heating[cte.HOUR] = pd.DataFrame(values, columns=['dummy'])
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building.cooling[cte.HOUR] = pd.DataFrame(values, columns=['dummy'])
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self.assertIsNotNone(building.heating_peak_load)
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self.assertIsNotNone(building.cooling_peak_load)
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pd.testing.assert_series_equal(
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building.heating_peak_load[cte.YEAR][cte.HEATING_PEAK_LOAD],
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expected_yearly['expected'],
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check_names=False
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)
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pd.testing.assert_series_equal(
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building.cooling_peak_load[cte.YEAR][cte.COOLING_PEAK_LOAD],
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expected_yearly['expected'],
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check_names=False
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)
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pd.testing.assert_series_equal(
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building.heating_peak_load[cte.MONTH][cte.HEATING_PEAK_LOAD],
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expected_monthly['expected'],
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check_names=False
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)
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pd.testing.assert_series_equal(
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building.cooling_peak_load[cte.MONTH][cte.COOLING_PEAK_LOAD],
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expected_monthly['expected'],
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check_names=False
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)
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